Repository: liangliangzhuang/R_example
Branch: master
Commit: 14a9ba56c52e
Files: 607
Total size: 154.8 MB

Directory structure:
gitextract_2nr_zcnv/

├── 2020年/
│   ├── 2020.07.13Network_igraph/
│   │   ├── edge.csv
│   │   ├── graph.r
│   │   └── vertices.csv
│   ├── 2020.07.14China_map/
│   │   ├── bou2_4p.dbf
│   │   ├── bou2_4p.shp
│   │   ├── bou2_4p.shx
│   │   ├── china_map.r
│   │   └── data_dt.csv
│   ├── 2020.07.15World_map/
│   │   ├── Country_Data.csv
│   │   ├── cores.r
│   │   └── world_data.csv
│   ├── 2020.07.22Movielense/
│   │   ├── ch11-task.Rmd
│   │   └── ch11-task.log
│   ├── 2020.07.22坐标轴截断画图/
│   │   ├── code_truncation .R
│   │   ├── code_truncation .Rmd
│   │   └── code_truncation-.docx
│   ├── 2020.07.24饼图与圆环图/
│   │   └── pie.rmd
│   ├── 2020.07.29温大招生/
│   │   └── code_ggimage.r
│   ├── 2020.08.14amazon/
│   │   ├── RECOM.Rmd
│   │   ├── code.Rmd
│   │   └── 亚马逊产品推荐算法.xmind
│   ├── 2020.08.15分面/
│   │   ├── .R数据科学.pdf.icloud
│   │   ├── facet.log
│   │   └── facet.rmd
│   ├── 2020.08.23好玩的图/
│   │   ├── fun.r
│   │   └── readme.txt
│   ├── 2020.08.25马赛克/
│   │   ├── 马赛克.html
│   │   └── 马赛克.rmd
│   ├── 2020.08.26混合多个图形/
│   │   ├── 混合图.html
│   │   └── 混合图.rmd
│   ├── 2020.09.15reticulate/
│   │   └── reticulate.rmd
│   ├── 2020.09.27tidyverse数据清洗/
│   │   └── Tidy_data.rmd
│   ├── 2020.10.09散点图系列一/
│   │   ├── scater_plot1.html
│   │   └── scater_plot1.rmd
│   ├── 2020.10.27散点图系列二/
│   │   ├── HighDensity_Scatter_Data.csv
│   │   ├── scater_plot2.html
│   │   └── scater_plot2.rmd
│   ├── 2020.10.30ggpubr/
│   │   └── ggpubr.rmd
│   ├── 2020.11.05气泡图/
│   │   ├── 1.txt
│   │   ├── Bubble_plot.html
│   │   └── Bubble_plot.rmd
│   ├── 2020.11.14gghalves/
│   │   ├── gghalves.html
│   │   └── gghalves.rmd
│   ├── 2020.11.16flexdashboard/
│   │   ├── flexdashboard.md
│   │   ├── test.html
│   │   ├── test.rmd
│   │   ├── 例子/
│   │   │   └── 09_rbokeh-iris-dataset/
│   │   │       ├── dashboard-pandoc2.0.3.html
│   │   │       └── dashboard.Rmd
│   │   ├── 教程.html
│   │   ├── 教程.rmd
│   │   └── 链接.txt
│   ├── 2020.11.22三维散点图/
│   │   ├── 3d_scatter.html
│   │   ├── 3d_scatter.rmd
│   │   └── ThreeD_Scatter_Data.csv
│   ├── 2020.11.22数据处理ntile()/
│   │   └── 数据处理数据按从小到大分成n类.md
│   ├── 2020.11.28等高线/
│   │   ├── .RData
│   │   ├── .Rhistory
│   │   ├── 等高线.html
│   │   ├── 等高线.r
│   │   ├── 等高线.rmd
│   │   └── 等高线.txt
│   ├── 2020.12.05日历/
│   │   ├── .My_calendar7(1).pdf.icloud
│   │   ├── 日历_EasyShu.rmd
│   │   ├── 日历教程.md
│   │   ├── 日历教程.rmd
│   │   └── 背景/
│   │       ├── .1.png.icloud
│   │       ├── .2.png.icloud
│   │       └── .3.jpg.icloud
│   ├── 2020.12.07瀑布图/
│   │   ├── Facting_Data.csv
│   │   └── 瀑布图.rmd
│   ├── 2020.12.21esquisse包/
│   │   └── esquisse包.md
│   ├── 2020.12.24Mandalas/
│   │   └── Mandalas/
│   │       ├── .Rproj.user/
│   │       │   ├── 9B97F8EE/
│   │       │   │   └── sources/
│   │       │   │       ├── prop/
│   │       │   │       │   ├── 34FB05E2
│   │       │   │       │   ├── 5C967418
│   │       │   │       │   ├── B38F6FEB
│   │       │   │       │   ├── D25723BC
│   │       │   │       │   └── INDEX
│   │       │   │       └── s-75E8F375/
│   │       │   │           ├── 17CC9D9C
│   │       │   │           ├── 17CC9D9C-contents
│   │       │   │           ├── 20B6EE7D
│   │       │   │           ├── 20B6EE7D-contents
│   │       │   │           ├── 28FAC3B5
│   │       │   │           ├── 28FAC3B5-contents
│   │       │   │           ├── 4326A9CE
│   │       │   │           ├── 4326A9CE-contents
│   │       │   │           ├── C7D0C74B
│   │       │   │           ├── C7D0C74B-contents
│   │       │   │           └── lock_file
│   │       │   └── shared/
│   │       │       └── notebooks/
│   │       │           ├── 40CC0FD6-Mandalas/
│   │       │           │   └── 1/
│   │       │           │       ├── 9B97F8EE87974FA4/
│   │       │           │       │   └── chunks.json
│   │       │           │       └── s/
│   │       │           │           ├── cct2o5lt4yw4d/
│   │       │           │           │   └── 000011.csv
│   │       │           │           └── chunks.json
│   │       │           ├── 540673E1-峰峦图/
│   │       │           │   └── 1/
│   │       │           │       ├── 9B97F8EE75E8F375/
│   │       │           │       │   └── chunks.json
│   │       │           │       ├── 9B97F8EE87974FA4/
│   │       │           │       │   └── chunks.json
│   │       │           │       └── s/
│   │       │           │           └── chunks.json
│   │       │           ├── patch-chunk-names
│   │       │           └── paths
│   │       ├── Mandalas.Rproj
│   │       ├── Mandalas.html
│   │       └── Mandalas.rmd
│   ├── 2020.12.24圣诞节/
│   │   ├── 1.json
│   │   └── marry.R
│   ├── 2020.12.25峰峦图/
│   │   └── 峰峦图.rmd
│   ├── 2020.12.30ggvis/
│   │   ├── ggvis.html
│   │   ├── ggvis.md
│   │   └── ggvis.rmd
│   └── 2020年R数据科学系列/
│       ├── 2020.08.23几何对象/
│       │   └── 几何对象.md
│       ├── 2020.08.23速查表/
│       │   ├── 让微信排版变 Nice.html
│       │   └── 速查表.html
│       ├── 2020.09.27tidy data/
│       │   └── Tidy_data.rmd
│       └── 《R数据科学》源代码/
│           ├── DESCRIPTION
│           ├── EDA.Rmd
│           ├── LICENSE
│           ├── README.md
│           ├── _bookdown.yml
│           ├── _common.R
│           ├── _output.yaml
│           ├── communicate-plots.Rmd
│           ├── communicate.Rmd
│           ├── contribs.txt
│           ├── contribute.rmd
│           ├── datetimes.Rmd
│           ├── explore.Rmd
│           ├── factors.Rmd
│           ├── figures.R
│           ├── functions.Rmd
│           ├── hierarchy.Rmd
│           ├── import.Rmd
│           ├── index.rmd
│           ├── intro.Rmd
│           ├── issues.json
│           ├── iteration.Rmd
│           ├── model-assess.Rmd
│           ├── model-basics.Rmd
│           ├── model-building.Rmd
│           ├── model-many.Rmd
│           ├── model.Rmd
│           ├── pipes.Rmd
│           ├── program.Rmd
│           ├── r4ds.Rproj
│           ├── r4ds.css
│           ├── relational-data.Rmd
│           ├── rmarkdown-formats.Rmd
│           ├── rmarkdown-workflow.Rmd
│           ├── rmarkdown.Rmd
│           ├── strings.Rmd
│           ├── tibble.Rmd
│           ├── transform.Rmd
│           ├── vectors.Rmd
│           ├── visualize.Rmd
│           ├── workflow-basics.Rmd
│           ├── workflow-projects.Rmd
│           ├── workflow-scripts.Rmd
│           └── wrangle.Rmd
├── 2021年/
│   ├── 2021.01.31主成分结果可视化/
│   │   ├── pac_visual.log
│   │   └── pac_visual.rmd
│   ├── 2021.02.21克利夫兰点图系列/
│   │   ├── DotPlots_Data.csv
│   │   ├── cleveland's .Rmd
│   │   └── cleveland-s-.log
│   └── 2021.02.28常用主题风格/
│       ├── .R可视乎｜ggplot常用主题风格汇总.pdf.icloud
│       └── R可视乎｜ggplot常用主题风格汇总.md
├── 2022年/
│   ├── 2022.01.14 如何使用 ggplot2 绘制双轴分离图？/
│   │   └── 双轴分离.R
│   ├── 2022.01.28 如何绘制省市级地图？/
│   │   ├── df_China4.csv
│   │   ├── 各区县经营效率平均值.csv
│   │   ├── 温州地图绘制.r
│   │   ├── 温州市.json
│   │   └── 绘制浙江省地图.R
│   ├── 2022.02.08读者投稿｜绘制一系列黑白印刷风格图表/
│   │   └── acchist.r
│   ├── 2022.03.14绘制混合密度函数图以及添加分位数线/
│   │   └── mix-quantile.r
│   ├── 2022.04.08R 案例｜绘制不同分布的 QQ 图/
│   │   └── qqplot.r
│   ├── 2022.05.15老板让你复现一个图片，你会使用什么软件？/
│   │   └── example.r
│   ├── 2022.08.08 ggplot 分面的细节调整汇总/
│   │   └── ggplot_facet.r
│   ├── 2022.09.09 如何在分面中添加数学表达式标签？ /
│   │   └── add_math_label.r
│   ├── 2022.09.24 中国地图绘制/
│   │   ├── .RData
│   │   ├── .Rhistory
│   │   └── .Rproj.user/
│   │       ├── 161F88A0/
│   │       │   ├── pcs/
│   │       │   │   ├── files-pane.pper
│   │       │   │   ├── packages-pane.pper
│   │       │   │   ├── source-pane.pper
│   │       │   │   ├── windowlayoutstate.pper
│   │       │   │   └── workbench-pane.pper
│   │       │   ├── rmd-outputs
│   │       │   ├── saved_source_markers
│   │       │   └── sources/
│   │       │       └── prop/
│   │       │           ├── 212A8ABB
│   │       │           ├── 21BA30B6
│   │       │           ├── 702BC91B
│   │       │           ├── 9F335933
│   │       │           ├── D8AA2E2E
│   │       │           └── INDEX
│   │       └── shared/
│   │           └── notebooks/
│   │               ├── patch-chunk-names
│   │               └── paths
│   ├── 2022.10.12使用 ggplot2 绘制单个和多个省份地图/
│   │   ├── china_shp/
│   │   │   ├── CHN_adm0.cpg
│   │   │   ├── CHN_adm0.csv
│   │   │   ├── CHN_adm0.dbf
│   │   │   ├── CHN_adm0.prj
│   │   │   ├── CHN_adm0.shp
│   │   │   ├── CHN_adm0.shx
│   │   │   ├── CHN_adm1.cpg
│   │   │   ├── CHN_adm1.csv
│   │   │   ├── CHN_adm1.dbf
│   │   │   ├── CHN_adm1.prj
│   │   │   ├── CHN_adm1.shp
│   │   │   ├── CHN_adm1.shx
│   │   │   ├── CHN_adm2.cpg
│   │   │   ├── CHN_adm2.csv
│   │   │   ├── CHN_adm2.dbf
│   │   │   ├── CHN_adm2.prj
│   │   │   ├── CHN_adm2.shp
│   │   │   ├── CHN_adm2.shx
│   │   │   ├── CHN_adm3.cpg
│   │   │   ├── CHN_adm3.csv
│   │   │   ├── CHN_adm3.dbf
│   │   │   ├── CHN_adm3.prj
│   │   │   ├── CHN_adm3.shp
│   │   │   ├── CHN_adm3.shx
│   │   │   ├── license.txt
│   │   │   └── my_file.txt
│   │   ├── colour.csv
│   │   ├── map.Rproj
│   │   ├── province.csv
│   │   ├── 南海.geojson
│   │   ├── 地图十段线.R
│   │   ├── 测试数据.xlsx
│   │   └── 省份地图.R
│   ├── 2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/
│   │   ├── .RData
│   │   ├── .Rhistory
│   │   ├── .Rproj.user/
│   │   │   ├── 161F88A0/
│   │   │   │   ├── build_options
│   │   │   │   ├── build_options 2
│   │   │   │   ├── pcs/
│   │   │   │   │   ├── .files-pane 2.pper.icloud
│   │   │   │   │   ├── .packages-pane 2.pper.icloud
│   │   │   │   │   ├── .source-pane 2.pper.icloud
│   │   │   │   │   ├── .windowlayoutstate 2.pper.icloud
│   │   │   │   │   ├── .workbench-pane 2.pper.icloud
│   │   │   │   │   ├── files-pane.pper
│   │   │   │   │   ├── packages-pane.pper
│   │   │   │   │   ├── source-pane.pper
│   │   │   │   │   ├── windowlayoutstate.pper
│   │   │   │   │   └── workbench-pane.pper
│   │   │   │   ├── rmd-outputs
│   │   │   │   ├── saved_source_markers
│   │   │   │   └── sources/
│   │   │   │       ├── per/
│   │   │   │       │   ├── t/
│   │   │   │       │   │   ├── CD5422DD
│   │   │   │       │   │   ├── CD5422DD 2
│   │   │   │       │   │   ├── CD5422DD-contents
│   │   │   │       │   │   └── CD5422DD-contents 2
│   │   │   │       │   └── u/
│   │   │   │       │       ├── 86D463FB
│   │   │   │       │       ├── 86D463FB 2
│   │   │   │       │       ├── 86D463FB-contents
│   │   │   │       │       └── 86D463FB-contents 2
│   │   │   │       └── prop/
│   │   │   │           ├── 6244B089
│   │   │   │           ├── 6244B089 2
│   │   │   │           ├── 66C45745
│   │   │   │           ├── 66C45745 2
│   │   │   │           ├── DCED28DF
│   │   │   │           ├── DCED28DF 2
│   │   │   │           ├── INDEX
│   │   │   │           └── INDEX 2
│   │   │   ├── C6560784/
│   │   │   │   ├── pcs/
│   │   │   │   │   ├── .files-pane 2.pper.icloud
│   │   │   │   │   ├── .source-pane 2.pper.icloud
│   │   │   │   │   ├── .windowlayoutstate 2.pper.icloud
│   │   │   │   │   ├── .workbench-pane 2.pper.icloud
│   │   │   │   │   ├── files-pane.pper
│   │   │   │   │   ├── source-pane.pper
│   │   │   │   │   ├── windowlayoutstate.pper
│   │   │   │   │   └── workbench-pane.pper
│   │   │   │   ├── rmd-outputs
│   │   │   │   ├── saved_source_markers
│   │   │   │   └── sources/
│   │   │   │       ├── per/
│   │   │   │       │   └── t/
│   │   │   │       │       ├── C36CFFC0
│   │   │   │       │       ├── C36CFFC0 2
│   │   │   │       │       ├── C36CFFC0-contents
│   │   │   │       │       └── C36CFFC0-contents 2
│   │   │   │       └── prop/
│   │   │   │           ├── F9089F45
│   │   │   │           ├── F9089F45 2
│   │   │   │           ├── INDEX
│   │   │   │           └── INDEX 2
│   │   │   └── shared/
│   │   │       └── notebooks/
│   │   │           ├── CED44E14-50pictures_ggplot/
│   │   │           │   └── 1/
│   │   │           │       ├── C656078445C2030A/
│   │   │           │       │   ├── chunks 2.json
│   │   │           │       │   └── chunks.json
│   │   │           │       └── s/
│   │   │           │           ├── cg7cix78cerjz/
│   │   │           │           │   ├── .00000f 2.metadata.icloud
│   │   │           │           │   ├── .00000f 2.rdf.icloud
│   │   │           │           │   ├── 00000f.metadata
│   │   │           │           │   └── 00000f.rdf
│   │   │           │           ├── chunks 2.json
│   │   │           │           ├── chunks.json
│   │   │           │           └── csetup_chunk/
│   │   │           │               ├── .00000f 2.csv.icloud
│   │   │           │               └── 00000f.csv
│   │   │           ├── patch-chunk-names
│   │   │           ├── patch-chunk-names 2
│   │   │           ├── paths
│   │   │           └── paths 2
│   │   ├── 50pictures_ggplot.Rproj
│   │   ├── 50pictures_ggplot.html
│   │   ├── 50pictures_ggplot.rmd
│   │   ├── midwest.csv
│   │   └── readme.txt
│   ├── 2022.11.15 R绘图案例｜基于分面的折线图绘制/
│   │   ├── .RData
│   │   ├── .Rhistory
│   │   ├── .Rproj.user/
│   │   │   ├── C6560784/
│   │   │   │   ├── pcs/
│   │   │   │   │   ├── files-pane.pper
│   │   │   │   │   ├── packages-pane.pper
│   │   │   │   │   ├── source-pane.pper
│   │   │   │   │   ├── windowlayoutstate.pper
│   │   │   │   │   └── workbench-pane.pper
│   │   │   │   ├── rmd-outputs
│   │   │   │   ├── saved_source_markers
│   │   │   │   └── sources/
│   │   │   │       ├── prop/
│   │   │   │       │   ├── 3664F9C7
│   │   │   │       │   ├── A35D258F
│   │   │   │       │   ├── E600AE88
│   │   │   │       │   └── INDEX
│   │   │   │       └── session-417D82C1/
│   │   │   │           ├── 3B4320B1
│   │   │   │           ├── 3B4320B1-contents
│   │   │   │           └── lock_file
│   │   │   └── shared/
│   │   │       └── notebooks/
│   │   │           ├── patch-chunk-names
│   │   │           └── paths
│   │   ├── data_city.csv
│   │   ├── line_point.R
│   │   └── plot.Rproj
│   ├── 2022.11.19 分面中添加不同的直线/
│   │   ├── facet-line.r
│   │   ├── plot.Rproj
│   │   └── test.xlsx
│   ├── 2022.11.29 R绘图案例｜基于分面的面积图绘制/
│   │   ├── .RData
│   │   ├── .Rhistory
│   │   ├── .Rproj.user/
│   │   │   ├── C6560784/
│   │   │   │   ├── jobs/
│   │   │   │   │   └── 7897C193-output.json
│   │   │   │   ├── pcs/
│   │   │   │   │   ├── files-pane.pper
│   │   │   │   │   ├── source-pane.pper
│   │   │   │   │   ├── windowlayoutstate.pper
│   │   │   │   │   └── workbench-pane.pper
│   │   │   │   ├── rmd-outputs
│   │   │   │   ├── saved_source_markers
│   │   │   │   └── sources/
│   │   │   │       ├── prop/
│   │   │   │       │   ├── 0508C42F
│   │   │   │       │   ├── 0D0D59B5
│   │   │   │       │   ├── 19008281
│   │   │   │       │   ├── A62E60CA
│   │   │   │       │   └── INDEX
│   │   │   │       ├── session-23478791/
│   │   │   │       │   ├── 0BC0007C
│   │   │   │       │   ├── 0BC0007C-contents
│   │   │   │       │   ├── AF358D71-contents
│   │   │   │       │   ├── EB6BB15A
│   │   │   │       │   ├── EB6BB15A-contents
│   │   │   │       │   └── lock_file
│   │   │   │       └── session-51C83C65/
│   │   │   │           ├── 8752579D
│   │   │   │           ├── 8752579D-contents
│   │   │   │           └── lock_file
│   │   │   └── shared/
│   │   │       └── notebooks/
│   │   │           ├── patch-chunk-names
│   │   │           └── paths
│   │   ├── R-plot.Rproj
│   │   ├── rev_plot.R
│   │   └── test.xlsx
│   └── 2022.12.31 2023年日历大派送/
│       ├── .RData
│       ├── .Rhistory
│       ├── .Rproj.user/
│       │   ├── C6560784/
│       │   │   ├── jobs/
│       │   │   │   └── E803742C-output.json
│       │   │   ├── pcs/
│       │   │   │   ├── files-pane.pper
│       │   │   │   ├── source-pane.pper
│       │   │   │   ├── windowlayoutstate.pper
│       │   │   │   └── workbench-pane.pper
│       │   │   ├── rmd-outputs
│       │   │   ├── saved_source_markers
│       │   │   └── sources/
│       │   │       ├── per/
│       │   │       │   └── t/
│       │   │       │       ├── 13B3FE41
│       │   │       │       ├── 13B3FE41-contents
│       │   │       │       ├── 873D867D
│       │   │       │       └── 873D867D-contents
│       │   │       └── prop/
│       │   │           ├── 26BD393F
│       │   │           ├── 8A1E9267
│       │   │           ├── F0AB5896
│       │   │           └── INDEX
│       │   └── shared/
│       │       └── notebooks/
│       │           ├── patch-chunk-names
│       │           └── paths
│       ├── version1.R
│       ├── version2.R
│       └── 未命名.Rproj
├── 2023年/
│   ├── 2022.08.31 分面+双轴/
│   │   ├── .RData
│   │   ├── .Rhistory
│   │   ├── .Rproj.user/
│   │   │   ├── C6560784/
│   │   │   │   ├── pcs/
│   │   │   │   │   ├── files-pane.pper
│   │   │   │   │   ├── source-pane.pper
│   │   │   │   │   ├── windowlayoutstate.pper
│   │   │   │   │   └── workbench-pane.pper
│   │   │   │   ├── rmd-outputs
│   │   │   │   ├── saved_source_markers
│   │   │   │   └── sources/
│   │   │   │       ├── per/
│   │   │   │       │   └── t/
│   │   │   │       │       ├── EA22C3C6
│   │   │   │       │       └── EA22C3C6-contents
│   │   │   │       └── prop/
│   │   │   │           ├── D4B371E0
│   │   │   │           ├── F02804FC
│   │   │   │           └── INDEX
│   │   │   └── shared/
│   │   │       └── notebooks/
│   │   │           ├── patch-chunk-names
│   │   │           └── paths
│   │   ├── Add secondary axis.R
│   │   └── df.csv
│   ├── 2023.03.07 高亮柱状图/
│   │   └── 高亮柱状图.R
│   ├── 2023.03.08 基于 ggridges 绘制剩余使用寿命密度图/
│   │   └── rul_ggbridges.R
│   ├── 2023.03.09基于 ggdensity 包的等高线绘制/
│   │   └── 等高线绘制.R
│   ├── 2023.03.16使用 ggTimeSeries 包构建日历图/
│   │   ├── timeseries.R
│   │   ├── 日历图.html
│   │   ├── 日历图.qmd
│   │   └── 日历图_files/
│   │       └── libs/
│   │           ├── bootstrap/
│   │           │   └── bootstrap-icons.css
│   │           └── quarto-html/
│   │               ├── quarto-syntax-highlighting.css
│   │               ├── quarto.js
│   │               └── tippy.css
│   ├── 2023.04.01 基于 R 语言的科研论文绘图技巧汇总/
│   │   ├── .RData
│   │   ├── .Rhistory
│   │   ├── .Rproj.user/
│   │   │   ├── C6560784/
│   │   │   │   ├── pcs/
│   │   │   │   │   ├── files-pane.pper
│   │   │   │   │   ├── packages-pane.pper
│   │   │   │   │   ├── source-pane.pper
│   │   │   │   │   ├── windowlayoutstate.pper
│   │   │   │   │   └── workbench-pane.pper
│   │   │   │   ├── rmd-outputs
│   │   │   │   ├── saved_source_markers
│   │   │   │   └── sources/
│   │   │   │       ├── per/
│   │   │   │       │   └── t/
│   │   │   │       │       ├── 7696DA26
│   │   │   │       │       ├── 7696DA26-contents
│   │   │   │       │       ├── 83B6CCD0
│   │   │   │       │       ├── 83B6CCD0-contents
│   │   │   │       │       ├── A32C5A91
│   │   │   │       │       ├── A32C5A91-contents
│   │   │   │       │       ├── E41305B6
│   │   │   │       │       └── E41305B6-contents
│   │   │   │       └── prop/
│   │   │   │           ├── 31AEA8EB
│   │   │   │           ├── 51574A35
│   │   │   │           ├── 53B7DAA0
│   │   │   │           ├── 5916C35B
│   │   │   │           ├── 5B81A5B2
│   │   │   │           ├── 75B7761D
│   │   │   │           ├── 86D189DF
│   │   │   │           ├── ABCABC27
│   │   │   │           ├── BCA3F795
│   │   │   │           ├── C757E81D
│   │   │   │           ├── E86E28FA
│   │   │   │           └── INDEX
│   │   │   └── shared/
│   │   │       └── notebooks/
│   │   │           ├── 18DAA490-A/
│   │   │           │   └── 1/
│   │   │           │       └── s/
│   │   │           │           └── chunks.json
│   │   │           ├── patch-chunk-names
│   │   │           └── paths
│   │   ├── LICENSE
│   │   ├── Panel_C.R
│   │   ├── Panel_D.R
│   │   ├── Panel_E.R
│   │   ├── Panel_F.R
│   │   ├── README.md
│   │   ├── data_B.csv
│   │   ├── data_Cmu_E10.5.csv
│   │   ├── data_Cmu_E11.5.csv
│   │   ├── data_Cmu_E8.5.csv
│   │   ├── data_Cmu_E9.5.csv
│   │   ├── data_Cwt_E10.5.csv
│   │   ├── data_Cwt_E11.5.csv
│   │   ├── data_Cwt_E8.5.csv
│   │   ├── data_Cwt_E9.5.csv
│   │   ├── data_D1.csv
│   │   ├── data_D2.csv
│   │   ├── data_Ea.csv
│   │   ├── data_Eb.csv
│   │   ├── data_Ec.csv
│   │   ├── data_F.csv
│   │   ├── figure_example.R
│   │   └── figure_example.Rproj
│   ├── 2023.04.12 Rmarkdown重复性报告/
│   │   ├── .RData
│   │   ├── .Rhistory
│   │   ├── .Rproj.user/
│   │   │   ├── C6560784/
│   │   │   │   ├── pcs/
│   │   │   │   │   ├── files-pane.pper
│   │   │   │   │   ├── source-pane.pper
│   │   │   │   │   ├── windowlayoutstate.pper
│   │   │   │   │   └── workbench-pane.pper
│   │   │   │   ├── quarto-crossref/
│   │   │   │   │   ├── AA033108
│   │   │   │   │   └── INDEX
│   │   │   │   ├── rmd-outputs
│   │   │   │   ├── saved_source_markers
│   │   │   │   └── sources/
│   │   │   │       ├── per/
│   │   │   │       │   ├── t/
│   │   │   │       │   │   ├── 24D937C1
│   │   │   │       │   │   ├── 24D937C1-contents
│   │   │   │       │   │   ├── 47C21D39
│   │   │   │       │   │   └── 47C21D39-contents
│   │   │   │       │   └── u/
│   │   │   │       │       ├── F684F954
│   │   │   │       │       └── F684F954-contents
│   │   │   │       └── prop/
│   │   │   │           ├── 07CCB8CA
│   │   │   │           ├── 55F7E8FD
│   │   │   │           ├── 5A14198B
│   │   │   │           ├── 998B2979
│   │   │   │           ├── CA24840E
│   │   │   │           ├── DD741787
│   │   │   │           ├── EAE6CDE4
│   │   │   │           ├── FD4997C5
│   │   │   │           └── INDEX
│   │   │   └── shared/
│   │   │       └── notebooks/
│   │   │           ├── 72C89A46-中文/
│   │   │           │   └── 1/
│   │   │           │       ├── C65607844eefea2c/
│   │   │           │       │   └── chunks.json
│   │   │           │       ├── C6560784b90da11a/
│   │   │           │       │   └── chunks.json
│   │   │           │       ├── C6560784f87258bb/
│   │   │           │       │   └── chunks.json
│   │   │           │       └── s/
│   │   │           │           ├── chunks.json
│   │   │           │           ├── cq8zbuk6wx2qd/
│   │   │           │           │   ├── 00000f.metadata
│   │   │           │           │   └── 00000f.rdf
│   │   │           │           └── csetup_chunk/
│   │   │           │               └── 00000f.csv
│   │   │           ├── patch-chunk-names
│   │   │           └── paths
│   │   ├── Project.Rproj
│   │   ├── data/
│   │   │   ├── A中学.csv
│   │   │   ├── B中学.csv
│   │   │   └── C中学.csv
│   │   ├── exploratory_A中学.log
│   │   ├── exploratory_B中学.log
│   │   ├── libs/
│   │   │   ├── header-attrs-2.20/
│   │   │   │   └── header-attrs.js
│   │   │   └── remark-css-0.0.1/
│   │   │       └── default.css
│   │   ├── test.R
│   │   ├── zh-CN.css
│   │   └── 中文.Rmd
│   ├── 2023.05.04 如何一步步提高图形 B 格？以 ggplot 绘图为例/
│   │   ├── .Rhistory
│   │   └── line.R
│   ├── 2023.05.08个性化树状图/
│   │   └── ggparty.R
│   ├── 2023.05.13 高级棒棒图/
│   │   └── 棒棒图.R
│   ├── 2023.06.29 旭日图/
│   │   ├── .Rproj.user/
│   │   │   ├── C6560784/
│   │   │   │   ├── jobs/
│   │   │   │   │   └── 4152B7D6-output.json
│   │   │   │   └── sources/
│   │   │   │       ├── prop/
│   │   │   │       │   ├── 1E4518E3
│   │   │   │       │   ├── 5E635AFA
│   │   │   │       │   └── INDEX
│   │   │   │       └── session-ef7e8bd6/
│   │   │   │           ├── 3A928347-contents
│   │   │   │           ├── F749EB52
│   │   │   │           ├── F749EB52-contents
│   │   │   │           └── lock_file
│   │   │   └── shared/
│   │   │       └── notebooks/
│   │   │           ├── patch-chunk-names
│   │   │           └── paths
│   │   ├── d.csv
│   │   ├── 旭日图.R
│   │   ├── 未命名.Rproj
│   │   └── 未命名.html
│   ├── 2023.08.05 局部细节放大图/
│   │   └── 局部细节放大图.R
│   ├── 2023.08.24 绘制足球数据/
│   │   └── ggsoccer.R
│   ├── 2023.10.08 灯芯柱状图/
│   │   ├── .RData
│   │   ├── .Rhistory
│   │   ├── .Rproj.user/
│   │   │   ├── C6560784/
│   │   │   │   ├── pcs/
│   │   │   │   │   ├── debug-breakpoints.pper
│   │   │   │   │   ├── files-pane.pper
│   │   │   │   │   ├── source-pane.pper
│   │   │   │   │   ├── windowlayoutstate.pper
│   │   │   │   │   └── workbench-pane.pper
│   │   │   │   ├── persistent-state
│   │   │   │   ├── rmd-outputs
│   │   │   │   ├── saved_source_markers
│   │   │   │   └── sources/
│   │   │   │       ├── prop/
│   │   │   │       │   ├── 00943EE4
│   │   │   │       │   ├── 59990D2B
│   │   │   │       │   └── INDEX
│   │   │   │       └── session-6c0d0c41/
│   │   │   │           ├── 4BEAD114
│   │   │   │           ├── 4BEAD114-contents
│   │   │   │           └── lock_file
│   │   │   └── shared/
│   │   │       └── notebooks/
│   │   │           ├── patch-chunk-names
│   │   │           └── paths
│   │   ├── animal_rescues.txt
│   │   ├── df_animals_sum.csv
│   │   ├── 未命名.Rproj
│   │   └── 灯芯柱状图.r
│   ├── 2023.12.02 分面中添加不同表格/
│   │   ├── .RData
│   │   ├── .Rhistory
│   │   ├── .Rproj.user/
│   │   │   ├── C6560784/
│   │   │   │   ├── pcs/
│   │   │   │   │   ├── files-pane.pper
│   │   │   │   │   ├── source-pane.pper
│   │   │   │   │   ├── windowlayoutstate.pper
│   │   │   │   │   └── workbench-pane.pper
│   │   │   │   ├── rmd-outputs
│   │   │   │   ├── saved_source_markers
│   │   │   │   └── sources/
│   │   │   │       ├── per/
│   │   │   │       │   └── t/
│   │   │   │       │       ├── 23FB0CA3
│   │   │   │       │       └── 23FB0CA3-contents
│   │   │   │       └── prop/
│   │   │   │           ├── 276F3DD0
│   │   │   │           └── INDEX
│   │   │   └── shared/
│   │   │       └── notebooks/
│   │   │           └── patch-chunk-names
│   │   ├── project.Rproj
│   │   └── 分面中添加不同表格.R
│   ├── 2023.12.16 分面中添加拟合曲线/
│   │   ├── .Rproj.user/
│   │   │   ├── C6560784/
│   │   │   │   ├── pcs/
│   │   │   │   │   ├── files-pane.pper
│   │   │   │   │   ├── source-pane.pper
│   │   │   │   │   ├── windowlayoutstate.pper
│   │   │   │   │   └── workbench-pane.pper
│   │   │   │   └── sources/
│   │   │   │       ├── prop/
│   │   │   │       │   ├── DAA25768
│   │   │   │       │   └── INDEX
│   │   │   │       └── session-427bca66/
│   │   │   │           ├── AB1928BC
│   │   │   │           ├── AB1928BC-contents
│   │   │   │           ├── C6EFA56A-contents
│   │   │   │           └── lock_file
│   │   │   └── shared/
│   │   │       └── notebooks/
│   │   │           └── patch-chunk-names
│   │   ├── data_fit.RData
│   │   ├── project.Rproj
│   │   ├── true_data.RData
│   │   └── 分面中添加拟合曲线.R
│   └── 合并图形+共享图例/
│       └── 合并图形-共享图例.R
├── 2024年/
│   ├── 2024.02.07 多分类的条形图并标记数字/
│   │   ├── .RData
│   │   ├── .Rhistory
│   │   ├── .Rproj.user/
│   │   │   ├── C6560784/
│   │   │   │   ├── pcs/
│   │   │   │   │   ├── files-pane.pper
│   │   │   │   │   ├── source-pane.pper
│   │   │   │   │   ├── windowlayoutstate.pper
│   │   │   │   │   └── workbench-pane.pper
│   │   │   │   ├── rmd-outputs
│   │   │   │   ├── saved_source_markers
│   │   │   │   └── sources/
│   │   │   │       ├── per/
│   │   │   │       │   └── t/
│   │   │   │       │       ├── AF66B3A8
│   │   │   │       │       └── AF66B3A8-contents
│   │   │   │       └── prop/
│   │   │   │           ├── 0418CBEF
│   │   │   │           ├── 048F8B4D
│   │   │   │           ├── 1A4EC317
│   │   │   │           └── INDEX
│   │   │   └── shared/
│   │   │       └── notebooks/
│   │   │           ├── patch-chunk-names
│   │   │           └── paths
│   │   ├── project.Rproj
│   │   └── 多分类条形图并标记数字.R
│   ├── 2024.02.12 TiKZ绘图/
│   │   ├── tutorial.aux
│   │   ├── tutorial.log
│   │   └── tutorial.tex
│   └── 2024.02.19 柱状图+分面/
│       ├── .Rproj.user/
│       │   ├── C6560784/
│       │   │   ├── pcs/
│       │   │   │   ├── files-pane.pper
│       │   │   │   ├── source-pane.pper
│       │   │   │   ├── windowlayoutstate.pper
│       │   │   │   └── workbench-pane.pper
│       │   │   └── sources/
│       │   │       ├── prop/
│       │   │       │   ├── 23624D4E
│       │   │       │   └── INDEX
│       │   │       └── session-fd2b58b4/
│       │   │           ├── 1FF7DD4B
│       │   │           ├── 1FF7DD4B-contents
│       │   │           └── lock_file
│       │   └── shared/
│       │       └── notebooks/
│       │           ├── patch-chunk-names
│       │           └── paths
│       ├── demo_dat.csv
│       ├── project.Rproj
│       └── 柱状图+分面.R
├── README.md
├── 公众号资料/
│   ├── nice主题.docx
│   ├── ~$创意大气述职报告.pptx
│   ├── 封面制作模板.pptx
│   ├── 年度总结/
│   │   └── 2020年公众号文章汇总.md
│   ├── 数据科学模板.docx
│   └── 资料获取.md
└── 资料分享.md

================================================
FILE CONTENTS
================================================

================================================
FILE: 2020年/2020.07.13Network_igraph/edge.csv
================================================
V1,V2
24,25
1,23
13,18
23,24
14,25
9,14
14,26
16,22
1,16
2,23
18,32
9,17
24,31
7,8
2,21
23,32
7,35
19,27
7,23
1,19
3,17
10,11
15,30
10,20
15,18
11,34
16,20
10,33
6,34
12,21
4,29
29,34
5,30
13,28
5,36


================================================
FILE: 2020年/2020.07.13Network_igraph/graph.r
================================================


library(igraph)

#1)导入边数据和节点数据=======
setwd("")
edges <- read.table('edge.csv', header=T, sep=',') #导入边数据，里面可以包含每个边的频次数据或权重
vertices <- read.table('vertices.csv', header=T, sep=',') #导入节点数据，可以包含属性数据，如分类
edges ;vertices
#2)导入数据后，要转化成图数据才能用R作图，不同数据格式用不同方式=======
graph <- graph_from_data_frame(edges, directed = F, vertices=vertices) #directed = TRUE表示有方向,如果不需要点数据，可以设置vertices=NULL

#生成方式1（没有颜色分类）：======
igraph.options(vertex.size=3, vertex.label=NA, edge.arrow.size=0.5)

V(graph)$color <- colrs[V(graph)$color]
plot(graph,  
     layout=layout.reingold.tilford(graph,circular=T),  #layout.fruchterman.reingold表示弹簧式发散的布局，
     #其他还有环形布局layout.circle，分层布局layout.reingold.tilford，中心向外发散layout.reingold.tilford(graph,circular=T) ，核心布局layout_as_star，大型网络可视化layout_with_drl
     vertex.size=5,     #节点大小  
     vertex.shape='circle',    #节点不带边框none,,圆形边框circle,方块形rectangle  
     vertex.color="lightgreen",#设置颜色，其他如red,blue,cyan,yellow等
     vertex.label=vertices$name, #NULL表示不设置，为默认状态，即默认显示数据中点的名称，可以是中文。如果是NA则表示不显示任何点信息	 
     vertex.label.cex=0.8,    #节点字体大小  
     vertex.label.color='black',  #节点字体颜色,red  
     vertex.label.dist=0.4,   #标签和节点位置错开
     edge.arrow.size=0,#连线的箭头的大小,若为0即为无向图，当然有些数据格式不支持有向图  
     edge.width = 0.5, #连接线宽度
     edge.label=NA, #不显示连接线标签，默认为频次
     edge.color="gray")  #连线颜色 

#生成方式2（有颜色分类）：==========
#set.seed() #生成随机数，这样图的布局就会可重复，而不是每次生成的时候都变
#l<-layout.fruchterman.reingold(graph) #设置图的布局方式为弹簧式发散的布局
l = layout.reingold.tilford(graph,circular=T)

#具体修改过程
V(graph)$size <- 8  #节点大小与点中心度成正比，中心度即与该点相连的点的总数
colrs <- c('#0096ff', "lightblue", "azure3","firebrick1")
V(graph)$color <- colrs[vertices$color] #根据类型设置颜色,按照类型分组
V(graph)$label.color <- 'black' #设置节点标记的颜色
V(graph)$label <- V(graph)$name 
#E(graph)$width <- E(graph)$fre #根据频次列设置边宽度
#E(graph)$label <- E(graph)$fre #根据频次列设置边标签
E(graph)$arrow.size=0.3 #设置箭头大小
#生成图
plot(graph, layout=l)



================================================
FILE: 2020年/2020.07.13Network_igraph/vertices.csv
================================================
id,name,color
1,,1
2,,1
3,,2
4,ҵ,3
5,ͳƵʵ,1
6,ְҵķչ,3
7,,4
8,һ,4
9,,2
10,ʵ,3
11,¡,3
12,ݷ,1
13,ԪͳƷ,1
14,,2
15,˼ͷ,1
16,ߵȴ(),1
17,ߵȴ(һ),2
18,ѧ,1
19,ͳƷ뽨ģ,1
20,˼,3
21,ʱз,1
22,ʵ,1
23,ͳ,1
24,ѧ(),2
25,ѧ(),2
26,ѧ(һ),2
27,ͳƷ׫д,1
28,ͳƽģ,1
29,,3
30,ͳƶӦ,1
31,ͳѧ,2
32,Ӧ,1
33,й˼,3
34,йִʷҪ,3
35,רҵ,4
36,רҵʵϰ,1


================================================
FILE: 2020年/2020.07.14China_map/china_map.r
================================================
#9.1中国疫情图============
#（https://my.oschina.net/u/2306127/blog/473842）
library(mapdata)
library(maptools)
library(ggplot2)
library(plyr)
china_map = readShapePoly("bou2_4p.shp")#导入shp格式的中国地图
x<-china_map@data
xs<-data.frame(x,id=seq(0:924)-1)#地图中共计有925个地域信息
china_map1<-fortify(china_map)  #转化为数据框
china_map_data<-join(china_map1,xs,type="full")#基于id进行连接
a = data.frame(unique(china_map@data$NAME))
#准备数据
mydata<-read.csv("data_dt.csv",header=T,as.is=T)
china_data <- join(china_map_data, mydata, type="full")#基于NAME字段进行连接，NAME字段来自于地图文件中

#3、绘制地图

## 版本一(无省份名称的当日各省确认人数)
ggplot(china_data, aes(x = long, y = lat, group = group, fill = people)) +    
    geom_polygon(colour="grey40") +
    scale_fill_gradient(low="white",high="steelblue") +#指定渐变填充色，可使用RGB
    theme( #清除不需要的元素
        panel.grid = element_blank(),
        panel.background = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        axis.title = element_blank(),
        legend.position = c(0.2,0.3)
      )    

## 版本二（有省份名称的当日各省确认人数）
midpos <- function(x) mean(range(x,na.rm=TRUE)) #取形状内的平均坐标
centres <- ddply(china_data,.(NAME),colwise(midpos,.(long,lat)))

ggplot(china_data,aes(long,lat))+      #此处语法与前面不同，参考ggplot2一书P85
    geom_polygon(aes(group=group,fill=people),colour="black")+
    scale_fill_gradient(low="white",high="steelblue") +
    geom_text(aes(label=NAME),data=centres) +
    theme(
         panel.grid = element_blank(),
        panel.background = element_blank(),
         axis.text = element_blank(),
         axis.ticks = element_blank(),
         axis.title = element_blank()
        )




================================================
FILE: 2020年/2020.07.14China_map/data_dt.csv
================================================
NAME,ratio,people,province
ʡ,68135,1405,
㶫ʡ,1637,732,㶫
ʡ,1276,667,
㽭ʡ,1269,572,㽭
ʡ,1019,532,
ʡ,991,510,
ʡ,947,452,
ʡ,932,340,
,905,322,
ɽʡ,792,297,ɽ
Ϻ,707,270,Ϻ
ʡ,654,242,
Ĵʡ,589,205,Ĵ
,582,201,
ʡ,363,176,
ӱʡ,349,166,ӱ
ʡ,320,154,
׳,254,103,
ɹ,238,93,ɹ
ʡ,198,84,
ɽʡ,198,77,ɽ
ʡ,185,54,
ʡ,171,49,
ʡ,163,45,
ʡ,155,45,
ʡ,154,42,
ʡ,147,40,
½ά,76,27,½
Ļ,75,9,
ຣʡ,18,7,ຣ
,1,5,
ر,1197,1,
̨ʡ,447,100,̨


================================================
FILE: 2020年/2020.07.15World_map/Country_Data.csv
================================================
"Country	",Scale
Aruba,
Afghanistan,14337184953
Angola,122686093
Anguilla,
Albania,52348966
Finland,
Andorra,
United Arab Emirates,1420302
Argentina,4793871
Armenia,118083985
American Samoa,1706789
Antarctica,
Australia,
French Southern and Antarctic Lands,
Antigua,1024000
Barbuda,
Austria,
Azerbaijan,99645874
Burundi,71626580
Belgium,4143675
Benin,72834739
Burkina Faso,291773051
Bangladesh,657712016
Bulgaria,40346336
Bahrain,22984534
Bahamas,5530627
Bosnia and Herzegovina,113820206
Saint Barthelemy,
Belarus,33366555
Belize,19013177
Bermuda,
Bolivia,40456906
Brazil,47003710
Barbados,1195377
Brunei,336196
Bhutan,827607
Botswana,108084913
Central African Republic,163301353
Canada,25171012
Switzerland,1182463
Chile,9166231
China,109468893
Ivory Coast,230099050
Cameroon,107714146
Democratic Republic of the Congo,774104905
Republic of Congo,9313958
Cook Islands,
Colombia,1008396303
Comoros,1162798
Cape Verde,
Costa Rica,23807023
Cuba,19674492
Curacao,
Cayman Islands,
Cyprus,5878004
Czech Republic,12429517
Germany,627585
Djibouti,43437611
Dominica,958000
Denmark,
Dominican Republic,105256853
Algeria,21999637
Ecuador,66630571
Egypt,285844459
Eritrea,1113076
Canary Islands,
Spain,5227911
Estonia,12470092
Ethiopia,1421649545
Fiji,4602905
Falkland Islands,
Reunion,
Mayotte,
French Guiana,
Martinique,
Guadeloupe,
France,1464862
Faroe Islands,
Micronesia,302553791
Gabon,13844417
UK,244658
Georgia,495691015
Guernsey,
Ghana,307723748
Guinea,62034795
Gambia,4398110
Guinea-Bissau,3299809
Equatorial Guinea,335973
Greece,1347158
Grenada,991550
Greenland,
Guatemala,304130054
Guam,
Guyana,18365479
Heard Island,
Honduras,197701659
Croatia,14439801
Haiti,702753017
Hungary,32842750
Indonesia,425241264
Isle of Man,
India,261495537
Cocos Islands,
Christmas Island,
Chagos Archipelago,
Ireland,7389871
Iran,2064968
Iraq,817392816
Iceland,
Israel,6249503135
Italy,2758463
San Marino,
Jamaica,63406681
Jersey,
Jordan,2229268225
Japan,5583196
Siachen Glacier,
Kazakhstan,284994227
Kenya,1757291419
Kyrgyzstan,142055012
Cambodia,215972560
Kiribati,
Nevis,
Saint Kitts,
South Korea,2457582
Kosovo,133600340
Kuwait,92372
Laos,50370856
Lebanon,639875848
Liberia,456462263
Libya,70061408
Saint Lucia,
Liechtenstein,
Sri Lanka,81459320
Lesotho,108025026
Lithuania,18158581
Luxembourg,
Latvia,9766796
Saint Martin,
Morocco,170856475
Monaco,
Moldova,186466454
Madagascar,125564678
Maldives,4842109
Mexico,557646557
Marshall Islands,128170833
Macedonia,56725720
Mali,361809062
Malta,1602646
Myanmar,225938871
Montenegro,9798308
Mongolia,30992965
Northern Mariana Islands,
Mozambique,777992540
Mauritania,41288351
Montserrat,
Mauritius,2569586
Malawi,440212898
Malaysia,25398180
Namibia,231306565
New Caledonia,
Niger,217698383
Norfolk Island,
Nigeria,1165516940
Nicaragua,94025520
Niue,
Bonaire,
Sint Eustatius,
Saba,
Netherlands,80365
Norway,
Nepal,169303579
Nauru,
New Zealand,7154
Oman,26659268
Pakistan,1972822305
Panama,24522344
Pitcairn Islands,
Peru,304607830
Philippines,728003613
Palau,28769020
Papua New Guinea,14424130
Poland,93171533
Puerto Rico,
North Korea,3997348
Madeira Islands,
Azores,
Portugal,916654
Paraguay,36856601
Palestine,884730488
French Polynesia,
Qatar,3098
Romania,76362825
Russia,297186633
Rwanda,343709226
Western Sahara,
Saudi Arabia,713501
Sudan,412033512
South Sudan,1480315209
Senegal,389694501
Singapore,1040834
South Sandwich Islands,
South Georgia,
Saint Helena,
Ascension Island,
Solomon Islands,2817980
Sierra Leone,53212837
El Salvador,153467672
Somalia,699889670
Saint Pierre and Miquelon,
Serbia,75712324
Sao Tome and Principe,933472
Suriname,1453709
Slovakia,4589332
Slovenia,7086955
Sweden,45798
Swaziland,91046517
Sint Maarten,
Seychelles,552879
Syria,1581881739
Turks and Caicos Islands,
Chad,245554164
Togo,7650651
Thailand,132772482
Tajikistan,132816030
Turkmenistan,17402234
Timor-Leste,72044958
Tonga,3221962
Trinidad,
Tobago,
Tunisia,137888152
Turkey,190530825
Taiwan,2722516
Tanzania,1187669230
Uganda,1058084960
Ukraine,500475016
Uruguay,2884931
USA,
Uzbekistan,104348366
Vatican,
Grenadines,
Saint Vincent,
Venezuela,14047441
Virgin Islands,
Vietnam,240210318
Vanuatu,5726365
Wallis and Futuna,
Samoa,
Yemen,485596030
South Africa,1072163281
Zambia,1011659748
Zimbabwe,357528936


================================================
FILE: 2020年/2020.07.15World_map/cores.r
================================================
#========================================================================
#============================= Task =====================================
#========================================================================

setwd("C:/Users/DELL/Desktop/wechat/2020.07.15World_map")

# 世界疫情图==========
library(maps)
library(ggplot2)
library(RColorBrewer)
library(plyr)
colormap<-c(rev(brewer.pal(9,"Greens")[c(4,6)]), brewer.pal(9,"YlOrRd")[c(3,4,5,6,7,8,9)])
mydata1<-read.csv("Country_Data.csv",stringsAsFactors=FALSE)#这个是全球数据
names(mydata1)=c("Country","Scale") #重新命名
mydata2 =  read.csv("world_data.csv",header=TRUE)  #我们的数据（疫情）
head(mydata2)
#将两个表格匹配
mydata <- join(mydata1, mydata2, type="full") 
head(mydata)
#把ratio参数设置成分类型，以便于好绘制
mydata$fan<-cut(mydata$ratio,
breaks=c(min(mydata$million,na.rm=TRUE),
0,1000,5000,10000,50000,200000,500000,2000000,
max(mydata$ratio,na.rm=TRUE)),
labels=c(" <=0","0~1000","1000~5000","5000~10000","10000~50000","50000~200000",
"200000~500000","500000~2000000"," >=2000000"),
order=TRUE)
#定义地图用全球的
world_map <- map_data("world")
#绘图
ggplot()+
geom_map(data=mydata,aes(map_id=Country,fill=fan),map=world_map)+
geom_path(data=world_map,aes(x=long,y=lat,group=group),colour="black",size=.2)+
scale_y_continuous(breaks=(-3:3)*30) +
scale_x_continuous(breaks=(-6:6)*30) +
scale_fill_manual(name="Ratio",values= colormap,na.value="grey75")+
guides(fill=guide_legend(reverse=TRUE)) +
theme_minimal()



================================================
FILE: 2020年/2020.07.15World_map/world_data.csv
================================================
Country,long,lat,ratio
Andorra,1.601554,42.546245,855
United Arab Emirates,53.847818,23.424076,NA
Afghanistan,67.709953,33.93911,30175
Antigua and Barbuda,-61.796428,17.060816,26
Anguilla,-63.068615,18.220554,3
Albania,20.168331,41.153332,2192
Armenia,45.038189,40.069099,22488
Netherlands Antilles,-69.060087,12.226079,NA
Angola,17.873887,-11.202692,189
Antarctica,-0.071389,-75.250973,NA
Argentina,-63.616672,-38.416097,49851
American Samoa,-170.132217,-14.270972,NA
Austria,14.550072,47.516231,17477
Australia,133.775136,-25.274398,7558
Aruba,-69.968338,12.52111,101
Azerbaijan,47.576927,40.143105,14852
Bosnia and Herzegovina,17.679076,43.915886,NA
Barbados,-59.543198,13.193887,97
Bangladesh,90.356331,23.684994,126606
Belgium,4.469936,50.503887,61007
Burkina Faso,-1.561593,12.238333,NA
Bulgaria,25.48583,42.733883,4242
Bahrain,50.637772,25.930414,23570
Burundi,29.918886,-3.373056,144
Benin,2.315834,9.30769,1017
Bermuda,-64.75737,32.321384,141
Brunei,114.727669,4.535277,141
Bolivia,-63.588653,-16.290154,28631
Brazil,-51.92528,-14.235004,1193609
Bahamas,-77.39628,25.03428,104
Bhutan,90.433601,27.514162,70
Bouvet Island,3.413194,-54.423199,NA
Botswana,24.684866,-22.328474,89
Belarus,27.953389,53.709807,60382
Belize,-88.49765,17.189877,23
Canada,-106.346771,56.130366,102242
Cocos [Keeling] Islands,96.870956,-12.164165,NA
Congo [DRC],21.758664,-4.038333,NA
Central African Republic,20.939444,6.611111,NA
Congo [Republic],15.827659,-0.228021,NA
Switzerland,8.227512,46.818188,31428
C?te d'Ivoire,-5.54708,7.539989,NA
Cook Islands,-159.777671,-21.236736,NA
Chile,-71.542969,-35.675147,254416
Cameroon,12.354722,7.369722,12592
China,104.195397,35.86166,85260
Colombia,-74.297333,4.570868,77113
Costa Rica,-83.753428,9.748917,2515
Cuba,-77.781167,21.521757,2319
Cape Verde,-24.013197,16.002082,NA
Christmas Island,105.690449,-10.447525,NA
Cyprus,33.429859,35.126413,991
Czech Republic,15.472962,49.817492,NA
Germany,10.451526,51.165691,193987
Djibouti,42.590275,11.825138,4630
Denmark,9.501785,56.26392,12636
Dominica,-61.370976,15.414999,NA
Dominican Republic,-70.162651,18.735693,NA
Algeria,1.659626,28.033886,12248
Ecuador,-78.183406,-1.831239,51643
Estonia,25.013607,58.595272,1984
Egypt,30.802498,26.820553,59561
Western Sahara,-12.885834,24.215527,9
Eritrea,39.782334,15.179384,143
Spain,-3.74922,40.463667,247086
Ethiopia,40.489673,9.145,5034
Finland,25.748151,61.92411,7172
Fiji,179.414413,-16.578193,18
Falkland Islands [Islas Malvinas],-59.523613,-51.796253,NA
Micronesia,150.550812,7.425554,NA
Faroe Islands,-6.911806,61.892635,187
France,2.213749,46.227638,161348
Gabon,11.609444,-0.803689,4956
United Kingdom,-3.435973,55.378051,306862
Grenada,-61.604171,12.262776,23
Georgia,43.356892,42.315407,917
French Guiana,-53.125782,3.933889,2827
Guernsey,-2.585278,49.465691,252
Ghana,-1.023194,7.946527,15013
Gibraltar,-5.345374,36.137741,176
Greenland,-42.604303,71.706936,13
Gambia,-15.310139,13.443182,42
Guinea,-9.696645,9.945587,5174
Guadeloupe,-62.067641,16.995971,174
Equatorial Guinea,10.267895,1.650801,NA
Greece,21.824312,39.074208,3310
South Georgia and the South Sandwich Islands,-36.587909,-54.429579,NA
Guatemala,-90.230759,15.783471,14819
Guam,144.793731,13.444304,226
Guinea-Bissau,-15.180413,11.803749,1556
Guyana,-58.93018,4.860416,206
Gaza Strip,34.308825,31.354676,NA
Hong Kong,114.109497,22.396428,NA
Heard Island and McDonald Islands,73.504158,-53.08181,NA
Honduras,-86.241905,15.199999,14571
Croatia,15.2,45.1,2483
Haiti,-72.285215,18.971187,5429
Hungary,19.503304,47.162494,4123
Indonesia,113.921327,-0.789275,51087
Ireland,-8.24389,53.41291,25396
Israel,34.851612,31.046051,22139
Isle of Man,-4.548056,54.236107,NA
India,78.96288,20.593684,473634
British Indian Ocean Territory,71.876519,-6.343194,NA
Iraq,43.679291,33.223191,39139
Iran,53.688046,32.427908,215096
Iceland,-19.020835,64.963051,1824
Italy,12.56738,41.87194,239410
Jersey,-2.13125,49.214439,308
Jamaica,-77.297508,18.109581,678
Jordan,36.238414,30.585164,1071
Japan,138.252924,36.204824,18212
Kenya,37.906193,-0.023559,5384
Kyrgyzstan,74.766098,41.20438,3954
Cambodia,104.990963,12.565679,130
Kiribati,-168.734039,-3.370417,NA
Comoros,43.872219,-11.875001,265
Saint Kitts and Nevis,-62.782998,17.357822,15
North Korea,127.510093,40.339852,NA
South Korea,127.766922,35.907757,NA
Kuwait,47.481766,29.31166,42788
Cayman Islands,-80.566956,19.513469,NA
Kazakhstan,66.923684,48.019573,19285
Laos,102.495496,19.85627,19
Lebanon,35.862285,33.854721,1644
Saint Lucia,-60.978893,13.909444,NA
Liechtenstein,9.555373,47.166,86
Sri Lanka,80.771797,7.873054,NA
Liberia,-9.429499,6.428055,662
Lesotho,28.233608,-29.609988,17
Lithuania,23.881275,55.169438,1806
Luxembourg,6.129583,49.815273,4140
Latvia,24.603189,56.879635,1111
Libya,17.228331,26.3351,670
Morocco,-7.09262,31.791702,11279
Monaco,7.412841,43.750298,101
Moldova,28.369885,47.411631,15078
Montenegro,19.37439,42.708678,389
Madagascar,46.869107,-18.766947,1829
Marshall Islands,171.184478,7.131474,NA
Macedonia [FYROM],21.745275,41.608635,NA
Mali,-3.996166,17.570692,2005
Myanmar [Burma],95.956223,21.913965,NA
Mongolia,103.846656,46.862496,213
Macau,113.543873,22.198745,NA
Northern Mariana Islands,145.38469,17.33083,31
Martinique,-61.024174,14.641528,236
Mauritania,-10.940835,21.00789,3519
Montserrat,-62.187366,16.742498,11
Malta,14.375416,35.937496,668
Mauritius,57.552152,-20.348404,341
Maldives,73.22068,3.202778,2261
Malawi,34.301525,-13.254308,941
Mexico,-102.552784,23.634501,196847
Malaysia,101.975766,4.210484,8600
Mozambique,35.529562,-18.665695,762
Namibia,18.49041,-22.95764,76
New Caledonia,165.618042,-20.904305,21
Niger,8.081666,17.607789,1051
Norfolk Island,167.954712,-29.040835,NA
Nigeria,8.675277,9.081999,22020
Nicaragua,-85.207229,12.865416,2170
Netherlands,5.291266,52.132633,49914
Norway,8.468946,60.472024,8788
Nepal,84.124008,28.394857,11162
Nauru,166.931503,-0.522778,NA
Niue,-169.867233,-19.054445,NA
New Zealand,174.885971,-40.900557,1519
Oman,55.923255,21.512583,34902
Panama,-80.782127,8.537981,28030
Peru,-75.015152,-9.189967,264689
French Polynesia,-149.406843,-17.679742,60
Papua New Guinea,143.95555,-6.314993,NA
Philippines,121.774017,12.879721,33069
Pakistan,69.345116,30.375321,192970
Poland,19.145136,51.919438,33119
Saint Pierre and Miquelon,-56.27111,46.941936,NA
Pitcairn Islands,-127.439308,-24.703615,NA
Puerto Rico,-66.590149,18.220833,6877
Palestinian Territories,35.233154,31.952162,NA
Portugal,-8.224454,39.399872,40415
Palau,134.58252,7.51498,NA
Paraguay,-58.443832,-23.442503,1528
Qatar,51.183884,25.354826,91838
Runion,55.536384,-21.115141,NA
Romania,24.96676,45.943161,NA
Serbia,21.005859,44.016521,13372
Russia,105.318756,61.52401,613994
Rwanda,29.873888,-1.940278,830
Saudi Arabia,45.079162,23.885942,170639
Solomon Islands,160.156194,-9.64571,NA
Seychelles,55.491977,-4.679574,11
Sudan,30.217636,12.862807,8889
Sweden,18.643501,60.128161,62324
Singapore,103.819836,1.352083,42736
Saint Helena,-10.030696,-24.143474,NA
Slovenia,14.995463,46.151241,1547
Svalbard and Jan Mayen,23.670272,77.553604,NA
Slovakia,19.699024,48.669026,1630
Sierra Leone,-11.779889,8.460555,NA
San Marino,12.457777,43.94236,NA
Senegal,-14.452362,14.497401,6233
Somalia,46.199616,5.152149,2835
Suriname,-56.027783,3.919305,357
S?o Tom and Prncipe,6.613081,0.18636,NA
El Salvador,-88.89653,13.794185,5336
Syria,38.996815,34.802075,231
Swaziland,31.465866,-26.522503,690
Turks and Caicos Islands,-71.797928,21.694025,NA
Chad,18.732207,15.454166,860
French Southern Territories,69.348557,-49.280366,NA
Togo,0.824782,8.619543,583
Thailand,100.992541,15.870032,3158
Tajikistan,71.276093,38.861034,5630
Tokelau,-171.855881,-8.967363,NA
Timor-Leste,125.727539,-8.874217,NA
Turkmenistan,59.556278,38.969719,NA
Tunisia,9.537499,33.886917,1160
Tonga,-175.198242,-21.178986,NA
Turkey,35.243322,38.963745,191657
Trinidad and Tobago,-61.222503,10.691803,123
Tuvalu,177.64933,-7.109535,NA
Taiwan,120.960515,23.69781,NA
Tanzania,34.888822,-6.369028,509
Ukraine,31.16558,48.379433,40008
Uganda,32.290275,1.373333,805
U.S. Minor Outlying Islands,NA,NA,NA
United States,-95.712891,37.09024,NA
Uruguay,-55.765835,-32.522779,902
Uzbekistan,64.585262,41.377491,6990
Vatican City,12.453389,41.902916,NA
Saint Vincent and the Grenadines,-61.287228,12.984305,29
Venezuela,-66.58973,6.42375,4366
British Virgin Islands,-64.639968,18.420695,NA
U.S. Virgin Islands,-64.896335,18.335765,NA
Vietnam,108.277199,14.058324,NA
Vanuatu,166.959158,-15.376706,NA
Wallis and Futuna,-177.156097,-13.768752,NA
Samoa,-172.104629,-13.759029,NA
Kosovo,20.902977,42.602636,2216
Yemen,48.516388,15.552727,1015
Mayotte,45.166244,-12.8275,2467
South Africa,22.937506,-30.559482,111796
Zambia,27.849332,-13.133897,1497
Zimbabwe,29.154857,-19.015438,530
USA,NA,NA,2462554
Bosnia,NA,NA,3676
Burkina faso,NA,NA,934
Burma,NA,NA,293
Bvi,NA,NA,8
Cape verde,NA,NA,999
Cayman islands,NA,NA,195
Congo (Bu),NA,NA,1087
Congo (DRC),NA,NA,6213
Curacao,NA,NA,20
Czech,NA,NA,10780
Diamond Princess,NA,NA,712
Dominic,NA,NA,18
Dominican,NA,NA,28631
East timor,NA,NA,24
Equatorial guinea,NA,NA,1664
Falkland islands,NA,NA,13
Isle,NA,NA,336
Ivory coast,NA,NA,8164
Korea,NA,NA,12563
Lucia,NA,NA,19
Netherlands Caribbean,NA,NA,7
Non -,NA,NA,3099
North Macedonia,NA,NA,5445
Palestinian,NA,NA,1328
Papua new guinea,NA,NA,9
Reunion,NA,NA,426
Romanian,NA,NA,25286
Saint Barthelemi,NA,NA,6
Saint-Pierre and Miquelon Islands,NA,NA,1
San marino,NA,NA,698
Sao Tome and Principe,NA,NA,710
Sierra leone,NA,NA,1354
South Sudan,NA,NA,1942
Sri lanka,NA,NA,2007
"St. Martin, France",NA,NA,117
"St. Martin, Netherlands",NA,NA,77
Tci,NA,NA,14
U.A.E,NA,NA,46563
Usvi,NA,NA,76
Vatican,NA,NA,12
Viet Nam,NA,NA,352


================================================
FILE: 2020年/2020.07.22Movielense/ch11-task.Rmd
================================================
---
title: "第11章作业MovieLense数据集分析"
author:
documentclass: ctexart
output:
  word_document: default
  html_document: default
  pdf_document: default
  rticles::ctex:
    fig_caption: yes
    number_sections: yes
    toc: yes
classoption: "hyperref,"
---

# 前言

R的recommenderlab包可以实现协同过滤算法。这个包中有许多关于推荐算法建立、处理及可视化的函数。选用recommenderlab包中内置的MovieLense数据集进行分析，该数据集收集了网站MovieLens（movielens.umn.edu）从1997年9月19日到1998年4月22日的数据，包括943名用户对1664部电影的评分。

```{r message=FALSE, warning=FALSE}
library(recommenderlab)
library(ggplot2)
```



# 数据处理与数据探索性分析
```{r  message=FALSE, warning=FALSE}
data(MovieLense)
image(MovieLense)
# 获取评分
ratings.movie <- data.frame(ratings = getRatings(MovieLense))
summary(ratings.movie$ratings)
ggplot(ratings.movie, aes(x = ratings)) + 
  geom_histogram(fill = "beige", color = "black",
    binwidth = 1, alpha = 0.7) + xlab("rating") + ylab("count")

```
利用`summary()`获取评分数据，可知最大值为5，最小值为1，平均值为3.53。并将其柱状图进行绘制，如下所示。


## 数据标准化

在进行数据分析前，利用`normalize()`我们将数据进行标准化，并进行绘制。
```{r message=FALSE, warning=FALSE}
ratings.movie1 <- data.frame(ratings = 
    getRatings(normalize(MovieLense, method = "Z-score")))
summary(ratings.movie1$ratings)

ggplot(ratings.movie1, aes(x = ratings)) + 
  geom_histogram(fill = "beige", color = "black",
    alpha = 0.7) + xlab("rating") + ylab("count")
```

## 用户的电影点评数

我们还对用户的电影点评数进行描述性分析，具体结果如下所示。

```{r message=FALSE, warning=FALSE}
movie.count <- data.frame(count = rowCounts(MovieLense))
ggplot(movie.count, aes(x = count)) + 
  geom_histogram(fill = "beige", color = "black",
    alpha = 0.7) + xlab("counts of users") + ylab("counts of movies rated")

rating.mean <- data.frame(rating = colMeans(MovieLense))
ggplot(rating.mean, aes(x = rating)) + 
  geom_histogram(fill = "beige", color = "black",
    alpha = 0.7) + xlab("rating") + ylab("counts of movies ")
```

# 建立推荐模型与模型评估

对于realRatingMatrix有六种方法：IBCF(基于物品的推荐)、UBCF（基于用户的推荐）、SVD（矩阵因子化）、PCA（主成分分析）、 RANDOM（随机推荐）、POPULAR（基于流行度的推荐）。

模型评估主要使用：recommenderlab包中自带的评估方案，对应的函数是evaluationScheme，能够设置采用n-fold交叉验证还是简单的training/train分开验证，本文采用后一种方法，即将数据集简单分为training和test，在training训练模型，然后在test上评估。接下来我们使用三种不同技术进行构建推荐系统，并利用评估方案比较三种技术的好坏。
```{r  message=FALSE, warning=FALSE}
library(recommenderlab)
data(MovieLense)
scheme <- evaluationScheme(MovieLense, method = "split", 
  train = 0.9, k = 1,  given = 10, goodRating = 4)
algorithms <- list(popular = list(name = "POPULAR", 
  param = list(normalize = "Z-score")),
    ubcf = list(name = "UBCF", param = list(normalize = "Z-score", 
      method = "Cosine",nn = 25, minRating = 3)), 
  ibcf = list(name = "IBCF", param = list(normalize = "Z-score")))
results <- evaluate(scheme, algorithms, n = c(1, 3, 5, 10, 15, 20))
plot(results, annotate = 1:3, legend = "topleft") #ROC
plot(results, "prec/rec", annotate = 3)#precision-recall
```

```{r}
# 按照评价方案建立推荐模型
model.popular <- Recommender(getData(scheme, "train"), method = "POPULAR")
model.ibcf <- Recommender(getData(scheme, "train"), method = "IBCF")
model.ubcf <- Recommender(getData(scheme, "train"), method = "UBCF")
# 对推荐模型进行预测
predict.popular <- predict(model.popular, getData(scheme, "known"), type = "ratings")
predict.ibcf <- predict(model.ibcf, getData(scheme, "known"), type = "ratings")
predict.ubcf <- predict(model.ubcf, getData(scheme, "known"), type = "ratings")
# 做误差的计算
predict.err <- rbind(calcPredictionAccuracy(predict.popular, 
  getData(scheme, "unknown")),calcPredictionAccuracy(predict.ubcf, getData(scheme,
    "unknown")), calcPredictionAccuracy(predict.ibcf,getData(scheme, "unknown")))
rownames(predict.err) <- c("POPULAR", "UBCF", "IBCF")
predict.err
```


通过结果我们可以看到：基于流行度推荐系统对于本案例数据的效果最好，RMSE，MSE，MAE都是三者中的最小值。其次是基于用户的推荐，最后是基于项目协同过滤。


# 参考资料{-}

1. [Recommenderlab包实现电影评分预测(R语言)
](https://blog.csdn.net/seuponder/article/details/21040917)

2. [R语言：recommenderlab包的总结与应用案例](https://www.cnblogs.com/yjd_hycf_space/p/6702764.html)

3. [recommender system handbook](https://www.amazon.com/Recommender-Systems-Handbook-Francesco-Ricci/dp/1489976361)

4. [Item-Based Collaborative Filtering Recommendation Algorithms](http://www.ra.ethz.ch/CDstore/www10/papers/pdf/p519.pdf)

5. [recommenderlab: A Framework for Developing and Testing Recommendation Algorithms](https://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf)



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================================================
FILE: 2020年/2020.07.22坐标轴截断画图/code_truncation .R
================================================

##  [ggplot坐标轴截断](https://www.jianshu.com/p/0e4fa8849479)
library(ggplot2)
set.seed(2019-01-19)
d <- data.frame(
    x = 1:20, 
    y = c(rnorm(5) + 4, rnorm(5) + 20, rnorm(5) + 5, rnorm(5) + 22)
)

ggplot(d, aes(x, y)) + geom_col()

library(dplyr)

breaks = c(7, 17)
d$.type <- NA
d$.type[d$y < breaks[1]] = "small"
d$.type[d$y > breaks[2]] = "big"

d <- filter(d, .type == 'big') %>% 
    mutate(.type = "small", y = breaks[1]) %>% 
    bind_rows(d)

mymin = function(y) ifelse(y <= breaks[1], 0, breaks[2])               
p <- ggplot(d, aes(x, y)) + 
    geom_rect(aes(xmin = x - .4, xmax = x + .4, ymin = mymin(y), ymax = y)) +
    facet_grid(.type ~ ., scales = "free") + 
    theme(strip.text=element_blank())
p

## [R语言作图——坐标轴截断画图](http://blog.sina.com.cn/s/blog_6a4ee1ad0102x5at.html)

library(plotrix)

w <- c(75, 64.4, 47.3, 66.9, 456, 80.6, 70, 55.8, 57.9, 561, 58.6, 61.2, 50.3, 54.6, 57.8)
x <- c(1:15)
gap.barplot(w,gap=c(90,420),xaxlab=x,ytics=c(50,70,450,500),col=rainbow(15),xlab ="mumbers", ylab = "height", main="test image")

axis.break(2,90,breakcol="snow",style="gap")##去掉中间的那两道横线；
axis.break(2,90*(1+0.02),breakcol="black",style="slash")##在左侧Y轴把gap位置换成slash；
axis.break(4,90*(1+0.02),breakcol="black",style="slash")##在右侧Y轴把gap位置换成slash；

### 案例
twogrp<-c(rnorm(10)+4,rnorm(10)+20)
gap.barplot(twogrp,gap=c(8,16),xlab="Index",ytics=c(3,6,17,20),
  ylab="Group values",main="Barplot with gap")
gap.barplot(twogrp,gap=c(8,16),xlab="Index",ytics=c(3,6,17,20),
  ylab="Group values",horiz=TRUE,main="Horizontal barplot with gap")





================================================
FILE: 2020年/2020.07.22坐标轴截断画图/code_truncation .Rmd
================================================
---
title: "R中坐标轴截断的不同实现方式"
author:
  - 庄闪闪
documentclass: ctexart
output:
  word_document: default
  html_document: default
  pdf_document: default
  rticles::ctex:
    fig_caption: yes
    number_sections: yes
    toc: yes
classoption: "hyperref,"
---

# plotrix包

[R语言作图——坐标轴截断画图](http://blog.sina.com.cn/s/blog_6a4ee1ad0102x5at.html)

利用gap.barplot()进进行绘制，将gap参数设置为90，420进行y轴截断，可加入参数axis.break()对截断形状进行修改
```{r}
library(plotrix)

w <- c(75, 64.4, 47.3, 66.9, 456, 80.6, 70, 55.8, 57.9, 561, 58.6, 61.2, 50.3, 54.6, 57.8)
x <- c(1:15)
gap.barplot(w,gap=c(90,420),xaxlab=x,ytics=c(50,70,450,500),col=rainbow(15),xlab ="mumbers", ylab = "height", main="test image")

axis.break(2,90,breakcol="snow",style="gap")##去掉中间的那两道横线；
axis.break(2,90*(1+0.02),breakcol="black",style="slash")##在左侧Y轴把gap位置换成slash；
axis.break(4,90*(1+0.02),breakcol="black",style="slash")##在右侧Y轴把gap位置换成slash；

```

- 其他案例

```{r}
twogrp<-c(rnorm(10)+4,rnorm(10)+20)
gap.barplot(twogrp,gap=c(8,16),xlab="Index",ytics=c(3,6,17,20),
  ylab="Group values",main="Barplot with gap")
gap.barplot(twogrp,gap=c(8,16),xlab="Index",ytics=c(3,6,17,20),
  ylab="Group values",horiz=TRUE,main="Horizontal barplot with gap")

```


# ggplot包

[ggplot坐标轴截断](https://www.jianshu.com/p/0e4fa8849479)

```{r message=FALSE, warning=FALSE}
library(ggplot2)
set.seed(123)
d <- data.frame(
    x = 1:20, 
    y = c(rnorm(5) + 4, rnorm(5) + 20, rnorm(5) + 5, rnorm(5) + 22)
)

ggplot(d, aes(x, y)) + geom_col()

library(dplyr)

breaks = c(7, 17)
d$type <- NA
d$type[d$y < breaks[1]] = "small"
d$type[d$y > breaks[2]] = "big"

d <- filter(d, type == 'big') %>% 
    mutate(type = "small", y = breaks[1]) %>% 
    bind_rows(d)

mymin = function(y) ifelse(y <= breaks[1], 0, breaks[2])               
p <- ggplot(d, aes(x, y)) + 
    geom_rect(aes(xmin = x - .4, xmax = x + .4, ymin = mymin(y), ymax = y)) +
    facet_grid(type ~ ., scales = "free") + 
    theme(strip.text=element_blank())#去除text
p

```









================================================
FILE: 2020年/2020.07.24饼图与圆环图/pie.rmd
================================================
---
title: "饼状图"
date: 2020-08-05
author:
  - 庄闪闪
documentclass: ctexart
output:
  word_document: default
  pdf_document: default
  html_document: default
classoption: "hyperref,"
---

# 饼图

饼图（pie chart）被广泛地应用于各个领域，用于表示不同分类的占比情况，通过弧度大小来对比各种分类。饼图通过将一个圆饼按照分类的占比划分成多个切片，整个圆饼代表数据的总量，每个切片（圆弧）表示该分类占总体的比例，所有切片（圆弧）的加和等于100%。

## graphics绘制饼图

```{r message=FALSE, warning=FALSE}
library(RColorBrewer)  
library(dplyr)
library(graphics)
library(ggplot2)
```

```{r}
sessionInfo(
)
``` 

init.angle可设定饼图的初始角度，labels可添加标签。颜色用了brewer.pal函数，第一个参数为个数，第二个参数为名字，这里用的是BrBG，具体可help一下。
```{r message=FALSE, warning=FALSE}
df <- data.frame(value = c(24.20,30.90,12.50,12.30,8.10,12.10), 
                 group = c('LVS','SJM','MCE','Galaxy','MGM','Wynn'))
df <-arrange(df,value)

labs <- paste0(df$group," \n(", round(df$value/sum(df$value)*100,2), "%)") #标签
lab <- paste0(round(df$value/sum(df$value)*100,2), "%") #标签
pie(df$value,labels=labs, init.angle=90,col =  brewer.pal(nrow(df), "BrBG"),
    border="black")
pie(df$value,labels=lab, init.angle=90,col =  brewer.pal(nrow(df), "Blues"),
    border="black")
```

## ggplot2包绘制

使用R中ggplot2包的geom_bar()函数绘制堆积柱形图，然后将直角坐标系转换成极坐标系，
就可以显示为饼图，但还是需要使用geom_text()函数添加数据标签。注意的是：ymax，ymin也需要自己计算得到。
```{r message=FALSE, warning=FALSE}
df$fraction = df$value / sum(df$value)
df$ymax = cumsum(df$fraction)
df$ymin = c(0, head(df$ymax, n = -1))
ggplot(data = df, aes(fill = group, ymax = ymax, ymin = ymin, xmax = 4, xmin = 3)) +
  geom_rect(show.legend = F,alpha=0.8) +
  scale_fill_brewer(palette = 'Set3')+
  coord_polar(theta = "y") +
  labs(x = "", y = "", title = "",fill='地区') + 
  theme_light() +
  theme(panel.grid=element_blank()) + ## 去掉白色外框
  theme(axis.text=element_blank()) + ## 把图旁边的标签去掉
  theme(axis.ticks=element_blank()) + ## 去掉左上角的坐标刻度线
  theme(panel.border=element_blank()) + ## 去掉最外层的正方形边框
 geom_text(aes(x = 3.5, y = ((ymin+ymax)/2),label = labs) ,size=3.6)
```

但是可以看到：由于缺乏饼图与数据标签之间的引导线，总感觉美观度不够，所以推荐使用graphics 包的pie()函数绘制饼图。

# 圆环图

## ggplot绘制圆环图

在刚才的gglpot绘制饼图的基础上，我们只要再加一条代码即可完成：xlim(c(0, 5))，即将x轴范围控制在0-5。

```{r}
df$fraction = df$value / sum(df$value)
df$ymax = cumsum(df$fraction)
df$ymin = c(0, head(df$ymax, n = -1))
ggplot(data = df, aes(fill = group, ymax = ymax, ymin = ymin, xmax = 4, xmin = 3)) +
  geom_rect(show.legend = F,alpha=0.8) +
  scale_fill_brewer(palette = 'Set3')+
  coord_polar(theta = "y") +
  labs(x = "", y = "", title = "",fill='地区') + 
  xlim(c(0, 5)) +
  theme_light() +
  theme(panel.grid=element_blank()) + ## 去掉白色外框
  theme(axis.text=element_blank()) + ## 把图旁边的标签去掉
  theme(axis.ticks=element_blank()) + ## 去掉左上角的坐标刻度线
  theme(panel.border=element_blank()) + ## 去掉最外层的正方形边框
 geom_text(aes(x = 3.5, y = ((ymin+ymax)/2),label = labs) ,size=3.6)
```


# 复合饼图系列

散点复合饼图（compound scatter and pie chart）可以展示三个数据变量的信息：(x, y, P)，其中x
和y 决定气泡在直角坐标系中的位置，P 表示饼图的数据信息，决定饼图中各个类别的占比情况，
如图(a)所示。
气泡复合饼图（compound bubble and pie chart）可以展示四个数据变量的信息：(x, y, z, P)，其中
x 和y 决定气泡在直角坐标系中的位置，z 决定气泡的大小，P 表示饼图的数据信息，决定饼图中各
个类别的占比情况，如图(b)所示。


```{r}
library(ggplot2)
library(scatterpie)
library(RColorBrewer)

crime <- read.csv("C:/Users/DELL/Desktop/我的书籍/R语言数据可视化之美/第7章 局部整体型图表/crimeRatesByState2005.tsv",header = TRUE, sep = "\t", stringsAsFactors = F)
radius <- sqrt(crime$population / pi)
Max_radius<-max(radius)
Bubble_Scale<-0.1
crime$radius <- Bubble_Scale * radius/Max_radius

mydata<-crime[,c(2,4,3,5:8)]  #数据集构造
Col_Mean<-apply(mydata,2,mean)
Col_Sort<-sort(Col_Mean,index.return=TRUE,decreasing = TRUE)
mydata<-mydata[,Col_Sort$ix]
x<-(mydata$murder-min(mydata$murder))/(max(mydata$murder)-min(mydata$murder))+0.00001
y<-(mydata$Robbery-min(mydata$Robbery))/(max(mydata$Robbery)-min(mydata$Robbery))+0.00001

xlabel<-seq(0,10,2)
xbreak<-(xlabel-min(mydata$murder))/(max(mydata$murder)-min(mydata$murder))+0.00001
ylabel<-seq(0,260,50)
ybreak<-(ylabel-min(mydata$Robbery))/(max(mydata$Robbery)-min(mydata$Robbery))+0.00001

mydata2<-data.frame(x,y,radius=crime$radius)
mydata2<-cbind(mydata2,mydata)

Legnd_label<-colnames(mydata2)[4:10]
colnames(mydata2)[4:10]<-LETTERS[1:7]
```



## 散点复合饼图系列(a)
```{r}
ggplot() + 
  geom_scatterpie(aes(x=x, y=y,r=0.05), data=mydata2, cols=colnames(mydata2)[4:10],alpha=0.9,size=0.1) +
  scale_fill_manual(values=colorRampPalette(brewer.pal(7, "Set2"))(7),labels=Legnd_label)+
  #geom_scatterpie_legend(mydata2$radius, x=0.1, y=0.95, n=5,labeller=function(x) round((x* Max_radius/ Bubble_Scale)^2*pi))+
  #geom_scatterpie_legend(mydata2$radius, x=0.009758116, y=0.090868067, n=4,labeller=function(x) round((x* Max_radius/ Bubble_Scale)^2*pi))+
  scale_x_continuous(breaks=xbreak, labels=xlabel)+
  scale_y_continuous(breaks=ybreak, labels=ylabel)+
  xlab("murder")+
  ylab("Robbery")+
  coord_fixed()+
  theme(
    axis.title=element_text(size=15,face="plain",color="black"),
    axis.text = element_text(size=13,face="plain",color="black"),
    legend.title=element_text(size=15,face="plain",color="black"),
    legend.text = element_text(size=14,face="plain",color="black")
  )
```

## 散点复合饼图系列(b)
```{r}
ggplot() + 
  geom_scatterpie(aes(x=x, y=y,r=radius), data=mydata2, cols=colnames(mydata2)[4:10],alpha=0.9,size=0.25) +
  scale_fill_manual(values=colorRampPalette(brewer.pal(7, "Set2"))(7),labels=Legnd_label)+
  geom_scatterpie_legend(mydata2$radius, x=0.1, y=0.95, n=5,
                         labeller=function(x) round((x* Max_radius/ Bubble_Scale)^2*pi))+
  #geom_scatterpie_legend(mydata2$radius, x=0.009758116, y=0.090868067, n=4,labeller=function(x) round((x* Max_radius/ Bubble_Scale)^2*pi))+
  scale_x_continuous(breaks=xbreak, labels=xlabel)+
  scale_y_continuous(breaks=ybreak, labels=ylabel)+
  xlab("murder")+
  ylab("Robbery")+
  coord_fixed()+
  theme(
    axis.title=element_text(size=15,face="plain",color="black"),
    axis.text = element_text(size=13,face="plain",color="black"),
    legend.title=element_text(size=15,face="plain",color="black"),
    legend.text = element_text(size=14,face="plain",color="black")
  )

```



参考资料

https://zhuanlan.zhihu.com/p/69617844







================================================
FILE: 2020年/2020.07.29温大招生/code_ggimage.r
================================================
require(magick)
require(ggplot2)
require(ggplotify)
require(shadowtext)
require(ggimage)
windows()
x = image_read("C:/Users/DELL/Desktop/bing/3.jpg")
p = as.ggplot(x)
#图上嵌图
smu = "http://www.wzu.edu.cn/dfiles/9987/template/default/newzhuzhan/wenda/images/logo.jpg"
#smu = "C:/Users/DELL/Desktop/bing/2.jpg"

p <- p + geom_rect(xmin=.2, xmax=.8, ymin=.4,
              ymax=.6, fill='steelblue', alpha=.5) +
	geom_image(x=.5, y=.5, image=smu, size=.4)
#寄语
msg = "填志愿一定要遵从本心\n第一眼看到哪个，就报哪个！"

p  + geom_shadowtext(
          x=.5, y=.8, label=msg,
          size=10, color='firebrick')

#参考https://mp.weixin.qq.com/s?__biz=MzI5NjUyNzkxMg==&mid=2247489599&idx=1&sn=e689331bc5500222ec7d0c1c61942362&chksm=ec43a978db34206ed932e7e086db00f707e5cb9f9aa4e38254e40c3ef642dee5693a03532994&mpshare=1&scene=1&srcid=07293Jcp2dA2gLL6If2GGh8s&sharer_sharetime=1596004011941&sharer_shareid=ee38888b33e1d0070e96aeb454518587&key=6614a0a10b7b6719e9a2b6aaf9b5a70639efbf3c7c8e8e92b20a32c4badbf01c713092a60b32600156388a8c91a282772e89bcfe2a7eb0236746b8e10cee021cae0a3722f18222f708988e462583107f&ascene=1&uin=OTk1MTUyNzI2&devicetype=Windows+10+x64&version=62090529&lang=zh_CN&exportkey=A%2FUKJTXVcz2E27Ve0FcZuIk%3D&pass_ticket=GHX0j6fsfiEATjqcMrcVQQYSihtF3L6yDim2tm78a1XP0v2qucpofrFRF8%2Bz4zjt





================================================
FILE: 2020年/2020.08.14amazon/RECOM.Rmd
================================================
---
title: "亚马逊产品的推荐算法"
author:
documentclass: ctexart
always_allow_html: true
output:
  word_document: default
  pdf_document: default
  html_document: default
classoption: "hyperref,"
---

# 前言

R的recommenderlab包有许多关于推荐算法建立、处理及可视化的函数。上一次也利用这个包对Movielisence进行了分析，但是这个数据集来源于包本身。本文对于一个实际数据进行分析，该数据集来源于亚马逊网站，我们的目标是利用recommenderlab包构建相应的推荐系统，利用用户对产品的打分，做到给用户个性化推荐，包括

1. 构建多个不同方法的推荐系统，并进行比较，选取最优推荐系统。

2. 给出每个用户Top3的产品推荐。

3. 对于某个产品，预测出用户的评分情况。



# 数据处理与数据探索性分析
```{r  message=FALSE, warning=FALSE}
library(recommenderlab)
library(reshape)
```

## 数据处理

选取有用数据，包括：用户名，产品名称,打分情况构建新的数据集。并删除含有缺失值的行，最后数据仅剩下34621行。
```{r  message=FALSE, warning=FALSE}
data = read.csv("C:/Users/DELL/Desktop/2020.08.12亚马逊/data.csv",header = T)
data = data[,c(21,3,15)] #userid,product,rating
#删除na的行
data = na.omit(data)
dim(data)
names(data) = c('V1','V2','V3')
unique(data$V2)
```

## 数据探索性分析

利用`summary()`获取评分数据，可知最大值为5，最小值为1，平均值为4.58。并将其柱状图进行绘制，如下所示。

```{r message=FALSE, warning=FALSE}
summary(data[, 3])

barplot(prop.table(table(data[, 3])),col="skyblue",
  main="各评分分数占比情况",xlab="rating",ylab="proportion")

length(unique(data[, 2]))
factor(unique(data[, 2]))
barplot(table(data[, 2]),col="skyblue",
  main="",xlab="Product",ylab="Frequent")
names(table(data[, 2]))

fre = as.numeric(table(data[, 2]))
type = unique(data[, 2])

levels(type)[28]<-'B01E6AO69U'
fre = fre[fre!=0]
library(tidyverse)
product <- tibble(
  type = factor(unique(data[, 2])),
  freq = fre
)
p = ggplot(data = product, mapping = aes(
  x = fct_reorder(type, desc(freq)),
  y = freq ))
p + geom_col() +
  coord_flip()
plot(as.numeric(table(data[, 2])),)
```

## 数据格式构造

构造新的数据类型`realRatingMatrix`，以便更好的分析。生成一个以v1为行，v2为列的矩阵，使用v3进行填充。最后生成26762 x 39稀疏矩阵。
```{r message=FALSE, warning=FALSE}
mydata <- cast(data,V1~V2,value="V3",fun.aggregate=mean) 
#生成一个以v1为行，v2为列的矩阵，使用v3进行填充
mydata <- mydata[,-1] #第一列数字为序列，可以删除
class(mydata)
class(mydata)<-"data.frame"  #只选取data.frame
mydata<-as.matrix(mydata)
mydata<-as(mydata,"realRatingMatrix") 
mydata
```


# 模型评估与构建最优模型

- 对于realRatingMatrix有六种方法：IBCF(基于物品的推荐)、UBCF（基于用户的推荐）、SVD（矩阵因子化）、PCA（主成分分析）、 RANDOM（随机推荐）、POPULAR（基于流行度的推荐）。

## 模型评估

主要使用：`recommenderlab`包中自带的评估方案，对应的函数是`evaluationScheme`，能够设置采用`n-fold`交叉验证还是简单的`training/train`分开验证，本文采用后一种方法，即将数据集简单分为`training`和`test`，在`training`训练模型，然后在`test`上评估。接下来我们使用三种不同技术进行构建推荐系统，并利用评估方案比较三种技术的好坏。

- 在此我们比较三种方法的结果：IBCF(基于物品的推荐)，RANDOM（随机推荐）,POPULAR（基于流行度的推荐）。结果如下
```{r  message=FALSE, warning=FALSE}
scheme <- evaluationScheme(mydata, method = "split",
  train = 0.9, k = 1, given = 1, goodRating = 4)
  algorithms <- list(popular = list(name = "POPULAR",
  param = list(normalize = "Z-score")),random =
  list(name = "RANDOM",param = list(normalize = "Z-score", method = "Cosine",nn = 25, minRating = 3)),
  ibcf = list(name = "IBCF", param = list(normalize = "Z-score")))
results <- evaluate(scheme, algorithms, n = c(1, 3, 5, 10, 15, 20))

plot(results, annotate = 1:3, legend = "topleft") #ROC
plot(results, "prec/rec", annotate = 3)#precision-recall
```

- 按照评价方案建立推荐模型

```{r message=FALSE, warning=FALSE}
# 按照评价方案建立推荐模型
model.popular <- Recommender(getData(scheme, "train"), method = "POPULAR")
model.ibcf <- Recommender(getData(scheme, "train"), method = "IBCF")
model.random <- Recommender(getData(scheme, "train"), method = "RANDOM")
```

- 对推荐模型进行预测

```{r message=FALSE, warning=FALSE}
predict.popular <- predict(model.popular, getData(scheme, "known"), 
  type = "ratings")
predict.ibcf <- predict(model.ibcf, getData(scheme, "known"), 
  type = "ratings")
predict.random <- predict(model.random, getData(scheme, "known"), 
  type = "ratings")
```

- 做误差的计算

```{r message=FALSE, warning=FALSE}
predict.err <- rbind(calcPredictionAccuracy(predict.popular,
  getData(scheme, "unknown")),calcPredictionAccuracy(predict.random,
    getData(scheme,"unknown")),
  calcPredictionAccuracy(predict.ibcf,getData(scheme, "unknown")))
rownames(predict.err) <- c("POPULAR", "RANDOM", "IBCF")
predict.err
```


通过结果我们可以看到：三种方法的比较**基于随机推荐系统**对于本案例数据的效果最好，RMSE，MSE，MAE都是三者中的最小值。其次是基于物品的推荐，最后是基于流行度过滤。

## 构建最优模型

利用以上结果，我们构建最优模型：**基于随机推荐系统**。首先先对系数矩阵的行列名进行定义。
```{r message=FALSE, warning=FALSE}
	colnames(mydata)<-paste0("asins",1:dim(mydata)[2],sep="")
mydata.model <- Recommender(mydata[1:dim(mydata)[1]], method = "RANDOM")
```
数据处理完毕，接来下是进行预测，可以显示三个用户的Top3推荐列表.

### TopN推荐

给出users201，202，203每人前三个产品的推荐。
```{r message=FALSE, warning=FALSE}
##TopN推荐
mydata.predict1 <- predict(mydata.model,mydata[201:203], n = 3)
#n指数量
as(mydata.predict1,"list")
```

### 用户对产品的评分预测

给出前三个users对前6个产品的评分预测。
```{r message=FALSE, warning=FALSE}
mydata.predict2 <- predict(mydata.model, mydata[201:403], type = "ratings")
mydata.predict2
a = as(mydata.predict2, "matrix")[1:3, 1:6] 
knitr::kable(a)
```


# 参考资料{-}

1. [基于协同过滤算法的电影推荐系统](https://blog.csdn.net/weixin_44035441/article/details/90728889)

2. [R语言：recommenderlab包的总结与应用案例](https://www.cnblogs.com/yjd_hycf_space/p/6702764.html)

3. [recommenderlab: A Framework for Developing and Testing Recommendation Algorithms](https://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf)



================================================
FILE: 2020年/2020.08.14amazon/code.Rmd
================================================
---
title: "亚马逊产品的推荐算法"
author:
documentclass: ctexart
always_allow_html: true
output:
  word_document: default
  pdf_document: default
  html_document: default
classoption: "hyperref,"
---

# 前言

R的recommenderlab包有许多关于推荐算法建立、处理及可视化的函数。上一次也利用这个包对Movielisence进行了分析，但是这个数据集来源于包本身。本文对于一个实际数据进行分析，该数据集来源于亚马逊网站，我们的目标是利用recommenderlab包构建相应的推荐系统，利用用户对产品的打分，做到给用户个性化推荐，包括

1. 构建多个不同方法的推荐系统，并进行比较，选取最优推荐系统。

2. 给出每个用户Top3的产品推荐。

3. 对于某个产品，预测出用户的评分情况。



# 数据处理与数据探索性分析
```{r  message=FALSE, warning=FALSE}
library(recommenderlab)
library(reshape)
```

## 数据处理

选取有用数据，包括：用户名，产品名称,打分情况构建新的数据集。并删除含有缺失值的行，最后数据仅剩下34621行。
```{r  message=FALSE, warning=FALSE}
data = read.csv("C:/Users/DELL/Desktop/2020.08.12亚马逊/data.csv",header = T)
data = data[,c(21,3,15)] #userid,product,rating
#删除na的行
data = na.omit(data)
dim(data)
names(data) = c('V1','V2','V3')
unique(data$V2)
```

## 数据探索性分析

利用`summary()`获取评分数据，可知最大值为5，最小值为1，平均值为4.58。并将其柱状图进行绘制，如下所示。

```{r message=FALSE, warning=FALSE}
summary(data[, 3])

barplot(prop.table(table(data[, 3])),col="skyblue",
  main="各评分分数占比情况",xlab="rating",ylab="proportion")

length(unique(data[, 2]))
unique(data[, 2])
barplot(table(data[, 2]),col="skyblue",
  main="",xlab="Product",ylab="Frequent")
names(table(data[, 2]))
plot(as.numeric(table(data[, 2])),)
```

## 数据格式构造

构造新的数据类型`realRatingMatrix`，以便更好的分析。生成一个以v1为行，v2为列的矩阵，使用v3进行填充。最后生成26762 x 39稀疏矩阵。
```{r message=FALSE, warning=FALSE}
mydata <- cast(data,V1~V2,value="V3",fun.aggregate=mean) 
#生成一个以v1为行，v2为列的矩阵，使用v3进行填充
mydata <- mydata[,-1] #第一列数字为序列，可以删除
class(mydata)
class(mydata)<-"data.frame"  #只选取data.frame
mydata<-as.matrix(mydata)
mydata<-as(mydata,"realRatingMatrix") 
mydata
```


# 模型评估与构建最优模型

- 对于realRatingMatrix有六种方法：IBCF(基于物品的推荐)、UBCF（基于用户的推荐）、SVD（矩阵因子化）、PCA（主成分分析）、 RANDOM（随机推荐）、POPULAR（基于流行度的推荐）。

## 模型评估

主要使用：`recommenderlab`包中自带的评估方案，对应的函数是`evaluationScheme`，能够设置采用`n-fold`交叉验证还是简单的`training/train`分开验证，本文采用后一种方法，即将数据集简单分为`training`和`test`，在`training`训练模型，然后在`test`上评估。接下来我们使用三种不同技术进行构建推荐系统，并利用评估方案比较三种技术的好坏。

- 在此我们比较三种方法的结果：IBCF(基于物品的推荐)，RANDOM（随机推荐）,POPULAR（基于流行度的推荐）。结果如下
```{r  message=FALSE, warning=FALSE}
scheme <- evaluationScheme(mydata, method = "split",
  train = 0.9, k = 1, given = 1, goodRating = 4)
  algorithms <- list(popular = list(name = "POPULAR",
  param = list(normalize = "Z-score")),random =
  list(name = "RANDOM",param = list(normalize = "Z-score", method = "Cosine",nn = 25, minRating = 3)),
  ibcf = list(name = "IBCF", param = list(normalize = "Z-score")))
results <- evaluate(scheme, algorithms, n = c(1, 3, 5, 10, 15, 20))

plot(results, annotate = 1:3, legend = "topleft") #ROC
plot(results, "prec/rec", annotate = 3)#precision-recall
```

- 按照评价方案建立推荐模型

```{r message=FALSE, warning=FALSE}
# 按照评价方案建立推荐模型
model.popular <- Recommender(getData(scheme, "train"), method = "POPULAR")
model.ibcf <- Recommender(getData(scheme, "train"), method = "IBCF")
model.random <- Recommender(getData(scheme, "train"), method = "RANDOM")
```

- 对推荐模型进行预测

```{r message=FALSE, warning=FALSE}
predict.popular <- predict(model.popular, getData(scheme, "known"), 
  type = "ratings")
predict.ibcf <- predict(model.ibcf, getData(scheme, "known"), 
  type = "ratings")
predict.random <- predict(model.random, getData(scheme, "known"), 
  type = "ratings")
```

- 做误差的计算

```{r message=FALSE, warning=FALSE}
predict.err <- rbind(calcPredictionAccuracy(predict.popular,
  getData(scheme, "unknown")),calcPredictionAccuracy(predict.random,
    getData(scheme,"unknown")),
  calcPredictionAccuracy(predict.ibcf,getData(scheme, "unknown")))
rownames(predict.err) <- c("POPULAR", "RANDOM", "IBCF")
predict.err
```


通过结果我们可以看到：三种方法的比较**基于随机推荐系统**对于本案例数据的效果最好，RMSE，MSE，MAE都是三者中的最小值。其次是基于物品的推荐，最后是基于流行度过滤。

## 构建最优模型

利用以上结果，我们构建最优模型：**基于随机推荐系统**。首先先对系数矩阵的行列名进行定义。
```{r message=FALSE, warning=FALSE}
	colnames(mydata)<-paste0("asins",1:dim(mydata)[2],sep="")
mydata.model <- Recommender(mydata[1:dim(mydata)[1]], method = "RANDOM")
```
数据处理完毕，接来下是进行预测，可以显示三个用户的Top3推荐列表.

### TopN推荐

给出users201，202，203每人前三个产品的推荐。
```{r message=FALSE, warning=FALSE}
##TopN推荐
mydata.predict1 <- predict(mydata.model,mydata[201:203], n = 3)
#n指数量
as(mydata.predict1,"list")
```

### 用户对产品的评分预测

给出前三个users对前6个产品的评分预测。
```{r message=FALSE, warning=FALSE}
mydata.predict2 <- predict(mydata.model, mydata[201:403], type = "ratings")
mydata.predict2
a = as(mydata.predict2, "matrix")[1:3, 1:6] 
knitr::kable(a)
```


# 参考资料{-}

1. [基于协同过滤算法的电影推荐系统](https://blog.csdn.net/weixin_44035441/article/details/90728889)

2. [R语言：recommenderlab包的总结与应用案例](https://www.cnblogs.com/yjd_hycf_space/p/6702764.html)

3. [recommenderlab: A Framework for Developing and Testing Recommendation Algorithms](https://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf)



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Output written on facet.pdf (10 pages).


================================================
FILE: 2020年/2020.08.15分面/facet.rmd
================================================
---
title: "分面|一页多图"
date: 2020-08-15
author:
  - 庄闪闪
documentclass: ctexart
output:
  rticles::ctex:
    fig_caption: yes
    number_sections: yes
    toc: yes
classoption: "hyperref,"
---

# 前言

双变量数据可视化可能对于我们比较简单， 但是如果变量是三个或者更多，怎么在一幅图一起显示呢？今天我们就来讨论这个问题，解决方案有两种。


# 使用图形属性

**使用图形属性**，比如说：散点图点的形状/透明度/颜色用第三个属性表示。

```{r}
library(ggplot2)
head(mpg)
#散点图的点的形状表示第三个属性（离散）
ggplot(data=mpg)+
	geom_point(mapping = aes(x=displ,y=cty,shape=as.factor(cyl)),size=2,color='skyblue')
#散点图点的透明度表示第三个属性
ggplot(data=mpg)+
	geom_point(mapping = aes(x=displ,y=cty,alpha=cyl),size=5,color='purple')
```

`geom_point()`中可以改变的参数alpha，colour，fill，group，shape，size，stroke（边缘的厚度）。所以我们还可以通过其他参数来引进更多的属性，但是越多图就显得越复杂。看下面这个图，但是可读性不是很高。

```{r}
ggplot(data=mpg)+
	geom_point(mapping = aes(x=displ,y=cty,shape=drv,color=fl,alpha=cyl), size=2)
```

# 分面

我们可以将图片按照第三个属性进行**分面**处理。ggplot2的分面有两种方式，分别使用 facet_wrap 或 facet_grid 函数。

## facet_wrap()

当想通过单个变量进行分面，则可以使用函数`facet_wrap()`其第一个参数是一个公式，创建公式的方式是在~符号后面加一个变量名，并且该变量应该是离散的。facet_wrap的参数如下

```{r eval=FALSE, include=TRUE}
facet_wrap(facets, nrow = NULL, ncol = NULL, scales = "fixed",
           shrink = TRUE, as.table = TRUE, drop = TRUE)
```


facets：分面参数如 ~cut，表示用 cut 变量进行数据分类

nrow：绘制图形的行数

ncol：绘制图形的列数，一般nrow/ncol只设定一个即可

scales：坐标刻度的范围，可以设定四种类型。fixed
表示所有小图均使用统一坐标范围；free表示每个小图按照各自数据范围自由调整坐标刻度范围；free_x为自由调整x轴刻度范围；free_y为自由调整y轴刻度范围。

shrinks：也和坐标轴刻度有关，如果为TRUE（默认值）则按统计后的数据调整刻度范围，否则按统计前的数据设定坐标。

as.table：和小图排列顺序有关的选项。如果为TRUE（默认）则按表格方式排列，即最大值（指分组level值）排在表格最后即右下角，否则排在左上角。

drop：是否丢弃没有数据的分组，如果为TRUE（默认），则空数据组不绘图。

strip.position： 条子位置，默认为"top"，可改为bottom", "left", "right"

具体例子如下：

x轴是displ，y轴是hwy，用class（离散，7个分类）进行分面。

```{r}
ggplot(data=mpg)+
	geom_point(mapping = aes(x=displ,y=hwy))+
	facet_wrap(~class,nrow = 2)
```


## facet_grid()

如果想通过两个变量对图进行分面，则使用`facet_grid()`。这个函数第一个参数也是公式，但该公式包含由~隔开的两个变量。

```{r eval=FALSE, include=TRUE}
facet_grid(facets, margins = FALSE, scales = "fixed", space = "fixed", shrink = TRUE,
           labeller = "label_value", as.table = TRUE, drop = TRUE)
```

和facet_wrap比较，除不用设置ncol和nrow外（facets公式已经包含）外还有几个参数不同：

margins：这不是设定图形边界的参数。它是指用于分面的包含每个变量元素所有数据的数据组。很好用的参数！

具体例子如下：用drv与cyl变量进行分面，x轴方向是cyl，y轴方向是drv的值。注意的是俩都是分类型变量。
```{r}
ggplot(data=mpg)+
	geom_point(mapping = aes(x=displ,y=hwy))+
	facet_grid(drv~cyl)
```


# 思考及拓展

1. 如果使用连续变量进行分面，得到的图会非常的多，每个数值分一次面，可读性很差，不建议使用该方法。

2. 使用`facet_grid(drv~cyl)`生成的图中，空白单元的意义说明drv与cyl在该单元没有关系。以下代码可以看出两者之间的关系。

```{r}
ggplot(data=mpg)+
	geom_point(mapping = aes(drv,cyl))
```

3. facet_grid()可以转换为facet_wrap图，只需改为facet_grid(drv~.)或facet_grid(.~cyl)。
```{r}
ggplot(data=mpg)+
	geom_point(mapping = aes(x=displ,y=hwy))+
	facet_grid(drv~.)

ggplot(data=mpg)+
	geom_point(mapping = aes(x=displ,y=hwy))+
	facet_grid(.~cyl)
```

4.要在每个面板中重复相同的数据，只需构造一个不包含faceting变量的数据框架。

```{r}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(data = transform(mpg, class = NULL), colour = "grey85") +
  geom_point(color='purple') +
  facet_wrap(~class)
```

5. 去除条子框以及改变条子位置

加入参数：strip.position = "top"（默认），可改为其他（见上面参数详解）并加入theme将strip.placement="outside"就可以去除条子的框了
```{r}
ggplot(economics_long, aes(date, value)) +
  geom_line() +
  facet_wrap(vars(variable), scales = "free_y", nrow = 2, strip.position = "top") +
  theme(strip.background = element_blank(), strip.placement = "outside")
```



# 参考资料{-}

[ggplot2作图详解4：分面（faceting）](https://blog.csdn.net/u014801157/article/details/24372507)

[R数据科学](R数据科学.pdf)


================================================
FILE: 2020年/2020.08.23好玩的图/fun.r
================================================
# 有趣的图=====

# 1.画爱心蛋糕======
require(grid)
require(gridExtra)
require(yyplot)
library(devtools)
#install_github("GuangchuangYu/yyplot")
require(ggplot2)

t <- seq(0,2*pi, by=0.2)
x <- 16*sin(t)^3
y <- 13*cos(t) - 5*cos(2*t) - 2*cos(3*t) - cos(4*t)
d <- data.frame(x=x, y=y)

ggplot(d, aes(x, y)) + geom_cake(size=0.1)


# 2.构建自己的二维码，只要输入链接即可==========
require(ggplot2)
require(ggimage)
require(yyplot)

ggqrcode("https://mp.weixin.qq.com/mp/profile_ext?action=home&__biz=MzI1NjUwMjQxMQ==&scene=124#wechat_redirect") #自己公众号


#表白用=======
ggqrcode('I miss you!')

d <- data.frame(x=1, y=1,
    img="C:/Users/DELL/Desktop/avatar.jpg")
p <- ggplot(d, aes(x,y)) + geom_image(aes(image=img), size=Inf) + theme_void()


p2 <- ggqrcode("http://mp.weixin.qq.com/s/oLgpTGdQgcka-OD757_3lA", "blue", alpha=.8)
p + geom_subview(p2, width=Inf, height=Inf, x=1, y=1)



pg <- ggqrcode("https://mp.weixin.qq.com/s/tposUJYrPuRnptXyNX03mA")
d <- data.frame(x=15, y=15,
    img="C:/Users/DELL/Desktop/avatar.jpg")
pg + geom_image(aes(x,y, image=img), data=d, size=.2)


geom_emoji("heart_eyes" )
ggplot() + geom_emoji("duck") + theme_void()

require(ggplot2)
require(magick)
require(purrr)

x <- search_emoji("heart")

plot_heart=function(x) {
 p = ggplot() + geom_emoji(x)
 o = paste0(x, ".png")
 ggsave(o, p, width=5, height=5)
 o
 }

x %>% map(plot_heart) %>% 
map(image_read) %>% 
image_join() %>% 
image_animate(fps=1) %>% 
image_write("heart.gif")



================================================
FILE: 2020年/2020.08.23好玩的图/readme.txt
================================================


================================================
FILE: 2020年/2020.08.25马赛克/马赛克.html
================================================
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1nJtTNAj6jJKAAZEs8WgqihJRgX9wJHOkYoXkf8iwR2RiKKqRRiitWw3lYdnr30cDzNae/8Tw/1L3sS5gFALINXpKDQgmp1pQxW86M3O8aoqMTlNtTGnSjATM2tjXEgCYfS3hKyuCkFHkzBeScI6WKhFVxLuD+EQLt4TkOo6CU5f1drrhvrrVly/dspDayfe+8EtQx7fuJG0HcbZLyyc1r+5qXbojtE1xa0dt4x/5c31r9hA6MYtP5DrVgijoiV5Po6KKs3MBOCVStFlgez8bG57v8/vq4tZ/Gilfr8pX7VqJm1EzJQGeg3j5/xX8ruWMbrG4oduFyXxMEFyQlkpkMeJTvhKbCMY1j/o2ykPlEmSr335KxvYPvbZydev29P65KNrX58+c92zfxv6+Kil76PnU1Sl6fe+l694//zIweMjUO1ZPnH2TU3fxqa09+l/6OHXAQgEAaSZuhddMDiaZ1epkRAzpTKAxyVzrnGh7JLreGi7qF1VqO5WvoGQ0DwF584uo3cpz4sCBzc9T9SAQPKgoqI082X2QfxhshCzXmZ5Jmoo6MvOYAk7gCWH6cudN5+98oSroZZNBoRWbuEw1ygDmqI9OZ36aJrbbTPYqIFmZrldRpdFA27ONADF4/HXxjyKYhkRU9LgYsIJ6e+pgHAkGUjkgUhLSBg2N9w3IMwpylMaKScT/n6efcC+PLN8xActmMGOhu+4bH6EpsV/yAgOoO0n9/+HnR2B5h7hr455LAPJ1+wc+1i1AYGhXOs6eQf4IR+uigYUp8WSlweZTnAWFNpz6mJ2u4d60kbEPGnUwENEvUTbVJbqTCjIAQJlPo8IXEUNdQEJcCAhMvd/gvy8Q3E6TmsbErv++Z2tRuuN/7f1X+zsNyv/vYhoN066sbVlcRuZiq/iWvuP7rEb/7LuhyPfsFPLMffdxfMnz7+1fu5qEc0RPdM6QIHLo14FgCDKRFYNMiWU1MaoAsLfupYpQwobhpDby4OfkoJ4iZQWPyy9jNLm8wLSdEtUyzvBB3lwOVwbLXYqnl6U+o3+Qo/Hnp1ttBtL+ihOZyBQXGwBS0Z9zJIGwfoYXGwTYYlLnVeWdKFwoCSqAj0/LqoW8qk7kShFiku3kK9cfCPVHyDedt/qpeyLL06zk4uXtU1DyfXfE2fPmrng0Ccjbhg+flxtq7zz3ZUzXhrU/O6sjqN73mrbXD2iY/Kzm89vbBp7Y/3VcwaOI3vqq674XdnlYysH1Ym8GajvcgekQQFURnOzZJfFEgyCCwqLtNy6mKZRrzd9RMyrUkMdR+Nfdbfu7DIBzCIaw0J5kS16edcXuNOdBXwbyU1J1ewxtvTOqxtHP/3+JIOl3xOz3v0nmr9Y+f2d8VNjp4xrbbm7jQ5mdazJdtYzasufW2r+83/H0fEE+3DTXbdNum1+Hfd4stOSZuvMURh1OXnyAPjtnsaYXeumMPAnaOwXTOb4NVYT72PqU+xG7xcf6mPNQAQX6/IUcHKmcllV1UUlBRXFZdIaYyZNUjgzJ6Rpm8u6mKrApzM0vUgYbrTrbF2SFHbS18Xa5GhSmF5P7JYqZODSiqKajIK/VYNEqQIEZRigFxShVFwJURhGD6JU0ZlDP443kvW7ccNSPH2abWFfCns140peoYDeNeZHHSqlRgkMcp00ViJSV30QKhkjagSue7JMQH4304/FkrTgKC9Tjh69VLueUScBrhFPNVAUJJTKEur6Ce0u1dCFuorNZH28UayJb2IaDjjNtKWsWmioXPicrpB365FYFc3LTU9PA+B2dlqdhUV2QCMFCAazGmNBl900ImaXkg7mVCR4KJVkyfpRJFR5F86oRckaXOFoe0m/7W6YevPVY5uWvzf1w3P7vm99YGyIHU4139VjH6ob1tLvqqpxR9u2r5m2onVI9RVXsHUX9eMTLkxQdnCc6AuVEIv2VCsq3G5XOGzt77rMZaWBtEDvNOgN0au8hkhEMg3QTPzqkVUq5feAklS7rOucMleiPU7ivc6kQtuiYCqrfNTdlVF8fxLxCKgtj3iUQC44+jrzOa06UfyDSESH3x2j106vnpWmTXnhlT1o+UfT/qt9NdGau79/Zhf73+exCP2T2Pz/ZefZXez6I/gIyv/EkRs7Yf3IFpM1FG27n5x++NQ9Q/otPPTGQSQBH/Pd/9Yf/vjjne1sx152gh0p6f3eKHwYW3/EZZ93sA627uCCpcfMzwj7AIC8WN4IKljh6miAWKkBQZHNZgqip6CSZLOSmpjVSs0yBZocIpTouZRiZWGortKL8gsDiITjI5Uik+LHJ7FXiYTziRJnywoMgWdwNFstbzxXRcbikdvy72CqiPvXAaQznI/t4Idczsm9VLdbktKzzeY83vfZ7QGDlqalDY9ZNLRSTbODPb0mZneCvyYG9BLcSxY9KQVDSTe5ArmSp7voCQYwWfE4HPqnwOu4AyOYNn/C/fPZh2fjx7C84/aZ8xev2nXHraxT3vDKpkVrHaacdQ++/xGdXTuy8Zr4NrZo3PgNgDCXI/UBnh9eKI36VZeLN+NWnxscUBNzSKpskmtiJleyNBOvSfVEKuQRD2+0Iw4l2BUdoTI+ZiikBS+9h9OfOtrxL7aJvdiOkQOHDrc2tEs72U/HmW846xyGi3DSZ3j9azd1FvUDImwoz+E2NIBd1OtGAIdVkjTZUhOTqWTlLbMzaamUcEELnGVzAbVA0BHKleew8ew2Ng534wR8gL3Dxq5ZjO/xGuQP7A55A7ubrcHDnUMBdY8RLs0Mg6L5BgnAqphMiBbFWBOzKNxLAnII3zehaKqJofOXXkp5iCsitPAkbol0bqDV8RN4ijmIm4tl7zK2BLqkUsalGqFvNN1AqVkBQDQJoSl5QlZS0MVSLhaCX7P9dHD8OHKMEwKWxLu8KBdxL6ZDTbQo3e8nNquVEFemy2DIsGlmjQdbOr9BNkt+r+zlsmTu1FB3wd0z5VlnstgW8BBwKLpv9YJL5RlPdMKNOALkU1L14E93sr+yVfg43vTxgZtW/GXnd1vevKGVHafhuOnyAlyMU3AcPjDybB377rOT591Y2mUHeYJu/Ug004jIzW+QJFm2GGhNrMaABoNsUijK3QmbMnfKFN2XPIHtjr/NdmE5uRrDZG78Xj5t2EIGAOCFiawBT+ozgRw+bSAGXiPLwM0MRsr79e4NCw4Rxa5IJL6kRnJurq0bOKEZy79hDV4k7gVL5JHn1l4AdgYS+tfxVS0wMJpjIcRkNiOAzUBl2cq/UrNZoXwP3VtwpgBXF1eWAOXEQAdVfSMRDKBcx1awhYvEZm7FB7CZETKxJf4D39CN6/Hf8XkJ6VIlly6LPUkqBVCQArccJKJUl6GXoPq6r3PD1MsbzldfSPxvRcyR3dAvmukGo9nI1bbxUPHKisdJjEQxq9QGilBcN36X0mUp6hA6Y9DpEYujXuXykscVRBpkK4wudhzbcaSC07GdfUgtRrZEms9Wzok3cw1WSi3nqklH6R3oPr8kYcedOm6WR9NMYETFagVwUFlRVM1MVW5RVLtHv11adI/EnAKwL1KEcM/JO9nv43fpSiwh81U7+qQGdrQtXseFv4FZvycdQPQ8+VKfDHgE0jgAfBZF8RpdNTGjRO01Mer6daQROSBexQQy16Hxpkj+kj3BXubXE3gz1vNr/PlDb76Bs9nSNzaSY+xxdivejVP5tZCj0mP/OYvf4smfoAvtpHU62rkEFkhGowdsNrvdbQXBV3ZNM9TENGr/TSzoRn/ZLXHoEyAo4ckJSx+au+BBspEdYacX8yA6iCb0UGXmlKkTd504Fz8rb/gchAXYat0CdkjjEZynUFmSCDVIJg9AhmYypVOVEwBXRFK5UWSV22N7Ev4uHU92T9OQe+LX7PPaKziWzWZnfL9pJMZW1bO5OPS3LSUP1S3lg9poocvnk0ySppm8njQw8cTzu4wWMA6PAZgtFm40C/WaRcikzJbSWfPzuXKqQ0sxKLdfgl3BF0A82brsgaXLW7gB12EPzH7oTqxuZWvZKtp73M0Tm+Pz4vvlDUeOLdxZwVwPk1KRVS2cQX0ce4s4n+RlpKcHICC7LeCGy4rdAbAELNlGX3ZNzCdRYyq+uhvwVHHWrRpn+IvGGoVFl/MhDadWMcJP9LZen9cr+din7JuOx/ZeN2FqnzFL7767DtWvZu2f2TrnyermlsJrn977BC7f/lkz5g4srx3e8+orqypveeqmzf8qL/13n8KGgcUDKqrHbRP6FwNIYiqrimdLCgBFNBhVKlHOuxSdv3y2lARgcoLtYrOlOn53IGEMEF7k+dXC13JCQdThQHSbDQaX08hRhsdSYuuXVBAOtyLx4BHI6+6CYLnlEXbyLfYFex/D9zz7BAf0ztqVZ+7EwHn6YufCPz33/DraBqjXfyHBI2K+RonRKAOiVZYkC3BDJ+q9VNpUJOaj+sXtVx6h57CC2dmLTMMKdPlKFXO0a4DY+dTwvZeN/qJLhrqRy8gSsx+T0e52yQh+v2ynlszMrKwci9mcnemSzdRvt6NJiOSi+EtCbgo1UyM3WkiKOMKJUtMlGvCIi78nPihD2fPbzWFJ6WPdxqngfix9q9Sr9HQdwoJDth5mUy/nm1hKoRixV/mpUJxwVT85trLi1EAa6twb+aS+9uuhNBsStmnSbVMVzTXLnPpUo6oYTYpJ0C2VLGYDkWXJqFCUkhDL9evG+ooUZ3VpjZj8Izex59h6fnXg56wfNmF/DGMtC5Pi+GHyHdka/47Y4j27dJCYyF2B7wZVlZEQEERvNFFF4QqiSgVDdslOjEH5Z65AarLLowIDZAGWchEZbA/LwDo6mozsXBTfQUqoXleVJiZ0RugfzTJISFUVEExmlYuSRP1I0IAGUcZdOgxNpl1qFqqPbALSzPPvkbfjTVJ6vIrs30m/RXi/0ykkLWUbyWw9T7KjVgXRIIFRJlTBfN2EuvH0BNZX4iUpmc0y8bOPPmIblXMHz60Xa1gA6MDkVFt/ZIKYnGpfnBa6sUmAHY9/mJhqI4S4fJ+QL55xoKIY+VYNoOZTiaaCvQtCfCFHMMy1CH34IX7GMmfKjQd/UoR8AzFIA+R3QIHeUTdBWVYkSTznFd6SVJko0DW+xLKLeyTRZYcwiGjADQ/jqVO8uP6KGOiGzmqyKN4maq1OtpHWXhja9SRIRonoRhEaJZ5K0NrOFyl//vMAAGKNdIQ+qATAwK1gBjVKRVTIdwCUpB/rioP0XWLww7EvHPD6PGRL5ZkqbKpcLx3ptW2gZ/z7GYIdmjju9pfm6E8Zq6OFTovBQvLy/P78LIMhaEkbFrNYZLfbPjjm5jWdnDM4JnvBk0Az/y+ZVYSeXlcUJWdMvMcN9+1u8h0omny9N6YT+huGr1r0xzd+Or/5xbv/On7T8Y9PswO/X3znY5MWPHHDsNfXvfono1K6rn7f+K3vx32E27h55MJbxwOBFVznDsUNTsjh7BvIojRg1Mw2n89szrWA2WPUFFDSh8QUL7iGxEC7mCz83SHi7H5mUeZ0aISzRVANCgTlw1AfH9d2D8WobftHX+7YNsMT+hpLLZbJM2ZOJJNvaZk+Q5rNdrPv2XH2t6XzFTdbPuiJ9jP3rwh0PPOXNWvWAMLoCyfoMWk2eDi6esRYymclxCubh8RkDexcM++lZZJuOTk32SdwmnJoYkjgUBQyIf4DZqJx81Mjh9525cmTzcuHVf/BTQZgFvauOZFVwBH49ZIydr4kH4iQK81M2CcaDRi9Gi+obTZhqFy7xwIOIyi6fTTdPt5ft4+oT4Q+ecShOXlPGioU/BLkji3iOnVPiAnZ9vHnOw9ON/mw7Jv+1omT5kyVp7dNmDnLjWVoRx7zq9vG4YSfTjyy5vt7ViWNk9BynD61y+DMEKROSUpzOLKcJlOm3+OkzuoYFVUUVMesmuoZHFNTel5aloiry3bI3RbgrbNeR4XKwOMJ6AVAxMMtOP2GaQZcT2aVs+/Y3zDt7LdoiJfID985vmNc3Qb61PyZM+d3NmAPdGAahth3Jx+789Eel5+4rCjB7nSOkgMeuCKa7SZElSn1+qwAPhndyHVz283akJgZqJ4bgp8v7QVDiRwWFgxH9KfOeieocBWpiZ1l+9eu3bj/ufm1o2uv6ocGOq9zCZ23rKHh3ZdLPsoafsVgoKAwtzSV26sYyiEKd0SrzFlZAwZIfRwOUqzmSkGUpIHpPXr4fJFg8Kp0K1jRqlj7qv2GxYy5Eke5wr7FpDpWXFxYWDksVqi5e1fH3BkXz+n4pxIOWz79gRHv0LneqJs2FQ76ewKfPao+pSsqEvmsj+ykQFfCF6ZeRcGFyUQK8v26El/4WGzqS33OfxjpXbL2ndc3sTfYvm9+vP3WksHVg5tvOnmsZKGTFc2buvrNabOfa5w5/drrmura10otT/ceNqZjJ5Xzew187smt/1i1bPw9We5Roeh1xYVrZ732vkM6L1UOHVlb2WcEHT5q0qRRuwBhBYC0lmeDB8LRdATw2Y0Wg8Fo9Nolp1MaEnNqJkCjR6D/JfU5336yUOPaKqJJEuCQeFQirWX7O+6YxfZjqapqE/61bQ958LsXt8S/40CwpeDekav/vh0ILAPAD7lsA1jEZFcyGsFksprtJg9Rr4kR6DJ/ZWoO7uobKtNnnyJUlrW3X3ttO14phMgLHn98yIjzPqkFgFxoY259XSt4oSTqd/L0JgaDT/NcE9PAaBctOk/sjOTEKYEwCRGJxwB6tajQpMDBcxoHXzN8CJbum6GLZe60066mRmnd+eJXN6mThXRIWPMH/Un+NdGgxLmTUKrIsmYzWa0Gg8lkN4P41WCzUcXkofbu2oTf3cjSZdpuokXRuGOyi1dx22KswGZWhYd5AffOIrF9jYxdh40sI74Et93MVivueDXr0gYPcG0ouF4DRIkAevQioLvExgPivyvuhO7qQJ5BQRgeLXS7XPrsKDMzI6PAajSaTPkuq9WRKzu46XwOzWzPRJNH7+G7krl7+OC8ePqbjJDCRIiEfKFykdziVfBd8q+ke9n++uvnTGL7vy529F437Xwso/dL097ZwvbVXz9jOnlw3rz12+LfSS1Lh1+/urZpy+F4kfhtxYuQjGCut1tMFxHAq6vrscoOoatQFU0Xx29SyV/XLRG8TS0ierkyof+ZtWWXEPbn7boC9dce3JHE5yf0pzhpostXLJYMcLnSvcYhMa9mp0Nidu8vu/xUrvPeVQMOCCQs6MzrxGVT5986ecr8W6dQmX3ELvzxh7swGyl/I6Xt6/70Qnv7mhfYKbbnQTS8jE7s8wA7B4LrOep1cC1ckMMn1Hl+RVFNlKpZmqrlcuQEq9U9hBOEwa5mQEaKzBKmSBWoSQVlTvPepDFCnPndRKFJtuemosq2GZrG9p/taZv8wfaPbt58TGf7vePdSx/wsv5K9SPtbB87/T/s7H10mU722JDgM67pTN1euaIq8dIsyh+TpOUZ+fg6PcNnz/ZanE5V4I0FhsQsv8m6iSfIBUmS5S2dL8HBXl8ook+LIkFBaLdMkafPPzxZ2v7R5zsmPXeFIQMJ22e1lq48uri9oOMZ9uLa9lNYiho3Z9+6xqU/bcBDAybXN3ZFFJ3LddVEh0mcejw5BCxZZVnUS7wGFxqlMrTMRy+JIqpdWewrCD+6iu3/sre97yvSbCP7xLR8SXyH1LKxZTYkqp/1XIZ4dpmjpLktAEU5bnchWNw5lhxTli9rcMynUdPgGPX+vJ2/2BgiqPTHK2HB5clePsGgXCkPt082oetPnbx1/bDrDtW395oycuG8yJd/3/Xu6MZHa5Zcv2zRrf2wZn1HILfzsvKx+b0rCstHz73+8VXN/8y//JriK/qHR/+30LeE6xuRa8AjToRYDHa7y2UyEIfB4fWZnHbn4JjVYrfL3HVyQt3QpktOVnRhgnBcxKOXvoLpIyFPwCO6cjK3bsas9tdeeHRt8xasYDuu+TD4aeiNN0jGwgknTn4e//yqK4UOT/Gc4zM+cENZ1E8cDrfby3t/j9NoJ7JNtumyPcmJ1sVDgItr7tQYgH+grxdrpR2zt72PpSLjsXRp7XUHt5Mj8dki4Ynt/EpI9JkPcrlm6BV1m0GWiYgIK0G0GNEuC5llKWndDU1X/x0SbTfiOtaElf/INyryZYexkjVJLfFF86aMXUzaumS4AZRtXEaWOMsoSyaOIVng81ETVTMyMjNzVEXJ9plMVLbbMxQ7yDqidR3RdPz2LIDSIO1WQ8wBsin/pGskRZpuUfew19lm7LMwJ1eRcrT7sG6R5NCsqBgvN92NPdk7uARPdt4vtTDH4m9q1lxH/PGvvE03jMkcer4XnuKKI5gApOW6bWqi+YoMaKSUSAQlGWWzQVWtfIZmMSoUAA1mj4T2S2cBqaROkYZeq3KlhdkClOu/mD2BI48cxZHsMWxja46fYO2kPwmyZ7A1fiy+DRewhcJLzK17ycs1KTC73ZrXK0koahm/Jgob/pNT8no0p9XJMTHDAFyVskQJkKKvhBlTUzxHyokifvTqgNsSaw9mmBRz7n4cwoqu+vcfR9RErqqfl+fkfr2/YcZNo8ic866XXnR8Z72xNZI450HXce2MIn+oKqkIYDYgmvQhAm8c7YR/MwyOoefSIULSSMJGySlCWEwR6LrOB4nC0uhAZiCmDrLp6+3xekDI4T38Id7D54ipCHUbcnIcfn+uNTMzIFGXy8qjKd9qSbTzYosp2hbbF7bnuBrm+REWRw08Coc18VTQ4xFQ6+EJhDmL2m6/c/OZG4cpn31T3XpmM9quH32qucGAVz7Z9jEdXMUObcyzBF8xskNVg+knbU8BIO5gJWSlYgMK7tcIpZJMAaCyhONDYlbqCOKOo0cV29lA1ylOauB7yBN7yOHlOmgGQ75bkoI52TabW3Z7qCzl/3/2IIuHzuFynuSi2BZnlftyiBSnzxyCyzwcrImh4e0Xbhz2+9mfKtWtL7xTP39x26LeM2aFPyFVQ7CnuWmyw5K3EXsOrqIfh2dPY5tNjY2nGm7QTxGQIqmCtoEHIlG/Ag4zmKnd7qNeu82mSJSaHQ5QoCRU1lYi9ElBdqqp5pwa1sv/RAMmELwQB0baym968pqFwxaOC99ePv7pgf89chFZcXX5l1NzcyPRii+nphf8lzhBwpbiQanl0rP6Dg26zurbad4v56mukCugE0Wi7Vh7JsTasSV5lIO0dJbKBcljHAhLOdJqfN6cwad7QYchPV3OyCA+n4mYMrPSXCNiBtuIGMiGNH4pGWmKygXqpwH4S8+ePzvOII575nOCTh4R15lS69q26gmSEBt94OCr7YtF6z7vlm8b7mpdcN+rL/fHcyhjZk77c8arjmflv/Bn9kZObzbAuFFEB4A0ST+d2BztZXeaidFqTfd6iV/zO51ado7Fn+avjxnT0sDFqcleG3P6QR7xs+NNXUfUIJTSVqjbjT+pBpRfbpXXFSKawsFwiBuQbNyyZcyzs2sbcS679w9k3/mvbhr+6qufy7sbvojGrt10dOm6WtZ5ttes1keObtl5BAjMBCYFpHXcnkW8R87TLC6j7EsnBrDZ8jIhM/OyYp9LSycWo2xQPZ4ctYBHz/YyHc11H2qb9S+iA4oURXyC3SM+0WGqPrVIoJJaFCmMXFRdbixfuGzBqEk3j1qwfGE43Pbogt+Nn93Y9siC8v1T6+qnzxxRO50cnPC7BcsWhCMLly6MTZs8uu2RtlBo/iNtYyYOnz6ttm7aDBHpCoDEp+PghZnR/7I53U6Plce2UaYyMYkJqxeRED/HBp/idDkbYkCRuuwmm93WEFPtdgt6FMsl5xX9mtiW3kNfypcpEhAfkgPKkCfoEXdAGF7cGCBD0YAVbOGWH374gX38448/vsOW4BViZBv3vHrfq8eO8RdyHMhFiKNCMGoniiKGmUaJSlTVsUcEbCpFdAhyJGBIAFHnAbag8wAAgUm89lnw/0o5D7g2jvTvPzOzu9KCJNSFaAKEBMYHAokSuQpiY04OODjYsWxCcjbkNaluuPdyiXuaS0jHpPfeE0N68fVO/ObSe+8uy39mVlqEzr76oeyi+bG7U3bK83yfkUZBGZwCMyKlaRaXRRTLC6E4JyfkAld4DKmpsbkrK0ttpSafxzc15nHqTVNjepQycUvmivi5NiuyMYtA0qyNo3NOVr9OFfZJmt75WUW7VMhOWtE4fsubj9zRP33SzuaW6LxFB3rWTJj4xSuvXdHyYsOAb/bpj257c+OS5s4tvmrim7appHXPputbn8kPlVdURssit194/xklXdGr7p3261Hh7uKKUGH0uu2nzi8Pxya1V5qmAUYu4UfygiRwVi0/YrQaWIvIdGcQ4pBB7dzU9snCdpLZJF/SOXJNjdRPPa0uMhVd2TKurqk5Mq5FXFPXEB0/7ucNExvqGieOb6wDIIw7lSbR99oBPqhmvm9ikm0mm7/c7yzPc+bV1IrpYEmnX1mlhbZglpActKMVbEo36zBrHWyifBGnSASrw44ZvIhr6bwgFCxiuH4R45HIul+c91p4c3j55tf/fvilPddGFx5b8zJqf5X9DCi9v/m10vvcrj6U09uHsg/0Ke/29invHSBfX7VJ+TAv99nwkcNvfNd82xjlI/4/Su+rLyi3/ObXaPaLTJb0b6xlBfCX+DHKMLqgAOoieZk65HLlmXXU56PLK/RmGI2e9HQbys4GEGweShSEA0F1mAtak3BQbR1SPGxVVo3K6irbp3YM1ToJV3pGr452r7n58XnrWi6tr79h3tY9yqTy/KbYvMvxsYvGRLrPu/BCWegef0l+cNcmpeGP/qIz6oqkNPas06Fd6BEEkMAIbZHRaUaDTKd2RMKCgERqGDdkGNkrBpBGCE4XBIMoIpOMsR4lWko4kLBqJI+K5j8Faab66Q897w8yR4ALIR3yqYfpaPGg8hFyDSo70RG06A12/oayC49HL1E/s9K3DL2QNXzKGb8fhTCZCCJkRZgzSkcQkogAAdYJoQTf6LXQWZQQHjx2hLz1I7pgEIaGErEHWAIzAAhaezTEW+S5kUqBYFHUgcViJEbamxB9uT/ROLFE8QLBIegdsp5+naSN8spKbara53ErgY4FlFnoIwadmhP5X7VaYcvuz5QHAu8h/cO3K+s89eFTJuceP+dft9utd0xUFqDpyj3kqh3K1+H6uhrlzX/ZctHQEckuSNLhJG8MjPTGCNLRbwWDZH+Fr/6Jm7D5hAmyIDMiQ0ZGTrbVkMkqRQ3FUq17vL06HSowmDyctbXd2N5201ln3XjW5a88G6uvnz2nLjJHWMg+7W0766bZL10emd02YWJ7G+NFAYSwiCGdcx+ZGTqdRB35BoSomd9sMRrSZYQkAYOKeoYC8S5MM5WnxriwyfZwnAs9I2/h3kG0RVlFY12UNylYiiCAo/gZTriVRKwOA5LAgiyuTNnkwQ4Hyucer4lJXb96j39EPHUF+JnjK/5+briipGXeqiuf3np9+4YudA6O3jbYEQv6S2bt37Cle8be7rMBwVgcxo+Ir4APJkRy7enY7QbIl/LTzVK65C8mdrvDIed4PSa5IIE5pbQ8dlABTRX6S6xu1DgHrezj3QjuuaN9/n1P7N541ards5oXtJ3REgwFWsOdE/b9v3W9wlu7a432i6at2N7wzOzzq6tvrAr76ePuDExYn+qLI0JEDyCnCdwXdyjui3uFjR/VNMjMIUk6ao6YiGZWHZ0i/DX75U5H1aEgAOK2LmrkhkxmMUmXJFnOsjrBQR/drXNlOGl7yiCq4Y2Z+zTTkbYwT8qwtv73xo0CxS6XhZtDZ7WvpVaAD0ZnlC6fNWF+vigy+yj67YoVdz/PrAF7Z8wo/9mM65SDUhQQLFSOCbslO2RAIOJINwsiAoTMFr0emUykKWYSWc8XiHtk4gMlbe5qgAb7UsMIa0IFwu6bbumd0PqX1/72IW5Tjkmn/3QfCVmPHEWCwiKd8Cj0e7KGEUURmUU6Ebk1RiCQCHSypSLhfEr/+2Eqe2hQsaNeALBCVcRlNjI7Fh1Y7Gaz0W60ySYW9pXNXt9QQI0EXB1/3PjAIiZPQYprQ3RWgnr3Xd88KXuOu/GW5v7s6Kwj6xc5btOZJpzh7hmf2cktXDiKGxPRSYI8MjopD+WfMDoJeePRSb4QbvyciNkVzReismdxFD2z4Oyi0vHr6MwOwnTUfEt8ic9KPBFjIvYqgzhkDw/xTGK3kxc9YlKPgt969IarH3/wwP4nFG9dY+PEiY2NdULbnf0v3Hr7wAu3dHR2dnTMm5cy6s2OlKZTy49OL2AW1Ib01FNiGh70BD7YIdHEB79/Oej1B9UBL+6NL0aoFonqQehRdg4ip/LxIFqsSMPn2KuMXYbaUNsyJZw1fMrGrnIA6Qpa2n5Y+TuAYvg1fgUA6eAP5Nrjj4L8IMFW+uJUVye0D51Au5h8T7W6B7CZSZlyNlXeJ75ClUs8XEnM8as+Eb9qmXpVwDBeWUH+LLTzNU5DpKiQug4YJk0jh0pMoyDbnI1lQp0JPk9rzJdhoRy8xZvKwaN4g9Cm5HHsnddbrUub3bCVWHLF4ldiF1wYPjM27aFzzp37w3lvHP3F7rOrUcnw6jY6d1dT86yJ4eiY0sOnTO6//YLru+j0cyyamXhHhoZU2lu3GPuhiOexHiQ0HfQPYqfoh9HVJ1B0w2//heIgzFQV2SMV52iKgYTCOlIxU1N0cUXaQwR7uWRYkxbXSNDfPYvXhpfEa4MpdD7OPtrg4sg4yUbMNmIRLCjNZEJsvgbgEETRbiYUvqb4syENGQkj/JFkkzkxTAQrMmlscsKiQLvUAAeUNb8G7yQ062PCs0QKkEYsI9rR6nzH9imOvcoLeLew9/ghbKIUT+hoLlq5jiPvcYqZDnXNrC6WKXZGjNP8+VlGYAXOBfY556p5+ZaodTT0KC89ZE+UXqqiG9pSFPdShT1JcXDoO1XhHnmNmZqia+gnXgMYFag1wGbucZ7cAJnQGCmivUCW3ep0GlBamtthAIqVWwGovcRJi9eKLYy8TgmP0+BgddahWmkscQqUlpiPo4MhBwPPA1tV5FzFz7cKwm9+d+CzzzahATIdd1Du/G5GoOPWnR9+ofQoyl1qHsRXeDuriLez36eUA+dUeTlUxtt7N1fgvJMpulHDv1AchOdUhXek4hxNMZBQZI1UzNQUXVzB2vvoeGkj2IAMglnogXTIjaRLBGTZYORGZXcgqMUn8260FqnLBlSM7lL+uB+Vocqr6Rhetkf5tfL7vfj3qKxH+SMavZf++VuaSiUAhD7DLeIHkgA2yIZCCEdyXJ4cuz0tB9LAW+TMK3Ab3QxXJQWpdOWImbyK8arGGFaJqpEG2V2IO/yqihEFV1Wm94Xts3tnv8iA1RevaL1x1sDRP56CjrR2UWL1/ZBiOG0+WqzyvXWXXHDpANrEwNWGNfM3DSi/fHYJ/rbsp+8e6j5uKR4aUmlIXgO18Vocrdaz1uOkKrqR6V8oDkKPqsgfqZipKbq4gr0RJcl9kqDwq4yNv3kb1KtYuCSJSmbrqZpIDiOjjbIoSpJTMDbFZEdTTJAFWdIRyZowKGrdjOZBjePIDroW0tZGwh2UUz1yNcPaH1CQ4fikjst3rbt0NcHv/agMUij5c2Vc18rz5/NZJM3JfMkD1dAaGU3tegXFxQDlWSZTbXkgUGPKKtBBcbEui2SWhkqnxEIQcFgyozFLwnGq7ZUx0g03TH/aTYLqcnOkuuX8iaFL8zhXsVAn4a3SSDRSWl1/RVfoo3fmXTau+ubIbfnTo2vnNjQ0TVjXsWQjbb4+hL9FfuGvkV+cNqai1JldVTJn7srmu+7JLfy6KLhqVGhcaeOylsh5lbWnl49r6TrnKPVMv/LO/azH5ASbVEBr5VQ+UtQfAPb2jbbEazY1vfvCE6Xna+kHfxhi6RUj001a+kAasPTikemClt4lAX+3T+GCYcUDmqJ/lKrwqwogTCEpQjeUQBBOgS2RydU1JDM/P2g3GoNBuabG7/GMKZPlsC/fW50fjVVXsyDp7OxQNJZtNo6aSoF3p+S0NFDHPHgbYiBJgQZGv/ERLZmZ0t5q6wkJKnqMhzBz8MufZG0ZXsZRzHYYrWJk1TDShwoZfiVWbn2rce4L19/03NdfPRtr2nHzvKc/emdx/d3LDyM4XkaJq+cfm/bY8bqFq1fv6FyOvX+1oHvwefbOru7Y0zcz5q91cn3Tq52bInXKZx9RCGvWp8UlOEsQzpxD6T/05acLVrNap952xtZhP0xWx0+0iY+fnCrjtT1FbQ2389oqStRWanr34n+eflDP00eNTBe09C6rWpeVidoeugYAvcGv8LTaXynTgF0DGRLXuBwA/y5J0T00eaRi6JdU8UmS4qDyuqqwJBTvUMXlkqApuriC9Vdu9UkSBIfk5fPVpZGx4MYuV46oJ+kEY0tOTnr6qEKLpcQNmZh+SJ2ImdjppB56CnnSKS02+RpiJifBU2MEnYC8izsQ2clwI9I+1YYLf3Gtkw8SVgdtm4XAwyNdtX46hDAvXCL2GCmnN3ZetuitjjuuvUr5/0PfKX9DwuFDDfpT17zfga0rz19x8fIFq84TXdXF99Wdtr1n/m5lz4fKh8pLyPrJR8gyV+hdtuva4/Mv2Lj1ih27+lg74MwMf2tPV9/aEPAZUHI97ucl3KK2k5t4PReeOJ319ZfAyRW8pRiS+gUt3aSlD6jpeSPTBS29y6C2pIDWK8yCw0JYeIl7wbKhNGJ1pqWZBQEIyYUcNwVKAXHz0vPBYdBQiw8WTxJRTWOGj2+K1tf/PFpXNzVaf2ojO+KOwcEvTpva/POG6c1EmNrUMqWhpRkIfcaHKAN0OZ81eEfOGnzxWQOjb0jBFAZx/C+zhmCNsJ9hQWsvOLVn0n5GBm1eUrt/zK5jR21o/OiJKy9AhwzKa/6alefjSoYJlXV2dVyL7IwUqpp+Qes1ytH2RjTouvnWlnFKMOP2oSGVpeD1c2ZST4ByefGmpvMavgVOruA1XMnTC0emC1p6V0B9A0u1np97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<section class="page-header">
<h1 class="title toc-ignore project-name">马赛克图</h1>
<h4 class="author project-author">庄亮亮</h4>
<h4 class="date project-date">2020/11/10</h4>
</section>



<section class="main-content">
<p>记得安装prettydoc包，html模板在该包渲染而成。</p>
<div id="前言" class="section level2">
<h2>1.前言</h2>
<p><strong>马赛克图</strong>（mosaic plot），显示分类数据中一对变量之间的关系，原理类似双向的100%堆叠式条形图，但其中所有条形在数值/标尺轴上具有相等长度，并会被划分成段。可以通过这两个变量来检测类别与其子类别之间的关系。</p>
<div id="主要优点" class="section level3">
<h3>主要优点</h3>
<p>马赛克图能按行或按列展示多个类别的比较关系。</p>
</div>
<div id="主要缺点" class="section level3">
<h3>主要缺点</h3>
<p>难以阅读，特别是当含有大量分段的时候。此外，我们也很难准确地对每个分段进行比较，因为它们并非沿着共同基线排列在一起。</p>
</div>
<div id="适用" class="section level3">
<h3>适用</h3>
<p>马赛克图比较适合提供数据概览。</p>
</div>
<div id="注意" class="section level3">
<h3>注意</h3>
<p><strong>非坐标轴非均匀的马赛克图</strong>也是统计学领域标准的马赛克图，一个非均匀的马赛克图包含以下构成元素：①非均匀的分类坐标轴；②面积、颜色均有含义的矩形块；③图例。对于非均匀的马赛克图，关注的数据维度非常多，一般的用户很难直观理解，在多数情况下可以被拆解成多个不同的图表，以下我们会对其进行绘制。</p>
</div>
</div>
<div id="数据介绍" class="section level2">
<h2>2.数据介绍</h2>
<p>数据构建代码来源《R数据可视化之美》，任意拟定一个数据框。并用melt()函数将数据转化成以下结果：</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="kw">library</span>(ggplot2)</a>
<a class="sourceLine" id="cb1-2" title="2"><span class="kw">library</span>(RColorBrewer)</a>
<a class="sourceLine" id="cb1-3" title="3"><span class="kw">library</span>(reshape2)  <span class="co">#提供melt()函数</span></a>
<a class="sourceLine" id="cb1-4" title="4"><span class="kw">library</span>(plyr)      <span class="co">#提供ddply()函数,join()函数</span></a>
<a class="sourceLine" id="cb1-5" title="5"></a>
<a class="sourceLine" id="cb1-6" title="6">df &lt;-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">segment =</span> <span class="kw">c</span>(<span class="st">&quot;A&quot;</span>, <span class="st">&quot;B&quot;</span>, <span class="st">&quot;C&quot;</span>,<span class="st">&quot;D&quot;</span>),<span class="dt">Alpha =</span> <span class="kw">c</span>(<span class="dv">2400</span>  ,<span class="dv">1200</span>,  <span class="dv">600</span> ,<span class="dv">250</span>), </a>
<a class="sourceLine" id="cb1-7" title="7">         <span class="dt">Beta =</span> <span class="kw">c</span>(<span class="dv">1000</span>  ,<span class="dv">900</span>,   <span class="dv">600</span>,    <span class="dv">250</span>),</a>
<a class="sourceLine" id="cb1-8" title="8">         <span class="dt">Gamma =</span> <span class="kw">c</span>(<span class="dv">400</span>, <span class="dv">600</span> ,<span class="dv">400</span>,   <span class="dv">250</span>), </a>
<a class="sourceLine" id="cb1-9" title="9">         <span class="dt">Delta =</span> <span class="kw">c</span>(<span class="dv">200</span>, <span class="dv">300</span> ,<span class="dv">400</span>,   <span class="dv">250</span>))</a>
<a class="sourceLine" id="cb1-10" title="10"></a>
<a class="sourceLine" id="cb1-11" title="11">melt_df&lt;-<span class="kw">melt</span>(df,<span class="dt">id=</span><span class="st">&quot;segment&quot;</span>)</a>
<a class="sourceLine" id="cb1-12" title="12"><span class="kw">str</span>(melt_df)</a></code></pre></div>
<pre><code>## &#39;data.frame&#39;:    16 obs. of  3 variables:
##  $ segment : chr  &quot;A&quot; &quot;B&quot; &quot;C&quot; &quot;D&quot; ...
##  $ variable: Factor w/ 4 levels &quot;Alpha&quot;,&quot;Beta&quot;,..: 1 1 1 1 2 2 2 2 3 3 ...
##  $ value   : num  2400 1200 600 250 1000 900 600 250 400 600 ...</code></pre>
<p>计算出每行的最大，最小值，并计算每行各数的百分比。ddply()对data.frame分组计算，并利用join()函数进行两个表格连接。</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1">segpct&lt;-<span class="kw">rowSums</span>(df[,<span class="dv">2</span><span class="op">:</span><span class="kw">ncol</span>(df)])</a>
<a class="sourceLine" id="cb3-2" title="2"><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="op">:</span><span class="kw">nrow</span>(df)){</a>
<a class="sourceLine" id="cb3-3" title="3">  <span class="cf">for</span> (j <span class="cf">in</span> <span class="dv">2</span><span class="op">:</span><span class="kw">ncol</span>(df)){</a>
<a class="sourceLine" id="cb3-4" title="4">    df[i,j]&lt;-df[i,j]<span class="op">/</span>segpct[i]<span class="op">*</span><span class="dv">100</span>  <span class="co">#将数字转换成百分比</span></a>
<a class="sourceLine" id="cb3-5" title="5">  }</a>
<a class="sourceLine" id="cb3-6" title="6">}</a>
<a class="sourceLine" id="cb3-7" title="7"></a>
<a class="sourceLine" id="cb3-8" title="8">segpct&lt;-segpct<span class="op">/</span><span class="kw">sum</span>(segpct)<span class="op">*</span><span class="dv">100</span></a>
<a class="sourceLine" id="cb3-9" title="9">df<span class="op">$</span>xmax &lt;-<span class="st"> </span><span class="kw">cumsum</span>(segpct)</a>
<a class="sourceLine" id="cb3-10" title="10">df<span class="op">$</span>xmin &lt;-<span class="st"> </span>(df<span class="op">$</span>xmax <span class="op">-</span><span class="st"> </span>segpct)</a>
<a class="sourceLine" id="cb3-11" title="11"></a>
<a class="sourceLine" id="cb3-12" title="12">dfm &lt;-<span class="st"> </span><span class="kw">melt</span>(df, <span class="dt">id =</span> <span class="kw">c</span>(<span class="st">&quot;segment&quot;</span>, <span class="st">&quot;xmin&quot;</span>, <span class="st">&quot;xmax&quot;</span>),<span class="dt">value.name=</span><span class="st">&quot;percentage&quot;</span>)</a>
<a class="sourceLine" id="cb3-13" title="13"><span class="kw">colnames</span>(dfm)[<span class="kw">ncol</span>(dfm)]&lt;-<span class="st">&quot;percentage&quot;</span></a>
<a class="sourceLine" id="cb3-14" title="14"></a>
<a class="sourceLine" id="cb3-15" title="15"><span class="co">#ddply()函数使用自定义统计函数，对data.frame分组计算</span></a>
<a class="sourceLine" id="cb3-16" title="16">dfm1 &lt;-<span class="st"> </span><span class="kw">ddply</span>(dfm, .(segment), transform, <span class="dt">ymax =</span> <span class="kw">cumsum</span>(percentage))</a>
<a class="sourceLine" id="cb3-17" title="17">dfm1 &lt;-<span class="st"> </span><span class="kw">ddply</span>(dfm1, .(segment), transform,<span class="dt">ymin =</span> ymax <span class="op">-</span><span class="st"> </span>percentage)</a>
<a class="sourceLine" id="cb3-18" title="18">dfm1<span class="op">$</span>xtext &lt;-<span class="st"> </span><span class="kw">with</span>(dfm1, xmin <span class="op">+</span><span class="st"> </span>(xmax <span class="op">-</span><span class="st"> </span>xmin)<span class="op">/</span><span class="dv">2</span>)</a>
<a class="sourceLine" id="cb3-19" title="19">dfm1<span class="op">$</span>ytext &lt;-<span class="st"> </span><span class="kw">with</span>(dfm1, ymin <span class="op">+</span><span class="st"> </span>(ymax <span class="op">-</span><span class="st"> </span>ymin)<span class="op">/</span><span class="dv">2</span>)</a>
<a class="sourceLine" id="cb3-20" title="20"></a>
<a class="sourceLine" id="cb3-21" title="21"><span class="co">#join()函数，连接两个表格data.frame</span></a>
<a class="sourceLine" id="cb3-22" title="22">dfm2&lt;-<span class="kw">join</span>(melt_df, dfm1, <span class="dt">by =</span> <span class="kw">c</span>(<span class="st">&quot;segment&quot;</span>, <span class="st">&quot;variable&quot;</span>), <span class="dt">type =</span> <span class="st">&quot;left&quot;</span>, <span class="dt">match =</span> <span class="st">&quot;all&quot;</span>)</a>
<a class="sourceLine" id="cb3-23" title="23"><span class="kw">str</span>(dfm2)</a></code></pre></div>
<pre><code>## &#39;data.frame&#39;:    16 obs. of  10 variables:
##  $ segment   : chr  &quot;A&quot; &quot;B&quot; &quot;C&quot; &quot;D&quot; ...
##  $ variable  : Factor w/ 4 levels &quot;Alpha&quot;,&quot;Beta&quot;,..: 1 1 1 1 2 2 2 2 3 3 ...
##  $ value     : num  2400 1200 600 250 1000 900 600 250 400 600 ...
##  $ xmin      : num  0 40 70 90 0 40 70 90 0 40 ...
##  $ xmax      : num  40 70 90 100 40 70 90 100 40 70 ...
##  $ percentage: num  60 40 30 25 25 30 30 25 10 20 ...
##  $ ymax      : num  60 40 30 25 85 70 60 50 95 90 ...
##  $ ymin      : num  0 0 0 0 60 40 30 25 85 70 ...
##  $ xtext     : num  20 55 80 95 20 55 80 95 20 55 ...
##  $ ytext     : num  30 20 15 12.5 72.5 55 45 37.5 90 80 ...</code></pre>
</div>
<div id="方法" class="section level2">
<h2>2.方法</h2>
<p>绘制马赛克图可以使用ggplot2包的geom_rect()函数、ggmosaic包的geom_mosai()函数、graphics包的mosaicplot()函数，或者vcd包的mosaic()函数绘制马赛克图。接下来对他们进行一一实现。</p>
<div id="ggplot2包的geom_rect函数" class="section level3">
<h3>2.1 ggplot2包的geom_rect()函数</h3>
<p>这个方法比较复杂，图层一层一层叠加得到的，不过灵活性比较强，可根据自己喜好进行修改。</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1"><span class="kw">ggplot</span>()<span class="op">+</span></a>
<a class="sourceLine" id="cb5-2" title="2"><span class="st">  </span><span class="kw">geom_rect</span>(<span class="kw">aes</span>(<span class="dt">ymin =</span> ymin, <span class="dt">ymax =</span> ymax, <span class="dt">xmin =</span> xmin, <span class="dt">xmax =</span> xmax, <span class="dt">fill =</span> variable),dfm2,<span class="dt">colour =</span> <span class="st">&quot;black&quot;</span>) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-3" title="3"><span class="st">  </span><span class="kw">geom_text</span>(<span class="kw">aes</span>(<span class="dt">x =</span> xtext, <span class="dt">y =</span> ytext,  <span class="dt">label =</span> value),dfm2 ,<span class="dt">size =</span> <span class="dv">4</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb5-4" title="4"><span class="st">  </span><span class="kw">geom_text</span>(<span class="kw">aes</span>(<span class="dt">x =</span> xtext, <span class="dt">y =</span> <span class="dv">103</span>, <span class="dt">label =</span> <span class="kw">paste</span>(<span class="st">&quot;Seg &quot;</span>, segment)),dfm2 ,<span class="dt">size =</span> <span class="dv">4</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb5-5" title="5"><span class="st">  </span><span class="kw">geom_text</span>(<span class="kw">aes</span>(<span class="dt">x =</span> <span class="dv">102</span>, <span class="dt">y =</span> <span class="kw">seq</span>(<span class="fl">12.5</span>,<span class="dv">100</span>,<span class="dv">25</span>), <span class="dt">label =</span> <span class="kw">c</span>(<span class="st">&quot;Alpha&quot;</span>,<span class="st">&quot;Beta&quot;</span>,<span class="st">&quot;Gamma&quot;</span>,<span class="st">&quot;Delta&quot;</span>)), <span class="dt">size =</span> <span class="dv">4</span>,<span class="dt">hjust =</span> <span class="dv">0</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb5-6" title="6"><span class="st">  </span><span class="kw">scale_x_continuous</span>(<span class="dt">breaks=</span><span class="kw">seq</span>(<span class="dv">0</span>,<span class="dv">100</span>,<span class="dv">25</span>),<span class="dt">limits=</span><span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">110</span>))<span class="op">+</span></a>
<a class="sourceLine" id="cb5-7" title="7"><span class="st">  </span><span class="kw">theme</span>(<span class="dt">panel.background=</span><span class="kw">element_rect</span>(<span class="dt">fill=</span><span class="st">&quot;white&quot;</span>,<span class="dt">colour=</span><span class="ot">NA</span>),</a>
<a class="sourceLine" id="cb5-8" title="8">        <span class="dt">panel.grid.major =</span> <span class="kw">element_line</span>(<span class="dt">colour =</span> <span class="st">&quot;grey60&quot;</span>,<span class="dt">size=</span>.<span class="dv">25</span>,<span class="dt">linetype =</span><span class="st">&quot;dotted&quot;</span> ),</a>
<a class="sourceLine" id="cb5-9" title="9">        <span class="dt">panel.grid.minor =</span> <span class="kw">element_line</span>(<span class="dt">colour =</span> <span class="st">&quot;grey60&quot;</span>,<span class="dt">size=</span>.<span class="dv">25</span>,<span class="dt">linetype =</span><span class="st">&quot;dotted&quot;</span> ),</a>
<a class="sourceLine" id="cb5-10" title="10">        <span class="dt">text=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>),</a>
<a class="sourceLine" id="cb5-11" title="11">        <span class="dt">legend.position=</span><span class="st">&quot;none&quot;</span>)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p><strong>图形解释：</strong>这个马赛克图，从A这列纵向看可以看出各个指标(Delta,Gamma等)的占比情况，从横向来看，不同变量（A,B等）的宽度代表该变量占所有数据的占比情况，越宽说明该变量数据总和越大。</p>
</div>
<div id="vcd包的mosaic函数" class="section level3">
<h3>2.2 vcd包的mosaic()函数</h3>
<p>用该函数，我们就不用前面那么复杂的数据集进行绘制了，只要使用xtabs转换成以下数据格式即可，方便简单。</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" title="1"><span class="kw">library</span>(vcd)</a></code></pre></div>
<pre><code>## Warning: package &#39;vcd&#39; was built under R version 4.0.3</code></pre>
<pre><code>## Loading required package: grid</code></pre>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" title="1">table&lt;-<span class="kw">xtabs</span>(value <span class="op">~</span>variable<span class="op">+</span>segment, melt_df)</a>
<a class="sourceLine" id="cb9-2" title="2"><span class="kw">mosaic</span>( <span class="op">~</span>segment<span class="op">+</span>variable,table,<span class="dt">shade=</span><span class="ot">TRUE</span>,<span class="dt">legend=</span><span class="ot">TRUE</span>,<span class="dt">color=</span><span class="ot">TRUE</span>)</a></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="graphics包的mosaicplot函数" class="section level3">
<h3>2.3 graphics包的mosaicplot()函数</h3>
<p>该方法和上面类似，掉包就行，数据类型与上面相同。</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" title="1"><span class="kw">library</span>(graphics)</a>
<a class="sourceLine" id="cb10-2" title="2"><span class="kw">library</span>(wesanderson) <span class="co">#颜色提取</span></a></code></pre></div>
<pre><code>## Warning: package &#39;wesanderson&#39; was built under R version 4.0.3</code></pre>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" title="1"><span class="kw">mosaicplot</span>( <span class="op">~</span>segment<span class="op">+</span>variable,table, <span class="dt">color =</span> <span class="kw">wes_palette</span>(<span class="st">&quot;GrandBudapest1&quot;</span>),<span class="dt">main =</span> <span class="st">&#39;&#39;</span>)</a></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
</div>
<div id="参考" class="section level2">
<h2>参考</h2>
<ol style="list-style-type: decimal">
<li><p>《R数据可视化之美》</p></li>
<li><p><a href="https://blog.csdn.net/tobeyourlover/article/details/52704333?%3E" class="uri">https://blog.csdn.net/tobeyourlover/article/details/52704333?%3E</a></p></li>
</ol>
<p><img src="data:image/jpeg;base64,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" 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================================================
FILE: 2020年/2020.08.25马赛克/马赛克.rmd
================================================
---
title: "马赛克图"
author:
  - 庄亮亮
date: "2020/11/10"
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
---

记得安装prettydoc包，html模板在该包渲染而成。

## 1.前言

**马赛克图**（mosaic plot），显示分类数据中一对变量之间的关系，原理类似双向的100%堆叠式条形图，但其中所有条形在数值/标尺轴上具有相等长度，并会被划分成段。可以通过这两个变量来检测类别与其子类别之间的关系。

### 主要优点

马赛克图能按行或按列展示多个类别的比较关系。

### 主要缺点 

难以阅读，特别是当含有大量分段的时候。此外，我们也很难准确地对每个分段进行比较，因为它们并非沿着共同基线排列在一起。


### 适用

马赛克图比较适合提供数据概览。

### 注意

**非坐标轴非均匀的马赛克图**也是统计学领域标准的马赛克图，一个非均匀的马赛克图包含以下构成元素：①非均匀的分类坐标轴；②面积、颜色均有含义的矩形块；③图例。对于非均匀的马赛克图，关注的数据维度非常多，一般的用户很难直观理解，在多数情况下可以被拆解成多个不同的图表，以下我们会对其进行绘制。

## 2.数据介绍

数据构建代码来源《R数据可视化之美》，任意拟定一个数据框。并用melt()函数将数据转化成以下结果：

```{r}
library(ggplot2)
library(RColorBrewer)
library(reshape2)  #提供melt()函数
library(plyr)      #提供ddply()函数,join()函数

df <- data.frame(segment = c("A", "B", "C","D"),Alpha = c(2400	,1200,	600	,250), 
         Beta = c(1000	,900,	600,	250),
         Gamma = c(400,	600	,400,	250), 
         Delta = c(200,	300	,400,	250))

melt_df<-melt(df,id="segment")
str(melt_df)
```


计算出每行的最大，最小值，并计算每行各数的百分比。ddply()对data.frame分组计算，并利用join()函数进行两个表格连接。

```{r}
segpct<-rowSums(df[,2:ncol(df)])
for (i in 1:nrow(df)){
  for (j in 2:ncol(df)){
    df[i,j]<-df[i,j]/segpct[i]*100  #将数字转换成百分比
  }
}

segpct<-segpct/sum(segpct)*100
df$xmax <- cumsum(segpct)
df$xmin <- (df$xmax - segpct)

dfm <- melt(df, id = c("segment", "xmin", "xmax"),value.name="percentage")
colnames(dfm)[ncol(dfm)]<-"percentage"

#ddply()函数使用自定义统计函数，对data.frame分组计算
dfm1 <- ddply(dfm, .(segment), transform, ymax = cumsum(percentage))
dfm1 <- ddply(dfm1, .(segment), transform,ymin = ymax - percentage)
dfm1$xtext <- with(dfm1, xmin + (xmax - xmin)/2)
dfm1$ytext <- with(dfm1, ymin + (ymax - ymin)/2)

#join()函数，连接两个表格data.frame
dfm2<-join(melt_df, dfm1, by = c("segment", "variable"), type = "left", match = "all")
str(dfm2)
```

## 2.方法

绘制马赛克图可以使用ggplot2包的geom_rect()函数、ggmosaic包的geom_mosai()函数、graphics包的mosaicplot()函数，或者vcd包的mosaic()函数绘制马赛克图。接下来对他们进行一一实现。

### 2.1 ggplot2包的geom_rect()函数

这个方法比较复杂，图层一层一层叠加得到的，不过灵活性比较强，可根据自己喜好进行修改。

```{r}
ggplot()+
  geom_rect(aes(ymin = ymin, ymax = ymax, xmin = xmin, xmax = xmax, fill = variable),dfm2,colour = "black") + 
  geom_text(aes(x = xtext, y = ytext,  label = value),dfm2 ,size = 4)+
  geom_text(aes(x = xtext, y = 103, label = paste("Seg ", segment)),dfm2 ,size = 4)+
  geom_text(aes(x = 102, y = seq(12.5,100,25), label = c("Alpha","Beta","Gamma","Delta")), size = 4,hjust = 0)+
  scale_x_continuous(breaks=seq(0,100,25),limits=c(0,110))+
  theme(panel.background=element_rect(fill="white",colour=NA),
        panel.grid.major = element_line(colour = "grey60",size=.25,linetype ="dotted" ),
        panel.grid.minor = element_line(colour = "grey60",size=.25,linetype ="dotted" ),
        text=element_text(size=15),
        legend.position="none")
```

**图形解释：**这个马赛克图，从A这列纵向看可以看出各个指标(Delta,Gamma等)的占比情况，从横向来看，不同变量（A,B等）的宽度代表该变量占所有数据的占比情况，越宽说明该变量数据总和越大。




### 2.2 vcd包的mosaic()函数

用该函数，我们就不用前面那么复杂的数据集进行绘制了，只要使用xtabs转换成以下数据格式即可，方便简单。

```{r}
library(vcd)
table<-xtabs(value ~variable+segment, melt_df)
mosaic( ~segment+variable,table,shade=TRUE,legend=TRUE,color=TRUE)
```


### 2.3 graphics包的mosaicplot()函数

该方法和上面类似，掉包就行，数据类型与上面相同。

```{r}
library(graphics)
library(wesanderson) #颜色提取
mosaicplot( ~segment+variable,table, color = wes_palette("GrandBudapest1"),main = '')
```



## 参考

1.  《R数据可视化之美》 

2.  https://blog.csdn.net/tobeyourlover/article/details/52704333?%3E

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1nJtTNAj6jJKAAZEs8WgqihJRgX9wJHOkYoXkf8iwR2RiKKqRRiitWw3lYdnr30cDzNae/8Tw/1L3sS5gFALINXpKDQgmp1pQxW86M3O8aoqMTlNtTGnSjATM2tjXEgCYfS3hKyuCkFHkzBeScI6WKhFVxLuD+EQLt4TkOo6CU5f1drrhvrrVly/dspDayfe+8EtQx7fuJG0HcbZLyyc1r+5qXbojtE1xa0dt4x/5c31r9hA6MYtP5DrVgijoiV5Po6KKs3MBOCVStFlgez8bG57v8/vq4tZ/Gilfr8pX7VqJm1EzJQGeg3j5/xX8ruWMbrG4oduFyXxMEFyQlkpkMeJTvhKbCMY1j/o2ykPlEmSr335KxvYPvbZydev29P65KNrX58+c92zfxv6+Kil76PnU1Sl6fe+l694//zIweMjUO1ZPnH2TU3fxqa09+l/6OHXAQgEAaSZuhddMDiaZ1epkRAzpTKAxyVzrnGh7JLreGi7qF1VqO5WvoGQ0DwF584uo3cpz4sCBzc9T9SAQPKgoqI082X2QfxhshCzXmZ5Jmoo6MvOYAk7gCWH6cudN5+98oSroZZNBoRWbuEw1ygDmqI9OZ36aJrbbTPYqIFmZrldRpdFA27ONADF4/HXxjyKYhkRU9LgYsIJ6e+pgHAkGUjkgUhLSBg2N9w3IMwpylMaKScT/n6efcC+PLN8xActmMGOhu+4bH6EpsV/yAgOoO0n9/+HnR2B5h7hr455LAPJ1+wc+1i1AYGhXOs6eQf4IR+uigYUp8WSlweZTnAWFNpz6mJ2u4d60kbEPGnUwENEvUTbVJbqTCjIAQJlPo8IXEUNdQEJcCAhMvd/gvy8Q3E6TmsbErv++Z2tRuuN/7f1X+zsNyv/vYhoN066sbVlcRuZiq/iWvuP7rEb/7LuhyPfsFPLMffdxfMnz7+1fu5qEc0RPdM6QIHLo14FgCDKRFYNMiWU1MaoAsLfupYpQwobhpDby4OfkoJ4iZQWPyy9jNLm8wLSdEtUyzvBB3lwOVwbLXYqnl6U+o3+Qo/Hnp1ttBtL+ihOZyBQXGwBS0Z9zJIGwfoYXGwTYYlLnVeWdKFwoCSqAj0/LqoW8qk7kShFiku3kK9cfCPVHyDedt/qpeyLL06zk4uXtU1DyfXfE2fPmrng0Ccjbhg+flxtq7zz3ZUzXhrU/O6sjqN73mrbXD2iY/Kzm89vbBp7Y/3VcwaOI3vqq674XdnlYysH1Ym8GajvcgekQQFURnOzZJfFEgyCCwqLtNy6mKZRrzd9RMyrUkMdR+Nfdbfu7DIBzCIaw0J5kS16edcXuNOdBXwbyU1J1ewxtvTOqxtHP/3+JIOl3xOz3v0nmr9Y+f2d8VNjp4xrbbm7jQ5mdazJdtYzasufW2r+83/H0fEE+3DTXbdNum1+Hfd4stOSZuvMURh1OXnyAPjtnsaYXeumMPAnaOwXTOb4NVYT72PqU+xG7xcf6mPNQAQX6/IUcHKmcllV1UUlBRXFZdIaYyZNUjgzJ6Rpm8u6mKrApzM0vUgYbrTrbF2SFHbS18Xa5GhSmF5P7JYqZODSiqKajIK/VYNEqQIEZRigFxShVFwJURhGD6JU0ZlDP443kvW7ccNSPH2abWFfCns140peoYDeNeZHHSqlRgkMcp00ViJSV30QKhkjagSue7JMQH4304/FkrTgKC9Tjh69VLueUScBrhFPNVAUJJTKEur6Ce0u1dCFuorNZH28UayJb2IaDjjNtKWsWmioXPicrpB365FYFc3LTU9PA+B2dlqdhUV2QCMFCAazGmNBl900ImaXkg7mVCR4KJVkyfpRJFR5F86oRckaXOFoe0m/7W6YevPVY5uWvzf1w3P7vm99YGyIHU4139VjH6ob1tLvqqpxR9u2r5m2onVI9RVXsHUX9eMTLkxQdnCc6AuVEIv2VCsq3G5XOGzt77rMZaWBtEDvNOgN0au8hkhEMg3QTPzqkVUq5feAklS7rOucMleiPU7ivc6kQtuiYCqrfNTdlVF8fxLxCKgtj3iUQC44+jrzOa06UfyDSESH3x2j106vnpWmTXnhlT1o+UfT/qt9NdGau79/Zhf73+exCP2T2Pz/ZefZXez6I/gIyv/EkRs7Yf3IFpM1FG27n5x++NQ9Q/otPPTGQSQBH/Pd/9Yf/vjjne1sx152gh0p6f3eKHwYW3/EZZ93sA627uCCpcfMzwj7AIC8WN4IKljh6miAWKkBQZHNZgqip6CSZLOSmpjVSs0yBZocIpTouZRiZWGortKL8gsDiITjI5Uik+LHJ7FXiYTziRJnywoMgWdwNFstbzxXRcbikdvy72CqiPvXAaQznI/t4Idczsm9VLdbktKzzeY83vfZ7QGDlqalDY9ZNLRSTbODPb0mZneCvyYG9BLcSxY9KQVDSTe5ArmSp7voCQYwWfE4HPqnwOu4AyOYNn/C/fPZh2fjx7C84/aZ8xev2nXHraxT3vDKpkVrHaacdQ++/xGdXTuy8Zr4NrZo3PgNgDCXI/UBnh9eKI36VZeLN+NWnxscUBNzSKpskmtiJleyNBOvSfVEKuQRD2+0Iw4l2BUdoTI+ZiikBS+9h9OfOtrxL7aJvdiOkQOHDrc2tEs72U/HmW846xyGi3DSZ3j9azd1FvUDImwoz+E2NIBd1OtGAIdVkjTZUhOTqWTlLbMzaamUcEELnGVzAbVA0BHKleew8ew2Ng534wR8gL3Dxq5ZjO/xGuQP7A55A7ubrcHDnUMBdY8RLs0Mg6L5BgnAqphMiBbFWBOzKNxLAnII3zehaKqJofOXXkp5iCsitPAkbol0bqDV8RN4ijmIm4tl7zK2BLqkUsalGqFvNN1AqVkBQDQJoSl5QlZS0MVSLhaCX7P9dHD8OHKMEwKWxLu8KBdxL6ZDTbQo3e8nNquVEFemy2DIsGlmjQdbOr9BNkt+r+zlsmTu1FB3wd0z5VlnstgW8BBwKLpv9YJL5RlPdMKNOALkU1L14E93sr+yVfg43vTxgZtW/GXnd1vevKGVHafhuOnyAlyMU3AcPjDybB377rOT591Y2mUHeYJu/Ug004jIzW+QJFm2GGhNrMaABoNsUijK3QmbMnfKFN2XPIHtjr/NdmE5uRrDZG78Xj5t2EIGAOCFiawBT+ozgRw+bSAGXiPLwM0MRsr79e4NCw4Rxa5IJL6kRnJurq0bOKEZy79hDV4k7gVL5JHn1l4AdgYS+tfxVS0wMJpjIcRkNiOAzUBl2cq/UrNZoXwP3VtwpgBXF1eWAOXEQAdVfSMRDKBcx1awhYvEZm7FB7CZETKxJf4D39CN6/Hf8XkJ6VIlly6LPUkqBVCQArccJKJUl6GXoPq6r3PD1MsbzldfSPxvRcyR3dAvmukGo9nI1bbxUPHKisdJjEQxq9QGilBcN36X0mUp6hA6Y9DpEYujXuXykscVRBpkK4wudhzbcaSC07GdfUgtRrZEms9Wzok3cw1WSi3nqklH6R3oPr8kYcedOm6WR9NMYETFagVwUFlRVM1MVW5RVLtHv11adI/EnAKwL1KEcM/JO9nv43fpSiwh81U7+qQGdrQtXseFv4FZvycdQPQ8+VKfDHgE0jgAfBZF8RpdNTGjRO01Mer6daQROSBexQQy16Hxpkj+kj3BXubXE3gz1vNr/PlDb76Bs9nSNzaSY+xxdivejVP5tZCj0mP/OYvf4smfoAvtpHU62rkEFkhGowdsNrvdbQXBV3ZNM9TENGr/TSzoRn/ZLXHoEyAo4ckJSx+au+BBspEdYacX8yA6iCb0UGXmlKkTd504Fz8rb/gchAXYat0CdkjjEZynUFmSCDVIJg9AhmYypVOVEwBXRFK5UWSV22N7Ev4uHU92T9OQe+LX7PPaKziWzWZnfL9pJMZW1bO5OPS3LSUP1S3lg9poocvnk0ySppm8njQw8cTzu4wWMA6PAZgtFm40C/WaRcikzJbSWfPzuXKqQ0sxKLdfgl3BF0A82brsgaXLW7gB12EPzH7oTqxuZWvZKtp73M0Tm+Pz4vvlDUeOLdxZwVwPk1KRVS2cQX0ce4s4n+RlpKcHICC7LeCGy4rdAbAELNlGX3ZNzCdRYyq+uhvwVHHWrRpn+IvGGoVFl/MhDadWMcJP9LZen9cr+din7JuOx/ZeN2FqnzFL7767DtWvZu2f2TrnyermlsJrn977BC7f/lkz5g4srx3e8+orqypveeqmzf8qL/13n8KGgcUDKqrHbRP6FwNIYiqrimdLCgBFNBhVKlHOuxSdv3y2lARgcoLtYrOlOn53IGEMEF7k+dXC13JCQdThQHSbDQaX08hRhsdSYuuXVBAOtyLx4BHI6+6CYLnlEXbyLfYFex/D9zz7BAf0ztqVZ+7EwHn6YufCPz33/DraBqjXfyHBI2K+RonRKAOiVZYkC3BDJ+q9VNpUJOaj+sXtVx6h57CC2dmLTMMKdPlKFXO0a4DY+dTwvZeN/qJLhrqRy8gSsx+T0e52yQh+v2ynlszMrKwci9mcnemSzdRvt6NJiOSi+EtCbgo1UyM3WkiKOMKJUtMlGvCIi78nPihD2fPbzWFJ6WPdxqngfix9q9Sr9HQdwoJDth5mUy/nm1hKoRixV/mpUJxwVT85trLi1EAa6twb+aS+9uuhNBsStmnSbVMVzTXLnPpUo6oYTYpJ0C2VLGYDkWXJqFCUkhDL9evG+ooUZ3VpjZj8Izex59h6fnXg56wfNmF/DGMtC5Pi+GHyHdka/47Y4j27dJCYyF2B7wZVlZEQEERvNFFF4QqiSgVDdslOjEH5Z65AarLLowIDZAGWchEZbA/LwDo6mozsXBTfQUqoXleVJiZ0RugfzTJISFUVEExmlYuSRP1I0IAGUcZdOgxNpl1qFqqPbALSzPPvkbfjTVJ6vIrs30m/RXi/0ykkLWUbyWw9T7KjVgXRIIFRJlTBfN2EuvH0BNZX4iUpmc0y8bOPPmIblXMHz60Xa1gA6MDkVFt/ZIKYnGpfnBa6sUmAHY9/mJhqI4S4fJ+QL55xoKIY+VYNoOZTiaaCvQtCfCFHMMy1CH34IX7GMmfKjQd/UoR8AzFIA+R3QIHeUTdBWVYkSTznFd6SVJko0DW+xLKLeyTRZYcwiGjADQ/jqVO8uP6KGOiGzmqyKN4maq1OtpHWXhja9SRIRonoRhEaJZ5K0NrOFyl//vMAAGKNdIQ+qATAwK1gBjVKRVTIdwCUpB/rioP0XWLww7EvHPD6PGRL5ZkqbKpcLx3ptW2gZ/z7GYIdmjju9pfm6E8Zq6OFTovBQvLy/P78LIMhaEkbFrNYZLfbPjjm5jWdnDM4JnvBk0Az/y+ZVYSeXlcUJWdMvMcN9+1u8h0omny9N6YT+huGr1r0xzd+Or/5xbv/On7T8Y9PswO/X3znY5MWPHHDsNfXvfono1K6rn7f+K3vx32E27h55MJbxwOBFVznDsUNTsjh7BvIojRg1Mw2n89szrWA2WPUFFDSh8QUL7iGxEC7mCz83SHi7H5mUeZ0aISzRVANCgTlw1AfH9d2D8WobftHX+7YNsMT+hpLLZbJM2ZOJJNvaZk+Q5rNdrPv2XH2t6XzFTdbPuiJ9jP3rwh0PPOXNWvWAMLoCyfoMWk2eDi6esRYymclxCubh8RkDexcM++lZZJuOTk32SdwmnJoYkjgUBQyIf4DZqJx81Mjh9525cmTzcuHVf/BTQZgFvauOZFVwBH49ZIydr4kH4iQK81M2CcaDRi9Gi+obTZhqFy7xwIOIyi6fTTdPt5ft4+oT4Q+ecShOXlPGioU/BLkji3iOnVPiAnZ9vHnOw9ON/mw7Jv+1omT5kyVp7dNmDnLjWVoRx7zq9vG4YSfTjyy5vt7ViWNk9BynD61y+DMEKROSUpzOLKcJlOm3+OkzuoYFVUUVMesmuoZHFNTel5aloiry3bI3RbgrbNeR4XKwOMJ6AVAxMMtOP2GaQZcT2aVs+/Y3zDt7LdoiJfID985vmNc3Qb61PyZM+d3NmAPdGAahth3Jx+789Eel5+4rCjB7nSOkgMeuCKa7SZElSn1+qwAPhndyHVz283akJgZqJ4bgp8v7QVDiRwWFgxH9KfOeieocBWpiZ1l+9eu3bj/ufm1o2uv6ocGOq9zCZ23rKHh3ZdLPsoafsVgoKAwtzSV26sYyiEKd0SrzFlZAwZIfRwOUqzmSkGUpIHpPXr4fJFg8Kp0K1jRqlj7qv2GxYy5Eke5wr7FpDpWXFxYWDksVqi5e1fH3BkXz+n4pxIOWz79gRHv0LneqJs2FQ76ewKfPao+pSsqEvmsj+ykQFfCF6ZeRcGFyUQK8v26El/4WGzqS33OfxjpXbL2ndc3sTfYvm9+vP3WksHVg5tvOnmsZKGTFc2buvrNabOfa5w5/drrmura10otT/ceNqZjJ5Xzew187smt/1i1bPw9We5Roeh1xYVrZ732vkM6L1UOHVlb2WcEHT5q0qRRuwBhBYC0lmeDB8LRdATw2Y0Wg8Fo9Nolp1MaEnNqJkCjR6D/JfU5336yUOPaKqJJEuCQeFQirWX7O+6YxfZjqapqE/61bQ958LsXt8S/40CwpeDekav/vh0ILAPAD7lsA1jEZFcyGsFksprtJg9Rr4kR6DJ/ZWoO7uobKtNnnyJUlrW3X3ttO14phMgLHn98yIjzPqkFgFxoY259XSt4oSTqd/L0JgaDT/NcE9PAaBctOk/sjOTEKYEwCRGJxwB6tajQpMDBcxoHXzN8CJbum6GLZe60066mRmnd+eJXN6mThXRIWPMH/Un+NdGgxLmTUKrIsmYzWa0Gg8lkN4P41WCzUcXkofbu2oTf3cjSZdpuokXRuGOyi1dx22KswGZWhYd5AffOIrF9jYxdh40sI74Et93MVivueDXr0gYPcG0ouF4DRIkAevQioLvExgPivyvuhO7qQJ5BQRgeLXS7XPrsKDMzI6PAajSaTPkuq9WRKzu46XwOzWzPRJNH7+G7krl7+OC8ePqbjJDCRIiEfKFykdziVfBd8q+ke9n++uvnTGL7vy529F437Xwso/dL097ZwvbVXz9jOnlw3rz12+LfSS1Lh1+/urZpy+F4kfhtxYuQjGCut1tMFxHAq6vrscoOoatQFU0Xx29SyV/XLRG8TS0ierkyof+ZtWWXEPbn7boC9dce3JHE5yf0pzhpostXLJYMcLnSvcYhMa9mp0Nidu8vu/xUrvPeVQMOCCQs6MzrxGVT5986ecr8W6dQmX3ELvzxh7swGyl/I6Xt6/70Qnv7mhfYKbbnQTS8jE7s8wA7B4LrOep1cC1ckMMn1Hl+RVFNlKpZmqrlcuQEq9U9hBOEwa5mQEaKzBKmSBWoSQVlTvPepDFCnPndRKFJtuemosq2GZrG9p/taZv8wfaPbt58TGf7vePdSx/wsv5K9SPtbB87/T/s7H10mU722JDgM67pTN1euaIq8dIsyh+TpOUZ+fg6PcNnz/ZanE5V4I0FhsQsv8m6iSfIBUmS5S2dL8HBXl8ook+LIkFBaLdMkafPPzxZ2v7R5zsmPXeFIQMJ22e1lq48uri9oOMZ9uLa9lNYiho3Z9+6xqU/bcBDAybXN3ZFFJ3LddVEh0mcejw5BCxZZVnUS7wGFxqlMrTMRy+JIqpdWewrCD+6iu3/sre97yvSbCP7xLR8SXyH1LKxZTYkqp/1XIZ4dpmjpLktAEU5bnchWNw5lhxTli9rcMynUdPgGPX+vJ2/2BgiqPTHK2HB5clePsGgXCkPt082oetPnbx1/bDrDtW395oycuG8yJd/3/Xu6MZHa5Zcv2zRrf2wZn1HILfzsvKx+b0rCstHz73+8VXN/8y//JriK/qHR/+30LeE6xuRa8AjToRYDHa7y2UyEIfB4fWZnHbn4JjVYrfL3HVyQt3QpktOVnRhgnBcxKOXvoLpIyFPwCO6cjK3bsas9tdeeHRt8xasYDuu+TD4aeiNN0jGwgknTn4e//yqK4UOT/Gc4zM+cENZ1E8cDrfby3t/j9NoJ7JNtumyPcmJ1sVDgItr7tQYgH+grxdrpR2zt72PpSLjsXRp7XUHt5Mj8dki4Ynt/EpI9JkPcrlm6BV1m0GWiYgIK0G0GNEuC5llKWndDU1X/x0SbTfiOtaElf/INyryZYexkjVJLfFF86aMXUzaumS4AZRtXEaWOMsoSyaOIVng81ETVTMyMjNzVEXJ9plMVLbbMxQ7yDqidR3RdPz2LIDSIO1WQ8wBsin/pGskRZpuUfew19lm7LMwJ1eRcrT7sG6R5NCsqBgvN92NPdk7uARPdt4vtTDH4m9q1lxH/PGvvE03jMkcer4XnuKKI5gApOW6bWqi+YoMaKSUSAQlGWWzQVWtfIZmMSoUAA1mj4T2S2cBqaROkYZeq3KlhdkClOu/mD2BI48cxZHsMWxja46fYO2kPwmyZ7A1fiy+DRewhcJLzK17ycs1KTC73ZrXK0koahm/Jgob/pNT8no0p9XJMTHDAFyVskQJkKKvhBlTUzxHyokifvTqgNsSaw9mmBRz7n4cwoqu+vcfR9RErqqfl+fkfr2/YcZNo8ic866XXnR8Z72xNZI450HXce2MIn+oKqkIYDYgmvQhAm8c7YR/MwyOoefSIULSSMJGySlCWEwR6LrOB4nC0uhAZiCmDrLp6+3xekDI4T38Id7D54ipCHUbcnIcfn+uNTMzIFGXy8qjKd9qSbTzYosp2hbbF7bnuBrm+REWRw08Coc18VTQ4xFQ6+EJhDmL2m6/c/OZG4cpn31T3XpmM9quH32qucGAVz7Z9jEdXMUObcyzBF8xskNVg+knbU8BIO5gJWSlYgMK7tcIpZJMAaCyhONDYlbqCOKOo0cV29lA1ylOauB7yBN7yOHlOmgGQ75bkoI52TabW3Z7qCzl/3/2IIuHzuFynuSi2BZnlftyiBSnzxyCyzwcrImh4e0Xbhz2+9mfKtWtL7xTP39x26LeM2aFPyFVQ7CnuWmyw5K3EXsOrqIfh2dPY5tNjY2nGm7QTxGQIqmCtoEHIlG/Ag4zmKnd7qNeu82mSJSaHQ5QoCRU1lYi9ElBdqqp5pwa1sv/RAMmELwQB0baym968pqFwxaOC99ePv7pgf89chFZcXX5l1NzcyPRii+nphf8lzhBwpbiQanl0rP6Dg26zurbad4v56mukCugE0Wi7Vh7JsTasSV5lIO0dJbKBcljHAhLOdJqfN6cwad7QYchPV3OyCA+n4mYMrPSXCNiBtuIGMiGNH4pGWmKygXqpwH4S8+ePzvOII575nOCTh4R15lS69q26gmSEBt94OCr7YtF6z7vlm8b7mpdcN+rL/fHcyhjZk77c8arjmflv/Bn9kZObzbAuFFEB4A0ST+d2BztZXeaidFqTfd6iV/zO51ado7Fn+avjxnT0sDFqcleG3P6QR7xs+NNXUfUIJTSVqjbjT+pBpRfbpXXFSKawsFwiBuQbNyyZcyzs2sbcS679w9k3/mvbhr+6qufy7sbvojGrt10dOm6WtZ5ttes1keObtl5BAjMBCYFpHXcnkW8R87TLC6j7EsnBrDZ8jIhM/OyYp9LSycWo2xQPZ4ctYBHz/YyHc11H2qb9S+iA4oURXyC3SM+0WGqPrVIoJJaFCmMXFRdbixfuGzBqEk3j1qwfGE43Pbogt+Nn93Y9siC8v1T6+qnzxxRO50cnPC7BcsWhCMLly6MTZs8uu2RtlBo/iNtYyYOnz6ttm7aDBHpCoDEp+PghZnR/7I53U6Plce2UaYyMYkJqxeRED/HBp/idDkbYkCRuuwmm93WEFPtdgt6FMsl5xX9mtiW3kNfypcpEhAfkgPKkCfoEXdAGF7cGCBD0YAVbOGWH374gX38448/vsOW4BViZBv3vHrfq8eO8RdyHMhFiKNCMGoniiKGmUaJSlTVsUcEbCpFdAhyJGBIAFHnAbag8wAAgUm89lnw/0o5D7g2jvTvPzOzu9KCJNSFaAKEBMYHAokSuQpiY04OODjYsWxCcjbkNaluuPdyiXuaS0jHpPfeE0N68fVO/ObSe+8uy39mVlqEzr76oeyi+bG7U3bK83yfkUZBGZwCMyKlaRaXRRTLC6E4JyfkAld4DKmpsbkrK0ttpSafxzc15nHqTVNjepQycUvmivi5NiuyMYtA0qyNo3NOVr9OFfZJmt75WUW7VMhOWtE4fsubj9zRP33SzuaW6LxFB3rWTJj4xSuvXdHyYsOAb/bpj257c+OS5s4tvmrim7appHXPputbn8kPlVdURssit194/xklXdGr7p3261Hh7uKKUGH0uu2nzi8Pxya1V5qmAUYu4UfygiRwVi0/YrQaWIvIdGcQ4pBB7dzU9snCdpLZJF/SOXJNjdRPPa0uMhVd2TKurqk5Mq5FXFPXEB0/7ucNExvqGieOb6wDIIw7lSbR99oBPqhmvm9ikm0mm7/c7yzPc+bV1IrpYEmnX1mlhbZglpActKMVbEo36zBrHWyifBGnSASrw44ZvIhr6bwgFCxiuH4R45HIul+c91p4c3j55tf/fvilPddGFx5b8zJqf5X9DCi9v/m10vvcrj6U09uHsg/0Ke/29invHSBfX7VJ+TAv99nwkcNvfNd82xjlI/4/Su+rLyi3/ObXaPaLTJb0b6xlBfCX+DHKMLqgAOoieZk65HLlmXXU56PLK/RmGI2e9HQbys4GEGweShSEA0F1mAtak3BQbR1SPGxVVo3K6irbp3YM1ToJV3pGr452r7n58XnrWi6tr79h3tY9yqTy/KbYvMvxsYvGRLrPu/BCWegef0l+cNcmpeGP/qIz6oqkNPas06Fd6BEEkMAIbZHRaUaDTKd2RMKCgERqGDdkGNkrBpBGCE4XBIMoIpOMsR4lWko4kLBqJI+K5j8Faab66Q897w8yR4ALIR3yqYfpaPGg8hFyDSo70RG06A12/oayC49HL1E/s9K3DL2QNXzKGb8fhTCZCCJkRZgzSkcQkogAAdYJoQTf6LXQWZQQHjx2hLz1I7pgEIaGErEHWAIzAAhaezTEW+S5kUqBYFHUgcViJEbamxB9uT/ROLFE8QLBIegdsp5+naSN8spKbara53ErgY4FlFnoIwadmhP5X7VaYcvuz5QHAu8h/cO3K+s89eFTJuceP+dft9utd0xUFqDpyj3kqh3K1+H6uhrlzX/ZctHQEckuSNLhJG8MjPTGCNLRbwWDZH+Fr/6Jm7D5hAmyIDMiQ0ZGTrbVkMkqRQ3FUq17vL06HSowmDyctbXd2N5201ln3XjW5a88G6uvnz2nLjJHWMg+7W0766bZL10emd02YWJ7G+NFAYSwiCGdcx+ZGTqdRB35BoSomd9sMRrSZYQkAYOKeoYC8S5MM5WnxriwyfZwnAs9I2/h3kG0RVlFY12UNylYiiCAo/gZTriVRKwOA5LAgiyuTNnkwQ4Hyucer4lJXb96j39EPHUF+JnjK/5+briipGXeqiuf3np9+4YudA6O3jbYEQv6S2bt37Cle8be7rMBwVgcxo+Ir4APJkRy7enY7QbIl/LTzVK65C8mdrvDIed4PSa5IIE5pbQ8dlABTRX6S6xu1DgHrezj3QjuuaN9/n1P7N541ards5oXtJ3REgwFWsOdE/b9v3W9wlu7a432i6at2N7wzOzzq6tvrAr76ePuDExYn+qLI0JEDyCnCdwXdyjui3uFjR/VNMjMIUk6ao6YiGZWHZ0i/DX75U5H1aEgAOK2LmrkhkxmMUmXJFnOsjrBQR/drXNlOGl7yiCq4Y2Z+zTTkbYwT8qwtv73xo0CxS6XhZtDZ7WvpVaAD0ZnlC6fNWF+vigy+yj67YoVdz/PrAF7Z8wo/9mM65SDUhQQLFSOCbslO2RAIOJINwsiAoTMFr0emUykKWYSWc8XiHtk4gMlbe5qgAb7UsMIa0IFwu6bbumd0PqX1/72IW5Tjkmn/3QfCVmPHEWCwiKd8Cj0e7KGEUURmUU6Ebk1RiCQCHSypSLhfEr/+2Eqe2hQsaNeALBCVcRlNjI7Fh1Y7Gaz0W60ySYW9pXNXt9QQI0EXB1/3PjAIiZPQYprQ3RWgnr3Xd88KXuOu/GW5v7s6Kwj6xc5btOZJpzh7hmf2cktXDiKGxPRSYI8MjopD+WfMDoJeePRSb4QbvyciNkVzReismdxFD2z4Oyi0vHr6MwOwnTUfEt8ic9KPBFjIvYqgzhkDw/xTGK3kxc9YlKPgt969IarH3/wwP4nFG9dY+PEiY2NdULbnf0v3Hr7wAu3dHR2dnTMm5cy6s2OlKZTy49OL2AW1Ib01FNiGh70BD7YIdHEB79/Oej1B9UBL+6NL0aoFonqQehRdg4ip/LxIFqsSMPn2KuMXYbaUNsyJZw1fMrGrnIA6Qpa2n5Y+TuAYvg1fgUA6eAP5Nrjj4L8IMFW+uJUVye0D51Au5h8T7W6B7CZSZlyNlXeJ75ClUs8XEnM8as+Eb9qmXpVwDBeWUH+LLTzNU5DpKiQug4YJk0jh0pMoyDbnI1lQp0JPk9rzJdhoRy8xZvKwaN4g9Cm5HHsnddbrUub3bCVWHLF4ldiF1wYPjM27aFzzp37w3lvHP3F7rOrUcnw6jY6d1dT86yJ4eiY0sOnTO6//YLru+j0cyyamXhHhoZU2lu3GPuhiOexHiQ0HfQPYqfoh9HVJ1B0w2//heIgzFQV2SMV52iKgYTCOlIxU1N0cUXaQwR7uWRYkxbXSNDfPYvXhpfEa4MpdD7OPtrg4sg4yUbMNmIRLCjNZEJsvgbgEETRbiYUvqb4syENGQkj/JFkkzkxTAQrMmlscsKiQLvUAAeUNb8G7yQ062PCs0QKkEYsI9rR6nzH9imOvcoLeLew9/ghbKIUT+hoLlq5jiPvcYqZDnXNrC6WKXZGjNP8+VlGYAXOBfY556p5+ZaodTT0KC89ZE+UXqqiG9pSFPdShT1JcXDoO1XhHnmNmZqia+gnXgMYFag1wGbucZ7cAJnQGCmivUCW3ep0GlBamtthAIqVWwGovcRJi9eKLYy8TgmP0+BgddahWmkscQqUlpiPo4MhBwPPA1tV5FzFz7cKwm9+d+CzzzahATIdd1Du/G5GoOPWnR9+ofQoyl1qHsRXeDuriLez36eUA+dUeTlUxtt7N1fgvJMpulHDv1AchOdUhXek4hxNMZBQZI1UzNQUXVzB2vvoeGkj2IAMglnogXTIjaRLBGTZYORGZXcgqMUn8260FqnLBlSM7lL+uB+Vocqr6Rhetkf5tfL7vfj3qKxH+SMavZf++VuaSiUAhD7DLeIHkgA2yIZCCEdyXJ4cuz0tB9LAW+TMK3Ab3QxXJQWpdOWImbyK8arGGFaJqpEG2V2IO/yqihEFV1Wm94Xts3tnv8iA1RevaL1x1sDRP56CjrR2UWL1/ZBiOG0+WqzyvXWXXHDpANrEwNWGNfM3DSi/fHYJ/rbsp+8e6j5uKR4aUmlIXgO18Vocrdaz1uOkKrqR6V8oDkKPqsgfqZipKbq4gr0RJcl9kqDwq4yNv3kb1KtYuCSJSmbrqZpIDiOjjbIoSpJTMDbFZEdTTJAFWdIRyZowKGrdjOZBjePIDroW0tZGwh2UUz1yNcPaH1CQ4fikjst3rbt0NcHv/agMUij5c2Vc18rz5/NZJM3JfMkD1dAaGU3tegXFxQDlWSZTbXkgUGPKKtBBcbEui2SWhkqnxEIQcFgyozFLwnGq7ZUx0g03TH/aTYLqcnOkuuX8iaFL8zhXsVAn4a3SSDRSWl1/RVfoo3fmXTau+ubIbfnTo2vnNjQ0TVjXsWQjbb4+hL9FfuGvkV+cNqai1JldVTJn7srmu+7JLfy6KLhqVGhcaeOylsh5lbWnl49r6TrnKPVMv/LO/azH5ASbVEBr5VQ+UtQfAPb2jbbEazY1vfvCE6Xna+kHfxhi6RUj001a+kAasPTikemClt4lAX+3T+GCYcUDmqJ/lKrwqwogTCEpQjeUQBBOgS2RydU1JDM/P2g3GoNBuabG7/GMKZPlsC/fW50fjVVXsyDp7OxQNJZtNo6aSoF3p+S0NFDHPHgbYiBJgQZGv/ERLZmZ0t5q6wkJKnqMhzBz8MufZG0ZXsZRzHYYrWJk1TDShwoZfiVWbn2rce4L19/03NdfPRtr2nHzvKc/emdx/d3LDyM4XkaJq+cfm/bY8bqFq1fv6FyOvX+1oHvwefbOru7Y0zcz5q91cn3Tq52bInXKZx9RCGvWp8UlOEsQzpxD6T/05acLVrNap952xtZhP0xWx0+0iY+fnCrjtT1FbQ2389oqStRWanr34n+eflDP00eNTBe09C6rWpeVidoeugYAvcGv8LTaXynTgF0DGRLXuBwA/y5J0T00eaRi6JdU8UmS4qDyuqqwJBTvUMXlkqApuriC9Vdu9UkSBIfk5fPVpZGx4MYuV46oJ+kEY0tOTnr6qEKLpcQNmZh+SJ2ImdjppB56CnnSKS02+RpiJifBU2MEnYC8izsQ2clwI9I+1YYLf3Gtkw8SVgdtm4XAwyNdtX46hDAvXCL2GCmnN3ZetuitjjuuvUr5/0PfKX9DwuFDDfpT17zfga0rz19x8fIFq84TXdXF99Wdtr1n/m5lz4fKh8pLyPrJR8gyV+hdtuva4/Mv2Lj1ih27+lg74MwMf2tPV9/aEPAZUHI97ucl3KK2k5t4PReeOJ319ZfAyRW8pRiS+gUt3aSlD6jpeSPTBS29y6C2pIDWK8yCw0JYeIl7wbKhNGJ1pqWZBQEIyYUcNwVKAXHz0vPBYdBQiw8WTxJRTWOGj2+K1tf/PFpXNzVaf2ojO+KOwcEvTpva/POG6c1EmNrUMqWhpRkIfcaHKAN0OZ81eEfOGnzxWQOjb0jBFAZx/C+zhmCNsJ9hQWsvOLVn0n5GBm1eUrt/zK5jR21o/OiJKy9AhwzKa/6alefjSoYJlXV2dVyL7IwUqpp+Qes1ytH2RjTouvnWlnFKMOP2oSGVpeD1c2ZST4ByefGmpvMavgVOruA1XMnTC0emC1p6V0B9A0u1np97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</style>





</head>

<body>




<section class="page-header">
<h1 class="title toc-ignore project-name">混合图</h1>
<h4 class="author project-author">庄亮亮</h4>
<h4 class="date project-date">2020/11/10</h4>
</section>



<section class="main-content">
<p>记得安装prettydoc包，html模板在该包渲染而成。</p>
<p><img 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" /><br />
欢迎<strong>关注</strong>我的<strong>公众号</strong>，<strong>点赞，在看，收藏~~~</strong></p>
<p>创作不易！感谢大家支持。</p>
<hr />
<div id="前言" class="section level2">
<h2>前言</h2>
<p>在同一页面上混合多个图形是一种常见的做法。它可以在同一数字上 总结大量信息，例如，它被广泛用于科学出版物。</p>
</div>
<div id="基础包par" class="section level2">
<h2>基础包——par()</h2>
<p>一页多图用mfrow参数或mfcol参数规定，这也是我几年前经常用的一种方法。</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1">x &lt;-<span class="st"> </span><span class="kw">rnorm</span>(<span class="dv">50</span>)</a>
<a class="sourceLine" id="cb1-2" title="2">y &lt;-<span class="st"> </span><span class="kw">rnorm</span>(<span class="dv">50</span>,<span class="dv">2</span>,<span class="dv">2</span>)</a></code></pre></div>
<p>随便模拟产生数据，并对数据绘制一些简单的图，用该函数将一页中对他们进行全部展示。</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1"><span class="kw">par</span>(<span class="dt">mfrow=</span><span class="kw">c</span>(<span class="dv">2</span>,<span class="dv">2</span>))</a>
<a class="sourceLine" id="cb2-2" title="2"><span class="kw">plot</span>(x, y, <span class="dt">xlab =</span> <span class="st">&quot;&quot;</span>, <span class="dt">ylab =</span> <span class="st">&quot;&quot;</span>)</a>
<a class="sourceLine" id="cb2-3" title="3"><span class="kw">hist</span>(x,<span class="dt">main=</span><span class="st">&#39;&#39;</span>)</a>
<a class="sourceLine" id="cb2-4" title="4"><span class="kw">qqnorm</span>(x,<span class="dt">main =</span> <span class="st">&#39;&#39;</span>);<span class="kw">qqline</span>(x)</a>
<a class="sourceLine" id="cb2-5" title="5"><span class="kw">barplot</span>(x, <span class="dt">axes =</span> <span class="ot">FALSE</span>, <span class="dt">space =</span> <span class="dv">0</span>,<span class="dt">col=</span><span class="st">&#39;white&#39;</span>)</a></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="基础包layout" class="section level2">
<h2>基础包——layout()</h2>
<pre><code>layout(mat, widths = rep.int(1, ncol(mat)),
       heights = rep.int(1, nrow(mat)), respect = FALSE)</code></pre>
<ul>
<li><p>mat 参数为一个矩阵，提供了作图的顺序以及图形版面的安排。0代表空缺，不绘制图形，大于0 的数代表绘图顺序，相同数字代表占位符。</p></li>
<li><p>widths 和 heights 参数提供了各个矩形作图区域的长和宽的比例。</p></li>
<li><p>respect 参数控制着各图形内的横纵轴刻度长度的比例尺是否一样。</p></li>
<li><p>n 参数为欲显示的区域的序号。</p></li>
</ul>
<p>生成2行2列的版面，并设置宽度和高度。par()中oma参数指四个外边空的行数</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1"><span class="kw">par</span>(<span class="dt">oma =</span> <span class="kw">c</span>(<span class="dv">2</span>,<span class="dv">2</span>,<span class="dv">2</span>,<span class="dv">2</span>))</a>
<a class="sourceLine" id="cb4-2" title="2">nf &lt;-<span class="st"> </span><span class="kw">layout</span>(<span class="kw">matrix</span>(<span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">1</span>,<span class="dv">3</span>),<span class="dv">2</span>,<span class="dv">2</span>),<span class="dt">widths =</span> <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">3</span>), <span class="dt">heights =</span> <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">2</span>))</a>
<a class="sourceLine" id="cb4-3" title="3"><span class="kw">layout.show</span>(nf)</a></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>再将各个图进行填充</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1"><span class="kw">plot</span>(x, y, <span class="dt">xlab =</span> <span class="st">&quot;&quot;</span>, <span class="dt">ylab =</span> <span class="st">&quot;&quot;</span>)</a></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" title="1"><span class="kw">hist</span>(x,<span class="dt">main=</span><span class="st">&#39;&#39;</span>)</a></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAMAAABNUi8GAAAAVFBMVEUAAAAAADoAAGYAOjoAOpAAZrY6AAA6ADo6AGY6kNtmAABmADpmZmZmtv+QOgCQ2/+2ZgC2/7a2///T09PbkDrb/9vb////tmb/25D//7b//9v////3MyrVAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAOoklEQVR4nO3d63LrNhJFYfqMfTJWJrKSMKYlvf97ji6+UFbUBxDY6A1yfT9SqRpU2wOu0BItk90eENZFfwOAhUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhjUAhLS/Qvuu6P07/8uMvl28HuJQVaP/wst+unvYEilpyAt2tn0//fHwlUFSSE+h2dfrxvt88vhIo6sg/gx5snggUdeS9Bn3PcrvqCBRV5L6LP/+Q360JFFVwHRTSpgm0+zTJOBmdq+j/d02YeJdmtund3znyVv89s71yQqAWAg2Xdx3068fTjTdJM9t0Ag2XtUu/fvM+s00n0HB5u7RbP005Th6BhsvcpeH9QuhE49QRaDjeJFkINByBWgg0HIFaCDQcgVoINByBWgg0HIFaCDQcgVoINByBWgg0HIFaCDQcgVoINByBWgg0HIFaCDQcgVoINByBWgg0HIFaCDQcgVoINByBWgg0HIFaCDQcgVoINByBWgg0HIFaCDQcgVoINByBWgg0HIFaCDQcgVoINByBWgg0HIFaCDQcgVoINByBWgg0HIFaCDQcgVoINByBWgg0XO4dls071BMogU4t71GI3fvDOoePfykap49Aw931MNlDqo+vxeMaQKDh8h5D83mD+oHH0BBoFZxBLQQaLvM16PsplNegBFpJ3i59PGvuxvmTQAl0alwHtRBouGl2aa6PmCbQcJxBLQQajkAtBBou7zooj+Mm0MqydonHcRNobbkfFuFx3ARaVeYu8ThuAq2LN0kWAg1HoBYCDUegFgINR6AWAg1HoBYCDUegFgINR6AWAg1HoBYCDUegFgINR6AWAg1HoBYCDUegFgINR6AWAg1HoBYCDUegFgINR6AWAg1HoBYCDUegFgINR6AWAg1HoBYCDUegFgINR6AWAg1HoBYCDUegFgINR6AWAg1HoBYCDUegFgINR6AWAg1HoBYCDUegFgINR6AWAg1HoBYCDUegFgINR6AWAg1HoBYCDUegFgINR6AWAg2Xt0ubrnvarbvu5rM+ZrbpBBoua5f6x9dDo0/HZ87xvHgCrSJnl3brQ5bDw8v+nGrpuAYQaLicXdquDj/Zh9PTEIfLZyLyOG4CdcIZ1EKg4e57DXpKtXicPgINx7t4C4GG4zqohUDDEaiFQMMRqIVAwxGohUDDEaiFQMMRqIVAw33u0nZ1vMA52bh5INBwo13qu667cf39nnFzQKDhLnepuNGZbTqBhvu+S8dELz8IUjSucQQa7mKXhtMvMXfrG58EyR3XPgIN97VLh3dJ3bnM4f5T6Mw2nUDDjd7Fnz5HN9W4eSDQcFwHtRBouNEubQ4/4IfCC00z23QCDfe1S5vTC9Dtquhy/cw2nUDDjV6Dnj+EXPAOaU+gBDq1z136+CuOnkC/EGi4r13qT3/H8faT3yR9IdBwo116+9l1XeG1pgk2vcvkOpxAo+ldZnI9zLnDHVcTaBICNYc7ribQJF+7dPpz4rKPihBo1vLyvVqA0XXQojKvxt0/wvMwE2hrRtdBSz+sfDGuYITnYSbQ1lxdqJ9oXMEIz8NMoK0ZXaif4E+SCDRnefleLcDXLg03b7h017j7R3geZgJtzfivOnkXfzXccTWBJuE6qDnccTWBJiFQc7jjagJNMtqlww/5x9dN+AeWCRQjozdJDy/942vh5VACzVhevlcLcPF50OMtvsM/D0qgGLm4UH8MNPwT9QSKkasz6Ob+uzbsCTRrefleLcD316B92eV6As1YXr5XC3D5Ll7iE/Weh5lAW8N1UHO442oCTUKg5nDH1QSaJO938ccPNR//tu7mCwECzVhevlcL8H2XzKtMpz5/ezE+PEqgGcvL92oBrnZpc/tjoaffMp3/d8eHyRIoRq52yTiFHs+b7zcgcXwcN4Fi5GqXrF91Hs+ePWfQaVYTaJLvu2Te3W67+vHX6KHxCePu+Y48DzOBtubqXbz9m87hvOhmxASasbx8rxaA66DmcMfVBJqEQM3hjqsJNMn1hfqiv5sj0Izl5Xu1AN//7JhPM10Md1xNoElGnwc9l3nrAlLmuIIRnoeZQFvz9SP+9/OVIz5RPx7uuJpAk1ydQflE/Xi442oCTXLxifrDP/uyTywTaMby8r1agO+fqC86fxJo1vLyvVoAroOawx1XE2gSAjWHO64m0CTc+sYc7riaQJNw6xtzuONqAk3CrW/M4Y6rCTQJt74xhzuuJtAk3PrGHO64mkCTcOsbc7jjagJNwq1vzOGOqwk0CddBzeGOqwk0ycVr0AnHFYzwPMwE2pqLd/ETjisY4XmYCbQ1ozdJPEz2erjjagJNknfzsPRxBSM8DzOBtoY3SeZwx9UEmoRAzeGOqwk0yXmXpnmHtCfQrOXle7UAo0CnuNBEoBnLy/dqAQjUHO64mkCTEKg53HE1gSYhUHO442oCTUKg5nDH1QSahEDN4Y6rCTTJR6BT3NpuT6BZy8v3agG4UG8Od1xNoEkI1BzuuJpAkxCoOdxxNYEmIVBzuONqAk1CoOZwx9UEmoRAzeGOqwk0CYGawx1XE2iSe3bp7efND+cRaMby8r1agJxdSricT6AZy8v3agGydmnoTr8M5Qw6xWoCTZK3S9vV8c5N14FGPo47T+Zwx9UEmiR3lzYPL2JnUMfVBBove5f67plApxlevlcLkL9Lbz//Q6CTDC/fqwW4Y5d269u3aCTQjOXle7UA7V+od1xNoPEING54+V4tAIHGDS/fqwUg0Ljh5Xu1AAQaN7x8rxaAQOOGl+/VAhBo3PDyvVoAAo0bXr5XC0CgccPL92oBCDRuePleLQCBxg0v36sFINC44eV7tQAEGje8fK8WgEDjhpfv1QIQaNzw8r1aAAKNG16+VwtAoHHDy/dqAQg0bnj5Xi0AgcYNz1S+tS0i0EaGL/WES6CNDCdQlXHtNkSgDgi0keEEqjKu3YYI1AGBNjKcQFXGtdsQgTog0EaGE6jKuHYbIlAHBNrIcAJVGdduQwTqgEAbGU6gKuPabYhAHRBoI8MJVGVcuw0RqAMCbWQ4gaqMa7chAnVAoI0MJ1CVce02RKAOCLSR4QSaou+68zOSep2nHTuuVhpOoAn6h5f9dvW0J9D6wwn013br59M/H18JtPpwAv217er8CMTN4+u3QCMfx+24Wml4ZqCz+aP7/DPoweaJM2jt4bmBeg6vKe816HuW21VHoJWHE2iK/v05x7s1gVYeTqAq49ptiEAdEGgjwwlUZVy7DRGoAwJtZDiBqoxrtyECdUCgjQwnUJVx7TZEoA4ItJHhBKoyrt2GCNQBgTYynEBVxrXbEIE6INBGhhOoyrh2GyJQBwTayHACVRnXbkME6oBAGxlOoCrj2m2IQB0QaCPDCVRlXLsNEaiDGoFm/gVs5tY6rlYanvv07szheUc50+RFTTxO6DC3O9z5e8k7yp7DU4qaeJzQYW53OIFOgkBFlhNoxjihw9zucAKdBIGKLCfQjHFCh7nd4QQ6CQIVWU6gGeOEDnO7wwl0EgQqspxAM8YJHeZ2hxPoJAhUZDmBZowTOsztDifQSRCoyHICzRgndJjbHU6gkyBQkeUEmjFO6DC3O5xAJ0GgIssJNGOc0GFudziBToJARZYvNNDd+vxHJjeekkSgMsuXGWjfvT8Lcfj4l6RxQoe53eEE+mufz+o8pPr4mj5O6DC3O5xAf+3jaccHQ87TjnP/ShUzc2eb53gy1iacQYFpZb4GfT+F3nwNCkwr7/S7XZ3P2Zw/UYnuTXmAPYFCHIFCGoFCGoFCGoFCGoFCGoFCWo1AY38RjOlViOYznta/BsNnNjzga7W7WQyvPzzga7W7WQyvPzzga7W7WQyvPzzga7W7WQyvPzzga7W7WQyvPzzga7W7WQyvPzzga7W7WQyvPzzga7W7WQyvPzzwawHZCBTSCBTSCBTSCBTSCBTSCBTSCBTSCBTSCBTSCBTSCBTSCBTSKgR6ejSI4w1v3/5765kjZYaue3hxmXzi9W17b3jvuy3f+Qe6Wx/+//Tdk9f87ermQ3GKDIdve/A7FF7ftveG94dv23FbrvgH+vbzeN/w3utwDMZTm0qcb8i/8frvyuvb3jtv+Hb1fNwbt9PNlVqvQb3+oxu658HlUPj+d+X2bX99Bcez3BwD3fgdDqdAf3txm33iHKjjhu/7Wf2IP/F8LIjPkT6fgRzPQ76BOm744PqW97s6gQ5+75EI9F+He2744Ud8vce8VAnU97FK/Ii/nu18jqv4Nt410P58Pa532a7+42Jfi2+S9q6B+mz4yHlzqqhwBv18Pp0TnyPtfJnJM1DPDT+n6fwGb6zGdVDn/5yddsv5Qr3fQfbd8M3h5efooa3u/APtzzflbe/dhvPv9NwCdd7wzWF2tR/wfFgE4ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0ggU0gjU1/EWzadnC+E+BOrreK/Xvt4jB+aHQJ0NP/78veKjLWeHQL1tXJ8IM3sE6m2oecPs+SFQZ7v1/+o9EmOGCNRZ//hPxWdizA+B+tqu/qj5VKH5IVBf58cK8TbpbgQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaf8HMjzI6C0L8jYAAAAASUVORK5CYII=" /><!-- --></p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1"><span class="kw">qqnorm</span>(x,<span class="dt">main =</span> <span class="st">&#39;&#39;</span>);<span class="kw">qqline</span>(x)</a></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAMAAABNUi8GAAAAZlBMVEUAAAAAADoAAGYAOpAAZrY6AAA6ADo6AGY6OpA6kNtmAABmADpmkJBmtrZmtv+QOgCQkGaQ2/+2ZgC2Zjq225C2/7a2///bkDrbtmbb25Db2//b/7bb////tmb/25D//7b//9v///++ibChAAAACXBIWXMAAA7DAAAOwwHHb6hkAAASGUlEQVR4nO3di3LjuIFGYbhtz0SejR0zSSu7a9nS+79kxDuoC0WCIPgDOF9VO12OWrSlMyQBXmROgDCz9Q8AjCFQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSCNQSPMcqAEm2SpQv0+HVBEopBEopBEopBEopBEopBEopBEoVNyc9CRQiDCnW1kQKDQY6+vVtyf/e28IFEMECmkECm3sg0Ibo3jEh0AhjUAhjUAhjUAhbc1AD8Y8ffp7OuRopUALY3bff/s6/by9+3g6ZGudQIvnr1NRrT33578tfjrka5VAq/Xm9x9loIdfvwfPMvtqUuRtpUB356/H/zuxBsVC62zi9+16s0516dMhXysNkvb18P1g7oyRCBTTMA8KaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKaQQKZdPvMkegmMfDLQzPz0CgWMftz+Wa9QyznoJAMYexvro9wcxnIFDMsTDQbveAQLGKRYFae68EinW474MOBlcEipU4juIv/hWBQslV1AQKHTfWuQQKFTd3CQgUGu7ssRIoFNwdUBEoFvHyoYEjz0GgWGL5EfcHZywRKBZYfMT94Ql1BIoFfJ0S8mgJk38Ubwg0Cb5OCXm0hMk/ijcEmoYl+6CThlcEikWcR/ET/x2BYguTsyZQhDdjrUugCG3WTgGBIqyZ+6wEipBmD6kIFOE4jPgJFKE4TUitFOjeGPNe/eXXbw9Ph/g5zpeuE+j+6fP08/ZyIlDUnE/KWyXQ48eu+vr8RaA4LchzpUB/3qrN+6l4/roI1HRmPB3itujNXnENela8sAbN3cJ10Ur7oE2WP2+GQLO2eFO52ii+3sgfPwg0Yx725JgHxVq8DDQIFOvwNA4mUKzB2zQNgcI/j7OIDoH+vO3KQ5nPX0GWi+h4neR2CLR4/vp+fTkVL0GWi8h4PgYzP9DyMNHBnP/cmUDyvFxExfshQrdAi3Oc944ReV4uIrLCEWyXTfzLz9vzV32y0vrLRTRWOcHCaZBknj6PH4v6JNDkrHT+D9NM8GG109MIFMutePakS6DnbfzzV7ELs1zIW/XkXodAD0+f+3KQtKhQAk3Fyueezw+0PBt5P3Ixh+flQtrql0a4zYOWgTJRjwBX7rivQYtFB+MJNEbDa8mCXFjmvA/anjO/9nKhw5ysNy7QdY+Oo3hjnj7DLBcyjP011BvIPCim6gMNeNU4gWKqNtCgNzUgUExWrzzDvnUzA612PxtMM2VngzvCsAbFZFvcsIhAMdE299MiUEyy1e3eZu+D7rrdUPZB87Hd3QhZg2JEPSra8maZDieL/FUfQ+JkkfRtMa9040eY98A2UE63S55An7MDLfp5UE5YTl190CiuQPs1aKDlYjv1yjO2QAMvF1s55zk4v26jn2L+A48fTDOlr1l7bv5pFy53FllU5tzlYgubd9lxuSZp2QXHM5eL8HTydL1oLuByEdr2m3Wby0Vzy+7KNHO5CMsIDIxsLhfNLbtcbuZyEVK79tR5e1w28Zwskqh+Wl7n7WEeFA17Wl7n7SFQVLqRUfz7oO02nk18OuzPn459FF99yPbL6fuVO4ukwqgN3S1uE/WH8u523JspDc0xd9H3xG2i/vvP39WfAMvFuqyVp+R74jJRv6tOuSPQBAymPSXfE4d90PJU+mLHJj5+1sDopPqWuEwzFS/lSH7ZOU2ar0ZWLu71KfqOMA+aKdUgLxFolmLJk2PxWYonzwVr0GWXxRPodmLKc8kmns+Lj1IcQ6Oee6Ajq9AJdxGVf2HSNAxSd3Kp5x7o2J1Fjh/3uuxMXS78aV91o3lm3U3OgY5/XvzD60LEX5YUXZ1Ql2ig7fZ7/EDSo+tCxF+W9AyPGlVfEw008HLhQ3MXxYste9r7oIGWi+X6MC+27BGMBVzOZjLVB82VZzUFWC6WujpjKYYVZ2d2oPv6tot7jiTFoTwd+fKczwhWnJ25gR7az+jcGybq9bUXEscyJLo2M9Bu+ug8lud8UFntcGhwoXtUW/bOzEC7GzPtn//DJl5PvzU37cUc1r5nRFv2jmugfIiCIivJ7iY2kW7aW66beE4WEdQnaUxfZpyb9pbzIOn7j0W3qo/3FRNl+quHBx9uZCLdtLfcp5mW3eIu4pdMkp1kux8a96qz4TpRv2gIP2e5eMAeqpuLg5oxrzobHOqM3HConkKSQwQat+FQPcFXlUDjZg3VU8yTQGNmDdzT2N+8hUCj1e99mkTXniUCjZU1Lko3T7dAqxNFimUf55XwSxpIugP3AYdAD0+f++evhR84l/SLujr7sNHGP8raXCbq61svjl127HG5uNbtfSafp0ug5QlNZaCczbQFe+Se+Ma95r4GLThhObzBIfdtf5RAnPdBF54tkser61vkJx+7cBzFG/O06Gw7AnVinU+XywvIPGhM+kCzef0INCp5jNxtBBqXjNadtZmBTrjzp+flomFMyofc72INGoFmUJRhngQag/6EpQxfNedppoUXJWX4UrvoJpTsOydmxW2i/vx1v2wiNMOXeqa2zfpKOAKd/MDjR30IiUOd/rVXtJt2u25985Tni+Zwsshf9aqTk0W8s86R77frbZ/ZTTDV3NegfNqxb+byaz2xlG2bFZd90Oo0EU4W8e4q0FxH7jaX80F9zNVn/rLbuh3PqzTzXnfWmAfdUjdS73c8u7vR0WaFQDdkjdTrr/0onjobDoG223hG8QtdzcB3Lwp5dlxG8UvvbDdruQm7Fyh5WlwGScvuDDpzuQmzLuAYzMtv9xMJclmDLrtlw8zlpsyegTfW3Dx6DvugC2/+PXO5SbscqZPnFZdAXxkkLXZrEok8b3DZxC/6eI+5y03TjVM/yPMmBknBXRwzar+5zQ8jj0FSaJez89Xf8n05HmGQFNjV5Cd5jlpwsgiDJBf25Gf912xfikk4Fh/Q1afBkedDBBrO5TEj8pxgpZNF6o+jG3lMZu/Mzdt6kucEDoEWz1/7l9P368hsU/OBnuXZ93eG/Nm8N/0VcJyx5MJlkLQ7HcpbgN8/qcmaibr3qFzenas0WXvO4zZR//3n7+rPHdZc/sW1n/3VInN/0DgNT0ce/hVTuE3Ul5cejwTKGrQ1XHdyxtJ8Dvug5cd7FLvRy467Sz5z3we9WneS50wu00zFSzmSH52nb0f6dyPO5V1q0mTk7op50NUMTkRuv7XZTxMrAl3L9Rl15OlgdqDfr7ty15JP+Xjg6ow68nQyN9Bqfr68+c3oRL3H5caKaXk/5gZalKfTF+UAqVh0Yn3S75d9YLP5xoY/TdxmBlp9xnF90Qe3X7zn8qQQ8lxgdqDvTaUEeg/T8j65BFrvfnJ/0DvYuPvksg9a3Z5+4cWdCb9vVqDkudjcQA9Pnz9v5bqzWLSFTznQbu+TPD2YPQ96qI5ffr/yMTT3meb2s1iOI0nrIE9PCHQF2ZzuGgCBekedPhGoZ+TpF4F6RZ6+EahH5OkfgXpDnmsgUD+6A++p/WJbI9CF2gs76kuPTgn9ZhoIdBn7sqMb96XFUgS6SNMnga6GQF01N12yDrsT6AoI1FFz2nw9NOoufz8l8JtpIdDZuvvVnUx/YQej+JUQ6FzdFUfGDO4ZglUQ6Ezt7qZhcx4Egc7UBXqK+JeICIHO1O1wsm0PgkDn6u/KgAAIdKaMbg8tgUBnoc3QCHQG8gyPQCcjzy0Q6ETkuQ0CnYQ8t0KgE5Dndgj0IfLcEoE+QJ7bItBR9jFNJui3QKAjujNC7D8IikDvas+Wr79yOcc2CPSO7rMLm68Eug0Cvanf3STQbRHoDYPREPugmyLQK5eDdUbxWyLQC1SohUAHyFMNgVrIUw+BdshT0SqB/ryZzp3P+5KLgTw1rbMGPX48+hw6sRzIU9VKm/iHn+QpFQR56lprH/Rg3n0+3Zru5Mm0p4TAg6R+39TL03lw7yfhwJGGzEfxV3l2h42sr9jQmoEeP3Y+n86/7oylrtNuvUmgIjIO9LpK6yuBisg20OsT6q7/uvkPiVwDtfc9bwfKKF5DloEO07u9dYeGDEfxzUfD9T+BXSXrTTHZBWpu7GFSpa7MAmWWMzZZBTqYWCLQKGQTqLnc6yTQKOQS6MVHHzBaj0UegZp+aDT8DtTlEKjhtgvxSj9Q6zPhCDQ+iQZq+hOV+uXRZ4TSDLTN0R64s9MZpSQD5WyPdKQbKHkmIdVA+cTXRCQZaH1r+VWXgEBSDLRce7KBT0R6gZJmUqIO9MZ6kjwTE3Og15Pv5JmciAO9OnxJnglKJ1DyTFIqgZJnoiIO1NoHJc9kxRxodx4yeaZLKVC32XXyTJpQoE6nbJJn4nQCdTnpnTyTF3Og5JmBeAMlzyzoBDpvH5Q8MyEU6IxRPHlmQynQyf+WPPMRX6DkmZXYAiXPzMQVKHlmJ6ZAyTND8QRKnlmKJVDyzFQcgZJntjYLFJhko0Cdhf05WFo0SyNQlia9NAJladJLI1CWJr00AmVp0ksjUJYmvTQCZWnSSyNQlia9NAJladJLUwkUuIlAIY1AIY1AIY1AIY1AIY1AIY1AIY1AIY1AIY1AIY1AIY1AIU0k0OOHMWYXcIHff/4Os6CDMU+fYRZVCfaLBXrTNAI9fpzfxL15CbbAn7dfYd7Hw/kXOwQsNNgvFupN0wj0+/X9/HUf7LU9r9bCLOv4Ua5himD/5QX7xU6h3jSNQGvB1jQHszuEeR8D/5cX7hfrF7nym6YUaBHwtQ0V6B+f4RZWCR3o2m+aUKCHkKOkQO9jvX4JuRMaONDV3zSdQA8Bx0gE6mtpq79pMoEGXX+yife0sPXftM0D3ddzafswfe7bmbs0B0mnsIGGeNM2D7S2N+9hFxjofQw9zRQ00CBvmkag369Bt++ncO9j6In6gIGGedM0At3Xd91NcSxx/tWCHuoMF2iYN00jUOAOAoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0AoU0Ar3n562+95D59e+3iXdxO7yf/9WNx/bfPDy+mVHzJDefKEMEOqa+/+zUVu4+rv0/jh/lnb3G7wrXPpZAawQ6xnOgzY1s67vaPngSAq0R6Jg20P95NdXNWvsbNNd/+fnrH+XnEtXf/j4/6Pn/39rHlTetLUz175rYft6aVWfxUn+r+tI95u/nfYr37kmq/69dXvnd0Lf4FUGgY9pAy49Ua/5UG+iDKQsqM3v+OnXf7nYdy2+UNZZ3Vq7//t4/26m6h2cXaP+YegHtk5R/2ieuVrmHPAsl0DFtoLtqu1yvAc911Tf2PpQ17doVYxNd/adeU/789Vlvz5tAuzstH+oOy+9bj2kX0gXaPXHozz5SQqBjrH3Q85c6sHNE9U5kU9Op+3bblr2PeTBmNNDhY6oqu0CtJ863UAIdcxFoM+/UBNrUdOq+3QXabsrPu5C//tWvQW9u4gePuQi0feL6c4Wz3MIT6Khba9BTOwwfrkGbx9lr0Op/rU18P0h6/mqfc/iYW2vQVhH0VvcyCHTMMNBu5sfaB323JoQu90GrXcdDv4mvppmOH8//Ww2o6l3O4WOGgQ5nmjKddyLQMcNAq1F1tSbrR/HVpFLz7TK6bhR//HipV4xm17dVVIWa89D/3OnX+W/vw8c0IyN7FF8+cbUqDfphNjoIdMxFoNW8ZDVeaedB6/LabxcX86DVR9AUzYq20uxWnh9cHkn9e70P2j+mmne6mAetV7Em7IfZ6CDQLfwzz9hcECikESikESikESikESikESikESikESikESikESikESikESikESikESikESikESikESikESikESikESikESikESikESik/RdWX1HQ3o4kUAAAAABJRU5ErkJggg==" /><!-- --></p>
<p>前面两种方法，说实话可以实现，但是比较费劲。那么下面看看gridExtra包。</p>
</div>
<div id="gridextra包混合多个图形" class="section level2">
<h2>gridExtra包——混合多个图形</h2>
<p>gridExtra包让混合多个图片变得轻而易举。它提供了grid.arrange() 函数来完成这个任务。它的nrow参数允许指定如何安排布局。</p>
<p>对于更复杂的布局，arrangeGrob() 函数允许做一些嵌套。这里有 4 个 例子来说明 gridExtra 是如何工作的:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" title="1"><span class="kw">library</span>(ggplot2)</a>
<a class="sourceLine" id="cb8-2" title="2"><span class="kw">library</span>(gridExtra)</a></code></pre></div>
<p>这里我们用ggplot绘图，并存在变量名称（g1,g2,g3）中，然后用grid.arrange()将各个变量名称展现出来。</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" title="1"><span class="co"># Make 3 simple graphics:</span></a>
<a class="sourceLine" id="cb9-2" title="2">g1 &lt;-<span class="st"> </span><span class="kw">ggplot</span>(mtcars, <span class="kw">aes</span>(<span class="dt">x=</span>qsec)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_density</span>(<span class="dt">fill=</span><span class="st">&quot;slateblue&quot;</span>)</a>
<a class="sourceLine" id="cb9-3" title="3">g2 &lt;-<span class="st"> </span><span class="kw">ggplot</span>(mtcars, <span class="kw">aes</span>(<span class="dt">x=</span>drat, <span class="dt">y=</span>qsec, <span class="dt">color=</span>cyl)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">size=</span><span class="dv">5</span>) <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position=</span><span class="st">&quot;none&quot;</span>)</a>
<a class="sourceLine" id="cb9-4" title="4">g3 &lt;-<span class="st"> </span><span class="kw">ggplot</span>(mtcars, <span class="kw">aes</span>(<span class="dt">x=</span><span class="kw">factor</span>(cyl), <span class="dt">y=</span>qsec, <span class="dt">fill=</span>cyl)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_boxplot</span>() <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">legend.position=</span><span class="st">&quot;none&quot;</span>)</a>
<a class="sourceLine" id="cb9-5" title="5">g4 &lt;-<span class="st"> </span><span class="kw">ggplot</span>(mtcars , <span class="kw">aes</span>(<span class="dt">x=</span><span class="kw">factor</span>(cyl), <span class="dt">fill=</span><span class="kw">factor</span>(cyl))) <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>()</a>
<a class="sourceLine" id="cb9-6" title="6"><span class="co"># Plots</span></a>
<a class="sourceLine" id="cb9-7" title="7"><span class="kw">grid.arrange</span>(g1, g2, g3, <span class="dt">nrow =</span> <span class="dv">3</span>)</a></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" title="1"><span class="kw">grid.arrange</span>(g2, <span class="kw">arrangeGrob</span>(g3, g4, <span class="dt">ncol=</span><span class="dv">2</span>), <span class="dt">nrow =</span> <span class="dv">2</span>)</a></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" title="1"><span class="kw">grid.arrange</span>(g2, <span class="kw">arrangeGrob</span>(g3, g4,g1, <span class="dt">ncol=</span><span class="dv">3</span>), <span class="dt">nrow =</span> <span class="dv">2</span>)</a></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
</section>



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================================================
FILE: 2020年/2020.08.26混合多个图形/混合图.rmd
================================================
---
title: "混合图"
author:
  - 庄亮亮
date: "2020/11/10"
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
---

记得安装prettydoc包，html模板在该包渲染而成。

![](https://mmbiz.qpic.cn/mmbiz_jpg/MIcgkkEyTHgfkvXafZE9scXp4icvdcNFyic0z7THajQBAyLNRiau3CKnZ3L9Y9K2YXObhaiblBm0Jbnicaq9lW3pz4g/640?wx_fmt=jpeg)  
欢迎**关注**我的**公众号**，**点赞，在看，收藏\~\~\~**


创作不易！感谢大家支持。

-----------------------


## 前言

在同一页面上混合多个图形是一种常见的做法。它可以在同一数字上
总结大量信息，例如，它被广泛用于科学出版物。

## 基础包——par()

一页多图用mfrow参数或mfcol参数规定，这也是我几年前经常用的一种方法。
```{r}
x <- rnorm(50)
y <- rnorm(50,2,2)
```
随便模拟产生数据，并对数据绘制一些简单的图，用该函数将一页中对他们进行全部展示。
```{r}
par(mfrow=c(2,2))
plot(x, y, xlab = "", ylab = "")
hist(x,main='')
qqnorm(x,main = '');qqline(x)
barplot(x, axes = FALSE, space = 0,col='white')
```



## 基础包——layout()

```
layout(mat, widths = rep.int(1, ncol(mat)),
       heights = rep.int(1, nrow(mat)), respect = FALSE)
```      
       
- mat 参数为一个矩阵，提供了作图的顺序以及图形版面的安排。0代表空缺，不绘制图形，大于0 的数代表绘图顺序，相同数字代表占位符。

- widths 和 heights 参数提供了各个矩形作图区域的长和宽的比例。

- respect 参数控制着各图形内的横纵轴刻度长度的比例尺是否一样。

- n 参数为欲显示的区域的序号。

生成2行2列的版面，并设置宽度和高度。par()中oma参数指四个外边空的行数
```{r}
par(oma = c(2,2,2,2))
nf <- layout(matrix(c(1,2,1,3),2,2),widths = c(1, 3), heights = c(1, 2))
layout.show(nf)
```


再将各个图进行填充

```{r}
plot(x, y, xlab = "", ylab = "")
hist(x,main='')
qqnorm(x,main = '');qqline(x)
```


前面两种方法，说实话可以实现，但是比较费劲。那么下面看看gridExtra包。

## gridExtra包——混合多个图形

gridExtra包让混合多个图片变得轻而易举。它提供了grid.arrange() 函数来完成这个任务。它的nrow参数允许指定如何安排布局。

对于更复杂的布局，arrangeGrob() 函数允许做一些嵌套。这里有 4 个
例子来说明 gridExtra 是如何工作的:

```{r}
library(ggplot2)
library(gridExtra)
```

这里我们用ggplot绘图，并存在变量名称（g1,g2,g3）中，然后用grid.arrange()将各个变量名称展现出来。



```{r}
# Make 3 simple graphics:
g1 <- ggplot(mtcars, aes(x=qsec)) + geom_density(fill="slateblue")
g2 <- ggplot(mtcars, aes(x=drat, y=qsec, color=cyl)) + geom_point(size=5) + theme(legend.position="none")
g3 <- ggplot(mtcars, aes(x=factor(cyl), y=qsec, fill=cyl)) + geom_boxplot() + theme(legend.position="none")
g4 <- ggplot(mtcars , aes(x=factor(cyl), fill=factor(cyl))) + geom_bar()
# Plots
grid.arrange(g1, g2, g3, nrow = 3)

```

```{r}
grid.arrange(g2, arrangeGrob(g3, g4, ncol=2), nrow = 2)
```

```{r}
grid.arrange(g2, arrangeGrob(g3, g4,g1, ncol=3), nrow = 2)

```



================================================
FILE: 2020年/2020.09.15reticulate/reticulate.rmd
================================================
---
title: "reticulate包"
author:
  - 庄亮亮
documentclass: ctexart
output:
  rticles::ctex:
    fig_caption: yes
    number_sections: yes
    toc: yes
classoption: "hyperref,"
---

```{r}
library("reticulate")
```

- 下载python模块
```{r}
py_install("math")
py_install("matplotlib.pyplot")
py_install("math")

#py_install("scipy") #安装
py_module_available("math") #查看是否安装成功

repl_python()# 查看路径 
#use_python("D:/anaconda/python.exe") #更换路径

```


```{python}
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from math import pi
from matplotlib.pyplot import figure, show, rc 
plt.rcParams["patch.force_edgecolor"] = True


df = pd.DataFrame(dict(categories=['var1', 'var2', 'var3', 'var4', 'var5'],
                       group_A=[38.0, 29, 8, 7, 28],
                       group_B=[1.5, 10, 39, 31, 15]))
N = df.shape[0]
 
angles = [n / float(N) * 2 * pi for n in range(N)]
angles += angles[:1]
 
fig = figure(figsize=(4,4),dpi =90)  
ax = fig.add_axes([0.1, 0.1, 0.6, 0.6], polar=True)
 
ax.set_theta_offset(pi / 2)
ax.set_theta_direction(-1)
ax.set_rlabel_position(0)

plt.xticks(angles[:-1], df['categories'], color="black", size=12)
plt.ylim(0,45)
plt.yticks(np.arange(10,50,10),color="black", size=12,verticalalignment='center',horizontalalignment='right')

plt.grid(which='major',axis ="x", linestyle='-', linewidth='0.5', color='gray',alpha=0.5)
plt.grid(which='major',axis ="y", linestyle='-', linewidth='0.5', color='gray',alpha=0.5)

values=df['group_A'].values.flatten().tolist()
values += values[:1]
ax.fill(angles, values, '#7FBC41', alpha=0.3)
ax.plot(angles, values, marker='o', markerfacecolor='#7FBC41', markersize=8, color='k', linewidth=0.25,label="group A")

values=df['group_B'].values.flatten().tolist()
values += values[:1]
ax.fill(angles, values, '#C51B7D', alpha=0.3)
ax.plot(angles, values, marker='o', markerfacecolor='#C51B7D', markersize=8, color='k', linewidth=0.25,label="group B")

plt.legend(loc="center",bbox_to_anchor=(1.25, 0, 0, 1))
```



```{python}
import numpy as np 
import matplotlib.pyplot as plt 
# 计算正弦曲线上点的 x 和 y 坐标
x = np.arange(0,  3  * np.pi,  0.1) 
y = np.sin(x)
plt.title("sine wave form")  # 使用 matplotlib 来绘制点
plt.plot(x, y) 
plt.show()
```



```{python}
import pandas as pd
#df = pd.read_csv("C:/Users/DELL/Desktop/test.csv")
#df.head
```




================================================
FILE: 2020年/2020.09.27tidyverse数据清洗/Tidy_data.rmd
================================================
---
title: "[R数据科学]Tidy data案例"
author:
  - 庄亮亮
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# Tidy data介绍

在本章中，你将学习在R中组织数据的一种一致的方法，这种组织被称为tidy data。将数据转换为这种格式需要一些前期工作，但这些工作从长远来看是值得的。一旦你有了整洁的数据和一些包提供的整洁工具，您将花费很少时间将数据从一种表示转换到另一种，从而可以将更多的时间花在分析问题上。

本文将为您提供整理数据的实用介绍以及tidyr包中附带的工具。如果你想了解更多的基本理论，你可能会喜欢发表在《统计软件杂志》上的整洁数据论文<http://www.jstatsoft.org/v59/i10/paper>


# 数据清洗案例

我们主要通过一个案例,来了解如何整洁数据,并将案例中的各个有用函数进行详细解读.该案例数据来自`tidyr::who`,其包含按年份，国家，年龄，性别和诊断方法细分的结核病（TB）病例。数据来自2014年世界卫生组织《全球结核病报告》，网址为<http://www.who.int/tb/country/data/download/en/>。

```{r}
library(tidyverse) 
who
```


这是一个非常典型的现实示例数据集。它包含**冗余列，奇数变量代码和许多缺失值**。我们需要采取多个步骤来对其进行整理。

## 不是变量的列汇集在一起

首先将不是变量的列聚集在一起。所包含的列包括：

- country，iso2和iso3是三个指定国家/地区的变量。

- year是一个变量。

- 变量名中给出的结构（例如new_sp_m014，new_ep_m014，new_ep_f014）可能是值，而不是变量。

因此，我们需要将从`new_sp_m014`到`newrel_f65`的所有列汇总在一起。我们用通用名称"key"来表示他们。我们知道单元格代表案件数，因此我们将变量数存储在`cases`中,并用`na.rm`去除含有缺失值的行。

这里使用`pivot_longer()`将数据变长,具体见后面函数详情.


```{r}
who1 <- who %>% 
	pivot_longer(
		cols = new_sp_m014:newrel_f65,
		names_to = 'key',
		values_to = 'cases',
		values_drop_na = T
)

who1
```

对key进行计数，我们可以得到一些有关值结构的提示：

```{r}
who1 %>% count(key)
```

其中key的具体含义，查阅可得：

每列的前三个字母：新、旧病例。

之后两个字母：结核的类型。

- rel 代表复发病例

- ep 代表肺外结核病例

- sn 代表无法通过肺部涂片诊断（涂片阴性）的肺结核病例

- sp 代表可被诊断为肺部涂片（涂片阳性）的肺结核病例

第六字母：结核病患者的性别。男性（m）和女性（f）

其余数字给出了年龄段。数据集将案例分为七个年龄组：

- 014 = 0 – 14岁

- 1524 = 15 – 24岁

- 2534 = 25 – 34岁

- 3544 = 35 – 44岁

- 4554 = 45 – 54岁

- 5564 = 55 – 64岁

- 65 = 65岁或以上

## 替换数据

我们需要对列名称的格式进行较小的修正：将new_rel替换为newrel（很难在这里找到它，但是如果您不修正它，我们将在后续步骤中出错）。这里使用了stringr包中的str_replace(),将`newrel`替换`new_rel`.

```{r}
who2 <- who1 %>% 
	mutate( names_from = stringr::str_replace(key,'newrel','new_rel')
	)
who2
```

## 字符分割

接下来就是将`key`中的字符进行分割,我们使用`separate()`对字符进行两次分割.

1.将在每个下划线处拆分代码。

```{r}
who3 <- who2 %>% 
	separate(key,c('new','type','sexage'),sep = '_')
who3
```

利用select()删除没用的列:new,iso2，iso3.

```{r}
who3 %>% count(new)
who4 <- who3 %>% select(-new,-iso2,-iso3)
who4
```

2. 将分离sexage到sex和age通过的第一个字符后拆分：

```{r}
who5 <- who4 %>% 
	separate(sexage,c('sex','age'),sep=1)
who5
```


这时,who数据集是目前整洁！

## 可视化

数据清洗完毕,就可以做一些初步的可视化,探索性分析.这里简单绘制了
前几个国家不同年份,不同性别的结核病病例总数。

```{r}
who5 %>% group_by(country,year,sex)  %>% filter(year<2003) %>% 
	count() %>% 
	head(100) %>%
	ggplot(aes(x=as.factor(year),y=n,fill=country))+geom_col() +facet_wrap(~sex,nrow = 1)+
	  scale_fill_brewer(palette = "Paired")

```

## 复杂的管道函数

事实上你可以直接只用管道函数构建一个复杂的函数：

```{r eval=FALSE, include=T}
who %>%
  pivot_longer(
    cols = new_sp_m014:newrel_f65, 
    names_to = "key", 
    values_to = "cases", 
    values_drop_na = TRUE
  ) %>% 
  mutate(
    key = stringr::str_replace(key, "newrel", "new_rel")
  ) %>%
  separate(key, c("new", "var", "sexage")) %>% 
  select(-new, -iso2, -iso3) %>% 
  separate(sexage, c("sex", "age"), sep = 1)
```

# 所用函数详细解释

## pivot_longer()、poivot_wider()

`pivot_longer()` 将在列中列名（数值）转换到一列上。具体可见下图,将列变量转化为数据存在year列名中,相当于把数据变长(longer).


![pivot_longer，从左到右](36.jpg)

函数主要参数:

> cols选取的列
> names_to 字符串，指定要从数据的列名中存储的数据创建的列的名称。
> values_to 字符串，指定要从存储在单元格值中的数据创建的列的名称。
> values_drop_na 如果为真，将删除value_to列中只包含NAs的行。这有效地将显式缺失值转换为隐式缺失值，通常只在由其结构创建的数据中缺失值时使用.

例子如上面例子:将new_sp_m014到newrel_f65之间的列选取,汇总到`key`列名中,值存在`cases`列名中,并将含有缺失值的行进行删除.

```{r}
who1 <- who %>% 
	pivot_longer(
		cols = new_sp_m014:newrel_f65,
		names_to = 'key',
		values_to = 'cases',
		values_drop_na = T
)
```


当然还有一个和他相反功能的函数`poivot_wider()`。具体见下图,相当于把key中的值变为列名,对应的values数据转化到population中.下面是简单的例子.


![pivot_wider，从右到左](37.jpg)

```{r}
library(tidyverse)
stocks <- tibble(
  year   = c(2015, 2015, 2016, 2016),
  half  = c(   1,    2,     1,    2),
  return = c(1.88, 0.59, 0.92, 0.17)
)
stocks
```

我们将数据变宽,将year变为列名,对应在return中的数据进行填充.

```{r}
stocks %>%
	pivot_wider(names_from = year,values_from = return) 
```


## separate()

该函数可将字符进行分割,具体案例如上.

默认情况下，当separate()看到非字母数字字符(即不是数字或字母的字符)时，它将分割值。可以用里面的参数`sep`。比如：`sep='_'`。他还有一个功能,当`sep=2`时,可通过第二个位置进行分割,使用在省份市级,等数据上.例如以下函数,其中into = c("century", "year")将原始分割后的数据导入两个新列上,分别叫century和year.


```
table3 %>% 
  separate(year, into = c("century", "year"), sep = 2)
```

**注意**:默认情况下，会转化成字符形式，你可以用参数`convert=T`，将数据转化最佳结构.

![](38.jpg)

## unite

是`separate()`的反函数,这里做个补充

![unite](39.jpg)

默认情况下，`sep='_'`如果我们不需要任何分隔符可以使用`sep=''`.


# 缺失值处理

两种情况会出现缺失值: 


1. **显式**: 标记为`NA`.

2. **隐式**: 没出在书中的.

以下为例子

```{r}
stocks <- tibble(
  year   = c(2015, 2015, 2015, 2015, 2016, 2016, 2016),
  qtr    = c(   1,    2,    3,    4,    2,    3,    4),
  return = c(1.88, 0.59, 0.35,   NA, 0.92, 0.17, 2.66)
)
```

在这个数据集中有两个缺失的值:

1. 2015年第四季度出现显式丢失，因为它的值应该在的单元格中包含NA。

2. 2016年第一季度的收益是隐式缺失的，因为它根本没有出现在数据集中。

对于隐式缺失值,我们可以将数据进行变化,显示隐性缺失.

```{r}
stocks %>% 
	pivot_wider(names_from = year,values_from = return)
```
这样就可以看到原来隐型的缺失值了.

这些显式缺失的值在数据的其他表示中可能并不重要，你可以在pivot_longer()中设置values_drop_na = TRUE来将显式缺失的值变成隐式缺失值:


```{r}
stocks %>% 
  pivot_wider(names_from = year, values_from = return) %>% 
  pivot_longer(
    cols = c(`2015`, `2016`), 
    names_to = "year", 
    values_to = "return", 
    values_drop_na = TRUE
  )
```

#### complete()

在整洁数据中显式显示缺失值的另一个重要工具是complete().

complete()获取一组列，并查找所有唯一的组合。然后，它确保原始数据集包含所有这些值，并在必要时显式填充NAs。

```{r}
stocks %>% 
	complete(year,qtr)
```

有时，当一个数据源主要用于数据输入时，丢失的值表明以前的值应该结转:

```{r}
treatment <- tribble(
	~person,~treatment,~response,
	'A',1,7,
	NA,2,10,
	NA,3,9,
	'B',1,4
)
```


#### fill()

您可以使用fill()填充这些缺失的值。它采用一组列，在这些列中，您希望用最近的非缺失值(有时称为最后一次观察结转)替换缺失值。

```{r}
treatment %>% fill(person)
```
直接将缺失值填写为前面的字符('A').


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1nJtTNAj6jJKAAZEs8WgqihJRgX9wJHOkYoXkf8iwR2RiKKqRRiitWw3lYdnr30cDzNae/8Tw/1L3sS5gFALINXpKDQgmp1pQxW86M3O8aoqMTlNtTGnSjATM2tjXEgCYfS3hKyuCkFHkzBeScI6WKhFVxLuD+EQLt4TkOo6CU5f1drrhvrrVly/dspDayfe+8EtQx7fuJG0HcbZLyyc1r+5qXbojtE1xa0dt4x/5c31r9hA6MYtP5DrVgijoiV5Po6KKs3MBOCVStFlgez8bG57v8/vq4tZ/Gilfr8pX7VqJm1EzJQGeg3j5/xX8ruWMbrG4oduFyXxMEFyQlkpkMeJTvhKbCMY1j/o2ykPlEmSr335KxvYPvbZydev29P65KNrX58+c92zfxv6+Kil76PnU1Sl6fe+l694//zIweMjUO1ZPnH2TU3fxqa09+l/6OHXAQgEAaSZuhddMDiaZ1epkRAzpTKAxyVzrnGh7JLreGi7qF1VqO5WvoGQ0DwF584uo3cpz4sCBzc9T9SAQPKgoqI082X2QfxhshCzXmZ5Jmoo6MvOYAk7gCWH6cudN5+98oSroZZNBoRWbuEw1ygDmqI9OZ36aJrbbTPYqIFmZrldRpdFA27ONADF4/HXxjyKYhkRU9LgYsIJ6e+pgHAkGUjkgUhLSBg2N9w3IMwpylMaKScT/n6efcC+PLN8xActmMGOhu+4bH6EpsV/yAgOoO0n9/+HnR2B5h7hr455LAPJ1+wc+1i1AYGhXOs6eQf4IR+uigYUp8WSlweZTnAWFNpz6mJ2u4d60kbEPGnUwENEvUTbVJbqTCjIAQJlPo8IXEUNdQEJcCAhMvd/gvy8Q3E6TmsbErv++Z2tRuuN/7f1X+zsNyv/vYhoN066sbVlcRuZiq/iWvuP7rEb/7LuhyPfsFPLMffdxfMnz7+1fu5qEc0RPdM6QIHLo14FgCDKRFYNMiWU1MaoAsLfupYpQwobhpDby4OfkoJ4iZQWPyy9jNLm8wLSdEtUyzvBB3lwOVwbLXYqnl6U+o3+Qo/Hnp1ttBtL+ihOZyBQXGwBS0Z9zJIGwfoYXGwTYYlLnVeWdKFwoCSqAj0/LqoW8qk7kShFiku3kK9cfCPVHyDedt/qpeyLL06zk4uXtU1DyfXfE2fPmrng0Ccjbhg+flxtq7zz3ZUzXhrU/O6sjqN73mrbXD2iY/Kzm89vbBp7Y/3VcwaOI3vqq674XdnlYysH1Ym8GajvcgekQQFURnOzZJfFEgyCCwqLtNy6mKZRrzd9RMyrUkMdR+Nfdbfu7DIBzCIaw0J5kS16edcXuNOdBXwbyU1J1ewxtvTOqxtHP/3+JIOl3xOz3v0nmr9Y+f2d8VNjp4xrbbm7jQ5mdazJdtYzasufW2r+83/H0fEE+3DTXbdNum1+Hfd4stOSZuvMURh1OXnyAPjtnsaYXeumMPAnaOwXTOb4NVYT72PqU+xG7xcf6mPNQAQX6/IUcHKmcllV1UUlBRXFZdIaYyZNUjgzJ6Rpm8u6mKrApzM0vUgYbrTrbF2SFHbS18Xa5GhSmF5P7JYqZODSiqKajIK/VYNEqQIEZRigFxShVFwJURhGD6JU0ZlDP443kvW7ccNSPH2abWFfCns140peoYDeNeZHHSqlRgkMcp00ViJSV30QKhkjagSue7JMQH4304/FkrTgKC9Tjh69VLueUScBrhFPNVAUJJTKEur6Ce0u1dCFuorNZH28UayJb2IaDjjNtKWsWmioXPicrpB365FYFc3LTU9PA+B2dlqdhUV2QCMFCAazGmNBl900ImaXkg7mVCR4KJVkyfpRJFR5F86oRckaXOFoe0m/7W6YevPVY5uWvzf1w3P7vm99YGyIHU4139VjH6ob1tLvqqpxR9u2r5m2onVI9RVXsHUX9eMTLkxQdnCc6AuVEIv2VCsq3G5XOGzt77rMZaWBtEDvNOgN0au8hkhEMg3QTPzqkVUq5feAklS7rOucMleiPU7ivc6kQtuiYCqrfNTdlVF8fxLxCKgtj3iUQC44+jrzOa06UfyDSESH3x2j106vnpWmTXnhlT1o+UfT/qt9NdGau79/Zhf73+exCP2T2Pz/ZefZXez6I/gIyv/EkRs7Yf3IFpM1FG27n5x++NQ9Q/otPPTGQSQBH/Pd/9Yf/vjjne1sx152gh0p6f3eKHwYW3/EZZ93sA627uCCpcfMzwj7AIC8WN4IKljh6miAWKkBQZHNZgqip6CSZLOSmpjVSs0yBZocIpTouZRiZWGortKL8gsDiITjI5Uik+LHJ7FXiYTziRJnywoMgWdwNFstbzxXRcbikdvy72CqiPvXAaQznI/t4Idczsm9VLdbktKzzeY83vfZ7QGDlqalDY9ZNLRSTbODPb0mZneCvyYG9BLcSxY9KQVDSTe5ArmSp7voCQYwWfE4HPqnwOu4AyOYNn/C/fPZh2fjx7C84/aZ8xev2nXHraxT3vDKpkVrHaacdQ++/xGdXTuy8Zr4NrZo3PgNgDCXI/UBnh9eKI36VZeLN+NWnxscUBNzSKpskmtiJleyNBOvSfVEKuQRD2+0Iw4l2BUdoTI+ZiikBS+9h9OfOtrxL7aJvdiOkQOHDrc2tEs72U/HmW846xyGi3DSZ3j9azd1FvUDImwoz+E2NIBd1OtGAIdVkjTZUhOTqWTlLbMzaamUcEELnGVzAbVA0BHKleew8ew2Ng534wR8gL3Dxq5ZjO/xGuQP7A55A7ubrcHDnUMBdY8RLs0Mg6L5BgnAqphMiBbFWBOzKNxLAnII3zehaKqJofOXXkp5iCsitPAkbol0bqDV8RN4ijmIm4tl7zK2BLqkUsalGqFvNN1AqVkBQDQJoSl5QlZS0MVSLhaCX7P9dHD8OHKMEwKWxLu8KBdxL6ZDTbQo3e8nNquVEFemy2DIsGlmjQdbOr9BNkt+r+zlsmTu1FB3wd0z5VlnstgW8BBwKLpv9YJL5RlPdMKNOALkU1L14E93sr+yVfg43vTxgZtW/GXnd1vevKGVHafhuOnyAlyMU3AcPjDybB377rOT591Y2mUHeYJu/Ug004jIzW+QJFm2GGhNrMaABoNsUijK3QmbMnfKFN2XPIHtjr/NdmE5uRrDZG78Xj5t2EIGAOCFiawBT+ozgRw+bSAGXiPLwM0MRsr79e4NCw4Rxa5IJL6kRnJurq0bOKEZy79hDV4k7gVL5JHn1l4AdgYS+tfxVS0wMJpjIcRkNiOAzUBl2cq/UrNZoXwP3VtwpgBXF1eWAOXEQAdVfSMRDKBcx1awhYvEZm7FB7CZETKxJf4D39CN6/Hf8XkJ6VIlly6LPUkqBVCQArccJKJUl6GXoPq6r3PD1MsbzldfSPxvRcyR3dAvmukGo9nI1bbxUPHKisdJjEQxq9QGilBcN36X0mUp6hA6Y9DpEYujXuXykscVRBpkK4wudhzbcaSC07GdfUgtRrZEms9Wzok3cw1WSi3nqklH6R3oPr8kYcedOm6WR9NMYETFagVwUFlRVM1MVW5RVLtHv11adI/EnAKwL1KEcM/JO9nv43fpSiwh81U7+qQGdrQtXseFv4FZvycdQPQ8+VKfDHgE0jgAfBZF8RpdNTGjRO01Mer6daQROSBexQQy16Hxpkj+kj3BXubXE3gz1vNr/PlDb76Bs9nSNzaSY+xxdivejVP5tZCj0mP/OYvf4smfoAvtpHU62rkEFkhGowdsNrvdbQXBV3ZNM9TENGr/TSzoRn/ZLXHoEyAo4ckJSx+au+BBspEdYacX8yA6iCb0UGXmlKkTd504Fz8rb/gchAXYat0CdkjjEZynUFmSCDVIJg9AhmYypVOVEwBXRFK5UWSV22N7Ev4uHU92T9OQe+LX7PPaKziWzWZnfL9pJMZW1bO5OPS3LSUP1S3lg9poocvnk0ySppm8njQw8cTzu4wWMA6PAZgtFm40C/WaRcikzJbSWfPzuXKqQ0sxKLdfgl3BF0A82brsgaXLW7gB12EPzH7oTqxuZWvZKtp73M0Tm+Pz4vvlDUeOLdxZwVwPk1KRVS2cQX0ce4s4n+RlpKcHICC7LeCGy4rdAbAELNlGX3ZNzCdRYyq+uhvwVHHWrRpn+IvGGoVFl/MhDadWMcJP9LZen9cr+din7JuOx/ZeN2FqnzFL7767DtWvZu2f2TrnyermlsJrn977BC7f/lkz5g4srx3e8+orqypveeqmzf8qL/13n8KGgcUDKqrHbRP6FwNIYiqrimdLCgBFNBhVKlHOuxSdv3y2lARgcoLtYrOlOn53IGEMEF7k+dXC13JCQdThQHSbDQaX08hRhsdSYuuXVBAOtyLx4BHI6+6CYLnlEXbyLfYFex/D9zz7BAf0ztqVZ+7EwHn6YufCPz33/DraBqjXfyHBI2K+RonRKAOiVZYkC3BDJ+q9VNpUJOaj+sXtVx6h57CC2dmLTMMKdPlKFXO0a4DY+dTwvZeN/qJLhrqRy8gSsx+T0e52yQh+v2ynlszMrKwci9mcnemSzdRvt6NJiOSi+EtCbgo1UyM3WkiKOMKJUtMlGvCIi78nPihD2fPbzWFJ6WPdxqngfix9q9Sr9HQdwoJDth5mUy/nm1hKoRixV/mpUJxwVT85trLi1EAa6twb+aS+9uuhNBsStmnSbVMVzTXLnPpUo6oYTYpJ0C2VLGYDkWXJqFCUkhDL9evG+ooUZ3VpjZj8Izex59h6fnXg56wfNmF/DGMtC5Pi+GHyHdka/47Y4j27dJCYyF2B7wZVlZEQEERvNFFF4QqiSgVDdslOjEH5Z65AarLLowIDZAGWchEZbA/LwDo6mozsXBTfQUqoXleVJiZ0RugfzTJISFUVEExmlYuSRP1I0IAGUcZdOgxNpl1qFqqPbALSzPPvkbfjTVJ6vIrs30m/RXi/0ykkLWUbyWw9T7KjVgXRIIFRJlTBfN2EuvH0BNZX4iUpmc0y8bOPPmIblXMHz60Xa1gA6MDkVFt/ZIKYnGpfnBa6sUmAHY9/mJhqI4S4fJ+QL55xoKIY+VYNoOZTiaaCvQtCfCFHMMy1CH34IX7GMmfKjQd/UoR8AzFIA+R3QIHeUTdBWVYkSTznFd6SVJko0DW+xLKLeyTRZYcwiGjADQ/jqVO8uP6KGOiGzmqyKN4maq1OtpHWXhja9SRIRonoRhEaJZ5K0NrOFyl//vMAAGKNdIQ+qATAwK1gBjVKRVTIdwCUpB/rioP0XWLww7EvHPD6PGRL5ZkqbKpcLx3ptW2gZ/z7GYIdmjju9pfm6E8Zq6OFTovBQvLy/P78LIMhaEkbFrNYZLfbPjjm5jWdnDM4JnvBk0Az/y+ZVYSeXlcUJWdMvMcN9+1u8h0omny9N6YT+huGr1r0xzd+Or/5xbv/On7T8Y9PswO/X3znY5MWPHHDsNfXvfono1K6rn7f+K3vx32E27h55MJbxwOBFVznDsUNTsjh7BvIojRg1Mw2n89szrWA2WPUFFDSh8QUL7iGxEC7mCz83SHi7H5mUeZ0aISzRVANCgTlw1AfH9d2D8WobftHX+7YNsMT+hpLLZbJM2ZOJJNvaZk+Q5rNdrPv2XH2t6XzFTdbPuiJ9jP3rwh0PPOXNWvWAMLoCyfoMWk2eDi6esRYymclxCubh8RkDexcM++lZZJuOTk32SdwmnJoYkjgUBQyIf4DZqJx81Mjh9525cmTzcuHVf/BTQZgFvauOZFVwBH49ZIydr4kH4iQK81M2CcaDRi9Gi+obTZhqFy7xwIOIyi6fTTdPt5ft4+oT4Q+ecShOXlPGioU/BLkji3iOnVPiAnZ9vHnOw9ON/mw7Jv+1omT5kyVp7dNmDnLjWVoRx7zq9vG4YSfTjyy5vt7ViWNk9BynD61y+DMEKROSUpzOLKcJlOm3+OkzuoYFVUUVMesmuoZHFNTel5aloiry3bI3RbgrbNeR4XKwOMJ6AVAxMMtOP2GaQZcT2aVs+/Y3zDt7LdoiJfID985vmNc3Qb61PyZM+d3NmAPdGAahth3Jx+789Eel5+4rCjB7nSOkgMeuCKa7SZElSn1+qwAPhndyHVz283akJgZqJ4bgp8v7QVDiRwWFgxH9KfOeieocBWpiZ1l+9eu3bj/ufm1o2uv6ocGOq9zCZ23rKHh3ZdLPsoafsVgoKAwtzSV26sYyiEKd0SrzFlZAwZIfRwOUqzmSkGUpIHpPXr4fJFg8Kp0K1jRqlj7qv2GxYy5Eke5wr7FpDpWXFxYWDksVqi5e1fH3BkXz+n4pxIOWz79gRHv0LneqJs2FQ76ewKfPao+pSsqEvmsj+ykQFfCF6ZeRcGFyUQK8v26El/4WGzqS33OfxjpXbL2ndc3sTfYvm9+vP3WksHVg5tvOnmsZKGTFc2buvrNabOfa5w5/drrmura10otT/ceNqZjJ5Xzew187smt/1i1bPw9We5Roeh1xYVrZ732vkM6L1UOHVlb2WcEHT5q0qRRuwBhBYC0lmeDB8LRdATw2Y0Wg8Fo9Nolp1MaEnNqJkCjR6D/JfU5336yUOPaKqJJEuCQeFQirWX7O+6YxfZjqapqE/61bQ958LsXt8S/40CwpeDekav/vh0ILAPAD7lsA1jEZFcyGsFksprtJg9Rr4kR6DJ/ZWoO7uobKtNnnyJUlrW3X3ttO14phMgLHn98yIjzPqkFgFxoY259XSt4oSTqd/L0JgaDT/NcE9PAaBctOk/sjOTEKYEwCRGJxwB6tajQpMDBcxoHXzN8CJbum6GLZe60066mRmnd+eJXN6mThXRIWPMH/Un+NdGgxLmTUKrIsmYzWa0Gg8lkN4P41WCzUcXkofbu2oTf3cjSZdpuokXRuGOyi1dx22KswGZWhYd5AffOIrF9jYxdh40sI74Et93MVivueDXr0gYPcG0ouF4DRIkAevQioLvExgPivyvuhO7qQJ5BQRgeLXS7XPrsKDMzI6PAajSaTPkuq9WRKzu46XwOzWzPRJNH7+G7krl7+OC8ePqbjJDCRIiEfKFykdziVfBd8q+ke9n++uvnTGL7vy529F437Xwso/dL097ZwvbVXz9jOnlw3rz12+LfSS1Lh1+/urZpy+F4kfhtxYuQjGCut1tMFxHAq6vrscoOoatQFU0Xx29SyV/XLRG8TS0ierkyof+ZtWWXEPbn7boC9dce3JHE5yf0pzhpostXLJYMcLnSvcYhMa9mp0Nidu8vu/xUrvPeVQMOCCQs6MzrxGVT5986ecr8W6dQmX3ELvzxh7swGyl/I6Xt6/70Qnv7mhfYKbbnQTS8jE7s8wA7B4LrOep1cC1ckMMn1Hl+RVFNlKpZmqrlcuQEq9U9hBOEwa5mQEaKzBKmSBWoSQVlTvPepDFCnPndRKFJtuemosq2GZrG9p/taZv8wfaPbt58TGf7vePdSx/wsv5K9SPtbB87/T/s7H10mU722JDgM67pTN1euaIq8dIsyh+TpOUZ+fg6PcNnz/ZanE5V4I0FhsQsv8m6iSfIBUmS5S2dL8HBXl8ook+LIkFBaLdMkafPPzxZ2v7R5zsmPXeFIQMJ22e1lq48uri9oOMZ9uLa9lNYiho3Z9+6xqU/bcBDAybXN3ZFFJ3LddVEh0mcejw5BCxZZVnUS7wGFxqlMrTMRy+JIqpdWewrCD+6iu3/sre97yvSbCP7xLR8SXyH1LKxZTYkqp/1XIZ4dpmjpLktAEU5bnchWNw5lhxTli9rcMynUdPgGPX+vJ2/2BgiqPTHK2HB5clePsGgXCkPt082oetPnbx1/bDrDtW395oycuG8yJd/3/Xu6MZHa5Zcv2zRrf2wZn1HILfzsvKx+b0rCstHz73+8VXN/8y//JriK/qHR/+30LeE6xuRa8AjToRYDHa7y2UyEIfB4fWZnHbn4JjVYrfL3HVyQt3QpktOVnRhgnBcxKOXvoLpIyFPwCO6cjK3bsas9tdeeHRt8xasYDuu+TD4aeiNN0jGwgknTn4e//yqK4UOT/Gc4zM+cENZ1E8cDrfby3t/j9NoJ7JNtumyPcmJ1sVDgItr7tQYgH+grxdrpR2zt72PpSLjsXRp7XUHt5Mj8dki4Ynt/EpI9JkPcrlm6BV1m0GWiYgIK0G0GNEuC5llKWndDU1X/x0SbTfiOtaElf/INyryZYexkjVJLfFF86aMXUzaumS4AZRtXEaWOMsoSyaOIVng81ETVTMyMjNzVEXJ9plMVLbbMxQ7yDqidR3RdPz2LIDSIO1WQ8wBsin/pGskRZpuUfew19lm7LMwJ1eRcrT7sG6R5NCsqBgvN92NPdk7uARPdt4vtTDH4m9q1lxH/PGvvE03jMkcer4XnuKKI5gApOW6bWqi+YoMaKSUSAQlGWWzQVWtfIZmMSoUAA1mj4T2S2cBqaROkYZeq3KlhdkClOu/mD2BI48cxZHsMWxja46fYO2kPwmyZ7A1fiy+DRewhcJLzK17ycs1KTC73ZrXK0koahm/Jgob/pNT8no0p9XJMTHDAFyVskQJkKKvhBlTUzxHyokifvTqgNsSaw9mmBRz7n4cwoqu+vcfR9RErqqfl+fkfr2/YcZNo8ic866XXnR8Z72xNZI450HXce2MIn+oKqkIYDYgmvQhAm8c7YR/MwyOoefSIULSSMJGySlCWEwR6LrOB4nC0uhAZiCmDrLp6+3xekDI4T38Id7D54ipCHUbcnIcfn+uNTMzIFGXy8qjKd9qSbTzYosp2hbbF7bnuBrm+REWRw08Coc18VTQ4xFQ6+EJhDmL2m6/c/OZG4cpn31T3XpmM9quH32qucGAVz7Z9jEdXMUObcyzBF8xskNVg+knbU8BIO5gJWSlYgMK7tcIpZJMAaCyhONDYlbqCOKOo0cV29lA1ylOauB7yBN7yOHlOmgGQ75bkoI52TabW3Z7qCzl/3/2IIuHzuFynuSi2BZnlftyiBSnzxyCyzwcrImh4e0Xbhz2+9mfKtWtL7xTP39x26LeM2aFPyFVQ7CnuWmyw5K3EXsOrqIfh2dPY5tNjY2nGm7QTxGQIqmCtoEHIlG/Ag4zmKnd7qNeu82mSJSaHQ5QoCRU1lYi9ElBdqqp5pwa1sv/RAMmELwQB0baym968pqFwxaOC99ePv7pgf89chFZcXX5l1NzcyPRii+nphf8lzhBwpbiQanl0rP6Dg26zurbad4v56mukCugE0Wi7Vh7JsTasSV5lIO0dJbKBcljHAhLOdJqfN6cwad7QYchPV3OyCA+n4mYMrPSXCNiBtuIGMiGNH4pGWmKygXqpwH4S8+ePzvOII575nOCTh4R15lS69q26gmSEBt94OCr7YtF6z7vlm8b7mpdcN+rL/fHcyhjZk77c8arjmflv/Bn9kZObzbAuFFEB4A0ST+d2BztZXeaidFqTfd6iV/zO51ado7Fn+avjxnT0sDFqcleG3P6QR7xs+NNXUfUIJTSVqjbjT+pBpRfbpXXFSKawsFwiBuQbNyyZcyzs2sbcS679w9k3/mvbhr+6qufy7sbvojGrt10dOm6WtZ5ttes1keObtl5BAjMBCYFpHXcnkW8R87TLC6j7EsnBrDZ8jIhM/OyYp9LSycWo2xQPZ4ctYBHz/YyHc11H2qb9S+iA4oURXyC3SM+0WGqPrVIoJJaFCmMXFRdbixfuGzBqEk3j1qwfGE43Pbogt+Nn93Y9siC8v1T6+qnzxxRO50cnPC7BcsWhCMLly6MTZs8uu2RtlBo/iNtYyYOnz6ttm7aDBHpCoDEp+PghZnR/7I53U6Plce2UaYyMYkJqxeRED/HBp/idDkbYkCRuuwmm93WEFPtdgt6FMsl5xX9mtiW3kNfypcpEhAfkgPKkCfoEXdAGF7cGCBD0YAVbOGWH374gX38448/vsOW4BViZBv3vHrfq8eO8RdyHMhFiKNCMGoniiKGmUaJSlTVsUcEbCpFdAhyJGBIAFHnAbag8wAAgUm89lnw/0o5D7g2jvTvPzOzu9KCJNSFaAKEBMYHAokSuQpiY04OODjYsWxCcjbkNaluuPdyiXuaS0jHpPfeE0N68fVO/ObSe+8uy39mVlqEzr76oeyi+bG7U3bK83yfkUZBGZwCMyKlaRaXRRTLC6E4JyfkAld4DKmpsbkrK0ttpSafxzc15nHqTVNjepQycUvmivi5NiuyMYtA0qyNo3NOVr9OFfZJmt75WUW7VMhOWtE4fsubj9zRP33SzuaW6LxFB3rWTJj4xSuvXdHyYsOAb/bpj257c+OS5s4tvmrim7appHXPputbn8kPlVdURssit194/xklXdGr7p3261Hh7uKKUGH0uu2nzi8Pxya1V5qmAUYu4UfygiRwVi0/YrQaWIvIdGcQ4pBB7dzU9snCdpLZJF/SOXJNjdRPPa0uMhVd2TKurqk5Mq5FXFPXEB0/7ucNExvqGieOb6wDIIw7lSbR99oBPqhmvm9ikm0mm7/c7yzPc+bV1IrpYEmnX1mlhbZglpActKMVbEo36zBrHWyifBGnSASrw44ZvIhr6bwgFCxiuH4R45HIul+c91p4c3j55tf/fvilPddGFx5b8zJqf5X9DCi9v/m10vvcrj6U09uHsg/0Ke/29invHSBfX7VJ+TAv99nwkcNvfNd82xjlI/4/Su+rLyi3/ObXaPaLTJb0b6xlBfCX+DHKMLqgAOoieZk65HLlmXXU56PLK/RmGI2e9HQbys4GEGweShSEA0F1mAtak3BQbR1SPGxVVo3K6irbp3YM1ToJV3pGr452r7n58XnrWi6tr79h3tY9yqTy/KbYvMvxsYvGRLrPu/BCWegef0l+cNcmpeGP/qIz6oqkNPas06Fd6BEEkMAIbZHRaUaDTKd2RMKCgERqGDdkGNkrBpBGCE4XBIMoIpOMsR4lWko4kLBqJI+K5j8Faab66Q897w8yR4ALIR3yqYfpaPGg8hFyDSo70RG06A12/oayC49HL1E/s9K3DL2QNXzKGb8fhTCZCCJkRZgzSkcQkogAAdYJoQTf6LXQWZQQHjx2hLz1I7pgEIaGErEHWAIzAAhaezTEW+S5kUqBYFHUgcViJEbamxB9uT/ROLFE8QLBIegdsp5+naSN8spKbara53ErgY4FlFnoIwadmhP5X7VaYcvuz5QHAu8h/cO3K+s89eFTJuceP+dft9utd0xUFqDpyj3kqh3K1+H6uhrlzX/ZctHQEckuSNLhJG8MjPTGCNLRbwWDZH+Fr/6Jm7D5hAmyIDMiQ0ZGTrbVkMkqRQ3FUq17vL06HSowmDyctbXd2N5201ln3XjW5a88G6uvnz2nLjJHWMg+7W0766bZL10emd02YWJ7G+NFAYSwiCGdcx+ZGTqdRB35BoSomd9sMRrSZYQkAYOKeoYC8S5MM5WnxriwyfZwnAs9I2/h3kG0RVlFY12UNylYiiCAo/gZTriVRKwOA5LAgiyuTNnkwQ4Hyucer4lJXb96j39EPHUF+JnjK/5+briipGXeqiuf3np9+4YudA6O3jbYEQv6S2bt37Cle8be7rMBwVgcxo+Ir4APJkRy7enY7QbIl/LTzVK65C8mdrvDIed4PSa5IIE5pbQ8dlABTRX6S6xu1DgHrezj3QjuuaN9/n1P7N541ards5oXtJ3REgwFWsOdE/b9v3W9wlu7a432i6at2N7wzOzzq6tvrAr76ePuDExYn+qLI0JEDyCnCdwXdyjui3uFjR/VNMjMIUk6ao6YiGZWHZ0i/DX75U5H1aEgAOK2LmrkhkxmMUmXJFnOsjrBQR/drXNlOGl7yiCq4Y2Z+zTTkbYwT8qwtv73xo0CxS6XhZtDZ7WvpVaAD0ZnlC6fNWF+vigy+yj67YoVdz/PrAF7Z8wo/9mM65SDUhQQLFSOCbslO2RAIOJINwsiAoTMFr0emUykKWYSWc8XiHtk4gMlbe5qgAb7UsMIa0IFwu6bbumd0PqX1/72IW5Tjkmn/3QfCVmPHEWCwiKd8Cj0e7KGEUURmUU6Ebk1RiCQCHSypSLhfEr/+2Eqe2hQsaNeALBCVcRlNjI7Fh1Y7Gaz0W60ySYW9pXNXt9QQI0EXB1/3PjAIiZPQYprQ3RWgnr3Xd88KXuOu/GW5v7s6Kwj6xc5btOZJpzh7hmf2cktXDiKGxPRSYI8MjopD+WfMDoJeePRSb4QbvyciNkVzReismdxFD2z4Oyi0vHr6MwOwnTUfEt8ic9KPBFjIvYqgzhkDw/xTGK3kxc9YlKPgt969IarH3/wwP4nFG9dY+PEiY2NdULbnf0v3Hr7wAu3dHR2dnTMm5cy6s2OlKZTy49OL2AW1Ib01FNiGh70BD7YIdHEB79/Oej1B9UBL+6NL0aoFonqQehRdg4ip/LxIFqsSMPn2KuMXYbaUNsyJZw1fMrGrnIA6Qpa2n5Y+TuAYvg1fgUA6eAP5Nrjj4L8IMFW+uJUVye0D51Au5h8T7W6B7CZSZlyNlXeJ75ClUs8XEnM8as+Eb9qmXpVwDBeWUH+LLTzNU5DpKiQug4YJk0jh0pMoyDbnI1lQp0JPk9rzJdhoRy8xZvKwaN4g9Cm5HHsnddbrUub3bCVWHLF4ldiF1wYPjM27aFzzp37w3lvHP3F7rOrUcnw6jY6d1dT86yJ4eiY0sOnTO6//YLru+j0cyyamXhHhoZU2lu3GPuhiOexHiQ0HfQPYqfoh9HVJ1B0w2//heIgzFQV2SMV52iKgYTCOlIxU1N0cUXaQwR7uWRYkxbXSNDfPYvXhpfEa4MpdD7OPtrg4sg4yUbMNmIRLCjNZEJsvgbgEETRbiYUvqb4syENGQkj/JFkkzkxTAQrMmlscsKiQLvUAAeUNb8G7yQ062PCs0QKkEYsI9rR6nzH9imOvcoLeLew9/ghbKIUT+hoLlq5jiPvcYqZDnXNrC6WKXZGjNP8+VlGYAXOBfY556p5+ZaodTT0KC89ZE+UXqqiG9pSFPdShT1JcXDoO1XhHnmNmZqia+gnXgMYFag1wGbucZ7cAJnQGCmivUCW3ep0GlBamtthAIqVWwGovcRJi9eKLYy8TgmP0+BgddahWmkscQqUlpiPo4MhBwPPA1tV5FzFz7cKwm9+d+CzzzahATIdd1Du/G5GoOPWnR9+ofQoyl1qHsRXeDuriLez36eUA+dUeTlUxtt7N1fgvJMpulHDv1AchOdUhXek4hxNMZBQZI1UzNQUXVzB2vvoeGkj2IAMglnogXTIjaRLBGTZYORGZXcgqMUn8260FqnLBlSM7lL+uB+Vocqr6Rhetkf5tfL7vfj3qKxH+SMavZf++VuaSiUAhD7DLeIHkgA2yIZCCEdyXJ4cuz0tB9LAW+TMK3Ab3QxXJQWpdOWImbyK8arGGFaJqpEG2V2IO/yqihEFV1Wm94Xts3tnv8iA1RevaL1x1sDRP56CjrR2UWL1/ZBiOG0+WqzyvXWXXHDpANrEwNWGNfM3DSi/fHYJ/rbsp+8e6j5uKR4aUmlIXgO18Vocrdaz1uOkKrqR6V8oDkKPqsgfqZipKbq4gr0RJcl9kqDwq4yNv3kb1KtYuCSJSmbrqZpIDiOjjbIoSpJTMDbFZEdTTJAFWdIRyZowKGrdjOZBjePIDroW0tZGwh2UUz1yNcPaH1CQ4fikjst3rbt0NcHv/agMUij5c2Vc18rz5/NZJM3JfMkD1dAaGU3tegXFxQDlWSZTbXkgUGPKKtBBcbEui2SWhkqnxEIQcFgyozFLwnGq7ZUx0g03TH/aTYLqcnOkuuX8iaFL8zhXsVAn4a3SSDRSWl1/RVfoo3fmXTau+ubIbfnTo2vnNjQ0TVjXsWQjbb4+hL9FfuGvkV+cNqai1JldVTJn7srmu+7JLfy6KLhqVGhcaeOylsh5lbWnl49r6TrnKPVMv/LO/azH5ASbVEBr5VQ+UtQfAPb2jbbEazY1vfvCE6Xna+kHfxhi6RUj001a+kAasPTikemClt4lAX+3T+GCYcUDmqJ/lKrwqwogTCEpQjeUQBBOgS2RydU1JDM/P2g3GoNBuabG7/GMKZPlsC/fW50fjVVXsyDp7OxQNJZtNo6aSoF3p+S0NFDHPHgbYiBJgQZGv/ERLZmZ0t5q6wkJKnqMhzBz8MufZG0ZXsZRzHYYrWJk1TDShwoZfiVWbn2rce4L19/03NdfPRtr2nHzvKc/emdx/d3LDyM4XkaJq+cfm/bY8bqFq1fv6FyOvX+1oHvwefbOru7Y0zcz5q91cn3Tq52bInXKZx9RCGvWp8UlOEsQzpxD6T/05acLVrNap952xtZhP0xWx0+0iY+fnCrjtT1FbQ2389oqStRWanr34n+eflDP00eNTBe09C6rWpeVidoeugYAvcGv8LTaXynTgF0DGRLXuBwA/y5J0T00eaRi6JdU8UmS4qDyuqqwJBTvUMXlkqApuriC9Vdu9UkSBIfk5fPVpZGx4MYuV46oJ+kEY0tOTnr6qEKLpcQNmZh+SJ2ImdjppB56CnnSKS02+RpiJifBU2MEnYC8izsQ2clwI9I+1YYLf3Gtkw8SVgdtm4XAwyNdtX46hDAvXCL2GCmnN3ZetuitjjuuvUr5/0PfKX9DwuFDDfpT17zfga0rz19x8fIFq84TXdXF99Wdtr1n/m5lz4fKh8pLyPrJR8gyV+hdtuva4/Mv2Lj1ih27+lg74MwMf2tPV9/aEPAZUHI97ucl3KK2k5t4PReeOJ319ZfAyRW8pRiS+gUt3aSlD6jpeSPTBS29y6C2pIDWK8yCw0JYeIl7wbKhNGJ1pqWZBQEIyYUcNwVKAXHz0vPBYdBQiw8WTxJRTWOGj2+K1tf/PFpXNzVaf2ojO+KOwcEvTpva/POG6c1EmNrUMqWhpRkIfcaHKAN0OZ81eEfOGnzxWQOjb0jBFAZx/C+zhmCNsJ9hQWsvOLVn0n5GBm1eUrt/zK5jR21o/OiJKy9AhwzKa/6alefjSoYJlXV2dVyL7IwUqpp+Qes1ytH2RjTouvnWlnFKMOP2oSGVpeD1c2ZST4ByefGmpvMavgVOruA1XMnTC0emC1p6V0B9A0u1np97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</head>

<body>




<section class="page-header">
<h1 class="title toc-ignore project-name">散点图系列(1)</h1>
<h4 class="author project-author">庄亮亮</h4>
<h4 class="date project-date">2020/10/09</h4>
</section>



<section class="main-content">
<p>记得安装prettydoc包，html模板在该包渲染而成。</p>
<p><img 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" /><br />
欢迎<strong>关注</strong>我的<strong>公众号</strong>，<strong>点赞，在看，收藏~~~</strong></p>
<hr />
<div id="简介" class="section level2">
<h2>简介</h2>
<p><strong>散点图</strong>（scatter graph、point graph、X-Y plot、scatter chart ）是科研绘图中最常见的图表类型之一，通常用于显示和比较数值。散点图是使用一系列的散点在直角坐标系中展示变量的数值分布。在二维散点图中，可以通过观察两个变量的数据变化，发现两者的关系与相关性。</p>
<p>散点图可以提供三类关键信息：</p>
<p>（1）变量之间是否存在数量关联趋势；</p>
<p>（2）如果存在关联趋势，那么其是线性还是非线性的；</p>
<p>（3）观察是否有存在离群值，从而分析这些离群值对建模分析的影响。</p>
<hr />
<p>本文可以看作是<a href="https://github.com/EasyChart/Beautiful-Visualization-with-R" title="《R语言数据可视化之美》">《R语言数据可视化之美》</a>的学习笔记。该书第四章——<strong>数据关系型图表</strong>中展示的散点图系列包括以下四个方面：</p>
<ol style="list-style-type: decimal">
<li><p>趋势显示的二维散点图</p></li>
<li><p>分布显示的二维散点图</p></li>
<li><p>气泡图</p></li>
<li><p>三维散点图</p></li>
</ol>
<p>本文主要对第一部分进行介绍，并加上小编自己的理解。下面几个部分也会在这星期陆续推出，敬请关注。</p>
</div>
<div id="本文框架" class="section level2">
<h2>2.本文框架</h2>
<p><img src="data:application/json;base64,eyJSZXRDb2RlIjotMTQ4NjYwLCAiRXJyTXNnIjoicmVxdWVzdCBleHBpcmVkIn0=" /></p>
</div>
<div id="数据介绍" class="section level2">
<h2>2.数据介绍</h2>
<p>随机产生2列20行的数据，列名分别为x，y。x为序号，y由标准正态分布中产生。</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="kw">library</span>(ggplot2)</a>
<a class="sourceLine" id="cb1-2" title="2">mydata =<span class="st"> </span><span class="kw">data.frame</span>(<span class="st">&#39;x&#39;</span>=<span class="st"> </span><span class="dv">1</span><span class="op">:</span><span class="dv">20</span>,<span class="st">&#39;y&#39;</span>=<span class="kw">sort</span>(<span class="kw">rnorm</span>(<span class="dv">20</span>)))</a>
<a class="sourceLine" id="cb1-3" title="3"><span class="kw">head</span>(mydata)</a></code></pre></div>
<pre><code>##   x       y
## 1 1 -1.8720
## 2 2 -1.3863
## 3 3 -1.3316
## 4 4 -0.6965
## 5 5 -0.6241
## 6 6 -0.5514</code></pre>
</div>
<div id="不同类型拟合曲线的绘制" class="section level2">
<h2>3. 不同类型拟合曲线的绘制</h2>
<div id="loess数据平滑曲线" class="section level3">
<h3>3.1 loess数据平滑曲线</h3>
<p><strong>局部加权回归</strong>（Locally Weighted Scatterplot Smoothing，LOESS）主要思想是取一定比例的局部数据，在这部分子集中拟合多项式回归曲线，这样就可以观察到数据在局部展现出来的规律和趋势。曲线的光滑程度与选取数据比例有关：比例越少，拟合越不光滑，反之越光滑。</p>
<p>ggplot2绘制时，使用<code>geom_point</code>绘制散点图，<code>geom_smooth</code>加入拟合曲线，<code>method</code>选择为loess,<code>se=TRUE</code>表示加入置信带，<code>span</code>控制loess平滑的平滑量，较小的数字产生波动线，较大的数字产生平滑线。其他参数对颜色，填充色以及透明度进行了修改。</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1"><span class="kw">ggplot</span>(<span class="dt">data =</span> mydata, <span class="kw">aes</span>(x,y)) <span class="op">+</span></a>
<a class="sourceLine" id="cb3-2" title="2"><span class="kw">geom_point</span>(<span class="dt">fill=</span><span class="st">&quot;black&quot;</span>,<span class="dt">colour=</span><span class="st">&quot;black&quot;</span>,<span class="dt">size=</span><span class="dv">3</span>,<span class="dt">shape=</span><span class="dv">21</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb3-3" title="3"><span class="kw">geom_smooth</span>(<span class="dt">method =</span> <span class="st">&#39;loess&#39;</span>,<span class="dt">span=</span><span class="fl">0.4</span>,<span class="dt">se=</span><span class="ot">TRUE</span>,</a>
<a class="sourceLine" id="cb3-4" title="4"><span class="dt">colour=</span><span class="st">&quot;#00A5FF&quot;</span>,<span class="dt">fill=</span><span class="st">&quot;#00A5FF&quot;</span>,<span class="dt">alpha=</span><span class="fl">0.2</span>)</a></code></pre></div>
<pre><code>## `geom_smooth()` using formula &#39;y ~ x&#39;</code></pre>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="样条数据平滑曲线" class="section level3">
<h3>3.2 样条数据平滑曲线</h3>
<p>这里使用了splines包中的样条函数，df=5，样条具有五个基函数，其他参数变化不大。具体非线性模型相关资料可参考：<a href="http://tecdat.cn/r%e8%af%ad%e8%a8%80%e9%87%8c%e7%9a%84%e9%9d%9e%e7%ba%bf%e6%80%a7%e6%a8%a1%e5%9e%8b%ef%bc%9a%e5%a4%9a%e9%a1%b9%e5%bc%8f%e5%9b%9e%e5%bd%92%e3%80%81%e5%b1%80%e9%83%a8%e6%a0%b7%e6%9d%a1%e3%80%81%e5%b9%b3/" title="R语言里的非线性模型：多项式回归、局部样条、平滑样条、广义加性模型分析">R语言里的非线性模型：多项式回归、局部样条、平滑样条、广义加性模型分析</a></p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1"><span class="kw">ggplot</span>(<span class="dt">data =</span> mydata, <span class="kw">aes</span>(x,y)) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-2" title="2"><span class="kw">geom_point</span>(<span class="dt">fill=</span><span class="st">&quot;black&quot;</span>,<span class="dt">colour=</span><span class="st">&quot;black&quot;</span>,<span class="dt">size=</span><span class="dv">3</span>,<span class="dt">shape=</span><span class="dv">21</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-3" title="3"><span class="kw">geom_smooth</span>(<span class="dt">method=</span><span class="st">&quot;lm&quot;</span>,<span class="dt">se=</span><span class="ot">TRUE</span>,</a>
<a class="sourceLine" id="cb5-4" title="4"><span class="dt">formula=</span>y <span class="op">~</span><span class="st"> </span>splines<span class="op">::</span><span class="kw">bs</span>(x, <span class="dv">5</span>),<span class="dt">colour=</span><span class="st">&quot;red&quot;</span>)</a></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAMAAABNUi8GAAAAqFBMVEUAAAAAADoAAGYAOpAAZrYzMzM6ADo6AGY6kNs9PT1NTU1NTW5NTY5NbqtNjshmAABmtv9uTU1uTW5uTY5ubqtuq+SOTU2OTW6OTY6OyP+QOgCQtpCQ2/+rbk2rbm6rbo6ryKur5P+2ZgC2/7a2///Ijk3I///KysrW1tbbkDrb///kq27k///r6+v/AAD/tmb/yI7/25D/5Kv//7b//8j//9v//+T///8Q9BYZAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAaSklEQVR4nO2dDZfctpFFO060mx7bchzHdpSNdi0nEunYIytjRfP//9n2d/MDBAGyCvWKeHVyfOT0+M4r4gpgs9nE7pnFAq6ddQAWK1YUlAVdFJQFXRSUBV0UlAVdFJQFXTmCPkrVtkmQody1R0HVSJCh3LVHQdVIkKHctUdB1UiQody1R0HVSJCh3LVHQdVIkKHctUdB1UiQody1R0HVSJCh3LVHQdVIkKHctUdB1UiQody1R0HVSJCh3LVHQdVIkKHctUdB1UiQody1R0HVSJCh3LVHQdVIkKHctUdB1UiQody1R0HVSJCh3LVHQdVIkKHctUdB1UiQody1R0HVSJCh3LVHQdVIkKHctUdB1UiQoRDbayioCQkyFGJ7FNSGBBkKsL2GgtqQIEMBtkdBjUiQofDaa+QFZbHkqm3blB/jDCpOggwF195hAt3vdpHfQkG1SJCh4Npr9vuDoNOKUlA1EmQotPYaCmpGggyF1t7Rz5OgU4ZSUDUSZCiw9hoKakeCDIXVXkNBDUmQobDaaxqeg9qRIENBtddQUEsSZCio9ppz8TqoDQkyFFJ7zbX4WbwJCTIUUnsU1JYEGQqovYaC2pIgQ+G011BQYxJkKJz2KKg1CTIUTHsNBbUmQYaCaY+CmpMgQ6G011BQcxJkKJD2en6+fx/7LRRUiwQZCqS9vp+cQU1IkKEw2utPoFzibUiQoSDaGyzwFNSGBBkKob2hnxTUhgQZCqG9oZ8U1IYEGQqgvcEJKAW1IkGGsm9vtMBTUCMSZCjz9gJ+UlAbEmQo6/ZCflJQGxJkKOv2+n5SUEsSZCjj9oJ+UlAbEmQo2/bCflJQGxJkKNP2giegFNSKBBnKsr0pPymoDQkylGF7Az8pqDUJMpRde9N+UlAbEmQos/YiflJQGxJkKKv2Yn5SUBsSZCij9qJ+Sgj69PXDwysKaoVCJOWg4n4KCPrx+zfPT39+Q0GNUIikDFQzELQRF/TDy8M/3l6n0PW9XX/5pkmQoSzam/NT6Bz0OIs+P7841OyPslj3ant1WODbYaVQZgX99Prb6x8F/vZd/nZsmgQZqnx7w/lzPIGKzKAfv7v5iXi0EEmQoYq3l+BnlJQo6NPXr+7/ItPfI+Zxp6CiqBQ/BQTt+Yl4tBBJkKEKtzfv5xwpTdCfH47Fd/FWKERSCmqoZ9hPiSW+VzL9zQTzT4IMVbS9WT8TSBRUjQQZqmR7Scv7HImCqpEgQ5VrL+30c5ZEQdVIkKGKtTfrZyKJgqqRIEOVai/HTwpqQ4IMVaa9Zs7P9FAUVI0EGapIe5l+UlAbEmSoAu0NF/NZPymoDQkylH57+X5SUBsSZCj19hb4SUFtSJChlNsb6pnkJwW1IUGG0m1v3s/sUBRUjQQZSrO9kJ4pflJQGxJkKMX2FvtJQW1IkKH02lt2+jkbioKqkSBDabU30jPDTwpqQ4IMpdTeKj8pqA0JMpRKe2M9s/ykoDYkyFAK7YX1THt7NB+KgqqRIEOJtxfQM9dPCmpDggwl3F5Iz7zlfS4UBVUjQYYSbS+oZ76fFNSGBBlKsr10P3e73dJQFFSNBBlKjtS0aaefzVHPXVRRCmpCggwlRTqI1xd0v98H/Xx8pKCgJMhQMqSTem1fz0OFps+bnxFDKagJCTKUCKkJCxr2k4KikiBDCZCu8rVjP/cBPykoKgky1GrS3b6+oMfpc78P+MlzUFQSZKiVpK5+PUHPfu5DflJQUBJkqFWk/vzYEfSi5z6k57F4HRSRBBlqBWlwftkR9OrnfsLPNaEoqBoJMtRS0tDOjqDnN++R+XNVKAqqRoIMtZAU8PMi6PvpByeLhKKgaiTIUItIIT3Pgob1zPRTXFBWVXXdbuuwiA824DrpOdqWK21jrtziDCpOggyVTbpOiucPM7vnmROzZ+70OROKgqqRIENlku7aDQWd0nOBnxTUhgQZKovU0W6/7xs6pecSPymoDQkyVDqp711f0JOewftBxUNRUDUSZKhU0lC8rqCX1T0kqHwoCqpGggyVRgqod/Xz/e3kMyCoQigKqkaCDJVACp5adm5Jvp58jgRVCUVB1UiQoWZJYT3PivbfuQ8F1QlFQdVIkKHipGk7m/GFpYGgSqEoqBoJMlSEFLUzcGGpJ6hWKAqqR4IMNUW6LOLD72xM69kXVCfU6TUKqkWCDBUiXSQbf5jZ03N0Xf4uqEao22sUVIsEGWpMumk2Jej7iU81r4JqhOq8RkG1SJChhqS7b8MPM2f0vAqqEar3GgXVIkGG6pF6vgUFnbTzIqhCqOFrFFSLBBnqThr6NhZ0evK8CCofKvAaBdUiQYY6k4LCDfyc0fMgqHCoidcoqBYJMtTz9PXO4e1KUTsPq3uZ9iioGgkxVPgeuY6iNznjdjaSoSioDQkq1G1djnv3PsnORijUtSioCQknVEetiKCJct7eulNQ5ySMUAO5JgVNlLNzZYmCOifZhprQKyxovp3LQk0UBTUhGYWKChb8rH2RnhTUPckiVFyw0GeZyXaOLstTUOek0qHmFQt/VLTIzmLtUVA1UtFQPQvjft6++ZZsZ/gzTQrqnFQsVGySDLyWKefkHSEU1DmpTKiwhFOC5pkZsbNUexRUj6QfanKSjNwXP96KY5GdJdo7v0ZBtUjKoYJGTQp6lXNyes2TU7+922sUVItU5D74JEHvC3uqnrbt9V6joFqkIvfBTxgakvNUMzeLpMqp2N7wNQqqRdIINWvXTdDOlaTI0xaWyqnUXug1CqpFkg81P/udFY1c54wIat5e+DUKqkUSDpVkZzP38dCUoObtTb5GQbVIkqFk7JwQ1L692GsUVIskhkp6a5P4ybrcA5XQBH365h0FLY+anvcW2NlIPq8GTNAPD19Q0OKo4Ly3WM47SaA5MEHffv4DZ9DSqNC8d6zYdc4EQUV6ewQT9LbEvzjU3I+yBCqwgdttw7dj3XZ6C+71NlXWXS0vnoOKk9agggvzbf7s3tOZOnVeFnaM9jJIFFSNtBwVWph7fi6/aw6hvSwSBVUjLUSF9OoJuuqeTvP2ckkUVI20CBU27CZo5665fDmXZpooCuqctAA1ZVk7tDPNT5FMkwUmaKeKBPNPykVFTGub+/eDY1/qiMu5IFOsKKhzUh4qalvbO+2c11MoU7woqHNSDiqqm8h3MPMzzRUFdU5KRsnJWfKLRBTUOykRNa9n4t1MgpkKoyioCSkFlWJnkyKoYKbyKApqQppFRY3rLe1xQSUzmaAoqAlpDpVqZ1xQ2Uw2KApqQoqjZuSMbCy8WM7ZTFYoCmpCiqGS585pQcUz2aEoqAlpGpVnZ0BQhUyWKApqQppCZes5FFQhky2KgpqQwqionlMvdgVVyGSNoqAmpABqweTZF1QhEwCKgpqQRqilel4EVckEgaKgJqQBarGdTamNhc1QFNSE1EUtnzxPUyd6e4okCqpGuqOy7bzf8SkdioJKBfNPuqKCcl6/+zah5/meeYVQFFQqmH/SCRWeOyN2XgXd7VRCUVCpYP5JR1RsbZ/64sbJzmNphKKgUsH8k6ae6jnz1czH3bU0QlFQqWD+ScFbPLrfHA4JevjvKGjvNQqqQ2pC9yBdTj0ndzM6/6cjPwHbE0VR0OKkk25DQe9vjIJ+3v5jCtp9jYLKky7GtSM7O99sHwjaB/T1RGtPHEVBS5Lu0rUTel4UnZBTJZQCiYK6JHUnzf4jv4bnm+l6ArWng6KghUgD9dp5PQuE0iJRUGeksX33Z9KtsnNNKEUSBfVFCgh4fSbdWj0R2lNFUVB1UlDBNrK6lwilS6KgfkjhKXL6hpAiobRJFNQLSV1PzANFQX2QInoGP20vEqoECVhQ1q2Cu2V1bwjZyj5a5sUZNJ8UW9tD94MUCVWMBDyDFgmGT5rWs2kCNyyVCVWOREGxSVE9R4IWClWSREGRSTN6NoMblsqEKkuioLikeT17gpYJVZpEQWFJXQkn9Oy8WihUcRIFRSAN7xx+7Ot5nSPDF+VbIT0xDxQFtSeNv3vRXd3vgk4+dlYjFAqJgtqTRoI2Yz/PjwgJnpWitweDoqDLSKPv/w5PMa96hvzUCgVDoqDmpIGgAwOjejZqoWBIFNSc1BN07GBkdVcMBUOioPaku58hC/dTD1jSDYVCoqD2pJugIT/P94RM64nfHgyKgi4mxfWcXNx1Q2GQKCgIKaTn1JXPYqEASBQUgrRcTxftYaAo6FLSpJ4BP4uFAiFRUHNSjp6hTzXB28NB5Qr621e/+zsFDeqZurprhQIimc6gP+52v/9X1YKu1xO6PSzUkiX+t692uz/WK2jO6l4sFBbJ/Bz0qOhn/6xT0Aw9I7fUwbaHhloi6E+73R8OS31woS8SzJAksLrLh4IjWQr6n7/tdn86/uHX4BRaJJgdSUhP1PYAUfnv4sNLexWCTu4dk6snZnubEHSuigQzIk3tHZM9fUqGgiRRUANSE9jcaKmegO2JkihocdJJvFZKT7j2hEkUtDDpot54c6OFeoK1J06ioGVJzVjQxSefYqGQSRS0KKkZCbr+CclA7SmQKGhBUke/uc2NyoUCJ1HQcqRmJKjI7hwo7emQKGgpUt/A6OZG5ULhkyhoIdLAwVbMT4z21EgUtAxpKGErpSdGe3okClqCNNZw47sbbVXQj989fPnL9gTV1BOgPVUSlKCfXr96/vnl1gQ9uxd/TPIKP63bUyZBCfrxr++en755ty1Br3re9jo46Rm43a5kKD8kKEGf/vLL88fv3xz+9OJQ0R91U+e93/a3DeGOeob2jbPOyTpVXNAPX14FPVaRvznKpKbpTqCHKfS6urcys+eiUK5IoDPoRgRtBoLeTz5bMT8htdqooBs7B70LOH7MfCulJ6ZWGxX00+tvN/QuvjtFjnZBaMX8hNRqo4Ju6jpoMxC0f22pldITU6utCtqrIsHUSMOLSINLn62UnphaUVCpYFqksZ79S/OtmJ+QWlFQqWBKpDk/BfcvhNSKgkoFUyHN6im5fyGkVhRUKpgGKcFPwf0LIbVy115Fgg5VDH+j2N0IWpEoqDApZfoUzQSplbv2ahE0MH2O/BTOBKmVu/bqEDRZT4cjaEWioHKkDD/9jaAViYKKkRLPPqUzQWrlrr3tCxrQc3L6FM0EqZW79jYvaNb0KZoJUit37W1c0Fw9HY6gFYmCCpASVvfhJ5vuRtCKREHXk7KnT9FMkFq5a2/Dgi7R0+EIWpEo6ErSvJ/KmSC1ctfeVgVdNn2KZoLUyl17GxV0sZ/+RtCKREFXkEZ+purpcAStSBR0MWn59CmaCVIrd+1tUNB5P8tkgtTKXXubE7QZ+ZkxfYpmgtTKXXtbE3Tez2KZILVy197GBJ31s2AmSK3ctbcpQcd6vs/U0+EIWpEoaDZJwk9/I2hFoqCZJBE9HY6gFYmC5pGE/PQ3glYkCppFmnt3ZJAJUit37W1D0MGDu0fTp0UmTK3ctbcJQQdPll/jp78RtCJR0NQ6KthG9Mx7XJ27EbQiUdDEavqCrpo+pTJJoxBJwIIi1XDbrdFuXIef2e28dse6ldMZ9DpNttPT5+5c5TIpoBBJwDNokWApdRexnV7eKagOiYLOVdfEdvLd0W6XZ6i7EbQiUdCZagaCTrx7p6BKJAoar2Yg6NSddRRUiURBYzVw8bRdXNBPnoNqkShopAJ6vt8H9aSgWiQKOlmDqbI5+7kP+/l4UlQ9ky4KkURBpyrk52lT7Qk/S2RSRiGSKOhEDf1srn7uBfR0OIJWJAoarJGedz/3En76G0ErEgXt1eU8MqRn38+CmcqhEEkUtFOXd+LB6fN90xW0XKaSKEQSBe3U2c99yM/z+ySp+dPhCFqRKOi9Ln7u9yM9r1fnWyk//Y2gFYmC3uvq537Kz4ugJTMVRSGSKOi9rn7uB37e/6WV8tPfCFqRKGinxn4O7l1qhfR0OIJWJAraqf6FzrGfB0GLZyqJQiRR0Ht136eH9DwIWjxTURQiiYLeqhlU4KHJFY+gFYmCXivg5/D/qnkErUgU9FIpftY8glYkCnqu+eU9lSSXqTgKkURBj5Vw+plIkstkgEIkUdDHxOU9iSSXyQKFSKKgKX6mkuQymaAQSRQ0w8+aR9CKREEz/Kx5BK1I1Qs61jPy2M+KR9CKVLmggekz9ljaikfQilS3oDnLe5wkl8kQhUiqWtBcP2seQStSzYJm+1nzCFqRKhY038+aR9CKVK+gYz3nd+2oeAStSLUKGpg+E3aVqXgErUhogj59866EoAuW9wmSXCZ7FCIJTNAPD1+UEHShnzWPoBUJS9C3n/9QYAYd6pnsZ80jaEXCEvS2xL841NyPLq3hplzjXblarV/NQi+Ac9Blb49CJLlMIChEEswM+vbh4WUBQUPLe/qmsBWPoBUJRtAyM+jy088hSS4TDgqRVJegK/2seQStSFUJutbPmkfQioQmaKfEg632s+YRtCJVJGhAz0w/ax5BK1I9ggr4WfMIWpGqEXR+eU94tGLFI2hFqkVQET9rHkErUiWCyvhZ8whakeoQdP70M+3RyRWPoBWpCkGl/Kx5BK1INQjaiizvpzbEMrkbQSvS9gW9bb+1dvp8rHoErUibF7TpC7rKz5pH0Iq0dUGbvqABPXN2lql4BK1IGxe06Qu60s+aR9CKtG1Bm56goeU9b2euikfQirRpQZueoOtOP89trM8kToIM5a49E0GbnqCrp8/HqkfQirRhQZueoBJ+1jyCVqTtCtr0BBXxs+YRtCJtVtCmJ2jHz9t+sQugFY+gFWmrgjY9Qe9+3nfcXkKteAStSBsVtLeUt/358yToMmzFI2hF2qag/XPNsZ/73TJuxSNoRdqioLezzJGfV0F3FNQLaXuC7m5nmWM/m4ueFNQNaXuC7vuCHh9d1/QMXe5nzSNoRdqcoNezzP19/uwLuqOgnkhbE7TpCXr++L0r6OEUYLGeVY+gFWljgjY9QS+3h7RdP1dVxSNoRdqUoINL8de3R62UnzWPoBVpS4L2L8Xf3763Un7WPIJWpO0I2nun3ru81Er5WfMIWpG2ImgzrM7lz1bKz5pH0Iq0DUFDet4vz7dCelY9glakLQg60nPw7Y5Wys+aR9CK5F/QsZ7Du5NbKT9rHkErkndBA3qO7p5vpfyseQStSMCCJtRo0zhuHMdaVaIzaGj2DH37CHFicDfFWJGAZ9CZXxfWc+wn5nF3N4JWJKeCTtgZ9BPyuLsbQSvSxgQNbryJeNzdjaAVaVuChjeGRTzu7kbQirQpQSc2LkY87u5G0Iq0JUGnNtZGPO7uRtCKtCFBh34mBcsrRBJkKHftFRB00k/I4+5uBK1ImxF02k/I4+5uBK1IWxE04ifkcXc3glakjQga8xPyuLsbQSvSNgSN+gl53N2NoBVpE4LG/YQ87u5G0Iq0AUFHmyPkBMsrRBJkKHftKQo63rwjJ1heIZIgQ7lrT0/Q8e2fWcHyCpEEGcpde1qCBvY+yguWV4gkyFDu2lMSNMlPyOPubgStSK4FTdw6DvG4uxtBK5JnQVO3NkQ87u5G0IrkWNDkrTcRj7u7EbQi+RU0fWtYxOPubgStSG4FzdjbEPG4uxtBK5JXQXP23kQ87u5G0IrkU9Bd1t6wiMfd3QhakTwKutsdBO1v1hXzE/K4uxtBK5JTQXtbdc34CXnc3Y2gFcmhoLuLoPtEPyGPu7sRtCJtRNDFwfIKkQQZyl17yoIuD5ZXiCTIUO7akxQ0cA66PFheIZIgQ7lrT1fQFcHyCpEEGcpde6KCnrbcTp4/MY+7uxG0IvkUtPedpFXB8gqRBBnKXXuKgq4LlleIJMhQ7tqjoGokyFDu2ksU9Onrh4dXeYKuDJZXiCTIUO7aSxP04/dvnp/+/CZH0LXB8gqRBBnKXXtpgn54efjH2+sUGvtdOX5CHnd3I2hFghL0Oos+P784VOynuEMXS6VmBf30+tvrH2N/GXImUMiJwd0UY0WCmUHfPjwcFviP3938TBBUIFheIZIgQ7lrL3EGffr61f1fYr+LgmqgEElQgvb8nBdUIlheIZIgQ7lrL03Qnx+OlfouXiRYXiGSIEO5ay9xie9V7HdRUA0UIsmvoDLB8gqRBBnKXXvyggoFyytEEmQod+1RUDUSZCh37YkLKhUsrxBJkKHctSctqFgw/yTIUO7ao6BqJMhQ7tqjoGokyFDu2qOgaiTIUO7ao6BqJMhQ7tqjoGokyFDu2qOgaiTIUO7ao6BqJMhQ7tqjoGokyFDu2qOgaiTIUO7ao6BqJMhQ7tqjoGokyFDu2qOgaiTIUO7ao6BqJMhQ7tqjoGokyFDu2qOgaiTIUO7ao6BqJMhQ7tqjoGokyFDu2qOgaiTIUO7aWyIoYEWfuGdViKEQMyWFoqDyhRgKMRMFNSrEUIiZKKhRIYZCzFSDoKytFwVlQRcFZUEXBWVBFwVlQZdnQY/Pf/7inXWKfj198+6488TDl79YJ7nXKRPWwbrsYJhwoDwL+vbV/M8Urg9HCT69fvX880vrKLc6ZcI6WJcdDFMOlGNBP/3vm/kfKltvP//hMFt9/Ou786wFUedMWAfrsoNhyoFyLOhhfejsdAtSx+P99JdfLvvzYdQxE97BOhyhlAPlWNDjLrdYE8PzWYYPX+IJCnewjjsYphwox4KeCurU6hl3Bj0V0ME67WC48Rn0VEDH/FRPcOegkIKed4jb+DnocYH49H84HpzqeLyPqxfQu/jbaQfOwbrsYJhyoBwLery09znOQnou5OugOAfruoPhxq+DsiooCsqCLgrKgi4KyoIuCsqCLgrKgi4KyoIuCsqCLgrKgi4Kqlo//e7vz7999UfrGI6LgurWj384/o+1uCiobv37v/7nv/9pHcJzUVDl+mn3J+sIrouCKtePO67wa4qC6tavn/3jK06hK4qCqtZvBzt//YwnocuLgqrWj7//1/N//sZFfnlRUBZ0UVAWdFFQFnRRUBZ0UVAWdFFQFnRRUBZ0UVAWdFFQFnRRUBZ0/T8+4IUDn8zIEwAAAABJRU5ErkJggg==" /><!-- --></p>
</div>
<div id="gam-数据平滑曲线" class="section level3">
<h3>3.3 GAM 数据平滑曲线</h3>
<p>GAM 模型的拟合是通过一个迭代过程（向后拟合算法）对每个预测变量进行样条平滑的。其算法要在拟合误差和自由度之间进行权衡最终达到最优。</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" title="1"><span class="kw">ggplot</span>(<span class="dt">data =</span> mydata, <span class="kw">aes</span>(x,y)) <span class="op">+</span></a>
<a class="sourceLine" id="cb6-2" title="2"><span class="kw">geom_point</span>(<span class="dt">fill=</span><span class="st">&quot;black&quot;</span>,<span class="dt">colour=</span><span class="st">&quot;black&quot;</span>,<span class="dt">size=</span><span class="dv">3</span>,<span class="dt">shape=</span><span class="dv">21</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb6-3" title="3"><span class="kw">geom_smooth</span>(<span class="dt">method =</span> <span class="st">&#39;gam&#39;</span>,<span class="dt">formula=</span>y <span class="op">~</span><span class="kw">s</span>(x))</a></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
</div>
<div id="残差分析图" class="section level2">
<h2>4. 残差分析图</h2>
<p><strong>残差分析</strong>（residual analysis）就是通过残差所提供 的信息，分析出数据的可靠性、周期性或其他干扰。用于分析模型的假定正确与否的方法。所谓残 差是指观测值与预测值（拟合值）之间的差，即实际观察值与回归估计值的差。以下给出两种拟合方法的残差分析图。<strong>注意：</strong> 这里还是使用前面随机模拟产生的数据。</p>
<div id="线性拟合" class="section level3">
<h3>4.1 线性拟合</h3>
<p>通过lm函数进行回归分析，公式为 <span class="math inline">\(y = ax+b\)</span>。并将预测值 <span class="math inline">\(\hat{y}\)</span>，残差<span class="math inline">\(\varepsilon\)</span>，残差的绝对值<span class="math inline">\(|\varepsilon|\)</span>进行存储，结果如下所示。</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1">fit &lt;-<span class="st"> </span><span class="kw">lm</span>(y <span class="op">~</span><span class="st"> </span>x, <span class="dt">data =</span> mydata)</a>
<a class="sourceLine" id="cb7-2" title="2">mydata<span class="op">$</span>predicted &lt;-<span class="st"> </span><span class="kw">predict</span>(fit) <span class="co"># Save the predicted values</span></a>
<a class="sourceLine" id="cb7-3" title="3">mydata<span class="op">$</span>residuals &lt;-<span class="st"> </span><span class="kw">residuals</span>(fit) <span class="co"># Save the residual values</span></a>
<a class="sourceLine" id="cb7-4" title="4">mydata<span class="op">$</span>Abs_Residuals &lt;-<span class="st"> </span><span class="kw">abs</span>(mydata<span class="op">$</span>residuals)</a>
<a class="sourceLine" id="cb7-5" title="5"><span class="kw">head</span>(mydata)</a></code></pre></div>
<pre><code>##   x       y predicted residuals Abs_Residuals
## 1 1 -1.8720   -1.4250  -0.44700       0.44700
## 2 2 -1.3863   -1.2344  -0.15189       0.15189
## 3 3 -1.3316   -1.0437  -0.28792       0.28792
## 4 4 -0.6965   -0.8531   0.15655       0.15655
## 5 5 -0.6241   -0.6624   0.03835       0.03835
## 6 6 -0.5514   -0.4718  -0.07963       0.07963</code></pre>
<p>完整代码如下所示：</p>
<div id="代码详解" class="section level4">
<h4>代码详解</h4>
<p>绘制的方式比较简单，根据ggplot的思想不断叠加图层。我们对以下代码进行详细分析：</p>
<ol style="list-style-type: decimal">
<li>以x为横坐标，y为纵坐标，<code>geom_point()</code>绘制散点图，以Abs_Residuals的大小来填充点和尺寸，颜色为黑色。<code>scale_fill_continuous()</code>将填充色从“black”到“red”渐变。<code>geom_smooth()</code>给数据加入拟合曲线，这里使用<code>lm()</code>方法，置信带不展示，颜色为“lightgrey”。这时候的图形如下：</li>
</ol>
<p><img src="data:application/json;base64,eyJSZXRDb2RlIjotMTQ4NjYwLCAiRXJyTXNnIjoicmVxdWVzdCBleHBpcmVkIn0=" /></p>
<ol start="2" style="list-style-type: decimal">
<li>将预测值的点进行绘制，<code>geom_segment()</code>可加入线段，其中<code>xend = x, yend = predicted</code>表示从x到x，y到predicted，所以就会产生下图中的竖直线了。</li>
</ol>
<p><img src="data:application/json;base64,eyJSZXRDb2RlIjotMTQ4NjYwLCAiRXJyTXNnIjoicmVxdWVzdCBleHBpcmVkIn0=" /></p>
<ol start="3" style="list-style-type: decimal">
<li>这时残差图基本完成，但是可以看到横纵坐标的标题有问题，右边的legend太累赘了以及字体颜色和大小还可以再做修改。最后图形如下所示：</li>
</ol>
<p><img src="data:application/json;base64,eyJSZXRDb2RlIjotMTQ4NjYwLCAiRXJyTXNnIjoicmVxdWVzdCBleHBpcmVkIn0=" /></p>
</div>
</div>
<div id="非线性拟合" class="section level3">
<h3>4.2 非线性拟合</h3>
<p>非线性拟合绘制残差图与线性拟合类似，唯一不同的点在：利用lm函数拟合不同的回归模型，以下使用了公式：<span class="math inline">\(y = ax+bx^2+c\)</span>，后面的绘制与上面相同。</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" title="1">d&lt;-mydata</a>
<a class="sourceLine" id="cb9-2" title="2">fit &lt;-<span class="st"> </span><span class="kw">lm</span>(y <span class="op">~</span><span class="st"> </span>x<span class="op">+</span><span class="kw">I</span>(x<span class="op">^</span><span class="dv">2</span>), <span class="dt">data =</span> d)</a>
<a class="sourceLine" id="cb9-3" title="3">d<span class="op">$</span>predicted &lt;-<span class="st"> </span><span class="kw">predict</span>(fit) </a>
<a class="sourceLine" id="cb9-4" title="4">d<span class="op">$</span>residuals0 &lt;-<span class="st"> </span><span class="kw">residuals</span>(fit)</a>
<a class="sourceLine" id="cb9-5" title="5">d<span class="op">$</span>Residuals&lt;-<span class="kw">abs</span>(d<span class="op">$</span>residuals0 )</a></code></pre></div>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" title="1"><span class="kw">ggplot</span>(d, <span class="kw">aes</span>(<span class="dt">x =</span> x, <span class="dt">y =</span> y)) <span class="op">+</span></a>
<a class="sourceLine" id="cb10-2" title="2"><span class="kw">geom_smooth</span>(<span class="dt">method =</span> <span class="st">&quot;lm&quot;</span>,<span class="dt">formula =</span> y <span class="op">~</span><span class="st"> </span>x<span class="op">+</span><span class="kw">I</span>(x<span class="op">^</span><span class="dv">2</span>), <span class="dt">se =</span> <span class="ot">FALSE</span>, <span class="dt">color =</span> <span class="st">&quot;lightgrey&quot;</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb10-3" title="3"><span class="kw">geom_segment</span>(<span class="kw">aes</span>(<span class="dt">xend =</span> x, <span class="dt">yend =</span> predicted), <span class="dt">alpha =</span> <span class="fl">.2</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb10-4" title="4"><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">fill =</span>Residuals, <span class="dt">size =</span> Residuals),<span class="dt">shape=</span><span class="dv">21</span>,<span class="dt">colour=</span><span class="st">&quot;black&quot;</span>) <span class="op">+</span><span class="st"> </span><span class="co"># size also mapped</span></a>
<a class="sourceLine" id="cb10-5" title="5"><span class="kw">scale_fill_continuous</span>(<span class="dt">low =</span> <span class="st">&quot;black&quot;</span>, <span class="dt">high =</span> <span class="st">&quot;red&quot;</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb10-6" title="6"><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">y =</span> predicted), <span class="dt">shape =</span> <span class="dv">1</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb10-7" title="7"><span class="kw">xlab</span>(<span class="st">&quot;X-Axis&quot;</span>)<span class="op">+</span><span class="st"> </span><span class="kw">ylab</span>(<span class="st">&quot;Y-Axis&quot;</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb10-8" title="8"><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">y =</span> predicted), <span class="dt">shape =</span> <span class="dv">1</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb10-9" title="9"><span class="kw">guides</span>(<span class="dt">fill =</span> <span class="kw">guide_legend</span>((<span class="dt">title=</span><span class="st">&quot;Rresidual&quot;</span>)),</a>
<a class="sourceLine" id="cb10-10" title="10"><span class="dt">size =</span> <span class="kw">guide_legend</span>((<span class="dt">title=</span><span class="st">&quot;Rresidual&quot;</span>)))<span class="op">+</span></a>
<a class="sourceLine" id="cb10-11" title="11"><span class="kw">theme</span>(<span class="dt">text=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>,<span class="dt">face=</span><span class="st">&quot;plain&quot;</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb10-12" title="12"><span class="dt">axis.title=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">10</span>,<span class="dt">face=</span><span class="st">&quot;plain&quot;</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb10-13" title="13"><span class="dt">axis.text =</span> <span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">10</span>,<span class="dt">face=</span><span class="st">&quot;plain&quot;</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb10-14" title="14"><span class="dt">legend.position =</span> <span class="st">&quot;right&quot;</span>,</a>
<a class="sourceLine" id="cb10-15" title="15"><span class="dt">legend.title =</span> <span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">13</span>,<span class="dt">face=</span><span class="st">&quot;plain&quot;</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb10-16" title="16"><span class="dt">legend.text =</span> <span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">10</span>,<span class="dt">face=</span><span class="st">&quot;plain&quot;</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb10-17" title="17"><span class="dt">legend.background =</span> <span class="kw">element_rect</span>(<span class="dt">fill=</span><span class="kw">alpha</span>(<span class="st">&quot;white&quot;</span>,<span class="dv">0</span>)))</a></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>这两个图采用黑色到红色渐变颜色和气泡面积大小两个视觉暗示对应残差的绝对值大小，用于实际数据点的表示；而拟合数据点则用小空心圆圈表示，并放置在灰色的拟合曲线上。用直线连接实际数据点和拟合数据点。残差的绝对值越大，颜色越红、气泡也越大，连接直线越长，这样可以很清晰地观察数据的拟合效果。</p>
</div>
<div id="有趣的拓展" class="section level3">
<h3>4.3 有趣的拓展</h3>
<p>R 中的<a href="https://cran.r-project.org/web/packages/ggimage/vignettes/ggimage.html" title="ggimage">ggimage</a>包提供了<code>geom_image()</code>函数可以将对应的圆形数据点使用图片替代展示。我们将其运用到上面的数据集中，就可以得到有趣的图了。</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" title="1"><span class="kw">library</span>(ggimage)</a></code></pre></div>
<pre><code>## Warning: package &#39;ggimage&#39; was built under R version 4.0.3</code></pre>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" title="1">mydata<span class="op">$</span>image =<span class="st"> &quot;https://www.r-project.org/logo/Rlogo.png&quot;</span></a>
<a class="sourceLine" id="cb13-2" title="2"><span class="kw">ggplot</span>(mydata, <span class="kw">aes</span>(x, y)) <span class="op">+</span><span class="st"> </span><span class="kw">geom_image</span>(<span class="kw">aes</span>(<span class="dt">image=</span>image))<span class="op">+</span></a>
<a class="sourceLine" id="cb13-3" title="3"><span class="st">  </span><span class="kw">geom_smooth</span>(<span class="dt">method =</span> <span class="st">&#39;lm&#39;</span>)</a></code></pre></div>
<pre><code>## `geom_smooth()` using formula &#39;y ~ x&#39;</code></pre>
<p><img 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================================================
FILE: 2020年/2020.10.09散点图系列一/scater_plot1.rmd
================================================
---
title: "散点图系列(1)"
author:
  - 庄亮亮
date: "2020/10/09"
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
---


记得安装prettydoc包，html模板在该包渲染而成。

![](https://mmbiz.qpic.cn/mmbiz_jpg/MIcgkkEyTHgfkvXafZE9scXp4icvdcNFyic0z7THajQBAyLNRiau3CKnZ3L9Y9K2YXObhaiblBm0Jbnicaq9lW3pz4g/640?wx_fmt=jpeg)  
欢迎**关注**我的**公众号**，**点赞，在看，收藏\~\~\~**

---------

## 简介

**散点图**（scatter graph、point graph、X-Y plot、scatter chart ）是科研绘图中最常见的图表类型之一，通常用于显示和比较数值。散点图是使用一系列的散点在直角坐标系中展示变量的数值分布。在二维散点图中，可以通过观察两个变量的数据变化，发现两者的关系与相关性。

散点图可以提供三类关键信息：

（1）变量之间是否存在数量关联趋势；

（2）如果存在关联趋势，那么其是线性还是非线性的；

（3）观察是否有存在离群值，从而分析这些离群值对建模分析的影响。

-----

本文可以看作是[《R语言数据可视化之美》](https://github.com/EasyChart/Beautiful-Visualization-with-R "《R语言数据可视化之美》")的学习笔记。该书第四章——**数据关系型图表**中展示的散点图系列包括以下四个方面：

1. 趋势显示的二维散点图 

2. 分布显示的二维散点图

3. 气泡图 

4. 三维散点图

本文主要对第一部分进行介绍，并加上小编自己的理解。下面几个部分也会在这星期陆续推出，敬请关注。

## 2.本文框架 


![](https://imgkr2.cn-bj.ufileos.com/0dce4c7a-cf88-4cd7-a5fb-a387a81ca46a.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=yw7%252BqqJH9pFaw0RcxoVcXlJTpc4%253D&Expires=1602321049)



## 2.数据介绍

随机产生2列20行的数据，列名分别为x，y。x为序号，y由标准正态分布中产生。

```{r}
library(ggplot2)
mydata = data.frame('x'= 1:20,'y'=sort(rnorm(20)))
head(mydata)
```


## 3. 不同类型拟合曲线的绘制

### 3.1 loess数据平滑曲线

**局部加权回归**（Locally Weighted Scatterplot Smoothing，LOESS）主要思想是取一定比例的局部数据，在这部分子集中拟合多项式回归曲线，这样就可以观察到数据在局部展现出来的规律和趋势。曲线的光滑程度与选取数据比例有关：比例越少，拟合越不光滑，反之越光滑。

ggplot2绘制时，使用`geom_point`绘制散点图，`geom_smooth`加入拟合曲线，`method`选择为loess,`se=TRUE`表示加入置信带，`span`控制loess平滑的平滑量，较小的数字产生波动线，较大的数字产生平滑线。其他参数对颜色，填充色以及透明度进行了修改。

```{r}
ggplot(data = mydata, aes(x,y)) +
geom_point(fill="black",colour="black",size=3,shape=21) +
geom_smooth(method = 'loess',span=0.4,se=TRUE,
colour="#00A5FF",fill="#00A5FF",alpha=0.2)
```



### 3.2 样条数据平滑曲线

这里使用了splines包中的样条函数，df=5，样条具有五个基函数，其他参数变化不大。具体非线性模型相关资料可参考：[R语言里的非线性模型：多项式回归、局部样条、平滑样条、广义加性模型分析](http://tecdat.cn/r%e8%af%ad%e8%a8%80%e9%87%8c%e7%9a%84%e9%9d%9e%e7%ba%bf%e6%80%a7%e6%a8%a1%e5%9e%8b%ef%bc%9a%e5%a4%9a%e9%a1%b9%e5%bc%8f%e5%9b%9e%e5%bd%92%e3%80%81%e5%b1%80%e9%83%a8%e6%a0%b7%e6%9d%a1%e3%80%81%e5%b9%b3/ "R语言里的非线性模型：多项式回归、局部样条、平滑样条、广义加性模型分析")

```{r}
ggplot(data = mydata, aes(x,y)) +
geom_point(fill="black",colour="black",size=3,shape=21) +
geom_smooth(method="lm",se=TRUE,
formula=y ~ splines::bs(x, 5),colour="red")
```



### 3.3 GAM 数据平滑曲线

GAM 模型的拟合是通过一个迭代过程（向后拟合算法）对每个预测变量进行样条平滑的。其算法要在拟合误差和自由度之间进行权衡最终达到最优。

```{r}
ggplot(data = mydata, aes(x,y)) +
geom_point(fill="black",colour="black",size=3,shape=21) +
geom_smooth(method = 'gam',formula=y ~s(x))
```



## 4. 残差分析图

**残差分析**（residual analysis）就是通过残差所提供
的信息，分析出数据的可靠性、周期性或其他干扰。用于分析模型的假定正确与否的方法。所谓残
差是指观测值与预测值（拟合值）之间的差，即实际观察值与回归估计值的差。以下给出两种拟合方法的残差分析图。**注意：** 这里还是使用前面随机模拟产生的数据。


### 4.1 线性拟合

通过lm函数进行回归分析，公式为 $y = ax+b$。并将预测值 $\hat{y}$，残差$\varepsilon$，残差的绝对值$|\varepsilon|$进行存储，结果如下所示。

```{r}
fit <- lm(y ~ x, data = mydata)
mydata$predicted <- predict(fit) # Save the predicted values
mydata$residuals <- residuals(fit) # Save the residual values
mydata$Abs_Residuals <- abs(mydata$residuals)
head(mydata)
```


完整代码如下所示：

```{r include=FALSE}
ggplot(mydata, aes(x = x, y = y)) +
  geom_point(aes(fill =Abs_Residuals, size = Abs_Residuals),shape=21,colour="black") + # size also mapped
  scale_fill_continuous(low = "black", high = "red") +
  geom_smooth(method = "lm", se = FALSE, color = "lightgrey") +
  geom_point(aes(y = predicted), shape = 1) +
  geom_segment(aes(xend = x, yend = predicted), alpha = .2) +
  guides(fill = guide_legend((title="Rresidual")),
         size = guide_legend((title="Rresidual")))+
  xlab("X-Axis")+
  ylab("Y-Axis")+
  theme(text=element_text(size=15,face="plain",color="black"),
        axis.title=element_text(size=10,face="plain",color="black"),
        axis.text = element_text(size=10,face="plain",color="black"),
        legend.position = "right",
        legend.title  = element_text(size=13,face="plain",color="black"),
        legend.text = element_text(size=10,face="plain",color="black"),
        legend.background = element_rect(fill=alpha("white",0)))
```

#### 代码详解

绘制的方式比较简单，根据ggplot的思想不断叠加图层。我们对以下代码进行详细分析：

1. 以x为横坐标，y为纵坐标，`geom_point()`绘制散点图，以Abs_Residuals的大小来填充点和尺寸，颜色为黑色。`scale_fill_continuous()`将填充色从"black"到"red"渐变。`geom_smooth()`给数据加入拟合曲线，这里使用`lm()`方法，置信带不展示，颜色为"lightgrey"。这时候的图形如下：

![](https://imgkr2.cn-bj.ufileos.com/07b50a94-50de-4f7d-a995-b825789986d7.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=0Hkgl4JPfi%252FokI6OtholM0UxFBg%253D&Expires=1602318448)


2. 将预测值的点进行绘制，`geom_segment()`可加入线段，其中`xend = x, yend = predicted`表示从x到x，y到predicted，所以就会产生下图中的竖直线了。

![](https://imgkr2.cn-bj.ufileos.com/10d02b70-707a-4361-ac15-dfb1096e5757.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=AGI6HCOJsJf6kxDDBHUpjpeh%252B6A%253D&Expires=1602318537)

3. 这时残差图基本完成，但是可以看到横纵坐标的标题有问题，右边的legend太累赘了以及字体颜色和大小还可以再做修改。最后图形如下所示：

![](https://imgkr2.cn-bj.ufileos.com/4c9a07c1-b7d0-41fa-ac8b-c3c92439c30f.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=xoAbaDqw7uzKX3nZP%252FHpEus%252BjxI%253D&Expires=1602315306)





### 4.2 非线性拟合

非线性拟合绘制残差图与线性拟合类似，唯一不同的点在：利用lm函数拟合不同的回归模型，以下使用了公式：$y = ax+bx^2+c$，后面的绘制与上面相同。

```{r}
d<-mydata
fit <- lm(y ~ x+I(x^2), data = d)
d$predicted <- predict(fit) 
d$residuals0 <- residuals(fit)
d$Residuals<-abs(d$residuals0 )
```

```{r}
ggplot(d, aes(x = x, y = y)) +
geom_smooth(method = "lm",formula = y ~ x+I(x^2), se = FALSE, color = "lightgrey") +
geom_segment(aes(xend = x, yend = predicted), alpha = .2) +
geom_point(aes(fill =Residuals, size = Residuals),shape=21,colour="black") + # size also mapped
scale_fill_continuous(low = "black", high = "red") +
geom_point(aes(y = predicted), shape = 1) +
xlab("X-Axis")+ ylab("Y-Axis")+
geom_point(aes(y = predicted), shape = 1) +
guides(fill = guide_legend((title="Rresidual")),
size = guide_legend((title="Rresidual")))+
theme(text=element_text(size=15,face="plain",color="black"),
axis.title=element_text(size=10,face="plain",color="black"),
axis.text = element_text(size=10,face="plain",color="black"),
legend.position = "right",
legend.title = element_text(size=13,face="plain",color="black"),
legend.text = element_text(size=10,face="plain",color="black"),
legend.background = element_rect(fill=alpha("white",0)))
```



这两个图采用黑色到红色渐变颜色和气泡面积大小两个视觉暗示对应残差的绝对值大小，用于实际数据点的表示；而拟合数据点则用小空心圆圈表示，并放置在灰色的拟合曲线上。用直线连接实际数据点和拟合数据点。残差的绝对值越大，颜色越红、气泡也越大，连接直线越长，这样可以很清晰地观察数据的拟合效果。

### 4.3 有趣的拓展

R 中的[ggimage](https://cran.r-project.org/web/packages/ggimage/vignettes/ggimage.html "ggimage")包提供了`geom_image()`函数可以将对应的圆形数据点使用图片替代展示。我们将其运用到上面的数据集中，就可以得到有趣的图了。

```{r}
library(ggimage)
mydata$image = "https://www.r-project.org/logo/Rlogo.png"
ggplot(mydata, aes(x, y)) + geom_image(aes(image=image))+
  geom_smooth(method = 'lm')
```




================================================
FILE: 2020年/2020.10.27散点图系列二/HighDensity_Scatter_Data.csv
================================================
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1nJtTNAj6jJKAAZEs8WgqihJRgX9wJHOkYoXkf8iwR2RiKKqRRiitWw3lYdnr30cDzNae/8Tw/1L3sS5gFALINXpKDQgmp1pQxW86M3O8aoqMTlNtTGnSjATM2tjXEgCYfS3hKyuCkFHkzBeScI6WKhFVxLuD+EQLt4TkOo6CU5f1drrhvrrVly/dspDayfe+8EtQx7fuJG0HcbZLyyc1r+5qXbojtE1xa0dt4x/5c31r9hA6MYtP5DrVgijoiV5Po6KKs3MBOCVStFlgez8bG57v8/vq4tZ/Gilfr8pX7VqJm1EzJQGeg3j5/xX8ruWMbrG4oduFyXxMEFyQlkpkMeJTvhKbCMY1j/o2ykPlEmSr335KxvYPvbZydev29P65KNrX58+c92zfxv6+Kil76PnU1Sl6fe+l694//zIweMjUO1ZPnH2TU3fxqa09+l/6OHXAQgEAaSZuhddMDiaZ1epkRAzpTKAxyVzrnGh7JLreGi7qF1VqO5WvoGQ0DwF584uo3cpz4sCBzc9T9SAQPKgoqI082X2QfxhshCzXmZ5Jmoo6MvOYAk7gCWH6cudN5+98oSroZZNBoRWbuEw1ygDmqI9OZ36aJrbbTPYqIFmZrldRpdFA27ONADF4/HXxjyKYhkRU9LgYsIJ6e+pgHAkGUjkgUhLSBg2N9w3IMwpylMaKScT/n6efcC+PLN8xActmMGOhu+4bH6EpsV/yAgOoO0n9/+HnR2B5h7hr455LAPJ1+wc+1i1AYGhXOs6eQf4IR+uigYUp8WSlweZTnAWFNpz6mJ2u4d60kbEPGnUwENEvUTbVJbqTCjIAQJlPo8IXEUNdQEJcCAhMvd/gvy8Q3E6TmsbErv++Z2tRuuN/7f1X+zsNyv/vYhoN066sbVlcRuZiq/iWvuP7rEb/7LuhyPfsFPLMffdxfMnz7+1fu5qEc0RPdM6QIHLo14FgCDKRFYNMiWU1MaoAsLfupYpQwobhpDby4OfkoJ4iZQWPyy9jNLm8wLSdEtUyzvBB3lwOVwbLXYqnl6U+o3+Qo/Hnp1ttBtL+ihOZyBQXGwBS0Z9zJIGwfoYXGwTYYlLnVeWdKFwoCSqAj0/LqoW8qk7kShFiku3kK9cfCPVHyDedt/qpeyLL06zk4uXtU1DyfXfE2fPmrng0Ccjbhg+flxtq7zz3ZUzXhrU/O6sjqN73mrbXD2iY/Kzm89vbBp7Y/3VcwaOI3vqq674XdnlYysH1Ym8GajvcgekQQFURnOzZJfFEgyCCwqLtNy6mKZRrzd9RMyrUkMdR+Nfdbfu7DIBzCIaw0J5kS16edcXuNOdBXwbyU1J1ewxtvTOqxtHP/3+JIOl3xOz3v0nmr9Y+f2d8VNjp4xrbbm7jQ5mdazJdtYzasufW2r+83/H0fEE+3DTXbdNum1+Hfd4stOSZuvMURh1OXnyAPjtnsaYXeumMPAnaOwXTOb4NVYT72PqU+xG7xcf6mPNQAQX6/IUcHKmcllV1UUlBRXFZdIaYyZNUjgzJ6Rpm8u6mKrApzM0vUgYbrTrbF2SFHbS18Xa5GhSmF5P7JYqZODSiqKajIK/VYNEqQIEZRigFxShVFwJURhGD6JU0ZlDP443kvW7ccNSPH2abWFfCns140peoYDeNeZHHSqlRgkMcp00ViJSV30QKhkjagSue7JMQH4304/FkrTgKC9Tjh69VLueUScBrhFPNVAUJJTKEur6Ce0u1dCFuorNZH28UayJb2IaDjjNtKWsWmioXPicrpB365FYFc3LTU9PA+B2dlqdhUV2QCMFCAazGmNBl900ImaXkg7mVCR4KJVkyfpRJFR5F86oRckaXOFoe0m/7W6YevPVY5uWvzf1w3P7vm99YGyIHU4139VjH6ob1tLvqqpxR9u2r5m2onVI9RVXsHUX9eMTLkxQdnCc6AuVEIv2VCsq3G5XOGzt77rMZaWBtEDvNOgN0au8hkhEMg3QTPzqkVUq5feAklS7rOucMleiPU7ivc6kQtuiYCqrfNTdlVF8fxLxCKgtj3iUQC44+jrzOa06UfyDSESH3x2j106vnpWmTXnhlT1o+UfT/qt9NdGau79/Zhf73+exCP2T2Pz/ZefZXez6I/gIyv/EkRs7Yf3IFpM1FG27n5x++NQ9Q/otPPTGQSQBH/Pd/9Yf/vjjne1sx152gh0p6f3eKHwYW3/EZZ93sA627uCCpcfMzwj7AIC8WN4IKljh6miAWKkBQZHNZgqip6CSZLOSmpjVSs0yBZocIpTouZRiZWGortKL8gsDiITjI5Uik+LHJ7FXiYTziRJnywoMgWdwNFstbzxXRcbikdvy72CqiPvXAaQznI/t4Idczsm9VLdbktKzzeY83vfZ7QGDlqalDY9ZNLRSTbODPb0mZneCvyYG9BLcSxY9KQVDSTe5ArmSp7voCQYwWfE4HPqnwOu4AyOYNn/C/fPZh2fjx7C84/aZ8xev2nXHraxT3vDKpkVrHaacdQ++/xGdXTuy8Zr4NrZo3PgNgDCXI/UBnh9eKI36VZeLN+NWnxscUBNzSKpskmtiJleyNBOvSfVEKuQRD2+0Iw4l2BUdoTI+ZiikBS+9h9OfOtrxL7aJvdiOkQOHDrc2tEs72U/HmW846xyGi3DSZ3j9azd1FvUDImwoz+E2NIBd1OtGAIdVkjTZUhOTqWTlLbMzaamUcEELnGVzAbVA0BHKleew8ew2Ng534wR8gL3Dxq5ZjO/xGuQP7A55A7ubrcHDnUMBdY8RLs0Mg6L5BgnAqphMiBbFWBOzKNxLAnII3zehaKqJofOXXkp5iCsitPAkbol0bqDV8RN4ijmIm4tl7zK2BLqkUsalGqFvNN1AqVkBQDQJoSl5QlZS0MVSLhaCX7P9dHD8OHKMEwKWxLu8KBdxL6ZDTbQo3e8nNquVEFemy2DIsGlmjQdbOr9BNkt+r+zlsmTu1FB3wd0z5VlnstgW8BBwKLpv9YJL5RlPdMKNOALkU1L14E93sr+yVfg43vTxgZtW/GXnd1vevKGVHafhuOnyAlyMU3AcPjDybB377rOT591Y2mUHeYJu/Ug004jIzW+QJFm2GGhNrMaABoNsUijK3QmbMnfKFN2XPIHtjr/NdmE5uRrDZG78Xj5t2EIGAOCFiawBT+ozgRw+bSAGXiPLwM0MRsr79e4NCw4Rxa5IJL6kRnJurq0bOKEZy79hDV4k7gVL5JHn1l4AdgYS+tfxVS0wMJpjIcRkNiOAzUBl2cq/UrNZoXwP3VtwpgBXF1eWAOXEQAdVfSMRDKBcx1awhYvEZm7FB7CZETKxJf4D39CN6/Hf8XkJ6VIlly6LPUkqBVCQArccJKJUl6GXoPq6r3PD1MsbzldfSPxvRcyR3dAvmukGo9nI1bbxUPHKisdJjEQxq9QGilBcN36X0mUp6hA6Y9DpEYujXuXykscVRBpkK4wudhzbcaSC07GdfUgtRrZEms9Wzok3cw1WSi3nqklH6R3oPr8kYcedOm6WR9NMYETFagVwUFlRVM1MVW5RVLtHv11adI/EnAKwL1KEcM/JO9nv43fpSiwh81U7+qQGdrQtXseFv4FZvycdQPQ8+VKfDHgE0jgAfBZF8RpdNTGjRO01Mer6daQROSBexQQy16Hxpkj+kj3BXubXE3gz1vNr/PlDb76Bs9nSNzaSY+xxdivejVP5tZCj0mP/OYvf4smfoAvtpHU62rkEFkhGowdsNrvdbQXBV3ZNM9TENGr/TSzoRn/ZLXHoEyAo4ckJSx+au+BBspEdYacX8yA6iCb0UGXmlKkTd504Fz8rb/gchAXYat0CdkjjEZynUFmSCDVIJg9AhmYypVOVEwBXRFK5UWSV22N7Ev4uHU92T9OQe+LX7PPaKziWzWZnfL9pJMZW1bO5OPS3LSUP1S3lg9poocvnk0ySppm8njQw8cTzu4wWMA6PAZgtFm40C/WaRcikzJbSWfPzuXKqQ0sxKLdfgl3BF0A82brsgaXLW7gB12EPzH7oTqxuZWvZKtp73M0Tm+Pz4vvlDUeOLdxZwVwPk1KRVS2cQX0ce4s4n+RlpKcHICC7LeCGy4rdAbAELNlGX3ZNzCdRYyq+uhvwVHHWrRpn+IvGGoVFl/MhDadWMcJP9LZen9cr+din7JuOx/ZeN2FqnzFL7767DtWvZu2f2TrnyermlsJrn977BC7f/lkz5g4srx3e8+orqypveeqmzf8qL/13n8KGgcUDKqrHbRP6FwNIYiqrimdLCgBFNBhVKlHOuxSdv3y2lARgcoLtYrOlOn53IGEMEF7k+dXC13JCQdThQHSbDQaX08hRhsdSYuuXVBAOtyLx4BHI6+6CYLnlEXbyLfYFex/D9zz7BAf0ztqVZ+7EwHn6YufCPz33/DraBqjXfyHBI2K+RonRKAOiVZYkC3BDJ+q9VNpUJOaj+sXtVx6h57CC2dmLTMMKdPlKFXO0a4DY+dTwvZeN/qJLhrqRy8gSsx+T0e52yQh+v2ynlszMrKwci9mcnemSzdRvt6NJiOSi+EtCbgo1UyM3WkiKOMKJUtMlGvCIi78nPihD2fPbzWFJ6WPdxqngfix9q9Sr9HQdwoJDth5mUy/nm1hKoRixV/mpUJxwVT85trLi1EAa6twb+aS+9uuhNBsStmnSbVMVzTXLnPpUo6oYTYpJ0C2VLGYDkWXJqFCUkhDL9evG+ooUZ3VpjZj8Izex59h6fnXg56wfNmF/DGMtC5Pi+GHyHdka/47Y4j27dJCYyF2B7wZVlZEQEERvNFFF4QqiSgVDdslOjEH5Z65AarLLowIDZAGWchEZbA/LwDo6mozsXBTfQUqoXleVJiZ0RugfzTJISFUVEExmlYuSRP1I0IAGUcZdOgxNpl1qFqqPbALSzPPvkbfjTVJ6vIrs30m/RXi/0ykkLWUbyWw9T7KjVgXRIIFRJlTBfN2EuvH0BNZX4iUpmc0y8bOPPmIblXMHz60Xa1gA6MDkVFt/ZIKYnGpfnBa6sUmAHY9/mJhqI4S4fJ+QL55xoKIY+VYNoOZTiaaCvQtCfCFHMMy1CH34IX7GMmfKjQd/UoR8AzFIA+R3QIHeUTdBWVYkSTznFd6SVJko0DW+xLKLeyTRZYcwiGjADQ/jqVO8uP6KGOiGzmqyKN4maq1OtpHWXhja9SRIRonoRhEaJZ5K0NrOFyl//vMAAGKNdIQ+qATAwK1gBjVKRVTIdwCUpB/rioP0XWLww7EvHPD6PGRL5ZkqbKpcLx3ptW2gZ/z7GYIdmjju9pfm6E8Zq6OFTovBQvLy/P78LIMhaEkbFrNYZLfbPjjm5jWdnDM4JnvBk0Az/y+ZVYSeXlcUJWdMvMcN9+1u8h0omny9N6YT+huGr1r0xzd+Or/5xbv/On7T8Y9PswO/X3znY5MWPHHDsNfXvfono1K6rn7f+K3vx32E27h55MJbxwOBFVznDsUNTsjh7BvIojRg1Mw2n89szrWA2WPUFFDSh8QUL7iGxEC7mCz83SHi7H5mUeZ0aISzRVANCgTlw1AfH9d2D8WobftHX+7YNsMT+hpLLZbJM2ZOJJNvaZk+Q5rNdrPv2XH2t6XzFTdbPuiJ9jP3rwh0PPOXNWvWAMLoCyfoMWk2eDi6esRYymclxCubh8RkDexcM++lZZJuOTk32SdwmnJoYkjgUBQyIf4DZqJx81Mjh9525cmTzcuHVf/BTQZgFvauOZFVwBH49ZIydr4kH4iQK81M2CcaDRi9Gi+obTZhqFy7xwIOIyi6fTTdPt5ft4+oT4Q+ecShOXlPGioU/BLkji3iOnVPiAnZ9vHnOw9ON/mw7Jv+1omT5kyVp7dNmDnLjWVoRx7zq9vG4YSfTjyy5vt7ViWNk9BynD61y+DMEKROSUpzOLKcJlOm3+OkzuoYFVUUVMesmuoZHFNTel5aloiry3bI3RbgrbNeR4XKwOMJ6AVAxMMtOP2GaQZcT2aVs+/Y3zDt7LdoiJfID985vmNc3Qb61PyZM+d3NmAPdGAahth3Jx+789Eel5+4rCjB7nSOkgMeuCKa7SZElSn1+qwAPhndyHVz283akJgZqJ4bgp8v7QVDiRwWFgxH9KfOeieocBWpiZ1l+9eu3bj/ufm1o2uv6ocGOq9zCZ23rKHh3ZdLPsoafsVgoKAwtzSV26sYyiEKd0SrzFlZAwZIfRwOUqzmSkGUpIHpPXr4fJFg8Kp0K1jRqlj7qv2GxYy5Eke5wr7FpDpWXFxYWDksVqi5e1fH3BkXz+n4pxIOWz79gRHv0LneqJs2FQ76ewKfPao+pSsqEvmsj+ykQFfCF6ZeRcGFyUQK8v26El/4WGzqS33OfxjpXbL2ndc3sTfYvm9+vP3WksHVg5tvOnmsZKGTFc2buvrNabOfa5w5/drrmura10otT/ceNqZjJ5Xzew187smt/1i1bPw9We5Roeh1xYVrZ732vkM6L1UOHVlb2WcEHT5q0qRRuwBhBYC0lmeDB8LRdATw2Y0Wg8Fo9Nolp1MaEnNqJkCjR6D/JfU5336yUOPaKqJJEuCQeFQirWX7O+6YxfZjqapqE/61bQ958LsXt8S/40CwpeDekav/vh0ILAPAD7lsA1jEZFcyGsFksprtJg9Rr4kR6DJ/ZWoO7uobKtNnnyJUlrW3X3ttO14phMgLHn98yIjzPqkFgFxoY259XSt4oSTqd/L0JgaDT/NcE9PAaBctOk/sjOTEKYEwCRGJxwB6tajQpMDBcxoHXzN8CJbum6GLZe60066mRmnd+eJXN6mThXRIWPMH/Un+NdGgxLmTUKrIsmYzWa0Gg8lkN4P41WCzUcXkofbu2oTf3cjSZdpuokXRuGOyi1dx22KswGZWhYd5AffOIrF9jYxdh40sI74Et93MVivueDXr0gYPcG0ouF4DRIkAevQioLvExgPivyvuhO7qQJ5BQRgeLXS7XPrsKDMzI6PAajSaTPkuq9WRKzu46XwOzWzPRJNH7+G7krl7+OC8ePqbjJDCRIiEfKFykdziVfBd8q+ke9n++uvnTGL7vy529F437Xwso/dL097ZwvbVXz9jOnlw3rz12+LfSS1Lh1+/urZpy+F4kfhtxYuQjGCut1tMFxHAq6vrscoOoatQFU0Xx29SyV/XLRG8TS0ierkyof+ZtWWXEPbn7boC9dce3JHE5yf0pzhpostXLJYMcLnSvcYhMa9mp0Nidu8vu/xUrvPeVQMOCCQs6MzrxGVT5986ecr8W6dQmX3ELvzxh7swGyl/I6Xt6/70Qnv7mhfYKbbnQTS8jE7s8wA7B4LrOep1cC1ckMMn1Hl+RVFNlKpZmqrlcuQEq9U9hBOEwa5mQEaKzBKmSBWoSQVlTvPepDFCnPndRKFJtuemosq2GZrG9p/taZv8wfaPbt58TGf7vePdSx/wsv5K9SPtbB87/T/s7H10mU722JDgM67pTN1euaIq8dIsyh+TpOUZ+fg6PcNnz/ZanE5V4I0FhsQsv8m6iSfIBUmS5S2dL8HBXl8ook+LIkFBaLdMkafPPzxZ2v7R5zsmPXeFIQMJ22e1lq48uri9oOMZ9uLa9lNYiho3Z9+6xqU/bcBDAybXN3ZFFJ3LddVEh0mcejw5BCxZZVnUS7wGFxqlMrTMRy+JIqpdWewrCD+6iu3/sre97yvSbCP7xLR8SXyH1LKxZTYkqp/1XIZ4dpmjpLktAEU5bnchWNw5lhxTli9rcMynUdPgGPX+vJ2/2BgiqPTHK2HB5clePsGgXCkPt082oetPnbx1/bDrDtW395oycuG8yJd/3/Xu6MZHa5Zcv2zRrf2wZn1HILfzsvKx+b0rCstHz73+8VXN/8y//JriK/qHR/+30LeE6xuRa8AjToRYDHa7y2UyEIfB4fWZnHbn4JjVYrfL3HVyQt3QpktOVnRhgnBcxKOXvoLpIyFPwCO6cjK3bsas9tdeeHRt8xasYDuu+TD4aeiNN0jGwgknTn4e//yqK4UOT/Gc4zM+cENZ1E8cDrfby3t/j9NoJ7JNtumyPcmJ1sVDgItr7tQYgH+grxdrpR2zt72PpSLjsXRp7XUHt5Mj8dki4Ynt/EpI9JkPcrlm6BV1m0GWiYgIK0G0GNEuC5llKWndDU1X/x0SbTfiOtaElf/INyryZYexkjVJLfFF86aMXUzaumS4AZRtXEaWOMsoSyaOIVng81ETVTMyMjNzVEXJ9plMVLbbMxQ7yDqidR3RdPz2LIDSIO1WQ8wBsin/pGskRZpuUfew19lm7LMwJ1eRcrT7sG6R5NCsqBgvN92NPdk7uARPdt4vtTDH4m9q1lxH/PGvvE03jMkcer4XnuKKI5gApOW6bWqi+YoMaKSUSAQlGWWzQVWtfIZmMSoUAA1mj4T2S2cBqaROkYZeq3KlhdkClOu/mD2BI48cxZHsMWxja46fYO2kPwmyZ7A1fiy+DRewhcJLzK17ycs1KTC73ZrXK0koahm/Jgob/pNT8no0p9XJMTHDAFyVskQJkKKvhBlTUzxHyokifvTqgNsSaw9mmBRz7n4cwoqu+vcfR9RErqqfl+fkfr2/YcZNo8ic866XXnR8Z72xNZI450HXce2MIn+oKqkIYDYgmvQhAm8c7YR/MwyOoefSIULSSMJGySlCWEwR6LrOB4nC0uhAZiCmDrLp6+3xekDI4T38Id7D54ipCHUbcnIcfn+uNTMzIFGXy8qjKd9qSbTzYosp2hbbF7bnuBrm+REWRw08Coc18VTQ4xFQ6+EJhDmL2m6/c/OZG4cpn31T3XpmM9quH32qucGAVz7Z9jEdXMUObcyzBF8xskNVg+knbU8BIO5gJWSlYgMK7tcIpZJMAaCyhONDYlbqCOKOo0cV29lA1ylOauB7yBN7yOHlOmgGQ75bkoI52TabW3Z7qCzl/3/2IIuHzuFynuSi2BZnlftyiBSnzxyCyzwcrImh4e0Xbhz2+9mfKtWtL7xTP39x26LeM2aFPyFVQ7CnuWmyw5K3EXsOrqIfh2dPY5tNjY2nGm7QTxGQIqmCtoEHIlG/Ag4zmKnd7qNeu82mSJSaHQ5QoCRU1lYi9ElBdqqp5pwa1sv/RAMmELwQB0baym968pqFwxaOC99ePv7pgf89chFZcXX5l1NzcyPRii+nphf8lzhBwpbiQanl0rP6Dg26zurbad4v56mukCugE0Wi7Vh7JsTasSV5lIO0dJbKBcljHAhLOdJqfN6cwad7QYchPV3OyCA+n4mYMrPSXCNiBtuIGMiGNH4pGWmKygXqpwH4S8+ePzvOII575nOCTh4R15lS69q26gmSEBt94OCr7YtF6z7vlm8b7mpdcN+rL/fHcyhjZk77c8arjmflv/Bn9kZObzbAuFFEB4A0ST+d2BztZXeaidFqTfd6iV/zO51ado7Fn+avjxnT0sDFqcleG3P6QR7xs+NNXUfUIJTSVqjbjT+pBpRfbpXXFSKawsFwiBuQbNyyZcyzs2sbcS679w9k3/mvbhr+6qufy7sbvojGrt10dOm6WtZ5ttes1keObtl5BAjMBCYFpHXcnkW8R87TLC6j7EsnBrDZ8jIhM/OyYp9LSycWo2xQPZ4ctYBHz/YyHc11H2qb9S+iA4oURXyC3SM+0WGqPrVIoJJaFCmMXFRdbixfuGzBqEk3j1qwfGE43Pbogt+Nn93Y9siC8v1T6+qnzxxRO50cnPC7BcsWhCMLly6MTZs8uu2RtlBo/iNtYyYOnz6ttm7aDBHpCoDEp+PghZnR/7I53U6Plce2UaYyMYkJqxeRED/HBp/idDkbYkCRuuwmm93WEFPtdgt6FMsl5xX9mtiW3kNfypcpEhAfkgPKkCfoEXdAGF7cGCBD0YAVbOGWH374gX38448/vsOW4BViZBv3vHrfq8eO8RdyHMhFiKNCMGoniiKGmUaJSlTVsUcEbCpFdAhyJGBIAFHnAbag8wAAgUm89lnw/0o5D7g2jvTvPzOzu9KCJNSFaAKEBMYHAokSuQpiY04OODjYsWxCcjbkNaluuPdyiXuaS0jHpPfeE0N68fVO/ObSe+8uy39mVlqEzr76oeyi+bG7U3bK83yfkUZBGZwCMyKlaRaXRRTLC6E4JyfkAld4DKmpsbkrK0ttpSafxzc15nHqTVNjepQycUvmivi5NiuyMYtA0qyNo3NOVr9OFfZJmt75WUW7VMhOWtE4fsubj9zRP33SzuaW6LxFB3rWTJj4xSuvXdHyYsOAb/bpj257c+OS5s4tvmrim7appHXPputbn8kPlVdURssit194/xklXdGr7p3261Hh7uKKUGH0uu2nzi8Pxya1V5qmAUYu4UfygiRwVi0/YrQaWIvIdGcQ4pBB7dzU9snCdpLZJF/SOXJNjdRPPa0uMhVd2TKurqk5Mq5FXFPXEB0/7ucNExvqGieOb6wDIIw7lSbR99oBPqhmvm9ikm0mm7/c7yzPc+bV1IrpYEmnX1mlhbZglpActKMVbEo36zBrHWyifBGnSASrw44ZvIhr6bwgFCxiuH4R45HIul+c91p4c3j55tf/fvilPddGFx5b8zJqf5X9DCi9v/m10vvcrj6U09uHsg/0Ke/29invHSBfX7VJ+TAv99nwkcNvfNd82xjlI/4/Su+rLyi3/ObXaPaLTJb0b6xlBfCX+DHKMLqgAOoieZk65HLlmXXU56PLK/RmGI2e9HQbys4GEGweShSEA0F1mAtak3BQbR1SPGxVVo3K6irbp3YM1ToJV3pGr452r7n58XnrWi6tr79h3tY9yqTy/KbYvMvxsYvGRLrPu/BCWegef0l+cNcmpeGP/qIz6oqkNPas06Fd6BEEkMAIbZHRaUaDTKd2RMKCgERqGDdkGNkrBpBGCE4XBIMoIpOMsR4lWko4kLBqJI+K5j8Faab66Q897w8yR4ALIR3yqYfpaPGg8hFyDSo70RG06A12/oayC49HL1E/s9K3DL2QNXzKGb8fhTCZCCJkRZgzSkcQkogAAdYJoQTf6LXQWZQQHjx2hLz1I7pgEIaGErEHWAIzAAhaezTEW+S5kUqBYFHUgcViJEbamxB9uT/ROLFE8QLBIegdsp5+naSN8spKbara53ErgY4FlFnoIwadmhP5X7VaYcvuz5QHAu8h/cO3K+s89eFTJuceP+dft9utd0xUFqDpyj3kqh3K1+H6uhrlzX/ZctHQEckuSNLhJG8MjPTGCNLRbwWDZH+Fr/6Jm7D5hAmyIDMiQ0ZGTrbVkMkqRQ3FUq17vL06HSowmDyctbXd2N5201ln3XjW5a88G6uvnz2nLjJHWMg+7W0766bZL10emd02YWJ7G+NFAYSwiCGdcx+ZGTqdRB35BoSomd9sMRrSZYQkAYOKeoYC8S5MM5WnxriwyfZwnAs9I2/h3kG0RVlFY12UNylYiiCAo/gZTriVRKwOA5LAgiyuTNnkwQ4Hyucer4lJXb96j39EPHUF+JnjK/5+briipGXeqiuf3np9+4YudA6O3jbYEQv6S2bt37Cle8be7rMBwVgcxo+Ir4APJkRy7enY7QbIl/LTzVK65C8mdrvDIed4PSa5IIE5pbQ8dlABTRX6S6xu1DgHrezj3QjuuaN9/n1P7N541ards5oXtJ3REgwFWsOdE/b9v3W9wlu7a432i6at2N7wzOzzq6tvrAr76ePuDExYn+qLI0JEDyCnCdwXdyjui3uFjR/VNMjMIUk6ao6YiGZWHZ0i/DX75U5H1aEgAOK2LmrkhkxmMUmXJFnOsjrBQR/drXNlOGl7yiCq4Y2Z+zTTkbYwT8qwtv73xo0CxS6XhZtDZ7WvpVaAD0ZnlC6fNWF+vigy+yj67YoVdz/PrAF7Z8wo/9mM65SDUhQQLFSOCbslO2RAIOJINwsiAoTMFr0emUykKWYSWc8XiHtk4gMlbe5qgAb7UsMIa0IFwu6bbumd0PqX1/72IW5Tjkmn/3QfCVmPHEWCwiKd8Cj0e7KGEUURmUU6Ebk1RiCQCHSypSLhfEr/+2Eqe2hQsaNeALBCVcRlNjI7Fh1Y7Gaz0W60ySYW9pXNXt9QQI0EXB1/3PjAIiZPQYprQ3RWgnr3Xd88KXuOu/GW5v7s6Kwj6xc5btOZJpzh7hmf2cktXDiKGxPRSYI8MjopD+WfMDoJeePRSb4QbvyciNkVzReismdxFD2z4Oyi0vHr6MwOwnTUfEt8ic9KPBFjIvYqgzhkDw/xTGK3kxc9YlKPgt969IarH3/wwP4nFG9dY+PEiY2NdULbnf0v3Hr7wAu3dHR2dnTMm5cy6s2OlKZTy49OL2AW1Ib01FNiGh70BD7YIdHEB79/Oej1B9UBL+6NL0aoFonqQehRdg4ip/LxIFqsSMPn2KuMXYbaUNsyJZw1fMrGrnIA6Qpa2n5Y+TuAYvg1fgUA6eAP5Nrjj4L8IMFW+uJUVye0D51Au5h8T7W6B7CZSZlyNlXeJ75ClUs8XEnM8as+Eb9qmXpVwDBeWUH+LLTzNU5DpKiQug4YJk0jh0pMoyDbnI1lQp0JPk9rzJdhoRy8xZvKwaN4g9Cm5HHsnddbrUub3bCVWHLF4ldiF1wYPjM27aFzzp37w3lvHP3F7rOrUcnw6jY6d1dT86yJ4eiY0sOnTO6//YLru+j0cyyamXhHhoZU2lu3GPuhiOexHiQ0HfQPYqfoh9HVJ1B0w2//heIgzFQV2SMV52iKgYTCOlIxU1N0cUXaQwR7uWRYkxbXSNDfPYvXhpfEa4MpdD7OPtrg4sg4yUbMNmIRLCjNZEJsvgbgEETRbiYUvqb4syENGQkj/JFkkzkxTAQrMmlscsKiQLvUAAeUNb8G7yQ062PCs0QKkEYsI9rR6nzH9imOvcoLeLew9/ghbKIUT+hoLlq5jiPvcYqZDnXNrC6WKXZGjNP8+VlGYAXOBfY556p5+ZaodTT0KC89ZE+UXqqiG9pSFPdShT1JcXDoO1XhHnmNmZqia+gnXgMYFag1wGbucZ7cAJnQGCmivUCW3ep0GlBamtthAIqVWwGovcRJi9eKLYy8TgmP0+BgddahWmkscQqUlpiPo4MhBwPPA1tV5FzFz7cKwm9+d+CzzzahATIdd1Du/G5GoOPWnR9+ofQoyl1qHsRXeDuriLez36eUA+dUeTlUxtt7N1fgvJMpulHDv1AchOdUhXek4hxNMZBQZI1UzNQUXVzB2vvoeGkj2IAMglnogXTIjaRLBGTZYORGZXcgqMUn8260FqnLBlSM7lL+uB+Vocqr6Rhetkf5tfL7vfj3qKxH+SMavZf++VuaSiUAhD7DLeIHkgA2yIZCCEdyXJ4cuz0tB9LAW+TMK3Ab3QxXJQWpdOWImbyK8arGGFaJqpEG2V2IO/yqihEFV1Wm94Xts3tnv8iA1RevaL1x1sDRP56CjrR2UWL1/ZBiOG0+WqzyvXWXXHDpANrEwNWGNfM3DSi/fHYJ/rbsp+8e6j5uKR4aUmlIXgO18Vocrdaz1uOkKrqR6V8oDkKPqsgfqZipKbq4gr0RJcl9kqDwq4yNv3kb1KtYuCSJSmbrqZpIDiOjjbIoSpJTMDbFZEdTTJAFWdIRyZowKGrdjOZBjePIDroW0tZGwh2UUz1yNcPaH1CQ4fikjst3rbt0NcHv/agMUij5c2Vc18rz5/NZJM3JfMkD1dAaGU3tegXFxQDlWSZTbXkgUGPKKtBBcbEui2SWhkqnxEIQcFgyozFLwnGq7ZUx0g03TH/aTYLqcnOkuuX8iaFL8zhXsVAn4a3SSDRSWl1/RVfoo3fmXTau+ubIbfnTo2vnNjQ0TVjXsWQjbb4+hL9FfuGvkV+cNqai1JldVTJn7srmu+7JLfy6KLhqVGhcaeOylsh5lbWnl49r6TrnKPVMv/LO/azH5ASbVEBr5VQ+UtQfAPb2jbbEazY1vfvCE6Xna+kHfxhi6RUj001a+kAasPTikemClt4lAX+3T+GCYcUDmqJ/lKrwqwogTCEpQjeUQBBOgS2RydU1JDM/P2g3GoNBuabG7/GMKZPlsC/fW50fjVVXsyDp7OxQNJZtNo6aSoF3p+S0NFDHPHgbYiBJgQZGv/ERLZmZ0t5q6wkJKnqMhzBz8MufZG0ZXsZRzHYYrWJk1TDShwoZfiVWbn2rce4L19/03NdfPRtr2nHzvKc/emdx/d3LDyM4XkaJq+cfm/bY8bqFq1fv6FyOvX+1oHvwefbOru7Y0zcz5q91cn3Tq52bInXKZx9RCGvWp8UlOEsQzpxD6T/05acLVrNap952xtZhP0xWx0+0iY+fnCrjtT1FbQ2389oqStRWanr34n+eflDP00eNTBe09C6rWpeVidoeugYAvcGv8LTaXynTgF0DGRLXuBwA/y5J0T00eaRi6JdU8UmS4qDyuqqwJBTvUMXlkqApuriC9Vdu9UkSBIfk5fPVpZGx4MYuV46oJ+kEY0tOTnr6qEKLpcQNmZh+SJ2ImdjppB56CnnSKS02+RpiJifBU2MEnYC8izsQ2clwI9I+1YYLf3Gtkw8SVgdtm4XAwyNdtX46hDAvXCL2GCmnN3ZetuitjjuuvUr5/0PfKX9DwuFDDfpT17zfga0rz19x8fIFq84TXdXF99Wdtr1n/m5lz4fKh8pLyPrJR8gyV+hdtuva4/Mv2Lj1ih27+lg74MwMf2tPV9/aEPAZUHI97ucl3KK2k5t4PReeOJ319ZfAyRW8pRiS+gUt3aSlD6jpeSPTBS29y6C2pIDWK8yCw0JYeIl7wbKhNGJ1pqWZBQEIyYUcNwVKAXHz0vPBYdBQiw8WTxJRTWOGj2+K1tf/PFpXNzVaf2ojO+KOwcEvTpva/POG6c1EmNrUMqWhpRkIfcaHKAN0OZ81eEfOGnzxWQOjb0jBFAZx/C+zhmCNsJ9hQWsvOLVn0n5GBm1eUrt/zK5jR21o/OiJKy9AhwzKa/6alefjSoYJlXV2dVyL7IwUqpp+Qes1ytH2RjTouvnWlnFKMOP2oSGVpeD1c2ZST4ByefGmpvMavgVOruA1XMnTC0emC1p6V0B9A0u1np97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</head>

<body>




<section class="page-header">
<h1 class="title toc-ignore project-name">散点图系列(2)</h1>
<h4 class="author project-author">庄亮亮</h4>
<h4 class="date project-date">2020/10/27</h4>
</section>



<section class="main-content">
<p>记得安装prettydoc包，html模板在该包渲染而成。</p>
<p><img 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" /><br />
欢迎<strong>关注</strong>我的<strong>公众号</strong>，<strong>点赞，在看，收藏~~~</strong></p>
<hr />
<div id="前言" class="section level2">
<h2>1.前言</h2>
<p><strong>散点图</strong>（scatter graph、point graph、X-Y plot、scatter chart ）是科研绘图中最常见的图表类型之一，通常用于显示和比较数值。散点图是使用一系列的散点在直角坐标系中展示变量的数值分布。在二维散点图中，可以通过观察两个变量的数据变化，发现两者的关系与相关性。</p>
<p>散点图可以提供三类关键信息：</p>
<p>（1）变量之间是否存在数量关联趋势；</p>
<p>（2）如果存在关联趋势，那么其是线性还是非线性的；</p>
<p>（3）观察是否有存在离群值，从而分析这些离群值对建模分析的影响。</p>
<hr />
<p>本文可以看作是<a href="https://github.com/EasyChart/Beautiful-Visualization-with-R" title="《R语言数据可视化之美》">《R语言数据可视化之美》</a>的学习笔记。该书第四章——<strong>数据关系型图表</strong>中展示的散点图系列包括以下四个方面：</p>
<ol style="list-style-type: decimal">
<li><p><a href="https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&amp;mid=2247485142&amp;idx=1&amp;sn=564bffc9e7765ebae9b9b81a17a188d9&amp;chksm=ea24f932dd5370241a05c75975ff24a34423a8f182bf6c716c8c9ed981788d0492dcb268248a&amp;token=1544929502&amp;lang=zh_CN#rd">趋势显示的二维散点图</a></p></li>
<li><p>分布显示的二维散点图</p></li>
<li><p>气泡图</p></li>
<li><p>三维散点图</p></li>
</ol>
<p>本文主要对<strong>第二部分</strong>进行介绍，并加上小编自己的理解。下面几个部分也会在最近陆续推出，敬请关注。</p>
</div>
<div id="单数据系列" class="section level2">
<h2>3.单数据系列</h2>
<div id="数据格式" class="section level3">
<h3>3.1数据格式</h3>
<p>这里我们使用正态分布随机产生250个数据（这个就是实际我们采集的一维数据）。<code>step</code>是指按照多少的区间进行划分类别。我们通过<code>hist()</code>将直方图内部数据进行存储(我也是第一次见这种操作，以后学起来)。输出<code>hg</code>.</p>
<p>其中breaks表示边界点，counts表示每个区间内的个数，density表示密度函数值。mids表示区间的中间点，并利用这些参数来构建后续绘图所需要的数据。通过循环语句，计算出x，y坐标数据。 完整代码如下：</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="co">#加载包</span></a>
<a class="sourceLine" id="cb1-2" title="2"><span class="kw">library</span>(ggplot2)</a>
<a class="sourceLine" id="cb1-3" title="3"><span class="kw">library</span>(RColorBrewer) <span class="co">#颜色</span></a>
<a class="sourceLine" id="cb1-4" title="4"><span class="kw">library</span>(scales)</a>
<a class="sourceLine" id="cb1-5" title="5"></a>
<a class="sourceLine" id="cb1-6" title="6">x &lt;-<span class="st"> </span><span class="kw">rnorm</span>(<span class="dv">250</span> , <span class="dt">mean=</span><span class="dv">10</span> , <span class="dt">sd=</span><span class="dv">1</span>) </a>
<a class="sourceLine" id="cb1-7" title="7">step&lt;-<span class="fl">0.2</span></a>
<a class="sourceLine" id="cb1-8" title="8">breaks&lt;-<span class="st"> </span><span class="kw">seq</span>(<span class="kw">min</span>(x)<span class="op">-</span>step,<span class="kw">max</span>(x)<span class="op">+</span>step,step)</a>
<a class="sourceLine" id="cb1-9" title="9"></a>
<a class="sourceLine" id="cb1-10" title="10">hg &lt;-<span class="st"> </span><span class="kw">hist</span>(x, <span class="dt">breaks =</span> breaks , <span class="dt">plot =</span> <span class="ot">FALSE</span>)<span class="co">#使用直方图数据，但不绘图</span></a>
<a class="sourceLine" id="cb1-11" title="11"></a>
<a class="sourceLine" id="cb1-12" title="12">bins &lt;-<span class="st"> </span><span class="kw">length</span>(hg<span class="op">$</span>counts) <span class="co"># bin类别数</span></a>
<a class="sourceLine" id="cb1-13" title="13">yvals &lt;-<span class="st"> </span><span class="kw">numeric</span>(<span class="dv">0</span>)       </a>
<a class="sourceLine" id="cb1-14" title="14">xvals &lt;-<span class="st"> </span><span class="kw">numeric</span>(<span class="dv">0</span>) </a>
<a class="sourceLine" id="cb1-15" title="15"><span class="cf">for</span>(i <span class="cf">in</span> <span class="dv">1</span><span class="op">:</span>bins) {       </a>
<a class="sourceLine" id="cb1-16" title="16">  yvals &lt;-<span class="st"> </span><span class="kw">c</span>(yvals, hg<span class="op">$</span>counts[i]<span class="op">:</span><span class="dv">0</span>)  </a>
<a class="sourceLine" id="cb1-17" title="17">  xvals &lt;-<span class="st"> </span><span class="kw">c</span>(xvals, <span class="kw">rep</span>(hg<span class="op">$</span>mids[i], hg<span class="op">$</span>counts[i]<span class="op">+</span><span class="dv">1</span>))  </a>
<a class="sourceLine" id="cb1-18" title="18">}    </a>
<a class="sourceLine" id="cb1-19" title="19">                                                   </a>
<a class="sourceLine" id="cb1-20" title="20">dat &lt;-<span class="st"> </span><span class="kw">data.frame</span>(xvals, yvals)  <span class="co"># 变成dataframe格式</span></a>
<a class="sourceLine" id="cb1-21" title="21">dat &lt;-<span class="st"> </span>dat[yvals <span class="op">&gt;</span><span class="st"> </span><span class="dv">0</span>, ]          <span class="co"># 去除小于0的数</span></a>
<a class="sourceLine" id="cb1-22" title="22"></a>
<a class="sourceLine" id="cb1-23" title="23">colormap &lt;-<span class="st"> </span><span class="kw">colorRampPalette</span>(<span class="kw">rev</span>(<span class="kw">brewer.pal</span>(<span class="dv">11</span>,<span class="st">&#39;Spectral&#39;</span>)))(<span class="dv">32</span>) <span class="co">#颜色选择</span></a></code></pre></div>
</div>
<div id="原始数据绘制" class="section level3">
<h3>3.2原始数据绘制</h3>
<p>接下来我们使用该数据（单数据）进行绘制：</p>
<ul>
<li><strong>柱状图（正态分布）</strong></li>
</ul>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1"><span class="kw">ggplot</span>(dat, <span class="kw">aes</span>(<span class="dt">x=</span>xvals,<span class="dt">y=</span>yvals,<span class="dt">fill=</span>yvals))<span class="op">+</span></a>
<a class="sourceLine" id="cb2-2" title="2"><span class="st">  </span><span class="kw">geom_tile</span>(<span class="dt">colour=</span><span class="st">&quot;black&quot;</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb2-3" title="3"><span class="st">  </span><span class="kw">scale_fill_gradientn</span>(<span class="dt">colours=</span>colormap)<span class="op">+</span></a>
<a class="sourceLine" id="cb2-4" title="4"><span class="st">  </span><span class="kw">ylim</span> (<span class="dv">0</span>, <span class="kw">max</span>(yvals)<span class="op">*</span><span class="fl">1.3</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb2-5" title="5"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb2-6" title="6">    <span class="dt">text=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb2-7" title="7">    <span class="dt">plot.title=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>,<span class="dt">family=</span><span class="st">&quot;myfont&quot;</span>,</a>
<a class="sourceLine" id="cb2-8" title="8">      <span class="dt">face=</span><span class="st">&quot;bold.italic&quot;</span>,<span class="dt">hjust=</span>.<span class="dv">5</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb2-9" title="9">    <span class="dt">legend.background =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb2-10" title="10">    <span class="dt">legend.position=</span><span class="kw">c</span>(<span class="fl">0.9</span>,<span class="fl">0.75</span>)</a>
<a class="sourceLine" id="cb2-11" title="11">  )</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p><code>geom_tile(colour=&quot;black&quot;)</code>改为<code>geom_point(colour=&quot;black&quot;,shape=21,size=4)</code>会得到以下图片</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1"><span class="kw">ggplot</span>(dat, <span class="kw">aes</span>(<span class="dt">x=</span>xvals,<span class="dt">y=</span>yvals,<span class="dt">fill=</span>yvals))<span class="op">+</span></a>
<a class="sourceLine" id="cb3-2" title="2"><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">colour=</span><span class="st">&quot;black&quot;</span>,<span class="dt">shape=</span><span class="dv">21</span>,<span class="dt">size=</span><span class="dv">4</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb3-3" title="3"><span class="st">  </span><span class="kw">scale_fill_gradientn</span>(<span class="dt">colours=</span>colormap)<span class="op">+</span></a>
<a class="sourceLine" id="cb3-4" title="4"><span class="st">  </span><span class="kw">ylim</span> (<span class="dv">0</span>, <span class="kw">max</span>(yvals)<span class="op">*</span><span class="fl">1.3</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb3-5" title="5"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb3-6" title="6">    <span class="dt">text=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb3-7" title="7">    <span class="dt">plot.title=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>,<span class="dt">family=</span><span class="st">&quot;myfont&quot;</span>,<span class="dt">face=</span><span class="st">&quot;bold.italic&quot;</span>,<span class="dt">hjust=</span>.<span class="dv">5</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb3-8" title="8">    <span class="dt">legend.background =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb3-9" title="9">    <span class="dt">legend.position=</span><span class="kw">c</span>(<span class="fl">0.9</span>,<span class="fl">0.75</span>)</a>
<a class="sourceLine" id="cb3-10" title="10">  )</a></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="q-q图的绘制" class="section level3">
<h3>3.3 Q-Q图的绘制</h3>
<p>在R中可以使用<code>CircStats</code>包的<code>pp.plot()</code>函数绘制P-P图；<code>ggplot2</code> 包的<code>geom_qq()</code>函数和<code>geom_qq_line()</code>函数结合可以绘制Q-Q 图；另外，<code>ggplot2</code>包结合<code>ggpubr</code>包也可以绘制，<a href="https://rpkgs.datanovia.com/ggpubr/index.html" title="ggpubr简介">当然改包还有其他好用的功能</a>。</p>
<p>下面对第三种方式进行实现： <code>ggpubr</code>包中的<code>ggqqplot</code>相应参数如下，包括了非常多的参数，前两个参数分别表示：数据，要绘制的变量。当然其他数据包括设置主题（<code>ggtheme</code>）;添加qqline（<code>add = c(&quot;qqline&quot;)）</code>等。</p>
<pre><code>ggqqplot(
  data,  x,  combine = FALSE,  merge = FALSE,  color = &quot;black&quot;,  palette = NULL,  size = NULL,  shape = NULL,  add = c(&quot;qqline&quot;, &quot;none&quot;),  add.params = list(linetype = &quot;solid&quot;),  conf.int = TRUE,  conf.int.level = 0.95,
  title = NULL,  xlab = NULL,  ylab = NULL,  facet.by = NULL,  panel.labs = NULL,  short.panel.labs = TRUE,  ggtheme = theme_pubr(),  ...
)</code></pre>
<p>为了更好解释这个函数，我们重新模拟一个数据集。</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1"><span class="kw">library</span>(ggpubr)</a>
<a class="sourceLine" id="cb5-2" title="2"><span class="co"># 创建一个数据集</span></a>
<a class="sourceLine" id="cb5-3" title="3"><span class="kw">set.seed</span>(<span class="dv">1234</span>)</a>
<a class="sourceLine" id="cb5-4" title="4">wdata =<span class="st"> </span><span class="kw">data.frame</span>(</a>
<a class="sourceLine" id="cb5-5" title="5">   <span class="dt">sex =</span> <span class="kw">factor</span>(<span class="kw">rep</span>(<span class="kw">c</span>(<span class="st">&quot;F&quot;</span>, <span class="st">&quot;M&quot;</span>), <span class="dt">each=</span><span class="dv">200</span>)),</a>
<a class="sourceLine" id="cb5-6" title="6">   <span class="dt">weight =</span> <span class="kw">c</span>(<span class="kw">rnorm</span>(<span class="dv">200</span>, <span class="dv">55</span>), <span class="kw">rnorm</span>(<span class="dv">200</span>, <span class="dv">58</span>)))</a>
<a class="sourceLine" id="cb5-7" title="7"><span class="kw">head</span>(wdata, <span class="dv">4</span>)</a></code></pre></div>
<pre><code>##   sex weight
## 1   F  53.79
## 2   F  55.28
## 3   F  56.08
## 4   F  52.65</code></pre>
<pre><code># 基本的Q-Q图
ggqqplot(wdata, x = &quot;weight&quot;)</code></pre>
<pre><code># 按性别改变颜色和形状
ggqqplot(wdata, x = &quot;weight&quot;,
   color = &quot;sex&quot;,
  ggtheme = ggplot2::theme_grey())#更改主题（灰色）当然可以用其他主题</code></pre>
</div>
<div id="带透明度设置的散点图" class="section level3">
<h3>3.4 带透明度设置的散点图</h3>
<ul>
<li>数据设定</li>
</ul>
<p>这个数据是张杰老师书中的数据，是经过一定处理得到的，结果图可以看下面。</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" title="1"><span class="kw">library</span>(ggplot2)</a>
<a class="sourceLine" id="cb9-2" title="2"><span class="kw">library</span>(RColorBrewer)  </a>
<a class="sourceLine" id="cb9-3" title="3"></a>
<a class="sourceLine" id="cb9-4" title="4">mydata&lt;-<span class="kw">read.csv</span>(<span class="st">&quot;HighDensity_Scatter_Data.csv&quot;</span>,<span class="dt">stringsAsFactors=</span><span class="ot">FALSE</span>)</a>
<a class="sourceLine" id="cb9-5" title="5"><span class="kw">head</span>(mydata)</a></code></pre></div>
<pre><code>##         x       y
## 1 -0.6035 -0.4126
## 2  0.1387  0.1736
## 3  0.5422 -0.4600
## 4 -1.1728 -0.1437
## 5  0.2146 -0.2756
## 6  0.2530  0.4243</code></pre>
<p>我们利用<code>ggplot()</code>简单绘制二维数据的散点图，之后在对该数据进行聚类。</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" title="1"><span class="kw">ggplot</span>(<span class="dt">data =</span> mydata, <span class="kw">aes</span>(x,y)) <span class="op">+</span></a>
<a class="sourceLine" id="cb11-2" title="2"><span class="st">  </span><span class="kw">geom_point</span>( <span class="dt">colour=</span><span class="st">&quot;black&quot;</span>,<span class="dt">alpha=</span><span class="fl">0.1</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb11-3" title="3"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">&quot;Axis X&quot;</span>,<span class="dt">y=</span><span class="st">&quot;Axis Y&quot;</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb11-4" title="4"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb11-5" title="5">    <span class="dt">text=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb11-6" title="6">    <span class="dt">plot.title=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>,<span class="dt">family=</span><span class="st">&quot;myfont&quot;</span>,<span class="dt">face=</span><span class="st">&quot;bold.italic&quot;</span>,<span class="dt">hjust=</span>.<span class="dv">5</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb11-7" title="7">    <span class="dt">legend.position=</span><span class="st">&quot;none&quot;</span></a>
<a class="sourceLine" id="cb11-8" title="8">  )</a></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="kmeans聚类的散点图" class="section level3">
<h3>3.5 kmeans聚类的散点图</h3>
<p>对上面数据进行kmeans聚类，这里使用base中的 <code>kmeans()</code>进行分类（两类），然后将分类情况(<code>cluster</code>)进行存储，并转化成因子类型。绘图时，cluster来设置颜色，这样就可以很明显看出分类结果。</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" title="1">kmeansResult&lt;-<span class="st"> </span><span class="kw">kmeans</span>(mydata, <span class="dv">2</span>, <span class="dt">nstart =</span> <span class="dv">20</span>)</a>
<a class="sourceLine" id="cb12-2" title="2">mydata<span class="op">$</span>cluster &lt;-<span class="st"> </span><span class="kw">as.factor</span>(kmeansResult<span class="op">$</span>cluster)</a>
<a class="sourceLine" id="cb12-3" title="3"><span class="kw">ggplot</span>(<span class="dt">data =</span> mydata, <span class="kw">aes</span>(x,y,<span class="dt">color=</span>cluster)) <span class="op">+</span></a>
<a class="sourceLine" id="cb12-4" title="4"><span class="st">  </span><span class="kw">geom_point</span>( <span class="dt">alpha=</span><span class="fl">0.2</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb12-5" title="5"><span class="st">  </span><span class="kw">scale_color_manual</span>(<span class="dt">values=</span><span class="kw">c</span>(<span class="st">&quot;#00AFBB&quot;</span>,  <span class="st">&quot;#FC4E07&quot;</span>))<span class="op">+</span></a>
<a class="sourceLine" id="cb12-6" title="6"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">&quot;Axis X&quot;</span>,<span class="dt">y=</span><span class="st">&quot;Axis Y&quot;</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb12-7" title="7"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb12-8" title="8">    <span class="dt">text=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb12-9" title="9">    <span class="dt">plot.title=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>,<span class="dt">family=</span><span class="st">&quot;myfont&quot;</span>,<span class="dt">face=</span><span class="st">&quot;bold.italic&quot;</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb12-10" title="10">    <span class="dt">legend.background=</span><span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb12-11" title="11">    <span class="dt">legend.position=</span><span class="kw">c</span>(<span class="fl">0.85</span>,<span class="fl">0.15</span>)</a>
<a class="sourceLine" id="cb12-12" title="12">  )</a></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="带椭圆标定的聚类散点图" class="section level3">
<h3>3.6 带椭圆标定的聚类散点图</h3>
<p>有时候我们想突出聚类结果会在结果上画上椭圆，那么可以使用<code>stat_ellipse()</code>。</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" title="1"><span class="kw">ggplot</span>(<span class="dt">data =</span> mydata, <span class="kw">aes</span>(x,y,<span class="dt">color=</span>cluster)) <span class="op">+</span></a>
<a class="sourceLine" id="cb13-2" title="2"><span class="st">  </span><span class="kw">geom_point</span> (<span class="dt">alpha=</span><span class="fl">0.2</span>)<span class="op">+</span><span class="st">  </span><span class="co"># 绘制透明度为0.2 的散点图</span></a>
<a class="sourceLine" id="cb13-3" title="3"><span class="st">  </span><span class="kw">stat_ellipse</span>(<span class="kw">aes</span>(<span class="dt">x=</span>x,<span class="dt">y=</span>y,<span class="dt">fill=</span> cluster), <span class="dt">geom=</span><span class="st">&quot;polygon&quot;</span>, <span class="dt">level=</span><span class="fl">0.95</span>, <span class="dt">alpha=</span><span class="fl">0.2</span>)<span class="op">+</span><span class="co">#绘制椭圆标定不同类别</span></a>
<a class="sourceLine" id="cb13-4" title="4"><span class="st">  </span><span class="kw">scale_color_manual</span>(<span class="dt">values=</span><span class="kw">c</span>(<span class="st">&quot;#00AFBB&quot;</span>,<span class="st">&quot;#FC4E07&quot;</span>))<span class="op">+</span><span class="co">#使用不同颜色标定不同数据类别</span></a>
<a class="sourceLine" id="cb13-5" title="5"><span class="st">  </span><span class="kw">scale_fill_manual</span>(<span class="dt">values=</span><span class="kw">c</span>(<span class="st">&quot;#00AFBB&quot;</span>,<span class="st">&quot;#FC4E07&quot;</span>))<span class="op">+</span><span class="co">#使用不同颜色标定不同椭类别</span></a>
<a class="sourceLine" id="cb13-6" title="6"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">&quot;Axis X&quot;</span>,<span class="dt">y=</span><span class="st">&quot;Axis Y&quot;</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb13-7" title="7"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb13-8" title="8">    <span class="dt">text=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb13-9" title="9">    <span class="dt">plot.title=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>,<span class="dt">family=</span><span class="st">&quot;myfont&quot;</span>,<span class="dt">face=</span><span class="st">&quot;bold.italic&quot;</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb13-10" title="10">    <span class="dt">legend.background=</span><span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb13-11" title="11">    <span class="dt">legend.position=</span><span class="kw">c</span>(<span class="fl">0.85</span>,<span class="fl">0.15</span>)</a>
<a class="sourceLine" id="cb13-12" title="12">  )</a></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
</div>
<div id="多数据系列" class="section level2">
<h2>3.7 多数据系列</h2>
<p>多数据系列，其实在聚类散点图就已经涉及到。这里对较少数据进行绘制，这样看起来结果更加美观。</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" title="1">mydata&lt;-mydata[<span class="kw">round</span>(<span class="kw">runif</span>(<span class="dv">300</span>,<span class="dv">0</span>,<span class="dv">10000</span>)),]</a>
<a class="sourceLine" id="cb14-2" title="2">kmeansResult&lt;-<span class="st"> </span><span class="kw">kmeans</span>(mydata, <span class="dv">2</span>, <span class="dt">nstart =</span> <span class="dv">20</span>)</a>
<a class="sourceLine" id="cb14-3" title="3">mydata<span class="op">$</span>cluster &lt;-<span class="st"> </span><span class="kw">as.factor</span>(kmeansResult<span class="op">$</span>cluster)</a>
<a class="sourceLine" id="cb14-4" title="4"><span class="kw">ggplot</span>(<span class="dt">data =</span> mydata, <span class="kw">aes</span>(x,y,<span class="dt">fill=</span>cluster,<span class="dt">shape=</span>cluster)) <span class="op">+</span></a>
<a class="sourceLine" id="cb14-5" title="5"><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">size=</span><span class="dv">4</span>,<span class="dt">colour=</span><span class="st">&quot;black&quot;</span>,<span class="dt">alpha=</span><span class="fl">0.7</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb14-6" title="6"><span class="st">  </span><span class="kw">scale_shape_manual</span>(<span class="dt">values=</span><span class="kw">c</span>(<span class="dv">21</span>,<span class="dv">23</span>))<span class="op">+</span></a>
<a class="sourceLine" id="cb14-7" title="7"><span class="st">  </span><span class="kw">scale_fill_manual</span>(<span class="dt">values=</span><span class="kw">c</span>(<span class="st">&quot;#00AFBB&quot;</span>,  <span class="st">&quot;#FC4E07&quot;</span>))<span class="op">+</span></a>
<a class="sourceLine" id="cb14-8" title="8"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">&quot;Axis X&quot;</span>,<span class="dt">y=</span><span class="st">&quot;Axis Y&quot;</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb14-9" title="9"><span class="st">  </span><span class="kw">scale_y_continuous</span>(<span class="dt">limits =</span> <span class="kw">c</span>(<span class="op">-</span><span class="dv">5</span>, <span class="dv">10</span>))<span class="op">+</span></a>
<a class="sourceLine" id="cb14-10" title="10"><span class="st">  </span><span class="kw">scale_x_continuous</span>(<span class="dt">limits =</span> <span class="kw">c</span>(<span class="op">-</span><span class="dv">5</span>, <span class="dv">10</span>))<span class="op">+</span></a>
<a class="sourceLine" id="cb14-11" title="11"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb14-12" title="12">    <span class="dt">text=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb14-13" title="13">    <span class="dt">plot.title=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>,<span class="dt">family=</span><span class="st">&quot;myfont&quot;</span>,<span class="dt">face=</span><span class="st">&quot;bold.italic&quot;</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb14-14" title="14">    <span class="dt">legend.background=</span><span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb14-15" title="15">    <span class="dt">legend.position=</span><span class="kw">c</span>(<span class="fl">0.85</span>,<span class="fl">0.15</span>)</a>
<a class="sourceLine" id="cb14-16" title="16">  )</a></code></pre></div>
<p><img 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" 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================================================
FILE: 2020年/2020.10.27散点图系列二/scater_plot2.rmd
================================================
---
title: "散点图系列(2)"
author:
  - 庄亮亮
date: "2020/10/27"
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
---


记得安装prettydoc包，html模板在该包渲染而成。

![](https://mmbiz.qpic.cn/mmbiz_jpg/MIcgkkEyTHgfkvXafZE9scXp4icvdcNFyic0z7THajQBAyLNRiau3CKnZ3L9Y9K2YXObhaiblBm0Jbnicaq9lW3pz4g/640?wx_fmt=jpeg)  
欢迎**关注**我的**公众号**，**点赞，在看，收藏\~\~\~**

---------


## 1.前言

**散点图**（scatter graph、point graph、X-Y plot、scatter chart ）是科研绘图中最常见的图表类型之一，通常用于显示和比较数值。散点图是使用一系列的散点在直角坐标系中展示变量的数值分布。在二维散点图中，可以通过观察两个变量的数据变化，发现两者的关系与相关性。

散点图可以提供三类关键信息：

（1）变量之间是否存在数量关联趋势；

（2）如果存在关联趋势，那么其是线性还是非线性的；

（3）观察是否有存在离群值，从而分析这些离群值对建模分析的影响。

-----

本文可以看作是[《R语言数据可视化之美》](https://github.com/EasyChart/Beautiful-Visualization-with-R "《R语言数据可视化之美》")的学习笔记。该书第四章——**数据关系型图表**中展示的散点图系列包括以下四个方面：

1. [趋势显示的二维散点图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485142&idx=1&sn=564bffc9e7765ebae9b9b81a17a188d9&chksm=ea24f932dd5370241a05c75975ff24a34423a8f182bf6c716c8c9ed981788d0492dcb268248a&token=1544929502&lang=zh_CN#rd)

2. 分布显示的二维散点图

3. 气泡图 

4. 三维散点图

本文主要对**第二部分**进行介绍，并加上小编自己的理解。下面几个部分也会在最近陆续推出，敬请关注。


## 3.单数据系列

### 3.1数据格式

这里我们使用正态分布随机产生250个数据（这个就是实际我们采集的一维数据）。`step`是指按照多少的区间进行划分类别。我们通过`hist()`将直方图内部数据进行存储(我也是第一次见这种操作，以后学起来)。输出`hg`.

其中breaks表示边界点，counts表示每个区间内的个数，density表示密度函数值。mids表示区间的中间点，并利用这些参数来构建后续绘图所需要的数据。通过循环语句，计算出x，y坐标数据。
完整代码如下：

```{r message=FALSE, warning=FALSE}
#加载包
library(ggplot2)
library(RColorBrewer) #颜色
library(scales)

x <- rnorm(250 , mean=10 , sd=1) 
step<-0.2
breaks<- seq(min(x)-step,max(x)+step,step)

hg <- hist(x, breaks = breaks , plot = FALSE)#使用直方图数据，但不绘图

bins <- length(hg$counts) # bin类别数
yvals <- numeric(0)       
xvals <- numeric(0) 
for(i in 1:bins) {       
  yvals <- c(yvals, hg$counts[i]:0)  
  xvals <- c(xvals, rep(hg$mids[i], hg$counts[i]+1))  
}    
                                                   
dat <- data.frame(xvals, yvals)  # 变成dataframe格式
dat <- dat[yvals > 0, ]          # 去除小于0的数

colormap <- colorRampPalette(rev(brewer.pal(11,'Spectral')))(32) #颜色选择
```




### 3.2原始数据绘制

接下来我们使用该数据（单数据）进行绘制：

- **柱状图（正态分布）**

```{r message=FALSE, warning=FALSE}
ggplot(dat, aes(x=xvals,y=yvals,fill=yvals))+
  geom_tile(colour="black")+
  scale_fill_gradientn(colours=colormap)+
  ylim (0, max(yvals)*1.3)+
  theme(
    text=element_text(size=15,color="black"),
    plot.title=element_text(size=15,family="myfont",
      face="bold.italic",hjust=.5,color="black"),
    legend.background = element_blank(),
    legend.position=c(0.9,0.75)
  )
```


`geom_tile(colour="black")`改为`geom_point(colour="black",shape=21,size=4)`会得到以下图片

```{r message=FALSE, warning=FALSE}
ggplot(dat, aes(x=xvals,y=yvals,fill=yvals))+
  geom_point(colour="black",shape=21,size=4)+
  scale_fill_gradientn(colours=colormap)+
  ylim (0, max(yvals)*1.3)+
  theme(
    text=element_text(size=15,color="black"),
    plot.title=element_text(size=15,family="myfont",face="bold.italic",hjust=.5,color="black"),
    legend.background = element_blank(),
    legend.position=c(0.9,0.75)
  )
```

### 3.3 Q-Q图的绘制

在R中可以使用`CircStats`包的`pp.plot()`函数绘制P-P图；`ggplot2` 包的`geom_qq()`函数和`geom_qq_line()`函数结合可以绘制Q-Q 图；另外，`ggplot2`包结合`ggpubr`包也可以绘制，[当然改包还有其他好用的功能](https://rpkgs.datanovia.com/ggpubr/index.html "ggpubr简介")。

下面对第三种方式进行实现：
`ggpubr`包中的`ggqqplot`相应参数如下，包括了非常多的参数，前两个参数分别表示：数据，要绘制的变量。当然其他数据包括设置主题（`ggtheme`）;添加qqline（`add = c("qqline")）`等。

```
ggqqplot(
  data,  x,  combine = FALSE,  merge = FALSE,  color = "black",  palette = NULL,  size = NULL,  shape = NULL,  add = c("qqline", "none"),  add.params = list(linetype = "solid"),  conf.int = TRUE,  conf.int.level = 0.95,
  title = NULL,  xlab = NULL,  ylab = NULL,  facet.by = NULL,  panel.labs = NULL,  short.panel.labs = TRUE,  ggtheme = theme_pubr(),  ...
)
```

为了更好解释这个函数，我们重新模拟一个数据集。

```{r message=FALSE, warning=FALSE}
library(ggpubr)
# 创建一个数据集
set.seed(1234)
wdata = data.frame(
   sex = factor(rep(c("F", "M"), each=200)),
   weight = c(rnorm(200, 55), rnorm(200, 58)))
head(wdata, 4)
```


```
# 基本的Q-Q图
ggqqplot(wdata, x = "weight")
```



```
# 按性别改变颜色和形状
ggqqplot(wdata, x = "weight",
   color = "sex",
  ggtheme = ggplot2::theme_grey())#更改主题（灰色）当然可以用其他主题
```


### 3.4 带透明度设置的散点图

- 数据设定

这个数据是张杰老师书中的数据，是经过一定处理得到的，结果图可以看下面。

```{r message=FALSE, warning=FALSE}
library(ggplot2)
library(RColorBrewer)  

mydata<-read.csv("HighDensity_Scatter_Data.csv",stringsAsFactors=FALSE)
head(mydata)
```


我们利用`ggplot()`简单绘制二维数据的散点图，之后在对该数据进行聚类。

```{r message=FALSE, warning=FALSE}
ggplot(data = mydata, aes(x,y)) +
  geom_point( colour="black",alpha=0.1)+
  labs(x = "Axis X",y="Axis Y")+
  theme(
    text=element_text(size=15,color="black"),
    plot.title=element_text(size=15,family="myfont",face="bold.italic",hjust=.5,color="black"),
    legend.position="none"
  )
```



### 3.5 kmeans聚类的散点图

对上面数据进行kmeans聚类，这里使用base中的 `kmeans()`进行分类（两类），然后将分类情况(`cluster`)进行存储，并转化成因子类型。绘图时，cluster来设置颜色，这样就可以很明显看出分类结果。

```{r message=FALSE, warning=FALSE}
kmeansResult<- kmeans(mydata, 2, nstart = 20)
mydata$cluster <- as.factor(kmeansResult$cluster)
ggplot(data = mydata, aes(x,y,color=cluster)) +
  geom_point( alpha=0.2)+
  scale_color_manual(values=c("#00AFBB",  "#FC4E07"))+
  labs(x = "Axis X",y="Axis Y")+
  theme(
    text=element_text(size=15,color="black"),
    plot.title=element_text(size=15,family="myfont",face="bold.italic",color="black"),
    legend.background=element_blank(),
    legend.position=c(0.85,0.15)
  )
```



### 3.6 带椭圆标定的聚类散点图

有时候我们想突出聚类结果会在结果上画上椭圆，那么可以使用`stat_ellipse()`。

```{r message=FALSE, warning=FALSE}
ggplot(data = mydata, aes(x,y,color=cluster)) +
  geom_point (alpha=0.2)+  # 绘制透明度为0.2 的散点图
  stat_ellipse(aes(x=x,y=y,fill= cluster), geom="polygon", level=0.95, alpha=0.2)+#绘制椭圆标定不同类别
  scale_color_manual(values=c("#00AFBB","#FC4E07"))+#使用不同颜色标定不同数据类别
  scale_fill_manual(values=c("#00AFBB","#FC4E07"))+#使用不同颜色标定不同椭类别
  labs(x = "Axis X",y="Axis Y")+
  theme(
    text=element_text(size=15,color="black"),
    plot.title=element_text(size=15,family="myfont",face="bold.italic",color="black"),
    legend.background=element_blank(),
    legend.position=c(0.85,0.15)
  )
```



## 3.7 多数据系列

多数据系列，其实在聚类散点图就已经涉及到。这里对较少数据进行绘制，这样看起来结果更加美观。

```{r message=FALSE, warning=FALSE}
mydata<-mydata[round(runif(300,0,10000)),]
kmeansResult<- kmeans(mydata, 2, nstart = 20)
mydata$cluster <- as.factor(kmeansResult$cluster)
ggplot(data = mydata, aes(x,y,fill=cluster,shape=cluster)) +
  geom_point(size=4,colour="black",alpha=0.7)+
  scale_shape_manual(values=c(21,23))+
  scale_fill_manual(values=c("#00AFBB",  "#FC4E07"))+
  labs(x = "Axis X",y="Axis Y")+
  scale_y_continuous(limits = c(-5, 10))+
  scale_x_continuous(limits = c(-5, 10))+
  theme(
    text=element_text(size=15,color="black"),
    plot.title=element_text(size=15,family="myfont",face="bold.italic",color="black"),
    legend.background=element_blank(),
    legend.position=c(0.85,0.15)
  )
```



================================================
FILE: 2020年/2020.10.30ggpubr/ggpubr.rmd
================================================
---
title: "ggpubr"
author: "庄闪闪"
date: "2020/10/1"
documentclass: ctexart
always_allow_html: true
output:
  rticles::ctex:
    fig_caption: yes
    number_sections: yes
    toc: yes
classoption: "hyperref,"
---



```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,warning = F, message= F)
```

# 简介

Hadley Wickham撰写的[ggplot2](https://ggplot2.tidyverse.org/)是出色且灵活的软件包，可用于R中的优雅数据可视化。但是，默认生成的绘图需要先进行一些格式化，然后才能发送它们进行发布。此外，要自定义ggplot，语法是不透明的，这对没有高级R编程技能的研究人员增加了难度。

“ ggpubr”软件包提供了一些易于使用的功能，用于创建和自定义基于“ ggplot2”的可发布出版物的图表。

该文章来自[ggpubr: Publication Ready Plots](http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/)

# 安装

- 从CRAN安装如下：
```{r}
#install.packages("ggpubr")
```

- 从GitHub安装最新版本，如下所示：
```{r}
#安装
#if(!require(devtools))install.packages("devtools")
# devtools :: install_github("kassambara / ggpubr")
```

# 分布

```{r message=FALSE, warning=FALSE}
library(ggpubr)
#> Loading required package: ggplot2
#> Loading required package: magrittr
# Create some data format
# :::::::::::::::::::::::::::::::::::::::::::::::::::
set.seed(1234)
wdata = data.frame(
   sex = factor(rep(c("F", "M"), each=200)),
   weight = c(rnorm(200, 55), rnorm(200, 58)))
head(wdata, 4)
#>   sex   weight
#> 1   F 53.79293
#> 2   F 55.27743
#> 3   F 56.08444
#> 4   F 52.65430

# Density plot with mean lines and marginal rug
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change outline and fill colors by groups ("sex")
# Use custom palette
ggdensity(wdata, x = "weight",
   add = "mean", rug = TRUE,
   color = "sex", fill = "sex",
   palette = c("#00AFBB", "#E7B800"))
```



```{r message=FALSE, warning=FALSE}
# Histogram plot with mean lines and marginal rug
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change outline and fill colors by groups ("sex")
# Use custom color palette
gghistogram(wdata, x = "weight",
   add = "mean", rug = TRUE,
   color = "sex", fill = "sex",
   palette = c("#00AFBB", "#E7B800"))
```


# 箱型图
```{r fig.align='center', fig.pos='h', message=FALSE, out.width='100%'}
# Load data
data("ToothGrowth")
df <- ToothGrowth
head(df, 4)
#>    len supp dose
#> 1  4.2   VC  0.5
#> 2 11.5   VC  0.5
#> 3  7.3   VC  0.5
#> 4  5.8   VC  0.5

# Box plots with jittered points
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change outline colors by groups: dose
# Use custom color palette
# Add jitter points and change the shape by groups
 p <- ggboxplot(df, x = "dose", y = "len",
                color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"),
                add = "jitter", shape = "dose")
 p
```


```{r message=FALSE, warning=FALSE}
 # Add p-values comparing groups
 # Specify the comparisons you want
my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
p + stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
  stat_compare_means(label.y = 50)                   # Add global p-value
```

# 小提琴图


```{r message=FALSE, warning=FALSE}
# Violin plots with box plots inside
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change fill color by groups: dose
# add boxplot with white fill color
ggviolin(df, x = "dose", y = "len", fill = "dose",
         palette = c("#00AFBB", "#E7B800", "#FC4E07"),
         add = "boxplot", add.params = list(fill = "white"))+
  stat_compare_means(comparisons = my_comparisons, label = "p.signif")+ # Add significance levels
  stat_compare_means(label.y = 50)                                      # Add global the p-value 
```

# 条形图

## 数据集
```{r message=FALSE, warning=FALSE}
# Load data
data("mtcars")
dfm <- mtcars
# Convert the cyl variable to a factor
dfm$cyl <- as.factor(dfm$cyl)
# Add the name colums
dfm$name <- rownames(dfm)
# Inspect the data
head(dfm[, c("name", "wt", "mpg", "cyl")])
#>                                name    wt  mpg cyl
#> Mazda RX4                 Mazda RX4 2.620 21.0   6
#> Mazda RX4 Wag         Mazda RX4 Wag 2.875 21.0   6
#> Datsun 710               Datsun 710 2.320 22.8   4
#> Hornet 4 Drive       Hornet 4 Drive 3.215 21.4   6
#> Hornet Sportabout Hornet Sportabout 3.440 18.7   8
#> Valiant                     Valiant 3.460 18.1   6
```

通过分组变量“cyl”改变填充颜色。排序将全局执行，而不是按组执行。

## 有序的条形图
```{r message=FALSE, warning=FALSE}
ggbarplot(dfm, x = "name", y = "mpg",
          fill = "cyl",               # change fill color by cyl
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "desc",          # Sort the value in dscending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 90           # Rotate vertically x axis texts
          )
```

Sort bars inside each group. Use the argument sort.by.groups = TRUE.
```{r message=FALSE, warning=FALSE}
ggbarplot(dfm, x = "name", y = "mpg",
          fill = "cyl",               # change fill color by cyl
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "asc",           # Sort the value in dscending order
          sort.by.groups = TRUE,      # Sort inside each group
          x.text.angle = 90           # Rotate vertically x axis texts
          )
```


# Deviation graphs

The deviation graph shows the deviation of quantitatives values to a reference value. In the R code below, we’ll plot the mpg z-score from the mtcars dataset.

Calculate the z-score of the mpg data:
```{r message=FALSE, warning=FALSE}
# Calculate the z-score of the mpg data
dfm$mpg_z <- (dfm$mpg -mean(dfm$mpg))/sd(dfm$mpg)
dfm$mpg_grp <- factor(ifelse(dfm$mpg_z < 0, "low", "high"),
                     levels = c("low", "high"))
# Inspect the data
head(dfm[, c("name", "wt", "mpg", "mpg_z", "mpg_grp", "cyl")])
#>                                name    wt  mpg      mpg_z mpg_grp cyl
#> Mazda RX4                 Mazda RX4 2.620 21.0  0.1508848    high   6
#> Mazda RX4 Wag         Mazda RX4 Wag 2.875 21.0  0.1508848    high   6
#> Datsun 710               Datsun 710 2.320 22.8  0.4495434    high   4
#> Hornet 4 Drive       Hornet 4 Drive 3.215 21.4  0.2172534    high   6
#> Hornet Sportabout Hornet Sportabout 3.440 18.7 -0.2307345     low   8
#> Valiant                     Valiant 3.460 18.1 -0.3302874     low   6
```


创建一个有序的barplot，根据mpg级别着色:
```{r message=FALSE, warning=FALSE}
ggbarplot(dfm, x = "name", y = "mpg_z",
          fill = "mpg_grp",           # change fill color by mpg_level
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "asc",           # Sort the value in ascending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 90,          # Rotate vertically x axis texts
          ylab = "MPG z-score",
          xlab = FALSE,
          legend.title = "MPG Group"
          )
```
Rotate the plot: use rotate = TRUE and sort.val = “desc”
```{r message=FALSE, warning=FALSE}
ggbarplot(dfm, x = "name", y = "mpg_z",
          fill = "mpg_grp",           # change fill color by mpg_level
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "desc",          # Sort the value in descending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 90,          # Rotate vertically x axis texts
          ylab = "MPG z-score",
          legend.title = "MPG Group",
          rotate = TRUE,
          ggtheme = theme_minimal()
          )
```


# Dot charts

## Lollipop chart

Lollipop chart is an alternative to bar plots, when you have a large set of values to visualize.

Lollipop chart colored by the grouping variable “cyl”:

```{r message=FALSE, warning=FALSE}
ggdotchart(dfm, x = "name", y = "mpg",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "ascending",                        # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           ggtheme = theme_pubr()                        # ggplot2 theme
           )
```

- Sort in decending order. sorting = “descending”.

- Rotate the plot vertically, using rotate = TRUE.

- Sort the mpg value inside each group by using group = “cyl”.
- Set dot.size to 6.

- Add mpg values as label. label = “mpg” or label = round(dfm$mpg).
```{r fig.height=8, fig.width=6, message=FALSE, warning=FALSE}
ggdotchart(dfm, x = "name", y = "mpg",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = TRUE,                                # Rotate vertically
           group = "cyl",                                # Order by groups
           dot.size = 6,                                 # Large dot size
           label = round(dfm$mpg),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 9,
                             vjust = 0.5),               # Adjust label parameters
           ggtheme = theme_pubr()                        # ggplot2 theme
           )
```

Deviation graph:

- Use y = “mpg_z”

- Change segment color and size: add.params = list(color = “lightgray”, size = 2)


```{r fig.align='center', fig.height=6, fig.pos='h', fig.width=8, message=FALSE, warning=FALSE, out.width='100%'}
ggdotchart(dfm, x = "name", y = "mpg_z",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           add.params = list(color = "lightgray", size = 2), # Change segment color and size
           group = "cyl",                                # Order by groups
           dot.size = 6,                                 # Large dot size
           label = round(dfm$mpg_z,1),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 9,
                             vjust = 0.5),               # Adjust label parameters
           ggtheme = theme_pubr()                        # ggplot2 theme
           )+
  geom_hline(yintercept = 0, linetype = 2, color = "lightgray")
```

# Cleveland’s dot plot

Color y text by groups. Use y.text.col = TRUE.

```{r fig.align='center', fig.height=8, fig.pos='h', fig.width=6, message=FALSE, warning=FALSE, fig_height=8, out.width='80%'}
ggdotchart(dfm, x = "name", y = "mpg",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "descending",                       # Sort value in descending order
           rotate = TRUE,                                # Rotate vertically
           dot.size = 2,                                 # Large dot size
           y.text.col = TRUE,                            # Color y text by groups
           ggtheme = theme_pubr()                        # ggplot2 theme
           )+
  theme_cleveland()                                      # Add dashed grids
```






================================================
FILE: 2020年/2020.11.05气泡图/1.txt
================================================


================================================
FILE: 2020年/2020.11.05气泡图/Bubble_plot.html
================================================
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<title>气泡图</title>



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<body>




<section class="page-header">
<h1 class="title toc-ignore project-name">气泡图</h1>
<h4 class="author project-author">庄亮亮</h4>
<h4 class="date project-date">2020/11/10</h4>
</section>



<section class="main-content">
<div id="气泡图" class="section level1">
<h1>气泡图</h1>
<p>气泡图是一种多变量图表，是散点图的变体，也可以认为是 <a href="R语言数据可视化之美">散点图和百分比区域图的组合</a>。气泡图最基本的用法是使用三个值来确定每个数据序列，和散点图一样。气泡图通过气泡的位置及面积大小，可分析数据之间的相关性。</p>
<p>本文可以看作是《R语言数据可视化之美》的学习笔记。前两部分可见（跳转）：</p>
<ul>
<li><p><a href="https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&amp;mid=2247485142&amp;idx=1&amp;sn=564bffc9e7765ebae9b9b81a17a188d9&amp;chksm=ea24f932dd5370241a05c75975ff24a34423a8f182bf6c716c8c9ed981788d0492dcb268248a&amp;token=1544929502&amp;lang=zh_CN&amp;scene=21#wechat_redirect">趋势显示的二维散点图</a></p></li>
<li><p><a href="https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&amp;mid=2247485276&amp;idx=1&amp;sn=f98a2aede13555fa1c372f08c3cdec44&amp;chksm=ea24f8b8dd5371ae9e13f3df41ff73e070775eb1ab871370783bb3f396a0a9c29d42c6a89210&amp;token=682523778&amp;lang=zh_CN#rd">分布显示的二维散点图</a></p></li>
</ul>
<p>该书对气泡图的绘制并不是非常详细，小编将内容进行了大量拓展。下面的例子将一步步带你完成气泡图的绘制。本文内容丰富，希望大家都能学到自己想要的内容。</p>
<div id="本文框架" class="section level2">
<h2>本文框架</h2>
<p><img src="data:image/png;base64,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" /></p>
</div>
<div id="数据介绍" class="section level2">
<h2>数据介绍</h2>
<p>数据集来源gapminder包中，包含了1704行和6个变量。其中6个变量含义分别为：</p>
<ul>
<li><p>country 国家142个水平</p></li>
<li><p>continent 大陆5个水平</p></li>
<li><p>year 1952年-2007年（间隔为5年）</p></li>
<li><p>lifeExp 出生预期寿命，以年计数</p></li>
<li><p>pop 人口数</p></li>
<li><p>gdpPercap 人均国内生产总值(扣除通货膨胀因素后的美元)</p></li>
</ul>
<p>由于数据过多，我们感兴趣的是年份为2007年的数据，所以使用dplyr包进行数据处理，具体数据处理案例可见我写的另一篇推送：<a href="https://mp.weixin.qq.com/s/ozH-TsltnLwp51i3aDNpSQ">[R数据科学]tidyverse数据清洗案例详解</a>。数据缩略版如下：</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="kw">library</span>(ggplot2)</a>
<a class="sourceLine" id="cb1-2" title="2"><span class="kw">library</span>(dplyr)</a>
<a class="sourceLine" id="cb1-3" title="3"><span class="kw">library</span>(gapminder)</a>
<a class="sourceLine" id="cb1-4" title="4">data &lt;-<span class="st"> </span>gapminder <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(year<span class="op">==</span><span class="st">&quot;2007&quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span>dplyr<span class="op">::</span><span class="kw">select</span>(<span class="op">-</span>year)</a></code></pre></div>
</div>
<div id="手把手绘制" class="section level2">
<h2>手把手绘制</h2>
<div id="geom_point函数构建" class="section level3">
<h3>geom_point()函数构建</h3>
<p>气泡图是添加了第三维度的散点图：附加数值变量的值通过点的大小表示。(来源:<a href="https://www.data-to-viz.com/graph/bubble.html" title="data-to-viz">data-to-viz</a>)。 使用ggplot2，可以通过<code>geom_point()</code>函数构建气泡图。<code>aes()</code>设定至少三个变量:x、y和size。其实就是散点图绘制的升级版吧，<code>aes()</code>中多了一个参数。</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1"><span class="kw">ggplot</span>(data, <span class="kw">aes</span>(<span class="dt">x=</span>gdpPercap, <span class="dt">y=</span>lifeExp, <span class="dt">size =</span> pop)) <span class="op">+</span></a>
<a class="sourceLine" id="cb2-2" title="2"><span class="st">    </span><span class="kw">geom_point</span>(<span class="dt">alpha=</span><span class="fl">0.7</span>)</a></code></pre></div>
<p><img 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fIlaJ22AG2DEfXLwH2Cuqr4wtTfj80XU9GXpaiZPt+4WLp5eWDyuNlob59CUOA83Ceos5NUSvd4C789bgtLU56amfTdEtQ4fDnoJUFQ4DzcK6jrNlP1HoeOoE1zc9PU7LZB24JUVtA8X0HQReJ+Qe036rd+Gvs+//nUVPim8Vm2QS831XmzMUYJWo0QOfiTz0/llwG4CycE7af56XnVyDw2/fbzpk6v25nlly+2ZWWz8fKgu1FGUHN3abXKIOjy8DGCBkhQQZtHSKs/efxUfhmAu3C7EPMStC46s2zl8VP5ZQDuwu1CzErQPMvryj3Pm9GgtiH1ui8DcBduF2JOglZjmNqnm9VQEZETBTwNbhdiboKax+/ES8B1XwbgLtwuxJwErYYzucrNcCcKeBrcLsSsBKUXUAhxooCnwe1CRBa0urHqewG+ghMFPA2uQdBz86Cp93h/jKBELa/7MgB34QoEbVYPGT4P3UvQ3H77M9iJAp4GX5ig7lJU92UA7sIJQR+YSFfxl/Xj/GlVfP1GEXcpqvsyAHfhfkEfOMaDBhW0GkZax1WIcj5K3jxIchiq+zIAd+FeQR+4p3wEFJQR5ieHoMvD7UL0/dwY2v74fFPYbcfZjZ92/Pl/QgnqW89L92UA7sI9gj7pCPqkJ+jb9p5lM7V42rTjpvX5+c+TOkl10ElaGu4RdOtnW4S26v3aDkDuzPWYMO340pS0l84GKCEo6zmS9ssA3IWPEbSZbFxsJ8ZNnHZc7dC30qLno9CrdIY5UcDT4GMEbSYbF/WcpNLAydOOy3aq5yaTT1DGOsdhThTwNLhHUHcb1KRqh/ZL0LHTjj+/PLh/abEYggIf14vfCNpvg46cdvzpWX2jflwbFFX8snGfoK77oM1k42IztXjatONP/1R//W90koCHeZJUTzZu+u2d+6Cpph3HOFHA0+B+QeWfxVcjRaoSduJgEekTBTwNTgjaDwQFDkEhKPDuBggKXDMOQYGrxhchKD3pWPtlAO7ClyAoNR0pyIkCngZPLyg5nB6C3mU8taC8+D8KBF0wbhdiXoKiDbpg3C7ERskoszrNo9Pj4ZhRvqAxThTwNLhX0AfNs3jhSXNvv/zfZ8efX3pGLCs4UcDT4D5BvcPtwgladuRNX77txd++Xn/zpihuflo/veAKiip+sbhHUOeIellB350UH59e3L4+Kd5/xxSUMyZU92UA7sLHCNqs8tH+5+TVjs9NFd++WeTml7P2y/WPZyxBWaPqdV8G4C7cLWjXz8bQDtEMTA6z2nE1i6mZlHT9/D9MFX/9/KK4+bms6ouvytiPsk21juxhGT+GLCekoM0kDoHVjq+/PzF2lrV8I6gJ8buW169lcpSidQNVdzkB3IWPFLQpL6evdtw0QDuC1kXntgRlCGocdAraNFB1XwbgLnycoO0suOmrHQ8EvfnXykx2G7Rd18MlaNtA1X0ZgLvwcYJedpWbttrx+e6z+HdVFX/7+gWrF58fVSt8fHA+ToKg88bdgvpuM73tvmZh6mrH/RLU3AD99ox9H3QrqCuo4meNjxJ083ImBasdV+twe+8xoZM0Z9wjqPtJUl0iBlntuGqDThtuxxkqov0yAHfhPkFjPYuno+BEAU+D24WIPJqJjIITBTwNbhfCPhxU64j6frCY7KLw1ILysscndw0c0X0ZgLtwuxBzErTfR3IOHNF9GYC7cLsQMxJ05wkSBF0YbhdivoKiil8YbhdixoKik7Qs3C7EbATN27Ei4icKeBrcLkRr5BNzG/SJXkFZM+LDnCjgaXC7EI2QD/pvAIegwDUJul3loy1Ep4vZBoIC5+F2Ibrlp/i0Y0acH4XZAA1wooCnwe1C7PgpO+2YEQUnCnga3C6EqeB30hfUDLUzCbHaMQQF7sadgvZnfOxO+Wic46x2/OyYnHYMQYG78FGCvv3iL3UJyl3t2DvlA4ICd+OjBN1U8YzVjiEo8Cn4NEEZqx1XVfxgzgd68cB5uFNQVieJsdqx6ST9/a9Cgia6Dzr8rdB9keeMOwX132a6srVBrdOON9+LCkqXpAHPq2XAlO6LPGfcLaj3Rn1jHmO14+rVN4N30wYWlFGSQtB54h5BfY86jaD81Y49r76ZJmhbcsYVFFV8RNwj6AwGi7SJLCjweLhdiLkMt9skahsUeETcLsRsBizHO1HA0+B2IeYkKEbULxq3CzEjQbm3QnVfBuAu3C4EBAWuBLcLAUGBK8HtQsxIULRBl43bhdgo2Xu5nU5BI50o4GlwuxC1j0/q94Nu74ROF7NNFEF7ZavuywDchduFqEvPGUya86XfOtV9GYC7cLsQO37Oc9IcBF0Cbhfib9R40KmBoMB5uF2I3QJ0uFbnxKANCpyH24UgBW2nHV89rJY9Pre9wTvgtGNmsJzsXcnAz9rQzc/bacfVlI6vXzWLHvcTf9qxd9l43eUEcBc+StDNtONLM1T+7XG7sFfiaccQdIH4KEG3VXxRbGbLHReJph1vm5q+Z0q6LwNwFz5ZUDP1qFn0OMG04w/sh/G6LwNwF+4SlNlJKmv1dl3Zt8fxpx2bVILSz+N1XwbgLnyioFcPN92jetHj2tpY045NjKAruhDVfRmAu3CnoP4b9e2LG2o/m0WP4087rpJXCx5j0twycaeg9Isbyn/Pq+bmcWfR48jTjus4F0cKeKKAp8Hdgs5psIj3BlOYEwU8DW4XYnbD7dBJWipuFwIDloErwe1CzGnKR6QTBTwNbhcCggJXgtuFgKDAleDhnNsrEBQ4D5fxj0wwQdnvANd9GYC7cBn/yAR7PyjztQ15rvsyAHfhMv6RCfiGZY6hJXc48UQBT4PL+EcGggLn4TL+kQkq6IphKKr4meIy/pEJ2QZljLULcKKAp8Fl/CMTsBfPGWsX4EQBT4PL+Ecm7IDlLFuJnyjgaXAZ/8gEHbCc5RldhOq+DMBduIx/ZII+SVpljDpe92UA7sJl/CMTVFDW3XrdlwG4C5fxj0zgKR+M5526LwNwFy7jH5mwk+ZinCjgaXAZ/8iE7cVHWkTB85ug+yLPGZfxj0zYVT7i3Kj3/SbovshzxmX8IwNBgfNwGf/IoIoHzsNl/CODThJwHi7jHxlM+QDOw2X8IwNBgfNwGf/IQFDgPFzGPzJzFdTV3tV9keeMy/hHJryg1OPOIOfVecdA90WeMy7jH5nggpIDRiDoPHEZ/8jICOorRVHFzxOX8Y+MiKArXymq+zIAd+Ey/pHxCPp+vV5/e1bc/LR+etFuY3wUU3xC0OXhMWy0xCPouxPz5+3rk+L9d+023ieHoAvE5V20xi3o7e9vzJebX86K6x/Pmo3MTy7fBgV+5wUtq/b1+qS4fn5R3PxsXP2qTLwDQxATt6DXP7wxpejHp62gJgp+k4GnweP4OAjRi393si1BTRwfhf3uxQAnCngaXFxFe0hBOW1Q7rsX9zlRrfK6r9odwsVVtMctqKnbb/96dvv6BdmLFxB086hI91W7Q3gUHYfx3wf95k3BuQ8aXtDt/BHdV+0O4TFstCTMk6TgbVAIqg6X8Y+M1uF2qOK14TL+kdEqKDpJ2nAZ/8ioFRS4MlzGPzIQFDgPl/GPDAQFzsNl/CMDQYHzcBn/yEBQ4Dxcxj8yAd/NVPe7yVuiui8DcBcu4x+ZcKt81Cso0A+VdF8G4C5cxj8y4QTN8vIfCLpYXMY/MhAUOA+X8Y9MuDboKqvWoEEbdKG4jH9kQr4fFK9fXDIu4x8Z3GYCzsNl/CMDQYHzcBn/yEBQ4Dxcxj8yEBQ4D5fxjwwEBc7DZfwjIyKotzuv+zIAd+Ey/pERENS8OwyvvlkcLuMfGQlB/St66b4MwF24jH9kBF4BfnTkNVT3ZQDuwmX8IxNE0N4zJAi6TFzGPzIhBN0ZIIIqfpG4jH9kAgh6aIQ0Y0GboJO0SFzGPzKBBM3MaLuNlbjNtEBcxj8y4QT1rpwQ8EQBT4PL+EcmVBt05V85IeCJAp4Gl/GPTLhePARdNi7jHxkMWAbOw2X8I4PBIsB5uIx/ZEK9HzTeiQKeBpfxj0yYNugeL1hWfhmAu3AZ/8hAUOA8XMY/MqjigfNwGf/IoJMEnIfL+EcGggLn4TL+kYGgwHm4jH9kIChwHi7jH5k9BXXn8PAw1K4QZJNQJSh/sTnd5QRwFy7jHxkICpyHy/hHBoIC5+Ey/pEJ9wJb7s163ZcBuAuX8Y8MevHAebiMf2QgKHAeLuMfmZCCYsDyknEZ/8iEHFGPKR9LxmX8IxNwIS//+xqCnSjgaXAZ/8hAUOA8XMY/MoEENTeZUMUvGpfxj0ygActV4YlO0pJxGf/IhBQ0zokCngaX8Y8MBAXOw2X8IxOwDRrpRAFPg8v4RwZPkoDzcBn/yEBQ4Dxcxj8ykQUtmwK6LwNwFy7jH5m4gprO1OHEEwU8DS7jH5ngb7fz9pYg6HxxGf/IhH3UucpX3vtNEHS+uIx/ZIIKmmXmH28RijboXHEZ/8iEE3RV2VkWosInCngaXMY/MuHmJB1lK9Y6CrovA3AXLuMfmZCzOo9yzgMl3ZcBuAuX8Y9MWEFjnCjgaXAZ/8gErOIxL37RuIx/ZAL24jEvftG4jH9k8CweOA+X8Y8MBAXOw2X8IwNBgfNwGf/IQFDgPFzGPzIQFDgPl/GPDAQFzsNl/CMDQYHzcBn/yEBQ4Dxcxj8yEBQ4D5fxjwwEBc7DZfwjA0GB83AZ/8hAUOA8XMY/MhAUOA+X8Y+MV9Db1ydFcfPT+ulFu0XBiQKeBpd30RqvoO/XJ5Wk779rtyg4UcDT4PIuWuMT9Pqf/+WkuPnlrLj+8azZpOBEAU+Dx7DREo+gt7//V1l6Xj+/KG5+flN+/1WZaMeFIFU8gr5/Yar3j09bQU0U/CYDT4NH0XEYt6Bl0XnbK0FNpn3yalaI7ssA3IXH8XEQt6Dv1yYvArZB63l1ui8DcBcex8dByNtMt69fhOrFQ9A54/IuWhP1Piiq+Bnj8i5agydJwHm4jH9kIChwHi7jHxkICpyHy/hHBoIC5+Ey/pGBoMB5uIx/ZCAocB4u4x8ZCAqch8v4RwaCAufhMv6RgaDAebiMf2QgKHAeLuMfGQgKnIfL+EcGggLn4TL+kYGgwHm4jH9kIChwHi7jHxkICpyHy/hHBoIC5+Ey/pGBoMB5uIx/ZPYU1BbZucjY+xL3zg8Exd4T7J0fCIq9J9g7PxAUe0+wd34CCIogcoGgiOpAUER1ICiiOhAUUZ2pgvZejBMk1ZvKmt1av0zZ9/fr9YnY3j+u19/KHXv/TUSh925eFVcevNixj85EQfsvCA+Rj+Y8Nbu1fpkQ8xrJ6x/eCO3d/Ga5dhvkPHXeyB587+9OzJ9yxz46EwXtv5wxQN5985/l7prdWr9M2PlHc5rfnQjt3cS12xB7776RPfTeb3+v3gAreGbGZqKg/dfbBok5F81urV8m7t612zB7L0saqb333sgeeu9lLW4aP4JnZmwmCtp/QXiQGEGb3Vq/TNu7ed2p2N6vv//mjdjee29kD733suFjSlG58z46d6wEvfnpRTHP8rn/RnaRMu7dyfJKUIH2ybVcG7Qs4kxfQLKlJdbC7b+RfV7HPiWTe/EvQvfwzLlodmv9MmXX3zd9VZG9N1Wh0N6L3hvZJY799q9ncsc+OnfqPmhdCp0I3kss26CzvQ8qeuyjgydJiOpAUER1ICiiOhAUUR0IiqgOBEVUB4JacnnvtP3Pzy8PTL54lfJ47nIgqCU9QR+bL+fbLUjUQFBLhoJ+enac7nDudCBok0/PDr74y6PT6utv906vvv7t4ODLP7qCmtq+NPfq0b+VX8w398sfXD0sGwCPixZHQgeC1vn07HH5773T9uvVQyPh/VbQt/V3xfmXf1w9vG8K1vuVtFXJer7FkdCBoHWqWr00rf169bA07+rrV00nqdxa/aQUsv1J9df+zxSa5Te9jUjAQNA656Z+vnp02n6tZCt1bEpQQ1SmHjyufrJtpV5WffwWT3X4yw0ErcMQtGli9gT99OyLV1UJCkGFAkHrVMZdNlX8ZVvFPzrdCnrZ3AutXGxr80tjbfmTFk909AsOBK2z7STdt3WSCtMvKmW8bGrz+kf3K2uvHhpB0UmSCQRt0txe6t1mul90BK1uM9UVevON8fFtue3fy55TgyOhA0E7udzeydyzR44OvFQgaB1TV3fraAiqJBC0ibmL1KmjIaiSQFBEdSAoojoQFFEdCIqoDgRFVAeCIqoDQRHV+X+lgdL2kPV9rgAAAABJRU5ErkJggg==" /><!-- --></p>
<p>上图展示了世界各国的预期寿命(y)与人均国内生产总值(x)之间的关系。每个国家的人口用圆的大小表示。但是这个图不是非常美观，而且圆的大小并不是很直观，大家都差不多大。接下来对圆的大小进行设定。</p>
</div>
<div id="scale_size控制圆的大小" class="section level3">
<h3>scale_size()控制圆的大小</h3>
<p>scale_size()允许使用range参数设置最小和最大的圆的大小，用<code>name</code>改变图例名称(<code>scale_size(range = c(0.1, 24), name=&quot;Population (M)&quot;)</code>)。</p>
<ul>
<li><p>图中可以看到，有些圆圈重叠了。k可将点的透明度进行调整（<code>geom_point(alpha=0.5)</code>）</p></li>
<li><p>为了避免在图表顶部出现大的圆圈，可以将数据集进行排序(<code>arrange(desc(pop))</code>)，代码如下。</p></li>
</ul>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1">data <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb3-2" title="2"><span class="st">  </span><span class="kw">arrange</span>(<span class="kw">desc</span>(pop)) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb3-3" title="3"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">country =</span> <span class="kw">factor</span>(country)) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb3-4" title="4"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x=</span>gdpPercap, <span class="dt">y=</span>lifeExp, <span class="dt">size =</span> pop)) <span class="op">+</span></a>
<a class="sourceLine" id="cb3-5" title="5"><span class="st">    </span><span class="kw">geom_point</span>(<span class="dt">alpha=</span><span class="fl">0.5</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb3-6" title="6"><span class="st">    </span><span class="kw">scale_size</span>(<span class="dt">range =</span> <span class="kw">c</span>(.<span class="dv">1</span>, <span class="dv">24</span>), <span class="dt">name=</span><span class="st">&quot;Population (M)&quot;</span>)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p>现在图可读性有所提高，但如果数据集中还有一个变量想加入图中该怎么办呢？</p>
</div>
<div id="添加第四个维度颜色" class="section level3">
<h3>添加第四个维度：颜色</h3>
<p>这里可以用每个国家的洲来控制圆圈的颜色（<code>aes(x=gdpPercap, y=lifeExp, size=pop, color=continent)</code>）:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1">data <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-2" title="2"><span class="st">  </span><span class="kw">arrange</span>(<span class="kw">desc</span>(pop)) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-3" title="3"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">country =</span> <span class="kw">factor</span>(country, country)) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-4" title="4"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x=</span>gdpPercap, <span class="dt">y=</span>lifeExp, <span class="dt">size=</span>pop, <span class="dt">color=</span>continent)) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-5" title="5"><span class="st">    </span><span class="kw">geom_point</span>(<span class="dt">alpha=</span><span class="fl">0.5</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-6" title="6"><span class="st">    </span><span class="kw">scale_size</span>(<span class="dt">range =</span> <span class="kw">c</span>(.<span class="dv">1</span>, <span class="dv">24</span>), <span class="dt">name=</span><span class="st">&quot;Population (M)&quot;</span>)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p>该图基本可以满足我们日常生活的气泡图的可视化了。相信大家通过前面的详细的介绍，应该可以自行绘制，只要换个数据，懂得各个代码的含义即可。后面是图表美化的过程，参考<a href="https://www.r-graph-gallery.com/320-the-basis-of-bubble-plot.html" title="thr R Graph Gallery: Bubble plot with ggplot2">thr R Graph Gallery: Bubble plot with ggplot2</a>。</p>
</div>
</div>
<div id="美化气泡图" class="section level2">
<h2>美化气泡图</h2>
<div id="一些经典的改进" class="section level3">
<h3>一些经典的改进</h3>
<ul>
<li><p>使用viridis包的调色板：（<code>scale_fill_viridis(discrete=TRUE, guide=FALSE, option=&quot;A&quot;)</code>）</p></li>
<li><p>使用<code>hrbrthemes</code>包的<code>theme_ipsum()</code></p></li>
<li><p>使用xlab和ylab自定义轴标题</p></li>
<li><p>为圆添加描边：将形状改为21，并指定颜色(描边)和填充</p></li>
</ul>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1"><span class="co"># Libraries</span></a>
<a class="sourceLine" id="cb5-2" title="2"><span class="kw">library</span>(hrbrthemes)</a>
<a class="sourceLine" id="cb5-3" title="3"><span class="kw">library</span>(viridis)</a>
<a class="sourceLine" id="cb5-4" title="4"></a>
<a class="sourceLine" id="cb5-5" title="5"><span class="co"># Most basic bubble plot</span></a>
<a class="sourceLine" id="cb5-6" title="6">data <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb5-7" title="7"><span class="st">  </span><span class="kw">arrange</span>(<span class="kw">desc</span>(pop)) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb5-8" title="8"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">country =</span> <span class="kw">factor</span>(country, country)) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb5-9" title="9"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x=</span>gdpPercap, <span class="dt">y=</span>lifeExp, <span class="dt">size=</span>pop, <span class="dt">fill=</span>continent)) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-10" title="10"><span class="st">    </span><span class="kw">geom_point</span>(<span class="dt">alpha=</span><span class="fl">0.5</span>, <span class="dt">shape=</span><span class="dv">21</span>, <span class="dt">color=</span><span class="st">&quot;black&quot;</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-11" title="11"><span class="st">    </span><span class="kw">scale_size</span>(<span class="dt">range =</span> <span class="kw">c</span>(.<span class="dv">1</span>, <span class="dv">24</span>), <span class="dt">name=</span><span class="st">&quot;Population (M)&quot;</span>) <span class="co">#+</span></a></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" title="1">    <span class="co">#scale_fill_viridis(discrete=TRUE, guide=FALSE, option=&quot;A&quot;) +</span></a>
<a class="sourceLine" id="cb6-2" title="2">    <span class="co">#theme_ipsum() +</span></a>
<a class="sourceLine" id="cb6-3" title="3">    <span class="co">#theme(legend.position=&quot;bottom&quot;) +</span></a>
<a class="sourceLine" id="cb6-4" title="4">    <span class="co">#ylab(&quot;Life Expectancy&quot;) +</span></a>
<a class="sourceLine" id="cb6-5" title="5">    <span class="co">#xlab(&quot;欢迎关注：庄闪闪的成长手册 \nq  Gdp per Capita&quot;) +</span></a>
<a class="sourceLine" id="cb6-6" title="6">    <span class="co">#theme(legend.position = &quot;none&quot;)</span></a></code></pre></div>
</div>
<div id="带数据标签" class="section level3">
<h3>带数据标签</h3>
<p>这里使用ggrepel包中的(<code>geom_text_repel()</code>)，可以给每个点自动加入标签，我这里是加入了各个国家名字，其他可以根据你实际需求进行设置。</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1"><span class="kw">library</span>(ggrepel)</a>
<a class="sourceLine" id="cb7-2" title="2"><span class="kw">library</span>(viridis)</a>
<a class="sourceLine" id="cb7-3" title="3"><span class="kw">library</span>(hrbrthemes)</a>
<a class="sourceLine" id="cb7-4" title="4">data1 =<span class="st"> </span>data <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb7-5" title="5"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">country =</span> <span class="kw">factor</span>(country)) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">head</span>(<span class="dv">20</span>)</a>
<a class="sourceLine" id="cb7-6" title="6"><span class="kw">attach</span>(data1)</a>
<a class="sourceLine" id="cb7-7" title="7">  <span class="kw">ggplot</span>(data1,<span class="kw">aes</span>(<span class="dt">x=</span>gdpPercap, <span class="dt">y=</span>lifeExp, <span class="dt">size=</span>pop, <span class="dt">fill=</span>continent)) <span class="op">+</span></a>
<a class="sourceLine" id="cb7-8" title="8"><span class="st">    </span><span class="kw">geom_point</span>(<span class="dt">alpha=</span><span class="fl">0.5</span>, <span class="dt">shape=</span><span class="dv">21</span>, <span class="dt">color=</span><span class="st">&quot;black&quot;</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb7-9" title="9"><span class="st">    </span><span class="kw">scale_size</span>(<span class="dt">range =</span> <span class="kw">c</span>(.<span class="dv">1</span>, <span class="dv">24</span>), <span class="dt">name=</span><span class="st">&quot;Population (M)&quot;</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb7-10" title="10"><span class="st">    </span><span class="kw">geom_text_repel</span>(<span class="dt">label =</span> country,<span class="dt">size=</span><span class="dv">5</span>)<span class="co">#+</span></a></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" title="1">    <span class="co">#scale_fill_viridis(discrete=TRUE, guide=FALSE, option=&quot;A&quot;) +</span></a>
<a class="sourceLine" id="cb8-2" title="2">    <span class="co">#theme_ipsum() +</span></a>
<a class="sourceLine" id="cb8-3" title="3">    <span class="co">#theme(legend.position=&quot;bottom&quot;) #+</span></a>
<a class="sourceLine" id="cb8-4" title="4">    <span class="co">#ylab(&quot;Life Expectancy&quot;) +</span></a>
<a class="sourceLine" id="cb8-5" title="5">    <span class="co">#xlab(&quot;欢迎关注：庄闪闪的成长手册 \nq  Gdp per Capita&quot;) +</span></a>
<a class="sourceLine" id="cb8-6" title="6">    <span class="co">#theme(legend.position = &quot;none&quot;)</span></a></code></pre></div>
<p>如果不喜欢圆形的气泡图，可以将代码中的<code>shape=21</code>进行更改，正方形是<code>shape=22</code>，得到的图如下：</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" title="1"><span class="kw">library</span>(ggrepel)</a>
<a class="sourceLine" id="cb9-2" title="2">data1 =<span class="st"> </span>data <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb9-3" title="3"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">country =</span> <span class="kw">factor</span>(country)) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">head</span>(<span class="dv">20</span>)</a>
<a class="sourceLine" id="cb9-4" title="4"><span class="kw">attach</span>(data1)</a>
<a class="sourceLine" id="cb9-5" title="5">  <span class="kw">ggplot</span>(data1,<span class="kw">aes</span>(<span class="dt">x=</span>gdpPercap, <span class="dt">y=</span>lifeExp, <span class="dt">size=</span>pop, <span class="dt">fill=</span>continent)) <span class="op">+</span></a>
<a class="sourceLine" id="cb9-6" title="6"><span class="st">    </span><span class="kw">geom_point</span>(<span class="dt">alpha=</span><span class="fl">0.5</span>, <span class="dt">shape=</span><span class="dv">22</span>, <span class="dt">color=</span><span class="st">&quot;black&quot;</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb9-7" title="7"><span class="st">    </span><span class="kw">scale_size</span>(<span class="dt">range =</span> <span class="kw">c</span>(.<span class="dv">1</span>, <span class="dv">24</span>), <span class="dt">name=</span><span class="st">&quot;Population (M)&quot;</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb9-8" title="8"><span class="st">    </span><span class="kw">geom_text_repel</span>(<span class="dt">label =</span> country,<span class="dt">size=</span><span class="dv">5</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb9-9" title="9"><span class="st">    </span><span class="kw">scale_fill_viridis</span>(<span class="dt">discrete=</span><span class="ot">TRUE</span>, <span class="dt">guide=</span><span class="ot">FALSE</span>, <span class="dt">option=</span><span class="st">&quot;A&quot;</span>) <span class="co">#+</span></a></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" title="1">    <span class="co">#theme_ipsum() +</span></a>
<a class="sourceLine" id="cb10-2" title="2">    <span class="co">#theme(legend.position=&quot;bottom&quot;) +</span></a>
<a class="sourceLine" id="cb10-3" title="3">    <span class="co">#ylab(&quot;Life Expectancy&quot;) +</span></a>
<a class="sourceLine" id="cb10-4" title="4">    <span class="co">#xlab(&quot;欢迎关注：庄闪闪的成长手册 \nq  Gdp per Capita&quot;) +</span></a>
<a class="sourceLine" id="cb10-5" title="5">    <span class="co">#theme(legend.position = &quot;none&quot;)</span></a></code></pre></div>
</div>
</div>
<div id="拓展知识" class="section level2">
<h2>拓展知识</h2>
<p>其他扩展可自行学（小编做推送的时候已经学过啦，但是篇幅有限，就没继续整理下去了）</p>
<ul>
<li><a href="https://wencke.github.io/" title="GOplot包">GOplot包</a>提供了直接做气泡图的方法，函数是：GOBubble。</li>
</ul>
<p><img 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" /></p>
<ul>
<li><a href="https://www.data-to-viz.com/graph/bubble.html" title="BUBBLE PLOT理论定义">BUBBLE PLOT理论定义</a></li>
</ul>
<p><img 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" /></p>
<ul>
<li><a href="https://plotly.com/r/bubble-charts/" title="plotly包">plotly包</a>绘制可以互动的气泡图</li>
</ul>
<p><img 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" /></p>
</div>
<div id="r语言数可视化书内代码" class="section level2">
<h2>R语言数可视化书内代码</h2>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" title="1"><span class="kw">library</span>(ggplot2)</a>
<a class="sourceLine" id="cb11-2" title="2"><span class="kw">library</span>(RColorBrewer)</a>
<a class="sourceLine" id="cb11-3" title="3"><span class="kw">library</span>(ggrepel)</a>
<a class="sourceLine" id="cb11-4" title="4"><span class="kw">attach</span>(mtcars)</a>
<a class="sourceLine" id="cb11-5" title="5"><span class="kw">head</span>(mtcars)</a></code></pre></div>
<pre><code>##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1</code></pre>
<div id="带数据标签的气泡图" class="section level3">
<h3>带数据标签的气泡图</h3>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" title="1"> <span class="kw">ggplot</span>(<span class="dt">data=</span>mtcars, <span class="kw">aes</span>(<span class="dt">x=</span>wt,<span class="dt">y=</span>mpg))<span class="op">+</span></a>
<a class="sourceLine" id="cb13-2" title="2"><span class="st">   </span><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">size=</span>disp,<span class="dt">fill=</span>disp),<span class="dt">shape=</span><span class="dv">21</span>,<span class="dt">colour=</span><span class="st">&quot;black&quot;</span>,<span class="dt">alpha=</span><span class="fl">0.8</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb13-3" title="3"><span class="st">   </span><span class="kw">scale_fill_gradient2</span>(<span class="dt">low=</span><span class="st">&quot;#377EB8&quot;</span>,<span class="dt">high=</span><span class="st">&quot;#E41A1C&quot;</span>,<span class="dt">midpoint =</span> <span class="kw">mean</span>(mtcars<span class="op">$</span>disp))<span class="op">+</span></a>
<a class="sourceLine" id="cb13-4" title="4"><span class="st">   </span><span class="kw">geom_text_repel</span>(<span class="dt">label =</span> disp )<span class="op">+</span></a>
<a class="sourceLine" id="cb13-5" title="5"><span class="st">   </span><span class="kw">scale_size_area</span>(<span class="dt">max_size=</span><span class="dv">12</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb13-6" title="6"><span class="st">   </span><span class="kw">guides</span>(<span class="dt">size =</span> <span class="kw">guide_legend</span>((<span class="dt">title=</span><span class="st">&quot;Value&quot;</span>)),</a>
<a class="sourceLine" id="cb13-7" title="7">          <span class="dt">fill =</span> <span class="kw">guide_legend</span>((<span class="dt">title=</span><span class="st">&quot;Value&quot;</span>)))<span class="op">+</span></a>
<a class="sourceLine" id="cb13-8" title="8"><span class="st">   </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb13-9" title="9">     <span class="dt">legend.text=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">10</span>,<span class="dt">face=</span><span class="st">&quot;plain&quot;</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb13-10" title="10">     <span class="dt">axis.title=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">10</span>,<span class="dt">face=</span><span class="st">&quot;plain&quot;</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb13-11" title="11">     <span class="dt">axis.text =</span> <span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">10</span>,<span class="dt">face=</span><span class="st">&quot;plain&quot;</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb13-12" title="12">     <span class="dt">legend.position =</span> <span class="st">&quot;right&quot;</span></a>
<a class="sourceLine" id="cb13-13" title="13">   )</a></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="方块状的气泡图" class="section level3">
<h3>方块状的气泡图</h3>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" title="1"><span class="kw">ggplot</span>(mtcars, <span class="kw">aes</span>(wt,mpg))<span class="op">+</span></a>
<a class="sourceLine" id="cb14-2" title="2"><span class="st">  </span><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">size=</span>disp,<span class="dt">fill=</span>disp),<span class="dt">shape=</span><span class="dv">22</span>,<span class="dt">colour=</span><span class="st">&quot;black&quot;</span>,<span class="dt">alpha=</span><span class="fl">0.8</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb14-3" title="3"><span class="st">  </span><span class="kw">scale_fill_gradient2</span>(<span class="dt">low=</span><span class="kw">brewer.pal</span>(<span class="dv">7</span>,<span class="st">&quot;Set1&quot;</span>)[<span class="dv">2</span>],<span class="dt">high=</span><span class="kw">brewer.pal</span>(<span class="dv">7</span>,<span class="st">&quot;Set1&quot;</span>)[<span class="dv">1</span>],</a>
<a class="sourceLine" id="cb14-4" title="4">                       <span class="dt">midpoint =</span> <span class="kw">mean</span>(mtcars<span class="op">$</span>disp))<span class="op">+</span></a>
<a class="sourceLine" id="cb14-5" title="5"><span class="st">  </span><span class="kw">scale_size_area</span>(<span class="dt">max_size=</span><span class="dv">12</span>)<span class="op">+</span></a>
<a class="sourceLine" id="cb14-6" title="6"><span class="st">  </span><span class="kw">guides</span>(<span class="dt">fill =</span> <span class="kw">guide_legend</span>((<span class="dt">title=</span><span class="st">&quot;Value&quot;</span>)),</a>
<a class="sourceLine" id="cb14-7" title="7">         <span class="dt">size =</span>  <span class="kw">guide_legend</span>((<span class="dt">title=</span><span class="st">&quot;Value&quot;</span>)))<span class="op">+</span></a>
<a class="sourceLine" id="cb14-8" title="8"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb14-9" title="9">    <span class="dt">text=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</a>
<a class="sourceLine" id="cb14-10" title="10">    <span class="dt">plot.title=</span><span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>,<span class="dt">family=</span><span class="st">&quot;myfont&quot;</span>,<span class="dt">face=</span><span class="st">&quot;bold.italic&quot;</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>)<span class="co">#,</span></a>
<a class="sourceLine" id="cb14-11" title="11">    <span class="co">#legend.position=c(0.9,0.05)</span></a>
<a class="sourceLine" id="cb14-12" title="12">  )</a></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAMAAABNUi8GAAAC7lBMVEUAAAAAADoAAGYAOjoAOmYAOpAAZpAAZrYGBwkICQkJBwcJCQkKBwYKCAgKCQkKCgofJCsfJSsgJSsgJiwiJiwjJScjJSgjJywjJy0kKC0lKC0mKS4nCggnKi4oKy4oKy8pKy8qKioqLC8rIR4rIR8rIh8rKiwrLS8rLTAuCwkuDg4vDg8vERAvEhAvFRIvLy8vMDEwFhMwMDAxHBgxJiMyHBkyIR4yIh8yIx8yJyQyKSYyKScyLCsyLy4yLy8yMjIyMzMzIh8zIyAzJiMzJyQzJyUzKCUzKSczLSwzMzM6AAA6ADo6AGY6OgA6OpA6ZpA6ZrY6kJA6kNtNTU1NTW5NTY5NbqtNjshmAABmOgBmOjpmZjpmZmZmZpBmkJBmkLZmkNtmtttmtv9uTU1uTW5uTY5ubm5uq+SAl7WEmraOTU2OyP+QOgCQZgCQZjqQZmaQkGaQtraQttuQ29uQ2/+SpLyZtdWZtdect9aeuNeeudeivNuivdupv9urbk2rjk2r5P+tw96tw9+uuMWvucawt8Owucawusexxd6zx+G1yeK2ZgC2Zjq2kJC2tma2tra2ttu227a229u22/+2//+4yuG5yuG8zuXDMSnEMSnF0+bH1efIjk3I1efI///J1+rL2evOnZLOoJTQ2ufR2+rS09TVpZnVpZrVqJ3VuLHV3+zWo5fWpJjWpZnWpprWubHX4u/Y0dvY3+vY4+/Z0tvZ4/DbkDrbkGbbtmbbtrbb25Db27bb29vb2//b/9vb///kq27k///lREXnNS3oNi7pSEnpVk7q7/Xr6+vtWlLtalzxbmDy8vL1inn3jHr3u633va/3v7H4+fn5jn37ppX7rJ37rZ37wrX7y8D7zcL73db76eX76+j8z8T87On8/f39xLb+wLH+w7X+xbf/qpn/sKH/tmb/wLL/wrT/w7T/xLb/xbf/xrj/xrn/yI7/z8T/25D/27b/29v/4dr/5Kv//7b//8j//9v//+T///+b6ypiAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAaa0lEQVR4nO2de5wb11XHxyHLAkkLfdCWAAFKwW2BLhhwCcVAIS1dhRLw2N4N0Da13Wy7XkKB8KaLKSkBtsVgpzyFeYZHaEpCQUlpimXe5U0wlEcglIcKCYh1u17t/Me9M5Jm9Jw7umfuPWf0+30+uytp97tz5uirOw9JV0GEIIwT+C4AQaYFgiKsA0ER1skVtBmGR06rn7vrocqKg5IQJE2eoM2j56O2NnTn5GknBSFINjmC7q6vRdHepho428pUBHEdk33QWNDm4Nb9j8dl/K1GmR2VRQord5AsR8HpMRG0qTfx9dvULuhafH1ZpdyqEKSbfEHbsZe768cuKkvXercSP1IZDA9uSGHlyhhB9za1nDrpjihxIxh03w0prFwZgiaH8To7J051byJuBIPuuyGFlStE0L6X6bkm4kYw6L4bUli57AVN1FRb9v6F7i+IG8Gg+25IYeWyFzSqq93PvU11bFTXp5lwkGRLCiuXv6BRPQzDUwMX4hA3gkH33ZDCyhUg6IQQN4JB992QwsqFoPaoLFJYuRDUHpVFCisXgtqjskhh5c6boB+VzVIZPeROCit37gT9lkwgqDSS1DzDQFC3pLByISh1D7mTwsqFoNQ95E4KKxeCUveQOymsXAhK3UPupLByISh1D7mTwsqFoNQ95E4KKxeCUveQOymsXAhK3UPupLByISh1D7mTwsqdN0GXBlJGD7mTwsqdN0FNO1FdUli5ENQelUUKKxeC2qOySGHlQlB7VBYprFwIao/KIoWVC0HtUVmksHIhqD0qixRWLgS1R2WRwsqFoPaoLFJYuRDUHpVFCisXgtqjskhh5ULQSehTs1kai1gvFIIWJEnNMwxbQb89EwjKgyQ1zzAQ1C0prFwIOgmFoAxJUvMMA0HdksLKlSto2Xnad2aCjw2b32AEdUsKK1fuCErcCAgqgSQ1zzAQ1C0prFwIOgmFoAxJUvMMA0HdksLKhaCTUAjKkCQ1zzAQ1C0prFwIOgmdPMUD3UIhaEGS1DzDcBW0qqSwciGoPSqLFFYuBLVHZZHCyoWg9qgsUli5ENQelUUKKxeC2qOySGHlQlB7VBYprFwIao/KIoWVC0HtUVmksHIhqD0qixRWLgS1R2WRwsqFoPaoLFJYuRDUHpVFCisXgtqjskhh5UJQe1QWKaxcCGqPyiKFlQtB7VFZpLByqyPo03We9fRupr4EvtQecieFlVshQd+g8sY3xHntaz/sKf0Yuuq/+25IYeVWVNBvfcq39QNB/S8UgkJQ1guFoBCU9UIhKARlvVAICkFZLxSCQlDWC4WgEJT1QiEoBGW90EoL2gzDI6f1hXbvQpzhNYGgjBdaZUGbR89HbS2m/tZODR1eEwjKeKEVFnR3fS2K9jZX1Je6ENVXer8YXhMIynihFRY0jhZ058SpKBlPkwyvCQRlvNCqC9pUW/adk3rr3o4FXVYZ/ptnvTHN6173kctpyEtG5in5gqqDo7Wou/uZ7oQOP9TieWaXjWacLfVBzp0UVq6MEXRv89jFPEEtG8Gg+25IYeXKEFR7md3E6xA3gkH33ZDCyhUiqDpCyjtIsmwEg+67IYWVy17QxEs1cOadZrJsBIPuuyGFlcte0Kh+7GJyDjTnRL1lIxh03w0prFz+gkb1MAz1KJo+5xmHuBEMuu+GFFauAEEnhLgRDLrvhhRWLgS1R2WRwsqFoPaoLFJYuRDUHpVFCisXgtqjskhh5UJQe1QWKaxcCGqPyiKFlQtB7VFZpLByIag9KosUVi4EtUdlkcLKhaD2qCxSWLkQ1B6VRQorF4Lao7JIYeVCUHtUFimsXAhqj8oihZULQe1RWaSwciGoPSqLFFYuBLVHZZHCyoWg9qgsUli5ENQelUUKKxeC2qOySGHlQlB7VBYprFwIao/KIoWVC0HtUVmksHIhqD0qixRWLgS1R2WRwsqFoPaoLFJYuRDUHpVFCisXgtqjskhh5c6xoE8dyJL/7rshhZU7z4J+RzYQVAJJap5hIKhbUli5EBSCCiJJzTPMrIJa52nflQ0+7gsZH4ygbklh5codQa0bAUHlkaTmGQaCuiWFlQtBIaggktQ8w0BQt6SwciEoBBVEkppnGAjqlhRWLgSFoIJIUvMM403QpcH4774bUli5cywoGSqLFFaugaCdjeB49+KV1X13ZH9x1d0z2pUJBHVLCivXZARtBIvdS61g4UJ6OwSVSAor10TQ7VpPxK3+WKoDQSWSwso12gfteXlldcBICCqRFFaukaC9LXsr3tZf/uogCK7+xkTQRNIrq/oPOm8Ogn2vujDhn0BQHqSwco0E7R0bxSNpK0hyfFjQK6vx7QsFDeUm6HVDWSrYw1mW6ZIUVq7ZaaZGcDjqatjZ2KcHzy01mA4JuhV85QU9ih4WLuhbBwNBGZGTZNiu6XGxlXGvNSLoldX4WL+zUXAIhaBuSWHlmgmqhk21jd/qngT9wDvf8jW1EUG3a91tf8EjJwjqlhRWruEzSXrwTI6Eosu3JB4OC9rbN4WgxZfpkhRWrqGgiYH6ZFNnI7j6FWfOjm7it2uLE3EIyocUVq7pc/Fq874VD41dDxsDgup91KGTpMaBoG5JYeWaCrpd+6rkIGi7dtW5+IxnIqg6dj8cXd4I4qP4hXNR9FANB0mFl+mSFFauqaBqy548naQvdM93xoLGu54fupGeB82+nASC8iOFlWsqqNqmd0fGzu1Kwpt/ZLW3eX+wFtz8O/G5pc7t6kD+5nMFRYOgbklh5RoLWlogqFtSWLkQdBiFoIxJUvMMA0HdksLKhaDDKARlTJKaZxgI6pYUVi4EHUYhKGOS1DzDcBN0aTgFezjLMl2SwsqFoPaoLFJYuRDUHpVFCisXgtqjskhh5fIXdG8zDMM1dWF3XV0IV3q3EzeCQffdkMLKNRH0z4xTgqB7m0dOR03t5c7J09lfEDeCQffdkMLKZS/ozolT6nvz6Pmorb4gqDUprFz2giZp62F0ZeAm4kYw6L4bUli5QgStq9Gzflt3ZzSKllVmWRZSyQxK+MyBHHAiaFuJubt+7KIyda13G/Ej1f/wcP1IpjxHIHlFLUgjQb8nGyeCttNj93RHlLgR/rt//U8PB4LKELQd9ofN7jETBJW8ohYkraDbXx6/zbPVe59Sa+wblnIFbWb8zJxrIm6E/+5D0HySVNDu+5BbSkr9lV4oJmgzTMbMZOzEJt52mf5X1IKkFLSVzDHS2dATOm0tpheKCbpzojd+1vWOaJUPkiBoLkkoaCs43ErmdNDvVm5cdXf/QjFBm2GcI2rLXlc/T/V/QdwI/92HoPkkoaDa0VjQm+5ILvYvFBN0Yogb4b/7EDSfLEHQZK9Tfe9fgKDjSQiaT0JQjyQEzSdLEBSbeFMSguaTZQhqeZAEQYmX6X9FLcgSBLU9zQRBiZfpf0UtyBIEtT5RD0Fpl+l/RS3IMgSNGr1nOBuzPdUJQUmX6X9FLUgTQQ8MZoqgZoGg3UDQfNJE0GmBoBYkBM0nIahHEoLmkxDUIzky5860WXckr6gFCUGrTworF4Lao7JIYeVCUHtUFimsXAhqj8oihZULQe1RWaSwcgsK+pljcgCCSiKFlVtU0D8aDQQVRQorF4LaowTkxw8nb+JxqStqSXoXtPP1X9vPK8/yEXRwvp9nDupDIehPDgWCjiW9C9r9QNpuFqd9bjJxI3IE/f6BQFBPJKGg8aci65coF5tZpPPmfbe+O/rAQ7Xg1ndtJDwELVptWah/kk7QzoZysREsFn3BciP50G8l88KF7akfPU/cCAgqgaQTtP8OpGJv+ehOl5Nc6F+BoMWqLQv1T9IJmkQNmMXeNAdBIeg0klrQrcy7jc3edrwVLPZ/YhM/Y7Vlof5JYkFb6iin4MQN27XgQ1515vW3qMMpdUSPg6SZqi0L9U/SCtrqHSMVmVnk8i3xGaarz6mt/LQBFIJaL1QgSSpoKx4Ai88s0nnXmR+eZiYEtVomA81mJykFbSQb6OIzi3TeeeYsBLWptizUP0koaO+EZtGZRbqb+K84B0FnrrYs1D9JJ+h2rXeAU+xEffcg6eXBtDNMEBSC2graSJ5N1z4Wmlkke5qJUT7mBwdC/ilin/AzQ8HnlBXIzKeZjDJ0or6r8HYtbwglfqRiBJVAMhA080wSBJ2x2rJQ/6R3QTMvFsnbxBM3Yjo6dUYFCOqMzBX0wLhQCho19t16IYoeqi2cffjhh9/NRdCyySKTilguk4Fms5O5gubEWtDBFyxPPZQnbgSD7rshhZXLTNDsWz5UXsHlxSJOyEPjM3YwFb2is5PeBS0Q4kYw6P6hfxobCJq5BkENc10mH6u+8vYZTZYJQXNJBoJ2zrwlf/POQNC3pvkx9TXOouuGM/JHELQg6V/QVs3kAEmIoD86FAhqS3oXtLMRXP0NZ+LkveSOuBEQVALpXdD+U535IW4EBJVAehe0s5H7KiYIWrxaItQ/mSPoiyblAJWg/ac6ISgEHUPmCfrPE0InaPTgTTeL3Ad99kiWUkG/OcmHZ387ukwImkv6F7Rzu9Cj+Gf/4nCygv5AnOzfQNBZSP+CbgX7DJ7mhKAQ1I+gco/iIagLkoGgUo/iIagL0rugco/iIagL0r+gUWPfqx+OM+3FyhAUgloL2hiZt9ZgAtvMC5ZxFA9BR0hCQfUUIoNvhzd5X3zmBcs4ioegIySdoFdWD+snLhcLzyxiHOJGFESzbx9a1t8gqAuSTtA4WtDiczOJEHSEhKAuSGJBG2qDXnx2u8oImqb3HCcEtSRJBW3Fn/JRdH7QCgma/sXoLyHoTCSpoHoTv3ABgo79JQSdiSQWNP4QhfndxKd/YSbo6OwNk+dwgKAkgqoDozk+SEr/wkxQN9Uy0Gx2kk7QRMdW0c9JgqClV8tAs9lJOkGjrYULiZPFJrCFoGVXy0Cz2UlCQaOtIEhe+FFoAluxgk7dd5y0ZwlBC5KUgpqmIoKKIYWVC0HtUVmksHL5C7q3GYbhmr7UDsMjp8UIev1IRs8XQdCCJENB9zaVlM1wRfmpLrRTQ4kbQS/oTw0HglqTDAXdOXFKfW8ePb+3qYfR+goEtSMZaDY7mSPo2AnAs7OAlyBoEjVy9k2FoFPJz56WpZIW6orMETQ3pQlaP3p+56TeurdjQZdVZlmWy3zizw7HTcmf+zdTwr5rM8W/oG11lJTsfqY7ocSP1OqMoH8+JRhBSxG03TtGgqD5JAR1Lmg7PsuU3cRDUAjKR9BmchZ0vg+S9g/lufv3T5gWH4I6FrQZnop/zvdppv33Dua37r0XgrIQdOfEWvfSXJ+oh6DJNX6CNsM4WsymrKc6ISg9yVDQiSFuBGNBD6p89JtGcq26eYykEBSCGpGEgl66dOlTHxjMbz7wgL4ZgkLQWUkIWgI5VdBpz/EegKDDgaAlkNMF/YvJgaAjgaAlkBCUjoSgJZAQlI40mYEBghYkIShHEoKm1yAoQxKCptcgKEMSgqbXIChDEoKm1yAoQxKCptcgKEMyFvT7RvLkQ4cOXTP61xDUXFA9gVixj6GBoGOSCPobwzn06KOPQlAbQVuBFnROZrcrkYSg6TVKQa+sBr0pGOdgftASSS3oQb1Bz+QL1RcEtRO0sfB1StB5mWG5RFILeknrmMm/JD8g6OyCbt90h94HnZc56kskIWh6jU5QvUXXgs7Lp3yUSMb7oM8Yfl/nfmzirQRtKDkhKAmJg6T0Gpmg8QYdm3gSspigkz7Mpv+CKr4rmk/SCdrofqj23HwMTYlkMUGJFsqTpBM0zhZOM1GQEDS9Ri8oTtRbkxA0vVaCoHPyMTQlkgaCfsGEjJvcge+K5pPEghoFgubFRNB/HB8ICkEhqEMSgnIk9emhZ3zvLwznk+655x4ICkF5kPvvun8g77j//i965JFHegJCUAjql4SgyTUIypSEoMk1CMqUhKDJtWmCTv4Yr/4neUHQskgImlybJqhBXAo6X3nuXe8Yzhc/9thjvQ/metkHx6eaH9w1HJ6CEj9SGQwP07L/zsHcdeed12Sme8IICkH9kjlz1ENQCOqXhKDJNQjKlBx+4fHy4GyOEBSCsibnW9ByA0EJSFNB9UvwXmb4srwSy52ZJDXPMBCUgDQWVN32QcORtsRyZyZJzTMMBCUgIWh5gaAEJAQtLxCUgISg5QWCEpAQtLxAUAISgpYXCEpAQtDyAkFfODkTxYGgzgJBX/i3E2Mq6LS5mLKBoMUDQQkENQ0ELR4ICkGNSVLzDANBIagxSWqeYSCouaA39PPSGwZiJhgEnSUQtICg7+3lf987EAhaXiAoBDUmSc0zDASFoMYkqXmGgaAQ1JgkNc8wEBSCGpOk5hkGguYJ+vn9fPKNA7lhSND8eZYhaPFA0FxB/76Xt/1bL4/rbyOCjlFvUD8IWjwQFIIak6TmGQaCFhX0JSov1d+e85JePuVFOk+GoCUEghYW9Iknnvgf9fXE2/+9l1/5Bx0IWkYgqENBe5OS5L0sr5wVHZMXTM0SBKVABQnqd0XH5AV/Mi0QlASFoLOTENS+h6WTEBSC2vSwdBKCQlCbHpZOQlDpgu685rz6vrseqqz0biRuIQQtf0XHpBKC7q4f1YLunDydvZW4hRC0/BUdkyoI2g7DWNB2/L0f4hbKEfQvb/zXbn7pr3r5+ft0PuJXISh98gRth2uJms2VgduJWyhIUD2Cxnn7f/Vy3/t0ngRBS4jBPmgiaP02tQu6Ft+wrFJuVS7zJf85MfFaftn/9fK2x1Uygv53L/e9X+dJvy7u05I+60+nhUXhpoLurh+7qCxd691I/Bj3OLBMnBak+xQkRtD0/3iI8Qg6dJG4hbzut4FA0PT/eEgxQXdOnOpeIm4hr/ttIBA0/T8eUlDQ/rkm4hbyut8GAkHT/+MhpoImY+d8b+J/WefnPuevu0lOLsWBoKXF/Chen2aq4kFSXlJB4/zajY93k2iZBoKWEfNNfD0Mw1P9G4lbyOt+GwgETf+Ph+DFInmBoOn/8RAImhdjQa+9xmSeZVYrCkHte+idHLLt2ucMHhqlOWj01iJWKwpB7XvIjTz4pcrF979vTCBoGYGgBQNB3QaCFsxBnRcfHBcIWkIgaMEcvKTyH5fGRQv6eZOz5KPc6SQEte8hNzJP0L+bGAg6SyBowUBQt4GgBQNB3QaCFgwEdRsIWjAQ1G0gaMFUStBpb3eJn6OFoPYoBHVGkppnGAhaMBDUbSBowUBQt4GgBQNB3QaCFszSxHm8lyBoCYGgtCQEJQ4EpSUhKHEgKC0JQYkDQWlJCEocCEpLQlDiQFBaEoISB4LSkhCUOBCUloSgxIGgtCQEJQ4EpSUhKHEgKC0JQYkDQWlJCEocCEpLQlDiQFBaEoISB4LSkhCUOLMKiozP8pT4rk1kMIK6JYWVK3cEJW4Eg+67IYWVC0HtUVmksHIhqD0qixRWLgS1R2WRwsqFoPaoLFJYuRDUHpVFCisXgtqjskhh5UJQe1QWKaxcCGqPyiKFlQtB7VFZpLByIag9KosUVi4EtUdlkcLKhaD2qCxSWLkQ1B6VRQorF4Lao7JIYeVCUHtUFimsXAhqj8oiGZT7/OlZmkySmmcYCOqWZFDu8393NM9L83GfnslnJD+60pKaZxgI6pZkUO5YQX+vnx//w0zek/yAoAzuNzckg3IhqH0Pq0syKBeC2vewuiSDciGofQ+rSzIoF4La97C6JINyIah9D6tLMigXgtr3sLokg3IhqH0Pq0syKBeC2vewuiSDciGofQ+rSzIoF4La97C6JINyIah9D6tLMigXgtr3sLokg3IhqH0Pq0syKBeC2vewuiSDcisn6M5rzusf7TA8crp/I3ELGdxvbkgG5VZN0N31o1rQtrKznRpK3EIG95sbkkG5FRNUDZxa0L3NNXWlvtK7mbiFDO43NySDcqslaDtca2tBd06cUtea8WCqQ9xCBvebG5JBudUSVCUR9OTp3sUIn/kjOp/2B6N53u/38xPvGY3Hu9tU0GT3M90JJX6MMxhY3JAMyq3mCApBaUgG5VZT0OwmXoe4hQzuNzckg3IrKigOkkhIBuVWU1CcZqIhGZRbTUFxop6GZFBuRQWNmniqk4BkUG7lBB0b4hYyuN/ckAzKhaD2PawuyaDcpTG5JpPs7cvdn93/4yEQ1C0prNxBktQ8w0BQt6SwciGoPSqLFFauXEGp4+PlCF5eAjE3K0oUCDoHC4Wg9pmb+21uVpQoEHQOFgpBEaSkQFCEdSAowjoQFGEdCIqwDgdB9zbDMFxzvNDBVw+6S/3YRafL211XzQ1X8v+QaRgIurepTGk67qF+80rbg6Ht0LGgyZvJ5IaBoMNvd3KR3fU1/chwPrCo8cyxoG2nfaUPA0GTeBjOPAjaPPbdjgVtyt26x2EjaN39I73p/DGhtreu90Hrt3nYwScMF0HbzpvYdn+/6bfGOhZ0d10vry7XUCaCtn0cZ+5tut7cquW5HkHjCN4R5SGo+/EzWazbbXx8QO1F0OQ4VGRYCNr0tJPk+H5rhkncyyL4XBMHQZvu77JETR9bPscjqL8VJQoDQXdOeBg/tSfJdD4eFux0eXrnHgdJNulu+Ryf86l72dZ62Af1taJEYSAogkwOBEVYB4IirANBEdaBoAjrQFCEdSAowjoQdMZ0zvquYD4CQWfL5VsWfZcwH4Ggs2W7BkGdBILOFgjqKBDUKJ2Nq+5WP7aCw5GW83ArUDnuu6p5CAQ1S0Pr2NkIFi5EUSs4DkFdBYKaJd6kb9cCPZBuqW/YxDsKBDVLZ0ONna19r1fD5pXVReyDOgsENczWvjuirYXfVl629LYdgjoKBDWM0rKzsagH0oZSFYK6CgQ1jNqwb9eOq4H0m/TGHoK6CgQ1zdZVP6SGzlbwylpyqgmCOgkENU0jeLkaOtWBvN7Cq596HEVKDwQ1jVJzMT4VGp+yv7IaBK/2XdI8BIKaRqmpz8w3gmTb/mAtflYJKTkQFGEdCIqwDgRFWAeCIqwDQRHWgaAI60BQhHUgKMI6EBRhHQiKsA4ERVjn/wGj+zQ5uNSGTwAAAABJRU5ErkJggg==" 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================================================
FILE: 2020年/2020.11.05气泡图/Bubble_plot.rmd
================================================
---
title: "气泡图"
author:
  - 庄亮亮
date: "2020/11/10"
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
---


# 气泡图

气泡图是一种多变量图表，是散点图的变体，也可以认为是
[散点图和百分比区域图的组合](R语言数据可视化之美 )。气泡图最基本的用法是使用三个值来确定每个数据序列，和散点图一样。气泡图通过气泡的位置及面积大小，可分析数据之间的相关性。

本文可以看作是《R语言数据可视化之美》的学习笔记。前两部分可见（跳转）：

- [趋势显示的二维散点图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485142&idx=1&sn=564bffc9e7765ebae9b9b81a17a188d9&chksm=ea24f932dd5370241a05c75975ff24a34423a8f182bf6c716c8c9ed981788d0492dcb268248a&token=1544929502&lang=zh_CN&scene=21#wechat_redirect)

- [分布显示的二维散点图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485276&idx=1&sn=f98a2aede13555fa1c372f08c3cdec44&chksm=ea24f8b8dd5371ae9e13f3df41ff73e070775eb1ab871370783bb3f396a0a9c29d42c6a89210&token=682523778&lang=zh_CN#rd)

该书对气泡图的绘制并不是非常详细，小编将内容进行了大量拓展。下面的例子将一步步带你完成气泡图的绘制。本文内容丰富，希望大家都能学到自己想要的内容。


## 本文框架

![](https://imgkr2.cn-bj.ufileos.com/b4382ada-392f-4868-be6b-53e619921374.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=qppb6xaOmm72QdYDTIA9aO34REA%253D&Expires=1605239247)



## 数据介绍

数据集来源gapminder包中，包含了1704行和6个变量。其中6个变量含义分别为：

- country 国家142个水平

- continent 大陆5个水平

- year 1952年-2007年（间隔为5年）

- lifeExp 出生预期寿命，以年计数

- pop 人口数

- gdpPercap 人均国内生产总值(扣除通货膨胀因素后的美元)

由于数据过多，我们感兴趣的是年份为2007年的数据，所以使用dplyr包进行数据处理，具体数据处理案例可见我写的另一篇推送：[[R数据科学]tidyverse数据清洗案例详解](https://mp.weixin.qq.com/s/ozH-TsltnLwp51i3aDNpSQ)。数据缩略版如下：


```{r message=FALSE, warning=FALSE}
library(ggplot2)
library(dplyr)
library(gapminder)
data <- gapminder %>% filter(year=="2007") %>% dplyr::select(-year)
```

## 手把手绘制

### geom_point()函数构建

气泡图是添加了第三维度的散点图：附加数值变量的值通过点的大小表示。(来源:[data-to-viz](https://www.data-to-viz.com/graph/bubble.html "data-to-viz"))。
使用ggplot2，可以通过`geom_point()`函数构建气泡图。`aes()`设定至少三个变量:x、y和size。其实就是散点图绘制的升级版吧，`aes()`中多了一个参数。

```{r}
ggplot(data, aes(x=gdpPercap, y=lifeExp, size = pop)) +
    geom_point(alpha=0.7)
```

上图展示了世界各国的预期寿命(y)与人均国内生产总值(x)之间的关系。每个国家的人口用圆的大小表示。但是这个图不是非常美观，而且圆的大小并不是很直观，大家都差不多大。接下来对圆的大小进行设定。

### scale_size()控制圆的大小

scale_size()允许使用range参数设置最小和最大的圆的大小，用`name`改变图例名称(`scale_size(range = c(0.1, 24), name="Population (M)")`)。

- 图中可以看到，有些圆圈重叠了。k可将点的透明度进行调整（`geom_point(alpha=0.5)`）

- 为了避免在图表顶部出现大的圆圈，可以将数据集进行排序(`arrange(desc(pop))`)，代码如下。

```{r}
data %>%
  arrange(desc(pop)) %>%
  mutate(country = factor(country)) %>%
  ggplot(aes(x=gdpPercap, y=lifeExp, size = pop)) +
    geom_point(alpha=0.5) +
    scale_size(range = c(.1, 24), name="Population (M)")
```


现在图可读性有所提高，但如果数据集中还有一个变量想加入图中该怎么办呢？

### 添加第四个维度：颜色

这里可以用每个国家的洲来控制圆圈的颜色（`aes(x=gdpPercap, y=lifeExp, size=pop, color=continent)`）:

```{r}
data %>%
  arrange(desc(pop)) %>%
  mutate(country = factor(country, country)) %>%
  ggplot(aes(x=gdpPercap, y=lifeExp, size=pop, color=continent)) +
    geom_point(alpha=0.5) +
    scale_size(range = c(.1, 24), name="Population (M)")
```

该图基本可以满足我们日常生活的气泡图的可视化了。相信大家通过前面的详细的介绍，应该可以自行绘制，只要换个数据，懂得各个代码的含义即可。后面是图表美化的过程，参考[thr R Graph Gallery: Bubble plot with ggplot2](https://www.r-graph-gallery.com/320-the-basis-of-bubble-plot.html "thr R Graph Gallery: Bubble plot with ggplot2")。


## 美化气泡图

### 一些经典的改进

- 使用viridis包的调色板：（`scale_fill_viridis(discrete=TRUE, guide=FALSE, option="A")`）

- 使用`hrbrthemes`包的`theme_ipsum()`

- 使用xlab和ylab自定义轴标题

- 为圆添加描边：将形状改为21，并指定颜色(描边)和填充


```{r message=FALSE, warning=FALSE}
# Libraries
library(hrbrthemes)
library(viridis)

# Most basic bubble plot
data %>%
  arrange(desc(pop)) %>%
  mutate(country = factor(country, country)) %>%
  ggplot(aes(x=gdpPercap, y=lifeExp, size=pop, fill=continent)) +
    geom_point(alpha=0.5, shape=21, color="black") +
    scale_size(range = c(.1, 24), name="Population (M)") #+
    #scale_fill_viridis(discrete=TRUE, guide=FALSE, option="A") +
    #theme_ipsum() +
    #theme(legend.position="bottom") +
    #ylab("Life Expectancy") +
    #xlab("欢迎关注：庄闪闪的成长手册 \nq  Gdp per Capita") +
    #theme(legend.position = "none")
```

### 带数据标签

这里使用ggrepel包中的(`geom_text_repel()`)，可以给每个点自动加入标签，我这里是加入了各个国家名字，其他可以根据你实际需求进行设置。

```{r message=FALSE, warning=FALSE}
library(ggrepel)
library(viridis)
library(hrbrthemes)
data1 = data %>%
  mutate(country = factor(country)) %>% head(20)
attach(data1)
  ggplot(data1,aes(x=gdpPercap, y=lifeExp, size=pop, fill=continent)) +
    geom_point(alpha=0.5, shape=21, color="black") +
    scale_size(range = c(.1, 24), name="Population (M)") +
    geom_text_repel(label = country,size=5)#+
    #scale_fill_viridis(discrete=TRUE, guide=FALSE, option="A") +
    #theme_ipsum() +
    #theme(legend.position="bottom") #+
    #ylab("Life Expectancy") +
    #xlab("欢迎关注：庄闪闪的成长手册 \nq  Gdp per Capita") +
    #theme(legend.position = "none")
```
如果不喜欢圆形的气泡图，可以将代码中的`shape=21`进行更改，正方形是`shape=22`，得到的图如下：
```{r message=FALSE, warning=FALSE}
library(ggrepel)
data1 = data %>%
  mutate(country = factor(country)) %>% head(20)
attach(data1)
  ggplot(data1,aes(x=gdpPercap, y=lifeExp, size=pop, fill=continent)) +
    geom_point(alpha=0.5, shape=22, color="black") +
    scale_size(range = c(.1, 24), name="Population (M)") +
    geom_text_repel(label = country,size=5)+
    scale_fill_viridis(discrete=TRUE, guide=FALSE, option="A") #+
    #theme_ipsum() +
    #theme(legend.position="bottom") +
    #ylab("Life Expectancy") +
    #xlab("欢迎关注：庄闪闪的成长手册 \nq  Gdp per Capita") +
    #theme(legend.position = "none")
```

## 拓展知识

其他扩展可自行学（小编做推送的时候已经学过啦，但是篇幅有限，就没继续整理下去了）

- [GOplot包](https://wencke.github.io/ "GOplot包")提供了直接做气泡图的方法，函数是：GOBubble。


![](https://imgkr2.cn-bj.ufileos.com/bdf4074a-8cb1-440d-9194-6e8d487e8ae5.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=190m6qR3V1exwcuPayCDULY0Ios%253D&Expires=1605238660)


- [BUBBLE PLOT理论定义](https://www.data-to-viz.com/graph/bubble.html "BUBBLE PLOT理论定义")

![](https://imgkr2.cn-bj.ufileos.com/40810411-d8fc-413f-b665-ece61d55531c.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=9tj23TfNB23j468EBKqr46wN1Ec%253D&Expires=1605238689)

- [plotly包](https://plotly.com/r/bubble-charts/ "plotly包")绘制可以互动的气泡图


![](https://imgkr2.cn-bj.ufileos.com/47d4f17c-9b83-4340-9a09-ad122467565b.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=2uehzuNnkzKRhgkrmPmOmnEpGWg%253D&Expires=1605238713)


## R语言数可视化书内代码

```{r message=FALSE, warning=FALSE}
library(ggplot2)
library(RColorBrewer)
library(ggrepel)
attach(mtcars)
head(mtcars)
```

### 带数据标签的气泡图
```{r message=FALSE, warning=FALSE}
 ggplot(data=mtcars, aes(x=wt,y=mpg))+
   geom_point(aes(size=disp,fill=disp),shape=21,colour="black",alpha=0.8)+
   scale_fill_gradient2(low="#377EB8",high="#E41A1C",midpoint = mean(mtcars$disp))+
   geom_text_repel(label = disp )+
   scale_size_area(max_size=12)+
   guides(size = guide_legend((title="Value")),
          fill = guide_legend((title="Value")))+
   theme(
     legend.text=element_text(size=10,face="plain",color="black"),
     axis.title=element_text(size=10,face="plain",color="black"),
     axis.text = element_text(size=10,face="plain",color="black"),
     legend.position = "right"
   )
```



### 方块状的气泡图

```{r message=FALSE, warning=FALSE}
ggplot(mtcars, aes(wt,mpg))+
  geom_point(aes(size=disp,fill=disp),shape=22,colour="black",alpha=0.8)+
  scale_fill_gradient2(low=brewer.pal(7,"Set1")[2],high=brewer.pal(7,"Set1")[1],
                       midpoint = mean(mtcars$disp))+
  scale_size_area(max_size=12)+
  guides(fill = guide_legend((title="Value")),
         size =  guide_legend((title="Value")))+
  theme(
    text=element_text(size=15,color="black"),
    plot.title=element_text(size=15,family="myfont",face="bold.italic",color="black")#,
    #legend.position=c(0.9,0.05)
  )
```


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1nJtTNAj6jJKAAZEs8WgqihJRgX9wJHOkYoXkf8iwR2RiKKqRRiitWw3lYdnr30cDzNae/8Tw/1L3sS5gFALINXpKDQgmp1pQxW86M3O8aoqMTlNtTGnSjATM2tjXEgCYfS3hKyuCkFHkzBeScI6WKhFVxLuD+EQLt4TkOo6CU5f1drrhvrrVly/dspDayfe+8EtQx7fuJG0HcbZLyyc1r+5qXbojtE1xa0dt4x/5c31r9hA6MYtP5DrVgijoiV5Po6KKs3MBOCVStFlgez8bG57v8/vq4tZ/Gilfr8pX7VqJm1EzJQGeg3j5/xX8ruWMbrG4oduFyXxMEFyQlkpkMeJTvhKbCMY1j/o2ykPlEmSr335KxvYPvbZydev29P65KNrX58+c92zfxv6+Kil76PnU1Sl6fe+l694//zIweMjUO1ZPnH2TU3fxqa09+l/6OHXAQgEAaSZuhddMDiaZ1epkRAzpTKAxyVzrnGh7JLreGi7qF1VqO5WvoGQ0DwF584uo3cpz4sCBzc9T9SAQPKgoqI082X2QfxhshCzXmZ5Jmoo6MvOYAk7gCWH6cudN5+98oSroZZNBoRWbuEw1ygDmqI9OZ36aJrbbTPYqIFmZrldRpdFA27ONADF4/HXxjyKYhkRU9LgYsIJ6e+pgHAkGUjkgUhLSBg2N9w3IMwpylMaKScT/n6efcC+PLN8xActmMGOhu+4bH6EpsV/yAgOoO0n9/+HnR2B5h7hr455LAPJ1+wc+1i1AYGhXOs6eQf4IR+uigYUp8WSlweZTnAWFNpz6mJ2u4d60kbEPGnUwENEvUTbVJbqTCjIAQJlPo8IXEUNdQEJcCAhMvd/gvy8Q3E6TmsbErv++Z2tRuuN/7f1X+zsNyv/vYhoN066sbVlcRuZiq/iWvuP7rEb/7LuhyPfsFPLMffdxfMnz7+1fu5qEc0RPdM6QIHLo14FgCDKRFYNMiWU1MaoAsLfupYpQwobhpDby4OfkoJ4iZQWPyy9jNLm8wLSdEtUyzvBB3lwOVwbLXYqnl6U+o3+Qo/Hnp1ttBtL+ihOZyBQXGwBS0Z9zJIGwfoYXGwTYYlLnVeWdKFwoCSqAj0/LqoW8qk7kShFiku3kK9cfCPVHyDedt/qpeyLL06zk4uXtU1DyfXfE2fPmrng0Ccjbhg+flxtq7zz3ZUzXhrU/O6sjqN73mrbXD2iY/Kzm89vbBp7Y/3VcwaOI3vqq674XdnlYysH1Ym8GajvcgekQQFURnOzZJfFEgyCCwqLtNy6mKZRrzd9RMyrUkMdR+Nfdbfu7DIBzCIaw0J5kS16edcXuNOdBXwbyU1J1ewxtvTOqxtHP/3+JIOl3xOz3v0nmr9Y+f2d8VNjp4xrbbm7jQ5mdazJdtYzasufW2r+83/H0fEE+3DTXbdNum1+Hfd4stOSZuvMURh1OXnyAPjtnsaYXeumMPAnaOwXTOb4NVYT72PqU+xG7xcf6mPNQAQX6/IUcHKmcllV1UUlBRXFZdIaYyZNUjgzJ6Rpm8u6mKrApzM0vUgYbrTrbF2SFHbS18Xa5GhSmF5P7JYqZODSiqKajIK/VYNEqQIEZRigFxShVFwJURhGD6JU0ZlDP443kvW7ccNSPH2abWFfCns140peoYDeNeZHHSqlRgkMcp00ViJSV30QKhkjagSue7JMQH4304/FkrTgKC9Tjh69VLueUScBrhFPNVAUJJTKEur6Ce0u1dCFuorNZH28UayJb2IaDjjNtKWsWmioXPicrpB365FYFc3LTU9PA+B2dlqdhUV2QCMFCAazGmNBl900ImaXkg7mVCR4KJVkyfpRJFR5F86oRckaXOFoe0m/7W6YevPVY5uWvzf1w3P7vm99YGyIHU4139VjH6ob1tLvqqpxR9u2r5m2onVI9RVXsHUX9eMTLkxQdnCc6AuVEIv2VCsq3G5XOGzt77rMZaWBtEDvNOgN0au8hkhEMg3QTPzqkVUq5feAklS7rOucMleiPU7ivc6kQtuiYCqrfNTdlVF8fxLxCKgtj3iUQC44+jrzOa06UfyDSESH3x2j106vnpWmTXnhlT1o+UfT/qt9NdGau79/Zhf73+exCP2T2Pz/ZefZXez6I/gIyv/EkRs7Yf3IFpM1FG27n5x++NQ9Q/otPPTGQSQBH/Pd/9Yf/vjjne1sx152gh0p6f3eKHwYW3/EZZ93sA627uCCpcfMzwj7AIC8WN4IKljh6miAWKkBQZHNZgqip6CSZLOSmpjVSs0yBZocIpTouZRiZWGortKL8gsDiITjI5Uik+LHJ7FXiYTziRJnywoMgWdwNFstbzxXRcbikdvy72CqiPvXAaQznI/t4Idczsm9VLdbktKzzeY83vfZ7QGDlqalDY9ZNLRSTbODPb0mZneCvyYG9BLcSxY9KQVDSTe5ArmSp7voCQYwWfE4HPqnwOu4AyOYNn/C/fPZh2fjx7C84/aZ8xev2nXHraxT3vDKpkVrHaacdQ++/xGdXTuy8Zr4NrZo3PgNgDCXI/UBnh9eKI36VZeLN+NWnxscUBNzSKpskmtiJleyNBOvSfVEKuQRD2+0Iw4l2BUdoTI+ZiikBS+9h9OfOtrxL7aJvdiOkQOHDrc2tEs72U/HmW846xyGi3DSZ3j9azd1FvUDImwoz+E2NIBd1OtGAIdVkjTZUhOTqWTlLbMzaamUcEELnGVzAbVA0BHKleew8ew2Ng534wR8gL3Dxq5ZjO/xGuQP7A55A7ubrcHDnUMBdY8RLs0Mg6L5BgnAqphMiBbFWBOzKNxLAnII3zehaKqJofOXXkp5iCsitPAkbol0bqDV8RN4ijmIm4tl7zK2BLqkUsalGqFvNN1AqVkBQDQJoSl5QlZS0MVSLhaCX7P9dHD8OHKMEwKWxLu8KBdxL6ZDTbQo3e8nNquVEFemy2DIsGlmjQdbOr9BNkt+r+zlsmTu1FB3wd0z5VlnstgW8BBwKLpv9YJL5RlPdMKNOALkU1L14E93sr+yVfg43vTxgZtW/GXnd1vevKGVHafhuOnyAlyMU3AcPjDybB377rOT591Y2mUHeYJu/Ug004jIzW+QJFm2GGhNrMaABoNsUijK3QmbMnfKFN2XPIHtjr/NdmE5uRrDZG78Xj5t2EIGAOCFiawBT+ozgRw+bSAGXiPLwM0MRsr79e4NCw4Rxa5IJL6kRnJurq0bOKEZy79hDV4k7gVL5JHn1l4AdgYS+tfxVS0wMJpjIcRkNiOAzUBl2cq/UrNZoXwP3VtwpgBXF1eWAOXEQAdVfSMRDKBcx1awhYvEZm7FB7CZETKxJf4D39CN6/Hf8XkJ6VIlly6LPUkqBVCQArccJKJUl6GXoPq6r3PD1MsbzldfSPxvRcyR3dAvmukGo9nI1bbxUPHKisdJjEQxq9QGilBcN36X0mUp6hA6Y9DpEYujXuXykscVRBpkK4wudhzbcaSC07GdfUgtRrZEms9Wzok3cw1WSi3nqklH6R3oPr8kYcedOm6WR9NMYETFagVwUFlRVM1MVW5RVLtHv11adI/EnAKwL1KEcM/JO9nv43fpSiwh81U7+qQGdrQtXseFv4FZvycdQPQ8+VKfDHgE0jgAfBZF8RpdNTGjRO01Mer6daQROSBexQQy16Hxpkj+kj3BXubXE3gz1vNr/PlDb76Bs9nSNzaSY+xxdivejVP5tZCj0mP/OYvf4smfoAvtpHU62rkEFkhGowdsNrvdbQXBV3ZNM9TENGr/TSzoRn/ZLXHoEyAo4ckJSx+au+BBspEdYacX8yA6iCb0UGXmlKkTd504Fz8rb/gchAXYat0CdkjjEZynUFmSCDVIJg9AhmYypVOVEwBXRFK5UWSV22N7Ev4uHU92T9OQe+LX7PPaKziWzWZnfL9pJMZW1bO5OPS3LSUP1S3lg9poocvnk0ySppm8njQw8cTzu4wWMA6PAZgtFm40C/WaRcikzJbSWfPzuXKqQ0sxKLdfgl3BF0A82brsgaXLW7gB12EPzH7oTqxuZWvZKtp73M0Tm+Pz4vvlDUeOLdxZwVwPk1KRVS2cQX0ce4s4n+RlpKcHICC7LeCGy4rdAbAELNlGX3ZNzCdRYyq+uhvwVHHWrRpn+IvGGoVFl/MhDadWMcJP9LZen9cr+din7JuOx/ZeN2FqnzFL7767DtWvZu2f2TrnyermlsJrn977BC7f/lkz5g4srx3e8+orqypveeqmzf8qL/13n8KGgcUDKqrHbRP6FwNIYiqrimdLCgBFNBhVKlHOuxSdv3y2lARgcoLtYrOlOn53IGEMEF7k+dXC13JCQdThQHSbDQaX08hRhsdSYuuXVBAOtyLx4BHI6+6CYLnlEXbyLfYFex/D9zz7BAf0ztqVZ+7EwHn6YufCPz33/DraBqjXfyHBI2K+RonRKAOiVZYkC3BDJ+q9VNpUJOaj+sXtVx6h57CC2dmLTMMKdPlKFXO0a4DY+dTwvZeN/qJLhrqRy8gSsx+T0e52yQh+v2ynlszMrKwci9mcnemSzdRvt6NJiOSi+EtCbgo1UyM3WkiKOMKJUtMlGvCIi78nPihD2fPbzWFJ6WPdxqngfix9q9Sr9HQdwoJDth5mUy/nm1hKoRixV/mpUJxwVT85trLi1EAa6twb+aS+9uuhNBsStmnSbVMVzTXLnPpUo6oYTYpJ0C2VLGYDkWXJqFCUkhDL9evG+ooUZ3VpjZj8Izex59h6fnXg56wfNmF/DGMtC5Pi+GHyHdka/47Y4j27dJCYyF2B7wZVlZEQEERvNFFF4QqiSgVDdslOjEH5Z65AarLLowIDZAGWchEZbA/LwDo6mozsXBTfQUqoXleVJiZ0RugfzTJISFUVEExmlYuSRP1I0IAGUcZdOgxNpl1qFqqPbALSzPPvkbfjTVJ6vIrs30m/RXi/0ykkLWUbyWw9T7KjVgXRIIFRJlTBfN2EuvH0BNZX4iUpmc0y8bOPPmIblXMHz60Xa1gA6MDkVFt/ZIKYnGpfnBa6sUmAHY9/mJhqI4S4fJ+QL55xoKIY+VYNoOZTiaaCvQtCfCFHMMy1CH34IX7GMmfKjQd/UoR8AzFIA+R3QIHeUTdBWVYkSTznFd6SVJko0DW+xLKLeyTRZYcwiGjADQ/jqVO8uP6KGOiGzmqyKN4maq1OtpHWXhja9SRIRonoRhEaJZ5K0NrOFyl//vMAAGKNdIQ+qATAwK1gBjVKRVTIdwCUpB/rioP0XWLww7EvHPD6PGRL5ZkqbKpcLx3ptW2gZ/z7GYIdmjju9pfm6E8Zq6OFTovBQvLy/P78LIMhaEkbFrNYZLfbPjjm5jWdnDM4JnvBk0Az/y+ZVYSeXlcUJWdMvMcN9+1u8h0omny9N6YT+huGr1r0xzd+Or/5xbv/On7T8Y9PswO/X3znY5MWPHHDsNfXvfono1K6rn7f+K3vx32E27h55MJbxwOBFVznDsUNTsjh7BvIojRg1Mw2n89szrWA2WPUFFDSh8QUL7iGxEC7mCz83SHi7H5mUeZ0aISzRVANCgTlw1AfH9d2D8WobftHX+7YNsMT+hpLLZbJM2ZOJJNvaZk+Q5rNdrPv2XH2t6XzFTdbPuiJ9jP3rwh0PPOXNWvWAMLoCyfoMWk2eDi6esRYymclxCubh8RkDexcM++lZZJuOTk32SdwmnJoYkjgUBQyIf4DZqJx81Mjh9525cmTzcuHVf/BTQZgFvauOZFVwBH49ZIydr4kH4iQK81M2CcaDRi9Gi+obTZhqFy7xwIOIyi6fTTdPt5ft4+oT4Q+ecShOXlPGioU/BLkji3iOnVPiAnZ9vHnOw9ON/mw7Jv+1omT5kyVp7dNmDnLjWVoRx7zq9vG4YSfTjyy5vt7ViWNk9BynD61y+DMEKROSUpzOLKcJlOm3+OkzuoYFVUUVMesmuoZHFNTel5aloiry3bI3RbgrbNeR4XKwOMJ6AVAxMMtOP2GaQZcT2aVs+/Y3zDt7LdoiJfID985vmNc3Qb61PyZM+d3NmAPdGAahth3Jx+789Eel5+4rCjB7nSOkgMeuCKa7SZElSn1+qwAPhndyHVz283akJgZqJ4bgp8v7QVDiRwWFgxH9KfOeieocBWpiZ1l+9eu3bj/ufm1o2uv6ocGOq9zCZ23rKHh3ZdLPsoafsVgoKAwtzSV26sYyiEKd0SrzFlZAwZIfRwOUqzmSkGUpIHpPXr4fJFg8Kp0K1jRqlj7qv2GxYy5Eke5wr7FpDpWXFxYWDksVqi5e1fH3BkXz+n4pxIOWz79gRHv0LneqJs2FQ76ewKfPao+pSsqEvmsj+ykQFfCF6ZeRcGFyUQK8v26El/4WGzqS33OfxjpXbL2ndc3sTfYvm9+vP3WksHVg5tvOnmsZKGTFc2buvrNabOfa5w5/drrmura10otT/ceNqZjJ5Xzew187smt/1i1bPw9We5Roeh1xYVrZ732vkM6L1UOHVlb2WcEHT5q0qRRuwBhBYC0lmeDB8LRdATw2Y0Wg8Fo9Nolp1MaEnNqJkCjR6D/JfU5336yUOPaKqJJEuCQeFQirWX7O+6YxfZjqapqE/61bQ958LsXt8S/40CwpeDekav/vh0ILAPAD7lsA1jEZFcyGsFksprtJg9Rr4kR6DJ/ZWoO7uobKtNnnyJUlrW3X3ttO14phMgLHn98yIjzPqkFgFxoY259XSt4oSTqd/L0JgaDT/NcE9PAaBctOk/sjOTEKYEwCRGJxwB6tajQpMDBcxoHXzN8CJbum6GLZe60066mRmnd+eJXN6mThXRIWPMH/Un+NdGgxLmTUKrIsmYzWa0Gg8lkN4P41WCzUcXkofbu2oTf3cjSZdpuokXRuGOyi1dx22KswGZWhYd5AffOIrF9jYxdh40sI74Et93MVivueDXr0gYPcG0ouF4DRIkAevQioLvExgPivyvuhO7qQJ5BQRgeLXS7XPrsKDMzI6PAajSaTPkuq9WRKzu46XwOzWzPRJNH7+G7krl7+OC8ePqbjJDCRIiEfKFykdziVfBd8q+ke9n++uvnTGL7vy529F437Xwso/dL097ZwvbVXz9jOnlw3rz12+LfSS1Lh1+/urZpy+F4kfhtxYuQjGCut1tMFxHAq6vrscoOoatQFU0Xx29SyV/XLRG8TS0ierkyof+ZtWWXEPbn7boC9dce3JHE5yf0pzhpostXLJYMcLnSvcYhMa9mp0Nidu8vu/xUrvPeVQMOCCQs6MzrxGVT5986ecr8W6dQmX3ELvzxh7swGyl/I6Xt6/70Qnv7mhfYKbbnQTS8jE7s8wA7B4LrOep1cC1ckMMn1Hl+RVFNlKpZmqrlcuQEq9U9hBOEwa5mQEaKzBKmSBWoSQVlTvPepDFCnPndRKFJtuemosq2GZrG9p/taZv8wfaPbt58TGf7vePdSx/wsv5K9SPtbB87/T/s7H10mU722JDgM67pTN1euaIq8dIsyh+TpOUZ+fg6PcNnz/ZanE5V4I0FhsQsv8m6iSfIBUmS5S2dL8HBXl8ook+LIkFBaLdMkafPPzxZ2v7R5zsmPXeFIQMJ22e1lq48uri9oOMZ9uLa9lNYiho3Z9+6xqU/bcBDAybXN3ZFFJ3LddVEh0mcejw5BCxZZVnUS7wGFxqlMrTMRy+JIqpdWewrCD+6iu3/sre97yvSbCP7xLR8SXyH1LKxZTYkqp/1XIZ4dpmjpLktAEU5bnchWNw5lhxTli9rcMynUdPgGPX+vJ2/2BgiqPTHK2HB5clePsGgXCkPt082oetPnbx1/bDrDtW395oycuG8yJd/3/Xu6MZHa5Zcv2zRrf2wZn1HILfzsvKx+b0rCstHz73+8VXN/8y//JriK/qHR/+30LeE6xuRa8AjToRYDHa7y2UyEIfB4fWZnHbn4JjVYrfL3HVyQt3QpktOVnRhgnBcxKOXvoLpIyFPwCO6cjK3bsas9tdeeHRt8xasYDuu+TD4aeiNN0jGwgknTn4e//yqK4UOT/Gc4zM+cENZ1E8cDrfby3t/j9NoJ7JNtumyPcmJ1sVDgItr7tQYgH+grxdrpR2zt72PpSLjsXRp7XUHt5Mj8dki4Ynt/EpI9JkPcrlm6BV1m0GWiYgIK0G0GNEuC5llKWndDU1X/x0SbTfiOtaElf/INyryZYexkjVJLfFF86aMXUzaumS4AZRtXEaWOMsoSyaOIVng81ETVTMyMjNzVEXJ9plMVLbbMxQ7yDqidR3RdPz2LIDSIO1WQ8wBsin/pGskRZpuUfew19lm7LMwJ1eRcrT7sG6R5NCsqBgvN92NPdk7uARPdt4vtTDH4m9q1lxH/PGvvE03jMkcer4XnuKKI5gApOW6bWqi+YoMaKSUSAQlGWWzQVWtfIZmMSoUAA1mj4T2S2cBqaROkYZeq3KlhdkClOu/mD2BI48cxZHsMWxja46fYO2kPwmyZ7A1fiy+DRewhcJLzK17ycs1KTC73ZrXK0koahm/Jgob/pNT8no0p9XJMTHDAFyVskQJkKKvhBlTUzxHyokifvTqgNsSaw9mmBRz7n4cwoqu+vcfR9RErqqfl+fkfr2/YcZNo8ic866XXnR8Z72xNZI450HXce2MIn+oKqkIYDYgmvQhAm8c7YR/MwyOoefSIULSSMJGySlCWEwR6LrOB4nC0uhAZiCmDrLp6+3xekDI4T38Id7D54ipCHUbcnIcfn+uNTMzIFGXy8qjKd9qSbTzYosp2hbbF7bnuBrm+REWRw08Coc18VTQ4xFQ6+EJhDmL2m6/c/OZG4cpn31T3XpmM9quH32qucGAVz7Z9jEdXMUObcyzBF8xskNVg+knbU8BIO5gJWSlYgMK7tcIpZJMAaCyhONDYlbqCOKOo0cV29lA1ylOauB7yBN7yOHlOmgGQ75bkoI52TabW3Z7qCzl/3/2IIuHzuFynuSi2BZnlftyiBSnzxyCyzwcrImh4e0Xbhz2+9mfKtWtL7xTP39x26LeM2aFPyFVQ7CnuWmyw5K3EXsOrqIfh2dPY5tNjY2nGm7QTxGQIqmCtoEHIlG/Ag4zmKnd7qNeu82mSJSaHQ5QoCRU1lYi9ElBdqqp5pwa1sv/RAMmELwQB0baym968pqFwxaOC99ePv7pgf89chFZcXX5l1NzcyPRii+nphf8lzhBwpbiQanl0rP6Dg26zurbad4v56mukCugE0Wi7Vh7JsTasSV5lIO0dJbKBcljHAhLOdJqfN6cwad7QYchPV3OyCA+n4mYMrPSXCNiBtuIGMiGNH4pGWmKygXqpwH4S8+ePzvOII575nOCTh4R15lS69q26gmSEBt94OCr7YtF6z7vlm8b7mpdcN+rL/fHcyhjZk77c8arjmflv/Bn9kZObzbAuFFEB4A0ST+d2BztZXeaidFqTfd6iV/zO51ado7Fn+avjxnT0sDFqcleG3P6QR7xs+NNXUfUIJTSVqjbjT+pBpRfbpXXFSKawsFwiBuQbNyyZcyzs2sbcS679w9k3/mvbhr+6qufy7sbvojGrt10dOm6WtZ5ttes1keObtl5BAjMBCYFpHXcnkW8R87TLC6j7EsnBrDZ8jIhM/OyYp9LSycWo2xQPZ4ctYBHz/YyHc11H2qb9S+iA4oURXyC3SM+0WGqPrVIoJJaFCmMXFRdbixfuGzBqEk3j1qwfGE43Pbogt+Nn93Y9siC8v1T6+qnzxxRO50cnPC7BcsWhCMLly6MTZs8uu2RtlBo/iNtYyYOnz6ttm7aDBHpCoDEp+PghZnR/7I53U6Plce2UaYyMYkJqxeRED/HBp/idDkbYkCRuuwmm93WEFPtdgt6FMsl5xX9mtiW3kNfypcpEhAfkgPKkCfoEXdAGF7cGCBD0YAVbOGWH374gX38448/vsOW4BViZBv3vHrfq8eO8RdyHMhFiKNCMGoniiKGmUaJSlTVsUcEbCpFdAhyJGBIAFHnAbag8wAAgUm89lnw/0o5D7g2jvTvPzOzu9KCJNSFaAKEBMYHAokSuQpiY04OODjYsWxCcjbkNaluuPdyiXuaS0jHpPfeE0N68fVO/ObSe+8uy39mVlqEzr76oeyi+bG7U3bK83yfkUZBGZwCMyKlaRaXRRTLC6E4JyfkAld4DKmpsbkrK0ttpSafxzc15nHqTVNjepQycUvmivi5NiuyMYtA0qyNo3NOVr9OFfZJmt75WUW7VMhOWtE4fsubj9zRP33SzuaW6LxFB3rWTJj4xSuvXdHyYsOAb/bpj257c+OS5s4tvmrim7appHXPputbn8kPlVdURssit194/xklXdGr7p3261Hh7uKKUGH0uu2nzi8Pxya1V5qmAUYu4UfygiRwVi0/YrQaWIvIdGcQ4pBB7dzU9snCdpLZJF/SOXJNjdRPPa0uMhVd2TKurqk5Mq5FXFPXEB0/7ucNExvqGieOb6wDIIw7lSbR99oBPqhmvm9ikm0mm7/c7yzPc+bV1IrpYEmnX1mlhbZglpActKMVbEo36zBrHWyifBGnSASrw44ZvIhr6bwgFCxiuH4R45HIul+c91p4c3j55tf/fvilPddGFx5b8zJqf5X9DCi9v/m10vvcrj6U09uHsg/0Ke/29invHSBfX7VJ+TAv99nwkcNvfNd82xjlI/4/Su+rLyi3/ObXaPaLTJb0b6xlBfCX+DHKMLqgAOoieZk65HLlmXXU56PLK/RmGI2e9HQbys4GEGweShSEA0F1mAtak3BQbR1SPGxVVo3K6irbp3YM1ToJV3pGr452r7n58XnrWi6tr79h3tY9yqTy/KbYvMvxsYvGRLrPu/BCWegef0l+cNcmpeGP/qIz6oqkNPas06Fd6BEEkMAIbZHRaUaDTKd2RMKCgERqGDdkGNkrBpBGCE4XBIMoIpOMsR4lWko4kLBqJI+K5j8Faab66Q897w8yR4ALIR3yqYfpaPGg8hFyDSo70RG06A12/oayC49HL1E/s9K3DL2QNXzKGb8fhTCZCCJkRZgzSkcQkogAAdYJoQTf6LXQWZQQHjx2hLz1I7pgEIaGErEHWAIzAAhaezTEW+S5kUqBYFHUgcViJEbamxB9uT/ROLFE8QLBIegdsp5+naSN8spKbara53ErgY4FlFnoIwadmhP5X7VaYcvuz5QHAu8h/cO3K+s89eFTJuceP+dft9utd0xUFqDpyj3kqh3K1+H6uhrlzX/ZctHQEckuSNLhJG8MjPTGCNLRbwWDZH+Fr/6Jm7D5hAmyIDMiQ0ZGTrbVkMkqRQ3FUq17vL06HSowmDyctbXd2N5201ln3XjW5a88G6uvnz2nLjJHWMg+7W0766bZL10emd02YWJ7G+NFAYSwiCGdcx+ZGTqdRB35BoSomd9sMRrSZYQkAYOKeoYC8S5MM5WnxriwyfZwnAs9I2/h3kG0RVlFY12UNylYiiCAo/gZTriVRKwOA5LAgiyuTNnkwQ4Hyucer4lJXb96j39EPHUF+JnjK/5+briipGXeqiuf3np9+4YudA6O3jbYEQv6S2bt37Cle8be7rMBwVgcxo+Ir4APJkRy7enY7QbIl/LTzVK65C8mdrvDIed4PSa5IIE5pbQ8dlABTRX6S6xu1DgHrezj3QjuuaN9/n1P7N541ards5oXtJ3REgwFWsOdE/b9v3W9wlu7a432i6at2N7wzOzzq6tvrAr76ePuDExYn+qLI0JEDyCnCdwXdyjui3uFjR/VNMjMIUk6ao6YiGZWHZ0i/DX75U5H1aEgAOK2LmrkhkxmMUmXJFnOsjrBQR/drXNlOGl7yiCq4Y2Z+zTTkbYwT8qwtv73xo0CxS6XhZtDZ7WvpVaAD0ZnlC6fNWF+vigy+yj67YoVdz/PrAF7Z8wo/9mM65SDUhQQLFSOCbslO2RAIOJINwsiAoTMFr0emUykKWYSWc8XiHtk4gMlbe5qgAb7UsMIa0IFwu6bbumd0PqX1/72IW5Tjkmn/3QfCVmPHEWCwiKd8Cj0e7KGEUURmUU6Ebk1RiCQCHSypSLhfEr/+2Eqe2hQsaNeALBCVcRlNjI7Fh1Y7Gaz0W60ySYW9pXNXt9QQI0EXB1/3PjAIiZPQYprQ3RWgnr3Xd88KXuOu/GW5v7s6Kwj6xc5btOZJpzh7hmf2cktXDiKGxPRSYI8MjopD+WfMDoJeePRSb4QbvyciNkVzReismdxFD2z4Oyi0vHr6MwOwnTUfEt8ic9KPBFjIvYqgzhkDw/xTGK3kxc9YlKPgt969IarH3/wwP4nFG9dY+PEiY2NdULbnf0v3Hr7wAu3dHR2dnTMm5cy6s2OlKZTy49OL2AW1Ib01FNiGh70BD7YIdHEB79/Oej1B9UBL+6NL0aoFonqQehRdg4ip/LxIFqsSMPn2KuMXYbaUNsyJZw1fMrGrnIA6Qpa2n5Y+TuAYvg1fgUA6eAP5Nrjj4L8IMFW+uJUVye0D51Au5h8T7W6B7CZSZlyNlXeJ75ClUs8XEnM8as+Eb9qmXpVwDBeWUH+LLTzNU5DpKiQug4YJk0jh0pMoyDbnI1lQp0JPk9rzJdhoRy8xZvKwaN4g9Cm5HHsnddbrUub3bCVWHLF4ldiF1wYPjM27aFzzp37w3lvHP3F7rOrUcnw6jY6d1dT86yJ4eiY0sOnTO6//YLru+j0cyyamXhHhoZU2lu3GPuhiOexHiQ0HfQPYqfoh9HVJ1B0w2//heIgzFQV2SMV52iKgYTCOlIxU1N0cUXaQwR7uWRYkxbXSNDfPYvXhpfEa4MpdD7OPtrg4sg4yUbMNmIRLCjNZEJsvgbgEETRbiYUvqb4syENGQkj/JFkkzkxTAQrMmlscsKiQLvUAAeUNb8G7yQ062PCs0QKkEYsI9rR6nzH9imOvcoLeLew9/ghbKIUT+hoLlq5jiPvcYqZDnXNrC6WKXZGjNP8+VlGYAXOBfY556p5+ZaodTT0KC89ZE+UXqqiG9pSFPdShT1JcXDoO1XhHnmNmZqia+gnXgMYFag1wGbucZ7cAJnQGCmivUCW3ep0GlBamtthAIqVWwGovcRJi9eKLYy8TgmP0+BgddahWmkscQqUlpiPo4MhBwPPA1tV5FzFz7cKwm9+d+CzzzahATIdd1Du/G5GoOPWnR9+ofQoyl1qHsRXeDuriLez36eUA+dUeTlUxtt7N1fgvJMpulHDv1AchOdUhXek4hxNMZBQZI1UzNQUXVzB2vvoeGkj2IAMglnogXTIjaRLBGTZYORGZXcgqMUn8260FqnLBlSM7lL+uB+Vocqr6Rhetkf5tfL7vfj3qKxH+SMavZf++VuaSiUAhD7DLeIHkgA2yIZCCEdyXJ4cuz0tB9LAW+TMK3Ab3QxXJQWpdOWImbyK8arGGFaJqpEG2V2IO/yqihEFV1Wm94Xts3tnv8iA1RevaL1x1sDRP56CjrR2UWL1/ZBiOG0+WqzyvXWXXHDpANrEwNWGNfM3DSi/fHYJ/rbsp+8e6j5uKR4aUmlIXgO18Vocrdaz1uOkKrqR6V8oDkKPqsgfqZipKbq4gr0RJcl9kqDwq4yNv3kb1KtYuCSJSmbrqZpIDiOjjbIoSpJTMDbFZEdTTJAFWdIRyZowKGrdjOZBjePIDroW0tZGwh2UUz1yNcPaH1CQ4fikjst3rbt0NcHv/agMUij5c2Vc18rz5/NZJM3JfMkD1dAaGU3tegXFxQDlWSZTbXkgUGPKKtBBcbEui2SWhkqnxEIQcFgyozFLwnGq7ZUx0g03TH/aTYLqcnOkuuX8iaFL8zhXsVAn4a3SSDRSWl1/RVfoo3fmXTau+ubIbfnTo2vnNjQ0TVjXsWQjbb4+hL9FfuGvkV+cNqai1JldVTJn7srmu+7JLfy6KLhqVGhcaeOylsh5lbWnl49r6TrnKPVMv/LO/azH5ASbVEBr5VQ+UtQfAPb2jbbEazY1vfvCE6Xna+kHfxhi6RUj001a+kAasPTikemClt4lAX+3T+GCYcUDmqJ/lKrwqwogTCEpQjeUQBBOgS2RydU1JDM/P2g3GoNBuabG7/GMKZPlsC/fW50fjVVXsyDp7OxQNJZtNo6aSoF3p+S0NFDHPHgbYiBJgQZGv/ERLZmZ0t5q6wkJKnqMhzBz8MufZG0ZXsZRzHYYrWJk1TDShwoZfiVWbn2rce4L19/03NdfPRtr2nHzvKc/emdx/d3LDyM4XkaJq+cfm/bY8bqFq1fv6FyOvX+1oHvwefbOru7Y0zcz5q91cn3Tq52bInXKZx9RCGvWp8UlOEsQzpxD6T/05acLVrNap952xtZhP0xWx0+0iY+fnCrjtT1FbQ2389oqStRWanr34n+eflDP00eNTBe09C6rWpeVidoeugYAvcGv8LTaXynTgF0DGRLXuBwA/y5J0T00eaRi6JdU8UmS4qDyuqqwJBTvUMXlkqApuriC9Vdu9UkSBIfk5fPVpZGx4MYuV46oJ+kEY0tOTnr6qEKLpcQNmZh+SJ2ImdjppB56CnnSKS02+RpiJifBU2MEnYC8izsQ2clwI9I+1YYLf3Gtkw8SVgdtm4XAwyNdtX46hDAvXCL2GCmnN3ZetuitjjuuvUr5/0PfKX9DwuFDDfpT17zfga0rz19x8fIFq84TXdXF99Wdtr1n/m5lz4fKh8pLyPrJR8gyV+hdtuva4/Mv2Lj1ih27+lg74MwMf2tPV9/aEPAZUHI97ucl3KK2k5t4PReeOJ319ZfAyRW8pRiS+gUt3aSlD6jpeSPTBS29y6C2pIDWK8yCw0JYeIl7wbKhNGJ1pqWZBQEIyYUcNwVKAXHz0vPBYdBQiw8WTxJRTWOGj2+K1tf/PFpXNzVaf2ojO+KOwcEvTpva/POG6c1EmNrUMqWhpRkIfcaHKAN0OZ81eEfOGnzxWQOjb0jBFAZx/C+zhmCNsJ9hQWsvOLVn0n5GBm1eUrt/zK5jR21o/OiJKy9AhwzKa/6alefjSoYJlXV2dVyL7IwUqpp+Qes1ytH2RjTouvnWlnFKMOP2oSGVpeD1c2ZST4ByefGmpvMavgVOruA1XMnTC0emC1p6V0B9A0u1np97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</head>

<body>




<section class="page-header">
<h1 class="title toc-ignore project-name">Vignette Title</h1>
<h4 class="author project-author">Vignette Author</h4>
<h4 class="date project-date">2020-11-14</h4>
</section>



<section class="main-content">
<div id="gghalves包" class="section level1">
<h1>gghalves包</h1>
<div id="介绍" class="section level2">
<h2>介绍</h2>
<p><code>gghalves</code>可以通过<code>ggplot2</code>轻松地编写自己想要的一半一半（half-half plots）的图片。比如：在散点旁边显示箱线图、在小提琴图旁边显示点图。</p>
<p>gghalves将<code>_half_</code>扩展添加到选定的<code>geom</code>。比如：<code>geom_half_violin()</code>函数，相当于<code>geom_violin()</code>函数的变体，该函数主要作用就是展示一半的小提琴图，然后与其他图形组合。还包含以下函数：</p>
<ul>
<li>geom_half_boxplot</li>
<li>geom_half_violin</li>
<li>geom_half_point</li>
</ul>
</div>
<div id="安装" class="section level2">
<h2>安装</h2>
<p>gghalves通过GitHub安装:</p>
<pre><code>if (!require(devtools)) {
    install.packages(&#39;devtools&#39;)
}
devtools::install_github(&#39;erocoar/gghalves&#39;)</code></pre>
</div>
<div id="函数介绍" class="section level2">
<h2>函数介绍</h2>
<pre><code>geom_half_violin(mapping = NULL, data = NULL, stat = &quot;half_ydensity&quot;,
  position = &quot;dodge&quot;, ..., side = &quot;l&quot;, nudge = 0,
  draw_quantiles = NULL, trim = TRUE, scale = &quot;area&quot;,
  na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)</code></pre>
<p>其参数包括：翻译来源<a href="https://blog.csdn.net/weixin_43700050/article/details/107512448">生信玩家</a></p>
<table>
<colgroup>
<col width="50%" />
<col width="50%" />
</colgroup>
<thead>
<tr class="header">
<th>参数</th>
<th>解释</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>mapping</code></td>
<td>通过<code>aes()</code>指定图形属性映射。默认为<code>NULL</code>，使用<code>ggplot()</code>中<code>aes()</code>指定的映射。</td>
</tr>
<tr class="even">
<td><code>data</code></td>
<td>指定数据框。默认为<code>NULL</code>，使用<code>ggplot()</code>中的数据。</td>
</tr>
<tr class="odd">
<td><code>stat</code></td>
<td>覆盖geom_density()和stat_density()之间的默认连接。</td>
</tr>
<tr class="even">
<td><code>position</code></td>
<td>位置调整，可以是字符串，默认为<code>&quot;dodge&quot;</code>，也可以是位置调整函数的调用结果。</td>
</tr>
<tr class="odd">
<td><code>side</code></td>
<td>画半小提琴图的一侧。 “ l”代表左，“ r”代表右，默认为“ l”。</td>
</tr>
<tr class="even">
<td><code>nudge</code></td>
<td>在小提琴图和分配给x轴上给定因子的空间中间之间添加空间。</td>
</tr>
<tr class="odd">
<td><code>draw_quantiles</code></td>
<td>如果不是<code>MULL</code>（默认为<code>NULL</code>），在给定的密度估计分位数处绘制水平线。</td>
</tr>
<tr class="even">
<td><code>trim</code></td>
<td>若为<code>TRUE</code>（默认），将小提琴的尾部修整到数据范围。 若为<code>FALSE</code>，不修剪尾巴。</td>
</tr>
<tr class="odd">
<td><code>scale</code></td>
<td>如果为<code>&quot;area&quot;</code>（默认），则所有小提琴都具有相同的面积（修剪尾部之前）。</td>
</tr>
<tr class="even">
<td><code>na.rm</code></td>
<td>如果为<code>FALSE</code>（默认），则会使用警告删除缺失值。如果为<code>TRUE</code>，则会自动删除缺少的值。</td>
</tr>
<tr class="odd">
<td><code>show.legend</code></td>
<td>逻辑值，默认为<code>NA</code>，若为<code>FALSE</code>，不显示该图层的图例； 若为<code>TRUE</code>，则显示该图层的图例。 它也可以是带有名称（图形属性）的逻辑向量，用来选择要显示的图形属性。 如<code>show.legend = c(size = TRUE,color = FALSE)</code>表示显示<code>size</code>对应的图例，而不显示<code>color</code>对应的图例。</td>
</tr>
<tr class="even">
<td><code>inherit.aes</code></td>
<td>默认为<code>TRUE</code>，若为<code>FALSE</code>，覆盖<code>ggplot()</code>中<code>aes()</code>默认属性，而不是与他们组合。</td>
</tr>
<tr class="odd">
<td><code>geom</code></td>
<td>覆盖<code>geom_density()</code>和<code>stat_density()</code>之间的默认连接。</td>
</tr>
<tr class="even">
<td><code>bw</code></td>
<td>要使用的平滑带宽度。如果是数字，则为平滑内核的标准差。</td>
</tr>
<tr class="odd">
<td><code>adjust</code></td>
<td>多次带宽调整。这使得可以在仍使用带宽估计器的情况下调整带宽。例如，<code>adjust = 1/2</code>表示使用默认带宽的一半。</td>
</tr>
</tbody>
</table>
</div>
<div id="示例" class="section level2">
<h2>示例</h2>
<div id="单个函数" class="section level3">
<h3>单个函数</h3>
<p>我们以iris数据集作为本例数据，先使用单个函数进行绘制。</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="fu">require</span>(devtools)) {</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>    <span class="fu">install.packages</span>(<span class="st">&#39;devtools&#39;</span>)</span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>devtools<span class="sc">::</span><span class="fu">install_github</span>(<span class="st">&#39;erocoar/gghalves&#39;</span>)</span></code></pre></div>
</div>
<div id="geom_half_boxplot" class="section level3">
<h3>geom_half_boxplot</h3>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(gghalves)</span></code></pre></div>
<pre><code>## Loading required package: ggplot2</code></pre>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(iris, <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Petal.Width, <span class="at">fill =</span> Species)) <span class="sc">+</span> </span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_half_boxplot</span>() </span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>默认为箱子在右，使用<code>center = TRUE</code>将箱子居中。下面函数参数调整类似，就不再绘制结果了，就把最原始的进行展示。</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(iris, <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Petal.Width, <span class="at">fill =</span> Species)) <span class="sc">+</span> </span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_half_boxplot</span>(<span class="at">center =</span> <span class="cn">TRUE</span>) </span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="geom_half_violin" class="section level3">
<h3>geom_half_violin</h3>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(iris, <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Petal.Width, <span class="at">fill =</span> Species)) <span class="sc">+</span> </span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_half_violin</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="geom_half_point" class="section level3">
<h3>geom_half_point</h3>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(iris, <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Petal.Width, <span class="at">fill =</span> Species)) <span class="sc">+</span> </span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_half_point</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="综合案例" class="section level3">
<h3>综合案例</h3>
<div id="云雨图" class="section level4">
<h4>云雨图</h4>
<p>该案例来自<strong>生信玩家</strong>公众号<a href="https://blog.csdn.net/weixin_43700050/article/details/107512448">ggplot扩展包–gghalves</a>但并没有对代码进行详细解释。</p>
<p>这里小编对代码进行详细解释，喜欢的伙伴，可以按照解释自己理解，并用到自己实际所需的复合图中。</p>
<p>先将数据的统计摘要进行计算存到了<code>summ_iris</code>中，包含了均值，标准差，数量标准误差。<code>iris_plot</code>为所需数据，这里将<code>Species</code>变量设置为因子，因为要用它作为分类变量。</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span></code></pre></div>
<pre><code>## Warning: package &#39;tidyverse&#39; was built under R version 4.0.3</code></pre>
<pre><code>## -- Attaching packages --------------------------------- tidyverse 1.3.0 --</code></pre>
<pre><code>## √ tibble  3.0.3     √ dplyr   1.0.2
## √ tidyr   1.1.2     √ stringr 1.4.0
## √ readr   1.3.1     √ forcats 0.5.0
## √ purrr   0.3.4</code></pre>
<pre><code>## Warning: package &#39;tidyr&#39; was built under R version 4.0.3</code></pre>
<pre><code>## -- Conflicts ------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()</code></pre>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="co"># 统计摘要</span></span>
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>summ_iris <span class="ot">&lt;-</span> iris <span class="sc">%&gt;%</span> </span>
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>    <span class="fu">group_by</span>(Species) <span class="sc">%&gt;%</span> </span>
<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a>    <span class="fu">summarise</span>(</span>
<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a>        <span class="at">mean =</span> <span class="fu">mean</span>(Sepal.Length),</span>
<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a>        <span class="at">sd =</span> <span class="fu">sd</span>(Sepal.Length),</span>
<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a>        <span class="at">n =</span> <span class="fu">n</span>()</span>
<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a>    ) <span class="sc">%&gt;%</span> </span>
<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">se =</span> sd<span class="sc">/</span><span class="fu">sqrt</span>(n),</span>
<span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a>                 <span class="at">Species =</span> <span class="fu">factor</span>(Species, <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">&#39;versicolor&#39;</span>, <span class="st">&#39;setosa&#39;</span>, <span class="st">&#39;virginica&#39;</span>)))</span></code></pre></div>
<pre><code>## `summarise()` ungrouping output (override with `.groups` argument)</code></pre>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>summ_iris</span></code></pre></div>
<pre><code>## # A tibble: 3 x 5
##   Species     mean    sd     n     se
##   &lt;fct&gt;      &lt;dbl&gt; &lt;dbl&gt; &lt;int&gt;  &lt;dbl&gt;
## 1 setosa      5.01 0.352    50 0.0498
## 2 versicolor  5.94 0.516    50 0.0730
## 3 virginica   6.59 0.636    50 0.0899</code></pre>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="co"># 数据转换  </span></span>
<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>iris_plot <span class="ot">&lt;-</span> iris <span class="sc">%&gt;%</span> </span>
<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">Species =</span> <span class="fu">factor</span>(Species, <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">&#39;versicolor&#39;</span>, <span class="st">&#39;setosa&#39;</span>, <span class="st">&#39;virginica&#39;</span>)))</span>
<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(iris_plot) </span></code></pre></div>
<pre><code>##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa</code></pre>
<p>接下来进行绘图，我们想要得到<code>Species</code>与<code>Sepal.Length</code>的关系，其中<code>Species</code>为离散变量，<code>Sepal.Length</code>为连续变量。并绘制了半边的小提琴图，并将该图往右移了0.15，上下位置不变（<code>position_nudge(x = .15, y = 0)</code>），为了后面绘制其他图形留位置。</p>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(gghalves)</span>
<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggsignif)</span></code></pre></div>
<pre><code>## Warning: package &#39;ggsignif&#39; was built under R version 4.0.3</code></pre>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggsci)</span></code></pre></div>
<pre><code>## Warning: package &#39;ggsci&#39; was built under R version 4.0.3</code></pre>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggpubr)</span></code></pre></div>
<pre><code>## Warning: package &#39;ggpubr&#39; was built under R version 4.0.3</code></pre>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(iris_plot , <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Sepal.Length, <span class="at">fill =</span> Species))<span class="sc">+</span></span>
<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_half_violin</span>(<span class="fu">aes</span>(<span class="at">fill =</span> Species),</span>
<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a>                                     <span class="at">position =</span> <span class="fu">position_nudge</span>(<span class="at">x =</span> .<span class="dv">15</span>, <span class="at">y =</span> <span class="dv">0</span>),</span>
<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a>                                      <span class="at">side =</span> <span class="st">&#39;r&#39;</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --> 接下来加入散点图，并使x坐标往左移动0.1（<code>x = as.numeric(Species)-0.1</code>），使用<code>position_jitter</code>使得重复的点分散开。</p>
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(iris_plot , <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Sepal.Length, <span class="at">fill =</span> Species))<span class="sc">+</span></span>
<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_half_violin</span>(<span class="fu">aes</span>(<span class="at">fill =</span> Species),</span>
<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>                                     <span class="at">position =</span> <span class="fu">position_nudge</span>(<span class="at">x =</span> .<span class="dv">15</span>, <span class="at">y =</span> <span class="dv">0</span>),</span>
<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a>                                     <span class="at">side =</span> <span class="st">&#39;r&#39;</span>) <span class="sc">+</span></span>
<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">x =</span> <span class="fu">as.numeric</span>(Species)<span class="sc">-</span><span class="fl">0.1</span>,</span>
<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a>                                 <span class="at">y =</span> Sepal.Length,<span class="at">color =</span> Species),</span>
<span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a>                         <span class="at">position =</span> <span class="fu">position_jitter</span>(<span class="at">width =</span> .<span class="dv">05</span>),<span class="at">size =</span> .<span class="dv">25</span>, <span class="at">shape =</span> <span class="dv">20</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>在原来基础上加入箱子图，位置放在正中间</p>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(iris_plot , <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Sepal.Length, <span class="at">fill =</span> Species))<span class="sc">+</span></span>
<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_half_violin</span>(<span class="fu">aes</span>(<span class="at">fill =</span> Species),</span>
<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a>                                     <span class="at">position =</span> <span class="fu">position_nudge</span>(<span class="at">x =</span> .<span class="dv">15</span>, <span class="at">y =</span> <span class="dv">0</span>),</span>
<span id="cb30-4"><a href="#cb30-4" aria-hidden="true" tabindex="-1"></a>                                     <span class="at">adjust=</span><span class="fl">1.5</span>, <span class="at">trim=</span><span class="cn">FALSE</span>, <span class="at">colour=</span><span class="cn">NA</span>, <span class="at">side =</span> <span class="st">&#39;r&#39;</span>) <span class="sc">+</span></span>
<span id="cb30-5"><a href="#cb30-5" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">x =</span> <span class="fu">as.numeric</span>(Species)<span class="sc">-</span><span class="fl">0.1</span>,</span>
<span id="cb30-6"><a href="#cb30-6" aria-hidden="true" tabindex="-1"></a>                                 <span class="at">y =</span> Sepal.Length,<span class="at">color =</span> Species),</span>
<span id="cb30-7"><a href="#cb30-7" aria-hidden="true" tabindex="-1"></a>                         <span class="at">position =</span> <span class="fu">position_jitter</span>(<span class="at">width =</span> .<span class="dv">05</span>),<span class="at">size =</span> .<span class="dv">25</span>, <span class="at">shape =</span> <span class="dv">20</span>) <span class="sc">+</span></span>
<span id="cb30-8"><a href="#cb30-8" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_boxplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> Species,<span class="at">y =</span> Sepal.Length, <span class="at">fill =</span> Species),</span>
<span id="cb30-9"><a href="#cb30-9" aria-hidden="true" tabindex="-1"></a>                             <span class="at">outlier.shape =</span> <span class="cn">NA</span>,</span>
<span id="cb30-10"><a href="#cb30-10" aria-hidden="true" tabindex="-1"></a>                             <span class="at">width =</span> .<span class="dv">05</span>,</span>
<span id="cb30-11"><a href="#cb30-11" aria-hidden="true" tabindex="-1"></a>                             <span class="at">color =</span> <span class="st">&quot;black&quot;</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>这里比较有趣的是，作者还通过<code>geom_point</code>和<code>geom_errorbar</code>加入和汇总信息以及对应的误差项。</p>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(iris_plot , <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Sepal.Length, <span class="at">fill =</span> Species))<span class="sc">+</span></span>
<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_half_violin</span>(<span class="fu">aes</span>(<span class="at">fill =</span> Species),</span>
<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a>                                     <span class="at">position =</span> <span class="fu">position_nudge</span>(<span class="at">x =</span> .<span class="dv">15</span>, <span class="at">y =</span> <span class="dv">0</span>),</span>
<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a>                                     <span class="at">adjust=</span><span class="fl">1.5</span>, <span class="at">trim=</span><span class="cn">FALSE</span>, <span class="at">colour=</span><span class="cn">NA</span>, <span class="at">side =</span> <span class="st">&#39;r&#39;</span>) <span class="sc">+</span></span>
<span id="cb31-5"><a href="#cb31-5" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">x =</span> <span class="fu">as.numeric</span>(Species)<span class="sc">-</span><span class="fl">0.1</span>,</span>
<span id="cb31-6"><a href="#cb31-6" aria-hidden="true" tabindex="-1"></a>                                 <span class="at">y =</span> Sepal.Length,<span class="at">color =</span> Species),</span>
<span id="cb31-7"><a href="#cb31-7" aria-hidden="true" tabindex="-1"></a>                         <span class="at">position =</span> <span class="fu">position_jitter</span>(<span class="at">width =</span> .<span class="dv">05</span>),<span class="at">size =</span> .<span class="dv">25</span>, <span class="at">shape =</span> <span class="dv">20</span>) <span class="sc">+</span></span>
<span id="cb31-8"><a href="#cb31-8" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_boxplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> Species,<span class="at">y =</span> Sepal.Length, <span class="at">fill =</span> Species),</span>
<span id="cb31-9"><a href="#cb31-9" aria-hidden="true" tabindex="-1"></a>                             <span class="at">outlier.shape =</span> <span class="cn">NA</span>,</span>
<span id="cb31-10"><a href="#cb31-10" aria-hidden="true" tabindex="-1"></a>                             <span class="at">width =</span> .<span class="dv">05</span>,</span>
<span id="cb31-11"><a href="#cb31-11" aria-hidden="true" tabindex="-1"></a>                             <span class="at">color =</span> <span class="st">&quot;black&quot;</span>)<span class="sc">+</span></span>
<span id="cb31-12"><a href="#cb31-12" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="at">data=</span>summ_iris,</span>
<span id="cb31-13"><a href="#cb31-13" aria-hidden="true" tabindex="-1"></a>                         <span class="fu">aes</span>(<span class="at">x=</span>Species,<span class="at">y =</span> mean,<span class="at">group =</span> Species, <span class="at">color =</span> Species),</span>
<span id="cb31-14"><a href="#cb31-14" aria-hidden="true" tabindex="-1"></a>                         <span class="at">shape=</span><span class="dv">18</span>,</span>
<span id="cb31-15"><a href="#cb31-15" aria-hidden="true" tabindex="-1"></a>                         <span class="at">size =</span> <span class="fl">1.5</span>,</span>
<span id="cb31-16"><a href="#cb31-16" aria-hidden="true" tabindex="-1"></a>                         <span class="at">position =</span> <span class="fu">position_nudge</span>(<span class="at">x =</span> .<span class="dv">1</span>,<span class="at">y =</span> <span class="dv">0</span>)) <span class="sc">+</span></span>
<span id="cb31-17"><a href="#cb31-17" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_errorbar</span>(<span class="at">data =</span> summ_iris,</span>
<span id="cb31-18"><a href="#cb31-18" aria-hidden="true" tabindex="-1"></a>                                <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> mean, <span class="at">group =</span> Species, <span class="at">colour =</span> Species,</span>
<span id="cb31-19"><a href="#cb31-19" aria-hidden="true" tabindex="-1"></a>                                        <span class="at">ymin =</span> mean<span class="sc">-</span>se, <span class="at">ymax =</span> mean<span class="sc">+</span>se),</span>
<span id="cb31-20"><a href="#cb31-20" aria-hidden="true" tabindex="-1"></a>                                <span class="at">width=</span>.<span class="dv">05</span>,</span>
<span id="cb31-21"><a href="#cb31-21" aria-hidden="true" tabindex="-1"></a>                                <span class="at">position=</span><span class="fu">position_nudge</span>(<span class="at">x =</span> .<span class="dv">1</span>, <span class="at">y =</span> <span class="dv">0</span>)</span>
<span id="cb31-22"><a href="#cb31-22" aria-hidden="true" tabindex="-1"></a>    )</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>这里使用<code>ggsci</code>包的<code>scale_color_aaas()</code>,<code>scale_fill_aaas()</code>将尺度的颜色进行改变（非常好用！)</p>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(iris_plot , <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Sepal.Length, <span class="at">fill =</span> Species))<span class="sc">+</span></span>
<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_half_violin</span>(<span class="fu">aes</span>(<span class="at">fill =</span> Species),</span>
<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a>                                     <span class="at">position =</span> <span class="fu">position_nudge</span>(<span class="at">x =</span> .<span class="dv">15</span>, <span class="at">y =</span> <span class="dv">0</span>),</span>
<span id="cb32-4"><a href="#cb32-4" aria-hidden="true" tabindex="-1"></a>                                     <span class="at">adjust=</span><span class="fl">1.5</span>, <span class="at">trim=</span><span class="cn">FALSE</span>, <span class="at">colour=</span><span class="cn">NA</span>, <span class="at">side =</span> <span class="st">&#39;r&#39;</span>) <span class="sc">+</span></span>
<span id="cb32-5"><a href="#cb32-5" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">x =</span> <span class="fu">as.numeric</span>(Species)<span class="sc">-</span><span class="fl">0.1</span>,</span>
<span id="cb32-6"><a href="#cb32-6" aria-hidden="true" tabindex="-1"></a>                                 <span class="at">y =</span> Sepal.Length,<span class="at">color =</span> Species),</span>
<span id="cb32-7"><a href="#cb32-7" aria-hidden="true" tabindex="-1"></a>                         <span class="at">position =</span> <span class="fu">position_jitter</span>(<span class="at">width =</span> .<span class="dv">05</span>),<span class="at">size =</span> .<span class="dv">25</span>, <span class="at">shape =</span> <span class="dv">20</span>) <span class="sc">+</span></span>
<span id="cb32-8"><a href="#cb32-8" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_boxplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> Species,<span class="at">y =</span> Sepal.Length, <span class="at">fill =</span> Species),</span>
<span id="cb32-9"><a href="#cb32-9" aria-hidden="true" tabindex="-1"></a>                             <span class="at">outlier.shape =</span> <span class="cn">NA</span>,</span>
<span id="cb32-10"><a href="#cb32-10" aria-hidden="true" tabindex="-1"></a>                             <span class="at">width =</span> .<span class="dv">05</span>,</span>
<span id="cb32-11"><a href="#cb32-11" aria-hidden="true" tabindex="-1"></a>                             <span class="at">color =</span> <span class="st">&quot;black&quot;</span>)<span class="sc">+</span></span>
<span id="cb32-12"><a href="#cb32-12" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="at">data=</span>summ_iris,</span>
<span id="cb32-13"><a href="#cb32-13" aria-hidden="true" tabindex="-1"></a>                         <span class="fu">aes</span>(<span class="at">x=</span>Species,<span class="at">y =</span> mean,<span class="at">group =</span> Species, <span class="at">color =</span> Species),</span>
<span id="cb32-14"><a href="#cb32-14" aria-hidden="true" tabindex="-1"></a>                         <span class="at">shape=</span><span class="dv">18</span>,</span>
<span id="cb32-15"><a href="#cb32-15" aria-hidden="true" tabindex="-1"></a>                         <span class="at">size =</span> <span class="fl">1.5</span>,</span>
<span id="cb32-16"><a href="#cb32-16" aria-hidden="true" tabindex="-1"></a>                         <span class="at">position =</span> <span class="fu">position_nudge</span>(<span class="at">x =</span> .<span class="dv">1</span>,<span class="at">y =</span> <span class="dv">0</span>)) <span class="sc">+</span></span>
<span id="cb32-17"><a href="#cb32-17" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_errorbar</span>(<span class="at">data =</span> summ_iris,</span>
<span id="cb32-18"><a href="#cb32-18" aria-hidden="true" tabindex="-1"></a>                                <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> mean, <span class="at">group =</span> Species, <span class="at">colour =</span> Species,</span>
<span id="cb32-19"><a href="#cb32-19" aria-hidden="true" tabindex="-1"></a>                                        <span class="at">ymin =</span> mean<span class="sc">-</span>se, <span class="at">ymax =</span> mean<span class="sc">+</span>se),</span>
<span id="cb32-20"><a href="#cb32-20" aria-hidden="true" tabindex="-1"></a>                                <span class="at">width=</span>.<span class="dv">05</span>,</span>
<span id="cb32-21"><a href="#cb32-21" aria-hidden="true" tabindex="-1"></a>                                <span class="at">position=</span><span class="fu">position_nudge</span>(<span class="at">x =</span> .<span class="dv">1</span>, <span class="at">y =</span> <span class="dv">0</span>)</span>
<span id="cb32-22"><a href="#cb32-22" aria-hidden="true" tabindex="-1"></a>    ) <span class="sc">+</span></span>
<span id="cb32-23"><a href="#cb32-23" aria-hidden="true" tabindex="-1"></a>    <span class="fu">scale_color_aaas</span>() <span class="sc">+</span></span>
<span id="cb32-24"><a href="#cb32-24" aria-hidden="true" tabindex="-1"></a>    <span class="fu">scale_fill_aaas</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>最后使用<code>ggpubr</code>包的<code>geom_signif</code>加入显著性结果，<code>ggsave</code>保存图片。</p>
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a><span class="co"># 绘图</span></span>
<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(iris_plot , <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Sepal.Length, <span class="at">fill =</span> Species))<span class="sc">+</span></span>
<span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_half_violin</span>(<span class="fu">aes</span>(<span class="at">fill =</span> Species),</span>
<span id="cb33-4"><a href="#cb33-4" aria-hidden="true" tabindex="-1"></a>                                     <span class="at">position =</span> <span class="fu">position_nudge</span>(<span class="at">x =</span> .<span class="dv">15</span>, <span class="at">y =</span> <span class="dv">0</span>),</span>
<span id="cb33-5"><a href="#cb33-5" aria-hidden="true" tabindex="-1"></a>                                     <span class="at">adjust=</span><span class="fl">1.5</span>, <span class="at">trim=</span><span class="cn">FALSE</span>, <span class="at">colour=</span><span class="cn">NA</span>, <span class="at">side =</span> <span class="st">&#39;r&#39;</span>) <span class="sc">+</span></span>
<span id="cb33-6"><a href="#cb33-6" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">x =</span> <span class="fu">as.numeric</span>(Species)<span class="sc">-</span><span class="fl">0.1</span>,</span>
<span id="cb33-7"><a href="#cb33-7" aria-hidden="true" tabindex="-1"></a>                                 <span class="at">y =</span> Sepal.Length,<span class="at">color =</span> Species),</span>
<span id="cb33-8"><a href="#cb33-8" aria-hidden="true" tabindex="-1"></a>                         <span class="at">position =</span> <span class="fu">position_jitter</span>(<span class="at">width =</span> .<span class="dv">05</span>),<span class="at">size =</span> .<span class="dv">25</span>, <span class="at">shape =</span> <span class="dv">20</span>) <span class="sc">+</span></span>
<span id="cb33-9"><a href="#cb33-9" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_boxplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> Species,<span class="at">y =</span> Sepal.Length, <span class="at">fill =</span> Species),</span>
<span id="cb33-10"><a href="#cb33-10" aria-hidden="true" tabindex="-1"></a>                             <span class="at">outlier.shape =</span> <span class="cn">NA</span>,</span>
<span id="cb33-11"><a href="#cb33-11" aria-hidden="true" tabindex="-1"></a>                             <span class="at">width =</span> .<span class="dv">05</span>,</span>
<span id="cb33-12"><a href="#cb33-12" aria-hidden="true" tabindex="-1"></a>                             <span class="at">color =</span> <span class="st">&quot;black&quot;</span>)<span class="sc">+</span></span>
<span id="cb33-13"><a href="#cb33-13" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="at">data=</span>summ_iris,</span>
<span id="cb33-14"><a href="#cb33-14" aria-hidden="true" tabindex="-1"></a>                         <span class="fu">aes</span>(<span class="at">x=</span>Species,<span class="at">y =</span> mean,<span class="at">group =</span> Species, <span class="at">color =</span> Species),</span>
<span id="cb33-15"><a href="#cb33-15" aria-hidden="true" tabindex="-1"></a>                         <span class="at">shape=</span><span class="dv">18</span>,</span>
<span id="cb33-16"><a href="#cb33-16" aria-hidden="true" tabindex="-1"></a>                         <span class="at">size =</span> <span class="fl">1.5</span>,</span>
<span id="cb33-17"><a href="#cb33-17" aria-hidden="true" tabindex="-1"></a>                         <span class="at">position =</span> <span class="fu">position_nudge</span>(<span class="at">x =</span> .<span class="dv">1</span>,<span class="at">y =</span> <span class="dv">0</span>)) <span class="sc">+</span></span>
<span id="cb33-18"><a href="#cb33-18" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_errorbar</span>(<span class="at">data =</span> summ_iris,</span>
<span id="cb33-19"><a href="#cb33-19" aria-hidden="true" tabindex="-1"></a>                                <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> mean, <span class="at">group =</span> Species, <span class="at">colour =</span> Species,</span>
<span id="cb33-20"><a href="#cb33-20" aria-hidden="true" tabindex="-1"></a>                                        <span class="at">ymin =</span> mean<span class="sc">-</span>se, <span class="at">ymax =</span> mean<span class="sc">+</span>se),</span>
<span id="cb33-21"><a href="#cb33-21" aria-hidden="true" tabindex="-1"></a>                                <span class="at">width=</span>.<span class="dv">05</span>,</span>
<span id="cb33-22"><a href="#cb33-22" aria-hidden="true" tabindex="-1"></a>                                <span class="at">position=</span><span class="fu">position_nudge</span>(<span class="at">x =</span> .<span class="dv">1</span>, <span class="at">y =</span> <span class="dv">0</span>)</span>
<span id="cb33-23"><a href="#cb33-23" aria-hidden="true" tabindex="-1"></a>    ) <span class="sc">+</span></span>
<span id="cb33-24"><a href="#cb33-24" aria-hidden="true" tabindex="-1"></a>    <span class="fu">scale_color_jco</span>() <span class="sc">+</span></span>
<span id="cb33-25"><a href="#cb33-25" aria-hidden="true" tabindex="-1"></a>    <span class="fu">scale_fill_jco</span>() <span class="sc">+</span></span>
<span id="cb33-26"><a href="#cb33-26" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_signif</span>(<span class="at">comparisons =</span> <span class="fu">list</span>(<span class="fu">c</span>(<span class="st">&quot;versicolor&quot;</span>, <span class="st">&quot;setosa&quot;</span>),</span>
<span id="cb33-27"><a href="#cb33-27" aria-hidden="true" tabindex="-1"></a>                                                                 <span class="fu">c</span>(<span class="st">&quot;versicolor&quot;</span>, <span class="st">&quot;virginica&quot;</span>),</span>
<span id="cb33-28"><a href="#cb33-28" aria-hidden="true" tabindex="-1"></a>                                                                 <span class="fu">c</span>(<span class="st">&quot;setosa&quot;</span>, <span class="st">&quot;virginica&quot;</span>)),</span>
<span id="cb33-29"><a href="#cb33-29" aria-hidden="true" tabindex="-1"></a>                            <span class="at">y_position =</span> <span class="fu">c</span>(<span class="fl">8.2</span>, <span class="fl">8.6</span>, <span class="fl">8.4</span>),</span>
<span id="cb33-30"><a href="#cb33-30" aria-hidden="true" tabindex="-1"></a>                            <span class="at">map_signif_level =</span> <span class="fu">c</span>(<span class="st">&quot;***&quot;</span> <span class="ot">=</span> <span class="fl">0.001</span>, <span class="st">&quot;**&quot;</span> <span class="ot">=</span> <span class="fl">0.01</span>, <span class="st">&quot;*&quot;</span> <span class="ot">=</span> <span class="fl">0.05</span>)) <span class="sc">+</span></span>
<span id="cb33-31"><a href="#cb33-31" aria-hidden="true" tabindex="-1"></a>    <span class="fu">ggsave</span>(<span class="st">&#39;云雨图.pdf&#39;</span>, <span class="at">width =</span> <span class="dv">6</span>, <span class="at">height =</span> <span class="dv">8</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAMAAABNUi8GAAABJlBMVEUAAAAAADoAAF4AAGYANYQAOmYAOpAAXqgAZpAAZrYAc8IzMzM1AF41hIQ1hMk6AAA6ADo6AGY6OmY6OpA6kJA6kNtNTU1NTW5NTY5Nbo5NbqtNjqtNjsheAABeXl5eqOtmAABmADpmAGZmOgBmOjpmtv9uTU1uTW5uTY5ubo5ubqtuq+SEXgCGhoaOTU2OTW6OTY6Obk2Obm6ObquOyP+QOgCQOjqQZgCQkGaQtpCQ2/+oXgCo6+urbk2rbm6rbo6rjk2ryKur5OSr5P+2ZgC225C2///Ijk3I5KvI///bkDrb25Db/7bb/9vb///kq27k5Kvk/8jk/+Tk///rqF7ryYTr6+vvwADy8vL/tmb/yI7/25D/5Kv//7b//8j//9v//+T///8pm6vNAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAcnUlEQVR4nO3dj3vUyH3H8SU0XIGmHKUYQtOWJIW7QuAuLc4PcndJa9cEN8VtuUCNjY3+/3+iGv3WamY00sxn5ivt5/08AVvnZ/WVeEXaXa92NxljgtukHoAxWwTKREegTHQEykRHoEx0BMpE5wr0HTj4CjyTPh98QKhCSwTqlvT5CBS9/egVeCZ9PgJFbz96BZ5Jn49A0duPXoFn0ucjUPT2o1cwt+8/K/683n4tMwIFbz96BfP6fvMPP/r7v87//Nviz/zr1BOZIlDw9qNXMLff/eAb9efVb5qvZUag4O1Hr2Bm3//FLz5Tf/76s/proREoePvRK5jZ7/7xz3/1Tf7nWfGn+lpoBArefvQKPJM+H4Gitx+9As+kz0eg6O1Hr8Az6fMRKHr70SvwTPp8BIrefvQKPJM+H4Gitx+9As+kz0eg6O1Hr8Az6fMRKHr70SvwTPp8BIrefvQKPJM+H4GC2zDPwP9AUIWWxABFr8Az8UdQAsVGoJ4RKLadAVq9ALrzZ5gIFNuOAG1eAP2j0C+AJlBsOwK0eQH0D0K/AJpAse0K0PoF0L8I/QJoAsW2K0DrF0D/OfQLoAkU264AhbXrQNFJmWOxrXUHSjmCSj9CSZ+Pv+pEbz96BZ5Jn49A0duPXoFn0ucjUPT2o1fgmfT5CBS9/egVeCZ9PgJFbz96BZ5Jn49A0duPXoFn0ucjUPT2o1fgWeqXI48H3gFQhZYI1C3pv+kyDvgs0O1DFVpaHVDQ6y0JNFErAwp7vSWBJmplQGGvtyTQRK0NKOr1lgSaqLUBRb3ekkATtTagqAg0UQTqFoEmikDdItBEEahb0uczDkiggbYfvQLPpM9nBhpIKFShJQJ1S/p8BApOOgDp8xEoOOkApM9HoOCkA5A+H4GCkw5A+nwECi71y31HQ+8A38xAwwiFKrQkBih6BZ5Jn49AwUkHIH0+AgUnHYD0+QgUnHQA0ucjUHDSAUifj0DBSQcgfT4b0CBCoQotEahb0ucjUHDSAUifj0DBSQcgfT4CBSf9V4nS5yNQcNIBSJ/PCjSEUKhCSwTqlvT5dhHo+eO9+6/rbwJson370SvwTPp8Owj04quD7OTB2+q7AJto3370CjyTPt8OAj1/8ja7+Lo+hAbYRPv2o1fgmfT5dhBoewS9mRdvIha0Auiz1FPMz3If9OL5XnOG5xE09QBjWY+gAQ6hMTDqspzivzjIPtznKb5M+nw7CPRDfvhUp/ky/y0c2X70CjyTPt8OAuURtJv0+UaA+guNw3GY5T7oh729e/UBlEBTDzDWDgLt5b2BY9uPXoFn0ucjUHDSAUifj0DBSQcgZj6TtRGg3kKhCi0RqFti5jNRM76BLYEGSQwAQ9LnI1Bw0gFIn49AwUkHIH0+AgUnHYD0+UaB+gqFKrREoG5Jn49AwUkHIH0+AgUnHYD0+QgUnHQA0ucbB+opFKrQEoG6JX0+AgUnHYD0+QgUnHQA0ucjUHDSAUifj0DBSQcgfT4CBScdgND5WnbjQP2EQhVaIlC3hM5HoHVeG+ey/egVeCZmvrmvByVQv8QAMCRmvtZZXxyBYhMDwJDA+Qi0m9fGuWw/egWeSZ/PBaiXUKhCSwTqlvT5CBScdADS5yNQcKYV3ECv2DECTRSBukWgiSJQtwg0UQTqFoEmikDdkgPU4MwFqI9QqEJLBOoWgSaKQN2SA9QQgWIjUM8IFBuBekag2AjUMwLFRqCe7TrQVN1IPcBSMv1D9oBGnShMPIK6tY4jqMchFKrQEoG6RaCJEg9UiFACTRSBukWgiSJQtwg0UQTqFoEmikDdItBEEahbBJooAnWLQBNFoG6JAerx1jdeQqEKLRGoWwSaKPlAZQgVA9QUgWIjUM8IFBuBekag2AjUMwLFRqDT63EjUGwEOr05QGcLhSq0tACgIoRKBNqLQLERqGcEio1APSNQbATqllEYgWIjULcI1NDc7XLefsPyG0KESgGq0ipzBTpXKFShJQJ1SyjQSZ80R6AeEeisCLRu5ma5b79hOYE6RqDYCNQzAsVmBSpA6HqAzhQKVWiJQN0i0EQRqFsEmqhlAE0vlEATRaBuiQTaxeYOdJ5QqEJLBOoWgSaKQN0SCbQbgWIbAZpcKIEmikDdWhPQWUKhCi0RqFsEmqh0QPvoxoCmFkqgiTIDPdlTvai+89h1hgg0aFOAzhEah+Mw+xH0w4O31Vc++85p+w3LCdSxnQR68dVB/aXPvnPafsPyG0KEEmiirEBPflL8dTMv1RwtUPQEC8/pk+aaoo7mlQ1o5wAKP4CYDgA8gjo26Qg64xCKp6jPBrS9B0qgBJooG9BXT9uvvXaeQ+NA0wol0ERZgH76rj3DB/v3MTkjUM+mAZ0uFE9RnwVo9y6oBKBJhRJooqT8qpNA3fO7Ln6mUKhCSwTqFoEmikDdkgR00KZO898INEwuQFMKlQm0crap0E0BOlUoVKElAnWLQBNFoG7JBFpFoEKAJhS6PqAThUIVWiJQtwg0UQmAap25AU0nlEATRaBuiQPauyZ+FtBpQqEKLS3rFE+gTQTaz29vjkegU9rCRaBigCYTukagk4RCFVoiULdkAd0qJdDjzWZz5aUZztnnlv/oUA/o2a3iN7pXj4Y/F2hXGnMFmkoogeo7VlhON4+8ENrqAr3cv278uUC7UuXzKJ5Ae1XI5gKdIlTP4nK/oHl47U1IlN26QD8+NP8fIcwOLSLQYAkAerf66uzz32w2yunlfnkOVn9fL0/x9aLiDD3xYNs/gkYBqo1AXdOwSgc0P7tvSqJnt64eqVNwcRo+vvZG/a2OeDnQelFxd/Ts1jShvfugp7p7nwCgGmbOQBMJlQnU59VME4UaBalHSdcreDnBwlAus35w1F1028jLAejHh83LXuEPkgg0UBKAFnaqw2Ou8Lg0dLc+2OXL60XZYUF5JtCRwuxQ4zVzE4CmESoNaM/WfKDuQq06coUN0OrxUhdo+xAqPwyaz9KjQKsHSdoTvfceLSPQQFmBdvVhgVYncgW0OMXfPjq98rL7X4pTfPd5UtsjcQFAjRGoR7ojaE0PfAQ9VPjUo6D2QVJ+vMxJ1g+YigdJ5aIC1tQn7lugx819UN39hKA7VHMkJVCPEgIt2dytnmZSctRzSqXa3tNMatHpyG+d7ECjPQ+q8gGaROhagToLHYPk+ytNU0v7XTyBatoVoO0zTcOTfNg9OmwK0BRClwb02RqBlr/5//jwruaX8oF36eAcT6AeDYG29MaAugqF6HNI/yh++EA+9D4l0IANgHbkEWiYCNSjXTmCNqf47Hjw8qnwe7XfJKAJhC4MqPt90EUBLZ6o2jzKjodPVoXep9sRqEc+j+L9gP6fPhhQc4F36SACnZX59aDOQB2F6lno/3kINL5QAtUWGegSrkki0E71y+007w8aHegGD9R2TRK68Q/y6hV1Nmm1n8NVfdUA7f5Q8yNj+YwSF2jE38UXdQ+EE4+g0Q+hko6gg4Oex8vtFnYEJVBTkoC+2zbq8YJlb6CbYbj7oLGuSdJEoJMSBHTwT4MDWr9YZAEPknYdaL9dAWor6A7VNBVobKErBuomVM9iFtDtqzutV3sSqFuCgNYPfto/FwZ0uwlAjzebR8Pfw6OA+jxIii1UFNBn7xYB9OzOm+zyVy/LKznP/ubnV49Oi/uPCmS+8MrL6iLP6vv6hyxAD6/96eEj/bOhwXZvJwKd1+jTTP0fxgId1gDNcSqkh3ez4+vFGzt8/NlR8eXtI/WE0ek19Z/yPxXQ6qvh245sPQ+qngoFX9Wpp0Wgbmk5CT2CZsc5zbsff/pSySwOkz8tL1K+fVSe1RXYfFn+TfsVgc6LQGcAPbvzX8UZXl3LWdg7u6VO7AronTelVHWYrcCWX9mAZsfqFK9eD4oEqm860LhCpQDVJvVppstf/fbOG3VwzNpHQuUpfd4RtHo9qM4ngUZd28SkAs0fdOeY2juap+oNHIov1X3Q/K/BfdAxoOaC7lBNBDq9xpZYoNUbNlXn9Uy9e5j9UbwT0EhvfdMXNgNoVKECgT6TATTm7+IjA/V7molARQDtHDib1gHU93nQnQfaRqDY+6C6F4TX2YDGFCoGaOdyzWYZgUKANsDqTSNQhxpR0oB2ItAdBhr0QxTcfaa/7Djme9RvRaCepQP6XtvaXg86G2hEoQSqjUAJ1CERQDfRgMZ/FE+gk+t/iILlaZBAPiUB1ea/S/v1ngOdDTSeUGFAh7JMTyQT6KxuEKhXQ1ipgGp+1bkOoJ1vCDRAM4E6376eRe/A2RxK+TRTfKEEmhroSGH2or4lA30fa4Cx5AOd9VmyBOrYSoG6374IoHHefrFU1bFFoAESCbS8zLh8NXL7R7NceRv75M7+m4fdvdx/pH+PuyA7sawG2uDyARpLKIEagA5rgVaXGWfVhcfNlR3VcnWR5+hhdfvtF8tbQAKtTRGof91XNBl/JIjPWUfQ6jLj9sLj6tq4anlteBLQHHb0y44J1L0+KuFAq8uM2wuPq6uLq+X1NUrOQLPDQqf2vW9m7EvnvIBGEmoEGlmoEdUcoFNWPAtoltVn9Kx4kFQdQevlDx9NO8WrO6E5au0HJk/bjdMi0ABJBFpdZpxtX13cXn48/hGfS3+aiUCrJAKtT+HlNcaX+82j+Gr58Wbzw5+PPIwnULfWCHTS7RuBRv1d/LG6fS1p/z3YbeuaeAL1LxXQzoGzCQa0+azOok/f7t07kA80jlAzUCFCpwOddvsSgLafdlx8++pF9uHB29BANaAINEC7B/Ti69ed/+a9A+scgHblEahjyYG+xwPNTq+8bE/x50/+UJ3ib+YFXOVwhhpoZ1n9WXIOQJGjjZb/c0Rfp/bD4UyPdo0+g4wS99OO+y8JPX/8Ikca/BRfpQ589cFPd4q/0fycjEOo5Qga/RCqfxNb4w8HOYCmvy5+kMJ58VX9KGni5oxGoAHqKpsKdOq69Ej0tx0H6MW/IoF2I9CZiQK6ab+EPg/afgzNK+QpvpsG6I32NU8E6tREoJNvXwTQ/sfQXDzfu988kPfbe/aGQFt3SwAqQuguAI3zKR/Dln4EXSDQ6bdvAqr5Vef6gGreGGNJ90FFCE0GdHDbOKCpPobGF2gMoeKAbiObBHTG+kQATfQxNL6P4gn0nWCg/Zckt985XuQZ/+V2bpd8LAuogHP8FKBzbn82UM9SAN0W5fm7eAJVCQRaftrxb28fFR9i/PHh5of/VFyYdOfX6jWd7aclWa8+7gA9rq5vgp/ix4D2f3YRQNMLTQV0WAO0/LTj/8xJqg8xVld7lFfO3brb+by502t/sl593AI9LV6Vfz1L8CCJQP2bAHTW7ZuA2k7xxacdVxdzquvl2g+OLf9qWZqvPm6Aqgvmig/1zqJc1Rno7RfBQFt4Y0BjCQ1xVeesFc8BWnzacU2yON9vAb1TSrNdfdx5d7tH1evtojwPWqiqaBGoewGAzlvxHKDFpx2PHkHtVx/3gRYHz3hP1IsH2iYFqDGJQItPO64htvdBG6DlZx7/h/Xq484p/lFW/hr+cFlv3BAHqOnf/70Uoc5AZ97+LKDKXQ00f/z9l1tH0OpRvPXq4/ZBUn70LO6Cnmof8s/dby4R6IRM732TDOiE38XPeQPGztNMh+oZpst97RuLhH93u07rABpJqC/Ques1Ae0cOJt0QC/3N/r3rHEHam3udmkiUETSgc5t+e8sEhqoXpoD0LRCCTRkQd+4gUBVjkBn376exeAe7uKB3ug+AVrlCzTUbEUGZy5AkwpNAzTlVZ39Zm/YVjpPBBogN6Dzbz+guUklvg9a6BIF1JAT0CQP5KsINFwdUgQ6o/lAPVYKVWgpMdCi9QBNeJInUFwrAppOKIHiWsLbL64CqM/tQxVaSgnU9t5M7c8sC2gyoQQavlUCTSXUAajX7UMVWuIp3nFGw3ICBSfiV52aN25of1KEzwlAEwk1/QsR6JyadwZrlpgAEKhj40A9bz9RSU/xIYBCBhs2AWgUoQNvBBokI6g1AY0hdDpQzxVCFVoiULcmAU1xlidQbHOBoucam49AwRGo33x6oAmEEmiwtKpWBjS+0DGg3refqIUDDTjWVlvGpgKNLnTXgQZM+9lwpjlGgCLmKz87busT5EzzGYEiJptTDTT1HHNLdh906+g37wgafCqV9ug3+Qga+xA6cgT1v/1EJXvBslyg2qYDjSyUQMOlgzULaMihRpoBNK5QAsW2RqBRhdqBBrj9RC0ZKGCMHJVeFoEmikB7hQUaUyiBYpsBFDZLAcv3edDYQq1AQ9x+ogjUUBig8YQSaJhMrqYDDTWRW3OBRhNKoGHaOaCxhBIotslA0QNtNR9oJKEEim3FQOMItQENcvuJWipQ9Dzb+QCNIpRAsa0aaAyhBIptIlDgJPPfAjypUALFtnKgeKEEim0aUPQ0w3yBwoVagIa5/UQRqFveQNFECRTbJKDoYTQRaKII1K0AQLFECTRY3ld1BpzF1ABTEKBIoQQarB0GChRKoNgmAEWPoi0QUJxQAsW2K0BhQgkUmztQ9CR1rSXbO0BPBooSagYa6PYTRaCmWkybilYYoCCiBIrNGSh6kE4E2r39RBHoeKGBQogSaMB0H8lt/NGkd0GLP8MDBQgl0IB5AA06h6bGDhZoeKEEik0c0E4E+o5AHYGixyjafuMGBNDgQgkUm0SgqEfxEKEEis0NKHqKfgTavf1EEai2nh8M0MBCCRTbDgINK5RAsTkBRQ9haBtoFxiBgrMAPdnb27v/uvomzFaakwK08/Rnk+4I2j5dSqDQLEBfveh8E2YrzckEWv5NoO8kAv303UHnuzBb2WnGp3wEn0FTn00FdPhyu/ctYQKFZgZ68Tw/xRcH0Zt5wVe89SFcLh/kFXwGTf1P4Cq/a4B2lr+vftAHaIztyRb7CV5VZqDnXxx0jqJh/m+4Veeg6HAEhYxgrj3GaU7xCzqChrr9RI08im/uhwbazH7LBbqg+6Chbj9RSYF2kgV0TY/iQ91+osxAPzx4m336vZynmdATdLMD7QKT4nMHgarnQe81D+QDbaYxWUD7LftXnaFuP1FpfpM05CYRaI1I4ItFhk8eEWjIZgANO4BTBNq9/UQt5nfx6AE6beNZ9guWQ91+oqQANa0gxQuZYgANPjaBYpMEdHvJsq/qDHX7iVoKUPT6bRHoOwLdLaCAIQkUm1CgmOviEZMSKDZhQHsvCF32ezOFuv1ELQQoevXbIa9JwkxMoNiEAe0VFihoSALFJhpoyDewRQ1JoNjsQNFr79Qh1NW03PeoD3X7iSLQrTpveQMAipubQLGJAfrO8A53xp+V4ZNAwUkCqmu5nzQX6vYTRaDDUEdQ6NAEis0KFL3yrTBAoSMTaKDNNG+/YbmQMzyBQhVaIlC3vIGiByRQbGsHCh+QQLHZgKLXba7zcYjGn5Dhk0DBrRtohFEJFJtMoG1eQGMMSKDYxADt/Kqzmw9Q6Lx1BIptxUCh4zYRKDYpQE2e5gOFjdqPQLFZgKJX3Ss4UNikW6H/haAKLRGoW3OBRhuQQMHbb1hOoI4RKHj7DctlvFRkNlDgnFsRKHj7DcuXDRQ45nYECt5+w/JFn+JjDkig4O03LBficxbQqAMSKHj7DcuFnOEJFKrQEoFuFQ4obkZduw40VXE+X248034y+4w63nrjEdSt6UfQqOPxCAre/PUBjTrdOwIFb/7qgEYdTkWg4O03LF8o0KizFREoePsNywnUMQIFb79huRCfE4FGHa2MQMHbb1i+SKBRJ6siUPD2G5aneZ5+iGwKUOyEhggUvP2G5QTqGIGCt9+wfIGn+KhzNREoePsNy5cHNOpYbQQK3n7D8sUBjTpVJwIFb79hOYE6RqDg7TcsTwNU84nHxp+U4ZNA0dtvWL4woOC5LBEoePsNy5d1io86Uj8CBW+/YbkUoKb5CBQcgbrlBDTqRFsRKHj7DcuXBDTqQNsRKHj70SvwzAFo1HkGESh4+9Er8IxAE0Wgbo0DjTrOMAIFbz96BZ6NAo06jSYCBW8/egWeEWiiCNStMaBRh9FFoODtR6/AMwJNFIG6NQI06izaCBS8/egVeEagiSJQt+xAo46ij0DB249egWcEmigCdcsKNOokhggUvP3oFXhGoIkiULdsQKMOYopAwduPXoFnBJooAnXLAjTqHMYIFLz96BV4RqCJIlC3CDRRBOoWgSbKCvTTty/qL8Gbv2CgUccwt5NAT/YItIpAE2UDev7PvyTQKgJNlAXop+/+vTzF38yLNc/S4ifKgbMAPXnK+6BNPIImygz0/MlbAm0yAo06haXdA3qyp3pafQfefAL1bfeAZnyaqROBJopA3SLQRPE3SW5Jn49A0duPXoFn0ucjUPT2o1fgmfT5CBS9/egVeCZ9PgJFbz96BZ5Jn49A0duPXoFn0ucjUPT2o1fgmfT5CBS9/egVeCZ9PgJFbz96BZ5Jn49A0duPXoFn0ucjUPT2o1fgmfT5CBS9/egVeCZ9PgJFbz96BZ5Jn49A0duPXoFn0ucjUPT2o1fgmfT5CBS9/egVeCZ9PgJFbz96BZ5Jn49A0duPXoFn0ucjUPT2o1fgmfT5dh4oOunvDCF9PvkDzoxA3ZI+n/wBZ0agbkmfT/6AMyNQt6TPJ3/AmUkBypg2AmWiI1AmOgJloiNQJjoJQM+/fG39PnHCxinqz9R+J3FWvyQA3U7YXhY2zo4VE+j5k7fZp+8OLp7v3X+tPqHh/usPe8WXuYB84b2DrPpP5ff1D0WcsK6cqxyi/aNZfv54r/38k8iVO/Hfvnxd7Jt8rh//y0G+w86f/EHN1O7KlDOGLCbQHKfav6+eZic/yXfgi+zi69fFl1++Vu9E+uGB+k/5n2ovV189TrKPq7myatJmnGr5xVcH6Q6r5U7875yk2jdqrnsF0Mf1rit35f+knDFkUU/xJ/k/+FP1z5v/Sxf/X8+/zIpzaLkvFYB8Wf5N+1XM+eqqudpJq3Gq5Vk1apqKnVjtMjVFLrb6rvyr3WfpZgxYVKDnT/63OMPv7d0r7eXnoXvF/9PViau8t9fu7/KrmPO1gz6u7nBUk1bjVMvzA1d5qk8zm9qJNcnyTlMfaLEr084YsKhA8/tOT95W/7+u7ZXnJVlH0GqudtJqnHr58xcJT5/FThw9gqadMWBxH8WfqE8Nae9o5n917jjlfw3ug6bZxdVc2fY47bzZ+RcH47eDSe3EGmJ7H7QBWu7KP6adMVxxgRa7rHqUWezfsUfxiY4Br5pH8flQn75tHsVXy0/29n78y2QPkdVOrIHmc/3d1hG0mjrtjOGS+Dwom9IqTuTmCHTJffp2r3zUtt4IlImOQJnoCJSJjkCZ6Ai01/Fms7ny0vzfzz63/EcGiEC7HV89yrLTzaPUc7AmAu10uV/QPLz2JvUkrI5AO13u362+Ovv8N5uNcnq5v9mow6r6+3p5iq8Xnd3K7w/wYAuOQLudbjYl0bNbV48u969n6n/Z8bU36u+PDx8poPWi4u7o2S0KxUag/dSjpOsVvJzgqTpU5jLrB0fdRbePEs+6ExHooI8Pq8NjrlB5zbtbqMwKoPWi7LCgzLAR6LBcYQO0erzUBdo+hPr4cHOVh1FsBNqpOpEroMUp/vbR6ZWX3f9SnOK7z5Oq+6UMGYF2O1T41KOg9kFSfrzMSdYPmIoHSeWi4qDKJ+7REWiv4/LuZfk0k7qHqZ5TKtX2nmZSi05HfuvEQkSg2nhklBKBaiNQKRGoNgKVEoEy0REoEx2BMtERKBMdgTLRESgTHYEy0f0/XUb59UjzCckAAAAASUVORK5CYII=" /><!-- --></p>
</div>
</div>
<div id="混合图" class="section level3">
<h3>混合图</h3>
<p>最后是混合图，根据自己想要的图，可以自行添加。相信这个代码简单的图给大家学术作图上省了不少时间。</p>
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>() <span class="sc">+</span></span>
<span id="cb34-3"><a href="#cb34-3" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_half_boxplot</span>(</span>
<span id="cb34-4"><a href="#cb34-4" aria-hidden="true" tabindex="-1"></a>        <span class="at">data =</span> iris <span class="sc">%&gt;%</span> <span class="fu">filter</span>(Species<span class="sc">==</span><span class="st">&quot;setosa&quot;</span>), </span>
<span id="cb34-5"><a href="#cb34-5" aria-hidden="true" tabindex="-1"></a>        <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Sepal.Length, <span class="at">fill =</span> Species), <span class="at">outlier.color =</span> <span class="cn">NA</span>) <span class="sc">+</span></span>
<span id="cb34-6"><a href="#cb34-6" aria-hidden="true" tabindex="-1"></a>    ggbeeswarm<span class="sc">::</span><span class="fu">geom_beeswarm</span>(</span>
<span id="cb34-7"><a href="#cb34-7" aria-hidden="true" tabindex="-1"></a>        <span class="at">data =</span> iris <span class="sc">%&gt;%</span> <span class="fu">filter</span>(Species<span class="sc">==</span><span class="st">&quot;setosa&quot;</span>),</span>
<span id="cb34-8"><a href="#cb34-8" aria-hidden="true" tabindex="-1"></a>        <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Sepal.Length, <span class="at">fill =</span> Species, <span class="at">color =</span> Species), <span class="at">beeswarmArgs=</span><span class="fu">list</span>(<span class="at">side=</span><span class="sc">+</span><span class="dv">1</span>)</span>
<span id="cb34-9"><a href="#cb34-9" aria-hidden="true" tabindex="-1"></a>    ) <span class="sc">+</span></span>
<span id="cb34-10"><a href="#cb34-10" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_half_violin</span>(</span>
<span id="cb34-11"><a href="#cb34-11" aria-hidden="true" tabindex="-1"></a>        <span class="at">data =</span> iris <span class="sc">%&gt;%</span> <span class="fu">filter</span>(Species<span class="sc">==</span><span class="st">&quot;versicolor&quot;</span>), </span>
<span id="cb34-12"><a href="#cb34-12" aria-hidden="true" tabindex="-1"></a>        <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Sepal.Length, <span class="at">fill =</span> Species), <span class="at">side=</span><span class="st">&quot;r&quot;</span>) <span class="sc">+</span></span>
<span id="cb34-13"><a href="#cb34-13" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_half_dotplot</span>(</span>
<span id="cb34-14"><a href="#cb34-14" aria-hidden="true" tabindex="-1"></a>        <span class="at">data =</span> iris <span class="sc">%&gt;%</span> <span class="fu">filter</span>(Species<span class="sc">==</span><span class="st">&quot;versicolor&quot;</span>), </span>
<span id="cb34-15"><a href="#cb34-15" aria-hidden="true" tabindex="-1"></a>        <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Sepal.Length, <span class="at">fill =</span> Species), <span class="at">method=</span><span class="st">&quot;histodot&quot;</span>, <span class="at">stackdir=</span><span class="st">&quot;down&quot;</span>) <span class="sc">+</span></span>
<span id="cb34-16"><a href="#cb34-16" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_half_boxplot</span>(</span>
<span id="cb34-17"><a href="#cb34-17" aria-hidden="true" tabindex="-1"></a>        <span class="at">data =</span> iris <span class="sc">%&gt;%</span> <span class="fu">filter</span>(Species<span class="sc">==</span><span class="st">&quot;virginica&quot;</span>), </span>
<span id="cb34-18"><a href="#cb34-18" aria-hidden="true" tabindex="-1"></a>        <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Sepal.Length, <span class="at">fill =</span> Species), <span class="at">side =</span> <span class="st">&quot;r&quot;</span>, <span class="at">errorbar.draw =</span> <span class="cn">TRUE</span>,</span>
<span id="cb34-19"><a href="#cb34-19" aria-hidden="true" tabindex="-1"></a>        <span class="at">outlier.color =</span> <span class="cn">NA</span>) <span class="sc">+</span></span>
<span id="cb34-20"><a href="#cb34-20" aria-hidden="true" tabindex="-1"></a>    <span class="fu">geom_half_point</span>(</span>
<span id="cb34-21"><a href="#cb34-21" aria-hidden="true" tabindex="-1"></a>        <span class="at">data =</span> iris <span class="sc">%&gt;%</span> <span class="fu">filter</span>(Species<span class="sc">==</span><span class="st">&quot;virginica&quot;</span>), </span>
<span id="cb34-22"><a href="#cb34-22" aria-hidden="true" tabindex="-1"></a>        <span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Sepal.Length, <span class="at">fill =</span> Species, <span class="at">color =</span> Species), <span class="at">side =</span> <span class="st">&quot;l&quot;</span>) <span class="sc">+</span></span>
<span id="cb34-23"><a href="#cb34-23" aria-hidden="true" tabindex="-1"></a>    <span class="fu">scale_fill_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;setosa&quot;</span> <span class="ot">=</span> <span class="st">&quot;#cba1d2&quot;</span>, <span class="st">&quot;versicolor&quot;</span><span class="ot">=</span><span class="st">&quot;#7067CF&quot;</span>,<span class="st">&quot;virginica&quot;</span><span class="ot">=</span><span class="st">&quot;#B7C0EE&quot;</span>)) <span class="sc">+</span></span>
<span id="cb34-24"><a href="#cb34-24" aria-hidden="true" tabindex="-1"></a>    <span class="fu">scale_color_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;setosa&quot;</span> <span class="ot">=</span> <span class="st">&quot;#cba1d2&quot;</span>, <span class="st">&quot;versicolor&quot;</span><span class="ot">=</span><span class="st">&quot;#7067CF&quot;</span>,<span class="st">&quot;virginica&quot;</span><span class="ot">=</span><span class="st">&quot;#B7C0EE&quot;</span>)) <span class="sc">+</span></span>
<span id="cb34-25"><a href="#cb34-25" aria-hidden="true" tabindex="-1"></a>    <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span>   </span>
<span id="cb34-26"><a href="#cb34-26" aria-hidden="true" tabindex="-1"></a>    <span class="fu">ggsave</span>(<span class="st">&#39;综合图.pdf&#39;</span>, <span class="at">width =</span> <span class="dv">6</span>, <span class="at">height =</span> <span class="dv">8</span>)</span></code></pre></div>
<pre><code>## Warning: Ignoring unknown parameters: beeswarmArgs</code></pre>
<pre><code>## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.</code></pre>
<p><img 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" /><!-- --></p>
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================================================
FILE: 2020年/2020.11.14gghalves/gghalves.rmd
================================================
---
title: "Vignette Title"
author: "Vignette Author"
date: "`r Sys.Date()`"
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
vignette: >
  %\VignetteIndexEntry{Vignette Title}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
# gghalves包

## 介绍

`gghalves`可以通过`ggplot2`轻松地编写自己想要的一半一半（half-half plots）的图片。比如：在散点旁边显示箱线图、在小提琴图旁边显示点图。


[gghalves](https://github.com/erocoar/gghalves)将`_half_`扩展添加到选定的`geom`。比如：`geom_half_violin()`函数，相当于`geom_violin()`函数的变体，该函数主要作用就是展示一半的小提琴图，然后与其他图形组合。还包含以下函数：

- geom_half_boxplot
- geom_half_violin
- geom_half_point

## 安装

gghalves通过GitHub安装:
```
if (!require(devtools)) {
    install.packages('devtools')
}
devtools::install_github('erocoar/gghalves')
```


## 函数介绍

```
geom_half_violin(mapping = NULL, data = NULL, stat = "half_ydensity",
  position = "dodge", ..., side = "l", nudge = 0,
  draw_quantiles = NULL, trim = TRUE, scale = "area",
  na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
```
其参数包括：翻译来源[生信玩家](https://blog.csdn.net/weixin_43700050/article/details/107512448)

| 参数 | 解释 |
| --- | --- |
| `mapping` | 通过`aes()`指定图形属性映射。默认为`NULL`，使用`ggplot()`中`aes()`指定的映射。 |
| `data` | 指定数据框。默认为`NULL`，使用`ggplot()`中的数据。 |
| `stat` | 覆盖geom\_density\(\)和stat\_density\(\)之间的默认连接。 |
| `position` | 位置调整，可以是字符串，默认为`"dodge"`，也可以是位置调整函数的调用结果。 |
| `side` | 画半小提琴图的一侧。 “ l”代表左，“ r”代表右，默认为“ l”。 |
| `nudge` | 在小提琴图和分配给x轴上给定因子的空间中间之间添加空间。 |
| `draw_quantiles` | 如果不是`MULL`（默认为`NULL`），在给定的密度估计分位数处绘制水平线。 |
| `trim` | 若为`TRUE`（默认），将小提琴的尾部修整到数据范围。 若为`FALSE`，不修剪尾巴。 |
| `scale` | 如果为`"area"`（默认），则所有小提琴都具有相同的面积（修剪尾部之前）。  
| `na.rm` | 如果为`FALSE`（默认），则会使用警告删除缺失值。如果为`TRUE`，则会自动删除缺少的值。 |
| `show.legend` | 逻辑值，默认为`NA`，若为`FALSE`，不显示该图层的图例；  若为`TRUE`，则显示该图层的图例。  它也可以是带有名称（图形属性）的逻辑向量，用来选择要显示的图形属性。 如`show.legend = c(size = TRUE,color = FALSE)`表示显示`size`对应的图例，而不显示`color`对应的图例。 |
| `inherit.aes` | 默认为`TRUE`，若为`FALSE`，覆盖`ggplot()`中`aes()`默认属性，而不是与他们组合。 |
| `geom` | 覆盖`geom_density()`和`stat_density()`之间的默认连接。 |
| `bw` | 要使用的平滑带宽度。如果是数字，则为平滑内核的标准差。 |
| `adjust` | 多次带宽调整。这使得可以在仍使用带宽估计器的情况下调整带宽。例如，`adjust = 1/2`表示使用默认带宽的一半。 |

## 示例

### 单个函数

我们以iris数据集作为本例数据，先使用单个函数进行绘制。

```{r eval=FALSE, message=FALSE, warning=FALSE, include=TRUE}
if (!require(devtools)) {
	install.packages('devtools')
}
devtools::install_github('erocoar/gghalves')
```


### geom_half_boxplot




```{r}
library(gghalves) # Compose Half-Half Plots Using Your Favourite Geoms # Compose Half-Half Plots Using Your Favourite Geoms

ggplot(iris, aes(x = Species, y = Petal.Width, fill = Species)) + 
	geom_half_boxplot() 
```

默认为箱子在右，使用`center = TRUE`将箱子居中。下面函数参数调整类似，就不再绘制结果了，就把最原始的进行展示。
```{r}
ggplot(iris, aes(x = Species, y = Petal.Width, fill = Species)) + 
	geom_half_boxplot(center = TRUE) 
```

### geom_half_violin


```{r}
ggplot(iris, aes(x = Species, y = Petal.Width, fill = Species)) + 
	geom_half_violin()
```


### geom_half_point

```{r}
ggplot(iris, aes(x = Species, y = Petal.Width, fill = Species)) + 
	geom_half_point()
```

### 综合案例

#### 云雨图

该案例来自**生信玩家**公众号[ggplot扩展包--gghalves](https://blog.csdn.net/weixin_43700050/article/details/107512448)但并没有对代码进行详细解释。

这里小编对代码进行详细解释，喜欢的伙伴，可以按照解释自己理解，并用到自己实际所需的复合图中。

先将数据的统计摘要进行计算存到了`summ_iris`中，包含了均值，标准差，数量标准误差。`iris_plot`为所需数据，这里将`Species`变量设置为因子，因为要用它作为分类变量。
```{r}
library(tidyverse) # Easily Install and Load the 'Tidyverse' # Easily Install and Load the 'Tidyverse'
# 统计摘要
summ_iris <- iris %>% 
	group_by(Species) %>% 
	summarise(
		mean = mean(Sepal.Length),
		sd = sd(Sepal.Length),
		n = n()
	) %>% 
	mutate(se = sd/sqrt(n),
				 Species = factor(Species, levels = c('versicolor', 'setosa', 'virginica')))
summ_iris
# 数据转换  
iris_plot <- iris %>% 
	mutate(Species = factor(Species, levels = c('versicolor', 'setosa', 'virginica')))
head(iris_plot) 
```
接下来进行绘图，我们想要得到`Species`与`Sepal.Length`的关系，其中`Species`为离散变量，`Sepal.Length`为连续变量。并绘制了半边的小提琴图，并将该图往右移了0.15，上下位置不变（`position_nudge(x = .15, y = 0)`），为了后面绘制其他图形留位置。

```{r}
library(gghalves) # Compose Half-Half Plots Using Your Favourite Geoms # Compose Half-Half Plots Using Your Favourite Geoms
library(ggsignif) # Significance Brackets for 'ggplot2'
library(ggsci) # Scientific Journal and Sci-Fi Themed Color Palettes for
'ggplot2'
library(ggpubr) # 'ggplot2' Based Publication Ready Plots
ggplot(iris_plot , aes(x = Species, y = Sepal.Length, fill = Species))+
	geom_half_violin(aes(fill = Species),
									 position = position_nudge(x = .15, y = 0),
									  side = 'r')
```


接下来加入散点图，并使x坐标往左移动0.1（`x = as.numeric(Species)-0.1`），使用`position_jitter`使得重复的点分散开。


```{r}
ggplot(iris_plot , aes(x = Species, y = Sepal.Length, fill = Species))+
	geom_half_violin(aes(fill = Species),
									 position = position_nudge(x = .15, y = 0),
									 side = 'r') +
	geom_point(aes(x = as.numeric(Species)-0.1,
								 y = Sepal.Length,color = Species),
						 position = position_jitter(width = .05),size = .25, shape = 20)
```

在原来基础上加入箱子图，位置放在正中间


```{r}
ggplot(iris_plot , aes(x = Species, y = Sepal.Length, fill = Species))+
	geom_half_violin(aes(fill = Species),
									 position = position_nudge(x = .15, y = 0),
									 adjust=1.5, trim=FALSE, colour=NA, side = 'r') +
	geom_point(aes(x = as.numeric(Species)-0.1,
								 y = Sepal.Length,color = Species),
						 position = position_jitter(width = .05),size = .25, shape = 20) +
	geom_boxplot(aes(x = Species,y = Sepal.Length, fill = Species),
							 outlier.shape = NA,
							 width = .05,
							 color = "black")
```

这里比较有趣的是，作者还通过`geom_point`和`geom_errorbar`加入和汇总信息以及对应的误差项。

```{r}
ggplot(iris_plot , aes(x = Species, y = Sepal.Length, fill = Species))+
	geom_half_violin(aes(fill = Species),
									 position = position_nudge(x = .15, y = 0),
									 adjust=1.5, trim=FALSE, colour=NA, side = 'r') +
	geom_point(aes(x = as.numeric(Species)-0.1,
								 y = Sepal.Length,color = Species),
						 position = position_jitter(width = .05),size = .25, shape = 20) +
	geom_boxplot(aes(x = Species,y = Sepal.Length, fill = Species),
							 outlier.shape = NA,
							 width = .05,
							 color = "black")+
	geom_point(data=summ_iris,
						 aes(x=Species,y = mean,group = Species, color = Species),
						 shape=18,
						 size = 1.5,
						 position = position_nudge(x = .1,y = 0)) +
	geom_errorbar(data = summ_iris,
								aes(x = Species, y = mean, group = Species, colour = Species,
										ymin = mean-se, ymax = mean+se),
								width=.05,
								position=position_nudge(x = .1, y = 0)
	)
```

这里使用`ggsci`包的`scale_color_aaas()`,`scale_fill_aaas()`将尺度的颜色进行改变（非常好用！)在下面展示另外一种配色（`scale_color_jco`）


```{r}
ggplot(iris_plot , aes(x = Species, y = Sepal.Length, fill = Species))+
	geom_half_violin(aes(fill = Species),
									 position = position_nudge(x = .15, y = 0),
									 adjust=1.5, trim=FALSE, colour=NA, side = 'r') +
	geom_point(aes(x = as.numeric(Species)-0.1,
								 y = Sepal.Length,color = Species),
						 position = position_jitter(width = .05),size = .25, shape = 20) +
	geom_boxplot(aes(x = Species,y = Sepal.Length, fill = Species),
							 outlier.shape = NA,
							 width = .05,
							 color = "black")+
	geom_point(data=summ_iris,
						 aes(x=Species,y = mean,group = Species, color = Species),
						 shape=18,
						 size = 1.5,
						 position = position_nudge(x = .1,y = 0)) +
	geom_errorbar(data = summ_iris,
								aes(x = Species, y = mean, group = Species, colour = Species,
										ymin = mean-se, ymax = mean+se),
								width=.05,
								position=position_nudge(x = .1, y = 0)
	) +
	scale_color_aaas() +
	scale_fill_aaas()
```


最后使用`ggpubr`包的`geom_signif`加入显著性结果，`ggsave`保存图片。

```{r}
# 绘图
ggplot(iris_plot , aes(x = Species, y = Sepal.Length, fill = Species))+
	geom_half_violin(aes(fill = Species),
									 position = position_nudge(x = .15, y = 0),
									 adjust=1.5, trim=FALSE, colour=NA, side = 'r') +
	geom_point(aes(x = as.numeric(Species)-0.1,
								 y = Sepal.Length,color = Species),
						 position = position_jitter(width = .05),size = .25, shape = 20) +
	geom_boxplot(aes(x = Species,y = Sepal.Length, fill = Species),
							 outlier.shape = NA,
							 width = .05,
							 color = "black")+
	geom_point(data=summ_iris,
						 aes(x=Species,y = mean,group = Species, color = Species),
						 shape=18,
						 size = 1.5,
						 position = position_nudge(x = .1,y = 0)) +
	geom_errorbar(data = summ_iris,
								aes(x = Species, y = mean, group = Species, colour = Species,
										ymin = mean-se, ymax = mean+se),
								width=.05,
								position=position_nudge(x = .1, y = 0)
	) +
	scale_color_jco() +
	scale_fill_jco() +
	geom_signif(comparisons = list(c("versicolor", "setosa"),
																 c("versicolor", "virginica"),
																 c("setosa", "virginica")),
							y_position = c(8.2, 8.6, 8.4),
							map_signif_level = c("***" = 0.001, "**" = 0.01, "*" = 0.05)) +
	ggsave('云雨图.pdf', width = 6, height = 8)
```

### 混合图

最后是混合图，根据自己想要的图，可以自行添加。相信这个代码简单的图给大家学术作图上省了不少时间。

```{r}
library(tidyverse) # Easily Install and Load the 'Tidyverse' # Easily Install and Load the 'Tidyverse'
ggplot() +
	geom_half_boxplot(
		data = iris %>% filter(Species=="setosa"), 
		aes(x = Species, y = Sepal.Length, fill = Species), outlier.color = NA) +
	ggbeeswarm::geom_beeswarm(
		data = iris %>% filter(Species=="setosa"),
		aes(x = Species, y = Sepal.Length, fill = Species, color = Species), beeswarmArgs=list(side=+1)
	) +
	geom_half_violin(
		data = iris %>% filter(Species=="versicolor"), 
		aes(x = Species, y = Sepal.Length, fill = Species), side="r") +
	geom_half_dotplot(
		data = iris %>% filter(Species=="versicolor"), 
		aes(x = Species, y = Sepal.Length, fill = Species), method="histodot", stackdir="down") +
	geom_half_boxplot(
		data = iris %>% filter(Species=="virginica"), 
		aes(x = Species, y = Sepal.Length, fill = Species), side = "r", errorbar.draw = TRUE,
		outlier.color = NA) +
	geom_half_point(
		data = iris %>% filter(Species=="virginica"), 
		aes(x = Species, y = Sepal.Length, fill = Species, color = Species), side = "l") +
	scale_fill_manual(values = c("setosa" = "#cba1d2", "versicolor"="#7067CF","virginica"="#B7C0EE")) +
	scale_color_manual(values = c("setosa" = "#cba1d2", "versicolor"="#7067CF","virginica"="#B7C0EE")) +
	theme(legend.position = "none") +	
	ggsave('综合图.pdf', width = 8, height = 6)
```
### 其他参考资料

[Using gghalves--Frederik Tiedemann](https://erocoar.github.io/gghalves/)

[gghalves: Compose Half-Half Plots Using Your Favourite Geoms](https://cran.r-project.org/web/packages/gghalves/index.html)

[CRAN](https://rdrr.io/cran/gghalves/f/vignettes/gghalves.Rmd)




================================================
FILE: 2020年/2020.11.16flexdashboard/flexdashboard.md
================================================
## 简介

- 使用R Markdown可以将一组相关的数据可视化发布为仪表板。

- 支持多种组件，包括htmlwidgets; 基本，晶格和网格图形；表格数据 量表和值箱；和文字注释。

- 灵活且易于指定基于行和列的布局。可以智能地调整组件的大小以填充浏览器并适合在移动设备上显示。

- 演示图板布局，用于呈现可视化效果序列和相关评论。

- 使用Shiny动态驱动可视化。

去年师兄用这个包做了一个不错的应用（企业可靠性统计方向的项目）。今天正好需要学习下数据可视化仪表盘的制作。尝试了下，还不错，比Tableau还要优秀。最近出一期入门，有机会可以把自己的例子介绍一下。

下载方式如下：
```
install.packages("flexdashboard")
```

## 官网案例分享

今天分享下官网的一些[小案例](https://rmarkdown.rstudio.com/flexdashboard/examples.html "小案例")。主要是截图呈现，当然你可以把他的[github](https://github.com/rstudio/flexdashboard "github")克隆到本地，有个文件夹专门放例子的代码，尝试修改代码，应用到自己实际项目中。

### 2008年NBA运动员得分情况



![](https://imgkr2.cn-bj.ufileos.com/20176c63-2213-4ead-b45c-fa378b217eef.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=w%252FdtpUnaHBk1BklxKe6%252BPq1ry70%253D&Expires=1605537297)


### 各种散点图

![](https://imgkr2.cn-bj.ufileos.com/bde1939f-1930-49c4-bb44-81d716a09ce1.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=jLDVKbiSgrpJXlKfSDVTeK7sClo%253D&Expires=1605537248)


![](https://imgkr2.cn-bj.ufileos.com/42b53872-fcfe-4603-ba3c-9ee6b775ca96.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=Z7jj50JIv3HzDzImGtaiUVT2szk%253D&Expires=1605537256)

### 其他例子


![](https://imgkr2.cn-bj.ufileos.com/6f819551-f375-4cb5-8d5b-b4a5a03a551a.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=zQoPXU9PUkadIF6hYSYn2KXFb5U%253D&Expires=1605537340)


![](https://imgkr2.cn-bj.ufileos.com/ff9d45a7-a1ab-4d93-9a09-427b4eaf29e6.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=ig0Bp6ORQ8cXVxHXOUpE2ZY7rbw%253D&Expires=1605537186)


![](https://imgkr2.cn-bj.ufileos.com/2e94d2a6-85f7-4bab-8402-929a47d70de0.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=ICP%252FysUWGCUXKEhwXpUmYtLwZW0%253D&Expires=1605537355)

当然这些都是可以交互的。大家可以去上面的网站访问下。



================================================
FILE: 2020年/2020.11.16flexdashboard/test.html
================================================
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>

<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />



<title>庄闪闪的成长手册</title>

<meta property="og:title" content="庄闪闪的成长手册" />

<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />

<link href="data:image/x-icon;" rel="icon" type="image/x-icon">


<script type="text/javascript">
window.FlexDashboardComponents = [];
</script>

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ke2C8vfjgV/Y9kkRQOJIn/xM9INZSpiBnqX0Q9GlQPpPKAyO5y+W5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W/N6l54qOynCqD3DpWQ+mpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE+7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv/8lbTIkkYpqKM9VOhp65ktYk+Q46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI+ejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5+7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu+vtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY/iOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v+6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ/qDQK+bfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam+WpHG+0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3+J1eCBay8IYZ0wQRKGAqvCuZ/UgbQPyllosq+XtfKIZOzmeJqRazpmmoP/76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t/I4Jktu0XSgifO2ozFOiwd/0SssJDn0dn4xqk4GDTTKX73/wQyBLdqgJ+Wx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy/qqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh/wkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml/R4yrzow1Q2A5G+kzo/RhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj/UMMc34IBqTKLDTp76WzL/dMjCxK7MjhiGjeYAC/kj/jY/Rde7hpSM1xChrog6yZ7OWTuD56xBJnGFE+pT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G+9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36/dBySis4m9/DR8izaLJW6bWCkVgm5T+ius3ZXq4xI+GnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te/r4dPYMCl5qtiHNTn+TPbh1jCBHH+dMJNhwNgs3nT+OhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF+uRIWyXjQMAs2chdpaKPNaB+kSezYt0+CA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6/TzoA1/ZBG9bIUVHLAbi/kdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh+epgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF+zkJHIA7PwCDk1gGVmGUZSSoPhNf+Tklauz98QofOlCIQ/tCD4dosHYPqtPCXB3agggQQIqQJsSkB+qn0rkQ1toJjON/OtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU+TQ6NIw3ej+AtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb++W6Uk4q6F7/rg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK+EfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l+wM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l+DMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg+EWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb+Mw76Qy29iQ5up/X7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa/xnsMYcIO/vEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz/Chp/Vlpj2P7jJQmQRwGnltkTV5dbF9fE3/fxoSqTROgq9wFUlbuYzYcasE0ouzBo+dDCDzxKAfhbAZYxQiHrLzV2iVexnDX/QnT1fsT/xuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR+CtGdkPwYN2o7DOw/VGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO/f9Qua+pDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD/prQ84B1pVGkIpVUAHCG+iz3Bn3qm2AVrYcYWhock4jso5+J7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk+UILT7+VoX5mdxxA5fS42gISQVi/HTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8/6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk/wdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt+Su9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ/BfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk/gc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B//lHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO+CvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd/p1gi/07h8qfwHrByuSxglc9cI4QIg2oqvC/qm0i7tjPLTgDhoWTAKDO2ONW5oe+/eKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2/j4ODUwRkqrtBBCrDsDpt8jhZdXoy/1BCqw3sSGhgGGy0a5Jw6BP/TExoCmNFYjZl248A0osgPyGEmRA+fAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd/ocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE/EZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ/Ugd/JZQK8lvAm43uDRAbyW8gZ+ZGq0EVerVGUKUSm/Idn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ+QATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm+oOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724+zUQ7+vkCpZB+pGA562hYQiDxHVWOq0oDQl/QsoiY+cuI7iWq/ZIBtHcXJ7kks+h2fCNUPA82BzjnqktNts+RLdk1VSu+tqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy/44XYXdI5noQoRcvjZ1RMPACRqYg2V1+OwOepcOknRLLFdYgTkT5UApt/JhLM3jeFYprZV+Zow2g8fP+U68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr/A1SY9dXFz4RjzoU9ExfJCmx/I9FKEGT3n2cmzl2X42L3Jh+AbQq6sA+Ss1kitoa4TAYgKHaoybHUDJ51oETdeI/9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB+Un44zExFE4vLytcu5NwpWrUxO/0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL+BDqMyaN3RJPh/anbx+Iv+qgQdAa3M9Z5JmvYlh4qop+Ho1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs+gs37sFvi0PPVvA5dnCBgILTwoKd/+DoL9F6inlM7H4rOTzD79KJgKlZO/Zgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno+gBoKVXgIL/VI8dB1O5o/R3Suez/gD7M781ShjKpIIORM/nxG+jjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4/QftDbEn+AucIr1oxrLabRj9q4ae0+fXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd+eNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz/6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT+mAUmiYbV3YQVqFVp9dorv+TsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp+xJyYLv1OsxQCZwTB4a8BZ/5EdxTBJthApqyfd9u3ifr/WILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj/qn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9+W8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4/YtowhEmTs0vrvlD/CrisnoBNDAcUi/teY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O/4DcgV/dZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk+tgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb/n7qwhvGnrHuf5bX6Vh/n3xffU3PeHdR+FA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E+YbfL0adwNtHP7dT7t7b46DVZIkzaRJOM+S6KcrzYVg+T3wSRFRQashjfU18NutrKa/7PXbtuJvpIjbgPeqd+pjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir/8tNXJ/OsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG+FZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx/GdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD/Qi/EmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4/asthNMK5UQKCOhU97oaOYNGsTah+jfCKsZnTRn5TbhFX8ghg8CBYt/BjeYYYUrtUZ5jVij/op7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM+3SW6Opll/wgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy+QbSKVJcqkia+GvDefFwMOmgnD7h81TUtMn+mRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d/QK7Cr4uoCeOQ7/8JfKT77KiDzLImESHw/0wf73QeHu74hxv7uihi4fTX+XEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo/oVH5ww5OzLFsiTPDns7fS6EURSSWd/92BxMYQ8sBaH+j+wthQPdVgDGpTfi+JQIWMD8xKqULliRH01rTeyF8x8q/GBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE+/7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV/yTDRRP8Y2ww5RO6d2D94o+6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt+kqdae76ViWe3STan69yaF9+fESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw/A1zEdjWquIsQXXGIVEH0thC5M+W9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF+RVmI8L4HUYk4x+67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8/p5qIQrEo/H+1l/0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud+tJUfdaZ4CWNijzZtlRa8+CkmO/EwHYfPZFU/hzjFWH7vnzHRMo+aF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce/+/9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs/GWJS6SwEN/ULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e+G2zor8l+YaMxSEomDdLHGcD6YVQPegTaA74H8+V4WvJkFUrjMLGLlvSZQWvi8/QA7yzQ8GPno//5SJHRP/OqKObPCo81s/+6WgLqykYpGAgQZhVDEBPXWgU/WzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M+GWn6ASobIWC+LbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg/kpf3+CnAXKiMgIE8Jk/Mhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo+RJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B+SkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE+VMd3b1fhCynD0pQNhCG6/WCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp/YbHjo++7/Wj8S4YNa+ZdqAw1hDrKWFXv9+zaXpf8ZTDSbiqsxnwN/CzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m/NCW/HILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO+5EJ7Z6bCiRoPedRZ/P0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn/LvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl+11PoFYnNv2HwAODeNRkHj+8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij+bsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur/eWHjiB7SOVdqMAVmpBvfRiebsFjger7DC+8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l+kXRZ0KLZaGsFSIdQz/HXDxf3/TE30+DgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH+RptvRMVRaahu4cySjS3P5wxAUCPkmn+rhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik/zbrTQk5KmgxCg/f45L0jywebOWUYFJQAJia7XzCV0x89rpp/f3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl+5tfUWZNwBpEPXghzbBggYCw/dhy0ntds2yeHCDKkF/YxQjNIL/F/37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD+qNOhwMlfARQUdJ2tUX+MNJqOwIciWalZsmEjt07tfa8ma4cji9sqz+Q9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe+jGDiNJQLWnfx+drTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf/bkvo8PLVBsZl152y5S8+HRDfZIMCbYZ1WDp4yrdchOJw8k6R+/2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB+M4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5/iYp3ZdrCf7fL+en+sIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv+NRiJc8JAKqqgCA/PNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN/hCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj+bYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5/zBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8/i+jHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2+JrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk+Mz7wwstg6RFZb+TZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm/7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk+9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx+whVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC+XYuqMBMUun5YezKbRKmEPwuK+CLzijPEQgfhahQswBBLfg/GBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX+RCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk/4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug/AbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM+Zu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX/pcsfwXbLze2+D+u33OGBoJyAAL3jn3RuEcdp5If8O+a4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT++tm+haBCikRUUMrMhYKZJKYoVuv/bsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV/DMUxd9uFZmBfKXMCn/SqkWJyKPnT6lq+4zBZni6fYRByJn6OK+OgPBGRAJluwGSk4wxjOOzyce/PKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO/cEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm+bddRQu5F9s0XftGEJ9JSuSk+ZachCbdU45fEqbugzTIUokwoAKvpUQF/CvLbWW5BNQFqFkJg2f30E/48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J+1pT0tLkvFVZaNzfhs/Kd3+A9YsImlO4XK4vpCo/elHQi/9gkFg07xxnuXLt21unCIpDV+bbRxb7FC6nWYTsMFF8+1LUg4JFjVt3vqbuhHmDKbgQ4e+RGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB/KVijd1ARWkFEf3yiUw1v/WaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G+ZManTqDLPjyrOse7WiiwOJCG+J0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj/6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j/N46f+S2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb/0jQbaHJ2YRK8A+ls6WMhWmpCXYG5jqapGs5/eOJErxi2/2KWVHiPellTgh/fNl/2KYPKb7DUcAg+mCOPQFCiU9Mq/WLcU1xxC8aLePFZZlE+PCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh/nFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW+oKFLvpyvTBMM69tN1Ydwv1LIEhHsC+ueVG+w+kyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw+H/AuOx+aH+tBL88H57D0MsqyiZxhOEQkF/8DR1d2hSPMj/sNOa5rxcUnBgH8ictv2J+cb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd/PjMMtQfyDNZsOPd6XcAsnBE/mRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl/XPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG/VanIvcwycVA7+BE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP/MVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX/5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c/F1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J/5wkjpkre727p5PTRX5FGrSBIfJqhJE/IS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug+oRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U/5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7/BQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN+nA7pvF78/RII5ZHA09OAiE/66MF6HQ+qVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe+hXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz+JV/4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB+K3wBP/ineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q/9uocGsx41O4IZhViw/2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY/cQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE/om7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi/caHSM3FPRGRf7dB7YC+cD2ho6oL2zGDCkjlf/DFoQVl8GS/56wur3rdV6ggtzZW60MRB3g+U1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg/gBQIZMG/YcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK/G7F3mUc3GOAKqh60zM0v34v+ELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND+/GTk6M56Ig4yMsU6LUW1EWE+fIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP/IoRIZ/F6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg/RrPD/d3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl+Mu4xf0ezqeXD2PtPDnwMPo86sbwDV+9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD/OwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d/UfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH+14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC+OA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6+vAUUBoGhY3CT2tgwehdPqU/4Q7ZLYvhRl/ogOvR9O2+wkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn/dFSWBWzQ/VYk+Gezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n/yJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET/Hh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j+DXfpi12m1RbzYLg9J2wFergEwOxFyD0/JstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT/cUP6pE/mujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB+HEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu+vqQ02+KpJBjaLt9ye1Ab+BbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC+UwkXOoAjneU/xHplMQo2cXUlrVNqJYczgYlaOEczVCs/OCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38+xsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9+b8fH6+b8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj/0Q7PFUcC8hDrxESWdfgFRm+7vvWbkEppHB4T/1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y+g3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR/Rrs/JLKXgEx+qkmeDlFOD1/yTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW+xz+5FElFxWB28VjYIGZ0Yd+5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ+lT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ+2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1+JpI9psyNYIFuJogZa0/1AhOWdlHQxdAgbwacsHqPZo8u/ngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2+RdM+MAaYaZ0Y/ADkbNCZuAyAVQa2OcXOeICmDn9Q/eFkDeFQg5MgHEDXq/tVjj+jtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2/Bc0UxvseQCO2pQ2i+Krfhu/WeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt/U0Wf+phiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7+ExseJauyqo30hs+1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j/e1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la/QCTiVelFnU6O/GCvykqS/wZJDhKN9gBtSOp/1SP5VRgJcoVj+kmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn+8p6+vBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H+gDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D/GvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P+jAgN5TB3haSifDcApp6yymEi6Ij/GsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x/pChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11/yxyw0UnGig3MFdZklN5FI/qiT65T+jOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5+bqWiAYiAv6Jsf79/VUs4cIl+n6+WOjcgB/2l5TreoAV2717JzZbQIR0W1cl/dEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW/PhoawJDrGAP0JYWHgAVUByo/bGdiv2T2EMg8gsS14/rAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq+SCcTSx5NDtbFwNlh8VhjGGDu7JG5/TAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9+ogD8Kk/W7QoRN1UWPqM4+xdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c+4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS+o3F8YVVeikw13w+OEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX++7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9/O53DYi/5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD+P8sNh6e+XYHJXT/lkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp+pT0KYV5dGtknU64JhabdqcVQbG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) 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<style type="text/css">

/*
 Dashboard CSS from Keen IO Dashboards
 (https://github.com/keen/dashboards)
*/

body {
  background: #f2f2f2;
  padding: 60px 0 0 8px; /* padding-top overridden by theme */
}

body hr {
  border-color: #d7d7d7;
  margin: 10px 0;
}

.navbar-inverse .navbar-nav > li > a,
.navbar .navbar-brand {
  text-decoration: none;
}

.navbar-logo {
  margin-top: 1px;
}

.navbar-logo img {
  margin-right: 12px;
}

.navbar-author {
  margin-left: 10px;
  font-size: 15px;
}

.navbar .dropdown-menu .fa {
  min-width: 20px;
}

.navbar .dropdown-menu {
  min-width: 150px;
  max-height: 500px;
  overflow: auto;
}

.chart-wrapper,
.nav-tabs-custom,
.sbframe-commentary
{
  background: #fff;
  border: 1px solid #e2e2e2;
  border-radius: 3px;
  margin-bottom: 8px;
  margin-right: 8px;
}

.chart-title {
  border-bottom: 1px solid #d7d7d7;
  color: #666;
  font-size: 14px;
  font-weight: 300;
  padding: 7px 10px 4px;
}

.chart-wrapper .chart-title:empty {
  display: none;
}

.chart-wrapper .chart-stage {
  overflow: hidden;
  padding: 5px 10px;
  position: relative;
}

.chart-wrapper .chart-notes {
  background: #fbfbfb;
  border-top: 1px solid #e2e2e2;
  color: #808080;
  font-size: 12px;
  padding: 8px 10px 5px;
}

/*
 CSS for handling flexbox layout
*/

#dashboard-container {
  visibility: hidden;
}

.tab-content>.dashboard-page-wrapper.active {
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
}

.dashboard-page-wrapper {
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
  -webkit-flex-direction: column;
      -ms-flex-direction: column;
          flex-direction: column;
}

.dashboard-row-orientation {
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
  -webkit-flex-direction: column;
      -ms-flex-direction: column;
          flex-direction: column;
}

.dashboard-row {
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
  -webkit-flex-direction: row;
      -ms-flex-direction: row;
          flex-direction: row;
}

.dashboard-row-flex {
  -webkit-box-flex: 1;
  -webkit-flex: 1;
      -ms-flex: 1;
          flex: 1;
}

.dashboard-column-orientation {
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
  -webkit-flex-direction: row;
      -ms-flex-direction: row;
          flex-direction: row;
}

.dashboard-column {
  -webkit-box-flex: 1;
  -webkit-flex: 1;
      -ms-flex: 1;
          flex: 1;
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
  -webkit-flex-direction: column;
      -ms-flex-direction: column;
          flex-direction: column;
}

.chart-wrapper-flex {
  -webkit-box-flex: 1;
  -webkit-flex: 1;
      -ms-flex: 1;
          flex: 1;
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
  -webkit-flex-direction: column;
      -ms-flex-direction: column;
          flex-direction: column;
}

.chart-stage-flex {
  -webkit-box-flex: 1;
  -webkit-flex: 1;
      -ms-flex: 1;
          flex: 1;
  position: relative;
}

.chart-shim {
  position: absolute;
  left: 8px; top: 8px; right: 8px; bottom: 8px;
}

.no-padding .chart-shim {
  left: 0; top: 0; right: 0; bottom: 0;
}

.flowing-content-shim {
  overflow: auto;
  left: 0; top: 0; right: 0; bottom: 0;
  padding-left: 8px;
  padding-right: 8px;
}

.flowing-content-container {
  padding-top: 8px;
  padding-bottom: 8px;
}

.chart-stage .table-bordered {
  border: none;
}

.chart-stage .table-bordered > tbody > tr > th,
.chart-stage .table-bordered > tfoot > tr > th,
.chart-stage .table-bordered > tbody > tr > td,
.chart-stage .table-bordered > tfoot > tr > td {
  border: none;
}

.chart-stage .table-bordered > tbody > tr > td {
  border-top: 1px solid #dddddd;
}

.chart-stage .table-bordered > thead > tr > th {
  border: none;
  border-bottom: 2px solid #dddddd;
}

.bootstrap-table table>thead {
  background-color: #fff;
}

.bootstrap-table table.data,
.bootstrap-table table.shiny-table {
  width: inherit !important;
}

.bootstrap-table table.data>tbody>tr>th,
.bootstrap-table table.shiny-table>tbody>tr>th {
  border-top: none;
  border-bottom: 2px solid #dddddd;
  padding: 5px;
}

.bootstrap-table .data td[align=right] {
  font-family: inherit;
}

.chart-wrapper form {
  padding-left: 5px;
  padding-right: 5px;
}

.shiny-input-container label {
  font-weight: normal;
}

.chart-stage .html-widget {
  width: 100% !important;
  height: 100% !important;
}

.chart-stage .html-widget-static-bound {
  width: 100% !important;
  height: 100% !important;
}

.chart-stage .shiny-bound-output {
  width: 100% !important;
  height: 100% !important;
}

/* Omit display of empty paragraphs */

.chart-shim>p:empty {
  display: none;
}

.chart-stage>p:empty {
  display: none;
}

/* Omit display of special knitr options div*/

.chart-stage .knitr-options {
  display: none;
}

/* Automatically resizing images */

.image-container {
  position: absolute;
  top: 0;
  left: 0;
  right: 0;
  bottom: 0;
  margin: 0;
}

.image-container img {
  opacity: 0;
  overflow: hidden;
}

/* Value box */

.value-box {
  border-radius: 2px;
  position: relative;
  display: block;
  margin-right: 8px;
  margin-bottom: 8px;
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.1);
}

.value-box > .inner {
  padding: 10px;
  padding-left: 20px;
  padding-right: 20px;
}

.value-box .value {
  font-size: 38px;
  font-weight: bold;
  margin: 0 0 3px 0;
  white-space: nowrap;
  padding: 0;
}

.value-box .caption {
  font-size: 15px;
}
.value-box .caption > small {
  display: block;
  font-size: 13px;
  margin-top: 5px;
}

.value-box .icon i {
  position: absolute;
  top: 15px;
  right: 15px;
  font-size: 80px;
  color: rgba(0, 0, 0, 0.15);
}

.linked-value:hover {
  cursor: pointer;
}

.value-box.linked-value:hover {
  box-shadow: 0 2px 2px rgba(0, 0, 0, 0.3);
}

/* STORYBOARD */

.storyboard-nav button {
  background: transparent;
  border: 0;
  opacity: .3;
  outline: none;
  padding: 0;
}

.storyboard-nav button:hover,
.storyboard-nav button:hover {
  opacity: .5;
}

.storyboard-nav button:disabled,
.storyboard-nav button:disabled {
  opacity: .1;
}

.storyboard-nav .sbnext,
.storyboard-nav .sbprev {
  float: left;
  width: 2%;
  height: 120px;
  font-size: 50px;
}

.storyboard-nav .sbprev {
  text-align: left;
  width: 2%;
}

.storyboard-nav .sbnext {
  float: right;
  text-align: right;
  margin-right: 8px;
}

.storyboard-nav .sbframelist {
  margin: 0 auto;
  width: 94%;
  height: 120px;
  overflow: hidden;
  text-shadow: none;
  margin-bottom: 8px;
}

.storyboard-nav .sbframelist ul {
  list-style: none;
  margin: 0;
  padding: 0;
  height: 100%;
}

.storyboard-nav .sbframelist ul li {
  float: left;
  width: 270px;
  height: 100%;
  padding: 10px 10px 10px 10px;
  margin-right: 8px;
  background: #fff;
  border: 1px solid #e2e2e2;
  border-radius: 3px;
  color: #3a3c47;
  text-align: left;
  font-size: 14px;
  cursor: pointer;
}

.storyboard-nav .sbframelist ul li:last-child {
  margin-right: 0px;
}

.storyboard-nav .sbframelist ul li.active {
  color: #fff;
  background: #B8B8B8;
}

.sbframe-commentary {
  width: 300px;
  background: #fbfbfb;
  font-size: 14px;
}

.sbframe-commentary ul {
  padding-left: 22px;
}

.sbframe.active {
  display: flex;
}

.sbframe:not(.active) {
  display: none;
}

/* NAV TABS */

.nav-tabs-custom > .nav-tabs {
  margin: 0;
  border-bottom: 1px solid #d7d7d7;
  color: #666;
  font-size: 14px;
  font-weight: 300;
  border-top-right-radius: 3px;
  border-top-left-radius: 3px;
}
.nav-tabs-custom > .nav-tabs > li {
  border-top: 3px solid transparent;
  margin-bottom: -1px;
  margin-right: 5px;
}
.nav-tabs-custom > .nav-tabs > li > a,
.nav-tabs-custom > .nav-tabs > li > a:active {
  color: #666;
  font-weight: 300;
  font-size: 14px;
  border-radius: 0;
  padding: 3px 10px 5px;
  text-transform: none;
}

.nav-tabs-custom > .nav-tabs > li:not(.active) > a {
  border-bottom-color: transparent;
}

.nav-tabs-custom > .nav-tabs > li > a.text-muted {
  color: #999;
}
.nav-tabs-custom > .nav-tabs > li > a,
.nav-tabs-custom > .nav-tabs > li > a:hover {
  background: transparent;
  margin: 0;
}
.nav-tabs-custom > .nav-tabs > li > a:hover {
  color: #999;
}
.nav-tabs-custom > .nav-tabs > li:not(.active) > a:hover,
.nav-tabs-custom > .nav-tabs > li:not(.active) > a:focus,
.nav-tabs-custom > .nav-tabs > li:not(.active) > a:active {
  border-color: transparent;
}
.nav-tabs-custom > .nav-tabs > li.active {
  border-top-color: #3c8dbc;
}
.nav-tabs-custom > .nav-tabs > li.active > a,
.nav-tabs-custom > .nav-tabs > li.active:hover > a {
  background-color: #fff;
  color: #666;
}
.nav-tabs-custom > .nav-tabs > li.active > a {
  border-top-color: transparent;
  border-left-color: #d7d7d7;
  border-right-color: #d7d7d7;
}
.nav-tabs-custom > .nav-tabs > li:first-of-type {
  margin-left: 0;
}
.nav-tabs-custom > .nav-tabs > li:first-of-type.active > a {
  border-left-color: transparent;
}
.nav-tabs-custom > .nav-tabs.pull-right {
  float: none !important;
}
.nav-tabs-custom > .nav-tabs.pull-right > li {
  float: right;
}
.nav-tabs-custom > .nav-tabs.pull-right > li:first-of-type {
  margin-right: 0;
}
.nav-tabs-custom > .nav-tabs.pull-right > li:first-of-type > a {
  border-left-width: 1px;
}
.nav-tabs-custom > .nav-tabs.pull-right > li:first-of-type.active > a {
  border-left-color: #d7d7d7;
  border-right-color: transparent;
}
.nav-tabs-custom > .nav-tabs > li.header {
  line-height: 35px;
  padding: 0 10px;
  font-size: 20px;
  color: #666;
}
.nav-tabs-custom > .nav-tabs > li.header > .fa,
.nav-tabs-custom > .nav-tabs > li.header > .glyphicon,
.nav-tabs-custom > .nav-tabs > li.header > .ion {
  margin-right: 5px;
}
.nav-tabs-custom > .tab-content {
  background: #fff;
  padding: 0;
  border-bottom-right-radius: 3px;
  border-bottom-left-radius: 3px;
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
}

.nav-tabs-custom > .tab-content > .chart-wrapper {
  background: #fff;
  border: none;
  margin: 0;
}


.nav-tabs-custom > .tab-content > .active {
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
}

.nav-tabs-custom .dropdown.open > a:active,
.nav-tabs-custom .dropdown.open > a:focus {
  background: transparent;
  color: #999;
}


@media (max-width: 767px) {
  .value-box {
    text-align: center;
    margin-right: 8px;
  }
  .value-box .icon {
    display: none;
  }
}

/* Fixed position sidebar */

.section.sidebar {
  position: fixed;
  top: 51px; /* overridden by theme */
  left: 0;
  bottom: 0;
  border-right: 1px solid #e2e2e2;
  background-color: white; /* overridden by theme */
  padding-left: 10px;
  padding-right: 10px;
  visibility: hidden;
  overflow: auto;
}

.section.sidebar form p:first-child {
  margin-top: 10px;
}

/* Embedded source code */

#flexdashboard-source-code {
  display: none;
}

.featherlight-content #flexdashboard-source-code {
  display: inline-block;
}

.featherlight-content {
  width: 80%;
  max-width: 800px;
  padding: 0 !important;
  border-bottom: none !important;
}

.featherlight:last-of-type {
  background: rgba(0, 0, 0, 0.7);
}

.featherlight-inner {
  width: 100%;
}

.featherlight-content pre {
  margin: 0;
  border: 0;
  background: #fff;
  font-size: 12px;
}

.unselectable {
  -ms-user-select: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: -moz-none;
  -o-user-select: none;
  user-select: none;
}

#flexdashboard-source-code code {
  -ms-user-select: text;
  -webkit-user-select: text;
  -khtml-user-select: text;
  -moz-user-select: text;
  -o-user-select: text;
  user-select: text;
}

/* DataTables Tweaks */

.dataTables_filter input[type="search"] {
  -webkit-appearance: searchfield;
  outline: none;
}

.dataTables_wrapper.no-footer
.dataTables_info {
  padding-top: 0;
}


table.dataTable thead th {
  border-bottom: 1px solid #d7d7d7;
}

.dataTables_wrapper.no-footer .dataTables_scrollBody {
  border-bottom: 1px solid #d7d7d7;
}

/* Mobile phone only CSS */

.desktop-layout div.section.mobile {
  display: none;
}

.mobile-layout div.section.no-mobile {
  display: none;
}

.mobile-figure {
  display: none;
}



body {
  padding-top: 60px;
}

.section.sidebar {
  top: 51px;
  background-color: rgba(39, 128, 227, 0.1);
}

.value-box {
  color: #f9f9f9;
}

.bg-primary {
  background-color: rgba(39, 128, 227, 0.7);
}

.storyboard-nav .sbframelist ul li.active {
  background-color: rgba(39, 128, 227, 0.7);
}

.nav-tabs-custom > .nav-tabs > li.active {
  border-top-color: rgba(39, 128, 227, 0.7);
}

.bg-info {
  background-color: rgba(153, 84, 187, 0.7);
}

.bg-warning {
  background-color: rgba(255, 117, 24, 0.7);
}

.bg-danger {
  background-color: rgba(255, 0, 57, 0.7);
}

.bg-success {
  background-color: rgba(63, 182, 24, 0.7);
}

.chart-title {
  font-weight: 500;
}

.nav-tabs-custom > .nav-tabs > li > a,
.nav-tabs-custom > .nav-tabs > li > a:active {
  font-weight: 500;
}

@media only screen and (min-width: 768px) {
html, body {
  height: 100%;
}

#dashboard-container {
  height: 100%;
}
}
</style>



</head>

<body>

<div class="navbar navbar-inverse navbar-fixed-top" role="navigation">
<div class="container-fluid">
<div class="navbar-header">

<button id="navbar-button" type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar">
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>

<span class="navbar-logo pull-left">
  
</span>
<span class="navbar-brand">
  庄闪闪的成长手册
  <span class="navbar-author">
        </span>
</span>

</div>
<div id="navbar" class="navbar-collapse collapse">
<ul class="nav navbar-nav navbar-left">
</ul>
<ul class="nav navbar-nav navbar-right">
</ul>
</div><!--/.nav-collapse-->
</div><!--/.container-->
</div><!--/.navbar-->

<script type="text/javascript">


var FlexDashboard = (function () {

  // initialize options
  var _options = {};

  var FlexDashboard = function() {

    // default options
    _options = $.extend(_options, {
      theme: "cosmo",
      fillPage: false,
      orientation: 'columns',
      storyboard: false,
      defaultFigWidth: 576,
      defaultFigHeight: 461,
      defaultFigWidthMobile: 360,
      defaultFigHeightMobile: 461,
      isMobile: false,
      isPortrait: false,
      resize_reload: true
    });
  };

  function init(options) {

    // extend default options
    $.extend(true, _options, options);

    // add ids to sections that don't have them (pandoc won't assign ids
    // to e.g. sections with titles consisting of only chinese characters)
    var nextId = 1;
    $('.level1:not([id]),.level2:not([id]),.level3:not([id])').each(function() {
      $(this).attr('id', 'dashboard-' + nextId++);
    });

    // find navbar items
    var navbarItems = $('#flexdashboard-navbar');
    if (navbarItems.length)
      navbarItems = JSON.parse(navbarItems.html());
    addNavbarItems(navbarItems);

    // find the main dashboard container
    var dashboardContainer = $('#dashboard-container');

    // resolve mobile classes
    resolveMobileClasses(dashboardContainer);

    // one time global initialization for components
    componentsInit(dashboardContainer);

    // look for a global sidebar
    var globalSidebar = dashboardContainer.find(".section.level1.sidebar");
    if (globalSidebar.length > 0) {

      // global layout for fullscreen displays
      if (!isMobilePhone()) {

         // hoist it up to the top level
         globalSidebar.insertBefore(dashboardContainer);

         // lay it out (set width/positions)
         layoutSidebar(globalSidebar, dashboardContainer);

      // tuck sidebar into first page for mobile phones
      } else {

        // convert it into a level3 section
        globalSidebar.removeClass('sidebar');
        globalSidebar.removeClass('level1');
        globalSidebar.addClass('level3');
        var h1 = globalSidebar.children('h1');
        var h3 = $('<h3></h3>');
        h3.append(h1.contents());
        h3.insertBefore(h1);
        h1.detach();

        // move it into the first page
        var page = dashboardContainer.find('.section.level1').first();
        if (page.length > 0)
          page.prepend(globalSidebar);
      }
    }

    // look for pages to layout
    var pages = $('div.section.level1');
    if (pages.length > 0) {

        // find the navbar and collapse on clicked
        var navbar = $('#navbar');
        navbar.on("click", "a[data-toggle!=dropdown]", null, function () {
           navbar.collapse('hide');
        });

        // envelop the dashboard container in a tab content div
        dashboardContainer.wrapInner('<div class="tab-content"></div>');

        pages.each(function(index) {

          // lay it out
          layoutDashboardPage($(this));

          // add it to the navbar
          addToNavbar($(this), index === 0);

        });

    } else {

      // remove the navbar and navbar button if we don't
      // have any navbuttons
      if (navbarItems.length === 0) {
        $('#navbar').remove();
        $('#navbar-button').remove();
      }

      // add the storyboard class if requested
      if (_options.storyboard)
        dashboardContainer.addClass('storyboard');

      // layout the entire page
      layoutDashboardPage(dashboardContainer);
    }

    // if we are in shiny we need to trigger a window resize event to
    // force correct layout of shiny-bound-output elements
    if (isShinyDoc())
      $(window).trigger('resize');

    // make main components visible
    $('.section.sidebar').css('visibility', 'visible');
    dashboardContainer.css('visibility', 'visible');

    // handle location hash
    handleLocationHash();

    // intialize prism highlighting
    initPrismHighlighting();

    // record mobile and orientation state then register a handler
    // to refresh if resize_reload is set to true and it changes
    _options.isMobile = isMobilePhone();
    _options.isPortrait = isPortrait();
    if (_options.resize_reload) {
      $(window).on('resize', function() {
        if (_options.isMobile !== isMobilePhone() ||
            _options.isPortrait !== isPortrait()) {
          window.location.reload();
        }
      });
    } else {
      // if in desktop mode and resizing to mobile, make sure the heights are 100%
      // This enforces what `fillpage.css` does for "wider" pages.
      // Since we are not reloading once the page becomes small, we need to force the height to 100%
      // This is a new situation introduced when `_options.resize_reload` is `false`
      if (! _options.isMobile) {
        // only add if `fillpage.css` was added in the first place
        if (_options.fillPage) {
          // fillpage.css
          $("html,body,#dashboard").css("height", "100%");
        }
      }
    }
    // trigger layoutcomplete event
    dashboardContainer.trigger('flexdashboard:layoutcomplete');
  }

  function resolveMobileClasses(dashboardContainer) {
     // add top level layout class
    dashboardContainer.addClass(isMobilePhone() ? 'mobile-layout' :
                                                  'desktop-layout');

    // look for .mobile sections and add .no-mobile to their peers
    var mobileSections = $('.section.mobile');
    mobileSections.each(function() {
       var id = $(this).attr('id');
       var nomobileId = id.replace(/-\d+$/, '');
       $('#' + nomobileId).addClass('no-mobile');
    });
  }

  function addNavbarItems(navbarItems) {

    var navbarLeft = $('ul.navbar-left');
    var navbarRight = $('ul.navbar-right');

    for (var i = 0; i<navbarItems.length; i++) {

      // get the item
      var item = navbarItems[i];

      // determine the container
      var container = null;
      if (item.align === "left")
        container = navbarLeft;
      else
        container = navbarRight;

      // navbar menu if we have multiple items
      if (item.items) {
        var menu = navbarMenu(null, item.icon, item.title, container);
        for (var j = 0; j<item.items.length; j++) {
          var subItem = item.items[j];
          var li = $('<li></li>');
          li.append(navbarLink(subItem.icon, subItem.title, subItem.href, subItem.target));
          menu.append(li);
        }
      } else {
        var li = $('<li></li>');
        li.append(navbarLink(item.icon, item.title, item.href, item.target));
        container.append(li);
      }
    }
  }

  // create or get a reference to an existing dropdown menu
  function navbarMenu(id, icon, title, container) {
    var existingMenu = [];
    if (id)
      existingMenu = container.children('#' + id);
    if (existingMenu.length > 0) {
      return existingMenu.children('ul');
    } else {
      var li = $('<li></li>');
      if (id)
        li.attr('id', id);
      li.addClass('dropdown');
      // auto add "Share" title on mobile if necessary
      if (!title && icon && (icon === "fa-share-alt") && isMobilePhone())
        title = "Share";
      if (title) {
        title = title + ' <span class="caret"></span>';
      }
      var a = navbarLink(icon, title, "#");
      a.addClass('dropdown-toggle');
      a.attr('data-toggle', 'dropdown');
      a.attr('role', 'button');
      a.attr('aria-expanded', 'false');
      li.append(a);
      var ul = $('<ul class="dropdown-menu"></ul>');
      ul.attr('role', 'menu');
      li.append(ul);
      container.append(li);
      return ul;
    }
  }

  function addToNavbar(page, active) {

    // capture the id and data-icon attribute (if any)
    var id = page.attr('id');
    var icon = page.attr('data-icon');
    var navmenu = page.attr('data-navmenu');

    // get hidden state (transfer this to navbar)
    var hidden = page.hasClass('hidden');
    page.removeClass('hidden');

    // sanitize the id for use with bootstrap tabs
    id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
    page.attr('id', id);

    // get the wrapper
    var wrapper = page.closest('.dashboard-page-wrapper');

    // move the id to the wrapper
    page.removeAttr('id');
    wrapper.attr('id', id);

    // add the tab-pane class to the wrapper
    wrapper.addClass('tab-pane');
    if (active)
      wrapper.addClass('active');

    // get a reference to the h1, discover its inner contens, then detach it
    var h1 = wrapper.find('h1').first();
    var title = h1.contents();
    h1.detach();

    // create a navbar item
    var li = $('<li></li>');
    var a = navbarLink(icon, title, '#' + id);
    a.attr('data-toggle', 'tab');
    li.append(a);

    // add it to the navbar (or navbar menu if specified)
    var container = $('ul.navbar-left');
    if (navmenu) {
      var menuId = navmenu.replace(/\s+/g, '');
      var menu = navbarMenu(menuId, null, navmenu, container);
      menu.append(li);
    } else {
      container.append(li);
    }

    // hide it if requested
    if (hidden)
      li.addClass('hidden');
  }

  function navbarLink(icon, title, href, target) {

    var a = $('<a></a>');
    if (icon) {

      // get the name of the icon set and icon
      var dashPos = icon.indexOf("-");
      var iconSet = null;
      var iconSplit = icon.split(" ");
      if (iconSplit.length > 1) {
        iconSet = iconSplit[0];
        icon = iconSplit.slice(1).join(" ");
      } else {
        iconSet = icon.substring(0, dashPos);
      }
      var iconName = icon.substring(dashPos + 1);

      // create the icon
      var iconElement = $('<span class="' + iconSet + ' ' + icon + '"></span>');
      if (title)
        iconElement.css('margin-right', '7px');
      a.append(iconElement);
      // if href is null see if we can auto-generate based on icon (e.g. social)
      if (!href)
        maybeGenerateLinkFromIcon(iconName, a);
    }
    if (title)
      a.append(title);

    // add the href.
    if (href) {
      if (href === "source_embed") {
        a.attr('href', '#');
        a.attr('data-featherlight', "#flexdashboard-source-code");
        a.featherlight({
            beforeOpen: function(event){
              $('body').addClass('unselectable');
            },
            afterClose: function(event){
              $('body').removeClass('unselectable');
            }
        });
      } else {
        a.attr('href', href);
      }
    }

    // add the arget
    if (target)
      a.attr('target', target);

    return a;
  }

  // auto generate a link from an icon name (e.g. twitter) when possible
  function maybeGenerateLinkFromIcon(iconName, a) {

     var serviceLinks = {
      "twitter": "https://twitter.com/share?text=" + encodeURIComponent(document.title) + "&url="+encodeURIComponent(location.href),
      "facebook": "https://www.facebook.com/sharer/sharer.php?s=100&p[url]="+encodeURIComponent(location.href),
      "google-plus": "https://plus.google.com/share?url="+encodeURIComponent(location.href),
      "linkedin": "https://www.linkedin.com/shareArticle?mini=true&url="+encodeURIComponent(location.href) + "&title=" + encodeURIComponent(document.title),
      "pinterest": "https://pinterest.com/pin/create/link/?url="+encodeURIComponent(location.href) + "&description=" + encodeURIComponent(document.title)
    };

    var makeSocialLink = function(a, href) {
      a.attr('href', '#');
      a.on('click', function(e) {
        e.preventDefault();
        window.open(href);
      });
    };

    $.each(serviceLinks, function(key, value) {
      if (iconName.indexOf(key) !== -1)
        makeSocialLink(a, value);
    });
  }

  // layout a dashboard page
  function layoutDashboardPage(page) {

    // use a page wrapper so that free form content above the
    // dashboard appears at the top rather than the side (as it
    // would without the wrapper in a column orientation)
    var wrapper = $('<div class="dashboard-page-wrapper"></div>');
    page.wrap(wrapper);

    // if there are no level2 or level3 headers synthesize a level3
    // header to contain the (e.g. frame it, scroll container, etc.)
    var headers = page.find('h2,h3');
    if (headers.length === 0)
      page.wrapInner('<div class="section level3"></div>');

    // hoist up any content before level 2 or level 3 headers
    var children = page.children();
    children.each(function(index) {
      if ($(this).hasClass('level2') || $(this).hasClass('level3'))
        return false;
      $(this).insertBefore(page);
    });

    // determine orientation and fillPage behavior for distinct media
    var orientation, fillPage, storyboard;

    // media: mobile phone
    if (isMobilePhone()) {

      // if there is a sidebar we need to ensure it's content
      // is properly framed as an h3
      var sidebar = page.find('.section.sidebar');
      sidebar.removeClass('sidebar');
      sidebar.wrapInner('<div class="section level3"></div>');
      var h2 = sidebar.find('h2');
      var h3 = $('<h3></h3>');
      h3.append(h2.contents());
      h3.insertBefore(h2);
      h2.detach();

      // wipeout h2 elements then enclose them in a single h2
      var level2 = page.find('div.section.level2');
      level2.each(function() {
        level2.children('h2').remove();
        level2.children().unwrap();
      });
      page.wrapInner('<div class="section level2"></div>');

      // substitute mobile images
      if (isPortrait()) {
        var mobileFigures = $('img.mobile-figure');
        mobileFigures.each(function() {
          // get the src (might be base64 encoded)
          var src = $(this).attr('src');

          // find it's peer
          var id = $(this).attr('data-mobile-figure-id');
          var img = $('img[data-figure-id=' + id + "]");
          img.attr('src', src)
             .attr('width', _options.defaultFigWidthMobile)
             .attr('height', _options.defaultFigHeightMobile);
        });
      }

      // hoist storyboard commentary into it's own section
      if (page.hasClass('storyboard')) {
        var commentaryHR = page.find('div.section.level3 hr');
        commentaryHR.each(function() {
          var commentary = $(this).nextAll().detach();
          var commentarySection = $('<div class="section level3"></div>');
          commentarySection.append(commentary);
          commentarySection.insertAfter($(this).closest('div.section.level3'));
          $(this).remove();
        });
      }

      // force a non full screen layout by columns
      orientation = _options.orientation = 'columns';
      fillPage = _options.fillPage = false;
      storyboard = _options.storyboard = false;

    // media: desktop
    } else {

      // determine orientation
      orientation = page.attr('data-orientation');
      if (orientation !== 'rows' && orientation != 'columns')
        orientation = _options.orientation;

      // determine storyboard mode
      storyboard = page.hasClass('storyboard');

      // fillPage based on options (force for storyboard)
      fillPage = _options.fillPage || storyboard;

      // handle sidebar
      var sidebar = page.find('.section.level2.sidebar');
      if (sidebar.length > 0)
        layoutSidebar(sidebar, page);
    }

    // give it and it's parent divs height: 100% if we are in fillPage mode
    if (fillPage) {
      page.addClass('vertical-layout-fill');
      page.css('height', '100%');
      page.parents('div').css('height', '100%');
    } else {
      page.addClass('vertical-layout-scroll');
    }

    // perform the layout
    if (storyboard)
      layoutPageAsStoryboard(page);
    else if (orientation === 'rows')
      layoutPageByRows(page, fillPage);
    else if (orientation === 'columns')
      layoutPageByColumns(page, fillPage);
  }

  function layoutSidebar(sidebar, content) {

    // get it out of the header hierarchy
    sidebar = sidebar.first();
    if (sidebar.hasClass('level1')) {
      sidebar.removeClass('level1');
      sidebar.children('h1').remove();
    } else if (sidebar.hasClass('level2')) {
      sidebar.removeClass('level2');
      sidebar.children('h2').remove();
    }

    // determine width
    var sidebarWidth = isTablet() ? 220 : 250;
    var dataWidth = parseInt(sidebar.attr('data-width'));
    if (dataWidth)
      sidebarWidth = dataWidth;

    // set the width and shift the page right to accomodate the sidebar
    sidebar.css('width', sidebarWidth + 'px');
    content.css('padding-left', sidebarWidth + 'px');

    // wrap it's contents in a form
    sidebar.wrapInner($('<form></form>'));
  }

  function layoutPageAsStoryboard(page) {

    // create storyboard navigation
    var nav = $('<div class="storyboard-nav"></div>');

    // add navigation buttons
    var prev = $('<button class="sbprev"><i class="fa fa-angle-left"></i></button>');
    nav.append(prev);
    var next= $('<button class="sbnext"><i class="fa fa-angle-right"></i></button>');
    nav.append(next);

    // add navigation frame
    var frameList = $('<div class="sbframelist"></div>');
    nav.append(frameList);
    var ul = $('<ul></ul>');
    frameList.append(ul);

     // find all the level3 sections (those are the storyboard frames)
    var frames = page.find('div.section.level3');
    frames.each(function() {

      // mark it
      $(this).addClass('sbframe');

      // divide it into chart content and (optional) commentary
      $(this).addClass('dashboard-column-orientation');

      // stuff the chart into it's own div w/ flex
      $(this).wrapInner('<div class="sbframe-component"></div>');
      setFlex($(this), 1);
      var frame = $(this).children('.sbframe-component');

      // extract the title from the h3
      var li = $('<li></li>');
      var h3 = frame.children('h3');
      li.append(h3.contents());
      h3.detach();
      ul.append(li);

      // extract commentary
      var hr = frame.children('hr');
      if (hr.length) {
        var commentary = hr.nextAll().detach();
        hr.remove();
        var commentaryFrame = $('<div class="sbframe-commentary"></div>');
        commentaryFrame.addClass('flowing-content-shim');
        commentaryFrame.addClass('flowing-content-container');
        commentaryFrame.append(commentary);
        $(this).append(commentaryFrame);

        // look for a data-commentary-width attribute
        var commentaryWidth = $(this).attr('data-commentary-width');
        if (commentaryWidth)
          commentaryFrame.css('width', commentaryWidth + 'px');
      }

      // layout the chart (force flex)
      var result = layoutChart(frame, true);

      // ice the notes if there are none
      if (!result.notes)
        frame.find('.chart-notes').remove();

      // set flex on chart
      setFlex(frame, 1);
    });

    // create a div to hold all the frames
    var frameContent = $('<div class="sbframe-content"></div>');
    frameContent.addClass('dashboard-row-orientation');
    frameContent.append(frames.detach());

    // row orientation to stack nav and frame content
    page.addClass('dashboard-row-orientation');
    page.append(nav);
    page.append(frameContent);
    setFlex(frameContent, 1);

    // initialize sly
    var sly = new Sly(frameList, {
    		horizontal: true,
    		itemNav: 'basic',
    		smart: true,
    		activateOn: 'click',
    		startAt: 0,
    		scrollBy: 1,
    		activatePageOn: 'click',
    		speed: 200,
    		moveBy: 600,
    		dragHandle: true,
    		dynamicHandle: true,
    		clickBar: true,
    		keyboardNavBy: 'items',
    		next: next,
    		prev: prev
    	}).init();

    // make first frame active
    frames.removeClass('active');
    frames.first().addClass('active');

    // subscribe to frame changed events
    sly.on('active', function (eventName, itemIndex) {
      frames.removeClass('active');
      frames.eq(itemIndex).addClass('active')
                          .trigger('shown');
    });
  }

  function layoutPageByRows(page, fillPage) {

    // row orientation
    page.addClass('dashboard-row-orientation');

    // find all the level2 sections (those are the rows)
    var rows = page.find('div.section.level2');

    // if there are no level2 sections then treat the
    // entire page as if it's a level 2 section
    if (rows.length === 0) {
      page.wrapInner('<div class="section level2"></div>');
      rows = page.find('div.section.level2');
    }

    rows.each(function () {

      // flags
      var haveNotes = false;
      var haveFlexHeight = true;

      // remove the h2
      $(this).children('h2').remove();

      // check for a tabset
      var isTabset = $(this).hasClass('tabset');
      if (isTabset)
        layoutTabset($(this));

      // give it row layout semantics if it's not a tabset
      if (!isTabset)
        $(this).addClass('dashboard-row');

      // find all of the level 3 subheads
      var columns = $(this).find('div.section.level3');

      // determine figureSizes sizes
      var figureSizes = chartFigureSizes(columns);

      // fixup the columns
      columns.each(function(index) {

        // layout the chart (force flex if we are in a tabset)
        var result = layoutChart($(this), isTabset);

        // update flexHeight state
        if (!result.flex)
          haveFlexHeight = false;

        // update state
        if (result.notes)
          haveNotes = true;

        // set the column flex based on the figure width
        // (value boxes will just get the default figure width)
        var chartWidth = figureSizes[index].width;
        setFlex($(this), chartWidth + ' ' + chartWidth + ' 0px');

      });

      // remove empty chart note divs
      if (isTabset)
        $(this).find('.chart-notes').filter(function() {
            return $(this).html() === "&nbsp;";
        }).remove();
      if (!haveNotes)
        $(this).find('.chart-notes').remove();

       // make it a flexbox row
      if (haveFlexHeight)
        $(this).addClass('dashboard-row-flex');

      // now we can set the height on all the wrappers (based on maximum
      // figure height + room for title and notes, or data-height on the
      // container if specified). However, don't do this if there is
      // no flex on any of the constituent columns
      var flexHeight = null;
      var dataHeight = parseInt($(this).attr('data-height'));
      if (dataHeight)
        flexHeight = adjustedHeight(dataHeight, columns.first());
      else if (haveFlexHeight)
        flexHeight = maxChartHeight(figureSizes, columns);
      if (flexHeight) {
        if (fillPage)
          setFlex($(this), flexHeight + ' ' + flexHeight + ' 0px');
        else {
          $(this).css('height', flexHeight + 'px');
          setFlex($(this), '0 0 ' + flexHeight + 'px');
        }
      }

    });
  }

  function layoutPageByColumns(page, fillPage) {

    // column orientation
    page.addClass('dashboard-column-orientation');

    // find all the level2 sections (those are the columns)
    var columns = page.find('div.section.level2');

    // if there are no level2 sections then treat the
    // entire page as if it's a level 2 section
    if (columns.length === 0) {
      page.wrapInner('<div class="section level2"></div>');
      columns = page.find('div.section.level2');
    }

    // layout each column
    columns.each(function (index) {

      // remove the h2
      $(this).children('h2').remove();

      // make it a flexbox column
      $(this).addClass('dashboard-column');

      // check for a tabset
      var isTabset = $(this).hasClass('tabset');
      if (isTabset)
        layoutTabset($(this));

      // find all the h3 elements
      var rows = $(this).find('div.section.level3');

      // get the figure sizes for the rows
      var figureSizes = chartFigureSizes(rows);

      // column flex is the max row width (or data-width if specified)
      var flexWidth;
      var dataWidth = parseInt($(this).attr('data-width'));
      if (dataWidth)
        flexWidth = dataWidth;
      else
        flexWidth = maxChartWidth(figureSizes);
      setFlex($(this), flexWidth + ' ' + flexWidth + ' 0px');

      // layout each chart
      rows.each(function(index) {

        // perform the layout
        var result = layoutChart($(this), false);

        // ice the notes if there are none
        if (!result.notes)
          $(this).find('.chart-notes').remove();

        // set flex height based on figHeight, then adjust
        if (result.flex) {
          var chartHeight = figureSizes[index].height;
          chartHeight = adjustedHeight(chartHeight, $(this));
          if (fillPage)
            setFlex($(this), chartHeight + ' ' + chartHeight + ' 0px');
          else {
            $(this).css('height', chartHeight + 'px');
            setFlex($(this), chartHeight + ' ' + chartHeight + ' ' + chartHeight + 'px');
          }
        }
      });
    });
  }

  function chartFigureSizes(charts) {

    // sizes
    var figureSizes = new Array(charts.length);

    // check each chart
    charts.each(function(index) {

      // start with default
      figureSizes[index] = {
        width: _options.defaultFigWidth,
        height: _options.defaultFigHeight
      };

      // look for data-height or data-width then knit options
      var dataWidth = parseInt($(this).attr('data-width'));
      var dataHeight = parseInt($(this).attr('data-height'));
      var knitrOptions = $(this).find('.knitr-options:first');
      var knitrWidth, knitrHeight;
      if (knitrOptions) {
        knitrWidth = parseInt(knitrOptions.attr('data-fig-width'));
        knitrHeight =  parseInt(knitrOptions.attr('data-fig-height'));
      }

      // width
      if (dataWidth)
        figureSizes[index].width = dataWidth;
      else if (knitrWidth)
        figureSizes[index].width = knitrWidth;

      // height
      if (dataHeight)
        figureSizes[index].height = dataHeight;
      else if (knitrHeight)
        figureSizes[index].height = knitrHeight;
    });

    // return sizes
    return figureSizes;
  }

  function maxChartHeight(figureSizes, charts) {

    // first compute the maximum height
    var maxHeight = _options.defaultFigHeight;
    for (var i = 0; i<figureSizes.length; i++)
      if (figureSizes[i].height > maxHeight)
        maxHeight = figureSizes[i].height;

    // now add offests for chart title and chart notes
    if (charts.length)
      maxHeight = adjustedHeight(maxHeight, charts.first());

    return maxHeight;
  }

  function adjustedHeight(height, chart) {
    if (chart.length > 0) {
      var chartTitle = chart.find('.chart-title');
      if (chartTitle.length)
        height += chartTitle.first().outerHeight();
      var chartNotes = chart.find('.chart-notes');
      if (chartNotes.length)
        height += chartNotes.first().outerHeight();
    }
    return height;
  }

  function maxChartWidth(figureSizes) {
    var maxWidth = _options.defaultFigWidth;
    for (var i = 0; i<figureSizes.length; i++)
      if (figureSizes[i].width > maxWidth)
        maxWidth = figureSizes[i].width;
    return maxWidth;
  }

  // layout a chart
  function layoutChart(chart, forceFlex) {

    // state to return
    var result = {
      notes: false,
      flex: false
    };

    // extract the title
    var title = extractTitle(chart);

    // find components that apply to this container
    var components = componentsFind(chart);

    // if it's a custom component then call it and return
    var customComponents = componentsCustom(components);
    if (customComponents.length) {
      componentsLayout(customComponents, title, chart);
      result.notes = false;
      result.flex = forceFlex || componentsFlex(customComponents);
      return result;
    }

    // put all the content in a chart wrapper div
    chart.addClass('chart-wrapper');
    chart.wrapInner('<div class="chart-stage"></div>');
    var chartContent = chart.children('.chart-stage');

    // flex the content if appropriate
    result.flex = forceFlex || componentsFlex(components);
    if (result.flex) {
      // add flex classes
      chart.addClass('chart-wrapper-flex');
      chartContent.addClass('chart-stage-flex');

      // additional shim to break out of flexbox sizing
      chartContent.wrapInner('<div class="chart-shim"></div>');
      chartContent = chartContent.children('.chart-shim');
    }

    // set custom data-padding attribute
    var pad = chart.attr('data-padding');
    if (pad) {
      if (pad === "0")
        chart.addClass('no-padding');
      else {
        pad = pad + 'px';
        chartContent.css('left', pad)
                    .css('top', pad)
                    .css('right', pad)
                    .css('bottom', pad)
      }
    }

    // call compoents
    componentsLayout(components, title, chartContent);

    // also activate components on shiny output
    findShinyOutput(chartContent).on('shiny:value',
      function(event) {
        var element = $(event.target);
        setTimeout(function() {

          // see if we opted out of flex based on our output (for shiny
          // we can't tell what type of output we have until after the
          // value is bound)
          var components = componentsFind(element);
          var flex = forceFlex || componentsFlex(components);
          if (!flex) {
            chart.css('height', "");
            setFlex(chart, "");
            chart.removeClass('chart-wrapper-flex');
            chartContent.removeClass('chart-stage-flex');
            chartContent.children().unwrap();
          }

          // perform layout
          componentsLayout(components, title, element.parent());
        }, 10);
      });

    // add the title
    var chartTitle = $('<div class="chart-title"></div>');
    chartTitle.append(title);
    chart.prepend(chartTitle);

    // add the notes section
    var chartNotes = $('<div class="chart-notes"></div>');
    chartNotes.html('&nbsp;');
    chart.append(chartNotes);

    // attempt to extract notes if we have a component
    if (components.length)
      result.notes = extractChartNotes(chartContent, chartNotes);

    // return result
    return result;
  }

  // build a tabset from a section div with the .tabset class
  function layoutTabset(tabset) {

    // check for fade option
    var fade = tabset.hasClass("tabset-fade");
    var navClass = "nav-tabs";

    // determine the heading level of the tabset and tabs
    var match = tabset.attr('class').match(/level(\d) /);
    if (match === null)
      return;
    var tabsetLevel = Number(match[1]);
    var tabLevel = tabsetLevel + 1;

    // find all subheadings immediately below
    var tabs = tabset.find("div.section.level" + tabLevel);
    if (!tabs.length)
      return;

    // create tablist and tab-content elements
    var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
    $(tabs[0]).before(tabList);
    var tabContent = $('<div class="tab-content"></div>');
    $(tabs[0]).before(tabContent);

    // build the tabset
    tabs.each(function(i) {

      // get the tab div
      var tab = $(tabs[i]);

      // get the id then sanitize it for use with bootstrap tabs
      var id = tab.attr('id');

      // sanitize the id for use with bootstrap tabs
      id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
      tab.attr('id', id);

      // get the heading element within it and grab it's text
      var heading = tab.find('h' + tabLevel + ':first');
      var headingDom = heading.contents();

      // build and append the tab list item
      var a = $('<a role="tab" data-toggle="tab"></a>');
      a.append(headingDom);
      a.attr('href', '#' + id);
      a.attr('aria-controls', id);
      var li = $('<li role="presentation"></li>');
      li.append(a);
      if (i === 0)
        li.attr('class', 'active');
      tabList.append(li);

      // set it's attributes
      tab.attr('role', 'tabpanel');
      tab.addClass('tab-pane');
      tab.addClass('tabbed-pane');
      tab.addClass('no-title');
      if (fade)
        tab.addClass('fade');
      if (i === 0) {
        tab.addClass('active');
        if (fade)
          tab.addClass('in');
      }

      // move it into the tab content div
      tab.detach().appendTo(tabContent);
    });

    // add nav-tabs-custom
    tabset.addClass('nav-tabs-custom');

    // internal layout is dashboard-column with tab-content flexing
    tabset.addClass('dashboard-column');
    setFlex(tabContent, 1);
  }

  // one time global initialization for components
  function componentsInit(dashboardContainer) {
    for (var i=0; i<window.FlexDashboardComponents.length; i++) {
      var component = window.FlexDashboardComponents[i];
      if (component.init)
        component.init(dashboardContainer);
    }
  }

  // find components that apply within a container
  function componentsFind(container) {

    // look for components
    var components = [];
    for (var i=0; i<window.FlexDashboardComponents.length; i++) {
      var component = window.FlexDashboardComponents[i];
      if (component.find(container).length)
        components.push(component);
    }

    // if there were none then use a special flowing content component
    // that just adds a scrollbar in fillPage mode
    if (components.length == 0) {
      components.push({
        find: function(container) {
          return container;
        },

        flex: function(fillPage) {
          return fillPage;
        },

        layout: function(title, container, element, fillPage) {
          if (fillPage) {
            container.addClass('flowing-content-shim');
            container.addClass('flowing-content-container');
          }
        }
      });
    }

    return components;
  }

  // if there is a custom component then pick it out
  function componentsCustom(components) {
    var customComponent = [];
    for (var i=0; i<components.length; i++)
      if (components[i].type === "custom") {
        customComponent.push(components[i]);
        break;
      }
    return customComponent;
  }

  // query all components for flex
  function componentsFlex(components) {

    // no components at all means no flex
    if (components.length === 0)
      return false;

    // otherwise query components (assume true unless we see false)
    var isMobile = isMobilePhone();
    for (var i=0; i<components.length; i++)
      if (components[i].flex && !components[i].flex(_options.fillPage))
        return false;
    return true;
  }

  // layout all components
  function componentsLayout(components, title, container) {
    var isMobile = isMobilePhone();
    for (var i=0; i<components.length; i++) {
      var element = components[i].find(container);
      if (components[i].layout) {
        // call layout (don't call other components if it returns false)
        var result = components[i].layout(title, container, element, _options.fillPage);
        if (result === false)
          return;
      }
    }
  }

  // get a reference to the h3, discover it's inner html, and remove it
  function extractTitle(container) {
    var h3 = container.children('h3').first();
    var title = '';
    if (!container.hasClass('no-title'))
      title = h3.contents();
    h3.detach();
    return title;
  }

  // extract chart notes
  function extractChartNotes(chartContent, chartNotes) {
    // look for a terminating blockquote or image caption
    var blockquote = chartContent.children('blockquote:last-child');
    var caption = chartContent.children('div.image-container')
                              .children('p.caption');
    if (blockquote.length) {
      chartNotes.empty().append(blockquote.children('p:first-child').contents());
      blockquote.remove();
      return true;
    } else if (caption.length) {
      chartNotes.empty().append(caption.contents());
      caption.remove();
      return true;
    } else {
      return false;
    }
  }

  function findShinyOutput(chartContent) {
    return chartContent.find('.shiny-text-output, .shiny-html-output');
  }

  // safely detect rendering on a mobile phone
  function isMobilePhone() {
    try
    {
      return ! window.matchMedia("only screen and (min-width: 768px)").matches;
    }
    catch(e) {
      return false;
    }
  }

  function isFillPage() {
    return _options.fillPage;
  }

  // detect portrait mode
  function isPortrait() {
    return ($(window).width() < $(window).height());
  }

  // safely detect rendering on a tablet
  function isTablet() {
    try
    {
      return window.matchMedia("only screen and (min-width: 769px) and (max-width: 992px)").matches;
    }
    catch(e) {
      return false;
    }
  }

  // test whether this is a shiny doc
  function isShinyDoc() {
    return (typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings);
  }

  // set flex using vendor specific prefixes
  function setFlex(el, flex) {
    el.css('-webkit-box-flex', flex)
      .css('-webkit-flex', flex)
      .css('-ms-flex', flex)
      .css('flex', flex);
  }

  // support bookmarking of pages
  function handleLocationHash() {

    // restore tab/page from bookmark
    var hash = window.decodeURIComponent(window.location.hash);
    if (hash.length > 0)
      $('ul.nav a[href="' + hash + '"]').tab('show');
    FlexDashboardUtils.manageActiveNavbarMenu();

    // navigate to a tab when the history changes
    window.addEventListener("popstate", function(e) {
      var hash = window.decodeURIComponent(window.location.hash);
      var activeTab = $('ul.nav a[href="' + hash + '"]');
      if (activeTab.length) {
        activeTab.tab('show');
      } else {
        $('ul.nav a:first').tab('show');
      }
      FlexDashboardUtils.manageActiveNavbarMenu();
    });

    // add a hash to the URL when the user clicks on a tab/page
    $('.navbar-nav a[data-toggle="tab"]').on('click', function(e) {
      var baseUrl = FlexDashboardUtils.urlWithoutHash(window.location.href);
      var hash = FlexDashboardUtils.urlHash($(this).attr('href'));
      var href = baseUrl + hash;
      FlexDashboardUtils.setLocation(href);
    });

    // handle clicks of other links that should activate pages
    var navPages = $('ul.navbar-nav li a[data-toggle=tab]');
    navPages.each(function() {
      var href =  $(this).attr('href');
      var links = $('a[href="' + href + '"][data-toggle!=tab]');
      links.each(function() {
        $(this).on('click', function(e) {
          window.FlexDashboardUtils.showPage(href);
        });
      });
    });
  }

  // tweak Prism highlighting
  function initPrismHighlighting() {

    if (window.Prism) {
      Prism.languages.insertBefore('r', 'comment', {
        'heading': [
          {
            // title 1
        	  // =======

        	  // title 2
        	  // -------
        	  pattern: /\w+.*(?:\r?\n|\r)(?:====+|----+)/,
            alias: 'operator'
          },
          {
            // ### title 3
            pattern: /(^\s*)###[^#].+/m,
            lookbehind: true,
            alias: 'operator'
          }
        ]
      });

      // prism highlight
      Prism.highlightAll();
    }
  }


  // get theme color
  var themeColors = {
    bootstrap: {
      primary: "rgba(51, 122, 183, 0.4)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    },
    cerulean: {
      primary: "rgb(47, 164, 231)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    },
    journal: {
      primary: "rgba(235, 104, 100, 0.70)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    },
    flatly: {
      primary: "rgba(44, 62, 80, 0.70)",
      info: "rgba(52, 152, 219, 0.70)",
      success: "rgba(24, 188, 156, 0.70)",
      warning: "rgba(243, 156, 18, 0.70)",
      danger: "rgba(231, 76, 60, 0.70)"
    },
    readable: {
      primary: "rgba(69, 130, 236, 0.4)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    },
    spacelab: {
      primary: "rgba(68, 110, 155, 0.25)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    },
    united: {
      primary: "rgba(221, 72, 20, 0.30)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    },
    cosmo: {
      primary: "rgba(39, 128, 227, 0.7)",
      info: "rgba(153, 84, 187, 0.7)",
      success: "rgba(63, 182, 24, 0.7)",
      warning: "rgba(255, 117, 24, 0.7)",
      danger: "rgba(255, 0, 57, 0.7)"
    },
    lumen: {
      primary: "rgba(21, 140, 186, 0.70)",
      info: "rgba(117, 202, 235, 0.90)",
      success: "rgba(40, 182, 44, 0.70)",
      warning: "rgba(255, 133, 27, 0.70)",
      danger: "rgba(255, 65, 54, 0.70)"
    },
    paper: {
      primary: "rgba(33, 150, 243, 0.35)",
      info: "rgb(225, 190, 231)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(255, 224, 178)",
      danger: "rgb(249, 189, 187)"
    },
    sandstone: {
      primary: "rgba(50, 93, 136, 0.3)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    },
    simplex: {
      primary: "rgba(217, 35, 15, 0.25)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    },
    yeti: {
      primary: "rgba(0, 140, 186, 0.298039)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    }
  }
  function themeColor(color) {
    return themeColors[_options.theme][color];
  }

  FlexDashboard.prototype = {
    constructor: FlexDashboard,
    init: init,
    isMobilePhone: isMobilePhone,
    isFillPage: isFillPage,
    themeColor: themeColor
  };

  return FlexDashboard;

})();

// utils
window.FlexDashboardUtils = {
  resizableImage: function(img) {
    var src = img.attr('src');
    var url = 'url("' + src + '")';
    img.parent().css('background', url)
                .css('background-size', 'contain')
                .css('background-repeat', 'no-repeat')
                .css('background-position', 'center')
                .addClass('image-container');
  },
  setLocation: function(href) {
    if (history && history.pushState) {
      history.pushState(null, null, href);
    } else {
      window.location.replace(href);
    }
    setTimeout(function() {
        window.scrollTo(0, 0);
    }, 10);
    this.manageActiveNavbarMenu();
  },
  showPage: function(href) {
    $('ul.navbar-nav li a[href="' + href + '"]').tab('show');
    var baseUrl = this.urlWithoutHash(window.location.href);
    var loc = baseUrl + href;
    this.setLocation(loc);
  },
  showLinkedValue: function(href) {
    // check for a page link
    if ($('ul.navbar-nav li a[data-toggle=tab][href="' + href + '"]').length > 0)
      this.showPage(href);
    else
      window.open(href);
  },
  urlWithoutHash: function(url) {
    var hashLoc = url.indexOf('#');
    if (hashLoc != -1)
      return url.substring(0, hashLoc);
    else
      return url;
  },
  urlHash: function(url) {
    var hashLoc = url.indexOf('#');
    if (hashLoc != -1)
      return url.substring(hashLoc);
    else
      return "";
  },
  manageActiveNavbarMenu: function () {
    // remove active from anyone currently active
    $('.navbar ul.nav').find('li').removeClass('active');
    // find the active tab
    var activeTab = $('.dashboard-page-wrapper.tab-pane.active');
    if (activeTab.length > 0) {
      var tabId = activeTab.attr('id');
      if (tabId)
        $(".navbar ul.nav a[href='#" + tabId + "']").parents('li').addClass('active');
    }
  }
};

window.FlexDashboard = new FlexDashboard();

// empty content
window.FlexDashboardComponents.push({
  find: function(container) {
    if (container.find('p').length == 0)
      return container;
    else
      return $();
  }
})

// plot image
window.FlexDashboardComponents.push({

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  find: function(container) {
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<div id="dashboard-container">

<div id="row" class="section level2" data-width="450">
<h2 data-width="450">Row</h2>
<div id="chart-a" class="section level3">
<h3>Chart A</h3>
<div class="knitr-options" data-fig-width="576" data-fig-height="460">

</div>
</div>
<div id="chart-e" class="section level3">
<h3>Chart E</h3>
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<div id="column" class="section level2" data-width="350">
<h2 data-width="350">Column</h2>
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<div class="knitr-options" data-fig-width="576" data-fig-height="460">

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<div id="chart-c" class="section level3">
<h3>Chart C</h3>
<div class="knitr-options" data-fig-width="576" data-fig-height="460">

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</div>
<div id="chart-d" class="section level3">
<h3>Chart D</h3>
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</div>
<div id="column-1" class="section level2" data-width="350">
<h2 data-width="350">Column</h2>
<div id="chart-f" class="section level3">
<h3>Chart F</h3>
</div>
</div>

</div>

<script>

$(document).ready(function () {

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$(document).ready(function () {
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    defaultFigHeight: 460,
    defaultFigWidthMobile: 360,
    defaultFigHeightMobile: 460,
    resize_reload: true
  });
});
</script>

</body>
</html>


================================================
FILE: 2020年/2020.11.16flexdashboard/test.rmd
================================================
---
title: "庄闪闪的成长手册"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
---


Row {data-width=450}
-----------------------------------------------------------------------

### Chart A

```{r}

```

### Chart E


Column {data-width=350}
-----------------------------------------------------------------------

### Chart B

```{r}

```

### Chart C

```{r}

```

### Chart D 

Column {data-width=350}
-----------------------------------------------------------------------

### Chart F


================================================
FILE: 2020年/2020.11.16flexdashboard/例子/09_rbokeh-iris-dataset/dashboard-pandoc2.0.3.html
================================================
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>

<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<meta name="author" content="Ryan Hafen" />



<title>rbokeh iris dataset</title>

<meta property="og:title" content="rbokeh iris dataset" />

<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />

<link href="data:image/x-icon;" rel="icon" type="image/x-icon">


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window.FlexDashboardComponents = [];
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[{"icon":"fa-share-alt","items":[{"title":"Twitter","icon":"fa-twitter"},{"title":"Facebook","icon":"fa-facebook"},{"title":"Google+","icon":"fa-google-plus"},{"title":"LinkedIn","icon":"fa-linkedin"},{"title":"Pinterest","icon":"fa-pinterest"}]},{"title":"Source Code","icon":"fa-code","href":"source_embed","align":"right"}]
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) 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) format('truetype');
font-weight: normal;
font-style: normal;
}
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linear}.fa-pulse{-webkit-animation:fa-spin 1s infinite steps(8);animation:fa-spin 1s infinite steps(8)}@-webkit-keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}100%{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}@keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}100%{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}.fa-rotate-90{filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=1);-webkit-transform:rotate(90deg);-ms-transform:rotate(90deg);transform:rotate(90deg)}.fa-rotate-180{filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=2);-webkit-transform:rotate(180deg);-ms-transform:rotate(180deg);transform:rotate(180deg)}.fa-rotate-270{filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=3);-webkit-transform:rotate(270deg);-ms-transform:rotate(270deg);transform:rotate(270deg)}.fa-flip-horizontal{filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=0, 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  // See if we're running in a viewer pane. If not, we're in a web browser.
  var viewerMode = window.HTMLWidgets.viewerMode =
      /\bviewer_pane=1\b/.test(window.location);

  // See if we're running in Shiny mode. If not, it's a static document.
  // Note that static widgets can appear in both Shiny and static modes, but
  // obviously, Shiny widgets can only appear in Shiny apps/documents.
  var shinyMode = window.HTMLWidgets.shinyMode =
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  // We can't count on jQuery being available, so we implement our own
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  // Add a method to delegator that, when invoked, calls
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  // For example:
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  // var a = {
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  // delegateMethod(a, b, "method2");
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  // The output would be "b1", "a2".
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      if (method) {
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  // Implement a vague facsimilie of jQuery's data method
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        arguments.length);
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  // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
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  function hasClass(el, className) {
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  // elements - array (or array-like object) of HTML elements
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    else if (obj.detachEvent) {
      obj.detachEvent(eventName, func);
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  // Translate array of values to top/right/bottom/left, as usual with
  // the "padding" CSS property
  // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
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    }
  }

  // Convert an unpacked padding object to a CSS value
  function paddingToCss(paddingObj) {
    return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
  }

  // Makes a number suitable for CSS
  function px(x) {
    if (typeof(x) === "number")
      return x + "px";
    else
      return x;
  }

  // Retrieves runtime widget sizing information for an element.
  // The return value is either null, or an object with fill, padding,
  // defaultWidth, defaultHeight fields.
  function sizingPolicy(el) {
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      return null;
    var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
    if (viewerMode) {
      return sp.viewer;
    } else {
      return sp.browser;
    }
  }

  // @param tasks Array of strings (or falsy value, in which case no-op).
  //   Each element must be a valid JavaScript expression that yields a
  //   function. Or, can be an array of objects with "code" and "data"
  //   properties; in this case, the "code" property should be a string
  //   of JS that's an expr that yields a function, and "data" should be
  //   an object that will be added as an additional argument when that
  //   function is called.
  // @param target The object that will be "this" for each function
  //   execution.
  // @param args Array of arguments to be passed to the functions. (The
  //   same arguments will be passed to all functions.)
  function evalAndRun(tasks, target, args) {
    if (tasks) {
      forEach(tasks, function(task) {
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        if (typeof(task) === "object") {
          theseArgs = theseArgs.concat([task.data]);
          task = task.code;
        }
        var taskFunc = eval("(" + task + ")");
        if (typeof(taskFunc) !== "function") {
          throw new Error("Task must be a function! Source:\n" + task);
        }
        taskFunc.apply(target, theseArgs);
      });
    }
  }

  function initSizing(el) {
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      return;

    var cel = document.getElementById("htmlwidget_container");
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        cel.style.bottom = pad.bottom + "px";
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      return {
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        getHeight: function() { return el.offsetHeight; }
      };
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  // Default implementations for methods
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      return querySelectorAll(scope, "." + this.name);
    },
    renderError: function(el, err) {
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      this.clearError(el);

      // Add all these error classes, as Shiny does
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      if (err.type !== null) {
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          return errClass + "-" + type;
        }).join(" ");
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      errClass = errClass + " htmlwidgets-error";

      // Is el inline or block? If inline or inline-block, just display:none it
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          errorSpan.text(err.message);
          $el.after(errorSpan);
        }
      } else if (display === "block") {
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        // el, and position the error to be on top of the el.
        // Mark it with a unique ID and CSS class so we can remove it later.
        $el.css("visibility", "hidden");
        if (err.message !== "") {
          var errorDiv = $("<div>").addClass(errClass).css("position", "absolute")
            .css("top", el.offsetTop)
            .css("left", el.offsetLeft)
            // setting width can push out the page size, forcing otherwise
            // unnecessary scrollbars to appear and making it impossible for
            // the element to shrink; so use max-width instead
            .css("maxWidth", el.offsetWidth)
            .css("height", el.offsetHeight);
          errorDiv.text(err.message);
          $el.after(errorDiv);

          // Really dumb way to keep the size/position of the error in sync with
          // the parent element as the window is resized or whatever.
          var intId = setInterval(function() {
            if (!errorDiv[0].parentElement) {
              clearInterval(intId);
              return;
            }
            errorDiv
              .css("top", el.offsetTop)
              .css("left", el.offsetLeft)
              .css("maxWidth", el.offsetWidth)
              .css("height", el.offsetHeight);
          }, 500);
        }
      }
    },
    clearError: function(el) {
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      var display = $el.data("restore-display-mode");
      $el.data("restore-display-mode", null);

      if (display === "inline" || display === "inline-block") {
        if (display)
          $el.css("display", display);
        $(el.nextSibling).filter(".htmlwidgets-error").remove();
      } else if (display === "block"){
        $el.css("visibility", "inherit");
        $(el.nextSibling).filter(".htmlwidgets-error").remove();
      }
    },
    sizing: {}
  };

  // Called by widget bindings to register a new type of widget. The definition
  // object can contain the following properties:
  // - name (required) - A string indicating the binding name, which will be
  //   used by default as the CSS classname to look for.
  // - initialize (optional) - A function(el) that will be called once per
  //   widget element; if a value is returned, it will be passed as the third
  //   value to renderValue.
  // - renderValue (required) - A function(el, data, initValue) that will be
  //   called with data. Static contexts will cause this to be called once per
  //   element; Shiny apps will cause this to be called multiple times per
  //   element, as the data changes.
  window.HTMLWidgets.widget = function(definition) {
    if (!definition.name) {
      throw new Error("Widget must have a name");
    }
    if (!definition.type) {
      throw new Error("Widget must have a type");
    }
    // Currently we only support output widgets
    if (definition.type !== "output") {
      throw new Error("Unrecognized widget type '" + definition.type + "'");
    }
    // TODO: Verify that .name is a valid CSS classname

    // Support new-style instance-bound definitions. Old-style class-bound
    // definitions have one widget "object" per widget per type/class of
    // widget; the renderValue and resize methods on such widget objects
    // take el and instance arguments, because the widget object can't
    // store them. New-style instance-bound definitions have one widget
    // object per widget instance; the definition that's passed in doesn't
    // provide renderValue or resize methods at all, just the single method
    //   factory(el, width, height)
    // which returns an object that has renderValue(x) and resize(w, h).
    // This enables a far more natural programming style for the widget
    // author, who can store per-instance state using either OO-style
    // instance fields or functional-style closure variables (I guess this
    // is in contrast to what can only be called C-style pseudo-OO which is
    // what we required before).
    if (definition.factory) {
      definition = createLegacyDefinitionAdapter(definition);
    }

    if (!definition.renderValue) {
      throw new Error("Widget must have a renderValue function");
    }

    // For static rendering (non-Shiny), use a simple widget registration
    // scheme. We also use this scheme for Shiny apps/documents that also
    // contain static widgets.
    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
    // Merge defaults into the definition; don't mutate the original definition.
    var staticBinding = extend({}, defaults, definition);
    overrideMethod(staticBinding, "find", function(superfunc) {
      return function(scope) {
        var results = superfunc(scope);
        // Filter out Shiny outputs, we only want the static kind
        return filterByClass(results, "html-widget-output", false);
      };
    });
    window.HTMLWidgets.widgets.push(staticBinding);

    if (shinyMode) {
      // Shiny is running. Register the definition with an output binding.
      // The definition itself will not be the output binding, instead
      // we will make an output binding object that delegates to the
      // definition. This is because we foolishly used the same method
      // name (renderValue) for htmlwidgets definition and Shiny bindings
      // but they actually have quite different semantics (the Shiny
      // bindings receive data that includes lots of metadata that it
      // strips off before calling htmlwidgets renderValue). We can't
      // just ignore the difference because in some widgets it's helpful
      // to call this.renderValue() from inside of resize(), and if
      // we're not delegating, then that call will go to the Shiny
      // version instead of the htmlwidgets version.

      // Merge defaults with definition, without mutating either.
      var bindingDef = extend({}, defaults, definition);

      // This object will be our actual Shiny binding.
      var shinyBinding = new Shiny.OutputBinding();

      // With a few exceptions, we'll want to simply use the bindingDef's
      // version of methods if they are available, otherwise fall back to
      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
      // methods in the future, and we want them to be overrideable by
      // HTMLWidget binding definitions, then we'll need to add them to this
      // list.
      delegateMethod(shinyBinding, bindingDef, "getId");
      delegateMethod(shinyBinding, bindingDef, "onValueChange");
      delegateMethod(shinyBinding, bindingDef, "onValueError");
      delegateMethod(shinyBinding, bindingDef, "renderError");
      delegateMethod(shinyBinding, bindingDef, "clearError");
      delegateMethod(shinyBinding, bindingDef, "showProgress");

      // The find, renderValue, and resize are handled differently, because we
      // want to actually decorate the behavior of the bindingDef methods.

      shinyBinding.find = function(scope) {
        var results = bindingDef.find(scope);

        // Only return elements that are Shiny outputs, not static ones
        var dynamicResults = results.filter(".html-widget-output");

        // It's possible that whatever caused Shiny to think there might be
        // new dynamic outputs, also caused there to be new static outputs.
        // Since there might be lots of different htmlwidgets bindings, we
        // schedule execution for later--no need to staticRender multiple
        // times.
        if (results.length !== dynamicResults.length)
          scheduleStaticRender();

        return dynamicResults;
      };

      // Wrap renderValue to handle initialization, which unfortunately isn't
      // supported natively by Shiny at the time of this writing.

      shinyBinding.renderValue = function(el, data) {
        Shiny.renderDependencies(data.deps);
        // Resolve strings marked as javascript literals to objects
        if (!(data.evals instanceof Array)) data.evals = [data.evals];
        for (var i = 0; data.evals && i < data.evals.length; i++) {
          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
        }
        if (!bindingDef.renderOnNullValue) {
          if (data.x === null) {
            el.style.visibility = "hidden";
            return;
          } else {
            el.style.visibility = "inherit";
          }
        }
        if (!elementData(el, "initialized")) {
          initSizing(el);

          elementData(el, "initialized", true);
          if (bindingDef.initialize) {
            var result = bindingDef.initialize(el, el.offsetWidth,
              el.offsetHeight);
            elementData(el, "init_result", result);
          }
        }
        bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
        evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
      };

      // Only override resize if bindingDef implements it
      if (bindingDef.resize) {
        shinyBinding.resize = function(el, width, height) {
          // Shiny can call resize before initialize/renderValue have been
          // called, which doesn't make sense for widgets.
          if (elementData(el, "initialized")) {
            bindingDef.resize(el, width, height, elementData(el, "init_result"));
          }
        };
      }

      Shiny.outputBindings.register(shinyBinding, bindingDef.name);
    }
  };

  var scheduleStaticRenderTimerId = null;
  function scheduleStaticRender() {
    if (!scheduleStaticRenderTimerId) {
      scheduleStaticRenderTimerId = setTimeout(function() {
        scheduleStaticRenderTimerId = null;
        window.HTMLWidgets.staticRender();
      }, 1);
    }
  }

  // Render static widgets after the document finishes loading
  // Statically render all elements that are of this widget's class
  window.HTMLWidgets.staticRender = function() {
    var bindings = window.HTMLWidgets.widgets || [];
    forEach(bindings, function(binding) {
      var matches = binding.find(document.documentElement);
      forEach(matches, function(el) {
        var sizeObj = initSizing(el, binding);

        if (hasClass(el, "html-widget-static-bound"))
          return;
        el.className = el.className + " html-widget-static-bound";

        var initResult;
        if (binding.initialize) {
          initResult = binding.initialize(el,
            sizeObj ? sizeObj.getWidth() : el.offsetWidth,
            sizeObj ? sizeObj.getHeight() : el.offsetHeight
          );
          elementData(el, "init_result", initResult);
        }

        if (binding.resize) {
          var lastSize = {};
          var resizeHandler = function(e) {
            var size = {
              w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
              h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
            };
            if (size.w === 0 && size.h === 0)
              return;
            if (size.w === lastSize.w && size.h === lastSize.h)
              return;
            lastSize = size;
            binding.resize(el, size.w, size.h, initResult);
          };

          on(window, "resize", resizeHandler);

          // This is needed for cases where we're running in a Shiny
          // app, but the widget itself is not a Shiny output, but
          // rather a simple static widget. One example of this is
          // an rmarkdown document that has runtime:shiny and widget
          // that isn't in a render function. Shiny only knows to
          // call resize handlers for Shiny outputs, not for static
          // widgets, so we do it ourselves.
          if (window.jQuery) {
            window.jQuery(document).on(
              "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
              resizeHandler
            );
            window.jQuery(document).on(
              "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
              resizeHandler
            );
          }

          // This is needed for the specific case of ioslides, which
          // flips slides between display:none and display:block.
          // Ideally we would not have to have ioslide-specific code
          // here, but rather have ioslides raise a generic event,
          // but the rmarkdown package just went to CRAN so the
          // window to getting that fixed may be long.
          if (window.addEventListener) {
            // It's OK to limit this to window.addEventListener
            // browsers because ioslides itself only supports
            // such browsers.
            on(document, "slideenter", resizeHandler);
            on(document, "slideleave", resizeHandler);
          }
        }

        var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
        if (scriptData) {
          var data = JSON.parse(scriptData.textContent || scriptData.text);
          // Resolve strings marked as javascript literals to objects
          if (!(data.evals instanceof Array)) data.evals = [data.evals];
          for (var k = 0; data.evals && k < data.evals.length; k++) {
            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
          }
          binding.renderValue(el, data.x, initResult);
          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
        }
      });
    });

    invokePostRenderHandlers();
  }

  // Wait until after the document has loaded to render the widgets.
  if (document.addEventListener) {
    document.addEventListener("DOMContentLoaded", function() {
      document.removeEventListener("DOMContentLoaded", arguments.callee, false);
      window.HTMLWidgets.staticRender();
    }, false);
  } else if (document.attachEvent) {
    document.attachEvent("onreadystatechange", function() {
      if (document.readyState === "complete") {
        document.detachEvent("onreadystatechange", arguments.callee);
        window.HTMLWidgets.staticRender();
      }
    });
  }


  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
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      key = 1;

    var link = document.getElementById(depname + "-" + key + "-attachment");
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    }
    return link.getAttribute("href");
  };

  window.HTMLWidgets.dataframeToD3 = function(df) {
    var names = [];
    var length;
    for (var name in df) {
        if (df.hasOwnProperty(name))
            names.push(name);
        if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
            throw new Error("All fields must be arrays");
        } else if (typeof(length) !== "undefined" && length !== df[name].length) {
            throw new Error("All fields must be arrays of the same length");
        }
        length = df[name].length;
    }
    var results = [];
    var item;
    for (var row = 0; row < length; row++) {
        item = {};
        for (var col = 0; col < names.length; col++) {
            item[names[col]] = df[names[col]][row];
        }
        results.push(item);
    }
    return results;
  };

  window.HTMLWidgets.transposeArray2D = function(array) {
      if (array.length === 0) return array;
      var newArray = array[0].map(function(col, i) {
          return array.map(function(row) {
              return row[i]
          })
      });
      return newArray;
  };
  // Split value at splitChar, but allow splitChar to be escaped
  // using escapeChar. Any other characters escaped by escapeChar
  // will be included as usual (including escapeChar itself).
  function splitWithEscape(value, splitChar, escapeChar) {
    var results = [];
    var escapeMode = false;
    var currentResult = "";
    for (var pos = 0; pos < value.length; pos++) {
      if (!escapeMode) {
        if (value[pos] === splitChar) {
          results.push(currentResult);
          currentResult = "";
        } else if (value[pos] === escapeChar) {
          escapeMode = true;
        } else {
          currentResult += value[pos];
        }
      } else {
        currentResult += value[pos];
        escapeMode = false;
      }
    }
    if (currentResult !== "") {
      results.push(currentResult);
    }
    return results;
  }
  // Function authored by Yihui/JJ Allaire
  window.HTMLWidgets.evaluateStringMember = function(o, member) {
    var parts = splitWithEscape(member, '.', '\\');
    for (var i = 0, l = parts.length; i < l; i++) {
      var part = parts[i];
      // part may be a character or 'numeric' member name
      if (o !== null && typeof o === "object" && part in o) {
        if (i == (l - 1)) { // if we are at the end of the line then evalulate
          if (typeof o[part] === "string")
            o[part] = eval("(" + o[part] + ")");
        } else { // otherwise continue to next embedded object
          o = o[part];
        }
      }
    }
  };

  // Retrieve the HTMLWidget instance (i.e. the return value of an
  // HTMLWidget binding's initialize() or factory() function)
  // associated with an element, or null if none.
  window.HTMLWidgets.getInstance = function(el) {
    return elementData(el, "init_result");
  };

  // Finds the first element in the scope that matches the selector,
  // and returns the HTMLWidget instance (i.e. the return value of
  // an HTMLWidget binding's initialize() or factory() function)
  // associated with that element, if any. If no element matches the
  // selector, or the first matching element has no HTMLWidget
  // instance associated with it, then null is returned.
  //
  // The scope argument is optional, and defaults to window.document.
  window.HTMLWidgets.find = function(scope, selector) {
    if (arguments.length == 1) {
      selector = scope;
      scope = document;
    }

    var el = scope.querySelector(selector);
    if (el === null) {
      return null;
    } else {
      return window.HTMLWidgets.getInstance(el);
    }
  };

  // Finds all elements in the scope that match the selector, and
  // returns the HTMLWidget instances (i.e. the return values of
  // an HTMLWidget binding's initialize() or factory() function)
  // associated with the elements, in an array. If elements that
  // match the selector don't have an associated HTMLWidget
  // instance, the returned array will contain nulls.
  //
  // The scope argument is optional, and defaults to window.document.
  window.HTMLWidgets.findAll = function(scope, selector) {
    if (arguments.length == 1) {
      selector = scope;
      scope = document;
    }

    var nodes = scope.querySelectorAll(selector);
    var results = [];
    for (var i = 0; i < nodes.length; i++) {
      results.push(window.HTMLWidgets.getInstance(nodes[i]));
    }
    return results;
  };

  var postRenderHandlers = [];
  function invokePostRenderHandlers() {
    while (postRenderHandlers.length) {
      var handler = postRenderHandlers.shift();
      if (handler) {
        handler();
      }
    }
  }

  // Register the given callback function to be invoked after the
  // next time static widgets are rendered.
  window.HTMLWidgets.addPostRenderHandler = function(callback) {
    postRenderHandlers.push(callback);
  };

  // Takes a new-style instance-bound definition, and returns an
  // old-style class-bound definition. This saves us from having
  // to rewrite all the logic in this file to accomodate both
  // types of definitions.
  function createLegacyDefinitionAdapter(defn) {
    var result = {
      name: defn.name,
      type: defn.type,
      initialize: function(el, width, height) {
        return defn.factory(el, width, height);
      },
      renderValue: function(el, x, instance) {
        return instance.renderValue(x);
      },
      resize: function(el, width, height, instance) {
        return instance.resize(width, height);
      }
    };

    if (defn.find)
      result.find = defn.find;
    if (defn.renderError)
      result.renderError = defn.renderError;
    if (defn.clearError)
      result.clearError = defn.clearError;

    return result;
  }
})();

</script>
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</style>
<style type="text/css">.bk-loader {
margin: auto;
font-size: 10px;
position: relative;
text-indent: -9999em;
border-top: 8px solid rgba(102,102,102, 0.2);
border-right: 8px solid rgba(102,102,102, 0.2);
border-bottom: 8px solid rgba(102,102,102, 0.2);
border-left: 8px solid #666666;
-webkit-transform: translateZ(0);
-ms-transform: translateZ(0);
transform: translateZ(0);
-webkit-animation: bk-load8 1.1s infinite linear;
animation: bk-load8 1.1s infinite linear;
-webkit-transition: opacity 0.7s step-end;
transition: opacity 0.7s step-end;
position: absolute;
}
.bk-loader,
.bk-loader:after {
border-radius: 50%;
width: 70px;
height: 70px;
}
@-webkit-keyframes bk-load8 {
0% {
-webkit-transform: rotate(0deg);
transform: rotate(0deg);
}
100% {
-webkit-transform: rotate(360deg);
transform: rotate(360deg);
}
}
@keyframes bk-load8 {
0% {
-webkit-transform: rotate(0deg);
transform: rotate(0deg);
}
100% {
-webkit-transform: rotate(360deg);
transform: rotate(360deg);
}
}
</style>
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binary"):(this._fragments.push(t.data),3===this._fragments.length&&(this._partial=u.assemble(this._fragments[0],this._fragments[1],this._fragments[2]),this._fragments=[],r=this._partial.problem(),null!==r&&this._close_bad_protocol(r))),null!=this._partial&&this._partial.complete())return e=this._partial,this._partial=null,this._current_handler(e)},t.prototype._on_close=function(t){var e,r;for(f.info("Lost websocket "+this._number+" connection, "+t.code+" ("+t.reason+")"),this.socket=null,null!=this._pending_ack&&(this._pending_ack[1](new Error("Lost websocket connection, "+t.code+" ("+t.reason+")")),this._pending_ack=null),e=function(){var t,e,r;e=this._pending_replies;for(r in e)return t=e[r],delete this._pending_replies[r],t;return null},r=e();null!==r;)r[1]("Disconnected"),r=e();if(!this.closed_permanently)return this._schedule_reconnect(2e3)},t.prototype._on_error=function(t){return f.debug("Websocket error on socket  "+this._number),t(new Error("Could not open websocket"))},t.prototype._close_bad_protocol=function(t){if(f.error("Closing connection: "+t),null!=this.socket)return this.socket.close(1002,t)},t.prototype._awaiting_ack_handler=function(t){return"ACK"!==t.msgtype()?this._close_bad_protocol("First message was not an ACK"):(this._current_handler=function(t){return function(e){return t._steady_state_handler(e)}}(this),this._repull_session_doc(),null!=this._pending_ack?(this._pending_ack[0](this),this._pending_ack=null):void 0)},t.prototype._steady_state_handler=function(t){var e;return t.reqid()in this._pending_replies?(e=this._pending_replies[t.reqid()],delete this._pending_replies[t.reqid()],e[0](t)):t.msgtype()in m?m[t.msgtype()](this,t):f.debug("Doing nothing with message "+t.msgtype())},t}(),o=function(){function t(t,e,r){this._connection=t,this.document=e,this.id=r,this._current_patch=null,this.document_listener=function(t){return function(e){return t._document_changed(e)}}(this),this.document.on_change(this.document_listener)}return t.prototype.close=function(){return this._connection.close()},t.prototype._connection_closed=function(){return this.document.remove_on_change(this.document_listener)},t.prototype.request_server_info=function(){var t,e;return t=u.create("SERVER-INFO-REQ",{}),e=this._connection.send_with_reply(t),e.then(function(t){return t.content})},t.prototype.force_roundtrip=function(){return this.request_server_info().then(function(t){})},t.prototype._should_suppress_on_change=function(t,e){var r,n,o,i,s,a,u,c,f,m,g,y,v,b;if(e instanceof h){for(g=t.content.events,n=0,a=g.length;n<a;n++)if(r=g[n],"ModelChanged"===r.kind&&r.model.id===e.model.id&&r.attr===e.attr)if(m=r["new"],e.new_ instanceof l){if("object"==typeof m&&"id"in m&&m.id===e.new_.id)return!0}else if(d.isEqual(m,e.new_))return!0}else if(e instanceof p){for(y=t.content.events,o=0,u=y.length;o<u;o++)if(r=y[o],"RootAdded"===r.kind&&r.model.id===e.model.id)return!0}else if(e instanceof _){for(v=t.content.events,i=0,c=v.length;i<c;i++)if(r=v[i],"RootRemoved"===r.kind&&r.model.id===e.model.id)return!0}else if(e instanceof TitleChangedEvent)for(b=t.content.events,s=0,f=b.length;s<f;s++)if(r=b[s],"TitleChanged"===r.kind&&r.title===e.title)return!0;return!1},t.prototype._document_changed=function(t){var e;if(!(null!=this._current_patch&&this._should_suppress_on_change(this._current_patch,t)||t instanceof h&&!(t.attr in t.model.serializable_attributes())))return e=u.create("PATCH-DOC",{},this.document.create_json_patch([t])),this._connection.send(e)},t.prototype._handle_patch=function(t){this._current_patch=t;try{return this.document.apply_json_patch(t.content)}finally{this._current_patch=null}},t}(),g=function(t,e){var r,o,i;return i=null,r=null,o=new c(function(o,i){return r=new n(t,e,function(t){var e,r;try{return o(t)}catch(r){throw e=r,f.error("Promise handler threw an error, closing session "+error),t.close(),e}},function(){return i(new Error("Connection was closed before we successfully pulled a session"))}),r.connect().then(function(t){},function(t){throw f.error("Failed to connect to Bokeh server "+t),t})}),o.close=function(){return r.close()},o},e.exports={pull_session:g,DEFAULT_SERVER_WEBSOCKET_URL:i,DEFAULT_SESSION_ID:s}},{"./core/has_props":"core/has_props","./core/logging":"core/logging","./document":"document","es6-promise":"es6-promise",underscore:"underscore"}],"common/build_views":[function(t,e,r){var n,o,i;n=t("underscore"),o=function(t,e,r,o){var s,a,l,u,h,c,p,_,d,f,m;for(null==o&&(o=[]),s=[],d=n.filter(e,function(e){return!n.has(t,e.id)}),l=a=0,c=d.length;a<c;l=++a)_=d[l],m=n.extend({},r,{model:_}),l<o.length?t[_.id]=new o[l](m):t[_.id]=new _.default_view(m),t[_.id].$el.find("*[class*='ui-']").each(function(t,e){return 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g_ticker"),MonthsTicker:t("../models/tickers/months_ticker"),SingleIntervalTicker:t("../models/tickers/single_interval_ticker"),YearsTicker:t("../models/tickers/years_ticker"),TileRenderer:t("../models/tiles/tile_renderer"),TileSource:t("../models/tiles/tile_source"),TMSTileSource:t("../models/tiles/tms_tile_source"),WMTSTileSource:t("../models/tiles/wmts_tile_source"),QUADKEYTileSource:t("../models/tiles/quadkey_tile_source"),BBoxTileSource:t("../models/tiles/bbox_tile_source"),DynamicImageRenderer:t("../models/tiles/dynamic_image_renderer"),ImageSource:t("../models/tiles/image_source"),Toolbar:t("../models/tools/toolbar"),ToolbarBox:t("../models/tools/toolbar_box"),ButtonTool:t("../models/tools/button_tool"),ActionTool:t("../models/tools/actions/action_tool"),SaveTool:t("../models/tools/actions/save_tool"),UndoTool:t("../models/tools/actions/undo_tool"),RedoTool:t("../models/tools/actions/redo_tool"),ResetTool:t("../models/tools/actions/reset_tool"),HelpTool:t("../models/tools/actions/help_tool"),BoxSelectTool:t("../models/tools/gestures/box_select_tool"),BoxZoomTool:t("../models/tools/gestures/box_zoom_tool"),GestureTool:t("../models/tools/gestures/gesture_tool"),LassoSelectTool:t("../models/tools/gestures/lasso_select_tool"),PanTool:t("../models/tools/gestures/pan_tool"),PolySelectTool:t("../models/tools/gestures/poly_select_tool"),SelectTool:t("../models/tools/gestures/select_tool"),ResizeTool:t("../models/tools/gestures/resize_tool"),TapTool:t("../models/tools/gestures/tap_tool"),WheelZoomTool:t("../models/tools/gestures/wheel_zoom_tool"),CrosshairTool:t("../models/tools/inspectors/crosshair_tool"),HoverTool:t("../models/tools/inspectors/hover_tool"),InspectTool:t("../models/tools/inspectors/inspect_tool")}},{"../models/annotations/arrow":"models/annotations/arrow","../models/annotations/arrow_head":"models/annotations/arrow_head","../models/annotations/box_annotation":"models/annotations/box_annotation","../models/annotations/color_bar":"models/annotations/color_bar","../models/annotations/label":"models/annotations/label","../models/annotations/label_set":"models/annotations/label_set","../models/annotations/legend":"models/annotations/legend","../models/annotations/poly_annotation":"models/annotations/poly_annotation","../models/annotations/span":"models/annotations/span","../models/annotations/title":"models/annotations/title","../models/annotations/tooltip":"models/annotations/tooltip","../models/axes/categorical_axis":"models/axes/categorical_axis","../models/axes/datetime_axis":"models/axes/datetime_axis","../models/axes/linear_axis":"models/axes/linear_axis","../models/axes/log_axis":"models/axes/log_axis","../models/callbacks/customjs":"models/callbacks/customjs","../models/callbacks/open_url":"models/callbacks/open_url","../models/canvas/canvas":"models/canvas/canvas","../models/canvas/cartesian_frame":"models/canvas/cartesian_frame","../models/formatters/basic_tick_formatter":"models/formatters/basic_tick_formatter","../models/formatters/categorical_tick_formatter":"models/formatters/categorical_tick_formatter","../models/formatters/datetime_tick_formatter":"models/formatters/datetime_tick_formatter","../models/formatters/func_tick_formatter":"models/formatters/func_tick_formatter","../models/formatters/log_tick_formatter":"models/formatters/log_tick_formatter","../models/formatters/numeral_tick_formatter":"models/formatters/numeral_tick_formatter","../models/formatters/printf_tick_formatter":"models/formatters/printf_tick_formatter","../models/glyphs/annular_wedge":"models/glyphs/annular_wedge","../models/glyphs/annulus":"models/glyphs/annulus","../models/glyphs/arc":"models/glyphs/arc","../models/glyphs/bezier":"models/glyphs/bezier","../models/glyphs/circle":"models/glyphs/circle","../models/glyphs/ellipse":"models/glyphs/ellipse","../models/glyphs/gear":"models/glyphs/gear","../models/glyphs/hbar":"models/glyphs/hbar","../models/glyphs/image":"models/glyphs/image","../models/glyphs/image_rgba":"models/glyphs/image_rgba","../models/glyphs/image_url":"models/glyphs/image_url","../models/glyphs/line":"models/glyphs/line","../models/glyphs/multi_line":"models/glyphs/multi_line","../models/glyphs/oval":"models/glyphs/oval","../models/glyphs/patch":"models/glyphs/patch","../models/glyphs/patches":"models/glyphs/patches","../models/glyphs/quad":"models/glyphs/quad","../models/glyphs/quadratic":"models/glyphs/quadratic","../models/glyphs/ray":"models/glyphs/ray","../models/glyphs/rect":"models/glyphs/rect","../models/glyphs/segment":"models/glyphs/segment","../models/glyphs/text":"models/glyphs/text","../models/glyphs/vbar":"models/glyphs/vbar","../models/glyphs/wedge":"models/glyphs/wedge","../models/grids/grid":"models/grids/grid","../models/layouts/column":"models/layouts/column","../models/layouts/row":"models/layouts/row","../models/layouts/spacer":"models/layouts/spacer","../models/layouts/widget_box":"models/layouts/widget_box","../models/mappers/categorical_mapper":"models/mappers/categ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t,e,r,o,i,s,a,l,u,h;for(i=this._roots,e=0,r=i.length;e<r;e++)if(s=i[e],s.layoutable===!0&&(u=s.get_constrained_variables(),null!=u.width||null!=u.height)){for(a=n("#modelid_"+s.id),l=0,o=a;0===l;)o=o.parent(),l=o.height();h=o.width(),t=l,null!=u.width&&(w.debug("Suggest width on Document -- "+h),this._solver.suggest_value(this._doc_width,h)),null!=u.height&&(w.debug("Suggest height on Document -- "+t),this._solver.suggest_value(this._doc_height,t))}return this._solver.update_variables(!1),this._solver.trigger("resize")},t.prototype.clear=function(){var t;this._push_all_models_freeze();try{for(t=[];this._roots.length>0;)t.push(this.remove_root(this._roots[0]));return t}finally{this._pop_all_models_freeze()}},t.prototype._destructively_move=function(t){var e,r,n,o,i,s;if(t===this)throw new Error("Attempted to overwrite a document with 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e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t("underscore"),n=t("./annotation"),a=t("../../core/properties"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.$el.appendTo(this.plot_view.$el.find("div.bk-canvas-overlays")),this.$el.addClass("bk-shading"),this.$el.hide()},e.prototype.bind_bokeh_events=function(){return"css"===this.mget("render_mode")?(this.listenTo(this.model,"change",this.render),this.listenTo(this.model,"data_update",this.render)):(this.listenTo(this.model,"change",this.plot_view.request_render),this.listenTo(this.model,"data_update",this.plot_view.request_render))},e.prototype.render=function(){var t,e,r,n;return null==this.mget("left")&&null==this.mget("right")&&null==this.mget("top")&&null==this.mget("bottom")?(this.$el.hide(),null):(this.frame=this.plot_model.get("frame"),this.canvas=this.plot_model.get("canvas"),this.xmapper=this.plot_view.frame.get("x_mappers")[this.mget("x_range_name")],this.ymapper=this.plot_view.frame.get("y_mappers")[this.mget("y_range_name")],e=this.canvas.vx_to_sx(this._calc_dim("left",this.xmapper,this.frame.get("h_range").get("start"))),r=this.canvas.vx_to_sx(this._calc_dim("right",this.xmapper,this.frame.get("h_range").get("end"))),t=this.canvas.vy_to_sy(this._calc_dim("bottom",this.ymapper,this.frame.get("v_range").get("start"))),n=this.canvas.vy_to_sy(this._calc_dim("top",this.ymapper,this.frame.get("v_range").get("end"))),"css"===this.mget("render_mode")?this._css_box(e,r,t,n):this._canvas_box(e,r,t,n))},e.prototype._css_box=function(t,e,r,n){var o,i,a,l,u,h,c,p;return p=Math.abs(e-t),h=Math.abs(r-n),u=this.mget("line_width").value,a=this.mget("line_color").value,i=this.mget("fill_color").value,o=this.mget("fill_alpha").value,c="left:"+t+"px; width:"+p+"px; top:"+n+"px; height:"+h+"px; border-width:"+u+"px; border-color:"+a+"; background-color:"+i+"; opacity:"+o+";",l=this.mget("line_dash"),s.isArray(l)&&(l=l.length<2?"solid":"dashed"),s.isString(l)&&(c+=" border-style:"+l+";"),this.$el.attr("style",c),this.$el.show()},e.prototype._canvas_box=function(t,e,r,n){var o;return o=this.plot_view.canvas_view.ctx,o.save(),o.beginPath(),o.rect(t,n,e-t,r-n),this.visuals.fill.set_value(o),o.fill(),this.visuals.line.set_value(o),o.stroke(),o.restore()},e.prototype._calc_dim=function(t,e,r){var n;return n=null!=this.mget(t)?"data"===this.mget(t+"_units")?e.map_to_target(this.mget(t)):this.mget(t):r},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type="BoxAnnotation",e.mixins(["line","fill"]),e.define({render_mode:[a.RenderMode,"canvas"],x_range_name:[a.String,"default"],y_range_name:[a.String,"default"],top:[a.Number,null],top_units:[a.SpatialUnits,"data"],bottom:[a.Number,null],bottom_units:[a.SpatialUnits,"data"],left:[a.Number,null],left_units:[a.SpatialUnits,"data"],right:[a.Number,null],right_units:[a.SpatialUnits,"data"]}),e.override({fill_color:"#fff9ba",fill_alpha:.4,line_color:"#cccccc",line_alpha:.3}),e.prototype.update=function(t){var e,r,n,o;return r=t.left,n=t.right,o=t.top,e=t.bottom,this.set({left:r,right:n,top:o,bottom:e},{silent:!0}),this.trigger("data_update")},e}(n.Model),e.exports={Model:o,View:i}},{"../../core/properties":"core/properties","./annotation":"models/annotations/annotation",underscore:"underscore"}],"models/annotations/color_bar":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y=function(t,e){function r(){this.constructor=t}for(var n in e)v.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},v={}.hasOwnProperty;f=t("underscore"),n=t("./annotation"),i=t("../tickers/basic_ticker"),o=t("../formatters/basic_tick_formatter"),h=t("../mappers/linear_color_mapper"),c=t("../mappers/linear_mapper"),p=t("../mappers/log_mapper"),_=t("../ranges/range1d"),m=t("../../core/properties"),g=t("../../core/util/text"),d=25,u=.3,l=.8,a=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return y(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this._set_canvas_image()},e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,"change:visible",this.plot_view.request_render),this.listenTo(this.model.ticker,"change",this.plot_view.request_render),this.listenTo(this.model.formatter,"change",this.plot_view.request_render),this.listenTo(this.model.color_mapper,"change",function(){return this._set_canvas_image(),this.plot_view.request_render()})},e.prototype._get_panel_offset=function(){var t,e;return t=this.model.panel._left._value,e=this.model.panel._top._value,{x:t,y:-e}},e.prototype._get_size=function(){var t,e;return t=this.compute_legend_dimensions(),e=this.model.panel.side,"above"===e||"below"===e?t.height:"left"===e||"right"===e?t.width:void 0},e.prototype._set_canvas_image=function(){var t,e,r,n,o,i,s,a,l,u,c,p,_;switch(a=this.model.color_mapper.palette,"vertical"===this.model.orientation&&(a=a.slice(0).reverse()),this.model.orientation){case"vertical":l=[1,a.length],_=l[0],o=l[1];break;case"horizontal":u=[a.length,1],_=u[0],o=u[1]}return r=document.createElement("canvas"),c=[_,o],r.width=c[0],r.height=c[1],i=r.getContext("2d"),s=i.getImageData(0,0,_,o),n=new h.Model({palette:a}),t=n.v_map_screen(function(){p=[];for(var t=0,e=a.length;0<=e?t<e:t>e;0<=e?t++:t--)p.push(t);return p}.apply(this)),e=new Uint8ClampedArray(t),s.data.set(e),i.putImageData(s,0,0),this.image=r},e.prototype.compute_legend_dimensions=function(){var t,e,r,n,o,i,s,a,l,u;switch(t=this.model._computed_image_dimensions(),a=[t.height,t.width],e=a[0],r=a[1],n=this._get_label_extent(),u=this.model._title_extent(),l=this.model._tick_extent(),s=this.model.padding,this.model.orientation){case"vertical":o=e+u+2*s,i=r+l+n+2*s;break;case"horizontal":o=e+u+l+n+2*s,i=r+2*s}return{height:o,width:i}},e.prototype.compute_legend_location=function(){var t,e,r,n,o,i,s,a,l,u,h,c;if(e=this.compute_legend_dimensions(),s=[e.height,e.width],r=s[0],o=s[1],n=this.model.margin,i=this.model.location,t=this.plot_view.frame.get("h_range"),u=this.plot_view.frame.get("v_range"),f.isString(i))switch(i){case"top_left":h=t.get("start")+n,c=u.get("end")-n;break;case"top_center":h=(t.get("end")+t.get("start"))/2-o/2,c=u.get("end")-n;break;case"top_right":h=t.get("end")-n-o,c=u.get("end")-n;break;case"right_center":h=t.get("end")-n-o,c=(u.get("end")+u.get("start"))/2+r/2;break;case"bottom_right":h=t.get("end")-n-o,c=u.get("start")+n+r;break;case"bottom_center":h=(t.get("end")+t.get("start"))/2-o/2,c=u.get("start")+n+r;break;case"bottom_left":h=t.get("start")+n,c=u.get("start")+n+r;break;case"left_center":h=t.get("start")+n,c=(u.get("end")+u.get("start"))/2+r/2;break;case"center":h=(t.get("end")+t.get("start"))/2-o/2,c=(u.get("end")+u.get("start"))/2+r/2}else f.isArray(i)&&2===i.length&&(h=i[0],c=i[1]);return a=this.plot_view.canvas.vx_to_sx(h),l=this.plot_view.canvas.vy_to_sy(c),{sx:a,sy:l}},e.prototype.render=function(){var t,e,r,n,o;if(this.model.visible!==!1)return t=this.plot_view.canvas_view.ctx,t.save(),null!=this.model.panel&&(o=this._get_panel_offset(),t.translate(o.x,o.y),e=this._get_frame_offset(),t.translate(e.x,e.y)),n=this.compute_legend_location(),t.translate(n.sx,n.sy),this._draw_bbox(t),r=this._get_image_offset(),t.translate(r.x,r.y),this._draw_image(t),null!=this.model.color_mapper.low&&null!=this.model.color_mapper.high&&(this._draw_major_ticks(t),this._draw_minor_ticks(t),this._draw_major_labels(t)),this.model.title&&this._draw_title(t),t.restore()},e.prototype._draw_bbox=function(t){var e;return e=this.compute_legend_dimensions(),t.save(),this.visuals.background_fill.doit&&(this.visuals.background_fill.set_value(t),t.fillRect(0,0,e.width,e.height)),this.visuals.border_line.doit&&(this.visuals.border_line.set_value(t),t.strokeRect(0,0,e.width,e.height)),t.restore()},e.prototype._draw_image=function(t){var e;return e=this.model._computed_image_dimensions(),t.save(),t.setImageSmoothingEnabled(!1),t.globalAlpha=this.model.scale_alpha,t.drawImage(this.image,0,0,e.width,e.height),this.visuals.bar_line.doit&&(this.visuals.bar_line.set_value(t),t.strokeRect(0,0,e.width,e.height)),t.restore()},e.prototype._draw_major_ticks=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f;if(this.visuals.major_tick_line.doit){for(s=this.model._normals(),o=s[0],i=s[1],r=this.model._computed_image_dimensions(),a=[r.width*o,r.height*i],d=a[0],f=a[1],l=this.model._tick_coordinates().major,h=l[0],c=l[1],p=this.model.major_tick_in,_=this.model.major_tick_out,t.save(),t.translate(d,f),this.visuals.major_tick_line.set_value(t),e=n=0,u=h.length;0<=u?n<u:n>u;e=0<=u?++n:--n)t.beginPath(),t.moveTo(Math.round(h[e]+o*_),Math.round(c[e]+i*_)),t.lineTo(Math.round(h[e]-o*p),Math.round(c[e]-i*p)),t.stroke();return t.restore()}},e.prototype._draw_minor_ticks=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f;if(this.visuals.minor_tick_line.doit){for(s=this.model._normals(),o=s[0],i=s[1],r=this.model._computed_image_dimensions(),a=[r.width*o,r.height*i],d=a[0],f=a[1],l=this.model._tick_coordinates().minor,h=l[0],c=l[1],p=this.model.minor_tick_in,_=this.model.minor_tick_out,t.save(),t.translate(d,f),this.visuals.minor_tick_line.set_value(t),e=n=0,u=h.length;0<=u?n<u:n>u;e=0<=u?++n:--n)t.beginPath(),t.moveTo(Math.round(h[e]+o*_),Math.round(c[e]+i*_)),t.lineTo(Math.round(h[e]-o*p),Math.round(c[e]-i*p)),t.stroke();return t.restore()}},e.prototype._draw_major_labels=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v;if(this.visuals.major_label_text.doit){for(l=this.model._normals(),s=l[0],a=l[1],n=this.model._computed_image_dimensions(),u=[n.width*s,n.height*a],m=u[0],y=u[1],_=this.model.label_standoff+this.model._tick_extent(),h=[_*s,_*a],g=h[0],v=h[1],c=this.model._tick_coordinates().major,d=c[0],f=c[1],i=this.model._tick_coordinates().major_labels,e=this.mget("formatter").doFormat(i),this.visuals.major_label_text.set_value(t),t.save(),t.translate(m+g,y+v),r=o=0,p=d.length;0<=p?o<p:o>p;r=0<=p?++o:--o)t.fillText(e[r],Math.round(d[r]+s*this.model.label_standoff),Math.round(f[r]+a*this.model.label_standoff));return t.restore()}},e.prototype._draw_title=function(t){if(this.visuals.title_text.doit)return t.save(),this.visuals.title_text.set_value(t),t.fillText(this.model.title,0,-this.model.title_standoff),t.restore()},e.prototype._get_label_extent=function(){var t,e,r,n;if(null!=this.model.color_mapper.low&&null!=this.model.color_mapper.high){switch(t=this.plot_view.canvas_view.ctx,t.save(),this.visuals.major_label_text.set_value(t),this.model.orientation){case"vertical":e=this.model.formatter.doFormat(this.model._tick_coordinates().major_labels),n=f.max(function(){var n,o,i;for(i=[],n=0,o=e.length;n<o;n++)r=e[n],i.push(t.measureText(r.toString()).width);return i}());break;case"horizontal":n=g.get_text_height(this.visuals.major_label_text.font_value()).height}n+=this.model.label_standoff,t.restore()}else n=0;return n},e.prototype._get_frame_offset=function(){var t,e,r,n,o;switch(r=[0,0],n=r[0],o=r[1],e=this.model.panel,t=this.plot_view.frame,e.side){case"left":case"right":o=Math.abs(e.get("top")-t.get("top"));break;case"above":case"below":n=Math.abs(t.get("left"))}return{x:n,y:o}},e.prototype._get_image_offset=function(){var t,e;return t=this.model.padding,e=this.model.padding+this.model._title_extent(),{x:t,y:e}},e}(n.View),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return y(e,t),e.prototype.default_view=a,e.prototype.type="ColorBar",e.mixins(["text:major_label_","text:title_","line:major_tick_","line:minor_tick_","line:border_","line:bar_","fill:background_"]),e.define({location:[m.Any,"top_right"],orientation:[m.Orientation,"vertical"],title:[m.String],title_standoff:[m.Number,2],height:[m.Any,"auto"],width:[m.Any,"auto"],scale_alpha:[m.Number,1],ticker:[m.Instance,function(){return new i.Model}],formatter:[m.Instance,function(){return new o.Model}],color_mapper:[m.Instance],label_standoff:[m.Number,5],margin:[m.Number,30],padding:[m.Number,10],major_tick_in:[m.Number,5],major_tick_out:[m.Number,0],minor_tick_in:[m.Number,0],minor_tick_out:[m.Number,0]}),e.override({background_fill_color:"#ffffff",background_fill_alpha:.95,bar_line_color:null,border_line_color:null,major_label_text_align:"center",major_label_text_baseline:"middle",major_label_text_font_size:"8pt",major_tick_line_color:"#ffffff",minor_tick_line_color:null,title_text_font_size:"10pt",title_text_font_style:"italic"}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r)},e.prototype._normals=function(){var t,e,r,n;return"vertical"===this.orientation?(r=[1,0],t=r[0],e=r[1]):(n=[0,1],t=n[0],e=n[1]),[t,e]},e.prototype._title_extent=function(){var t,e;return t=this.title_text_font+" "+this.title_text_font_size+" "+this.title_text_font_style,e=this.title?g.get_text_height(t).height+this.title_standoff:0},e.prototype._tick_extent=function(){var t;return t=null!=this.color_mapper.low&&null!=this.color_mapper.high?f.max([this.major_tick_out,this.minor_tick_out]):0},e.prototype._computed_image_dimensions=function(){var t,e,r,n,o;switch(t=this.plot.plot_canvas.frame.get("height"),e=this.plot.plot_canvas.frame.get("width"),n=this._title_extent(),this.orientation){case"vertical":"auto"===this.height?null!=this.panel?r=t-2*this.padding-n:(r=f.max([this.color_mapper.palette.length*d,t*u]),r=f.min([r,t*l-2*this.padding-n])):r=this.height,o="auto"===this.width?d:this.width;break;case"horizontal":r="auto"===this.height?d:this.height,"auto"===this.width?null!=this.panel?o=e-2*this.padding:(o=f.max([this.color_mapper.palette.length*d,e*u]),o=f.min([o,e*l-2*this.padding])):o=this.width}return{height:r,width:o}},e.prototype._tick_coordinate_mapper=function(t){var e,r;switch(r={source_range:new _.Model({start:this.color_mapper.low,end:this.color_mapper.high}),target_range:new _.Model({start:0,end:t})},this.color_mapper.type){case"LinearColorMapper":e=new c.Model(r);break;case"LogColorMapper":e=new p.Model(r)}return e},e.prototype._tick_coordinates=function(){var t,e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b;switch(o=this._computed_image_dimensions(),this.orientation){case"vertical":y=o.height;break;case"horizontal":y=o.width}for(c=this._tick_coordinate_mapper(y),d=this._normals(),r=d[0],i=d[1],f=[this.color_mapper.low,this.color_mapper.high],v=f[0],e=f[1],b=this.ticker.get_ticks(v,e,null,this.ticker.desired_num_ticks),h=b.major,_=b.minor,l=[[],[]],p=[[],[]],n=s=0,m=h.length;0<=m?s<m:s>m;n=0<=m?++s:--s)h[n]<v||h[n]>e||(l[r].push(h[n]),l[i].push(0));for(n=a=0,g=_.length;0<=g?a<g:a>g;n=0<=g?++a:--a)_[n]<v||_[n]>e||(p[r].push(_[n]),p[i].push(0));return u=l[r].slice(0),l[r]=c.v_map_to_target(l[r]),p[r]=c.v_map_to_target(p[r]),"vertical"===this.orientation&&(l[r]=new Float64Array(function(){var e,n,o,i;for(o=l[r],i=[],n=0,e=o.length;n<e;n++)t=o[n],i.push(y-t);return i}()),p[r]=new Float64Array(function(){var e,n,o,i;for(o=p[r],i=[],n=0,e=o.length;n<e;n++)t=o[n],i.push(y-t);return i}())),{major:l,minor:p,major_labels:u}},e}(n.Model),e.exports={Model:s,View:a}},{"../../core/properties":"core/properties","../../core/util/text":"core/util/text","../formatters/basic_tick_formatter":"models/formatters/basic_tick_formatter","../mappers/linear_color_mapper":"models/mappers/linear_color_mapper","../mappers/linear_mapper":"models/mappers/linear_mapper","../mappers/log_mapper":"models/mappers/log_mapper","../ranges/range1d":"models/ranges/range1d","../tickers/basic_ticker":"models/tickers/basic_ticker","./annotation":"models/annotations/annotation",underscore:"underscore"}],"models/annotations/label":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;i=t("./text_annotation"),s=t("../../core/properties"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.initialize=function(t){var r,n,o,i;e.__super__.initialize.call(this,t),this.canvas=this.plot_model.get("canvas"),this.xmapper=this.plot_view.frame.get("x_mappers")[this.mget("x_range_name")],this.ymapper=this.plot_view.frame.get("y_mappers")[this.mget("y_range_name")],o=this.visuals,i=[];for(r in o)n=o[r],i.push(n.warm_cache(null));return i},e.prototype._get_size=function(){var t,e,r,n;return t=this.plot_view.canvas_view.ctx,this.visuals.text.set_value(t),r=this.model.panel.side,"above"===r||"below"===r?e=t.measureText(this.mget("text")).ascent:"left"===r||"right"===r?n=t.measureText(this.mget("text")).width:void 0},e.prototype.render=function(){var t,e,r,n,o,i,s;switch(e=this.plot_view.canvas_view.ctx,this.mget("angle_units")){case"rad":t=-1*this.mget("angle");break;case"deg":t=-1*this.mget("angle")*Math.PI/180}return i="data"===this.mget("x_units")?this.xmapper.map_to_target(this.mget("x")):this.mget("x"),n=this.canvas.vx_to_sx(i),s="data"===this.mget("y_units")?this.ymapper.map_to_target(this.mget("y")):this.mget("y"),o=this.canvas.vy_to_sy(s),null!=this.model.panel&&(r=this._get_panel_offset(),n+=r.x,o+=r.y),"canvas"===this.mget("render_mode")?this._canvas_text(e,this.mget("text"),n+this.mget("x_offset"),o-this.mget("y_offset"),t):this._css_text(e,this.mget("text"),n+this.mget("x_offset"),o-this.mget("y_offset"),t)},e}(i.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.default_view=o,e.prototype.type="Label",e.mixins(["text","line:border_","fill:background_"]),e.define({x:[s.Number],x_units:[s.SpatialUnits,"data"],y:[s.Number],y_units:[s.SpatialUnits,"data"],text:[s.String],angle:[s.Angle,0],angle_units:[s.AngleUnits,"rad"],x_offset:[s.Number,0],y_offset:[s.Number,0],x_range_name:[s.String,"default"],y_range_name:[s.String,"default"],render_mode:[s.RenderMode,"canvas"]}),e.override({background_fill_color:null,border_line_color:null}),e}(i.Model),e.exports={Model:n,View:o}},{"../../core/properties":"core/properties","./text_annotation":"models/annotations/text_annotation"}],"models/annotations/label_set":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;l=t("underscore"),n=t("jquery"),a=t("./text_annotation"),o=t("../sources/column_data_source"),u=t("../../core/properties"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.initialize=function(t){var r,o,i,s;if(e.__super__.initialize.call(this,t),this.xmapper=this.plot_view.frame.get("x_mappers")[this.model.x_range_name],this.ymapper=this.plot_view.frame.get("y_mappers")[this.model.y_range_name],this.set_data(),"css"===this.model.render_mode){for(s=[],r=o=0,i=this._text.length;0<=i?o<i:o>i;r=0<=i?++o:--o)this.title_div=n("<div>").addClass("bk-annotation-child").hide(),
s.push(this.title_div.appendTo(this.$el));return s}},e.prototype.bind_bokeh_events=function(){return"css"===this.model.render_mode?(this.listenTo(this.model,"change",function(){return this.set_data(),this.render()}),this.listenTo(this.model.source,"change",function(){return this.set_data(),this.render()})):(this.listenTo(this.model,"change",function(){return this.set_data(),this.plot_view.request_render()}),this.listenTo(this.model.source,"change",function(){return this.set_data(),this.plot_view.request_render()}))},e.prototype.set_data=function(){return e.__super__.set_data.call(this,this.model.source),this.set_visuals(this.model.source)},e.prototype._map_data=function(){var t,e,r,n;return r="data"===this.model.x_units?this.xmapper.v_map_to_target(this._x):this._x.slice(0),t=this.canvas.v_vx_to_sx(r),n="data"===this.model.y_units?this.ymapper.v_map_to_target(this._y):this._y.slice(0),e=this.canvas.v_vy_to_sy(n),[t,e]},e.prototype.render=function(){var t,e,r,n,o,i,s,a,l,u,h;if(t=this.plot_view.canvas_view.ctx,o=this._map_data(),u=o[0],h=o[1],"canvas"===this.model.render_mode){for(a=[],e=r=0,i=this._text.length;0<=i?r<i:r>i;e=0<=i?++r:--r)a.push(this._v_canvas_text(t,e,this._text[e],u[e]+this._x_offset[e],h[e]-this._y_offset[e],this._angle[e]));return a}for(l=[],e=n=0,s=this._text.length;0<=s?n<s:n>s;e=0<=s?++n:--n)l.push(this._v_css_text(t,e,this._text[e],u[e]+this._x_offset[e],h[e]-this._y_offset[e],this._angle[e]));return l},e.prototype._get_size=function(){var t,e,r,n;return t=this.plot_view.canvas_view.ctx,this.visuals.text.set_value(t),r=this.model.panel.side,"above"===r||"below"===r?e=t.measureText(this._text[0]).ascent:"left"===r||"right"===r?n=t.measureText(this._text[0]).width:void 0},e.prototype._v_canvas_text=function(t,e,r,n,o,i){var s;return this.visuals.text.set_vectorize(t,e),s=this._calculate_bounding_box_dimensions(t,r),t.save(),t.beginPath(),t.translate(n,o),t.rotate(i),t.rect(s[0],s[1],s[2],s[3]),this.visuals.background_fill.doit&&(this.visuals.background_fill.set_vectorize(t,e),t.fill()),this.visuals.border_line.doit&&(this.visuals.border_line.set_vectorize(t,e),t.stroke()),this.visuals.text.doit&&(this.visuals.text.set_vectorize(t,e),t.fillText(r,0,0)),t.restore()},e.prototype._v_css_text=function(t,e,r,n,o,i){var s,a,u,h;return this.visuals.text.set_vectorize(t,e),s=this._calculate_bounding_box_dimensions(t,r),u=this.visuals.border_line.line_dash.value(),l.isArray(u)&&(h=u.length<2?"solid":"dashed"),l.isString(u)&&(h=u),this.visuals.border_line.set_vectorize(t,e),this.visuals.background_fill.set_vectorize(t,e),a={position:"absolute",left:n+s[0]+"px",top:o+s[1]+"px",color:""+this.visuals.text.text_color.value(),opacity:""+this.visuals.text.text_alpha.value(),font:""+this.visuals.text.font_value(),"line-height":"normal"},i&&l.extend(a,{transform:"rotate("+i+"rad)"}),this.visuals.background_fill.doit&&l.extend(a,{"background-color":""+this.visuals.background_fill.color_value()}),this.visuals.border_line.doit&&l.extend(a,{"border-style":""+h,"border-width":""+this.visuals.border_line.line_width.value(),"border-color":""+this.visuals.border_line.color_value()}),this.$el.children().eq(e).html(r).css(a).show()},e}(a.View),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=s,e.prototype.type="Label",e.mixins(["text","line:border_","fill:background_"]),e.coords([["x","y"]]),e.define({x_units:[u.SpatialUnits,"data"],y_units:[u.SpatialUnits,"data"],text:[u.StringSpec,{field:"text"}],angle:[u.AngleSpec,0],x_offset:[u.NumberSpec,{value:0}],y_offset:[u.NumberSpec,{value:0}],source:[u.Instance,function(){return new o.Model}],x_range_name:[u.String,"default"],y_range_name:[u.String,"default"],render_mode:[u.RenderMode,"canvas"]}),e.override({background_fill_color:null,border_line_color:null}),e}(a.Model),e.exports={Model:i,View:s}},{"../../core/properties":"core/properties","../sources/column_data_source":"models/sources/column_data_source","./text_annotation":"models/annotations/text_annotation",jquery:"jquery",underscore:"underscore"}],"models/annotations/legend":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t("underscore"),n=t("./annotation"),l=t("../../core/properties"),a=t("../../core/util/text").get_text_height,i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t)},e.prototype.compute_legend_bbox=function(){var t,e,r,n,o,i,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T;for(d=function(){var t,e,r,o,i;for(r=this.mget("legends"),i=[],t=0,e=r.length;t<e;t++)o=r[t],_=o[0],n=o[1],i.push(_);return i}.call(this),e=this.mget("glyph_height"),r=this.mget("glyph_width"),l=this.mget("label_height"),h=this.mget("label_width"),this.max_label_height=s.max([a(this.visuals.label_text.font_value()).height,l,e]),t=this.plot_view.canvas_view.ctx,t.save(),this.visuals.label_text.set_value(t),this.text_widths={},i=0,y=d.length;i<y;i++)x=d[i],this.text_widths[x]=s.max([t.measureText(x).width,h]);if(t.restore(),b=s.max(s.values(this.text_widths)),p=this.mget("legend_margin"),f=this.mget("legend_padding"),m=this.mget("legend_spacing"),u=this.mget("label_standoff"),"vertical"===this.mget("orientation"))c=d.length*this.max_label_height+(d.length-1)*m+2*f,g=b+r+u+2*f;else{g=2*f+(d.length-1)*m,w=this.text_widths;for(x in w)k=w[x],g+=s.max([k,h])+r+u;c=this.max_label_height+2*f}if(v=this.mget("location"),o=this.plot_view.frame.get("h_range"),M=this.plot_view.frame.get("v_range"),s.isString(v))switch(v){case"top_left":j=o.get("start")+p,T=M.get("end")-p;break;case"top_center":j=(o.get("end")+o.get("start"))/2-g/2,T=M.get("end")-p;break;case"top_right":j=o.get("end")-p-g,T=M.get("end")-p;break;case"right_center":j=o.get("end")-p-g,T=(M.get("end")+M.get("start"))/2+c/2;break;case"bottom_right":j=o.get("end")-p-g,T=M.get("start")+p+c;break;case"bottom_center":j=(o.get("end")+o.get("start"))/2-g/2,T=M.get("start")+p+c;break;case"bottom_left":j=o.get("start")+p,T=M.get("start")+p+c;break;case"left_center":j=o.get("start")+p,T=(M.get("end")+M.get("start"))/2+c/2;break;case"center":j=(o.get("end")+o.get("start"))/2-g/2,T=(M.get("end")+M.get("start"))/2+c/2}else s.isArray(v)&&2===v.length&&(j=v[0],T=v[1]);return j=this.plot_view.canvas.vx_to_sx(j),T=this.plot_view.canvas.vy_to_sy(T),{x:j,y:T,width:g,height:c}},e.prototype.render=function(){var t,e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j;if(0!==this.model.legends.length){for(e=this.compute_legend_bbox(),n=this.mget("glyph_height"),o=this.mget("glyph_width"),d=this.mget("orientation"),r=this.plot_view.canvas_view.ctx,r.save(),null!=this.model.panel&&(f=this._get_panel_offset(),r.translate(f.x,f.y)),r.beginPath(),r.rect(e.x,e.y,e.width,e.height),this.visuals.background_fill.set_value(r),r.fill(),this.visuals.border_line.doit&&(this.visuals.border_line.set_value(r),r.stroke()),t=this.mget("legends").length,c=this.mget("legend_spacing"),u=this.mget("label_standoff"),w=j=this.mget("legend_padding"),m=this.mget("legends"),a=s=0,p=m.length;s<p;a=++s)for(g=m[a],h=g[0],i=g[1],b=e.x+w,M=e.y+j,x=b+o,k=M+n,"vertical"===d?j+=this.max_label_height+c:w+=this.text_widths[h]+o+u+c,this.visuals.label_text.set_value(r),r.fillText(h,x+u,M+this.max_label_height/2),l=0,_=i.length;l<_;l++)y=i[l],v=this.plot_view.renderer_views[y.id],v.draw_legend(r,b,x,M,k);return r.restore()}},e.prototype._get_size=function(){var t,e;return t=this.compute_legend_bbox(),e=this.model.panel.side,"above"===e||"below"===e?t.height:"left"===e||"right"===e?t.width:void 0},e.prototype._get_panel_offset=function(){var t,e;return t=this.model.panel._left._value,e=this.model.panel._top._value,{x:t,y:-e}},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=i,e.prototype.type="Legend",e.mixins(["text:label_","line:border_","fill:background_"]),e.define({legends:[l.Array,[]],orientation:[l.Orientation,"vertical"],location:[l.Any,"top_right"],label_standoff:[l.Number,5],glyph_height:[l.Number,20],glyph_width:[l.Number,20],label_height:[l.Number,20],label_width:[l.Number,20],legend_margin:[l.Number,10],legend_padding:[l.Number,10],legend_spacing:[l.Number,3]}),e.override({border_line_color:"#e5e5e5",border_line_alpha:.5,border_line_width:1,background_fill_color:"#ffffff",background_fill_alpha:.95,label_text_font_size:"10pt",label_text_baseline:"middle"}),e}(n.Model),e.exports={Model:o,View:i}},{"../../core/properties":"core/properties","../../core/util/text":"core/util/text","./annotation":"models/annotations/annotation",underscore:"underscore"}],"models/annotations/poly_annotation":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t("underscore"),n=t("./annotation"),a=t("../../core/properties"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,"change",this.plot_view.request_render),this.listenTo(this.model,"data_update",this.plot_view.request_render)},e.prototype.render=function(t){var e,r,n,o,i,s,a,l,u,h;if(u=this.mget("xs"),h=this.mget("ys"),u.length!==h.length)return null;if(u.length<3||h.length<3)return null;for(e=this.plot_view.canvas,t=this.plot_view.canvas_view.ctx,r=n=0,o=u.length;0<=o?n<o:n>o;r=0<=o?++n:--n)"screen"===this.mget("xs_units")&&(a=u[r]),"screen"===this.mget("ys_units")&&(l=h[r]),i=e.vx_to_sx(a),s=e.vy_to_sy(l),0===r?(t.beginPath(),t.moveTo(i,s)):t.lineTo(i,s);return t.closePath(),this.visuals.line.doit&&(this.visuals.line.set_value(t),t.stroke()),this.visuals.fill.doit?(this.visuals.fill.set_value(t),t.fill()):void 0},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type="PolyAnnotation",e.mixins(["line","fill"]),e.define({xs:[a.Array,[]],xs_units:[a.SpatialUnits,"data"],ys:[a.Array,[]],ys_units:[a.SpatialUnits,"data"],x_range_name:[a.String,"default"],y_range_name:[a.String,"default"]}),e.override({fill_color:"#fff9ba",fill_alpha:.4,line_color:"#cccccc",line_alpha:.3}),e.prototype.update=function(t){var e,r;return e=t.xs,r=t.ys,this.set({xs:e,ys:r},{silent:!0}),this.trigger("data_update")},e}(n.Model),e.exports={Model:o,View:i}},{"../../core/properties":"core/properties","./annotation":"models/annotations/annotation",underscore:"underscore"}],"models/annotations/span":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t("underscore"),n=t("./annotation"),a=t("../../core/properties"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.$el.appendTo(this.plot_view.$el.find("div.bk-canvas-overlays")),this.$el.css({position:"absolute"}),this.$el.hide()},e.prototype.bind_bokeh_events=function(){return this.mget("for_hover")?this.listenTo(this.model,"change:computed_location",this._draw_span):"canvas"===this.mget("render_mode")?this.listenTo(this.model,"change:location",this.plot_view.request_render):this.listenTo(this.model,"change:location",this._draw_span)},e.prototype.render=function(){return this._draw_span()},e.prototype._draw_span=function(){var t,e,r,n,o,i,s,a,l,u;return o=this.mget("for_hover")?this.mget("computed_location"):this.mget("location"),null==o?void this.$el.hide():(r=this.plot_model.get("frame"),t=this.plot_model.get("canvas"),l=this.plot_view.frame.get("x_mappers")[this.mget("x_range_name")],u=this.plot_view.frame.get("y_mappers")[this.mget("y_range_name")],"width"===this.mget("dimension")?(s=t.vy_to_sy(this._calc_dim(o,u)),i=t.vx_to_sx(r.get("left")),a=r.get("width"),n=this.model.properties.line_width.value()):(s=t.vy_to_sy(r.get("top")),i=t.vx_to_sx(this._calc_dim(o,l)),a=this.model.properties.line_width.value(),n=r.get("height")),"css"===this.mget("render_mode")?(this.$el.css({top:s,left:i,width:a+"px",height:n+"px","z-index":1e3,"background-color":this.model.properties.line_color.value(),opacity:this.model.properties.line_alpha.value()}),this.$el.show()):"canvas"===this.mget("render_mode")?(e=this.plot_view.canvas_view.ctx,e.save(),e.beginPath(),this.visuals.line.set_value(e),e.moveTo(i,s),"width"===this.mget("dimension")?e.lineTo(i+a,s):e.lineTo(i,s+n),e.stroke(),e.restore()):void 0)},e.prototype._calc_dim=function(t,e){var r;return r="data"===this.mget("location_units")?e.map_to_target(t):t},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type="Span",e.mixins(["line"]),e.define({render_mode:[a.RenderMode,"canvas"],x_range_name:[a.String,"default"],y_range_name:[a.String,"default"],location:[a.Number,null],location_units:[a.SpatialUnits,"data"],dimension:[a.Dimension,"width"]}),e.override({line_color:"black"}),e.internal({for_hover:[a.Boolean,!1],computed_location:[a.Number,null]}),e}(n.Model),e.exports={Model:o,View:i}},{"../../core/properties":"core/properties","./annotation":"models/annotations/annotation",underscore:"underscore"}],"models/annotations/text_annotation":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new 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r,n,o,i,s;switch(n=this._calculate_text_dimensions(t,e),o=n[0],r=n[1],t.textAlign){case"left":i=0;break;case"center":i=-o/2;break;case"right":i=-o}switch(t.textBaseline){case"top":s=0;break;case"middle":s=-.5*r;break;case"bottom":s=-1*r;break;case"alphabetic":s=-.8*r;break;case"hanging":s=-.17*r;break;case"ideographic":s=-.83*r}return[i,s,o,r]},e.prototype._get_size=function(){var t;return t=this.plot_view.canvas_view.ctx,this.visuals.text.set_value(t),t.measureText(this.mget("text")).ascent},e.prototype.render=function(){return null},e.prototype._canvas_text=function(t,e,r,n,o){var i;return this.visuals.text.set_value(t),i=this._calculate_bounding_box_dimensions(t,e),t.save(),t.beginPath(),t.translate(r,n),o&&t.rotate(o),t.rect(i[0],i[1],i[2],i[3]),this.visuals.background_fill.doit&&(this.visuals.background_fill.set_value(t),t.fill()),this.visuals.border_line.doit&&(this.visuals.border_line.set_value(t),t.stroke()),this.visuals.text.doit&&(this.visuals.text.set_value(t),t.fillText(e,0,0)),t.restore()},e.prototype._css_text=function(t,e,r,n,o){var i,a,l,u;return this.$el.hide(),this.visuals.text.set_value(t),i=this._calculate_bounding_box_dimensions(t,e),l=this.visuals.border_line.line_dash.value(),s.isArray(l)&&(u=l.length<2?"solid":"dashed"),s.isString(l)&&(u=l),this.visuals.border_line.set_value(t),this.visuals.background_fill.set_value(t),a={position:"absolute",left:r+i[0]+"px",top:n+i[1]+"px",color:""+this.visuals.text.text_color.value(),opacity:""+this.visuals.text.text_alpha.value(),font:""+this.visuals.text.font_value(),"line-height":"normal"},o&&s.extend(a,{transform:"rotate("+o+"rad)"}),this.visuals.background_fill.doit&&s.extend(a,{"background-color":""+this.visuals.background_fill.color_value()}),this.visuals.border_line.doit&&s.extend(a,{"border-style":""+u,"border-width":""+this.visuals.border_line.line_width.value(),"border-color":""+this.visuals.border_line.color_value()}),this.$el.html(e).css(a).show()},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.type="TextAnnotation",e.prototype.default_view=i,e}(n.Model),e.exports={Model:o,View:i}},{"../../core/properties":"core/properties","../../core/util/text":"core/util/text","./annotation":"models/annotations/annotation",underscore:"underscore"}],"models/annotations/title":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;n=t("./text_annotation"),a=t("../../core/properties"),s=t("../renderers/renderer").Visuals,i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.initialize=function(t){var r;return e.__super__.initialize.call(this,t),this.visuals.text=new s.text({obj:this.model,prefix:""}),r=this.plot_view.canvas_view.ctx,r.save(),this.model.panel.apply_label_text_heuristics(r,"justified"),this.model.text_baseline=r.textBaseline,this.model.text_align=this.model.align,r.restore()},e.prototype._get_computed_location=function(){var t,e,r,n,o,i,s;switch(e=this._calculate_text_dimensions(this.plot_view.canvas_view.ctx,this.text),s=e[0],t=e[1],this.model.panel.side){case"left":o=0,i=this._get_text_location(this.mget("align"),this.frame.get("v_range"))+this.mget("offset");break;case"right":o=this.canvas.get("right")-1,i=this.canvas.get("height")-this._get_text_location(this.mget("align"),this.frame.get("v_range"))-this.mget("offset");break;case"above":o=this._get_text_location(this.mget("align"),this.frame.get("h_range"))+this.mget("offset"),i=this.canvas.get("top")-10;break;case"below":o=this._get_text_location(this.mget("align"),this.frame.get("h_range"))+this.mget("offset"),i=0}return r=this.canvas.vx_to_sx(o),n=this.canvas.vy_to_sy(i),[r,n]},e.prototype._get_text_location=function(t,e){var r;switch(t){case"left":r=e.get("start");break;case"center":r=(e.get("end")+e.get("start"))/2;break;case"right":r=e.get("end")}return r},e.prototype.render=function(){var t,e,r,n,o;if(t=this.model.panel.get_label_angle_heuristic("parallel"),r=this._get_computed_location(),n=r[0],o=r[1],e=this.plot_view.canvas_view.ctx,""!==this.model.text&&null!==this.model.text)return"canvas"===this.model.render_mode?this._canvas_text(e,this.model.text,n,o,t):this._css_text(e,this.model.text,n,o,t)},e.prototype._get_size=function(){var t,e;return e=this.model.text,""===e||null===e?0:(t=this.plot_view.canvas_view.ctx,this.visuals.text.set_value(t),t.measureText(e).ascent+10)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type="Title",e.mixins(["line:border_","fill:background_"]),e.define({text:[a.String],text_font:[a.Font,"helvetica"],text_font_size:[a.FontSizeSpec,"10pt"],text_font_style:[a.FontStyle,"bold"],text_color:[a.ColorSpec,"#444444"],text_alpha:[a.NumberSpec,1],align:[a.TextAlign,"left"],offset:[a.Number,0],render_mode:[a.RenderMode,"canvas"]}),e.override({background_fill_color:null,border_line_color:null}),e.internal({text_align:[a.TextAlign,"left"],text_baseline:[a.TextBaseline,"bottom"]}),e}(n.Model),e.exports={Model:o,View:i}},{"../../core/properties":"core/properties","../renderers/renderer":"models/renderers/renderer","./text_annotation":"models/annotations/text_annotation"}],"models/annotations/tooltip":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;n=t("jquery"),a=t("underscore"),o=t("./annotation"),l=t("../../core/logging").logger,u=t("../../core/properties"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.className="bk-tooltip",e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.$el.appendTo(this.plot_view.$el.find("div.bk-canvas-overlays")),this.$el.css({"z-index":1010}),this.$el.hide()},e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,"change:data",this._draw_tips)},e.prototype.render=function(){return this._draw_tips()},e.prototype._draw_tips=function(){var t,e,r,o,i,s,l,u,h,c,p,_,d,f,m,g,y,v;if(i=this.model.data,this.$el.empty(),this.$el.hide(),this.$el.toggleClass("bk-tooltip-custom",this.mget("custom")),!a.isEmpty(i)){for(l=0,h=i.length;l<h;l++)m=i[l],g=m[0],y=m[1],o=m[2],this.mget("inner_only")&&!this.plot_view.frame.contains(g,y)||(d=n("<div />").appendTo(this.$el),d.append(o));switch(p=this.plot_view.mget("canvas").vx_to_sx(g),_=this.plot_view.mget("canvas").vy_to_sy(y),e=this.model.attachment){case"horizontal":v=this.plot_view.frame.get("width"),u=this.plot_view.frame.get("left"),c=g-u<v/2?"right":"left";break;case"vertical":s=this.plot_view.frame.get("height"),r=this.plot_view.frame.get("bottom"),c=y-r<s/2?"below":"above";break;default:c=e}switch(this.$el.removeClass("bk-right bk-left bk-above bk-below"),t=10,c){case"right":this.$el.addClass("bk-left"),u=p+(this.$el.outerWidth()-this.$el.innerWidth())+t,f=_-this.$el.outerHeight()/2;break;case"left":this.$el.addClass("bk-right"),u=p-this.$el.outerWidth()-t,f=_-this.$el.outerHeight()/2;break;case"above":this.$el.addClass("bk-above"),f=_+(this.$el.outerHeight()-this.$el.innerHeight())+t,u=Math.round(p-this.$el.outerWidth()/2);break;case"below":this.$el.addClass("bk-below"),f=_-this.$el.outerHeight()-t,u=Math.round(p-this.$el.outerWidth()/2)}return this.model.show_arrow&&this.$el.addClass("bk-tooltip-arrow"),this.$el.children().length>0?(this.$el.css({top:f,left:u}),this.$el.show()):void 0}},e}(o.View),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=s,e.prototype.type="Tooltip",e.define({attachment:[u.String,"horizontal"],inner_only:[u.Bool,!0],show_arrow:[u.Bool,!0]}),e.override({level:"overlay"}),e.internal({data:[u.Any,[]],custom:[u.Any]}),e.prototype.clear=function(){return this.data=[]},e.prototype.add=function(t,e,r){var n;return n=this.data,n.push([t,e,r]),this.data=n,this.trigger("change:data")},e}(o.Model),e.exports={Model:i,View:s}},{"../../core/logging":"core/logging","../../core/properties":"core/properties","./annotation":"models/annotations/annotation",jquery:"jquery",underscore:"underscore"}],"models/axes/axis":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p=function(t,e){function r(){this.constructor=t}for(var n in e)_.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},_={}.hasOwnProperty;u=t("underscore"),l=t("../../core/layout/side_panel"),s=t("../renderers/guide_renderer"),a=t("../renderers/renderer"),i=t("../../core/layout/solver").GE,h=t("../../core/logging").logger,c=t("../../core/properties"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return p(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this._x_range_name=this.mget("x_range_name"),this._y_range_name=this.mget("y_range_name")},e.prototype.render=function(){var t;if(this.model.visible!==!1)return t=this.plot_view.canvas_view.ctx,t.save(),this._draw_rule(t),this._draw_major_ticks(t),this._draw_minor_ticks(t),this._draw_major_labels(t),this._draw_axis_label(t),t.restore()},e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,"change",this.plot_view.request_render)},e.prototype._get_size=function(){return this._tick_extent()+this._tick_label_extent()+this._axis_label_extent()},e.prototype._draw_rule=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m;if(this.visuals.axis_line.doit){for(s=e=this.mget("rule_coords"),_=s[0],f=s[1],a=this.plot_view.map_to_screen(_,f,this._x_range_name,this._y_range_name),c=a[0],p=a[1],l=this.mget("normals"),o=l[0],i=l[1],u=this.mget("offsets"),d=u[0],m=u[1],this.visuals.axis_line.set_value(t),t.beginPath(),t.moveTo(Math.round(c[0]+o*d),Math.round(p[0]+i*m)),r=n=1,h=c.length;1<=h?n<h:n>h;r=1<=h?++n:--n)t.lineTo(Math.round(c[r]+o*d),Math.round(p[r]+i*m));return t.stroke()}},e.prototype._draw_major_ticks=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v;if(this.visuals.major_tick_line.doit){for(e=this.mget("tick_coords"),s=e.major,m=s[0],y=s[1],a=this.plot_view.map_to_screen(m,y,this._x_range_name,this._y_range_name),p=a[0],_=a[1],l=this.mget("normals"),o=l[0],i=l[1],u=this.mget("offsets"),g=u[0],v=u[1],d=this.mget("major_tick_in"),f=this.mget("major_tick_out"),this.visuals.major_tick_line.set_value(t),c=[],r=n=0,h=p.length;0<=h?n<h:n>h;r=0<=h?++n:--n)t.beginPath(),t.moveTo(Math.round(p[r]+o*f+o*g),Math.round(_[r]+i*f+i*v)),t.lineTo(Math.round(p[r]-o*d+o*g),Math.round(_[r]-i*d+i*v)),c.push(t.stroke());return c}},e.prototype._draw_minor_ticks=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v;if(this.visuals.minor_tick_line.doit){for(e=this.mget("tick_coords"),s=e.minor,m=s[0],y=s[1],a=this.plot_view.map_to_screen(m,y,this._x_range_name,this._y_range_name),p=a[0],_=a[1],l=this.mget("normals"),o=l[0],i=l[1],u=this.mget("offsets"),g=u[0],v=u[1],d=this.mget("minor_tick_in"),f=this.mget("minor_tick_out"),this.visuals.minor_tick_line.set_value(t),c=[],r=n=0,h=p.length;0<=h?n<h:n>h;r=0<=h?++n:--n)t.beginPath(),t.moveTo(Math.round(p[r]+o*f+o*g),Math.round(_[r]+i*f+i*v)),t.lineTo(Math.round(p[r]-o*d+o*g),Math.round(_[r]-i*d+i*v)),c.push(t.stroke());return c}},e.prototype._draw_major_labels=function(t){var e,r,n,o,i,s,a,l,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k;for(r=this.mget("tick_coords"),c=r.major,x=c[0],M=c[1],p=this.plot_view.map_to_screen(x,M,this._x_range_name,this._y_range_name),v=p[0],b=p[1],_=this.mget("normals"),a=_[0],l=_[1],d=this.mget("offsets"),w=d[0],k=d[1],n=this.mget("dimension"),g=this.mget("panel_side"),h=this.mget("major_label_orientation"),e=u.isString(h)?this.model.panel.get_label_angle_heuristic(h):-h,y=this._tick_extent()+this.mget("major_label_standoff"),s=this.mget("formatter").doFormat(r.major[n]),this.visuals.major_label_text.set_value(t),this.model.panel.apply_label_text_heuristics(t,h),m=[],o=i=0,f=v.length;0<=f?i<f:i>f;o=0<=f?++i:--i)e?(t.translate(v[o]+a*y+a*w,b[o]+l*y+l*k),t.rotate(e),t.fillText(s[o],0,0),t.rotate(-e),m.push(t.translate(-v[o]-a*y+a*w,-b[o]-l*y+l*k))):m.push(t.fillText(s[o],Math.round(v[o]+a*y+a*w),Math.round(b[o]+l*y+l*k)));return m},e.prototype._draw_axis_label=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g;if(r=this.mget("axis_label"),null!=r&&(s=this.mget("rule_coords"),d=s[0],m=s[1],a=this.plot_view.map_to_screen(d,m,this._x_range_name,this._y_range_name),p=a[0],_=a[1],l=this.mget("normals"),n=l[0],o=l[1],u=this.mget("offsets"),f=u[0],g=u[1],h=this.mget("panel_side"),i="parallel",e=this.model.panel.get_label_angle_heuristic(i),c=this._tick_extent()+this._tick_label_extent()+this.mget("axis_label_standoff"),p=(p[0]+p[p.length-1])/2,_=(_[0]+_[_.length-1])/2,this.visuals.axis_label_text.set_value(t),this.model.panel.apply_label_text_heuristics(t,i),d=p+n*c+n*f,m=_+o*c+o*g,!isNaN(d)&&!isNaN(m)))return e?(t.translate(d,m),t.rotate(e),t.fillText(r,0,0),t.rotate(-e),t.translate(-d,-m)):t.fillText(r,d,m)},e.prototype._tick_extent=function(){return this.mget("major_tick_out")},e.prototype._tick_label_extent=function(){var t,e,r,n,o,i,s,a,l,h,c,p,_,d,f,m,g,y,v;for(i=0,n=this.plot_view.canvas_view.ctx,o=this.mget("dimension"),r=this.mget("tick_coords").major,m=this.mget("panel_side"),_=this.mget("major_label_orientation"),p=this.mget("formatter").doFormat(r[o]),this.visuals.major_label_text.set_value(n),u.isString(_)?(l=1,t=this.model.panel.get_label_angle_heuristic(_)):(l=2,t=-_),t=Math.abs(t),e=Math.cos(t),f=Math.sin(t),"above"===m||"below"===m?(v=f,a=e):(v=e,a=f),h=c=0,d=p.length;0<=d?c<d:c>d;h=0<=d?++c:--c)null!=p[h]&&(y=1.1*n.measureText(p[h]).width,s=.9*n.measureText(p[h]).ascent,g=y*v+s/l*a,g>i&&(i=g));return i>0&&(i+=this.mget("major_label_standoff")),i},e.prototype._axis_label_extent=function(){var t,e,r,n,o,i,s,a,l,u;return o=0,l=this.mget("panel_side"),e=this.mget("axis_label"),s="parallel",n=this.plot_view.canvas_view.ctx,this.visuals.axis_label_text.set_value(n),t=Math.abs(this.model.panel.get_label_angle_heuristic(s)),r=Math.cos(t),a=Math.sin(t),e&&(o+=this.mget("axis_label_standoff"),this.visuals.axis_label_text.set_value(n),u=1.1*n.measureText(e).width,i=.9*n.measureText(e).ascent,o+="above"===l||"below"===l?u*a+i*r:u*r+i*a),o},e}(a.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return p(e,t),e.prototype.default_view=o,e.prototype.type="Axis",e.mixins(["line:axis_","line:major_tick_","line:minor_tick_","text:major_label_","text:axis_label_"]),e.define({bounds:[c.Any,"auto"],ticker:[c.Instance,null],formatter:[c.Instance,null],x_range_name:[c.String,"default"],y_range_name:[c.String,"default"],axis_label:[c.String,""],axis_label_standoff:[c.Int,5],major_label_standoff:[c.Int,5],major_label_orientation:[c.Any,"horizontal"],major_tick_in:[c.Number,2],major_tick_out:[c.Number,6],minor_tick_in:[c.Number,0],minor_tick_out:[c.Number,4]}),e.override({axis_line_color:"black",major_tick_line_color:"black",minor_tick_line_color:"black",major_label_text_font_size:"8pt",major_label_text_align:"center",major_label_text_baseline:"alphabetic",axis_label_text_font_size:"10pt",axis_label_text_font_style:"italic"}),e.internal({panel_side:[c.Any]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.define_computed_property("computed_bounds",this._computed_bounds,!1),this.add_dependencies("computed_bounds",this,["bounds"]),this.add_dependencies("computed_bounds",this.get("plot"),["x_range","y_range"]),this.define_computed_property("rule_coords",this._rule_coords,!1),this.add_dependencies("rule_coords",this,["computed_bounds","side"]),this.define_computed_property("tick_coords",this._tick_coords,!1),this.add_dependencies("tick_coords",this,["computed_bounds","panel_side"]),this.define_computed_property("ranges",this._ranges,!0),this.define_computed_property("normals",function(){return this.panel._normals},!0),this.define_computed_property("dimension",function(){return this.panel._dim},!0),this.define_computed_property("offsets",this._offsets,!0)},e.prototype.add_panel=function(t){return this.panel=new l.Model({side:t}),this.panel.attach_document(this.document),this.set("panel_side",t)},e.prototype._offsets=function(){var t,e,r,n,o;return r=this.get("panel_side"),e=[0,0],n=e[0],o=e[1],t=this.plot.plot_canvas.get("frame"),"below"===r?o=Math.abs(this.panel.get("top")-t.get("bottom")):"above"===r?o=Math.abs(this.panel.get("bottom")-t.get("top")):"right"===r?n=Math.abs(this.panel.get("left")-t.get("right")):"left"===r&&(n=Math.abs(this.panel.get("right")-t.get("left"))),[n,o]},e.prototype._ranges=function(){var t,e,r,n;return e=this.get("dimension"),r=(e+1)%2,t=this.plot.plot_canvas.get("frame"),n=[t.get("x_ranges")[this.get("x_range_name")],t.get("y_ranges")[this.get("y_range_name")]],[n[e],n[r]]},e.prototype._computed_bounds=function(){var t,e,r,n,o,i,s,a;return o=this.get("ranges"),r=o[0],t=o[1],a=null!=(i=this.get("bounds"))?i:"auto",n=[r.get("min"),r.get("max")],"auto"===a?n:u.isArray(a)?(Math.abs(a[0]-a[1])>Math.abs(n[0]-n[1])?(s=Math.max(Math.min(a[0],a[1]),n[0]),e=Math.min(Math.max(a[0],a[1]),n[1])):(s=Math.min(a[0],a[1]),
e=Math.max(a[0],a[1])),[s,e]):(h.error("user bounds '"+a+"' not understood"),null)},e.prototype._rule_coords=function(){var t,e,r,n,o,i,s,a,l,u,h,c;return n=this.get("dimension"),o=(n+1)%2,a=this.get("ranges"),s=a[0],e=a[1],l=this.get("computed_bounds"),u=l[0],r=l[1],h=new Array(2),c=new Array(2),t=[h,c],i=this._get_loc(e),t[n][0]=Math.max(u,s.get("min")),t[n][1]=Math.min(r,s.get("max")),t[n][0]>t[n][1]&&(t[n][0]=t[n][1]=NaN),t[o][0]=i,t[o][1]=i,t},e.prototype._tick_coords=function(){var t,e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S;if(n=this.get("dimension"),i=(n+1)%2,y=this.get("ranges"),f=y[0],e=y[1],v=this.get("computed_bounds"),k=v[0],r=v[1],j=this.get("ticker").get_ticks(k,r,f,{}),h=j.major,d=j.minor,l=this._get_loc(e),T=[],S=[],t=[T,S],p=[],_=[],c=[p,_],"FactorRange"===f.type)for(o=s=0,b=h.length;0<=b?s<b:s>b;o=0<=b?++s:--s)t[n].push(h[o]),t[i].push(l);else{for(x=[f.get("min"),f.get("max")],g=x[0],m=x[1],o=a=0,w=h.length;0<=w?a<w:a>w;o=0<=w?++a:--a)h[o]<g||h[o]>m||(t[n].push(h[o]),t[i].push(l));for(o=u=0,M=d.length;0<=M?u<M:u>M;o=0<=M?++u:--u)d[o]<g||d[o]>m||(c[n].push(d[o]),c[i].push(l))}return{major:t,minor:c}},e.prototype._get_loc=function(t){var e,r,n,o;return r=t.get("start"),e=t.get("end"),o=this.get("panel_side"),"left"===o||"below"===o?n="start":"right"!==o&&"above"!==o||(n="end"),t.get(n)},e}(s.Model),e.exports={Model:n,View:o}},{"../../core/layout/side_panel":"core/layout/side_panel","../../core/layout/solver":"core/layout/solver","../../core/logging":"core/logging","../../core/properties":"core/properties","../renderers/guide_renderer":"models/renderers/guide_renderer","../renderers/renderer":"models/renderers/renderer",underscore:"underscore"}],"models/axes/categorical_axis":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;l=t("underscore"),n=t("./axis"),s=t("../formatters/categorical_tick_formatter"),a=t("../tickers/categorical_ticker"),u=t("../../core/logging").logger,i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return 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u=null!=(l=this.get_reference_point())?l:0,s=[u],p={},p[u]=(e+r)/2,_={},_[u]=(n+o)/2,a=.5*Math.min(Math.abs(r-e),Math.abs(o-n)),h={},h[u]=.4*a,c={},c[u]=.8*a,i={sx:p,sy:_,sinner_radius:h,souter_radius:c},this._render(t,s,i)},e}(i.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=o,e.prototype.type="Annulus",e.coords([["x","y"]]),e.mixins(["line","fill"]),e.define({inner_radius:[l.DistanceSpec],outer_radius:[l.DistanceSpec]}),e}(i.Model),e.exports={Model:n,View:o}},{"../../common/hittest":"common/hittest","../../core/properties":"core/properties","./glyph":"models/glyphs/glyph",underscore:"underscore"}],"models/glyphs/arc":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new 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e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=o,e.prototype.type="Arc",e.coords([["x","y"]]),e.mixins(["line"]),e.define({direction:[a.Direction,"anticlock"],radius:[a.DistanceSpec],start_angle:[a.AngleSpec],end_angle:[a.AngleSpec]}),e}(i.Model),e.exports={Model:n,View:o}},{"../../core/properties":"core/properties","./glyph":"models/glyphs/glyph",underscore:"underscore"}],"models/glyphs/bezier":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t("underscore"),l=t("rbush"),i=t("./glyph"),a=function(t,e,r,n,o,i,s,a){var l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k;for(w=[],c=[[],[]],_=m=0;m<=2;_=++m)if(0===_?(u=6*t-12*r+6*o,l=-3*t+9*r-9*o+3*s,p=3*r-3*t):(u=6*e-12*n+6*i,l=-3*e+9*n-9*i+3*a,p=3*n-3*e),Math.abs(l)<1e-12){if(Math.abs(u)<1e-12)continue;v=-p/u,0<v&&v<1&&w.push(v)}else h=u*u-4*p*l,y=Math.sqrt(h),h<0||(b=(-u+y)/(2*l),0<b&&b<1&&w.push(b),x=(-u-y)/(2*l),0<x&&x<1&&w.push(x));for(d=w.length,f=d;d--;)v=w[d],g=1-v,M=g*g*g*t+3*g*g*v*r+3*g*v*v*o+v*v*v*s,c[0][d]=M,k=g*g*g*e+3*g*g*v*n+3*g*v*v*i+v*v*v*a,c[1][d]=k;return c[0][f]=t,c[1][f]=e,c[0][f+1]=s,c[1][f+1]=a,[Math.min.apply(null,c[0]),Math.max.apply(null,c[1]),Math.max.apply(null,c[0]),Math.min.apply(null,c[1])]},o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype._index_data=function(){var t,e,r,n,o,i,s,u,h,c;for(e=l(),n=[],t=r=0,o=this._x0.length;0<=o?r<o:r>o;t=0<=o?++r:--r)isNaN(this._x0[t]+this._x1[t]+this._y0[t]+this._y1[t]+this._cx0[t]+this._cy0[t]+this._cx1[t]+this._cy1[t])||(i=a(this._x0[t],this._y0[t],this._x1[t],this._y1[t],this._cx0[t],this._cy0[t],this._cx1[t],this._cy1[t]),s=i[0],h=i[1],u=i[2],c=i[3],n.push({minX:s,minY:h,maxX:u,maxY:c,i:t}));return e.load(n),e},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f;if(p=r.sx0,d=r.sy0,_=r.sx1,f=r.sy1,a=r.scx,l=r.scx0,h=r.scy0,u=r.scx1,c=r.scy1,this.visuals.line.doit){for(s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(p[n]+d[n]+_[n]+f[n]+l[n]+h[n]+u[n]+c[n])||(t.beginPath(),t.moveTo(p[n],d[n]),t.bezierCurveTo(l[n],h[n],u[n],c[n],_[n],f[n]),this.visuals.line.set_vectorize(t,n),s.push(t.stroke()));return s}},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_line_legend(t,e,r,n,o)},e}(i.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=o,e.prototype.type="Bezier",e.coords([["x0","y0"],["x1","y1"],["cx0","cy0"],["cx1","cy1"]]),e.mixins(["line"]),e}(i.Model),e.exports={Model:n,View:o}},{"./glyph":"models/glyphs/glyph",rbush:"rbush",underscore:"underscore"}],"models/glyphs/circle":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t("underscore"),i=t("./glyph"),a=t("../../common/hittest"),l=t("../../core/properties"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype._index_data=function(){return this._xy_index()},e.prototype._map_data=function(){var t,e;return null!=this._radius?"data"===this.model.properties.radius.spec.units?(t=this.model.properties.radius_dimension.spec.value,this.sradius=this.sdist(this.renderer[t+"mapper"],this["_"+t],this._radius)):(this.sradius=this._radius,this.max_size=2*this.max_radius):this.sradius=function(){var t,r,n,o;for(n=this._size,o=[],t=0,r=n.length;t<r;t++)e=n[t],o.push(e/2);return o}.call(this)},e.prototype._mask_data=function(t){var e,r,n,o,i,s,l,u,h,c,p,_,d,f,m,g;return r=this.renderer.plot_view.frame.get("h_range"),p=this.renderer.plot_view.frame.get("v_range"),null!=this._radius&&"data"===this.model.properties.radius.units?(l=r.get("start"),u=r.get("end"),n=this.renderer.xmapper.v_map_from_target([l,u],!0),d=n[0],f=n[1],d-=this.max_radius,f+=this.max_radius,h=p.get("start"),c=p.get("end"),o=this.renderer.ymapper.v_map_from_target([h,c],!0),m=o[0],g=o[1],m-=this.max_radius,g+=this.max_radius):(l=r.get("start")-this.max_size,u=r.get("end")+this.max_size,i=this.renderer.xmapper.v_map_from_target([l,u],!0),d=i[0],f=i[1],h=p.get("start")-this.max_size,c=p.get("end")+this.max_size,s=this.renderer.ymapper.v_map_from_target([h,c],!0),m=s[0],g=s[1]),e=a.validate_bbox_coords([d,f],[m,g]),function(){var t,r,n,o;for(n=this.index.search(e),o=[],t=0,r=n.length;t<r;t++)_=n[t],o.push(_.i);return o}.call(this)},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u;for(l=r.sx,u=r.sy,a=r.sradius,s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(l[n]+u[n]+a[n])||(t.beginPath(),t.arc(l[n],u[n],a[n],0,2*Math.PI,!1),this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,n),t.fill()),this.visuals.line.doit?(this.visuals.line.set_vectorize(t,n),s.push(t.stroke())):s.push(void 0));return s},e.prototype._hit_point=function(t){var e,r,n,o,i,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C,N,O,q,D,I;if(d=[t.vx,t.vy],T=d[0],P=d[1],C=this.renderer.xmapper.map_from_target(T,!0),q=this.renderer.ymapper.map_from_target(P,!0),null!=this._radius&&"data"===this.model.properties.radius.units?(N=C-this.max_radius,O=C+this.max_radius,D=q-this.max_radius,I=q+this.max_radius):(S=T-this.max_size,z=T+this.max_size,f=this.renderer.xmapper.v_map_from_target([S,z],!0),N=f[0],O=f[1],m=[Math.min(N,O),Math.max(N,O)],N=m[0],O=m[1],E=P-this.max_size,A=P+this.max_size,g=this.renderer.ymapper.v_map_from_target([E,A],!0),D=g[0],I=g[1],y=[Math.min(D,I),Math.max(D,I)],D=y[0],I=y[1]),e=a.validate_bbox_coords([N,O],[D,I]),r=function(){var t,r,n,o;for(n=this.index.search(e),o=[],t=0,r=n.length;t<r;t++)p=n[t],o.push(p.i);return o}.call(this),o=[],null!=this._radius&&"data"===this.model.properties.radius.units)for(l=0,h=r.length;l<h;l++)i=r[l],_=Math.pow(this.sradius[i],2),x=this.renderer.xmapper.map_to_target(C,!0),w=this.renderer.xmapper.map_to_target(this._x[i],!0),k=this.renderer.ymapper.map_to_target(q,!0),j=this.renderer.ymapper.map_to_target(this._y[i],!0),n=Math.pow(x-w,2)+Math.pow(k-j,2),n<=_&&o.push([i,n]);else for(b=this.renderer.plot_view.canvas.vx_to_sx(T),M=this.renderer.plot_view.canvas.vy_to_sy(P),u=0,c=r.length;u<c;u++)i=r[u],_=Math.pow(this.sradius[i],2),n=Math.pow(this.sx[i]-b,2)+Math.pow(this.sy[i]-M,2),n<=_&&o.push([i,n]);return o=s.chain(o).sortBy(function(t){return t[1]}).map(function(t){return t[0]}).value(),v=a.create_hit_test_result(),v["1d"].indices=o,v},e.prototype._hit_span=function(t){var e,r,n,o,i,s,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T;return u=[t.vx,t.vy],m=u[0],v=u[1],h=this.bounds(),i=h.minX,s=h.minY,n=h.maxX,o=h.maxY,f=a.create_hit_test_result(),"h"===t.direction?(j=s,T=o,null!=this._radius&&"data"===this.model.properties.radius.units?(g=m-this.max_radius,y=m+this.max_radius,c=this.renderer.xmapper.v_map_from_target([g,y]),w=c[0],M=c[1]):(l=this.max_size/2,g=m-l,y=m+l,p=this.renderer.xmapper.v_map_from_target([g,y],!0),w=p[0],M=p[1])):(w=i,M=n,null!=this._radius&&"data"===this.model.properties.radius.units?(b=v-this.max_radius,x=v+this.max_radius,_=this.renderer.ymapper.v_map_from_target([b,x]),j=_[0],T=_[1]):(l=this.max_size/2,b=v-l,x=v+l,d=this.renderer.ymapper.v_map_from_target([b,x],!0),j=d[0],T=d[1])),e=a.validate_bbox_coords([w,M],[j,T]),r=function(){var t,r,n,o;for(n=this.index.search(e),o=[],t=0,r=n.length;t<r;t++)k=n[t],o.push(k.i);return o}.call(this),f["1d"].indices=r,f},e.prototype._hit_rect=function(t){var e,r,n,o,i,s,l,u,h;return r=this.renderer.xmapper.v_map_from_target([t.vx0,t.vx1],!0),s=r[0],l=r[1],n=this.renderer.ymapper.v_map_from_target([t.vy0,t.vy1],!0),u=n[0],h=n[1],e=a.validate_bbox_coords([s,l],[u,h]),o=a.create_hit_test_result(),o["1d"].indices=function(){var t,r,n,o;for(n=this.index.search(e),o=[],t=0,r=n.length;t<r;t++)i=n[t],o.push(i.i);return o}.call(this),o},e.prototype._hit_poly=function(t){var e,r,n,o,i,s,l,u,h,c,p,_,d;for(s=[t.vx,t.vy],_=s[0],d=s[1],c=this.renderer.plot_view.canvas.v_vx_to_sx(_),p=this.renderer.plot_view.canvas.v_vy_to_sy(d),e=function(){h=[];for(var t=0,e=this.sx.length;0<=e?t<e:t>e;0<=e?t++:t--)h.push(t);return h}.apply(this),r=[],n=i=0,l=e.length;0<=l?i<l:i>l;n=0<=l?++i:--i)o=e[n],a.point_in_poly(this.sx[n],this.sy[n],c,p)&&r.push(o);return u=a.create_hit_test_result(),u["1d"].indices=r,u},e.prototype.draw_legend=function(t,e,r,n,o){var i,s,a,l,u,h,c;return l=null!=(a=this.get_reference_point())?a:0,s=[l],h={},h[l]=(e+r)/2,c={},c[l]=(n+o)/2,u={},u[l]=.2*Math.min(Math.abs(r-e),Math.abs(o-n)),i={sx:h,sy:c,sradius:u},this._render(t,s,i)},e}(i.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=o,e.prototype.type="Circle",e.coords([["x","y"]]),e.mixins(["line","fill"]),e.define({angle:[l.AngleSpec,0],size:[l.DistanceSpec,{units:"screen",value:4}],radius:[l.DistanceSpec,null],radius_dimension:[l.String,"x"]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.properties.radius.optional=!0},e}(i.Model),e.exports={Model:n,View:o}},{"../../common/hittest":"common/hittest","../../core/properties":"core/properties","./glyph":"models/glyphs/glyph",underscore:"underscore"}],"models/glyphs/ellipse":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t("underscore"),i=t("./glyph"),a=t("../../core/properties"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._set_data=function(){if(this.max_w2=0,"data"===this.model.properties.width.units&&(this.max_w2=this.max_width/2),this.max_h2=0,"data"===this.model.properties.height.units)return this.max_h2=this.max_height/2},e.prototype._index_data=function(){return this._xy_index()},e.prototype._map_data=function(){return"data"===this.model.properties.width.units?this.sw=this.sdist(this.renderer.xmapper,this._x,this._width,"center"):this.sw=this._width,"data"===this.model.properties.height.units?this.sh=this.sdist(this.renderer.ymapper,this._y,this._height,"center"):this.sh=this._height},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h;for(u=r.sx,h=r.sy,l=r.sw,a=r.sh,s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(u[n]+h[n]+l[n]+a[n]+this._angle[n])||(t.beginPath(),t.ellipse(u[n],h[n],l[n]/2,a[n]/2,this._angle[n],0,2*Math.PI),this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,n),t.fill()),this.visuals.line.doit?(this.visuals.line.set_vectorize(t,n),s.push(t.stroke())):s.push(void 0));return s},e.prototype.draw_legend=function(t,e,r,n,o){var i,s,a,l,u,h,c,p,_,d;return u=null!=(l=this.get_reference_point())?l:0,a=[u],_={},_[u]=(e+r)/2,d={},d[u]=(n+o)/2,h=this.sw[u]/this.sh[u],i=.8*Math.min(Math.abs(r-e),Math.abs(o-n)),p={},c={},h>1?(p[u]=i,c[u]=i/h):(p[u]=i*h,c[u]=i),s={sx:_,sy:d,sw:p,sh:c},this._render(t,a,s)},e.prototype._bounds=function(t){return this.max_wh2_bounds(t)},e}(i.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=o,e.prototype.type="Ellipse",e.coords([["x","y"]]),e.mixins(["line","fill"]),e.define({angle:[a.AngleSpec,0],width:[a.DistanceSpec],height:[a.DistanceSpec]}),e}(i.Model),e.exports={Model:n,View:o}},{"../../core/properties":"core/properties","./glyph":"models/glyphs/glyph",underscore:"underscore"}],"models/glyphs/gear":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;l=t("underscore"),a=t("./glyph"),i=t("gear_utils"),u=t("../../core/properties"),n=t("../../util/bezier"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype._index_data=function(){return this._xy_index()},e.prototype._map_data=function(){return this.smodule=this.sdist(this.renderer.xmapper,this._x,this._module,"edge")},e.prototype._render=function(t,e,r){var n,o,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z;for(j=r.sx,T=r.sy,k=r.smodule,o=r._angle,u=r._teeth,a=r._pressure_angle,l=r._shaft_size,s=r._internal,_=0,f=e.length;_<f;_++)if(c=e[_],!isNaN(j[c]+T[c]+o[c]+k[c]+u[c]+a[c]+l[c]+s[c])){for(m=k[c]*u[c]/2,h=s[c]?i.create_internal_gear_tooth:i.create_gear_tooth,w=h(k[c],u[c],a[c]),g=w.slice(0,3),n=g[0],S=g[1],z=g[2],x=w.slice(3),t.save(),t.translate(j[c],T[c]),t.rotate(o[c]),t.beginPath(),b=2*Math.PI/u[c],t.moveTo(S,z),p=d=0,y=u[c];0<=y?d<y:d>y;p=0<=y?++d:--d)this._render_seq(t,x),t.rotate(b);t.closePath(),s[c]?(v=m+2.75*k[c],t.moveTo(v,0),t.arc(0,0,v,0,2*Math.PI,!0)):l[c]>0&&(M=m*l[c],t.moveTo(M,0),t.arc(0,0,M,0,2*Math.PI,!0)),this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,c),t.fill()),this.visuals.line.doit&&(this.visuals.line.set_vectorize(t,c),t.stroke()),t.restore()}},e.prototype._render_seq=function(t,e){var r,o,i,s,a,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C;for(u=0;u<e.length;)switch(l.isString(e[u])&&(r=e[u],u+=1),r){case"M":f=e.slice(u,u+2),E=f[0],C=f[1],t.moveTo(E,C),m=[E,C],_=m[0],d=m[1],u+=2;break;case"L":y=e.slice(u,u+2),E=y[0],C=y[1],t.lineTo(E,C),v=[E,C],_=v[0],d=v[1],u+=2;break;case"C":b=e.slice(u,u+6),o=b[0],s=b[1],i=b[2],a=b[3],E=b[4],C=b[5],t.bezierCurveTo(o,s,i,a,E,C),x=[E,C],_=x[0],d=x[1],u+=6;break;case"Q":w=e.slice(u,u+4),o=w[0],s=w[1],E=w[2],C=w[3],t.quadraticCurveTo(o,s,E,C),M=[E,C],_=M[0],d=M[1],u+=4;break;case"A":for(k=e.slice(u,u+7),T=k[0],S=k[1],A=k[2],c=k[3],P=k[4],E=k[5],C=k[6],z=n.arc_to_bezier(_,d,T,S,-A,c,1-P,E,C),h=0,p=z.length;h<p;h++)j=z[h],o=j[0],s=j[1],i=j[2],a=j[3],E=j[4],C=j[5],t.bezierCurveTo(o,s,i,a,E,C);g=[E,C],_=g[0],d=g[1],u+=7;break;default:throw new Error("unexpected command: "+r)}},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_area_legend(t,e,r,n,o)},e}(a.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=s,e.prototype.type="Gear",e.coords([["x","y"]]),e.mixins(["line","fill"]),e.define({angle:[u.AngleSpec,0],module:[u.NumberSpec,null],pressure_angle:[u.NumberSpec,20],shaft_size:[u.NumberSpec,.3],teeth:[u.NumberSpec,null],internal:[u.NumberSpec,!1]}),e}(a.Model),e.exports={Model:o,View:s}},{"../../core/properties":"core/properties","../../util/bezier":"util/bezier","./glyph":"models/glyphs/glyph",gear_utils:"gear_utils",underscore:"underscore"}],"models/glyphs/glyph":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_=function(t,e){function r(){this.constructor=t}for(var n in e)d.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},d={}.hasOwnProperty;l=t("underscore"),p=t("rbush"),n=t("../mappers/categorical_mapper"),a=t("../renderers/renderer"),c=t("../../core/properties"),u=t("../../core/util/bbox"),s=t("../../model"),h=t("./webgl/main"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return _(e,t),e.prototype.initialize=function(t){var r,n,o;if(e.__super__.initialize.call(this,t),this.renderer=t.renderer,null!=(null!=(o=this.renderer)?o.plot_view:void 0)&&(n=this.renderer.plot_view.canvas_view.ctx,null!=n.glcanvas&&(r=h[this.model.type+"GLGlyph"])))return this.glglyph=new r(n.glcanvas.gl,this)},e.prototype.render=function(t,e,r){if(this.mget("visible")){if(t.beginPath(),null!=this.glglyph&&this._render_gl(t,e,r))return;this._render(t,e,r)}},e.prototype._render_gl=function(t,e,r){var n,o,i,s,a,l,u,h,c,p;return c=p=1,i=this.renderer.map_to_screen([0*c,1*c,2*c],[0*p,1*p,2*p]),n=i[0],o=i[1],c=100/Math.min(Math.max(Math.abs(n[1]-n[0]),1e-12),1e12),p=100/Math.min(Math.max(Math.abs(o[1]-o[0]),1e-12),1e12),s=this.renderer.map_to_screen([0*c,1*c,2*c],[0*p,1*p,2*p]),n=s[0],o=s[1],!(Math.abs(n[1]-n[0]-(n[2]-n[1]))>1e-6||Math.abs(o[1]-o[0]-(o[2]-o[1]))>1e-6)&&(a=[(n[1]-n[0])/c,(o[1]-o[0])/p],l=a[0],u=a[1],h={pixel_ratio:t.pixel_ratio,width:t.glcanvas.width,height:t.glcanvas.height,dx:n[0]/l,dy:o[0]/u,sx:l,sy:u},this.glglyph.draw(e,r,h),!0)},e.prototype.bounds=function(){var t,e;return null==this.index?u.empty():(e=this.index.data,t={minX:e.minX,minY:e.minY,maxX:e.maxX,maxY:e.maxY},this._bounds(t))},e.prototype.max_wh2_bounds=function(t){return{minX:t.minX-this.max_w2,maxX:t.maxX+this.max_w2,minY:t.minY-this.max_h2,maxY:t.maxY+this.max_h2}},e.prototype.get_anchor_point=function(t,e,r){var n,o;switch(n=r[0],o=r[1],t){case"center":return{x:this.scx(e,n,o),y:this.scy(e,n,o)};default:return null}},e.prototype.scx=function(t){return this.sx[t]},e.prototype.scy=function(t){return this.sy[t]},e.prototype._xy_index=function(){var t,e,r,o,i,s,a,l,u;for(e=p(),o=[],a=this.renderer.xmapper instanceof n.Model?this.renderer.xmapper.v_map_to_target(this._x,!0):this._x,u=this.renderer.ymapper instanceof n.Model?this.renderer.ymapper.v_map_to_target(this._y,!0):this._y,t=r=0,i=a.length;0<=i?r<i:r>i;t=0<=i?++r:--r)s=a[t],!isNaN(s)&&isFinite(s)&&(l=u[t],!isNaN(l)&&isFinite(l)&&o.push({minX:s,minY:l,maxX:s,maxY:l,i:t}));return e.load(o),e},e.prototype.sdist=function(t,e,r,n,o){var i,s,a,u,h,c,p;return null==n&&(n="edge"),null==o&&(o=!1),l.isString(e[0])&&(e=t.v_map_to_target(e)),"center"===n?(s=function(){var t,e,n;for(n=[],t=0,e=r.length;t<e;t++)i=r[t],n.push(i/2);return n}(),u=function(){var t,r,n;for(n=[],a=t=0,r=e.length;0<=r?t<r:t>r;a=0<=r?++t:--t)n.push(e[a]-s[a]);return n}(),h=function(){var t,r,n;for(n=[],a=t=0,r=e.length;0<=r?t<r:t>r;a=0<=r?++t:--t)n.push(e[a]+s[a]);return n}()):(u=e,h=function(){var t,e,n;for(n=[],a=t=0,e=u.length;0<=e?t<e:t>e;a=0<=e?++t:--t)n.push(u[a]+r[a]);return n}()),c=t.v_map_to_target(u),p=t.v_map_to_target(h),o?function(){var t,e,r;for(r=[],a=t=0,e=c.length;0<=e?t<e:t>e;a=0<=e?++t:--t)r.push(Math.ceil(Math.abs(p[a]-c[a])));return r}():function(){var t,e,r;for(r=[],a=t=0,e=c.length;0<=e?t<e:t>e;a=0<=e?++t:--t)r.push(Math.abs(p[a]-c[a]));return r}()},e.prototype.get_reference_point=function(){},e.prototype.draw_legend=function(t,e,r,n,o){return null},e.prototype._generic_line_legend=function(t,e,r,n,o){var i,s;return s=null!=(i=this.get_reference_point())?i:0,t.save(),t.beginPath(),t.moveTo(e,(n+o)/2),t.lineTo(r,(n+o)/2),this.visuals.line.doit&&(this.visuals.line.set_vectorize(t,s),t.stroke()),t.restore()},e.prototype._generic_area_legend=function(t,e,r,n,o){var i,s,a,l,u,h,c,p,_,d,f;if(h=null!=(u=this.get_reference_point())?u:0,l=[h],f=Math.abs(r-e),s=.1*f,a=Math.abs(o-n),i=.1*a,c=e+s,p=r-s,_=n+i,d=o-i,this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,h),t.fillRect(c,_,p-c,d-_)),this.visuals.line.doit)return t.beginPath(),t.rect(c,_,p-c,d-_),this.visuals.line.set_vectorize(t,h),t.stroke()},e}(a.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return _(e,t),e.define({visible:[c.Bool,!0]}),e.internal({x_range_name:[c.String,"default"],y_range_name:[c.String,"default"]}),e}(s),e.exports={Model:o,View:i}},{"../../core/properties":"core/properties","../../core/util/bbox":"core/util/bbox","../../model":"model","../mappers/categorical_mapper":"models/mappers/categorical_mapper","../renderers/renderer":"models/renderers/renderer","./webgl/main":"models/glyphs/webgl/main",rbush:"rbush",underscore:"underscore"}],"models/glyphs/hbar":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p=function(t,e){function r(){this.constructor=t}for(var n in e)_.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},_={}.hasOwnProperty;l=t("underscore"),c=t("rbush"),a=t("./quad"),o=t("./glyph"),n=t("../mappers/categorical_mapper"),u=t("../../common/hittest"),h=t("../../core/properties"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return p(e,t),e.prototype._map_data=function(){var t,e,r,n,o,i;for(i=this.renderer.ymapper.v_map_to_target(this._y),this.sy=this.plot_view.canvas.v_vy_to_sy(i),o=this.renderer.xmapper.v_map_to_target(this._right),n=this.renderer.xmapper.v_map_to_target(this._left),this.sright=this.plot_view.canvas.v_vx_to_sx(o),this.sleft=this.plot_view.canvas.v_vx_to_sx(n),this.stop=[],this.sbottom=[],this.sh=this.sdist(this.renderer.ymapper,this._y,this._height,"center"),t=e=0,r=this.sy.length;0<=r?e<r:e>r;t=0<=r?++e:--e)this.stop.push(this.sy[t]-this.sh[t]/2),this.sbottom.push(this.sy[t]+this.sh[t]/2);return null},e.prototype._index_data=function(){var t,e,r,o,i,s,a,l,u,h,p,_,d,f;for(l=function(t,e){return t instanceof n.Model?t.v_map_to_target(e,!0):e},a=l(this.renderer.xmapper,this._left),_=l(this.renderer.xmapper,this._right),f=l(this.renderer.ymapper,this._y),e=l(this.renderer.ymapper,this._height),o=c(),u=[],r=i=0,p=f.length;0<=p?i<p:i>p;r=0<=p?++i:--i)s=a[r],h=_[r],d=f[r]+.5*e[r],t=f[r]-.5*e[r],!isNaN(s+h+d+t)&&isFinite(s+h+d+t)&&u.push({minX:s,minY:t,maxX:h,maxY:d,i:r});return o.load(u),o},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h;for(l=r.sleft,u=r.sright,h=r.stop,a=r.sbottom,s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(l[n]+h[n]+u[n]+a[n])||(this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,n),t.fillRect(l[n],h[n],u[n]-l[n],a[n]-h[n])),this.visuals.line.doit?(t.beginPath(),t.rect(l[n],h[n],u[n]-l[n],a[n]-h[n]),this.visuals.line.set_vectorize(t,n),s.push(t.stroke())):s.push(void 0));return s},e.prototype._hit_point=function(t){var e,r,n,o,i,s,a;return r=[t.vx,t.vy],o=r[0],i=r[1],s=this.renderer.xmapper.map_from_target(o,!0),a=this.renderer.ymapper.map_from_target(i,!0),e=function(){var t,e,r,n;for(r=this.index.search({minX:s,minY:a,maxX:s,maxY:a}),n=[],t=0,e=r.length;t<e;t++)s=r[t],n.push(s.i);return n}.call(this),n=u.create_hit_test_result(),n["1d"].indices=e,n},e.prototype.scx=function(t){return(this.sleft[t]+this.sright[t])/2},e}(o.View),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return p(e,t),e.prototype.default_view=s,e.prototype.type="HBar",e.mixins(["line","fill"]),e.define({y:[h.NumberSpec],height:[h.DistanceSpec],left:[h.NumberSpec,0],right:[h.NumberSpec]}),e}(o.Model),e.exports={Model:i,View:s}},{"../../common/hittest":"common/hittest","../../core/properties":"core/properties","../mappers/categorical_mapper":"models/mappers/categorical_mapper","./glyph":"models/glyphs/glyph","./quad":"models/glyphs/quad",rbush:"rbush",underscore:"underscore"}],"models/glyphs/image":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;l=t("underscore"),n=t("../glyphs/glyph"),a=t("../mappers/linear_color_mapper"),u=t("../../core/properties"),o=t("../../palettes/palettes").Greys,s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.listenTo(this.model.color_mapper,"change",this._update_image)},e.prototype._update_image=function(){if(null!=this.image_data)return this._set_data(),this.plot_view.request_render()},e.prototype._index_data=function(){return this._xy_index()},e.prototype._set_data=function(){var t,e,r,n,o,i,s,a,u,h,c;for(null!=this.image_data&&this.image_data.length===this._image.length||(this.image_data=new Array(this._image.length)),null!=this._width&&this._width.length===this._image.length||(this._width=new Array(this._image.length)),null!=this._height&&this._height.length===this._image.length||(this._height=new Array(this._image.length)),c=[],i=u=0,h=this._image.length;0<=h?u<h:u>h;i=0<=h?++u:--u)null!=this._rows?(this._height[i]=this._rows[i],this._width[i]=this._cols[i]):(this._height[i]=this._image[i].length,this._width[i]=this._image[i][0].length),r=document.createElement("canvas"),r.width=this._width[i],r.height=this._height[i],o=r.getContext("2d"),s=o.getImageData(0,0,this._width[i],this._height[i]),n=this.mget("color_mapper"),a=null!=this._rows?this._image[i]:l.flatten(this._image[i]),t=n.v_map_screen(a),e=new Uint8ClampedArray(t),s.data.set(e),o.putImageData(s,0,0),this.image_data[i]=r,this.max_dw=0,"data"===this._dw.units&&(this.max_dw=l.max(this._dw)),this.max_dh=0,"data"===this._dh.units&&(this.max_dh=l.max(this._dh)),c.push(this._xy_index());return c},e.prototype._map_data=function(){switch(this.model.properties.dw.units){case"data":this.sw=this.sdist(this.renderer.xmapper,this._x,this._dw,"edge",this.mget("dilate"));break;case"screen":this.sw=this._dw}switch(this.model.properties.dh.units){case"data":return this.sh=this.sdist(this.renderer.ymapper,this._y,this._dh,"edge",this.mget("dilate"));case"screen":return this.sh=this._dh}},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p;for(o=r.image_data,h=r.sx,c=r.sy,u=r.sw,l=r.sh,a=t.getImageSmoothingEnabled(),t.setImageSmoothingEnabled(!1),i=0,s=e.length;i<s;i++)n=e[i],null!=o[n]&&(isNaN(h[n]+c[n]+u[n]+l[n])||(p=c[n],t.translate(0,p),t.scale(1,-1),t.translate(0,-p),t.drawImage(o[n],0|h[n],0|c[n],u[n],l[n]),t.translate(0,p),t.scale(1,-1),t.translate(0,-p)));return t.setImageSmoothingEnabled(a)},e.prototype.bounds=function(){var t;return t=this.index.data,{minX:t.minX,minY:t.minY,maxX:t.maxX+this.max_dw,maxY:t.maxY+this.max_dh}},e}(n.View),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=s,e.prototype.type="Image",e.coords([["x","y"]]),e.mixins([]),e.define({image:[u.NumberSpec],dw:[u.DistanceSpec],dh:[u.DistanceSpec],dilate:[u.Bool,!1],color_mapper:[u.Instance,function(){return new a.Model({palette:o.Greys9})}]}),e}(n.Model),e.exports={Model:i,View:s}},{"../../core/properties":"core/properties","../../palettes/palettes":"palettes/palettes","../glyphs/glyph":"models/glyphs/glyph","../mappers/linear_color_mapper":"models/mappers/linear_color_mapper",
underscore:"underscore"}],"models/glyphs/image_rgba":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t("underscore"),n=t("./glyph"),a=t("../../core/properties"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._index_data=function(){return this._xy_index()},e.prototype._set_data=function(t,e){var r,n,o,i,a,l,u,h,c,p,_,d,f,m;for(null!=this.image_data&&this.image_data.length===this._image.length||(this.image_data=new Array(this._image.length)),null!=this._width&&this._width.length===this._image.length||(this._width=new Array(this._image.length)),null!=this._height&&this._height.length===this._image.length||(this._height=new Array(this._image.length)),m=[],u=p=0,d=this._image.length;0<=d?p<d:p>d;u=0<=d?++p:--p)if(null==e||u===e){if(null!=this._rows?(this._height[u]=this._rows[u],this._width[u]=this._cols[u]):(this._height[u]=this._image[u].length,this._width[u]=this._image[u][0].length),o=document.createElement("canvas"),o.width=this._width[u],o.height=this._height[u],a=o.getContext("2d"),h=a.getImageData(0,0,this._width[u],this._height[u]),null!=this._rows)h.data.set(new Uint8ClampedArray(this._image[u]));else{for(l=s.flatten(this._image[u]),r=new ArrayBuffer(4*l.length),i=new Uint32Array(r),c=_=0,f=l.length;0<=f?_<f:_>f;c=0<=f?++_:--_)i[c]=l[c];n=new Uint8ClampedArray(r),h.data.set(n)}a.putImageData(h,0,0),this.image_data[u]=o,this.max_dw=0,"data"===this._dw.units&&(this.max_dw=s.max(this._dw)),this.max_dh=0,"data"===this._dh.units?m.push(this.max_dh=s.max(this._dh)):m.push(void 0)}return m},e.prototype._map_data=function(){switch(this.model.properties.dw.units){case"data":this.sw=this.sdist(this.renderer.xmapper,this._x,this._dw,"edge",this.mget("dilate"));break;case"screen":this.sw=this._dw}switch(this.model.properties.dh.units){case"data":return this.sh=this.sdist(this.renderer.ymapper,this._y,this._dh,"edge",this.mget("dilate"));case"screen":return this.sh=this._dh}},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p;for(o=r.image_data,h=r.sx,c=r.sy,u=r.sw,l=r.sh,a=t.getImageSmoothingEnabled(),t.setImageSmoothingEnabled(!1),i=0,s=e.length;i<s;i++)n=e[i],isNaN(h[n]+c[n]+u[n]+l[n])||(p=c[n],t.translate(0,p),t.scale(1,-1),t.translate(0,-p),t.drawImage(o[n],0|h[n],0|c[n],u[n],l[n]),t.translate(0,p),t.scale(1,-1),t.translate(0,-p));return t.setImageSmoothingEnabled(a)},e.prototype.bounds=function(){var t;return t=this.index.data,{minX:t.minX,minY:t.minY,maxX:t.maxX+this.max_dw,maxY:t.maxY+this.max_dh}},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type="ImageRGBA",e.coords([["x","y"]]),e.mixins([]),e.define({image:[a.NumberSpec],rows:[a.NumberSpec],cols:[a.NumberSpec],dw:[a.DistanceSpec],dh:[a.DistanceSpec],dilate:[a.Bool,!1]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.properties.rows.optional=!0,this.properties.cols.optional=!0},e}(n.Model),e.exports={Model:o,View:i}},{"../../core/properties":"core/properties","./glyph":"models/glyphs/glyph",underscore:"underscore"}],"models/glyphs/image_url":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t("underscore"),n=t("./glyph"),a=t("../../core/logging").logger,l=t("../../core/properties"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.listenTo(this.model,"change:global_alpha",this.renderer.request_render)},e.prototype._index_data=function(){},e.prototype._set_data=function(){var t,e,r,n,o,i,s;for(null!=this.image&&this.image.length===this._url.length||(this.image=function(){var t,r,n,o;for(n=this._url,o=[],t=0,r=n.length;t<r;t++)e=n[t],o.push(null);return o}.call(this)),i=this.model.retry_attempts,s=this.model.retry_timeout,this.retries=function(){var t,r,n,o;for(n=this._url,o=[],t=0,r=n.length;t<r;t++)e=n[t],o.push(i);return o}.call(this),o=[],t=r=0,n=this._url.length;0<=n?r<n:r>n;t=0<=n?++r:--r)null!=this._url[t]&&(e=new Image,e.onerror=function(t){return function(e,r){return function(){return t.retries[e]>0?(a.trace("ImageURL failed to load "+t._url[e]+" image, retrying in "+s+" ms"),setTimeout(function(){return r.src=t._url[e]},s)):a.warn("ImageURL unable to load "+t._url[e]+" image after "+i+" retries"),t.retries[e]-=1}}}(this)(t,e),e.onload=function(t){return function(e,r){return function(){return t.image[r]=e,t.renderer.request_render()}}}(this)(e,t),o.push(e.src=this._url[t]));return o},e.prototype._map_data=function(){var t,e,r;switch(e=function(){var t,e,n,o;if(null!=this._w)return this._w;for(n=this._x,o=[],t=0,e=n.length;t<e;t++)r=n[t],o.push(NaN);return o}.call(this),t=function(){var t,e,n,o;if(null!=this._h)return this._h;for(n=this._x,o=[],t=0,e=n.length;t<e;t++)r=n[t],o.push(NaN);return o}.call(this),this.model.properties.w.units){case"data":this.sw=this.sdist(this.renderer.xmapper,this._x,e,"edge",this.model.dilate);break;case"screen":this.sw=e}switch(this.model.properties.h.units){case"data":return this.sh=this.sdist(this.renderer.ymapper,this._y,t,"edge",this.model.dilate);case"screen":return this.sh=t}},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d;for(o=r._url,a=r.image,_=r.sx,d=r.sy,p=r.sw,c=r.sh,n=r._angle,i=this.renderer.plot_view.frame,t.rect(i.get("left")+1,i.get("bottom")+1,i.get("width")-2,i.get("height")-2),t.clip(),h=[],l=0,u=e.length;l<u;l++)s=e[l],isNaN(_[s]+d[s]+n[s])||this.retries[s]!==-1&&null!=a[s]&&h.push(this._render_image(t,s,a[s],_,d,p,c,n));return h},e.prototype._final_sx_sy=function(t,e,r,n,o){switch(t){case"top_left":return[e,r];case"top_center":return[e-n/2,r];case"top_right":return[e-n,r];case"right_center":return[e-n,r-o/2];case"bottom_right":return[e-n,r-o];case"bottom_center":return[e-n/2,r-o];case"bottom_left":return[e,r-o];case"left_center":return[e,r-o/2];case"center":return[e-n/2,r-o/2]}},e.prototype._render_image=function(t,e,r,n,o,i,s,a){var l,u;return isNaN(i[e])&&(i[e]=r.width),isNaN(s[e])&&(s[e]=r.height),l=this.model.anchor,u=this._final_sx_sy(l,n[e],o[e],i[e],s[e]),n=u[0],o=u[1],t.save(),t.globalAlpha=this.model.global_alpha,a[e]?(t.translate(n,o),t.rotate(a[e]),t.drawImage(r,0,0,i[e],s[e]),t.rotate(-a[e]),t.translate(-n,-o)):t.drawImage(r,n,o,i[e],s[e]),t.restore()},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=i,e.prototype.type="ImageURL",e.coords([["x","y"]]),e.mixins([]),e.define({url:[l.StringSpec],anchor:[l.Anchor,"top_left"],global_alpha:[l.Number,1],angle:[l.AngleSpec,0],w:[l.DistanceSpec],h:[l.DistanceSpec],dilate:[l.Bool,!1],retry_attempts:[l.Number,0],retry_timeout:[l.Number,0]}),e}(n.Model),e.exports={Model:o,View:i}},{"../../core/logging":"core/logging","../../core/properties":"core/properties","./glyph":"models/glyphs/glyph",underscore:"underscore"}],"models/glyphs/line":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t("underscore"),n=t("./glyph"),a=t("../../common/hittest"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._index_data=function(){return this._xy_index()},e.prototype._render=function(t,e,r){var n,o,i,s,a,l;for(a=r.sx,l=r.sy,n=!1,this.visuals.line.set_value(t),i=0,s=e.length;i<s;i++)o=e[i],isFinite(a[o]+l[o])||!n?n?t.lineTo(a[o],l[o]):(t.beginPath(),t.moveTo(a[o],l[o]),n=!0):(t.stroke(),t.beginPath(),n=!1);if(n)return t.stroke()},e.prototype._hit_point=function(t){var e,r,n,o,i,s,l,u,h,c,p;for(h=a.create_hit_test_result(),s={x:this.renderer.plot_view.canvas.vx_to_sx(t.vx),y:this.renderer.plot_view.canvas.vy_to_sy(t.vy)},c=9999,p=Math.max(2,this.visuals.line.line_width.value()/2),r=n=0,l=this.sx.length-1;0<=l?n<l:n>l;r=0<=l?++n:--n)u=[{x:this.sx[r],y:this.sy[r]},{x:this.sx[r+1],y:this.sy[r+1]}],o=u[0],i=u[1],e=a.dist_to_segment(s,o,i),e<p&&e<c&&(c=e,h["0d"].glyph=this.model,h["0d"].get_view=function(){return this}.bind(this),h["0d"].flag=!0,h["0d"].indices=[r]);return h},e.prototype._hit_span=function(t){var e,r,n,o,i,s,l,u,h;for(n=[t.vx,t.vy],u=n[0],h=n[1],i=a.create_hit_test_result(),"v"===t.direction?(s=this.renderer.ymapper.map_from_target(h),l=this._y):(s=this.renderer.xmapper.map_from_target(u),l=this._x),e=r=0,o=l.length-1;0<=o?r<o:r>o;e=0<=o?++r:--r)l[e]<=s&&s<=l[e+1]&&(i["0d"].glyph=this.model,i["0d"].get_view=function(){return this}.bind(this),i["0d"].flag=!0,i["0d"].indices.push(e));return i},e.prototype.get_interpolation_hit=function(t,e){var r,n,o,i,s,l,u,h,c,p,_,d,f,m,g,y,v,b,x;return r=[e.vx,e.vy],p=r[0],_=r[1],n=[this._x[t],this._y[t],this._x[t+1],this._y[t+1]],m=n[0],b=n[1],g=n[2],x=n[3],"point"===e.type?(o=this.renderer.ymapper.v_map_from_target([_-1,_+1]),y=o[0],v=o[1],i=this.renderer.xmapper.v_map_from_target([p-1,p+1]),d=i[0],f=i[1]):"v"===e.direction?(s=this.renderer.ymapper.v_map_from_target([_,_]),y=s[0],v=s[1],l=[m,g],d=l[0],f=l[1]):(u=this.renderer.xmapper.v_map_from_target([p,p]),d=u[0],f=u[1],h=[b,x],y=h[0],v=h[1]),c=a.check_2_segments_intersect(d,y,f,v,m,b,g,x),[c.x,c.y]},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_line_legend(t,e,r,n,o)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type="Line",e.coords([["x","y"]]),e.mixins(["line"]),e}(n.Model),e.exports={Model:o,View:i}},{"../../common/hittest":"common/hittest","./glyph":"models/glyphs/glyph",underscore:"underscore"}],"models/glyphs/multi_line":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t("underscore"),l=t("rbush"),a=t("../../common/hittest"),n=t("./glyph"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype._index_data=function(){var t,e,r,n,o,i,a,u,h;for(e=l(),n=[],t=r=0,o=this._xs.length;0<=o?r<o:r>o;t=0<=o?++r:--r)a=function(){var e,r,n,o;for(n=this._xs[t],o=[],e=0,r=n.length;e<r;e++)i=n[e],s.isNaN(i)||o.push(i);return o}.call(this),h=function(){var e,r,n,o;for(n=this._ys[t],o=[],e=0,r=n.length;e<r;e++)u=n[e],s.isNaN(u)||o.push(u);return o}.call(this),0!==a.length&&n.push({minX:s.min(a),minY:s.min(h),maxX:s.max(a),maxY:s.max(h),i:t});return e.load(n),e},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d;for(p=r.sxs,d=r.sys,h=[],i=0,a=e.length;i<a;i++){for(n=e[i],l=[p[n],d[n]],c=l[0],_=l[1],this.visuals.line.set_vectorize(t,n),o=s=0,u=c.length;0<=u?s<u:s>u;o=0<=u?++s:--s)0!==o?isNaN(c[o])||isNaN(_[o])?(t.stroke(),t.beginPath()):t.lineTo(c[o],_[o]):(t.beginPath(),t.moveTo(c[o],_[o]));h.push(t.stroke())}return h},e.prototype._hit_point=function(t){var e,r,n,o,i,l,u,h,c,p,_,d,f,m,g,y;for(m=a.create_hit_test_result(),c={x:this.renderer.plot_view.canvas.vx_to_sx(t.vx),y:this.renderer.plot_view.canvas.vy_to_sy(t.vy)},g=9999,y=Math.max(2,this.visuals.line.line_width.value()/2),r={},n=i=0,_=this.sxs.length;0<=_?i<_:i>_;n=0<=_?++i:--i){for(p=null,o=l=0,d=this.sxs[n].length-1;0<=d?l<d:l>d;o=0<=d?++l:--l)f=[{x:this.sxs[n][o],y:this.sys[n][o]},{x:this.sxs[n][o+1],y:this.sys[n][o+1]}],u=f[0],h=f[1],e=a.dist_to_segment(c,u,h),e<y&&e<g&&(g=e,p=[o]);p&&(r[n]=p)}return m["1d"].indices=s.keys(r),m["2d"]=r,m},e.prototype._hit_span=function(t){var e,r,n,o,i,l,u,h,c,p,_,d,f,m;for(u=[t.vx,t.vy],f=u[0],m=u[1],p=a.create_hit_test_result(),"v"===t.direction?(_=this.renderer.ymapper.map_from_target(m),d=this._ys):(_=this.renderer.xmapper.map_from_target(f),d=this._xs),e={},r=o=0,h=d.length;0<=h?o<h:o>h;r=0<=h?++o:--o){for(l=[],n=i=0,c=d[r].length-1;0<=c?i<c:i>c;n=0<=c?++i:--i)d[r][n]<=_&&_<=d[r][n+1]&&l.push(n);l.length>0&&(e[r]=l)}return p["1d"].indices=s.keys(e),p["2d"]=e,p},e.prototype.get_interpolation_hit=function(t,e,r){var n,o,i,s,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w;return n=[r.vx,r.vy],_=n[0],d=n[1],o=[this._xs[t][e],this._ys[t][e],this._xs[t][e+1],this._ys[t][e+1]],g=o[0],x=o[1],y=o[2],w=o[3],"point"===r.type?(i=this.renderer.ymapper.v_map_from_target([d-1,d+1]),v=i[0],b=i[1],s=this.renderer.xmapper.v_map_from_target([_-1,_+1]),f=s[0],m=s[1]):"v"===r.direction?(l=this.renderer.ymapper.v_map_from_target([d,d]),v=l[0],b=l[1],u=[g,y],f=u[0],m=u[1]):(h=this.renderer.xmapper.v_map_from_target([_,_]),f=h[0],m=h[1],c=[x,w],v=c[0],b=c[1]),p=a.check_2_segments_intersect(f,v,m,b,g,x,y,w),[p.x,p.y]},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_line_legend(t,e,r,n,o)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=i,e.prototype.type="MultiLine",e.coords([["xs","ys"]]),e.mixins(["line"]),e}(n.Model),e.exports={Model:o,View:i}},{"../../common/hittest":"common/hittest","./glyph":"models/glyphs/glyph",rbush:"rbush",underscore:"underscore"}],"models/glyphs/oval":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t("underscore"),n=t("./glyph"),a=t("../../core/properties"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._set_data=function(){if(this.max_w2=0,"data"===this.model.properties.width.units&&(this.max_w2=this.max_width/2),this.max_h2=0,"data"===this.model.properties.height.units)return this.max_h2=this.max_height/2},e.prototype._index_data=function(){return this._xy_index()},e.prototype._map_data=function(){return"data"===this.model.properties.width.units?this.sw=this.sdist(this.renderer.xmapper,this._x,this._width,"center"):this.sw=this._width,"data"===this.model.properties.height.units?this.sh=this.sdist(this.renderer.ymapper,this._y,this._height,"center"):this.sh=this._height},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h;for(u=r.sx,h=r.sy,l=r.sw,a=r.sh,s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(u[n]+h[n]+l[n]+a[n]+this._angle[n])||(t.translate(u[n],h[n]),t.rotate(this._angle[n]),t.beginPath(),t.moveTo(0,-a[n]/2),t.bezierCurveTo(l[n]/2,-a[n]/2,l[n]/2,a[n]/2,0,a[n]/2),t.bezierCurveTo(-l[n]/2,a[n]/2,-l[n]/2,-a[n]/2,0,-a[n]/2),t.closePath(),this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,n),t.fill()),this.visuals.line.doit&&(this.visuals.line.set_vectorize(t,n),t.stroke()),t.rotate(-this._angle[n]),s.push(t.translate(-u[n],-h[n])));return s},e.prototype.draw_legend=function(t,e,r,n,o){var i,s,a,l,u,h,c,p,_,d;return u=null!=(l=this.get_reference_point())?l:0,a=[u],_={},_[u]=(e+r)/2,d={},d[u]=(n+o)/2,h=this.sw[u]/this.sh[u],i=.8*Math.min(Math.abs(r-e),Math.abs(o-n)),p={},c={},h>1?(p[u]=i,c[u]=i/h):(p[u]=i*h,c[u]=i),s={sx:_,sy:d,sw:p,sh:c},this._render(t,a,s)},e.prototype._bounds=function(t){return this.max_wh2_bounds(t)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type="Oval",e.coords([["x","y"]]),e.mixins(["line","fill"]),e.define({angle:[a.AngleSpec,0],width:[a.DistanceSpec],height:[a.DistanceSpec]}),e}(n.Model),e.exports={Model:o,View:i}},{"../../core/properties":"core/properties","./glyph":"models/glyphs/glyph",underscore:"underscore"}],"models/glyphs/patch":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;s=t("underscore"),n=t("./glyph"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype._index_data=function(){return this._xy_index()},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u;if(l=r.sx,u=r.sy,this.visuals.fill.doit){for(this.visuals.fill.set_value(t),o=0,s=e.length;o<s;o++)n=e[o],0!==n?isNaN(l[n]+u[n])?(t.closePath(),t.fill(),t.beginPath()):t.lineTo(l[n],u[n]):(t.beginPath(),t.moveTo(l[n],u[n]));t.closePath(),t.fill()}if(this.visuals.line.doit){for(this.visuals.line.set_value(t),i=0,a=e.length;i<a;i++)n=e[i],0!==n?isNaN(l[n]+u[n])?(t.closePath(),t.stroke(),t.beginPath()):t.lineTo(l[n],u[n]):(t.beginPath(),t.moveTo(l[n],u[n]));return t.closePath(),t.stroke()}},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_area_legend(t,e,r,n,o)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.default_view=i,e.prototype.type="Patch",e.coords([["x","y"]]),e.mixins(["line","fill"]),e}(n.Model),e.exports={Model:o,View:i}},{"./glyph":"models/glyphs/glyph",underscore:"underscore"}],"models/glyphs/patches":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t("underscore"),l=t("rbush"),n=t("./glyph"),a=t("../../common/hittest"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype._build_discontinuous_object=function(t){var e,r,n,o,i,a,l,u,h;for(r={},n=o=0,h=t.length;0<=h?o<h:o>h;n=0<=h?++o:--o)for(r[n]=[],l=s.toArray(t[n]);l.length>0;)i=s.findLastIndex(l,function(t){return s.isNaN(t)}),i>=0?u=l.splice(i):(u=l,l=[]),e=function(){var t,e,r;for(r=[],t=0,e=u.length;t<e;t++)a=u[t],s.isNaN(a)||r.push(a);return r}(),r[n].push(e);return r},e.prototype._index_data=function(){var t,e,r,n,o,i,a,u,h,c,p,_;for(e=l(),i=[],c=this._build_discontinuous_object(this._xs),_=this._build_discontinuous_object(this._ys),t=n=0,a=this._xs.length;0<=a?n<a:n>a;t=0<=a?++n:--n)for(r=o=0,u=c[t].length;0<=u?o<u:o>u;r=0<=u?++o:--o)h=c[t][r],p=_[t][r],0!==h.length&&i.push({minX:s.min(h),minY:s.min(p),maxX:s.max(h),maxY:s.max(p),i:t});return e.load(i),e},e.prototype._mask_data=function(t){var e,r,n,o,i,s,l,u,h,c;return l=this.renderer.plot_view.x_range,r=[l.get("min"),l.get("max")],i=r[0],s=r[1],c=this.renderer.plot_view.y_range,n=[c.get("min"),c.get("max")],u=n[0],h=n[1],e=a.validate_bbox_coords([i,s],[u,h]),function(){var t,r,n,i;for(n=this.index.search(e),i=[],t=0,r=n.length;t<r;t++)o=n[t],i.push(o.i);return i}.call(this)},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m;for(d=r.sxs,m=r.sys,this.renderer.sxss=this._build_discontinuous_object(d),this.renderer.syss=this._build_discontinuous_object(m),p=[],i=0,a=e.length;i<a;i++){if(n=e[i],u=[d[n],m[n]],_=u[0],f=u[1],this.visuals.fill.doit){for(this.visuals.fill.set_vectorize(t,n),o=s=0,h=_.length;0<=h?s<h:s>h;o=0<=h?++s:--s)0!==o?isNaN(_[o]+f[o])?(t.closePath(),t.fill(),t.beginPath()):t.lineTo(_[o],f[o]):(t.beginPath(),t.moveTo(_[o],f[o]));t.closePath(),t.fill()}if(this.visuals.line.doit){for(this.visuals.line.set_vectorize(t,n),o=l=0,c=_.length;0<=c?l<c:l>c;o=0<=c?++l:--l)0!==o?isNaN(_[o]+f[o])?(t.closePath(),t.stroke(),t.beginPath()):t.lineTo(_[o],f[o]):(t.beginPath(),t.moveTo(_[o],f[o]));t.closePath(),p.push(t.stroke())}else p.push(void 0)}return p},e.prototype._hit_point=function(t){var e,r,n,o,i,s,l,u,h,c,p,_,d,f,m,g,y,v,b;for(u=[t.vx,t.vy],g=u[0],y=u[1],_=this.renderer.plot_view.canvas.vx_to_sx(g),f=this.renderer.plot_view.canvas.vy_to_sy(y),v=this.renderer.xmapper.map_from_target(g,!0),b=this.renderer.ymapper.map_from_target(y,!0),e=function(){var t,e,r,n;for(r=this.index.search({minX:v,minY:b,maxX:v,maxY:b}),n=[],t=0,e=r.length;t<e;t++)v=r[t],n.push(v.i);return n}.call(this),r=[],n=s=0,h=e.length;0<=h?s<h:s>h;n=0<=h?++s:--s)for(o=e[n],d=this.renderer.sxss[o],m=this.renderer.syss[o],i=l=0,c=d.length;0<=c?l<c:l>c;i=0<=c?++l:--l)a.point_in_poly(_,f,d[i],m[i])&&r.push(o);return p=a.create_hit_test_result(),p["1d"].indices=r,p},e.prototype._get_snap_coord=function(t){var e,r,n,o;for(o=0,e=0,r=t.length;e<r;e++)n=t[e],o+=n;return o/t.length},e.prototype.scx=function(t,e,r){var n,o,i,s,l;if(1===this.renderer.sxss[t].length)return this._get_snap_coord(this.sxs[t]);for(s=this.renderer.sxss[t],l=this.renderer.syss[t],n=o=0,i=s.length;0<=i?o<i:o>i;n=0<=i?++o:--o)if(a.point_in_poly(e,r,s[n],l[n]))return this._get_snap_coord(s[n]);return null},e.prototype.scy=function(t,e,r){var n,o,i,s,l;if(1===this.renderer.syss[t].length)return this._get_snap_coord(this.sys[t]);for(s=this.renderer.sxss[t],l=this.renderer.syss[t],n=o=0,i=s.length;0<=i?o<i:o>i;n=0<=i?++o:--o)if(a.point_in_poly(e,r,s[n],l[n]))return this._get_snap_coord(l[n])},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_area_legend(t,e,r,n,o)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=i,e.prototype.type="Patches",e.coords([["xs","ys"]]),e.mixins(["line","fill"]),e}(n.Model),e.exports={Model:o,View:i}},{"../../common/hittest":"common/hittest","./glyph":"models/glyphs/glyph",rbush:"rbush",underscore:"underscore"}],"models/glyphs/quad":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;a=t("underscore"),u=t("rbush"),o=t("./glyph"),n=t("../mappers/categorical_mapper"),l=t("../../common/hittest"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype._index_data=function(){var t,e,r,o,i,s,a,l,h,c,p,_,d,f;for(l=function(t,e){return t instanceof n.Model?t.v_map_to_target(e,!0):e},a=l(this.renderer.xmapper,this._left),_=l(this.renderer.xmapper,this._right),f=l(this.renderer.ymapper,this._top),e=l(this.renderer.ymapper,this._bottom),o=u(),h=[],r=i=0,p=a.length;0<=p?i<p:i>p;r=0<=p?++i:--i)s=a[r],c=_[r],d=f[r],t=e[r],!isNaN(s+c+d+t)&&isFinite(s+c+d+t)&&h.push({minX:s,minY:t,maxX:c,maxY:d,i:r});return o.load(h),o},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h;for(l=r.sleft,u=r.sright,h=r.stop,a=r.sbottom,s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(l[n]+h[n]+u[n]+a[n])||(this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,n),t.fillRect(l[n],h[n],u[n]-l[n],a[n]-h[n])),this.visuals.line.doit?(t.beginPath(),t.rect(l[n],h[n],u[n]-l[n],a[n]-h[n]),this.visuals.line.set_vectorize(t,n),s.push(t.stroke())):s.push(void 0));return s},e.prototype._hit_point=function(t){var e,r,n,o,i,s,a;return r=[t.vx,t.vy],o=r[0],i=r[1],s=this.renderer.xmapper.map_from_target(o,!0),a=this.renderer.ymapper.map_from_target(i,!0),e=function(){var t,e,r,n;for(r=this.index.search({minX:s,minY:a,maxX:s,maxY:a}),n=[],t=0,e=r.length;t<e;t++)s=r[t],n.push(s.i);return n}.call(this),n=l.create_hit_test_result(),n["1d"].indices=e,n},e.prototype.get_anchor_point=function(t,e,r){var n,o,i,s;switch(o=Math.min(this.sleft[e],this.sright[e]),i=Math.max(this.sright[e],this.sleft[e]),s=Math.min(this.stop[e],this.sbottom[e]),n=Math.max(this.sbottom[e],this.stop[e]),t){case"top_left":return{x:o,y:s};case"top_center":return{x:(o+i)/2,y:s};case"top_right":return{x:i,y:s};case"right_center":return{x:i,y:(s+n)/2};case"bottom_right":return{x:i,y:n};case"bottom_center":return{x:(o+i)/2,y:n};case"bottom_left":return{x:o,y:n};case"left_center":return{x:o,y:(s+n)/2};case"center":return{x:(o+i)/2,y:(s+n)/2}}},e.prototype.scx=function(t){return(this.sleft[t]+this.sright[t])/2},e.prototype.scy=function(t){return(this.stop[t]+this.sbottom[t])/2},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_area_legend(t,e,r,n,o)},e}(o.View),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=s,e.prototype.type="Quad",e.coords([["right","bottom"],["left","top"]]),e.mixins(["line","fill"]),e}(o.Model),e.exports={Model:i,View:s}},{"../../common/hittest":"common/hittest","../mappers/categorical_mapper":"models/mappers/categorical_mapper","./glyph":"models/glyphs/glyph",rbush:"rbush",underscore:"underscore"}],"models/glyphs/quadratic":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t("underscore"),l=t("rbush"),n=t("./glyph"),a=function(t,e,r){var n,o;return e===(t+r)/2?[t,r]:(o=(t-e)/(t-2*e+r),n=t*Math.pow(1-o,2)+2*e*(1-o)*o+r*Math.pow(o,2),[Math.min(t,r,n),Math.max(t,r,n)])},i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype._index_data=function(){var t,e,r,n,o,i,s,u,h,c,p;for(e=l(),n=[],t=r=0,o=this._x0.length;0<=o?r<o:r>o;t=0<=o?++r:--r)isNaN(this._x0[t]+this._x1[t]+this._y0[t]+this._y1[t]+this._cx[t]+this._cy[t])||(i=a(this._x0[t],this._cx[t],this._x1[t]),u=i[0],h=i[1],s=a(this._y0[t],this._cy[t],this._y1[t]),c=s[0],p=s[1],n.push({minX:u,minY:c,maxX:h,maxY:p,i:t}));return e.load(n),e},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p;if(u=r.sx0,c=r.sy0,h=r.sx1,p=r.sy1,a=r.scx,l=r.scy,this.visuals.line.doit){for(s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(u[n]+c[n]+h[n]+p[n]+a[n]+l[n])||(t.beginPath(),t.moveTo(u[n],c[n]),t.quadraticCurveTo(a[n],l[n],h[n],p[n]),this.visuals.line.set_vectorize(t,n),s.push(t.stroke()));return s}},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_line_legend(t,e,r,n,o)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=i,e.prototype.type="Quadratic",e.coords([["x0","y0"],["x1","y1"],["cx","cy"]]),e.mixins(["line"]),e}(n.Model),e.exports={Model:o,View:i}},{"./glyph":"models/glyphs/glyph",rbush:"rbush",underscore:"underscore"}],"models/glyphs/ray":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t("underscore"),n=t("./glyph"),a=t("../../core/properties"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._index_data=function(){return this._xy_index()},e.prototype._map_data=function(){return this.slength=this.sdist(this.renderer.xmapper,this._x,this._length)},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f;if(_=r.sx,d=r.sy,p=r.slength,n=r._angle,this.visuals.line.doit){for(f=this.renderer.plot_view.frame.get("width"),o=this.renderer.plot_view.frame.get("height"),s=2*(f+o),i=a=0,h=p.length;0<=h?a<h:a>h;i=0<=h?++a:--a)0===p[i]&&(p[i]=s);for(c=[],l=0,u=e.length;l<u;l++)i=e[l],isNaN(_[i]+d[i]+n[i]+p[i])||(t.translate(_[i],d[i]),t.rotate(n[i]),t.beginPath(),t.moveTo(0,0),t.lineTo(p[i],0),this.visuals.line.set_vectorize(t,i),t.stroke(),t.rotate(-n[i]),c.push(t.translate(-_[i],-d[i])));return c}},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_line_legend(t,e,r,n,o)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type="Ray",e.coords([["x","y"]]),e.mixins(["line"]),e.define({length:[a.DistanceSpec],angle:[a.AngleSpec]}),e}(n.Model),e.exports={Model:o,View:i}},{"../../core/properties":"core/properties","./glyph":"models/glyphs/glyph",underscore:"underscore"}],"models/glyphs/rect":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t("underscore"),n=t("./glyph"),a=t("../../common/hittest"),l=t("../../core/properties"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype._set_data=function(){if(this.max_w2=0,"data"===this.model.properties.width.units&&(this.max_w2=this.max_width/2),this.max_h2=0,"data"===this.model.properties.height.units)return this.max_h2=this.max_height/2},e.prototype._index_data=function(){return this._xy_index()},e.prototype._map_data=function(){return"data"===this.model.properties.width.units?this.sw=this.sdist(this.renderer.xmapper,this._x,this._width,"center",this.mget("dilate")):this.sw=this._width,"data"===this.model.properties.height.units?this.sh=this.sdist(this.renderer.ymapper,this._y,this._height,"center",this.mget("dilate")):this.sh=this._height},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p;if(c=r.sx,p=r.sy,h=r.sw,u=r.sh,n=r._angle,this.visuals.fill.doit)for(i=0,a=e.length;i<a;i++)o=e[i],isNaN(c[o]+p[o]+h[o]+u[o]+n[o])||(this.visuals.fill.set_vectorize(t,o),n[o]?(t.translate(c[o],p[o]),t.rotate(n[o]),t.fillRect(-h[o]/2,-u[o]/2,h[o],u[o]),t.rotate(-n[o]),t.translate(-c[o],-p[o])):t.fillRect(c[o]-h[o]/2,p[o]-u[o]/2,h[o],u[o]));if(this.visuals.line.doit){for(t.beginPath(),s=0,l=e.length;s<l;s++)o=e[s],isNaN(c[o]+p[o]+h[o]+u[o]+n[o])||0!==h[o]&&0!==u[o]&&(n[o]?(t.translate(c[o],p[o]),t.rotate(n[o]),t.rect(-h[o]/2,-u[o]/2,h[o],u[o]),t.rotate(-n[o]),t.translate(-c[o],-p[o])):t.rect(c[o]-h[o]/2,p[o]-u[o]/2,h[o],u[o]),this.visuals.line.set_vectorize(t,o),t.stroke(),t.beginPath());return t.stroke()}},e.prototype._hit_rect=function(t){var e,r,n,o,i,s,l,u,h;return r=this.renderer.xmapper.v_map_from_target([t.vx0,t.vx1],!0),s=r[0],l=r[1],n=this.renderer.ymapper.v_map_from_target([t.vy0,t.vy1],!0),u=n[0],h=n[1],e=a.validate_bbox_coords([s,l],[u,h]),o=a.create_hit_test_result(),o["1d"].indices=function(){var t,r,n,o;for(n=this.index.search(e),o=[],t=0,r=n.length;t<r;t++)i=n[t],o.push(i.i);return o}.call(this),o},e.prototype._hit_point=function(t){var 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o}.call(this),l=0,u=m.length;l<u;l++)s=m[l],v=this.renderer.plot_view.canvas.vx_to_sx(x),b=this.renderer.plot_view.canvas.vy_to_sy(k),this._angle[s]&&(n=Math.sqrt(Math.pow(v-this.sx[s],2)+Math.pow(b-this.sy[s],2)),y=Math.sin(-this._angle[s]),r=Math.cos(-this._angle[s]),c=r*(v-this.sx[s])-y*(b-this.sy[s])+this.sx[s],p=y*(v-this.sx[s])+r*(b-this.sy[s])+this.sy[s],v=c,b=p),S=Math.abs(this.sx[s]-v)<=this.sw[s]/2,o=Math.abs(this.sy[s]-b)<=this.sh[s]/2,o&&S&&i.push(s);return g=a.create_hit_test_result(),g["1d"].indices=i,g},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_area_legend(t,e,r,n,o)},e.prototype._bounds=function(t){return this.max_wh2_bounds(t)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=i,e.prototype.type="Rect",e.coords([["x","y"]]),e.mixins(["line","fill"]),e.define({angle:[l.AngleSpec,0],width:[l.DistanceSpec],height:[l.DistanceSpec],dilate:[l.Bool,!1]}),e}(n.Model),e.exports={Model:o,View:i}},{"../../common/hittest":"common/hittest","../../core/properties":"core/properties","./glyph":"models/glyphs/glyph",underscore:"underscore"}],"models/glyphs/segment":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,
t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t("underscore"),a=t("rbush"),n=t("./glyph"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._index_data=function(){var t,e,r,n,o;for(e=a(),n=[],t=r=0,o=this._x0.length;0<=o?r<o:r>o;t=0<=o?++r:--r)isNaN(this._x0[t]+this._x1[t]+this._y0[t]+this._y1[t])||n.push({minX:Math.min(this._x0[t],this._x1[t]),minY:Math.min(this._y0[t],this._y1[t]),maxX:Math.max(this._x0[t],this._x1[t]),maxY:Math.max(this._y0[t],this._y1[t]),i:t});return e.load(n),e},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h;if(a=r.sx0,u=r.sy0,l=r.sx1,h=r.sy1,this.visuals.line.doit){for(s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(a[n]+u[n]+l[n]+h[n])||(t.beginPath(),t.moveTo(a[n],u[n]),t.lineTo(l[n],h[n]),this.visuals.line.set_vectorize(t,n),s.push(t.stroke()));return s}},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_line_legend(t,e,r,n,o)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type="Segment",e.coords([["x0","y0"],["x1","y1"]]),e.mixins(["line"]),e}(n.Model),e.exports={Model:o,View:i}},{"./glyph":"models/glyphs/glyph",rbush:"rbush",underscore:"underscore"}],"models/glyphs/text":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t("underscore"),n=t("./glyph"),a=t("../../core/properties"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._index_data=function(){return this._xy_index()},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p;for(c=r.sx,p=r.sy,i=r._x_offset,s=r._y_offset,n=r._angle,o=r._text,h=[],l=0,u=e.length;l<u;l++)a=e[l],isNaN(c[a]+p[a]+i[a]+s[a]+n[a])||null==o[a]||(this.visuals.text.doit?(t.save(),t.translate(c[a]+i[a],p[a]+s[a]),t.rotate(n[a]),this.visuals.text.set_vectorize(t,a),t.fillText(o[a],0,0),h.push(t.restore())):h.push(void 0));return h},e.prototype.draw_legend=function(t,e,r,n,o){return t.save(),this.text_props.set_value(t),t.font=this.text_props.font_value(),t.font=t.font.replace(/\b[\d\.]+[\w]+\b/,"10pt"),t.textAlign="right",t.textBaseline="middle",t.fillText("text",r,(n+o)/2),t.restore()},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type="Text",e.coords([["x","y"]]),e.mixins(["text"]),e.define({text:[a.StringSpec,{field:"text"}],angle:[a.AngleSpec,0],x_offset:[a.NumberSpec,0],y_offset:[a.NumberSpec,0]}),e}(n.Model),e.exports={Model:o,View:i}},{"../../core/properties":"core/properties","./glyph":"models/glyphs/glyph",underscore:"underscore"}],"models/glyphs/vbar":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p=function(t,e){function r(){this.constructor=t}for(var n in e)_.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},_={}.hasOwnProperty;l=t("underscore"),c=t("rbush"),i=t("./quad"),o=t("./glyph"),n=t("../mappers/categorical_mapper"),u=t("../../common/hittest"),h=t("../../core/properties"),a=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return p(e,t),e.prototype._map_data=function(){var t,e,r,n,o;for(this.sx=this.renderer.xmapper.v_map_to_target(this._x),o=this.renderer.ymapper.v_map_to_target(this._top),n=this.renderer.ymapper.v_map_to_target(this._bottom),this.stop=this.plot_view.canvas.v_vy_to_sy(o),this.sbottom=this.plot_view.canvas.v_vy_to_sy(n),this.sleft=[],this.sright=[],this.sw=this.sdist(this.renderer.xmapper,this._x,this._width,"center"),t=e=0,r=this.sx.length;0<=r?e<r:e>r;t=0<=r?++e:--e)this.sleft.push(this.sx[t]-this.sw[t]/2),this.sright.push(this.sx[t]+this.sw[t]/2);return null},e.prototype._index_data=function(){var t,e,r,o,i,s,a,l,u,h,p,_,d,f;for(a=function(t,e){return t instanceof n.Model?t.v_map_to_target(e,!0):e},f=a(this.renderer.xmapper,this._x),d=a(this.renderer.xmapper,this._width),_=a(this.renderer.ymapper,this._top),e=a(this.renderer.ymapper,this._bottom),o=c(),l=[],r=i=0,h=f.length;0<=h?i<h:i>h;r=0<=h?++i:--i)s=f[r]-d[r]/2,u=f[r]+d[r]/2,p=_[r],t=e[r],!isNaN(s+u+p+t)&&isFinite(s+u+p+t)&&l.push({minX:s,minY:t,maxX:u,maxY:p,i:r});return o.load(l),o},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h;for(l=r.sleft,u=r.sright,h=r.stop,a=r.sbottom,s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(l[n]+h[n]+u[n]+a[n])||(this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,n),t.fillRect(l[n],h[n],u[n]-l[n],a[n]-h[n])),this.visuals.line.doit?(t.beginPath(),t.rect(l[n],h[n],u[n]-l[n],a[n]-h[n]),this.visuals.line.set_vectorize(t,n),s.push(t.stroke())):s.push(void 0));return s},e.prototype._hit_point=function(t){var e,r,n,o,i,s,a;return r=[t.vx,t.vy],o=r[0],i=r[1],s=this.renderer.xmapper.map_from_target(o,!0),a=this.renderer.ymapper.map_from_target(i,!0),e=function(){var t,e,r,n;for(r=this.index.search({minX:s,minY:a,maxX:s,maxY:a}),n=[],t=0,e=r.length;t<e;t++)s=r[t],n.push(s.i);return n}.call(this),n=u.create_hit_test_result(),n["1d"].indices=e,n},e.prototype.scy=function(t){return(this.stop[t]+this.sbottom[t])/2},e}(o.View),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return p(e,t),e.prototype.default_view=a,e.prototype.type="VBar",e.mixins(["line","fill"]),e.define({x:[h.NumberSpec],width:[h.DistanceSpec],top:[h.NumberSpec],bottom:[h.NumberSpec,0]}),e}(o.Model),e.exports={Model:s,View:a}},{"../../common/hittest":"common/hittest","../../core/properties":"core/properties","../mappers/categorical_mapper":"models/mappers/categorical_mapper","./glyph":"models/glyphs/glyph","./quad":"models/glyphs/quad",rbush:"rbush",underscore:"underscore"}],"models/glyphs/webgl/base":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p;o=t("underscore"),a=t("../../../core/util/color"),l=a.color2rgba,n=function(){function t(t,e){this.gl=t,this.glyph=e,this.nvertices=0,this.size_changed=!1,this.data_changed=!1,this.visuals_changed=!1,this.init()}return t.prototype.GLYPH="",t.prototype.VERT="",t.prototype.FRAG="",t.prototype.set_data_changed=function(t){return t!==this.nvertices&&(this.nvertices=t,this.size_changed=!0),this.data_changed=!0},t.prototype.set_visuals_changed=function(){return this.visuals_changed=!0},t}(),c=function(t){return t<2&&(t=Math.sqrt(2*t)),t},u=function(t,e){var r,n,o,i;for(r=new Float32Array(t),n=o=0,i=t;0<=i?o<i:o>i;n=0<=i?++o:--o)r[n]=e;return r},h=function(t,e,r){var n,o,i,s,a,l,u;for(n=new Float32Array(t*e),o=s=0,l=t;0<=l?s<l:s>l;o=0<=l?++s:--s)for(i=a=0,u=e;0<=u?a<u:a>u;i=0<=u?++a:--a)n[o*e+i]=r[i];return n},p=function(t,e){return!o.isUndefined(t[e].spec.value)},s=function(t,e,r,n,o,i){var s;return o.doit?p(o,i)?(e.used=!1,t.set_attribute(r,"float",o[i].value())):(e.used=!0,s=new Float32Array(o.cache[i+"_array"]),e.set_size(4*n),e.set_data(0,s),t.set_attribute(r,"float",e)):(e.used=!1,t.set_attribute(r,"float",[0]))},i=function(t,e,r,n,o,i){var s,a,h,c,_,d,f,m,g,y,v,b,x;if(y=4,c=i+"_color",a=i+"_alpha",o.doit){if(p(o,c)&&p(o,a))return e.used=!1,x=l(o[c].value(),o[a].value()),t.set_attribute(r,"vec4",x);for(e.used=!0,_=p(o,c)?function(){var t,e,r;for(r=[],d=t=0,e=n;0<=e?t<e:t>e;d=0<=e?++t:--t)r.push(o[c].value());return r}():o.cache[c+"_array"],h=p(o,a)?u(n,o[a].value()):o.cache[a+"_array"],s=new Float32Array(n*y),d=m=0,v=n;0<=v?m<v:m>v;d=0<=v?++m:--m)for(x=l(_[d],h[d]),f=g=0,b=y;0<=b?g<b:g>b;f=0<=b?++g:--g)s[d*y+f]=x[f];return e.set_size(n*y*4),e.set_data(0,s),t.set_attribute(r,"vec4",e)}return e.used=!1,t.set_attribute(r,"vec4",[0,0,0,0])},e.exports={BaseGLGlyph:n,line_width:c,attach_float:s,attach_color:i,color2rgba:l}},{"../../../core/util/color":"core/util/color",underscore:"underscore"}],"models/glyphs/webgl/line":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_=function(t,e){function r(){this.constructor=t}for(var n in e)d.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},d={}.hasOwnProperty;u=t("gloo2"),c=t("../../../core/logging").logger,p=t("./base"),n=p.BaseGLGlyph,h=p.line_width,a=p.attach_float,s=p.attach_color,l=p.color2rgba,o=function(){function t(t){this._atlas={},this._index=0,this._width=256,this._height=256,this.tex=new u.Texture2D(t),this.tex.set_wrapping(t.REPEAT,t.REPEAT),this.tex.set_interpolation(t.NEAREST,t.NEAREST),this.tex.set_size([this._height,this._width],t.RGBA),this.tex.set_data([0,0],[this._height,this._width],new Uint8Array(this._height*this._width*4)),this.get_atlas_data([1])}return t.prototype.get_atlas_data=function(t){var e,r,n,o,i,s;return n=t.join("-"),r=this._atlas[n],void 0===r&&(i=this.make_pattern(t),e=i[0],o=i[1],this.tex.set_data([this._index,0],[1,this._width],new Uint8Array(function(){var t,r,n;for(n=[],t=0,r=e.length;t<r;t++)s=e[t],n.push(s+10);return n}())),this._atlas[n]=[this._index/this._height,o],this._index+=1),this._atlas[n]},t.prototype.make_pattern=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j;for(t.length>1&&t.length%2&&(t=t.concat(t)),m=0,p=0,_=t.length;p<_;p++)w=t[p],m+=w;for(e=[],i=0,u=f=0,v=t.length+2;f<v;u=f+=2)n=Math.max(1e-4,t[u%t.length]),o=Math.max(1e-4,t[(u+1)%t.length]),e.push.apply(e,[i,i+n]),i+=n+o;for(d=this._width,r=new Float32Array(4*d),u=g=0,b=d;0<=b?g<b:g>b;u=0<=b?++g:--g){for(j=m*u/(d-1),h=0,k=1e16,c=y=0,x=e.length;0<=x?y<x:y>x;c=0<=x?++y:--y)M=Math.abs(e[c]-j),M<k&&(h=c,k=M);h%2===0?(l=j<=e[h]?1:0,a=e[h],s=e[h+1]):(l=j>e[h]?-1:0,a=e[h-1],s=e[h]),r[4*u+0]=e[h],r[4*u+1]=l,r[4*u+2]=a,r[4*u+3]=s}return[r,m]},t}(),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return _(e,t),e.prototype.GLYPH="line",e.prototype.JOINS={miter:0,round:1,bevel:2},e.prototype.CAPS={"":0,none:0,".":0,round:1,")":1,"(":1,o:1,"triangle in":2,"<":2,"triangle out":3,">":3,square:4,"[":4,"]":4,"=":4,butt:5,"|":5},e.prototype.VERT="precision mediump float;\n\nconst float PI = 3.14159265358979323846264;\nconst float THETA = 15.0 * 3.14159265358979323846264/180.0;\n\nuniform float u_pixel_ratio;\nuniform vec2 u_canvas_size, u_offset;\nuniform vec2 u_scale_aspect;\nuniform float u_scale_length;\n\nuniform vec4 u_color;\nuniform float u_antialias;\nuniform float u_length;\nuniform float u_linewidth;\nuniform float u_dash_index;\nuniform float u_closed;\n\nattribute vec2 a_position;\nattribute vec4 a_tangents;\nattribute vec2 a_segment;\nattribute vec2 a_angles;\nattribute vec2 a_texcoord;\n\nvarying vec4  v_color;\nvarying vec2  v_segment;\nvarying vec2  v_angles;\nvarying vec2  v_texcoord;\nvarying vec2  v_miter;\nvarying float v_length;\nvarying float v_linewidth;\n\nfloat cross(in vec2 v1, in vec2 v2)\n{\n    return v1.x*v2.y - v1.y*v2.x;\n}\n\nfloat signed_distance(in vec2 v1, in vec2 v2, in vec2 v3)\n{\n    return cross(v2-v1,v1-v3) / length(v2-v1);\n}\n\nvoid rotate( in vec2 v, in float alpha, out vec2 result )\n{\n    float c = cos(alpha);\n    float s = sin(alpha);\n    result = vec2( c*v.x - s*v.y,\n                   s*v.x + c*v.y );\n}\n\nvoid main()\n{\n    bool closed = (u_closed > 0.0);\n\n    // Attributes and uniforms to varyings\n    v_color = u_color;\n    v_linewidth = u_linewidth;\n    v_segment = a_segment * u_scale_length;\n    v_length = u_length * u_scale_length;\n\n    // Scale to map to pixel coordinates. The original algorithm from the paper\n    // assumed isotropic scale. We obviously do not have this.\n    vec2 abs_scale_aspect = abs(u_scale_aspect);\n    vec2 abs_scale = u_scale_length * abs_scale_aspect;\n\n    // Correct angles for aspect ratio\n    vec2 av;\n    av = vec2(1.0, tan(a_angles.x)) / abs_scale_aspect;\n    v_angles.x = atan(av.y, av.x);\n    av = vec2(1.0, tan(a_angles.y)) / abs_scale_aspect;\n    v_angles.y = atan(av.y, av.x);\n\n    // Thickness below 1 pixel are represented using a 1 pixel thickness\n    // and a modified alpha\n    v_color.a = min(v_linewidth, v_color.a);\n    v_linewidth = max(v_linewidth, 1.0);\n\n    // If color is fully transparent we just will discard the fragment anyway\n    if( v_color.a <= 0.0 ) {\n        gl_Position = vec4(0.0,0.0,0.0,1.0);\n        return;\n    }\n\n    // This is the actual half width of the line\n    float w = ceil(u_antialias+v_linewidth)/2.0;\n\n    vec2 position = (a_position + u_offset) * abs_scale;\n\n    vec2 t1 = normalize(a_tangents.xy * abs_scale_aspect);  // note the scaling for aspect ratio here\n    vec2 t2 = normalize(a_tangents.zw * abs_scale_aspect);\n    float u = a_texcoord.x;\n    float v = a_texcoord.y;\n    vec2 o1 = vec2( +t1.y, -t1.x);\n    vec2 o2 = vec2( +t2.y, -t2.x);\n\n    // This is a join\n    // ----------------------------------------------------------------\n    if( t1 != t2 ) {\n        float angle = atan (t1.x*t2.y-t1.y*t2.x, t1.x*t2.x+t1.y*t2.y);  // Angle needs recalculation for some reason\n        vec2 t  = normalize(t1+t2);\n        vec2 o  = vec2( + t.y, - t.x);\n\n        if ( u_dash_index > 0.0 )\n        {\n            // Broken angle\n            // ----------------------------------------------------------------\n            if( (abs(angle) > THETA) ) {\n                position += v * w * o / cos(angle/2.0);\n                float s = sign(angle);\n                if( angle < 0.0 ) {\n                    if( u == +1.0 ) {\n                        u = v_segment.y + v * w * tan(angle/2.0);\n                        if( v == 1.0 ) {\n                            position -= 2.0 * w * t1 / sin(angle);\n                            u -= 2.0 * w / sin(angle);\n                        }\n                    } else {\n                        u = v_segment.x - v * w * tan(angle/2.0);\n                        if( v == 1.0 ) {\n                            position += 2.0 * w * t2 / sin(angle);\n                            u += 2.0*w / sin(angle);\n                        }\n                    }\n                } else {\n                    if( u == +1.0 ) {\n                        u = v_segment.y + v * w * tan(angle/2.0);\n                        if( v == -1.0 ) {\n                            position += 2.0 * w * t1 / sin(angle);\n                            u += 2.0 * w / sin(angle);\n                        }\n                    } else {\n                        u = v_segment.x - v * w * tan(angle/2.0);\n                        if( v == -1.0 ) {\n                            position -= 2.0 * w * t2 / sin(angle);\n                            u -= 2.0*w / sin(angle);\n                        }\n                    }\n                }\n                // Continuous angle\n                // ------------------------------------------------------------\n            } else {\n                position += v * w * o / cos(angle/2.0);\n                if( u == +1.0 ) u = v_segment.y;\n                else            u = v_segment.x;\n            }\n        }\n\n        // Solid line\n        // --------------------------------------------------------------------\n        else\n        {\n            position.xy += v * w * o / cos(angle/2.0);\n            if( angle < 0.0 ) {\n                if( u == +1.0 ) {\n                    u = v_segment.y + v * w * tan(angle/2.0);\n                } else {\n                    u = v_segment.x - v * w * tan(angle/2.0);\n                }\n            } else {\n                if( u == +1.0 ) {\n                    u = v_segment.y + v * w * tan(angle/2.0);\n                } else {\n                    u = v_segment.x - v * w * tan(angle/2.0);\n                }\n            }\n        }\n\n    // This is a line start or end (t1 == t2)\n    // ------------------------------------------------------------------------\n    } else {\n        position += v * w * o1;\n        if( u == -1.0 ) {\n            u = v_segment.x - w;\n            position -= w * t1;\n        } else {\n            u = v_segment.y + w;\n            position += w * t2;\n        }\n    }\n\n    // Miter distance\n    // ------------------------------------------------------------------------\n    vec2 t;\n    vec2 curr = a_position * abs_scale;\n    if( a_texcoord.x < 0.0 ) {\n        vec2 next = curr + t2*(v_segment.y-v_segment.x);\n\n        rotate( t1, +v_angles.x/2.0, t);\n        v_miter.x = signed_distance(curr, curr+t, position);\n\n        rotate( t2, +v_angles.y/2.0, t);\n        v_miter.y = signed_distance(next, next+t, position);\n    } else {\n        vec2 prev = curr - t1*(v_segment.y-v_segment.x);\n\n        rotate( t1, -v_angles.x/2.0,t);\n        v_miter.x = signed_distance(prev, prev+t, position);\n\n        rotate( t2, -v_angles.y/2.0,t);\n        v_miter.y = signed_distance(curr, curr+t, position);\n    }\n\n    if (!closed && v_segment.x <= 0.0) {\n        v_miter.x = 1e10;\n    }\n    if (!closed && v_segment.y >= v_length)\n    {\n        v_miter.y = 1e10;\n    }\n\n    v_texcoord = vec2( u, v*w );\n\n    // Calculate position in device coordinates. Note that we\n    // already scaled with abs scale above.\n    vec2 normpos = position * sign(u_scale_aspect);\n    normpos += 0.5;  // make up for Bokeh's offset\n    normpos /= u_canvas_size / u_pixel_ratio;  // in 0..1\n    gl_Position = vec4(normpos*2.0-1.0, 0.0, 1.0);\n    gl_Position.y *= -1.0;\n}\n",e.prototype.FRAG_="// Fragment shader that can be convenient during debugging to show the line skeleton.\nprecision mediump float;\nuniform vec4  u_color;\nvoid main () {\n  gl_FragColor = u_color;\n}",e.prototype.FRAG="precision mediump float;\n\nconst float PI = 3.14159265358979323846264;\nconst float THETA = 15.0 * 3.14159265358979323846264/180.0;\n\nuniform sampler2D u_dash_atlas;\n\nuniform vec2 u_linecaps;\nuniform float u_miter_limit;\nuniform float u_linejoin;\nuniform float u_antialias;\nuniform float u_dash_phase;\nuniform float u_dash_period;\nuniform float u_dash_index;\nuniform vec2 u_dash_caps;\nuniform float u_closed;\n\nvarying vec4  v_color;\nvarying vec2  v_segment;\nvarying vec2  v_angles;\nvarying vec2  v_texcoord;\nvarying vec2  v_miter;\nvarying float v_length;\nvarying float v_linewidth;\n\n// Compute distance to cap ----------------------------------------------------\nfloat cap( int type, float dx, float dy, float t, float linewidth )\n{\n    float d = 0.0;\n    dx = abs(dx);\n    dy = abs(dy);\n    if      (type == 0)  discard;  // None\n    else if (type == 1)  d = sqrt(dx*dx+dy*dy);  // Round\n    else if (type == 3)  d = (dx+abs(dy));  // Triangle in\n    else if (type == 2)  d = max(abs(dy),(t+dx-abs(dy)));  // Triangle out\n    else if (type == 4)  d = max(dx,dy);  // Square\n    else if (type == 5)  d = max(dx+t,dy);  // Butt\n    return d;\n}\n\n// Compute distance to join -------------------------------------------------\nfloat join( in int type, in float d, in vec2 segment, in vec2 texcoord, in vec2 miter,\n           in float linewidth )\n{\n    // texcoord.x is distance from start\n    // texcoord.y is distance from centerline\n    // segment.x and y indicate the limits (as for texcoord.x) for this segment\n\n    float dx = texcoord.x;\n\n    // Round join\n    if( type == 1 ) {\n        if (dx < segment.x) {\n            d = max(d,length( texcoord - vec2(segment.x,0.0)));\n            //d = length( texcoord - vec2(segment.x,0.0));\n        } else if (dx > segment.y) {\n            d = max(d,length( texcoord - vec2(segment.y,0.0)));\n            //d = length( texcoord - vec2(segment.y,0.0));\n        }\n    }\n    // Bevel join\n    else if ( type == 2 ) {\n        if (dx < segment.x) {\n            vec2 x = texcoord - vec2(segment.x,0.0);\n            d = max(d, max(abs(x.x), abs(x.y)));\n\n        } else if (dx > segment.y) {\n            vec2 x = texcoord - vec2(segment.y,0.0);\n            d = max(d, max(abs(x.x), abs(x.y)));\n        }\n        /*  Original code for bevel which does not work for us\n        if( (dx < segment.x) ||  (dx > segment.y) )\n            d = max(d, min(abs(x.x),abs(x.y)));\n        */\n    }\n\n    return d;\n}\n\nvoid main()\n{\n    // If color is fully transparent we just discard the fragment\n    if( v_color.a <= 0.0 ) {\n        discard;\n    }\n\n    // Test if dash pattern is the solid one (0)\n    bool solid =  (u_dash_index == 0.0);\n\n    // Test if path is closed\n    bool closed = (u_closed > 0.0);\n\n    vec4 color = v_color;\n    float dx = v_texcoord.x;\n    float dy = v_texcoord.y;\n    float t = v_linewidth/2.0-u_antialias;\n    float width = 1.0;  //v_linewidth; original code had dashes scale with line width, we do not\n    float d = 0.0;\n\n    vec2 linecaps = u_linecaps;\n    vec2 dash_caps = u_dash_caps;\n    float line_start = 0.0;\n    float line_stop = v_length;\n\n    // Apply miter limit; fragments too far into the miter are simply discarded\n    if( (dx < v_segment.x) || (dx > v_segment.y) ) {\n        float into_miter = max(v_segment.x - dx, dx - v_segment.y);\n        if (into_miter > u_miter_limit*v_linewidth/2.0)\n          discard;\n    }\n\n    // Solid line --------------------------------------------------------------\n    if( solid ) {\n        d = abs(dy);\n        if( (!closed) && (dx < line_start) ) {\n            d = cap( int(u_linecaps.x), abs(dx), abs(dy), t, v_linewidth );\n        }\n        else if( (!closed) &&  (dx > line_stop) ) {\n            d = cap( int(u_linecaps.y), abs(dx)-line_stop, abs(dy), t, v_linewidth );\n        }\n        else {\n            d = join( int(u_linejoin), abs(dy), v_segment, v_texcoord, v_miter, v_linewidth );\n        }\n\n    // Dash line --------------------------------------------------------------\n    } else {\n        float segment_start = v_segment.x;\n        float segment_stop  = v_segment.y;\n        float segment_center= (segment_start+segment_stop)/2.0;\n        float freq          = u_dash_period*width;\n        float u = mod( dx + u_dash_phase*width, freq);\n        vec4 tex = texture2D(u_dash_atlas, vec2(u/freq, u_dash_index)) * 255.0 -10.0;  // conversion to int-like\n        float dash_center= tex.x * width;\n        float dash_type  = tex.y;\n        float _start = tex.z * width;\n        float _stop  = tex.a * width;\n        float dash_start = dx - u + _start;\n        float dash_stop  = dx - u + _stop;\n\n        // Compute extents of the first dash (the one relative to v_segment.x)\n        // Note: this could be computed in the vertex shader\n        if( (dash_stop < segment_start) && (dash_caps.x != 5.0) ) {\n            float u = mod(segment_start + u_dash_phase*width, freq);\n            vec4 tex = texture2D(u_dash_atlas, vec2(u/freq, u_dash_index)) * 255.0 -10.0;  // conversion to int-like\n            dash_center= tex.x * width;\n            //dash_type  = tex.y;\n            float _start = tex.z * width;\n            float _stop  = tex.a * width;\n            dash_start = segment_start - u + _start;\n            dash_stop = segment_start - u + _stop;\n        }\n\n        // Compute extents of the last dash (the one relatives to v_segment.y)\n        // Note: This could be computed in the vertex shader\n        else if( (dash_start > segment_stop)  && (dash_caps.y != 5.0) ) {\n            float u = mod(segment_stop + u_dash_phase*width, freq);\n            vec4 tex = texture2D(u_dash_atlas, vec2(u/freq, u_dash_index)) * 255.0 -10.0;  // conversion to int-like\n            dash_center= tex.x * width;\n            //dash_type  = tex.y;\n            float _start = tex.z * width;\n            float _stop  = tex.a * width;\n            dash_start = segment_stop - u + _start;\n            dash_stop  = segment_stop - u + _stop;\n        }\n\n        // This test if the we are dealing with a discontinuous angle\n        bool discontinuous = ((dx <  segment_center) && abs(v_angles.x) > THETA) ||\n                             ((dx >= segment_center) && abs(v_angles.y) > THETA);\n        //if( dx < line_start) discontinuous = false;\n        //if( dx > line_stop)  discontinuous = false;\n\n        float d_join = join( int(u_linejoin), abs(dy),\n                            v_segment, v_texcoord, v_miter, v_linewidth );\n\n        // When path is closed, we do not have room for linecaps, so we make room\n        // by shortening the total length\n        if (closed) {\n             line_start += v_linewidth/2.0;\n             line_stop  -= v_linewidth/2.0;\n        }\n\n        // We also need to take antialias area into account\n        //line_start += u_antialias;\n        //line_stop  -= u_antialias;\n\n        // Check is dash stop is before line start\n        if( dash_stop <= line_start ) {\n            discard;\n        }\n        // Check is dash start is beyond line stop\n        if( dash_start >= line_stop ) {\n            discard;\n        }\n\n        // Check if current dash start is beyond segment stop\n        if( discontinuous ) {\n            // Dash start is beyond segment, we discard\n            if( (dash_start > segment_stop) ) {\n                discard;\n                //gl_FragColor = vec4(1.0,0.0,0.0,.25); return;\n            }\n\n            // Dash stop is before segment, we discard\n            if( (dash_stop < segment_start) ) {\n                discard;  //gl_FragColor = vec4(0.0,1.0,0.0,.25); return;\n            }\n\n            // Special case for round caps (nicer with this)\n            if( dash_caps.x == 1.0 ) {\n                if( (u > _stop) && (dash_stop > segment_stop )  && (abs(v_angles.y) < PI/2.0)) {\n                    discard;\n                }\n            }\n\n            // Special case for round caps  (nicer with this)\n            if( dash_caps.y == 1.0 ) {\n                if( (u < _start) && (dash_start < segment_start )  && (abs(v_angles.x) < PI/2.0)) {\n                    discard;\n                }\n            }\n\n            // Special case for triangle caps (in & out) and square\n            // We make sure the cap stop at crossing frontier\n            if( (dash_caps.x != 1.0) && (dash_caps.x != 5.0) ) {\n                if( (dash_start < segment_start )  && (abs(v_angles.x) < PI/2.0) ) {\n                    float a = v_angles.x/2.0;\n                    float x = (segment_start-dx)*cos(a) - dy*sin(a);\n                    float y = (segment_start-dx)*sin(a) + dy*cos(a);\n                    if( x > 0.0 ) discard;\n                    // We transform the cap into square to avoid holes\n                    dash_caps.x = 4.0;\n                }\n            }\n\n            // Special case for triangle caps (in & out) and square\n            // We make sure the cap stop at crossing frontier\n            if( (dash_caps.y != 1.0) && (dash_caps.y != 5.0) ) {\n                if( (dash_stop > segment_stop )  && (abs(v_angles.y) < PI/2.0) ) {\n                    float a = v_angles.y/2.0;\n                    float x = (dx-segment_stop)*cos(a) - dy*sin(a);\n                    float y = (dx-segment_stop)*sin(a) + dy*cos(a);\n                    if( x > 0.0 ) discard;\n                    // We transform the caps into square to avoid holes\n                    dash_caps.y = 4.0;\n                }\n            }\n        }\n\n        // Line cap at start\n        if( (dx < line_start) && (dash_start < line_start) && (dash_stop > line_start) ) {\n            d = cap( int(linecaps.x), dx-line_start, dy, t, v_linewidth);\n        }\n        // Line cap at stop\n        else if( (dx > line_stop) && (dash_stop > line_stop) && (dash_start < line_stop) ) {\n            d = cap( int(linecaps.y), dx-line_stop, dy, t, v_linewidth);\n        }\n        // Dash cap left - dash_type = -1, 0 or 1, but there may be roundoff errors\n        else if( dash_type < -0.5 ) {\n            d = cap( int(dash_caps.y), abs(u-dash_center), dy, t, v_linewidth);\n            if( (dx > line_start) && (dx < line_stop) )\n                d = max(d,d_join);\n        }\n        // Dash cap right\n        else if( dash_type > 0.5 ) {\n            d = cap( int(dash_caps.x), abs(dash_center-u), dy, t, v_linewidth);\n            if( (dx > line_start) && (dx < line_stop) )\n                d = max(d,d_join);\n        }\n        // Dash body (plain)\n        else {// if( dash_type > -0.5 &&  dash_type < 0.5) {\n            d = abs(dy);\n        }\n\n        // Line join\n        if( (dx > line_start) && (dx < line_stop)) {\n            if( (dx <= segment_start) && (dash_start <= segment_start)\n                && (dash_stop >= segment_start) ) {\n                d = d_join;\n                // Antialias at outer border\n                float angle = PI/2.+v_angles.x;\n                float f = abs( (segment_start - dx)*cos(angle) - dy*sin(angle));\n                d = max(f,d);\n            }\n            else if( (dx > segment_stop) && (dash_start <= segment_stop)\n                     && (dash_stop >= segment_stop) ) {\n                d = d_join;\n                // Antialias at outer border\n                float angle = PI/2.+v_angles.y;\n                float f = abs((dx - segment_stop)*cos(angle) - dy*sin(angle));\n                d = max(f,d);\n            }\n            else if( dx < (segment_start - v_linewidth/2.)) {\n                discard;\n            }\n            else if( dx > (segment_stop + v_linewidth/2.)) {\n                discard;\n            }\n        }\n        else if( dx < (segment_start - v_linewidth/2.)) {\n            discard;\n        }\n        else if( dx > (segment_stop + v_linewidth/2.)) {\n            discard;\n        }\n    }\n\n    // Distance to border ------------------------------------------------------\n    d = d - t;\n    if( d < 0.0 ) {\n        gl_FragColor = color;\n    } else {\n        d /= u_antialias;\n        gl_FragColor = vec4(color.rgb, exp(-d*d)*color.a);\n    }\n}",e.prototype.init=function(){var t;return t=this.gl,this._scale_aspect=0,this.prog=new u.Program(t),this.prog.set_shaders(this.VERT,this.FRAG),this.index_buffer=new u.IndexBuffer(t),this.vbo_position=new u.VertexBuffer(t),this.vbo_tangents=new u.VertexBuffer(t),this.vbo_segment=new u.VertexBuffer(t),this.vbo_angles=new u.VertexBuffer(t),this.vbo_texcoord=new u.VertexBuffer(t),this.dash_atlas=new o(t)},e.prototype.draw=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w;if(u=e.glglyph,u.data_changed){if(!isFinite(r.dx)||!isFinite(r.dy))return;u._baked_offset=[r.dx,r.dy],u._set_data(),u.data_changed=!1}if(this.visuals_changed&&(this._set_visuals(),this.visuals_changed=!1),v=r.sx,b=r.sy,y=Math.sqrt(v*v+b*b),v/=y,b/=y,Math.abs(this._scale_aspect-b/v)>Math.abs(.001*this._scale_aspect)&&(u._update_scale(v,b),this._scale_aspect=b/v),this.prog.set_attribute("a_position","vec2",u.vbo_position),this.prog.set_attribute("a_tangents","vec4",u.vbo_tangents),this.prog.set_attribute("a_segment","vec2",u.vbo_segment),this.prog.set_attribute("a_angles","vec2",u.vbo_angles),this.prog.set_attribute("a_texcoord","vec2",u.vbo_texcoord),this.prog.set_uniform("u_length","float",[u.cumsum]),this.prog.set_texture("u_dash_atlas",this.dash_atlas.tex),n=u._baked_offset,this.prog.set_uniform("u_pixel_ratio","float",[r.pixel_ratio]),this.prog.set_uniform("u_canvas_size","vec2",[r.width,r.height]),this.prog.set_uniform("u_offset","vec2",[r.dx-n[0],r.dy-n[1]]),this.prog.set_uniform("u_scale_aspect","vec2",[v,b]),this.prog.set_uniform("u_scale_length","float",[y]),this.I_triangles=u.I_triangles,this.I_triangles.length<65535)return this.index_buffer.set_size(2*this.I_triangles.length),this.index_buffer.set_data(0,new Uint16Array(this.I_triangles)),this.prog.draw(this.gl.TRIANGLES,this.index_buffer);for(t=this.I_triangles,h=this.I_triangles.length,s=64008,i=[],a=l=0,d=Math.ceil(h/s);0<=d?l<d:l>d;a=0<=d?++l:--l)i.push([]);for(a=p=0,f=t.length;0<=f?p<f:p>f;a=0<=f?++p:--p)w=t[a]%s,o=Math.floor(t[a]/s),i[o].push(w);for(g=[],o=_=0,m=i.length;0<=m?_<m:_>m;o=0<=m?++_:--_)x=new Uint16Array(i[o]),c=o*s*4,0!==x.length&&(this.prog.set_attribute("a_position","vec2",u.vbo_position,0,2*c),this.prog.set_attribute("a_tangents","vec4",u.vbo_tangents,0,4*c),this.prog.set_attribute("a_segment","vec2",u.vbo_segment,0,2*c),
this.prog.set_attribute("a_angles","vec2",u.vbo_angles,0,2*c),this.prog.set_attribute("a_texcoord","vec2",u.vbo_texcoord,0,2*c),this.index_buffer.set_size(2*x.length),this.index_buffer.set_data(0,x),g.push(this.prog.draw(this.gl.TRIANGLES,this.index_buffer)));return g},e.prototype._set_data=function(){return this._bake(),this.vbo_position.set_size(4*this.V_position.length),this.vbo_position.set_data(0,this.V_position),this.vbo_tangents.set_size(4*this.V_tangents.length),this.vbo_tangents.set_data(0,this.V_tangents),this.vbo_angles.set_size(4*this.V_angles.length),this.vbo_angles.set_data(0,this.V_angles),this.vbo_texcoord.set_size(4*this.V_texcoord.length),this.vbo_texcoord.set_data(0,this.V_texcoord)},e.prototype._set_visuals=function(){var t,e,r,n,o,i,s;return e=l(this.glyph.visuals.line.line_color.value(),this.glyph.visuals.line.line_alpha.value()),t=this.CAPS[this.glyph.visuals.line.line_cap.value()],i=this.JOINS[this.glyph.visuals.line.line_join.value()],this.prog.set_uniform("u_color","vec4",e),this.prog.set_uniform("u_linewidth","float",[this.glyph.visuals.line.line_width.value()]),this.prog.set_uniform("u_antialias","float",[.9]),this.prog.set_uniform("u_linecaps","vec2",[t,t]),this.prog.set_uniform("u_linejoin","float",[i]),this.prog.set_uniform("u_miter_limit","float",[10]),n=this.glyph.visuals.line.line_dash.value(),r=0,o=1,n.length&&(s=this.dash_atlas.get_atlas_data(n),r=s[0],o=s[1]),this.prog.set_uniform("u_dash_index","float",[r]),this.prog.set_uniform("u_dash_phase","float",[this.glyph.visuals.line.line_dash_offset.value()]),this.prog.set_uniform("u_dash_period","float",[o]),this.prog.set_uniform("u_dash_caps","vec2",[t,t]),this.prog.set_uniform("u_closed","float",[0])},e.prototype._bake=function(){var t,e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C,N,O,q,D,I,R,F,B,L;for(x=this.nvertices,_=new Float64Array(this.glyph._x),d=new Float64Array(this.glyph._y),i=c=new Float32Array(2*x),n=new Float32Array(2*x),a=p=new Float32Array(4*x),u=new Float32Array(2*x),m=v=0,S=x;0<=S?v<S:v>S;m=0<=S?++v:--v)i[2*m+0]=_[m]+this._baked_offset[0],i[2*m+1]=d[m]+this._baked_offset[1];for(this.tangents=r=new Float32Array(2*x-2),m=k=0,z=x-1;0<=z?k<z:k>z;m=0<=z?++k:--k)r[2*m+0]=c[2*(m+1)+0]-c[2*m+0],r[2*m+1]=c[2*(m+1)+1]-c[2*m+1];for(m=j=0,P=x-1;0<=P?j<P:j>P;m=0<=P?++j:--j)a[4*(m+1)+0]=r[2*m+0],a[4*(m+1)+1]=r[2*m+1],a[4*m+2]=r[2*m+0],a[4*m+3]=r[2*m+1];for(a[0]=r[0],a[1]=r[1],a[4*(x-1)+2]=r[2*(x-2)+0],a[4*(x-1)+3]=r[2*(x-2)+1],t=new Float32Array(x),m=T=0,E=x;0<=E?T<E:T>E;m=0<=E?++T:--T)t[m]=Math.atan2(p[4*m+0]*p[4*m+3]-p[4*m+1]*p[4*m+2],p[4*m+0]*p[4*m+2]+p[4*m+1]*p[4*m+3]);for(m=D=0,A=x-1;0<=A?D<A:D>A;m=0<=A?++D:--D)n[2*m+0]=t[m],n[2*m+1]=t[m+1];for(b=4*x-4,this.V_position=s=new Float32Array(2*b),this.V_angles=o=new Float32Array(2*b),this.V_tangents=l=new Float32Array(4*b),this.V_texcoord=h=new Float32Array(2*b),M=2,m=I=0,C=x;0<=C?I<C:I>C;m=0<=C?++I:--I)for(g=R=0;R<4;g=++R){for(y=F=0;F<2;y=++F)s[2*(4*m+g-M)+y]=i[2*m+y],o[2*(4*m+g)+y]=n[2*m+y];for(y=B=0;B<4;y=++B)l[4*(4*m+g-M)+y]=a[4*m+y]}for(m=L=0,N=x;0<=N?L<=N:L>=N;m=0<=N?++L:--L)h[2*(4*m+0)+0]=-1,h[2*(4*m+1)+0]=-1,h[2*(4*m+2)+0]=1,h[2*(4*m+3)+0]=1,h[2*(4*m+0)+1]=-1,h[2*(4*m+1)+1]=1,h[2*(4*m+2)+1]=-1,h[2*(4*m+3)+1]=1;for(w=6*(x-1),this.I_triangles=e=new Uint32Array(w),q=[],m=f=0,O=x;0<=O?f<O:f>O;m=0<=O?++f:--f)e[6*m+0]=0+4*m,e[6*m+1]=1+4*m,e[6*m+2]=3+4*m,e[6*m+3]=2+4*m,e[6*m+4]=0+4*m,q.push(e[6*m+5]=3+4*m);return q},e.prototype._update_scale=function(t,e){var r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v;for(p=this.nvertices,c=4*p-4,n=this.tangents,r=new Float32Array(p-1),o=new Float32Array(2*p),this.V_segment=i=new Float32Array(2*c),a=h=0,m=p-1;0<=m?h<m:h>m;a=0<=m?++h:--h)r[a]=Math.sqrt(Math.pow(n[2*a+0]*t,2)+Math.pow(n[2*a+1]*e,2));for(s=0,a=_=0,g=p-1;0<=g?_<g:_>g;a=0<=g?++_:--_)s+=r[a],o[2*(a+1)+0]=s,o[2*a+1]=s;for(a=d=0,y=p;0<=y?d<y:d>y;a=0<=y?++d:--d)for(l=f=0;f<4;l=++f)for(u=v=0;v<2;u=++v)i[2*(4*a+l)+u]=o[2*a+u];return this.cumsum=s,this.vbo_segment.set_size(4*this.V_segment.length),this.vbo_segment.set_data(0,this.V_segment)},e}(n),e.exports={LineGLGlyph:i}},{"../../../core/logging":"core/logging","./base":"models/glyphs/webgl/base",gloo2:"gloo2"}],"models/glyphs/webgl/main":[function(t,e,r){var n,o;n=t("./line"),o=t("./markers"),e.exports={LineGLGlyph:n.LineGLGlyph,CircleGLGlyph:o.CircleGLGlyph,SquareGLGlyph:o.SquareGLGlyph,AnnulusGLGlyph:o.AnnulusGLGlyph,DiamondGLGlyph:o.DiamondGLGlyph,TriangleGLGlyph:o.TriangleGLGlyph,InvertedTriangleGLGlyph:o.InvertedTriangleGLGlyph,CrossGLGlyph:o.CrossGLGlyph,CircleCrossGLGlyph:o.CircleCrossGLGlyph,SquareCrossGLGlyph:o.SquareCrossGLGlyph,DiamondCrossGLGlyph:o.DiamondCrossGLGlyph,XGLGlyph:o.XGLGlyph,CircleXGLGlyph:o.CircleXGLGlyph,SquareXGLGlyph:o.SquareXGLGlyph,AsteriskGLGlyph:o.AsteriskGLGlyph}},{"./line":"models/glyphs/webgl/line","./markers":"models/glyphs/webgl/markers"}],"models/glyphs/webgl/markers":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j=function(t,e){function r(){this.constructor=t}for(var n in e)T.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},T={}.hasOwnProperty;x=t("gloo2"),M=t("../../../core/logging").logger,k=t("./base"),i=k.BaseGLGlyph,w=k.line_width,b=k.attach_float,v=k.attach_color,_=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.VERT="precision mediump float;\nconst float SQRT_2 = 1.4142135623730951;\n//\nuniform float u_pixel_ratio;\nuniform vec2 u_canvas_size;\nuniform vec2 u_offset;\nuniform vec2 u_scale;\nuniform float u_antialias;\n//\nattribute float a_x;\nattribute float a_y;\nattribute float a_size;\nattribute float a_angle;  // in radians\nattribute float a_linewidth;\nattribute vec4  a_fg_color;\nattribute vec4  a_bg_color;\n//\nvarying float v_linewidth;\nvarying float v_size;\nvarying vec4  v_fg_color;\nvarying vec4  v_bg_color;\nvarying vec2  v_rotation;\n\nvoid main (void)\n{\n    v_size = a_size * u_pixel_ratio;\n    v_linewidth = a_linewidth * u_pixel_ratio;\n    v_fg_color = a_fg_color;\n    v_bg_color = a_bg_color;\n    v_rotation = vec2(cos(-a_angle), sin(-a_angle));\n    // Calculate position - the -0.5 is to correct for canvas origin\n    vec2 pos = (vec2(a_x, a_y) + u_offset) * u_scale; // in pixels\n    pos += 0.5;  // make up for Bokeh's offset\n    pos /= u_canvas_size / u_pixel_ratio;  // in 0..1\n    gl_Position = vec4(pos*2.0-1.0, 0.0, 1.0);\n    gl_Position.y *= -1.0;\n    gl_PointSize = SQRT_2 * v_size + 2.0 * (v_linewidth + 1.5*u_antialias);\n}",e.prototype.FRAG="precision mediump float;\nconst float SQRT_2 = 1.4142135623730951;\nconst float PI = 3.14159265358979323846264;\n//\nuniform float u_antialias;\n//\nvarying vec4  v_fg_color;\nvarying vec4  v_bg_color;\nvarying float v_linewidth;\nvarying float v_size;\nvarying vec2  v_rotation;\n\nMARKERCODE\n\nvec4 outline(float distance, float linewidth, float antialias, vec4 fg_color, vec4 bg_color)\n{\n    vec4 frag_color;\n    float t = linewidth/2.0 - antialias;\n    float signed_distance = distance;\n    float border_distance = abs(signed_distance) - t;\n    float alpha = border_distance/antialias;\n    alpha = exp(-alpha*alpha);\n\n    // If fg alpha is zero, it probably means no outline. To avoid a dark outline\n    // shining through due to aa, we set the fg color to the bg color. Avoid if (i.e. branching).\n    float select = float(bool(fg_color.a));\n    fg_color.rgb = select * fg_color.rgb + (1.0  - select) * bg_color.rgb;\n    // Similarly, if we want a transparent bg\n    select = float(bool(bg_color.a));\n    bg_color.rgb = select * bg_color.rgb + (1.0  - select) * fg_color.rgb;\n\n    if( border_distance < 0.0)\n        frag_color = fg_color;\n    else if( signed_distance < 0.0 ) {\n        frag_color = mix(bg_color, fg_color, sqrt(alpha));\n    } else {\n        if( abs(signed_distance) < (linewidth/2.0 + antialias) ) {\n            frag_color = vec4(fg_color.rgb, fg_color.a * alpha);\n        } else {\n            discard;\n        }\n    }\n    return frag_color;\n}\n\nvoid main()\n{\n    vec2 P = gl_PointCoord.xy - vec2(0.5, 0.5);\n    P = vec2(v_rotation.x*P.x - v_rotation.y*P.y,\n             v_rotation.y*P.x + v_rotation.x*P.y);\n    float point_size = SQRT_2*v_size  + 2.0 * (v_linewidth + 1.5*u_antialias);\n    float distance = marker(P*point_size, v_size);\n    gl_FragColor = outline(distance, v_linewidth, u_antialias, v_fg_color, v_bg_color);\n    //gl_FragColor.rgb *= gl_FragColor.a;  // pre-multiply alpha\n}",e.prototype.MARKERCODE="<defined in subclasses>",e.prototype.init=function(){var t,e;return e=this.gl,t=this.FRAG.replace(/MARKERCODE/,this.MARKERCODE),this.last_trans={},this.prog=new x.Program(e),this.prog.set_shaders(this.VERT,t),this.vbo_x=new x.VertexBuffer(e),this.prog.set_attribute("a_x","float",this.vbo_x),this.vbo_y=new x.VertexBuffer(e),this.prog.set_attribute("a_y","float",this.vbo_y),this.vbo_s=new x.VertexBuffer(e),this.prog.set_attribute("a_size","float",this.vbo_s),this.vbo_a=new x.VertexBuffer(e),this.prog.set_attribute("a_angle","float",this.vbo_a),this.vbo_linewidth=new x.VertexBuffer(e),this.vbo_fg_color=new x.VertexBuffer(e),this.vbo_bg_color=new x.VertexBuffer(e),this.index_buffer=new x.IndexBuffer(e)},e.prototype.draw=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x;if(c=e.glglyph,p=c.nvertices,c.data_changed){if(!isFinite(r.dx)||!isFinite(r.dy))return;c._baked_offset=[r.dx,r.dy],c._set_data(p),c.data_changed=!1}else null==this.glyph._radius||r.sx===this.last_trans.sx&&r.sy===this.last_trans.sy||(this.last_trans=r,this.vbo_s.set_data(0,new Float32Array(function(){var t,e,r,n;for(r=this.glyph.sradius,n=[],t=0,e=r.length;t<e;t++)y=r[t],n.push(2*y);return n}.call(this))));if(this.visuals_changed&&(this._set_visuals(p),this.visuals_changed=!1),n=c._baked_offset,this.prog.set_uniform("u_pixel_ratio","float",[r.pixel_ratio]),this.prog.set_uniform("u_canvas_size","vec2",[r.width,r.height]),this.prog.set_uniform("u_offset","vec2",[r.dx-n[0],r.dy-n[1]]),this.prog.set_uniform("u_scale","vec2",[r.sx,r.sy]),this.prog.set_attribute("a_x","float",c.vbo_x),this.prog.set_attribute("a_y","float",c.vbo_y),this.prog.set_attribute("a_size","float",c.vbo_s),this.prog.set_attribute("a_angle","float",c.vbo_a),0!==t.length){if(t.length===p)return this.prog.draw(this.gl.POINTS,[0,p]);if(p<65535)return b=window.navigator.userAgent,b.indexOf("MSIE ")+b.indexOf("Trident/")+b.indexOf("Edge/")>0&&M.warn("WebGL warning: IE is known to produce 1px sprites whith selections."),this.index_buffer.set_size(2*t.length),this.index_buffer.set_data(0,new Uint16Array(t)),this.prog.draw(this.gl.POINTS,this.index_buffer);for(s=64e3,i=[],a=l=0,d=Math.ceil(p/s);0<=d?l<d:l>d;a=0<=d?++l:--l)i.push([]);for(a=u=0,f=t.length;0<=f?u<f:u>f;a=0<=f?++u:--u)x=t[a]%s,o=Math.floor(t[a]/s),i[o].push(x);for(g=[],o=h=0,m=i.length;0<=m?h<m:h>m;o=0<=m?++h:--h)v=new Uint16Array(i[o]),_=o*s*4,0!==v.length&&(this.prog.set_attribute("a_x","float",c.vbo_x,0,_),this.prog.set_attribute("a_y","float",c.vbo_y,0,_),this.prog.set_attribute("a_size","float",c.vbo_s,0,_),this.prog.set_attribute("a_angle","float",c.vbo_a,0,_),this.vbo_linewidth.used&&this.prog.set_attribute("a_linewidth","float",this.vbo_linewidth,0,_),this.vbo_fg_color.used&&this.prog.set_attribute("a_fg_color","vec4",this.vbo_fg_color,0,4*_),this.vbo_bg_color.used&&this.prog.set_attribute("a_bg_color","vec4",this.vbo_bg_color,0,4*_),this.index_buffer.set_size(2*v.length),this.index_buffer.set_data(0,v),g.push(this.prog.draw(this.gl.POINTS,this.index_buffer)));return g}},e.prototype._set_data=function(t){var e,r,n,o,i,s,a;for(n=4*t,this.vbo_x.set_size(n),this.vbo_y.set_size(n),this.vbo_a.set_size(n),this.vbo_s.set_size(n),s=new Float64Array(this.glyph._x),a=new Float64Array(this.glyph._y),e=r=0,o=t;0<=o?r<o:r>o;e=0<=o?++r:--r)s[e]+=this._baked_offset[0],a[e]+=this._baked_offset[1];return this.vbo_x.set_data(0,new Float32Array(s)),this.vbo_y.set_data(0,new Float32Array(a)),null!=this.glyph._angle&&this.vbo_a.set_data(0,new Float32Array(this.glyph._angle)),null!=this.glyph._radius?this.vbo_s.set_data(0,new Float32Array(function(){var t,e,r,n;for(r=this.glyph.sradius,n=[],t=0,e=r.length;t<e;t++)i=r[t],n.push(2*i);return n}.call(this))):this.vbo_s.set_data(0,new Float32Array(this.glyph._size))},e.prototype._set_visuals=function(t){return b(this.prog,this.vbo_linewidth,"a_linewidth",t,this.glyph.visuals.line,"line_width"),v(this.prog,this.vbo_fg_color,"a_fg_color",t,this.glyph.visuals.line,"line"),v(this.prog,this.vbo_bg_color,"a_bg_color",t,this.glyph.visuals.fill,"fill"),this.prog.set_uniform("u_antialias","float",[.8])},e}(i),a=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH="circle",e.prototype.MARKERCODE="// --- disc\nfloat marker(vec2 P, float size)\n{\n    return length(P) - size/2.0;\n}",e}(_),f=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH="square",e.prototype.MARKERCODE="// --- square\nfloat marker(vec2 P, float size)\n{\n    return max(abs(P.x), abs(P.y)) - size/2.0;\n}",e}(_),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH="annulus",e.prototype.MARKERCODE="float marker(vec2 P, float size)\n{\n    float r1 = length(P) - size/2.0;\n    float r2 = length(P) - size/4.0;  // half width\n    return max(r1, -r2);\n}",e}(_),c=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH="diamond",e.prototype.MARKERCODE="// --- diamond\nfloat marker(vec2 P, float size)\n{\n    float x = SQRT_2 / 2.0 * (P.x * 1.5 - P.y);\n    float y = SQRT_2 / 2.0 * (P.x * 1.5 + P.y);\n    float r1 = max(abs(x), abs(y)) - size / (2.0 * SQRT_2);\n    return r1 / SQRT_2;\n}",e}(_),g=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH="triangle",e.prototype.MARKERCODE="float marker(vec2 P, float size)\n{\n    P.y -= size * 0.3;\n    float x = SQRT_2 / 2.0 * (P.x * 1.7 - P.y);\n    float y = SQRT_2 / 2.0 * (P.x * 1.7 + P.y);\n    float r1 = max(abs(x), abs(y)) - size / 1.6;\n    float r2 = P.y;\n    return max(r1 / SQRT_2, r2);  // Instersect diamond with rectangle\n}",e}(_),p=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH="invertedtriangle",e.prototype.MARKERCODE="float marker(vec2 P, float size)\n{\n    P.y += size * 0.3;\n    float x = SQRT_2 / 2.0 * (P.x * 1.7 - P.y);\n    float y = SQRT_2 / 2.0 * (P.x * 1.7 + P.y);\n    float r1 = max(abs(x), abs(y)) - size / 1.6;\n    float r2 = - P.y;\n    return max(r1 / SQRT_2, r2);  // Instersect diamond with rectangle\n}",e}(_),u=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH="cross",e.prototype.MARKERCODE='float marker(vec2 P, float size)\n{\n    float square = max(abs(P.x), abs(P.y)) - size / 2.5;  // 2.5 is a tweak\n    float cross = min(abs(P.x), abs(P.y)) - size / 100.0;  // bit of "width" for aa\n    return max(square, cross);\n}',e}(_),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH="circlecross",e.prototype.MARKERCODE="float marker(vec2 P, float size)\n{\n    // Define quadrants\n    float qs = size / 2.0;  // quadrant size\n    float s1 = max(abs(P.x - qs), abs(P.y - qs)) - qs;\n    float s2 = max(abs(P.x + qs), abs(P.y - qs)) - qs;\n    float s3 = max(abs(P.x - qs), abs(P.y + qs)) - qs;\n    float s4 = max(abs(P.x + qs), abs(P.y + qs)) - qs;\n    // Intersect main shape with quadrants (to form cross)\n    float circle = length(P) - size/2.0;\n    float c1 = max(circle, s1);\n    float c2 = max(circle, s2);\n    float c3 = max(circle, s3);\n    float c4 = max(circle, s4);\n    // Union\n    return min(min(min(c1, c2), c3), c4);\n}",e}(_),d=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH="squarecross",e.prototype.MARKERCODE="float marker(vec2 P, float size)\n{\n    // Define quadrants\n    float qs = size / 2.0;  // quadrant size\n    float s1 = max(abs(P.x - qs), abs(P.y - qs)) - qs;\n    float s2 = max(abs(P.x + qs), abs(P.y - qs)) - qs;\n    float s3 = max(abs(P.x - qs), abs(P.y + qs)) - qs;\n    float s4 = max(abs(P.x + qs), abs(P.y + qs)) - qs;\n    // Intersect main shape with quadrants (to form cross)\n    float square = max(abs(P.x), abs(P.y)) - size/2.0;\n    float c1 = max(square, s1);\n    float c2 = max(square, s2);\n    float c3 = max(square, s3);\n    float c4 = max(square, s4);\n    // Union\n    return min(min(min(c1, c2), c3), c4);\n}",e}(_),h=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH="diamondcross",e.prototype.MARKERCODE="float marker(vec2 P, float size)\n{\n    // Define quadrants\n    float qs = size / 2.0;  // quadrant size\n    float s1 = max(abs(P.x - qs), abs(P.y - qs)) - qs;\n    float s2 = max(abs(P.x + qs), abs(P.y - qs)) - qs;\n    float s3 = max(abs(P.x - qs), abs(P.y + qs)) - qs;\n    float s4 = max(abs(P.x + qs), abs(P.y + qs)) - qs;\n    // Intersect main shape with quadrants (to form cross)\n    float x = SQRT_2 / 2.0 * (P.x * 1.5 - P.y);\n    float y = SQRT_2 / 2.0 * (P.x * 1.5 + P.y);\n    float diamond = max(abs(x), abs(y)) - size / (2.0 * SQRT_2);\n    diamond /= SQRT_2;\n    float c1 = max(diamond, s1);\n    float c2 = max(diamond, s2);\n    float c3 = max(diamond, s3);\n    float c4 = max(diamond, s4);\n    // Union\n    return min(min(min(c1, c2), c3), c4);\n}",e}(_),y=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH="x",e.prototype.MARKERCODE='float marker(vec2 P, float size)\n{\n    float circle = length(P) - size / 1.6;\n    float X = min(abs(P.x - P.y), abs(P.x + P.y)) - size / 100.0;  // bit of "width" for aa\n    return max(circle, X);\n}',e}(_),l=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH="circlex",e.prototype.MARKERCODE='float marker(vec2 P, float size)\n{\n    float x = P.x - P.y;\n    float y = P.x + P.y;\n    // Define quadrants\n    float qs = size / 2.0;  // quadrant size\n    float s1 = max(abs(x - qs), abs(y - qs)) - qs;\n    float s2 = max(abs(x + qs), abs(y - qs)) - qs;\n    float s3 = max(abs(x - qs), abs(y + qs)) - qs;\n    float s4 = max(abs(x + qs), abs(y + qs)) - qs;\n    // Intersect main shape with quadrants (to form cross)\n    float circle = length(P) - size/2.0;\n    float c1 = max(circle, s1);\n    float c2 = max(circle, s2);\n    float c3 = max(circle, s3);\n    float c4 = max(circle, s4);\n    // Union\n    float almost = min(min(min(c1, c2), c3), c4);\n    // In this case, the X is also outside of the main shape\n    float Xmask = length(P) - size / 1.6;  // a circle\n    float X = min(abs(P.x - P.y), abs(P.x + P.y)) - size / 100.0;  // bit of "width" for aa\n    return min(max(X, Xmask), almost);\n}',e}(_),m=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH="squarex",e.prototype.MARKERCODE="float marker(vec2 P, float size)\n{\n    float x = P.x - P.y;\n    float y = P.x + P.y;\n    // Define quadrants\n    float qs = size / 2.0;  // quadrant size\n    float s1 = max(abs(x - qs), abs(y - qs)) - qs;\n    float s2 = max(abs(x + qs), abs(y - qs)) - qs;\n    float s3 = max(abs(x - qs), abs(y + qs)) - qs;\n    float s4 = max(abs(x + qs), abs(y + qs)) - qs;\n    // Intersect main shape with quadrants (to form cross)\n    float square = max(abs(P.x), abs(P.y)) - size/2.0;\n    float c1 = max(square, s1);\n    float c2 = max(square, s2);\n    float c3 = max(square, s3);\n    float c4 = max(square, s4);\n    // Union\n    return min(min(min(c1, c2), c3), c4);\n}",e}(_),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH="asterisk",e.prototype.MARKERCODE='float marker(vec2 P, float size)\n{\n    // Masks\n    float diamond = max(abs(SQRT_2 / 2.0 * (P.x - P.y)), abs(SQRT_2 / 2.0 * (P.x + P.y))) - size / (2.0 * SQRT_2);\n    float square = max(abs(P.x), abs(P.y)) - size / (2.0 * SQRT_2);\n    // Shapes\n    float X = min(abs(P.x - P.y), abs(P.x + P.y)) - size / 100.0;  // bit of "width" for aa\n    float cross = min(abs(P.x), abs(P.y)) - size / 100.0;  // bit of "width" for aa\n    // Result is union of masked shapes\n    return min(max(X, diamond), max(cross, square));\n}',e}(_),e.exports={CircleGLGlyph:a,SquareGLGlyph:f,AnnulusGLGlyph:n,DiamondGLGlyph:c,TriangleGLGlyph:g,InvertedTriangleGLGlyph:p,CrossGLGlyph:u,CircleCrossGLGlyph:s,SquareCrossGLGlyph:d,DiamondCrossGLGlyph:h,XGLGlyph:y,CircleXGLGlyph:l,SquareXGLGlyph:m,AsteriskGLGlyph:o}},{"../../../core/logging":"core/logging","./base":"models/glyphs/webgl/base",gloo2:"gloo2"}],"models/glyphs/wedge":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;s=t("underscore"),n=t("./glyph"),l=t("../../common/hittest"),u=t("../../core/properties"),a=t("../../core/util/math").angle_between,i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype._index_data=function(){return this._xy_index()},e.prototype._map_data=function(){return"data"===this.model.properties.radius.units?this.sradius=this.sdist(this.renderer.xmapper,this._x,this._radius):this.sradius=this._radius},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p;for(c=r.sx,p=r.sy,h=r.sradius,o=r._start_angle,n=r._end_angle,i=this.model.properties.direction.value(),u=[],a=0,l=e.length;a<l;a++)s=e[a],isNaN(c[s]+p[s]+h[s]+o[s]+n[s])||(t.beginPath(),t.arc(c[s],p[s],h[s],o[s],n[s],i),t.lineTo(c[s],p[s]),t.closePath(),this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,s),t.fill()),this.visuals.line.doit?(this.visuals.line.set_vectorize(t,s),u.push(t.stroke())):u.push(void 0));return u},e.prototype._hit_point=function(t){var e,r,n,o,i,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C,N,O,q,D,I,R,F,B;for(g=[t.vx,t.vy],P=g[0],C=g[1],q=this.renderer.xmapper.map_from_target(P,!0),R=this.renderer.ymapper.map_from_target(C,!0),"data"===this.model.properties.radius.units?(D=q-this.max_radius,I=q+this.max_radius,F=R-this.max_radius,B=R+this.max_radius):(E=P-this.max_radius,A=P+this.max_radius,y=this.renderer.xmapper.v_map_from_target([E,A],!0),D=y[0],I=y[1],N=C-this.max_radius,O=C+this.max_radius,v=this.renderer.ymapper.v_map_from_target([N,O],!0),F=v[0],B=v[1]),n=[],r=l.validate_bbox_coords([D,I],[F,B]),b=function(){var t,e,n,o;for(n=this.index.search(r),o=[],t=0,e=n.length;t<e;t++)f=n[t],o.push(f.i);return o}.call(this),c=0,_=b.length;c<_;c++)h=b[c],m=Math.pow(this.sradius[h],2),k=this.renderer.xmapper.map_to_target(q,!0),j=this.renderer.xmapper.map_to_target(this._x[h],!0),S=this.renderer.ymapper.map_to_target(R,!0),z=this.renderer.ymapper.map_to_target(this._y[h],!0),i=Math.pow(k-j,2)+Math.pow(S-z,2),i<=m&&n.push([h,i]);for(o=this.model.properties.direction.value(),u=[],p=0,d=n.length;p<d;p++)x=n[p],h=x[0],i=x[1],M=this.renderer.plot_view.canvas.vx_to_sx(P),T=this.renderer.plot_view.canvas.vy_to_sy(C),e=Math.atan2(T-this.sy[h],M-this.sx[h]),a(-e,-this._start_angle[h],-this._end_angle[h],o)&&u.push([h,i]);return w=l.create_hit_test_result(),w["1d"].indices=s.chain(u).sortBy(function(t){return t[1]}).map(function(t){return t[0]}).value(),w},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_area_legend(t,e,r,n,o)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=i,e.prototype.type="Wedge",e.coords([["x","y"]]),e.mixins(["line","fill"]),e.define({direction:[u.Direction,"anticlock"],radius:[u.DistanceSpec],start_angle:[u.AngleSpec],end_angle:[u.AngleSpec]}),e}(n.Model),e.exports={Model:o,View:i}},{"../../common/hittest":"common/hittest","../../core/properties":"core/properties","../../core/util/math":"core/util/math","./glyph":"models/glyphs/glyph",underscore:"underscore"}],"models/grids/grid":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;a=t("underscore"),i=t("../renderers/guide_renderer"),s=t("../renderers/renderer"),l=t("../../core/properties"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this._x_range_name=this.mget("x_range_name"),this._y_range_name=this.mget("y_range_name")},e.prototype.render=function(){var t;if(this.model.visible!==!1)return t=this.plot_view.canvas_view.ctx,t.save(),this._draw_regions(t),this._draw_minor_grids(t),this._draw_grids(t),t.restore()},e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,"change",this.request_render)},e.prototype._draw_regions=function(t){var e,r,n,o,i,s,a,l,u,h,c,p;if(this.visuals.band_fill.doit)for(n=this.model.grid_coords("major",!1),c=n[0],p=n[1],this.visuals.band_fill.set_value(t),e=r=0,o=c.length-1;0<=o?r<o:r>o;e=0<=o?++r:--r)e%2===1&&(i=this.plot_view.map_to_screen(c[e],p[e],this._x_range_name,this._y_range_name),a=i[0],u=i[1],s=this.plot_view.map_to_screen(c[e+1],p[e+1],this._x_range_name,this._y_range_name),l=s[0],h=s[1],t.fillRect(a[0],u[0],l[1]-a[0],h[1]-u[0]),t.fill())},e.prototype._draw_grids=function(t){var e,r,n;if(this.visuals.grid_line.doit)return e=this.model.grid_coords("major"),r=e[0],n=e[1],this._draw_grid_helper(t,this.visuals.grid_line,r,n)},e.prototype._draw_minor_grids=function(t){var e,r,n;if(this.visuals.minor_grid_line.doit)return e=this.model.grid_coords("minor"),r=e[0],n=e[1],this._draw_grid_helper(t,this.visuals.minor_grid_line,r,n)},e.prototype._draw_grid_helper=function(t,e,r,n){var o,i,s,a,l,u,h,c;for(e.set_value(t),o=i=0,a=r.length;0<=a?i<a:i>a;o=0<=a?++i:--i){for(l=this.plot_view.map_to_screen(r[o],n[o],this._x_range_name,this._y_range_name),h=l[0],c=l[1],t.beginPath(),t.moveTo(Math.round(h[0]),Math.round(c[0])),o=s=1,u=h.length;1<=u?s<u:s>u;o=1<=u?++s:--s)t.lineTo(Math.round(h[o]),Math.round(c[o]));t.stroke()}},e}(s.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=o,e.prototype.type="Grid",e.mixins(["line:grid_","line:minor_grid_","fill:band_"]),e.define({bounds:[l.Any,"auto"],dimension:[l.Number,0],ticker:[l.Instance],x_range_name:[l.String,"default"],y_range_name:[l.String,"default"]}),e.override({level:"underlay",band_fill_color:null,band_fill_alpha:0,grid_line_color:"#e5e5e5",minor_grid_line_color:null}),e.prototype.ranges=function(){var t,e,r,n;return e=this.get("dimension"),r=(e+1)%2,t=this.plot.plot_canvas.get("frame"),n=[t.get("x_ranges")[this.get("x_range_name")],t.get("y_ranges")[this.get("y_range_name")]],[n[e],n[r]]},e.prototype.computed_bounds=function(){var t,e,r,n,o,i,s;return o=this.ranges(),r=o[0],t=o[1],s=this.get("bounds"),n=[r.get("min"),r.get("max")],a.isArray(s)?(i=Math.min(s[0],s[1]),e=Math.max(s[0],s[1]),i<n[0]?i=n[0]:i>n[1]&&(i=null),e>n[1]?e=n[1]:e<n[0]&&(e=null)):(i=n[0],e=n[1]),[i,e]},e.prototype.grid_coords=function(t,e){var r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T;for(null==e&&(e=!0),h=this.get("dimension"),p=(h+1)%2,b=this.ranges(),v=b[0],s=b[1],x=this.computed_bounds(),k=x[0],u=x[1],T=Math.min(k,u),u=Math.max(k,u),k=T,j=this.get("ticker").get_ticks(k,u,v,{})[t],g=v.get("min"),m=v.get("max"),o=s.get("min"),n=s.get("max"),i=[[],[]],c=_=0,w=j.length;0<=w?_<w:_>w;c=0<=w?++_:--_)if(j[c]!==g&&j[c]!==m||!e){for(a=[],l=[],r=2,y=d=0,M=r;0<=M?d<M:d>M;y=0<=M?++d:--d)f=o+(n-o)/(r-1)*y,a.push(j[c]),l.push(f);i[h].push(a),i[p].push(l)}return i},e}(i.Model),e.exports={Model:n,View:o}},{"../../core/properties":"core/properties","../renderers/guide_renderer":"models/renderers/guide_renderer","../renderers/renderer":"models/renderers/renderer",underscore:"underscore"}],"models/layouts/box":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d=function(t,e){function r(){this.constructor=t}for(var n in e)f.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},f={}.hasOwnProperty,m=[].indexOf||function(t){for(var e=0,r=this.length;e<r;e++)if(e in this&&this[e]===t)return e;return-1};c=t("underscore"),_=t("../../core/layout/solver"),i=_.EQ,s=_.GE,l=_.Strength,u=_.Variable,h=_.WEAK_EQ,p=t("../../core/properties"),a=t("./layout_dom"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return d(e,t),e.prototype.className="bk-grid",e.prototype.bind_bokeh_events=function(){return e.__super__.bind_bokeh_events.call(this),this.listenTo(this.model,"change:children",this.build_child_views)},e.prototype.get_height=function(){var t,e,r;return e=this.model.get_layoutable_children(),t=c.map(e,function(t){return t._height._value}),r=this.model._horizontal?c.reduce(t,function(t,e){return Math.max(t,e)}):c.reduce(t,function(t,e){return t+e})},e.prototype.get_width=function(){var t,e,r;return e=this.model.get_layoutable_children(),t=c.map(e,function(t){return t._width._value}),r=this.model._horizontal?c.reduce(t,function(t,e){return t+e}):c.reduce(t,function(t,e){return Math.max(t,e)})},e}(a.View),n=function(t){function e(t,r){e.__super__.constructor.call(this,t,r),this._child_equal_size_width=new u,this._child_equal_size_height=new u,this._box_equal_size_top=new u,this._box_equal_size_bottom=new u,this._box_equal_size_left=new u,this._box_equal_size_right=new u,this._box_cell_align_top=new u,this._box_cell_align_bottom=new u,this._box_cell_align_left=new u,this._box_cell_align_right=new u}return d(e,t),e.prototype.default_view=o,e.define({children:[p.Array,[]]}),e.internal({spacing:[p.Number,6]}),e.prototype.get_layoutable_children=function(){return this.get("children")},e.prototype.variables_updated=function(){var t,e,r,n;for(n=this.get_layoutable_children(),e=0,r=n.length;e<r;e++)t=n[e],t.trigger("change");return this.trigger("change")},e.prototype.get_edit_variables=function(){var t,r,n,o,i;for(r=e.__super__.get_edit_variables.call(this),i=this.get_layoutable_children(),n=0,o=i.length;n<o;n++)t=i[n],r=r.concat(t.get_edit_variables());return r},e.prototype.get_constrained_variables=function(){var t;return t=e.__super__.get_constrained_variables.call(this),t=c.extend(t,{"box-equal-size-top":this._box_equal_size_top,"box-equal-size-bottom":this._box_equal_size_bottom,"box-equal-size-left":this._box_equal_size_left,"box-equal-size-right":this._box_equal_size_right,"box-cell-align-top":this._box_cell_align_top,"box-cell-align-bottom":this._box_cell_align_bottom,"box-cell-align-left":this._box_cell_align_left,"box-cell-align-right":this._box_cell_align_right})},e.prototype.get_constraints=function(){var t,e,r,n,o,a,l,u,p,_,d,f,m;if(r=[],e=this.get_layoutable_children(),0===e.length)return r;for(o=0,u=e.length;o<u;o++)t=e[o],this._test_layoutable(t),m=t.get_constrained_variables(),f=c.keys(m),_=this._child_rect(m),this._horizontal?this._has_var("height",f)&&r.push(i(_.height,[-1,this._height])):this._has_var("width",f)&&r.push(i(_.width,[-1,this._width])),this._horizontal?this._has_var(["box-equal-size-left","box-equal-size-right","width"],f)&&r.push(i([-1,m["box-equal-size-left"]],[-1,m["box-equal-size-right"]],m.width,this._child_equal_size_width)):this._has_var(["box-equal-size-top","box-equal-size-bottom","height"],f)&&r.push(i([-1,m["box-equal-size-top"]],[-1,m["box-equal-size-bottom"]],m.height,this._child_equal_size_height)),r=r.concat(t.get_constraints());for(l=this._info(e[0].get_constrained_variables()),r.push(i(l.span.start,0)),n=a=1,d=e.length;1<=d?a<d:a>d;n=1<=d?++a:--a)p=this._info(e[n].get_constrained_variables()),l.span.size&&r.push(i(l.span.start,l.span.size,[-1,p.span.start])),
r.push(h(l.whitespace.after,p.whitespace.before,0-this.spacing)),r.push(s(l.whitespace.after,p.whitespace.before,0-this.spacing)),l=p;return this._horizontal?this._has_var("width",f)&&r.push(i(l.span.start,l.span.size,[-1,this._width])):this._has_var("height",f)&&r.push(i(l.span.start,l.span.size,[-1,this._height])),r=r.concat(this._align_outer_edges_constraints(!0)),r=r.concat(this._align_outer_edges_constraints(!1)),r=r.concat(this._align_inner_cell_edges_constraints()),r=r.concat(this._box_equal_size_bounds(!0)),r=r.concat(this._box_equal_size_bounds(!1)),r=r.concat(this._box_cell_align_bounds(!0)),r=r.concat(this._box_cell_align_bounds(!1)),r=r.concat(this._box_whitespace(!0)),r=r.concat(this._box_whitespace(!1))},e.prototype._has_var=function(t,e){var r;return r="string"==typeof t?[t]:t,c.every(r,function(t){return m.call(e,t)>=0})},e.prototype._test_layoutable=function(t){var e,r,n,o,i;if(o=["origin-x","origin-y","whitespace-top","whitespace-right","whitespace-bottom","whitespace-left"],null==t.get_constrained_variables)throw new Error(t+" is missing get_constrained_variables method");for(i=t.get_constrained_variables(),e=0,n=o.length;e<n;e++){if(r=o[e],m.call(c.keys(i),r)<0)throw new Error(t+" is missing constrained_variable "+r);if(!i[r]instanceof u)throw new Error(t+" "+r+" is not a solver Variable")}return!0},e.prototype._child_rect=function(t){var e,r,n,o,i;return n=t.width,e=t.height,r=[t["origin-x"],t["origin-y"]],o=r[0],i=r[1],{x:o,y:i,width:n,height:e}},e.prototype._span=function(t){return this._horizontal?{start:t.x,size:t.width}:{start:t.y,size:t.height}},e.prototype._info=function(t){var e,r;return r=this._horizontal?{before:t["whitespace-left"],after:t["whitespace-right"]}:{before:t["whitespace-top"],after:t["whitespace-bottom"]},e=this._span(this._child_rect(t)),{span:e,whitespace:r}},e.prototype._flatten_cell_edge_variables=function(t){var r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w;for(x=t?e._top_bottom_inner_cell_edge_variables:e._left_right_inner_cell_edge_variables,r=t!==this._horizontal,l=this.get_layoutable_children(),o=l.length,h={},i=0,c=0,f=l.length;c<f;c++){for(a=l[c],s=a instanceof e?a._flatten_cell_edge_variables(t):{},n=a.get_constrained_variables(),p=0,m=x.length;p<m;p++)g=x[p],g in n&&(s[g]=[n[g]]);for(_ in s)w=s[_],r?(v=_.split(" "),d=v[0],b=v.length>1?v[1]:"",u=this._horizontal?"row":"col",y=d+" "+u+"-"+o+"-"+i+"-"+b):y=_,y in h?h[y]=h[y].concat(w):h[y]=w;i+=1}return h},e.prototype._align_inner_cell_edges_constraints=function(){var t,e,r,n,o,s,a,l;if(t=[],this._is_root){e=this._flatten_cell_edge_variables(this._horizontal);for(o in e)if(l=e[o],l.length>1)for(s=l[0],r=n=1,a=l.length;1<=a?n<a:n>a;r=1<=a?++n:--n)t.push(i(l[r],[-1,s]))}return t},e.prototype._find_edge_leaves=function(t){var r,n,o,i,s,a,l,u;if(o=this.get_layoutable_children(),a=[[],[]],o.length>0)if(this._horizontal===t)u=o[0],i=o[o.length-1],u instanceof e?a[0]=a[0].concat(u._find_edge_leaves(t)[0]):a[0].push(u),i instanceof e?a[1]=a[1].concat(i._find_edge_leaves(t)[1]):a[1].push(i);else for(s=0,l=o.length;s<l;s++)r=o[s],r instanceof e?(n=r._find_edge_leaves(t),a[0]=a[0].concat(n[0]),a[1]=a[1].concat(n[1])):(a[0].push(r),a[1].push(r));return a},e.prototype._align_outer_edges_constraints=function(t){var e,r,n,o,s,a,l,u,h,c;return a=this._find_edge_leaves(t),h=a[0],o=a[1],t?(c="on-edge-align-left",s="on-edge-align-right"):(c="on-edge-align-top",s="on-edge-align-bottom"),r=function(t,e){var r,n,o,i,s;for(r=[],n=0,i=t.length;n<i;n++)o=t[n],s=o.get_constrained_variables(),e in s&&r.push(s[e]);return r},u=r(h,c),n=r(o,s),l=[],e=function(t){var e,r,n,o,s;if(t.length>1){for(r=t[0],n=o=1,s=t.length;1<=s?o<s:o>s;n=1<=s?++o:--o)e=t[n],l.push(i([-1,r],e));return null}},e(u),e(n),l},e.prototype._box_insets_from_child_insets=function(t,e,r,n){var o,a,l,u,h,c,p,_,d;return c=this._find_edge_leaves(t),_=c[0],a=c[1],t?(d=e+"-left",l=e+"-right",h=this[r+"_left"],u=this[r+"_right"]):(d=e+"-top",l=e+"-bottom",h=this[r+"_top"],u=this[r+"_bottom"]),p=[],o=function(t,e,r){var o,a,l,u,h;for(o=[],a=0,u=e.length;a<u;a++)l=e[a],h=l.get_constrained_variables(),r in h&&(n?p.push(s([-1,t],h[r])):p.push(i([-1,t],h[r])));return null},o(h,_,d),o(u,a,l),p},e.prototype._box_equal_size_bounds=function(t){return this._box_insets_from_child_insets(t,"box-equal-size","_box_equal_size",!1)},e.prototype._box_cell_align_bounds=function(t){return this._box_insets_from_child_insets(t,"box-cell-align","_box_cell_align",!1)},e.prototype._box_whitespace=function(t){return this._box_insets_from_child_insets(t,"whitespace","_whitespace",!0)},e._left_right_inner_cell_edge_variables=["box-cell-align-left","box-cell-align-right"],e._top_bottom_inner_cell_edge_variables=["box-cell-align-top","box-cell-align-bottom"],e}(a.Model),e.exports={Model:n,View:o}},{"../../core/layout/solver":"core/layout/solver","../../core/properties":"core/properties","./layout_dom":"models/layouts/layout_dom",underscore:"underscore"}],"models/layouts/column":[function(t,e,r){var n,o,i,s=function(t,e){function r(){this.constructor=t}for(var n in e)a.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},a={}.hasOwnProperty;n=t("./box"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return s(e,t),e.prototype.className="bk-grid-column",e}(n.View),o=function(t){function e(t,r){e.__super__.constructor.call(this,t,r),this._horizontal=!1}return s(e,t),e.prototype.type="Column",e.prototype.default_view=i,e}(n.Model),e.exports={View:i,Model:o}},{"./box":"models/layouts/box"}],"models/layouts/layout_dom":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g=function(t,e){function r(){this.constructor=t}for(var n in e)y.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},y={}.hasOwnProperty;p=t("underscore"),n=t("jquery"),u=t("../../model"),f=t("../../core/properties"),m=t("../../core/layout/solver"),s=m.GE,i=m.EQ,h=m.Strength,c=m.Variable,_=t("../../common/build_views"),o=t("../../core/bokeh_view"),d=t("../../core/logging").logger,l=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return g(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.$el.attr("id","modelid_"+this.model.id),this.$el.addClass("bk-layout-"+this.model.sizing_mode),this.child_views={},this.build_child_views(!1)},e.prototype.build_child_views=function(t){var e,r,n,o,i;for(null==t&&(t=!0),this.unbind_bokeh_events(),t&&(this.model.document._invalidate_all_models(),this.model.document._init_solver()),n=this.model.get_layoutable_children(),this.child_views={},_(this.child_views,n),this.$el.empty(),o=0,i=n.length;o<i;o++)e=n[o],r=this.child_views[e.id],this.$el.append(r.$el);return this.bind_bokeh_events()},e.prototype.unbind_bokeh_events=function(){var t,e,r,n;this.stopListening(),e=this.child_views,r=[];for(t in e)n=e[t],n.stopListening(),r.push("function"==typeof n.unbind_bokeh_events?n.unbind_bokeh_events():void 0);return r},e.prototype.bind_bokeh_events=function(){var t;return this.listenTo(this.model,"change",this.render),"fixed"===this.model.sizing_mode?this.listenToOnce(this.model.document.solver(),"resize",function(t){return function(){return t.render()}}(this)):this.listenTo(this.model.document.solver(),"resize",function(t){return function(){return t.render()}}(this)),t="Changing sizing_mode after initialization is not currently supported.",this.listenTo(this.model,"change:sizing_mode",function(){return d.warn(t)})},e.prototype.render=function(){var t,e,r;if(e=this.model.document.solver(),"fixed"===this.model.sizing_mode&&(null!=this.model.width?r=this.model.width:(r=this.get_width(),this.model.width=r),null!=this.model.height?t=this.model.height:(t=this.get_height(),this.model.height=t),e.suggest_value(this.model._width,r),e.suggest_value(this.model._height,t),e.update_variables(),this.$el.css({width:r,height:t})),"scale_width"===this.model.sizing_mode&&(t=this.get_height(),e.suggest_value(this.model._height,t),e.update_variables(),this.$el.css({width:this.model._width._value,height:this.model._height._value})),"scale_height"===this.model.sizing_mode&&(r=this.get_width(),e.suggest_value(this.model._width,r),e.update_variables(),this.$el.css({width:this.model._width._value,height:this.model._height._value})),"stretch_both"===this.model.sizing_mode)return this.$el.css({position:"absolute",left:this.model._dom_left._value,top:this.model._dom_top._value,width:this.model._width._value,height:this.model._height._value})},e.prototype.get_height=function(){return null},e.prototype.get_width=function(){return null},e}(o),a=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return g(e,t),e.prototype.type="LayoutDOM",e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this._width=new c("_width "+this.id),this._height=new c("_height "+this.id),this._left=new c("_left "+this.id),this._right=new c("_right "+this.id),this._top=new c("_top "+this.id),this._bottom=new c("_bottom "+this.id),this._dom_top=new c("_dom_top "+this.id),this._dom_left=new c("_dom_left "+this.id),this._width_minus_right=new c("_width_minus_right "+this.id),this._height_minus_bottom=new c("_height_minus_bottom "+this.id),this._whitespace_top=new c,this._whitespace_bottom=new c,this._whitespace_left=new c,this._whitespace_right=new c},e.prototype.get_constraints=function(){var t;return t=[],t.push(s(this._dom_left)),t.push(s(this._dom_top)),t.push(s(this._left)),t.push(s(this._width,[-1,this._right])),t.push(s(this._top)),t.push(s(this._height,[-1,this._bottom])),t.push(i(this._width_minus_right,[-1,this._width],this._right)),t.push(i(this._height_minus_bottom,[-1,this._height],this._bottom)),t},e.prototype.get_layoutable_children=function(){return[]},e.prototype.get_edit_variables=function(){var t;return t=[],"fixed"===this.sizing_mode&&(t.push({edit_variable:this._height,strength:h.strong}),t.push({edit_variable:this._width,strength:h.strong})),"scale_width"===this.sizing_mode&&t.push({edit_variable:this._height,strength:h.strong}),"scale_height"===this.sizing_mode&&t.push({edit_variable:this._width,strength:h.strong}),t},e.prototype.get_constrained_variables=function(){var t;return t={"origin-x":this._dom_left,"origin-y":this._dom_top,"whitespace-top":this._whitespace_top,"whitespace-bottom":this._whitespace_bottom,"whitespace-left":this._whitespace_left,"whitespace-right":this._whitespace_right},"stretch_both"===this.sizing_mode&&(t=p.extend(t,{width:this._width,height:this._height})),"scale_width"===this.sizing_mode&&(t=p.extend(t,{width:this._width})),"scale_height"===this.sizing_mode&&(t=p.extend(t,{height:this._height})),t},e.define({height:[f.Number],width:[f.Number],disabled:[f.Bool,!1],sizing_mode:[f.SizingMode,"fixed"]}),e.internal({layoutable:[f.Bool,!0]}),e}(u),e.exports={Model:a,View:l}},{"../../common/build_views":"common/build_views","../../core/bokeh_view":"core/bokeh_view","../../core/layout/solver":"core/layout/solver","../../core/logging":"core/logging","../../core/properties":"core/properties","../../model":"model",jquery:"jquery",underscore:"underscore"}],"models/layouts/row":[function(t,e,r){var n,o,i,s=function(t,e){function r(){this.constructor=t}for(var n in e)a.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},a={}.hasOwnProperty;n=t("./box"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return s(e,t),e.prototype.className="bk-grid-row",e}(n.View),o=function(t){function e(t,r){e.__super__.constructor.call(this,t,r),this._horizontal=!0}return s(e,t),e.prototype.type="Row",e.prototype.default_view=i,e}(n.Model),e.exports={View:i,Model:o}},{"./box":"models/layouts/box"}],"models/layouts/spacer":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;s=t("underscore"),n=t("./layout_dom"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return 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t=e.__super__.get_constrained_variables.call(this),t=s.extend(t,{"on-edge-align-top":this._top,"on-edge-align-bottom":this._height_minus_bottom,"on-edge-align-left":this._left,"on-edge-align-right":this._width_minus_right,"box-cell-align-top":this._top,"box-cell-align-bottom":this._height_minus_bottom,"box-cell-align-left":this._left,"box-cell-align-right":this._width_minus_right,"box-equal-size-top":this._top,"box-equal-size-bottom":this._height_minus_bottom,"box-equal-size-left":this._left,"box-equal-size-right":this._width_minus_right})},e}(n.Model),e.exports={Model:o}},{"./layout_dom":"models/layouts/layout_dom",underscore:"underscore"}],"models/layouts/widget_box":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y=function(t,e){function r(){this.constructor=t}for(var n in e)v.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new 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r=this.model.document.solver(),"fixed"!==this.model.sizing_mode&&"scale_height"!==this.model.sizing_mode||(n=this.get_width(),this.model._width._value!==n&&(r.suggest_value(this.model._width,n),r.update_variables())),"fixed"!==this.model.sizing_mode&&"scale_width"!==this.model.sizing_mode||(e=this.get_height(),this.model._height._value!==e&&(r.suggest_value(this.model._height,e),r.update_variables())),t=this.model._width._value-20>0?this.model._width._value-20:"100%","stretch_both"===this.model.sizing_mode?this.$el.css({position:"absolute",left:this.model._dom_left._value,top:this.model._dom_top._value,width:this.model._width._value,height:this.model._height._value}):this.$el.css({width:t})},e.prototype.get_height=function(){var t,e,r,n;e=0,n=this.child_views;for(r in n)v.call(n,r)&&(t=n[r],e+=t.el.scrollHeight);return e+20},e.prototype.get_width=function(){var t,e,r,n,o;if(null!=this.model.width)return this.model.width;o=this.el.scrollWidth+20,n=this.child_views;for(r in n)v.call(n,r)&&(t=n[r],e=t.el.scrollWidth,e>o&&(o=e));return o},e}(a.View),c=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return y(e,t),e.prototype.type="WidgetBox",e.prototype.default_view=p,e.prototype.initialize=function(t){if(e.__super__.initialize.call(this,t),"fixed"===this.sizing_mode&&null===this.width&&(this.width=300,f.info("WidgetBox mode is fixed, but no width specified. Using default of 300.")),"scale_height"===this.sizing_mode)return f.warn("sizing_mode `scale_height` is not experimental for WidgetBox. Please report your results to the bokeh dev team so we can improve.")},e.prototype.get_edit_variables=function(){var t,r,n,o,i;for(r=e.__super__.get_edit_variables.call(this),i=this.get_layoutable_children(),n=0,o=i.length;n<o;n++)t=i[n],r=r.concat(t.get_edit_variables());return r},e.prototype.get_constraints=function(){var t,r,n,o,i;for(r=e.__super__.get_constraints.call(this),i=this.get_layoutable_children(),n=0,o=i.length;n<o;n++)t=i[n],r=r.concat(t.get_constraints());return r},e.prototype.get_constrained_variables=function(){var t;return t=e.__super__.get_constrained_variables.call(this),t=_.extend(t,{"on-edge-align-top":this._top,"on-edge-align-bottom":this._height_minus_bottom,"on-edge-align-left":this._left,"on-edge-align-right":this._width_minus_right,"box-cell-align-top":this._top,"box-cell-align-bottom":this._height_minus_bottom,"box-cell-align-left":this._left,"box-cell-align-right":this._width_minus_right,"box-equal-size-top":this._top,"box-equal-size-bottom":this._height_minus_bottom}),"fixed"!==this.sizing_mode&&(t=_.extend(t,{"box-equal-size-left":this._left,"box-equal-size-right":this._width_minus_right})),t},e.prototype.get_layoutable_children=function(){return this.children},e.define({children:[m.Array,[]]}),e}(a.Model),e.exports={Model:c}},{"../../common/build_views":"common/build_views","../../core/bokeh_view":"core/bokeh_view","../../core/layout/solver":"core/layout/solver","../../core/logging":"core/logging","../../core/properties":"core/properties","../layouts/layout_dom":"models/layouts/layout_dom",jquery:"jquery",underscore:"underscore"}],"models/mappers/categorical_mapper":[function(t,e,r){var n,o,i,s=function(t,e){function r(){this.constructor=t}for(var n in e)a.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},a={}.hasOwnProperty;i=t("underscore"),o=t("./linear_mapper"),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return s(e,t),e.prototype.map_to_target=function(t,r){var n,o,s,a,l,u;return 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See https://developers.google.com/maps/documentation/javascript/get-api-key for more information on how to obtain your own."),this._plot_canvas=new o.Model({plot:this}),this.plot_canvas.toolbar=this.toolbar},e.define({map_options:[u.Any],api_key:[u.String]}),e}(s.Model),e.exports={Model:n,View:i}},{"../../core/logging":"core/logging","../../core/properties":"core/properties","./gmap_plot_canvas":"models/plots/gmap_plot_canvas","./plot":"models/plots/plot",proj4:"proj4",underscore:"underscore"}],"models/plots/gmap_plot_canvas":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){return function(){return t.apply(e,arguments)}},c=function(t,e){function r(){this.constructor=t}for(var n in e)p.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},p={}.hasOwnProperty;s=t("underscore"),l=t("proj4"),u=l.defs("GOOGLE"),i=t("./plot_canvas"),a=t("../../core/properties"),o=function(t){function e(){return this.setRanges=h(this.setRanges,this),this.getProjectedBounds=h(this.getProjectedBounds,this),this.getLatLngBounds=h(this.getLatLngBounds,this),e.__super__.constructor.apply(this,arguments)}return c(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,s.defaults(t,this.default_options)),this.zoom_count=0},e.prototype.getLatLngBounds=function(){var t,e,r,n,o,i,s;return e=this.map.getBounds(),r=e.getNorthEast(),t=e.getSouthWest(),o=t.lng(),n=r.lng(),s=t.lat(),i=r.lat(),[o,n,s,i]},e.prototype.getProjectedBounds=function(){var t,e,r,n,o,i,s,a,h,c,p;return o=this.getLatLngBounds(),h=o[0],a=o[1],p=o[2],c=o[3],i=l(u,[h,p]),e=i[0],n=i[1],s=l(u,[a,c]),t=s[0],r=s[1],[e,t,n,r]},e.prototype.setRanges=function(){var t,e,r,n,o;return o=this.getProjectedBounds(),e=o[0],t=o[1],n=o[2],r=o[3],this.x_range.set({start:e,end:t}),this.y_range.set({start:n,end:r})},e.prototype.update_range=function(t){var r,n,o,i,s,a,l,u;if(this.pause(),null==t.sdx&&null==t.sdy||(this.map.panBy(t.sdx,t.sdy),e.__super__.update_range.call(this,t)),null!=t.factor){if(10!==this.zoom_count)return void(this.zoom_count+=1);this.zoom_count=0,e.__super__.update_range.call(this,t),u=t.factor<0?-1:1,n=this.map.getZoom(),r=n+u,r>=2&&(this.map.setZoom(r),l=this.getProjectedBounds(),i=l[0],o=l[1],a=l[2],s=l[3],o-i<0&&this.map.setZoom(n)),this.setRanges()}return this.unpause()},e.prototype.bind_bokeh_events=function(){var t,r,n,o,i,a;return e.__super__.bind_bokeh_events.call(this),a=this.frame.get("width"),r=this.frame.get("height"),n=this.canvas.vx_to_sx(this.frame.get("left")),i=this.canvas.vy_to_sy(this.frame.get("top")),this.canvas_view.map_div.attr("style","top: "+i+"px; left: "+n+"px; position: absolute"),this.canvas_view.map_div.attr("style","width:"+a+"px;"),this.canvas_view.map_div.attr("style","height:"+r+"px;"),this.canvas_view.map_div.width(a+"px").height(r+"px"),this.initial_zoom=this.model.plot.map_options.zoom,t=function(t){return function(){var e,r,n,o;return n=window.google.maps,r={satellite:n.MapTypeId.SATELLITE,terrain:n.MapTypeId.TERRAIN,roadmap:n.MapTypeId.ROADMAP,hybrid:n.MapTypeId.HYBRID},o=t.model.plot.map_options,e={center:new n.LatLng(o.lat,o.lng),zoom:o.zoom,disableDefaultUI:!0,mapTypeId:r[o.map_type]},null!=o.styles&&(e.styles=JSON.parse(o.styles)),t.map=new n.Map(t.canvas_view.map_div[0],e),n.event.addListenerOnce(t.map,"idle",t.setRanges)}}(this),null==window._bokeh_gmap_loads&&(window._bokeh_gmap_loads=[]),null!=window.google&&null!=window.google.maps?s.defer(t):null!=window._bokeh_gmap_callback?window._bokeh_gmap_loads.push(t):(window._bokeh_gmap_loads.push(t),window._bokeh_gmap_callback=function(){return s.each(window._bokeh_gmap_loads,s.defer)},o=document.createElement("script"),o.type="text/javascript",o.src="https://maps.googleapis.com/maps/api/js?key="+this.model.plot.api_key+"&callback=_bokeh_gmap_callback",document.body.appendChild(o))},e.prototype._map_hook=function(t,e){var r,n,o,i;return n=e[0],o=e[1],i=e[2],r=e[3],this.canvas_view.map_div.attr("style","top: "+o+"px; left: "+n+"px;"),this.canvas_view.map_div.width(i+"px").height(r+"px")},e.prototype._paint_empty=function(t,e){var r,n,o,i,s,a;return s=this.canvas.get("width"),i=this.canvas.get("height"),o=e[0],a=e[1],n=e[2],r=e[3],t.clearRect(0,0,s,i),t.beginPath(),t.moveTo(0,0),t.lineTo(0,i),t.lineTo(s,i),t.lineTo(s,0),t.lineTo(0,0),t.moveTo(o,a),t.lineTo(o+n,a),t.lineTo(o+n,a+r),t.lineTo(o,a+r),t.lineTo(o,a),t.closePath(),t.fillStyle=this.model.plot.border_fill_color,t.fill()},e}(i.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return c(e,t),e.prototype.type="GMapPlotCanvas",e.prototype.default_view=o,e.prototype.initialize=function(t,r){return this.use_map=!0,e.__super__.initialize.call(this,t,r)},e}(i.Model),e.exports={Model:n,View:o}},{"../../core/properties":"core/properties","./plot_canvas":"models/plots/plot_canvas",proj4:"proj4",underscore:"underscore"}],"models/plots/plot":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x=function(t,e){function r(){this.constructor=t}for(var n in e)w.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},w={}.hasOwnProperty,M=[].slice;g=t("underscore"),b=t("../../core/layout/solver"),m=b.WEAK_EQ,i=b.GE,o=b.EQ,c=b.Strength,f=b.Variable,y=t("../../core/logging").logger,v=t("../../core/properties"),a=t("../layouts/layout_dom"),p=t("../annotations/title"),d=t("../tools/toolbar"),_=t("../../common/tool_events"),u=t("./plot_canvas").Model,n=t("../sources/column_data_source"),s=t("../renderers/glyph_renderer"),p=t("../annotations/title"),h=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return x(e,t),e.prototype.className="bk-plot-layout",e.prototype.bind_bokeh_events=function(){var t;return e.__super__.bind_bokeh_events.call(this),t="Title object cannot be replaced. Try changing properties on title to update it after initialization.",this.listenTo(this.model,"change:title",function(e){return function(){return y.warn(t)}}(this))},e.prototype.render=function(){var t,r,n,o;if(e.__super__.render.call(this),"scale_both"===this.model.sizing_mode)return r=this.get_width_height(),o=r[0],t=r[1],n=this.model.document.solver(),n.suggest_value(this.model._width,o),n.suggest_value(this.model._height,t),this.$el.css({position:"absolute",left:this.model._dom_left._value,top:this.model._dom_top._value,width:this.model._width.value(),height:this.model._height.value()})},e.prototype.get_width_height=function(){var t,e,r,n,o,i,s,a,l;return s=this.el.parentNode.clientHeight,a=this.el.parentNode.clientWidth,t=this.model.get_aspect_ratio(),o=a,r=a/t,i=s*t,n=s,o<i?(l=o,e=r):(l=i,e=n),[l,e]},e.prototype.get_height=function(){return this.model._width._value/this.model.get_aspect_ratio()},e.prototype.get_width=function(){return this.model._height._value*this.model.get_aspect_ratio()},e}(a.View),l=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return x(e,t),e.prototype.type="Plot",e.prototype.default_view=h,e.prototype.initialize=function(t){var r,n,o,i,s,a,l,h,c,_,d;for(e.__super__.initialize.call(this,t),a=g.values(this.extra_x_ranges).concat(this.x_range),r=0,o=a.length;r<o;r++)_=a[r],s=_.get("plots"),g.isArray(s)&&(s=s.concat(this),_.set("plots",s));for(l=g.values(this.extra_y_ranges).concat(this.y_range),n=0,i=l.length;n<i;n++)d=l[n],s=d.get("plots"),g.isArray(s)&&(s=s.concat(this),d.set("plots",s));if(this._horizontal=!1,"left"!==(h=this.toolbar_location)&&"right"!==h||(this._horizontal=!0),null!=this.min_border&&(null==this.min_border_top&&(this.min_border_top=this.min_border),null==this.min_border_bottom&&(this.min_border_bottom=this.min_border),null==this.min_border_left&&(this.min_border_left=this.min_border),null==this.min_border_right&&(this.min_border_right=this.min_border)),null!=this.title&&(c=g.isString(this.title)?new p.Model({text:this.title}):this.title,this.add_layout(c,this.title_location)),this._plot_canvas=new u({plot:this}),this.toolbar.toolbar_location=this.toolbar_location,this.toolbar.toolbar_sticky=this.toolbar_sticky,this.plot_canvas.toolbar=this.toolbar,null==this.width&&(this.width=this.plot_width),null==this.height)return this.height=this.plot_height},Object.defineProperty(e.prototype,"plot_canvas",{get:function(){return this._plot_canvas}}),e.prototype._doc_attached=function(){var t,e,r,n,o,i,s,a;for(s=["above","below","left","right"],t=0,n=s.length;t<n;t++)for(a=s[t],r=this.get(a),e=0,o=r.length;e<o;e++)i=r[e],this.plot_canvas.add_renderer_to_canvas_side(i,a);return this.plot_canvas.attach_document(this.document),this._set_orientation_variables(this),this._set_orientation_variables(this.toolbar),this._set_orientation_variables(this.plot_canvas)},e.prototype.add_renderers=function(){var t,e;return t=1<=arguments.length?M.call(arguments,0):[],e=this.get("renderers"),e=e.concat(t),this.set("renderers",e)},e.prototype.add_layout=function(t,e){var r;if(null==e&&(e="center"),null!=t.props.plot&&(t.plot=this),this.add_renderers(t),"center"!==e)return r=this.get(e),r.push(t)},e.prototype.add_glyph=function(t,e,r){var o;return null==r&&(r={}),null==e&&(e=new n.Model),r=g.extend({},r,{data_source:e,glyph:t}),o=new s.Model(r),this.add_renderers(o),o},e.prototype.add_tools=function(){var t,e,r,n;return n=1<=arguments.length?M.call(arguments,0):[],e=function(){var e,o,i;for(i=[],e=0,o=n.length;e<o;e++)r=n[e],null!=r.overlay&&this.add_renderers(r.overlay),null!=r.plot?i.push(r):(t=g.clone(r.attributes),t.plot=this,i.push(new r.constructor(t)));return i}.call(this),this.toolbar.tools=this.toolbar.tools.concat(e)},e.prototype.get_aspect_ratio=function(){return this.width/this.height},e.prototype.get_layoutable_children=function(){var t;return t=[this.plot_canvas],null!=this.toolbar_location&&(t=[this.toolbar,this.plot_canvas]),t},e.prototype.get_edit_variables=function(){var t,r,n,o,i;for(r=e.__super__.get_edit_variables.call(this),"scale_both"===this.sizing_mode&&(r.push({edit_variable:this._width,strength:c.strong}),r.push({edit_variable:this._height,strength:c.strong})),i=this.get_layoutable_children(),n=0,o=i.length;n<o;n++)t=i[n],r=r.concat(t.get_edit_variables());return r},e.prototype.get_constraints=function(){var t,r,n,s,a,l,u,h;for(r=e.__super__.get_constraints.call(this),null!=this.toolbar_location&&(this.toolbar_sticky===!0?r.push(o(this._sizeable,[-1,this.plot_canvas._sizeable])):r.push(o(this._sizeable,[-1,this.plot_canvas._sizeable],[-1,this.toolbar._sizeable])),r.push(o(this._full,[-1,this.plot_canvas._full])),"above"===this.toolbar_location&&(h=this.toolbar_sticky===!0?this.plot_canvas._top:this.plot_canvas._dom_top,r.push(o(h,[-1,this.toolbar._dom_top],[-1,this.toolbar._height]))),"below"===this.toolbar_location&&(this.toolbar_sticky===!1&&r.push(o(this.toolbar._dom_top,[-1,this.plot_canvas._height],this.toolbar._bottom,[-1,this.toolbar._height])),this.toolbar_sticky===!0&&(r.push(i(this.plot_canvas.below_panel._height,[-1,this.toolbar._height])),r.push(m(this.toolbar._dom_top,[-1,this.plot_canvas._height],this.plot_canvas.below_panel._height)))),"left"===this.toolbar_location&&(h=this.toolbar_sticky===!0?this.plot_canvas._left:this.plot_canvas._dom_left,r.push(o(h,[-1,this.toolbar._dom_left],[-1,this.toolbar._width]))),"right"===this.toolbar_location&&(this.toolbar_sticky===!1&&r.push(o(this.toolbar._dom_left,[-1,this.plot_canvas._width],this.toolbar._right,[-1,this.toolbar._width])),this.toolbar_sticky===!0&&(r.push(i(this.plot_canvas.right_panel._width,[-1,this.toolbar._width])),r.push(m(this.toolbar._dom_left,[-1,this.plot_canvas._width],this.plot_canvas.right_panel._width)))),"above"!==(a=this.toolbar_location)&&"below"!==a||r.push(o(this._width,[-1,this.toolbar._width],[-1,this.plot_canvas._width_minus_right])),"left"!==(l=this.toolbar_location)&&"right"!==l||(r.push(o(this._height,[-1,this.toolbar._height],[-1,this.plot_canvas.above_panel._height])),r.push(o(this.toolbar._dom_top,[-1,this.plot_canvas.above_panel._height])))),null==this.toolbar_location&&(r.push(o(this._width,[-1,this.plot_canvas._width])),r.push(o(this._height,[-1,this.plot_canvas._height]))),u=this.get_layoutable_children(),n=0,s=u.length;n<s;n++)t=u[n],r=r.concat(t.get_constraints());return r},e.prototype.get_constrained_variables=function(){var t;return t=e.__super__.get_constrained_variables.call(this),t=g.extend(t,{"on-edge-align-top":this.plot_canvas._top,"on-edge-align-bottom":this.plot_canvas._height_minus_bottom,"on-edge-align-left":this.plot_canvas._left,"on-edge-align-right":this.plot_canvas._width_minus_right,"box-cell-align-top":this.plot_canvas._top,"box-cell-align-bottom":this.plot_canvas._height_minus_bottom,"box-cell-align-left":this.plot_canvas._left,"box-cell-align-right":this.plot_canvas._width_minus_right,"box-equal-size-top":this.plot_canvas._top,"box-equal-size-bottom":this.plot_canvas._height_minus_bottom}),"fixed"!==this.sizing_mode&&(t=g.extend(t,{"box-equal-size-left":this.plot_canvas._left,"box-equal-size-right":this.plot_canvas._width_minus_right})),t},e.prototype._set_orientation_variables=function(t){if(this._horizontal===!1&&(t._sizeable=t._height,t._full=t._width),this._horizontal===!0)return t._sizeable=t._width,t._full=t._height},e.mixins(["line:outline_","fill:background_","fill:border_"]),e.define({toolbar:[v.Instance,function(){return new d.Model}],toolbar_location:[v.Location,"right"],toolbar_sticky:[v.Bool,!0],plot_width:[v.Number,600],plot_height:[v.Number,600],title:[v.Any,function(){return new p.Model({text:""})}],title_location:[v.Location,"above"],h_symmetry:[v.Bool,!0],v_symmetry:[v.Bool,!1],above:[v.Array,[]],below:[v.Array,[]],left:[v.Array,[]],right:[v.Array,[]],renderers:[v.Array,[]],x_range:[v.Instance],extra_x_ranges:[v.Any,{}],y_range:[v.Instance],extra_y_ranges:[v.Any,{}],x_mapper_type:[v.String,"auto"],y_mapper_type:[v.String,"auto"],tool_events:[v.Instance,function(){return new _.Model}],lod_factor:[v.Number,10],lod_interval:[v.Number,300],lod_threshold:[v.Number,2e3],lod_timeout:[v.Number,500],webgl:[v.Bool,!1],hidpi:[v.Bool,!0],min_border:[v.Number,5],min_border_top:[v.Number,null],min_border_left:[v.Number,null],min_border_bottom:[v.Number,null],min_border_right:[v.Number,null]}),e.override({outline_line_color:"#e5e5e5",border_fill_color:"#ffffff",background_fill_color:"#ffffff"}),e}(a.Model),e.exports={View:h,Model:l}},{"../../common/tool_events":"common/tool_events","../../core/layout/solver":"core/layout/solver","../../core/logging":"core/logging","../../core/properties":"core/properties","../annotations/title":"models/annotations/title","../layouts/layout_dom":"models/layouts/layout_dom","../renderers/glyph_renderer":"models/renderers/glyph_renderer","../sources/column_data_source":"models/sources/column_data_source","../tools/toolbar":"models/tools/toolbar","./plot_canvas":"models/plots/plot_canvas",underscore:"underscore"}],"models/plots/plot_canvas":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j=function(t,e){return function(){return t.apply(e,arguments)}},T=function(t,e){function r(){this.constructor=t}for(var n in e)S.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},S={}.hasOwnProperty,z=[].indexOf||function(t){for(var e=0,r=this.length;e<r;e++)if(e in this&&this[e]===t)return e;return-1};m=t("underscore"),n=t("jquery"),o=t("../canvas/canvas"),i=t("../canvas/cartesian_frame"),s=t("../ranges/data_range1d"),u=t("../renderers/glyph_renderer"),c=t("../layouts/layout_dom"),d=t("../renderers/renderer"),g=t("../../common/build_views"),f=t("../../common/ui_events"),y=t("../../core/enums"),h=t("../../core/layout/layout_canvas"),w=t("../../core/layout/solver"),a=w.EQ,l=w.GE,b=t("../../core/logging").logger,x=t("../../core/properties"),M=t("../../core/util/throttle").throttle,k=t("../../core/layout/side_panel").update_constraints,v=null,_=function(t){function e(){return this.remove=j(this.remove,this),this.request_render=j(this.request_render,this),e.__super__.constructor.apply(this,arguments)}return T(e,t),e.prototype.className="bk-plot-wrapper",e.prototype.state={history:[],index:-1},e.prototype.view_options=function(){return m.extend({plot_model:this.model,plot_view:this},this.options)},e.prototype.pause=function(){return this.is_paused=!0},e.prototype.unpause=function(){return this.is_paused=!1,this.request_render()},e.prototype.request_render=function(){this.is_paused||this.throttled_render()},e.prototype.remove=function(){var t,r,n,o;e.__super__.remove.call(this),r=this.tool_views,n=[];for(t in r)o=r[t],n.push(o.remove());return n},e.prototype.initialize=function(t){var r,n,o,i,s,a,l,u,h,c,p;for(e.__super__.initialize.call(this,t),this.pause(),this.visuals={},u=this.model.plot.mixins,r=0,o=u.length;r<o;r++)p=u[r],h=p.split(":"),a=h[0],l=h[1],null==l&&(l=""),this.visuals[l+a]=new d.Visuals[a]({obj:this.model.plot,prefix:l});for(this._initial_state_info={range:null,selection:{},dimensions:{width:this.mget("canvas").get("width"),height:this.mget("canvas").get("height")}},this.frame=this.mget("frame"),this.x_range=this.frame.get("x_ranges")["default"],this.y_range=this.frame.get("y_ranges")["default"],this.xmapper=this.frame.get("x_mappers")["default"],this.ymapper=this.frame.get("y_mappers")["default"],this.canvas=this.mget("canvas"),this.canvas_view=new this.canvas.default_view({model:this.canvas}),this.$el.append(this.canvas_view.el),this.canvas_view.render(!0),(this.model.plot.webgl||window.location.search.indexOf("webgl=1")>0)&&window.location.search.indexOf("webgl=0")===-1&&this.init_webgl(),this.throttled_render=M(this.render,15),null==this.model.document._unrendered_plots&&(this.model.document._unrendered_plots={}),this.model.document._unrendered_plots[this.id]=!0,this.ui_event_bus=new f({toolbar:this.mget("toolbar"),hit_area:this.canvas_view.$el}),this.renderer_views={},this.tool_views={},this.levels={},c=y.RenderLevel,n=0,i=c.length;n<i;n++)s=c[n],this.levels[s]={};return this.build_levels(),this.bind_bokeh_events(),this.update_dataranges(),this.unpause(),b.debug("PlotView initialized"),this},e.prototype.get_canvas_element=function(){return this.canvas_view.ctx.canvas},e.prototype.init_webgl=function(){var t,e,r;return t=this.canvas_view.ctx,e=v,null==e&&(v=e=document.createElement("canvas"),r={premultipliedAlpha:!0},e.gl=e.getContext("webgl",r)||e.getContext("experimental-webgl",r)),null!=e.gl?t.glcanvas=e:b.warn("WebGL is not supported, falling back to 2D canvas.")},e.prototype.prepare_webgl=function(t,e){var r,n,o,i;if(n=this.canvas_view.ctx,r=this.canvas_view.get_canvas_element(),n.glcanvas)return n.glcanvas.width=r.width,n.glcanvas.height=r.height,i=n.glcanvas.gl,i.viewport(0,0,n.glcanvas.width,n.glcanvas.height),i.clearColor(0,0,0,0),i.clear(i.COLOR_BUFFER_BIT||i.DEPTH_BUFFER_BIT),i.enable(i.SCISSOR_TEST),o=n.glcanvas.height-t*(e[1]+e[3]),i.scissor(t*e[0],o,t*e[2],t*e[3]),i.enable(i.BLEND),i.blendFuncSeparate(i.SRC_ALPHA,i.ONE_MINUS_SRC_ALPHA,i.ONE_MINUS_DST_ALPHA,i.ONE)},e.prototype.blit_webgl=function(t){var e;if(e=this.canvas_view.ctx,e.glcanvas)return b.debug("drawing with WebGL"),e.restore(),e.drawImage(e.glcanvas,0,0),e.save(),e.scale(t,t),e.translate(.5,.5)},e.prototype.update_dataranges=function(){var t,e,r,n,o,i,a,l,u,h,c,p,_,d,f,g,y,v,x,w,M,k,j;n=this.model.frame,e={},f=this.renderer_views;for(a in f)M=f[a],t=null!=(g=M.glyph)&&"function"==typeof g.bounds?g.bounds():void 0,null!=t&&(e[a]=t);for(r=!1,o=!1,y=m.values(n.get("x_ranges")),i=0,u=y.length;i<u;i++)k=y[i],k instanceof s.Model&&(k.update(e,0,this.model.id),k.get("follow")&&(r=!0)),null!=k.get("bounds")&&(o=!0);for(v=m.values(n.get("y_ranges")),l=0,h=v.length;l<h;l++)j=v[l],j instanceof s.Model&&(j.update(e,1,this.model.id),j.get("follow")&&(r=!0)),null!=j.get("bounds")&&(o=!0);if(r&&o){for(b.warn("Follow enabled so bounds are unset."),x=m.values(n.get("x_ranges")),_=0,c=x.length;_<c;_++)k=x[_],k.set("bounds",null);for(w=m.values(n.get("y_ranges")),d=0,p=w.length;d<p;d++)j=w[d],j.set("bounds",null)}return this.range_update_timestamp=Date.now()},e.prototype.map_to_screen=function(t,e,r,n){return null==r&&(r="default"),null==n&&(n="default"),this.frame.map_to_screen(t,e,this.canvas,r,n)},e.prototype.push_state=function(t,e){var r,n;return r=(null!=(n=this.state.history[this.state.index])?n.info:void 0)||{},e=m.extend({},this._initial_state_info,r,e),this.state.history.slice(0,this.state.index+1),this.state.history.push({type:t,info:e}),this.state.index=this.state.history.length-1,this.trigger("state_changed")},e.prototype.clear_state=function(){return this.state={history:[],index:-1},this.trigger("state_changed")},e.prototype.can_undo=function(){return this.state.index>=0},e.prototype.can_redo=function(){return this.state.index<this.state.history.length-1},e.prototype.undo=function(){if(this.can_undo())return this.state.index-=1,this._do_state_change(this.state.index),this.trigger("state_changed")},e.prototype.redo=function(){if(this.can_redo())return this.state.index+=1,this._do_state_change(this.state.index),this.trigger("state_changed")},e.prototype._do_state_change=function(t){var e,r;if(e=(null!=(r=this.state.history[t])?r.info:void 0)||this._initial_state_info,null!=e.range&&this.update_range(e.range),null!=e.selection&&this.update_selection(e.selection),null!=e.dimensions)return this.canvas_view.set_dims([e.dimensions.width,e.dimensions.height])},e.prototype.reset_dimensions=function(){return this.update_dimensions(this.canvas.initial_width,this.canvas.initial_height)},e.prototype.update_dimensions=function(t,e){return this.pause(),this.model.plot.width=t,this.model.plot.height=e,this.model.document.resize(),this.unpause()},e.prototype.get_selection=function(){var t,e,r,n,o,i;for(i=[],r=this.model.plot.renderers,t=0,e=r.length;t<e;t++)n=r[t],n instanceof u.Model&&(o=n.get("data_source").get("selected"),i[n.id]=o);return i},e.prototype.update_selection=function(t){var e,r,n,o,i,s,a;for(o=this.model.plot.renderers,a=[],r=0,n=o.length;r<n;r++)s=o[r],s instanceof u.Model&&(e=s.get("data_source"),null!=t?(i=s.id,z.call(t,i)>=0?a.push(e.set("selected",t[s.id])):a.push(void 0)):a.push(e.get("selection_manager").clear()));return a},e.prototype.reset_selection=function(){return this.update_selection(null)},e.prototype._update_ranges_together=function(t){var e,r,n,o,i,s,a,l,u,h;for(h=1,e=0,n=t.length;e<n;e++)s=t[e],u=s[0],i=s[1],h=Math.min(h,this._get_weight_to_constrain_interval(u,i));if(h<1){for(l=[],r=0,o=t.length;r<o;r++)a=t[r],u=a[0],i=a[1],i.start=h*i.start+(1-h)*u.get("start"),l.push(i.end=h*i.end+(1-h)*u.get("end"));return l}},e.prototype._update_ranges_individually=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y;for(n=!1,o=0,s=t.length;o<s;o++)p=t[o],g=p[0],c=p[1],m=g.get("start")>g.get("end"),r||(y=this._get_weight_to_constrain_interval(g,c),y<1&&(c.start=y*c.start+(1-y)*g.get("start"),c.end=y*c.end+(1-y)*g.get("end"))),null!=g.get("bounds")&&(u=g.get("bounds")[0],l=g.get("bounds")[1],h=Math.abs(c.end-c.start),m?(null!=u&&u>=c.end&&(n=!0,c.end=u,null==e&&null==r||(c.start=u+h)),null!=l&&l<=c.start&&(n=!0,c.start=l,null==e&&null==r||(c.end=l-h))):(null!=u&&u>=c.start&&(n=!0,c.start=u,null==e&&null==r||(c.end=u+h)),null!=l&&l<=c.end&&(n=!0,c.end=l,null==e&&null==r||(c.start=l-h))));if(!r||!n){for(f=[],i=0,a=t.length;i<a;i++)_=t[i],g=_[0],c=_[1],g.have_updated_interactively=!0,g.get("start")!==c.start||g.get("end")!==c.end?(g.set(c),f.push(null!=(d=g.get("callback"))?d.execute(g):void 0)):f.push(void 0);return f}},e.prototype._get_weight_to_constrain_interval=function(t,e){var r,n,o,i,s,a,l,u,h;return s=t.get("min_interval"),n=t.get("max_interval"),h=1,null!=t.get("bounds")&&(u=t.get("bounds"),i=u[0],r=u[1],null!=i&&null!=r&&(o=Math.abs(r-i),n=null!=n?Math.min(n,o):o)),null==s&&null==n||(l=Math.abs(t.get("end")-t.get("start")),a=Math.abs(e.end-e.start),s>0&&a<s&&(h=(l-s)/(l-a)),n>0&&a>n&&(h=(n-l)/(a-l)),h=Math.max(0,Math.min(1,h))),h},e.prototype.update_range=function(t,e,r){var n,o,i,s,a,l,u,h,c;if(this.pause,null==t){i=this.frame.get("x_ranges");for(n in i)c=i[n],c.reset(),null!=(s=c.get("callback"))&&s.execute(c);a=this.frame.get("y_ranges");for(n in a)c=a[n],c.reset(),null!=(l=c.get("callback"))&&l.execute(c);this.update_dataranges()}else{o=[],u=this.frame.get("x_ranges");for(n in u)c=u[n],o.push([c,t.xrs[n]]);h=this.frame.get("y_ranges");for(n in h)c=h[n],o.push([c,t.yrs[n]]);r&&this._update_ranges_together(o),this._update_ranges_individually(o,e,r)}return this.unpause()},e.prototype.reset_range=function(){return this.update_range(null)},e.prototype.build_levels=function(){var t,e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,y,v,b,x;for(p=this.model.plot.renderers,c=this.model.plot.toolbar.tools,e=0,n=c.length;e<n;e++)f=c[e],d=f.get("synthetic_renderers"),p=p.concat(d);for(h=m.keys(this.renderer_views),x=g(this.renderer_views,p,this.view_options()),_=m.difference(h,m.pluck(p,"id")),r=0,o=_.length;r<o;r++)t=_[r],delete this.levels.glyph[t];for(v=g(this.tool_views,this.model.plot.toolbar.tools,this.view_options()),l=0,i=x.length;l<i;l++)b=x[l],a=b.mget("level"),this.levels[a][b.model.id]=b,b.bind_bokeh_events();for(u=0,s=v.length;u<s;u++)y=v[u],a=y.mget("level"),this.levels[a][y.model.id]=y,y.bind_bokeh_events(),this.ui_event_bus.register_tool(y);return this},e.prototype.bind_bokeh_events=function(){var t,e,r,n;e=this.model.frame.get("x_ranges");for(t in e)n=e[t],this.listenTo(n,"change",this.request_render);r=this.model.frame.get("y_ranges");for(t in r)n=r[t],this.listenTo(n,"change",this.request_render);return this.listenTo(this.model.plot,"change:renderers",this.build_levels),this.listenTo(this.model.plot.toolbar,"change:tools",this.build_levels),this.listenTo(this.model.plot,"change",this.request_render),this.listenTo(this.model.plot,"destroy",function(t){return function(){return t.remove()}}(this)),this.listenTo(this.model.plot.document.solver(),"layout_update",function(t){return function(){return t.request_render()}}(this)),this.listenTo(this.model.plot.document.solver(),"resize",function(t){return function(){return t.resize()}}(this)),this.listenTo(this.canvas,"change:pixel_ratio",function(t){return function(){return t.request_render()}}(this))},e.prototype.set_initial_range=function(){var t,e,r,n,o,i,s;t=!0,i={},r=this.frame.get("x_ranges");for(e in r){if(o=r[e],null==o.get("start")||null==o.get("end")||m.isNaN(o.get("start")+o.get("end"))){t=!1;break}i[e]={start:o.get("start"),end:o.get("end")}}if(t){s={},n=this.frame.get("y_ranges");for(e in n){if(o=n[e],null==o.get("start")||null==o.get("end")||m.isNaN(o.get("start")+o.get("end"))){t=!1;break}s[e]={start:o.get("start"),end:o.get("end")}}}return t?(this._initial_state_info.range=this.initial_range_info={xrs:i,yrs:s},b.debug("initial ranges set")):b.warn("could not set initial ranges")},e.prototype.render=function(t){var e,r,o,i,s,a,l;if(null==t&&(t=!1),b.trace("PlotCanvas.render(force_canvas="+t+") for "+this.model.id),null!=this.model.document){Date.now()-this.interactive_timestamp<this.model.plot.lod_interval?(this.interactive=!0,i=this.model.plot.lod_timeout,setTimeout(function(t){return function(){return t.interactive&&Date.now()-t.interactive_timestamp>i&&(t.interactive=!1),t.request_render()}}(this),i)):this.interactive=!1,a=this.renderer_views;for(o in a)if(l=a[o],null==this.range_update_timestamp||l.set_data_timestamp>this.range_update_timestamp){this.update_dataranges();break}return this.update_constraints(),this.model.get("frame")._update_mappers(),e=this.canvas_view.ctx,e.pixel_ratio=s=this.canvas_view.pixel_ratio,e.save(),e.scale(s,s),e.translate(.5,.5),r=[this.canvas.vx_to_sx(this.frame.get("left")),this.canvas.vy_to_sy(this.frame.get("top")),this.frame.get("width"),this.frame.get("height")],this._map_hook(e,r),this._paint_empty(e,r),this.prepare_webgl(s,r),e.save(),this.visuals.outline_line.doit&&(this.visuals.outline_line.set_value(e),e.strokeRect.apply(e,r)),e.restore(),this._render_levels(e,["image","underlay","glyph"],r),this.blit_webgl(s),this._render_levels(e,["annotation"],r),this._render_levels(e,["overlay","tool"]),null==this.initial_range_info&&this.set_initial_range(),e.restore(),null!=this.model.document._unrendered_plots&&(delete this.model.document._unrendered_plots[this.id],m.isEmpty(this.model.document._unrendered_plots))?(this.model.document._unrendered_plots=null,m.delay(n.proxy(this.model.document.resize,this.model.document),1)):void 0;
}},e.prototype.resize=function(){var t,e,r,n;n=this.model._width._value,e=this.model._height._value,this.canvas_view.set_dims([n,e],!0),this.canvas_view.prepare_canvas();try{this.update_constraints()}catch(t){r=t}return this.$el.css({position:"absolute",left:this.model._dom_left._value,top:this.model._dom_top._value,width:this.model._width._value,height:this.model._height._value})},e.prototype.update_constraints=function(){var t,e,r,n;r=this.model.document.solver(),r.suggest_value(this.frame._width,this.canvas.get("width")-1),r.suggest_value(this.frame._height,this.canvas.get("height")-1),e=this.renderer_views;for(t in e)n=e[t],null!=n.model.panel&&k(n);return r.update_variables(!1)},e.prototype._render_levels=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,g;for(t.save(),null!=r&&(t.beginPath(),t.rect.apply(t,r),t.clip(),t.beginPath()),o={},p=this.model.plot.renderers,n=i=0,a=p.length;i<a;n=++i)_=p[n],o[_.id]=n;for(g=function(t){return o[t.model.id]},s=0,l=e.length;s<l;s++)for(h=e[s],f=m.sortBy(m.values(this.levels[h]),g),c=0,u=f.length;c<u;c++)d=f[c],d.render();return t.restore()},e.prototype._map_hook=function(t,e){},e.prototype._paint_empty=function(t,e){if(t.clearRect(0,0,this.canvas_view.mget("width"),this.canvas_view.mget("height")),this.visuals.border_fill.doit&&(this.visuals.border_fill.set_value(t),t.fillRect(0,0,this.canvas_view.mget("width"),this.canvas_view.mget("height")),t.clearRect.apply(t,e)),this.visuals.background_fill.doit)return this.visuals.background_fill.set_value(t),t.fillRect.apply(t,e)},e}(d.View),p=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return T(e,t),e.prototype.type="PlotCanvas",e.prototype.default_view=_,e.prototype.initialize=function(t,r){var n;return e.__super__.initialize.call(this,t,r),this.canvas=new o.Model({map:null!=(n=this.use_map)&&n,initial_width:this.plot.plot_width,initial_height:this.plot.plot_height,use_hidpi:this.plot.hidpi}),this.frame=new i.Model({x_range:this.plot.x_range,extra_x_ranges:this.plot.extra_x_ranges,x_mapper_type:this.plot.x_mapper_type,y_range:this.plot.y_range,extra_y_ranges:this.plot.extra_y_ranges,y_mapper_type:this.plot.y_mapper_type}),this.above_panel=new h.Model,this.below_panel=new h.Model,this.left_panel=new h.Model,this.right_panel=new h.Model,b.debug("PlotCanvas initialized")},e.prototype.add_renderer_to_canvas_side=function(t,e){if("center"!==e)return t.add_panel(e)},e.prototype._doc_attached=function(){return this.canvas.attach_document(this.document),this.frame.attach_document(this.document),this.above_panel.attach_document(this.document),this.below_panel.attach_document(this.document),this.left_panel.attach_document(this.document),this.right_panel.attach_document(this.document),b.debug("PlotCanvas attached to document")},e.override({sizing_mode:"stretch_both"}),e.internal({plot:[x.Instance],toolbar:[x.Instance],canvas:[x.Instance],frame:[x.Instance]}),e.prototype.get_layoutable_children=function(){var t,e,r,n,o,i,s,a,l;for(t=[this.above_panel,this.below_panel,this.left_panel,this.right_panel,this.canvas,this.frame],a=["above","below","left","right"],e=0,o=a.length;e<o;e++)for(l=a[e],n=this.plot.get(l),r=0,i=n.length;r<i;r++)s=n[r],null!=s.panel&&t.push(s.panel);return t},e.prototype.get_edit_variables=function(){var t,e,r,n,o;for(e=[],o=this.get_layoutable_children(),r=0,n=o.length;r<n;r++)t=o[r],e=e.concat(t.get_edit_variables());return e},e.prototype.get_constraints=function(){var t,r,n,o,i;for(r=e.__super__.get_constraints.call(this),r=r.concat(this._get_constant_constraints()),r=r.concat(this._get_side_constraints()),i=this.get_layoutable_children(),n=0,o=i.length;n<o;n++)t=i[n],r=r.concat(t.get_constraints());return r},e.prototype._get_constant_constraints=function(){var t,e,r,n,o;return o=this.plot.min_border_top,e=this.plot.min_border_bottom,r=this.plot.min_border_left,n=this.plot.min_border_right,t=[],t.push(l(this.above_panel._height,-o)),t.push(l(this.below_panel._height,-e)),t.push(l(this.left_panel._width,-r)),t.push(l(this.right_panel._width,-n)),t.push(a(this.above_panel._top,[-1,this.canvas._top])),t.push(a(this.above_panel._bottom,[-1,this.frame._top])),t.push(a(this.below_panel._bottom,[-1,this.canvas._bottom])),t.push(a(this.below_panel._top,[-1,this.frame._bottom])),t.push(a(this.left_panel._left,[-1,this.canvas._left])),t.push(a(this.left_panel._right,[-1,this.frame._left])),t.push(a(this.right_panel._right,[-1,this.canvas._right])),t.push(a(this.right_panel._left,[-1,this.frame._right])),t.push(a(this.above_panel._height,[-1,this._top])),t.push(a(this.above_panel._height,[-1,this.canvas._top],this.frame._top)),t.push(a(this.below_panel._height,[-1,this._height],this._bottom)),t.push(a(this.below_panel._height,[-1,this.frame._bottom])),t.push(a(this.left_panel._width,[-1,this._left])),t.push(a(this.left_panel._width,[-1,this.frame._left])),t.push(a(this.right_panel._width,[-1,this._width],this._right)),t.push(a(this.right_panel._width,[-1,this.canvas._right],this.frame._right)),t},e.prototype._get_side_constraints=function(){var t,e,r,n,o,i,s,l,u,h;for(t=[],u=["above","below","left","right"],e=0,i=u.length;e<i;e++){for(h=u[e],o=this.plot.get(h),n=this.frame,r=0,s=o.length;r<s;r++)l=o[r],"above"===h&&t.push(a(n.panel._top,[-1,l.panel._bottom])),"below"===h&&t.push(a(n.panel._bottom,[-1,l.panel._top])),"left"===h&&t.push(a(n.panel._left,[-1,l.panel._right])),"right"===h&&t.push(a(n.panel._right,[-1,l.panel._left])),n=l;0!==o.length&&("above"===h&&t.push(a(n.panel._top,[-1,this.above_panel._top])),"below"===h&&t.push(a(n.panel._bottom,[-1,this.below_panel._bottom])),"left"===h&&t.push(a(n.panel._left,[-1,this.left_panel._left])),"right"===h&&t.push(a(n.panel._right,[-1,this.right_panel._right])))}return t},e.prototype.plot_canvas=function(){return this},e}(c.Model),e.exports={Model:p,View:_}},{"../../common/build_views":"common/build_views","../../common/ui_events":"common/ui_events","../../core/enums":"core/enums","../../core/layout/layout_canvas":"core/layout/layout_canvas","../../core/layout/side_panel":"core/layout/side_panel","../../core/layout/solver":"core/layout/solver","../../core/logging":"core/logging","../../core/properties":"core/properties","../../core/util/throttle":"core/util/throttle","../canvas/canvas":"models/canvas/canvas","../canvas/cartesian_frame":"models/canvas/cartesian_frame","../layouts/layout_dom":"models/layouts/layout_dom","../ranges/data_range1d":"models/ranges/data_range1d","../renderers/glyph_renderer":"models/renderers/glyph_renderer","../renderers/renderer":"models/renderers/renderer",jquery:"jquery",underscore:"underscore"}],"models/ranges/data_range":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;i=t("underscore"),o=t("./range"),s=t("../../core/properties"),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.type="DataRange",e.define({names:[s.Array,[]],renderers:[s.Array,[]]}),e}(o.Model),e.exports={Model:n}},{"../../core/properties":"core/properties","./range":"models/ranges/range",underscore:"underscore"}],"models/ranges/data_range1d":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;i=t("underscore"),n=t("./data_range"),a=t("../../core/logging").logger,l=t("../../core/properties"),s=t("../../core/util/bbox"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.type="DataRange1d",e.define({start:[l.Number],end:[l.Number],range_padding:[l.Number,.1],flipped:[l.Bool,!1],follow:[l.String],follow_interval:[l.Number],default_span:[l.Number,2],bounds:[l.Any],min_interval:[l.Any],max_interval:[l.Any]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.define_computed_property("min",function(){return Math.min(this.get("start"),this.get("end"))},!0),this.add_dependencies("min",this,["start","end"]),this.define_computed_property("max",function(){return Math.max(this.get("start"),this.get("end"))},!0),this.add_dependencies("max",this,["start","end"]),this.plot_bounds={},this.have_updated_interactively=!1,this._initial_start=this.get("start"),this._initial_end=this.get("end"),this._initial_range_padding=this.get("range_padding"),this._initial_follow=this.get("follow"),this._initial_follow_interval=this.get("follow_interval"),this._initial_default_span=this.get("default_span")},e.prototype.computed_renderers=function(){var t,e,r,n,o,i,s,l,u,h,c;if(i=this.get("names"),h=this.get("renderers"),0===h.length)for(u=this.get("plots"),e=0,n=u.length;e<n;e++)s=u[e],t=s.get("renderers"),c=function(){var e,r,n;for(n=[],e=0,r=t.length;e<r;e++)l=t[e],"GlyphRenderer"===l.type&&n.push(l);return n}(),h=h.concat(c);for(i.length>0&&(h=function(){var t,e,r;for(r=[],t=0,e=h.length;t<e;t++)l=h[t],i.indexOf(l.get("name"))>=0&&r.push(l);return r}()),a.debug("computed "+h.length+" renderers for DataRange1d "+this.id),r=0,o=h.length;r<o;r++)l=h[r],a.trace(" - "+l.type+" "+l.id);return h},e.prototype._compute_plot_bounds=function(t,e){var r,n,o,i;for(i=s.empty(),r=0,n=t.length;r<n;r++)o=t[r],null!=e[o.id]&&(i=s.union(i,e[o.id]));return i},e.prototype._compute_min_max=function(t,e){var r,n,o,i,a,l,u;i=s.empty();for(r in t)u=t[r],i=s.union(i,u);return 0===e?(a=[i.minX,i.maxX],o=a[0],n=a[1]):(l=[i.minY,i.maxY],o=l[0],n=l[1]),[o,n]},e.prototype._compute_range=function(t,e){var r,n,o,i,s,a,l,u,h,c;return s=this.get("range_padding"),null!=s&&s>0?(h=e===t?this.get("default_span"):(e-t)*(1+s),r=(e+t)/2,a=[r-h/2,r+h/2],c=a[0],n=a[1]):(l=[t,e],c=l[0],n=l[1]),i=1,this.get("flipped")&&(u=[n,c],c=u[0],n=u[1],i=-1),o=this.get("follow_interval"),null!=o&&Math.abs(c-n)>o&&("start"===this.get("follow")?n=c+i*o:"end"===this.get("follow")&&(c=n-i*o)),[c,n]},e.prototype.update=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_;if(!this.have_updated_interactively)return p=this.computed_renderers(),this.plot_bounds[r]=this._compute_plot_bounds(p,t),u=this._compute_min_max(this.plot_bounds,e),a=u[0],s=u[1],h=this._compute_range(a,s),_=h[0],i=h[1],null!=this._initial_start&&(_=this._initial_start),null!=this._initial_end&&(i=this._initial_end),c=[this.get("start"),this.get("end")],o=c[0],n=c[1],_===o&&i===n||(l={},_!==o&&(l.start=_),i!==n&&(l.end=i),this.set(l)),"auto"===this.get("bounds")?this.set("bounds",[_,i]):void 0},e.prototype.reset=function(){return this.have_updated_interactively=!1,this.set({range_padding:this._initial_range_padding,follow:this._initial_follow,follow_interval:this._initial_follow_interval,default_span:this._initial_default_span})},e}(n.Model),e.exports={Model:o}},{"../../core/logging":"core/logging","../../core/properties":"core/properties","../../core/util/bbox":"core/util/bbox","./data_range":"models/ranges/data_range",underscore:"underscore"}],"models/ranges/factor_range":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;i=t("underscore"),o=t("./range"),s=t("../../core/properties"),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.type="FactorRange",e.define({offset:[s.Number,0],factors:[s.Array,[]],bounds:[s.Any],min_interval:[s.Any],max_interval:[s.Any]}),e.internal({_bounds_as_factors:[s.Any],start:[s.Number],end:[s.Number]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),null!=this.get("bounds")&&"auto"!==this.get("bounds")?this.set("_bounds_as_factors",this.get("bounds")):this.set("_bounds_as_factors",this.get("factors")),this._init(),this.define_computed_property("min",function(){return this.get("start")},!1),this.add_dependencies("min",this,["factors","offset"]),this.define_computed_property("max",function(){return this.get("end")},!1),this.add_dependencies("max",this,["factors","offset"]),this.listenTo(this,"change:factors",this._update_factors),this.listenTo(this,"change:offset",this._init)},e.prototype.reset=function(){return this._init()},e.prototype._update_factors=function(){return this.set("_bounds_as_factors",this.get("factors")),this._init()},e.prototype._init=function(){var t,e,r;if(e=this.get("factors"),null!=this.get("bounds")&&"auto"!==this.get("bounds")&&(e=this.get("_bounds_as_factors"),this.set("factors",e)),r=.5+this.get("offset"),t=e.length+r,this.set("start",r),this.set("end",t),null!=this.get("bounds"))return this.set("bounds",[r,t])},e}(o.Model),e.exports={Model:n}},{"../../core/properties":"core/properties","./range":"models/ranges/range",underscore:"underscore"}],"models/ranges/range":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new 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a(e,t),e.prototype.type="Range1d",e.define({start:[s.Number,0],end:[s.Number,1],bounds:[s.Any],min_interval:[s.Any],max_interval:[s.Any]}),e.prototype._set_auto_bounds=function(){var t,e;if("auto"===this.get("bounds"))return e=Math.min(this._initial_start,this._initial_end),t=Math.max(this._initial_start,this._initial_end),this.set("bounds",[e,t])},e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.define_computed_property("min",function(){return Math.min(this.get("start"),this.get("end"))},!0),this.add_dependencies("min",this,["start","end"]),this.define_computed_property("max",function(){return Math.max(this.get("start"),this.get("end"))},!0),this.add_dependencies("max",this,["start","end"]),this._initial_start=this.get("start"),this._initial_end=this.get("end"),this._set_auto_bounds()},e.prototype.reset=function(){return this.set({start:this._initial_start,end:this._initial_end}),this._set_auto_bounds()},e}(n.Model),e.exports={Model:o}},{"../../core/properties":"core/properties","./range":"models/ranges/range",underscore:"underscore"}],"models/renderers/glyph_renderer":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;a=t("underscore"),s=t("./renderer"),i=t("../sources/remote_data_source"),l=t("../../core/logging").logger,u=t("../../core/properties"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.initialize=function(t){var r,n,o,s,l,u,h,c,p;if(e.__super__.initialize.call(this,t),r=this.mget("glyph"),s=a.contains(r.mixins,"fill"),l=a.contains(r.mixins,"line"),o=a.omit(a.clone(r.attributes),"id"),h=function(t){var e;return e=a.clone(o),s&&a.extend(e,t.fill),l&&a.extend(e,t.line),new r.constructor(e)},this.glyph=this.build_glyph_view(r),p=this.mget("selection_glyph"),null==p&&(p=h(this.model.selection_defaults)),this.selection_glyph=this.build_glyph_view(p),c=this.mget("nonselection_glyph"),null==c&&(c=h(this.model.nonselection_defaults)),this.nonselection_glyph=this.build_glyph_view(c),u=this.mget("hover_glyph"),null!=u&&(this.hover_glyph=this.build_glyph_view(u)),n=h(this.model.decimated_defaults),this.decimated_glyph=this.build_glyph_view(n),this.xmapper=this.plot_view.frame.get("x_mappers")[this.mget("x_range_name")],this.ymapper=this.plot_view.frame.get("y_mappers")[this.mget("y_range_name")],this.set_data(!1),this.mget("data_source")instanceof i.Model)return this.mget("data_source").setup(this.plot_view,this.glyph)},e.prototype.build_glyph_view=function(t){return new t.default_view({model:t,renderer:this,plot_view:this.plot_view,plot_model:this.plot_model})},e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,"change",this.request_render),this.listenTo(this.mget("data_source"),"change",this.set_data),this.listenTo(this.mget("data_source"),"patch",this.set_data),this.listenTo(this.mget("data_source"),"stream",this.set_data),this.listenTo(this.mget("data_source"),"select",this.request_render),null!=this.hover_glyph&&this.listenTo(this.mget("data_source"),"inspect",this.request_render),this.listenTo(this.mget("glyph"),"propchange",function(){return this.glyph.set_visuals(this.mget("data_source")),this.request_render()})},e.prototype.have_selection_glyphs=function(){return null!=this.selection_glyph&&null!=this.nonselection_glyph},e.prototype.set_data=function(t,e){var r,n,o,i,s,a,u,h,c;for(null==t&&(t=!0),c=Date.now(),h=this.mget("data_source"),this.glyph.model.set({x_range_name:this.mget("x_range_name"),y_range_name:this.mget("y_range_name")},{silent:!0}),this.glyph.set_data(h,e),this.glyph.set_visuals(h),this.decimated_glyph.set_visuals(h),this.have_selection_glyphs()&&(this.selection_glyph.set_visuals(h),this.nonselection_glyph.set_visuals(h)),null!=this.hover_glyph&&this.hover_glyph.set_visuals(h),i=h.get_length(),null==i&&(i=1),this.all_indices=function(){u=[];for(var t=0;0<=i?t<i:t>i;0<=i?t++:t--)u.push(t);return u}.apply(this),s=this.plot_model.plot.lod_factor,this.decimated=[],n=o=0,a=Math.floor(this.all_indices.length/s);0<=a?o<a:o>a;n=0<=a?++o:--o)this.decimated.push(this.all_indices[n*s]);if(r=Date.now()-c,l.debug(this.glyph.model.type+" GlyphRenderer ("+this.model.id+"): set_data finished in "+r+"ms"),this.set_data_timestamp=Date.now(),t)return this.request_render()},e.prototype.render=function(){var t,e,r,n,o,i,s,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S;if(this.model.visible!==!1){if(M=Date.now(),s=this.glyph.glglyph,k=Date.now(),this.glyph.map_data(),e=Date.now()-M,j=Date.now(),c=s?this.all_indices:this.glyph._mask_data(this.all_indices),r=Date.now()-j,t=this.plot_view.canvas_view.ctx,t.save(),b=this.mget("data_source").get("selected"),b=b&&0!==b.length?b["0d"].glyph?c:b["1d"].indices.length>0?b["1d"].indices:[]:[],p=this.mget("data_source").get("inspected"),p=p&&0!==p.length?p["0d"].glyph?c:p["1d"].indices.length>0?p["1d"].indices:[]:[],g=this.plot_model.plot.lod_threshold,this.plot_view.interactive&&!s&&null!=g&&this.all_indices.length>g?(c=this.decimated,u=this.decimated_glyph,v=this.decimated_glyph,w=this.selection_glyph):(u=this.glyph,v=this.nonselection_glyph,w=this.selection_glyph),null!=this.hover_glyph&&p.length&&(c=a.without.bind(null,c).apply(null,p)),b.length&&this.have_selection_glyphs()){for(S=Date.now(),x={},_=0,f=b.length;_<f;_++)h=b[_],x[h]=!0;for(b=new Array,y=new 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n,o,i,s=function(t,e){function r(){this.constructor=t}for(var n in e)a.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},a={}.hasOwnProperty;i=t("underscore"),n=t("../../model"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return s(e,t),e.prototype.type="Ticker",e.prototype.get_ticks=function(t,e,r,n){var o;return o=n.desired_n_ticks,this.get_ticks_no_defaults(t,e,this.get("desired_num_ticks"))},e.prototype.get_ticks_no_defaults=function(t,e,r){var n,o,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w;if(l=this.get_interval(t,e,r),v=Math.floor(t/l),n=Math.ceil(e/l),s=i.isNaN(v)||i.isNaN(n)?[]:i.range(v,n+1),x=function(){var t,e,r;for(r=[],t=0,e=s.length;t<e;t++)o=s[t],r.push(o*l);return r}(),y=this.get("num_minor_ticks"),g=[],y>1){for(f=l/y,m=function(){var t,e,r;for(r=[],a=t=1,e=y;1<=e?t<=e:t>=e;a=1<=e?++t:--t)r.push(a*f);return r}(),u=0,p=m.length;u<p;u++)w=m[u],g.push(x[0]-w);for(h=0,_=x.length;h<_;h++)for(b=x[h],c=0,d=m.length;c<d;c++)w=m[c],g.push(b+w)}return{major:x,minor:g}},e}(n),e.exports={Model:o}},{"../../model":"model",underscore:"underscore"}],"models/tickers/util":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d;h=t("underscore"),i=1,l=1e3,s=60*l,o=60*s,n=24*o,a=30*n,u=365*n,c=function(t){var e;return e=h.min(h.range(t.length),function(e){return t[e]})},p=function(t){return new Date(t.getTime())},_=function(t){return t=p(t),t.setUTCDate(1),t.setUTCHours(0),t.setUTCMinutes(0),t.setUTCSeconds(0),t.setUTCMilliseconds(0),t},d=function(t){return t=_(t),t.setUTCMonth(0),t},e.exports={argmin:c,copy_date:p,last_month_no_later_than:_,last_year_no_later_than:d,ONE_MILLI:i,ONE_SECOND:l,ONE_MINUTE:s,ONE_HOUR:o,ONE_DAY:n,ONE_MONTH:a,ONE_YEAR:u}},{underscore:"underscore"}],"models/tickers/years_ticker":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;a=t("underscore"),n=t("./basic_ticker"),i=t("./single_interval_ticker"),u=t("./util"),l=u.last_year_no_later_than,o=u.ONE_YEAR,s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.type="YearsTicker",e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.set("interval",o),this.basic_ticker=new n.Model({num_minor_ticks:0})},e.prototype.get_ticks_no_defaults=function(t,e,r){var n,o,i,s,u,h;return i=l(new Date(t)).getUTCFullYear(),o=l(new Date(e)).getUTCFullYear(),h=this.basic_ticker.get_ticks_no_defaults(i,o,r).major,n=function(){var t,e,r;for(r=[],t=0,e=h.length;t<e;t++)u=h[t],r.push(Date.UTC(u,0,1));return r}(),s=a.filter(n,function(r){return t<=r&&r<=e}),{major:s,minor:[]}},e}(i.Model),e.exports={Model:s}},{"./basic_ticker":"models/tickers/basic_ticker","./single_interval_ticker":"models/tickers/single_interval_ticker","./util":"models/tickers/util",underscore:"underscore"}],"models/tiles/bbox_tile_source":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;i=t("underscore"),o=t("./mercator_tile_source"),s=t("../../core/properties"),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.type="BBoxTileSource",e.define({use_latlon:[s.Bool,!1]}),e.prototype.get_image_url=function(t,e,r){var n,o,i,s,a,l,u;return n=this.string_lookup_replace(this.get("url"),this.get("extra_url_vars")),this.get("use_latlon")?(o=this.get_tile_geographic_bounds(t,e,r),a=o[0],u=o[1],s=o[2],l=o[3]):(i=this.get_tile_meter_bounds(t,e,r),a=i[0],u=i[1],s=i[2],l=i[3]),n.replace("{XMIN}",a).replace("{YMIN}",u).replace("{XMAX}",s).replace("{YMAX}",l)},e}(o),e.exports={Model:n}},{"../../core/properties":"core/properties","./mercator_tile_source":"models/tiles/mercator_tile_source",underscore:"underscore"}],"models/tiles/dynamic_image_renderer":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){return function(){return t.apply(e,arguments)}},c=function(t,e){function r(){this.constructor=t}for(var n in e)p.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},p={}.hasOwnProperty;a=t("underscore"),i=t("./image_pool"),s=t("../renderers/renderer"),l=t("../../core/logging").logger,u=t("../../core/properties"),o=function(t){function e(){return this._on_image_error=h(this._on_image_error,this),this._on_image_load=h(this._on_image_load,this),e.__super__.constructor.apply(this,arguments)}return c(e,t),e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,"change",this.request_render)},e.prototype.get_extent=function(){return[this.x_range.get("start"),this.y_range.get("start"),this.x_range.get("end"),this.y_range.get("end")]},e.prototype._set_data=function(){return this.map_plot=this.plot_view.model.plot,this.map_canvas=this.plot_view.canvas_view.ctx,this.map_frame=this.plot_view.frame,this.x_range=this.map_plot.get("x_range"),this.x_mapper=this.map_frame.get("x_mappers")["default"],this.y_range=this.map_plot.get("y_range"),this.y_mapper=this.map_frame.get("y_mappers")["default"],this.lastImage=void 0,this.extent=this.get_extent()},e.prototype._map_data=function(){return this.initial_extent=this.get_extent()},e.prototype._on_image_load=function(t){var e;if(e=t.target.image_data,e.img=t.target,e.loaded=!0,this.lastImage=e,this.get_extent().join(":")===e.cache_key)return this.request_render()},e.prototype._on_image_error=function(t){var e;return l.error("Error loading image: #{e.target.src}"),e=t.target.image_data,this.mget("image_source").remove_image(e)},e.prototype._create_image=function(t){var e;return e=new Image,e.onload=this._on_image_load,e.onerror=this._on_image_error,e.alt="",e.image_data={bounds:t,loaded:!1,cache_key:t.join(":")},this.mget("image_source").add_image(e.image_data),e.src=this.mget("image_source").get_image_url(t[0],t[1],t[2],t[3],Math.ceil(this.map_frame.get("height")),Math.ceil(this.map_frame.get("width"))),e},e.prototype.render=function(t,e,r){var n,o;return null==this.map_initialized&&(this._set_data(),this._map_data(),this.map_initialized=!0),n=this.get_extent(),this.render_timer&&clearTimeout(this.render_timer),o=this.mget("image_source").images[n.join(":")],null!=o&&o.loaded?void this._draw_image(n.join(":")):(null!=this.lastImage&&this._draw_image(this.lastImage.cache_key),null==o?this.render_timer=setTimeout(function(t){return function(){return t._create_image(n)}}(this),125):void 0)},e.prototype._draw_image=function(t){var e,r,n,o,i,s,a,l,u,h,c;if(e=this.mget("image_source").images[t],null!=e)return this.map_canvas.save(),this._set_rect(),this.map_canvas.globalAlpha=this.mget("alpha"),r=this.plot_view.frame.map_to_screen([e.bounds[0]],[e.bounds[3]],this.plot_view.canvas),l=r[0],c=r[1],n=this.plot_view.frame.map_to_screen([e.bounds[2]],[e.bounds[1]],this.plot_view.canvas),a=n[0],h=n[1],l=l[0],c=c[0],a=a[0],h=h[0],i=a-l,o=h-c,s=l,u=c,this.map_canvas.drawImage(e.img,s,u,i,o),this.map_canvas.restore()},e.prototype._set_rect=function(){var t,e,r,n,o;return r=this.plot_model.plot.properties.outline_line_width.value(),e=this.plot_view.canvas.vx_to_sx(this.map_frame.get("left"))+r/2,n=this.plot_view.canvas.vy_to_sy(this.map_frame.get("top"))+r/2,o=this.map_frame.get("width")-r,t=this.map_frame.get("height")-r,this.map_canvas.rect(e,n,o,t),this.map_canvas.clip()},e}(s.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return c(e,t),e.prototype.default_view=o,e.prototype.type="DynamicImageRenderer",e.define({alpha:[u.Number,1],image_source:[u.Instance],render_parents:[u.Bool,!0]}),e.override({level:"underlay"}),e}(s.Model),e.exports={Model:n,View:o}},{"../../core/logging":"core/logging","../../core/properties":"core/properties","../renderers/renderer":"models/renderers/renderer","./image_pool":"models/tiles/image_pool",underscore:"underscore"}],"models/tiles/image_pool":[function(t,e,r){var n;n=function(){function t(){this.images=[]}return t.prototype.pop=function(){var t;return t=this.images.pop(),null!=t?t:new Image},t.prototype.push=function(t){if(!(this.images.length>50))return t.constructor===Array?Array.prototype.push.apply(this.images,t):this.images.push(t)},t}(),e.exports=n},{}],"models/tiles/image_source":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;i=t("underscore"),s=t("../../core/logging").logger,a=t("../../core/properties"),o=t("../../model"),n=function(t){function e(t){null==t&&(t={}),e.__super__.constructor.apply(this,arguments),this.images={},this.normalize_case()}return l(e,t),e.prototype.type="ImageSource",e.define({url:[a.String,""],extra_url_vars:[a.Any,{}]}),e.prototype.normalize_case=function(){"Note: should probably be refactored into subclasses.";var t;return t=this.get("url"),t=t.replace("{xmin}","{XMIN}"),t=t.replace("{ymin}","{YMIN}"),t=t.replace("{xmax}","{XMAX}"),t=t.replace("{ymax}","{YMAX}"),t=t.replace("{height}","{HEIGHT}"),t=t.replace("{width}","{WIDTH}"),this.set("url",t)},e.prototype.string_lookup_replace=function(t,e){var r,n,o;n=t;for(r in e)o=e[r],n=n.replace("{"+r+"}",o.toString());return n},e.prototype.add_image=function(t){return this.images[t.cache_key]=t},e.prototype.remove_image=function(t){return delete this.images[t.cache_key]},e.prototype.get_image_url=function(t,e,r,n,o,i){var s;return s=this.string_lookup_replace(this.get("url"),this.get("extra_url_vars")),
s.replace("{XMIN}",t).replace("{YMIN}",e).replace("{XMAX}",r).replace("{YMAX}",n).replace("{WIDTH}",i).replace("{HEIGHT}",o)},e}(o),e.exports={Model:n}},{"../../core/logging":"core/logging","../../core/properties":"core/properties","../../model":"model",underscore:"underscore"}],"models/tiles/mercator_tile_source":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;i=t("underscore"),o=t("./tile_source"),s=t("../../core/properties"),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.type="MercatorTileSource",e.define({wrap_around:[s.Bool,!0]}),e.override({x_origin_offset:20037508.34,y_origin_offset:20037508.34,initial_resolution:156543.03392804097}),e.prototype.initialize=function(t){var r;return e.__super__.initialize.call(this,t),this._resolutions=function(){var t,e;for(e=[],r=t=0;t<=30;r=++t)e.push(this.get_resolution(r));return e}.call(this)},e.prototype._computed_initial_resolution=function(){return null!=this.get("initial_resolution")?this.get("initial_resolution"):2*Math.PI*6378137/this.get("tile_size")},e.prototype.is_valid_tile=function(t,e,r){return!(!this.get("wrap_around")&&(t<0||t>=Math.pow(2,r)))&&!(e<0||e>=Math.pow(2,r))},e.prototype.retain_children=function(t){var e,r,n,o,i,s,a;o=t.quadkey,n=o.length,r=n+3,i=this.tiles,s=[];for(e in i)a=i[e],0===a.quadkey.indexOf(o)&&a.quadkey.length>n&&a.quadkey.length<=r?s.push(a.retain=!0):s.push(void 0);return s},e.prototype.retain_neighbors=function(t){var e,r,n,o,s,a,l,u,h,c,p,_,d;r=4,s=t.tile_coords,h=s[0],c=s[1],p=s[2],n=function(){var t,e,n,o;for(o=[],_=t=e=h-r,n=h+r;e<=n?t<=n:t>=n;_=e<=n?++t:--t)o.push(_);return o}(),o=function(){var t,e,n,o;for(o=[],d=t=e=c-r,n=c+r;e<=n?t<=n:t>=n;d=e<=n?++t:--t)o.push(d);return o}(),a=this.tiles,l=[];for(e in a)u=a[e],u.tile_coords[2]===p&&i.contains(n,u.tile_coords[0])&&i.contains(o,u.tile_coords[1])?l.push(u.retain=!0):l.push(void 0);return l},e.prototype.retain_parents=function(t){var e,r,n,o,i;r=t.quadkey,n=this.tiles,o=[];for(e in n)i=n[e],o.push(i.retain=0===r.indexOf(i.quadkey));return o},e.prototype.children_by_tile_xyz=function(t,e,r){var n,o,i,s,a,l,u,h,c;for(c=this.calculate_world_x_by_tile_xyz(t,e,r),0!==c&&(l=this.normalize_xyz(t,e,r),t=l[0],e=l[1],r=l[2]),a=this.tile_xyz_to_quadkey(t,e,r),o=[],i=s=0;s<=3;i=s+=1)u=this.quadkey_to_tile_xyz(a+i.toString()),t=u[0],e=u[1],r=u[2],0!==c&&(h=this.denormalize_xyz(t,e,r,c),t=h[0],e=h[1],r=h[2]),n=this.get_tile_meter_bounds(t,e,r),null!=n&&o.push([t,e,r,n]);return o},e.prototype.parent_by_tile_xyz=function(t,e,r){var n,o;return o=this.tile_xyz_to_quadkey(t,e,r),n=o.substring(0,o.length-1),this.quadkey_to_tile_xyz(n)},e.prototype.get_resolution=function(t){return this._computed_initial_resolution()/Math.pow(2,t)},e.prototype.get_resolution_by_extent=function(t,e,r){var n,o;return n=(t[2]-t[0])/r,o=(t[3]-t[1])/e,[n,o]},e.prototype.get_level_by_extent=function(t,e,r){var n,o,i,s,a,l,u,h;for(u=(t[2]-t[0])/r,h=(t[3]-t[1])/e,l=Math.max(u,h),n=0,a=this._resolutions,o=0,i=a.length;o<i;o++){if(s=a[o],l>s){if(0===n)return 0;if(n>0)return n-1}n+=1}},e.prototype.get_closest_level_by_extent=function(t,e,r){var n,o,i,s,a;return s=(t[2]-t[0])/r,a=(t[3]-t[1])/e,o=Math.max(s,a),i=this._resolutions,n=this._resolutions.reduce(function(t,e){return Math.abs(e-o)<Math.abs(t-o)?e:t}),this._resolutions.indexOf(n)},e.prototype.snap_to_zoom=function(t,e,r,n){var o,i,s,a,l,u,h,c,p;return o=this._resolutions[n],i=r*o,s=e*o,u=t[0],p=t[1],l=t[2],c=t[3],a=(i-(l-u))/2,h=(s-(c-p))/2,[u-a,p-h,l+a,c+h]},e.prototype.tms_to_wmts=function(t,e,r){"Note this works both ways";return[t,Math.pow(2,r)-1-e,r]},e.prototype.wmts_to_tms=function(t,e,r){"Note this works both ways";return[t,Math.pow(2,r)-1-e,r]},e.prototype.pixels_to_meters=function(t,e,r){var n,o,i;return i=this.get_resolution(r),n=t*i-this.get("x_origin_offset"),o=e*i-this.get("y_origin_offset"),[n,o]},e.prototype.meters_to_pixels=function(t,e,r){var n,o,i;return i=this.get_resolution(r),n=(t+this.get("x_origin_offset"))/i,o=(e+this.get("y_origin_offset"))/i,[n,o]},e.prototype.pixels_to_tile=function(t,e){var r,n;return r=Math.ceil(t/parseFloat(this.get("tile_size"))),r=0===r?r:r-1,n=Math.max(Math.ceil(e/parseFloat(this.get("tile_size")))-1,0),[r,n]},e.prototype.pixels_to_raster=function(t,e,r){var n;return n=this.get("tile_size")<<r,[t,n-e]},e.prototype.meters_to_tile=function(t,e,r){var n,o,i;return i=this.meters_to_pixels(t,e,r),n=i[0],o=i[1],this.pixels_to_tile(n,o)},e.prototype.get_tile_meter_bounds=function(t,e,r){var n,o,i,s,a,l;return n=this.pixels_to_meters(t*this.get("tile_size"),e*this.get("tile_size"),r),s=n[0],l=n[1],o=this.pixels_to_meters((t+1)*this.get("tile_size"),(e+1)*this.get("tile_size"),r),i=o[0],a=o[1],null!=s&&null!=l&&null!=i&&null!=a?[s,l,i,a]:void 0},e.prototype.get_tile_geographic_bounds=function(t,e,r){var n,o,i,s,a,l;return n=this.get_tile_meter_bounds(t,e,r),l=this.utils.meters_extent_to_geographic(n),a=l[0],s=l[1],i=l[2],o=l[3],[a,s,i,o]},e.prototype.get_tiles_by_extent=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x;for(null==r&&(r=1),v=t[0],x=t[1],y=t[2],b=t[3],i=this.meters_to_tile(v,x,e),d=i[0],g=i[1],s=this.meters_to_tile(y,b,e),_=s[0],m=s[1],d-=r,g-=r,_+=r,m+=r,c=[],f=n=a=m,l=g;n>=l;f=n+=-1)for(p=o=u=d,h=_;o<=h;p=o+=1)this.is_valid_tile(p,f,e)&&c.push([p,f,e,this.get_tile_meter_bounds(p,f,e)]);return c=this.sort_tiles_from_center(c,[d,g,_,m])},e.prototype.quadkey_to_tile_xyz=function(t){"Computes tile x, y and z values based on quadKey.";var e,r,n,o,i,s,a,l;for(i=0,s=0,a=t.length,e=r=o=a;r>0;e=r+=-1)if(l=t.charAt(a-e),n=1<<e-1,"0"!==l)if("1"===l)i|=n;else if("2"===l)s|=n;else{if("3"!==l)throw new TypeError("Invalid Quadkey: "+t);i|=n,s|=n}return[i,s,a]},e.prototype.tile_xyz_to_quadkey=function(t,e,r){"Computes quadkey value based on tile x, y and z values.";var n,o,i,s,a,l;for(a="",o=i=l=r;i>0;o=i+=-1)n=0,s=1<<o-1,0!==(t&s)&&(n+=1),0!==(e&s)&&(n+=2),a+=n.toString();return a},e.prototype.children_by_tile_xyz=function(t,e,r){var n,o,i,s,a,l;for(a=this.tile_xyz_to_quadkey(t,e,r),o=[],i=s=0;s<=3;i=s+=1)l=this.quadkey_to_tile_xyz(a+i.toString()),t=l[0],e=l[1],r=l[2],n=this.get_tile_meter_bounds(t,e,r),null!=n&&o.push([t,e,r,n]);return o},e.prototype.parent_by_tile_xyz=function(t,e,r){var n,o;return o=this.tile_xyz_to_quadkey(t,e,r),n=o.substring(0,o.length-1),this.quadkey_to_tile_xyz(n)},e.prototype.get_closest_parent_by_tile_xyz=function(t,e,r){var n,o,i,s,a;for(a=this.calculate_world_x_by_tile_xyz(t,e,r),o=this.normalize_xyz(t,e,r),t=o[0],e=o[1],r=o[2],n=this.tile_xyz_to_quadkey(t,e,r);n.length>0;)if(n=n.substring(0,n.length-1),i=this.quadkey_to_tile_xyz(n),t=i[0],e=i[1],r=i[2],s=this.denormalize_xyz(t,e,r,a),t=s[0],e=s[1],r=s[2],this.tile_xyz_to_key(t,e,r)in this.tiles)return[t,e,r];return[0,0,0]},e.prototype.normalize_xyz=function(t,e,r){var n;return this.get("wrap_around")?(n=Math.pow(2,r),[(t%n+n)%n,e,r]):[t,e,r]},e.prototype.denormalize_xyz=function(t,e,r,n){return[t+n*Math.pow(2,r),e,r]},e.prototype.denormalize_meters=function(t,e,r,n){return[t+2*n*Math.PI*6378137,e]},e.prototype.calculate_world_x_by_tile_xyz=function(t,e,r){return Math.floor(t/Math.pow(2,r))},e}(o),e.exports=n},{"../../core/properties":"core/properties","./tile_source":"models/tiles/tile_source",underscore:"underscore"}],"models/tiles/quadkey_tile_source":[function(t,e,r){var n,o,i=function(t,e){function r(){this.constructor=t}for(var n in e)s.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},s={}.hasOwnProperty;n=t("./mercator_tile_source"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return i(e,t),e.prototype.type="QUADKEYTileSource",e.prototype.get_image_url=function(t,e,r){var n,o,i;return n=this.string_lookup_replace(this.get("url"),this.get("extra_url_vars")),i=this.tms_to_wmts(t,e,r),t=i[0],e=i[1],r=i[2],o=this.tile_xyz_to_quadkey(t,e,r),n.replace("{Q}",o)},e}(n),e.exports={Model:o}},{"./mercator_tile_source":"models/tiles/mercator_tile_source"}],"models/tiles/tile_renderer":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p=function(t,e){return function(){return t.apply(e,arguments)}},_=function(t,e){function r(){this.constructor=t}for(var n in e)d.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},d={}.hasOwnProperty,f=[].indexOf||function(t){for(var e=0,r=this.length;e<r;e++)if(e in this&&this[e]===t)return e;return-1};l=t("underscore"),n=t("jquery"),o=t("./image_pool"),c=t("./wmts_tile_source"),i=t("../renderers/renderer"),u=t("../../core/logging").logger,h=t("../../core/properties"),a=function(t){function e(){return this._update=p(this._update,this),this._prefetch_tiles=p(this._prefetch_tiles,this),this._on_tile_error=p(this._on_tile_error,this),this._on_tile_cache_load=p(this._on_tile_cache_load,this),this._on_tile_load=p(this._on_tile_load,this),this._add_attribution=p(this._add_attribution,this),e.__super__.constructor.apply(this,arguments)}return _(e,t),e.prototype.initialize=function(t){return this.attributionEl=null,e.__super__.initialize.apply(this,arguments)},e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,"change",this.request_render)},e.prototype.get_extent=function(){return[this.x_range.get("start"),this.y_range.get("start"),this.x_range.get("end"),this.y_range.get("end")]},e.prototype._set_data=function(){return this.pool=new o,this.map_plot=this.plot_model.plot,this.map_canvas=this.plot_view.canvas_view.ctx,this.map_frame=this.plot_model.frame,this.x_range=this.map_plot.get("x_range"),this.x_mapper=this.map_frame.get("x_mappers")["default"],this.y_range=this.map_plot.get("y_range"),this.y_mapper=this.map_frame.get("y_mappers")["default"],this.extent=this.get_extent(),this._last_height=void 0,this._last_width=void 0},e.prototype._add_attribution=function(){var t,e,r,o,i,s;if(t=this.mget("tile_source").get("attribution"),l.isString(t)&&t.length>0)return null!=this.attributionEl?this.attributionEl.html(t):(e=this.map_plot.get("outline_line_width"),r=this.map_plot.get("min_border_bottom")+e,s=this.map_frame.get("right")-this.map_frame.get("width"),o=this.map_frame.get("width")-e,this.attributionEl=n("<div>").html(t).addClass("bk-tile-attribution").css({position:"absolute",bottom:r+"px",right:s+"px","max-width":o+"px","background-color":"rgba(255,255,255,0.8)","font-size":"9pt","font-family":"sans-serif"}),i=this.plot_view.$el.find("div.bk-canvas-events"),this.attributionEl.appendTo(i))},e.prototype._map_data=function(){var t,e;return this.initial_extent=this.get_extent(),e=this.mget("tile_source").get_level_by_extent(this.initial_extent,this.map_frame.get("height"),this.map_frame.get("width")),t=this.mget("tile_source").snap_to_zoom(this.initial_extent,this.map_frame.get("height"),this.map_frame.get("width"),e),this.x_range.set("start",t[0]),this.y_range.set("start",t[1]),this.x_range.set("end",t[2]),this.y_range.set("end",t[3]),this._add_attribution()},e.prototype._on_tile_load=function(t){var e;return e=t.target.tile_data,e.img=t.target,e.current=!0,e.loaded=!0,this.request_render()},e.prototype._on_tile_cache_load=function(t){var e;return e=t.target.tile_data,e.img=t.target,e.loaded=!0},e.prototype._on_tile_error=function(t){return""},e.prototype._create_tile=function(t,e,r,n,o){var i,s,a;return null==o&&(o=!1),i=this.mget("tile_source").normalize_xyz(t,e,r),a=this.pool.pop(),o?a.onload=this._on_tile_cache_load:a.onload=this._on_tile_load,a.onerror=this._on_tile_error,a.alt="",a.tile_data={tile_coords:[t,e,r],normalized_coords:i,quadkey:this.mget("tile_source").tile_xyz_to_quadkey(t,e,r),cache_key:this.mget("tile_source").tile_xyz_to_key(t,e,r),bounds:n,loaded:!1,x_coord:n[0],y_coord:n[3]},this.mget("tile_source").tiles[a.tile_data.cache_key]=a.tile_data,a.src=(s=this.mget("tile_source")).get_image_url.apply(s,i),a},e.prototype._enforce_aspect_ratio=function(){var t,e,r;return(this._last_height!==this.map_frame.get("height")||this._last_width!==this.map_frame.get("width"))&&(t=this.get_extent(),r=this.mget("tile_source").get_level_by_extent(t,this.map_frame.get("height"),this.map_frame.get("width")),e=this.mget("tile_source").snap_to_zoom(t,this.map_frame.get("height"),this.map_frame.get("width"),r),this.x_range.set({start:e[0],end:e[2]}),this.y_range.set({start:e[1],end:e[3]}),this.extent=e,this._last_height=this.map_frame.get("height"),this._last_width=this.map_frame.get("width"),!0)},e.prototype.render=function(t,e,r){if(null==this.map_initialized&&(this._set_data(),this._map_data(),this.map_initialized=!0),!this._enforce_aspect_ratio())return this._update(),null!=this.prefetch_timer&&clearTimeout(this.prefetch_timer),this.prefetch_timer=setTimeout(this._prefetch_tiles,500)},e.prototype._draw_tile=function(t){var e,r,n,o,i,s,a,l,u,h,c;if(c=this.mget("tile_source").tiles[t],null!=c)return e=this.plot_view.frame.map_to_screen([c.bounds[0]],[c.bounds[3]],this.plot_view.canvas),a=e[0],h=e[1],r=this.plot_view.frame.map_to_screen([c.bounds[2]],[c.bounds[1]],this.plot_view.canvas),s=r[0],u=r[1],a=a[0],h=h[0],s=s[0],u=u[0],o=s-a,n=u-h,i=a,l=h,this.map_canvas.drawImage(c.img,i,l,o,n)},e.prototype._set_rect=function(){var t,e,r,n,o;return r=this.plot_model.plot.properties.outline_line_width.value(),e=this.plot_view.canvas.vx_to_sx(this.map_frame.get("left"))+r/2,n=this.plot_view.canvas.vy_to_sy(this.map_frame.get("top"))+r/2,o=this.map_frame.get("width")-r,t=this.map_frame.get("height")-r,this.map_canvas.rect(e,n,o,t),this.map_canvas.clip()},e.prototype._render_tiles=function(t){var e,r,n;for(this.map_canvas.save(),this._set_rect(),this.map_canvas.globalAlpha=this.mget("alpha"),e=0,r=t.length;e<r;e++)n=t[e],this._draw_tile(n);return this.map_canvas.restore()},e.prototype._prefetch_tiles=function(){var t,e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v;for(_=this.mget("tile_source"),a=this.get_extent(),l=this.map_frame.get("height"),f=this.map_frame.get("width"),v=this.mget("tile_source").get_level_by_extent(a,l,f),d=this.mget("tile_source").get_tiles_by_extent(a,v),c=[],p=u=0,h=Math.min(10,d.length);u<=h;p=u+=1)m=p[0],g=p[1],y=p[2],t=p[3],n=this.mget("tile_source").children_by_tile_xyz(m,g,y),c.push(function(){var t,a,l;for(l=[],t=0,a=n.length;t<a;t++)e=n[t],o=e[0],i=e[1],s=e[2],r=e[3],_.tile_xyz_to_key(o,i,s)in _.tiles||l.push(this._create_tile(o,i,s,r,!0));return l}.call(this));return c},e.prototype._fetch_tiles=function(t){var e,r,n,o,i,s,a,l;for(o=[],r=0,n=t.length;r<n;r++)i=t[r],s=i[0],a=i[1],l=i[2],e=i[3],o.push(this._create_tile(s,a,l,e));return o},e.prototype._update=function(){var t,e,r,n,o,i,s,a,l,u,h,c,p,_,d,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C,N,O,q,D,I,R,F;for(C=this.mget("tile_source"),b=C.get("min_zoom"),v=C.get("max_zoom"),C.update(),u=this.get_extent(),F=this.extent[2]-this.extent[0]<u[2]-u[0],h=this.map_frame.get("height"),O=this.map_frame.get("width"),R=C.get_level_by_extent(u,h,O),P=!1,R<b?(u=this.extent,R=b,P=!0):R>v&&(u=this.extent,R=v,P=!0),P&&(this.x_range.set({x_range:{start:u[0],end:u[2]}}),this.y_range.set({start:u[1],end:u[3]}),this.extent=u),this.extent=u,N=C.get_tiles_by_extent(u,R),k=[],x=[],r=[],i=[],c=0,m=N.length;c<m;c++){if(E=N[c],q=E[0],D=E[1],I=E[2],t=E[3],d=C.tile_xyz_to_key(q,D,I),A=C.tiles[d],null!=A&&A.loaded===!0)r.push(d);else if(this.mget("render_parents")&&(z=C.get_closest_parent_by_tile_xyz(q,D,I),j=z[0],T=z[1],S=z[2],w=C.tile_xyz_to_key(j,T,S),M=C.tiles[w],null!=M&&M.loaded&&f.call(k,w)<0&&k.push(w),F))for(i=C.children_by_tile_xyz(q,D,I),p=0,g=i.length;p<g;p++)e=i[p],s=e[0],a=e[1],l=e[2],n=e[3],o=C.tile_xyz_to_key(s,a,l),o in C.tiles&&i.push(o);null==A&&x.push(E)}for(this._render_tiles(k),this._render_tiles(i),this._render_tiles(r),_=0,y=r.length;_<y;_++)E=r[_],C.tiles[E].current=!0;return null!=this.render_timer&&clearTimeout(this.render_timer),this.render_timer=setTimeout(function(t){return function(){return t._fetch_tiles(x)}}(this),65)},e}(i.View),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return _(e,t),e.prototype.default_view=a,e.prototype.type="TileRenderer",e.define({alpha:[h.Number,1],x_range_name:[h.String,"default"],y_range_name:[h.String,"default"],tile_source:[h.Instance,function(){return new c.Model}],render_parents:[h.Bool,!0]}),e.override({level:"underlay"}),e}(i.Model),e.exports={Model:s,View:a}},{"../../core/logging":"core/logging","../../core/properties":"core/properties","../renderers/renderer":"models/renderers/renderer","./image_pool":"models/tiles/image_pool","./wmts_tile_source":"models/tiles/wmts_tile_source",jquery:"jquery",underscore:"underscore"}],"models/tiles/tile_source":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;s=t("underscore"),n=t("./image_pool"),u=t("./tile_utils"),a=t("../../core/logging").logger,l=t("../../core/properties"),o=t("../../model"),i=function(t){function e(t){null==t&&(t={}),e.__super__.constructor.apply(this,arguments),this.utils=new u.ProjectionUtils,this.pool=new n,this.tiles={},this.normalize_case()}return h(e,t),e.prototype.type="TileSource",e.define({url:[l.String,""],tile_size:[l.Number,256],max_zoom:[l.Number,30],min_zoom:[l.Number,0],extra_url_vars:[l.Any,{}],attribution:[l.String,""],x_origin_offset:[l.Number],y_origin_offset:[l.Number],initial_resolution:[l.Number]}),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.normalize_case()},e.prototype.string_lookup_replace=function(t,e){var r,n,o;n=t;for(r in e)o=e[r],n=n.replace("{"+r+"}",o.toString());return n},e.prototype.normalize_case=function(){"Note: should probably be refactored into subclasses.";var t;return t=this.get("url"),t=t.replace("{x}","{X}"),t=t.replace("{y}","{Y}"),t=t.replace("{z}","{Z}"),t=t.replace("{q}","{Q}"),t=t.replace("{xmin}","{XMIN}"),t=t.replace("{ymin}","{YMIN}"),t=t.replace("{xmax}","{XMAX}"),t=t.replace("{ymax}","{YMAX}"),this.set("url",t)},e.prototype.update=function(){var t,e,r,n;a.debug("TileSource: tile cache count: "+Object.keys(this.tiles).length),e=this.tiles,r=[];for(t in e)n=e[t],n.current=!1,r.push(n.retain=!1);return r},e.prototype.tile_xyz_to_key=function(t,e,r){var n;return n=t+":"+e+":"+r},e.prototype.key_to_tile_xyz=function(t){var e;return function(){var r,n,o,i;for(o=t.split(":"),i=[],r=0,n=o.length;r<n;r++)e=o[r],i.push(parseInt(e));return i}()},e.prototype.sort_tiles_from_center=function(t,e){var r,n,o,i,s,a;return i=e[0],a=e[1],o=e[2],s=e[3],r=(o-i)/2+i,n=(s-a)/2+a,t.sort(function(t,e){var o,i;return o=Math.sqrt(Math.pow(r-t[0],2)+Math.pow(n-t[1],2)),i=Math.sqrt(Math.pow(r-e[0],2)+Math.pow(n-e[1],2)),o-i}),t},e.prototype.prune_tiles=function(){var t,e,r,n,o;e=this.tiles;for(t in e)o=e[t],o.retain=o.current||o.tile_coords[2]<3,o.current&&(this.retain_neighbors(o),this.retain_children(o),this.retain_parents(o));r=this.tiles,n=[];for(t in r)o=r[t],o.retain?n.push(void 0):n.push(this.remove_tile(t));return n},e.prototype.remove_tile=function(t){var e;if(e=this.tiles[t],null!=e)return this.pool.push(e.img),delete this.tiles[t]},e.prototype.get_image_url=function(t,e,r){var n;return n=this.string_lookup_replace(this.get("url"),this.get("extra_url_vars")),n.replace("{X}",t).replace("{Y}",e).replace("{Z}",r)},e.prototype.retain_neighbors=function(t){throw Error("Not Implemented")},e.prototype.retain_parents=function(t){throw Error("Not Implemented")},e.prototype.retain_children=function(t){throw Error("Not Implemented")},e.prototype.tile_xyz_to_quadkey=function(t,e,r){throw Error("Not Implemented")},e.prototype.quadkey_to_tile_xyz=function(t){throw Error("Not Implemented")},e}(o),e.exports=i},{"../../core/logging":"core/logging","../../core/properties":"core/properties","../../model":"model","./image_pool":"models/tiles/image_pool","./tile_utils":"models/tiles/tile_utils",underscore:"underscore"}],"models/tiles/tile_utils":[function(t,e,r){var n,o,i,s;i=t("proj4"),o=i.defs("GOOGLE"),s=i.defs("WGS84"),n=function(){function t(){this.origin_shift=2*Math.PI*6378137/2}return t.prototype.geographic_to_meters=function(t,e){return i(s,o,[t,e])},t.prototype.meters_to_geographic=function(t,e){return i(o,s,[t,e])},t.prototype.geographic_extent_to_meters=function(t){var e,r,n,o,i,s;return o=t[0],s=t[1],n=t[2],i=t[3],e=this.geographic_to_meters(o,s),o=e[0],s=e[1],r=this.geographic_to_meters(n,i),n=r[0],i=r[1],[o,s,n,i]},t.prototype.meters_extent_to_geographic=function(t){var e,r,n,o,i,s;return o=t[0],s=t[1],n=t[2],i=t[3],e=this.meters_to_geographic(o,s),o=e[0],s=e[1],r=this.meters_to_geographic(n,i),n=r[0],i=r[1],[o,s,n,i]},t}(),e.exports={ProjectionUtils:n}},{proj4:"proj4"}],"models/tiles/tms_tile_source":[function(t,e,r){var n,o,i=function(t,e){function r(){this.constructor=t}for(var n in e)s.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},s={}.hasOwnProperty;n=t("./mercator_tile_source"),o=function(t){function e(){return 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tool"))},!1),this.add_dependencies("tooltip",this,["dimensions"])},e.define({dimensions:[u.Array,["width","height"]],overlay:[u.Instance,s],match_aspect:[u.Bool,!1]}),e}(a.Model),e.exports={Model:o,View:i}},{"../../../core/properties":"core/properties","../../annotations/box_annotation":"models/annotations/box_annotation","./gesture_tool":"models/tools/gestures/gesture_tool",underscore:"underscore"}],"models/tools/gestures/gesture_tool":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;a=t("underscore"),n=t("../button_tool"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._clicked=function(){var t;return t=this.model.get("active"),this.model.set("active",!t)},e}(n.ButtonView),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return 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this._clear_overlay()},e.prototype._keyup=function(t){if(13===t.keyCode)return this._clear_overlay()},e.prototype._pan_start=function(t){var e,r,n;return e=this.plot_view.canvas,r=e.sx_to_vx(t.bokeh.sx),n=e.sy_to_vy(t.bokeh.sy),this.data={vx:[r],vy:[n]},null},e.prototype._pan=function(t){var e,r,n,o,i,s,a,l;if(r=this.plot_view.canvas,a=r.sx_to_vx(t.bokeh.sx),l=r.sy_to_vy(t.bokeh.sy),n=this.plot_model.get("frame").get("h_range"),s=this.plot_model.get("frame").get("v_range"),a>n.get("end")&&(a=n.get("end")),a<n.get("start")&&(a=n.get("start")),l>s.get("end")&&(l=s.get("end")),l<s.get("start")&&(l=s.get("start")),this.data.vx.push(a),this.data.vy.push(l),o=this.mget("overlay"),o.update({xs:this.data.vx,ys:this.data.vy}),this.mget("select_every_mousemove"))return e=null!=(i=t.srcEvent.shiftKey)&&i,this._select(this.data.vx,this.data.vy,!1,e)},e.prototype._pan_end=function(t){var e,r;return this._clear_overlay(),e=null!=(r=t.srcEvent.shiftKey)&&r,this._select(this.data.vx,this.data.vy,!0,e),this.plot_view.push_state("lasso_select",{selection:this.plot_view.get_selection()})},e.prototype._clear_overlay=function(){return this.mget("overlay").update({xs:[],ys:[]})},e.prototype._select=function(t,e,r,n){var o,i,s,a,l,u,h;for(i={type:"poly",vx:t,vy:e},u=this.mget("computed_renderers"),s=0,a=u.length;s<a;s++)l=u[s],o=l.get("data_source"),h=o.get("selection_manager"),h.select(this,this.plot_view.renderer_views[l.id],i,r,n);return null!=this.mget("callback")&&this._emit_callback(i),this._save_geometry(i,r,n),null},e.prototype._emit_callback=function(t){var e,r,n,o,i;n=this.mget("computed_renderers")[0],e=this.plot_model.get("canvas"),r=this.plot_model.get("frame"),t.sx=e.v_vx_to_sx(t.vx),t.sy=e.v_vy_to_sy(t.vy),o=r.get("x_mappers")[n.get("x_range_name")],i=r.get("y_mappers")[n.get("y_range_name")],t.x=o.v_map_from_target(t.vx),t.y=i.v_map_from_target(t.vy),this.mget("callback").execute(this.model,{geometry:t})},e}(a.View),n=function(){return new s.Model({level:"overlay",xs_units:"screen",ys_units:"screen",fill_color:"lightgrey",fill_alpha:.5,line_color:"black",line_alpha:1,line_width:2,line_dash:[4,4]})},o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=i,e.prototype.type="LassoSelectTool",e.prototype.tool_name="Lasso Select",e.prototype.icon="bk-tool-icon-lasso-select",e.prototype.event_type="pan",e.prototype.default_order=12,e.define({select_every_mousemove:[u.Bool,!0],callback:[u.Instance],overlay:[u.Instance,n]}),e}(a.Model),e.exports={Model:o,View:i}},{"../../../core/properties":"core/properties","../../annotations/poly_annotation":"models/annotations/poly_annotation","./select_tool":"models/tools/gestures/select_tool",underscore:"underscore"}],"models/tools/gestures/pan_tool":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t("underscore"),n=t("./gesture_tool"),a=t("../../../core/properties"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._pan_start=function(t){var e,r,n,o,i,s;return this.last_dx=0,this.last_dy=0,e=this.plot_view.canvas,r=this.plot_view.frame,i=e.sx_to_vx(t.bokeh.sx),s=e.sy_to_vy(t.bokeh.sy),r.contains(i,s)||(n=r.get("h_range"),o=r.get("v_range"),(i<n.get("start")||i>n.get("end"))&&(this.v_axis_only=!0),(s<o.get("start")||s>o.get("end"))&&(this.h_axis_only=!0)),this.plot_view.interactive_timestamp=Date.now()},e.prototype._pan=function(t){return this._update(t.deltaX,-t.deltaY),this.plot_view.interactive_timestamp=Date.now()},e.prototype._pan_end=function(t){if(this.h_axis_only=!1,this.v_axis_only=!1,null!=this.pan_info)return this.plot_view.push_state("pan",{range:this.pan_info})},e.prototype._update=function(t,e){var r,n,o,i,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P;o=this.plot_view.frame,h=t-this.last_dx,c=e-this.last_dy,i=s.clone(o.get("h_range")),w=i.get("start")-h,x=i.get("end")-h,S=s.clone(o.get("v_range")),T=S.get("start")-c,j=S.get("end")-c,r=this.mget("dimensions"),r.indexOf("width")>-1&&!this.v_axis_only?(v=w,b=x,m=-h):(v=i.get("start"),b=i.get("end"),m=0),r.indexOf("height")>-1&&!this.h_axis_only?(M=T,k=j,g=c):(M=S.get("start"),k=S.get("end"),g=0),this.last_dx=t,this.last_dy=e,z={},p=o.get("x_mappers");for(u in p)l=p[u],_=l.v_map_from_target([v,b],!0),y=_[0],n=_[1],z[u]={start:y,end:n};P={},d=o.get("y_mappers");for(u in d)l=d[u],f=l.v_map_from_target([M,k],!0),y=f[0],n=f[1],P[u]={start:y,end:n};return this.pan_info={xrs:z,yrs:P,sdx:m,sdy:g},this.plot_view.update_range(this.pan_info,a=!0),null},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type="PanTool",e.prototype.tool_name="Pan",e.prototype.icon="bk-tool-icon-pan",e.prototype.event_type="pan",e.prototype.default_order=10,e.define({dimensions:[a.Array,["width","height"]]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.override_computed_property("tooltip",function(){return this._get_dim_tooltip("Pan",this._check_dims(this.get("dimensions"),"pan tool"))},!1),this.add_dependencies("tooltip",this,["dimensions"])},e}(n.Model),e.exports={Model:o,View:i}},{"../../../core/properties":"core/properties","./gesture_tool":"models/tools/gestures/gesture_tool",underscore:"underscore"}],"models/tools/gestures/poly_select_tool":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;l=t("underscore"),a=t("./select_tool"),o=t("../../annotations/poly_annotation"),u=t("../../../core/properties"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.listenTo(this.model,"change:active",this._active_change),this.data=null},e.prototype._active_change=function(){if(!this.mget("active"))return this._clear_data()},e.prototype._keyup=function(t){if(13===t.keyCode)return this._clear_data()},e.prototype._doubletap=function(t){var e,r;return e=null!=(r=t.srcEvent.shiftKey)&&r,this._select(this.data.vx,this.data.vy,!0,e),this._clear_data()},e.prototype._clear_data=function(){return this.data=null,this.mget("overlay").update({xs:[],ys:[]})},e.prototype._tap=function(t){var e,r,n,o,i;return e=this.plot_view.canvas,o=e.sx_to_vx(t.bokeh.sx),i=e.sy_to_vy(t.bokeh.sy),null==this.data?(this.data={vx:[o],vy:[i]},null):(this.data.vx.push(o),this.data.vy.push(i),n=this.mget("overlay"),r={},r.vx=l.clone(this.data.vx),r.vy=l.clone(this.data.vy),n.update({xs:this.data.vx,ys:this.data.vy}))},e.prototype._select=function(t,e,r,n){var o,i,s,a,l,u,h;for(i={type:"poly",vx:t,vy:e},u=this.mget("computed_renderers"),s=0,a=u.length;s<a;s++)l=u[s],o=l.get("data_source"),h=o.get("selection_manager"),h.select(this,this.plot_view.renderer_views[l.id],i,r,n);return this._save_geometry(i,r,n),this.plot_view.push_state("poly_select",{selection:this.plot_view.get_selection()}),null},e}(a.View),n=function(){return new o.Model({level:"overlay",xs_units:"screen",ys_units:"screen",fill_color:"lightgrey",fill_alpha:.5,line_color:"black",line_alpha:1,line_width:2,line_dash:[4,4]})},i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=s,e.prototype.type="PolySelectTool",e.prototype.tool_name="Poly Select",e.prototype.icon="bk-tool-icon-polygon-select",e.prototype.event_type="tap",e.prototype.default_order=11,e.define({overlay:[u.Instance,n]}),e}(a.Model),e.exports={Model:i,View:s}},{"../../../core/properties":"core/properties","../../annotations/poly_annotation":"models/annotations/poly_annotation","./select_tool":"models/tools/gestures/select_tool",underscore:"underscore"}],"models/tools/gestures/resize_tool":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t("underscore"),n=t("./gesture_tool"),a=t("../../../core/properties"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.className="bk-resize-popup",e.prototype.initialize=function(t){var r;return e.__super__.initialize.call(this,t),r=this.plot_view.$el.find("div.bk-canvas-wrapper"),this.$el.appendTo(r),this.$el.hide(),this.active=!1,null},e.prototype.activate=function(){return this.active=!0,this.render(),null},e.prototype.deactivate=function(){return this.active=!1,this.render(),null},e.prototype.render=function(t){var e,r,n,o;return this.active?(e=this.plot_view.canvas,r=this.plot_view.frame,n=e.vx_to_sx(r.get("h_range").get("end")-40),o=e.vy_to_sy(r.get("v_range").get("start")+40),this.$el.attr("style","position:absolute; top:"+o+"px; left:"+n+"px;"),this.$el.show()):this.$el.hide(),this},e.prototype._pan_start=function(t){var e;return e=this.plot_view.canvas,this.ch=e.get("height"),this.cw=e.get("width"),this.plot_view.interactive_timestamp=Date.now(),null},e.prototype._pan=function(t){return this._update(t.deltaX,t.deltaY),this.plot_view.interactive_timestamp=Date.now(),null},e.prototype._pan_end=function(t){return this.plot_view.push_state("resize",{dimensions:{width:this.plot_view.canvas.get("width"),height:this.plot_view.canvas.get("height")}})},e.prototype._update=function(t,e){var r,n;n=this.cw+t,r=this.cw+e,n<100||r<100||this.plot_view.update_dimensions(n,r)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type="ResizeTool",e.prototype.tool_name="Resize",e.prototype.icon="bk-tool-icon-resize",e.prototype.event_type="pan",e.prototype.default_order=40,e}(n.Model),e.exports={Model:o,View:i}},{"../../../core/properties":"core/properties","./gesture_tool":"models/tools/gestures/gesture_tool",underscore:"underscore"}],"models/tools/gestures/select_tool":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;a=t("underscore"),n=t("./gesture_tool"),o=t("../../renderers/glyph_renderer"),l=t("../../../core/logging").logger,u=t("../../../core/properties"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype._keyup=function(t){var e,r,n,o,i,s,a;if(27===t.keyCode){for(i=this.mget("computed_renderers"),s=[],r=0,n=i.length;r<n;r++)o=i[r],e=o.get("data_source"),a=e.get("selection_manager"),s.push(a.clear());return s}},e.prototype._save_geometry=function(t,e,r){var n,o,i,s,u,h,c,p;if(n=a.clone(t),c=this.plot_view.frame.get("x_mappers")["default"],p=this.plot_view.frame.get("y_mappers")["default"],"point"===n.type)n.x=c.map_from_target(n.vx),n.y=p.map_from_target(n.vy);else if("rect"===n.type)n.x0=c.map_from_target(n.vx0),n.y0=p.map_from_target(n.vy0),n.x1=c.map_from_target(n.vx1),n.y1=p.map_from_target(n.vy1);else if("poly"===n.type)for(n.x=new Array(n.vx.length),n.y=new Array(n.vy.length),i=s=0,u=n.vx.length;0<=u?s<u:s>u;i=0<=u?++s:--s)n.x[i]=c.map_from_target(n.vx[i]),n.y[i]=p.map_from_target(n.vy[i]);else l.debug("Unrecognized selection geometry type: '"+n.type+"'");return e&&(h=this.plot_model.plot.tool_events,r?(o=h.get("geometries"),o.push(n)):o=[n],h.set("geometries",o)),null},e}(n.View),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.define({renderers:[u.Array,[]],names:[u.Array,[]]}),e.internal({multi_select_modifier:[u.String,"shift"]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.define_computed_property("computed_renderers",function(){var t,e,r,n;return n=this.get("renderers"),e=this.get("names"),0===n.length&&(t=this.get("plot").get("renderers"),n=function(){var e,n,i;for(i=[],e=0,n=t.length;e<n;e++)r=t[e],r instanceof o.Model&&i.push(r);return i}()),e.length>0&&(n=function(){var t,o,i;for(i=[],t=0,o=n.length;t<o;t++)r=n[t],e.indexOf(r.get("name"))>=0&&i.push(r);return i}()),n},!0),this.add_dependencies("computed_renderers",this,["renderers","names","plot"]),this.add_dependencies("computed_renderers",this.get("plot"),["renderers"]),null},e}(n.Model),e.exports={Model:i,View:s}},{"../../../core/logging":"core/logging","../../../core/properties":"core/properties","../../renderers/glyph_renderer":"models/renderers/glyph_renderer","./gesture_tool":"models/tools/gestures/gesture_tool",underscore:"underscore"}],"models/tools/gestures/tap_tool":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t("underscore"),n=t("./select_tool"),a=t("../../../core/properties"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._tap=function(t){var e,r,n,o,i;return r=this.plot_view.canvas,o=r.sx_to_vx(t.bokeh.sx),i=r.sy_to_vy(t.bokeh.sy),e=null!=(n=t.srcEvent.shiftKey)&&n,this._select(o,i,!0,e)},e.prototype._select=function(t,e,r,n){var o,i,a,l,u,h,c,p,_,d,f;for(u={type:"point",vx:t,vy:e},o=this.mget("callback"),this._save_geometry(u,r,n),i={geometries:this.plot_model.plot.tool_events.get("geometries")},_=this.mget("computed_renderers"),h=0,c=_.length;h<c;h++)p=_[h],l=p.get("data_source"),d=l.get("selection_manager"),f=this.plot_view.renderer_views[p.id],a="select"===this.model.behavior?d.select(this,f,u,r,n):d.inspect(this,f,u,{geometry:u}),a&&null!=o&&(s.isFunction(o)?o(l,i):o.execute(l,i));return"select"===this.model.behavior&&this.plot_view.push_state("tap",{selection:this.plot_view.get_selection()}),null},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type="TapTool",e.prototype.tool_name="Tap",e.prototype.icon="bk-tool-icon-tap-select",e.prototype.event_type="tap",e.prototype.default_order=10,e.define({behavior:[a.String,"select"],callback:[a.Any]}),e}(n.Model),e.exports={Model:o,View:i}},{"../../../core/properties":"core/properties","./select_tool":"models/tools/gestures/select_tool",underscore:"underscore"}],"models/tools/gestures/wheel_zoom_tool":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t("underscore"),n=t("./gesture_tool"),l=t("../../../core/properties"),"undefined"!=typeof a&&null!==a||(a={}),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype._pinch=function(t){var e;return e=t.scale>=1?20*(t.scale-1):-20/t.scale,t.bokeh.delta=e,this._scroll(t)},e.prototype._scroll=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A;i=this.plot_model.get("frame"),a=i.get("h_range"),w=i.get("v_range"),M=this.plot_view.canvas.sx_to_vx(t.bokeh.sx),T=this.plot_view.canvas.sy_to_vy(t.bokeh.sy),(M<a.get("start")||M>a.get("end"))&&(x=!0),(T<w.get("start")||T>w.get("end"))&&(s=!0),u=navigator.userAgent.toLowerCase().indexOf("firefox")>-1?20:1,e=null!=(null!=(c=t.originalEvent)?c.deltaY:void 0)?-t.originalEvent.deltaY*u:t.bokeh.delta,o=this.mget("speed")*e,o>.9?o=.9:o<-.9&&(o=-.9),j=a.get("start"),k=a.get("end"),z=w.get("start"),S=w.get("end"),r=this.mget("dimensions"),r.indexOf("width")>-1&&!x?(g=j-(j-M)*o,y=k-(k-M)*o):(g=j,y=k),r.indexOf("height")>-1&&!s?(v=z-(z-T)*o,b=S-(S-T)*o):(v=z,b=S),P={},p=i.get("x_mappers");for(h in p)l=p[h],_=l.v_map_from_target([g,y],!0),m=_[0],n=_[1],P[h]={start:m,end:n};E={},d=i.get("y_mappers");for(h in d)l=d[h],f=l.v_map_from_target([v,b],!0),m=f[0],n=f[1],E[h]={start:m,end:n};return A={xrs:P,yrs:E,factor:o},this.plot_view.push_state("wheel_zoom",{range:A}),this.plot_view.update_range(A,!1,!0),this.plot_view.interactive_timestamp=Date.now(),null},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=i,e.prototype.type="WheelZoomTool",e.prototype.tool_name="Wheel Zoom",e.prototype.icon="bk-tool-icon-wheel-zoom",e.prototype.event_type="ontouchstart"in window||navigator.maxTouchPoints>0?"pinch":"scroll",e.prototype.default_order=10,e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.override_computed_property("tooltip",function(){return this._get_dim_tooltip(this.tool_name,this._check_dims(this.get("dimensions"),"wheel zoom tool"))},!1),this.add_dependencies("tooltip",this,["dimensions"])},e.define({dimensions:[l.Array,["width","height"]]}),e.internal({speed:[l.Number,1/600]}),e}(n.Model),e.exports={Model:o,View:i}},{"../../../core/properties":"core/properties","./gesture_tool":"models/tools/gestures/gesture_tool",underscore:"underscore"}],"models/tools/inspectors/crosshair_tool":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;a=t("underscore"),i=t("./inspect_tool"),s=t("../../annotations/span"),l=t("../../../core/properties"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype._move=function(t){var e,r,n,o,i,s,a,l,u,h;if(this.mget("active")){for(n=this.plot_model.get("frame"),e=this.plot_model.get("canvas"),u=e.sx_to_vx(t.bokeh.sx),h=e.sy_to_vy(t.bokeh.sy),s=this.mget("dimensions"),a=[],o=0,i=s.length;o<i;o++)r=s[o],l=this.mget("spans")[r],n.contains(u,h)?"width"===r?a.push(l.set("computed_location",h)):a.push(l.set("computed_location",u)):a.push(l.unset("computed_location"));return a}},e.prototype._move_exit=function(t){var e,r,n,o,i,s;for(o=this.mget("dimensions"),i=[],r=0,n=o.length;r<n;r++)e=o[r],s=this.mget("spans")[e],i.push(s.unset("computed_location"));return i},e}(i.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=o,e.prototype.type="CrosshairTool",e.prototype.tool_name="Crosshair",e.define({dimensions:[l.Array,["width","height"]],line_color:[l.Color,"black"],line_width:[l.Number,1],line_alpha:[l.Number,1]}),e.internal({location_units:[l.SpatialUnits,"screen"],render_mode:[l.RenderMode,"css"],spans:[l.Any]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.override_computed_property("tooltip",function(){return this._get_dim_tooltip("Crosshair",this._check_dims(this.get("dimensions"),"crosshair tool"))},!1),this.add_dependencies("tooltip",this,["dimensions"]),this.spans={width:new s.Model({for_hover:!0,dimension:"width",render_mode:this.get("render_mode"),location_units:this.get("location_units"),line_color:this.get("line_color"),line_width:this.get("line_width"),line_alpha:this.get("line_alpha")}),height:new s.Model({for_hover:!0,dimension:"height",render_mode:this.get("render_mode"),location_units:this.get("location_units"),line_color:this.get("line_color"),line_width:this.get("line_width"),line_alpha:this.get("line_alpha")})},this.override_computed_property("synthetic_renderers",function(t){return function(){return a.values(t.get("spans"))}}(this),!0)},e}(i.Model),e.exports={Model:n,View:o}},{"../../../core/properties":"core/properties","../../annotations/span":"models/annotations/span","./inspect_tool":"models/tools/inspectors/inspect_tool",underscore:"underscore"}],"models/tools/inspectors/hover_tool":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f=function(t,e){function r(){this.constructor=t}for(var n in e)m.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new 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e,r,n;if(this.mget("active"))return e=this.plot_view.canvas,r=e.sx_to_vx(t.bokeh.sx),n=e.sy_to_vy(t.bokeh.sy),this.plot_view.frame.contains(r,n)?this._inspect(r,n):this._clear()},e.prototype._move_exit=function(){return this._clear()},e.prototype._inspect=function(t,e,r){var n,o,i,s,a,l,u,h;for(n={type:"point",vx:t,vy:e},"mouse"===this.model.mode?n.type="point":(n.type="span","vline"===this.model.mode?n.direction="h":n.direction="v"),o=[],i=[],u=this.mget("computed_renderers"),s=0,a=u.length;s<a;s++)l=u[s],h=l.data_source.get("selection_manager"),h.inspect(this,this.plot_view.renderer_views[l.id],n,{geometry:n});null!=this.mget("callback")&&this._emit_callback(n)},e.prototype._update=function(t,e,r,n,o){var i,s,a,l,u,c,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C,N,O,q,D,I,R,F,B,L,G,V,U,Y,H,X,$,W,J,Q,K,Z,tt,et,rt,nt;if(g=o.geometry,J=null!=(z=this.mget("ttmodels")[r.model.id])?z:null,null!=J&&(J.clear(),null!==t["0d"].glyph||0!==t["1d"].indices.length)){for(K=g.vx,Z=g.vy,i=this.plot_model.get("canvas"),m=this.plot_model.get("frame"),$=i.vx_to_sx(K),W=i.vy_to_sy(Z),et=m.get("x_mappers")[r.mget("x_range_name")],nt=m.get("y_mappers")[r.mget("y_range_name")],tt=et.map_from_target(K),rt=nt.map_from_target(Z),P=t["0d"].indices,b=0,w=P.length;b<w;b++)y=P[b],c=r.glyph._x[y+1],_=r.glyph._y[y+1],"interp"===this.model.line_policy?(D=r.glyph.get_interpolation_hit(y,g),c=D[0],_=D[1],U=et.map_to_target(c),Y=nt.map_to_target(_)):"prev"===this.model.line_policy?(U=i.sx_to_vx(r.glyph.sx[y]),Y=i.sy_to_vy(r.glyph.sy[y])):"next"===this.model.line_policy?(U=i.sx_to_vx(r.glyph.sx[y+1]),Y=i.sy_to_vy(r.glyph.sy[y+1])):"nearest"===this.model.line_policy?(s=r.glyph.sx[y],a=r.glyph.sy[y],d=p.dist_2_pts(s,a,$,W),l=r.glyph.sx[y+1],u=r.glyph.sy[y+1],f=p.dist_2_pts(l,u,$,W),d<f?(I=[s,a],H=I[0],X=I[1]):(R=[l,u],H=R[0],X=R[1],y+=1),c=r.glyph._x[y],_=r.glyph._y[y],U=i.sx_to_vx(H),Y=i.sy_to_vy(X)):(F=[K,Z],U=F[0],Y=F[1]),Q={index:y,x:tt,y:rt,vx:K,vy:Z,sx:$,sy:W,data_x:c,data_y:_,rx:U,ry:Y},J.add(U,Y,this._render_tooltips(n,y,Q));for(B=t["1d"].indices,x=0,M=B.length;x<M;x++)if(y=B[x],h.isEmpty(t["2d"]))c=null!=(C=r.glyph._x)?C[y]:void 0,_=null!=(N=r.glyph._y)?N[y]:void 0,"snap_to_data"===this.model.point_policy?(S=r.glyph.get_anchor_point(this.model.anchor,y,[$,W]),null!=S?(tt=S.x,rt=S.y):(O=r.glyph.get_anchor_point("center",y,[$,W]),tt=O.x,rt=O.y),U=i.sx_to_vx(tt),Y=i.sy_to_vy(rt)):(q=[K,Z],U=q[0],Y=q[1]),Q={index:y,x:tt,y:rt,vx:K,vy:Z,sx:$,sy:W,data_x:c,data_y:_},J.add(U,Y,this._render_tooltips(n,y,Q));else for(L=h.pairs(t["2d"]),j=0,k=L.length;j<k;j++)T=L[j],G=[T[0],T[1][0]],y=G[0],v=G[1],c=r.glyph._xs[y][v],_=r.glyph._ys[y][v],"interp"===this.model.line_policy?(V=r.glyph.get_interpolation_hit(y,v,g),c=V[0],_=V[1],U=et.map_to_target(c),Y=nt.map_to_target(_)):"prev"===this.model.line_policy?(U=i.sx_to_vx(r.glyph.sxs[y][v]),Y=i.sy_to_vy(r.glyph.sys[y][v])):"next"===this.model.line_policy?(U=i.sx_to_vx(r.glyph.sxs[y][v+1]),Y=i.sy_to_vy(r.glyph.sys[y][v+1])):"nearest"===this.model.line_policy&&(s=r.glyph.sx[y][v],a=r.glyph.sy[y][v],d=p.dist_2_pts(s,a,$,W),l=r.glyph.sx[y][v+1],u=r.glyph.sy[y][v+1],f=p.dist_2_pts(l,u,$,W),d<f?(E=[s,a],H=E[0],X=E[1]):(A=[l,u],H=A[0],X=A[1],v+=1),c=r.glyph._x[y][v],_=r.glyph._y[y][v],U=i.sx_to_vx(H),Y=i.sy_to_vy(X)),Q={index:y,segment_index:v,x:tt,y:rt,vx:K,vy:Z,sx:$,sy:W,data_x:c,data_y:_
},J.add(U,Y,this._render_tooltips(n,y,Q));return null}},e.prototype._emit_callback=function(t){var e,r,n,o,i,s,a,l,u,c;a=this.mget("computed_renderers")[0],i=this.plot_view.renderer_views[a.id].hit_test(t),r=this.plot_model.canvas,o=this.plot_model.frame,t.sx=r.vx_to_sx(t.vx),t.sy=r.vy_to_sy(t.vy),u=o.get("x_mappers")[a.get("x_range_name")],c=o.get("y_mappers")[a.get("y_range_name")],t.x=u.map_from_target(t.vx),t.y=c.map_from_target(t.vy),e=this.model.callback,l=[e,{index:i,geometry:t}],s=l[0],n=l[1],h.isFunction(e)?e(s,n):e.execute(s,n)},e.prototype._render_tooltips=function(t,e,r){var o,i,s,a,l,p,_,d,f,m,g,y,v,b,x,w,M,k;if(M=this.mget("tooltips"),h.isString(M))return n("<div>").html(u.replace_placeholders(M,t,e,r));if(h.isFunction(M))return M(t,r);for(x=n("<table></table>"),l=0,_=M.length;l<_;l++){if(m=M[l],p=m[0],k=m[1],y=n("<tr></tr>"),y.append(n("<td class='bk-tooltip-row-label'>").text(p+": ")),w=n("<td class='bk-tooltip-row-value'></td>"),k.indexOf("$color")>=0){if(g=k.match(/\$color(\[.*\])?:(\w*)/),d=g[0],f=g[1],o=g[2],s=t.get_column(o),null==s){v=n("<span>").text(o+" unknown"),w.append(v);continue}if(a=(null!=f?f.indexOf("hex"):void 0)>=0,b=(null!=f?f.indexOf("swatch"):void 0)>=0,i=s[e],null==i){v=n("<span>(null)</span>"),w.append(v);continue}a&&(i=c(i)),v=n("<span>").text(i),w.append(v),b&&(v=n("<span class='bk-tooltip-color-block'> </span>"),v.css({backgroundColor:i})),w.append(v)}else k=k.replace("$~","$data_"),k=u.replace_placeholders(k,t,e,r),w.append(n("<span>").html(k));y.append(w),x.append(y)}return x},e}(a.View),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return f(e,t),e.prototype.default_view=s,e.prototype.type="HoverTool",e.prototype.tool_name="Hover Tool",e.prototype.icon="bk-tool-icon-hover",e.define({tooltips:[d.Any,[["index","$index"],["data (x, y)","($x, $y)"],["canvas (x, y)","($sx, $sy)"]]],renderers:[d.Array,[]],names:[d.Array,[]],mode:[d.String,"mouse"],point_policy:[d.String,"snap_to_data"],line_policy:[d.String,"prev"],show_arrow:[d.Boolean,!0],anchor:[d.String,"center"],attachment:[d.String,"horizontal"],callback:[d.Any]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.define_computed_property("computed_renderers",function(){var t,e,r,n;return n=this.get("renderers"),e=this.get("names"),0===n.length&&(t=this.get("plot").get("renderers"),n=function(){var e,n,i;for(i=[],e=0,n=t.length;e<n;e++)r=t[e],r instanceof o.Model&&i.push(r);return i}()),e.length>0&&(n=function(){var t,o,i;for(i=[],t=0,o=n.length;t<o;t++)r=n[t],e.indexOf(r.get("name"))>=0&&i.push(r);return i}()),n},!0),this.add_dependencies("computed_renderers",this,["renderers","names","plot"]),this.add_dependencies("computed_renderers",this.get("plot"),["renderers"]),this.define_computed_property("ttmodels",function(){var t,e,r,n,o,i,s;if(s={},i=this.get("tooltips"),null!=i)for(n=this.get("computed_renderers"),t=0,e=n.length;t<e;t++)r=n[t],o=new l.Model({custom:h.isString(i)||h.isFunction(i),attachment:this.attachment,show_arrow:this.show_arrow}),s[r.id]=o;return s},!0),this.add_dependencies("ttmodels",this,["computed_renderers","tooltips"]),this.override_computed_property("synthetic_renderers",function(){return h.values(this.get("ttmodels"))},!0),this.add_dependencies("synthetic_renderers",this,["ttmodels"])},e}(a.Model),e.exports={Model:i,View:s}},{"../../../common/hittest":"common/hittest","../../../core/logging":"core/logging","../../../core/properties":"core/properties","../../../util/util":"util/util","../../annotations/tooltip":"models/annotations/tooltip","../../renderers/glyph_renderer":"models/renderers/glyph_renderer","./inspect_tool":"models/tools/inspectors/inspect_tool",jquery:"jquery",underscore:"underscore"}],"models/tools/inspectors/inspect_tool":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;l=t("underscore"),n=t("backbone"),a=t("../tool"),u=t("./inspect_tool_list_item_template"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.className="bk-toolbar-inspector",e.prototype.template=u,e.prototype.events={'click [type="checkbox"]':"_clicked"},e.prototype.initialize=function(t){return this.listenTo(this.model,"change:active",this.render),this.render()},e.prototype.render=function(){return this.$el.html(this.template({model:this.model})),this},e.prototype._clicked=function(t){var e;return e=this.model.get("active"),this.model.set("active",!e)},e}(n.View),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e}(a.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.event_type="move",e.override({active:!0}),e.prototype.bind_bokeh_events=function(){return e.__super__.bind_bokeh_events.call(this),this.listenTo(events,"move",this._inspect)},e.prototype._inspect=function(t,e,r){},e.prototype._exit_inner=function(){},e.prototype._exit_outer=function(){},e}(a.Model),e.exports={Model:o,View:s,ListItemView:i}},{"../tool":"models/tools/tool","./inspect_tool_list_item_template":"models/tools/inspectors/inspect_tool_list_item_template",backbone:"backbone",underscore:"underscore"}],"models/tools/inspectors/inspect_tool_list_item_template":[function(t,e,r){e.exports=function(t){t||(t={});var e=[],r=function(t){return t&&t.ecoSafe?t:"undefined"!=typeof t&&null!=t?n(t):""},n=function(t){return(""+t).replace(/&/g,"&amp;").replace(/</g,"&lt;").replace(/>/g,"&gt;").replace(/"/g,"&quot;")};return function(){(function(){e.push('<input type="checkbox" '),this.model.active&&e.push("checked"),e.push(">"),e.push(r(this.model.tool_name)),e.push("</input>\n")}).call(this)}.call(t),e.join("")}},{}],"models/tools/tool":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;a=t("underscore"),o=t("../renderers/renderer"),l=t("../../core/logging").logger,u=t("../../core/properties"),n=t("../../model"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,"change:active",function(t){return function(){return t.mget("active")?t.activate():t.deactivate()}}(this))},e.prototype.activate=function(){},e.prototype.deactivate=function(){},e}(o.View),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.define_computed_property("synthetic_renderers",function(){return[]},!0)},e.define({plot:[u.Instance]}),e.internal({level:[u.RenderLevel,"overlay"],active:[u.Boolean,!1]}),e.prototype._check_dims=function(t,e){var r,n,o;return n=[!1,!1],o=n[0],r=n[1],0===t.length?l.warn(e+" given empty dimensions"):1===t.length?"width"!==t[0]&&"height"!==t[0]&&l.warn(e+" given unrecognized dimensions: "+t):2===t.length?(t.indexOf("width")<0||t.indexOf("height")<0)&&l.warn(e+" given unrecognized dimensions: "+t):l.warn(e+" given more than two dimensions: "+t),t.indexOf("width")>=0&&(o=!0),t.indexOf("height")>=0&&(r=!0),[o,r]},e.prototype._get_dim_tooltip=function(t,e){var r,n;return n=e[0],r=e[1],n&&!r?t+" (x-axis)":r&&!n?t+" (y-axis)":t},e.prototype._get_dim_limits=function(t,e,r,n){var o,i,s,l,u,h,c,p;return s=t[0],h=t[1],l=e[0],c=e[1],o=r.get("h_range"),n.indexOf("width")>=0?(u=[a.min([s,l]),a.max([s,l])],u=[a.max([u[0],o.get("min")]),a.min([u[1],o.get("max")])]):u=[o.get("min"),o.get("max")],i=r.get("v_range"),n.indexOf("height")>=0?(p=[a.min([h,c]),a.max([h,c])],p=[a.max([p[0],i.get("min")]),a.min([p[1],i.get("max")])]):p=[i.get("min"),i.get("max")],[u,p]},e}(n),e.exports={Model:i,View:s}},{"../../core/logging":"core/logging","../../core/properties":"core/properties","../../model":"model","../renderers/renderer":"models/renderers/renderer",underscore:"underscore"}],"models/tools/tool_proxy":[function(t,e,r){var n,o,i,s=function(t,e){function r(){this.constructor=t}for(var n in e)a.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},a={}.hasOwnProperty;i=t("../../core/properties"),n=t("../../model"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return s(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.listenTo(this,"do",this["do"]),this.listenTo(this,"change:active",this.set_active)},e.prototype["do"]=function(){var t,e,r,n;for(r=this.tools,t=0,e=r.length;t<e;t++)n=r[t],n.trigger("do");return null},e.prototype.set_active=function(){var t,e,r,n;for(r=this.tools,t=0,e=r.length;t<e;t++)n=r[t],n.active=this.active;return null},e.define({tools:[i.Array,[]],active:[i.Bool,!1],tooltip:[i.String],tool_name:[i.String],disabled:[i.Bool,!1],event_type:[i.String],icon:[i.String]}),e.prototype._clicked=function(){var t;return t=this.model.get("active"),this.model.set("active",!t)},e}(n),e.exports={ToolProxy:o}},{"../../core/properties":"core/properties","../../model":"model"}],"models/tools/toolbar":[function(t,e,r){var n,o,i,s,a,l,u,h,c=function(t,e){function r(){this.constructor=t}for(var n in e)p.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new 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t=e.__super__.get_constraints.call(this),t.push(s(this._sizeable,-30)),t},e.define({tools:[m.Array,[]],logo:[m.String,"normal"]}),e.internal({gestures:[m.Any,function(){return{pan:{tools:[],active:null},tap:{tools:[],active:null},doubletap:{tools:[],active:null},scroll:{tools:[],active:null},pinch:{tools:[],active:null},press:{tools:[],active:null},rotate:{tools:[],active:null}}}],actions:[m.Array,[]],inspectors:[m.Array,[]],help:[m.Array,[]],toolbar_location:[m.Location,"right"],toolbar_sticky:[m.Bool]}),e.override({sizing_mode:null}),e}(h.Model),e.exports={Model:c,View:p}},{"../../core/layout/solver":"core/layout/solver","../../core/logging":"core/logging","../../core/properties":"core/properties","../layouts/layout_dom":"models/layouts/layout_dom","./actions/action_tool":"models/tools/actions/action_tool","./actions/help_tool":"models/tools/actions/help_tool","./gestures/gesture_tool":"models/tools/gestures/gesture_tool","./inspectors/inspect_tool":"models/tools/inspectors/inspect_tool","./toolbar_template":"models/tools/toolbar_template","bootstrap/dropdown":"bootstrap/dropdown",jquery:"jquery",underscore:"underscore"}],"models/tools/toolbar_box":[function(t,e,r){var 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License regarding the Viridis, Magma, Plasma and Inferno color maps */
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r,n=0,o=t.length;n<o;n++)r=t[n],B.tweeners[r]=B.tweeners[r]||[],B.tweeners[r].unshift(e)},prefilters:[R],prefilter:function(t,e){e?B.prefilters.unshift(t):B.prefilters.push(t)}}),st.speed=function(t,e,r){var n=t&&"object"==typeof t?st.extend({},t):{complete:r||!r&&e||st.isFunction(t)&&t,duration:t,easing:r&&e||e&&!st.isFunction(e)&&e};return n.duration=st.fx.off?0:"number"==typeof n.duration?n.duration:n.duration in st.fx.speeds?st.fx.speeds[n.duration]:st.fx.speeds._default,null!=n.queue&&n.queue!==!0||(n.queue="fx"),n.old=n.complete,n.complete=function(){st.isFunction(n.old)&&n.old.call(this),n.queue&&st.dequeue(this,n.queue)},n},st.fn.extend({fadeTo:function(t,e,r,n){return this.filter(Nt).css("opacity",0).show().end().animate({opacity:e},t,r,n)},animate:function(t,e,r,n){var o=st.isEmptyObject(t),i=st.speed(e,r,n),s=function(){var e=B(this,st.extend({},t),i);(o||Tt.get(this,"finish"))&&e.stop(!0)};return s.finish=s,o||i.queue===!1?this.each(s):this.queue(i.queue,s)},stop:function(t,e,r){var n=function(t){var e=t.stop;delete t.stop,e(r)};return"string"!=typeof t&&(r=e,e=t,t=void 0),e&&t!==!1&&this.queue(t||"fx",[]),this.each(function(){var e=!0,o=null!=t&&t+"queueHooks",i=st.timers,s=Tt.get(this);if(o)s[o]&&s[o].stop&&n(s[o]);else for(o in s)s[o]&&s[o].stop&&le.test(o)&&n(s[o]);for(o=i.length;o--;)i[o].elem!==this||null!=t&&i[o].queue!==t||(i[o].anim.stop(r),e=!1,i.splice(o,1));!e&&r||st.dequeue(this,t)})},finish:function(t){return t!==!1&&(t=t||"fx"),this.each(function(){var e,r=Tt.get(this),n=r[t+"queue"],o=r[t+"queueHooks"],i=st.timers,s=n?n.length:0;for(r.finish=!0,st.queue(this,t,[]),o&&o.stop&&o.stop.call(this,!0),e=i.length;e--;)i[e].elem===this&&i[e].queue===t&&(i[e].anim.stop(!0),i.splice(e,1));for(e=0;e<s;e++)n[e]&&n[e].finish&&n[e].finish.call(this);delete r.finish})}}),st.each(["toggle","show","hide"],function(t,e){var r=st.fn[e];st.fn[e]=function(t,n,o){return 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t=J.createElement("input"),e=J.createElement("select"),r=e.appendChild(J.createElement("option"));t.type="checkbox",ot.checkOn=""!==t.value,ot.optSelected=r.selected,e.disabled=!0,ot.optDisabled=!r.disabled,t=J.createElement("input"),t.value="t",t.type="radio",ot.radioValue="t"===t.value}();var ue,he=st.expr.attrHandle;st.fn.extend({attr:function(t,e){return kt(this,st.attr,t,e,arguments.length>1)},removeAttr:function(t){return this.each(function(){st.removeAttr(this,t)})}}),st.extend({attr:function(t,e,r){var n,o,i=t.nodeType;if(3!==i&&8!==i&&2!==i)return"undefined"==typeof t.getAttribute?st.prop(t,e,r):(1===i&&st.isXMLDoc(t)||(e=e.toLowerCase(),o=st.attrHooks[e]||(st.expr.match.bool.test(e)?ue:void 0)),void 0!==r?null===r?void st.removeAttr(t,e):o&&"set"in o&&void 0!==(n=o.set(t,r,e))?n:(t.setAttribute(e,r+""),r):o&&"get"in o&&null!==(n=o.get(t,e))?n:(n=st.find.attr(t,e),null==n?void 0:n))},attrHooks:{type:{set:function(t,e){if(!ot.radioValue&&"radio"===e&&st.nodeName(t,"input")){var r=t.value;return t.setAttribute("type",e),r&&(t.value=r),e}}}},removeAttr:function(t,e){var r,n,o=0,i=e&&e.match(wt);if(i&&1===t.nodeType)for(;r=i[o++];)n=st.propFix[r]||r,st.expr.match.bool.test(r)&&(t[n]=!1),t.removeAttribute(r)}}),ue={set:function(t,e,r){return e===!1?st.removeAttr(t,r):t.setAttribute(r,r),r}},st.each(st.expr.match.bool.source.match(/\w+/g),function(t,e){var r=he[e]||st.find.attr;he[e]=function(t,e,n){var o,i;return n||(i=he[e],he[e]=o,o=null!=r(t,e,n)?e.toLowerCase():null,he[e]=i),o}});var ce=/^(?:input|select|textarea|button)$/i,pe=/^(?:a|area)$/i;st.fn.extend({prop:function(t,e){return kt(this,st.prop,t,e,arguments.length>1)},removeProp:function(t){return this.each(function(){delete this[st.propFix[t]||t]})}}),st.extend({prop:function(t,e,r){var n,o,i=t.nodeType;if(3!==i&&8!==i&&2!==i)return 1===i&&st.isXMLDoc(t)||(e=st.propFix[e]||e,o=st.propHooks[e]),void 0!==r?o&&"set"in o&&void 0!==(n=o.set(t,r,e))?n:t[e]=r:o&&"get"in o&&null!==(n=o.get(t,e))?n:t[e]},propHooks:{tabIndex:{get:function(t){var e=st.find.attr(t,"tabindex");return e?parseInt(e,10):ce.test(t.nodeName)||pe.test(t.nodeName)&&t.href?0:-1}}},propFix:{"for":"htmlFor","class":"className"}}),ot.optSelected||(st.propHooks.selected={get:function(t){var e=t.parentNode;return e&&e.parentNode&&e.parentNode.selectedIndex,null},set:function(t){var e=t.parentNode;e&&(e.selectedIndex,e.parentNode&&e.parentNode.selectedIndex)}}),st.each(["tabIndex","readOnly","maxLength","cellSpacing","cellPadding","rowSpan","colSpan","useMap","frameBorder","contentEditable"],function(){st.propFix[this.toLowerCase()]=this});var _e=/[\t\r\n\f]/g;st.fn.extend({addClass:function(t){var e,r,n,o,i,s,a,l=0;if(st.isFunction(t))return this.each(function(e){st(this).addClass(t.call(this,e,L(this)))});if("string"==typeof t&&t)for(e=t.match(wt)||[];r=this[l++];)if(o=L(r),n=1===r.nodeType&&(" "+o+" ").replace(_e," ")){for(s=0;i=e[s++];)n.indexOf(" "+i+" ")<0&&(n+=i+" ");a=st.trim(n),o!==a&&r.setAttribute("class",a)}return this},removeClass:function(t){var e,r,n,o,i,s,a,l=0;if(st.isFunction(t))return this.each(function(e){st(this).removeClass(t.call(this,e,L(this)))});if(!arguments.length)return this.attr("class","");if("string"==typeof t&&t)for(e=t.match(wt)||[];r=this[l++];)if(o=L(r),n=1===r.nodeType&&(" "+o+" ").replace(_e," ")){for(s=0;i=e[s++];)for(;n.indexOf(" "+i+" ")>-1;)n=n.replace(" "+i+" "," ");a=st.trim(n),o!==a&&r.setAttribute("class",a)}return this},toggleClass:function(t,e){var r=typeof t;return"boolean"==typeof e&&"string"===r?e?this.addClass(t):this.removeClass(t):st.isFunction(t)?this.each(function(r){st(this).toggleClass(t.call(this,r,L(this),e),e)}):this.each(function(){var e,n,o,i;if("string"===r)for(n=0,o=st(this),i=t.match(wt)||[];e=i[n++];)o.hasClass(e)?o.removeClass(e):o.addClass(e);else void 0!==t&&"boolean"!==r||(e=L(this),e&&Tt.set(this,"__className__",e),this.setAttribute&&this.setAttribute("class",e||t===!1?"":Tt.get(this,"__className__")||""))})},hasClass:function(t){var e,r,n=0;for(e=" "+t+" ";r=this[n++];)if(1===r.nodeType&&(" "+L(r)+" ").replace(_e," ").indexOf(e)>-1)return!0;return!1}});var de=/\r/g,fe=/[\x20\t\r\n\f]+/g;st.fn.extend({val:function(t){var e,r,n,o=this[0];{if(arguments.length)return n=st.isFunction(t),this.each(function(r){var o;1===this.nodeType&&(o=n?t.call(this,r,st(this).val()):t,null==o?o="":"number"==typeof o?o+="":st.isArray(o)&&(o=st.map(o,function(t){return null==t?"":t+""})),e=st.valHooks[this.type]||st.valHooks[this.nodeName.toLowerCase()],e&&"set"in e&&void 0!==e.set(this,o,"value")||(this.value=o))});if(o)return e=st.valHooks[o.type]||st.valHooks[o.nodeName.toLowerCase()],e&&"get"in e&&void 0!==(r=e.get(o,"value"))?r:(r=o.value,"string"==typeof r?r.replace(de,""):null==r?"":r)}}}),st.extend({valHooks:{option:{get:function(t){var e=st.find.attr(t,"value");return null!=e?e:st.trim(st.text(t)).replace(fe," ")}},select:{get:function(t){for(var e,r,n=t.options,o=t.selectedIndex,i="select-one"===t.type||o<0,s=i?null:[],a=i?o+1:n.length,l=o<0?a:i?o:0;l<a;l++)if(r=n[l],(r.selected||l===o)&&(ot.optDisabled?!r.disabled:null===r.getAttribute("disabled"))&&(!r.parentNode.disabled||!st.nodeName(r.parentNode,"optgroup"))){if(e=st(r).val(),i)return e;s.push(e)}return s},set:function(t,e){for(var r,n,o=t.options,i=st.makeArray(e),s=o.length;s--;)n=o[s],(n.selected=st.inArray(st.valHooks.option.get(n),i)>-1)&&(r=!0);return r||(t.selectedIndex=-1),i}}}}),st.each(["radio","checkbox"],function(){st.valHooks[this]={set:function(t,e){if(st.isArray(e))return t.checked=st.inArray(st(t).val(),e)>-1}},ot.checkOn||(st.valHooks[this].get=function(t){return null===t.getAttribute("value")?"on":t.value})});var me=/^(?:focusinfocus|focusoutblur)$/;st.extend(st.event,{trigger:function(t,r,n,o){var 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e=this.datum_params[0],r=this.datum_params[1],n=this.datum_params[2],s=this.datum_params[3],a=this.datum_params[4],l=this.datum_params[5],u=this.datum_params[6],h=(t.x-e)/u,c=(t.y-r)/u,p=(t.z-n)/u;t.x=h+l*c-a*p,t.y=-l*h+c+s*p,t.z=a*h-s*c+p}}},e.exports=p},{}],"proj4/lib/datum_transform":[function(t,e,r){var n=1,o=2,i=3,s=5,a=6378137,l=.006694379990141316;e.exports=function(t,e,r){function u(t){return t===n||t===o}var h,c,p;if(t.compare_datums(e))return r;if(t.datum_type===s||e.datum_type===s)return r;var _=t.a,d=t.es,f=e.a,m=e.es,g=t.datum_type;if(g===i)if(0===this.apply_gridshift(t,0,r))t.a=a,t.es=l;else{if(!t.datum_params)return t.a=_,t.es=t.es,r;for(h=1,c=0,p=t.datum_params.length;c<p;c++)h*=t.datum_params[c];if(0===h)return t.a=_,t.es=t.es,r;g=t.datum_params.length>3?o:n}return 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n=t("./constants/Datum"),o=t("./constants/Ellipsoid"),i=t("./extend"),s=t("./datum"),a=1e-10,l=.16666666666666666,u=.04722222222222222,h=.022156084656084655;e.exports=function(t){if(t.datumCode&&"none"!==t.datumCode){var e=n[t.datumCode];e&&(t.datum_params=e.towgs84?e.towgs84.split(","):null,t.ellps=e.ellipse,t.datumName=e.datumName?e.datumName:t.datumCode)}if(!t.a){var r=o[t.ellps]?o[t.ellps]:o.WGS84;i(t,r)}return t.rf&&!t.b&&(t.b=(1-1/t.rf)*t.a),(0===t.rf||Math.abs(t.a-t.b)<a)&&(t.sphere=!0,t.b=t.a),t.a2=t.a*t.a,t.b2=t.b*t.b,t.es=(t.a2-t.b2)/t.a2,t.e=Math.sqrt(t.es),t.R_A&&(t.a*=1-t.es*(l+t.es*(u+t.es*h)),t.a2=t.a*t.a,t.b2=t.b*t.b,t.es=0),t.ep2=(t.a2-t.b2)/t.b2,t.k0||(t.k0=1),t.axis||(t.axis="enu"),t.datum||(t.datum=s(t)),t}},{"./constants/Datum":"proj4/lib/constants/Datum","./constants/Ellipsoid":"proj4/lib/constants/Ellipsoid","./datum":"proj4/lib/datum","./extend":"proj4/lib/extend"}],"proj4/lib/extend":[function(t,e,r){e.exports=function(t,e){t=t||{};var r,n;if(!e)return t;for(n in e)r=e[n],void 0!==r&&(t[n]=r);return t}},{}],"proj4/lib/global":[function(t,e,r){e.exports=function(t){t("EPSG:4326","+title=WGS 84 (long/lat) +proj=longlat +ellps=WGS84 +datum=WGS84 +units=degrees"),t("EPSG:4269","+title=NAD83 (long/lat) +proj=longlat +a=6378137.0 +b=6356752.31414036 +ellps=GRS80 +datum=NAD83 +units=degrees"),t("EPSG:3857","+title=WGS 84 / Pseudo-Mercator +proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +no_defs"),t.WGS84=t["EPSG:4326"],t["EPSG:3785"]=t["EPSG:3857"],t.GOOGLE=t["EPSG:3857"],t["EPSG:900913"]=t["EPSG:3857"],t["EPSG:102113"]=t["EPSG:3857"]}},{}],"proj4/lib/includedProjections":[function(t,e,r){var n=[t("./projections/tmerc"),t("./projections/utm"),t("./projections/sterea"),t("./projections/stere"),t("./projections/somerc"),t("./projections/omerc"),t("./projections/lcc"),t("./projections/krovak"),t("./projections/cass"),t("./projections/laea"),t("./projections/aea"),t("./projections/gnom"),t("./projections/cea"),t("./projections/eqc"),t("./projections/poly"),t("./projections/nzmg"),t("./projections/mill"),t("./projections/sinu"),t("./projections/moll"),t("./projections/eqdc"),t("./projections/vandg"),t("./projections/aeqd")];e.exports=function(t){n.forEach(function(e){t.Proj.projections.add(e)})}},{"./projections/aea":"proj4/lib/projections/aea","./projections/aeqd":"proj4/lib/projections/aeqd","./projections/cass":"proj4/lib/projections/cass","./projections/cea":"proj4/lib/projections/cea","./projections/eqc":"proj4/lib/projections/eqc","./projections/eqdc":"proj4/lib/projections/eqdc","./projections/gnom":"proj4/lib/projections/gnom","./projections/krovak":"proj4/lib/projections/krovak","./projections/laea":"proj4/lib/projections/laea","./projections/lcc":"proj4/lib/projections/lcc","./projections/mill":"proj4/lib/projections/mill","./projections/moll":"proj4/lib/projections/moll","./projections/nzmg":"proj4/lib/projections/nzmg","./projections/omerc":"proj4/lib/projections/omerc","./projections/poly":"proj4/lib/projections/poly","./projections/sinu":"proj4/lib/projections/sinu","./projections/somerc":"proj4/lib/projections/somerc","./projections/stere":"proj4/lib/projections/stere","./projections/sterea":"proj4/lib/projections/sterea","./projections/tmerc":"proj4/lib/projections/tmerc","./projections/utm":"proj4/lib/projections/utm","./projections/vandg":"proj4/lib/projections/vandg"}],proj4:[function(t,e,r){var n=t("./core");n.defaultDatum="WGS84",n.Proj=t("./Proj"),n.WGS84=new n.Proj("WGS84"),n.Point=t("./Point"),n.toPoint=t("./common/toPoint"),n.defs=t("./defs"),n.transform=t("./transform"),n.mgrs=t("mgrs"),n.version=t("../package.json").version,t("./includedProjections")(n),e.exports=n},{"../package.json":"proj4/package.json","./Point":"proj4/lib/Point","./Proj":"proj4/lib/Proj","./common/toPoint":"proj4/lib/common/toPoint","./core":"proj4/lib/core","./defs":"proj4/lib/defs","./includedProjections":"proj4/lib/includedProjections","./transform":"proj4/lib/transform",mgrs:"proj4/node_modules/mgrs/mgrs"}],"proj4/lib/parseCode":[function(t,e,r){function n(t){return"string"==typeof t}function o(t){return t in l}function i(t){var e=["GEOGCS","GEOCCS","PROJCS","LOCAL_CS"];return e.reduce(function(e,r){return e+1+t.indexOf(r)},0)}function s(t){return"+"===t[0]}function a(t){return n(t)?o(t)?l[t]:i(t)?u(t):s(t)?h(t):void 0:t}var l=t("./defs"),u=t("./wkt"),h=t("./projString");e.exports=a},{"./defs":"proj4/lib/defs","./projString":"proj4/lib/projString","./wkt":"proj4/lib/wkt"}],"proj4/lib/projString":[function(t,e,r){var n=.017453292519943295,o=t("./constants/PrimeMeridian"),i=t("./constants/units");e.exports=function(t){var e={},r={};t.split("+").map(function(t){return t.trim()}).filter(function(t){return t}).forEach(function(t){var e=t.split("=");e.push(!0),r[e[0].toLowerCase()]=e[1]});var s,a,l,u={proj:"projName",datum:"datumCode",rf:function(t){e.rf=parseFloat(t)},lat_0:function(t){e.lat0=t*n},lat_1:function(t){e.lat1=t*n},lat_2:function(t){e.lat2=t*n},lat_ts:function(t){e.lat_ts=t*n},lon_0:function(t){e.long0=t*n},lon_1:function(t){e.long1=t*n},lon_2:function(t){e.long2=t*n},alpha:function(t){e.alpha=parseFloat(t)*n},lonc:function(t){e.longc=t*n},x_0:function(t){e.x0=parseFloat(t)},y_0:function(t){e.y0=parseFloat(t)},k_0:function(t){e.k0=parseFloat(t)},k:function(t){e.k0=parseFloat(t)},a:function(t){e.a=parseFloat(t)},b:function(t){e.b=parseFloat(t)},r_a:function(){e.R_A=!0},zone:function(t){e.zone=parseInt(t,10)},south:function(){e.utmSouth=!0},towgs84:function(t){e.datum_params=t.split(",").map(function(t){return parseFloat(t)})},to_meter:function(t){e.to_meter=parseFloat(t)},units:function(t){e.units=t,i[t]&&(e.to_meter=i[t].to_meter)},from_greenwich:function(t){e.from_greenwich=t*n},pm:function(t){e.from_greenwich=(o[t]?o[t]:parseFloat(t))*n},nadgrids:function(t){"@null"===t?e.datumCode="none":e.nadgrids=t},axis:function(t){var r="ewnsud";3===t.length&&r.indexOf(t.substr(0,1))!==-1&&r.indexOf(t.substr(1,1))!==-1&&r.indexOf(t.substr(2,1))!==-1&&(e.axis=t)}};for(s in r)a=r[s],s in u?(l=u[s],"function"==typeof l?l(a):e[l]=a):e[s]=a;return"string"==typeof e.datumCode&&"WGS84"!==e.datumCode&&(e.datumCode=e.datumCode.toLowerCase()),e}},{"./constants/PrimeMeridian":"proj4/lib/constants/PrimeMeridian","./constants/units":"proj4/lib/constants/units"}],"proj4/lib/projections":[function(t,e,r){function n(t,e){var r=s.length;return t.names?(s[r]=t,t.names.forEach(function(t){i[t.toLowerCase()]=r}),this):(console.log(e),!0)}var o=[t("./projections/merc"),t("./projections/longlat")],i={},s=[];r.add=n,r.get=function(t){if(!t)return!1;var e=t.toLowerCase();return"undefined"!=typeof i[e]&&s[i[e]]?s[i[e]]:void 0},r.start=function(){o.forEach(n)}},{"./projections/longlat":"proj4/lib/projections/longlat","./projections/merc":"proj4/lib/projections/merc"}],"proj4/lib/projections/aea":[function(t,e,r){var n=1e-10,o=t("../common/msfnz"),i=t("../common/qsfnz"),s=t("../common/adjust_lon"),a=t("../common/asinz");r.init=function(){Math.abs(this.lat1+this.lat2)<n||(this.temp=this.b/this.a,this.es=1-Math.pow(this.temp,2),this.e3=Math.sqrt(this.es),this.sin_po=Math.sin(this.lat1),this.cos_po=Math.cos(this.lat1),this.t1=this.sin_po,this.con=this.sin_po,this.ms1=o(this.e3,this.sin_po,this.cos_po),this.qs1=i(this.e3,this.sin_po,this.cos_po),this.sin_po=Math.sin(this.lat2),this.cos_po=Math.cos(this.lat2),this.t2=this.sin_po,this.ms2=o(this.e3,this.sin_po,this.cos_po),this.qs2=i(this.e3,this.sin_po,this.cos_po),this.sin_po=Math.sin(this.lat0),this.cos_po=Math.cos(this.lat0),this.t3=this.sin_po,this.qs0=i(this.e3,this.sin_po,this.cos_po),Math.abs(this.lat1-this.lat2)>n?this.ns0=(this.ms1*this.ms1-this.ms2*this.ms2)/(this.qs2-this.qs1):this.ns0=this.con,this.c=this.ms1*this.ms1+this.ns0*this.qs1,this.rh=this.a*Math.sqrt(this.c-this.ns0*this.qs0)/this.ns0)},r.forward=function(t){var e=t.x,r=t.y;this.sin_phi=Math.sin(r),this.cos_phi=Math.cos(r);var n=i(this.e3,this.sin_phi,this.cos_phi),o=this.a*Math.sqrt(this.c-this.ns0*n)/this.ns0,a=this.ns0*s(e-this.long0),l=o*Math.sin(a)+this.x0,u=this.rh-o*Math.cos(a)+this.y0;return t.x=l,t.y=u,t},r.inverse=function(t){var e,r,n,o,i,a;return t.x-=this.x0,t.y=this.rh-t.y+this.y0,this.ns0>=0?(e=Math.sqrt(t.x*t.x+t.y*t.y),n=1):(e=-Math.sqrt(t.x*t.x+t.y*t.y),n=-1),o=0,0!==e&&(o=Math.atan2(n*t.x,n*t.y)),n=e*this.ns0/this.a,this.sphere?a=Math.asin((this.c-n*n)/(2*this.ns0)):(r=(this.c-n*n)/this.ns0,a=this.phi1z(this.e3,r)),i=s(o/this.ns0+this.long0),t.x=i,t.y=a,t},r.phi1z=function(t,e){var r,o,i,s,l,u=a(.5*e);if(t<n)return u;for(var h=t*t,c=1;c<=25;c++)if(r=Math.sin(u),o=Math.cos(u),i=t*r,s=1-i*i,l=.5*s*s/o*(e/(1-h)-r/s+.5/t*Math.log((1-i)/(1+i))),u+=l,Math.abs(l)<=1e-7)return u;return null},r.names=["Albers_Conic_Equal_Area","Albers","aea"]},{"../common/adjust_lon":"proj4/lib/common/adjust_lon","../common/asinz":"proj4/lib/common/asinz","../common/msfnz":"proj4/lib/common/msfnz","../common/qsfnz":"proj4/lib/common/qsfnz"}],"proj4/lib/projections/aeqd":[function(t,e,r){var n=t("../common/adjust_lon"),o=Math.PI/2,i=1e-10,s=t("../common/mlfn"),a=t("../common/e0fn"),l=t("../common/e1fn"),u=t("../common/e2fn"),h=t("../common/e3fn"),c=t("../common/gN"),p=t("../common/asinz"),_=t("../common/imlfn");r.init=function(){this.sin_p12=Math.sin(this.lat0),this.cos_p12=Math.cos(this.lat0)},r.forward=function(t){var e,r,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C,N=t.x,O=t.y,q=Math.sin(t.y),D=Math.cos(t.y),I=n(N-this.long0);return this.sphere?Math.abs(this.sin_p12-1)<=i?(t.x=this.x0+this.a*(o-O)*Math.sin(I),t.y=this.y0-this.a*(o-O)*Math.cos(I),t):Math.abs(this.sin_p12+1)<=i?(t.x=this.x0+this.a*(o+O)*Math.sin(I),t.y=this.y0+this.a*(o+O)*Math.cos(I),t):(S=this.sin_p12*q+this.cos_p12*D*Math.cos(I),j=Math.acos(S),T=j/Math.sin(j),t.x=this.x0+this.a*T*D*Math.sin(I),t.y=this.y0+this.a*T*(this.cos_p12*q-this.sin_p12*D*Math.cos(I)),t):(e=a(this.es),r=l(this.es),p=u(this.es),_=h(this.es),Math.abs(this.sin_p12-1)<=i?(d=this.a*s(e,r,p,_,o),f=this.a*s(e,r,p,_,O),t.x=this.x0+(d-f)*Math.sin(I),t.y=this.y0-(d-f)*Math.cos(I),t):Math.abs(this.sin_p12+1)<=i?(d=this.a*s(e,r,p,_,o),f=this.a*s(e,r,p,_,O),t.x=this.x0+(d+f)*Math.sin(I),t.y=this.y0+(d+f)*Math.cos(I),t):(m=q/D,g=c(this.a,this.e,this.sin_p12),y=c(this.a,this.e,q),v=Math.atan((1-this.es)*m+this.es*g*this.sin_p12/(y*D)),b=Math.atan2(Math.sin(I),this.cos_p12*Math.tan(v)-this.sin_p12*Math.cos(I)),z=0===b?Math.asin(this.cos_p12*Math.sin(v)-this.sin_p12*Math.cos(v)):Math.abs(Math.abs(b)-Math.PI)<=i?-Math.asin(this.cos_p12*Math.sin(v)-this.sin_p12*Math.cos(v)):Math.asin(Math.sin(I)*Math.cos(v)/Math.sin(b)),x=this.e*this.sin_p12/Math.sqrt(1-this.es),w=this.e*this.cos_p12*Math.cos(b)/Math.sqrt(1-this.es),M=x*w,k=w*w,P=z*z,E=P*z,A=E*z,C=A*z,j=g*z*(1-P*k*(1-k)/6+E/8*M*(1-2*k)+A/120*(k*(4-7*k)-3*x*x*(1-7*k))-C/48*M),t.x=this.x0+j*Math.sin(b),t.y=this.y0+j*Math.cos(b),t))},r.inverse=function(t){t.x-=this.x0,t.y-=this.y0;var e,r,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C,N,O;if(this.sphere){if(e=Math.sqrt(t.x*t.x+t.y*t.y),e>2*o*this.a)return;return r=e/this.a,d=Math.sin(r),f=Math.cos(r),m=this.long0,Math.abs(e)<=i?g=this.lat0:(g=p(f*this.sin_p12+t.y*d*this.cos_p12/e),y=Math.abs(this.lat0)-o,m=n(Math.abs(y)<=i?this.lat0>=0?this.long0+Math.atan2(t.x,-t.y):this.long0-Math.atan2(-t.x,t.y):this.long0+Math.atan2(t.x*d,e*this.cos_p12*f-t.y*this.sin_p12*d))),t.x=m,t.y=g,t}return v=a(this.es),b=l(this.es),x=u(this.es),w=h(this.es),Math.abs(this.sin_p12-1)<=i?(M=this.a*s(v,b,x,w,o),e=Math.sqrt(t.x*t.x+t.y*t.y),k=M-e,g=_(k/this.a,v,b,x,w),m=n(this.long0+Math.atan2(t.x,-1*t.y)),t.x=m,t.y=g,t):Math.abs(this.sin_p12+1)<=i?(M=this.a*s(v,b,x,w,o),e=Math.sqrt(t.x*t.x+t.y*t.y),k=e-M,g=_(k/this.a,v,b,x,w),m=n(this.long0+Math.atan2(t.x,t.y)),t.x=m,t.y=g,t):(e=Math.sqrt(t.x*t.x+t.y*t.y),S=Math.atan2(t.x,t.y),j=c(this.a,this.e,this.sin_p12),z=Math.cos(S),P=this.e*this.cos_p12*z,E=-P*P/(1-this.es),A=3*this.es*(1-E)*this.sin_p12*this.cos_p12*z/(1-this.es),C=e/j,N=C-E*(1+E)*Math.pow(C,3)/6-A*(1+3*E)*Math.pow(C,4)/24,O=1-E*N*N/2-C*N*N*N/6,T=Math.asin(this.sin_p12*Math.cos(N)+this.cos_p12*Math.sin(N)*z),m=n(this.long0+Math.asin(Math.sin(S)*Math.sin(N)/Math.cos(T))),g=Math.atan((1-this.es*O*this.sin_p12/Math.sin(T))*Math.tan(T)/(1-this.es)),t.x=m,t.y=g,t)},r.names=["Azimuthal_Equidistant","aeqd"]},{"../common/adjust_lon":"proj4/lib/common/adjust_lon","../common/asinz":"proj4/lib/common/asinz","../common/e0fn":"proj4/lib/common/e0fn","../common/e1fn":"proj4/lib/common/e1fn","../common/e2fn":"proj4/lib/common/e2fn","../common/e3fn":"proj4/lib/common/e3fn","../common/gN":"proj4/lib/common/gN","../common/imlfn":"proj4/lib/common/imlfn","../common/mlfn":"proj4/lib/common/mlfn"}],"proj4/lib/projections/cass":[function(t,e,r){var n=t("../common/mlfn"),o=t("../common/e0fn"),i=t("../common/e1fn"),s=t("../common/e2fn"),a=t("../common/e3fn"),l=t("../common/gN"),u=t("../common/adjust_lon"),h=t("../common/adjust_lat"),c=t("../common/imlfn"),p=Math.PI/2,_=1e-10;r.init=function(){
this.sphere||(this.e0=o(this.es),this.e1=i(this.es),this.e2=s(this.es),this.e3=a(this.es),this.ml0=this.a*n(this.e0,this.e1,this.e2,this.e3,this.lat0))},r.forward=function(t){var e,r,o=t.x,i=t.y;if(o=u(o-this.long0),this.sphere)e=this.a*Math.asin(Math.cos(i)*Math.sin(o)),r=this.a*(Math.atan2(Math.tan(i),Math.cos(o))-this.lat0);else{var s=Math.sin(i),a=Math.cos(i),h=l(this.a,this.e,s),c=Math.tan(i)*Math.tan(i),p=o*Math.cos(i),_=p*p,d=this.es*a*a/(1-this.es),f=this.a*n(this.e0,this.e1,this.e2,this.e3,i);e=h*p*(1-_*c*(1/6-(8-c+8*d)*_/120)),r=f-this.ml0+h*s/a*_*(.5+(5-c+6*d)*_/24)}return t.x=e+this.x0,t.y=r+this.y0,t},r.inverse=function(t){t.x-=this.x0,t.y-=this.y0;var e,r,n=t.x/this.a,o=t.y/this.a;if(this.sphere){var i=o+this.lat0;e=Math.asin(Math.sin(i)*Math.cos(n)),r=Math.atan2(Math.tan(n),Math.cos(i))}else{var s=this.ml0/this.a+o,a=c(s,this.e0,this.e1,this.e2,this.e3);if(Math.abs(Math.abs(a)-p)<=_)return t.x=this.long0,t.y=p,o<0&&(t.y*=-1),t;var d=l(this.a,this.e,Math.sin(a)),f=d*d*d/this.a/this.a*(1-this.es),m=Math.pow(Math.tan(a),2),g=n*this.a/d,y=g*g;e=a-d*Math.tan(a)/f*g*g*(.5-(1+3*m)*g*g/24),r=g*(1-y*(m/3+(1+3*m)*m*y/15))/Math.cos(a)}return t.x=u(r+this.long0),t.y=h(e),t},r.names=["Cassini","Cassini_Soldner","cass"]},{"../common/adjust_lat":"proj4/lib/common/adjust_lat","../common/adjust_lon":"proj4/lib/common/adjust_lon","../common/e0fn":"proj4/lib/common/e0fn","../common/e1fn":"proj4/lib/common/e1fn","../common/e2fn":"proj4/lib/common/e2fn","../common/e3fn":"proj4/lib/common/e3fn","../common/gN":"proj4/lib/common/gN","../common/imlfn":"proj4/lib/common/imlfn","../common/mlfn":"proj4/lib/common/mlfn"}],"proj4/lib/projections/cea":[function(t,e,r){var n=t("../common/adjust_lon"),o=t("../common/qsfnz"),i=t("../common/msfnz"),s=t("../common/iqsfnz");r.init=function(){this.sphere||(this.k0=i(this.e,Math.sin(this.lat_ts),Math.cos(this.lat_ts)))},r.forward=function(t){var e,r,i=t.x,s=t.y,a=n(i-this.long0);if(this.sphere)e=this.x0+this.a*a*Math.cos(this.lat_ts),r=this.y0+this.a*Math.sin(s)/Math.cos(this.lat_ts);else{var l=o(this.e,Math.sin(s));e=this.x0+this.a*this.k0*a,r=this.y0+this.a*l*.5/this.k0}return t.x=e,t.y=r,t},r.inverse=function(t){t.x-=this.x0,t.y-=this.y0;var e,r;return this.sphere?(e=n(this.long0+t.x/this.a/Math.cos(this.lat_ts)),r=Math.asin(t.y/this.a*Math.cos(this.lat_ts))):(r=s(this.e,2*t.y*this.k0/this.a),e=n(this.long0+t.x/(this.a*this.k0))),t.x=e,t.y=r,t},r.names=["cea"]},{"../common/adjust_lon":"proj4/lib/common/adjust_lon","../common/iqsfnz":"proj4/lib/common/iqsfnz","../common/msfnz":"proj4/lib/common/msfnz","../common/qsfnz":"proj4/lib/common/qsfnz"}],"proj4/lib/projections/eqc":[function(t,e,r){var n=t("../common/adjust_lon"),o=t("../common/adjust_lat");r.init=function(){this.x0=this.x0||0,this.y0=this.y0||0,this.lat0=this.lat0||0,this.long0=this.long0||0,this.lat_ts=this.lat_ts||0,this.title=this.title||"Equidistant Cylindrical (Plate Carre)",this.rc=Math.cos(this.lat_ts)},r.forward=function(t){var e=t.x,r=t.y,i=n(e-this.long0),s=o(r-this.lat0);return t.x=this.x0+this.a*i*this.rc,t.y=this.y0+this.a*s,t},r.inverse=function(t){var e=t.x,r=t.y;return t.x=n(this.long0+(e-this.x0)/(this.a*this.rc)),t.y=o(this.lat0+(r-this.y0)/this.a),t},r.names=["Equirectangular","Equidistant_Cylindrical","eqc"]},{"../common/adjust_lat":"proj4/lib/common/adjust_lat","../common/adjust_lon":"proj4/lib/common/adjust_lon"}],"proj4/lib/projections/eqdc":[function(t,e,r){var n=t("../common/e0fn"),o=t("../common/e1fn"),i=t("../common/e2fn"),s=t("../common/e3fn"),a=t("../common/msfnz"),l=t("../common/mlfn"),u=t("../common/adjust_lon"),h=t("../common/adjust_lat"),c=t("../common/imlfn"),p=1e-10;r.init=function(){Math.abs(this.lat1+this.lat2)<p||(this.lat2=this.lat2||this.lat1,this.temp=this.b/this.a,this.es=1-Math.pow(this.temp,2),this.e=Math.sqrt(this.es),this.e0=n(this.es),this.e1=o(this.es),this.e2=i(this.es),this.e3=s(this.es),this.sinphi=Math.sin(this.lat1),this.cosphi=Math.cos(this.lat1),this.ms1=a(this.e,this.sinphi,this.cosphi),this.ml1=l(this.e0,this.e1,this.e2,this.e3,this.lat1),Math.abs(this.lat1-this.lat2)<p?this.ns=this.sinphi:(this.sinphi=Math.sin(this.lat2),this.cosphi=Math.cos(this.lat2),this.ms2=a(this.e,this.sinphi,this.cosphi),this.ml2=l(this.e0,this.e1,this.e2,this.e3,this.lat2),this.ns=(this.ms1-this.ms2)/(this.ml2-this.ml1)),this.g=this.ml1+this.ms1/this.ns,this.ml0=l(this.e0,this.e1,this.e2,this.e3,this.lat0),this.rh=this.a*(this.g-this.ml0))},r.forward=function(t){var e,r=t.x,n=t.y;if(this.sphere)e=this.a*(this.g-n);else{var o=l(this.e0,this.e1,this.e2,this.e3,n);e=this.a*(this.g-o)}var i=this.ns*u(r-this.long0),s=this.x0+e*Math.sin(i),a=this.y0+this.rh-e*Math.cos(i);return t.x=s,t.y=a,t},r.inverse=function(t){t.x-=this.x0,t.y=this.rh-t.y+this.y0;var e,r,n,o;this.ns>=0?(r=Math.sqrt(t.x*t.x+t.y*t.y),e=1):(r=-Math.sqrt(t.x*t.x+t.y*t.y),e=-1);var i=0;if(0!==r&&(i=Math.atan2(e*t.x,e*t.y)),this.sphere)return o=u(this.long0+i/this.ns),n=h(this.g-r/this.a),t.x=o,t.y=n,t;var s=this.g-r/this.a;return n=c(s,this.e0,this.e1,this.e2,this.e3),o=u(this.long0+i/this.ns),t.x=o,t.y=n,t},r.names=["Equidistant_Conic","eqdc"]},{"../common/adjust_lat":"proj4/lib/common/adjust_lat","../common/adjust_lon":"proj4/lib/common/adjust_lon","../common/e0fn":"proj4/lib/common/e0fn","../common/e1fn":"proj4/lib/common/e1fn","../common/e2fn":"proj4/lib/common/e2fn","../common/e3fn":"proj4/lib/common/e3fn","../common/imlfn":"proj4/lib/common/imlfn","../common/mlfn":"proj4/lib/common/mlfn","../common/msfnz":"proj4/lib/common/msfnz"}],"proj4/lib/projections/gauss":[function(t,e,r){var n=Math.PI/4,o=t("../common/srat"),i=Math.PI/2,s=20;r.init=function(){var t=Math.sin(this.lat0),e=Math.cos(this.lat0);e*=e,this.rc=Math.sqrt(1-this.es)/(1-this.es*t*t),this.C=Math.sqrt(1+this.es*e*e/(1-this.es)),this.phic0=Math.asin(t/this.C),this.ratexp=.5*this.C*this.e,this.K=Math.tan(.5*this.phic0+n)/(Math.pow(Math.tan(.5*this.lat0+n),this.C)*o(this.e*t,this.ratexp))},r.forward=function(t){var e=t.x,r=t.y;return t.y=2*Math.atan(this.K*Math.pow(Math.tan(.5*r+n),this.C)*o(this.e*Math.sin(r),this.ratexp))-i,t.x=this.C*e,t},r.inverse=function(t){for(var e=1e-14,r=t.x/this.C,a=t.y,l=Math.pow(Math.tan(.5*a+n)/this.K,1/this.C),u=s;u>0&&(a=2*Math.atan(l*o(this.e*Math.sin(t.y),-.5*this.e))-i,!(Math.abs(a-t.y)<e));--u)t.y=a;return u?(t.x=r,t.y=a,t):null},r.names=["gauss"]},{"../common/srat":"proj4/lib/common/srat"}],"proj4/lib/projections/gnom":[function(t,e,r){var n=t("../common/adjust_lon"),o=1e-10,i=t("../common/asinz");r.init=function(){this.sin_p14=Math.sin(this.lat0),this.cos_p14=Math.cos(this.lat0),this.infinity_dist=1e3*this.a,this.rc=1},r.forward=function(t){var e,r,i,s,a,l,u,h,c=t.x,p=t.y;return i=n(c-this.long0),e=Math.sin(p),r=Math.cos(p),s=Math.cos(i),l=this.sin_p14*e+this.cos_p14*r*s,a=1,l>0||Math.abs(l)<=o?(u=this.x0+this.a*a*r*Math.sin(i)/l,h=this.y0+this.a*a*(this.cos_p14*e-this.sin_p14*r*s)/l):(u=this.x0+this.infinity_dist*r*Math.sin(i),h=this.y0+this.infinity_dist*(this.cos_p14*e-this.sin_p14*r*s)),t.x=u,t.y=h,t},r.inverse=function(t){var e,r,o,s,a,l;return t.x=(t.x-this.x0)/this.a,t.y=(t.y-this.y0)/this.a,t.x/=this.k0,t.y/=this.k0,(e=Math.sqrt(t.x*t.x+t.y*t.y))?(s=Math.atan2(e,this.rc),r=Math.sin(s),o=Math.cos(s),l=i(o*this.sin_p14+t.y*r*this.cos_p14/e),a=Math.atan2(t.x*r,e*this.cos_p14*o-t.y*this.sin_p14*r),a=n(this.long0+a)):(l=this.phic0,a=0),t.x=a,t.y=l,t},r.names=["gnom"]},{"../common/adjust_lon":"proj4/lib/common/adjust_lon","../common/asinz":"proj4/lib/common/asinz"}],"proj4/lib/projections/krovak":[function(t,e,r){var n=t("../common/adjust_lon");r.init=function(){this.a=6377397.155,this.es=.006674372230614,this.e=Math.sqrt(this.es),this.lat0||(this.lat0=.863937979737193),this.long0||(this.long0=.4334234309119251),this.k0||(this.k0=.9999),this.s45=.785398163397448,this.s90=2*this.s45,this.fi0=this.lat0,this.e2=this.es,this.e=Math.sqrt(this.e2),this.alfa=Math.sqrt(1+this.e2*Math.pow(Math.cos(this.fi0),4)/(1-this.e2)),this.uq=1.04216856380474,this.u0=Math.asin(Math.sin(this.fi0)/this.alfa),this.g=Math.pow((1+this.e*Math.sin(this.fi0))/(1-this.e*Math.sin(this.fi0)),this.alfa*this.e/2),this.k=Math.tan(this.u0/2+this.s45)/Math.pow(Math.tan(this.fi0/2+this.s45),this.alfa)*this.g,this.k1=this.k0,this.n0=this.a*Math.sqrt(1-this.e2)/(1-this.e2*Math.pow(Math.sin(this.fi0),2)),this.s0=1.37008346281555,this.n=Math.sin(this.s0),this.ro0=this.k1*this.n0/Math.tan(this.s0),this.ad=this.s90-this.uq},r.forward=function(t){var e,r,o,i,s,a,l,u=t.x,h=t.y,c=n(u-this.long0);return e=Math.pow((1+this.e*Math.sin(h))/(1-this.e*Math.sin(h)),this.alfa*this.e/2),r=2*(Math.atan(this.k*Math.pow(Math.tan(h/2+this.s45),this.alfa)/e)-this.s45),o=-c*this.alfa,i=Math.asin(Math.cos(this.ad)*Math.sin(r)+Math.sin(this.ad)*Math.cos(r)*Math.cos(o)),s=Math.asin(Math.cos(r)*Math.sin(o)/Math.cos(i)),a=this.n*s,l=this.ro0*Math.pow(Math.tan(this.s0/2+this.s45),this.n)/Math.pow(Math.tan(i/2+this.s45),this.n),t.y=l*Math.cos(a)/1,t.x=l*Math.sin(a)/1,this.czech||(t.y*=-1,t.x*=-1),t},r.inverse=function(t){var e,r,n,o,i,s,a,l,u=t.x;t.x=t.y,t.y=u,this.czech||(t.y*=-1,t.x*=-1),s=Math.sqrt(t.x*t.x+t.y*t.y),i=Math.atan2(t.y,t.x),o=i/Math.sin(this.s0),n=2*(Math.atan(Math.pow(this.ro0/s,1/this.n)*Math.tan(this.s0/2+this.s45))-this.s45),e=Math.asin(Math.cos(this.ad)*Math.sin(n)-Math.sin(this.ad)*Math.cos(n)*Math.cos(o)),r=Math.asin(Math.cos(n)*Math.sin(o)/Math.cos(e)),t.x=this.long0-r/this.alfa,a=e,l=0;var h=0;do t.y=2*(Math.atan(Math.pow(this.k,-1/this.alfa)*Math.pow(Math.tan(e/2+this.s45),1/this.alfa)*Math.pow((1+this.e*Math.sin(a))/(1-this.e*Math.sin(a)),this.e/2))-this.s45),Math.abs(a-t.y)<1e-10&&(l=1),a=t.y,h+=1;while(0===l&&h<15);return h>=15?null:t},r.names=["Krovak","krovak"]},{"../common/adjust_lon":"proj4/lib/common/adjust_lon"}],"proj4/lib/projections/laea":[function(t,e,r){var n=Math.PI/2,o=Math.PI/4,i=1e-10,s=t("../common/qsfnz"),a=t("../common/adjust_lon");r.S_POLE=1,r.N_POLE=2,r.EQUIT=3,r.OBLIQ=4,r.init=function(){var t=Math.abs(this.lat0);if(Math.abs(t-n)<i?this.mode=this.lat0<0?this.S_POLE:this.N_POLE:Math.abs(t)<i?this.mode=this.EQUIT:this.mode=this.OBLIQ,this.es>0){var e;switch(this.qp=s(this.e,1),this.mmf=.5/(1-this.es),this.apa=this.authset(this.es),this.mode){case this.N_POLE:this.dd=1;break;case this.S_POLE:this.dd=1;break;case this.EQUIT:this.rq=Math.sqrt(.5*this.qp),this.dd=1/this.rq,this.xmf=1,this.ymf=.5*this.qp;break;case this.OBLIQ:this.rq=Math.sqrt(.5*this.qp),e=Math.sin(this.lat0),this.sinb1=s(this.e,e)/this.qp,this.cosb1=Math.sqrt(1-this.sinb1*this.sinb1),this.dd=Math.cos(this.lat0)/(Math.sqrt(1-this.es*e*e)*this.rq*this.cosb1),this.ymf=(this.xmf=this.rq)/this.dd,this.xmf*=this.dd}}else this.mode===this.OBLIQ&&(this.sinph0=Math.sin(this.lat0),this.cosph0=Math.cos(this.lat0))},r.forward=function(t){var e,r,l,u,h,c,p,_,d,f,m=t.x,g=t.y;if(m=a(m-this.long0),this.sphere){if(h=Math.sin(g),f=Math.cos(g),l=Math.cos(m),this.mode===this.OBLIQ||this.mode===this.EQUIT){if(r=this.mode===this.EQUIT?1+f*l:1+this.sinph0*h+this.cosph0*f*l,r<=i)return null;r=Math.sqrt(2/r),e=r*f*Math.sin(m),r*=this.mode===this.EQUIT?h:this.cosph0*h-this.sinph0*f*l}else if(this.mode===this.N_POLE||this.mode===this.S_POLE){if(this.mode===this.N_POLE&&(l=-l),Math.abs(g+this.phi0)<i)return null;r=o-.5*g,r=2*(this.mode===this.S_POLE?Math.cos(r):Math.sin(r)),e=r*Math.sin(m),r*=l}}else{switch(p=0,_=0,d=0,l=Math.cos(m),u=Math.sin(m),h=Math.sin(g),c=s(this.e,h),this.mode!==this.OBLIQ&&this.mode!==this.EQUIT||(p=c/this.qp,_=Math.sqrt(1-p*p)),this.mode){case this.OBLIQ:d=1+this.sinb1*p+this.cosb1*_*l;break;case this.EQUIT:d=1+_*l;break;case this.N_POLE:d=n+g,c=this.qp-c;break;case this.S_POLE:d=g-n,c=this.qp+c}if(Math.abs(d)<i)return null;switch(this.mode){case this.OBLIQ:case this.EQUIT:d=Math.sqrt(2/d),r=this.mode===this.OBLIQ?this.ymf*d*(this.cosb1*p-this.sinb1*_*l):(d=Math.sqrt(2/(1+_*l)))*p*this.ymf,e=this.xmf*d*_*u;break;case this.N_POLE:case this.S_POLE:c>=0?(e=(d=Math.sqrt(c))*u,r=l*(this.mode===this.S_POLE?d:-d)):e=r=0}}return t.x=this.a*e+this.x0,t.y=this.a*r+this.y0,t},r.inverse=function(t){t.x-=this.x0,t.y-=this.y0;var e,r,o,s,l,u,h,c=t.x/this.a,p=t.y/this.a;if(this.sphere){var _,d=0,f=0;if(_=Math.sqrt(c*c+p*p),r=.5*_,r>1)return null;switch(r=2*Math.asin(r),this.mode!==this.OBLIQ&&this.mode!==this.EQUIT||(f=Math.sin(r),d=Math.cos(r)),this.mode){case this.EQUIT:r=Math.abs(_)<=i?0:Math.asin(p*f/_),c*=f,p=d*_;break;case this.OBLIQ:r=Math.abs(_)<=i?this.phi0:Math.asin(d*this.sinph0+p*f*this.cosph0/_),c*=f*this.cosph0,p=(d-Math.sin(r)*this.sinph0)*_;break;case this.N_POLE:p=-p,r=n-r;break;case this.S_POLE:r-=n}e=0!==p||this.mode!==this.EQUIT&&this.mode!==this.OBLIQ?Math.atan2(c,p):0}else{if(h=0,this.mode===this.OBLIQ||this.mode===this.EQUIT){if(c/=this.dd,p*=this.dd,u=Math.sqrt(c*c+p*p),u<i)return t.x=0,t.y=this.phi0,t;s=2*Math.asin(.5*u/this.rq),o=Math.cos(s),c*=s=Math.sin(s),this.mode===this.OBLIQ?(h=o*this.sinb1+p*s*this.cosb1/u,l=this.qp*h,p=u*this.cosb1*o-p*this.sinb1*s):(h=p*s/u,l=this.qp*h,p=u*o)}else if(this.mode===this.N_POLE||this.mode===this.S_POLE){if(this.mode===this.N_POLE&&(p=-p),l=c*c+p*p,!l)return t.x=0,t.y=this.phi0,t;h=1-l/this.qp,this.mode===this.S_POLE&&(h=-h)}e=Math.atan2(c,p),r=this.authlat(Math.asin(h),this.apa)}return t.x=a(this.long0+e),t.y=r,t},r.P00=.3333333333333333,r.P01=.17222222222222222,r.P02=.10257936507936508,r.P10=.06388888888888888,r.P11=.0664021164021164,r.P20=.016415012942191543,r.authset=function(t){var e,r=[];return r[0]=t*this.P00,e=t*t,r[0]+=e*this.P01,r[1]=e*this.P10,e*=t,r[0]+=e*this.P02,r[1]+=e*this.P11,r[2]=e*this.P20,r},r.authlat=function(t,e){var r=t+t;return t+e[0]*Math.sin(r)+e[1]*Math.sin(r+r)+e[2]*Math.sin(r+r+r)},r.names=["Lambert Azimuthal Equal Area","Lambert_Azimuthal_Equal_Area","laea"]},{"../common/adjust_lon":"proj4/lib/common/adjust_lon","../common/qsfnz":"proj4/lib/common/qsfnz"}],"proj4/lib/projections/lcc":[function(t,e,r){var n=1e-10,o=t("../common/msfnz"),i=t("../common/tsfnz"),s=Math.PI/2,a=t("../common/sign"),l=t("../common/adjust_lon"),u=t("../common/phi2z");r.init=function(){if(this.lat2||(this.lat2=this.lat1),this.k0||(this.k0=1),this.x0=this.x0||0,this.y0=this.y0||0,!(Math.abs(this.lat1+this.lat2)<n)){var t=this.b/this.a;this.e=Math.sqrt(1-t*t);var e=Math.sin(this.lat1),r=Math.cos(this.lat1),s=o(this.e,e,r),a=i(this.e,this.lat1,e),l=Math.sin(this.lat2),u=Math.cos(this.lat2),h=o(this.e,l,u),c=i(this.e,this.lat2,l),p=i(this.e,this.lat0,Math.sin(this.lat0));Math.abs(this.lat1-this.lat2)>n?this.ns=Math.log(s/h)/Math.log(a/c):this.ns=e,isNaN(this.ns)&&(this.ns=e),this.f0=s/(this.ns*Math.pow(a,this.ns)),this.rh=this.a*this.f0*Math.pow(p,this.ns),this.title||(this.title="Lambert Conformal Conic")}},r.forward=function(t){var e=t.x,r=t.y;Math.abs(2*Math.abs(r)-Math.PI)<=n&&(r=a(r)*(s-2*n));var o,u,h=Math.abs(Math.abs(r)-s);if(h>n)o=i(this.e,r,Math.sin(r)),u=this.a*this.f0*Math.pow(o,this.ns);else{if(h=r*this.ns,h<=0)return null;u=0}var c=this.ns*l(e-this.long0);return t.x=this.k0*(u*Math.sin(c))+this.x0,t.y=this.k0*(this.rh-u*Math.cos(c))+this.y0,t},r.inverse=function(t){var e,r,n,o,i,a=(t.x-this.x0)/this.k0,h=this.rh-(t.y-this.y0)/this.k0;this.ns>0?(e=Math.sqrt(a*a+h*h),r=1):(e=-Math.sqrt(a*a+h*h),r=-1);var c=0;if(0!==e&&(c=Math.atan2(r*a,r*h)),0!==e||this.ns>0){if(r=1/this.ns,n=Math.pow(e/(this.a*this.f0),r),o=u(this.e,n),o===-9999)return null}else o=-s;return i=l(c/this.ns+this.long0),t.x=i,t.y=o,t},r.names=["Lambert Tangential Conformal Conic Projection","Lambert_Conformal_Conic","Lambert_Conformal_Conic_2SP","lcc"]},{"../common/adjust_lon":"proj4/lib/common/adjust_lon","../common/msfnz":"proj4/lib/common/msfnz","../common/phi2z":"proj4/lib/common/phi2z","../common/sign":"proj4/lib/common/sign","../common/tsfnz":"proj4/lib/common/tsfnz"}],"proj4/lib/projections/longlat":[function(t,e,r){function n(t){return t}r.init=function(){},r.forward=n,r.inverse=n,r.names=["longlat","identity"]},{}],"proj4/lib/projections/merc":[function(t,e,r){var n=t("../common/msfnz"),o=Math.PI/2,i=1e-10,s=57.29577951308232,a=t("../common/adjust_lon"),l=Math.PI/4,u=t("../common/tsfnz"),h=t("../common/phi2z");r.init=function(){var t=this.b/this.a;this.es=1-t*t,"x0"in this||(this.x0=0),"y0"in this||(this.y0=0),this.e=Math.sqrt(this.es),this.lat_ts?this.sphere?this.k0=Math.cos(this.lat_ts):this.k0=n(this.e,Math.sin(this.lat_ts),Math.cos(this.lat_ts)):this.k0||(this.k?this.k0=this.k:this.k0=1)},r.forward=function(t){var e=t.x,r=t.y;if(r*s>90&&r*s<-90&&e*s>180&&e*s<-180)return null;var n,h;if(Math.abs(Math.abs(r)-o)<=i)return null;if(this.sphere)n=this.x0+this.a*this.k0*a(e-this.long0),h=this.y0+this.a*this.k0*Math.log(Math.tan(l+.5*r));else{var c=Math.sin(r),p=u(this.e,r,c);n=this.x0+this.a*this.k0*a(e-this.long0),h=this.y0-this.a*this.k0*Math.log(p)}return t.x=n,t.y=h,t},r.inverse=function(t){var e,r,n=t.x-this.x0,i=t.y-this.y0;if(this.sphere)r=o-2*Math.atan(Math.exp(-i/(this.a*this.k0)));else{var s=Math.exp(-i/(this.a*this.k0));if(r=h(this.e,s),r===-9999)return null}return e=a(this.long0+n/(this.a*this.k0)),t.x=e,t.y=r,t},r.names=["Mercator","Popular Visualisation Pseudo Mercator","Mercator_1SP","Mercator_Auxiliary_Sphere","merc"]},{"../common/adjust_lon":"proj4/lib/common/adjust_lon","../common/msfnz":"proj4/lib/common/msfnz","../common/phi2z":"proj4/lib/common/phi2z","../common/tsfnz":"proj4/lib/common/tsfnz"}],"proj4/lib/projections/mill":[function(t,e,r){var n=t("../common/adjust_lon");r.init=function(){},r.forward=function(t){var e=t.x,r=t.y,o=n(e-this.long0),i=this.x0+this.a*o,s=this.y0+this.a*Math.log(Math.tan(Math.PI/4+r/2.5))*1.25;return t.x=i,t.y=s,t},r.inverse=function(t){t.x-=this.x0,t.y-=this.y0;var e=n(this.long0+t.x/this.a),r=2.5*(Math.atan(Math.exp(.8*t.y/this.a))-Math.PI/4);return t.x=e,t.y=r,t},r.names=["Miller_Cylindrical","mill"]},{"../common/adjust_lon":"proj4/lib/common/adjust_lon"}],"proj4/lib/projections/moll":[function(t,e,r){var n=t("../common/adjust_lon"),o=1e-10;r.init=function(){},r.forward=function(t){for(var e=t.x,r=t.y,i=n(e-this.long0),s=r,a=Math.PI*Math.sin(r),l=0;!0;l++){var u=-(s+Math.sin(s)-a)/(1+Math.cos(s));if(s+=u,Math.abs(u)<o)break}s/=2,Math.PI/2-Math.abs(r)<o&&(i=0);var h=.900316316158*this.a*i*Math.cos(s)+this.x0,c=1.4142135623731*this.a*Math.sin(s)+this.y0;return t.x=h,t.y=c,t},r.inverse=function(t){var e,r;t.x-=this.x0,t.y-=this.y0,r=t.y/(1.4142135623731*this.a),Math.abs(r)>.999999999999&&(r=.999999999999),e=Math.asin(r);var o=n(this.long0+t.x/(.900316316158*this.a*Math.cos(e)));o<-Math.PI&&(o=-Math.PI),o>Math.PI&&(o=Math.PI),r=(2*e+Math.sin(2*e))/Math.PI,Math.abs(r)>1&&(r=1);var i=Math.asin(r);return t.x=o,t.y=i,t},r.names=["Mollweide","moll"]},{"../common/adjust_lon":"proj4/lib/common/adjust_lon"}],"proj4/lib/projections/nzmg":[function(t,e,r){var n=484813681109536e-20;r.iterations=1,r.init=function(){this.A=[],this.A[1]=.6399175073,this.A[2]=-.1358797613,this.A[3]=.063294409,this.A[4]=-.02526853,this.A[5]=.0117879,this.A[6]=-.0055161,this.A[7]=.0026906,this.A[8]=-.001333,this.A[9]=67e-5,this.A[10]=-34e-5,this.B_re=[],this.B_im=[],this.B_re[1]=.7557853228,this.B_im[1]=0,this.B_re[2]=.249204646,this.B_im[2]=.003371507,this.B_re[3]=-.001541739,this.B_im[3]=.04105856,this.B_re[4]=-.10162907,this.B_im[4]=.01727609,this.B_re[5]=-.26623489,this.B_im[5]=-.36249218,this.B_re[6]=-.6870983,this.B_im[6]=-1.1651967,this.C_re=[],this.C_im=[],this.C_re[1]=1.3231270439,this.C_im[1]=0,this.C_re[2]=-.577245789,this.C_im[2]=-.007809598,this.C_re[3]=.508307513,this.C_im[3]=-.112208952,this.C_re[4]=-.15094762,this.C_im[4]=.18200602,this.C_re[5]=1.01418179,this.C_im[5]=1.64497696,this.C_re[6]=1.9660549,this.C_im[6]=2.5127645,this.D=[],this.D[1]=1.5627014243,this.D[2]=.5185406398,this.D[3]=-.03333098,this.D[4]=-.1052906,this.D[5]=-.0368594,this.D[6]=.007317,this.D[7]=.0122,this.D[8]=.00394,this.D[9]=-.0013},r.forward=function(t){var e,r=t.x,o=t.y,i=o-this.lat0,s=r-this.long0,a=i/n*1e-5,l=s,u=1,h=0;for(e=1;e<=10;e++)u*=a,h+=this.A[e]*u;var c,p,_=h,d=l,f=1,m=0,g=0,y=0;for(e=1;e<=6;e++)c=f*_-m*d,p=m*_+f*d,f=c,m=p,g=g+this.B_re[e]*f-this.B_im[e]*m,y=y+this.B_im[e]*f+this.B_re[e]*m;return t.x=y*this.a+this.x0,t.y=g*this.a+this.y0,t},r.inverse=function(t){var e,r,o,i=t.x,s=t.y,a=i-this.x0,l=s-this.y0,u=l/this.a,h=a/this.a,c=1,p=0,_=0,d=0;for(e=1;e<=6;e++)r=c*u-p*h,o=p*u+c*h,c=r,p=o,_=_+this.C_re[e]*c-this.C_im[e]*p,d=d+this.C_im[e]*c+this.C_re[e]*p;for(var f=0;f<this.iterations;f++){var m,g,y=_,v=d,b=u,x=h;for(e=2;e<=6;e++)m=y*_-v*d,g=v*_+y*d,y=m,v=g,b+=(e-1)*(this.B_re[e]*y-this.B_im[e]*v),x+=(e-1)*(this.B_im[e]*y+this.B_re[e]*v);y=1,v=0;var w=this.B_re[1],M=this.B_im[1];for(e=2;e<=6;e++)m=y*_-v*d,g=v*_+y*d,y=m,v=g,w+=e*(this.B_re[e]*y-this.B_im[e]*v),M+=e*(this.B_im[e]*y+this.B_re[e]*v);var k=w*w+M*M;_=(b*w+x*M)/k,d=(x*w-b*M)/k}var j=_,T=d,S=1,z=0;for(e=1;e<=9;e++)S*=j,z+=this.D[e]*S;var P=this.lat0+z*n*1e5,E=this.long0+T;return t.x=E,t.y=P,t},r.names=["New_Zealand_Map_Grid","nzmg"]},{}],"proj4/lib/projections/omerc":[function(t,e,r){var n=t("../common/tsfnz"),o=t("../common/adjust_lon"),i=t("../common/phi2z"),s=Math.PI/2,a=Math.PI/4,l=1e-10;r.init=function(){this.no_off=this.no_off||!1,this.no_rot=this.no_rot||!1,isNaN(this.k0)&&(this.k0=1);var t=Math.sin(this.lat0),e=Math.cos(this.lat0),r=this.e*t;this.bl=Math.sqrt(1+this.es/(1-this.es)*Math.pow(e,4)),this.al=this.a*this.bl*this.k0*Math.sqrt(1-this.es)/(1-r*r);var i=n(this.e,this.lat0,t),s=this.bl/e*Math.sqrt((1-this.es)/(1-r*r));s*s<1&&(s=1);var a,l;if(isNaN(this.longc)){var u=n(this.e,this.lat1,Math.sin(this.lat1)),h=n(this.e,this.lat2,Math.sin(this.lat2));this.lat0>=0?this.el=(s+Math.sqrt(s*s-1))*Math.pow(i,this.bl):this.el=(s-Math.sqrt(s*s-1))*Math.pow(i,this.bl);var c=Math.pow(u,this.bl),p=Math.pow(h,this.bl);a=this.el/c,l=.5*(a-1/a);var _=(this.el*this.el-p*c)/(this.el*this.el+p*c),d=(p-c)/(p+c),f=o(this.long1-this.long2);this.long0=.5*(this.long1+this.long2)-Math.atan(_*Math.tan(.5*this.bl*f)/d)/this.bl,this.long0=o(this.long0);var m=o(this.long1-this.long0);this.gamma0=Math.atan(Math.sin(this.bl*m)/l),this.alpha=Math.asin(s*Math.sin(this.gamma0))}else a=this.lat0>=0?s+Math.sqrt(s*s-1):s-Math.sqrt(s*s-1),this.el=a*Math.pow(i,this.bl),l=.5*(a-1/a),this.gamma0=Math.asin(Math.sin(this.alpha)/s),this.long0=this.longc-Math.asin(l*Math.tan(this.gamma0))/this.bl;this.no_off?this.uc=0:this.lat0>=0?this.uc=this.al/this.bl*Math.atan2(Math.sqrt(s*s-1),Math.cos(this.alpha)):this.uc=-1*this.al/this.bl*Math.atan2(Math.sqrt(s*s-1),Math.cos(this.alpha))},r.forward=function(t){var e,r,i,u=t.x,h=t.y,c=o(u-this.long0);if(Math.abs(Math.abs(h)-s)<=l)i=h>0?-1:1,r=this.al/this.bl*Math.log(Math.tan(a+i*this.gamma0*.5)),e=-1*i*s*this.al/this.bl;else{var p=n(this.e,h,Math.sin(h)),_=this.el/Math.pow(p,this.bl),d=.5*(_-1/_),f=.5*(_+1/_),m=Math.sin(this.bl*c),g=(d*Math.sin(this.gamma0)-m*Math.cos(this.gamma0))/f;r=Math.abs(Math.abs(g)-1)<=l?Number.POSITIVE_INFINITY:.5*this.al*Math.log((1-g)/(1+g))/this.bl,e=Math.abs(Math.cos(this.bl*c))<=l?this.al*this.bl*c:this.al*Math.atan2(d*Math.cos(this.gamma0)+m*Math.sin(this.gamma0),Math.cos(this.bl*c))/this.bl}return this.no_rot?(t.x=this.x0+e,t.y=this.y0+r):(e-=this.uc,t.x=this.x0+r*Math.cos(this.alpha)+e*Math.sin(this.alpha),t.y=this.y0+e*Math.cos(this.alpha)-r*Math.sin(this.alpha)),t},r.inverse=function(t){var e,r;this.no_rot?(r=t.y-this.y0,e=t.x-this.x0):(r=(t.x-this.x0)*Math.cos(this.alpha)-(t.y-this.y0)*Math.sin(this.alpha),e=(t.y-this.y0)*Math.cos(this.alpha)+(t.x-this.x0)*Math.sin(this.alpha),e+=this.uc);var n=Math.exp(-1*this.bl*r/this.al),a=.5*(n-1/n),u=.5*(n+1/n),h=Math.sin(this.bl*e/this.al),c=(h*Math.cos(this.gamma0)+a*Math.sin(this.gamma0))/u,p=Math.pow(this.el/Math.sqrt((1+c)/(1-c)),1/this.bl);return Math.abs(c-1)<l?(t.x=this.long0,t.y=s):Math.abs(c+1)<l?(t.x=this.long0,t.y=-1*s):(t.y=i(this.e,p),t.x=o(this.long0-Math.atan2(a*Math.cos(this.gamma0)-h*Math.sin(this.gamma0),Math.cos(this.bl*e/this.al))/this.bl)),t},r.names=["Hotine_Oblique_Mercator","Hotine Oblique Mercator","Hotine_Oblique_Mercator_Azimuth_Natural_Origin","Hotine_Oblique_Mercator_Azimuth_Center","omerc"]},{"../common/adjust_lon":"proj4/lib/common/adjust_lon","../common/phi2z":"proj4/lib/common/phi2z","../common/tsfnz":"proj4/lib/common/tsfnz"}],"proj4/lib/projections/poly":[function(t,e,r){var n=t("../common/e0fn"),o=t("../common/e1fn"),i=t("../common/e2fn"),s=t("../common/e3fn"),a=t("../common/adjust_lon"),l=t("../common/adjust_lat"),u=t("../common/mlfn"),h=1e-10,c=t("../common/gN"),p=20;r.init=function(){this.temp=this.b/this.a,this.es=1-Math.pow(this.temp,2),this.e=Math.sqrt(this.es),this.e0=n(this.es),this.e1=o(this.es),this.e2=i(this.es),this.e3=s(this.es),this.ml0=this.a*u(this.e0,this.e1,this.e2,this.e3,this.lat0)},r.forward=function(t){var e,r,n,o=t.x,i=t.y,s=a(o-this.long0);if(n=s*Math.sin(i),this.sphere)Math.abs(i)<=h?(e=this.a*s,r=-1*this.a*this.lat0):(e=this.a*Math.sin(n)/Math.tan(i),r=this.a*(l(i-this.lat0)+(1-Math.cos(n))/Math.tan(i)));else if(Math.abs(i)<=h)e=this.a*s,r=-1*this.ml0;else{var p=c(this.a,this.e,Math.sin(i))/Math.tan(i);e=p*Math.sin(n),r=this.a*u(this.e0,this.e1,this.e2,this.e3,i)-this.ml0+p*(1-Math.cos(n))}return t.x=e+this.x0,t.y=r+this.y0,t},r.inverse=function(t){var e,r,n,o,i,s,l,c,_;if(n=t.x-this.x0,o=t.y-this.y0,this.sphere)if(Math.abs(o+this.a*this.lat0)<=h)e=a(n/this.a+this.long0),r=0;else{s=this.lat0+o/this.a,l=n*n/this.a/this.a+s*s,c=s;var d;for(i=p;i;--i)if(d=Math.tan(c),_=-1*(s*(c*d+1)-c-.5*(c*c+l)*d)/((c-s)/d-1),c+=_,Math.abs(_)<=h){r=c;break}e=a(this.long0+Math.asin(n*Math.tan(c)/this.a)/Math.sin(r))}else if(Math.abs(o+this.ml0)<=h)r=0,e=a(this.long0+n/this.a);else{s=(this.ml0+o)/this.a,l=n*n/this.a/this.a+s*s,c=s;var f,m,g,y,v;for(i=p;i;--i)if(v=this.e*Math.sin(c),f=Math.sqrt(1-v*v)*Math.tan(c),m=this.a*u(this.e0,this.e1,this.e2,this.e3,c),g=this.e0-2*this.e1*Math.cos(2*c)+4*this.e2*Math.cos(4*c)-6*this.e3*Math.cos(6*c),y=m/this.a,_=(s*(f*y+1)-y-.5*f*(y*y+l))/(this.es*Math.sin(2*c)*(y*y+l-2*s*y)/(4*f)+(s-y)*(f*g-2/Math.sin(2*c))-g),c-=_,Math.abs(_)<=h){r=c;break}f=Math.sqrt(1-this.es*Math.pow(Math.sin(r),2))*Math.tan(r),e=a(this.long0+Math.asin(n*f/this.a)/Math.sin(r))}return t.x=e,t.y=r,t},r.names=["Polyconic","poly"]},{"../common/adjust_lat":"proj4/lib/common/adjust_lat","../common/adjust_lon":"proj4/lib/common/adjust_lon","../common/e0fn":"proj4/lib/common/e0fn","../common/e1fn":"proj4/lib/common/e1fn","../common/e2fn":"proj4/lib/common/e2fn","../common/e3fn":"proj4/lib/common/e3fn","../common/gN":"proj4/lib/common/gN","../common/mlfn":"proj4/lib/common/mlfn"}],"proj4/lib/projections/sinu":[function(t,e,r){var n=t("../common/adjust_lon"),o=t("../common/adjust_lat"),i=t("../common/pj_enfn"),s=20,a=t("../common/pj_mlfn"),l=t("../common/pj_inv_mlfn"),u=Math.PI/2,h=1e-10,c=t("../common/asinz");r.init=function(){this.sphere?(this.n=1,this.m=0,this.es=0,this.C_y=Math.sqrt((this.m+1)/this.n),this.C_x=this.C_y/(this.m+1)):this.en=i(this.es)},r.forward=function(t){var e,r,o=t.x,i=t.y;if(o=n(o-this.long0),this.sphere){if(this.m)for(var l=this.n*Math.sin(i),u=s;u;--u){var c=(this.m*i+Math.sin(i)-l)/(this.m+Math.cos(i));if(i-=c,Math.abs(c)<h)break}else i=1!==this.n?Math.asin(this.n*Math.sin(i)):i;e=this.a*this.C_x*o*(this.m+Math.cos(i)),r=this.a*this.C_y*i}else{var p=Math.sin(i),_=Math.cos(i);r=this.a*a(i,p,_,this.en),e=this.a*o*_/Math.sqrt(1-this.es*p*p)}return t.x=e,t.y=r,t},r.inverse=function(t){var e,r,i,s;return t.x-=this.x0,i=t.x/this.a,t.y-=this.y0,e=t.y/this.a,this.sphere?(e/=this.C_y,i/=this.C_x*(this.m+Math.cos(e)),this.m?e=c((this.m*e+Math.sin(e))/this.n):1!==this.n&&(e=c(Math.sin(e)/this.n)),i=n(i+this.long0),e=o(e)):(e=l(t.y/this.a,this.es,this.en),s=Math.abs(e),s<u?(s=Math.sin(e),r=this.long0+t.x*Math.sqrt(1-this.es*s*s)/(this.a*Math.cos(e)),i=n(r)):s-h<u&&(i=this.long0)),t.x=i,t.y=e,t},r.names=["Sinusoidal","sinu"]},{"../common/adjust_lat":"proj4/lib/common/adjust_lat","../common/adjust_lon":"proj4/lib/common/adjust_lon","../common/asinz":"proj4/lib/common/asinz","../common/pj_enfn":"proj4/lib/common/pj_enfn","../common/pj_inv_mlfn":"proj4/lib/common/pj_inv_mlfn","../common/pj_mlfn":"proj4/lib/common/pj_mlfn"}],"proj4/lib/projections/somerc":[function(t,e,r){r.init=function(){var t=this.lat0;this.lambda0=this.long0;var e=Math.sin(t),r=this.a,n=this.rf,o=1/n,i=2*o-Math.pow(o,2),s=this.e=Math.sqrt(i);this.R=this.k0*r*Math.sqrt(1-i)/(1-i*Math.pow(e,2)),this.alpha=Math.sqrt(1+i/(1-i)*Math.pow(Math.cos(t),4)),this.b0=Math.asin(e/this.alpha);var a=Math.log(Math.tan(Math.PI/4+this.b0/2)),l=Math.log(Math.tan(Math.PI/4+t/2)),u=Math.log((1+s*e)/(1-s*e));this.K=a-this.alpha*l+this.alpha*s/2*u},r.forward=function(t){var e=Math.log(Math.tan(Math.PI/4-t.y/2)),r=this.e/2*Math.log((1+this.e*Math.sin(t.y))/(1-this.e*Math.sin(t.y))),n=-this.alpha*(e+r)+this.K,o=2*(Math.atan(Math.exp(n))-Math.PI/4),i=this.alpha*(t.x-this.lambda0),s=Math.atan(Math.sin(i)/(Math.sin(this.b0)*Math.tan(o)+Math.cos(this.b0)*Math.cos(i))),a=Math.asin(Math.cos(this.b0)*Math.sin(o)-Math.sin(this.b0)*Math.cos(o)*Math.cos(i));return t.y=this.R/2*Math.log((1+Math.sin(a))/(1-Math.sin(a)))+this.y0,t.x=this.R*s+this.x0,t},r.inverse=function(t){for(var e=t.x-this.x0,r=t.y-this.y0,n=e/this.R,o=2*(Math.atan(Math.exp(r/this.R))-Math.PI/4),i=Math.asin(Math.cos(this.b0)*Math.sin(o)+Math.sin(this.b0)*Math.cos(o)*Math.cos(n)),s=Math.atan(Math.sin(n)/(Math.cos(this.b0)*Math.cos(n)-Math.sin(this.b0)*Math.tan(o))),a=this.lambda0+s/this.alpha,l=0,u=i,h=-1e3,c=0;Math.abs(u-h)>1e-7;){if(++c>20)return;l=1/this.alpha*(Math.log(Math.tan(Math.PI/4+i/2))-this.K)+this.e*Math.log(Math.tan(Math.PI/4+Math.asin(this.e*Math.sin(u))/2)),h=u,u=2*Math.atan(Math.exp(l))-Math.PI/2}return t.x=a,t.y=u,t},r.names=["somerc"]},{}],"proj4/lib/projections/stere":[function(t,e,r){var n=Math.PI/2,o=1e-10,i=t("../common/sign"),s=t("../common/msfnz"),a=t("../common/tsfnz"),l=t("../common/phi2z"),u=t("../common/adjust_lon");r.ssfn_=function(t,e,r){return e*=r,Math.tan(.5*(n+t))*Math.pow((1-e)/(1+e),.5*r)},r.init=function(){this.coslat0=Math.cos(this.lat0),this.sinlat0=Math.sin(this.lat0),this.sphere?1===this.k0&&!isNaN(this.lat_ts)&&Math.abs(this.coslat0)<=o&&(this.k0=.5*(1+i(this.lat0)*Math.sin(this.lat_ts))):(Math.abs(this.coslat0)<=o&&(this.lat0>0?this.con=1:this.con=-1),this.cons=Math.sqrt(Math.pow(1+this.e,1+this.e)*Math.pow(1-this.e,1-this.e)),1===this.k0&&!isNaN(this.lat_ts)&&Math.abs(this.coslat0)<=o&&(this.k0=.5*this.cons*s(this.e,Math.sin(this.lat_ts),Math.cos(this.lat_ts))/a(this.e,this.con*this.lat_ts,this.con*Math.sin(this.lat_ts))),this.ms1=s(this.e,this.sinlat0,this.coslat0),this.X0=2*Math.atan(this.ssfn_(this.lat0,this.sinlat0,this.e))-n,this.cosX0=Math.cos(this.X0),this.sinX0=Math.sin(this.X0))},r.forward=function(t){var e,r,i,s,l,h,c=t.x,p=t.y,_=Math.sin(p),d=Math.cos(p),f=u(c-this.long0);return Math.abs(Math.abs(c-this.long0)-Math.PI)<=o&&Math.abs(p+this.lat0)<=o?(t.x=NaN,t.y=NaN,t):this.sphere?(e=2*this.k0/(1+this.sinlat0*_+this.coslat0*d*Math.cos(f)),t.x=this.a*e*d*Math.sin(f)+this.x0,t.y=this.a*e*(this.coslat0*_-this.sinlat0*d*Math.cos(f))+this.y0,t):(r=2*Math.atan(this.ssfn_(p,_,this.e))-n,s=Math.cos(r),i=Math.sin(r),Math.abs(this.coslat0)<=o?(l=a(this.e,p*this.con,this.con*_),h=2*this.a*this.k0*l/this.cons,t.x=this.x0+h*Math.sin(c-this.long0),t.y=this.y0-this.con*h*Math.cos(c-this.long0),t):(Math.abs(this.sinlat0)<o?(e=2*this.a*this.k0/(1+s*Math.cos(f)),t.y=e*i):(e=2*this.a*this.k0*this.ms1/(this.cosX0*(1+this.sinX0*i+this.cosX0*s*Math.cos(f))),t.y=e*(this.cosX0*i-this.sinX0*s*Math.cos(f))+this.y0),t.x=e*s*Math.sin(f)+this.x0,t))},r.inverse=function(t){t.x-=this.x0,t.y-=this.y0;var e,r,i,s,a,h=Math.sqrt(t.x*t.x+t.y*t.y);if(this.sphere){var c=2*Math.atan(h/(.5*this.a*this.k0));return e=this.long0,r=this.lat0,h<=o?(t.x=e,t.y=r,t):(r=Math.asin(Math.cos(c)*this.sinlat0+t.y*Math.sin(c)*this.coslat0/h),e=u(Math.abs(this.coslat0)<o?this.lat0>0?this.long0+Math.atan2(t.x,-1*t.y):this.long0+Math.atan2(t.x,t.y):this.long0+Math.atan2(t.x*Math.sin(c),h*this.coslat0*Math.cos(c)-t.y*this.sinlat0*Math.sin(c))),t.x=e,t.y=r,t)}if(Math.abs(this.coslat0)<=o){if(h<=o)return r=this.lat0,e=this.long0,
t.x=e,t.y=r,t;t.x*=this.con,t.y*=this.con,i=h*this.cons/(2*this.a*this.k0),r=this.con*l(this.e,i),e=this.con*u(this.con*this.long0+Math.atan2(t.x,-1*t.y))}else s=2*Math.atan(h*this.cosX0/(2*this.a*this.k0*this.ms1)),e=this.long0,h<=o?a=this.X0:(a=Math.asin(Math.cos(s)*this.sinX0+t.y*Math.sin(s)*this.cosX0/h),e=u(this.long0+Math.atan2(t.x*Math.sin(s),h*this.cosX0*Math.cos(s)-t.y*this.sinX0*Math.sin(s)))),r=-1*l(this.e,Math.tan(.5*(n+a)));return t.x=e,t.y=r,t},r.names=["stere","Stereographic_South_Pole","Polar Stereographic (variant B)"]},{"../common/adjust_lon":"proj4/lib/common/adjust_lon","../common/msfnz":"proj4/lib/common/msfnz","../common/phi2z":"proj4/lib/common/phi2z","../common/sign":"proj4/lib/common/sign","../common/tsfnz":"proj4/lib/common/tsfnz"}],"proj4/lib/projections/sterea":[function(t,e,r){var n=t("./gauss"),o=t("../common/adjust_lon");r.init=function(){n.init.apply(this),this.rc&&(this.sinc0=Math.sin(this.phic0),this.cosc0=Math.cos(this.phic0),this.R2=2*this.rc,this.title||(this.title="Oblique Stereographic Alternative"))},r.forward=function(t){var e,r,i,s;return t.x=o(t.x-this.long0),n.forward.apply(this,[t]),e=Math.sin(t.y),r=Math.cos(t.y),i=Math.cos(t.x),s=this.k0*this.R2/(1+this.sinc0*e+this.cosc0*r*i),t.x=s*r*Math.sin(t.x),t.y=s*(this.cosc0*e-this.sinc0*r*i),t.x=this.a*t.x+this.x0,t.y=this.a*t.y+this.y0,t},r.inverse=function(t){var e,r,i,s,a;if(t.x=(t.x-this.x0)/this.a,t.y=(t.y-this.y0)/this.a,t.x/=this.k0,t.y/=this.k0,a=Math.sqrt(t.x*t.x+t.y*t.y)){var l=2*Math.atan2(a,this.R2);e=Math.sin(l),r=Math.cos(l),s=Math.asin(r*this.sinc0+t.y*e*this.cosc0/a),i=Math.atan2(t.x*e,a*this.cosc0*r-t.y*this.sinc0*e)}else s=this.phic0,i=0;return t.x=i,t.y=s,n.inverse.apply(this,[t]),t.x=o(t.x+this.long0),t},r.names=["Stereographic_North_Pole","Oblique_Stereographic","Polar_Stereographic","sterea","Oblique Stereographic Alternative"]},{"../common/adjust_lon":"proj4/lib/common/adjust_lon","./gauss":"proj4/lib/projections/gauss"}],"proj4/lib/projections/tmerc":[function(t,e,r){var n=t("../common/e0fn"),o=t("../common/e1fn"),i=t("../common/e2fn"),s=t("../common/e3fn"),a=t("../common/mlfn"),l=t("../common/adjust_lon"),u=Math.PI/2,h=1e-10,c=t("../common/sign"),p=t("../common/asinz");r.init=function(){this.e0=n(this.es),this.e1=o(this.es),this.e2=i(this.es),this.e3=s(this.es),this.ml0=this.a*a(this.e0,this.e1,this.e2,this.e3,this.lat0)},r.forward=function(t){var e,r,n,o=t.x,i=t.y,s=l(o-this.long0),u=Math.sin(i),h=Math.cos(i);if(this.sphere){var c=h*Math.sin(s);if(Math.abs(Math.abs(c)-1)<1e-10)return 93;r=.5*this.a*this.k0*Math.log((1+c)/(1-c)),e=Math.acos(h*Math.cos(s)/Math.sqrt(1-c*c)),i<0&&(e=-e),n=this.a*this.k0*(e-this.lat0)}else{var p=h*s,_=Math.pow(p,2),d=this.ep2*Math.pow(h,2),f=Math.tan(i),m=Math.pow(f,2);e=1-this.es*Math.pow(u,2);var g=this.a/Math.sqrt(e),y=this.a*a(this.e0,this.e1,this.e2,this.e3,i);r=this.k0*g*p*(1+_/6*(1-m+d+_/20*(5-18*m+Math.pow(m,2)+72*d-58*this.ep2)))+this.x0,n=this.k0*(y-this.ml0+g*f*(_*(.5+_/24*(5-m+9*d+4*Math.pow(d,2)+_/30*(61-58*m+Math.pow(m,2)+600*d-330*this.ep2)))))+this.y0}return t.x=r,t.y=n,t},r.inverse=function(t){var e,r,n,o,i,s,a=6;if(this.sphere){var _=Math.exp(t.x/(this.a*this.k0)),d=.5*(_-1/_),f=this.lat0+t.y/(this.a*this.k0),m=Math.cos(f);e=Math.sqrt((1-m*m)/(1+d*d)),i=p(e),f<0&&(i=-i),s=0===d&&0===m?this.long0:l(Math.atan2(d,m)+this.long0)}else{var g=t.x-this.x0,y=t.y-this.y0;for(e=(this.ml0+y/this.k0)/this.a,r=e,o=0;!0&&(n=(e+this.e1*Math.sin(2*r)-this.e2*Math.sin(4*r)+this.e3*Math.sin(6*r))/this.e0-r,r+=n,!(Math.abs(n)<=h));o++)if(o>=a)return 95;if(Math.abs(r)<u){var v=Math.sin(r),b=Math.cos(r),x=Math.tan(r),w=this.ep2*Math.pow(b,2),M=Math.pow(w,2),k=Math.pow(x,2),j=Math.pow(k,2);e=1-this.es*Math.pow(v,2);var T=this.a/Math.sqrt(e),S=T*(1-this.es)/e,z=g/(T*this.k0),P=Math.pow(z,2);i=r-T*x*P/S*(.5-P/24*(5+3*k+10*w-4*M-9*this.ep2-P/30*(61+90*k+298*w+45*j-252*this.ep2-3*M))),s=l(this.long0+z*(1-P/6*(1+2*k+w-P/20*(5-2*w+28*k-3*M+8*this.ep2+24*j)))/b)}else i=u*c(y),s=this.long0}return t.x=s,t.y=i,t},r.names=["Transverse_Mercator","Transverse Mercator","tmerc"]},{"../common/adjust_lon":"proj4/lib/common/adjust_lon","../common/asinz":"proj4/lib/common/asinz","../common/e0fn":"proj4/lib/common/e0fn","../common/e1fn":"proj4/lib/common/e1fn","../common/e2fn":"proj4/lib/common/e2fn","../common/e3fn":"proj4/lib/common/e3fn","../common/mlfn":"proj4/lib/common/mlfn","../common/sign":"proj4/lib/common/sign"}],"proj4/lib/projections/utm":[function(t,e,r){var n=.017453292519943295,o=t("./tmerc");r.dependsOn="tmerc",r.init=function(){this.zone&&(this.lat0=0,this.long0=(6*Math.abs(this.zone)-183)*n,this.x0=5e5,this.y0=this.utmSouth?1e7:0,this.k0=.9996,o.init.apply(this),this.forward=o.forward,this.inverse=o.inverse)},r.names=["Universal Transverse Mercator System","utm"]},{"./tmerc":"proj4/lib/projections/tmerc"}],"proj4/lib/projections/vandg":[function(t,e,r){var n=t("../common/adjust_lon"),o=Math.PI/2,i=1e-10,s=t("../common/asinz");r.init=function(){this.R=this.a},r.forward=function(t){var e,r,a=t.x,l=t.y,u=n(a-this.long0);Math.abs(l)<=i&&(e=this.x0+this.R*u,r=this.y0);var h=s(2*Math.abs(l/Math.PI));(Math.abs(u)<=i||Math.abs(Math.abs(l)-o)<=i)&&(e=this.x0,r=l>=0?this.y0+Math.PI*this.R*Math.tan(.5*h):this.y0+Math.PI*this.R*-Math.tan(.5*h));var c=.5*Math.abs(Math.PI/u-u/Math.PI),p=c*c,_=Math.sin(h),d=Math.cos(h),f=d/(_+d-1),m=f*f,g=f*(2/_-1),y=g*g,v=Math.PI*this.R*(c*(f-y)+Math.sqrt(p*(f-y)*(f-y)-(y+p)*(m-y)))/(y+p);u<0&&(v=-v),e=this.x0+v;var b=p+f;return v=Math.PI*this.R*(g*b-c*Math.sqrt((y+p)*(p+1)-b*b))/(y+p),r=l>=0?this.y0+v:this.y0-v,t.x=e,t.y=r,t},r.inverse=function(t){var e,r,o,s,a,l,u,h,c,p,_,d,f;return t.x-=this.x0,t.y-=this.y0,_=Math.PI*this.R,o=t.x/_,s=t.y/_,a=o*o+s*s,l=-Math.abs(s)*(1+a),u=l-2*s*s+o*o,h=-2*l+1+2*s*s+a*a,f=s*s/h+(2*u*u*u/h/h/h-9*l*u/h/h)/27,c=(l-u*u/3/h)/h,p=2*Math.sqrt(-c/3),_=3*f/c/p,Math.abs(_)>1&&(_=_>=0?1:-1),d=Math.acos(_)/3,r=t.y>=0?(-p*Math.cos(d+Math.PI/3)-u/3/h)*Math.PI:-(-p*Math.cos(d+Math.PI/3)-u/3/h)*Math.PI,e=Math.abs(o)<i?this.long0:n(this.long0+Math.PI*(a-1+Math.sqrt(1+2*(o*o-s*s)+a*a))/2/o),t.x=e,t.y=r,t},r.names=["Van_der_Grinten_I","VanDerGrinten","vandg"]},{"../common/adjust_lon":"proj4/lib/common/adjust_lon","../common/asinz":"proj4/lib/common/asinz"}],"proj4/lib/transform":[function(t,e,r){var n=.017453292519943295,o=57.29577951308232,i=1,s=2,a=t("./datum_transform"),l=t("./adjust_axis"),u=t("./Proj"),h=t("./common/toPoint");e.exports=function c(t,e,r){function p(t,e){return(t.datum.datum_type===i||t.datum.datum_type===s)&&"WGS84"!==e.datumCode}var _;return Array.isArray(r)&&(r=h(r)),t.datum&&e.datum&&(p(t,e)||p(e,t))&&(_=new 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sprintf() for JavaScript 0.7-beta1
http://www.diveintojavascript.com/projects/javascript-sprintf

Copyright (c) Alexandru Marasteanu <alexaholic [at) gmail (dot] com>
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
    * Redistributions of source code must retain the above copyright
      notice, this list of conditions and the following disclaimer.
    * Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      documentation and/or other materials provided with the distribution.
    * Neither the name of sprintf() for JavaScript nor the
      names of its contributors may be used to endorse or promote products
      derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL Alexandru Marasteanu BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


Changelog:
2010.11.07 - 0.7-beta1-node
  - converted it to a node.js compatible module

2010.09.06 - 0.7-beta1
  - features: vsprintf, support for named placeholders
  - enhancements: format cache, reduced global namespace pollution

2010.05.22 - 0.6:
 - reverted to 0.4 and fixed the bug regarding the sign of the number 0
 Note:
 Thanks to Raphael Pigulla <raph (at] n3rd [dot) org> (http://www.n3rd.org/)
 who warned me about a bug in 0.5, I discovered that the last update was
 a regress. I appologize for that.

2010.05.09 - 0.5:
 - bug fix: 0 is now preceeded with a + sign
 - bug fix: the sign was not at the right position on padded results (Kamal Abdali)
 - switched from GPL to BSD license

2007.10.21 - 0.4:
 - unit test and patch (David Baird)

2007.09.17 - 0.3:
 - bug fix: no longer throws exception on empty paramenters (Hans Pufal)

2007.09.11 - 0.2:
 - feature: added argument swapping

2007.04.03 - 0.1:
 - initial release
**/
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s=this.UTYPEMAP[e],this.activate(),k.call(e,"mat")?this._gl[s](a,!1,r):this._gl[s](a,r),null},o.prototype.set_attribute=function(t,e,r,n,o){var i,s,l,u,c,p,d,f;if(n=void 0===n?0:n,o=void 0===o?0:o,!g(this._linked))throw s=new Error("RuntimeError:Cannot set attribute when program has no code"),s.name="RuntimeError",s;return p=r instanceof a,c=v.call(this.locations,t,-1),g(c<0)?(_(t,this._known_invalid)||(y.call(this._known_invalid,t),g(p)&&g(o>0)||h.log("Variable "+t+" is not an active attribute")),null):(_(t,this._unset_variables)&&w.call(this._unset_variables,t),this.activate(),g(p)?(f=this.ATYPEINFO[e],d=f[0],u=f[1],l="vertexAttribPointer",i=[d,u,this._gl.FALSE,n,o],this._attributes[t]=[r.handle,c,l,i]):(l=this.ATYPEMAP[e],this._attributes[t]=[0,c,l,r]),null)},o.prototype._pre_draw=function(){var t,e,r,n,o,i,s,a,l,u,h,p;this.activate(),s=this._samplers;for(p in s)s.hasOwnProperty(p)&&(p=s[p],i=p,l=i[0],a=i[1],u=i[2],this._gl.activeTexture(c(this._gl.TEXTURE0,u)),this._gl.bindTexture(l,a));o=this._attributes;for(p in o)o.hasOwnProperty(p)&&(p=o[p],n=p,h=n[0],e=n[1],r=n[2],t=n[3],g(h)?(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,h),this._gl.enableVertexAttribArray(e),this._gl[r].apply(this._gl,[].concat([e],t))):(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,null),this._gl.disableVertexAttribArray(e),this._gl[r].apply(this._gl,[].concat([e],t))));return g(this._validated)||(this._validated=!0,this._validate()),null},o.prototype._validate=function(){var t;if(this._unset_variables.length&&h.log("Program has unset variables: "+this._unset_variables),this._gl.validateProgram(this.handle),!g(this._gl.getProgramParameter(this.handle,this._gl.VALIDATE_STATUS)))throw h.log(this._gl.getProgramInfoLog(this.handle)),t=new Error("RuntimeError:Program validation error"),t.name="RuntimeError",t;return null},o.prototype.draw=function(t,e){var r,o,i,s,a;if(!g(this._linked))throw o=new Error("RuntimeError:Cannot draw program if code has not been set"),o.name="RuntimeError",o;return u(this._gl,"before draw"),g(e instanceof n)?(this._pre_draw(),e.activate(),r=e._buffer_size/2,s=this._gl.UNSIGNED_SHORT,this._gl.drawElements(t,r,s,0),e.deactivate()):(a=e,i=a[0],r=a[1],g(r)&&(this._pre_draw(),this._gl.drawArrays(t,i,r))),u(this._gl,"after draw"),null},t=function(){f(this,arguments)},t.prototype=Object.create(r.prototype),t.prototype._base_class=r.prototype,t.prototype._class_name="Buffer",
t.prototype._target=null,t.prototype._usage=35048,t.prototype._create=function(){return this.handle=this._gl.createBuffer(),this._buffer_size=0,null},t.prototype["delete"]=function(){return this._gl.deleteBuffer(this.handle),null},t.prototype.activate=function(){return this._gl.bindBuffer(this._target,this.handle),null},t.prototype.deactivate=function(){return this._gl.bindBuffer(this._target,null),null},t.prototype.set_size=function(t){return d(t,this._buffer_size)||(this.activate(),this._gl.bufferData(this._target,t,this._usage),this._buffer_size=t),null},t.prototype.set_data=function(t,e){return this.activate(),this._gl.bufferSubData(this._target,t,e),null},a=function(){f(this,arguments)},a.prototype=Object.create(t.prototype),a.prototype._base_class=t.prototype,a.prototype._class_name="VertexBuffer",a.prototype._target=34962,n=function(){f(this,arguments)},n.prototype=Object.create(t.prototype),n.prototype._base_class=t.prototype,n.prototype._class_name="IndexBuffer",n.prototype._target=34963,i=function(){f(this,arguments)},i.prototype=Object.create(r.prototype),i.prototype._base_class=r.prototype,i.prototype._class_name="Texture2D",i.prototype._target=3553,i.prototype._types={Int8Array:5120,Uint8Array:5121,Int16Array:5122,Uint16Array:5123,Int32Array:5124,Uint32Array:5125,Float32Array:5126},i.prototype._create=function(){return this.handle=this._gl.createTexture(),this._shape_format=null,null},i.prototype["delete"]=function(){return this._gl.deleteTexture(this.handle),null},i.prototype.activate=function(){return this._gl.bindTexture(this._target,this.handle),null},i.prototype.deactivate=function(){return this._gl.bindTexture(this._target,0),null},i.prototype._get_alignment=function(t){var e,r,n,o;for(r=[4,8,2,1],n=r,"object"!=typeof n||Array.isArray(n)||(n=Object.keys(n)),o=0;o<n.length;o+=1)if(e=n[o],d(t%e,0))return e;return null},i.prototype.set_wrapping=function(t,e){return this.activate(),this._gl.texParameterf(this._target,this._gl.TEXTURE_WRAP_S,t),this._gl.texParameterf(this._target,this._gl.TEXTURE_WRAP_T,e),null},i.prototype.set_interpolation=function(t,e){return this.activate(),this._gl.texParameterf(this._target,this._gl.TEXTURE_MIN_FILTER,t),this._gl.texParameterf(this._target,this._gl.TEXTURE_MAG_FILTER,e),null},i.prototype.set_size=function(t,e){var r,n,o;return n=t,r=n[0],o=n[1],d([r,o,e],this._shape_format)||(this._shape_format=[r,o,e],this.activate(),this._gl.texImage2D(this._target,0,e,o,r,0,e,this._gl.UNSIGNED_BYTE,null)),this.u_shape=[r,o],null},i.prototype.set_data=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_;if(d(e.length,2)&&(e=[e[0],e[1],1]),this.activate(),s=this._shape_format[2],u=e,l=u[0],c=u[1],n=u[2],h=t,_=h[0],p=h[1],a=v.call(this._types,r.constructor.name,null),null===a)throw i=new Error("ValueError:"+("Type "+r.constructor.name+" not allowed for texture")),i.name="ValueError",i;return o=this._get_alignment(m(e[e.length-2],e[e.length-1])),d(o,4)||this._gl.pixelStorei(this._gl.UNPACK_ALIGNMENT,o),this._gl.texSubImage2D(this._target,0,p,_,c,l,s,a,r),d(o,4)||this._gl.pixelStorei(this._gl.UNPACK_ALIGNMENT,4),null},s=function(){f(this,arguments)},s.prototype=Object.create(i.prototype),s.prototype._base_class=i.prototype,s.prototype._class_name="Texture3DLike",s.prototype.GLSL_SAMPLE_NEAREST="\n        vec4 sample3D(sampler2D tex, vec3 texcoord, vec3 shape, vec2 tiles) {\n            shape.xyz = shape.zyx;  // silly row-major convention\n            float nrows = tiles.y, ncols = tiles.x;\n            // Don't let adjacent frames be interpolated into this one\n            texcoord.x = min(texcoord.x * shape.x, shape.x - 0.5);\n            texcoord.x = max(0.5, texcoord.x) / shape.x;\n            texcoord.y = min(texcoord.y * shape.y, shape.y - 0.5);\n            texcoord.y = max(0.5, texcoord.y) / shape.y;\n\n            float zindex = floor(texcoord.z * shape.z);\n\n            // Do a lookup in the 2D texture\n            float u = (mod(zindex, ncols) + texcoord.x) / ncols;\n            float v = (floor(zindex / ncols) + texcoord.y) / nrows;\n\n            return texture2D(tex, vec2(u,v));\n        }\n    ",s.prototype.GLSL_SAMPLE_LINEAR="\n        vec4 sample3D(sampler2D tex, vec3 texcoord, vec3 shape, vec2 tiles) {\n            shape.xyz = shape.zyx;  // silly row-major convention\n            float nrows = tiles.y, ncols = tiles.x;\n            // Don't let adjacent frames be interpolated into this one\n            texcoord.x = min(texcoord.x * shape.x, shape.x - 0.5);\n            texcoord.x = max(0.5, texcoord.x) / shape.x;\n            texcoord.y = min(texcoord.y * shape.y, shape.y - 0.5);\n            texcoord.y = max(0.5, texcoord.y) / shape.y;\n\n            float z = texcoord.z * shape.z;\n            float zindex1 = floor(z);\n            float u1 = (mod(zindex1, ncols) + texcoord.x) / ncols;\n            float v1 = (floor(zindex1 / ncols) + texcoord.y) / nrows;\n\n            float zindex2 = zindex1 + 1.0;\n            float u2 = (mod(zindex2, ncols) + texcoord.x) / ncols;\n            float v2 = (floor(zindex2 / ncols) + texcoord.y) / nrows;\n\n            vec4 s1 = texture2D(tex, vec2(u1, v1));\n            vec4 s2 = texture2D(tex, vec2(u2, v2));\n\n            return s1 * (zindex2 - z) + s2 * (z - zindex1);\n        }\n    ",s.prototype._get_tile_info=function(t){var e,r,n,o;if(r=this._gl.getParameter(this._gl.MAX_TEXTURE_SIZE),o=Math.floor(r/t[1]),o=Math.min(o,t[0]),n=window.Math.ceil(t[0]/o),g(m(n,t[2])>r))throw e=new Error("RuntimeError:"+("Cannot fit 3D data with shape "+t+" onto simulated 2D texture.")),e.name="RuntimeError",e;return[o,n]},s.prototype.set_size=function(t,e){var r,n,o,i;return i=this._get_tile_info(t),n=i[0],r=i[1],o=[m(t[1],n),m(t[2],r)],s.prototype._base_class.set_size.call(this,o,e),this.u_shape=[t[0],t[1],t[2]],this.u_tiles=[r,n],null},s.prototype.set_data=function(t,e,r){var n,o,i,a,l,u,h,c,_,f,g,y,v;if(d(e.length,3)&&(e=[e[0],e[1],e[2],1]),!p(function(){var e,r,n,o=[];for(r=t,"object"!=typeof r||Array.isArray(r)||(r=Object.keys(r)),n=0;n<r.length;n++)e=r[n],o.push(d(e,0));return o}.apply(this)))throw a=new Error("ValueError:Texture3DLike does not support nonzero offset (for now)"),a.name="ValueError",a;if(_=this._get_tile_info(e),u=_[0],l=_[1],c=[m(e[1],u),m(e[2],l),e[3]],d(l,1))s.prototype._base_class.set_data.call(this,[0,0],c,r);else for(n=r.constructor,v=new n(m(m(c[0],c[1]),c[2])),s.prototype._base_class.set_data.call(this,[0,0],c,v),y=0;y<e[0];y+=1)f=[Math.floor(y/l),y%l],h=f[0],o=f[1],i=Math.floor(r.length/e[0]),g=r.slice(m(y,i),m(y+1,i)),s.prototype._base_class.set_data.call(this,[m(h,e[1]),m(o,e[2])],e.slice(1),g);return null},{Buffer:t,GlooObject:r,IndexBuffer:n,Program:o,Texture2D:i,Texture3DLike:s,VertexBuffer:a,check_error:u,console:h}})}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{}],kiwi:[function(t,e,r){/*-----------------------------------------------------------------------------
| Copyright (c) 2014, Nucleic Development Team.
|
| Distributed under the terms of the Modified BSD License.
|
| The full license is in the file COPYING.txt, distributed with this software.
|----------------------------------------------------------------------------*/
var n;!function(t){function e(t){return t instanceof Array?new l(t):t.__iter__()}function r(t){return t instanceof Array?new u(t):t.__reversed__()}function n(t){return t.__next__()}function o(t){if(t instanceof Array)return t.slice();for(var e,r=[],n=t.__iter__();void 0!==(e=n.__next__());)r.push(e);return r}function i(t,e){if(t instanceof Array){for(var r=0,n=t.length;r<n;++r)if(e(t[r])===!1)return}else for(var o,i=t.__iter__();void 0!==(o=i.__next__());)if(e(o)===!1)return}function s(t,e){var r=[];if(t instanceof Array)for(var n=0,o=t.length;n<o;++n)r.push(e(t[n]));else for(var i,s=t.__iter__();void 0!==(i=s.__next__());)r.push(e(i));return r}function a(t,e){var r,n=[];if(t instanceof Array)for(var o=0,i=t.length;o<i;++o)r=t[o],e(r)&&n.push(r);else for(var s=t.__iter__();void 0!==(r=s.__next__());)e(r)&&n.push(r);return n}var l=function(){function t(t,e){"undefined"==typeof e&&(e=0),this._array=t,this._index=Math.max(0,Math.min(e,t.length))}return t.prototype.__next__=function(){return this._array[this._index++]},t.prototype.__iter__=function(){return this},t}();t.ArrayIterator=l;var u=function(){function t(t,e){"undefined"==typeof e&&(e=t.length),this._array=t,this._index=Math.max(0,Math.min(e,t.length))}return t.prototype.__next__=function(){return this._array[--this._index]},t.prototype.__iter__=function(){return this},t}();t.ReverseArrayIterator=u,t.iter=e,t.reversed=r,t.next=n,t.asArray=o,t.forEach=i,t.map=s,t.filter=a}(n||(n={}));/*-----------------------------------------------------------------------------
| Copyright (c) 2014, Nucleic Development Team.
|
| Distributed under the terms of the Modified BSD License.
|
| The full license is in the file COPYING.txt, distributed with this software.
|----------------------------------------------------------------------------*/
var n;!function(t){var e=function(){function t(t,e){this.first=t,this.second=e}return t.prototype.copy=function(){return new t(this.first,this.second)},t}();t.Pair=e}(n||(n={}));/*-----------------------------------------------------------------------------
| Copyright (c) 2014, Nucleic Development Team.
|
| Distributed under the terms of the Modified BSD License.
|
| The full license is in the file COPYING.txt, distributed with this software.
|----------------------------------------------------------------------------*/
var n;!function(t){function e(t,e,r){for(var n,o,i=0,s=t.length;s>0;)n=s>>1,o=i+n,r(t[o],e)<0?(i=o+1,s-=n+1):s=n;return i}function r(t,r,n){var o=e(t,r,n);if(o===t.length)return-1;var i=t[o];return 0!==n(i,r)?-1:o}function n(t,r,n){var o=e(t,r,n);if(o!==t.length){var i=t[o];if(0===n(i,r))return i}}function o(e,r){var n=t.asArray(e),o=n.length;if(o<=1)return n;n.sort(r);for(var i=[n[0]],s=1,a=0;s<o;++s){var l=n[s];0!==r(i[a],l)&&(i.push(l),++a)}return i}function i(t,e,r){for(var n=0,o=0,i=t.length,s=e.length;n<i&&o<s;){var a=r(t[n],e[o]);if(a<0)++n;else{if(!(a>0))return!1;++o}}return!0}function s(t,e,r){var n=t.length,o=e.length;if(n>o)return!1;for(var i=0,s=0;i<n&&s<o;){var a=r(t[i],e[s]);if(a<0)return!1;a>0?++s:(++i,++s)}return!(i<n)}function a(t,e,r){for(var n=0,o=0,i=t.length,s=e.length,a=[];n<i&&o<s;){var l=t[n],u=e[o],h=r(l,u);h<0?(a.push(l),++n):h>0?(a.push(u),++o):(a.push(l),++n,++o)}for(;n<i;)a.push(t[n]),++n;for(;o<s;)a.push(e[o]),++o;return a}function l(t,e,r){for(var n=0,o=0,i=t.length,s=e.length,a=[];n<i&&o<s;){var l=t[n],u=e[o],h=r(l,u);h<0?++n:h>0?++o:(a.push(l),++n,++o)}return a}function u(t,e,r){for(var n=0,o=0,i=t.length,s=e.length,a=[];n<i&&o<s;){var l=t[n],u=e[o],h=r(l,u);h<0?(a.push(l),++n):h>0?++o:(++n,++o)}for(;n<i;)a.push(t[n]),++n;return a}function h(t,e,r){for(var n=0,o=0,i=t.length,s=e.length,a=[];n<i&&o<s;){var l=t[n],u=e[o],h=r(l,u);h<0?(a.push(l),++n):h>0?(a.push(u),++o):(++n,++o)}for(;n<i;)a.push(t[n]),++n;for(;o<s;)a.push(e[o]),++o;return a}t.lowerBound=e,t.binarySearch=r,t.binaryFind=n,t.asSet=o,t.setIsDisjoint=i,t.setIsSubset=s,t.setUnion=a,t.setIntersection=l,t.setDifference=u,t.setSymmetricDifference=h}(n||(n={}));/*-----------------------------------------------------------------------------
| Copyright (c) 2014, Nucleic Development Team.
|
| Distributed under the terms of the Modified BSD License.
|
| The full license is in the file COPYING.txt, distributed with this software.
|----------------------------------------------------------------------------*/
var n;!function(t){var e=function(){function e(){this._array=[]}return e.prototype.size=function(){return this._array.length},e.prototype.empty=function(){return 0===this._array.length},e.prototype.itemAt=function(t){return this._array[t]},e.prototype.takeAt=function(t){return this._array.splice(t,1)[0]},e.prototype.clear=function(){this._array=[]},e.prototype.swap=function(t){var e=this._array;this._array=t._array,t._array=e},e.prototype.__iter__=function(){return t.iter(this._array)},e.prototype.__reversed__=function(){return t.reversed(this._array)},e}();t.ArrayBase=e}(n||(n={}));/*-----------------------------------------------------------------------------
| Copyright (c) 2014, Nucleic Development Team.
|
| Distributed under the terms of the Modified BSD License.
|
| The full license is in the file COPYING.txt, distributed with this software.
|----------------------------------------------------------------------------*/
var n,o=this.__extends||function(t,e){function r(){this.constructor=t}for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);r.prototype=e.prototype,t.prototype=new r};!function(t){function e(t){return function(e,r){return t(e.first,r)}}function r(t,e,r){for(var n=0,o=0,i=t.length,s=e.length,a=[];n<i&&o<s;){var l=t[n],u=e[o],h=r(l.first,u.first);h<0?(a.push(l.copy()),++n):h>0?(a.push(u.copy()),++o):(a.push(u.copy()),++n,++o)}for(;n<i;)a.push(t[n].copy()),++n;for(;o<s;)a.push(e[o].copy()),++o;return a}var n=function(n){function i(t){n.call(this),this._compare=t,this._wrapped=e(t)}return o(i,n),i.prototype.comparitor=function(){return this._compare},i.prototype.indexOf=function(e){return t.binarySearch(this._array,e,this._wrapped)},i.prototype.contains=function(e){return t.binarySearch(this._array,e,this._wrapped)>=0},i.prototype.find=function(e){return t.binaryFind(this._array,e,this._wrapped)},i.prototype.setDefault=function(e,r){var n=this._array,o=t.lowerBound(n,e,this._wrapped);if(o===n.length){var i=new t.Pair(e,r());return n.push(i),i}var s=n[o];if(0!==this._compare(s.first,e)){var i=new t.Pair(e,r());return n.splice(o,0,i),i}return s},i.prototype.insert=function(e,r){var n=this._array,o=t.lowerBound(n,e,this._wrapped);if(o===n.length){var i=new t.Pair(e,r);return n.push(i),i}var s=n[o];if(0!==this._compare(s.first,e)){var i=new t.Pair(e,r);return n.splice(o,0,i),i}return s.second=r,s},i.prototype.update=function(e){var n=this;if(e instanceof i){var o=e;this._array=r(this._array,o._array,this._compare)}else t.forEach(e,function(t){n.insert(t.first,t.second)})},i.prototype.erase=function(e){var r=this._array,n=t.binarySearch(r,e,this._wrapped);if(!(n<0))return r.splice(n,1)[0]},i.prototype.copy=function(){for(var t=new i(this._compare),e=t._array,r=this._array,n=0,o=r.length;n<o;++n)e.push(r[n].copy());return t},i}(t.ArrayBase);t.AssociativeArray=n}(n||(n={}));/*-----------------------------------------------------------------------------
| Copyright (c) 2014, Nucleic Development Team.
|
| Distributed under the terms of the Modified BSD License.
|
| The full license is in the file COPYING.txt, distributed with this software.
|----------------------------------------------------------------------------*/
var n;!function(t){function e(e,n){return e instanceof r?e._array:t.asSet(e,n)}var r=function(r){function n(t){r.call(this),this._compare=t}return o(n,r),n.prototype.comparitor=function(){return this._compare},n.prototype.indexOf=function(e){return t.binarySearch(this._array,e,this._compare)},n.prototype.contains=function(e){return t.binarySearch(this._array,e,this._compare)>=0},n.prototype.insert=function(e){var r=this._array,n=t.lowerBound(r,e,this._compare);return n===r.length?(r.push(e),!0):0!==this._compare(r[n],e)&&(r.splice(n,0,e),!0)},n.prototype.erase=function(e){var r=this._array,n=t.binarySearch(r,e,this._compare);return!(n<0)&&(r.splice(n,1),!0)},n.prototype.copy=function(){var t=new n(this._compare);return t._array=this._array.slice(),t},n.prototype.isDisjoint=function(r){var n=this._compare,o=e(r,n);return t.setIsDisjoint(this._array,o,n)},n.prototype.isSubset=function(r){var n=this._compare,o=e(r,n);return t.setIsSubset(this._array,o,n)},n.prototype.isSuperset=function(r){var n=this._compare,o=e(r,n);return t.setIsSubset(o,this._array,n)},n.prototype.union=function(r){var o=this._compare,i=new n(o),s=e(r,o);return i._array=t.setUnion(this._array,s,o),i},n.prototype.intersection=function(r){var o=this._compare,i=new n(o),s=e(r,o);return i._array=t.setIntersection(this._array,s,o),i},n.prototype.difference=function(r){var o=this._compare,i=new n(o),s=e(r,o);return i._array=t.setDifference(this._array,s,o),i},n.prototype.symmetricDifference=function(r){var o=this._compare,i=new n(o),s=e(r,o);return i._array=t.setSymmetricDifference(this._array,s,o),i},n.prototype.unionUpdate=function(r){var n=this._compare,o=e(r,n);this._array=t.setUnion(this._array,o,n)},n.prototype.intersectionUpdate=function(r){var n=this._compare,o=e(r,n);this._array=t.setIntersection(this._array,o,n)},n.prototype.differenceUpdate=function(r){var n=this._compare,o=e(r,n);this._array=t.setDifference(this._array,o,n)},n.prototype.symmetricDifferenceUpdate=function(r){var n=this._compare,o=e(r,n);this._array=t.setSymmetricDifference(this._array,o,n)},n}(t.ArrayBase);t.UniqueArray=r}(n||(n={}));/*-----------------------------------------------------------------------------
| Copyright (c) 2014, Nucleic Development Team.
|
| Distributed under the terms of the Modified BSD License.
|
| The full license is in the file COPYING.txt, distributed with this software.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) 2014, Nucleic Development Team.
|
| Distributed under the terms of the Modified BSD License.
|
| The full license is in the file COPYING.txt, distributed with this software.
|----------------------------------------------------------------------------*/
var i;!function(t){!function(t){t[t.Le=0]="Le",t[t.Ge=1]="Ge",t[t.Eq=2]="Eq"}(t.Operator||(t.Operator={}));var e=(t.Operator,function(){function e(e,n,o){"undefined"==typeof o&&(o=t.Strength.required),this._id=r++,this._operator=n,this._expression=e,this._strength=t.Strength.clip(o)}return e.Compare=function(t,e){return t.id()-e.id()},e.prototype.id=function(){return this._id},e.prototype.expression=function(){return this._expression},e.prototype.op=function(){return this._operator},e.prototype.strength=function(){return this._strength},e}());t.Constraint=e;var r=0}(i||(i={}));/*-----------------------------------------------------------------------------
| Copyright (c) 2014, Nucleic Development Team.
|
| Distributed under the terms of the Modified BSD License.
|
| The full license is in the file COPYING.txt, distributed with this software.
|----------------------------------------------------------------------------*/
var i;!function(t){function e(t){return new n.AssociativeArray(t)}t.createMap=e}(i||(i={}));/*-----------------------------------------------------------------------------
| Copyright (c) 2014, Nucleic Development Team.
|
| Distributed under the terms of the Modified BSD License.
|
| The full license is in the file COPYING.txt, distributed with this software.
|----------------------------------------------------------------------------*/
var i;!function(t){var e=function(){function t(t){"undefined"==typeof t&&(t=""),this._value=0,this._context=null,this._id=r++,this._name=t}return t.Compare=function(t,e){return t.id()-e.id()},t.prototype.id=function(){return this._id},t.prototype.name=function(){return this._name},t.prototype.setName=function(t){this._name=t},t.prototype.context=function(){return this._context},t.prototype.setContext=function(t){this._context=t},t.prototype.value=function(){return this._value},t.prototype.setValue=function(t){this._value=t},t}();t.Variable=e;var r=0}(i||(i={}));/*-----------------------------------------------------------------------------
| Copyright (c) 2014, Nucleic Development Team.
|
| Distributed under the terms of the Modified BSD License.
|
| The full license is in the file COPYING.txt, distributed with this software.
|----------------------------------------------------------------------------*/
var i;!function(t){function e(e){for(var r=0,n=function(){return 0},o=t.createMap(t.Variable.Compare),i=0,s=e.length;i<s;++i){var a=e[i];if("number"==typeof a)r+=a;else if(a instanceof t.Variable)o.setDefault(a,n).second+=1;else{if(!(a instanceof Array))throw new Error("invalid Expression argument: "+a);if(2!==a.length)throw new Error("array must have length 2");var l=a[0],u=a[1];if("number"!=typeof l)throw new Error("array item 0 must be a number");if(!(u instanceof t.Variable))throw new Error("array item 1 must be a variable");o.setDefault(u,n).second+=l}}return{terms:o,constant:r}}var r=function(){function t(){var t=e(arguments);this._terms=t.terms,this._constant=t.constant}return t.prototype.terms=function(){return this._terms},t.prototype.constant=function(){return this._constant},t.prototype.value=function(){var t=this._constant;return n.forEach(this._terms,function(e){t+=e.first.value()*e.second}),t},t}();t.Expression=r}(i||(i={}));/*-----------------------------------------------------------------------------
| Copyright (c) 2014, Nucleic Development Team.
|
| Distributed under the terms of the Modified BSD License.
|
| The full license is in the file COPYING.txt, distributed with this software.
|----------------------------------------------------------------------------*/
var i;!function(t){!function(t){function e(t,e,r,n){"undefined"==typeof n&&(n=1);var o=0;return o+=1e6*Math.max(0,Math.min(1e3,t*n)),o+=1e3*Math.max(0,Math.min(1e3,e*n)),o+=Math.max(0,Math.min(1e3,r*n))}function r(e){return Math.max(0,Math.min(t.required,e))}t.create=e,t.required=e(1e3,1e3,1e3),t.strong=e(1,0,0),t.medium=e(0,1,0),t.weak=e(0,0,1),t.clip=r}(t.Strength||(t.Strength={}));t.Strength}(i||(i={}));/*-----------------------------------------------------------------------------
| Copyright (c) 2014, Nucleic Development Team.
|
| Distributed under the terms of the Modified BSD License.
|
| The full license is in the file COPYING.txt, distributed with this software.
|----------------------------------------------------------------------------*/
var i;!function(t){function e(t){var e=1e-8;return t<0?-t<e:t<e}function r(){return t.createMap(t.Constraint.Compare)}function n(){return t.createMap(l.Compare)}function o(){return t.createMap(t.Variable.Compare)}function i(){return t.createMap(t.Variable.Compare)}var s=function(){function s(){this._cnMap=r(),this._rowMap=n(),this._varMap=o(),this._editMap=i(),this._infeasibleRows=[],this._objective=new h,this._artificial=null,this._idTick=0}return s.prototype.addConstraint=function(t){var r=this._cnMap.find(t);if(void 0!==r)throw new Error("duplicate constraint");var n=this._createRow(t),o=n.row,i=n.tag,s=this._chooseSubject(o,i);if(0===s.type()&&o.allDummies()){if(!e(o.constant()))throw new Error("unsatisfiable constraint");s=i.marker}if(0===s.type()){if(!this._addWithArtificialVariable(o)){for(var a,l="",u=0;a=t._expression._terms._array[u];u++)l+=a.first._name,l+=", ";var h=["LE","GE","EQ"];throw new Error("Unsatisfiable constraint ["+l.slice(0,-2)+"] operator: "+h[t._operator])}}else o.solveFor(s),this._substitute(s,o),this._rowMap.insert(s,o);this._cnMap.insert(t,i),this._optimize(this._objective)},s.prototype.removeConstraint=function(t){var e=this._cnMap.erase(t);if(void 0===e)throw new Error("unknown constraint");this._removeConstraintEffects(t,e.second);var r=e.second.marker,n=this._rowMap.erase(r);if(void 0===n){var o=this._getMarkerLeavingSymbol(r);if(0===o.type())throw new Error("failed to find leaving row");n=this._rowMap.erase(o),n.second.solveForEx(o,r),this._substitute(r,n.second)}this._optimize(this._objective)},s.prototype.hasConstraint=function(t){return this._cnMap.contains(t)},s.prototype.addEditVariable=function(e,r){var n=this._editMap.find(e);if(void 0!==n)throw new Error("duplicate edit variable");if(r=t.Strength.clip(r),r===t.Strength.required)throw new Error("bad required strength");var o=new t.Expression(e),i=new t.Constraint(o,2,r);this.addConstraint(i);var s=this._cnMap.find(i).second,a={tag:s,constraint:i,constant:0};this._editMap.insert(e,a)},s.prototype.removeEditVariable=function(t){var e=this._editMap.erase(t);if(void 0===e)throw new Error("unknown edit variable");this.removeConstraint(e.second.constraint)},s.prototype.hasEditVariable=function(t){return this._editMap.contains(t)},s.prototype.suggestValue=function(t,e){var r=this._editMap.find(t);if(void 0===r)throw new Error("unknown edit variable");var n=this._rowMap,o=r.second,i=e-o.constant;o.constant=e;var s=o.tag.marker,a=n.find(s);if(void 0!==a)return a.second.add(-i)<0&&this._infeasibleRows.push(s),void this._dualOptimize();var l=o.tag.other,a=n.find(l);if(void 0!==a)return a.second.add(i)<0&&this._infeasibleRows.push(l),void this._dualOptimize();for(var u=0,h=n.size();u<h;++u){var a=n.itemAt(u),c=a.second,p=c.coefficientFor(s);0!==p&&c.add(i*p)<0&&1!==a.first.type()&&this._infeasibleRows.push(a.first)}this._dualOptimize()},s.prototype.updateVariables=function(){for(var t=this._varMap,e=this._rowMap,r=0,n=t.size();r<n;++r){var o=t.itemAt(r),i=e.find(o.second);void 0!==i?o.first.setValue(i.second.constant()):o.first.setValue(0)}},s.prototype._getVarSymbol=function(t){var e=this,r=function(){return e._makeSymbol(1)};return this._varMap.setDefault(t,r).second},s.prototype._createRow=function(r){for(var n=r.expression(),o=new h(n.constant()),i=n.terms(),s=0,a=i.size();s<a;++s){var l=i.itemAt(s);if(!e(l.second)){var c=this._getVarSymbol(l.first),p=this._rowMap.find(c);void 0!==p?o.insertRow(p.second,l.second):o.insertSymbol(c,l.second)}}var _=this._objective,d=r.strength(),f={marker:u,other:u};switch(r.op()){case 0:case 1:var m=0===r.op()?1:-1,g=this._makeSymbol(2);if(f.marker=g,o.insertSymbol(g,m),d<t.Strength.required){var y=this._makeSymbol(3);f.other=y,o.insertSymbol(y,-m),_.insertSymbol(y,d)}break;case 2:if(d<t.Strength.required){var v=this._makeSymbol(3),b=this._makeSymbol(3);f.marker=v,f.other=b,o.insertSymbol(v,-1),o.insertSymbol(b,1),_.insertSymbol(v,d),_.insertSymbol(b,d)}else{var x=this._makeSymbol(4);f.marker=x,o.insertSymbol(x)}}return o.constant()<0&&o.reverseSign(),{row:o,tag:f}},s.prototype._chooseSubject=function(t,e){for(var r=t.cells(),n=0,o=r.size();n<o;++n){var i=r.itemAt(n);if(1===i.first.type())return i.first}var s=e.marker.type();return(2===s||3===s)&&t.coefficientFor(e.marker)<0?e.marker:(s=e.other.type(),(2===s||3===s)&&t.coefficientFor(e.other)<0?e.other:u)},s.prototype._addWithArtificialVariable=function(t){var r=this._makeSymbol(2);this._rowMap.insert(r,t.copy()),this._artificial=t.copy(),this._optimize(this._artificial);var n=e(this._artificial.constant());this._artificial=null;var o=this._rowMap.erase(r);if(void 0!==o){var i=o.second;if(i.isConstant())return n;var s=this._anyPivotableSymbol(i);if(0===s.type())return!1;i.solveForEx(r,s),this._substitute(s,i),this._rowMap.insert(s,i)}for(var a=this._rowMap,l=0,u=a.size();l<u;++l)a.itemAt(l).second.removeSymbol(r);return this._objective.removeSymbol(r),n},s.prototype._substitute=function(t,e){for(var r=this._rowMap,n=0,o=r.size();n<o;++n){var i=r.itemAt(n);i.second.substitute(t,e),i.second.constant()<0&&1!==i.first.type()&&this._infeasibleRows.push(i.first)}this._objective.substitute(t,e),this._artificial&&this._artificial.substitute(t,e)},s.prototype._optimize=function(t){for(;;){var e=this._getEnteringSymbol(t);if(0===e.type())return;var r=this._getLeavingSymbol(e);if(0===r.type())throw new Error("the objective is unbounded");var n=this._rowMap.erase(r).second;n.solveForEx(r,e),this._substitute(e,n),this._rowMap.insert(e,n)}},s.prototype._dualOptimize=function(){for(var t=this._rowMap,e=this._infeasibleRows;0!==e.length;){var r=e.pop(),n=t.find(r);if(void 0!==n&&n.second.constant()<0){var o=this._getDualEnteringSymbol(n.second);if(0===o.type())throw new Error("dual optimize failed");var i=n.second;t.erase(r),i.solveForEx(r,o),this._substitute(o,i),t.insert(o,i)}}},s.prototype._getEnteringSymbol=function(t){for(var e=t.cells(),r=0,n=e.size();r<n;++r){var o=e.itemAt(r),i=o.first;if(o.second<0&&4!==i.type())return i}return u},s.prototype._getDualEnteringSymbol=function(t){for(var e=Number.MAX_VALUE,r=u,n=t.cells(),o=0,i=n.size();o<i;++o){var s=n.itemAt(o),a=s.first,l=s.second;if(l>0&&4!==a.type()){var h=this._objective.coefficientFor(a),c=h/l;c<e&&(e=c,r=a)}}return r},s.prototype._getLeavingSymbol=function(t){for(var e=Number.MAX_VALUE,r=u,n=this._rowMap,o=0,i=n.size();o<i;++o){var s=n.itemAt(o),a=s.first;if(1!==a.type()){var l=s.second,h=l.coefficientFor(t);if(h<0){var c=-l.constant()/h;c<e&&(e=c,r=a)}}}return r},s.prototype._getMarkerLeavingSymbol=function(t){for(var e=Number.MAX_VALUE,r=e,n=e,o=u,i=o,s=o,a=o,l=this._rowMap,h=0,c=l.size();h<c;++h){var p=l.itemAt(h),_=p.second,d=_.coefficientFor(t);if(0!==d){var f=p.first;if(1===f.type())a=f;else if(d<0){var m=-_.constant()/d;m<r&&(r=m,i=f)}else{var m=_.constant()/d;m<n&&(n=m,s=f)}}}return i!==o?i:s!==o?s:a},s.prototype._removeConstraintEffects=function(t,e){3===e.marker.type()&&this._removeMarkerEffects(e.marker,t.strength()),3===e.other.type()&&this._removeMarkerEffects(e.other,t.strength())},s.prototype._removeMarkerEffects=function(t,e){var r=this._rowMap.find(t);void 0!==r?this._objective.insertRow(r.second,-e):this._objective.insertSymbol(t,-e)},s.prototype._anyPivotableSymbol=function(t){for(var e=t.cells(),r=0,n=e.size();r<n;++r){var o=e.itemAt(r),i=o.first.type();if(2===i||3===i)return o.first}return u},s.prototype._makeSymbol=function(t){return new l(t,(this._idTick++))},s}();t.Solver=s;var a;!function(t){t[t.Invalid=0]="Invalid",t[t.External=1]="External",t[t.Slack=2]="Slack",t[t.Error=3]="Error",t[t.Dummy=4]="Dummy"}(a||(a={}));var l=function(){function t(t,e){this._id=e,this._type=t}return t.Compare=function(t,e){return t.id()-e.id()},t.prototype.id=function(){return this._id},t.prototype.type=function(){return this._type},t}(),u=new l(0,(-1)),h=function(){function r(e){"undefined"==typeof e&&(e=0),this._cellMap=t.createMap(l.Compare),this._constant=e}return r.prototype.cells=function(){return this._cellMap},r.prototype.constant=function(){return this._constant},r.prototype.isConstant=function(){return this._cellMap.empty()},r.prototype.allDummies=function(){for(var t=this._cellMap,e=0,r=t.size();e<r;++e){var n=t.itemAt(e);if(4!==n.first.type())return!1}return!0},r.prototype.copy=function(){var t=new r(this._constant);return t._cellMap=this._cellMap.copy(),t},r.prototype.add=function(t){return this._constant+=t},r.prototype.insertSymbol=function(t,r){"undefined"==typeof r&&(r=1);var n=this._cellMap.setDefault(t,function(){return 0});e(n.second+=r)&&this._cellMap.erase(t)},r.prototype.insertRow=function(t,e){"undefined"==typeof e&&(e=1),this._constant+=t._constant*e;for(var r=t._cellMap,n=0,o=r.size();n<o;++n){var i=r.itemAt(n);this.insertSymbol(i.first,i.second*e)}},r.prototype.removeSymbol=function(t){this._cellMap.erase(t)},r.prototype.reverseSign=function(){this._constant=-this._constant;for(var t=this._cellMap,e=0,r=t.size();e<r;++e){var n=t.itemAt(e);n.second=-n.second}},r.prototype.solveFor=function(t){var e=this._cellMap,r=e.erase(t),n=-1/r.second;this._constant*=n;for(var o=0,i=e.size();o<i;++o)e.itemAt(o).second*=n},r.prototype.solveForEx=function(t,e){this.insertSymbol(t,-1),this.solveFor(e)},r.prototype.coefficientFor=function(t){var e=this._cellMap.find(t);return void 0!==e?e.second:0},r.prototype.substitute=function(t,e){var r=this._cellMap.erase(t);void 0!==r&&this.insertRow(e,r.second)},r}()}(i||(i={})),/*-----------------------------------------------------------------------------
| Copyright (c) 2014, Nucleic Development Team.
|
| Distributed under the terms of the Modified BSD License.
|
| The full license is in the file COPYING.txt, distributed with this software.
|----------------------------------------------------------------------------*/
e.exports=i},{}]},{},["main"])}();/*
Copyright (c) 2012, Continuum Analytics, Inc.
All rights reserved.

Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.

Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.

Neither the name of Continuum Analytics nor the names of any contributors
may be used to endorse or promote products derived from this software 
without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE 
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR 
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF 
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS 
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) 
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF 
THE POSSIBILITY OF SUCH DAMAGE.
*/
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}

.dashboard-page-wrapper {
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
  -webkit-flex-direction: column;
      -ms-flex-direction: column;
          flex-direction: column;
}

.dashboard-row-orientation {
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
  -webkit-flex-direction: column;
      -ms-flex-direction: column;
          flex-direction: column;
}

.dashboard-row {
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
  -webkit-flex-direction: row;
      -ms-flex-direction: row;
          flex-direction: row;
}

.dashboard-row-flex {
  -webkit-box-flex: 1;
  -webkit-flex: 1;
      -ms-flex: 1;
          flex: 1;
}

.dashboard-column-orientation {
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
  -webkit-flex-direction: row;
      -ms-flex-direction: row;
          flex-direction: row;
}

.dashboard-column {
  -webkit-box-flex: 1;
  -webkit-flex: 1;
      -ms-flex: 1;
          flex: 1;
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
  -webkit-flex-direction: column;
      -ms-flex-direction: column;
          flex-direction: column;
}

.chart-wrapper-flex {
  -webkit-box-flex: 1;
  -webkit-flex: 1;
      -ms-flex: 1;
          flex: 1;
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
  -webkit-flex-direction: column;
      -ms-flex-direction: column;
          flex-direction: column;
}

.chart-stage-flex {
  -webkit-box-flex: 1;
  -webkit-flex: 1;
      -ms-flex: 1;
          flex: 1;
  position: relative;
}

.chart-shim {
  position: absolute;
  left: 8px; top: 8px; right: 8px; bottom: 8px;
}

.no-padding .chart-shim {
  left: 0; top: 0; right: 0; bottom: 0;
}

.flowing-content-shim {
  overflow: auto;
  left: 0; top: 0; right: 0; bottom: 0;
  padding-left: 8px;
  padding-right: 8px;
}

.flowing-content-container {
  padding-top: 8px;
  padding-bottom: 8px;
}

.chart-stage .table-bordered {
  border: none;
}

.chart-stage .table-bordered > tbody > tr > th,
.chart-stage .table-bordered > tfoot > tr > th,
.chart-stage .table-bordered > tbody > tr > td,
.chart-stage .table-bordered > tfoot > tr > td {
  border: none;
}

.chart-stage .table-bordered > tbody > tr > td {
  border-top: 1px solid #dddddd;
}

.chart-stage .table-bordered > thead > tr > th {
  border: none;
  border-bottom: 2px solid #dddddd;
}

.bootstrap-table table>thead {
  background-color: #fff;
}

.bootstrap-table table.data,
.bootstrap-table table.shiny-table {
  width: inherit !important;
}

.bootstrap-table table.data>tbody>tr>th,
.bootstrap-table table.shiny-table>tbody>tr>th {
  border-top: none;
  border-bottom: 2px solid #dddddd;
  padding: 5px;
}

.bootstrap-table .data td[align=right] {
  font-family: inherit;
}

.chart-wrapper form {
  padding-left: 5px;
  padding-right: 5px;
}

.shiny-input-container label {
  font-weight: normal;
}

.chart-stage .html-widget {
  width: 100% !important;
  height: 100% !important;
}

.chart-stage .html-widget-static-bound {
  width: 100% !important;
  height: 100% !important;
}

.chart-stage .shiny-bound-output {
  width: 100% !important;
  height: 100% !important;
}

/* Omit display of empty paragraphs */

.chart-shim>p:empty {
  display: none;
}

.chart-stage>p:empty {
  display: none;
}

/* Omit display of special knitr options div*/

.chart-stage .knitr-options {
  display: none;
}

/* Automatically resizing images */

.image-container {
  position: absolute;
  top: 0;
  left: 0;
  right: 0;
  bottom: 0;
  margin: 0;
}

.image-container img {
  opacity: 0;
  overflow: hidden;
}

/* Value box */

.value-box {
  border-radius: 2px;
  position: relative;
  display: block;
  margin-right: 8px;
  margin-bottom: 8px;
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.1);
}

.value-box > .inner {
  padding: 10px;
  padding-left: 20px;
  padding-right: 20px;
}

.value-box .value {
  font-size: 38px;
  font-weight: bold;
  margin: 0 0 3px 0;
  white-space: nowrap;
  padding: 0;
}

.value-box .caption {
  font-size: 15px;
}
.value-box .caption > small {
  display: block;
  font-size: 13px;
  margin-top: 5px;
}

.value-box .icon i {
  position: absolute;
  top: 15px;
  right: 15px;
  font-size: 80px;
  color: rgba(0, 0, 0, 0.15);
}

.linked-value:hover {
  cursor: pointer;
}

.value-box.linked-value:hover {
  box-shadow: 0 2px 2px rgba(0, 0, 0, 0.3);
}

/* STORYBOARD */

.storyboard-nav button {
  background: transparent;
  border: 0;
  opacity: .3;
  outline: none;
  padding: 0;
}

.storyboard-nav button:hover,
.storyboard-nav button:hover {
  opacity: .5;
}

.storyboard-nav button:disabled,
.storyboard-nav button:disabled {
  opacity: .1;
}

.storyboard-nav .sbnext,
.storyboard-nav .sbprev {
  float: left;
  width: 2%;
  height: 120px;
  font-size: 50px;
}

.storyboard-nav .sbprev {
  text-align: left;
  width: 2%;
}

.storyboard-nav .sbnext {
  float: right;
  text-align: right;
  margin-right: 8px;
}

.storyboard-nav .sbframelist {
  margin: 0 auto;
  width: 94%;
  height: 120px;
  overflow: hidden;
  text-shadow: none;
  margin-bottom: 8px;
}

.storyboard-nav .sbframelist ul {
  list-style: none;
  margin: 0;
  padding: 0;
  height: 100%;
}

.storyboard-nav .sbframelist ul li {
  float: left;
  width: 270px;
  height: 100%;
  padding: 10px 10px 10px 10px;
  margin-right: 8px;
  background: #fff;
  border: 1px solid #e2e2e2;
  border-radius: 3px;
  color: #3a3c47;
  text-align: left;
  font-size: 14px;
  cursor: pointer;
}

.storyboard-nav .sbframelist ul li:last-child {
  margin-right: 0px;
}

.storyboard-nav .sbframelist ul li.active {
  color: #fff;
  background: #B8B8B8;
}

.sbframe-commentary {
  width: 300px;
  background: #fbfbfb;
  font-size: 14px;
}

.sbframe-commentary ul {
  padding-left: 22px;
}

.sbframe.active {
  display: flex;
}

.sbframe:not(.active) {
  display: none;
}

/* NAV TABS */

.nav-tabs-custom > .nav-tabs {
  margin: 0;
  border-bottom: 1px solid #d7d7d7;
  color: #666;
  font-size: 14px;
  font-weight: 300;
  border-top-right-radius: 3px;
  border-top-left-radius: 3px;
}
.nav-tabs-custom > .nav-tabs > li {
  border-top: 3px solid transparent;
  margin-bottom: -1px;
  margin-right: 5px;
}
.nav-tabs-custom > .nav-tabs > li > a,
.nav-tabs-custom > .nav-tabs > li > a:active {
  color: #666;
  font-weight: 300;
  font-size: 14px;
  border-radius: 0;
  padding: 3px 10px 5px;
  text-transform: none;
}

.nav-tabs-custom > .nav-tabs > li:not(.active) > a {
  border-bottom-color: transparent;
}

.nav-tabs-custom > .nav-tabs > li > a.text-muted {
  color: #999;
}
.nav-tabs-custom > .nav-tabs > li > a,
.nav-tabs-custom > .nav-tabs > li > a:hover {
  background: transparent;
  margin: 0;
}
.nav-tabs-custom > .nav-tabs > li > a:hover {
  color: #999;
}
.nav-tabs-custom > .nav-tabs > li:not(.active) > a:hover,
.nav-tabs-custom > .nav-tabs > li:not(.active) > a:focus,
.nav-tabs-custom > .nav-tabs > li:not(.active) > a:active {
  border-color: transparent;
}
.nav-tabs-custom > .nav-tabs > li.active {
  border-top-color: #3c8dbc;
}
.nav-tabs-custom > .nav-tabs > li.active > a,
.nav-tabs-custom > .nav-tabs > li.active:hover > a {
  background-color: #fff;
  color: #666;
}
.nav-tabs-custom > .nav-tabs > li.active > a {
  border-top-color: transparent;
  border-left-color: #d7d7d7;
  border-right-color: #d7d7d7;
}
.nav-tabs-custom > .nav-tabs > li:first-of-type {
  margin-left: 0;
}
.nav-tabs-custom > .nav-tabs > li:first-of-type.active > a {
  border-left-color: transparent;
}
.nav-tabs-custom > .nav-tabs.pull-right {
  float: none !important;
}
.nav-tabs-custom > .nav-tabs.pull-right > li {
  float: right;
}
.nav-tabs-custom > .nav-tabs.pull-right > li:first-of-type {
  margin-right: 0;
}
.nav-tabs-custom > .nav-tabs.pull-right > li:first-of-type > a {
  border-left-width: 1px;
}
.nav-tabs-custom > .nav-tabs.pull-right > li:first-of-type.active > a {
  border-left-color: #d7d7d7;
  border-right-color: transparent;
}
.nav-tabs-custom > .nav-tabs > li.header {
  line-height: 35px;
  padding: 0 10px;
  font-size: 20px;
  color: #666;
}
.nav-tabs-custom > .nav-tabs > li.header > .fa,
.nav-tabs-custom > .nav-tabs > li.header > .glyphicon,
.nav-tabs-custom > .nav-tabs > li.header > .ion {
  margin-right: 5px;
}
.nav-tabs-custom > .tab-content {
  background: #fff;
  padding: 0;
  border-bottom-right-radius: 3px;
  border-bottom-left-radius: 3px;
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
}

.nav-tabs-custom > .tab-content > .chart-wrapper {
  background: #fff;
  border: none;
  margin: 0;
}


.nav-tabs-custom > .tab-content > .active {
  display: -webkit-box;
  display: -webkit-flex;
  display: -ms-flexbox;
  display: flex;
}

.nav-tabs-custom .dropdown.open > a:active,
.nav-tabs-custom .dropdown.open > a:focus {
  background: transparent;
  color: #999;
}


@media (max-width: 767px) {
  .value-box {
    text-align: center;
    margin-right: 8px;
  }
  .value-box .icon {
    display: none;
  }
}

/* Fixed position sidebar */

.section.sidebar {
  position: fixed;
  top: 51px; /* overridden by theme */
  left: 0;
  bottom: 0;
  border-right: 1px solid #e2e2e2;
  background-color: white; /* overridden by theme */
  padding-left: 10px;
  padding-right: 10px;
  visibility: hidden;
  overflow: auto;
}

.section.sidebar form p:first-child {
  margin-top: 10px;
}

/* Embedded source code */

#flexdashboard-source-code {
  display: none;
}

.featherlight-content #flexdashboard-source-code {
  display: inline-block;
}

.featherlight-content {
  width: 80%;
  max-width: 800px;
  padding: 0 !important;
  border-bottom: none !important;
}

.featherlight:last-of-type {
  background: rgba(0, 0, 0, 0.7);
}

.featherlight-inner {
  width: 100%;
}

.featherlight-content pre {
  margin: 0;
  border: 0;
  background: #fff;
  font-size: 12px;
}

.unselectable {
  -ms-user-select: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: -moz-none;
  -o-user-select: none;
  user-select: none;
}

#flexdashboard-source-code code {
  -ms-user-select: text;
  -webkit-user-select: text;
  -khtml-user-select: text;
  -moz-user-select: text;
  -o-user-select: text;
  user-select: text;
}

/* DataTables Tweaks */

.dataTables_filter input[type="search"] {
  -webkit-appearance: searchfield;
  outline: none;
}

.dataTables_wrapper.no-footer
.dataTables_info {
  padding-top: 0;
}


table.dataTable thead th {
  border-bottom: 1px solid #d7d7d7;
}

.dataTables_wrapper.no-footer .dataTables_scrollBody {
  border-bottom: 1px solid #d7d7d7;
}

/* Mobile phone only CSS */

.desktop-layout div.section.mobile {
  display: none;
}

.mobile-layout div.section.no-mobile {
  display: none;
}

.mobile-figure {
  display: none;
}



body {
  padding-top: 60px;
}

.section.sidebar {
  top: 51px;
  background-color: rgba(39, 128, 227, 0.1);
}

.value-box {
  color: #f9f9f9;
}

.bg-primary {
  background-color: rgba(39, 128, 227, 0.7);
}

.storyboard-nav .sbframelist ul li.active {
  background-color: rgba(39, 128, 227, 0.7);
}

.nav-tabs-custom > .nav-tabs > li.active {
  border-top-color: rgba(39, 128, 227, 0.7);
}

.bg-info {
  background-color: rgba(153, 84, 187, 0.7);
}

.bg-warning {
  background-color: rgba(255, 117, 24, 0.7);
}

.bg-danger {
  background-color: rgba(255, 0, 57, 0.7);
}

.bg-success {
  background-color: rgba(63, 182, 24, 0.7);
}

.chart-title {
  font-weight: 500;
}

.nav-tabs-custom > .nav-tabs > li > a,
.nav-tabs-custom > .nav-tabs > li > a:active {
  font-weight: 500;
}

@media only screen and (min-width: 768px) {
html, body {
  height: 100%;
}

#dashboard-container {
  height: 100%;
}
}
</style>



</head>

<body>

<div class="navbar navbar-inverse navbar-fixed-top" role="navigation">
<div class="container-fluid">
<div class="navbar-header">

<button id="navbar-button" type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar">
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>

<span class="navbar-logo pull-left">
  
</span>
<span class="navbar-brand">
  rbokeh iris dataset
  <span class="navbar-author">
    Ryan Hafen
    </span>
</span>

</div>
<div id="navbar" class="navbar-collapse collapse">
<ul class="nav navbar-nav navbar-left">
</ul>
<ul class="nav navbar-nav navbar-right">
</ul>
</div><!--/.nav-collapse-->
</div><!--/.container-->
</div><!--/.navbar-->

<script type="text/javascript">


var FlexDashboard = (function () {

  // initialize options
  var _options = {};

  var FlexDashboard = function() {

    // default options
    _options = $.extend(_options, {
      theme: "cosmo",
      fillPage: false,
      orientation: 'columns',
      storyboard: false,
      defaultFigWidth: 576,
      defaultFigHeight: 461,
      defaultFigWidthMobile: 360,
      defaultFigHeightMobile: 461,
      isMobile: false,
      isPortrait: false
    });
  };

  function init(options) {

    // extend default options
    $.extend(true, _options, options);

    // add ids to sections that don't have them (pandoc won't assign ids
    // to e.g. sections with titles consisting of only chinese characters)
    var nextId = 1;
    $('.level1:not([id]),.level2:not([id]),.level3:not([id])').each(function() {
      $(this).attr('id', 'dashboard-' + nextId++);
    });

    // find navbar items
    var navbarItems = $('#flexdashboard-navbar');
    if (navbarItems.length)
      navbarItems = JSON.parse(navbarItems.html());
    addNavbarItems(navbarItems);

    // find the main dashboard container
    var dashboardContainer = $('#dashboard-container');

    // resolve mobile classes
    resolveMobileClasses(dashboardContainer);

    // one time global initialization for components
    componentsInit(dashboardContainer);

    // look for a global sidebar
    var globalSidebar = dashboardContainer.find(".section.level1.sidebar");
    if (globalSidebar.length > 0) {

      // global layout for fullscreen displays
      if (!isMobilePhone()) {

         // hoist it up to the top level
         globalSidebar.insertBefore(dashboardContainer);

         // lay it out (set width/positions)
         layoutSidebar(globalSidebar, dashboardContainer);

      // tuck sidebar into first page for mobile phones
      } else {

        // convert it into a level3 section
        globalSidebar.removeClass('sidebar');
        globalSidebar.removeClass('level1');
        globalSidebar.addClass('level3');
        var h1 = globalSidebar.children('h1');
        var h3 = $('<h3></h3>');
        h3.html(h1.html());
        h3.insertBefore(h1);
        h1.remove();

        // move it into the first page
        var page = dashboardContainer.find('.section.level1').first();
        if (page.length > 0)
          page.prepend(globalSidebar);
      }
    }

    // look for pages to layout
    var pages = $('div.section.level1');
    if (pages.length > 0) {

        // find the navbar and collapse on clicked
        var navbar = $('#navbar');
        navbar.on("click", "a[data-toggle!=dropdown]", null, function () {
           navbar.collapse('hide');
        });

        // envelop the dashboard container in a tab content div
        dashboardContainer.wrapInner('<div class="tab-content"></div>');

        pages.each(function(index) {

          // lay it out
          layoutDashboardPage($(this));

          // add it to the navbar
          addToNavbar($(this), index === 0);

        });

    } else {

      // remove the navbar and navbar button if we don't
      // have any navbuttons
      if (navbarItems.length === 0) {
        $('#navbar').remove();
        $('#navbar-button').remove();
      }

      // add the storyboard class if requested
      if (_options.storyboard)
        dashboardContainer.addClass('storyboard');

      // layout the entire page
      layoutDashboardPage(dashboardContainer);
    }

    // if we are in shiny we need to trigger a window resize event to
    // force correct layout of shiny-bound-output elements
    if (isShinyDoc())
      $(window).trigger('resize');

    // make main components visible
    $('.section.sidebar').css('visibility', 'visible');
    dashboardContainer.css('visibility', 'visible');

    // handle location hash
    handleLocationHash();

    // intialize prism highlighting
    initPrismHighlighting();

    // record mobile and orientation state then register a handler
    // to refresh if it changes
    _options.isMobile = isMobilePhone();
    _options.isPortrait = isPortrait();
    $(window).on('resize', function() {
      if (_options.isMobile !== isMobilePhone() ||
          _options.isPortrait !== isPortrait()) {
        window.location.reload();
      }
    });

    // trigger layoutcomplete event
    dashboardContainer.trigger('flexdashboard:layoutcomplete');
  }

  function resolveMobileClasses(dashboardContainer) {
     // add top level layout class
    dashboardContainer.addClass(isMobilePhone() ? 'mobile-layout' :
                                                  'desktop-layout');

    // look for .mobile sections and add .no-mobile to their peers
    var mobileSections = $('.section.mobile');
    mobileSections.each(function() {
       var id = $(this).attr('id');
       var nomobileId = id.replace(/-\d+$/, '');
       $('#' + nomobileId).addClass('no-mobile');
    });
  }

  function addNavbarItems(navbarItems) {

    var navbarLeft = $('ul.navbar-left');
    var navbarRight = $('ul.navbar-right');

    for (var i = 0; i<navbarItems.length; i++) {

      // get the item
      var item = navbarItems[i];

      // determine the container
      var container = null;
      if (item.align === "left")
        container = navbarLeft;
      else
        container = navbarRight;

      // navbar menu if we have multiple items
      if (item.items) {
        var menu = navbarMenu(null, item.icon, item.title, container);
        for (var j = 0; j<item.items.length; j++) {
          var subItem = item.items[j];
          var li = $('<li></li>');
          li.append(navbarLink(subItem.icon, subItem.title, subItem.href, subItem.target));
          menu.append(li);
        }
      } else {
        var li = $('<li></li>');
        li.append(navbarLink(item.icon, item.title, item.href, item.target));
        container.append(li);
      }
    }
  }

  // create or get a reference to an existing dropdown menu
  function navbarMenu(id, icon, title, container) {
    var existingMenu = [];
    if (id)
      existingMenu = container.children('#' + id);
    if (existingMenu.length > 0) {
      return existingMenu.children('ul');
    } else {
      var li = $('<li></li>');
      if (id)
        li.attr('id', id);
      li.addClass('dropdown');
      // auto add "Share" title on mobile if necessary
      if (!title && icon && (icon === "fa-share-alt") && isMobilePhone())
        title = "Share";
      if (title) {
        title = title + ' <span class="caret"></span>';
      }
      var a = navbarLink(icon, title, "#");
      a.addClass('dropdown-toggle');
      a.attr('data-toggle', 'dropdown');
      a.attr('role', 'button');
      a.attr('aria-expanded', 'false');
      li.append(a);
      var ul = $('<ul class="dropdown-menu"></ul>');
      ul.attr('role', 'menu');
      li.append(ul);
      container.append(li);
      return ul;
    }
  }

  function addToNavbar(page, active) {

    // capture the id and data-icon attribute (if any)
    var id = page.attr('id');
    var icon = page.attr('data-icon');
    var navmenu = page.attr('data-navmenu');

    // get hidden state (transfer this to navbar)
    var hidden = page.hasClass('hidden');
    page.removeClass('hidden');

    // sanitize the id for use with bootstrap tabs
    id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
    page.attr('id', id);

    // get the wrapper
    var wrapper = page.closest('.dashboard-page-wrapper');

    // move the id to the wrapper
    page.removeAttr('id');
    wrapper.attr('id', id);

    // add the tab-pane class to the wrapper
    wrapper.addClass('tab-pane');
    if (active)
      wrapper.addClass('active');

    // get a reference to the h1, discover it's id and title, then remove it
    var h1 = wrapper.find('h1').first();
    var title = h1.html();
    h1.remove();

    // create a navbar item
    var li = $('<li></li>');
    var a = navbarLink(icon, title, '#' + id);
    a.attr('data-toggle', 'tab');
    li.append(a);

    // add it to the navbar (or navbar menu if specified)
    var container = $('ul.navbar-left');
    if (navmenu) {
      var menuId = navmenu.replace(/\s+/g, '');
      var menu = navbarMenu(menuId, null, navmenu, container);
      menu.append(li);
    } else {
      container.append(li);
    }

    // hide it if requested
    if (hidden)
      li.addClass('hidden');
  }

  function navbarLink(icon, title, href, target) {

    var a = $('<a></a>');
    if (icon) {

      // get the name of the icon set and icon
      var dashPos = icon.indexOf("-");
      var iconSet = icon.substring(0, dashPos);
      var iconName = icon.substring(dashPos + 1);

      // create the icon
      var iconElement = $('<span class="' + iconSet + ' ' + icon + '"></span>');
      if (title)
        iconElement.css('margin-right', '7px');
      a.append(iconElement);
      // if href is null see if we can auto-generate based on icon (e.g. social)
      if (!href)
        maybeGenerateLinkFromIcon(iconName, a);
    }
    if (title)
      a.append(title);

    // add the href.
    if (href) {
      if (href === "source_embed") {
        a.attr('href', '#');
        a.attr('data-featherlight', "#flexdashboard-source-code");
        a.featherlight({
            beforeOpen: function(event){
              $('body').addClass('unselectable');
            },
            afterClose: function(event){
              $('body').removeClass('unselectable');
            }
        });
      } else {
        a.attr('href', href);
      }
    }

    // add the arget
    if (target)
      a.attr('target', target);

    return a;
  }

  // auto generate a link from an icon name (e.g. twitter) when possible
  function maybeGenerateLinkFromIcon(iconName, a) {

     var serviceLinks = {
      "twitter": "https://twitter.com/share?text=" + encodeURIComponent(document.title) + "&url="+encodeURIComponent(location.href),
      "facebook": "https://www.facebook.com/sharer/sharer.php?s=100&p[url]="+encodeURIComponent(location.href),
      "google-plus": "https://plus.google.com/share?url="+encodeURIComponent(location.href),
      "linkedin": "https://www.linkedin.com/shareArticle?mini=true&url="+encodeURIComponent(location.href) + "&title=" + encodeURIComponent(document.title),
      "pinterest": "https://pinterest.com/pin/create/link/?url="+encodeURIComponent(location.href) + "&description=" + encodeURIComponent(document.title)
    };

    var makeSocialLink = function(a, href) {
      a.attr('href', '#');
      a.on('click', function(e) {
        e.preventDefault();
        window.open(href);
      });
    };

    $.each(serviceLinks, function(key, value) {
      if (iconName.indexOf(key) !== -1)
        makeSocialLink(a, value);
    });
  }

  // layout a dashboard page
  function layoutDashboardPage(page) {

    // use a page wrapper so that free form content above the
    // dashboard appears at the top rather than the side (as it
    // would without the wrapper in a column orientation)
    var wrapper = $('<div class="dashboard-page-wrapper"></div>');
    page.wrap(wrapper);

    // if there are no level2 or level3 headers synthesize a level3
    // header to contain the (e.g. frame it, scroll container, etc.)
    var headers = page.find('h2,h3');
    if (headers.length === 0)
      page.wrapInner('<div class="section level3"></div>');

    // hoist up any content before level 2 or level 3 headers
    var children = page.children();
    children.each(function(index) {
      if ($(this).hasClass('level2') || $(this).hasClass('level3'))
        return false;
      $(this).insertBefore(page);
    });

    // determine orientation and fillPage behavior for distinct media
    var orientation, fillPage, storyboard;

    // media: mobile phone
    if (isMobilePhone()) {

      // if there is a sidebar we need to ensure it's content
      // is properly framed as an h3
      var sidebar = page.find('.section.sidebar');
      sidebar.removeClass('sidebar');
      sidebar.wrapInner('<div class="section level3"></div>');
      var h2 = sidebar.find('h2');
      var h3 = $('<h3></h3>');
      h3.html(h2.html());
      h3.insertBefore(h2);
      h2.remove();

      // wipeout h2 elements then enclose them in a single h2
      var level2 = page.find('div.section.level2');
      level2.each(function() {
        level2.children('h2').remove();
        level2.children().unwrap();
      });
      page.wrapInner('<div class="section level2"></div>');

      // substitute mobile images
      if (isPortrait()) {
        var mobileFigures = $('img.mobile-figure');
        mobileFigures.each(function() {
          // get the src (might be base64 encoded)
          var src = $(this).attr('src');

          // find it's peer
          var id = $(this).attr('data-mobile-figure-id');
          var img = $('img[data-figure-id=' + id + "]");
          img.attr('src', src)
             .attr('width', _options.defaultFigWidthMobile)
             .attr('height', _options.defaultFigHeightMobile);
        });
      }

      // hoist storyboard commentary into it's own section
      if (page.hasClass('storyboard')) {
        var commentaryHR = page.find('div.section.level3 hr');
        commentaryHR.each(function() {
          var commentary = $(this).nextAll().detach();
          var commentarySection = $('<div class="section level3"></div>');
          commentarySection.append(commentary);
          commentarySection.insertAfter($(this).closest('div.section.level3'));
          $(this).remove();
        });
      }

      // force a non full screen layout by columns
      orientation = _options.orientation = 'columns';
      fillPage = _options.fillPage = false;
      storyboard = _options.storyboard = false;

    // media: desktop
    } else {

      // determine orientation
      orientation = page.attr('data-orientation');
      if (orientation !== 'rows' && orientation != 'columns')
        orientation = _options.orientation;

      // determine storyboard mode
      storyboard = page.hasClass('storyboard');

      // fillPage based on options (force for storyboard)
      fillPage = _options.fillPage || storyboard;

      // handle sidebar
      var sidebar = page.find('.section.level2.sidebar');
      if (sidebar.length > 0)
        layoutSidebar(sidebar, page);
    }

    // give it and it's parent divs height: 100% if we are in fillPage mode
    if (fillPage) {
      page.addClass('vertical-layout-fill');
      page.css('height', '100%');
      page.parents('div').css('height', '100%');
    } else {
      page.addClass('vertical-layout-scroll');
    }

    // perform the layout
    if (storyboard)
      layoutPageAsStoryboard(page);
    else if (orientation === 'rows')
      layoutPageByRows(page, fillPage);
    else if (orientation === 'columns')
      layoutPageByColumns(page, fillPage);
  }

  function layoutSidebar(sidebar, content) {

    // get it out of the header hierarchy
    sidebar = sidebar.first();
    if (sidebar.hasClass('level1')) {
      sidebar.removeClass('level1');
      sidebar.children('h1').remove();
    } else if (sidebar.hasClass('level2')) {
      sidebar.removeClass('level2');
      sidebar.children('h2').remove();
    }

    // determine width
    var sidebarWidth = isTablet() ? 220 : 250;
    var dataWidth = parseInt(sidebar.attr('data-width'));
    if (dataWidth)
      sidebarWidth = dataWidth;

    // set the width and shift the page right to accomodate the sidebar
    sidebar.css('width', sidebarWidth + 'px');
    content.css('padding-left', sidebarWidth + 'px');

    // wrap it's contents in a form
    sidebar.wrapInner($('<form></form>'));
  }

  function layoutPageAsStoryboard(page) {

    // create storyboard navigation
    var nav = $('<div class="storyboard-nav"></div>');

    // add navigation buttons
    var prev = $('<button class="sbprev"><i class="fa fa-angle-left"></i></button>');
    nav.append(prev);
    var next= $('<button class="sbnext"><i class="fa fa-angle-right"></i></button>');
    nav.append(next);

    // add navigation frame
    var frameList = $('<div class="sbframelist"></div>');
    nav.append(frameList);
    var ul = $('<ul></ul>');
    frameList.append(ul);

     // find all the level3 sections (those are the storyboard frames)
    var frames = page.find('div.section.level3');
    frames.each(function() {

      // mark it
      $(this).addClass('sbframe');

      // divide it into chart content and (optional) commentary
      $(this).addClass('dashboard-column-orientation');

      // stuff the chart into it's own div w/ flex
      $(this).wrapInner('<div class="sbframe-component"></div>');
      setFlex($(this), 1);
      var frame = $(this).children('.sbframe-component');

      // extract the title from the h3
      var li = $('<li></li>');
      var h3 = frame.children('h3');
      li.html(h3.html());
      h3.remove();
      ul.append(li);

      // extract commentary
      var hr = frame.children('hr');
      if (hr.length) {
        var commentary = hr.nextAll().detach();
        hr.remove();
        var commentaryFrame = $('<div class="sbframe-commentary"></div>');
        commentaryFrame.addClass('flowing-content-shim');
        commentaryFrame.addClass('flowing-content-container');
        commentaryFrame.append(commentary);
        $(this).append(commentaryFrame);

        // look for a data-commentary-width attribute
        var commentaryWidth = $(this).attr('data-commentary-width');
        if (commentaryWidth)
          commentaryFrame.css('width', commentaryWidth + 'px');
      }

      // layout the chart (force flex)
      var result = layoutChart(frame, true);

      // ice the notes if there are none
      if (!result.notes)
        frame.find('.chart-notes').remove();

      // set flex on chart
      setFlex(frame, 1);
    });

    // create a div to hold all the frames
    var frameContent = $('<div class="sbframe-content"></div>');
    frameContent.addClass('dashboard-row-orientation');
    frameContent.append(frames.detach());

    // row orientation to stack nav and frame content
    page.addClass('dashboard-row-orientation');
    page.append(nav);
    page.append(frameContent);
    setFlex(frameContent, 1);

    // initialize sly
    var sly = new Sly(frameList, {
    		horizontal: true,
    		itemNav: 'basic',
    		smart: true,
    		activateOn: 'click',
    		startAt: 0,
    		scrollBy: 1,
    		activatePageOn: 'click',
    		speed: 200,
    		moveBy: 600,
    		dragHandle: true,
    		dynamicHandle: true,
    		clickBar: true,
    		keyboardNavBy: 'items',
    		next: next,
    		prev: prev
    	}).init();

    // make first frame active
    frames.removeClass('active');
    frames.first().addClass('active');

    // subscribe to frame changed events
    sly.on('active', function (eventName, itemIndex) {
      frames.removeClass('active');
      frames.eq(itemIndex).addClass('active')
                          .trigger('shown');
    });
  }

  function layoutPageByRows(page, fillPage) {

    // row orientation
    page.addClass('dashboard-row-orientation');

    // find all the level2 sections (those are the rows)
    var rows = page.find('div.section.level2');

    // if there are no level2 sections then treat the
    // entire page as if it's a level 2 section
    if (rows.length === 0) {
      page.wrapInner('<div class="section level2"></div>');
      rows = page.find('div.section.level2');
    }

    rows.each(function () {

      // flags
      var haveNotes = false;
      var haveFlexHeight = true;

      // remove the h2
      $(this).children('h2').remove();

      // check for a tabset
      var isTabset = $(this).hasClass('tabset');
      if (isTabset)
        layoutTabset($(this));

      // give it row layout semantics if it's not a tabset
      if (!isTabset)
        $(this).addClass('dashboard-row');

      // find all of the level 3 subheads
      var columns = $(this).find('div.section.level3');

      // determine figureSizes sizes
      var figureSizes = chartFigureSizes(columns);

      // fixup the columns
      columns.each(function(index) {

        // layout the chart (force flex if we are in a tabset)
        var result = layoutChart($(this), isTabset);

        // update flexHeight state
        if (!result.flex)
          haveFlexHeight = false;

        // update state
        if (result.notes)
          haveNotes = true;

        // set the column flex based on the figure width
        // (value boxes will just get the default figure width)
        var chartWidth = figureSizes[index].width;
        setFlex($(this), chartWidth + ' ' + chartWidth + ' 0px');

      });

      // remove empty chart note divs
      if (isTabset)
        $(this).find('.chart-notes').filter(function() {
            return $(this).html() === "&nbsp;";
        }).remove();
      if (!haveNotes)
        $(this).find('.chart-notes').remove();

       // make it a flexbox row
      if (haveFlexHeight)
        $(this).addClass('dashboard-row-flex');

      // now we can set the height on all the wrappers (based on maximum
      // figure height + room for title and notes, or data-height on the
      // container if specified). However, don't do this if there is
      // no flex on any of the constituent columns
      var flexHeight = null;
      var dataHeight = parseInt($(this).attr('data-height'));
      if (dataHeight)
        flexHeight = adjustedHeight(dataHeight, columns.first());
      else if (haveFlexHeight)
        flexHeight = maxChartHeight(figureSizes, columns);
      if (flexHeight) {
        if (fillPage)
          setFlex($(this), flexHeight + ' ' + flexHeight + ' 0px');
        else {
          $(this).css('height', flexHeight + 'px');
          setFlex($(this), '0 0 ' + flexHeight + 'px');
        }
      }

    });
  }

  function layoutPageByColumns(page, fillPage) {

    // column orientation
    page.addClass('dashboard-column-orientation');

    // find all the level2 sections (those are the columns)
    var columns = page.find('div.section.level2');

    // if there are no level2 sections then treat the
    // entire page as if it's a level 2 section
    if (columns.length === 0) {
      page.wrapInner('<div class="section level2"></div>');
      columns = page.find('div.section.level2');
    }

    // layout each column
    columns.each(function (index) {

      // remove the h2
      $(this).children('h2').remove();

      // make it a flexbox column
      $(this).addClass('dashboard-column');

      // check for a tabset
      var isTabset = $(this).hasClass('tabset');
      if (isTabset)
        layoutTabset($(this));

      // find all the h3 elements
      var rows = $(this).find('div.section.level3');

      // get the figure sizes for the rows
      var figureSizes = chartFigureSizes(rows);

      // column flex is the max row width (or data-width if specified)
      var flexWidth;
      var dataWidth = parseInt($(this).attr('data-width'));
      if (dataWidth)
        flexWidth = dataWidth;
      else
        flexWidth = maxChartWidth(figureSizes);
      setFlex($(this), flexWidth + ' ' + flexWidth + ' 0px');

      // layout each chart
      rows.each(function(index) {

        // perform the layout
        var result = layoutChart($(this), false);

        // ice the notes if there are none
        if (!result.notes)
          $(this).find('.chart-notes').remove();

        // set flex height based on figHeight, then adjust
        if (result.flex) {
          var chartHeight = figureSizes[index].height;
          chartHeight = adjustedHeight(chartHeight, $(this));
          if (fillPage)
            setFlex($(this), chartHeight + ' ' + chartHeight + ' 0px');
          else {
            $(this).css('height', chartHeight + 'px');
            setFlex($(this), chartHeight + ' ' + chartHeight + ' ' + chartHeight + 'px');
          }
        }
      });
    });
  }

  function chartFigureSizes(charts) {

    // sizes
    var figureSizes = new Array(charts.length);

    // check each chart
    charts.each(function(index) {

      // start with default
      figureSizes[index] = {
        width: _options.defaultFigWidth,
        height: _options.defaultFigHeight
      };

      // look for data-height or data-width then knit options
      var dataWidth = parseInt($(this).attr('data-width'));
      var dataHeight = parseInt($(this).attr('data-height'));
      var knitrOptions = $(this).find('.knitr-options:first');
      var knitrWidth, knitrHeight;
      if (knitrOptions) {
        knitrWidth = parseInt(knitrOptions.attr('data-fig-width'));
        knitrHeight =  parseInt(knitrOptions.attr('data-fig-height'));
      }

      // width
      if (dataWidth)
        figureSizes[index].width = dataWidth;
      else if (knitrWidth)
        figureSizes[index].width = knitrWidth;

      // height
      if (dataHeight)
        figureSizes[index].height = dataHeight;
      else if (knitrHeight)
        figureSizes[index].height = knitrHeight;
    });

    // return sizes
    return figureSizes;
  }

  function maxChartHeight(figureSizes, charts) {

    // first compute the maximum height
    var maxHeight = _options.defaultFigHeight;
    for (var i = 0; i<figureSizes.length; i++)
      if (figureSizes[i].height > maxHeight)
        maxHeight = figureSizes[i].height;

    // now add offests for chart title and chart notes
    if (charts.length)
      maxHeight = adjustedHeight(maxHeight, charts.first());

    return maxHeight;
  }

  function adjustedHeight(height, chart) {
    if (chart.length > 0) {
      var chartTitle = chart.find('.chart-title');
      if (chartTitle.length)
        height += chartTitle.first().outerHeight();
      var chartNotes = chart.find('.chart-notes');
      if (chartNotes.length)
        height += chartNotes.first().outerHeight();
    }
    return height;
  }

  function maxChartWidth(figureSizes) {
    var maxWidth = _options.defaultFigWidth;
    for (var i = 0; i<figureSizes.length; i++)
      if (figureSizes[i].width > maxWidth)
        maxWidth = figureSizes[i].width;
    return maxWidth;
  }

  // layout a chart
  function layoutChart(chart, forceFlex) {

    // state to return
    var result = {
      notes: false,
      flex: false
    };

    // extract the title
    var title = extractTitle(chart);

    // find components that apply to this container
    var components = componentsFind(chart);

    // if it's a custom component then call it and return
    var customComponents = componentsCustom(components);
    if (customComponents.length) {
      componentsLayout(customComponents, title, chart);
      result.notes = false;
      result.flex = forceFlex || componentsFlex(customComponents);
      return result;
    }

    // put all the content in a chart wrapper div
    chart.addClass('chart-wrapper');
    chart.wrapInner('<div class="chart-stage"></div>');
    var chartContent = chart.children('.chart-stage');

    // flex the content if appropriate
    result.flex = forceFlex || componentsFlex(components);
    if (result.flex) {
      // add flex classes
      chart.addClass('chart-wrapper-flex');
      chartContent.addClass('chart-stage-flex');

      // additional shim to break out of flexbox sizing
      chartContent.wrapInner('<div class="chart-shim"></div>');
      chartContent = chartContent.children('.chart-shim');
    }

    // set custom data-padding attribute
    var pad = chart.attr('data-padding');
    if (pad) {
      if (pad === "0")
        chart.addClass('no-padding');
      else {
        pad = pad + 'px';
        chartContent.css('left', pad)
                    .css('top', pad)
                    .css('right', pad)
                    .css('bottom', pad)
      }
    }

    // call compoents
    componentsLayout(components, title, chartContent);

    // also activate components on shiny output
    findShinyOutput(chartContent).on('shiny:value',
      function(event) {
        var element = $(event.target);
        setTimeout(function() {

          // see if we opted out of flex based on our output (for shiny
          // we can't tell what type of output we have until after the
          // value is bound)
          var components = componentsFind(element);
          var flex = forceFlex || componentsFlex(components);
          if (!flex) {
            chart.css('height', "");
            setFlex(chart, "");
            chart.removeClass('chart-wrapper-flex');
            chartContent.removeClass('chart-stage-flex');
            chartContent.children().unwrap();
          }

          // perform layout
          componentsLayout(components, title, element.parent());
        }, 10);
      });

    // add the title
    var chartTitle = $('<div class="chart-title"></div>');
    chartTitle.html(title);
    chart.prepend(chartTitle);

    // add the notes section
    var chartNotes = $('<div class="chart-notes"></div>');
    chartNotes.html('&nbsp;');
    chart.append(chartNotes);

    // attempt to extract notes if we have a component
    if (components.length)
      result.notes = extractChartNotes(chartContent, chartNotes);

    // return result
    return result;
  }

  // build a tabset from a section div with the .tabset class
  function layoutTabset(tabset) {

    // check for fade option
    var fade = tabset.hasClass("tabset-fade");
    var navClass = "nav-tabs";

    // determine the heading level of the tabset and tabs
    var match = tabset.attr('class').match(/level(\d) /);
    if (match === null)
      return;
    var tabsetLevel = Number(match[1]);
    var tabLevel = tabsetLevel + 1;

    // find all subheadings immediately below
    var tabs = tabset.find("div.section.level" + tabLevel);
    if (!tabs.length)
      return;

    // create tablist and tab-content elements
    var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
    $(tabs[0]).before(tabList);
    var tabContent = $('<div class="tab-content"></div>');
    $(tabs[0]).before(tabContent);

    // build the tabset
    tabs.each(function(i) {

      // get the tab div
      var tab = $(tabs[i]);

      // get the id then sanitize it for use with bootstrap tabs
      var id = tab.attr('id');

      // sanitize the id for use with bootstrap tabs
      id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
      tab.attr('id', id);

      // get the heading element within it and grab it's text
      var heading = tab.find('h' + tabLevel + ':first');
      var headingText = heading.html();

      // build and append the tab list item
      var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
      a.attr('href', '#' + id);
      a.attr('aria-controls', id);
      var li = $('<li role="presentation"></li>');
      li.append(a);
      if (i === 0)
        li.attr('class', 'active');
      tabList.append(li);

      // set it's attributes
      tab.attr('role', 'tabpanel');
      tab.addClass('tab-pane');
      tab.addClass('tabbed-pane');
      tab.addClass('no-title');
      if (fade)
        tab.addClass('fade');
      if (i === 0) {
        tab.addClass('active');
        if (fade)
          tab.addClass('in');
      }

      // move it into the tab content div
      tab.detach().appendTo(tabContent);
    });

    // add nav-tabs-custom
    tabset.addClass('nav-tabs-custom');

    // internal layout is dashboard-column with tab-content flexing
    tabset.addClass('dashboard-column');
    setFlex(tabContent, 1);
  }

  // one time global initialization for components
  function componentsInit(dashboardContainer) {
    for (var i=0; i<window.FlexDashboardComponents.length; i++) {
      var component = window.FlexDashboardComponents[i];
      if (component.init)
        component.init(dashboardContainer);
    }
  }

  // find components that apply within a container
  function componentsFind(container) {

    // look for components
    var components = [];
    for (var i=0; i<window.FlexDashboardComponents.length; i++) {
      var component = window.FlexDashboardComponents[i];
      if (component.find(container).length)
        components.push(component);
    }

    // if there were none then use a special flowing content component
    // that just adds a scrollbar in fillPage mode
    if (components.length == 0) {
      components.push({
        find: function(container) {
          return container;
        },

        flex: function(fillPage) {
          return fillPage;
        },

        layout: function(title, container, element, fillPage) {
          if (fillPage) {
            container.addClass('flowing-content-shim');
            container.addClass('flowing-content-container');
          }
        }
      });
    }

    return components;
  }

  // if there is a custom component then pick it out
  function componentsCustom(components) {
    var customComponent = [];
    for (var i=0; i<components.length; i++)
      if (components[i].type === "custom") {
        customComponent.push(components[i]);
        break;
      }
    return customComponent;
  }

  // query all components for flex
  function componentsFlex(components) {

    // no components at all means no flex
    if (components.length === 0)
      return false;

    // otherwise query components (assume true unless we see false)
    var isMobile = isMobilePhone();
    for (var i=0; i<components.length; i++)
      if (components[i].flex && !components[i].flex(_options.fillPage))
        return false;
    return true;
  }

  // layout all components
  function componentsLayout(components, title, container) {
    var isMobile = isMobilePhone();
    for (var i=0; i<components.length; i++) {
      var element = components[i].find(container);
      if (components[i].layout) {
        // call layout (don't call other components if it returns false)
        var result = components[i].layout(title, container, element, _options.fillPage);
        if (result === false)
          return;
      }
    }
  }

  // get a reference to the h3, discover it's inner html, and remove it
  function extractTitle(container) {
    var h3 = container.children('h3').first();
    var title = '';
    if (!container.hasClass('no-title'))
      title = h3.html();
    h3.remove();
    return title;
  }

  // extract chart notes
  function extractChartNotes(chartContent, chartNotes) {
    // look for a terminating blockquote or image caption
    var blockquote = chartContent.children('blockquote:last-child');
    var caption = chartContent.children('div.image-container')
                              .children('p.caption');
    if (blockquote.length) {
      chartNotes.html(blockquote.children('p:first-child').html());
      blockquote.remove();
      return true;
    } else if (caption.length) {
      chartNotes.html(caption.html());
      caption.remove();
      return true;
    } else {
      return false;
    }
  }

  function findShinyOutput(chartContent) {
    return chartContent.find('.shiny-text-output, .shiny-html-output');
  }

  // safely detect rendering on a mobile phone
  function isMobilePhone() {
    try
    {
      return ! window.matchMedia("only screen and (min-width: 768px)").matches;
    }
    catch(e) {
      return false;
    }
  }

  function isFillPage() {
    return _options.fillPage;
  }

  // detect portrait mode
  function isPortrait() {
    return ($(window).width() < $(window).height());
  }

  // safely detect rendering on a tablet
  function isTablet() {
    try
    {
      return window.matchMedia("only screen and (min-width: 769px) and (max-width: 992px)").matches;
    }
    catch(e) {
      return false;
    }
  }

  // test whether this is a shiny doc
  function isShinyDoc() {
    return (typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings);
  }

  // set flex using vendor specific prefixes
  function setFlex(el, flex) {
    el.css('-webkit-box-flex', flex)
      .css('-webkit-flex', flex)
      .css('-ms-flex', flex)
      .css('flex', flex);
  }

  // support bookmarking of pages
  function handleLocationHash() {

    // restore tab/page from bookmark
    var hash = window.location.hash;
    if (hash.length > 0)
      $('ul.nav a[href="' + hash + '"]').tab('show');
    FlexDashboardUtils.manageActiveNavbarMenu();

    // navigate to a tab when the history changes
    window.addEventListener("popstate", function(e) {
      var hash = window.location.hash;
      var activeTab = $('ul.nav a[href="' + hash + '"]');
      if (activeTab.length) {
        activeTab.tab('show');
      } else {
        $('ul.nav a:first').tab('show');
      }
      FlexDashboardUtils.manageActiveNavbarMenu();
    });

    // add a hash to the URL when the user clicks on a tab/page
    $('.navbar-nav a[data-toggle="tab"]').on('click', function(e) {
      var baseUrl = FlexDashboardUtils.urlWithoutHash(window.location.href);
      var hash = FlexDashboardUtils.urlHash($(this).attr('href'));
      var href = baseUrl + hash;
      FlexDashboardUtils.setLocation(href);
    });

    // handle clicks of other links that should activate pages
    var navPages = $('ul.navbar-nav li a[data-toggle=tab]');
    navPages.each(function() {
      var href =  $(this).attr('href');
      var links = $('a[href="' + href + '"][data-toggle!=tab]');
      links.each(function() {
        $(this).on('click', function(e) {
          window.FlexDashboardUtils.showPage(href);
        });
      });
    });
  }

  // tweak Prism highlighting
  function initPrismHighlighting() {

    if (window.Prism) {
      Prism.languages.insertBefore('r', 'comment', {
        'heading': [
          {
            // title 1
        	  // =======

        	  // title 2
        	  // -------
        	  pattern: /\w+.*(?:\r?\n|\r)(?:====+|----+)/,
            alias: 'operator'
          },
          {
            // ### title 3
            pattern: /(^\s*)###[^#].+/m,
            lookbehind: true,
            alias: 'operator'
          }
        ]
      });

      // prism highlight
      Prism.highlightAll();
    }
  }


  // get theme color
  var themeColors = {
    bootstrap: {
      primary: "rgba(51, 122, 183, 0.4)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    },
    cerulean: {
      primary: "rgb(47, 164, 231)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    },
    journal: {
      primary: "rgba(235, 104, 100, 0.70)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    },
    flatly: {
      primary: "rgba(44, 62, 80, 0.70)",
      info: "rgba(52, 152, 219, 0.70)",
      success: "rgba(24, 188, 156, 0.70)",
      warning: "rgba(243, 156, 18, 0.70)",
      danger: "rgba(231, 76, 60, 0.70)"
    },
    readable: {
      primary: "rgba(69, 130, 236, 0.4)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    },
    spacelab: {
      primary: "rgba(68, 110, 155, 0.25)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    },
    united: {
      primary: "rgba(221, 72, 20, 0.30)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    },
    cosmo: {
      primary: "rgba(39, 128, 227, 0.7)",
      info: "rgba(153, 84, 187, 0.7)",
      success: "rgba(63, 182, 24, 0.7)",
      warning: "rgba(255, 117, 24, 0.7)",
      danger: "rgba(255, 0, 57, 0.7)"
    },
    lumen: {
      primary: "rgba(21, 140, 186, 0.70)",
      info: "rgba(117, 202, 235, 0.90)",
      success: "rgba(40, 182, 44, 0.70)",
      warning: "rgba(255, 133, 27, 0.70)",
      danger: "rgba(255, 65, 54, 0.70)"
    },
    paper: {
      primary: "rgba(33, 150, 243, 0.35)",
      info: "rgb(225, 190, 231)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(255, 224, 178)",
      danger: "rgb(249, 189, 187)"
    },
    sandstone: {
      primary: "rgba(50, 93, 136, 0.3)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    },
    simplex: {
      primary: "rgba(217, 35, 15, 0.25)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    },
    yeti: {
      primary: "rgba(0, 140, 186, 0.298039)",
      info: "rgb(217, 237, 247)",
      success: "rgb(223, 240, 216)",
      warning: "rgb(252, 248, 227)",
      danger: "rgb(242, 222, 222)"
    }
  }
  function themeColor(color) {
    return themeColors[_options.theme][color];
  }

  FlexDashboard.prototype = {
    constructor: FlexDashboard,
    init: init,
    isMobilePhone: isMobilePhone,
    isFillPage: isFillPage,
    themeColor: themeColor
  };

  return FlexDashboard;

})();

// utils
window.FlexDashboardUtils = {
  resizableImage: function(img) {
    var src = img.attr('src');
    var url = 'url("' + src + '")';
    img.parent().css('background', url)
                .css('background-size', 'contain')
                .css('background-repeat', 'no-repeat')
                .css('background-position', 'center')
                .addClass('image-container');
  },
  setLocation: function(href) {
    if (history && history.pushState) {
      history.pushState(null, null, href);
    } else {
      window.location.replace(href);
    }
    setTimeout(function() {
        window.scrollTo(0, 0);
    }, 10);
    this.manageActiveNavbarMenu();
  },
  showPage: function(href) {
    $('ul.navbar-nav li a[href="' + href + '"]').tab('show');
    var baseUrl = this.urlWithoutHash(window.location.href);
    var loc = baseUrl + href;
    this.setLocation(loc);
  },
  showLinkedValue: function(href) {
    // check for a page link
    if ($('ul.navbar-nav li a[data-toggle=tab][href="' + href + '"]').length > 0)
      this.showPage(href);
    else
      window.open(href);
  },
  urlWithoutHash: function(url) {
    var hashLoc = url.indexOf('#');
    if (hashLoc != -1)
      return url.substring(0, hashLoc);
    else
      return url;
  },
  urlHash: function(url) {
    var hashLoc = url.indexOf('#');
    if (hashLoc != -1)
      return url.substring(hashLoc);
    else
      return "";
  },
  manageActiveNavbarMenu: function () {
    // remove active from anyone currently active
    $('.navbar ul.nav').find('li').removeClass('active');
    // find the active tab
    var activeTab = $('.dashboard-page-wrapper.tab-pane.active');
    if (activeTab.length > 0) {
      var tabId = activeTab.attr('id');
      if (tabId)
        $(".navbar ul.nav a[href='#" + tabId + "']").parents('li').addClass('active');
    }
  }
};

window.FlexDashboard = new FlexDashboard();

// empty content
window.FlexDashboardComponents.push({
  find: function(container) {
    if (container.find('p').length == 0)
      return container;
    else
      return $();
  }
})

// plot image
window.FlexDashboardComponents.push({

  find: function(container) {
    return container.children('p')
                    .children('img:only-child');
  },

  layout: function(title, container, element, fillPage) {
    FlexDashboardUtils.resizableImage(element);
  }
});

// plot image (figure style)
window.FlexDashboardComponents.push({

  find: function(container) {
    return container.children('div.figure').children('img');
  },

  layout: function(title, container, element, fillPage) {
    FlexDashboardUtils.resizableImage(element);
  }
});

// htmlwidget
window.FlexDashboardComponents.push({

  init: function(dashboardContainer) {
    // trigger "shown" after initial layout to force static htmlwidgets
    // in runtime: shiny to be resized after the dom has been transformed
    dashboardContainer.on('flexdashboard:layoutcomplete', function(event) {
      setTimeout(function() {
        dashboardContainer.trigger('shown');
      }, 200);
    });
  },

  find: function(container) {
    return container.children('div[id^="htmlwidget-"],div.html-widget');
  }
});

// gauge
window.FlexDashboardComponents.push({

  find: function(container) {
    return container.children('div.html-widget.gauge');
  },

  flex: function(fillPage) {
    return false;
  },

  layout: function(title, container, element, fillPage) {


  }

});

// shiny output
window.FlexDashboardComponents.push({
  find: function(container) {
    return container.children('div[class^="shiny-"]');
  }
});

// datatables
window.FlexDashboardComponents.push({
  find: function(container) {
    return container.find('.datatables');
  },
  flex: function(fillPage) {
    return fillPage;
  }
});

// bootstrap table
window.FlexDashboardComponents.push({

  find: function(container) {
    var bsTable = container.find('table.table');
    if (bsTable.length !== 0)
      return bsTable;
    else
      return container.find('tr.header').parent('thead').parent('table');
  },

  flex: function(fillPage) {
    return fillPage;
  },

  layout: function(title, container, element, fillPage) {

    // alias variables
    var bsTable = element;

    // fixup xtable generated tables with a proper thead
    var headerRow = bsTable.find('tbody > tr:first-child > th').parent();
    if (headerRow.length > 0) {
      var thead = $('<thead></thead>');
      bsTable.prepend(thead);
      headerRow.detach().appendTo(thead);
    }

    // improve appearance
    container.addClass('bootstrap-table');

    // for fill page provide scrolling w/ sticky headers
    if (fillPage) {
      // force scrollbar on overflow
      container.addClass('flowing-content-shim');

      // stable table headers when scrolling
      bsTable.stickyTableHeaders({
        scrollableArea: container
      });
    }
  }
});

// embedded shiny app
window.FlexDashboardComponents.push({

  find: function(container) {
    return container.find('iframe.shiny-frame');
  },

  flex: function(fillPage) {
    return fillPage;
  },

  layout: function(title, container, element, fillPage) {
    if (fillPage) {
      element.attr('height', '100%');
    } else {
      // provide default height if necessary
      var height = element.get(0).style.height;
      if (!height)
        height = element.attr('height');
      if (!height)
        element.attr('height', 500);
    }
  }
});

// shiny fillRow or fillCol
window.FlexDashboardComponents.push({

  find: function(container) {
    return container.find('.flexfill-container');
  },

  flex: function(fillPage) {
    return fillPage;
  },

  layout: function(title, container, element, fillPage) {
    if (fillPage)
      element.css('height', '100%');
    else {
      // provide default height if necessary
      var height = element.get(0).style.height;
      if (height === "100%" || height === "auto" || height === "initial" ||
          height === "inherit" || !height) {
        element.css('height', 500);
      }
    }
  }
});

// valueBox
window.FlexDashboardComponents.push({

  type: "custom",

  find: function(container) {
    if (container.find('span.value-output, .shiny-valuebox-output').length)
      return container;
    else
      return $();
  },

  flex: function(fillPage) {
    return false;
  },

  layout: function(title, container, element, fillPage) {

    // alias variables
    var chartTitle = title;
    var valueBox = element;

    // add value-box class to container
    container.addClass('value-box');

    // value paragraph
    var value = $('<p class="value"></p>');

    // if we have shiny-text-output then just move it in
    var valueOutputSpan = [];
    var shinyOutput = valueBox.find('.shiny-valuebox-output').detach();
    if (shinyOutput.length) {
      valueBox.children().remove();
      shinyOutput.html("&mdash;");
      value.append(shinyOutput);
    } else {
      // extract the value (remove leading vector index)
      var chartValue = valueBox.text().trim();
      chartValue = chartValue.replace("[1] ", "");
      valueOutputSpan = valueBox.find('span.value-output').detach();
      valueBox.children().remove();
      value.text(chartValue);
    }

    // caption
    var caption = $('<p class="caption"></p>');
    caption.html(chartTitle);

    // build inner div for value box and add it
    var inner = $('<div class="inner"></div>');
    inner.append(value);
    inner.append(caption);
    valueBox.append(inner);

    // add icon if specified
    var icon = $('<div class="icon"><i></i></div>');
    valueBox.append(icon);
    function setIcon(chartIcon) {
      var iconLib = "";
      var components = chartIcon.split("-");
      if (components.length > 1)
        iconLib = components[0];
      icon.children('i').attr('class', iconLib + ' ' + chartIcon);
    }
    var chartIcon = valueBox.attr('data-icon');
    if (chartIcon)
      setIcon(chartIcon);

    // set color based on data-background if necessary
    var dataBackground = valueBox.attr('data-background');
    if (dataBackground)
      valueBox.css('background-color', bgColor);
    else {
      // default to bg-primary if no other background is specified
      if (!valueBox.hasClass('bg-primary') &&
          !valueBox.hasClass('bg-info') &&
          !valueBox.hasClass('bg-warning') &&
          !valueBox.hasClass('bg-success') &&
          !valueBox.hasClass('bg-danger')) {
        valueBox.addClass('bg-primary');
      }
    }

    // handle data attributes in valueOutputSpan
    function handleValueOutput(valueOutput) {

      // caption
      var dataCaption = valueOutput.attr('data-caption');
      if (dataCaption)
        caption.html(dataCaption);

      // icon
      var dataIcon = valueOutput.attr('data-icon');
      if (dataIcon)
        setIcon(dataIcon);

      // color
      var dataColor = valueOutput.attr('data-color');
      if (dataColor) {
        if (dataColor.indexOf('bg-') === 0) {
          valueBox.css('background-color', '');
          if (!valueBox.hasClass(dataColor)) {
             valueBox.removeClass('bg-primary bg-info bg-warning bg-danger bg-success');
             valueBox.addClass(dataColor);
          }
        } else {
          valueBox.removeClass('bg-primary bg-info bg-warning bg-danger bg-success');
          valueBox.css('background-color', dataColor);
        }
      }

      // url
      var dataHref = valueOutput.attr('data-href');
      if (dataHref) {
        valueBox.addClass('linked-value');
        valueBox.off('click.value-box');
        valueBox.on('click.value-box', function(e) {
          window.FlexDashboardUtils.showLinkedValue(dataHref);
        });
      }
    }

    // check for a valueOutputSpan
    if (valueOutputSpan.length > 0) {
      handleValueOutput(valueOutputSpan);
    }

    // if we have a shinyOutput then bind a listener to handle
    // new valueOutputSpan values
    shinyOutput.on('shiny:value',
      function(event) {
        var element = $(event.target);
        setTimeout(function() {
          var valueOutputSpan = element.find('span.value-output');
          if (valueOutputSpan.length > 0)
            handleValueOutput(valueOutputSpan);
        }, 10);
      }
    );
  }
});


</script>

<div id="dashboard-container">

<div id="column" class="section level2" data-width="600">
<h2>Column</h2>
<div id="species" class="section level3">
<h3>Species</h3>
<div class="knitr-options" data-fig-width="576" data-fig-height="460">

</div>
<div id="htmlwidget-56c0ebf81cd6442ea262" style="width:576px;height:460.8px;" class="rbokeh html-widget"></div>
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<div id="column-1" class="section level2" data-width="400">
<h2>Column</h2>
<div id="species-quantile" class="section level3">
<h3>Species (Quantile)</h3>
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<div id="petal-width" class="section level3">
<h3>Petal Width</h3>
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</div>

<script>

$(document).ready(function () {

  // add bootstrap table styles to pandoc tables
  $('tr.header').parent('thead').parent('table').addClass('table table-condensed');

  // initialize mathjax
  var script = document.createElement("script");
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</script>

<div id="flexdashboard-source-code">
<pre class="line-numbers"><code class="language-r">---
title: &quot;rbokeh iris dataset&quot;
author: &quot;Ryan Hafen&quot;
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(rbokeh)
library(flexdashboard)
```

Column {data-width=600}
-----------------------------------------------------------------------

### Species

```{r}
figure(width = NULL, height = NULL) %&gt;%
  ly_points(Sepal.Length, Sepal.Width, data = iris, color = Species)
# figure() %&gt;%
#   ly_points(Sepal.Length, Sepal.Width, data = iris,
#     color = Species, glyph = Species)
```


Column {data-width=400}
-----------------------------------------------------------------------

### Species (Quantile)

```{r}
figure(width = NULL, height = NULL, legend_location = &quot;top_left&quot;) %&gt;%
  ly_quantile(Sepal.Length, group = Species, data = iris)
```

### Petal Width

```{r}
figure(width = NULL, height = NULL) %&gt;%
  ly_points(Sepal.Length, Sepal.Width, data = iris,
    color = Petal.Width)
```
</code></pre>
</div>
<script type="text/javascript">
$(document).ready(function () {
  FlexDashboard.init({
    theme: "cosmo",
    fillPage: true,
    orientation: "columns",
    storyboard: false,
    defaultFigWidth: 576,
    defaultFigHeight: 460,
    defaultFigWidthMobile: 360,
    defaultFigHeightMobile: 460
  });
});
</script>

</body>
</html>


================================================
FILE: 2020年/2020.11.16flexdashboard/例子/09_rbokeh-iris-dataset/dashboard.Rmd
================================================
---
title: "rbokeh iris dataset"
author: "Ryan Hafen"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(rbokeh)
library(flexdashboard)
```

Column {data-width=600}
-----------------------------------------------------------------------

### Species

```{r}
figure(width = NULL, height = NULL) %>%
  ly_points(Sepal.Length, Sepal.Width, data = iris, color = Species)
# figure() %>%
#   ly_points(Sepal.Length, Sepal.Width, data = iris,
#     color = Species, glyph = Species)
```


Column {data-width=400}
-----------------------------------------------------------------------

### Species (Quantile)

```{r}
figure(width = NULL, height = NULL, legend_location = "top_left") %>%
  ly_quantile(Sepal.Length, group = Species, data = iris)
```

### Petal Width

```{r}
figure(width = NULL, height = NULL) %>%
  ly_points(Sepal.Length, Sepal.Width, data = iris,
    color = Petal.Width)
```

================================================
FILE: 2020年/2020.11.16flexdashboard/教程.rmd
================================================
---
title: "Vignette Title"
author: "Vignette Author"
date: "`r Sys.Date()`"
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
vignette: >
  %\VignetteIndexEntry{Vignette Title}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

# 介绍

从CRAN安装flexdashboard软件包
```{r}
# install.packages("flexdashboard")
```

要编写flexdashboard，您可以使用输出格式创建R Markdown文档flexdashboard::flex_dashboard。您可以使用新的R Markdown对话框在RStudio中执行此操作：

![](1.jpg)

如果不使用RStudio，则可以flexdashboard从R控制台创建一个新的R Markdown文件：

```{r eval=FALSE, include=TRUE}
rmarkdown::draft("dashboard.Rmd", template = "flex_dashboard", package = "flexdashboard")
```


## 仪表板基础

### 组件

您可以使用flexdashboard将相关数据可视化组发布为仪表板。Flexdashboard可以是静态的（标准网页），也可以是动态的（Shiny交互式文档）。flexdashboard布局中可以包含各种各样的组件，包括：

基于htmlwidgets的交互式JavaScript数据可视化。

R图形输出，包括基础，晶格和网格图形。

表格数据（具有可选的排序，过滤和分页）。

用于突出显示重要摘要数据的值框。

用于在指定范围内的仪表上显示值的仪表。

各种文本注释。

有关每种组件类型的使用的更多详细信息，请参见仪表板组件文档。


## 布局

### 方向

- 按列布局

仪表板分为列和行，并使用3级降价标题（###）描绘了输出组件。默认情况下，仪表板布置在单列中，图表垂直堆叠在列中，其大小可填充可用的浏览器高度。

默认情况下，仪表板中的2级降价标题（）定义列，各个图表垂直堆叠在每一列中。这是两列仪表板的定义，左侧有一个图表，右侧有两个图表：




两列，第二列包含两行

![](3.jpg)

- 按行布局

通过指定`orientation:rows`选项，您还可以选择按行而不是按列定向仪表板。例如，此布局定义了两行，其中第一行有一个图表，第二行有两个图表：

![](4.jpg)

- vertical_layout: scroll 将列的图片进行滚动

- 通过data-width属性为该列提供了更大的尺寸

- orientation: rows 以行填充为默认仪表盘形式

### 滚动布局

默认情况下，Flexdashboard图表的布局可以自动填充浏览器的高度。这对于少数垂直堆叠的图表效果很好，但是，如果您有很多图表，则可能需要滚动而不是全部放置在页面上。您可以使用vertical_layout选项控制此行为。指定fill垂直调整图表大小，以便它们完全填满页面，并scroll以自然高度布置图表，并在必要时滚动页面。


### 标签集

如果您要在行或列中显示多个组件，则可以尝试将它们作为选项卡布局，而不是尝试同时将它们全部显示在屏幕上。当一个组件是主要组件（即，所有读者都应该看到）而其他组件提供仅某些读者可能感兴趣的次要信息时，这尤其合适。

在许多情况下，选项卡集比vertical_layout: scroll显示大量组件更好，因为它们很容易导航。

要将行或列布置为选项卡集，只需将{.tabset}属性添加到节标题即可。例如，以下代码列出了tabset中的第二列：

![](5.jpg)
行也可以作为选项卡布局，如下所示：

请注意，在此我们还应用了{.tabset-fade}在切换标签时导致淡入/淡出效果的属性。

![](6.jpg)

注意，这里我们还应用了{.tabset-fade}选项卡时产生淡入/淡出效果。

## 组件

### HTML小部件

所述htmlwidgets框架提供为JavaScript数据可视化库高级的R绑定。基于htmlwidgets的图表非常适合与flexdashboard一起使用，因为它们可以动态调整自身大小，因此几乎总是完美地适合其flexdashboard容器的范围。

可用的htmlwidgets包括：

Leaflet，一个用于创建支持平移和缩放的动态地图的库，带有各种注释，例如标记，多边形和弹出窗口。

dygraphs，它提供了用于绘制时间序列数据的丰富功能，并支持许多交互式功能，包括序列/点突出显示，缩放和平移。

Plotly，通过ggplotly界面，可以轻松地将ggplot2图形转换为基于Web的交互式版本。

rbokeh是Bokeh的接口，Bokeh是用于创建基于Web的绘图的强大的声明性Bokeh框架。

Highcharter，是流行的Highcharts JavaScript图形库的丰富R接口。

visNetwork，是vis.js库的网络可视化功能的接口。

CRAN上有30多个提供htmlwidgets的软件包。您可以在htmlwidgets展示柜中找到一些较流行的htmlwidget的示例用法，并浏览图库中所有可用的widget 。

您在R Markdown文档中包含htmlwidget，就像您包含R图一样。例如，以下是一个简单的仪表板定义，其中包含3个笔形时间序列图：
































================================================
FILE: 2020年/2020.11.16flexdashboard/链接.txt
================================================
https://rmarkdown.rstudio.com/flexdashboard/

================================================
FILE: 2020年/2020.11.22三维散点图/3d_scatter.html
================================================
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o/jNPY4W82Os88wiJ34K4tdAIQjAOQkx8YArcM2PaAOjSZBL8uolzAJFFvGDXd8ej67P2AvKpUkOYghcnK7zl300RBcsExwzJ/hbrd7GuYBwhgAIYtbTx/3+d4klJ3gtKCQnGIz9InYZEzqG8EkjSzNavCB/cXYlcQshhyMsZrI6PYLWc3lOG/vlA4rHr/3uTFD3r38/r+3fMKOke9W4oJ9G566u7au84CpOz/ct5R99wF7W6dIYjjnawrHIAh3hlungFOWgXoyzVKbHOr1eD19Il6vISsrrU8kSzbY+0QMGpdjgYh60zDTHJKHoyP4404pw27zB4o1o62gq+BLL299am8j+zv774zj995/dgTOZsOfWr3rnTWPj2h8qGbo1/M//kYYvmxfms7TtPrM54E7ns4vwBw0rFy/aNJjRRVTet31OgCBPABhongUDOCAzuE0h6gnxChToCJ1ulB0iH0jeqvscFBZotflk+hMQ5oJDqhrC/l//FxmAUlGYeK5Z6Jl5MDec2yJQdc+l5ViNduL1avoZ805eGll04jy6COKheT8S+U6kQwdw+lW6nPpXF4qtEoBziwAye3mMnRLkqlPRLqZdQlsKxTcLghkqhzjrLL5M+WgUwldSkjbL1HPLrCf51d8MHbv66zu/mcGl5Kz0YNZ0+mcf759kbEB29qGGrZiYWop2b2R9fYqnKnlWOVzqXqgNfQIB5LtRr8fQLLT7CyT0ZLaL2K0WFzU5e0TcfmojkckcgvcyhJ4pNlr8Bd63VyEhIbiGhfIBFGTq8R9lqcWB2Dl1G79Rn/9i8n08OU3L/760UX2E369YuvqVUPrI9VryFR8CXc5V/rYefbW7svv/YNdxUHv/OnFVQ1V8yse2Dde0UcAIY/zU4L0sA1FEQg3jJT0jVAJFBlqbOOrALk1dCOmkuHNF+mpaKOYunHhldNAlZhEyFGpz4R20C+c47Vmu+6gqXo9lewuq5TfXrLnZORk9Ink5JjAlNwvYvJBoF8E5N8qd9nN3jrmj7mOx8OPLDXqolpgwv0zZkpuzaeTynf+vWjNvnr22b+bsfDJR7+e+cL6dQ1bXlu3CDvOWfHIMytnrhJPHt7x4L7eg/48+8C5U0euLuu/f8ozr1xteHTRssdGru8V3kwfeHTMsN937/zksLEzFdlO5NQpNsMLWdAtnJlizzQYAAQu26AljUvWZbEQlyuJi1Ymcr8Iaal2jjKNg5qJ9Ctqx02jMyDFKHJw8TpUIvjHKhXZQlZ0/Iwe1eO++6/RVHpg2mv/uPbBuguPMtfKLU+tuXfjkIFraEVzg2tlMuZg6O57/vXBP1C3kZ3H9od2PPV81RMVE/aNAy3HEcaokRS34Ta+LAA8XotzQMRiizkRDVfN87X0JXae6NzkVR6Znehb6J8XL+Y3IKovXMjn0oEDMrkmmc2iXu9yGm0DIkab6hgTZklwj/T6FDccpXsmn6Rjlxv+knyrTFMR8+U/cF9+DiRwh/UCiChwdeXD58cDhSwsRjeikNNcTo83/0AtP2DDKLywji1nhxSezMTjgo9eVHOy3LBbJgIQ0OsEsToiIFRHrIjI4wHOlfxEz6a4ZOTXTLq9eTjdTofW1bEH6up+g5GIBDhGEr2BkRNVlMZTa/P3HKVyrMMKrF3H/KPYUAWjlGsXaRnXrxTIhrJwqp/bMtnphFYWIdgGoLWtddqASGuPzdA7YhNaqFZLvVJSEa48LZwUd4YSN4mJ+aq/ctSSXgtmD6gf2emV91/9KNj38bHd9l3PX0tq19dMnzFw3OSsgsWjj+zqPXn0w4On3e9nZ+NJLYFZ1yqkQ2ITFEM5zzwyA+1KLJ1kVwpAjsvSTgx3S+rQQeiisxv5Ky+9kGbnqUmllmSFEhOP6/G4ug6C2nJQUPdSt0td36R1IFMgbsUalrqlQAbw4KK1v1BwIH/udKqm8NCQbeMHP2LUtVk3rv7Fb4712N3Tt/DeaWvZt3+8wA7swe6Y/5cvjv3I1rHJn+AyhLM44ODVn14/7bBUDpq/hpxb8c388XfdM+rU3veu+Tws17Pv7O79aFvzMnvxc3aaHRq8sAZX4jgUsP7CfvYntoNhGYquJiAAAKJNPAIyWLjk0ojFqENR0SwqyILNaiG9I0bRYhFECoKD518xh6iplZYz+5W8H0OIlBsz/tURB6IHmnaT7itJORvb6A94cnbjGZYvHrnSg0zENwfPGTGddQIKJwCEo9xyW8ALGdA7nO0UUg1Wn89iEGQLjwd01iRrUlXEarWAxVcVsTjAWxUBevt4QnM9/gxBMbluwe4SAjxpj/mcgN0ef3cCt2IAhVVLsR/7+TIjjZjU9PTeY1ew4I9/Ovhn8cCeI/Nf9BnK2Pk3/kZ7TF00+6HoquhndauXPAGAMIdb09Oqr8gOu6jFpbdQb5IDekccglHi/HK2DL+4emRymUNIE3+Ro3WokKfbtNP37Cs0/7rxjQ0X2Cvs2Rex/NNLuysbxBB7lX3FPmdvl64rwyU44QusOVSzuj8AUTgmDuEc04FdsYcWQQ8COJyiuSoiUsFSFREct4ppwc9rSBlA+ZuAPZTBx2Az2Uo2CY/hIHysic/1z59PI/dU5CtWz+aJB9gi9gKmYebVKZgHgMq89Bc+r1GJWSSDAQXQoWAyS/reEUlCQsTeEUKRr3B03DZmUZBwxy/6S/MZmh+dTYZHt5OF4oH1LKc+eilhJj0UhpMlAKQ6pAbjTRPxSW45Q0CbAac3asPzwaNfrY9LTuyi2ilOhUvnI8SSohNapUJK7wiAaDLZe0dMgujtHRGdt4+8/HaphRyV9+rq5lT1xe9nfPc0a2IrDuKQL//9bve3DrL/so/Qj0kbVrGXCYuWZWXjUhzzD7xn/+D6GvYau8Q+Ze8H8LUY7WK6yuVQ2KdHBJ0giCCaTTraO6LTiQaJoshJV81RgnG/Qbydi5f/DYnpjc2ssZGSRrI3Ws1z7dXkYQC8NoLNxfFqVpwaNht1OotVT4GzFDJj9GrpGI15+JJiPpxLMg0v6dVv9AONx9jclFWuR6fyFGvI0TNxvRC+UjHmnkjBViRGg4Ix0Yn6RGzLWkgJZRVRDKHw1TvRrzc2NpL1J6JN5M0l0dc5snnk4+jCBF0QIT1soQCCJCMFzgtw3EBXxTekkO0+0aio0pV/bIp9V+KIgpPrUZJOFCUev/JSmsuNBjuVjDK1gKQgp2DnLbuZlRjwuJUAn2MY4nce4COtZjadZSsCntbhh6zRomMm0bbpo+bh4oGrVQLPOume7Uev/BCXo1IDsUG7sFsvcaytVpDB7jBS2aqjKCdypaUI4xPzabNJKZdj+WvNn+tsW4/RVB2xkGeEk582NR/nE3ZMwaxy2guAqFp99FZ5bu+IXqDW3hHqvLVNiOltBiTmueJRtpW9oZgjHIE9sBOOujo9+v1/fvn5h/9Eeb77LHuYa+94HIt1bArbxs6yU1iIuRjEAnYqZp+E8erqdUBRONnA+c75DE6XQaiKGAySLDuqIjKVEtavhpXmSgW/mlplYChutYXx7Ay7tLsRZ5PWUePGL949euKoYPr7t1HOh2jK6mdXrVC5wHaoXLBCCp+Zp8MeAIEa+OqmZtns6x0xC7KTL2yZM+MtlRs3J6I2pViG8q258sX7OOxndrH0tpz5ki3rzuqxivyf/DnN+WMCN1SGs8yIxKS3y0aDQdYTwePVm8EMVRGzmVDK5UepkSi6cntnp2Ku8ktw20SOf5bGNm4BcRXyGdhfcfkJ9jQ7/VXTzl2vfEZGRLeJB94/zf4+LjqZjFi9cuWqJwDVHIFw29ha4V6a0wSQ5BSFrGxTGvV4uH30CFSfoEoJiY4mt0CGlozy8D+o5jgx+6jmBbwy4BEI+9d3rHnZ0I/GN+7usnL1ey+xM389WLx/1+INHRbWXfoDLjz+6Z07su+YN73vyIFFvd959sV3qtf2nfFA35F3FQw8AoDgABCGcv7JvJ7iABSRUp1epgK3CYLmFeJ5qGYSi7k3IEsbWYFQyQrE9PWqJzjM14yPj2OHrLDdhgYZZafDrqOCmQ8UpzGUuFzsLkUnVHMYs4uij/2F/cJfFxrfee3ld8QDzf2vsC8wo5nuaa44+Mabh+ghQAAA4XW1/pMcNqJgMuooCJQqiPLlrxWvQhjgF8//SgXTwej3O6M/NmF1x8zWHdVaFh/5uU3bnwXkmg1yXz6aT6km+QwpyW6LRdQn2Q0U9TGTotqUGOKqNclWAjJldKcyenwSZ0h8cyc75y5CT3v2xU42u+nL9p6UYpSa0Nne7yy+1EQ/7PaW6/dbm0N88llHNx18ic5qnrv59RXv0YUK93QAQr1q9QNhhyCJ3ORLiskXFJMvtDT5KhocAz63Yu7rj/PIY0oTXmKdjuAkfHg/60QWROeQZnI4+gq5M9oX4lybrUY5GWGrIBJRpnoDiChTUeOcJmE+qKL+GCJdcNEhlrSb+Q6T8+R887zoCZJPFyv1ZQBBscZ6pWKmQyqDLKBgMIoCNwcUdUrMcuuKmVot8AvlzU6qi9roq82/0LSFwoaNC69OAIQGdoRMVnSRY2mRUFAYoxcJlTDIOdBSfeJRD5nMSvEEu4B+dkS6svyKX6HWC0A+i1c2Kd5c2XRy3h0mgYbo/4spg/KNEDuCzdrMFFACSacHOUgFevPMXj5rMb9CfMoLfOrSA+KF5b9KyigFJCgExOMgQVJYD1TWiQQEwrO+G5rpVFUTC3DfaPxsA1vG9pEg3dQ8jnwV9QJea2Zv0k3XKtUKsJLHIlEqwBgjmU/LQUfRp9mbCwCxTjhHHZIf9OA8AILRID2BkJ+s1ZoxwDW1OMStBHU83G1fm5MZ0+4QzhUdK3f33F8MRKk50lPCUEXzoVc4K1NnTEvz+Rw6yqMpYkzrFSFGI7jd1ooIt4LJFRHRA24o/98LVH4tX7NllapJZ7zS6LZn8QVeLKsVKjrQrxv43GPPvUychyc/VveH0F3HR77xCrNs/mPDWy89tOWB3js3Y1+b1GPe7Jq5dxTuORZ11TZuHC3LD00fOhwI7OVWtVZygRPSeVUt0+D1Wq2mVGqiGX4zmNwOu8HOhccRljzgqoiArYV5DSXF1SDB1sddEk825YBijeRQiVcrvHAqyJ5Pv/3+k0l/7GwKzGzQ6Wa811i/qXFjfb0wlJ1jP/DXxwMGLpdcbNHcsTuWvv7ll29fOPPJXwAQpnMOLxWGxbIaK6VuPU3ySmaOmQ0cHDPPzVmNGM9qlJ1DHgNzu6hmOGTcZXYV9f8d8HTbUOn8QrbvuW11Tz3swiw0oRPvyPQu96Sywe9+2mlNGRBlVqGU88fB+dM97E+VvGCx2CV7ht/htgIgmqhez9mjt1FnRYR6bscerSYTkLTqvTcUDPLPA6osi+JOiG7ST//n2W+/++TCTLMsNCxmTzdu3Ny4evOmNS9gNlr5647tA/rh0V+/mfny+4Gv3r54+i+fxLF0cN44IRk6hdOTDF4jpdzqtkrxGit4uRskyaUyyqIw6paZQyiRZQ632++JsUuivNbh53Kb+x/2JYp/e/+7qFl8eecf/zBk65bfb7WQLstc2AZl1GMH9v3fJxx/p2pttp/+c/eGrS8oUksFoBYpHVxK3cVlMjkJ4UaSuj0GvhQMgKIsVkScspUqq0GtY98IAxWmOZS1p2QNgeJSXkPW3DX3mE+zrxreeANH3lObN6LH8KHopW83l9G3+3TugmsDC9PnPNkLgEKQuYQCzplcKIVu8HC4a56vQ5YpvYtY4ESnSHIzW6Vn+Qzd72xlLbYWV0R0nXpFDJm6XKvOqvPk5pJekVxrm/JekTY2T7teEU9KnHUa+zj/8pXd+rzbxD1uragaVBdAqDC+jaAUkrJv/OXKcGMXmJOnbhQXF/F3QsHJVnf87VhB3sSqoa/te5X9jf3r7FdPzMgtC/ccNOnTtwb3ZPb6ZWdOPLzh7amPD50/4z8/1T4uVE5ICkzt9ewxXYdBbfPqVx54ddvqMauTndXFnYfmBnY+2PS66ypEhs2ZFOn5IO08/ZFvfn4cEPYCCD24nnuUzM5i0nFz7dF7vEkWvcMhVEQcNgOA3q0Y7xjlCatesVT2mALbtRUfM1P06cfm/+GZhgadoWD/jBMnyJuLfn/kk+jrfHXnDOow4N5XP4gWAxDYDoDjxAtAwcr9tZ3PJCDa7Ga5MmImVlQ04/3EwqZSIqAJJVQc3NDQ1CG3TceObXI7CJWYU1Zc0qFDaSkAubaKudSxTZAEd4Q9TqPRrNP5kj22yognrLcC1z6ISzW5xSTOhATTljhb3v2det7Zv/eNGZnLt9g16B6h+aqNHZHv0yaP8TSV89QGJTzetxgMRqNOEkSdYHeYAGw2nY7KRje1xiKGfD5zeUyFyuJsRTUiQi0bdclYkzcER73JeuD5E2zOnB07dKSgy2icydpGlxLpQTZOcjW/XTo9NjcO5nNT4GQCoiASQHfca2tMVBjHYVRo6SRfJQGoCAfcdruDiz+gdwRo66xWHrfb4RPMPm5p0302p1UPDkUPuCLEt534Igi1bHVIVIgEzfAqepHh1bRDypryyOa1DVNmblnVsDhFl79rIuIAXcHhmYdfJicWLNj3cnSLcv/zx9HjQmV99dDDg8e8+heuMZq2cnxdUBBOApeiri69x23S22xcWW02g/V2ytpSV72Jmrp7m4JG6NDUt95RNPXwJ+q8d0XUSWM2dhSfU9EknsU6wSyDnOwzeLgds1GbYvxvmcVylSHFilGFxE4PYRT74fKaf/wOTZcvobX5lZ3PPffii88/10Cy2I/swyeR/AFNmMfeZ1f/8rfzH545p1j5vdyW1apU+6E8nOEzCrKsS3foHJkBwQhWq7siYrXprboUaHXDzMdZ0GLBqpaeO2hPAhMUr62Y+gRHrThpU8Niry7c+PBf/+f7yzvryabGFc8+6xowcMRg1kUqqh9azT5h/1GcNr14+GTWl29fevfUeYVXHNNSlVexqMKW6qHJyT6bL8OfnOK1pqalecxOp8wtv80MFRHz/+Y2VT5yJ1l63Ul6r3vQ0njtQyL9GzaIW15cvXnjnI8uf/fJ57P0SQsajObpM/d9mHXp3YunT59birloRDO2a6z/9T38eEzFCzE9okGOpw1ywy6zXm8wEF4DsZrB4FYtg03rc2nRkaE5IY15ZEfvjt4eRQtfaahz6rrsFoaZNlk/fTbaJFSenDQjlrnS6XyW1twOtIplrqLzeuZaEfHYJKq/rj/5t8pdueG5kbsG25Hfpq50+j/e/+tjA/bXzF82+dmN88r/evSPL3Z6ftEjj7Yds+J13jSzsaHnpjbt7h4Uvrdr2aAH+yzaXLm4R1W3O7p2KO71FCCkX/uG7BQrwKPWJlwu3jPioEKS1+C0OXtFLGGbVeaCkj1xU3kqIVjV5ONWqo52xVGXhtxKNuHyEMcdA5NSJuSy17ZurRiBXdlrw2vN8lyzHQeQZdU9/83mRWePngiAsIOvrjKhElx8fh86ZZPJ4DS4PSaz2aZzWdVV7TFqEbMS/4daVmW0rJcrhBY127EvX9TPNNQl6UP7Z7zztlAZLeMO6GMSvnpozV2Dj54hp7RcjgiVau+HAQ0ms6hHK6jhiJZl+NX0NFTicIYQt7ER+76ptuiMte/tYyP4oI/8o0cx9iPtrx6K5UpSgI/Winsblz4lNc3rsZipYBZ0yQ7ubnTuxCyYK7c2A1U2Z2Rlk8LhUHSq1BmbsoRPKeSfcBbp2qSdPsY+3jNxsk5nLHCcaHqjg0snBF7dzc6QBZ3OvHR/dK5QyUaz6j5l+4tJbXTp7trW9eRvHClACAIIOpXGzLBdFiVAUWlxQZ3RLaD1pnQ4ngmjmhUfYgteQT9m/JktwFVH2Cn27hFSQLxsGO6IfhU9jUdYD0AgfL1LfHw3z/sVMqnHK5jB7OBLO0UHfIJCVam1GRJo46KKOdrSUrLvuwFOnfnuS/tYTsWfl/StKu2xq3cXzuCVn9wf+pn87mrGy5vtC03HtkAsZ6YPCZW3yJl7RUQr6npF0P2/5cz0oeZ/ksHR0+TL6D5y31Q6eN685sPxrixetlPl5/YlJxu9AFbZRbmnpqlpTq09K3F7TdV/bpXcPJZTfEtxCddDvj7d3EK4ZLfHjedrpx794PFH58/49MClCxdM44aRZaRxE+aPjywnw0Zg4ebdS6Xj7NzZoCl4FhAvMxuZrfluorSo0RSABN+tlHzx8nKeJv3cDAiV7Ijaw5Oq4OwWDQ4H8UFqqsXiE2laujso0QScEzYFFXSDxYr7U7DPVNCV5Dj2pcRw4eKhDx+Z/9jjp45OnvHwVFIePIvB49LSPRvZ+yPvJcsjvOq5cRenZNg4zJn2qEvdpyXVQg6tAS/XAzu1JvkcpuoIdVglCaojEuTngS3pjfw38rSkOlOZT8nQVNOmbD9lKoU5HFg8t2TMUz2mRrqPyi95omTcisrHK/sMJSfuLFn/UKvsVinhsvqH/RkZSeoOPFuKdcJwrcuYCALV8343AGpSu4xtNPOWXcZcCQNO1/Xt0PNKk/Gszp3Ly0IVZPfVC2Lfxb3C5ZVhQDjK7fd5dVemazjNozNTahCARxo62irVJxKnwUz4SzDKgg+07k9ljt9sw2apra1KOJCldLR6NAOuqD89OWHNwpPHcdniPisKChY+tHv7My8sX/FdifTO+xlov4LNXXfvoH7vstCH5z462QkQypUYSDzBpV4Zzk5y6s3mZI+dGD1OMS3dlORL6h/R+3xOcNr6RpxJIPa5uRWkRdPQzZ6Nm29lf5Lfinl2ypuduEqQxqONXTatnD0HG9jQblU05erVU2+99f/EEzUL+/1uGTs397MxS+7YtDz/xwtzsfO+U4psZqMkeIVtnHNByAibW0GmBSxtctLd7iwZeNSYn1gJchaVBku9il8r9co82Ja9clCxDnKwNLs0IXQ6VLV4+OLx8+eOq7t/UVXVgmF14+YuGrN42MKqeVtnzHh627QZW8mHj01aNmxh794Lhz059ZEFD/CHvfj7JZN+N2XbM1Onbd8BiscDEJT9Fw8MDrdzWGSj0WYS9URPTS6LW/YmGSwW2So5HBScbqsz3UmsTqvThG7JlATlWg+33RHrzL7lpjuGUOGj1uaovjBEKnH2HjYCJfY6dmGv72BvYGd+ARu7j1wgZ5vZ3Ma57Ec08RslQBKsgaxUVYkkUR726QUqUDlmFjgmiYqtbgjFLYRiI5p/YebmnxVpXPuF1kupUABdeGdcdiE4pdy0Dj5fmkmCgNS13E07lbRqK/n1/mCviN+tt/WK6OGGznh/s4t9I39VVFmLztSUlwuwZdCiRC2l/Kk33lG0dHD/qprTbw5/ZmTxqMV9Z8yYvelw/cCqjf/+6K9P9H9t4KLl7R+cvmJR99W/f6Ggbs3LPQbRnMF1WW0mD5q1NDW4IJjSKdy5prTH+klDl+fctXrZxm5rs9r27dWuY8e8oqHTRvWb0MVZPfnuKWXOMUCwWLTQ8eKH6u5TWpiTanKAI8lnpW495N90QCAhzctKeI/FxVnZpaXZWcU4pzgrq7Q0K6tYnFrUrl1RYUFBYfwOQGEM7xzvEdt5hxKeSwWDXmrNT0936a1esbSDZAKH1ZRuIuCwOYjJYXKk5AWcoRQByhNPBdhblgFRMxHuG90bnN2obu8KDjc3eYHM1py5DiFU2NqhNXTQOXMWz10weE77sRWvffDZq0880vHB5vXv4PB3les1tv2D02z76xP2YNvdezD3pT3s7N497JOXhMCeTTu3t/2dq9X3n575qfMjIXZI/Q7b/u6brOGD0zj0rT+wD/+wB3P2xr8GQKCCushU8W1OdzqUhlt5pRQDokeJazP8rQwGh88D1EYJNTvSOakf3feGku9qVGpqG4xTV8ojfbXWGSt18iYUtdZJXEnDlt0/edPztWvHjM+btnB+HauecmLUlAeov2bk6HHjJkhCcGFoRIcJs1jnI2OaCgRBqd8NhFraSI+CBGbICTupxI21YNTrBbMkWKwmUYegHGS5WbPRiyhjVuw2EAfPVEriM1kjLsUhtexzTK9lO0kQ1/dk29mzvXB9yo23qh9EHfeDXhAhJWwiKKAki0J1RCSQr20nattixUJOXfM71Bv9Hhc+CdeuaV3LRAIbAAjXdUoX16r7wqGgF3iOLui5Zpn1JodXKu1gsnFoi9Pi0DmtjnQHAR63E4fT4bythikCCP22ZKVVoUS+hp0Bqm51Fnr+L2UjHz5YPXLwfRNx36B+l3eeXrwWxYbNVy/8n+pGrtwd7tNtSfXsNFaLo9jTdPZ89ub/pXB47YrkEiRpzW3r+oJ09UfBJLnmAoG5dBi5LJ5U83Z/2GIGp7L7nGwzHPNQhS3J7yWaAKe27LkytvA6c/fPn39g4Oqa+fun195VPX3qwLunC2vmH9i/oGZlTdOCgdOm3l0zdZoiv/GASic8yQYLAMhwBiA6Q93NqCLLub9OUmpcstOLaHGCwAsItnQvZqjyadHEUVx6cz+0JMt+sjy645vIQH91edGont0XbPj9msiaPXiIVI2/NHhk35IePbMLh0yeP6V6/ZPPA4KflKlzBqAsnGkVRaCONIPUOstxn/MhJ+nrRKMzxUmcTl2yP92s88eVhKvIfTe2KDHRmKtlyd/2PpPpA3vsPbRzw4w1sz/8snbmA6Or7+w+pUPP8mXDl2wVvqx+wJu//YmVHWb32L5q0oAeXXrkBYa2LZl5056LnkfvwhP6xD0X5YAIN3pyAOvaT85494494cnCD133dnN3O1oEqNZDegiV4IHicLJoMOhs4HS6dC6+LeC2ulLMRKks6LWkMWHX6XqfaELKyMnTOhsGs13PNCxJNkz+Z/0Qg6GhAeewK698pKaNLwyr2caOScrsU1mzMEJygRWCYYcgIoBopDa7TidSq4jaQa/8RJkG7MortqVTEvILI6Z9PL1rzacn//ov0pY1S3t/raYhx5WrKDBA2ED6Yh0dqvitsEECMJuofkCEQsyAJOqq2jzatUOseZR82L1nz+7xMwlZzIVNAOBQIge7xQhgUfrILXa7jtog/71CzQq3qDNoZYbSkOzBpo31obZtOw24a8BDQx4ubWIXRk7UT9S1Kckrtu+bHgSEvqQKP1d3kPleHwFKDSZuX2mGBGlK3sc5EGO7FpnEzw8MXLlQ8pQsvpNv4K4ld9471NP2/hFAoDt1kaPi26q3zgo7lONnEnBvHfMfbr3iP964r4XTTjgzJSYsWHJ0V/3qF3eu3/B8lN07fsKwYRMeGCZM3nHw8LPP7T+w/TH+b/YjjwCBau4hdsY9BF+ZRr1AgMrEoJdu5R/4fBhELEUxdqM72c5aTGef1+IQVnvjPTGxCb3wfhzek01IufGW24c+AOIZzq8gnCYLACAbHrsGKMNHNDV6EPR/osTBA8ziYuCw7Tjs+ThseQz2CwV2Ou3PYeV9xMZBVchkAMkvnuAQM34FFf4CxEZ9KD5qXmxUIBBiM2mNMBxSoY3Sba1zpQWwlbVVwCXk5EIqmmhqKj93lzEgkm2zG3tH7IEWecP9w+9rGZ4ohslCYnXDUm9MGF2J0ihbnJBfkf59Rs7q4vv9Y9X1ozq9+dbRTwPhSMnYbk2zOnXtXqqkXKHH1tZM7NOvw5ip2e0XjzjcWDEhMjB/yIz70jFvcU/eGRvmVKrdoPJ0bltbq9R1v/YaDgTdn4hNzIa84ltA1MLCGETS7SCOQSAGkdoSIv86xGsg3HKMrOsQE6CUQxiaKGmtgtyAkWIwIMNxKIN5QK4xAIk3MIIVnNA/fAdPM+wIOhPaRNEtuvROycm7kHm7iMHM7wabASUqOtByowkglmHm5an5G8bOiYau9y/SAF7vYVQ2zqR5UUeUXdxLDtMT0SMkNXqR9Lhag0cfURpetbZG/AvZr2jRHOZSOkc5ztkqzrMIAf55rM9N5VmbON8PqhxBs8aRmyFqoTwG4b4dxLFrV2MQyS0hsq5DTACHylWC/hhXgUA+gFip9id54Z5wod3t1glmAKcgCUk+rogS11erXC6/JJ+WL8jcIsuyoNfbqiJ6Kri17tNEXW55EDWhHZV7uVhLarxnM5QhVqpNqbM3bcJ9eBf+bn/07S9xNlt4lIyKtaWSunqyntWxHSQcba5nhhhNYrmqS+3jurSmJdWx7jiVLwUx3sKsmLb5bgdRi4YYhP92EMegKQaR3RIiX4PgeGy65RhZ1yEmwMdxnW4b5z7CQrQJJmEDGMEX1st6ino0mXXgy0+0x2rMHLeOu0ewbTh8BHua7RiLw9m2MThS2DCa/3fbaLyfPTsaR+CIsWwrAOXzv877434CJ6RAQFkZnnRvmsAPExtcAA6rqFMCF0+a32f2945YHTpRoDazQHnjnES1lrm3+Fq4+YgL/ygm0lglwc7fxSoM1BZEj3qKzovZ1zsLv1479tEH9ykddGe2jnx04rGmh6Mjpu/9zy/NwbFk68SdWpPhmOUDNr2FDyl9dMMXV699l61D26bmvgOVZjp2ZRN9qTc7xVdOrI9LlUxpXLoVMfk7Nb7fDFELp2MQKbeDOAZzYhAZLSGyrkNMgA3xlRNMtEfCbHWUTvF5CmKjOFSQeO/frHjvH9+pMOtFUbKDBB6vWeALiC8fs96sl2LdkZoVarkRrHVH8v9lCDcaJGexM+zzQ42NZ9GHnuYrO3mL5LvvUdvFy4zXWq/B6ei/V+5Y9yQAqv0oW6R0aK94ppxcMTUAXpMJUu25YkGhw5Hbrl12RaQd5LrV3S5tj+vm0xpaZCBL2vZIQjWCo6Q2/2lnOTKUqE/1UYJv5ZAOKb36Lxv32p+OTCrfUnn27ofnjujZq094yVz2TcPf/v7+58IPi6dX3OnPyC0L3b917LZdPTcF8w/0mVQxcHZN+cTisqHF1YMuXO0r7Nv3562c52pXkOTnPL8TACXovgLUVWlXOH6L57V56vN2t3t+7FP1eajFc/Gz689fe+UW3xc/vP58whegruiOKsCNGRZehzj+cwyiTQwCqAIhKbtXOVDENWdkOJQLre3tedlIaF+WlJTe3ghi5y4pbYNtKyK+AqGgV6RD66BdECyZQU+xzqKriLgsNtBaO9R97viBxZsNL1corarUot3Jy/+qHSkOv7bLFExMz5TiAMaaVIb/wg7NmPnUc0VVb4+a/3xO8a6Hj/0reqcOO967tWbwurHswpy73lz03Mt7Jg1ZtfPpwzvoK7OWGon8BOY/+yddrEUqp/ie+4eMYP/9+yRWGwjyVpav5k5sXH9/5MVNo2XdQ6Sw4ektO5V1zXc4lW4kzreeMU+JFaqnVDtxVIn1ikl8vyqRVppEbn5e21993vp2z4/9rD7PafGcS1R7PsEQk1d7TaLX/gqAo9URXolZHHYXKGOgqI3xIgApTICovZYRgzDHIa79iUMMSoA4xl6IQTg0iG84RDrHQ4OYwA4CqBbHZ9d89VRlx1zyq6euqsJ5fsnUqhXwYN5jsTttkj7YRp9eETFSj91nsfLIR0+9LqSttY3QmLJw6/3b430QyITiIlAqxdlBMcj/lHpUk+6gRVqnV4kwil39+e/sK5T/9sUYXdkp9n3vr4YN77ll3OW+pzc8v7NpC3vppe0vPUtC7Ev2FzR/cQmlWcInr25+cGHXgtrefZ6cNHMlm8b+taaRbXjh4Aku21jXgbraqmOrzaLyJC1RNqNUrt0Vk/1HquySb/e8drD6PPN2z4+p45Ngi+d8fu35a9/f4vtcJtrzCSkx3Wh3fS2Ph2YhR9gJVO1CD4WTPAaDTSACKjsZTifKZjMqJ/QQ8tX1yhOfG8nPjUN6iccXE96Pp8ejezqVFHXsFCrqot3J8iefZP/q3KW8Y1m4nPwYfwOUY3tEGCUsjvv7PvxEa3orl8vQ6iZn76u47uxt1M+b2Kjnf3P2ZWVxBdGcfXw7QXSpTl4Si1SnX6L2X2yaUjNt+Dw0Xd40o6Z25NzmV4rxTJ9pvAljfYjl95r63Iuxboyetf0XbEBQGjL6zuy7cMOvu8aRRcWffLRjTHRO6DzXjNjutSq5e2KSf0PVDI8mmZuf107VNOfWz4851OeBFs+5ZLXnE/yxtZarrfrYDqw6wr2xGWIjpKsAWu+I2t+VyXex0jOkFJfNZpfsrQMOsKeYPHqqT+NdjB7q5euvRZPnb3oYUWsXUUomXo/W9JUVbx7J4HugOKR748Sz333/yd8fMwk63mSElTs38OYRzF9LmyID2Efsvwpjn83sV86KdcDaFQ1NOXQi58u3ce/ZMxo1nF6Nmgn7Y/TmxejV+puEyuv9TaJArLfsb+Iw6gkU6UvxFLggHe4Ot0uSrE5nKpjtqZKY4bc6eDxpBaOR51hGGj+Vwg8UUAc4b5zk4det2ia1fWVJO2TlvZF9aafq7NnSl1EYN4y9zJ7BYRgeN5RaonxdR8+Rfs09fmXXEH+ecs89LqzDiTgeF3ljSZmwlZ1m55QTGn6hNi32qy1yujAU0iAXCmBQuG26zkI8nqx8t7tVlk4oDOW1Mbbh0RHvSCKixdiunWg32pIyxcyKCIieFj7YoVjVRAeseV9R9a0q5rdyvYktTFkxnyvWs/Nzup6pu8B+ROnrBae6djz2+InL0aAOq4Y/e8+QDVf9G154buPm5xvWCb3mrjKRjN+7vp4xEwtQh3q8Y+a0KbPYz19MYDO5tw1mkLIPz3985rOPP/10x9NP7wBEE68Q7pH8YFF6wGWwWXmN0KJs3CSfKkwsE/Igzx1QzhIE0DR3nLfB89CcmUMWLuFF2u+WPJGTu3C+t3TBoiIAgpP5iG2lhdp+kEMyxSpMejflw753u9KSrHUfcfpp29njxj46a8zY3z3YPRTq3rmsqJu4b9TM2lGjps8c3qFLlw78AkQdn+k78TN1N5wPn+Szg2gC/nKrZc73En4mKLYb3o4vKU6BwvQ0olRTQpJEXXkDB/TOLAxZRpmn39tucP/KjIL21tHmqcL5rLZZnbvMquO3Tl1n1aldEci5Ff/FEyCCePMvngykw+K/eMIh5f8VUtYgffQ49lB7+R0HUNTpQenhP6WBBkscHEs5y+QZ1WF29yx63DMUTVyicNM3RdTpRZly061Rq55Od5RisXIk/bGKDPGARzmLjqmfcouq/e4LkcAKAEQZizSpY1khOWwS0KwXbHbQUZP2M1+x3pUgbyrhA/vjeGG9tcNjs9M6maNnb2B4FnXTeR1Tw7TF6DZldL0ZRcHuMIs2WRn9LW10DWe/ei9JQJ4ELUkjOsxJ7m6+QYbnXvbTY2Ow6D6FHh/7lTTBZZSVLOtqB8g4iCCHzeZK+dC1Y38ymWJ3vb5SBnteXszG7cAfyXB6EYzgPBD/URrIP3Wr6u+OqQ9OmDF94qRp5JtZj/9u9sx5C/icym8TiHvgB8gGOwAEwU4c/M4nELJA1RaoJelK5ZPTbBAIlYikk0WuCInpvPM3e2CJ+16ASv2UpGqjUBAIkMRRWhRNSeqtK6QAyGYBkJXxUyYgEkE7ZYLxAQJIVjbPWkkXx4+ZIJRzr1gnnuT0TQ2Xp3rTPZ5kI5Hl5NZ2wZDslYJtjN4kb/+ILklMTUvtHyFp1rT0tPw0qqdJaUlpzsxM6BvJlJ0W3iDhg5ZN3bwwdMsfKruRW2ZQbuRlt9evdcorVpPyolGwuJT/dUDsCHUKOz4AWfRHQvA065Z1snHLxtW7/oddaNewgZANO4LY+n9OPN+rQSxmD80rC7ed1/Rm9/puaEacl3tH9TwUsfXIpYPVzprl6o4iBXdYT0AUtDAtYc3y+EuJtrjkUwGEVlI650ylKvE+5ABA/HNTwuf9lc+BgItUcf0/AgZwQedwuks0ypTyaYjSqY+iqLe60l3E5aIWOZ1mxPuV70toergeGwR4g0v8V2eKi0otVJZJ05xV7GHcsHQO+0ESk9LSjDup6913x/KzVKdeX9THFGzb1v5TDDfpQ45bECoJ9+43cBcf0nCXXr/F8/43notvxJ6rVEnqc1TWG05X9cp+AAQRKWiHl2Knck80KgqljCAC4Aq1QvJpPHP6XaxCImp1FiUv6pwAUXstt2Ud9NrbHGJCAsQx9ufEKktsFtJBzroOMYF9EK/V+GK1mv8PflNJUQAAAAABAAAAARmahXJJOF8PPPUACQgAAAAAAMk1MYsAAAAAyehMTPua/dUJoghiAAAACQACAAAAAAAAeAFjYGRg4Oj9u4KBgXPN71n/qjkXAUVQwU0Ap6sHhAB4AW2SA6wYQRRF786+2d3atm3b9ldQ27atsG6D2mFt2zaC2ra2d/YbSU7u6C3OG7mIowAgGQFlKIBldiXM1CVQQRZiurMEffRtDLVOYqbqhBBSS/ohgnt9rG+ooxYiTOXDMvUBGbnWixwgPUgnUoLMJCOj5n1IP3Oe1ImajzZpD0YOtxzG6rSALoOzOiUm6ps4K8NJPs6vc/4cZ1UBv4u85FoRnHWr4azjkRqYKFej8hP3eqCfDER61uyT44DbBzlkBTwZD8h8/sMabOD3ZmFWkAiUs5f4f2SFNZfv6iTPscW+jOHynEzEcLULuaQbivCdW5SDNcrx50uFYLzFHYotZl1umvNM1tgNWX+V/3gdebi3ThTgVEMWKYci4kHZhxBie3TYx3rHbGr+Pdo7x4dIHTKe5DFn+O/j+W2VnE3ooW6isf0LIUENvZs1gf/LHojJwdpplCP5gn/5gi26FoYa19ZVFOJ6Sxuoz/q2Ti20IKVJdnqvYJwnhfPH/2f6YHoQF30aZaK9J8T026RxH5fA/WPW/8IW4zkpnIfoFLifGB86v0ffm5nbyRs5iaHR3hNBD0HSfTzoPugRM+hdN0x052KoHLBS0tdgpidAiEesDsgWYO73RWQz2LWIwjqnMe/uYISQtlbyf2NlT9Q9PoBcBnrO6I5ELoMeyHkNnIXGdv809H/DXNOTeAEc0jWMJFcQxvFnto/5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56//Cz+Vaqqrat5rY8x7xnzxl3nvo+27jFnz8c/mI9Nmh2XBdMsilrBitsnD9rI8aiN5DI/jSftC9mIf9pMfIB4kHiI+hWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW/gLbzNbnfwLt7DJ/p0TX4+Uucji1hCnY/U+cijVB7D46jzkb3Yh/3kB4gHiYeIT+EZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV/EaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK/UVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN+59b410iF0sUFO0l2UJtY/8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC/LzdhmV2XBvpBF25IlLJOvEFfRI+NjgCFGGGNK5Rs6Z7Ij/45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j/+58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go/lfr05F+Ua7CCzGx10sYA9tiWLxCWs2BfyN+Ia1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt/QOZPfmY3//Ss3Y5tNpTpL9ZQeGR8DDDHCGN/wbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h+1FeZTKY3gcT2KvTWUf9pMZIB4kHiI+xcQzxGfpfA7P4wW8yG4eT/kYYIgRxvgb9TWsYwObmOAITlI/xf7TOIOzOIfzuEDlIi7hMq7gFbyK1/A63sBbeJtvdwfv4j28zyaP8QmVL/imL/ENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI/hcTzJp73Yh/3kB4gHiYeIT+EZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe/gY/+egvq0YCAEoCNa1n+KVyTUl3Q0uIhoe+3DnRfV7nXGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOM8XZouTZemS1OAKcAUYAowBZgCTAHm3x31O7p3vNf5c1iXeBkEAQDFcbsJX0IqFBwK7tyEgkPC3R0K7hrXzsIhePPK/7c77jPM1yxSPua0WmuDzNcuNmuLtmq7sbyfsUu7De/xu9fvvvDNfN3ioN9j5pq0ximd1hmd1TmlX7iky7qiq7qmG3pgXYd6pMd6oqd6pud6oZd6pdd6p/f6oI/6pC/KSxvf9F0/1LFl1naRcwwzrAu7AHNarbW6oEu6rCu6qmu6ob9Y7xu+kbfHH1ZopCk25RVrhXKn4LCO6KiOGfvpd+R3is15xXmVWKGRptgaysQKpUwc1hEdVcpEysTI7xTbKHMcKzTSFDtCmVihkab4z0FdI0QQBAEUbRz6XLh3Lc7VcI/WN54IuxXFS97oH58+MBoclE1usbHHW77wlW985wcHHHLEMSecsUuPXMNRqfzib3pcllj5xd+0lSVW5nNIL3nF6389h+Y5NG3Thja0oQ1taEMb2tCGNrQn+QwjrcwxM93gJre4Y89mvsdb3vGeD3zkE5/5wle+8Z0fHHDIEceccMaOX67wNz3747gObCQAQhCKdjlRzBVD5be7rwAmfOMQsUvPLj279OzSYBks49Ibl97In/HCuNDGO+NOW6qlWqqlWqqlWqqlWqqYUkwpphTzifnEfII92IM92IM92IM92IM92IM92I/D4/A4PA6Pw+PwODwOj8M/f7kaaDXQyt7K3mqglcCVwNVAq4FWA60GWglZCVkJWQlZCVkJWQlZDbQyqhpoNdAPh3NAwCAAwwDM+7b2sg8kCjIO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO47AO67AO67AO67AO67AO67AO67AO67AO67AO67AO67AO63AO53AO53AO53AO53AO53AO53AO53AO53AO53AO53AO5xCHOMQhDnGIQxziEIc4xCEOcYhDHOIQhzjEIQ5xiEMd6lCHOtShDnWoQx3qUIc61KEOdahDHepQhzrUoQ6/h+P6RpIjiKEoyOPvCARUoK9LctP5ZqXTop7q/6H/0H+4P9yfPz82bdm2Y9ee/T355bS3/divDW9reFtDb4beDL0ZejP0ZujN0JuhN0Nvht4MvRl6M/Rm6M3w1of3PVnJSlaykpWsZCUrWclKVrKSlaxkJStZySpWsYpVrGIVq1jFKlaxilWsYhWrWMUqVrGa1axmNatZzWpWs5rVrGY1q1nNalazmtWsYQ1rWMMa1rCGNaxhDWtYwxrWsIY1rGENa1nLWtaylrWsZS1rWcta1rKWtaxlLWtZyzrWsY51rGMd61jHOtaxjnWsYx3rWMc61rEeTf1o6kdTP/84rpMqCKAYhmH8Cfy2JjuLCPiYPDH1Y+rH1I+pH1M/pn5M/Zh6FEZhFEZhFEZhFEZhFEZhFFZhFVZhFVZhFVZhFVZhFVbhFE7hFE7hFE7hFE7hFE7hFCKgCChPHQFlc7I52ZxsTgQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQti5bl63L1mXrsnXZuggoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCyt5GQBFQBPTlwD7OEIaBKAxSOrmJVZa2TsJcwJ6r0/+9sBOGnTDshOF+DndyXG7k7vfh9+n35fft978Thp2wKuqqqKtarmq58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<section class="page-header">
<h1 class="title toc-ignore project-name">三维散点图</h1>
<h4 class="author project-author">庄闪闪</h4>
<h4 class="date project-date">2020-11-22</h4>
</section>



<section class="main-content">
<p>上期我们说了<a href="https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&amp;mid=2247486060&amp;idx=1&amp;sn=b613c8d0239c93185641c1bb6a062f7b&amp;chksm=ea24f588dd537c9e2d7a33dd5e9f32b34d0a72d5396cdb52211c51f3998269a4d843011abf9b&amp;token=1457590009&amp;lang=zh_CN#rd">气泡图</a>。如果我们将<a href="R语言数据可视化之美" title="R语言数据可视化之美">气泡图的三维数据绘制到三维坐标系</a>中，通常称其为<strong>三维散点图</strong>，即用在三维X-Y-Z图上针对一个或多个数据序列绘出三个度量的一种图表。</p>
<p>有关散点图前几部分系列可见（可跳转）：</p>
<ul>
<li><p><a href="https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&amp;mid=2247485142&amp;idx=1&amp;sn=564bffc9e7765ebae9b9b81a17a188d9&amp;chksm=ea24f932dd5370241a05c75975ff24a34423a8f182bf6c716c8c9ed981788d0492dcb268248a&amp;token=1544929502&amp;lang=zh_CN&amp;scene=21#wechat_redirect">趋势显示的二维散点图</a></p></li>
<li><p><a href="https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&amp;mid=2247485276&amp;idx=1&amp;sn=f98a2aede13555fa1c372f08c3cdec44&amp;chksm=ea24f8b8dd5371ae9e13f3df41ff73e070775eb1ab871370783bb3f396a0a9c29d42c6a89210&amp;token=682523778&amp;lang=zh_CN#rd">分布显示的二维散点图</a></p></li>
<li><p><a href="https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&amp;mid=2247486060&amp;idx=1&amp;sn=b613c8d0239c93185641c1bb6a062f7b&amp;chksm=ea24f588dd537c9e2d7a33dd5e9f32b34d0a72d5396cdb52211c51f3998269a4d843011abf9b&amp;token=1457590009&amp;lang=zh_CN#rd">气泡图</a></p></li>
</ul>
<p>R 中<code>scatterplot3d</code>包的<code>scatterplot3d()</code>函数、<code>rgl</code>包的<a href="http://www.rforscience.com/rpackages/visualisation/oceanview/">plot3d()</a>函数、<code>plot3D</code>包的<code>scatter3D()</code>函数等都可以绘制三维散点图。</p>
<p>下面将从两个包（<code>scatter3D()</code>,<code>plot3d()</code>）入手，一步步带你完成三维散点图的绘制。本文内容丰富，希望大家都能学到自己想要的内容，学习不易，欢迎反馈建议。</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(plot3D)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(scales)</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(RColorBrewer)</span></code></pre></div>
<div id="数据介绍" class="section level2">
<h2>数据介绍</h2>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>df<span class="ot">&lt;-</span><span class="fu">read.csv</span>(<span class="st">&quot;ThreeD_Scatter_Data.csv&quot;</span>,<span class="at">header=</span>T)</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(df)</span></code></pre></div>
<pre><code>##   mph Gas_Mileage Power Weight Engine_Displacement
## 1  23          19    69    821              3687.7
## 2  13          17    80   1287              4261.4
## 3  13          22    55   1535              1983.2
## 4  22          34    55   1037              1770.1
## 5  14          29    55   1082              1589.8
## 6  19          26    49   1285              1294.8</code></pre>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>knitr<span class="sc">::</span><span class="fu">kable</span>(<span class="fu">head</span>(iris))</span></code></pre></div>
<table>
<thead>
<tr class="header">
<th align="right">Sepal.Length</th>
<th align="right">Sepal.Width</th>
<th align="right">Petal.Length</th>
<th align="right">Petal.Width</th>
<th align="left">Species</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">5.1</td>
<td align="right">3.5</td>
<td align="right">1.4</td>
<td align="right">0.2</td>
<td align="left">setosa</td>
</tr>
<tr class="even">
<td align="right">4.9</td>
<td align="right">3.0</td>
<td align="right">1.4</td>
<td align="right">0.2</td>
<td align="left">setosa</td>
</tr>
<tr class="odd">
<td align="right">4.7</td>
<td align="right">3.2</td>
<td align="right">1.3</td>
<td align="right">0.2</td>
<td align="left">setosa</td>
</tr>
<tr class="even">
<td align="right">4.6</td>
<td align="right">3.1</td>
<td align="right">1.5</td>
<td align="right">0.2</td>
<td align="left">setosa</td>
</tr>
<tr class="odd">
<td align="right">5.0</td>
<td align="right">3.6</td>
<td align="right">1.4</td>
<td align="right">0.2</td>
<td align="left">setosa</td>
</tr>
<tr class="even">
<td align="right">5.4</td>
<td align="right">3.9</td>
<td align="right">1.7</td>
<td align="right">0.4</td>
<td align="left">setosa</td>
</tr>
</tbody>
</table>
</div>
<div id="plot3d包scatter3d" class="section level2">
<h2>plot3D包scatter3D()</h2>
<pre><code>scatter3D (x, y, z, ..., colvar = z, phi = 40, theta = 40,
           col = NULL, NAcol = &quot;white&quot;, breaks = NULL,
           colkey = NULL, panel.first = NULL, 
           clim = NULL, clab = NULL, 
           bty = &quot;b&quot;, CI = NULL, surf = NULL, 
           add = FALSE, plot = TRUE)</code></pre>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co"># pmar &lt;- par(mar = c(5.1, 4.1, 4.1, 6.1))</span></span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="fu">with</span>(iris, <span class="fu">scatter3D</span>(<span class="at">x =</span> Sepal.Length, <span class="at">y =</span> Sepal.Width, <span class="at">z =</span> Petal.Length,</span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">pch =</span> <span class="dv">21</span>, <span class="at">cex =</span> <span class="fl">1.5</span>,<span class="at">col=</span><span class="st">&quot;black&quot;</span>,<span class="at">bg=</span><span class="st">&quot;#F57446&quot;</span>,</span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>                   <span class="at">xlab =</span> <span class="st">&quot;Sepal.Length&quot;</span>,</span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>                   <span class="at">ylab =</span> <span class="st">&quot;Sepal.Width&quot;</span>,</span>
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a>                   <span class="at">zlab =</span> <span class="st">&quot;Petal.Length&quot;</span>, </span>
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a>                   <span class="co"># zlim=c(40,180),</span></span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a>                   <span class="at">ticktype =</span> <span class="st">&quot;detailed&quot;</span>,<span class="at">bty =</span> <span class="st">&quot;f&quot;</span>,<span class="at">box =</span> <span class="cn">TRUE</span>,</span>
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a>                   <span class="co">#panel.first = panelfirst,</span></span>
<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a>                   <span class="at">theta =</span> <span class="dv">60</span>, <span class="at">phi =</span> <span class="dv">20</span>, <span class="at">d=</span><span class="dv">3</span>,</span>
<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a>                   <span class="at">colkey =</span> <span class="cn">FALSE</span>)<span class="co">#list(length = 0.5, width = 0.5, cex.clab = 0.75))</span></span>
<span id="cb6-12"><a href="#cb6-12" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>将Z轴变量数据“Power(KW)”映射到数据点颜色，这样可以更加清晰地观察Z 轴变量与X、Y 轴变量数据的变化关系。</p>
<p>我们先自己构造一个颜色映射的颜色条RdYlGn，再绘制三维散点图，然后根据映射的数值添加图例颜色条。</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>iris <span class="ot">=</span> iris <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">quan =</span> <span class="fu">ntile</span>(Petal.Width,<span class="dv">6</span>))</span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>colormap <span class="ot">&lt;-</span> <span class="fu">colorRampPalette</span>(<span class="fu">rev</span>(<span class="fu">brewer.pal</span>(<span class="dv">11</span>,<span class="st">&#39;RdYlGn&#39;</span>)))(<span class="dv">6</span>)<span class="co">#legend颜色配置</span></span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>pmar <span class="ot">&lt;-</span> <span class="fu">par</span>(<span class="at">mar =</span> <span class="fu">c</span>(<span class="fl">5.1</span>, <span class="fl">4.1</span>, <span class="fl">4.1</span>, <span class="fl">6.1</span>))</span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a><span class="co"># 绘图</span></span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a><span class="fu">with</span>(iris, <span class="fu">scatter3D</span>(<span class="at">x =</span> Sepal.Length, <span class="at">y =</span> Sepal.Width, <span class="at">z =</span> Petal.Length,<span class="at">pch =</span> <span class="dv">21</span>, <span class="at">cex =</span> <span class="fl">1.5</span>,<span class="at">col=</span><span class="st">&quot;black&quot;</span>,<span class="at">bg=</span>colormap[iris<span class="sc">$</span>quan],</span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a>     <span class="at">xlab =</span> <span class="st">&quot;Sepal.Length&quot;</span>,</span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a>     <span class="at">ylab =</span> <span class="st">&quot;Sepal.Width&quot;</span>,</span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a>     <span class="at">zlab =</span> <span class="st">&quot;Petal.Length&quot;</span>, </span>
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a>     <span class="at">ticktype =</span> <span class="st">&quot;detailed&quot;</span>,<span class="at">bty =</span> <span class="st">&quot;f&quot;</span>,<span class="at">box =</span> <span class="cn">TRUE</span>,</span>
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a>     <span class="at">theta =</span> <span class="dv">60</span>, <span class="at">phi =</span> <span class="dv">20</span>, <span class="at">d=</span><span class="dv">3</span>,</span>
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a>     <span class="at">colkey =</span> <span class="cn">FALSE</span>)</span>
<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a><span class="fu">colkey</span> (<span class="at">col=</span>colormap,<span class="at">clim=</span><span class="fu">range</span>(iris<span class="sc">$</span>quan),<span class="at">clab =</span> <span class="st">&quot;Petal.Width&quot;</span>, <span class="at">add=</span><span class="cn">TRUE</span>, <span class="at">length=</span><span class="fl">0.4</span>,<span class="at">side =</span> <span class="dv">4</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<p>三维散点图可以展示三维数据，如果添加一维数据，则使图表展示四维数据。第1种方法就是将图4-1-14(c)的填充颜色渐变映射到第四维数据，而不是原来的第三维数据</p>
<p>第2种方法就是将第四维数据映射到数据点的大小上，即三维气泡图</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>pmar <span class="ot">&lt;-</span> <span class="fu">par</span>(<span class="at">mar =</span> <span class="fu">c</span>(<span class="fl">5.1</span>, <span class="fl">4.1</span>, <span class="fl">4.1</span>, <span class="fl">6.1</span>))</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 绘图</span></span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="fu">with</span>(iris, <span class="fu">scatter3D</span>(<span class="at">x =</span> Sepal.Length, <span class="at">y =</span> Sepal.Width, <span class="at">z =</span> Petal.Length,<span class="at">pch =</span> <span class="dv">21</span>, </span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>                     <span class="at">cex =</span> <span class="fu">rescale</span>(iris<span class="sc">$</span>quan, <span class="fu">c</span>(.<span class="dv">5</span>, <span class="dv">4</span>)),<span class="at">col=</span><span class="st">&quot;black&quot;</span>,<span class="at">bg=</span>colormap[iris<span class="sc">$</span>quan],</span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>                     <span class="at">xlab =</span> <span class="st">&quot;Sepal.Length&quot;</span>,</span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a>                     <span class="at">ylab =</span> <span class="st">&quot;Sepal.Width&quot;</span>,</span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a>                     <span class="at">zlab =</span> <span class="st">&quot;Petal.Length&quot;</span>, </span>
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a>                     <span class="at">ticktype =</span> <span class="st">&quot;detailed&quot;</span>,<span class="at">bty =</span> <span class="st">&quot;f&quot;</span>,<span class="at">box =</span> <span class="cn">TRUE</span>,</span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a>                     <span class="at">theta =</span> <span class="dv">30</span>, <span class="at">phi =</span> <span class="dv">15</span>, <span class="at">d=</span><span class="dv">2</span>,</span>
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a>                     <span class="at">colkey =</span> <span class="cn">FALSE</span>)</span>
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a>breaks <span class="ot">=</span><span class="dv">1</span><span class="sc">:</span><span class="dv">6</span></span>
<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a><span class="fu">legend</span>(<span class="st">&quot;right&quot;</span>,<span class="at">title =</span>  <span class="st">&quot;Weight&quot;</span>,<span class="at">legend=</span>breaks,<span class="at">pch=</span><span class="dv">21</span>,</span>
<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a>       <span class="at">pt.cex=</span><span class="fu">rescale</span>(breaks, <span class="fu">c</span>(.<span class="dv">5</span>, <span class="dv">4</span>)),<span class="at">y.intersp=</span><span class="fl">1.6</span>,</span>
<span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a>       <span class="at">pt.bg =</span> colormap[<span class="dv">1</span><span class="sc">:</span><span class="dv">6</span>],<span class="at">bg=</span><span class="st">&quot;white&quot;</span>,<span class="at">bty=</span><span class="st">&quot;n&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(wesanderson)</span></code></pre></div>
<pre><code>## Warning: package &#39;wesanderson&#39; was built under R version 4.0.3</code></pre>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>pmar <span class="ot">&lt;-</span> <span class="fu">par</span>(<span class="at">mar =</span> <span class="fu">c</span>(<span class="fl">5.1</span>, <span class="fl">4.1</span>, <span class="fl">4.1</span>, <span class="fl">7.1</span>))</span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>colors0 <span class="ot">&lt;-</span>  <span class="fu">wes_palette</span>(<span class="at">n=</span><span class="dv">3</span>, <span class="at">name=</span><span class="st">&quot;Darjeeling1&quot;</span>)</span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>colors <span class="ot">&lt;-</span> colors0[<span class="fu">as.numeric</span>(iris<span class="sc">$</span>Species)]</span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a><span class="fu">with</span>(iris, <span class="fu">scatter3D</span>(<span class="at">x =</span> Sepal.Length, <span class="at">y =</span> Sepal.Width, <span class="at">z =</span> Petal.Length, <span class="co">#bgvar = mag,</span></span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>                   <span class="at">pch =</span> <span class="dv">21</span>, <span class="at">cex =</span> <span class="fl">1.5</span>,<span class="at">col=</span><span class="st">&quot;black&quot;</span>,<span class="at">bg=</span>colors,</span>
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a>                   <span class="at">xlab =</span> <span class="st">&quot;longitude&quot;</span>, <span class="at">ylab =</span> <span class="st">&quot;latitude&quot;</span>,</span>
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a>                   <span class="at">zlab =</span> <span class="st">&quot;depth, km&quot;</span>, </span>
<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a>                   <span class="at">ticktype =</span> <span class="st">&quot;detailed&quot;</span>,<span class="at">bty =</span> <span class="st">&quot;f&quot;</span>,<span class="at">box =</span> <span class="cn">TRUE</span>,</span>
<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a>                   <span class="co">#panel.first = panelfirst,</span></span>
<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a>                   <span class="at">theta =</span> <span class="dv">140</span>, <span class="at">phi =</span> <span class="dv">20</span>, <span class="at">d=</span><span class="dv">3</span>,</span>
<span id="cb11-11"><a href="#cb11-11" aria-hidden="true" tabindex="-1"></a>                   <span class="at">colkey =</span> <span class="cn">FALSE</span>)<span class="co">#list(length = 0.5, width = 0.5, cex.clab = 0.75))</span></span>
<span id="cb11-12"><a href="#cb11-12" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb11-13"><a href="#cb11-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-14"><a href="#cb11-14" aria-hidden="true" tabindex="-1"></a><span class="fu">legend</span>(<span class="st">&quot;right&quot;</span>,<span class="at">title =</span>  <span class="st">&quot;Species&quot;</span>,<span class="at">legend=</span><span class="fu">c</span>(<span class="st">&quot;setosa&quot;</span>, <span class="st">&quot;versicolor&quot;</span>, <span class="st">&quot;virginica&quot;</span>),<span class="at">pch=</span><span class="dv">21</span>,</span>
<span id="cb11-15"><a href="#cb11-15" aria-hidden="true" tabindex="-1"></a>       <span class="at">cex=</span><span class="dv">1</span>,<span class="at">y.intersp=</span><span class="dv">1</span>,<span class="at">pt.bg =</span> colors0,<span class="at">bg=</span><span class="st">&quot;white&quot;</span>,<span class="at">bty=</span><span class="st">&quot;n&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="rgl包-plot3d" class="section level2">
<h2>rgl包 plot3d()</h2>
<p><a href="https://www.rdocumentation.org/packages/rgl/versions/0.100.54">rgl包</a>是在r中构建3d图表的最佳选择，请参阅这篇介绍3d散点图使用它的文章。</p>
<pre><code>plot3d(x, y, z,  
    xlab, ylab, zlab, type = &quot;p&quot;, col,  
    size, lwd, radius,
    add = FALSE, aspect = !add, 
    xlim = NULL, ylim = NULL, zlim = NULL, 
    forceClipregion = FALSE, ...)</code></pre>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># library</span></span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(rgl)</span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Add a new column with color</span></span>
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a>mycolors <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&#39;royalblue1&#39;</span>, <span class="st">&#39;darkcyan&#39;</span>, <span class="st">&#39;oldlace&#39;</span>)</span>
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a>iris<span class="sc">$</span>color <span class="ot">&lt;-</span> mycolors[ <span class="fu">as.numeric</span>(iris<span class="sc">$</span>Species) ]</span>
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a><span class="fu">plot3d</span>( </span>
<span id="cb13-10"><a href="#cb13-10" aria-hidden="true" tabindex="-1"></a>  <span class="at">x=</span>iris<span class="sc">$</span><span class="st">`</span><span class="at">Sepal.Length</span><span class="st">`</span>, <span class="at">y=</span>iris<span class="sc">$</span><span class="st">`</span><span class="at">Sepal.Width</span><span class="st">`</span>, <span class="at">z=</span>iris<span class="sc">$</span><span class="st">`</span><span class="at">Petal.Length</span><span class="st">`</span>, </span>
<span id="cb13-11"><a href="#cb13-11" aria-hidden="true" tabindex="-1"></a>  <span class="at">col =</span> iris<span class="sc">$</span>color, </span>
<span id="cb13-12"><a href="#cb13-12" aria-hidden="true" tabindex="-1"></a>  <span class="at">type =</span> <span class="st">&#39;s&#39;</span>, </span>
<span id="cb13-13"><a href="#cb13-13" aria-hidden="true" tabindex="-1"></a>  <span class="at">radius =</span> .<span class="dv">1</span>,</span>
<span id="cb13-14"><a href="#cb13-14" aria-hidden="true" tabindex="-1"></a>  <span class="at">xlab=</span><span class="st">&quot;Sepal Length&quot;</span>, <span class="at">ylab=</span><span class="st">&quot;Sepal Width&quot;</span>, <span class="at">zlab=</span><span class="st">&quot;Petal Length&quot;</span>)</span>
<span id="cb13-15"><a href="#cb13-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-16"><a href="#cb13-16" aria-hidden="true" tabindex="-1"></a><span class="co"># writeWebGL( filename=&quot;HtmlWidget/3dscatter.html&quot; ,  width=600, height=600)</span></span></code></pre></div>
<p>它还提供了plot3d()和play3d()函数，允许将3d图表动画化，并最终以.gif格式导出结果。下面是著名iris数据集的一个应用程序，它具有一个很好的动画3d散点图。</p>
</div>
<div id="动态3d图" class="section level2">
<h2>动态3D图</h2>
<p><a href="https://www.r-graph-gallery.com/3-r-animated-cube.html">Animated 3d chart with R.</a></p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(rgl)</span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(magick)</span></code></pre></div>
<pre><code>## Warning: package &#39;magick&#39; was built under R version 4.0.3</code></pre>
<pre><code>## Linking to ImageMagick 6.9.11.34
## Enabled features: cairo, freetype, fftw, ghostscript, lcms, pango, rsvg, webp
## Disabled features: fontconfig, x11</code></pre>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="co"># This is ugly</span></span>
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>colors <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;royalblue1&quot;</span>, <span class="st">&quot;darkcyan&quot;</span>, <span class="st">&quot;oldlace&quot;</span>)</span>
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>iris<span class="sc">$</span>color <span class="ot">&lt;-</span> colors[ <span class="fu">as.numeric</span>( <span class="fu">as.factor</span>(iris<span class="sc">$</span>Species) ) ]</span>
<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Static chart</span></span>
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a><span class="co"># plot3d( iris[,1], iris[,2], iris[,3], col = iris$color, type = &quot;s&quot;, radius = .2 )</span></span>
<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a><span class="co"># We can indicate the axis and the rotation velocity</span></span>
<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a><span class="co"># play3d( spin3d( axis = c(0, 0, 1), rpm = 20), duration = 50 )</span></span>
<span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-11"><a href="#cb17-11" aria-hidden="true" tabindex="-1"></a><span class="co"># Save like gif</span></span>
<span id="cb17-12"><a href="#cb17-12" aria-hidden="true" tabindex="-1"></a><span class="co"># movie3d(</span></span>
<span id="cb17-13"><a href="#cb17-13" aria-hidden="true" tabindex="-1"></a><span class="co">#   movie=&quot;3dAnimatedScatterplot&quot;, </span></span>
<span id="cb17-14"><a href="#cb17-14" aria-hidden="true" tabindex="-1"></a><span class="co">#   spin3d( axis = c(0, 0, 1), rpm = 7),</span></span>
<span id="cb17-15"><a href="#cb17-15" aria-hidden="true" tabindex="-1"></a><span class="co">#   duration = 10, </span></span>
<span id="cb17-16"><a href="#cb17-16" aria-hidden="true" tabindex="-1"></a><span class="co">#   dir = &quot;~/Desktop&quot;,</span></span>
<span id="cb17-17"><a href="#cb17-17" aria-hidden="true" tabindex="-1"></a><span class="co">#   type = &quot;gif&quot;, </span></span>
<span id="cb17-18"><a href="#cb17-18" aria-hidden="true" tabindex="-1"></a><span class="co">#   clean = TRUE</span></span>
<span id="cb17-19"><a href="#cb17-19" aria-hidden="true" tabindex="-1"></a><span class="co"># )</span></span></code></pre></div>
</div>
</section>



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================================================
FILE: 2020年/2020.11.22三维散点图/3d_scatter.rmd
================================================
---
title: "三维散点图"
author: "庄闪闪"
date: "`r Sys.Date()`"
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
vignette: >
  %\VignetteIndexEntry{Vignette Title}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


上期我们说了[气泡图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486060&idx=1&sn=b613c8d0239c93185641c1bb6a062f7b&chksm=ea24f588dd537c9e2d7a33dd5e9f32b34d0a72d5396cdb52211c51f3998269a4d843011abf9b&token=1457590009&lang=zh_CN#rd)。如果我们将[气泡图的三维数据绘制到三维坐标系](R语言数据可视化之美 "R语言数据可视化之美")中，通常称其为**三维散点图**，即用在三维X-Y-Z图上针对一个或多个数据序列绘出三个度量的一种图表。


有关散点图前几部分系列可见（可跳转）：

- [趋势显示的二维散点图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485142&idx=1&sn=564bffc9e7765ebae9b9b81a17a188d9&chksm=ea24f932dd5370241a05c75975ff24a34423a8f182bf6c716c8c9ed981788d0492dcb268248a&token=1544929502&lang=zh_CN&scene=21#wechat_redirect)

- [分布显示的二维散点图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485276&idx=1&sn=f98a2aede13555fa1c372f08c3cdec44&chksm=ea24f8b8dd5371ae9e13f3df41ff73e070775eb1ab871370783bb3f396a0a9c29d42c6a89210&token=682523778&lang=zh_CN#rd)

- [气泡图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486060&idx=1&sn=b613c8d0239c93185641c1bb6a062f7b&chksm=ea24f588dd537c9e2d7a33dd5e9f32b34d0a72d5396cdb52211c51f3998269a4d843011abf9b&token=1457590009&lang=zh_CN#rd)

R 中`scatterplot3d`包的`scatterplot3d()`函数、`rgl`包的[plot3d()](http://www.rforscience.com/rpackages/visualisation/oceanview/)函数、`plot3D`包的`scatter3D()`函数等都可以绘制三维散点图。

下面将从两个包（`scatter3D()`,`plot3d()`）入手，一步步带你完成三维散点图的绘制。本文内容丰富，希望大家都能学到自己想要的内容，学习不易，欢迎反馈建议。


```{r message=FALSE, warning=FALSE}
library(plot3D)
library(scales)
library(RColorBrewer)
```

## 数据介绍
```{r}
df<-read.csv("ThreeD_Scatter_Data.csv",header=T)
head(df)
```
```{r}
knitr::kable(head(iris))
```


## plot3D包scatter3D()

```
scatter3D (x, y, z, ..., colvar = z, phi = 40, theta = 40,
           col = NULL, NAcol = "white", breaks = NULL,
           colkey = NULL, panel.first = NULL, 
           clim = NULL, clab = NULL, 
           bty = "b", CI = NULL, surf = NULL, 
           add = FALSE, plot = TRUE)
```



```{r}
# pmar <- par(mar = c(5.1, 4.1, 4.1, 6.1))
with(iris, scatter3D(x = Sepal.Length, y = Sepal.Width, z = Petal.Length,
  pch = 21, cex = 1.5,col="black",bg="#F57446",
                   xlab = "Sepal.Length",
                   ylab = "Sepal.Width",
                   zlab = "Petal.Length", 
                   # zlim=c(40,180),
                   ticktype = "detailed",bty = "f",box = TRUE,
                   #panel.first = panelfirst,
                   theta = 60, phi = 20, d=3,
                   colkey = FALSE)#list(length = 0.5, width = 0.5, cex.clab = 0.75))
)
```

将Z轴变量数据“Power(KW)”映射到数据点颜色，这样可以更加清晰地观察Z 轴变量与X、Y 轴变量数据的变化关系。

我们先自己构造一个颜色映射的颜色条RdYlGn，再绘制三维散点图，然后根据映射的数值添加图例颜色条。


```{r message=FALSE, warning=FALSE}
library(tidyverse)
iris = iris %>% mutate(quan = ntile(Petal.Width,6))
colormap <- colorRampPalette(rev(brewer.pal(11,'RdYlGn')))(6)#legend颜色配置
pmar <- par(mar = c(5.1, 4.1, 4.1, 6.1))
# 绘图
with(iris, scatter3D(x = Sepal.Length, y = Sepal.Width, z = Petal.Length,pch = 21, cex = 1.5,col="black",bg=colormap[iris$quan],
     xlab = "Sepal.Length",
     ylab = "Sepal.Width",
     zlab = "Petal.Length", 
     ticktype = "detailed",bty = "f",box = TRUE,
     theta = 60, phi = 20, d=3,
     colkey = FALSE)
)
colkey (col=colormap,clim=range(iris$quan),clab = "Petal.Width", add=TRUE, length=0.4,side = 4)
```

三维散点图可以展示三维数据，如果添加一维数据，则使图表展示四维数据。第1种方法就是将图4-1-14(c)的填充颜色渐变映射到第四维数据，而不是原来的第三维数据

第2种方法就是将第四维数据映射到数据点的大小上，即三维气泡图
```{r message=FALSE, warning=FALSE}
pmar <- par(mar = c(5.1, 4.1, 4.1, 6.1))
# 绘图
with(iris, scatter3D(x = Sepal.Length, y = Sepal.Width, z = Petal.Length,pch = 21, 
                     cex = rescale(iris$quan, c(.5, 4)),col="black",bg=colormap[iris$quan],
                     xlab = "Sepal.Length",
                     ylab = "Sepal.Width",
                     zlab = "Petal.Length", 
                     ticktype = "detailed",bty = "f",box = TRUE,
                     theta = 30, phi = 15, d=2,
                     colkey = FALSE)
)
breaks =1:6
legend("right",title =  "Weight",legend=breaks,pch=21,
       pt.cex=rescale(breaks, c(.5, 4)),y.intersp=1.6,
       pt.bg = colormap[1:6],bg="white",bty="n")
```

```{r}
library(wesanderson)
pmar <- par(mar = c(5.1, 4.1, 4.1, 7.1))
colors0 <-  wes_palette(n=3, name="Darjeeling1")
colors <- colors0[as.numeric(iris$Species)]
with(iris, scatter3D(x = Sepal.Length, y = Sepal.Width, z = Petal.Length, #bgvar = mag,
                   pch = 21, cex = 1.5,col="black",bg=colors,
                   xlab = "longitude", ylab = "latitude",
                   zlab = "depth, km", 
                   ticktype = "detailed",bty = "f",box = TRUE,
                   #panel.first = panelfirst,
                   theta = 140, phi = 20, d=3,
                   colkey = FALSE)#list(length = 0.5, width = 0.5, cex.clab = 0.75))
)

legend("right",title =  "Species",legend=c("setosa", "versicolor", "virginica"),pch=21,
       cex=1,y.intersp=1,pt.bg = colors0,bg="white",bty="n")
```



## rgl包 plot3d()

[rgl包](https://www.rdocumentation.org/packages/rgl/versions/0.100.54)是在r中构建3d图表的最佳选择，请参阅这篇介绍3d散点图使用它的文章。


```
plot3d(x, y, z,  
	xlab, ylab, zlab, type = "p", col,  
	size, lwd, radius,
	add = FALSE, aspect = !add, 
	xlim = NULL, ylim = NULL, zlim = NULL, 
	forceClipregion = FALSE, ...)
```

```{r webgl=TRUE, results='hide'}
# library
library(rgl)

# Add a new column with color
mycolors <- c('royalblue1', 'darkcyan', 'oldlace')
iris$color <- mycolors[ as.numeric(iris$Species) ]

# Plot
plot3d( 
  x=iris$`Sepal.Length`, y=iris$`Sepal.Width`, z=iris$`Petal.Length`, 
  col = iris$color, 
  type = 's', 
  radius = .1,
  xlab="Sepal Length", ylab="Sepal Width", zlab="Petal Length")

# writeWebGL( filename="HtmlWidget/3dscatter.html" ,  width=600, height=600)
```

它还提供了plot3d()和play3d()函数，允许将3d图表动画化，并最终以.gif格式导出结果。下面是著名iris数据集的一个应用程序，它具有一个很好的动画3d散点图。

## 动态3D图

[Animated 3d chart with R.](https://www.r-graph-gallery.com/3-r-animated-cube.html)

```{r }
library(rgl)
library(magick)

# This is ugly
colors <- c("royalblue1", "darkcyan", "oldlace")
iris$color <- colors[ as.numeric( as.factor(iris$Species) ) ]

# Static chart
# plot3d( iris[,1], iris[,2], iris[,3], col = iris$color, type = "s", radius = .2 )

# We can indicate the axis and the rotation velocity
# play3d( spin3d( axis = c(0, 0, 1), rpm = 20), duration = 50 )

# Save like gif
# movie3d(
#   movie="3dAnimatedScatterplot", 
#   spin3d( axis = c(0, 0, 1), rpm = 7),
#   duration = 10, 
#   dir = "~/Desktop",
#   type = "gif", 
#   clean = TRUE
# )
```

















================================================
FILE: 2020年/2020.11.22三维散点图/ThreeD_Scatter_Data.csv
================================================
mph,Gas_Mileage,Power,Weight,Engine_Displacement
23,19,69,821,3687.7
13,17,80,1287,4261.4
13,22,55,1535,1983.2
22,34,55,1037,1770.1
14,29,55,1082,1589.8
19,26,49,1285,1294.8
11,27,52,1246,2311
16,15,128,1655,6556
18,15,110,2121,5212
15,13,139,1680,6556
17,27,66,1124,2474.9
17,29,58,1187,1819.3
18,21,73,739,3687.7
10,14,161,1597,7441
20,20,71,883,1147.3
23,17,139,1675,6556
12,29,57,1036,1589.8
19,21,77,1457,3786.1
12,33,51,1005,1393.1
12,29,61,923,2212.6
13,17,124,1251,5736.5
18,21,73,1389,3802.5
17,20,79,1138,2556.8
15,22,80,1099,1983.2
19,33,52,1228,1753.7
12,19,102,1867,5212
12,19,121,1587,3786.1
16,24,69,970,1704.6
16,21,80,935,3687.7
16,30,49,902,1606.2
14,14,113,2152,5736.5
10,14,106,1417,5736.5
18,28,66,843,1901.2
14,35,38,1173,1491.5
13,19,106,1435,4998.9
16,27,61,1498,2474.9
15,24,69,1339,1852.1
13,20,74,1347,2130.7
17,26,61,987,2212.6
15,16,102,1352,4949.8
16,20,64,1979,1589.8
13,10,158,1531,5900.4
19,19,99,1505,5212
14,21,66,1432,3802.5
16,31,57,1509,1589.8
11,19,69,1387,4228.6
16,27,44,1613,1589.8
17,16,110,1487,6556
17,12,145,1426,7031.3
16,19,106,1052,4998.9


================================================
FILE: 2020年/2020.11.22数据处理ntile()/数据处理数据按从小到大分成n类.md
================================================
## 数据处理|数据按从小到大分成n类

最近做项目遇到了一个实际数据清洗的问题，如何将连续数据按从大到小分成n类？刚开始我是打算用tidyverse包的，但是找不到合适的函数。只能通过较为笨拙的方法进行了。
![](https://imgkr2.cn-bj.ufileos.com/dde70a2c-d9fa-4076-8b52-ae17d0aa96b3.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=xb9feNgbDRAYJbQM8t67howdNmo%253D&Expires=1606120382)

之后通过[stackoverflow网站](https://stackoverflow.com/questions/4126326/how-to-quickly-form-groups-quartiles-deciles-etc-by-ordering-columns-in-a?noredirect=1 "How to quickly form groups ")进行查询才发现原来有这么好用的**窗口函数**。

![](https://imgkr2.cn-bj.ufileos.com/c02dd36a-b258-4dc6-9350-3569d9980b3f.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=SAb7a4hlRpv3l43MWN6b%252Bi6ciaY%253D&Expires=1606121218)


## 较为笨拙的方法

使用Rbase包中的数据框操作进行，首先随机产生一个数据框作为模拟数据。
```{r}
temp <- data.frame(name=letters[1:12], value=rnorm(12), q=rep(NA, 12))
head(temp)
#    name       value quartile
# 1     a  2.55118169       NA
# 2     b  0.79755259       NA
# 3     c  0.16918905       NA
# 4     d  1.73359245       NA
# 5     e  0.41027113       NA
# 6     f  0.73012966       NA
```
```{r}
temp.sorted <- temp[order(temp$value), ]
temp.sorted$q <- rep(1:4, each=12/4)
temp <- temp.sorted[order(as.numeric(rownames(temp.sorted))), ]
head(temp)
#    name       value        q
# 1     a  2.55118169        4
# 2     b  0.79755259        3
# 3     c  0.16918905        2
# 4     d  1.73359245        4
# 5     e  0.41027113        2
# 6     f  0.73012966        3
```

## 使用dplyr包中的ntile()


首先构建一个数据框，包含a，b变量。以该数据框进行演示：

```{r}
foo <- data.frame(a = 1:100,
                  b = runif(100, 50, 200),
                  stringsAsFactors = FALSE)
```

载入[tidyverse包](https://www.tidyverse.org/ "tidyverse包")，内部包含了[dplyr包](https://dplyr.tidyverse.org/ "dplyr包")。然后使用管道函数，利用函数`ntile()`构建新的列，列名为q。或者不用通道函数，直接加载`dplyr`包也可以。
```{r}
library(tidyverse)
foo %>%
    mutate(q = ntile(b, 10))

#  a         b        q
#1 1  93.94754        2
#2 2 172.51323        8
#3 3  99.79261        3
#4 4  81.55288        2
#5 5 116.59942        5
#6 6 128.75947        6
```







================================================
FILE: 2020年/2020.11.28等高线/.Rhistory
================================================
install.packages("prettydoc")
library(directlabels)
library(reshape2)
library(ggplot2)
library(grDevices)
library(RColorBrewer)
library(directlabels)
library(reshape2)
library(ggplot2)
library(grDevices)
library(RColorBrewer)
install.packages("reshape2")
library(directlabels)
library(reshape2)
library(ggplot2)
library(grDevices)
library(RColorBrewer)
library(directlabels)
library(reshape2)
library(ggplot2)
library(grDevices)
library(RColorBrewer)
install.packages("Rcpp")
library(directlabels)
library(reshape2)
library(ggplot2)
library(grDevices)
library(RColorBrewer)
library(directlabels)
library(reshape2)
library(ggplot2)
library(grDevices)
library(RColorBrewer)
install.packages("reshape2")
library(directlabels)
library(reshape2)
library(ggplot2)
library(grDevices)
library(RColorBrewer)
install.packages("RCPP")
install.packages("Rcpp")
install.packages("Rcpp")
library(directlabels)
library(reshape2)
library(ggplot2)
library(grDevices)
library(RColorBrewer)
library(reshape)
library(reshape2)
rf <- colorRampPalette(rev(brewer.pal(11,'Spectral')))
colormap <- colorRampPalette(rev(brewer.pal(11,'Spectral')))(32)
# 数据
z <- as.matrix(read.table("等高线.txt",header=TRUE))
colnames(z) <- seq(1,ncol(z),by=1) #列名设置
max_z <- max(z)
min_z <- min(z)
breaks_lines <- seq(min(z),max(z),by=(max_z-min_z)/10)
map <- melt(z) #这个函数来自reshape2（处理数据的包）介绍一下这个函数，以及相关函数
colnames(map)<-c("Var1","Var2","value")
library(directlabels)
library(reshape2)
library(ggplot2)
library(grDevices)
library(RColorBrewer)


================================================
FILE: 2020年/2020.11.28等高线/等高线.html
================================================
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// apply pandoc div.sourceCode style to pre.sourceCode instead
(function() {
  var sheets = document.styleSheets;
  for (var i = 0; i < sheets.length; i++) {
    if (sheets[i].ownerNode.dataset["origin"] !== "pandoc") continue;
    try { var rules = sheets[i].cssRules; } catch (e) { continue; }
    for (var j = 0; j < rules.length; j++) {
      var rule = rules[j];
      // check if there is a div.sourceCode rule
      if (rule.type !== rule.STYLE_RULE || rule.selectorText !== "div.sourceCode") continue;
      var style = rule.style.cssText;
      // check if color or background-color is set
      if (rule.style.color === '' && rule.style.backgroundColor === '') continue;
      // replace div.sourceCode by a pre.sourceCode rule
      sheets[i].deleteRule(j);
      sheets[i].insertRule('pre.sourceCode{' + style + '}', j);
    }
  }
})();
</script>



<style type="text/css">@font-face{font-family:"Open Sans";font-style:normal;font-weight:400;src:local("Open 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<section class="page-header">
<h1 class="title toc-ignore project-name">等高线图</h1>
<h4 class="author project-author">庄闪闪</h4>
<h4 class="date project-date">2020-12-01</h4>
</section>



<section class="main-content">
<div id="等高线图" class="section level1">
<h1>等高线图</h1>
<p><strong>等高线图（contour map）</strong> 是<a href="https://github.com/EasyChart/Beautiful-Visualization-with-R" title="R语言数据可视化之美">可视化二维空间标量场的基本方法</a>，可以将三维数据使用二维的方法可视化，同时用颜色视觉特征表示第三维数据，如地图上的等高线、天气预报中的等压线和等温线等。假设 <span class="math inline">\(f(x, y)\)</span> 是在点 <span class="math inline">\((x, y)\)</span> 处的数值，等值线是在二维数据场中满足 <span class="math inline">\(f(x, y)=c\)</span> 的空间点集按一定的顺序连接而成的线。数值为c的等值线可以将二维空间标量场分为两部分：如果 函数大于c，则该点在等值线内；反之，则该点在等值线外。</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="kw">library</span>(directlabels)</span>
<span id="cb1-2"><a href="#cb1-2"></a><span class="kw">library</span>(reshape2)</span>
<span id="cb1-3"><a href="#cb1-3"></a><span class="kw">library</span>(ggplot2)</span>
<span id="cb1-4"><a href="#cb1-4"></a><span class="kw">library</span>(grDevices)</span>
<span id="cb1-5"><a href="#cb1-5"></a><span class="kw">library</span>(RColorBrewer)</span></code></pre></div>
<div id="数据介绍" class="section level2">
<h2>数据介绍</h2>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a>rf &lt;-<span class="st"> </span><span class="kw">colorRampPalette</span>(<span class="kw">rev</span>(<span class="kw">brewer.pal</span>(<span class="dv">11</span>,<span class="st">&#39;Spectral&#39;</span>)))</span>
<span id="cb2-2"><a href="#cb2-2"></a>colormap &lt;-<span class="st"> </span><span class="kw">colorRampPalette</span>(<span class="kw">rev</span>(<span class="kw">brewer.pal</span>(<span class="dv">11</span>,<span class="st">&#39;Spectral&#39;</span>)))(<span class="dv">32</span>)</span>
<span id="cb2-3"><a href="#cb2-3"></a><span class="co"># 数据</span></span>
<span id="cb2-4"><a href="#cb2-4"></a>z &lt;-<span class="st"> </span><span class="kw">as.matrix</span>(<span class="kw">read.table</span>(<span class="st">&quot;等高线.txt&quot;</span>,<span class="dt">header=</span><span class="ot">TRUE</span>))</span>
<span id="cb2-5"><a href="#cb2-5"></a><span class="kw">colnames</span>(z) &lt;-<span class="st"> </span><span class="kw">seq</span>(<span class="dv">1</span>,<span class="kw">ncol</span>(z),<span class="dt">by=</span><span class="dv">1</span>) <span class="co">#列名设置</span></span>
<span id="cb2-6"><a href="#cb2-6"></a>max_z &lt;-<span class="st"> </span><span class="kw">max</span>(z)</span>
<span id="cb2-7"><a href="#cb2-7"></a>min_z &lt;-<span class="st"> </span><span class="kw">min</span>(z)</span>
<span id="cb2-8"><a href="#cb2-8"></a>breaks_lines &lt;-<span class="st"> </span><span class="kw">seq</span>(<span class="kw">min</span>(z),<span class="kw">max</span>(z),<span class="dt">by=</span>(max_z<span class="op">-</span>min_z)<span class="op">/</span><span class="dv">10</span>)</span>
<span id="cb2-9"><a href="#cb2-9"></a>map &lt;-<span class="st"> </span><span class="kw">melt</span>(z) <span class="co">#这个函数来自reshape2（处理数据的包）介绍一下这个函数，以及相关函数</span></span>
<span id="cb2-10"><a href="#cb2-10"></a><span class="kw">colnames</span>(map)&lt;-<span class="kw">c</span>(<span class="st">&quot;Var1&quot;</span>,<span class="st">&quot;Var2&quot;</span>,<span class="st">&quot;value&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a>knitr<span class="op">::</span><span class="kw">kable</span>(<span class="kw">head</span>(z[,<span class="dv">1</span><span class="op">:</span><span class="dv">10</span>]),<span class="dt">caption =</span> <span class="st">&quot;转换前数据&quot;</span>)</span></code></pre></div>
<table>
<caption>转换前数据</caption>
<thead>
<tr class="header">
<th align="right">1</th>
<th align="right">2</th>
<th align="right">3</th>
<th align="right">4</th>
<th align="right">5</th>
<th align="right">6</th>
<th align="right">7</th>
<th align="right">8</th>
<th align="right">9</th>
<th align="right">10</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">1076</td>
<td align="right">1067</td>
<td align="right">1060</td>
<td align="right">1063</td>
<td align="right">1076</td>
<td align="right">1078</td>
<td align="right">1073</td>
<td align="right">1071</td>
<td align="right">1073</td>
<td align="right">1086</td>
</tr>
<tr class="even">
<td align="right">1083</td>
<td align="right">1074</td>
<td align="right">1065</td>
<td align="right">1075</td>
<td align="right">1091</td>
<td align="right">1090</td>
<td align="right">1082</td>
<td align="right">1083</td>
<td align="right">1087</td>
<td align="right">1097</td>
</tr>
<tr class="odd">
<td align="right">1087</td>
<td align="right">1085</td>
<td align="right">1079</td>
<td align="right">1083</td>
<td align="right">1101</td>
<td align="right">1102</td>
<td align="right">1094</td>
<td align="right">1098</td>
<td align="right">1105</td>
<td align="right">1108</td>
</tr>
<tr class="even">
<td align="right">1087</td>
<td align="right">1089</td>
<td align="right">1089</td>
<td align="right">1092</td>
<td align="right">1106</td>
<td align="right">1111</td>
<td align="right">1109</td>
<td align="right">1116</td>
<td align="right">1118</td>
<td align="right">1122</td>
</tr>
<tr class="odd">
<td align="right">1089</td>
<td align="right">1093</td>
<td align="right">1095</td>
<td align="right">1100</td>
<td align="right">1112</td>
<td align="right">1124</td>
<td align="right">1129</td>
<td align="right">1135</td>
<td align="right">1134</td>
<td align="right">1137</td>
</tr>
<tr class="even">
<td align="right">1094</td>
<td align="right">1104</td>
<td align="right">1103</td>
<td align="right">1103</td>
<td align="right">1117</td>
<td align="right">1138</td>
<td align="right">1149</td>
<td align="right">1154</td>
<td align="right">1155</td>
<td align="right">1150</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a>knitr<span class="op">::</span><span class="kw">kable</span>(<span class="kw">head</span>(map),<span class="dt">caption =</span> <span class="st">&quot;转换后数据&quot;</span>)</span></code></pre></div>
<table>
<caption>转换后数据</caption>
<thead>
<tr class="header">
<th align="right">Var1</th>
<th align="right">Var2</th>
<th align="right">value</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">1</td>
<td align="right">1</td>
<td align="right">1076</td>
</tr>
<tr class="even">
<td align="right">2</td>
<td align="right">1</td>
<td align="right">1083</td>
</tr>
<tr class="odd">
<td align="right">3</td>
<td align="right">1</td>
<td align="right">1087</td>
</tr>
<tr class="even">
<td align="right">4</td>
<td align="right">1</td>
<td align="right">1087</td>
</tr>
<tr class="odd">
<td align="right">5</td>
<td align="right">1</td>
<td align="right">1089</td>
</tr>
<tr class="even">
<td align="right">6</td>
<td align="right">1</td>
<td align="right">1094</td>
</tr>
</tbody>
</table>
</div>
<div id="热力分布图" class="section level2">
<h2>热力分布图</h2>
<p>使用ggplot2包中的<code>geom_tile()</code>或者<code>geom_raster()</code>绘制热 力分布图。其主要区别在于<code>geom_raster()</code>函数中存在<code>interpolate=TRUE/FALSE</code>这个参数，决定是否对热力图进行平滑处理。</p>
<p>这里使用<code>geom_tile()</code>进行演示，将三维数据<code>(x,y,z)</code>中<code>(x,y)</code>表示位置信息，<code>z</code>映射到颜色。这里的<code>scale_fill_gradientn()</code>将颜色填充呈n个梯度。</p>
<ul>
<li><strong>拓展：</strong><code>scale_*_gradient</code>创建一个双色梯度(低-高)，<code>scale_*_gradient2</code>创建一个渐变的颜色梯度(低-中-高)，<code>scale_*_gradientn</code>创建一个n色梯度。</li>
</ul>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a><span class="kw">ggplot</span>(map,<span class="kw">aes</span>(<span class="dt">x=</span>Var1,<span class="dt">y=</span>Var2,<span class="dt">z=</span>value))<span class="op">+</span></span>
<span id="cb5-2"><a href="#cb5-2"></a><span class="st">    </span><span class="kw">geom_tile</span>(<span class="kw">aes</span>(<span class="dt">fill=</span>value))<span class="op">+</span></span>
<span id="cb5-3"><a href="#cb5-3"></a><span class="st">    </span><span class="kw">scale_fill_gradientn</span>(<span class="dt">colours=</span>colormap)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="添加等高线" class="section level2">
<h2>添加等高线</h2>
<p>使用<code>geom_contour()</code>在上图基础上添加等高线，同一轮廓上的数值相同。</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="kw">ggplot</span>(map,<span class="kw">aes</span>(<span class="dt">x=</span>Var1,<span class="dt">y=</span>Var2,<span class="dt">z=</span>value))<span class="op">+</span></span>
<span id="cb6-2"><a href="#cb6-2"></a><span class="st">    </span><span class="kw">geom_tile</span>(<span class="kw">aes</span>(<span class="dt">fill=</span>value))<span class="op">+</span><span class="co">#根据高度填充</span></span>
<span id="cb6-3"><a href="#cb6-3"></a><span class="st">    </span><span class="kw">scale_fill_gradientn</span>(<span class="dt">colours=</span>colormap)<span class="op">+</span></span>
<span id="cb6-4"><a href="#cb6-4"></a><span class="st">    </span><span class="kw">geom_contour</span>(<span class="dt">breaks=</span>breaks_lines,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>)<span class="op">+</span><span class="co">#</span></span>
<span id="cb6-5"><a href="#cb6-5"></a><span class="st">    </span><span class="kw">labs</span>(<span class="dt">x=</span><span class="st">&quot;X-Axis&quot;</span>,<span class="dt">y=</span><span class="st">&quot;Y-Axis&quot;</span>,<span class="dt">fill=</span><span class="st">&quot;Z-Value&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="设置主题" class="section level2">
<h2>设置主题</h2>
<p>对主题进行稍微的调整。改变x轴题目(<code>axis.title</code>,大小为15，字体形式为常规体<code>face=&quot;plain&quot;</code>，颜色黑色)，x轴文字(<code>axis.text</code>),图例标题(<code>legend.title</code>)图例文字,(<code>legend.text</code>),(图例背景<code>legend.background</code>),图例位置(<code>legend.position</code>)</p>
<ul>
<li><em>拓展</em>：face还可以设置其他字体形式：plain（常规体）、bold（粗体）、italic（斜体）、bold.italic（粗斜体）</li>
</ul>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a>Contour &lt;-<span class="st"> </span><span class="kw">ggplot</span>(map,<span class="kw">aes</span>(<span class="dt">x=</span>Var1,<span class="dt">y=</span>Var2,<span class="dt">z=</span>value))<span class="op">+</span></span>
<span id="cb7-2"><a href="#cb7-2"></a><span class="st">    </span><span class="kw">geom_tile</span>(<span class="kw">aes</span>(<span class="dt">fill=</span>value))<span class="op">+</span><span class="co">#根据高度填充</span></span>
<span id="cb7-3"><a href="#cb7-3"></a><span class="st">    </span><span class="kw">scale_fill_gradientn</span>(<span class="dt">colours=</span>colormap)<span class="op">+</span></span>
<span id="cb7-4"><a href="#cb7-4"></a><span class="st">    </span><span class="kw">geom_contour</span>(<span class="kw">aes</span>(<span class="dt">colour=</span> ..level..),<span class="dt">breaks=</span>breaks_lines,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>)<span class="op">+</span><span class="co">#</span></span>
<span id="cb7-5"><a href="#cb7-5"></a><span class="st">    </span><span class="kw">labs</span>(<span class="dt">x=</span><span class="st">&quot;X-Axis&quot;</span>,<span class="dt">y=</span><span class="st">&quot;Y-Axis&quot;</span>,<span class="dt">fill=</span><span class="st">&quot;Z-Value&quot;</span>)<span class="op">+</span></span>
<span id="cb7-6"><a href="#cb7-6"></a><span class="st">    </span><span class="kw">theme</span>(</span>
<span id="cb7-7"><a href="#cb7-7"></a>        <span class="dt">axis.title =</span> <span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">15</span>,<span class="dt">face=</span><span class="st">&quot;plain&quot;</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</span>
<span id="cb7-8"><a href="#cb7-8"></a>        <span class="dt">axis.text =</span> <span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">13</span>,<span class="dt">face=</span><span class="st">&quot;plain&quot;</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</span>
<span id="cb7-9"><a href="#cb7-9"></a>        <span class="dt">legend.title =</span> <span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">13</span>,<span class="dt">face=</span><span class="st">&quot;plain&quot;</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</span>
<span id="cb7-10"><a href="#cb7-10"></a>        <span class="dt">legend.text =</span> <span class="kw">element_text</span>(<span class="dt">size=</span><span class="dv">11</span>,<span class="dt">face=</span><span class="st">&quot;plain&quot;</span>,<span class="dt">color=</span><span class="st">&quot;black&quot;</span>),</span>
<span id="cb7-11"><a href="#cb7-11"></a>        <span class="dt">legend.background =</span> <span class="kw">element_blank</span>(),</span>
<span id="cb7-12"><a href="#cb7-12"></a>        <span class="dt">legend.position =</span> <span class="kw">c</span>(<span class="fl">0.15</span>,<span class="fl">0.2</span>)</span>
<span id="cb7-13"><a href="#cb7-13"></a>    )</span>
<span id="cb7-14"><a href="#cb7-14"></a>Contour</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="添加等高线的具体数值" class="section level2">
<h2>添加等高线的具体数值</h2>
<p>在上面的图基础上，添加等高线的具体数值，从而不需要颜色映射的图例，同一轮廓上的数值相同。</p>
<ul>
<li><strong>作用</strong>：在二维屏幕上，等高线可以有效地表达相同数值的区域，揭示走势和陡峭程度及两者之间的关系，寻找坡、峰、谷等形状。</li>
</ul>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a><span class="kw">direct.label</span>(Contour, <span class="kw">list</span>(<span class="st">&quot;bottom.pieces&quot;</span>, <span class="dt">cex=</span><span class="fl">0.8</span>,                                                     <span class="dt">fontface=</span><span class="st">&quot;plain&quot;</span>, <span class="dt">fontfamily=</span><span class="st">&quot;serif&quot;</span>, <span class="dt">colour=</span><span class="st">&#39;black&#39;</span>))</span></code></pre></div>
<p><img 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" 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================================================
FILE: 2020年/2020.11.28等高线/等高线.r
================================================
# 等高线图

# 等高线图（contour map）是可视化二维空间标量场的基本方法，可以将三维数据使用二维的方
# 法可视化，同时用颜色视觉特征表示第三维数据，如地图上的等高线、天气预报中的等压线和等温
# 线等。假设f(x, y)是在点(x, y)处的数值，等值线是在二维数据场中满足f(x, y)=c 的空间点集按一定的
# 顺序连接而成的线。数值为c 的等值线可以将二维空间标量场分为两部分：如果f(x, y)<c，则该点在
# 等值线内；如果f(x, y)>c，则该点在等值线外。
# 图4-3-1(a)为热力分布图，只是将三维数据(x, y, z)中(x, y)表示位置信息，z 映射到颜色。图4-3-1(b)
# 是在图4-3-1(a)的基础上添加等高线，同一轮廓上的数值相同。图4-3-1(c)是在图4-3-1(b)的基础上添
# 加等高线的具体数值，从而不需要颜色映射的图例，同一轮廓上的数值相同。在二维屏幕上，等高
# 线可以有效地表达相同数值的区域，揭示走势和陡峭程度及两者之间的关系，寻找坡、峰、谷等
# 形状。


library(directlabels)
library(reshape2)
library(ggplot2)
library(grDevices)

rf <- colorRampPalette(rev(brewer.pal(11,'Spectral')))
colormap <- colorRampPalette(rev(brewer.pal(11,'Spectral')))(32)
# 数据
z <- as.matrix(read.table("等高线.txt",header=TRUE))
colnames(z) <- seq(1,ncol(z),by=1) #列名设置
head(z)
dim(z)
max_z <- max(z)
min_z <- min(z)
breaks_lines <- seq(min(z),max(z),by=(max_z-min_z)/10)
map <- melt(z) #这个函数来自reshape2（处理数据的包）介绍一下这个函数，以及相关函数
dim(map)
colnames(map)<-c("Var1","Var2","value")
head(map)
Contour <- ggplot(map,aes(x=Var1,y=Var2,z=value))+
	geom_tile(aes(fill=value))+#根据高度填充
	scale_fill_gradientn(colours=colormap)+
	geom_contour(aes(colour= ..level..),breaks=breaks_lines,color="black")+#
	labs(x="X-Axis",y="Y-Axis",fill="Z-Value")+
	theme(
		axis.title = element_text(size=15,face="plain",color="black"),
		axis.text = element_text(size=13,face="plain",color="black"),
		legend.title = element_text(size=13,face="plain",color="black"),
		legend.text = element_text(size=11,face="plain",color="black"),
		legend.background = element_blank(),
		legend.position = c(0.15,0.2)
	)
Contour

## 加维度
direct.label(Contour, list("bottom.pieces", cex=0.8, #"far.from.others.borders",
													 fontface="plain", fontfamily="serif", colour='black'))


================================================
FILE: 2020年/2020.11.28等高线/等高线.rmd
================================================
---
title: "等高线图"
author: "庄闪闪"
date: "`r Sys.Date()`"
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
vignette: >
  %\VignetteIndexEntry{Vignette Title}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---
# 等高线图

**等高线图（contour map）** 是[可视化二维空间标量场的基本方法](https://github.com/EasyChart/Beautiful-Visualization-with-R "R语言数据可视化之美")，可以将三维数据使用二维的方法可视化，同时用颜色视觉特征表示第三维数据，如地图上的等高线、天气预报中的等压线和等温线等。假设 $f(x, y)$ 是在点 $(x, y)$ 处的数值，等值线是在二维数据场中满足 $f(x, y)=c$ 的空间点集按一定的顺序连接而成的线。数值为c的等值线可以将二维空间标量场分为两部分：如果 函数大于c，则该点在等值线内；反之，则该点在等值线外。




```{r message=FALSE, warning=FALSE}
library(directlabels)
library(reshape2)
library(ggplot2)
library(grDevices)
library(RColorBrewer)
```

## 数据介绍

```{r message=FALSE, warning=FALSE}
rf <- colorRampPalette(rev(brewer.pal(11,'Spectral')))
colormap <- colorRampPalette(rev(brewer.pal(11,'Spectral')))(32)
# 数据
z <- as.matrix(read.table("等高线.txt",header=TRUE))
colnames(z) <- seq(1,ncol(z),by=1) #列名设置
max_z <- max(z)
min_z <- min(z)
breaks_lines <- seq(min(z),max(z),by=(max_z-min_z)/10)
map <- melt(z) #这个函数来自reshape2（处理数据的包）介绍一下这个函数，以及相关函数
colnames(map)<-c("Var1","Var2","value")
```

```{r}
knitr::kable(head(z[,1:10]),caption = "转换前数据")
```

```{r}
knitr::kable(head(map),caption = "转换后数据")
```

## 热力分布图


使用ggplot2包中的`geom_tile()`或者`geom_raster()`绘制热
力分布图。其主要区别在于`geom_raster()`函数中存在`interpolate=TRUE/FALSE`这个参数，决定是否对热力图进行平滑处理。

这里使用`geom_tile()`进行演示，将三维数据`(x,y,z)`中`(x,y)`表示位置信息，`z`映射到颜色。这里的`scale_fill_gradientn()`将颜色填充呈n个梯度。

- **拓展：**`scale_*_gradient`创建一个双色梯度(低-高)，`scale_*_gradient2`创建一个渐变的颜色梯度(低-中-高)，`scale_*_gradientn`创建一个n色梯度。

```{r}
ggplot(map,aes(x=Var1,y=Var2,z=value))+
	geom_tile(aes(fill=value))+
	scale_fill_gradientn(colours=colormap)
```

## 添加等高线

使用`geom_contour()`在上图基础上添加等高线，同一轮廓上的数值相同。

```{r}
ggplot(map,aes(x=Var1,y=Var2,z=value))+
	geom_tile(aes(fill=value))+#根据高度填充
	scale_fill_gradientn(colours=colormap)+
	geom_contour(breaks=breaks_lines,color="black")+#
	labs(x="X-Axis",y="Y-Axis",fill="Z-Value")
```

## 设置主题

对主题进行稍微的调整。改变x轴题目(`axis.title`,大小为15，字体形式为常规体`face="plain"`，颜色黑色)，x轴文字(`axis.text`),图例标题(`legend.title`)图例文字,(`legend.text`),(图例背景`legend.background`),图例位置(`legend.position`)

- *拓展*：face还可以设置其他字体形式：plain（常规体）、bold（粗体）、italic（斜体）、bold.italic（粗斜体）

```{r}
Contour <- ggplot(map,aes(x=Var1,y=Var2,z=value))+
	geom_tile(aes(fill=value))+#根据高度填充
	scale_fill_gradientn(colours=colormap)+
	geom_contour(aes(colour= ..level..),breaks=breaks_lines,color="black")+#
	labs(x="X-Axis",y="Y-Axis",fill="Z-Value")+
	theme(
		axis.title = element_text(size=15,face="plain",color="black"),
		axis.text = element_text(size=13,face="plain",color="black"),
		legend.title = element_text(size=13,face="plain",color="black"),
		legend.text = element_text(size=11,face="plain",color="black"),
		legend.background = element_blank(),
		legend.position = c(0.15,0.2)
	)
Contour
```


## 添加等高线的具体数值

在上面的图基础上，添加等高线的具体数值，从而不需要颜色映射的图例，同一轮廓上的数值相同。

- **作用**：在二维屏幕上，等高线可以有效地表达相同数值的区域，揭示走势和陡峭程度及两者之间的关系，寻找坡、峰、谷等形状。

```{r}
direct.label(Contour, list("bottom.pieces", cex=0.8, 													 fontface="plain", fontfamily="serif", colour='black'))
```

































================================================
FILE: 2020年/2020.11.28等高线/等高线.txt
================================================
1063	1059	1053	1058	1056	1063	1062	1066	1077	1090	1105	1118	1124	1121	1116	1113	1110	1098	1078	1063	1052	1047	1038	1030	1019	1013	1002	996	994	986	961	939	928	920	912	907	908	911	898	872	850	841	839	838	837	838	839	840	841	840	838	838	836	835	836	840	835	836	840	841	841	842	843	835	819	825	840	840	838	836	835	836	828	812	792	780	790	811	830	840	829	815	810	821	834	835	843	855	873	882	879	875	876	881	890	895	887	878	877	864	836	811	794	786	775	763	752	745	752	756	756	754	750	746	751	758	760	761	764	765	767	771	775	778	780	780	777	770	767	776	787	794	798	802	807	814	817	817	818	819	819	815	812	809	805	802	798	795	791	788	788	790	793	789	782	783	786	787	789	790	790	794	796	795	794	796	798	798	799	798	799	801	803	803	804	804	805	808	811	810	810	810	810	811	811	809	810	813	817	828	843	855	851	830	823	829	832	838	844	847	846
1076	1067	1060	1063	1076	1078	1073	1071	1073	1086	1104	1120	1128	1128	1128	1125	1118	1104	1085	1069	1054	1050	1041	1030	1023	1018	1011	1003	997	979	945	915	895	883	875	869	871	877	873	858	849	845	844	844	843	843	844	844	843	842	843	838	832	834	837	841	839	841	845	846	846	844	840	835	820	833	841	841	846	844	842	840	825	800	787	792	808	826	839	845	833	817	809	824	839	843	850	867	880	884	881	879	885	888	895	897	895	889	880	862	831	806	787	776	761	752	751	756	759	760	759	757	752	745	755	759	760	761	765	767	769	771	775	779	783	785	785	778	773	780	783	791	794	798	809	815	818	818	819	820	820	816	812	809	805	802	797	792	790	790	791	792	794	791	784	785	787	788	790	791	791	793	793	793	796	796	797	798	800	802	803	803	804	804	806	807	807	809	809	809	810	810	811	814	816	815	817	817	810	816	830	840	837	828	825	828	831	834	836	841	844
1083	1074	1065	1075	1091	1090	1082	1083	1087	1097	1107	1125	1135	1135	1132	1128	1120	1105	1085	1074	1065	1056	1048	1032	1029	1029	1022	1014	1002	974	928	892	872	864	859	854	854	855	852	846	848	850	850	850	849	849	847	846	844	846	850	844	838	839	841	844	844	847	849	850	852	848	844	837	823	840	845	844	845	845	845	842	825	801	786	800	824	841	848	852	843	829	815	824	839	844	853	874	882	883	883	886	892	895	894	891	893	892	881	861	830	805	788	775	760	753	753	754	760	760	759	756	751	747	760	765	764	762	767	771	774	776	781	783	788	791	794	790	785	779	781	793	792	796	809	817	820	820	823	823	823	818	814	809	804	800	797	794	791	792	792	793	793	790	787	787	785	786	789	790	791	792	792	793	796	796	796	799	802	803	802	801	801	801	804	806	808	809	810	811	812	812	812	813	814	812	810	812	812	815	820	822	823	825	825	827	831	835	836	840	842
1087	1085	1079	1083	1101	1102	1094	1098	1105	1108	1115	1131	1140	1139	1130	1123	1112	1097	1084	1072	1061	1055	1047	1035	1027	1031	1026	1019	1002	965	925	896	880	869	860	851	849	854	854	854	855	857	858	857	856	854	849	851	853	854	854	854	851	846	847	851	851	849	851	854	856	854	850	841	826	843	847	845	844	847	848	839	813	797	793	809	834	849	853	857	855	834	818	827	842	850	859	875	884	883	883	889	893	891	891	893	893	891	881	862	832	803	785	771	759	754	758	761	763	763	763	761	754	759	768	771	774	777	781	785	788	792	796	797	798	799	799	797	794	782	786	794	792	798	810	819	825	825	826	826	825	822	817	811	805	801	799	798	797	793	791	792	792	788	789	789	785	786	789	790	794	794	795	797	798	799	798	798	803	805	804	804	804	804	805	806	808	811	813	815	815	816	818	819	819	818	815	815	816	817	821	823	824	826	829	829	834	838	840	843	845
1087	1089	1089	1092	1106	1111	1109	1116	1118	1122	1128	1134	1144	1142	1128	1113	1103	1090	1079	1076	1067	1056	1047	1036	1031	1036	1032	1013	979	941	908	885	872	860	852	850	853	854	851	854	859	861	865	861	858	855	853	857	860	861	860	859	855	853	853	854	854	854	858	859	860	858	850	843	832	848	849	845	849	854	853	836	805	791	795	817	844	852	856	860	856	833	822	830	844	858	866	874	882	885	886	893	893	886	890	897	896	890	879	860	832	806	790	775	763	755	760	765	764	762	763	763	760	770	776	780	789	795	802	808	812	816	822	824	825	824	818	810	802	796	793	785	795	803	808	819	826	827	828	828	828	827	821	814	810	806	803	800	799	795	795	794	792	789	788	787	789	790	790	793	797	799	798	801	802	802	800	797	802	805	806	807	807	809	809	806	808	813	815	816	816	816	819	819	819	819	817	815	815	815	818	821	825	827	829	832	834	838	839	842	843
1089	1093	1095	1100	1112	1124	1129	1135	1134	1137	1143	1137	1143	1140	1125	1109	1100	1088	1076	1074	1065	1049	1042	1043	1035	1041	1043	1017	979	945	912	887	871	860	850	848	854	855	854	857	862	865	868	864	862	861	859	855	858	862	859	856	855	854	855	859	860	860	856	856	859	858	849	845	842	852	852	850	856	862	855	834	815	801	795	821	849	857	861	858	847	837	832	830	841	856	865	869	873	881	890	895	893	890	893	898	898	892	877	857	831	814	804	786	768	759	761	764	764	764	767	769	770	778	784	791	802	810	818	825	832	838	844	845	845	841	833	824	815	808	796	782	799	804	810	819	828	829	829	829	829	826	820	812	810	808	805	803	802	800	799	798	795	791	791	793	793	793	794	795	797	798	798	800	801	801	802	803	803	801	803	805	806	810	812	808	811	812	813	814	814	815	816	816	816	816	816	815	815	815	817	820	825	829	831	835	837	839	839	841	844
1094	1104	1103	1103	1117	1138	1149	1154	1155	1150	1151	1144	1137	1131	1120	1108	1099	1092	1084	1076	1061	1049	1045	1055	1049	1050	1053	1024	984	948	912	883	865	861	858	857	859	859	859	861	867	870	868	866	865	862	858	855	859	863	862	859	857	855	856	860	863	866	863	861	861	857	852	848	854	853	857	860	862	870	851	821	801	806	819	838	856	866	865	854	843	845	842	833	835	846	857	861	863	873	888	892	892	899	901	901	899	892	875	854	833	821	819	797	775	767	762	763	764	767	771	778	782	790	798	807	819	830	840	846	855	862	867	870	871	866	856	846	834	822	805	791	793	805	810	822	831	833	834	835	834	829	824	818	815	813	811	810	807	803	800	797	794	792	790	791	791	791	795	796	796	801	802	805	807	807	807	807	807	808	808	808	808	809	809	808	809	810	813	817	819	821	822	823	823	823	822	819	818	820	824	827	830	833	835	839	842	842	843	844	847
1100	1108	1107	1111	1122	1137	1153	1164	1169	1156	1153	1147	1136	1125	1114	1102	1097	1098	1094	1084	1067	1065	1060	1065	1066	1060	1056	1028	985	943	904	876	860	858	863	866	866	865	865	866	870	873	872	869	868	865	862	861	862	867	867	864	862	859	859	862	867	869	866	865	864	859	856	851	856	860	863	867	865	867	849	817	796	803	830	849	860	869	866	858	854	860	857	852	849	847	853	862	864	868	874	887	901	909	911	911	905	897	879	854	833	824	823	804	788	775	763	764	764	767	774	786	793	805	819	831	844	856	866	874	881	887	893	901	903	897	882	867	853	837	819	799	793	807	813	823	830	835	838	839	837	833	828	824	821	818	815	812	809	807	805	802	797	793	793	793	793	795	798	798	801	805	806	807	808	808	808	808	808	808	806	804	805	809	811	811	812	814	816	818	818	819	819	820	820	820	818	815	817	821	824	827	831	834	837	839	840	842	843	845	846
1108	1117	1126	1132	1135	1135	1144	1158	1165	1155	1147	1140	1129	1119	1108	1098	1100	1099	1090	1079	1076	1079	1070	1064	1068	1058	1053	1035	995	946	901	876	864	861	867	867	868	868	868	869	871	874	876	874	873	872	869	867	864	866	868	866	866	864	864	867	871	872	869	868	865	865	860	858	861	867	872	874	870	867	844	812	799	813	839	857	866	874	870	866	865	866	865	863	860	850	854	864	865	865	869	885	902	910	911	909	907	905	887	864	842	839	831	812	790	778	770	771	775	781	790	800	810	823	840	853	866	878	887	899	908	915	920	929	930	922	903	886	869	851	832	814	798	805	817	821	829	836	840	839	838	835	830	826	823	819	816	813	811	811	809	806	800	796	794	794	795	798	799	800	802	805	809	811	810	808	809	809	809	810	812	810	809	811	814	814	815	816	816	814	815	822	823	824	825	825	824	824	825	826	827	833	837	839	841	843	845	845	846	848	849
1115	1123	1133	1142	1138	1135	1142	1149	1155	1157	1147	1131	1124	1113	1101	1097	1100	1101	1098	1097	1096	1093	1078	1066	1068	1061	1055	1044	1000	945	896	872	865	869	874	875	874	869	867	869	872	874	874	874	875	874	870	869	865	864	867	866	867	869	870	872	875	875	873	870	865	869	863	865	866	871	878	879	878	871	848	817	806	825	851	865	872	877	873	869	870	870	868	865	865	860	858	866	867	868	869	884	904	913	916	915	915	909	892	873	857	851	839	823	802	790	780	775	770	787	801	811	825	843	860	873	888	902	917	932	939	944	950	956	957	950	927	905	883	864	846	824	804	807	817	822	834	841	843	844	843	837	830	828	825	820	818	817	815	812	809	804	801	799	796	796	798	801	803	803	803	806	812	814	813	811	812	812	812	814	816	814	809	811	814	814	814	816	816	815	818	823	825	826	827	827	828	827	827	828	830	834	836	839	841	843	847	845	845	848	848
1116	1124	1129	1135	1132	1134	1144	1142	1141	1146	1143	1131	1124	1115	1105	1102	1105	1106	1107	1104	1099	1092	1078	1062	1046	1027	1027	1036	995	947	908	882	867	869	872	872	874	873	873	874	876	877	876	874	874	875	874	872	868	869	871	868	871	875	875	876	877	876	875	869	867	870	866	867	864	873	880	882	881	874	854	832	817	831	859	872	877	878	876	872	871	872	870	868	870	868	864	872	873	873	872	880	902	920	923	920	922	918	904	881	864	854	845	829	814	802	788	776	779	797	814	828	843	859	878	899	916	930	944	958	968	973	975	975	973	963	941	917	896	878	858	831	809	808	822	829	839	846	849	847	842	837	832	829	826	824	822	820	818	815	813	808	802	800	800	802	804	807	810	807	806	808	813	814	815	816	816	815	814	817	818	816	810	811	813	815	815	817	817	820	822	822	823	825	827	828	829	829	830	831	832	834	836	840	842	845	850	850	850	853	853
1107	1112	1115	1116	1117	1124	1137	1140	1135	1133	1137	1130	1120	1116	1111	1109	1113	1115	1109	1101	1093	1086	1079	1055	1024	1004	1005	1018	998	950	910	884	871	875	877	876	877	877	875	875	875	877	879	877	874	874	877	876	871	871	875	875	875	876	878	880	880	877	873	876	872	873	873	866	870	879	882	882	882	874	854	834	827	846	869	879	880	877	877	877	875	873	874	875	875	870	871	881	880	879	875	876	893	923	929	926	927	926	917	889	867	854	851	841	827	812	796	781	785	805	827	847	862	877	897	922	942	958	972	982	990	993	991	984	979	968	948	927	910	893	867	837	811	807	817	827	836	845	847	847	846	844	839	834	829	827	826	822	817	814	812	809	804	800	802	804	804	805	807	808	808	809	812	815	816	816	817	818	818	820	824	823	818	815	814	818	819	819	817	821	823	824	824	825	829	830	831	832	834	835	835	837	839	842	844	846	849	852	854	857	858
1082	1084	1084	1087	1093	1102	1118	1133	1135	1132	1131	1126	1117	1114	1113	1112	1114	1117	1109	1098	1085	1071	1061	1037	1007	991	978	994	994	950	911	885	874	878	880	880	881	878	876	876	879	881	883	880	876	875	879	879	877	875	877	879	880	881	882	883	883	881	876	878	880	879	876	872	878	887	889	889	889	880	863	848	841	854	876	887	887	883	879	879	879	881	880	878	878	876	879	886	886	885	878	874	889	918	930	931	929	929	922	897	873	863	867	863	847	830	812	790	791	816	841	863	878	894	915	938	959	975	990	1000	1002	998	992	980	973	966	958	946	929	906	874	839	816	812	822	832	841	848	849	848	846	845	840	837	834	831	829	826	820	813	810	809	806	805	805	807	805	806	810	812	810	810	815	818	819	819	820	821	821	823	824	823	818	815	815	816	817	817	820	822	825	827	828	829	831	832	833	835	836	836	839	840	841	841	842	843	844	849	851	854	856
1060	1061	1060	1062	1073	1083	1098	1118	1132	1133	1129	1126	1124	1118	1113	1109	1108	1111	1107	1100	1083	1054	1026	1009	991	973	949	970	973	939	908	887	878	878	878	881	883	879	879	880	885	886	884	880	877	877	879	879	878	877	879	883	886	887	886	885	886	884	881	884	886	882	877	875	882	888	889	889	889	879	862	853	853	863	881	897	890	887	887	886	886	886	883	882	883	884	884	887	888	886	880	878	891	921	929	928	930	935	928	903	878	884	889	883	867	853	834	799	810	836	859	877	896	912	932	951	972	987	1001	1012	1007	997	987	975	965	966	971	967	947	917	880	844	819	813	828	839	848	854	855	854	852	848	840	837	835	832	828	824	819	814	811	809	806	805	803	802	802	802	807	811	814	815	817	822	823	824	824	825	825	823	823	826	825	821	819	818	820	822	823	824	825	827	829	831	832	833	834	837	839	840	841	842	842	842	843	844	844	847	848	851	854
1043	1041	1040	1043	1056	1073	1089	1105	1119	1128	1131	1130	1128	1121	1112	1111	1112	1107	1101	1094	1076	1042	1009	990	972	951	938	953	943	917	893	884	885	880	880	882	882	880	883	885	887	887	883	880	878	878	878	878	879	880	881	885	888	889	890	890	891	888	884	892	892	885	883	883	884	887	891	893	891	884	872	865	862	864	881	898	892	891	892	892	893	891	885	886	888	889	885	888	886	881	881	881	896	928	939	935	931	936	930	912	894	902	909	903	888	868	845	815	821	846	869	893	918	942	959	968	990	1007	1012	1014	1006	995	986	977	969	976	983	983	964	927	886	849	824	819	830	844	849	855	859	859	857	849	842	838	834	829	825	820	816	814	812	808	806	805	804	803	804	805	809	813	818	818	817	822	826	828	828	828	827	824	826	828	827	824	822	822	824	825	825	826	828	829	829	831	832	833	835	837	840	840	841	841	843	845	850	849	847	846	850	857	861
1024	1028	1034	1042	1051	1057	1068	1084	1104	1123	1128	1132	1132	1128	1121	1116	1111	1103	1090	1073	1048	1019	994	972	947	930	920	924	915	900	888	886	889	887	884	880	879	882	888	890	890	889	885	884	883	883	883	882	883	882	882	885	889	891	894	894	894	890	886	895	897	889	891	890	886	887	893	895	893	892	883	873	864	862	876	894	897	895	895	894	894	892	887	886	893	894	890	890	888	884	886	884	898	924	939	938	931	939	939	923	906	915	918	915	905	884	856	835	848	865	885	909	932	955	972	987	1003	1014	1012	1003	994	984	977	975	974	978	988	993	980	938	893	855	830	825	839	850	853	856	860	857	847	842	850	843	834	830	825	820	819	816	811	809	808	808	807	807	810	811	815	818	822	821	818	820	825	829	831	831	829	827	830	831	830	828	827	826	826	825	825	827	829	829	830	832	834	834	836	839	841	841	843	844	844	846	850	852	851	851	854	858	860
1000	1009	1022	1034	1041	1042	1050	1067	1095	1113	1118	1130	1136	1135	1132	1126	1118	1106	1084	1057	1023	992	967	947	930	919	914	910	899	891	891	892	891	890	886	882	882	886	891	892	891	888	884	884	888	889	888	886	880	879	885	888	893	898	899	897	895	887	888	896	895	894	895	891	889	890	891	893	894	893	886	872	866	865	875	893	901	901	901	897	894	894	891	887	896	900	895	893	893	889	893	890	899	925	944	942	935	944	946	934	922	921	920	917	903	885	865	847	861	880	899	923	945	965	986	1001	1011	1015	1010	1002	993	988	984	980	980	978	982	993	985	947	901	861	833	826	836	850	858	860	863	858	841	850	867	856	844	837	829	825	828	823	815	811	808	808	807	807	811	813	816	820	824	826	822	821	825	828	833	833	832	831	833	835	835	833	831	830	829	828	826	827	828	830	832	833	834	836	839	841	842	845	846	847	848	849	850	852	854	855	856	856	858
975	985	998	1011	1022	1029	1039	1058	1086	1103	1115	1128	1137	1139	1139	1131	1121	1106	1079	1047	1012	980	955	934	923	914	914	907	893	888	891	891	889	889	887	884	882	886	890	891	891	888	885	885	889	892	891	887	882	881	885	889	895	900	902	901	901	895	890	896	899	893	896	899	894	892	894	900	897	897	895	881	873	871	881	897	904	905	904	901	900	898	894	890	898	902	896	897	898	894	899	898	904	933	951	950	943	948	950	944	938	929	922	913	895	880	867	858	870	891	912	935	959	980	998	1009	1010	1006	1004	1004	1000	995	995	991	988	982	984	995	991	954	909	865	837	830	840	858	865	865	866	856	842	865	879	869	853	836	831	835	834	825	822	817	807	807	810	811	813	816	819	821	826	830	827	826	828	831	836	836	831	834	836	838	838	835	834	834	833	832	829	828	829	831	833	834	835	838	841	842	843	845	848	849	849	851	852	854	858	859	859	859	862
954	961	975	992	1005	1017	1033	1056	1080	1101	1120	1128	1139	1141	1136	1128	1116	1095	1066	1033	1001	969	944	927	918	913	913	904	895	892	895	893	892	892	890	886	885	888	890	891	891	890	889	889	893	894	893	890	887	888	889	892	900	905	905	905	905	900	895	901	900	895	897	899	899	896	896	897	898	900	894	882	879	879	889	903	912	911	908	906	904	901	894	895	901	901	900	899	898	904	905	902	914	936	952	954	952	953	952	948	939	930	917	904	892	879	866	872	884	903	924	947	971	991	1005	1011	1009	1004	1005	1007	1005	1001	999	1000	998	990	993	1001	992	958	916	876	846	832	844	863	868	868	868	853	853	887	893	877	863	846	843	849	845	835	838	834	820	813	815	815	814	819	820	820	827	832	832	830	828	830	835	835	834	835	835	839	842	841	839	839	837	833	832	831	831	832	832	833	835	839	841	842	844	845	847	848	848	849	853	858	861	862	862	864	867
932	935	958	986	1004	1019	1035	1057	1081	1105	1120	1133	1145	1143	1131	1123	1109	1086	1050	1018	986	954	932	918	914	913	912	903	899	899	898	898	900	898	894	889	889	892	894	894	894	895	895	895	895	894	894	894	894	895	894	896	902	906	908	908	908	906	903	903	905	902	901	904	905	903	903	903	905	906	904	898	892	890	895	907	913	914	913	913	910	907	902	900	906	906	900	899	902	906	909	907	920	944	958	959	957	953	953	940	923	910	900	893	887	879	872	880	897	915	935	957	980	998	1008	1010	1011	1012	1011	1008	1004	1001	999	1004	1005	1000	999	1005	993	959	918	882	854	839	846	865	871	871	869	853	874	903	896	874	868	864	863	866	861	854	855	849	835	832	831	822	814	823	821	820	832	834	836	834	830	831	836	839	839	836	835	841	845	845	843	842	840	837	835	834	833	834	836	838	839	841	843	844	847	848	849	849	850	852	856	857	859	860	862	866	868
913	926	956	984	1002	1021	1038	1060	1079	1096	1114	1136	1146	1144	1131	1122	1112	1090	1047	1013	985	960	938	922	917	916	914	910	907	904	898	901	903	899	896	894	887	892	898	898	898	898	898	897	894	894	894	896	898	899	901	903	904	907	910	911	910	909	906	905	908	908	907	909	907	906	909	911	911	912	912	910	904	900	903	912	917	918	915	911	913	911	906	907	911	904	900	904	904	913	912	911	926	945	960	959	958	958	957	945	920	901	891	885	877	878	880	886	903	924	946	972	995	1007	1011	1012	1011	1012	1012	1009	1003	1000	1001	1007	1008	1007	1002	1007	999	967	923	886	860	843	844	864	874	877	870	857	890	909	893	874	872	879	883	883	880	873	876	867	850	849	849	835	821	831	830	825	839	837	836	838	833	832	837	839	838	835	833	838	846	849	848	845	842	838	837	836	833	832	837	845	843	846	849	845	849	851	851	851	853	853	857	860	861	862	864	867	871
910	929	955	981	1001	1019	1034	1056	1073	1093	1114	1134	1145	1149	1133	1114	1092	1067	1027	993	965	945	930	924	921	920	917	914	913	909	907	907	902	901	900	898	889	891	897	898	898	898	897	896	896	896	895	896	899	901	904	909	908	913	915	915	912	908	907	907	907	909	911	910	907	907	912	914	915	915	915	914	910	907	909	916	919	919	918	913	913	914	911	914	918	909	906	908	912	913	910	912	927	949	962	963	962	963	961	942	919	903	892	886	880	879	879	887	908	932	954	978	997	1010	1012	1015	1019	1017	1013	1011	1014	1006	1000	1010	1013	1009	1004	1010	1002	977	936	896	867	847	842	855	873	883	875	861	891	904	897	892	884	894	902	903	899	892	898	890	873	865	865	850	839	846	847	835	847	841	835	839	835	833	839	839	837	839	840	843	851	852	851	847	842	839	837	832	832	836	845	852	850	849	851	850	850	850	852	854	856	857	859	861	863	866	867	871	875
907	923	944	972	999	1018	1037	1059	1079	1096	1114	1132	1148	1151	1126	1101	1067	1036	1004	976	951	935	926	921	920	921	919	915	911	906	903	903	902	901	903	904	901	899	897	896	896	896	895	895	897	899	901	901	902	900	896	902	911	917	919	919	915	912	911	909	909	911	913	913	910	911	913	914	914	916	917	917	916	913	915	920	921	924	924	919	917	919	916	919	926	922	916	915	918	910	909	914	928	951	964	969	969	970	967	945	921	908	894	888	884	884	886	896	913	938	962	985	1004	1017	1021	1022	1024	1021	1019	1018	1018	1013	1010	1013	1013	1011	1010	1010	1008	988	951	907	874	854	845	854	873	885	877	865	888	901	908	909	900	914	919	920	914	914	919	907	897	894	882	863	863	865	860	849	856	844	838	842	838	837	840	841	842	843	849	853	857	856	854	849	843	842	837	826	831	845	857	862	862	860	857	859	859	853	854	857	860	862	863	862	865	870	870	875	878
901	916	938	971	998	1020	1040	1061	1083	1096	1111	1128	1151	1149	1111	1077	1038	1009	985	962	942	931	925	920	919	919	918	912	908	908	909	909	908	904	904	904	902	898	896	897	899	900	901	902	902	906	908	906	903	898	898	906	914	919	921	924	920	917	919	918	917	915	913	913	913	915	915	916	918	920	921	921	919	918	926	925	925	931	931	926	927	925	920	923	932	933	928	927	922	915	914	917	930	950	967	976	976	977	974	952	927	914	899	894	889	890	897	909	924	947	969	989	1008	1022	1031	1034	1032	1029	1028	1027	1024	1022	1018	1016	1018	1014	1017	1019	1016	993	956	919	887	864	850	853	869	887	883	865	882	898	913	932	930	923	936	944	936	932	937	920	917	914	896	882	884	883	876	866	861	849	841	844	844	840	838	842	844	841	847	857	858	858	857	852	845	845	836	829	838	857	869	874	877	876	869	867	868	855	854	859	862	864	865	865	870	873	872	875	876
903	924	955	984	1005	1025	1042	1057	1077	1093	1113	1130	1147	1137	1098	1060	1024	998	974	956	939	932	925	921	919	918	917	912	908	911	912	912	909	905	905	904	901	897	895	895	896	899	903	904	905	907	908	909	907	906	908	908	909	914	920	924	923	923	924	924	923	919	916	917	920	921	919	918	922	922	921	923	924	926	931	931	928	935	939	938	933	930	928	926	931	936	935	930	931	929	922	919	928	949	975	981	981	982	978	957	933	916	905	902	894	897	905	920	937	956	976	994	1010	1024	1036	1043	1043	1041	1038	1035	1030	1025	1023	1022	1023	1016	1019	1025	1026	1005	971	935	902	877	861	857	866	884	888	869	878	894	908	933	946	939	950	963	960	955	955	942	943	934	915	909	906	900	891	876	869	846	845	850	849	844	840	842	843	844	849	859	860	860	860	854	849	845	831	840	852	869	883	890	891	885	881	877	867	856	857	860	861	863	864	870	874	876	875	875	876
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932	942	944	943	943	947	951	952	955	959	965	970	974	980	985	989	995	1005	1013	1021	1023	1025	1030	1033	1038	1044	1052	1056	1059	1061	1062	1063	1065	1068	1071	1075	1077	1079	1084	1087	1087	1089	1092	1097	1103	1107	1107	1110	1112	1112	1116	1118	1120	1120	1121	1122	1127	1131	1131	1133	1139	1141	1138	1134	1144	1143	1141	1142	1144	1144	1147	1146	1154	1161	1164	1165	1164	1164	1171	1172	1169	1177	1181	1192	1203	1210	1215	1219	1223	1226	1227	1224	1227	1235	1250	1252	1261	1271	1270	1262	1255	1256	1254	1242	1233	1227	1224	1217	1205	1189	1171	1153	1131	1110	1098	1100	1107	1110	1107	1105	1106	1107	1109	1110	1115	1118	1117	1117	1117	1117	1115	1114	1107	1096	1088	1086	1083	1082	1080	1081	1094	1105	1099	1096	1082	1084	1089	1080	1063	1069	1080	1086	1079	1061	1070	1069	1071	1081	1092	1096	1089	1086	1088	1083	1083	1088	1086	1082	1081	1080	1072	1063	1063	1061	1058	1062	1062	1064	1062	1056	1043	1047	1048	1041	1032	1020	1029	1034	1035	1034	1032	1031	1029	1028	1028	1027	1029	1026	1020	1013	1011
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944	950	950	952	957	959	962	969	971	972	979	977	983	987	993	998	1004	1010	1019	1023	1027	1035	1038	1045	1050	1055	1060	1068	1070	1068	1067	1069	1072	1076	1078	1079	1082	1085	1089	1094	1097	1098	1104	1108	1112	1115	1116	1118	1119	1119	1119	1123	1126	1127	1128	1131	1135	1137	1138	1138	1140	1143	1145	1147	1152	1158	1159	1158	1162	1162	1164	1160	1166	1164	1175	1178	1178	1179	1181	1186	1186	1192	1197	1201	1209	1215	1228	1245	1260	1266	1264	1256	1246	1239	1248	1266	1268	1268	1270	1272	1264	1253	1247	1244	1233	1227	1219	1204	1182	1159	1139	1126	1118	1112	1107	1110	1112	1114	1116	1114	1110	1109	1111	1121	1127	1130	1130	1130	1129	1125	1126	1125	1120	1108	1098	1092	1097	1097	1098	1103	1115	1121	1111	1107	1109	1107	1100	1105	1114	1103	1100	1093	1090	1091	1077	1079	1101	1116	1116	1116	1113	1109	1096	1102	1112	1118	1115	1119	1125	1125	1124	1113	1094	1070	1061	1070	1073	1070	1056	1059	1057	1055	1049	1043	1041	1037	1043	1043	1042	1040	1038	1036	1039	1038	1035	1029	1027	1021	1017	1020	1020
950	951	953	956	959	964	970	976	982	987	990	991	991	992	999	1007	1012	1016	1025	1033	1037	1043	1045	1050	1054	1057	1061	1066	1071	1074	1075	1076	1079	1081	1084	1086	1089	1090	1093	1097	1100	1101	1106	1111	1115	1120	1121	1121	1121	1121	1122	1130	1136	1136	1135	1134	1139	1143	1141	1142	1144	1148	1150	1148	1158	1165	1165	1165	1170	1169	1166	1165	1170	1172	1181	1184	1182	1183	1186	1189	1191	1193	1199	1207	1214	1225	1244	1262	1274	1281	1282	1273	1263	1247	1247	1264	1276	1275	1270	1272	1265	1256	1245	1239	1232	1225	1212	1191	1166	1142	1126	1121	1125	1121	1117	1116	1120	1123	1121	1123	1120	1118	1119	1124	1130	1134	1135	1134	1136	1134	1131	1126	1121	1110	1103	1094	1106	1116	1114	1120	1126	1124	1114	1115	1123	1114	1103	1115	1124	1118	1111	1101	1099	1105	1105	1110	1121	1126	1121	1119	1120	1118	1111	1126	1136	1140	1139	1138	1137	1133	1132	1128	1109	1088	1072	1070	1071	1069	1058	1060	1059	1055	1048	1047	1050	1046	1051	1049	1047	1045	1041	1041	1044	1043	1036	1031	1030	1026	1019	1018	1017
951	954	956	955	961	968	975	979	983	987	992	993	992	994	1000	1005	1011	1020	1026	1035	1043	1050	1055	1057	1060	1061	1066	1070	1074	1077	1078	1079	1080	1082	1086	1088	1090	1094	1097	1100	1103	1105	1109	1114	1119	1126	1126	1125	1125	1126	1128	1136	1142	1143	1143	1143	1146	1149	1146	1146	1149	1151	1157	1156	1162	1172	1173	1174	1176	1176	1174	1173	1174	1179	1181	1188	1189	1188	1194	1200	1201	1202	1206	1209	1219	1239	1263	1282	1295	1301	1297	1288	1280	1270	1261	1267	1279	1277	1273	1270	1264	1254	1243	1238	1230	1221	1205	1181	1159	1141	1130	1128	1134	1131	1127	1122	1129	1128	1124	1129	1129	1125	1122	1129	1135	1136	1141	1143	1143	1139	1135	1129	1123	1114	1105	1104	1118	1127	1131	1136	1135	1130	1116	1122	1129	1118	1111	1123	1130	1130	1123	1108	1100	1112	1123	1128	1130	1131	1127	1125	1125	1122	1126	1140	1149	1150	1149	1141	1135	1132	1131	1131	1117	1097	1074	1075	1068	1066	1065	1065	1060	1052	1054	1054	1052	1052	1053	1048	1045	1048	1048	1047	1047	1043	1037	1038	1033	1030	1029	1028	1026
955	958	964	969	974	978	985	989	994	1000	1004	1004	1004	1006	1010	1011	1016	1023	1028	1038	1050	1061	1066	1069	1070	1067	1064	1071	1075	1078	1079	1080	1082	1085	1088	1090	1093	1098	1102	1105	1108	1111	1118	1120	1125	1129	1132	1132	1132	1134	1138	1142	1143	1144	1149	1151	1153	1152	1153	1152	1152	1155	1167	1169	1168	1179	1182	1184	1185	1183	1180	1181	1184	1186	1185	1192	1198	1198	1204	1211	1213	1214	1214	1219	1234	1254	1278	1299	1313	1315	1309	1303	1295	1283	1264	1269	1283	1282	1279	1275	1269	1260	1246	1236	1225	1210	1192	1171	1154	1147	1148	1146	1142	1142	1136	1130	1126	1127	1127	1127	1125	1125	1130	1141	1140	1137	1144	1145	1146	1144	1138	1128	1121	1118	1106	1114	1129	1137	1143	1146	1143	1131	1114	1128	1131	1121	1117	1131	1135	1135	1133	1112	1101	1110	1123	1130	1133	1137	1136	1132	1128	1125	1137	1145	1150	1149	1145	1137	1134	1136	1137	1132	1122	1104	1081	1078	1069	1068	1070	1069	1063	1056	1062	1058	1054	1058	1056	1052	1049	1051	1050	1049	1047	1043	1039	1043	1037	1033	1035	1036	1034
954	960	970	978	983	986	989	992	999	1003	1006	1006	1007	1009	1013	1019	1023	1030	1035	1046	1057	1067	1073	1076	1072	1066	1072	1076	1078	1080	1082	1084	1086	1089	1093	1095	1097	1101	1105	1108	1111	1117	1122	1125	1128	1132	1138	1135	1134	1141	1145	1147	1143	1147	1152	1155	1158	1154	1157	1153	1152	1163	1175	1179	1183	1188	1189	1191	1193	1190	1184	1185	1195	1197	1198	1198	1205	1209	1215	1220	1222	1224	1222	1228	1245	1268	1292	1313	1323	1322	1318	1313	1307	1293	1275	1272	1285	1287	1285	1280	1273	1261	1242	1229	1217	1201	1182	1162	1151	1150	1158	1160	1159	1160	1154	1144	1135	1138	1136	1133	1130	1130	1142	1152	1151	1144	1144	1152	1154	1149	1140	1132	1124	1119	1110	1106	1134	1147	1152	1155	1148	1134	1122	1133	1137	1124	1120	1136	1138	1138	1141	1128	1116	1110	1128	1138	1142	1143	1142	1137	1133	1135	1147	1151	1152	1148	1141	1137	1140	1145	1145	1137	1127	1110	1088	1079	1075	1075	1073	1070	1069	1066	1066	1062	1060	1062	1059	1056	1056	1055	1053	1050	1048	1047	1043	1040	1040	1038	1038	1039	1037
955	964	975	983	988	991	993	996	1003	1006	1008	1011	1013	1016	1021	1027	1033	1041	1047	1054	1065	1074	1081	1083	1079	1071	1075	1079	1081	1083	1088	1092	1093	1096	1098	1101	1105	1107	1109	1114	1116	1119	1123	1128	1132	1138	1142	1141	1142	1146	1148	1150	1150	1153	1157	1162	1163	1163	1165	1168	1173	1177	1183	1190	1194	1199	1197	1194	1196	1196	1194	1195	1201	1203	1206	1207	1206	1219	1225	1228	1229	1230	1230	1231	1245	1275	1303	1322	1327	1326	1321	1320	1317	1306	1290	1278	1288	1292	1289	1282	1274	1257	1238	1224	1211	1194	1175	1161	1160	1160	1171	1175	1176	1175	1169	1158	1146	1142	1137	1130	1131	1139	1149	1158	1159	1150	1150	1156	1154	1154	1144	1126	1118	1125	1121	1111	1133	1153	1154	1160	1148	1139	1140	1141	1139	1124	1120	1138	1142	1142	1143	1131	1118	1126	1135	1141	1146	1143	1144	1144	1138	1147	1156	1157	1155	1149	1144	1142	1145	1147	1145	1139	1125	1107	1087	1083	1082	1081	1075	1070	1074	1072	1067	1066	1066	1065	1062	1060	1059	1059	1057	1054	1049	1053	1049	1037	1042	1045	1042	1039	1036
966	970	979	988	993	996	1001	1006	1008	1012	1016	1020	1023	1027	1032	1035	1044	1051	1057	1062	1073	1083	1087	1087	1080	1076	1076	1080	1082	1086	1090	1094	1096	1099	1100	1104	1108	1109	1111	1116	1119	1120	1126	1133	1137	1141	1146	1147	1148	1151	1153	1154	1155	1158	1163	1168	1170	1172	1170	1174	1181	1188	1193	1193	1201	1206	1200	1193	1201	1198	1194	1197	1206	1210	1214	1212	1214	1222	1230	1236	1236	1236	1237	1240	1253	1274	1301	1325	1330	1327	1322	1327	1324	1313	1294	1285	1293	1297	1294	1287	1274	1255	1237	1223	1206	1187	1171	1161	1169	1177	1180	1187	1189	1186	1182	1173	1161	1150	1141	1132	1133	1143	1153	1163	1169	1164	1160	1157	1156	1157	1149	1133	1126	1139	1137	1129	1136	1157	1161	1162	1150	1147	1153	1150	1143	1126	1120	1140	1147	1150	1148	1137	1127	1138	1144	1148	1150	1149	1150	1149	1145	1159	1165	1165	1158	1153	1151	1151	1150	1147	1145	1135	1120	1104	1091	1087	1085	1081	1076	1073	1074	1075	1070	1066	1069	1071	1067	1062	1061	1060	1058	1056	1051	1056	1050	1043	1045	1050	1046	1041	1039
963	970	978	987	989	991	1000	1008	1006	1008	1016	1017	1024	1030	1035	1039	1052	1060	1064	1071	1080	1087	1088	1086	1078	1078	1080	1084	1086	1087	1091	1095	1097	1102	1103	1106	1110	1112	1114	1119	1123	1125	1131	1135	1140	1143	1147	1150	1152	1154	1157	1157	1161	1164	1168	1173	1178	1182	1181	1181	1182	1190	1198	1201	1206	1211	1212	1207	1208	1210	1210	1209	1210	1211	1218	1221	1221	1224	1231	1238	1241	1244	1245	1247	1254	1270	1298	1329	1340	1340	1336	1335	1334	1326	1310	1297	1293	1302	1299	1290	1274	1251	1232	1215	1198	1183	1172	1161	1173	1189	1181	1192	1199	1199	1197	1187	1176	1161	1150	1144	1141	1147	1159	1166	1178	1182	1173	1161	1163	1160	1160	1151	1142	1154	1151	1145	1144	1162	1162	1164	1158	1154	1157	1154	1139	1122	1131	1147	1151	1156	1154	1142	1135	1145	1153	1152	1152	1156	1158	1150	1157	1167	1169	1166	1162	1159	1158	1158	1155	1149	1141	1127	1107	1094	1090	1089	1087	1082	1080	1080	1079	1077	1076	1074	1075	1074	1069	1066	1066	1065	1060	1060	1059	1053	1049	1049	1051	1050	1049	1048	1047
972	972	977	988	992	998	1005	1010	1013	1017	1027	1030	1034	1040	1043	1048	1058	1066	1073	1080	1085	1091	1093	1093	1091	1086	1086	1092	1093	1089	1094	1100	1103	1105	1107	1111	1115	1120	1123	1124	1127	1131	1134	1136	1142	1147	1149	1155	1157	1159	1161	1162	1166	1169	1170	1175	1181	1184	1186	1183	1182	1191	1201	1207	1211	1217	1221	1216	1213	1219	1223	1220	1212	1217	1228	1231	1227	1234	1239	1245	1249	1256	1258	1256	1263	1280	1310	1336	1348	1355	1358	1358	1354	1340	1324	1304	1296	1304	1304	1292	1270	1248	1231	1212	1193	1180	1183	1188	1188	1195	1203	1219	1224	1221	1210	1198	1187	1172	1157	1150	1150	1157	1164	1169	1180	1186	1184	1169	1165	1166	1171	1162	1155	1167	1163	1155	1153	1170	1160	1164	1164	1157	1158	1153	1133	1121	1141	1155	1160	1163	1161	1152	1142	1147	1160	1160	1162	1168	1171	1162	1164	1163	1165	1167	1165	1163	1161	1160	1157	1151	1140	1125	1107	1093	1095	1097	1092	1089	1088	1087	1083	1080	1076	1077	1079	1078	1072	1070	1068	1066	1063	1062	1057	1052	1052	1053	1053	1050	1050	1049	1048
978	978	979	987	992	999	1001	1005	1009	1019	1029	1035	1040	1046	1051	1057	1062	1068	1077	1085	1090	1097	1098	1096	1093	1088	1096	1101	1096	1091	1098	1103	1105	1107	1111	1116	1118	1123	1126	1127	1129	1135	1139	1143	1147	1152	1156	1161	1164	1167	1168	1169	1171	1172	1174	1176	1179	1182	1186	1183	1185	1193	1200	1207	1214	1222	1225	1221	1220	1224	1229	1228	1222	1227	1238	1242	1237	1243	1250	1255	1257	1263	1265	1264	1269	1289	1319	1345	1362	1375	1379	1376	1370	1351	1328	1308	1298	1308	1309	1295	1272	1251	1231	1212	1190	1185	1197	1213	1216	1202	1211	1233	1245	1245	1234	1219	1201	1184	1169	1158	1156	1157	1159	1172	1180	1184	1190	1179	1167	1171	1173	1170	1172	1185	1180	1169	1164	1173	1164	1163	1159	1160	1162	1153	1146	1142	1145	1164	1171	1171	1171	1166	1151	1147	1167	1176	1181	1184	1185	1179	1167	1158	1163	1170	1170	1168	1166	1165	1161	1150	1132	1117	1108	1100	1102	1105	1098	1095	1094	1093	1089	1086	1081	1082	1083	1080	1076	1072	1070	1068	1066	1063	1059	1056	1058	1058	1057	1054	1055	1054	1052
982	986	987	990	991	997	1001	1006	1011	1020	1031	1037	1043	1051	1058	1066	1073	1077	1084	1093	1099	1105	1108	1106	1106	1105	1108	1104	1098	1101	1102	1106	1111	1115	1118	1122	1124	1128	1131	1134	1137	1140	1143	1145	1146	1152	1159	1162	1169	1173	1173	1174	1176	1177	1179	1183	1187	1189	1192	1193	1193	1194	1201	1209	1219	1227	1229	1230	1231	1229	1234	1237	1240	1239	1244	1251	1247	1250	1259	1262	1266	1268	1268	1269	1274	1293	1322	1352	1373	1387	1387	1380	1374	1358	1331	1309	1302	1311	1317	1304	1281	1257	1234	1210	1193	1198	1216	1230	1237	1221	1229	1248	1255	1250	1238	1223	1207	1194	1180	1169	1163	1164	1167	1176	1185	1190	1194	1189	1180	1176	1175	1180	1195	1202	1195	1182	1180	1174	1173	1167	1158	1166	1162	1155	1167	1168	1164	1175	1182	1179	1180	1179	1163	1152	1177	1192	1196	1196	1196	1189	1176	1167	1170	1173	1175	1175	1174	1171	1163	1146	1121	1105	1106	1107	1108	1107	1100	1097	1098	1098	1096	1091	1087	1086	1086	1083	1080	1076	1073	1071	1068	1063	1061	1061	1063	1063	1062	1061	1061	1058	1056
987	993	994	993	994	1000	1006	1011	1016	1020	1031	1038	1043	1046	1057	1068	1079	1083	1090	1098	1105	1110	1116	1118	1118	1116	1112	1103	1097	1102	1107	1112	1117	1119	1125	1130	1131	1132	1137	1139	1140	1142	1145	1147	1149	1155	1164	1166	1170	1173	1175	1175	1177	1180	1183	1186	1190	1192	1194	1197	1199	1197	1206	1214	1225	1233	1230	1234	1237	1233	1237	1244	1252	1253	1253	1258	1251	1258	1265	1268	1275	1278	1275	1275	1283	1296	1323	1356	1379	1389	1386	1381	1376	1365	1339	1315	1308	1316	1324	1314	1289	1264	1238	1214	1200	1213	1231	1249	1259	1250	1255	1267	1274	1264	1247	1230	1214	1199	1186	1175	1168	1168	1172	1173	1186	1195	1198	1197	1191	1183	1176	1182	1203	1212	1208	1194	1183	1175	1179	1175	1167	1166	1168	1171	1189	1191	1189	1191	1196	1194	1193	1193	1179	1161	1179	1198	1204	1205	1204	1201	1196	1183	1173	1170	1177	1178	1175	1167	1154	1133	1119	1109	1112	1113	1110	1105	1101	1101	1102	1103	1098	1093	1090	1089	1088	1087	1084	1081	1077	1075	1069	1066	1065	1066	1067	1067	1066	1065	1064	1060	1058
990	996	993	995	1000	1003	1007	1012	1016	1019	1030	1040	1044	1043	1056	1070	1080	1086	1094	1102	1110	1116	1122	1127	1127	1124	1118	1112	1106	1107	1115	1120	1123	1126	1130	1137	1138	1138	1144	1146	1147	1148	1151	1153	1158	1165	1173	1174	1176	1175	1180	1185	1189	1190	1193	1197	1200	1202	1203	1206	1213	1218	1218	1222	1230	1234	1235	1240	1246	1246	1244	1247	1259	1262	1266	1268	1266	1265	1270	1277	1281	1287	1287	1287	1287	1297	1324	1363	1387	1393	1395	1392	1382	1369	1349	1332	1316	1321	1325	1318	1297	1272	1243	1220	1207	1221	1244	1266	1278	1274	1273	1284	1295	1287	1264	1241	1224	1207	1192	1180	1177	1175	1175	1174	1191	1202	1204	1197	1199	1189	1182	1191	1205	1214	1214	1202	1181	1184	1187	1187	1183	1176	1186	1197	1206	1206	1202	1205	1206	1202	1200	1200	1187	1166	1185	1201	1210	1209	1210	1212	1207	1189	1174	1178	1178	1179	1176	1172	1148	1123	1117	1117	1118	1118	1113	1107	1106	1105	1104	1102	1097	1094	1093	1091	1090	1087	1084	1082	1079	1077	1072	1070	1071	1071	1070	1069	1068	1064	1063	1061	1059
992	993	998	1001	1000	998	1007	1011	1015	1022	1034	1046	1053	1060	1070	1082	1087	1092	1099	1109	1119	1125	1130	1135	1138	1134	1128	1125	1123	1122	1124	1128	1131	1134	1138	1146	1149	1149	1150	1151	1151	1154	1158	1160	1166	1172	1180	1180	1179	1179	1187	1193	1196	1194	1198	1204	1207	1210	1208	1210	1222	1225	1222	1226	1234	1238	1241	1247	1251	1257	1253	1253	1264	1269	1273	1276	1277	1272	1282	1284	1287	1296	1295	1289	1293	1308	1334	1365	1385	1397	1403	1401	1392	1380	1366	1352	1336	1328	1330	1327	1308	1283	1250	1222	1224	1248	1265	1274	1284	1284	1279	1293	1303	1301	1278	1250	1232	1214	1198	1182	1187	1187	1181	1179	1199	1210	1211	1199	1206	1203	1196	1202	1210	1212	1215	1210	1190	1190	1195	1200	1194	1195	1212	1221	1217	1218	1218	1221	1215	1207	1207	1208	1194	1173	1188	1208	1220	1221	1218	1217	1212	1196	1182	1184	1187	1184	1180	1171	1151	1130	1120	1120	1119	1119	1114	1108	1108	1106	1103	1103	1100	1097	1094	1093	1092	1088	1085	1084	1080	1077	1074	1075	1076	1075	1071	1070	1070	1067	1065	1062	1059
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1172	1155	1144	1147	1173	1182	1172	1162	1156	1170	1180	1201	1228	1250	1273	1298	1321	1343	1351	1348	1356	1377	1395	1407	1416	1422	1423	1426	1432	1432	1449	1473	1486	1499	1492	1467	1472	1479	1473	1463	1459	1454	1451	1449	1442	1454	1479	1496	1514	1535	1560	1578	1587	1618	1662	1695	1709	1723	1740	1759	1779	1797	1815	1836	1861	1887	1918	1956	1991	2016	2045	2082	2117	2130	2135	2138	2145	2143	2141	2151	2169	2183	2201	2223	2245	2262	2271	2267	2261	2266	2273	2285	2299	2313	2324	2336	2344	2342	2338	2331	2327	2324	2321	2318	2315	2311	2309	2306	2301	2295	2284	2275	2267	2260	2255	2246	2235	2218	2202	2186	2171	2160	2147	2133	2120	2106	2087	2070	2052	2030	2016	2006	2000	2009	2026	2044	2044	2037	2031	2017	1995	1972	1949	1928	1904	1883	1865	1851	1835	1821	1809	1802	1792	1773	1749	1727	1705	1690	1673	1657	1639	1626	1613	1595	1579	1571	1558	1549	1537	1522	1505	1490	1476	1461	1453	1448	1439	1438	1439	1433	1430	1427	1423	1417	1408	1402	1393	1379	1371	1368	1364	1360	1356	1353	1352	1351	1350	1349	1348	1344	1343
1145	1143	1143	1152	1165	1154	1144	1147	1145	1160	1175	1201	1233	1258	1277	1295	1315	1326	1329	1338	1351	1364	1376	1384	1392	1396	1395	1398	1403	1409	1426	1456	1471	1478	1469	1441	1435	1432	1428	1426	1426	1425	1427	1430	1430	1443	1466	1491	1513	1531	1550	1567	1581	1612	1657	1694	1708	1724	1745	1764	1778	1796	1820	1844	1869	1897	1929	1962	1997	2029	2065	2100	2120	2123	2121	2133	2132	2132	2130	2141	2152	2163	2178	2199	2223	2242	2251	2243	2232	2244	2252	2260	2273	2285	2298	2313	2326	2329	2322	2315	2309	2304	2299	2294	2290	2289	2289	2285	2280	2273	2264	2259	2252	2247	2243	2234	2220	2202	2188	2173	2159	2149	2133	2117	2105	2088	2066	2053	2041	2016	2003	2000	1994	1999	2010	2019	2019	2016	2007	1996	1981	1960	1935	1915	1899	1884	1866	1848	1833	1819	1805	1795	1792	1783	1760	1735	1712	1696	1679	1661	1644	1635	1625	1608	1592	1580	1566	1553	1542	1528	1509	1493	1481	1470	1459	1444	1433	1430	1433	1427	1421	1418	1419	1417	1413	1411	1408	1396	1378	1367	1364	1362	1358	1355	1353	1351	1350	1349	1346	1345	1344
1129	1120	1135	1155	1159	1138	1130	1138	1135	1156	1184	1211	1234	1253	1271	1281	1293	1303	1319	1343	1347	1338	1341	1347	1353	1363	1365	1364	1377	1384	1401	1419	1429	1418	1415	1408	1407	1404	1399	1399	1398	1397	1409	1428	1443	1461	1482	1508	1536	1553	1567	1579	1595	1623	1659	1687	1704	1724	1749	1767	1779	1795	1816	1838	1864	1895	1929	1966	2003	2037	2068	2097	2107	2105	2115	2124	2122	2120	2116	2124	2133	2143	2159	2180	2202	2220	2227	2216	2205	2218	2226	2232	2241	2257	2275	2294	2308	2313	2311	2307	2294	2282	2272	2266	2260	2258	2261	2258	2253	2249	2240	2232	2227	2219	2212	2205	2192	2173	2159	2145	2133	2124	2114	2102	2087	2072	2052	2034	2017	1997	1983	1970	1968	1978	1990	1993	1993	1987	1978	1971	1961	1941	1916	1895	1882	1868	1852	1837	1824	1811	1796	1784	1781	1773	1755	1730	1709	1694	1678	1660	1648	1642	1632	1619	1603	1586	1570	1557	1546	1531	1512	1499	1489	1476	1465	1446	1434	1429	1426	1422	1415	1411	1414	1414	1415	1413	1407	1395	1378	1370	1367	1366	1363	1360	1358	1355	1354	1353	1351	1349	1348
1117	1103	1122	1138	1147	1131	1127	1138	1140	1161	1191	1213	1222	1228	1246	1261	1270	1295	1318	1321	1319	1316	1312	1316	1322	1330	1334	1340	1353	1353	1362	1366	1372	1376	1379	1379	1379	1379	1371	1370	1381	1395	1414	1442	1464	1489	1514	1541	1570	1587	1605	1622	1635	1654	1674	1695	1712	1731	1753	1765	1778	1792	1812	1832	1858	1891	1922	1961	1995	2024	2050	2071	2079	2082	2104	2112	2114	2107	2101	2106	2116	2127	2145	2164	2180	2193	2197	2183	2183	2191	2195	2200	2213	2234	2256	2281	2296	2302	2308	2304	2281	2265	2254	2248	2242	2238	2234	2235	2241	2237	2222	2213	2211	2204	2196	2189	2173	2153	2140	2128	2119	2110	2102	2093	2079	2062	2042	2022	2005	1986	1967	1956	1961	1969	1969	1969	1974	1967	1958	1949	1940	1928	1912	1888	1868	1853	1838	1825	1815	1803	1789	1776	1765	1755	1740	1720	1701	1683	1670	1658	1648	1641	1631	1619	1605	1589	1572	1560	1544	1527	1507	1490	1478	1465	1456	1439	1427	1420	1416	1411	1407	1403	1397	1395	1400	1393	1383	1377	1373	1370	1370	1368	1365	1362	1360	1357	1355	1353	1350	1346	1344
1092	1095	1111	1116	1122	1114	1117	1130	1150	1173	1198	1218	1222	1227	1244	1263	1277	1297	1300	1286	1282	1283	1286	1292	1297	1301	1309	1313	1317	1332	1339	1332	1329	1343	1348	1346	1342	1360	1369	1378	1394	1413	1436	1465	1491	1519	1543	1569	1591	1594	1615	1637	1653	1671	1689	1708	1722	1736	1745	1754	1770	1785	1803	1828	1856	1888	1917	1947	1974	1998	2020	2035	2047	2060	2083	2097	2101	2091	2088	2094	2099	2111	2131	2147	2163	2173	2174	2165	2164	2171	2171	2172	2191	2218	2247	2279	2294	2299	2311	2311	2272	2250	2235	2226	2215	2207	2205	2201	2208	2212	2200	2191	2185	2178	2171	2163	2152	2135	2120	2111	2099	2088	2078	2070	2055	2041	2024	2008	1989	1972	1954	1942	1944	1946	1948	1944	1949	1947	1937	1929	1920	1914	1907	1888	1865	1847	1832	1818	1810	1800	1786	1771	1756	1748	1736	1720	1700	1679	1666	1659	1649	1641	1633	1623	1613	1596	1579	1569	1554	1534	1514	1494	1476	1460	1449	1436	1425	1421	1417	1413	1408	1403	1398	1393	1392	1387	1380	1376	1375	1374	1373	1372	1371	1369	1366	1362	1358	1353	1350	1344	1341
1077	1082	1091	1098	1105	1102	1103	1121	1143	1170	1198	1216	1227	1237	1253	1267	1276	1279	1273	1265	1262	1261	1264	1269	1271	1272	1278	1282	1284	1296	1304	1301	1299	1307	1313	1318	1326	1350	1373	1394	1416	1441	1463	1488	1516	1544	1567	1587	1592	1587	1608	1630	1652	1675	1692	1708	1720	1732	1728	1735	1757	1771	1792	1820	1853	1884	1910	1930	1951	1970	1986	1998	2015	2035	2056	2070	2074	2068	2064	2074	2082	2095	2109	2123	2137	2147	2139	2132	2143	2144	2141	2147	2176	2207	2235	2268	2274	2270	2287	2295	2256	2233	2213	2201	2190	2184	2181	2179	2185	2194	2186	2178	2166	2156	2150	2139	2133	2123	2104	2093	2080	2068	2056	2046	2031	2022	2009	1992	1972	1955	1940	1925	1919	1920	1930	1922	1921	1923	1915	1911	1901	1891	1887	1877	1863	1847	1830	1814	1802	1791	1776	1761	1751	1741	1733	1723	1707	1689	1670	1655	1637	1631	1625	1617	1611	1603	1592	1579	1562	1539	1517	1496	1475	1457	1445	1432	1420	1411	1412	1408	1403	1398	1393	1389	1386	1384	1379	1375	1373	1372	1370	1369	1368	1366	1363	1362	1358	1352	1350	1344	1341
1075	1082	1087	1093	1102	1107	1112	1126	1144	1165	1188	1206	1221	1233	1245	1254	1260	1245	1234	1228	1228	1227	1229	1236	1240	1244	1251	1261	1270	1278	1282	1290	1296	1303	1312	1327	1347	1366	1389	1414	1439	1468	1491	1515	1541	1564	1577	1579	1577	1586	1609	1629	1651	1674	1684	1696	1707	1715	1714	1722	1746	1757	1788	1819	1850	1879	1897	1914	1932	1947	1961	1972	1990	2013	2031	2037	2036	2037	2046	2056	2064	2077	2090	2101	2112	2118	2109	2103	2116	2120	2120	2123	2155	2190	2222	2252	2249	2244	2257	2280	2256	2224	2197	2185	2173	2165	2161	2156	2162	2171	2168	2160	2148	2134	2128	2119	2108	2107	2091	2073	2062	2049	2037	2026	2015	2008	1998	1978	1958	1939	1925	1914	1906	1909	1916	1907	1900	1905	1900	1893	1882	1875	1868	1860	1852	1842	1828	1809	1794	1780	1768	1758	1749	1741	1734	1720	1710	1694	1671	1651	1634	1627	1621	1614	1605	1602	1591	1583	1567	1546	1523	1500	1477	1458	1447	1432	1417	1406	1407	1403	1398	1396	1393	1389	1386	1384	1380	1376	1373	1370	1368	1366	1364	1361	1359	1358	1356	1352	1348	1345	1344
1080	1080	1084	1093	1100	1110	1115	1123	1139	1158	1173	1183	1199	1213	1222	1228	1228	1212	1200	1193	1198	1203	1208	1218	1227	1236	1247	1261	1276	1286	1290	1302	1314	1325	1340	1359	1381	1398	1417	1441	1464	1489	1517	1545	1563	1573	1563	1548	1565	1589	1614	1630	1644	1662	1674	1683	1693	1690	1696	1715	1735	1752	1779	1802	1830	1853	1871	1886	1902	1916	1927	1942	1965	1985	2001	2007	2002	2014	2027	2041	2047	2056	2067	2074	2086	2082	2077	2080	2091	2100	2104	2114	2142	2175	2208	2231	2239	2233	2234	2263	2260	2222	2186	2169	2156	2149	2140	2132	2139	2146	2148	2137	2126	2110	2100	2096	2079	2081	2077	2052	2035	2021	2011	2000	1991	1983	1975	1962	1944	1920	1908	1898	1888	1887	1891	1885	1879	1883	1878	1869	1860	1851	1842	1836	1828	1821	1816	1808	1790	1772	1763	1753	1745	1737	1724	1710	1703	1691	1671	1652	1637	1628	1621	1607	1594	1592	1579	1571	1563	1551	1529	1504	1480	1461	1446	1430	1415	1404	1400	1397	1395	1392	1392	1390	1387	1382	1379	1377	1373	1371	1370	1369	1367	1364	1362	1361	1359	1355	1350	1347	1349
1094	1087	1086	1092	1098	1108	1116	1122	1135	1148	1158	1163	1176	1184	1191	1192	1193	1190	1187	1187	1197	1210	1222	1236	1249	1260	1274	1288	1301	1313	1322	1332	1340	1349	1363	1378	1400	1418	1440	1465	1489	1514	1537	1556	1563	1557	1543	1545	1571	1597	1614	1625	1640	1661	1665	1677	1686	1682	1685	1711	1738	1756	1774	1784	1808	1831	1852	1865	1878	1892	1904	1923	1947	1963	1977	1983	1982	1994	2007	2023	2028	2037	2049	2053	2062	2053	2050	2061	2076	2081	2088	2111	2138	2168	2194	2210	2232	2228	2215	2241	2245	2213	2178	2152	2137	2132	2120	2115	2122	2129	2133	2126	2111	2096	2080	2067	2061	2059	2058	2043	2021	2008	1996	1983	1974	1966	1966	1954	1931	1913	1902	1889	1882	1880	1875	1864	1860	1871	1863	1850	1844	1832	1823	1819	1809	1800	1801	1801	1786	1771	1761	1748	1743	1737	1720	1704	1693	1686	1674	1657	1641	1632	1622	1604	1586	1585	1571	1558	1547	1539	1526	1506	1484	1462	1444	1429	1417	1401	1394	1391	1390	1385	1384	1383	1382	1378	1375	1373	1370	1369	1369	1367	1364	1361	1359	1359	1357	1356	1354	1352	1352
1112	1104	1100	1101	1105	1110	1117	1122	1129	1137	1145	1148	1159	1159	1164	1162	1170	1180	1190	1201	1214	1231	1252	1266	1278	1291	1304	1319	1331	1341	1351	1361	1372	1384	1398	1415	1435	1453	1470	1493	1516	1533	1545	1554	1544	1527	1527	1549	1572	1591	1602	1619	1640	1649	1651	1663	1668	1668	1678	1700	1725	1734	1750	1762	1781	1802	1823	1837	1850	1862	1877	1899	1920	1934	1948	1955	1963	1971	1984	1997	2006	2017	2031	2038	2042	2034	2034	2044	2063	2060	2071	2100	2132	2162	2178	2189	2208	2212	2195	2225	2211	2186	2166	2141	2123	2111	2103	2094	2098	2105	2110	2104	2096	2079	2060	2045	2036	2031	2035	2025	2009	1992	1975	1961	1950	1941	1937	1937	1915	1900	1889	1877	1867	1862	1854	1843	1838	1846	1849	1836	1826	1814	1806	1800	1792	1784	1780	1783	1779	1765	1755	1745	1738	1733	1721	1708	1692	1684	1671	1658	1641	1631	1622	1606	1587	1582	1568	1550	1528	1512	1510	1503	1482	1459	1444	1432	1419	1404	1394	1394	1391	1385	1383	1381	1377	1376	1374	1372	1372	1371	1371	1368	1364	1359	1357	1355	1353	1354	1356	1360	1366
1129	1124	1121	1120	1120	1118	1114	1112	1119	1128	1134	1135	1143	1151	1166	1165	1176	1185	1196	1212	1227	1242	1265	1284	1299	1317	1331	1343	1357	1368	1377	1389	1398	1410	1425	1441	1460	1478	1492	1507	1521	1532	1539	1539	1522	1505	1525	1550	1566	1580	1592	1616	1632	1632	1641	1650	1653	1656	1671	1691	1707	1707	1726	1745	1759	1775	1794	1807	1821	1831	1846	1867	1885	1903	1914	1923	1935	1947	1960	1970	1982	1995	2006	2016	2019	2011	2018	2027	2039	2039	2057	2086	2118	2136	2147	2158	2169	2177	2170	2196	2192	2159	2150	2134	2105	2089	2088	2082	2077	2082	2093	2093	2080	2063	2040	2024	2015	2007	2010	2010	1997	1979	1959	1945	1928	1917	1918	1919	1905	1890	1872	1859	1850	1845	1836	1827	1822	1825	1839	1824	1806	1797	1789	1782	1778	1773	1762	1763	1767	1752	1746	1741	1729	1722	1718	1714	1699	1685	1660	1654	1640	1626	1617	1604	1587	1571	1553	1535	1517	1494	1490	1491	1475	1458	1441	1427	1416	1401	1385	1382	1385	1383	1379	1379	1375	1372	1371	1370	1369	1365	1364	1363	1361	1359	1359	1360	1360	1360	1358	1367	1376
1162	1152	1143	1140	1143	1140	1130	1125	1132	1143	1151	1154	1160	1165	1174	1182	1198	1214	1230	1246	1260	1275	1295	1314	1331	1349	1363	1376	1390	1402	1411	1422	1429	1438	1449	1462	1475	1489	1500	1507	1516	1525	1529	1518	1497	1490	1518	1540	1556	1570	1584	1608	1617	1621	1633	1637	1641	1648	1662	1679	1685	1690	1712	1729	1743	1758	1773	1785	1799	1812	1826	1842	1863	1885	1893	1899	1912	1926	1944	1951	1957	1967	1982	1994	1994	1987	1998	2005	2014	2020	2042	2071	2096	2113	2123	2125	2136	2143	2146	2156	2171	2139	2127	2117	2085	2062	2065	2066	2058	2056	2069	2068	2055	2042	2023	2006	1990	1978	1978	1986	1979	1962	1941	1929	1913	1902	1903	1896	1892	1878	1858	1841	1833	1831	1821	1814	1808	1813	1828	1813	1793	1783	1773	1766	1764	1761	1753	1749	1747	1740	1737	1730	1719	1712	1712	1706	1696	1685	1660	1648	1639	1626	1616	1602	1584	1568	1548	1530	1514	1480	1476	1482	1469	1456	1440	1424	1409	1398	1384	1377	1379	1379	1378	1376	1371	1372	1370	1370	1368	1362	1360	1358	1356	1356	1358	1360	1362	1364	1363	1370	1380
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1138	1105	1101	1115	1142	1161	1172	1180	1179	1178	1179	1176	1175	1175	1182	1192	1199	1200	1191	1189	1196	1202	1205	1197	1205	1210	1196	1184	1185	1191	1198	1204	1211	1222	1238	1255	1271	1283	1290	1291	1289	1284	1285	1292	1296	1298	1301	1300	1302	1308	1313	1320	1328	1326	1313	1324	1337	1340	1342	1355	1371	1376	1378	1370	1355	1337	1346	1374	1388	1398	1405	1406	1410	1414	1415	1418	1425	1431	1437	1443	1455	1464	1469	1469	1471	1473	1479	1484	1487	1491	1495	1500	1499	1499	1498	1496	1495	1494	1494	1494	1493	1490	1489	1494	1504	1508	1505	1499	1490	1499	1497	1482	1467	1436	1413	1431	1452	1448	1444	1434	1441	1439	1425	1427	1436	1434	1432	1425	1419	1419	1418	1422	1424	1417	1417	1423	1407	1407	1419	1412	1414	1427	1422	1424	1424	1421	1408	1398	1394	1394	1379	1348	1346	1365	1353	1340	1337	1332	1337	1341	1336	1333	1329	1336	1335	1328	1323	1318	1312	1302	1284	1258	1256	1260	1258	1264	1267	1268	1280	1302	1324	1304	1272	1226	1182	1144	1113	1089	1067	1048	1034	1017	1005	1016	1040	1052	1059	1055	1030	1012	987
1125	1097	1098	1122	1149	1169	1182	1189	1186	1183	1177	1169	1167	1171	1179	1187	1192	1189	1184	1188	1198	1206	1203	1201	1203	1197	1185	1184	1190	1198	1208	1219	1229	1241	1253	1266	1276	1283	1288	1285	1283	1286	1286	1288	1287	1290	1300	1300	1302	1312	1319	1324	1320	1315	1316	1324	1333	1334	1339	1350	1363	1370	1372	1365	1346	1335	1346	1371	1382	1387	1397	1397	1403	1409	1410	1413	1415	1417	1425	1428	1439	1450	1454	1456	1461	1464	1469	1474	1476	1481	1486	1489	1486	1484	1484	1482	1481	1480	1479	1478	1478	1475	1475	1473	1490	1497	1492	1485	1473	1480	1478	1464	1448	1431	1414	1410	1431	1434	1430	1421	1428	1424	1412	1415	1424	1422	1421	1418	1413	1410	1414	1409	1414	1411	1407	1410	1401	1396	1407	1397	1404	1417	1411	1413	1412	1412	1404	1392	1378	1379	1374	1353	1342	1346	1342	1332	1329	1330	1338	1339	1331	1322	1334	1336	1330	1327	1325	1322	1314	1305	1283	1260	1265	1265	1258	1259	1266	1268	1272	1286	1305	1311	1270	1223	1182	1146	1115	1089	1068	1048	1030	1005	993	1007	1016	1028	1041	1052	1032	986	940
1109	1090	1096	1121	1147	1165	1173	1175	1177	1173	1164	1158	1161	1168	1176	1180	1179	1176	1180	1189	1196	1200	1200	1197	1192	1182	1175	1177	1187	1196	1208	1219	1230	1242	1256	1268	1276	1280	1283	1279	1268	1277	1282	1285	1285	1286	1294	1295	1300	1313	1323	1326	1312	1308	1317	1320	1324	1325	1330	1346	1359	1367	1370	1360	1337	1331	1346	1362	1369	1375	1387	1385	1393	1399	1401	1405	1403	1403	1415	1422	1424	1432	1432	1441	1454	1456	1458	1465	1468	1472	1477	1479	1478	1476	1474	1469	1472	1475	1471	1466	1467	1464	1458	1462	1480	1489	1485	1474	1466	1475	1461	1452	1435	1422	1416	1399	1413	1418	1423	1412	1413	1406	1408	1413	1416	1414	1413	1413	1406	1403	1406	1396	1403	1403	1398	1398	1393	1389	1390	1387	1398	1410	1397	1404	1401	1403	1401	1392	1372	1357	1360	1356	1345	1331	1336	1332	1326	1318	1319	1327	1318	1317	1331	1334	1326	1322	1321	1324	1315	1303	1289	1274	1260	1258	1252	1254	1262	1267	1268	1276	1297	1316	1281	1235	1195	1161	1130	1102	1079	1059	1039	1018	1004	1003	988	982	1012	1040	1017	978	939
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1085	1098	1114	1135	1149	1153	1152	1149	1146	1142	1136	1136	1141	1148	1149	1146	1148	1159	1166	1162	1163	1167	1161	1156	1154	1154	1161	1170	1181	1193	1206	1221	1228	1235	1242	1244	1247	1248	1238	1244	1252	1257	1261	1262	1263	1280	1294	1302	1305	1305	1300	1285	1272	1287	1290	1288	1298	1311	1315	1329	1337	1342	1343	1336	1322	1311	1303	1306	1326	1344	1350	1361	1367	1368	1370	1364	1358	1362	1365	1368	1371	1376	1386	1405	1411	1412	1421	1431	1436	1443	1449	1449	1447	1447	1445	1441	1436	1433	1430	1431	1431	1431	1425	1428	1442	1448	1443	1435	1427	1420	1427	1419	1400	1394	1390	1375	1381	1388	1389	1386	1376	1385	1394	1392	1390	1389	1388	1387	1382	1378	1382	1377	1362	1369	1375	1367	1370	1365	1361	1373	1377	1363	1363	1370	1369	1363	1351	1354	1349	1337	1337	1337	1330	1322	1323	1319	1316	1313	1312	1310	1306	1302	1307	1309	1317	1319	1316	1316	1320	1319	1313	1302	1280	1253	1240	1252	1265	1272	1271	1273	1296	1327	1320	1288	1255	1216	1182	1146	1115	1091	1072	1052	1033	1017	994	974	954	954	955	937	927
1091	1113	1128	1141	1149	1151	1147	1146	1142	1136	1131	1137	1144	1145	1141	1138	1143	1155	1162	1156	1156	1155	1149	1146	1148	1152	1161	1171	1187	1202	1214	1225	1231	1237	1236	1228	1224	1236	1239	1246	1252	1253	1257	1264	1277	1292	1293	1300	1307	1301	1277	1263	1267	1279	1284	1288	1293	1306	1315	1329	1339	1340	1335	1325	1317	1307	1293	1300	1318	1328	1344	1355	1359	1354	1356	1353	1349	1354	1360	1364	1363	1360	1371	1387	1394	1403	1412	1419	1424	1430	1434	1435	1437	1437	1437	1432	1426	1422	1421	1419	1417	1416	1414	1415	1426	1432	1429	1417	1417	1408	1393	1399	1396	1385	1382	1371	1364	1373	1381	1379	1371	1370	1380	1379	1377	1376	1377	1375	1376	1369	1369	1375	1367	1362	1360	1354	1361	1354	1354	1370	1364	1346	1340	1349	1353	1347	1340	1338	1334	1332	1330	1327	1323	1318	1319	1315	1312	1311	1310	1308	1304	1297	1305	1312	1316	1315	1311	1312	1314	1313	1307	1295	1272	1243	1240	1255	1270	1269	1270	1282	1304	1326	1325	1291	1252	1224	1193	1159	1129	1104	1082	1059	1038	1014	994	974	953	933	916	895	892
1098	1122	1134	1136	1136	1138	1142	1142	1136	1129	1129	1137	1142	1141	1134	1132	1138	1148	1155	1156	1151	1146	1143	1137	1139	1146	1154	1165	1183	1201	1212	1218	1223	1220	1215	1208	1218	1229	1233	1238	1244	1246	1250	1261	1273	1283	1286	1293	1298	1289	1265	1253	1254	1267	1278	1284	1288	1296	1313	1326	1333	1332	1328	1317	1307	1299	1287	1299	1308	1315	1333	1341	1340	1333	1338	1340	1339	1343	1348	1356	1356	1351	1360	1374	1384	1394	1405	1412	1415	1417	1420	1422	1426	1427	1426	1422	1419	1415	1412	1409	1404	1400	1406	1410	1416	1424	1412	1400	1401	1390	1372	1378	1380	1377	1371	1356	1362	1370	1377	1376	1367	1365	1375	1375	1371	1370	1373	1373	1369	1361	1363	1370	1369	1356	1348	1349	1352	1344	1354	1363	1356	1343	1331	1335	1338	1337	1331	1327	1328	1328	1323	1319	1317	1315	1313	1311	1310	1311	1308	1302	1296	1293	1302	1308	1310	1310	1307	1304	1298	1296	1293	1287	1274	1250	1238	1247	1260	1266	1270	1283	1307	1327	1320	1291	1266	1241	1206	1174	1146	1121	1097	1074	1052	1032	1019	998	974	951	925	901	885
1107	1131	1147	1141	1128	1129	1133	1134	1131	1126	1128	1134	1136	1133	1127	1126	1131	1134	1139	1146	1139	1135	1134	1133	1138	1148	1160	1177	1193	1202	1204	1203	1197	1198	1209	1214	1214	1226	1232	1236	1242	1249	1255	1268	1280	1287	1283	1287	1292	1277	1253	1247	1248	1260	1269	1276	1284	1294	1309	1319	1322	1324	1321	1312	1297	1289	1282	1296	1302	1309	1315	1318	1314	1313	1320	1326	1327	1331	1333	1340	1345	1344	1354	1367	1379	1386	1396	1400	1401	1405	1408	1410	1411	1412	1412	1410	1406	1403	1401	1398	1394	1389	1386	1388	1398	1406	1395	1382	1374	1371	1361	1352	1359	1364	1364	1353	1351	1361	1367	1368	1360	1357	1364	1367	1364	1362	1361	1363	1360	1352	1350	1359	1361	1352	1340	1341	1342	1336	1353	1354	1349	1344	1338	1329	1325	1327	1324	1322	1324	1319	1320	1319	1317	1312	1306	1309	1309	1309	1308	1298	1289	1292	1298	1303	1307	1310	1309	1304	1293	1291	1295	1288	1274	1243	1233	1241	1255	1267	1274	1289	1313	1340	1330	1304	1268	1237	1211	1185	1159	1135	1112	1089	1068	1047	1030	1010	985	961	937	914	889
1108	1128	1135	1126	1120	1122	1125	1127	1127	1125	1126	1129	1129	1124	1118	1117	1125	1131	1136	1132	1124	1122	1123	1124	1130	1142	1158	1175	1185	1191	1187	1185	1178	1193	1212	1219	1208	1221	1229	1233	1239	1249	1261	1274	1286	1290	1280	1281	1288	1271	1245	1241	1246	1254	1256	1269	1282	1295	1305	1311	1315	1320	1314	1305	1291	1282	1284	1292	1302	1305	1302	1299	1305	1310	1317	1319	1320	1321	1324	1330	1338	1345	1352	1362	1372	1378	1383	1389	1394	1401	1404	1404	1402	1402	1401	1397	1395	1392	1387	1384	1381	1378	1368	1371	1381	1385	1377	1363	1351	1348	1347	1339	1348	1355	1357	1349	1341	1352	1359	1357	1350	1350	1354	1357	1357	1354	1347	1349	1351	1341	1335	1347	1349	1346	1334	1327	1332	1333	1347	1345	1342	1340	1336	1329	1314	1312	1320	1317	1310	1307	1310	1313	1311	1308	1307	1302	1301	1304	1302	1295	1291	1286	1285	1298	1304	1305	1306	1302	1293	1286	1282	1281	1268	1245	1231	1235	1245	1261	1279	1297	1320	1335	1319	1294	1264	1240	1218	1195	1172	1148	1124	1102	1081	1059	1037	1018	995	971	949	926	903
1107	1127	1127	1118	1116	1119	1122	1124	1125	1124	1126	1127	1125	1118	1110	1115	1126	1133	1128	1120	1117	1118	1120	1121	1125	1136	1149	1159	1165	1171	1173	1176	1184	1209	1216	1213	1210	1219	1227	1233	1240	1250	1264	1276	1280	1280	1268	1273	1280	1269	1249	1240	1237	1240	1247	1260	1275	1287	1294	1296	1301	1304	1300	1296	1286	1277	1272	1279	1283	1286	1287	1289	1293	1299	1305	1307	1308	1309	1314	1320	1326	1334	1340	1347	1354	1361	1367	1373	1381	1386	1388	1389	1388	1388	1387	1386	1385	1382	1377	1374	1370	1366	1356	1355	1357	1361	1359	1347	1338	1333	1331	1336	1342	1346	1347	1343	1336	1340	1351	1350	1339	1343	1349	1351	1350	1348	1344	1343	1343	1333	1334	1340	1342	1339	1329	1323	1334	1340	1341	1337	1336	1334	1333	1329	1312	1309	1319	1314	1301	1306	1311	1314	1313	1311	1304	1293	1294	1297	1298	1294	1289	1283	1283	1296	1302	1302	1302	1299	1288	1276	1281	1281	1265	1240	1222	1232	1243	1260	1281	1303	1318	1315	1294	1276	1262	1247	1227	1207	1185	1161	1136	1111	1088	1066	1048	1030	1009	988	966	942	919
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1094	1102	1105	1103	1102	1105	1110	1113	1116	1118	1120	1119	1112	1102	1103	1115	1118	1111	1108	1107	1110	1118	1122	1124	1126	1129	1136	1150	1167	1187	1203	1217	1214	1200	1193	1194	1200	1217	1229	1239	1246	1252	1258	1264	1265	1260	1250	1256	1265	1254	1235	1230	1229	1232	1247	1265	1276	1281	1280	1279	1283	1282	1277	1275	1269	1258	1256	1258	1263	1267	1270	1277	1282	1283	1285	1289	1295	1299	1305	1310	1315	1318	1321	1327	1335	1343	1350	1358	1366	1370	1371	1370	1370	1371	1368	1366	1364	1362	1360	1358	1353	1348	1342	1336	1332	1330	1327	1325	1326	1321	1321	1326	1326	1322	1326	1331	1323	1322	1338	1342	1335	1321	1330	1333	1332	1330	1327	1323	1324	1326	1316	1323	1322	1322	1317	1305	1307	1321	1325	1324	1322	1318	1317	1315	1311	1301	1295	1305	1303	1293	1283	1292	1294	1295	1293	1289	1288	1283	1276	1282	1285	1281	1277	1288	1297	1300	1301	1295	1280	1270	1259	1253	1246	1238	1229	1220	1229	1247	1263	1286	1297	1278	1262	1251	1238	1227	1217	1198	1180	1164	1142	1118	1096	1071	1052	1036	1017	1005	987	964	934
1092	1097	1098	1096	1097	1100	1103	1107	1110	1111	1110	1108	1101	1093	1102	1115	1111	1102	1103	1105	1111	1118	1120	1121	1124	1128	1133	1143	1162	1190	1211	1218	1198	1182	1184	1195	1207	1220	1239	1244	1251	1255	1259	1265	1262	1251	1238	1251	1259	1252	1229	1225	1225	1232	1247	1264	1270	1271	1274	1276	1274	1267	1267	1267	1264	1255	1254	1256	1262	1266	1267	1271	1272	1277	1280	1284	1288	1293	1299	1303	1307	1311	1317	1321	1326	1334	1340	1344	1349	1353	1355	1355	1356	1357	1358	1358	1356	1352	1349	1346	1343	1339	1332	1327	1323	1320	1318	1316	1312	1314	1314	1310	1314	1314	1316	1314	1311	1312	1325	1331	1331	1315	1317	1325	1325	1321	1317	1315	1315	1318	1315	1313	1311	1315	1313	1303	1294	1305	1315	1314	1312	1307	1306	1306	1303	1296	1294	1297	1299	1294	1286	1289	1288	1290	1289	1288	1290	1285	1276	1279	1280	1276	1279	1290	1297	1301	1301	1291	1267	1248	1239	1234	1232	1232	1231	1232	1241	1254	1266	1283	1283	1261	1242	1231	1220	1214	1202	1179	1170	1162	1146	1129	1109	1083	1063	1046	1028	1010	991	968	941
1090	1094	1092	1090	1093	1095	1098	1102	1103	1102	1101	1099	1095	1091	1100	1107	1101	1097	1100	1101	1104	1108	1111	1122	1137	1145	1140	1140	1150	1175	1198	1198	1183	1171	1177	1193	1209	1221	1227	1233	1240	1243	1248	1252	1251	1243	1233	1242	1251	1246	1225	1220	1221	1231	1247	1258	1263	1267	1273	1271	1262	1255	1264	1264	1258	1251	1249	1255	1256	1261	1265	1268	1269	1275	1281	1286	1288	1291	1293	1295	1297	1303	1310	1313	1319	1325	1330	1334	1338	1342	1345	1346	1348	1350	1350	1348	1347	1346	1344	1342	1335	1328	1325	1324	1321	1319	1316	1316	1309	1307	1307	1306	1308	1309	1312	1309	1304	1307	1318	1322	1321	1309	1315	1323	1321	1315	1311	1311	1309	1311	1309	1303	1305	1309	1308	1300	1282	1292	1306	1304	1299	1295	1295	1294	1293	1292	1289	1286	1288	1292	1286	1279	1277	1282	1283	1286	1286	1281	1278	1274	1268	1269	1268	1284	1290	1293	1287	1276	1261	1246	1238	1230	1225	1221	1219	1222	1233	1236	1250	1270	1258	1237	1222	1212	1202	1193	1179	1163	1152	1139	1125	1113	1100	1084	1063	1044	1022	999	977	952	922
1090	1092	1087	1086	1090	1093	1096	1097	1097	1097	1096	1093	1089	1096	1106	1100	1094	1097	1100	1096	1099	1105	1116	1137	1155	1166	1160	1147	1144	1158	1175	1175	1172	1176	1185	1206	1220	1226	1229	1233	1237	1242	1245	1246	1245	1236	1229	1235	1243	1236	1221	1216	1216	1229	1243	1250	1259	1266	1268	1261	1252	1252	1261	1261	1254	1245	1240	1245	1243	1251	1258	1260	1260	1264	1268	1272	1274	1277	1280	1283	1286	1291	1298	1303	1308	1314	1317	1323	1328	1332	1334	1336	1337	1338	1336	1335	1333	1331	1329	1328	1325	1320	1317	1312	1308	1306	1306	1303	1299	1298	1299	1302	1301	1303	1304	1303	1300	1298	1309	1309	1305	1302	1309	1317	1315	1309	1305	1307	1306	1303	1297	1298	1300	1302	1300	1292	1277	1290	1301	1299	1296	1293	1297	1295	1292	1291	1284	1281	1287	1286	1276	1277	1279	1280	1278	1280	1282	1280	1278	1274	1267	1263	1269	1277	1284	1288	1278	1271	1259	1240	1227	1225	1222	1219	1219	1220	1228	1231	1247	1265	1246	1222	1205	1197	1186	1172	1160	1147	1128	1107	1099	1093	1086	1074	1053	1029	1007	983	958	932	901
1082	1082	1082	1083	1086	1090	1090	1089	1092	1092	1089	1084	1084	1093	1099	1092	1087	1093	1096	1091	1095	1109	1124	1144	1158	1167	1169	1161	1154	1154	1160	1158	1162	1177	1197	1213	1223	1229	1232	1235	1236	1244	1248	1244	1241	1231	1222	1233	1238	1229	1213	1211	1218	1236	1245	1248	1257	1261	1261	1255	1250	1252	1259	1260	1255	1237	1231	1238	1242	1245	1249	1254	1256	1258	1260	1262	1266	1269	1275	1282	1287	1292	1296	1301	1306	1310	1313	1317	1323	1326	1326	1327	1324	1324	1327	1326	1322	1319	1318	1317	1316	1313	1310	1304	1298	1296	1295	1293	1292	1291	1292	1293	1292	1295	1294	1296	1294	1290	1297	1296	1291	1293	1295	1303	1304	1299	1293	1299	1298	1293	1292	1290	1292	1294	1292	1279	1271	1284	1293	1289	1287	1287	1287	1285	1281	1281	1279	1277	1276	1276	1273	1273	1272	1271	1272	1276	1278	1278	1274	1270	1267	1259	1256	1270	1278	1280	1269	1262	1250	1239	1233	1223	1216	1215	1215	1219	1222	1229	1242	1252	1234	1214	1198	1186	1170	1153	1138	1124	1106	1085	1076	1071	1061	1059	1047	1022	1000	980	955	931	907
1078	1077	1081	1080	1083	1082	1081	1083	1088	1086	1080	1077	1084	1092	1088	1087	1087	1089	1088	1087	1100	1121	1132	1143	1152	1164	1174	1178	1177	1173	1167	1163	1172	1182	1210	1221	1226	1228	1230	1234	1238	1242	1241	1233	1226	1221	1219	1225	1229	1223	1211	1208	1218	1230	1240	1244	1246	1252	1254	1251	1247	1246	1248	1252	1248	1236	1226	1231	1237	1238	1242	1245	1248	1250	1247	1250	1257	1260	1262	1267	1273	1278	1282	1285	1290	1297	1301	1306	1312	1314	1316	1317	1313	1313	1318	1318	1315	1313	1312	1309	1308	1306	1304	1301	1296	1292	1291	1291	1290	1287	1284	1284	1283	1286	1292	1295	1289	1290	1292	1293	1290	1283	1293	1297	1295	1291	1289	1289	1286	1281	1286	1288	1288	1289	1286	1273	1277	1287	1289	1287	1286	1284	1283	1283	1280	1276	1276	1278	1277	1276	1274	1270	1269	1269	1270	1272	1272	1272	1270	1268	1265	1253	1249	1263	1269	1271	1264	1258	1250	1247	1242	1219	1210	1212	1216	1218	1219	1227	1241	1247	1225	1205	1189	1174	1155	1136	1114	1098	1093	1079	1058	1041	1027	1044	1054	1032	1007	989	970	950	926
1074	1074	1075	1074	1078	1078	1080	1083	1082	1075	1070	1075	1085	1090	1082	1083	1087	1087	1081	1086	1106	1124	1130	1129	1136	1150	1164	1179	1190	1193	1194	1198	1210	1224	1231	1238	1237	1230	1229	1236	1243	1244	1237	1222	1212	1209	1216	1224	1227	1220	1206	1206	1222	1231	1232	1240	1250	1254	1251	1249	1248	1250	1251	1250	1242	1227	1225	1230	1236	1239	1236	1237	1241	1245	1242	1242	1248	1252	1254	1258	1262	1266	1270	1273	1277	1285	1291	1296	1300	1303	1306	1308	1307	1306	1307	1309	1308	1307	1306	1302	1301	1299	1296	1294	1290	1286	1285	1283	1280	1279	1277	1276	1274	1277	1284	1288	1282	1273	1282	1284	1275	1266	1283	1288	1284	1283	1282	1277	1279	1277	1274	1281	1283	1282	1280	1268	1269	1279	1282	1279	1277	1275	1275	1275	1273	1271	1272	1271	1270	1268	1269	1269	1268	1266	1265	1265	1267	1267	1267	1267	1262	1250	1246	1247	1247	1249	1252	1254	1255	1251	1236	1216	1208	1215	1223	1217	1217	1227	1244	1251	1226	1197	1173	1160	1142	1127	1108	1086	1075	1071	1056	1037	1028	1035	1041	1029	1009	991	970	945	920
1071	1070	1070	1068	1069	1076	1077	1077	1073	1067	1067	1073	1078	1076	1074	1078	1083	1083	1079	1082	1091	1103	1107	1111	1120	1134	1151	1171	1190	1206	1216	1223	1226	1231	1237	1244	1243	1236	1230	1230	1233	1231	1223	1212	1201	1193	1191	1202	1215	1214	1206	1203	1217	1225	1227	1232	1241	1245	1244	1243	1242	1243	1245	1245	1239	1224	1221	1226	1234	1238	1233	1229	1232	1235	1237	1236	1239	1245	1251	1257	1258	1261	1267	1272	1276	1281	1287	1292	1295	1297	1299	1301	1301	1296	1297	1301	1299	1297	1295	1294	1291	1288	1287	1286	1283	1280	1276	1276	1275	1274	1267	1267	1269	1272	1279	1282	1279	1267	1273	1277	1255	1264	1280	1282	1278	1273	1272	1273	1271	1270	1269	1274	1276	1278	1278	1269	1264	1275	1278	1275	1273	1271	1270	1270	1268	1268	1269	1261	1261	1263	1263	1264	1264	1262	1259	1260	1262	1263	1263	1261	1256	1250	1236	1224	1220	1221	1230	1238	1243	1236	1222	1215	1212	1211	1217	1217	1220	1232	1238	1230	1212	1195	1178	1155	1127	1110	1099	1081	1064	1055	1042	1027	1015	998	994	1003	998	983	965	943	920
1069	1070	1073	1070	1067	1072	1071	1068	1064	1062	1067	1074	1072	1069	1072	1075	1077	1078	1078	1080	1087	1095	1101	1108	1117	1131	1148	1170	1193	1214	1229	1240	1246	1251	1255	1253	1249	1238	1225	1218	1219	1221	1215	1204	1184	1173	1177	1190	1206	1209	1206	1208	1217	1220	1222	1228	1233	1237	1238	1237	1233	1234	1238	1239	1235	1226	1214	1220	1229	1235	1229	1222	1221	1221	1225	1228	1230	1234	1240	1245	1247	1248	1254	1262	1268	1271	1274	1279	1283	1285	1286	1287	1288	1289	1289	1287	1287	1289	1289	1283	1280	1278	1276	1275	1274	1272	1270	1268	1266	1265	1265	1264	1263	1268	1274	1275	1273	1267	1268	1272	1247	1256	1274	1273	1270	1267	1267	1267	1262	1264	1267	1269	1272	1272	1272	1268	1263	1268	1272	1273	1272	1270	1267	1266	1267	1268	1265	1257	1260	1265	1260	1256	1258	1261	1259	1259	1258	1257	1257	1255	1252	1245	1227	1218	1222	1222	1223	1223	1221	1213	1207	1211	1214	1213	1215	1219	1228	1245	1255	1239	1217	1194	1169	1146	1124	1103	1088	1075	1062	1046	1027	1022	1015	1002	995	996	991	976	959	939	918
1059	1063	1066	1069	1068	1064	1064	1065	1062	1059	1064	1069	1067	1067	1072	1073	1072	1074	1076	1079	1083	1089	1096	1103	1111	1124	1144	1167	1192	1213	1233	1249	1263	1268	1264	1256	1246	1231	1226	1218	1214	1211	1206	1198	1179	1169	1170	1174	1193	1202	1205	1215	1217	1215	1217	1226	1233	1235	1233	1231	1228	1233	1239	1238	1232	1219	1210	1217	1224	1231	1220	1215	1217	1217	1220	1227	1231	1233	1236	1239	1244	1249	1254	1257	1263	1267	1271	1274	1277	1281	1283	1282	1279	1280	1283	1282	1281	1281	1279	1276	1273	1272	1268	1267	1266	1266	1265	1261	1254	1256	1259	1258	1258	1262	1264	1267	1270	1263	1264	1265	1241	1249	1265	1261	1261	1265	1265	1257	1254	1259	1261	1264	1266	1265	1262	1262	1258	1254	1261	1266	1265	1262	1260	1259	1262	1265	1263	1258	1255	1253	1253	1255	1256	1257	1253	1256	1258	1254	1253	1256	1255	1247	1235	1226	1218	1211	1213	1216	1213	1208	1204	1203	1206	1208	1213	1222	1239	1257	1254	1230	1208	1185	1158	1135	1117	1096	1076	1060	1046	1028	1012	1009	1003	995	987	980	972	956	939	922	901
1060	1063	1064	1065	1062	1059	1060	1061	1058	1056	1061	1063	1063	1066	1069	1070	1069	1070	1072	1075	1079	1085	1089	1095	1105	1119	1139	1163	1187	1209	1229	1249	1268	1272	1259	1246	1229	1218	1228	1223	1212	1200	1191	1185	1171	1155	1147	1145	1172	1191	1203	1207	1204	1204	1212	1217	1225	1227	1222	1224	1223	1227	1229	1230	1222	1213	1201	1207	1215	1220	1217	1212	1211	1212	1215	1220	1224	1227	1229	1231	1236	1242	1246	1249	1253	1258	1261	1265	1270	1274	1275	1275	1272	1271	1269	1270	1274	1274	1272	1269	1266	1266	1262	1260	1259	1258	1258	1254	1245	1243	1246	1250	1254	1255	1256	1259	1265	1256	1256	1251	1234	1250	1259	1253	1255	1260	1259	1254	1248	1253	1256	1257	1259	1261	1260	1260	1253	1250	1261	1264	1263	1259	1256	1256	1262	1263	1262	1258	1250	1251	1253	1251	1246	1251	1253	1255	1255	1251	1251	1252	1252	1248	1236	1219	1212	1212	1212	1213	1207	1206	1207	1206	1206	1211	1219	1234	1253	1255	1234	1207	1183	1162	1139	1116	1098	1080	1060	1038	1019	1008	1001	992	984	975	962	952	946	931	916	906	895
1053	1055	1059	1062	1054	1050	1050	1052	1053	1053	1054	1054	1057	1060	1062	1063	1063	1067	1069	1071	1073	1078	1083	1088	1098	1114	1134	1156	1176	1198	1218	1239	1260	1267	1254	1234	1213	1208	1214	1211	1208	1198	1179	1159	1140	1124	1124	1139	1158	1179	1193	1198	1196	1204	1212	1217	1224	1224	1221	1224	1228	1230	1229	1227	1218	1205	1197	1202	1210	1219	1217	1206	1204	1209	1212	1214	1216	1218	1223	1225	1230	1235	1238	1242	1247	1249	1252	1256	1263	1265	1266	1266	1265	1260	1257	1260	1266	1267	1266	1262	1259	1257	1255	1254	1252	1251	1250	1246	1240	1232	1235	1243	1249	1252	1254	1254	1252	1246	1238	1239	1231	1247	1251	1246	1245	1245	1246	1247	1242	1247	1252	1252	1254	1255	1253	1250	1246	1243	1252	1257	1258	1255	1249	1246	1253	1258	1259	1254	1247	1244	1246	1248	1246	1247	1250	1252	1248	1246	1248	1248	1247	1243	1234	1216	1207	1207	1209	1211	1205	1203	1204	1206	1208	1216	1233	1258	1266	1245	1217	1191	1166	1145	1122	1100	1081	1065	1047	1029	1009	1006	1003	993	982	967	948	939	942	933	920	919	921
1046	1048	1051	1050	1046	1043	1044	1046	1051	1049	1047	1048	1053	1056	1058	1058	1058	1063	1065	1065	1065	1069	1074	1080	1091	1106	1126	1147	1166	1187	1210	1233	1249	1256	1246	1231	1218	1205	1188	1183	1191	1184	1168	1152	1136	1120	1113	1118	1137	1158	1178	1189	1191	1202	1206	1211	1220	1222	1220	1220	1224	1226	1227	1222	1208	1196	1184	1187	1204	1212	1209	1201	1194	1202	1208	1209	1209	1209	1214	1218	1223	1228	1233	1237	1240	1242	1244	1248	1253	1256	1257	1257	1256	1250	1251	1255	1260	1260	1256	1254	1253	1250	1248	1248	1247	1246	1244	1238	1233	1232	1234	1239	1243	1246	1246	1242	1247	1243	1232	1233	1238	1250	1247	1244	1248	1246	1243	1244	1246	1246	1245	1245	1246	1250	1250	1246	1242	1246	1250	1251	1251	1248	1244	1243	1249	1255	1254	1250	1244	1241	1242	1243	1246	1246	1246	1247	1243	1243	1242	1241	1240	1234	1224	1213	1205	1201	1204	1207	1205	1203	1198	1201	1208	1218	1237	1259	1255	1224	1203	1183	1164	1143	1121	1101	1084	1066	1054	1049	1041	1034	1023	1011	992	971	950	948	955	952	944	947	952
1045	1048	1045	1036	1038	1042	1045	1045	1042	1041	1044	1050	1054	1054	1057	1061	1062	1062	1059	1056	1061	1063	1067	1078	1092	1108	1126	1145	1164	1186	1210	1235	1256	1268	1254	1231	1212	1192	1175	1172	1174	1163	1144	1132	1124	1121	1117	1114	1132	1153	1170	1183	1190	1195	1203	1207	1212	1216	1217	1218	1221	1222	1223	1218	1199	1186	1177	1175	1189	1201	1199	1191	1184	1190	1197	1200	1202	1202	1205	1207	1213	1218	1223	1228	1231	1234	1237	1240	1241	1245	1248	1248	1248	1243	1242	1243	1252	1254	1249	1246	1244	1243	1243	1241	1238	1238	1238	1233	1229	1225	1221	1227	1234	1237	1235	1232	1236	1236	1224	1222	1230	1240	1241	1239	1238	1236	1235	1236	1237	1240	1238	1235	1237	1243	1242	1238	1234	1238	1243	1244	1245	1241	1239	1241	1247	1250	1248	1244	1239	1237	1237	1238	1241	1242	1241	1241	1239	1239	1237	1236	1233	1228	1211	1204	1206	1205	1205	1204	1197	1199	1202	1204	1211	1222	1245	1272	1261	1224	1195	1171	1153	1139	1123	1111	1098	1082	1073	1070	1064	1056	1040	1017	997	991	988	987	988	987	983	982	983
1040	1042	1040	1036	1035	1035	1031	1029	1025	1033	1039	1043	1046	1049	1053	1056	1056	1056	1056	1054	1055	1056	1060	1073	1090	1105	1122	1140	1160	1183	1209	1234	1254	1265	1257	1236	1217	1196	1177	1166	1162	1152	1134	1119	1113	1115	1116	1116	1127	1144	1159	1174	1190	1193	1198	1203	1203	1208	1213	1216	1220	1220	1219	1212	1191	1178	1174	1173	1183	1200	1199	1181	1180	1188	1191	1194	1196	1201	1206	1210	1214	1217	1219	1224	1228	1229	1230	1230	1231	1236	1242	1243	1239	1233	1236	1244	1249	1249	1247	1245	1241	1239	1237	1234	1231	1231	1231	1223	1220	1222	1223	1227	1229	1229	1229	1229	1233	1230	1219	1217	1224	1233	1237	1233	1229	1226	1228	1232	1231	1234	1234	1232	1235	1240	1234	1228	1229	1229	1234	1238	1239	1238	1235	1238	1243	1245	1244	1238	1233	1229	1230	1235	1236	1236	1235	1237	1232	1231	1234	1237	1232	1227	1214	1206	1201	1200	1199	1200	1195	1194	1199	1206	1214	1229	1252	1274	1259	1226	1197	1172	1158	1151	1135	1118	1107	1099	1093	1092	1085	1073	1053	1025	1005	1013	1021	1022	1019	1018	1012	1010	1011
1036	1036	1036	1035	1036	1037	1036	1036	1039	1042	1043	1042	1044	1045	1050	1053	1054	1053	1053	1047	1047	1053	1058	1069	1084	1099	1115	1134	1154	1177	1201	1226	1246	1262	1258	1236	1216	1197	1176	1158	1153	1146	1129	1115	1112	1114	1116	1116	1122	1138	1156	1172	1188	1194	1191	1197	1195	1201	1208	1213	1217	1218	1215	1201	1182	1171	1160	1166	1180	1199	1187	1169	1174	1183	1185	1187	1188	1191	1193	1194	1199	1203	1204	1206	1211	1215	1217	1218	1220	1223	1227	1231	1229	1225	1225	1234	1238	1238	1238	1237	1233	1231	1231	1229	1226	1226	1225	1220	1215	1218	1225	1227	1223	1221	1222	1224	1228	1224	1217	1214	1216	1224	1230	1226	1223	1221	1223	1227	1229	1229	1230	1230	1233	1236	1229	1225	1227	1228	1232	1236	1234	1233	1235	1236	1240	1243	1242	1235	1229	1230	1231	1231	1232	1238	1238	1235	1227	1229	1232	1236	1229	1220	1207	1193	1193	1194	1194	1196	1196	1196	1201	1208	1220	1239	1264	1278	1257	1224	1195	1179	1175	1165	1151	1140	1130	1127	1124	1119	1104	1081	1059	1046	1037	1043	1048	1048	1046	1045	1037	1035	1035
1029	1030	1029	1032	1035	1036	1036	1037	1035	1032	1036	1037	1038	1040	1044	1046	1046	1047	1047	1043	1044	1052	1058	1069	1082	1097	1114	1132	1152	1174	1197	1218	1236	1253	1251	1229	1210	1191	1173	1159	1151	1138	1120	1109	1110	1115	1120	1123	1127	1139	1158	1175	1182	1188	1190	1190	1193	1199	1205	1211	1215	1215	1209	1196	1176	1155	1149	1157	1178	1201	1193	1173	1172	1175	1178	1181	1184	1186	1189	1190	1192	1196	1199	1201	1204	1207	1209	1213	1216	1218	1220	1222	1222	1222	1223	1229	1230	1230	1229	1228	1225	1224	1224	1224	1222	1220	1219	1215	1213	1216	1222	1222	1219	1217	1216	1216	1220	1217	1211	1206	1206	1214	1221	1220	1216	1214	1215	1217	1220	1221	1221	1220	1219	1221	1223	1221	1218	1219	1223	1227	1227	1227	1229	1232	1234	1235	1235	1234	1229	1223	1221	1228	1230	1229	1228	1230	1229	1226	1224	1226	1228	1220	1208	1197	1192	1191	1193	1194	1195	1194	1200	1209	1226	1249	1276	1280	1261	1229	1197	1190	1195	1176	1165	1170	1159	1155	1155	1140	1116	1086	1064	1063	1071	1074	1077	1080	1080	1078	1074	1067	1060
1017	1019	1019	1020	1021	1023	1026	1027	1024	1021	1025	1029	1030	1032	1035	1036	1037	1040	1043	1043	1041	1050	1055	1065	1079	1094	1110	1129	1150	1172	1195	1217	1235	1248	1243	1227	1213	1201	1186	1171	1155	1136	1116	1105	1104	1109	1116	1118	1121	1133	1154	1172	1181	1184	1194	1189	1187	1194	1198	1203	1208	1207	1196	1180	1168	1154	1140	1143	1168	1182	1177	1164	1164	1169	1172	1174	1177	1181	1184	1187	1189	1190	1193	1196	1198	1200	1203	1207	1211	1213	1214	1215	1214	1215	1217	1221	1223	1221	1217	1218	1218	1219	1218	1217	1216	1215	1212	1206	1207	1212	1215	1216	1216	1216	1213	1211	1216	1210	1202	1202	1206	1215	1218	1218	1215	1212	1214	1218	1219	1219	1219	1219	1219	1218	1217	1214	1215	1216	1220	1222	1220	1223	1226	1228	1233	1233	1232	1231	1226	1220	1223	1228	1231	1228	1226	1227	1228	1222	1215	1218	1223	1217	1204	1192	1186	1190	1190	1191	1192	1194	1201	1215	1238	1264	1287	1283	1264	1238	1216	1210	1208	1190	1187	1197	1184	1180	1180	1155	1125	1099	1082	1080	1092	1095	1094	1105	1114	1118	1112	1100	1088
1014	1015	1015	1012	1012	1013	1015	1016	1019	1020	1022	1024	1025	1026	1029	1031	1031	1034	1041	1040	1038	1050	1056	1067	1080	1095	1112	1130	1152	1174	1199	1218	1235	1251	1244	1225	1214	1204	1193	1178	1159	1135	1113	1102	1102	1107	1113	1116	1122	1138	1158	1167	1171	1181	1187	1187	1188	1193	1197	1201	1206	1197	1189	1176	1160	1140	1129	1137	1161	1176	1168	1155	1158	1164	1166	1168	1171	1174	1177	1180	1181	1182	1184	1188	1190	1192	1196	1201	1203	1204	1206	1206	1205	1203	1200	1206	1209	1209	1207	1209	1210	1210	1210	1211	1211	1211	1210	1206	1199	1196	1203	1207	1208	1207	1208	1209	1213	1209	1202	1198	1196	1201	1201	1209	1211	1208	1208	1207	1204	1206	1212	1212	1213	1214	1213	1212	1208	1210	1215	1216	1215	1217	1221	1223	1225	1227	1226	1225	1223	1219	1220	1226	1228	1226	1223	1222	1223	1219	1214	1213	1214	1211	1198	1186	1183	1186	1182	1182	1188	1196	1210	1229	1252	1276	1297	1291	1266	1247	1242	1230	1208	1202	1215	1218	1205	1206	1204	1175	1137	1115	1114	1122	1130	1139	1137	1134	1134	1139	1133	1119	1102
1019	1020	1019	1016	1015	1016	1013	1014	1020	1021	1021	1023	1024	1028	1029	1031	1031	1032	1034	1035	1035	1042	1051	1061	1074	1089	1106	1125	1149	1174	1198	1218	1235	1247	1242	1226	1215	1210	1200	1190	1174	1151	1130	1113	1105	1104	1111	1116	1121	1137	1157	1169	1166	1179	1184	1186	1188	1190	1194	1198	1195	1180	1175	1171	1156	1134	1122	1129	1154	1173	1163	1151	1154	1157	1160	1163	1167	1168	1170	1171	1171	1173	1175	1179	1182	1183	1188	1194	1196	1196	1197	1197	1197	1196	1192	1194	1198	1206	1208	1209	1207	1203	1203	1208	1208	1207	1203	1197	1195	1196	1200	1202	1200	1198	1203	1204	1206	1202	1194	1192	1192	1196	1198	1201	1201	1203	1206	1202	1200	1202	1206	1207	1211	1213	1210	1204	1204	1207	1210	1212	1213	1216	1218	1219	1222	1223	1222	1221	1220	1216	1216	1220	1222	1221	1218	1218	1219	1217	1213	1207	1208	1206	1199	1194	1186	1183	1181	1183	1190	1203	1220	1241	1263	1286	1302	1296	1276	1266	1260	1240	1214	1208	1224	1231	1216	1220	1219	1189	1153	1131	1127	1138	1142	1151	1149	1149	1158	1159	1152	1137	1128
1021	1021	1019	1014	1014	1016	1017	1023	1024	1020	1019	1019	1020	1021	1024	1029	1030	1030	1029	1022	1031	1038	1048	1059	1072	1087	1103	1124	1150	1178	1204	1223	1240	1255	1247	1230	1225	1219	1212	1203	1190	1172	1145	1119	1104	1104	1110	1113	1119	1135	1154	1165	1163	1174	1183	1182	1180	1180	1185	1190	1180	1161	1155	1161	1151	1133	1120	1122	1146	1159	1152	1146	1146	1150	1153	1158	1163	1164	1163	1164	1164	1165	1167	1169	1172	1174	1178	1181	1184	1186	1187	1187	1189	1190	1188	1189	1190	1196	1198	1199	1197	1194	1194	1198	1200	1198	1197	1194	1189	1192	1197	1198	1197	1196	1195	1195	1197	1197	1192	1188	1187	1186	1184	1189	1191	1195	1198	1196	1196	1196	1198	1201	1206	1209	1205	1195	1198	1204	1205	1206	1209	1211	1213	1215	1220	1221	1220	1219	1216	1211	1213	1214	1216	1221	1219	1217	1214	1207	1199	1197	1197	1194	1190	1188	1181	1179	1185	1193	1202	1216	1232	1254	1277	1298	1313	1306	1289	1280	1267	1243	1230	1232	1247	1246	1238	1241	1230	1193	1166	1159	1159	1167	1175	1182	1182	1183	1190	1186	1174	1163	1156
1014	1015	1015	1015	1016	1012	1010	1016	1018	1018	1017	1018	1019	1022	1023	1025	1026	1026	1026	1024	1027	1034	1045	1057	1069	1083	1100	1124	1154	1184	1212	1232	1247	1257	1249	1234	1230	1224	1218	1211	1201	1181	1150	1121	1105	1106	1108	1110	1118	1136	1152	1159	1159	1170	1179	1178	1169	1171	1176	1180	1171	1148	1141	1149	1137	1121	1115	1127	1148	1160	1148	1136	1144	1147	1149	1153	1158	1161	1159	1159	1161	1163	1164	1166	1170	1174	1174	1173	1175	1177	1180	1185	1187	1188	1185	1182	1185	1190	1190	1191	1190	1189	1190	1193	1195	1194	1194	1189	1183	1186	1190	1193	1195	1193	1190	1190	1190	1194	1192	1185	1181	1178	1175	1183	1188	1189	1190	1191	1190	1189	1191	1194	1199	1200	1198	1191	1191	1196	1199	1201	1201	1201	1203	1206	1208	1210	1211	1211	1211	1209	1209	1205	1210	1212	1209	1205	1201	1196	1188	1184	1181	1178	1176	1177	1175	1181	1193	1206	1217	1228	1243	1267	1293	1314	1324	1317	1299	1286	1271	1257	1255	1260	1270	1268	1263	1263	1246	1204	1179	1183	1196	1205	1210	1215	1218	1214	1213	1210	1191	1173	1166
1011	1011	1011	1009	1010	1008	1008	1010	1013	1015	1017	1018	1019	1020	1021	1022	1024	1025	1025	1023	1024	1033	1043	1055	1069	1084	1103	1129	1161	1190	1211	1228	1240	1244	1239	1233	1229	1222	1215	1210	1200	1180	1149	1121	1106	1102	1104	1108	1117	1134	1149	1159	1154	1172	1175	1178	1168	1170	1172	1171	1159	1142	1131	1131	1124	1112	1111	1128	1149	1152	1140	1129	1136	1141	1142	1146	1148	1150	1152	1152	1156	1156	1155	1161	1165	1169	1169	1169	1171	1172	1172	1179	1183	1182	1180	1179	1181	1182	1182	1186	1185	1183	1182	1187	1191	1190	1188	1184	1180	1180	1180	1184	1189	1185	1186	1187	1185	1189	1185	1179	1177	1180	1181	1181	1186	1188	1188	1189	1190	1190	1192	1194	1197	1196	1192	1189	1190	1196	1201	1201	1201	1200	1201	1204	1206	1209	1211	1209	1208	1205	1208	1214	1215	1215	1210	1197	1185	1183	1178	1176	1170	1162	1168	1174	1180	1192	1205	1218	1230	1245	1263	1287	1313	1333	1342	1329	1299	1291	1284	1276	1272	1277	1287	1290	1286	1284	1263	1222	1204	1206	1215	1228	1233	1232	1237	1235	1229	1218	1193	1174	1180
1004	1004	1004	1004	1003	1003	1004	1005	1005	1006	1010	1013	1014	1013	1013	1015	1021	1024	1021	1018	1023	1032	1037	1044	1055	1072	1096	1126	1158	1187	1209	1225	1235	1239	1239	1233	1227	1219	1210	1201	1189	1167	1139	1112	1099	1097	1098	1107	1123	1139	1150	1153	1154	1165	1168	1169	1166	1166	1165	1161	1151	1130	1117	1118	1111	1108	1114	1132	1148	1145	1128	1129	1133	1136	1137	1142	1146	1147	1148	1145	1147	1151	1151	1157	1162	1165	1163	1165	1167	1168	1167	1171	1174	1174	1176	1176	1172	1172	1173	1181	1180	1176	1174	1178	1183	1183	1182	1181	1180	1174	1172	1177	1182	1180	1176	1177	1180	1182	1180	1172	1173	1177	1174	1170	1179	1183	1182	1182	1180	1180	1183	1186	1188	1188	1185	1183	1185	1191	1194	1192	1191	1193	1196	1198	1196	1199	1203	1202	1202	1200	1201	1200	1205	1206	1199	1190	1181	1174	1168	1168	1164	1162	1168	1179	1194	1209	1222	1234	1250	1268	1288	1310	1335	1352	1354	1333	1297	1301	1302	1292	1283	1292	1303	1305	1310	1302	1273	1236	1238	1245	1233	1249	1261	1250	1257	1267	1251	1216	1194	1201	1201
999	999	999	999	999	999	1002	1003	1002	1003	1007	1009	1011	1008	1008	1011	1016	1018	1015	1012	1022	1029	1032	1037	1046	1065	1093	1124	1154	1182	1204	1221	1231	1235	1233	1229	1222	1216	1210	1201	1188	1165	1136	1114	1097	1090	1089	1101	1125	1142	1149	1147	1151	1165	1169	1168	1164	1159	1157	1151	1143	1128	1118	1109	1097	1097	1115	1135	1142	1133	1122	1123	1128	1130	1130	1139	1142	1142	1143	1140	1139	1142	1146	1151	1157	1159	1159	1159	1158	1160	1162	1166	1169	1168	1170	1168	1163	1162	1164	1171	1175	1172	1170	1171	1174	1177	1179	1180	1176	1170	1169	1173	1177	1176	1174	1176	1175	1173	1175	1172	1168	1170	1174	1174	1180	1177	1171	1178	1182	1182	1183	1183	1182	1178	1177	1179	1180	1185	1190	1187	1184	1190	1194	1194	1191	1196	1200	1200	1198	1194	1195	1196	1199	1194	1185	1181	1176	1166	1162	1158	1161	1166	1178	1195	1215	1230	1245	1258	1272	1291	1307	1327	1353	1365	1353	1329	1313	1317	1318	1313	1303	1310	1318	1324	1322	1306	1285	1263	1256	1267	1261	1251	1276	1277	1267	1270	1255	1230	1204	1202	1204
998	998	997	995	994	996	1001	1003	1005	1010	1011	1012	1012	1013	1013	1014	1014	1013	1010	1008	1014	1019	1024	1033	1047	1066	1092	1119	1146	1174	1198	1216	1228	1231	1228	1224	1217	1209	1203	1196	1185	1163	1136	1114	1093	1083	1089	1106	1126	1140	1145	1146	1150	1161	1165	1164	1161	1154	1151	1147	1141	1133	1121	1103	1092	1098	1118	1135	1138	1129	1122	1121	1127	1128	1127	1135	1134	1135	1136	1137	1136	1134	1136	1144	1147	1150	1153	1151	1147	1150	1154	1161	1167	1164	1162	1163	1162	1158	1155	1161	1165	1165	1167	1162	1164	1169	1170	1171	1170	1166	1163	1165	1170	1171	1167	1165	1167	1168	1165	1163	1160	1163	1167	1171	1177	1170	1164	1166	1174	1178	1179	1182	1178	1173	1171	1174	1178	1182	1185	1184	1182	1187	1190	1188	1186	1190	1195	1195	1192	1189	1192	1201	1199	1190	1181	1175	1168	1163	1159	1156	1164	1177	1195	1217	1237	1256	1274	1292	1304	1317	1324	1340	1363	1376	1359	1335	1346	1348	1347	1337	1336	1344	1353	1356	1342	1317	1302	1301	1297	1296	1285	1285	1299	1300	1290	1280	1252	1227	1223	1205	1201
992	994	993	992	989	988	991	996	1000	1002	1003	1003	1004	1008	1011	1010	1010	1010	1006	1004	1004	1008	1014	1026	1044	1063	1086	1111	1137	1164	1191	1210	1221	1223	1217	1211	1206	1200	1192	1185	1177	1166	1145	1120	1096	1081	1090	1114	1131	1140	1142	1144	1148	1155	1157	1155	1152	1148	1146	1145	1142	1135	1118	1100	1091	1099	1119	1135	1137	1132	1120	1125	1131	1129	1128	1130	1132	1133	1130	1133	1135	1136	1135	1138	1145	1150	1146	1145	1146	1149	1153	1155	1156	1156	1157	1159	1161	1160	1156	1156	1160	1161	1163	1161	1162	1164	1165	1167	1167	1161	1159	1161	1166	1165	1159	1157	1163	1165	1161	1160	1156	1158	1161	1169	1174	1168	1165	1162	1166	1170	1173	1176	1172	1169	1168	1170	1175	1178	1177	1178	1181	1183	1182	1179	1179	1183	1185	1186	1185	1183	1188	1193	1193	1188	1178	1170	1165	1160	1156	1160	1173	1193	1216	1238	1255	1270	1289	1305	1317	1328	1340	1354	1370	1379	1372	1364	1373	1374	1375	1363	1361	1367	1368	1364	1352	1330	1312	1316	1318	1315	1302	1303	1317	1312	1302	1287	1266	1246	1242	1222	1213
992	996	996	994	991	989	993	1001	1005	1005	1006	1005	1005	1006	1007	1007	1007	1007	1005	1001	1003	1006	1012	1027	1045	1066	1089	1115	1141	1168	1193	1210	1215	1215	1212	1206	1205	1200	1192	1185	1179	1172	1154	1128	1102	1081	1087	1113	1129	1138	1139	1136	1141	1150	1152	1149	1145	1143	1143	1141	1138	1133	1120	1101	1086	1091	1113	1122	1124	1122	1115	1104	1116	1123	1122	1124	1125	1125	1126	1127	1125	1128	1132	1134	1138	1145	1144	1136	1133	1137	1143	1147	1149	1148	1145	1149	1155	1155	1151	1147	1148	1153	1156	1157	1157	1158	1159	1161	1162	1157	1153	1154	1159	1159	1153	1152	1156	1155	1155	1155	1149	1153	1159	1164	1164	1163	1165	1163	1161	1164	1168	1168	1165	1164	1164	1167	1169	1174	1171	1172	1176	1178	1177	1174	1180	1186	1187	1187	1179	1178	1186	1190	1187	1178	1163	1161	1160	1150	1156	1165	1185	1210	1237	1266	1272	1268	1274	1287	1298	1308	1320	1334	1351	1358	1367	1377	1386	1397	1400	1388	1377	1370	1356	1344	1349	1353	1348	1348	1343	1337	1323	1324	1337	1327	1312	1296	1287	1271	1254	1246	1240

================================================
FILE: 2020年/2020.12.05日历/日历_EasyShu.rmd
================================================
---
title: "Calendar plot in R using ggplot2"
author: "庄亮亮"
date: "`r Sys.Date()`"
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
vignette: >
  %\VignetteIndexEntry{Vignette Title}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---
  
  
# 1. ggTimeSeries绘图
  
R中ggTimeSeries包[1]的`ggplot_calendar_heatmap()`函数可以绘制如图6-2-2(a)所示的日历图，但是不能设定日历图每个时间单元的边框格式。

使用`stat_calendar_heatmap()`函数和`ggplot2`包的`ggplot()`函数可以调整日历图每个时间单元的边框格式，具体代码如下所示。其关键是使用`as.integer(strftime())`日期型处理组合函数获取某天对应所在的年份、月份、周数等数据信息。
```{r message=FALSE, warning=FALSE}
library(ggplot2)
library(data.table) # 数据格式依赖
library(ggTimeSeries)
library(RColorBrewer)
```


## 构造随机数据
```{r}
set.seed(2134)
dat <- data.table(
  date = seq(as.Date("2016-01-01"), as.Date("2019-12-31"), "days"),
  ValueCol = runif(1461)
)

dat[, ValueCol := ValueCol + (strftime(date, "%u") %in% c(6,7)*runif(1)*0.75)
][, ValueCol := ValueCol + (abs(as.numeric(strftime(date, "%m")) - 6.5))*runif(1)*0.75
][, ':='(Year = as.integer(strftime(date, "%Y")), # add new column
         month = as.integer(strftime(date, "%m")),
         week = as.integer(strftime(date, "%W")))] # 添加列

MonthLabels <- dat[, list(meanWkofYr = mean(week)), by = c("month")
][, month := month.abb[month]]
```


```{r}
ggplot(data = dat, aes(date = date, fill = ValueCol)) + 
  stat_calendar_heatmap() + 
  scale_fill_gradientn(colours = rev(brewer.pal(11, "Spectral"))) + 
  scale_y_continuous(name = NULL,
                     breaks = seq(7, 1, -1), 
                     labels = c("Mon", "Tue", "Wed", 
                                "Thu", "Fri", "Sat", "Sun")) + 
  scale_x_continuous(name = NULL, 
                     breaks = MonthLabels$meanWkofYr, 
                     labels = MonthLabels$month, 
                     expand = c(0,0)) + 
  facet_wrap(~Year, ncol = 1, strip.position = "right") + 
  theme(panel.background = element_blank(),
        panel.border = element_blank(),
        strip.background = element_blank(),
        strip.text = element_text(size = 13, face = "plain", color = "black"),
        axis.line = element_line(colour = "black", size = 0.25),
        axis.title = element_text(size = 10, face = "plain", color = "black"),
        axis.text = element_text(size = 10, face = "plain", color = "black"))

```


# 2.`geom_tile()`

使用R中`ggplot2`包的`geom_tile()`函数，借助`facet_wrap()`函数分面，就可以绘制如图6-2-2(b)所示的以月为单位的日历图，具体代码如下所示。

```{r}

label_mons <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", 
                "Aug", "Sep", "Oct", "Nov", "Dec")
label_wik <- c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")

dat19 <- dat[Year == 2017, list(date, ValueCol, month, week)
][, ':='(weekday = as.integer(strftime(date, "%u")), # 周数
         yearmonth = strftime(date, "%m%Y"),       # 月数
         day = strftime(date, "%d"))              # 天数
][, ':='(monthf = factor(x = month, levels = as.character(1:12),
                         labels = label_mons, ordered = TRUE),
         weekdayf = factor(x = weekday, levels = 1:7, 
                           labels = label_wik, ordered = TRUE),
         yearmonthf = factor(x = yearmonth))
][, ':='(monthweek = 1 + week - min(week)), by = .(monthf)] # 分组聚合
```

```{r}
ggplot(dat19, aes(weekdayf, monthweek, fill = ValueCol)) + 
  geom_tile(color = "white") + 
  geom_text(aes(label = day), size = 3) + 
  scale_fill_gradientn(colours = rev(brewer.pal(11, "Spectral"))) + 
  facet_wrap(~monthf, nrow = 3) + 
  scale_y_reverse(name = "Week of the month") + 
  xlab("Day") +
  theme(strip.text = element_text(size = 11, face = "plain", color = "black"),
        panel.grid = element_blank())
```













================================================
FILE: 2020年/2020.12.05日历/日历教程.md
================================================


## 前言

前两天给大家派送了小编自己定制的2021年日历和月历，看到好多读者下载了，小编表示很欣慰😁。上期推送可见：[R可视乎|2021年日历大派送](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487678&idx=1&sn=4ddebccf2214e8e20a2a798ab23d002f&chksm=ea24ef5add53664c274d47a677804251052135c609cd6207e17c94b49ed6b7908321c8b7890f&token=1431845296&lang=zh_CN#rd)

今天来说说这个包吧，非常简单，比起`ggplot2包`绘制日历要简单的多。

## R中的年历图

该软件包非常易容易使用，因为它仅包含一个命名函数`calendR`。默认情况下，如果未指定任何参数，则该函数将以**横向形式**创建**当年的日历**，并且所有文本将**使用系统的语言**，如下所示：

```{r message=FALSE, warning=FALSE}
# install.packages("calendR") #直接install安装包
library(calendR)
calendR() # 默认为当前年份
```

![](https://imgkr2.cn-bj.ufileos.com/4ff54b53-cba3-4bfa-8855-770096aed79d.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=gml5s7P6A4jqQATn1kadTtRImnc%253D&Expires=1608013809)

如果不希望日历使用操作系统的语言，则可以对其进行修改。例如，使用英语：
```{r}
Sys.setlocale("LC_ALL","English")
```


如果想创建其他年份的年度日历图，则可以在`year`参数上设定，如下所示：
```{r eval=FALSE, include=FALSE}
calendR(year = 2021) 
```


### 日历周开始的设定

默认情况下，日历的周数将从星期日开始。设定参数`start="M"`可以获得从星期一开始日历图。

```{r}
calendR(year = 2021, start = "M") 
```
![](https://imgkr2.cn-bj.ufileos.com/cc39ef13-5589-476a-8c61-a61b6faa4f2e.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=mu8GKTP6cC4RG7vsi2AKz708EhA%253D&Expires=1608013834)

### 为日子增添色彩

`special.days`参数可以为指定的日期添加颜色，`special.col`设置颜色，`low.col`设置其他日期的颜色。

比如下面就是将第9，19等日子设置为特定时间，其颜色为淡蓝色，其他颜色设置为白色。
```{r}
calendR(year = 2021,
        start = "M",
        special.days = c(9, 19, 56, 79, 102,  # Days to color
                         126, 257, 300, 342),
        special.col = "lightblue",            # Color of the specified days
        low.col = "white")                    # Background color of the rest of the days
```
![](https://imgkr2.cn-bj.ufileos.com/679fe6e8-6bfa-4d08-ab4e-1c8c5e57461d.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=mu%252B%252BgQHoZAcTbjr3uPtI1ZepZPY%253D&Expires=1608013854)


> **技巧**：如果要突出日历中所有的周末，可以将`special.days`参数设置为`"weekend"`，此快捷方式将立即为它们全部着色。


```{r}
calendR(year = 2025,
        start = "M",
        special.days = "weekend") # Color all weekends
```
![](https://imgkr2.cn-bj.ufileos.com/2a0d3258-eac2-43a3-a7ba-45f513feb48c.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=H2lVusnh33GCFRMU7hq7RFsg9iI%253D&Expires=1608013868)


为了添加多个事件，您将需要创建一个NA值向量，该向量的长度与相应年份的天数相同。然后将事件添加到相应的日期，你需要在`special.days`参数中指定。

```{r}
events <- rep(NA, 365)

# Set the corresponding events
events[40:45] <- "Trip"
events[213:240] <- "Holidays"
events[252] <- "Birthday"
events[359] <- "Christmas" 

# Creating the calendar with a legend
calendR(year = 2025,
        special.days = events,
        special.col = c("pink", "lightblue", # Colors
                        "lightgreen", "lightsalmon"),
        legend.pos = "right") # Legend to the right
```
![](https://imgkr2.cn-bj.ufileos.com/dfd4d4c3-a806-4c1b-b790-f7e0c9e94ca1.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=L%252FLyY8DPPAQS8IeNfMybOV0ttTk%253D&Expires=1608013883)


## R中的月历图

年度日历前面描述的功能也可用于月度日历中。但是月度日历还可以将文本添加到每月的某几天中。

为了创建月度日历，你需要指定年份和月份。

```{r}
calendR(year = 2021, month = 8)
```

![](https://imgkr2.cn-bj.ufileos.com/b9c9b5e2-1f38-4967-86a8-5ffd1657785d.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=%252FQBfqHVceMMdCIcefdoFM%252Bp%252BgEY%253D&Expires=1608013893)

### 为日子增添色彩

与前面相同，使用`special.days`参数给指定日期加上颜色。

```{r}
calendR(year = 2021, month = 8,
        special.days = c(1, 9, 12, 23, 28),
        special.col = "#bfe2f2",
        low.col = "white")
```

### 在日子里添加文字

使用月度日历图时，可以使用`text`参数向日期添加一些文本，并使用参数`text.pos`指定其位置。使用`text.size`和`text.col`参数修改文本的大小和颜色。

```{r}
calendR(year = 2021, month = 8,        # Year and month
        start = "M",                   # Start the week on Monday
        text = c("Running", "Running", # Add text (only for monthly calendars)
                 "Class"), 
        text.pos = c(5, 16, 25),       # Days of the month where to put the texts 
        text.size = 4.5,               # Font size of the text
        text.col = 4)                  # Color of the texts
```
![](https://imgkr2.cn-bj.ufileos.com/f7315f70-bc22-47f8-91a8-8eba7c2f1c86.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=9V2XPe84pqaIZ7j7Dl8w4%252FXjVUs%253D&Expires=1608013907)

### 添加月相

设置`lunar = TRUE`可以将月相添加到其日期中。`lunar.col`参数设置隐藏区域的颜色，`lunar.size `设置大小。


```{r}
calendR(month = 2,  
        lunar = TRUE,         # Add moons to the calendar
        lunar.col = "gray60", # Color of the non-visible area of the moons
        lunar.size = 7)       # Size of the moons
```
![](https://imgkr2.cn-bj.ufileos.com/1cc52168-4064-491c-ba8d-1192386950b6.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=mOBSYAaXy%252F27GUVhb7fes2z2CvU%253D&Expires=1608013914)


## 学术日历

使用`start_date`和`end_date`创建学术日历。如果你想设置某个时间段（下面是2020年9月-2021年5月31日）的日历，非常使用科研人员，学生。请参阅以下示例：

```{r}
calendR(start_date = "2020-09-01", # Custom start date
        end_date = "2021-05-31",   # Custom end date
        start = "M",               # Start the weeks on Monday
        mbg.col = 4,               # Color of the background of the names of the months
        months.col = "white",      # Color text of the names of the months
        special.days = "weekend",  # Color the weekends
        special.col = "lightblue", # Color of the special.days
        lty = 0,                   # Line type
        bg.col = "#f4f4f4",        # Background color
        title = "Academic calendar 2020-2021", # Title
        title.size = 30,                       # Title size
        orientation = "p")         # Vertical orientation
```
![](https://imgkr2.cn-bj.ufileos.com/87407c79-7a9a-4b88-9237-645d3284824d.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=FxuzUzaVlw2XTdNIiXqMNz8Beoo%253D&Expires=1608013924)

> 当然，我觉得打印出来可能效果更好用吧。这里只是给出一个简单的例子，你可以在这个基础上加上背景以及你喜欢的颜色，可以继续往下看。

## 私人定制

接下来，就是给日历加了背景以及根据**直男审美**把其他颜色进行了调整。下面给出上次大家说还不错的日历的源代码（具体pdf版本可在公众号中回复“**日历**”免费获得）。

> 可以使用`pdf = TRUE`将日历进行导出（默认为A4格式）。可以在`doc_name`参数中指定生成的PDF文件的名称。此外，你可以在几种纸张尺寸之间进行选择以保存日历，从"A6"到，"A0"。但是注意，可能需要微调一些字体尺寸来获得所需的输出。

如果想制作自己的日历，只需修改img的图片，存储的路径（默认在**我的文档**里）。

### 日历
```{r}
img <- "C:/Users/ZLL/Desktop/5.jpg"

calendR(year = 2021,
        start = "M", 
        title.col = "white",
        # Weeks start on Monday
        mbg.col = "#cd853f",               # Background color of the month names
        months.col = "white",      # Color of the text of the month names
        weeknames.col = "white",
        
        special.days = "weekend",  # Color the weekends
        special.col = "#a9a9a9", # Color of the special.days
        lty = 0,                   # Line type (no line)
        weeknames = c("Mo", "Tu",  # Week names
                      "We", "Th",
                      "Fr", "Sa",
                      "Su"),
        title.size = 40,   # Title size
        orientation = "p",
        bg.img = img,
        pdf = TRUE,
        doc_name = "My_calendar_brown"
)  
```
![](https://imgkr2.cn-bj.ufileos.com/e618a4ce-2ec8-4669-bae7-47a07cdf624d.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=blpbAmUFbNBMRoDDQLID3bRXz7A%253D&Expires=1608013937)


### 月历

这里和日历区别最大的点就是：月历要把每个月都输出，可以使用`sapply`函数，把所有月份输出。前面的`invisible`指的是：不显示图片（显示的话太费事了！保存再看更快）。

最后使用`paste0()`将字符串进行粘合（**这个方法非常好用！**），而这里的i是变化的，所以最后生成的pdf文件名不一样但很有规律（除了i不一样）。

```{r}
img <- "C:/Users/ZLL/Desktop/5.jpg"
invisible(sapply(1:12 , function(i) calendR(year = 2021,month = i, pdf = TRUE,                            title.col = "white",
                                            # Weeks start on Monday
                                            mbg.col = "#cd853f",  
                                            # Background color of the month names
                                            months.col = "white",      # Color of the text of the month names
                                            weeknames.col = "white",
                                            special.days = "weekend",  # Color the weekends
                                            special.col = "gray60", # Color of the special.days
                                            lty = 0,                   # Line type (no line)
                                            bg.img = img,
                                            doc_name = file.path("C:\\Users\\ZLL\\Documents\\calendar", paste0("Calendar(gray)_2021_", i))))
                                            )
```

![](https://imgkr2.cn-bj.ufileos.com/02f9a33d-7bc6-48c9-810d-8cdcbd749ff5.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=iiPYDJAkD5Ur%252F88xqhLUSpkuJKA%253D&Expires=1608013946)


### 拓展阅读

其他相关资料，小编收集了一些，想继续研究的可见：

[Calendar plot in R using ggplot2](https://r-coder.com/calendar-plot-r/ "Calendar plot in R using ggplot2")

[对应的github](https://rdrr.io/github/houyunhuang/ggcor/man/ "对应的github")

[calendar详细介绍](https://cran.r-project.org/web/packages/calendar/calendar.pdf "calendar详细介绍")

>> 各位可视化爱好者可以根据教程设置私人化日历，也欢迎后台和小编联系，分享你制作的日历，我们可以制作一期私人日历展示活动！

对应代码与相关数据，请在我的github中获取（文末原文）。喜欢请**一键三连，创作不易，感恩不尽**😄。


================================================
FILE: 2020年/2020.12.05日历/日历教程.rmd
================================================
---
title: "Calendar plot in R using ggplot2"
author: "庄亮亮"
date: "`r Sys.Date()`"
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
vignette: >
  %\VignetteIndexEntry{Vignette Title}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---



## 前言

前两天给大家派送了小编自己定制的2021年日历和月历，看到好多读者下载了，小编表示很欣慰😁。上期推送可见：[R可视乎|2021年日历大派送](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487678&idx=1&sn=4ddebccf2214e8e20a2a798ab23d002f&chksm=ea24ef5add53664c274d47a677804251052135c609cd6207e17c94b49ed6b7908321c8b7890f&token=1431845296&lang=zh_CN#rd)

今天来说说这个包吧，非常简单，比起`ggplot2包`绘制日历要简单很多。

## R中的年历图

该软件包非常易容易使用，因为它仅包含一个命名函数`calendR`。默认情况下，如果未指定任何参数，则该函数将以横向形式创建当年的日历，并且所有文本将使用系统的语言，如下所示：

```{r message=FALSE, warning=FALSE}
# install.packages("calendR") #直接install安装包
library(calendR)
calendR() # 默认为当前年份
```

如果您不希望日历使用操作系统的语言，则可以对其进行修改。例如，如果您希望日历使用英语，则在执行calendR之前输入：
```{r}
Sys.setlocale("LC_ALL","English")
```


如果您希望创建其他年份的年度日历图，则可以在`year`参数上设定，如下所示：
```{r eval=FALSE, include=FALSE}
calendR(year = 2021) 
```


### 日历周开始的设定

默认情况下，日历的周数将从星期日开始。设定参数`start="M"`可以获得从星期一开始日历图。

```{r}
calendR(year = 2021, start = "M") 
```

### 为日子增添色彩

`special.days`参数可以为指定的日期添加颜色，`special.col`设置颜色，`low.col`设置其他日期的颜色。

比如下面就是将第9，19等日子设置为特定时间，其颜色为淡蓝色，其他颜色设置为白色。
```{r}
calendR(year = 2021,
        start = "M",
        special.days = c(9, 19, 56, 79, 102,  # Days to color
                         126, 257, 300, 342),
        special.col = "lightblue",            # Color of the specified days
        low.col = "white")                    # Background color of the rest of the days
```


> **技巧**：如果要突出日历中所有的周末，可以将`special.days`参数设置为`"weekend"`，此快捷方式将立即为它们全部着色。


```{r}
calendR(year = 2025,
        start = "M",
        special.days = "weekend") # Color all weekends
```


为了添加多个事件，您将需要创建一个NA值向量，该向量的长度与相应年份的天数相同。然后将事件添加到相应的日期，你需要在`special.days`参数中指定。

```{r}
events <- rep(NA, 365)

# Set the corresponding events
events[40:45] <- "Trip"
events[213:240] <- "Holidays"
events[252] <- "Birthday"
events[359] <- "Christmas" 

# Creating the calendar with a legend
calendR(year = 2025,
        special.days = events,
        special.col = c("pink", "lightblue", # Colors
                        "lightgreen", "lightsalmon"),
        legend.pos = "right") # Legend to the right
```


## R中的月历图

年度日历前面描述的功能也可用于月度日历中。但是月度日历还可以将文本添加到每月的某几天中。

为了创建月度日历，您需要指定年份和月份，如下所示：

```{r}
calendR(year = 2021, month = 8)
```

### 为日子增添色彩

与前面相同，使用`special.days`参数给指定日期加上颜色。

```{r}
calendR(year = 2021, month = 8,
        special.days = c(1, 9, 12, 23, 28),
        special.col = "#bfe2f2",
        low.col = "white")
```

### 在日子里添加文字

使用月度日历图时，可以使用`text`参数向日期添加一些文本，并使用参数`text.pos`指定其位置。使用`text.size`和`text.col`参数修改文本的大小和颜色。

```{r}
calendR(year = 2021, month = 8,        # Year and month
        start = "M",                   # Start the week on Monday
        text = c("Running", "Running", # Add text (only for monthly calendars)
                 "Class"), 
        text.pos = c(5, 16, 25),       # Days of the month where to put the texts 
        text.size = 4.5,               # Font size of the text
        text.col = 4)                  # Color of the texts
```

### 添加月相

设置`lunar = TRUE`可以将月相添加到其日期中。`lunar.col`参数设置隐藏区域的颜色，`lunar.size `设置大小。


```{r}
calendR(month = 2,  
        lunar = TRUE,         # Add moons to the calendar
        lunar.col = "gray60", # Color of the non-visible area of the moons
        lunar.size = 7)       # Size of the moons
```


## 学术日历

使用`start_date`和`end_date`创建学术日历。如果你想设置某个时间段（下面是2020年9月-2021年5月31日）的日历，非常使用科研人员。请参阅以下示例：

```{r}
calendR(start_date = "2020-09-01", # Custom start date
        end_date = "2021-05-31",   # Custom end date
        start = "M",               # Start the weeks on Monday
        mbg.col = 4,               # Color of the background of the names of the months
        months.col = "white",      # Color text of the names of the months
        special.days = "weekend",  # Color the weekends
        special.col = "lightblue", # Color of the special.days
        lty = 0,                   # Line type
        bg.col = "#f4f4f4",        # Background color
        title = "Academic calendar 2020-2021", # Title
        title.size = 30,                       # Title size
        orientation = "p")         # Vertical orientation
```
> 当然，我觉得打印出来可能效果更好用吧。这里只是给出一个简单的例子，你可以在这个基础上加上背景以及你喜欢的颜色，可以继续往下看。

## 私人定制

接下来，这里就是给日历加了背景以及根据**直男审美**把其他颜色进行了调整。下面给出上次大家说还不错的日历的源代码（具体pdf版本可在公众号中回复“日历”免费获得）。

> 可以使用`pdf = TRUE`将日历进行导出（默认为A4格式）。可以在`doc_name`参数中指定生成的PDF文件的名称。此外，你可以在几种纸张尺寸之间进行选择以保存日历，从"A6"到，"A0"。但是注意，可能需要微调一些字体尺寸来获得所需的输出。

### 日历
```{r}
img <- "C:/Users/ZLL/Desktop/wechat/庄闪闪原创/R/R可视乎/2020.11.13日历/wechat/5.jpg"

calendR(year = 2021,
        start = "M", 
        title.col = "white",
        # Weeks start on Monday
        mbg.col = "#cd853f",               # Background color of the month names
        months.col = "white",      # Color of the text of the month names
        weeknames.col = "white",
        
        special.days = "weekend",  # Color the weekends
        special.col = "#a9a9a9", # Color of the special.days
        lty = 0,                   # Line type (no line)
        weeknames = c("Mo", "Tu",  # Week names
                      "We", "Th",
                      "Fr", "Sa",
                      "Su"),
        title.size = 40,   # Title size
        orientation = "p",
        bg.img = img,
        pdf = TRUE,
        doc_name = "My_calendar_brown"
)  
```


### 月历

这里和日历区别最大的点就是：月历要把每个月都输出，可以使用`sapply`函数，把所有月份输出。前面的`invisible`指的是：不显示图片（太费事了！保存再看更快）。

最后使用`paste0()`将字符串进行粘合（这个方法非常好用！），而这里的i是变化的，所以最后生成的pdf文件名不一样但很有规律。

```{r}
img <- "C:/Users/ZLL/Desktop/5.jpg"
invisible(sapply(1:12 , function(i) calendR(year = 2021,month = i, pdf = TRUE,                            title.col = "white",
                                            # Weeks start on Monday
                                            mbg.col = "#cd853f",  
                                            # Background color of the month names
                                            months.col = "white",      # Color of the text of the month names
                                            weeknames.col = "white",
                                            special.days = "weekend",  # Color the weekends
                                            special.col = "gray60", # Color of the special.days
                                            lty = 0,                   # Line type (no line)
                                            bg.img = img,
                                            doc_name = file.path("C:\\Users\\ZLL\\Documents\\calendar", paste0("Calendar(gray)_2021_", i))))
                                            )
```

### 拓展阅读

其他相关资料，小编收集了一些，想继续研究的可见：

[Calendar plot in R using ggplot2](https://r-coder.com/calendar-plot-r/ "Calendar plot in R using ggplot2")

[对应的github](https://rdrr.io/github/houyunhuang/ggcor/man/ "对应的github")

[calendar详细介绍](https://cran.r-project.org/web/packages/calendar/calendar.pdf "calendar详细介绍")







================================================
FILE: 2020年/2020.12.07瀑布图/Facting_Data.csv
================================================
X_Axis,60,55,50,45,40,35,30,25
360,7.6821,7.3053,6.0619,7.6628,8.3848,9.2971,9.2372,8.6967
370,7.1166,7.0108,6.5378,7.7374,8.4002,9.159,9.0612,7.8567
380,6.2423,6.3366,6.1613,6.9001,7.4425,8.1681,7.7771,6.9062
390,5.3379,5.5059,5.4587,5.8385,6.2378,6.7344,6.3799,5.7448
400,4.4369,4.5943,4.5685,4.7075,4.9636,5.3273,5.0153,4.4864
410,3.5337,3.6765,3.6402,3.7,3.8173,4.1257,3.8898,3.4693
420,2.8664,3.0402,3.0192,3.0142,3.0933,3.3515,3.1961,2.8586
430,2.4889,2.7031,2.677,2.6541,2.7015,2.9365,2.8073,2.5205
440,2.259,2.4977,2.4587,2.4136,2.4313,2.6138,2.5083,2.261
450,2.1903,2.4267,2.369,2.2875,2.259,2.4194,2.3193,2.0908
460,2.2336,2.4609,2.3726,2.2276,2.1406,2.2789,2.1713,1.9664
470,2.4132,2.6218,2.4909,2.252,2.0713,2.1753,2.0677,1.8747
480,2.8172,2.9937,2.7968,2.3905,2.0528,2.1075,1.9865,1.8026
490,3.2794,3.4094,3.1387,2.5632,2.0549,2.0669,1.931,1.759
500,4.0475,4.1306,3.7575,2.9351,2.1385,2.0811,1.909,1.7449
510,5.1939,5.2194,4.7275,3.5788,2.3427,2.1603,1.9435,1.7703
520,6.4268,6.4104,5.8418,4.4074,2.6653,2.3288,2.0293,1.8465
530,7.6299,7.5689,6.9601,5.3072,3.0684,2.5597,2.1563,1.9548
540,8.603,8.5318,7.9234,6.1732,3.503,2.8177,2.3077,2.0799
550,9.462,9.3866,8.7283,6.9871,3.948,3.0762,2.4634,2.2032
560,10.2074,10.0775,9.4402,7.7211,4.3929,3.3475,2.6319,2.3239
570,10.4668,10.38,9.7358,8.0937,4.6863,3.5456,2.7499,2.4151
580,10.6801,10.5651,9.9439,8.4002,4.9343,3.7077,2.8533,2.4826
590,10.8896,10.8426,10.3084,8.8249,5.2337,3.9041,2.9636,2.5708
600,10.9422,10.9354,10.3768,8.8843,5.266,3.8775,2.9292,2.5464
610,10.5836,10.5202,9.9223,8.3873,4.8423,3.4813,2.6388,2.2992
620,9.4338,9.3364,8.9144,7.0977,3.8649,2.7702,2.1032,1.8811
630,7.7169,7.6263,7.1268,5.3149,2.7358,1.9857,1.5521,1.4281
640,5.4489,5.4942,4.9482,3.4699,1.7938,1.3687,1.1237,1.0436
650,3.1697,3.3857,2.9798,2.0419,1.1621,0.9553,0.825,0.7715
660,1.6842,1.9811,1.7494,1.2512,0.827,0.7299,0.659,0.6182
670,0.8644,1.2072,1.0891,0.8506,0.6589,0.6025,0.561,0.5258
680,0.4683,0.8124,0.7565,0.6471,0.5664,0.5217,0.4929,0.4651
690,0.28,0.6149,0.5859,0.5363,0.5038,0.4631,0.4401,0.4158
700,0.1846,0.5057,0.4905,0.47,0.4586,0.4177,0.4004,0.3769
710,0.1373,0.4475,0.4384,0.4311,0.4304,0.3854,0.3691,0.3489
720,0.1095,0.4121,0.4062,0.405,0.4102,0.3616,0.3458,0.3268
730,0.0909,0.3897,0.3852,0.3868,0.3971,0.341,0.3276,0.3103
740,0.0791,0.3749,0.37,0.3743,0.3865,0.3293,0.3159,0.2969


================================================
FILE: 2020年/2020.12.07瀑布图/瀑布图.rmd
================================================
---
title: "瀑布图"
author:
  - 庄闪闪
documentclass: ctexart
keywords:
  - 中文
  - R Markdown
output:
  rticles::ctex:
    fig_caption: yes
    number_sections: yes
    toc: yes
editor_options: 
  chunk_output_type: console
---

# 简介

**瀑布图（waterfall plot）**用于展示拥有相同的X轴变量数据（如相同的时间序列）、不同的Y 轴离散型变量（如不同的类别变量）和Z轴数值变量，可以清晰地展示不同变量之间的数据变化关系。

# 三维瀑布图

三维瀑布图可以看成是多数据系列三维面积图。R中`plot3D`包的`polygon3D()`函数和`segments3D()`函数可以绘制三维面积图，`lines3D()`函数可以绘制三维曲线图，所以，综合这几个函数可以绘制三维瀑布图，该代码，数据来源[R语言书可视化之美](https://github.com/EasyChart/Beautiful-Visualization-with-R)。

> 这是一本非常棒的R可视化书籍，小编预计在年底进行一次抽奖送这本书的活动，进行期待。

## 数据介绍

原始数据如下所示：一共39行，9列数据。列表示不同组别，行表示不同x坐标下的数值大小，其中第一列表示x坐标位置。
```{r message=FALSE, warning=FALSE}
library(plot3D)
library(RColorBrewer)
mydata0 <- read.csv("Facting_Data.csv",check.names =FALSE)
head(mydata0)
```

之后对数据进行一个变换，变成我们绘图所需要的数据格式（这里最费时间了）。因为从行来看，数据是离散的绘制出来效果不是很好，于是使用插值样条函数(`spline`)对原始数据进行插值，变成了300行数据（`n=300`）。这里做了一个for循环，把所有数据都进行了插值，列名存在了`variable`中。`mydata`的前6行结果如下所示：

```{r message=FALSE, warning=FALSE}
N <- ncol(mydata0)-1
mydata <- data.frame(x=numeric(),y=numeric(),variable=character())

for(i in 1:N){
  newdata <- data.frame(spline(mydata0[,1],mydata0[,i+1],n=300,method= "natural")) #进行插值
  newdata$variable<-colnames(mydata0)[i+1]
  mydata <- rbind(mydata,newdata)
}

mydata$variable <- as.numeric(mydata$variable)
head(mydata)

group <- unique(mydata$variable) #每组的名称
M <- length(group) #组数
```

进行颜色的处理，以及图片版式的设置。
```{r message=FALSE, warning=FALSE}
gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}

colormap <- rev(gg_color_hue(M))#brewer.pal(M,'RdYlGn')

pmar <- par(mar = c(5.1, 4.1, 4.1, 6.1))
```


## 基础版本

这里先构建一个空的立方体，注意x，y，z轴的坐标范围，所以你得看看原始数据，再定范围，不能一股脑地拿来用。该函数的内部参数，在[R可视乎|等高线](等高线)有提过一些。或者将光标放在该函数，按F1寻求帮助文档，在这里就不做过多解释。 
```{r message=FALSE, warning=FALSE}
perspbox(z=as.vector(0),#add=TRUE,
          xlim=c(20,70),ylim=c(360,750),zlim=c(0,15),
          ticktype = "detailed",bty = "f",box = TRUE,
          colkey = FALSE,theta = -110, phi = 20, d=3)
```

使用`polygon3D`函数和`lines3D`函数将每一类的数据填充到立方体中。这里代码主要还是用base包写的，你可以试试tidyverse流写。
```{r message=FALSE, warning=FALSE}
for (i in 1:M){
  df0<-mydata[mydata$variable==group[i],]
  Ndf<-nrow(df0)
  df<-rbind(df0,c(df0$x[1],df0$y[Ndf],df0$variable[Ndf]))
  with(df,polygon3D(x=variable,y=x, z=y, alpha=0.6,
                     col=colormap[i],lwd = 3,add=TRUE,colkey = FALSE))
  with(df0,lines3D(x=variable,y=x, z=y, 
                  lwd = 0.5,col="black",add=TRUE))
}
```
在此，就完成这个三位瀑布图了，美观度极佳，可解释性也不错。

## 添加第四个变量

如果想加入第四变量也是没问题的，具体不再重复。完整代码可见[R语言书可视化之美](https://github.com/EasyChart/Beautiful-Visualization-with-R)或者我的github中。
```{r echo=FALSE, message=FALSE, warning=FALSE}
mydata0<-read.csv("Facting_Data.csv",check.names =FALSE)

N<-ncol(mydata0)-1
mydata<-data.frame(x=numeric(),y=numeric(),variable=character())

for (i in 1:N){
  newdata<-data.frame(spline(mydata0[,1],mydata0[,i+1],n=300,method= "natural"))
  newdata$variable<-colnames(mydata0)[i+1]
  mydata<-rbind(mydata,newdata)
}

mydata$variable<-as.numeric(mydata$variable)
group<-unique(mydata$variable)
M<-length(group)

#------------
colormap <- colorRampPalette(rev(brewer.pal(11,'Spectral')),alpha = TRUE)(32)
#colormap <- colorRampPalette(rev(brewer.pal(7,'RdYlGn')),alpha = TRUE)(32)
pmar <- par(mar = c(5.1, 4.1, 4.1, 6.1))

perspbox(z=as.vector(0),#add=TRUE,
         xlim=c(20,70),ylim=c(360,750),zlim=c(0,15),
         ticktype = "detailed",bty = "f",box = TRUE,
         colkey = FALSE,theta = -110, phi = 20, d=3)

for (i in 1:M){
  df0<-mydata[mydata$variable==group[i],]
  
  df<-cbind(df0,z0=rep(0,nrow(df0)))
  df<-df[df$y>0.05,]
  with(df,segments3D(x0=variable, y0=x,z0=z0,
                     x1=variable,y1=x, z1=y, colvar =y,
                     alpha=0.5,col=ramp.col(colormap,alpha = 0.9),lwd = 3,add=TRUE,colkey = FALSE))
  with(df0,lines3D(x=variable,y=x, z=y, 
                  lwd = 1.5,col="black",add=TRUE))
}

colkey (col=colormap,clim=range(mydata$y),clab = "Z Value", add=TRUE, length=0.5,side = 4)
```


# 行分面的带填充的曲线图

使用分面图的可视化方法也可以展示瀑布图的数据信息，关于分面图可视化方法我已经在[R可视乎|分面一页多图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484186&idx=1&sn=c913a65f88132b3611e580b0318404d9&chksm=ea24fcfedd5375e87adfc3028850ee4034a0a0d34dd3855cf155b28eea9c71bfedb381d2c9e9&token=1309493585&lang=zh_CN#rd)介绍过。

下面进行行分面的带填充的曲线图绘制，所有数据共用X轴坐标，每个数据类别是用的Y轴坐标。

>相对三维瀑布图，分面瀑布图的**优点**是：
可以更好地展示数据信息，避免不同类别之间数据重叠引起的遮挡问题，但是不能很直接地比较不同类别之间的数据差异。

## 基础版本

这里不做过多解释，用`geom_area()`绘制面积，用`facet_grid()`进行分面，最后就是对主题进行设置。主题的设置有很多有趣的技巧，以后整理一篇完整的。
```{r message=FALSE, warning=FALSE}
library(reshape2)
library(ggplot2)
mydata0<-read.csv("Facting_Data.csv",stringsAsFactors=FALSE)

colnames(mydata0)<-c("X_Axis",seq(60,25,-5))
mydata<-melt(mydata0,id.vars = "X_Axis")

ggplot(mydata,aes(X_Axis,value,fill=variable))+
  geom_area(color="black",size=0.25)+
  facet_grid(variable~.)+
  theme(
    text=element_text(size=15,face="plain",color="black"),
    axis.title=element_text(size=10,face="plain",color="black"),
    axis.text = element_text(size=10,face="plain",color="black"),
    legend.position="none"
  )
```


## 加入第四个变量

同理，在上图的基础上可以将每个数据的Z变量进行颜色映射，这样有利于比较不同类别之间的数据差异，该图如下所示：
```{r message=FALSE, warning=FALSE}
library(RColorBrewer)
colormap <- colorRampPalette(rev(brewer.pal(11,'Spectral')))(32)
mydata0<-read.csv("Facting_Data.csv",stringsAsFactors=FALSE)

N<-ncol(mydata0)-1

colnames(mydata0)<-c("X_Axis",seq(60,25,-5))

mydata<-data.frame(x=numeric(),y=numeric(),variable=character())

for (i in 1:N){
  newdata<-data.frame(spline(mydata0[,1],mydata0[,i+1],n=300,method= "natural"))
  newdata$variable<-colnames(mydata0)[i+1]
  mydata<-rbind(mydata,newdata)
}

mydata$variable<-factor(mydata$variable,levels=seq(60,25,-5))

ggplot(mydata,aes(x,y,group=variable))+
  geom_bar(aes(fill=y),color=NA,size=0.25,stat="identity")+
  geom_line(color="black",size=0.5)+
  scale_fill_gradientn(colours=colormap)+
  facet_grid(variable~.)+
  theme(
    text=element_text(size=15,face="plain",color="black"),
    axis.title=element_text(size=10,face="plain",color="black"),
    axis.text = element_text(size=10,face="plain",color="black"),
    legend.position="right"
  )
```




================================================
FILE: 2020年/2020.12.21esquisse包/esquisse包.md
================================================

## 简介

最近学习可视化时发现了一个好用的包，可以直接使用“拖拽”的方式生成绘图，**不需要写任何代码！** 
这个包是esquisse，具体介绍可以见[对应的github](https://github.com/dreamRs/esquisse "esquisse包")。这是建立在[ggplot2包](https://github.com/tidyverse/ggplot2 "ggplot2包")基础上设计的。 你可以通过生成ggplot2图表以交互方式探索esquisse环境中的数据。入门门槛极低，有点类似tableau的感觉。


## 安装
可以通过CRAN直接下载，也可以通过github中下载，然后将其进行加载即可。

```
# From CRAN
install.packages("esquisse")
# remotes::install_github("dreamRs/esquisse")
#Load the package in R
library(esquisse)
```

## 使用方式

使用方式由以下两种，推荐使用窗口操作，因为不用记住代码，也很好找到。

### 1. 输入以下代码
```
esquisse::esquisser() #helps in launching the add-in
```
![代码打开界面](https://imgkr2.cn-bj.ufileos.com/96e53f2f-a5cb-45a5-b674-19b07e840e7a.gif?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=dd6c5rVWW8v17z%252BdHGvcGZLF4xw%253D&Expires=1608642502)

### 2. 窗口操作

通过RStudio菜单启动插件（推荐）

>**注意**：如果您的环境中没有data.frame，则可以使用ggplot2中的数据集。推荐还是自己前面已经导入数据了，界面才会有显示可以使用的数据。

- 加载该包之后，在窗口的左上方有个Addins，点击打开找到对应包的函数点击即可。
    
![打开方式1](https://imgkr2.cn-bj.ufileos.com/46e23df4-76a5-422f-aa6f-56c0b7965fe6.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=Io88OxOXooyRyqfjqKFDl%252Bo%252BdL8%253D&Expires=1608642673)


    
    
## 窗口详细说明

导入自己的数据，就可以对其进行分析了！这里咱们对`iris`数据作为例子。窗口都是互动形式的，你可以根据自己所需进行绘制对应的图形，不需要输入代码。我们给出操作图，如下所示。之后对界面下面的四个小窗口进行详细介绍。

![具体操作](https://imgkr2.cn-bj.ufileos.com/31b82c98-1553-4a8f-9848-80c9f0e182c6.gif?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=h%252FoeHlvI%252F1S6VbTLI6Cm0RNZWkc%253D&Expires=1608646813)



### Lables&Title

![添加各种标签题目](https://imgkr2.cn-bj.ufileos.com/771f7c69-927e-4ca8-b008-a86c33c3a336.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=gr0ZI5D53%252BqEXYxkNkJQFGsvJg0%253D&Expires=1608644653)


### Plot options

可以设计geom_xxx中的各种参数（颜色，尺寸），legend摆放的位置，主题形式等等；

![设计各种参数](https://imgkr2.cn-bj.ufileos.com/d23c37df-10fd-49f9-9aa0-cceb3d26b255.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=u8pdjVoXk0IW3v3qrd%252FEX2Jt%252B44%253D&Expires=1608643705)

### Data


![改变输入数据的范围](https://imgkr2.cn-bj.ufileos.com/56bb4d45-fb81-4f95-be79-fc99c9d6cf5d.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=9mBnHsMSJhSjGVJoEYDIlJ%252B5fkQ%253D&Expires=1608644852)

### Export&code

这可以显示操作后图对应的ggplot的代码！（非常管用！）你可以按( Insert code in script )将自动导入你的代码中。

![代码、图片导出](https://imgkr2.cn-bj.ufileos.com/24149f1d-b729-4508-8061-38f5851a0311.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=s6mAPWoNrQhhTJvVS0bC%252BZ2r2%252F0%253D&Expires=1608643830)

当然可以导出pptx或者png格式，操作如下所示：

![导出pptx格式，在线修改图片](https://imgkr2.cn-bj.ufileos.com/b83417e0-9749-473d-9fd5-44e220f057a5.gif?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=hu3nMbXOCx2Qg%252FyF77XOGsn2r%252Bs%253D&Expires=1608644529)


> **注意**：导出pptx可能还需要两个包（`rvg , officer`），你可以先安装，在使用界面操作导出pptx格式。 pptx格式可以修改图片内部的任何地方，非常方便。

## 小编有话说

- 这个包对想学习ggplot语法的读者来说，也非常合适。可以直接导出你做图的代码，根据代码反过来学习对应语法，从实践中学习也是不错的选择。

- 小编最近在准备毕业的开题答辩，书籍翻译和论文撰写，所以更新的比较慢。不过可视化系列一直在逐步推进，已经准备几期了初稿了，但是感觉不够系统，所以还打算打磨下再发出来。[上次里的flag](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487940&idx=1&sn=02e087fdb8c7ec192ea3297dadda702d&chksm=ea24ee20dd536736997620b3a7b6d008475503a8997476d97941e71cc5d6adab9549a8f3b9a5&token=1926271128&lang=zh_CN#rd)也会继续下去的，尽情期待。也欢迎加小编微信一起沟通交流，也感谢各位博士大大们对小编的建议和厚爱❤。






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FILE: 2020年/2020.12.24Mandalas/Mandalas/.Rproj.user/9B97F8EE/sources/s-75E8F375/17CC9D9C-contents
================================================
---
title: "Mandalas"
author: "庄闪闪"
date: "`r Sys.Date()`"
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
vignette: >
  %\VignetteIndexEntry{Vignette Title}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

## 参考文献

[Mandalas](https://github.com/aschinchon/mandalas)

[R-blogger-Mandalas](https://www.r-bloggers.com/2018/02/mandalas/)

[Mandalas](https://fronkonstin.com/2018/02/14/mandalas/)

[Inspired by your work I created this](https://hrafnkelle.github.io/p5mandalas/)


## 简介

- 以（0,0）为中心的单位圆中获得n个等距点

- 对所有这些点重复该过程，再次获得每个点周围的n个点； 半径按比例缩放

- 舍弃了以前的（父）n分


我反复地重复这些步骤。如果我从n个点开始，迭代k次，最后我得到nk个点。然后，我计算它们的Voronoi镶嵌，并用ggplot绘制。

```{r message=FALSE, warning=FALSE}
# Load in libraries
library(ggplot2)
library(dplyr)
library(deldir)

# Parameters to change as you like
iter=5 # Number of iterations (depth)
points=8 # Number of points
radius=4.8 # Factor of expansion/compression

# Angles of points from center
angles=seq(0, 2*pi*(1-1/points), length.out = points)+pi/2

# Initial center
df=data.frame(x=0, y=0)

# Iterate over centers again and again
for (k in 1:iter)
{
  temp=data.frame()
  for (i in 1:nrow(df))
  {
    data.frame(x=df[i,"x"]+radius^(k-1)*cos(angles),
               y=df[i,"y"]+radius^(k-1)*sin(angles)) %>% rbind(temp) -> temp
  }
  df=temp
}

# Obtain Voronoi regions
df %>%
  select(x,y) %>%
  deldir(sort=TRUE) %>%
  .$dirsgs -> data

# Plot regions with geom_segmen
data %>%
  ggplot() +
  geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2), color="black") +
  scale_x_continuous(expand=c(0,0))+
  scale_y_continuous(expand=c(0,0))+
  coord_fixed() +
  theme(legend.position  = "none",
        panel.background = element_rect(fill="white"),
        panel.border     = element_rect(colour = "black", fill=NA),
        axis.ticks       = element_blank(),
        panel.grid       = element_blank(),
        axis.title       = element_blank(),
        axis.text        = element_blank())->plot

plot

# Do you like the result? Save it (change the name if you want)
ggsave("mandala4.png", height=5, width=5, units='in', dpi=600)

```

![](1.jpg)










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library(ggplot2)

# set the `rel_min_height` argument to remove tails
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(rel_min_height = 0.005) +
  scale_y_discrete(expand = c(0.01, 0)) +
  scale_x_continuous(expand = c(0.01, 0)) +
  theme_ridges()

# set the `scale` to determine how much overlap there is among the plots
ggplot(diamonds, aes(x = price, y = cut)) +
  geom_density_ridges(scale = 4) +
  scale_y_discrete(expand = c(0.01, 0)) +
  scale_x_continuous(expand = c(0.01, 0)) +
  theme_ridges()

# the same figure with colors, and using the ggplot2 density stat
ggplot(diamonds, aes(x = price, y = cut, fill = cut, height = ..density..)) +
  geom_density_ridges(scale = 4, stat = "density") +
  scale_y_discrete(expand = c(0.01, 0)) +
  scale_x_continuous(expand = c(0.01, 0)) +
  scale_fill_brewer(palette = 4) +
  theme_ridges() + theme(legend.position = "none")

# use geom_density_ridges2() instead of geom_density_ridges() for solid polygons
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges2() +
  scale_y_discrete(expand = c(0.01, 0)) +
  scale_x_continuous(expand = c(0.01, 0)) +
  theme_ridges()


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FILE: 2020年/2020.12.24Mandalas/Mandalas/.Rproj.user/9B97F8EE/sources/s-75E8F375/28FAC3B5-contents
================================================
---
title: "峰峦图"
author:
  - 庄闪闪
documentclass: ctexart
keywords:
  - 中文
  - R Markdown
output:
  rticles::ctex:
    fig_caption: yes
    number_sections: yes
    toc: yes
editor_options: 
  chunk_output_type: console
---



# 峰峦图

上次可视化系列说了[瀑布图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487383&idx=1&sn=43e2eaf6b7c6b24510ccadb79e766f07&chksm=ea24f073dd53796546fd4145ded4cddfe8779464ad9d0d674900a65484966cd5c3f6367dcecf&token=1431845296&lang=zh_CN#rd)。它可以用于展示拥有相同的X轴变量数据（如相同的时间序列）、不同的Y轴离散型变量（如不同的类别变量）和Z轴数值变量。

本节使用的**峰峦图**也可以很好地展示瀑布图的数据信息。它们对于可视化随时间或空间分布的变化非常有用。本节主要使用`ggridges`包中的`geom_density_ridges()`进行绘制峰峦图。详细介绍如下：

## 数据结构

这里使用`base`包中的`diamonds`数据集做例子。
```{r message=FALSE, warning=FALSE}
# library
library(ggridges) # Ridgeline Plots in 'ggplot2', CRAN v0.5.2
library(ggplot2) # Create Elegant Data Visualisations Using the Grammar of Graphics, CRAN v3.3.2 
head(diamonds)
```

## 绘图教程

### 基础版本

使用`price`作为x轴, `cut`为y轴，`fill`参数也是设定为`cut`。`geom_density_ridges()`内部全部使用默认参数。使用了ggridges包中的主题`theme_ridges()`。
```{r message=FALSE, warning=FALSE}
ggplot(diamonds, aes(x = price, y = cut, fill = cut)) +
	geom_density_ridges() +
	theme_ridges() + 
	theme(legend.position = "none")
```

### 形状变化

如果不想绘制密度图，则可以使用`stat="binline", bins=20`绘制柱形图，其中`bins=20`表示每格格子大小。为了防止上下图片重叠，这里使用了透明度参数：`alpha=0.6`。
```{r message=FALSE, warning=FALSE}
ggplot(diamonds, aes(x = price, y = cut, fill = cut)) +
	geom_density_ridges(alpha=0.7, stat="binline", bins=20) +
	theme_ridges() + 
	theme(legend.position = "none")
```

### 根据第三变量进行分面

```{r}
ggplot(diamonds, aes(x = price, y = cut,fill = cut)) +
	geom_density_ridges(alpha=0.7) +
	facet_wrap(~color) + 
  theme_ridges() + 
	theme(legend.position = "none")
```

### 加入统计量

通过设置选项`quantile_lines = TRUE`，我们可以使`stat_density_ridges`计算指示分位数的线的位置。 默认情况下，绘制了三行，分别对应于第一，第二和第三四分位数：
```{r}
ggplot(diamonds, aes(x = price, y = cut,fill = cut)) +
	geom_density_ridges(alpha=0.7,quantile_lines = TRUE) +
  theme_ridges() + 
	theme(legend.position = "none")
```

> **注意**：`quantiles=2`意味着在两个分位数之间的边界上有一条线即，(中位数)。

我们还可以通过切点而不是数字来指定分位数。例如，我们可以指出2.5％和97.5％的尾巴。

```{r}
ggplot(diamonds, aes(x = price, y = cut,fill = cut)) +
	geom_density_ridges(alpha=0.7,quantile_lines = TRUE,quantiles = c(0.025, 0.975)) +
  theme_ridges() + 
	theme(legend.position = "none")
```

使用`stat_density_ridges`，计算`stat(quantile)`，通过分位数进行着色，。 注意，仅当`calc_ecdf = TRUE`时才能计算。

```{r}
ggplot(diamonds, aes(x = price, y = cut,fill = factor(stat(quantile)))) +
	stat_density_ridges(
	  geom = "density_ridges_gradient", 
	  calc_ecdf = TRUE,
    quantiles = 4, quantile_lines = TRUE) +
  theme_ridges() +
  scale_fill_viridis_d(name = "Quartiles")
```

我们可以使用相同的方法来突出分布的尾部。
```{r}
ggplot(diamonds, aes(x = price, y = cut,fill = factor(stat(quantile)))) +
	stat_density_ridges(
	  geom = "density_ridges_gradient", 
	  calc_ecdf = TRUE,
    quantiles = c(0.025, 0.975)) +
  theme_ridges() +
    scale_fill_manual(
    name = "Probability", values = c("#FF0000A0", "#A0A0A0A0", "#0000FFA0"),
    labels = c("(0, 0.025]", "(0.025, 0.975]", "(0.975, 1]")
  )
```

最后，当`calc_ecdf = TRUE`时，我们还可以计算`stat(ecdf)`，它表示该分布的经验累积密度函数。 我们将其概率直接映射到颜色上。
```{r}
ggplot(diamonds, aes(x = price, y = cut,fill = 0.5 - abs(0.5 - stat(ecdf)))) +
	stat_density_ridges(geom = "density_ridges_gradient", calc_ecdf = TRUE) +
  scale_fill_viridis_c(name = "Tail probability", direction = -1)
```

### 加入抖动点

`stat_density_ridges()`还提供了可视化生成分布的原始数据点的选项。可以通过设置`jittered_points = TRUE`实现。为了只管我们这里使用`iris`包。

```{r}
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(jittered_points = TRUE)+
	theme_ridges() + 
	theme(legend.position = "none")
```
当然可以将其放在密度函数的下方，通过使用`position = "raincloud"`参数。
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(
    jittered_points = TRUE, position = "raincloud",
    alpha = 0.7, scale = 0.9
  )
```

我们还可以模拟地毯形式：
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(
    jittered_points = TRUE,
    position = position_points_jitter(width = 0.05, height = 0),
    point_shape = '|', point_size = 3, point_alpha = 1, alpha = 0.7,
  )
```


可以使用ggridges提供的特殊比例来设置抖动点的样式。 首先，`scale_discrete_manual()`可用于制作具有任意形状和比例的图形。 `scale_point_color_hue()`。 
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Species, fill = Species)) +
  geom_density_ridges(
    aes(point_color = Species, point_fill = Species, point_shape = Species),
    alpha = .2, point_alpha = 1, jittered_points = TRUE
  ) +
  scale_point_color_hue(l = 40) +
  scale_discrete_manual(aesthetics = "point_shape", values = c(21, 22, 23))
```

如果你还想再加入一个变量进行可视化，可以在`geom_density_ridges()`加入，例如：`point_shape = Species, point_fill = Species, point_size = Petal.Length`。
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Species, fill = Species)) +
  geom_density_ridges(
    aes(point_shape = Species, point_fill = Species, point_size = Petal.Length), 
    alpha = .2, point_alpha = 1, jittered_points = TRUE
  ) +
  scale_point_color_hue(l = 40) + scale_point_size_continuous(range = c(0.5, 4)) +
  scale_discrete_manual(aesthetics = "point_shape", values = c(21, 22, 23))
```

另外一种有趣的可视化是通过`vline_xxx`构造以下图形。
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(
    jittered_points = TRUE, quantile_lines = TRUE, scale = 0.9, alpha = 0.7,
    vline_size = 1, vline_color = "red",
    point_size = 0.4, point_alpha = 1,
    position = position_raincloud(adjust_vlines = TRUE)
  )
```

## 其他资料

对于该包的其他有趣函数与可视化可参考以下资料：
- [Introduction to ggridges](https://cran.r-project.org/web/packages/ggridges/vignettes/introduction.html "Introduction to ggridges")

- [RDocumentation-ggridges](https://www.rdocumentation.org/packages/ggridges/versions/0.5.2 "RDocumentation-ggridges")

- [Basic ridgeline plot](https://www.r-graph-gallery.com/294-basic-ridgeline-plot "Basic ridgeline plot")











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FILE: 2020年/2020.12.24Mandalas/Mandalas/.Rproj.user/9B97F8EE/sources/s-75E8F375/4326A9CE-contents
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---
title: "切面图"
author:
  - 庄亮亮
date: "2020/11/30"
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
---

# 切面图

切面图（slice chart）可以展示四维数据$v=f(x, y,z)$，将前三维数据展现在三维直角坐标系$f(x, y, z)$，
通过对图形的线型、立面、色彩、渲染、光线、视角等的控制，可形象地表现数据四维特性v。任何一个在三维坐标系中绘制的数据体，都可以使用分割得到平行于X-Y、X-Z和Y-Z的三个切面。然后每个切面上的数据点都可以通过3-D 插值获得，如图4-4-1 所示。

在切面图中，$v=f(x, y, z)$ 这个函数对于每一个给定的$(x, y, z)$都对应一个v，然后映射到渐变颜色，那么$v=f(x,y,z)$就可以得到一个有颜色变化（其颜色分布按给定的函数表达式变化）的立体图形。给定$(x,y,z)$中的某个值，就可以得到某一个切面上的颜色分布，根据颜色映射大致可以看出其函数值的变化。在图4-4-1中，展示了$x=0，y=(-4,0,4)$的4个切面颜色变化情况。


```{r message=FALSE, warning=FALSE}
library(grDevices)
library(plot3D)
library(RColorBrewer)
x <- y <- z <- seq(-4, 4, by = 0.2)
M <- mesh(x, y, z)  #mesh 3d网
R <- with (M, sqrt(x^2 + y^2 + z^2))
p <- sin(2*R) /(R+1e-3)
```


```{r}
colormap <- colorRampPalette(rev(brewer.pal(11,'Spectral')),alpha = TRUE)(32)
```


```{r}
slice3D(x, y, z, colvar = p, facets = FALSE,
				col = ramp.col(colormap,alpha = 0.9), 
				clab="p vlaue",
				xs = 0, ys = c(-4, 0, 4), zs = NULL, 
				ticktype = "detailed",bty = "f",box = TRUE,
				theta = -120, phi = 30, d=3,
				colkey = list(length = 0.5, width = 1, cex.clab = 1))
```

```{r}
slicecont3D(x, y, z, colvar = p, facets = FALSE,
				col = ramp.col(colormap,alpha = 0.9), 
				clab="p vlaue",
				xs = 0, ys = c(-4, 0, 4), zs = NULL, 
				ticktype = "detailed",bty = "f",box = TRUE,
				theta = -120, phi = 30, d=3,
				colkey = list(length = 0.5, width = 1, cex.clab = 1))

```


```{r}
# save plotting parameters
 pm <- par("mfrow")
 pmar <- par("mar")

## =======================================================================
## Simple slice3D examples
## =======================================================================

 par(mfrow = c(2, 2))
 x <- y <- z <- seq(-1, 1, by = 0.1)
 grid   <- mesh(x, y, z)
 colvar <- with(grid, x*exp(-x^2 - y^2 - z^2))

# default is just the panels
 slice3D  (x, y, z, colvar = colvar, theta = 60)

# contour slices
 slicecont3D (x, y, z, ys = seq(-1, 1, by = 0.5), colvar = colvar, 
           theta = 60, border = "black")
          
 slice3D  (x, y, z, xs = c(-1, -0.5, 0.5), ys = c(-1, 0, 1), 
           zs = c(-1, 0), colvar = colvar, 
           theta = 60, phi = 40)

## =======================================================================
## coloring on a surface
## =======================================================================

 XY <- mesh(x, y)
 ZZ <- XY$x*XY$y
 slice3D  (x, y, z, xs = XY$x, ys = XY$y, zs = ZZ, colvar = colvar, 
           lighting =  TRUE, lphi = 90, ltheta = 0)

## =======================================================================
## Specifying transparent colors
## =======================================================================

 par(mfrow = c(1, 1))
 x <- y <- z <- seq(-4, 4, by = 0.2)
 M <- mesh(x, y, z)

 R <- with (M, sqrt(x^2 + y^2 + z^2))
 p <- sin(2*R) /(R+1e-3)

## Not run: 
# This is very slow - alpha = 0.5 makes it transparent

 slice3D(x, y, z, colvar = p, col = jet.col(alpha = 0.5), 
         xs = 0, ys = c(-4, 0, 4), zs = NULL, d = 2) 

## End(Not run)

 slice3D(x, y, z, colvar = p, d = 2, theta = 60, border = "black",
         xs = c(-4, 0), ys = c(-4, 0, 4), zs = c(-4, 0))

## =======================================================================
## A section along a transect
## =======================================================================

 data(Oxsat)
 Ox <- Oxsat$val[,  Oxsat$lat > - 5 & Oxsat$lat < 5, ]
 slice3D(x = Oxsat$lon, z = -Oxsat$depth, y = 1:5, colvar = Ox, 
         ys = 1:5, zs = NULL, NAcol = "black", 
         expand = 0.4, theta = 45, phi = 45)

## =======================================================================
## isosurf3D example - rather slow
## =======================================================================

 par(mfrow = c(2, 2), mar  = c(2, 2, 2, 2))
 x <- y <- z <- seq(-2, 2, length.out = 15)
 xyz <- mesh(x, y, z)
 F <- with(xyz, log(x^2 + y^2 + z^2 + 
                10*(x^2 + y^2) * (y^2 + z^2) ^2))

# use shading for level = 1 - show triangulation with border
 isosurf3D(x, y, z, F, level = 1, shade = 0.9, 
           col = "yellow", border = "orange")

# lighting for level - 2
 isosurf3D(x, y, z, F, level = 2, lighting = TRUE,
           lphi = 0, ltheta = 0, col = "blue", shade = NA)  
 
# three levels, transparency added
 isosurf3D(x, y, z, F, level = seq(0, 4, by = 2), 
   col = c("red", "blue", "yellow"), 
   clab = "F", alpha = 0.2, theta = 0, lighting = TRUE)  

# transparency can also be added afterwards with plotdev()
## Not run: 
 isosurf3D(x, y, z, F, level = seq(0, 4, by = 2), 
   col = c("red", "blue", "yellow"), 
   shade = NA, plot = FALSE, clab = "F")  
 plotdev(lighting = TRUE, alpha = 0.2, theta = 0)

## End(Not run)
# use of creatisosurf
 iso <- createisosurf(x, y, z, F, level = 2)
 head(iso)
 triangle3D(iso, col = "green", shade = 0.3)

## Not run: 
 # higher resolution
  x <- y <- z <- seq(-2, 2, length.out = 50)
  xyz <- mesh(x, y, z)
  F <- with(xyz, log(x^2 + y^2 + z^2 + 
                10*(x^2 + y^2) * (y^2 + z^2) ^2))

# three levels
  isosurf3D(x, y, z, F, level = seq(0, 4, by = 2), 
    col = c("red", "blue", "yellow"), 
    shade = NA, plot = FALSE, clab = "F")  
  plotdev(lighting = TRUE, alpha = 0.2, theta = 0)

## End(Not run)

## =======================================================================
## voxel3D example
## =======================================================================

 par(mfrow = c(2, 2), mar  = c(2, 2, 2, 2))

# fast but needs high resolution grid
 x <- y <- z <- seq(-2, 2, length.out = 70)
 xyz <- mesh(x, y, z)
 F <- with(xyz, log(x^2 + y^2 + z^2 + 
                10*(x^2 + y^2) * (y^2 + z^2) ^2))

 voxel3D(x, y, z, F, level = 4, pch = ".", cex = 5)

## =======================================================================
## rotation 
## =======================================================================

 plotdev(theta = 45, phi = 0)
 plotdev(theta = 90, phi = 10)

# same using createvoxel -  more flexible for coloring
 vox <- createvoxel(x, y, z, F, level = 4)
 scatter3D(vox$x, vox$y, vox$z, colvar = vox$y, 
   bty = "g", colkey = FALSE)


## =======================================================================
## voxel3D to show hypox sites
## =======================================================================

 par(mfrow = c(1, 1), mar = c(2, 2, 2, 2))
 Hypox <- createvoxel(Oxsat$lon, Oxsat$lat, Oxsat$depth[1:19], 
                      Oxsat$val[,,1:19], level = 40, operator = "<")

 panel <- function(pmat) {  # an image at the bottom
   Nx <- length(Oxsat$lon)
   Ny <- length(Oxsat$lat)
   M <- mesh(Oxsat$lon, Oxsat$lat) 
   xy <- trans3D(pmat = pmat, x = as.vector(M$x), y = as.vector(M$y), 
        z = rep(-1000, length.out = Nx*Ny)) 
   x <- matrix(nrow = Nx, ncol = Ny, data = xy$x)
   y <- matrix(nrow = Nx, ncol = Ny, data = xy$y)
   Bat <- Oxsat$val[,,1]; Bat[!is.na(Bat)] <- 1
   image2D(x = x, y = y, z = Bat, NAcol = "black", col = "grey",
         add = TRUE, colkey = FALSE)
 }
   
 scatter3D(Hypox$x, Hypox$y, -Hypox$z, colvar = Hypox$cv, 
           panel.first = panel, pch = ".", bty = "b", 
           theta = 30, phi = 20, ticktype = "detailed",
           zlim = c(-1000,0), xlim = range(Oxsat$lon), 
           ylim = range(Oxsat$lat) )
           
# restore plotting parameters
 par(mfrow = pm)
 par(mar = pmar)
```









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#EasyCharts团队出品，如有商用必究，
#如需使用与深入学习，请联系微信：EasyCharts
setwd("F:\\我的书籍\\R书籍\\R语言数据可视化之美\\第4章 数据关系型图表")
library(ggplot2)
library(RColorBrewer)  

colormap <- colorRampPalette(rev(brewer.pal(11,'Spectral')))(32)

mydata0<-read.csv("Facting_Data.csv",check.names =FALSE)

N<-ncol(mydata0)-1
labels_Y<-colnames(mydata0)[1:N+1]
colnames(mydata0)<-c("x",seq(1,N,1))
mydata<-data.frame(x=numeric(),y=numeric(),variable=character()) #创建空的Data.Frame

for (i in 1:N){
  newdata<-data.frame(spline(mydata0[,1],mydata0[,i+1],n=300,method= "natural"))
  newdata$variable<-colnames(mydata0)[i+1]
  mydata<-rbind(mydata,newdata)
}

Step<-5
mydata$offest<--as.numeric(mydata$variable)*Step
mydata$V1_density_offest<-mydata$y+mydata$offest

p<-ggplot()
for (i in 1:N){
  p<-p+ geom_linerange(data=mydata[mydata$variable==i,],aes(x=x,ymin=offest,ymax=V1_density_offest,group=variable,color=y),size =1, alpha =1) +
    geom_line(data=mydata[mydata$variable==i,],aes(x=x, y=V1_density_offest),color="black",size=0.5)
}
#ggplot() + 
#  geom_linerange(aes(x=x,ymin=offest,ymax=V1_density_offest,group=variable,color=y),mydata,size =1, alpha =1) +
p+scale_color_gradientn(colours=colormap)+
  #geom_line(aes(x, V1_density_offest,group=variable),mydata,color="black")+
  scale_y_continuous(breaks=seq(-Step*N,-Step,Step),labels=rev(labels_Y))+
  xlab("Time")+
  ylab("Class")+
  theme(
    panel.background=element_rect(fill="white",colour=NA),
    panel.grid.major.x = element_line(colour = "grey80",size=.25),
    panel.grid.major.y = element_line(colour = "grey60",size=.25),
    axis.line = element_blank(),
    text=element_text(size=13),
    plot.title=element_text(size=15,hjust=.5),
    legend.position="right"
  )

#------------------------------------------------------------------------------------------------------

ggplot() + 
  geom_ribbon(aes(x, ymin=offest,ymax=V1_density_offest, fill=variable),mydata, alpha=1,colour=NA)+
  geom_line(aes(x, V1_density_offest, color=variable,group=variable),mydata, color="black")+
  scale_y_continuous(breaks=seq(-40,-5,5),labels=rev(labels_Y))+
  theme_classic()+
  theme(
    panel.background=element_rect(fill="white",colour=NA),
    panel.grid.major.x = element_line(colour = "grey80",size=.25),
    panel.grid.major.y = element_line(colour = "grey60",size=.25),
    axis.line = element_blank(),
    text=element_text(size=15),
    plot.title=element_text(size=15,hjust=.5),#family="myfont",
    legend.position="none"
  )



================================================
FILE: 2020年/2020.12.24Mandalas/Mandalas/.Rproj.user/9B97F8EE/sources/s-75E8F375/lock_file
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FILE: 2020年/2020.12.24Mandalas/Mandalas/.Rproj.user/shared/notebooks/40CC0FD6-Mandalas/1/s/cct2o5lt4yw4d/000011.csv
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"0","# Load in libraries"
"0","library(ggplot2)"
"0","library(dplyr)"
"0","library(deldir)"
"0",""
"0","# Parameters to change as you like"
"0","iter=5 # Number of iterations (depth)"
"0","points=8 # Number of points"
"0","radius=4.8 # Factor of expansion/compression"
"0",""
"0","# Angles of points from center"
"0","angles=seq(0, 2*pi*(1-1/points), length.out = points)+pi/2"
"0",""
"0","# Initial center"
"0","df=data.frame(x=0, y=0)"
"0",""
"0","# Iterate over centers again and again"
"0","for (k in 1:iter)"
"0","{"
"0","  temp=data.frame()"
"0","  for (i in 1:nrow(df))"
"0","  {"
"0","    data.frame(x=df[i,""x""]+radius^(k-1)*cos(angles),"
"0","               y=df[i,""y""]+radius^(k-1)*sin(angles)) %>% rbind(temp) -> temp"
"0","  }"
"0","  df=temp"
"0","}"
"0",""
"0","# Obtain Voronoi regions"
"0","df %>%"
"0","  select(x,y) %>%"
"0","  deldir(sort=TRUE) %>%"
"0","  .$dirsgs -> data"


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FILE: 2020年/2020.12.24Mandalas/Mandalas/.Rproj.user/shared/notebooks/patch-chunk-names
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================================================
FILE: 2020年/2020.12.24Mandalas/Mandalas/.Rproj.user/shared/notebooks/paths
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C:/Users/ZLL/Desktop/wechat/庄闪闪原创/R/未做推送/2020.12.07峰峦图/峰峦图.rmd="540673E1"
F:/我的学习/R/R_example/2020.12.24Mandalas/Mandalas/Mandalas.rmd="40CC0FD6"


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FILE: 2020年/2020.12.24Mandalas/Mandalas/Mandalas.html
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1nJtTNAj6jJKAAZEs8WgqihJRgX9wJHOkYoXkf8iwR2RiKKqRRiitWw3lYdnr30cDzNae/8Tw/1L3sS5gFALINXpKDQgmp1pQxW86M3O8aoqMTlNtTGnSjATM2tjXEgCYfS3hKyuCkFHkzBeScI6WKhFVxLuD+EQLt4TkOo6CU5f1drrhvrrVly/dspDayfe+8EtQx7fuJG0HcbZLyyc1r+5qXbojtE1xa0dt4x/5c31r9hA6MYtP5DrVgijoiV5Po6KKs3MBOCVStFlgez8bG57v8/vq4tZ/Gilfr8pX7VqJm1EzJQGeg3j5/xX8ruWMbrG4oduFyXxMEFyQlkpkMeJTvhKbCMY1j/o2ykPlEmSr335KxvYPvbZydev29P65KNrX58+c92zfxv6+Kil76PnU1Sl6fe+l694//zIweMjUO1ZPnH2TU3fxqa09+l/6OHXAQgEAaSZuhddMDiaZ1epkRAzpTKAxyVzrnGh7JLreGi7qF1VqO5WvoGQ0DwF584uo3cpz4sCBzc9T9SAQPKgoqI082X2QfxhshCzXmZ5Jmoo6MvOYAk7gCWH6cudN5+98oSroZZNBoRWbuEw1ygDmqI9OZ36aJrbbTPYqIFmZrldRpdFA27ONADF4/HXxjyKYhkRU9LgYsIJ6e+pgHAkGUjkgUhLSBg2N9w3IMwpylMaKScT/n6efcC+PLN8xActmMGOhu+4bH6EpsV/yAgOoO0n9/+HnR2B5h7hr455LAPJ1+wc+1i1AYGhXOs6eQf4IR+uigYUp8WSlweZTnAWFNpz6mJ2u4d60kbEPGnUwENEvUTbVJbqTCjIAQJlPo8IXEUNdQEJcCAhMvd/gvy8Q3E6TmsbErv++Z2tRuuN/7f1X+zsNyv/vYhoN066sbVlcRuZiq/iWvuP7rEb/7LuhyPfsFPLMffdxfMnz7+1fu5qEc0RPdM6QIHLo14FgCDKRFYNMiWU1MaoAsLfupYpQwobhpDby4OfkoJ4iZQWPyy9jNLm8wLSdEtUyzvBB3lwOVwbLXYqnl6U+o3+Qo/Hnp1ttBtL+ihOZyBQXGwBS0Z9zJIGwfoYXGwTYYlLnVeWdKFwoCSqAj0/LqoW8qk7kShFiku3kK9cfCPVHyDedt/qpeyLL06zk4uXtU1DyfXfE2fPmrng0Ccjbhg+flxtq7zz3ZUzXhrU/O6sjqN73mrbXD2iY/Kzm89vbBp7Y/3VcwaOI3vqq674XdnlYysH1Ym8GajvcgekQQFURnOzZJfFEgyCCwqLtNy6mKZRrzd9RMyrUkMdR+Nfdbfu7DIBzCIaw0J5kS16edcXuNOdBXwbyU1J1ewxtvTOqxtHP/3+JIOl3xOz3v0nmr9Y+f2d8VNjp4xrbbm7jQ5mdazJdtYzasufW2r+83/H0fEE+3DTXbdNum1+Hfd4stOSZuvMURh1OXnyAPjtnsaYXeumMPAnaOwXTOb4NVYT72PqU+xG7xcf6mPNQAQX6/IUcHKmcllV1UUlBRXFZdIaYyZNUjgzJ6Rpm8u6mKrApzM0vUgYbrTrbF2SFHbS18Xa5GhSmF5P7JYqZODSiqKajIK/VYNEqQIEZRigFxShVFwJURhGD6JU0ZlDP443kvW7ccNSPH2abWFfCns140peoYDeNeZHHSqlRgkMcp00ViJSV30QKhkjagSue7JMQH4304/FkrTgKC9Tjh69VLueUScBrhFPNVAUJJTKEur6Ce0u1dCFuorNZH28UayJb2IaDjjNtKWsWmioXPicrpB365FYFc3LTU9PA+B2dlqdhUV2QCMFCAazGmNBl900ImaXkg7mVCR4KJVkyfpRJFR5F86oRckaXOFoe0m/7W6YevPVY5uWvzf1w3P7vm99YGyIHU4139VjH6ob1tLvqqpxR9u2r5m2onVI9RVXsHUX9eMTLkxQdnCc6AuVEIv2VCsq3G5XOGzt77rMZaWBtEDvNOgN0au8hkhEMg3QTPzqkVUq5feAklS7rOucMleiPU7ivc6kQtuiYCqrfNTdlVF8fxLxCKgtj3iUQC44+jrzOa06UfyDSESH3x2j106vnpWmTXnhlT1o+UfT/qt9NdGau79/Zhf73+exCP2T2Pz/ZefZXez6I/gIyv/EkRs7Yf3IFpM1FG27n5x++NQ9Q/otPPTGQSQBH/Pd/9Yf/vjjne1sx152gh0p6f3eKHwYW3/EZZ93sA627uCCpcfMzwj7AIC8WN4IKljh6miAWKkBQZHNZgqip6CSZLOSmpjVSs0yBZocIpTouZRiZWGortKL8gsDiITjI5Uik+LHJ7FXiYTziRJnywoMgWdwNFstbzxXRcbikdvy72CqiPvXAaQznI/t4Idczsm9VLdbktKzzeY83vfZ7QGDlqalDY9ZNLRSTbODPb0mZneCvyYG9BLcSxY9KQVDSTe5ArmSp7voCQYwWfE4HPqnwOu4AyOYNn/C/fPZh2fjx7C84/aZ8xev2nXHraxT3vDKpkVrHaacdQ++/xGdXTuy8Zr4NrZo3PgNgDCXI/UBnh9eKI36VZeLN+NWnxscUBNzSKpskmtiJleyNBOvSfVEKuQRD2+0Iw4l2BUdoTI+ZiikBS+9h9OfOtrxL7aJvdiOkQOHDrc2tEs72U/HmW846xyGi3DSZ3j9azd1FvUDImwoz+E2NIBd1OtGAIdVkjTZUhOTqWTlLbMzaamUcEELnGVzAbVA0BHKleew8ew2Ng534wR8gL3Dxq5ZjO/xGuQP7A55A7ubrcHDnUMBdY8RLs0Mg6L5BgnAqphMiBbFWBOzKNxLAnII3zehaKqJofOXXkp5iCsitPAkbol0bqDV8RN4ijmIm4tl7zK2BLqkUsalGqFvNN1AqVkBQDQJoSl5QlZS0MVSLhaCX7P9dHD8OHKMEwKWxLu8KBdxL6ZDTbQo3e8nNquVEFemy2DIsGlmjQdbOr9BNkt+r+zlsmTu1FB3wd0z5VlnstgW8BBwKLpv9YJL5RlPdMKNOALkU1L14E93sr+yVfg43vTxgZtW/GXnd1vevKGVHafhuOnyAlyMU3AcPjDybB377rOT591Y2mUHeYJu/Ug004jIzW+QJFm2GGhNrMaABoNsUijK3QmbMnfKFN2XPIHtjr/NdmE5uRrDZG78Xj5t2EIGAOCFiawBT+ozgRw+bSAGXiPLwM0MRsr79e4NCw4Rxa5IJL6kRnJurq0bOKEZy79hDV4k7gVL5JHn1l4AdgYS+tfxVS0wMJpjIcRkNiOAzUBl2cq/UrNZoXwP3VtwpgBXF1eWAOXEQAdVfSMRDKBcx1awhYvEZm7FB7CZETKxJf4D39CN6/Hf8XkJ6VIlly6LPUkqBVCQArccJKJUl6GXoPq6r3PD1MsbzldfSPxvRcyR3dAvmukGo9nI1bbxUPHKisdJjEQxq9QGilBcN36X0mUp6hA6Y9DpEYujXuXykscVRBpkK4wudhzbcaSC07GdfUgtRrZEms9Wzok3cw1WSi3nqklH6R3oPr8kYcedOm6WR9NMYETFagVwUFlRVM1MVW5RVLtHv11adI/EnAKwL1KEcM/JO9nv43fpSiwh81U7+qQGdrQtXseFv4FZvycdQPQ8+VKfDHgE0jgAfBZF8RpdNTGjRO01Mer6daQROSBexQQy16Hxpkj+kj3BXubXE3gz1vNr/PlDb76Bs9nSNzaSY+xxdivejVP5tZCj0mP/OYvf4smfoAvtpHU62rkEFkhGowdsNrvdbQXBV3ZNM9TENGr/TSzoRn/ZLXHoEyAo4ckJSx+au+BBspEdYacX8yA6iCb0UGXmlKkTd504Fz8rb/gchAXYat0CdkjjEZynUFmSCDVIJg9AhmYypVOVEwBXRFK5UWSV22N7Ev4uHU92T9OQe+LX7PPaKziWzWZnfL9pJMZW1bO5OPS3LSUP1S3lg9poocvnk0ySppm8njQw8cTzu4wWMA6PAZgtFm40C/WaRcikzJbSWfPzuXKqQ0sxKLdfgl3BF0A82brsgaXLW7gB12EPzH7oTqxuZWvZKtp73M0Tm+Pz4vvlDUeOLdxZwVwPk1KRVS2cQX0ce4s4n+RlpKcHICC7LeCGy4rdAbAELNlGX3ZNzCdRYyq+uhvwVHHWrRpn+IvGGoVFl/MhDadWMcJP9LZen9cr+din7JuOx/ZeN2FqnzFL7767DtWvZu2f2TrnyermlsJrn977BC7f/lkz5g4srx3e8+orqypveeqmzf8qL/13n8KGgcUDKqrHbRP6FwNIYiqrimdLCgBFNBhVKlHOuxSdv3y2lARgcoLtYrOlOn53IGEMEF7k+dXC13JCQdThQHSbDQaX08hRhsdSYuuXVBAOtyLx4BHI6+6CYLnlEXbyLfYFex/D9zz7BAf0ztqVZ+7EwHn6YufCPz33/DraBqjXfyHBI2K+RonRKAOiVZYkC3BDJ+q9VNpUJOaj+sXtVx6h57CC2dmLTMMKdPlKFXO0a4DY+dTwvZeN/qJLhrqRy8gSsx+T0e52yQh+v2ynlszMrKwci9mcnemSzdRvt6NJiOSi+EtCbgo1UyM3WkiKOMKJUtMlGvCIi78nPihD2fPbzWFJ6WPdxqngfix9q9Sr9HQdwoJDth5mUy/nm1hKoRixV/mpUJxwVT85trLi1EAa6twb+aS+9uuhNBsStmnSbVMVzTXLnPpUo6oYTYpJ0C2VLGYDkWXJqFCUkhDL9evG+ooUZ3VpjZj8Izex59h6fnXg56wfNmF/DGMtC5Pi+GHyHdka/47Y4j27dJCYyF2B7wZVlZEQEERvNFFF4QqiSgVDdslOjEH5Z65AarLLowIDZAGWchEZbA/LwDo6mozsXBTfQUqoXleVJiZ0RugfzTJISFUVEExmlYuSRP1I0IAGUcZdOgxNpl1qFqqPbALSzPPvkbfjTVJ6vIrs30m/RXi/0ykkLWUbyWw9T7KjVgXRIIFRJlTBfN2EuvH0BNZX4iUpmc0y8bOPPmIblXMHz60Xa1gA6MDkVFt/ZIKYnGpfnBa6sUmAHY9/mJhqI4S4fJ+QL55xoKIY+VYNoOZTiaaCvQtCfCFHMMy1CH34IX7GMmfKjQd/UoR8AzFIA+R3QIHeUTdBWVYkSTznFd6SVJko0DW+xLKLeyTRZYcwiGjADQ/jqVO8uP6KGOiGzmqyKN4maq1OtpHWXhja9SRIRonoRhEaJZ5K0NrOFyl//vMAAGKNdIQ+qATAwK1gBjVKRVTIdwCUpB/rioP0XWLww7EvHPD6PGRL5ZkqbKpcLx3ptW2gZ/z7GYIdmjju9pfm6E8Zq6OFTovBQvLy/P78LIMhaEkbFrNYZLfbPjjm5jWdnDM4JnvBk0Az/y+ZVYSeXlcUJWdMvMcN9+1u8h0omny9N6YT+huGr1r0xzd+Or/5xbv/On7T8Y9PswO/X3znY5MWPHHDsNfXvfono1K6rn7f+K3vx32E27h55MJbxwOBFVznDsUNTsjh7BvIojRg1Mw2n89szrWA2WPUFFDSh8QUL7iGxEC7mCz83SHi7H5mUeZ0aISzRVANCgTlw1AfH9d2D8WobftHX+7YNsMT+hpLLZbJM2ZOJJNvaZk+Q5rNdrPv2XH2t6XzFTdbPuiJ9jP3rwh0PPOXNWvWAMLoCyfoMWk2eDi6esRYymclxCubh8RkDexcM++lZZJuOTk32SdwmnJoYkjgUBQyIf4DZqJx81Mjh9525cmTzcuHVf/BTQZgFvauOZFVwBH49ZIydr4kH4iQK81M2CcaDRi9Gi+obTZhqFy7xwIOIyi6fTTdPt5ft4+oT4Q+ecShOXlPGioU/BLkji3iOnVPiAnZ9vHnOw9ON/mw7Jv+1omT5kyVp7dNmDnLjWVoRx7zq9vG4YSfTjyy5vt7ViWNk9BynD61y+DMEKROSUpzOLKcJlOm3+OkzuoYFVUUVMesmuoZHFNTel5aloiry3bI3RbgrbNeR4XKwOMJ6AVAxMMtOP2GaQZcT2aVs+/Y3zDt7LdoiJfID985vmNc3Qb61PyZM+d3NmAPdGAahth3Jx+789Eel5+4rCjB7nSOkgMeuCKa7SZElSn1+qwAPhndyHVz283akJgZqJ4bgp8v7QVDiRwWFgxH9KfOeieocBWpiZ1l+9eu3bj/ufm1o2uv6ocGOq9zCZ23rKHh3ZdLPsoafsVgoKAwtzSV26sYyiEKd0SrzFlZAwZIfRwOUqzmSkGUpIHpPXr4fJFg8Kp0K1jRqlj7qv2GxYy5Eke5wr7FpDpWXFxYWDksVqi5e1fH3BkXz+n4pxIOWz79gRHv0LneqJs2FQ76ewKfPao+pSsqEvmsj+ykQFfCF6ZeRcGFyUQK8v26El/4WGzqS33OfxjpXbL2ndc3sTfYvm9+vP3WksHVg5tvOnmsZKGTFc2buvrNabOfa5w5/drrmura10otT/ceNqZjJ5Xzew187smt/1i1bPw9We5Roeh1xYVrZ732vkM6L1UOHVlb2WcEHT5q0qRRuwBhBYC0lmeDB8LRdATw2Y0Wg8Fo9Nolp1MaEnNqJkCjR6D/JfU5336yUOPaKqJJEuCQeFQirWX7O+6YxfZjqapqE/61bQ958LsXt8S/40CwpeDekav/vh0ILAPAD7lsA1jEZFcyGsFksprtJg9Rr4kR6DJ/ZWoO7uobKtNnnyJUlrW3X3ttO14phMgLHn98yIjzPqkFgFxoY259XSt4oSTqd/L0JgaDT/NcE9PAaBctOk/sjOTEKYEwCRGJxwB6tajQpMDBcxoHXzN8CJbum6GLZe60066mRmnd+eJXN6mThXRIWPMH/Un+NdGgxLmTUKrIsmYzWa0Gg8lkN4P41WCzUcXkofbu2oTf3cjSZdpuokXRuGOyi1dx22KswGZWhYd5AffOIrF9jYxdh40sI74Et93MVivueDXr0gYPcG0ouF4DRIkAevQioLvExgPivyvuhO7qQJ5BQRgeLXS7XPrsKDMzI6PAajSaTPkuq9WRKzu46XwOzWzPRJNH7+G7krl7+OC8ePqbjJDCRIiEfKFykdziVfBd8q+ke9n++uvnTGL7vy529F437Xwso/dL097ZwvbVXz9jOnlw3rz12+LfSS1Lh1+/urZpy+F4kfhtxYuQjGCut1tMFxHAq6vrscoOoatQFU0Xx29SyV/XLRG8TS0ierkyof+ZtWWXEPbn7boC9dce3JHE5yf0pzhpostXLJYMcLnSvcYhMa9mp0Nidu8vu/xUrvPeVQMOCCQs6MzrxGVT5986ecr8W6dQmX3ELvzxh7swGyl/I6Xt6/70Qnv7mhfYKbbnQTS8jE7s8wA7B4LrOep1cC1ckMMn1Hl+RVFNlKpZmqrlcuQEq9U9hBOEwa5mQEaKzBKmSBWoSQVlTvPepDFCnPndRKFJtuemosq2GZrG9p/taZv8wfaPbt58TGf7vePdSx/wsv5K9SPtbB87/T/s7H10mU722JDgM67pTN1euaIq8dIsyh+TpOUZ+fg6PcNnz/ZanE5V4I0FhsQsv8m6iSfIBUmS5S2dL8HBXl8ook+LIkFBaLdMkafPPzxZ2v7R5zsmPXeFIQMJ22e1lq48uri9oOMZ9uLa9lNYiho3Z9+6xqU/bcBDAybXN3ZFFJ3LddVEh0mcejw5BCxZZVnUS7wGFxqlMrTMRy+JIqpdWewrCD+6iu3/sre97yvSbCP7xLR8SXyH1LKxZTYkqp/1XIZ4dpmjpLktAEU5bnchWNw5lhxTli9rcMynUdPgGPX+vJ2/2BgiqPTHK2HB5clePsGgXCkPt082oetPnbx1/bDrDtW395oycuG8yJd/3/Xu6MZHa5Zcv2zRrf2wZn1HILfzsvKx+b0rCstHz73+8VXN/8y//JriK/qHR/+30LeE6xuRa8AjToRYDHa7y2UyEIfB4fWZnHbn4JjVYrfL3HVyQt3QpktOVnRhgnBcxKOXvoLpIyFPwCO6cjK3bsas9tdeeHRt8xasYDuu+TD4aeiNN0jGwgknTn4e//yqK4UOT/Gc4zM+cENZ1E8cDrfby3t/j9NoJ7JNtumyPcmJ1sVDgItr7tQYgH+grxdrpR2zt72PpSLjsXRp7XUHt5Mj8dki4Ynt/EpI9JkPcrlm6BV1m0GWiYgIK0G0GNEuC5llKWndDU1X/x0SbTfiOtaElf/INyryZYexkjVJLfFF86aMXUzaumS4AZRtXEaWOMsoSyaOIVng81ETVTMyMjNzVEXJ9plMVLbbMxQ7yDqidR3RdPz2LIDSIO1WQ8wBsin/pGskRZpuUfew19lm7LMwJ1eRcrT7sG6R5NCsqBgvN92NPdk7uARPdt4vtTDH4m9q1lxH/PGvvE03jMkcer4XnuKKI5gApOW6bWqi+YoMaKSUSAQlGWWzQVWtfIZmMSoUAA1mj4T2S2cBqaROkYZeq3KlhdkClOu/mD2BI48cxZHsMWxja46fYO2kPwmyZ7A1fiy+DRewhcJLzK17ycs1KTC73ZrXK0koahm/Jgob/pNT8no0p9XJMTHDAFyVskQJkKKvhBlTUzxHyokifvTqgNsSaw9mmBRz7n4cwoqu+vcfR9RErqqfl+fkfr2/YcZNo8ic866XXnR8Z72xNZI450HXce2MIn+oKqkIYDYgmvQhAm8c7YR/MwyOoefSIULSSMJGySlCWEwR6LrOB4nC0uhAZiCmDrLp6+3xekDI4T38Id7D54ipCHUbcnIcfn+uNTMzIFGXy8qjKd9qSbTzYosp2hbbF7bnuBrm+REWRw08Coc18VTQ4xFQ6+EJhDmL2m6/c/OZG4cpn31T3XpmM9quH32qucGAVz7Z9jEdXMUObcyzBF8xskNVg+knbU8BIO5gJWSlYgMK7tcIpZJMAaCyhONDYlbqCOKOo0cV29lA1ylOauB7yBN7yOHlOmgGQ75bkoI52TabW3Z7qCzl/3/2IIuHzuFynuSi2BZnlftyiBSnzxyCyzwcrImh4e0Xbhz2+9mfKtWtL7xTP39x26LeM2aFPyFVQ7CnuWmyw5K3EXsOrqIfh2dPY5tNjY2nGm7QTxGQIqmCtoEHIlG/Ag4zmKnd7qNeu82mSJSaHQ5QoCRU1lYi9ElBdqqp5pwa1sv/RAMmELwQB0baym968pqFwxaOC99ePv7pgf89chFZcXX5l1NzcyPRii+nphf8lzhBwpbiQanl0rP6Dg26zurbad4v56mukCugE0Wi7Vh7JsTasSV5lIO0dJbKBcljHAhLOdJqfN6cwad7QYchPV3OyCA+n4mYMrPSXCNiBtuIGMiGNH4pGWmKygXqpwH4S8+ePzvOII575nOCTh4R15lS69q26gmSEBt94OCr7YtF6z7vlm8b7mpdcN+rL/fHcyhjZk77c8arjmflv/Bn9kZObzbAuFFEB4A0ST+d2BztZXeaidFqTfd6iV/zO51ado7Fn+avjxnT0sDFqcleG3P6QR7xs+NNXUfUIJTSVqjbjT+pBpRfbpXXFSKawsFwiBuQbNyyZcyzs2sbcS679w9k3/mvbhr+6qufy7sbvojGrt10dOm6WtZ5ttes1keObtl5BAjMBCYFpHXcnkW8R87TLC6j7EsnBrDZ8jIhM/OyYp9LSycWo2xQPZ4ctYBHz/YyHc11H2qb9S+iA4oURXyC3SM+0WGqPrVIoJJaFCmMXFRdbixfuGzBqEk3j1qwfGE43Pbogt+Nn93Y9siC8v1T6+qnzxxRO50cnPC7BcsWhCMLly6MTZs8uu2RtlBo/iNtYyYOnz6ttm7aDBHpCoDEp+PghZnR/7I53U6Plce2UaYyMYkJqxeRED/HBp/idDkbYkCRuuwmm93WEFPtdgt6FMsl5xX9mtiW3kNfypcpEhAfkgPKkCfoEXdAGF7cGCBD0YAVbOGWH374gX38448/vsOW4BViZBv3vHrfq8eO8RdyHMhFiKNCMGoniiKGmUaJSlTVsUcEbCpFdAhyJGBIAFHnAbag8wAAgUm89lnw/0o5D7g2jvTvPzOzu9KCJNSFaAKEBMYHAokSuQpiY04OODjYsWxCcjbkNaluuPdyiXuaS0jHpPfeE0N68fVO/ObSe+8uy39mVlqEzr76oeyi+bG7U3bK83yfkUZBGZwCMyKlaRaXRRTLC6E4JyfkAld4DKmpsbkrK0ttpSafxzc15nHqTVNjepQycUvmivi5NiuyMYtA0qyNo3NOVr9OFfZJmt75WUW7VMhOWtE4fsubj9zRP33SzuaW6LxFB3rWTJj4xSuvXdHyYsOAb/bpj257c+OS5s4tvmrim7appHXPputbn8kPlVdURssit194/xklXdGr7p3261Hh7uKKUGH0uu2nzi8Pxya1V5qmAUYu4UfygiRwVi0/YrQaWIvIdGcQ4pBB7dzU9snCdpLZJF/SOXJNjdRPPa0uMhVd2TKurqk5Mq5FXFPXEB0/7ucNExvqGieOb6wDIIw7lSbR99oBPqhmvm9ikm0mm7/c7yzPc+bV1IrpYEmnX1mlhbZglpActKMVbEo36zBrHWyifBGnSASrw44ZvIhr6bwgFCxiuH4R45HIul+c91p4c3j55tf/fvilPddGFx5b8zJqf5X9DCi9v/m10vvcrj6U09uHsg/0Ke/29invHSBfX7VJ+TAv99nwkcNvfNd82xjlI/4/Su+rLyi3/ObXaPaLTJb0b6xlBfCX+DHKMLqgAOoieZk65HLlmXXU56PLK/RmGI2e9HQbys4GEGweShSEA0F1mAtak3BQbR1SPGxVVo3K6irbp3YM1ToJV3pGr452r7n58XnrWi6tr79h3tY9yqTy/KbYvMvxsYvGRLrPu/BCWegef0l+cNcmpeGP/qIz6oqkNPas06Fd6BEEkMAIbZHRaUaDTKd2RMKCgERqGDdkGNkrBpBGCE4XBIMoIpOMsR4lWko4kLBqJI+K5j8Faab66Q897w8yR4ALIR3yqYfpaPGg8hFyDSo70RG06A12/oayC49HL1E/s9K3DL2QNXzKGb8fhTCZCCJkRZgzSkcQkogAAdYJoQTf6LXQWZQQHjx2hLz1I7pgEIaGErEHWAIzAAhaezTEW+S5kUqBYFHUgcViJEbamxB9uT/ROLFE8QLBIegdsp5+naSN8spKbara53ErgY4FlFnoIwadmhP5X7VaYcvuz5QHAu8h/cO3K+s89eFTJuceP+dft9utd0xUFqDpyj3kqh3K1+H6uhrlzX/ZctHQEckuSNLhJG8MjPTGCNLRbwWDZH+Fr/6Jm7D5hAmyIDMiQ0ZGTrbVkMkqRQ3FUq17vL06HSowmDyctbXd2N5201ln3XjW5a88G6uvnz2nLjJHWMg+7W0766bZL10emd02YWJ7G+NFAYSwiCGdcx+ZGTqdRB35BoSomd9sMRrSZYQkAYOKeoYC8S5MM5WnxriwyfZwnAs9I2/h3kG0RVlFY12UNylYiiCAo/gZTriVRKwOA5LAgiyuTNnkwQ4Hyucer4lJXb96j39EPHUF+JnjK/5+briipGXeqiuf3np9+4YudA6O3jbYEQv6S2bt37Cle8be7rMBwVgcxo+Ir4APJkRy7enY7QbIl/LTzVK65C8mdrvDIed4PSa5IIE5pbQ8dlABTRX6S6xu1DgHrezj3QjuuaN9/n1P7N541ards5oXtJ3REgwFWsOdE/b9v3W9wlu7a432i6at2N7wzOzzq6tvrAr76ePuDExYn+qLI0JEDyCnCdwXdyjui3uFjR/VNMjMIUk6ao6YiGZWHZ0i/DX75U5H1aEgAOK2LmrkhkxmMUmXJFnOsjrBQR/drXNlOGl7yiCq4Y2Z+zTTkbYwT8qwtv73xo0CxS6XhZtDZ7WvpVaAD0ZnlC6fNWF+vigy+yj67YoVdz/PrAF7Z8wo/9mM65SDUhQQLFSOCbslO2RAIOJINwsiAoTMFr0emUykKWYSWc8XiHtk4gMlbe5qgAb7UsMIa0IFwu6bbumd0PqX1/72IW5Tjkmn/3QfCVmPHEWCwiKd8Cj0e7KGEUURmUU6Ebk1RiCQCHSypSLhfEr/+2Eqe2hQsaNeALBCVcRlNjI7Fh1Y7Gaz0W60ySYW9pXNXt9QQI0EXB1/3PjAIiZPQYprQ3RWgnr3Xd88KXuOu/GW5v7s6Kwj6xc5btOZJpzh7hmf2cktXDiKGxPRSYI8MjopD+WfMDoJeePRSb4QbvyciNkVzReismdxFD2z4Oyi0vHr6MwOwnTUfEt8ic9KPBFjIvYqgzhkDw/xTGK3kxc9YlKPgt969IarH3/wwP4nFG9dY+PEiY2NdULbnf0v3Hr7wAu3dHR2dnTMm5cy6s2OlKZTy49OL2AW1Ib01FNiGh70BD7YIdHEB79/Oej1B9UBL+6NL0aoFonqQehRdg4ip/LxIFqsSMPn2KuMXYbaUNsyJZw1fMrGrnIA6Qpa2n5Y+TuAYvg1fgUA6eAP5Nrjj4L8IMFW+uJUVye0D51Au5h8T7W6B7CZSZlyNlXeJ75ClUs8XEnM8as+Eb9qmXpVwDBeWUH+LLTzNU5DpKiQug4YJk0jh0pMoyDbnI1lQp0JPk9rzJdhoRy8xZvKwaN4g9Cm5HHsnddbrUub3bCVWHLF4ldiF1wYPjM27aFzzp37w3lvHP3F7rOrUcnw6jY6d1dT86yJ4eiY0sOnTO6//YLru+j0cyyamXhHhoZU2lu3GPuhiOexHiQ0HfQPYqfoh9HVJ1B0w2//heIgzFQV2SMV52iKgYTCOlIxU1N0cUXaQwR7uWRYkxbXSNDfPYvXhpfEa4MpdD7OPtrg4sg4yUbMNmIRLCjNZEJsvgbgEETRbiYUvqb4syENGQkj/JFkkzkxTAQrMmlscsKiQLvUAAeUNb8G7yQ062PCs0QKkEYsI9rR6nzH9imOvcoLeLew9/ghbKIUT+hoLlq5jiPvcYqZDnXNrC6WKXZGjNP8+VlGYAXOBfY556p5+ZaodTT0KC89ZE+UXqqiG9pSFPdShT1JcXDoO1XhHnmNmZqia+gnXgMYFag1wGbucZ7cAJnQGCmivUCW3ep0GlBamtthAIqVWwGovcRJi9eKLYy8TgmP0+BgddahWmkscQqUlpiPo4MhBwPPA1tV5FzFz7cKwm9+d+CzzzahATIdd1Du/G5GoOPWnR9+ofQoyl1qHsRXeDuriLez36eUA+dUeTlUxtt7N1fgvJMpulHDv1AchOdUhXek4hxNMZBQZI1UzNQUXVzB2vvoeGkj2IAMglnogXTIjaRLBGTZYORGZXcgqMUn8260FqnLBlSM7lL+uB+Vocqr6Rhetkf5tfL7vfj3qKxH+SMavZf++VuaSiUAhD7DLeIHkgA2yIZCCEdyXJ4cuz0tB9LAW+TMK3Ab3QxXJQWpdOWImbyK8arGGFaJqpEG2V2IO/yqihEFV1Wm94Xts3tnv8iA1RevaL1x1sDRP56CjrR2UWL1/ZBiOG0+WqzyvXWXXHDpANrEwNWGNfM3DSi/fHYJ/rbsp+8e6j5uKR4aUmlIXgO18Vocrdaz1uOkKrqR6V8oDkKPqsgfqZipKbq4gr0RJcl9kqDwq4yNv3kb1KtYuCSJSmbrqZpIDiOjjbIoSpJTMDbFZEdTTJAFWdIRyZowKGrdjOZBjePIDroW0tZGwh2UUz1yNcPaH1CQ4fikjst3rbt0NcHv/agMUij5c2Vc18rz5/NZJM3JfMkD1dAaGU3tegXFxQDlWSZTbXkgUGPKKtBBcbEui2SWhkqnxEIQcFgyozFLwnGq7ZUx0g03TH/aTYLqcnOkuuX8iaFL8zhXsVAn4a3SSDRSWl1/RVfoo3fmXTau+ubIbfnTo2vnNjQ0TVjXsWQjbb4+hL9FfuGvkV+cNqai1JldVTJn7srmu+7JLfy6KLhqVGhcaeOylsh5lbWnl49r6TrnKPVMv/LO/azH5ASbVEBr5VQ+UtQfAPb2jbbEazY1vfvCE6Xna+kHfxhi6RUj001a+kAasPTikemClt4lAX+3T+GCYcUDmqJ/lKrwqwogTCEpQjeUQBBOgS2RydU1JDM/P2g3GoNBuabG7/GMKZPlsC/fW50fjVVXsyDp7OxQNJZtNo6aSoF3p+S0NFDHPHgbYiBJgQZGv/ERLZmZ0t5q6wkJKnqMhzBz8MufZG0ZXsZRzHYYrWJk1TDShwoZfiVWbn2rce4L19/03NdfPRtr2nHzvKc/emdx/d3LDyM4XkaJq+cfm/bY8bqFq1fv6FyOvX+1oHvwefbOru7Y0zcz5q91cn3Tq52bInXKZx9RCGvWp8UlOEsQzpxD6T/05acLVrNap952xtZhP0xWx0+0iY+fnCrjtT1FbQ2389oqStRWanr34n+eflDP00eNTBe09C6rWpeVidoeugYAvcGv8LTaXynTgF0DGRLXuBwA/y5J0T00eaRi6JdU8UmS4qDyuqqwJBTvUMXlkqApuriC9Vdu9UkSBIfk5fPVpZGx4MYuV46oJ+kEY0tOTnr6qEKLpcQNmZh+SJ2ImdjppB56CnnSKS02+RpiJifBU2MEnYC8izsQ2clwI9I+1YYLf3Gtkw8SVgdtm4XAwyNdtX46hDAvXCL2GCmnN3ZetuitjjuuvUr5/0PfKX9DwuFDDfpT17zfga0rz19x8fIFq84TXdXF99Wdtr1n/m5lz4fKh8pLyPrJR8gyV+hdtuva4/Mv2Lj1ih27+lg74MwMf2tPV9/aEPAZUHI97ucl3KK2k5t4PReeOJ319ZfAyRW8pRiS+gUt3aSlD6jpeSPTBS29y6C2pIDWK8yCw0JYeIl7wbKhNGJ1pqWZBQEIyYUcNwVKAXHz0vPBYdBQiw8WTxJRTWOGj2+K1tf/PFpXNzVaf2ojO+KOwcEvTpva/POG6c1EmNrUMqWhpRkIfcaHKAN0OZ81eEfOGnzxWQOjb0jBFAZx/C+zhmCNsJ9hQWsvOLVn0n5GBm1eUrt/zK5jR21o/OiJKy9AhwzKa/6alefjSoYJlXV2dVyL7IwUqpp+Qes1ytH2RjTouvnWlnFKMOP2oSGVpeD1c2ZST4ByefGmpvMavgVOruA1XMnTC0emC1p6V0B9A0u1np97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<section class="page-header">
<h1 class="title toc-ignore project-name">Mandalas</h1>
<h4 class="author project-author">庄闪闪</h4>
<h4 class="date project-date">2020-12-24</h4>
</section>



<section class="main-content">
<div id="section" class="section level2">
<h2></h2>
<p><a href="https://github.com/aschinchon/mandalas">Mandalas</a></p>
<p><a href="https://www.r-bloggers.com/2018/02/mandalas/">R-blogger-Mandalas</a></p>
<p><a href="https://fronkonstin.com/2018/02/14/mandalas/">Mandalas</a></p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="co"># Load in libraries</span></span>
<span id="cb1-2"><a href="#cb1-2"></a><span class="kw">library</span>(ggplot2)</span>
<span id="cb1-3"><a href="#cb1-3"></a><span class="kw">library</span>(dplyr)</span>
<span id="cb1-4"><a href="#cb1-4"></a><span class="kw">library</span>(deldir)</span>
<span id="cb1-5"><a href="#cb1-5"></a></span>
<span id="cb1-6"><a href="#cb1-6"></a><span class="co"># Parameters to change as you like</span></span>
<span id="cb1-7"><a href="#cb1-7"></a>iter=<span class="dv">3</span> <span class="co"># Number of iterations (depth)</span></span>
<span id="cb1-8"><a href="#cb1-8"></a>points=<span class="dv">6</span> <span class="co"># Number of points</span></span>
<span id="cb1-9"><a href="#cb1-9"></a>radius=<span class="fl">3.8</span> <span class="co"># Factor of expansion/compression</span></span>
<span id="cb1-10"><a href="#cb1-10"></a></span>
<span id="cb1-11"><a href="#cb1-11"></a><span class="co"># Angles of points from center</span></span>
<span id="cb1-12"><a href="#cb1-12"></a>angles=<span class="kw">seq</span>(<span class="dv">0</span>, <span class="dv">2</span><span class="op">*</span>pi<span class="op">*</span>(<span class="dv">1-1</span><span class="op">/</span>points), <span class="dt">length.out =</span> points)<span class="op">+</span>pi<span class="op">/</span><span class="dv">2</span></span>
<span id="cb1-13"><a href="#cb1-13"></a></span>
<span id="cb1-14"><a href="#cb1-14"></a><span class="co"># Initial center</span></span>
<span id="cb1-15"><a href="#cb1-15"></a>df=<span class="kw">data.frame</span>(<span class="dt">x=</span><span class="dv">0</span>, <span class="dt">y=</span><span class="dv">0</span>)</span>
<span id="cb1-16"><a href="#cb1-16"></a></span>
<span id="cb1-17"><a href="#cb1-17"></a><span class="co"># Iterate over centers again and again</span></span>
<span id="cb1-18"><a href="#cb1-18"></a><span class="cf">for</span> (k <span class="cf">in</span> <span class="dv">1</span><span class="op">:</span>iter)</span>
<span id="cb1-19"><a href="#cb1-19"></a>{</span>
<span id="cb1-20"><a href="#cb1-20"></a>  temp=<span class="kw">data.frame</span>()</span>
<span id="cb1-21"><a href="#cb1-21"></a>  <span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="op">:</span><span class="kw">nrow</span>(df))</span>
<span id="cb1-22"><a href="#cb1-22"></a>  {</span>
<span id="cb1-23"><a href="#cb1-23"></a>    <span class="kw">data.frame</span>(<span class="dt">x=</span>df[i,<span class="st">&quot;x&quot;</span>]<span class="op">+</span>radius<span class="op">^</span>(k<span class="dv">-1</span>)<span class="op">*</span><span class="kw">cos</span>(angles),</span>
<span id="cb1-24"><a href="#cb1-24"></a>               <span class="dt">y=</span>df[i,<span class="st">&quot;y&quot;</span>]<span class="op">+</span>radius<span class="op">^</span>(k<span class="dv">-1</span>)<span class="op">*</span><span class="kw">sin</span>(angles)) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">rbind</span>(temp) -&gt;<span class="st"> </span>temp</span>
<span id="cb1-25"><a href="#cb1-25"></a>  }</span>
<span id="cb1-26"><a href="#cb1-26"></a>  df=temp</span>
<span id="cb1-27"><a href="#cb1-27"></a>}</span>
<span id="cb1-28"><a href="#cb1-28"></a></span>
<span id="cb1-29"><a href="#cb1-29"></a><span class="co"># Obtain Voronoi regions</span></span>
<span id="cb1-30"><a href="#cb1-30"></a>df <span class="op">%&gt;%</span></span>
<span id="cb1-31"><a href="#cb1-31"></a><span class="st">  </span><span class="kw">select</span>(x,y) <span class="op">%&gt;%</span></span>
<span id="cb1-32"><a href="#cb1-32"></a><span class="st">  </span><span class="kw">deldir</span>(<span class="dt">sort=</span><span class="ot">TRUE</span>) <span class="op">%&gt;%</span></span>
<span id="cb1-33"><a href="#cb1-33"></a><span class="st">  </span>.<span class="op">$</span>dirsgs -&gt;<span class="st"> </span>data</span>
<span id="cb1-34"><a href="#cb1-34"></a></span>
<span id="cb1-35"><a href="#cb1-35"></a><span class="co"># Plot regions with geom_segmen</span></span>
<span id="cb1-36"><a href="#cb1-36"></a>data <span class="op">%&gt;%</span></span>
<span id="cb1-37"><a href="#cb1-37"></a><span class="st">  </span><span class="kw">ggplot</span>() <span class="op">+</span></span>
<span id="cb1-38"><a href="#cb1-38"></a><span class="st">  </span><span class="kw">geom_segment</span>(<span class="kw">aes</span>(<span class="dt">x =</span> x1, <span class="dt">y =</span> y1, <span class="dt">xend =</span> x2, <span class="dt">yend =</span> y2), <span class="dt">color=</span><span class="st">&quot;black&quot;</span>) <span class="op">+</span></span>
<span id="cb1-39"><a href="#cb1-39"></a><span class="st">  </span><span class="kw">scale_x_continuous</span>(<span class="dt">expand=</span><span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>))<span class="op">+</span></span>
<span id="cb1-40"><a href="#cb1-40"></a><span class="st">  </span><span class="kw">scale_y_continuous</span>(<span class="dt">expand=</span><span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>))<span class="op">+</span></span>
<span id="cb1-41"><a href="#cb1-41"></a><span class="st">  </span><span class="kw">coord_fixed</span>() <span class="op">+</span></span>
<span id="cb1-42"><a href="#cb1-42"></a><span class="st">  </span><span class="kw">theme</span>(<span class="dt">legend.position  =</span> <span class="st">&quot;none&quot;</span>,</span>
<span id="cb1-43"><a href="#cb1-43"></a>        <span class="dt">panel.background =</span> <span class="kw">element_rect</span>(<span class="dt">fill=</span><span class="st">&quot;white&quot;</span>),</span>
<span id="cb1-44"><a href="#cb1-44"></a>        <span class="dt">panel.border     =</span> <span class="kw">element_rect</span>(<span class="dt">colour =</span> <span class="st">&quot;black&quot;</span>, <span class="dt">fill=</span><span class="ot">NA</span>),</span>
<span id="cb1-45"><a href="#cb1-45"></a>        <span class="dt">axis.ticks       =</span> <span class="kw">element_blank</span>(),</span>
<span id="cb1-46"><a href="#cb1-46"></a>        <span class="dt">panel.grid       =</span> <span class="kw">element_blank</span>(),</span>
<span id="cb1-47"><a href="#cb1-47"></a>        <span class="dt">axis.title       =</span> <span class="kw">element_blank</span>(),</span>
<span id="cb1-48"><a href="#cb1-48"></a>        <span class="dt">axis.text        =</span> <span class="kw">element_blank</span>())-&gt;plot</span>
<span id="cb1-49"><a href="#cb1-49"></a></span>
<span id="cb1-50"><a href="#cb1-50"></a>plot</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="co"># Do you like the result? Save it (change the name if you want)</span></span>
<span id="cb2-2"><a href="#cb2-2"></a><span class="kw">ggsave</span>(<span class="st">&quot;mandala.png&quot;</span>, <span class="dt">height=</span><span class="dv">5</span>, <span class="dt">width=</span><span class="dv">5</span>, <span class="dt">units=</span><span class="st">&#39;in&#39;</span>, <span class="dt">dpi=</span><span class="dv">600</span>)</span></code></pre></div>
<p><img 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" /></p>
</div>
</section>



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================================================
FILE: 2020年/2020.12.24Mandalas/Mandalas/Mandalas.rmd
================================================
---
title: "Mandalas"
author: "庄闪闪"
date: "`r Sys.Date()`"
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
vignette: >
  %\VignetteIndexEntry{Vignette Title}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

## 参考文献

[Mandalas](https://github.com/aschinchon/mandalas)

[R-blogger-Mandalas](https://www.r-bloggers.com/2018/02/mandalas/)

[Mandalas](https://fronkonstin.com/2018/02/14/mandalas/)

[Inspired by your work I created this](https://hrafnkelle.github.io/p5mandalas/)


## 简介

- 以（0,0）为中心的单位圆中获得n个等距点

- 对所有这些点重复该过程，再次获得每个点周围的n个点； 半径按比例缩放

- 舍弃了以前的（父）n分


我反复地重复这些步骤。如果我从n个点开始，迭代k次，最后我得到nk个点。然后，我计算它们的Voronoi镶嵌，并用ggplot绘制。

```{r message=FALSE, warning=FALSE}
# Load in libraries
library(ggplot2)
library(dplyr)
library(deldir)

# Parameters to change as you like
iter=5 # Number of iterations (depth)
points=8 # Number of points
radius=4.8 # Factor of expansion/compression

# Angles of points from center
angles=seq(0, 2*pi*(1-1/points), length.out = points)+pi/2

# Initial center
df=data.frame(x=0, y=0)

# Iterate over centers again and again
for (k in 1:iter)
{
  temp=data.frame()
  for (i in 1:nrow(df))
  {
    data.frame(x=df[i,"x"]+radius^(k-1)*cos(angles),
               y=df[i,"y"]+radius^(k-1)*sin(angles)) %>% rbind(temp) -> temp
  }
  df=temp
}

# Obtain Voronoi regions
df %>%
  select(x,y) %>%
  deldir(sort=TRUE) %>%
  .$dirsgs -> data

# Plot regions with geom_segmen
data %>%
  ggplot() +
  geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2), color="black") +
  scale_x_continuous(expand=c(0,0))+
  scale_y_continuous(expand=c(0,0))+
  coord_fixed() +
  theme(legend.position  = "none",
        panel.background = element_rect(fill="white"),
        panel.border     = element_rect(colour = "black", fill=NA),
        axis.ticks       = element_blank(),
        panel.grid       = element_blank(),
        axis.title       = element_blank(),
        axis.text        = element_blank())->plot

plot

# Do you like the result? Save it (change the name if you want)
ggsave("mandala4.png", height=5, width=5, units='in', dpi=600)

```

![](1.jpg)










================================================
FILE: 2020年/2020.12.24圣诞节/1.json
================================================
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


================================================
FILE: 2020年/2020.12.24圣诞节/marry.R
================================================
# https://www.data-imaginist.com/2016/Data-driven-x-mas-card/

library(jsonlite)
library(ggplot2)

greet <- fromJSON(readLines('F:/我的学习/R/R_example/2020.12.24圣诞节/1.json'))
layer_names <- greet$layers$name
heart <- greet$layers$paths[[which(layer_names == 'Heart')]]$points
merry <- greet$layers$paths[[which(layer_names == 'Merry')]]$points
x_mas <- greet$layers$paths[[which(layer_names == 'X-MAS')]]$points
range <- greet$layers$paths[[which(layer_names == 'back')]]$points[[1]]

ggplot(as.data.frame(heart[[1]])) +
  geom_polygon(aes(V1, V2))
#============
library(mgcv)
heart_points <- data.frame(
  x = runif(25000, min = min(range[,1]), max = max(range[, 1])),
  y = runif(25000, min = min(range[,2]), max = max(range[, 2])),
  colour = 'green',
  stringsAsFactors = FALSE
)
heart_points$colour[
  in.out(
    do.call(rbind, lapply(heart, rbind, c(NA, NA))),
    cbind(heart_points$x, heart_points$y)
  )
] <- 'red'

merry_points <- data.frame(
  x = runif(25000, min = min(range[,1]), max = max(range[, 1])),
  y = runif(25000, min = min(range[,2]), max = max(range[, 2])),
  colour = 'red',
  stringsAsFactors = FALSE
)
merry_points$colour[
  in.out(
    do.call(rbind, lapply(merry, rbind, c(NA, NA))),
    cbind(merry_points$x, merry_points$y)
  )
] <- 'green'

xmas_points <- data.frame(
  x = runif(25000, min = min(range[,1]), max = max(range[, 1])),
  y = runif(25000, min = min(range[,2]), max = max(range[, 2])),
  colour = 'red',
  stringsAsFactors = FALSE
)
xmas_points$colour[
  in.out(
    do.call(rbind, lapply(x_mas, rbind, c(NA, NA))),
    cbind(xmas_points$x, xmas_points$y)
  )
] <- 'green'

ggplot(merry_points) +
  geom_point(aes(x, y, colour = colour))

#===================

library(deldir)
library(ggforce)

heart_vor <- deldir(heart_points$x, heart_points$y)
heart_con <- split(c(heart_points$colour[heart_vor$delsgs$ind2],
                     heart_points$colour[heart_vor$delsgs$ind1]),
                   c(heart_vor$delsgs$ind1, heart_vor$delsgs$ind2))
heart_boring <- lengths(lapply(heart_con, unique)) == 1
heart_remove <- c(
  sample(which(heart_boring & heart_points$colour == 'red'),
         sum(heart_boring & heart_points$colour == 'red') -
           (1000 - sum(!heart_boring & heart_points$colour == 'red'))),
  sample(which(heart_boring & heart_points$colour == 'green'),
         sum(heart_boring & heart_points$colour == 'green') -
           (1000 - sum(!heart_boring & heart_points$colour == 'green')))
)
heart_points <- heart_points[-heart_remove, ]

merry_vor <- deldir(merry_points$x, merry_points$y)
merry_con <- split(c(merry_points$colour[merry_vor$delsgs$ind2],
                     merry_points$colour[merry_vor$delsgs$ind1]),
                   c(merry_vor$delsgs$ind1, merry_vor$delsgs$ind2))
merry_boring <- lengths(lapply(merry_con, unique)) == 1
merry_remove <- c(
  sample(which(merry_boring & merry_points$colour == 'red'),
         sum(merry_boring & merry_points$colour == 'red') -
           (1000 - sum(!merry_boring & merry_points$colour == 'red'))),
  sample(which(merry_boring & merry_points$colour == 'green'),
         sum(merry_boring & merry_points$colour == 'green') -
           (1000 - sum(!merry_boring & merry_points$colour == 'green')))
)
merry_points <- merry_points[-merry_remove, ]

xmas_vor <- deldir(xmas_points$x, xmas_points$y)
xmas_con <- split(c(xmas_points$colour[xmas_vor$delsgs$ind2],
                    xmas_points$colour[xmas_vor$delsgs$ind1]),
                  c(xmas_vor$delsgs$ind1, xmas_vor$delsgs$ind2))
xmas_boring <- lengths(lapply(xmas_con, unique)) == 1
xmas_remove <- c(
  sample(which(xmas_boring & xmas_points$colour == 'red'),
         sum(xmas_boring & xmas_points$colour == 'red') -
           (1000 - sum(!xmas_boring & xmas_points$colour == 'red'))),
  sample(which(xmas_boring & xmas_points$colour == 'green'),
         sum(xmas_boring & xmas_points$colour == 'green') -
           (1000 - sum(!xmas_boring & xmas_points$colour == 'green')))
)
xmas_points <- xmas_points[-xmas_remove, ]

ggplot(xmas_points) +
  geom_voronoi_tile(aes(x, y, fill = colour), colour = 'black')


#=====================
heart_points <- heart_points[order(heart_points$colour), ]
heart_points$id <- seq_len(nrow(heart_points))
merry_points <- merry_points[order(merry_points$colour), ]
merry_points$id <- seq_len(nrow(merry_points))
xmas_points <- xmas_points[order(xmas_points$colour), ]

eu_dist <- function(x, y) {
  ifelse(
    merry_points$colour[x] != xmas_points$colour[y],
    1e5,
    sqrt((merry_points$x[x] - xmas_points$x[y])^2 +
           (merry_points$y[x] - xmas_points$y[y])^2)
  )
}
distance <- outer(seq_len(nrow(merry_points)),
                  seq_len(nrow(merry_points)),
                  eu_dist
)

pair <- Reduce(function(l, r) {
  pair_order <- order(distance[r, ])
  c(l, pair_order[which(!pair_order %in% l)[1]])
}, seq_len(nrow(merry_points)), init = integer())

xmas_points$id[pair] <- seq_len(nrow(xmas_points))
xmas_points <- xmas_points[order(xmas_points$id), ]


#=======================
library(tweenr)
tween_points <- function(from, to, length, stagger) {
  leave <- sample(seq_len(stagger), nrow(from), replace = TRUE)
  arive <- sample(seq_len(stagger), nrow(from), replace = TRUE)
  x <- tween_t(Map(function(.f, .t) c(.f, .t), .f = from$x, .t = to$x),
               length - leave - arive)
  y <- tween_t(Map(function(.f, .t) c(.f, .t), .f = from$y, .t = to$y),
               length - leave - arive)
  points <- Map(function(x, y, l, a) {
    data.frame(x = x, y = y)[c(rep(1, l), seq_along(x), rep(length(x), a)), ]
  }, x = x, y = y, l = leave, a = arive)
  points <- do.call(rbind, points)
  points$frame <- rep(seq_len(length), nrow(from))
  cbind(
    points,
    from[rep(seq_len(nrow(from)), each = length), !names(from) %in% c('x', 'y')])
}


#================
n_frames_still <- 36
n_frames_trans <- 60
n_frames_stagger <- 20
n_frames_stage <- 96

heart_to_merry <- tween_points(heart_points,
                               merry_points,
                               n_frames_trans,
                               n_frames_stagger)
merry_to_xmas <- tween_points(merry_points,
                              xmas_points,
                              n_frames_trans,
                              n_frames_stagger)
xmas_to_heart <- tween_points(xmas_points,
                              heart_points,
                              n_frames_trans,
                              n_frames_stagger)


#================
heart_still <- heart_points[rep(seq_len(nrow(heart_points)), n_frames_still), ]
heart_still$frame <- rep(seq_len(n_frames_still), each = nrow(heart_points))
heart_to_merry$frame <- heart_to_merry$frame + n_frames_still

merry_still <- merry_points[rep(seq_len(nrow(merry_points)), n_frames_still), ]
merry_still$frame <- rep(seq_len(n_frames_still), each = nrow(merry_points)) + n_frames_stage
merry_to_xmas$frame <- merry_to_xmas$frame + n_frames_stage + n_frames_still

xmas_still <- xmas_points[rep(seq_len(nrow(xmas_points)), n_frames_still), ]
xmas_still$frame <- rep(seq_len(n_frames_still), each = nrow(xmas_points)) + 2*n_frames_stage
xmas_to_heart$frame <- xmas_to_heart$frame + 2*n_frames_stage + n_frames_still

frames <- rbind(
  heart_still,
  heart_to_merry,
  merry_still,
  merry_to_xmas,
  xmas_still,
  xmas_to_heart
)


christmas_card <- function(data) {
  bound <- c(range(range[,1]), range(-range[,2]))
  ggplot(data, aes(x = x, y = y)) +
    geom_voronoi_tile(aes(fill = colour), bound = bound) +
    geom_voronoi_segment(alpha = 0.3, bound = bound) +
    scale_y_reverse() +
    scale_fill_manual(values = c('forestgreen', 'firebrick')) +
    labs(caption = 'www.data-imaginist.com') +
    coord_fixed(expand = FALSE) +
    theme_void() +
    theme(legend.position = 'none')
}
plot(christmas_card(heart_points))

for (i in seq_len(max(frames$frame))) {
  data <- frames[frames$frame == i, ]
  plot(christmas_card(data))
}



================================================
FILE: 2020年/2020.12.25峰峦图/峰峦图.rmd
================================================
---
title: "峰峦图"
author:
  - 庄闪闪
documentclass: ctexart
keywords:
  - 中文
  - R Markdown
output:
  rticles::ctex:
    fig_caption: yes
    number_sections: yes
    toc: yes
editor_options: 
  chunk_output_type: console
---



# 峰峦图

上次可视化系列说了[瀑布图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487383&idx=1&sn=43e2eaf6b7c6b24510ccadb79e766f07&chksm=ea24f073dd53796546fd4145ded4cddfe8779464ad9d0d674900a65484966cd5c3f6367dcecf&token=1431845296&lang=zh_CN#rd)。它可以用于展示拥有相同的X轴变量数据（如相同的时间序列）、不同的Y轴离散型变量（如不同的类别变量）和Z轴数值变量。

本节使用的**峰峦图**也可以很好地展示瀑布图的数据信息。它们对于可视化随时间或空间分布的变化非常有用。本节主要使用`ggridges`包中的`geom_density_ridges()`进行绘制峰峦图。详细介绍如下：

## 数据结构

这里使用`base`包中的`diamonds`数据集做例子。
```{r message=FALSE, warning=FALSE}
# library
library(ggridges) # Ridgeline Plots in 'ggplot2', CRAN v0.5.2
library(ggplot2) # Create Elegant Data Visualisations Using the Grammar of Graphics, CRAN v3.3.2 
head(diamonds)
```

## 绘图教程

### 基础版本

使用`price`作为x轴, `cut`为y轴，`fill`参数也是设定为`cut`。`geom_density_ridges()`内部全部使用默认参数。使用了ggridges包中的主题`theme_ridges()`。
```{r message=FALSE, warning=FALSE}
ggplot(diamonds, aes(x = price, y = cut, fill = cut)) +
	geom_density_ridges() +
	theme_ridges() + 
	theme(legend.position = "none")
```

### 形状变化

如果不想绘制密度图，则可以使用`stat="binline", bins=20`绘制柱形图，其中`bins=20`表示每格格子大小。为了防止上下图片重叠，这里使用了透明度参数：`alpha=0.6`。
```{r message=FALSE, warning=FALSE}
ggplot(diamonds, aes(x = price, y = cut, fill = cut)) +
	geom_density_ridges(alpha=0.7, stat="binline", bins=20) +
	theme_ridges() + 
	theme(legend.position = "none")
```

### 根据第三变量进行分面

```{r}
ggplot(diamonds, aes(x = price, y = cut,fill = cut)) +
	geom_density_ridges(alpha=0.7) +
	facet_wrap(~color) + 
  theme_ridges() + 
	theme(legend.position = "none")
```

### 加入统计量

通过设置选项`quantile_lines = TRUE`，我们可以使`stat_density_ridges`计算指示分位数的线的位置。 默认情况下，绘制了三行，分别对应于第一，第二和第三四分位数：
```{r}
ggplot(diamonds, aes(x = price, y = cut,fill = cut)) +
	geom_density_ridges(alpha=0.7,quantile_lines = TRUE) +
  theme_ridges() + 
	theme(legend.position = "none")
```

> **注意**：`quantiles=2`意味着在两个分位数之间的边界上有一条线即，(中位数)。

我们还可以通过切点而不是数字来指定分位数。例如，我们可以指出2.5％和97.5％的尾巴。

```{r}
ggplot(diamonds, aes(x = price, y = cut,fill = cut)) +
	geom_density_ridges(alpha=0.7,quantile_lines = TRUE,quantiles = c(0.025, 0.975)) +
  theme_ridges() + 
	theme(legend.position = "none")
```

使用`stat_density_ridges`，计算`stat(quantile)`，通过分位数进行着色，。 注意，仅当`calc_ecdf = TRUE`时才能计算。

```{r}
ggplot(diamonds, aes(x = price, y = cut,fill = factor(stat(quantile)))) +
	stat_density_ridges(
	  geom = "density_ridges_gradient", 
	  calc_ecdf = TRUE,
    quantiles = 4, quantile_lines = TRUE) +
  theme_ridges() +
  scale_fill_viridis_d(name = "Quartiles")
```

我们可以使用相同的方法来突出分布的尾部。
```{r}
ggplot(diamonds, aes(x = price, y = cut,fill = factor(stat(quantile)))) +
	stat_density_ridges(
	  geom = "density_ridges_gradient", 
	  calc_ecdf = TRUE,
    quantiles = c(0.025, 0.975)) +
  theme_ridges() +
    scale_fill_manual(
    name = "Probability", values = c("#FF0000A0", "#A0A0A0A0", "#0000FFA0"),
    labels = c("(0, 0.025]", "(0.025, 0.975]", "(0.975, 1]")
  )
```

最后，当`calc_ecdf = TRUE`时，我们还可以计算`stat(ecdf)`，它表示该分布的经验累积密度函数。 我们将其概率直接映射到颜色上。
```{r}
ggplot(diamonds, aes(x = price, y = cut,fill = 0.5 - abs(0.5 - stat(ecdf)))) +
	stat_density_ridges(geom = "density_ridges_gradient", calc_ecdf = TRUE) +
  scale_fill_viridis_c(name = "Tail probability", direction = -1)
```

### 加入抖动点

`stat_density_ridges()`还提供了可视化生成分布的原始数据点的选项。可以通过设置`jittered_points = TRUE`实现。为了只管我们这里使用`iris`包。

```{r}
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(jittered_points = TRUE)+
	theme_ridges() + 
	theme(legend.position = "none")
```
当然可以将其放在密度函数的下方，通过使用`position = "raincloud"`参数。
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(
    jittered_points = TRUE, position = "raincloud",
    alpha = 0.7, scale = 0.9
  )
```

我们还可以模拟地毯形式：
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(
    jittered_points = TRUE,
    position = position_points_jitter(width = 0.05, height = 0),
    point_shape = '|', point_size = 3, point_alpha = 1, alpha = 0.7,
  )
```


可以使用ggridges提供的特殊比例来设置抖动点的样式。 首先，`scale_discrete_manual()`可用于制作具有任意形状和比例的图形。 `scale_point_color_hue()`。 
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Species, fill = Species)) +
  geom_density_ridges(
    aes(point_color = Species, point_fill = Species, point_shape = Species),
    alpha = .2, point_alpha = 1, jittered_points = TRUE
  ) +
  scale_point_color_hue(l = 40) +
  scale_discrete_manual(aesthetics = "point_shape", values = c(21, 22, 23))
```

如果你还想再加入一个变量进行可视化，可以在`geom_density_ridges()`加入，例如：`point_shape = Species, point_fill = Species, point_size = Petal.Length`。
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Species, fill = Species)) +
  geom_density_ridges(
    aes(point_shape = Species, point_fill = Species, point_size = Petal.Length), 
    alpha = .2, point_alpha = 1, jittered_points = TRUE
  ) +
  scale_point_color_hue(l = 40) + scale_point_size_continuous(range = c(0.5, 4)) +
  scale_discrete_manual(aesthetics = "point_shape", values = c(21, 22, 23))
```

另外一种有趣的可视化是通过`vline_xxx`构造以下图形。
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(
    jittered_points = TRUE, quantile_lines = TRUE, scale = 0.9, alpha = 0.7,
    vline_size = 1, vline_color = "red",
    point_size = 0.4, point_alpha = 1,
    position = position_raincloud(adjust_vlines = TRUE)
  )
```

## 其他资料

对于该包的其他有趣函数与可视化可参考以下资料：
- [Introduction to ggridges](https://cran.r-project.org/web/packages/ggridges/vignettes/introduction.html "Introduction to ggridges")

- [RDocumentation-ggridges](https://www.rdocumentation.org/packages/ggridges/versions/0.5.2 "RDocumentation-ggridges")

- [Basic ridgeline plot](https://www.r-graph-gallery.com/294-basic-ridgeline-plot "Basic ridgeline plot")











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<script>/**
* Detect Element Resize Plugin for jQuery
*
* https://github.com/sdecima/javascript-detect-element-resize
* Sebastian Decima
*
* version: 0.5.3
**/

(function ( $ ) {
	var attachEvent = document.attachEvent,
		stylesCreated = false;
	
	var jQuery_resize = $.fn.resize;
	
	$.fn.resize = function(callback) {
		return this.each(function() {
			if(this == window)
				jQuery_resize.call(jQuery(this), callback);
			else
				addResizeListener(this, callback);
		});
	}

	$.fn.removeResize = function(callback) {
		return this.each(function() {
			removeResizeListener(this, callback);
		});
	}
	
	if (!attachEvent) {
		var requestFrame = (function(){
			var raf = window.requestAnimationFrame || window.mozRequestAnimationFrame || window.webkitRequestAnimationFrame ||
								function(fn){ return window.setTimeout(fn, 20); };
			return function(fn){ return raf(fn); };
		})();
		
		var cancelFrame = (function(){
			var cancel = window.cancelAnimationFrame || window.mozCancelAnimationFrame || window.webkitCancelAnimationFrame ||
								   window.clearTimeout;
		  return function(id){ return cancel(id); };
		})();

		function resetTriggers(element){
			var triggers = element.__resizeTriggers__,
				expand = triggers.firstElementChild,
				contract = triggers.lastElementChild,
				expandChild = expand.firstElementChild;
			contract.scrollLeft = contract.scrollWidth;
			contract.scrollTop = contract.scrollHeight;
			expandChild.style.width = expand.offsetWidth + 1 + 'px';
			expandChild.style.height = expand.offsetHeight + 1 + 'px';
			expand.scrollLeft = expand.scrollWidth;
			expand.scrollTop = expand.scrollHeight;
		};

		function checkTriggers(element){
			return element.offsetWidth != element.__resizeLast__.width ||
						 element.offsetHeight != element.__resizeLast__.height;
		}
		
		function scrollListener(e){
			var element = this;
			resetTriggers(this);
			if (this.__resizeRAF__) cancelFrame(this.__resizeRAF__);
			this.__resizeRAF__ = requestFrame(function(){
				if (checkTriggers(element)) {
					element.__resizeLast__.width = element.offsetWidth;
					element.__resizeLast__.height = element.offsetHeight;
					element.__resizeListeners__.forEach(function(fn){
						fn.call(element, e);
					});
				}
			});
		};
		
		/* Detect CSS Animations support to detect element display/re-attach */
		var animation = false,
			animationstring = 'animation',
			keyframeprefix = '',
			animationstartevent = 'animationstart',
			domPrefixes = 'Webkit Moz O ms'.split(' '),
			startEvents = 'webkitAnimationStart animationstart oAnimationStart MSAnimationStart'.split(' '),
			pfx  = '';
		{
			var elm = document.createElement('fakeelement');
			if( elm.style.animationName !== undefined ) { animation = true; }    
			
			if( animation === false ) {
				for( var i = 0; i < domPrefixes.length; i++ ) {
					if( elm.style[ domPrefixes[i] + 'AnimationName' ] !== undefined ) {
						pfx = domPrefixes[ i ];
						animationstring = pfx + 'Animation';
						keyframeprefix = '-' + pfx.toLowerCase() + '-';
						animationstartevent = startEvents[ i ];
						animation = true;
						break;
					}
				}
			}
		}
		
		var animationName = 'resizeanim';
		var animationKeyframes = '@' + keyframeprefix + 'keyframes ' + animationName + ' { from { opacity: 0; } to { opacity: 0; } } ';
		var animationStyle = keyframeprefix + 'animation: 1ms ' + animationName + '; ';
	}
	
	function createStyles() {
		if (!stylesCreated) {
			//opacity:0 works around a chrome bug https://code.google.com/p/chromium/issues/detail?id=286360
			var css = (animationKeyframes ? animationKeyframes : '') +
					'.resize-triggers { ' + (animationStyle ? animationStyle : '') + 'visibility: hidden; opacity: 0; } ' +
					'.resize-triggers, .resize-triggers > div, .contract-trigger:before { content: \" \"; display: block; position: absolute; top: 0; left: 0; height: 100%; width: 100%; overflow: hidden; } .resize-triggers > div { background: #eee; overflow: auto; } .contract-trigger:before { width: 200%; height: 200%; }',
				head = document.head || document.getElementsByTagName('head')[0],
				style = document.createElement('style');
			
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				style.appendChild(document.createTextNode(css));
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			head.appendChild(style);
			stylesCreated = true;
		}
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	window.addResizeListener = function(element, fn){
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		else {
			if (!element.__resizeTriggers__) {
				if (getComputedStyle(element).position == 'static') element.style.position = 'relative';
				createStyles();
				element.__resizeLast__ = {};
				element.__resizeListeners__ = [];
				(element.__resizeTriggers__ = document.createElement('div')).className = 'resize-triggers';
				element.__resizeTriggers__.innerHTML = '<div class="expand-trigger"><div></div></div>' +
																						'<div class="contract-trigger"></div>';
				element.appendChild(element.__resizeTriggers__);
				resetTriggers(element);
				element.addEventListener('scroll', scrollListener, true);
				
				/* Listen for a css animation to detect element display/re-attach */
				animationstartevent && element.__resizeTriggers__.addEventListener(animationstartevent, function(e) {
					if(e.animationName == animationName)
						resetTriggers(element);
				});
			}
			element.__resizeListeners__.push(fn);
		}
	};
	
	window.removeResizeListener = function(element, fn){
		if (attachEvent) element.detachEvent('onresize', fn);
		else {
			element.__resizeListeners__.splice(element.__resizeListeners__.indexOf(fn), 1);
			if (!element.__resizeListeners__.length) {
					element.removeEventListener('scroll', scrollListener);
					element.__resizeTriggers__ = !element.removeChild(element.__resizeTriggers__);
			}
		}
	}
}( jQuery ));</script>
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)}.ui-widget-header 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<script>/*! jQuery UI - v1.11.4 - 2015-03-11
* http://jqueryui.com
* Includes: core.js, widget.js, mouse.js, position.js, accordion.js, autocomplete.js, button.js, datepicker.js, dialog.js, draggable.js, droppable.js, effect.js, effect-blind.js, effect-bounce.js, effect-clip.js, effect-drop.js, effect-explode.js, effect-fade.js, effect-fold.js, effect-highlight.js, effect-puff.js, effect-pulsate.js, effect-scale.js, effect-shake.js, effect-size.js, effect-slide.js, effect-transfer.js, menu.js, progressbar.js, resizable.js, selectable.js, selectmenu.js, slider.js, sortable.js, spinner.js, tabs.js, tooltip.js
* Copyright 2015 jQuery Foundation and other contributors; Licensed MIT */

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0)},_keydown:function(t){if(!t.altKey&&!t.ctrlKey){var i=e.ui.keyCode,s=this.headers.length,n=this.headers.index(t.target),a=!1;switch(t.keyCode){case i.RIGHT:case i.DOWN:a=this.headers[(n+1)%s];break;case i.LEFT:case i.UP:a=this.headers[(n-1+s)%s];break;case i.SPACE:case i.ENTER:this._eventHandler(t);break;case i.HOME:a=this.headers[0];break;case i.END:a=this.headers[s-1]}a&&(e(t.target).attr("tabIndex",-1),e(a).attr("tabIndex",0),a.focus(),t.preventDefault())}},_panelKeyDown:function(t){t.keyCode===e.ui.keyCode.UP&&t.ctrlKey&&e(t.currentTarget).prev().focus()},refresh:function(){var t=this.options;this._processPanels(),t.active===!1&&t.collapsible===!0||!this.headers.length?(t.active=!1,this.active=e()):t.active===!1?this._activate(0):this.active.length&&!e.contains(this.element[0],this.active[0])?this.headers.length===this.headers.find(".ui-state-disabled").length?(t.active=!1,this.active=e()):this._activate(Math.max(0,t.active-1)):t.active=this.headers.index(this.active),this._destroyIcons(),this._refresh()},_processPanels:function(){var e=this.headers,t=this.panels;this.headers=this.element.find(this.options.header).addClass("ui-accordion-header ui-state-default ui-corner-all"),this.panels=this.headers.next().addClass("ui-accordion-content ui-helper-reset ui-widget-content ui-corner-bottom").filter(":not(.ui-accordion-content-active)").hide(),t&&(this._off(e.not(this.headers)),this._off(t.not(this.panels)))},_refresh:function(){var t,i=this.options,s=i.heightStyle,n=this.element.parent();this.active=this._findActive(i.active).addClass("ui-accordion-header-active ui-state-active ui-corner-top").removeClass("ui-corner-all"),this.active.next().addClass("ui-accordion-content-active").show(),this.headers.attr("role","tab").each(function(){var t=e(this),i=t.uniqueId().attr("id"),s=t.next(),n=s.uniqueId().attr("id");t.attr("aria-controls",n),s.attr("aria-labelledby",i)}).next().attr("role","tabpanel"),this.headers.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}).next().attr({"aria-hidden":"true"}).hide(),this.active.length?this.active.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}).next().attr({"aria-hidden":"false"}):this.headers.eq(0).attr("tabIndex",0),this._createIcons(),this._setupEvents(i.event),"fill"===s?(t=n.height(),this.element.siblings(":visible").each(function(){var i=e(this),s=i.css("position");"absolute"!==s&&"fixed"!==s&&(t-=i.outerHeight(!0))}),this.headers.each(function(){t-=e(this).outerHeight(!0)}),this.headers.next().each(function(){e(this).height(Math.max(0,t-e(this).innerHeight()+e(this).height()))}).css("overflow","auto")):"auto"===s&&(t=0,this.headers.next().each(function(){t=Math.max(t,e(this).css("height","").height())}).height(t))},_activate:function(t){var i=this._findActive(t)[0];i!==this.active[0]&&(i=i||this.active[0],this._eventHandler({target:i,currentTarget:i,preventDefault:e.noop}))},_findActive:function(t){return"number"==typeof t?this.headers.eq(t):e()},_setupEvents:function(t){var i={keydown:"_keydown"};t&&e.each(t.split(" "),function(e,t){i[t]="_eventHandler"}),this._off(this.headers.add(this.headers.next())),this._on(this.headers,i),this._on(this.headers.next(),{keydown:"_panelKeyDown"}),this._hoverable(this.headers),this._focusable(this.headers)},_eventHandler:function(t){var i=this.options,s=this.active,n=e(t.currentTarget),a=n[0]===s[0],o=a&&i.collapsible,r=o?e():n.next(),h=s.next(),l={oldHeader:s,oldPanel:h,newHeader:o?e():n,newPanel:r};t.preventDefault(),a&&!i.collapsible||this._trigger("beforeActivate",t,l)===!1||(i.active=o?!1:this.headers.index(n),this.active=a?e():n,this._toggle(l),s.removeClass("ui-accordion-header-active ui-state-active"),i.icons&&s.children(".ui-accordion-header-icon").removeClass(i.icons.activeHeader).addClass(i.icons.header),a||(n.removeClass("ui-corner-all").addClass("ui-accordion-header-active ui-state-active ui-corner-top"),i.icons&&n.children(".ui-accordion-header-icon").removeClass(i.icons.header).addClass(i.icons.activeHeader),n.next().addClass("ui-accordion-content-active")))},_toggle:function(t){var i=t.newPanel,s=this.prevShow.length?this.prevShow:t.oldPanel;this.prevShow.add(this.prevHide).stop(!0,!0),this.prevShow=i,this.prevHide=s,this.options.animate?this._animate(i,s,t):(s.hide(),i.show(),this._toggleComplete(t)),s.attr({"aria-hidden":"true"}),s.prev().attr({"aria-selected":"false","aria-expanded":"false"}),i.length&&s.length?s.prev().attr({tabIndex:-1,"aria-expanded":"false"}):i.length&&this.headers.filter(function(){return 0===parseInt(e(this).attr("tabIndex"),10)}).attr("tabIndex",-1),i.attr("aria-hidden","false").prev().attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_animate:function(e,t,i){var s,n,a,o=this,r=0,h=e.css("box-sizing"),l=e.length&&(!t.length||e.index()<t.index()),u=this.options.animate||{},d=l&&u.down||u,c=function(){o._toggleComplete(i)};return"number"==typeof d&&(a=d),"string"==typeof d&&(n=d),n=n||d.easing||u.easing,a=a||d.duration||u.duration,t.length?e.length?(s=e.show().outerHeight(),t.animate(this.hideProps,{duration:a,easing:n,step:function(e,t){t.now=Math.round(e)}}),e.hide().animate(this.showProps,{duration:a,easing:n,complete:c,step:function(e,i){i.now=Math.round(e),"height"!==i.prop?"content-box"===h&&(r+=i.now):"content"!==o.options.heightStyle&&(i.now=Math.round(s-t.outerHeight()-r),r=0)}}),void 0):t.animate(this.hideProps,a,n,c):e.animate(this.showProps,a,n,c)},_toggleComplete:function(e){var t=e.oldPanel;t.removeClass("ui-accordion-content-active").prev().removeClass("ui-corner-top").addClass("ui-corner-all"),t.length&&(t.parent()[0].className=t.parent()[0].className),this._trigger("activate",null,e)}}),e.widget("ui.menu",{version:"1.11.4",defaultElement:"<ul>",delay:300,options:{icons:{submenu:"ui-icon-carat-1-e"},items:"> *",menus:"ul",position:{my:"left-1 top",at:"right 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i.siblings(".ui-state-active").removeClass("ui-state-active"),this.focus(t,i)}},mouseleave:"collapseAll","mouseleave .ui-menu":"collapseAll",focus:function(e,t){var i=this.active||this.element.find(this.options.items).eq(0);t||this.focus(e,i)},blur:function(t){this._delay(function(){e.contains(this.element[0],this.document[0].activeElement)||this.collapseAll(t)})},keydown:"_keydown"}),this.refresh(),this._on(this.document,{click:function(e){this._closeOnDocumentClick(e)&&this.collapseAll(e),this.mouseHandled=!1}})},_destroy:function(){this.element.removeAttr("aria-activedescendant").find(".ui-menu").addBack().removeClass("ui-menu ui-widget ui-widget-content ui-menu-icons 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e.ui.keyCode.LEFT:this.collapse(t);break;case e.ui.keyCode.RIGHT:this.active&&!this.active.is(".ui-state-disabled")&&this.expand(t);break;case e.ui.keyCode.ENTER:case e.ui.keyCode.SPACE:this._activate(t);break;case e.ui.keyCode.ESCAPE:this.collapse(t);break;default:o=!1,s=this.previousFilter||"",n=String.fromCharCode(t.keyCode),a=!1,clearTimeout(this.filterTimer),n===s?a=!0:n=s+n,i=this._filterMenuItems(n),i=a&&-1!==i.index(this.active.next())?this.active.nextAll(".ui-menu-item"):i,i.length||(n=String.fromCharCode(t.keyCode),i=this._filterMenuItems(n)),i.length?(this.focus(t,i),this.previousFilter=n,this.filterTimer=this._delay(function(){delete this.previousFilter},1e3)):delete this.previousFilter}o&&t.preventDefault()},_activate:function(e){this.active.is(".ui-state-disabled")||(this.active.is("[aria-haspopup='true']")?this.expand(e):this.select(e))},refresh:function(){var t,i,s=this,n=this.options.icons.submenu,a=this.element.find(this.options.menus);this.element.toggleClass("ui-menu-icons",!!this.element.find(".ui-icon").length),a.filter(":not(.ui-menu)").addClass("ui-menu ui-widget ui-widget-content ui-front").hide().attr({role:this.options.role,"aria-hidden":"true","aria-expanded":"false"}).each(function(){var t=e(this),i=t.parent(),s=e("<span>").addClass("ui-menu-icon ui-icon "+n).data("ui-menu-submenu-carat",!0);i.attr("aria-haspopup","true").prepend(s),t.attr("aria-labelledby",i.attr("id"))}),t=a.add(this.element),i=t.find(this.options.items),i.not(".ui-menu-item").each(function(){var t=e(this);s._isDivider(t)&&t.addClass("ui-widget-content ui-menu-divider")}),i.not(".ui-menu-item, .ui-menu-divider").addClass("ui-menu-item").uniqueId().attr({tabIndex:-1,role:this._itemRole()}),i.filter(".ui-state-disabled").attr("aria-disabled","true"),this.active&&!e.contains(this.element[0],this.active[0])&&this.blur()},_itemRole:function(){return{menu:"menuitem",listbox:"option"}[this.options.role]},_setOption:function(e,t){"icons"===e&&this.element.find(".ui-menu-icon").removeClass(this.options.icons.submenu).addClass(t.submenu),"disabled"===e&&this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this._super(e,t)},focus:function(e,t){var i,s;this.blur(e,e&&"focus"===e.type),this._scrollIntoView(t),this.active=t.first(),s=this.active.addClass("ui-state-focus").removeClass("ui-state-active"),this.options.role&&this.element.attr("aria-activedescendant",s.attr("id")),this.active.parent().closest(".ui-menu-item").addClass("ui-state-active"),e&&"keydown"===e.type?this._close():this.timer=this._delay(function(){this._close()},this.delay),i=t.children(".ui-menu"),i.length&&e&&/^mouse/.test(e.type)&&this._startOpening(i),this.activeMenu=t.parent(),this._trigger("focus",e,{item:t})},_scrollIntoView:function(t){var i,s,n,a,o,r;this._hasScroll()&&(i=parseFloat(e.css(this.activeMenu[0],"borderTopWidth"))||0,s=parseFloat(e.css(this.activeMenu[0],"paddingTop"))||0,n=t.offset().top-this.activeMenu.offset().top-i-s,a=this.activeMenu.scrollTop(),o=this.activeMenu.height(),r=t.outerHeight(),0>n?this.activeMenu.scrollTop(a+n):n+r>o&&this.activeMenu.scrollTop(a+n-o+r))},blur:function(e,t){t||clearTimeout(this.timer),this.active&&(this.active.removeClass("ui-state-focus"),this.active=null,this._trigger("blur",e,{item:this.active}))},_startOpening:function(e){clearTimeout(this.timer),"true"===e.attr("aria-hidden")&&(this.timer=this._delay(function(){this._close(),this._open(e)},this.delay))},_open:function(t){var i=e.extend({of:this.active},this.options.position);clearTimeout(this.timer),this.element.find(".ui-menu").not(t.parents(".ui-menu")).hide().attr("aria-hidden","true"),t.show().removeAttr("aria-hidden").attr("aria-expanded","true").position(i)},collapseAll:function(t,i){clearTimeout(this.timer),this.timer=this._delay(function(){var s=i?this.element:e(t&&t.target).closest(this.element.find(".ui-menu"));s.length||(s=this.element),this._close(s),this.blur(t),this.activeMenu=s},this.delay)},_close:function(e){e||(e=this.active?this.active.parent():this.element),e.find(".ui-menu").hide().attr("aria-hidden","true").attr("aria-expanded","false").end().find(".ui-state-active").not(".ui-state-focus").removeClass("ui-state-active")},_closeOnDocumentClick:function(t){return!e(t.target).closest(".ui-menu").length},_isDivider:function(e){return!/[^\-\u2014\u2013\s]/.test(e.text())},collapse:function(e){var t=this.active&&this.active.parent().closest(".ui-menu-item",this.element);t&&t.length&&(this._close(),this.focus(e,t))},expand:function(e){var t=this.active&&this.active.children(".ui-menu ").find(this.options.items).first();t&&t.length&&(this._open(t.parent()),this._delay(function(){this.focus(e,t)}))},next:function(e){this._move("next","first",e)},previous:function(e){this._move("prev","last",e)},isFirstItem:function(){return this.active&&!this.active.prevAll(".ui-menu-item").length},isLastItem:function(){return this.active&&!this.active.nextAll(".ui-menu-item").length},_move:function(e,t,i){var s;this.active&&(s="first"===e||"last"===e?this.active["first"===e?"prevAll":"nextAll"](".ui-menu-item").eq(-1):this.active[e+"All"](".ui-menu-item").eq(0)),s&&s.length&&this.active||(s=this.activeMenu.find(this.options.items)[t]()),this.focus(i,s)},nextPage:function(t){var i,s,n;return this.active?(this.isLastItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.nextAll(".ui-menu-item").each(function(){return i=e(this),0>i.offset().top-s-n}),this.focus(t,i)):this.focus(t,this.activeMenu.find(this.options.items)[this.active?"last":"first"]())),void 0):(this.next(t),void 0)},previousPage:function(t){var i,s,n;return this.active?(this.isFirstItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.prevAll(".ui-menu-item").each(function(){return i=e(this),i.offset().top-s+n>0}),this.focus(t,i)):this.focus(t,this.activeMenu.find(this.options.items).first())),void 0):(this.next(t),void 0)},_hasScroll:function(){return this.element.outerHeight()<this.element.prop("scrollHeight")},select:function(t){this.active=this.active||e(t.target).closest(".ui-menu-item");var i={item:this.active};this.active.has(".ui-menu").length||this.collapseAll(t,!0),this._trigger("select",t,i)},_filterMenuItems:function(t){var i=t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&"),s=RegExp("^"+i,"i");return this.activeMenu.find(this.options.items).filter(".ui-menu-item").filter(function(){return s.test(e.trim(e(this).text()))})}}),e.widget("ui.autocomplete",{version:"1.11.4",defaultElement:"<input>",options:{appendTo:null,autoFocus:!1,delay:300,minLength:1,position:{my:"left top",at:"left bottom",collision:"none"},source:null,change:null,close:null,focus:null,open:null,response:null,search:null,select:null},requestIndex:0,pending:0,_create:function(){var t,i,s,n=this.element[0].nodeName.toLowerCase(),a="textarea"===n,o="input"===n;this.isMultiLine=a?!0:o?!1:this.element.prop("isContentEditable"),this.valueMethod=this.element[a||o?"val":"text"],this.isNewMenu=!0,this.element.addClass("ui-autocomplete-input").attr("autocomplete","off"),this._on(this.element,{keydown:function(n){if(this.element.prop("readOnly"))return t=!0,s=!0,i=!0,void 0;t=!1,s=!1,i=!1;var a=e.ui.keyCode;switch(n.keyCode){case a.PAGE_UP:t=!0,this._move("previousPage",n);break;case a.PAGE_DOWN:t=!0,this._move("nextPage",n);break;case a.UP:t=!0,this._keyEvent("previous",n);break;case a.DOWN:t=!0,this._keyEvent("next",n);break;case a.ENTER:this.menu.active&&(t=!0,n.preventDefault(),this.menu.select(n));break;case a.TAB:this.menu.active&&this.menu.select(n);break;case a.ESCAPE:this.menu.element.is(":visible")&&(this.isMultiLine||this._value(this.term),this.close(n),n.preventDefault());break;default:i=!0,this._searchTimeout(n)}},keypress:function(s){if(t)return t=!1,(!this.isMultiLine||this.menu.element.is(":visible"))&&s.preventDefault(),void 0;if(!i){var n=e.ui.keyCode;switch(s.keyCode){case n.PAGE_UP:this._move("previousPage",s);break;case n.PAGE_DOWN:this._move("nextPage",s);break;case n.UP:this._keyEvent("previous",s);break;case n.DOWN:this._keyEvent("next",s)}}},input:function(e){return s?(s=!1,e.preventDefault(),void 0):(this._searchTimeout(e),void 0)},focus:function(){this.selectedItem=null,this.previous=this._value()},blur:function(e){return this.cancelBlur?(delete this.cancelBlur,void 0):(clearTimeout(this.searching),this.close(e),this._change(e),void 0)}}),this._initSource(),this.menu=e("<ul>").addClass("ui-autocomplete ui-front").appendTo(this._appendTo()).menu({role:null}).hide().menu("instance"),this._on(this.menu.element,{mousedown:function(t){t.preventDefault(),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur});var i=this.menu.element[0];e(t.target).closest(".ui-menu-item").length||this._delay(function(){var t=this;this.document.one("mousedown",function(s){s.target===t.element[0]||s.target===i||e.contains(i,s.target)||t.close()})})},menufocus:function(t,i){var s,n;return this.isNewMenu&&(this.isNewMenu=!1,t.originalEvent&&/^mouse/.test(t.originalEvent.type))?(this.menu.blur(),this.document.one("mousemove",function(){e(t.target).trigger(t.originalEvent)}),void 0):(n=i.item.data("ui-autocomplete-item"),!1!==this._trigger("focus",t,{item:n})&&t.originalEvent&&/^key/.test(t.originalEvent.type)&&this._value(n.value),s=i.item.attr("aria-label")||n.value,s&&e.trim(s).length&&(this.liveRegion.children().hide(),e("<div>").text(s).appendTo(this.liveRegion)),void 0)},menuselect:function(e,t){var i=t.item.data("ui-autocomplete-item"),s=this.previous;this.element[0]!==this.document[0].activeElement&&(this.element.focus(),this.previous=s,this._delay(function(){this.previous=s,this.selectedItem=i})),!1!==this._trigger("select",e,{item:i})&&this._value(i.value),this.term=this._value(),this.close(e),this.selectedItem=i}}),this.liveRegion=e("<span>",{role:"status","aria-live":"assertive","aria-relevant":"additions"}).addClass("ui-helper-hidden-accessible").appendTo(this.document[0].body),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_destroy:function(){clearTimeout(this.searching),this.element.removeClass("ui-autocomplete-input").removeAttr("autocomplete"),this.menu.element.remove(),this.liveRegion.remove()},_setOption:function(e,t){this._super(e,t),"source"===e&&this._initSource(),"appendTo"===e&&this.menu.element.appendTo(this._appendTo()),"disabled"===e&&t&&this.xhr&&this.xhr.abort()},_appendTo:function(){var t=this.options.appendTo;return t&&(t=t.jquery||t.nodeType?e(t):this.document.find(t).eq(0)),t&&t[0]||(t=this.element.closest(".ui-front")),t.length||(t=this.document[0].body),t},_initSource:function(){var t,i,s=this;e.isArray(this.options.source)?(t=this.options.source,this.source=function(i,s){s(e.ui.autocomplete.filter(t,i.term))}):"string"==typeof this.options.source?(i=this.options.source,this.source=function(t,n){s.xhr&&s.xhr.abort(),s.xhr=e.ajax({url:i,data:t,dataType:"json",success:function(e){n(e)},error:function(){n([])}})}):this.source=this.options.source},_searchTimeout:function(e){clearTimeout(this.searching),this.searching=this._delay(function(){var t=this.term===this._value(),i=this.menu.element.is(":visible"),s=e.altKey||e.ctrlKey||e.metaKey||e.shiftKey;(!t||t&&!i&&!s)&&(this.selectedItem=null,this.search(null,e))},this.options.delay)},search:function(e,t){return e=null!=e?e:this._value(),this.term=this._value(),e.length<this.options.minLength?this.close(t):this._trigger("search",t)!==!1?this._search(e):void 0},_search:function(e){this.pending++,this.element.addClass("ui-autocomplete-loading"),this.cancelSearch=!1,this.source({term:e},this._response())},_response:function(){var t=++this.requestIndex;return e.proxy(function(e){t===this.requestIndex&&this.__response(e),this.pending--,this.pending||this.element.removeClass("ui-autocomplete-loading")},this)},__response:function(e){e&&(e=this._normalize(e)),this._trigger("response",null,{content:e}),!this.options.disabled&&e&&e.length&&!this.cancelSearch?(this._suggest(e),this._trigger("open")):this._close()},close:function(e){this.cancelSearch=!0,this._close(e)},_close:function(e){this.menu.element.is(":visible")&&(this.menu.element.hide(),this.menu.blur(),this.isNewMenu=!0,this._trigger("close",e))},_change:function(e){this.previous!==this._value()&&this._trigger("change",e,{item:this.selectedItem})},_normalize:function(t){return t.length&&t[0].label&&t[0].value?t:e.map(t,function(t){return"string"==typeof t?{label:t,value:t}:e.extend({},t,{label:t.label||t.value,value:t.value||t.label})})},_suggest:function(t){var i=this.menu.element.empty();this._renderMenu(i,t),this.isNewMenu=!0,this.menu.refresh(),i.show(),this._resizeMenu(),i.position(e.extend({of:this.element},this.options.position)),this.options.autoFocus&&this.menu.next()},_resizeMenu:function(){var e=this.menu.element;e.outerWidth(Math.max(e.width("").outerWidth()+1,this.element.outerWidth()))},_renderMenu:function(t,i){var s=this;e.each(i,function(e,i){s._renderItemData(t,i)})},_renderItemData:function(e,t){return this._renderItem(e,t).data("ui-autocomplete-item",t)},_renderItem:function(t,i){return e("<li>").text(i.label).appendTo(t)},_move:function(e,t){return this.menu.element.is(":visible")?this.menu.isFirstItem()&&/^previous/.test(e)||this.menu.isLastItem()&&/^next/.test(e)?(this.isMultiLine||this._value(this.term),this.menu.blur(),void 0):(this.menu[e](t),void 0):(this.search(null,t),void 0)},widget:function(){return this.menu.element},_value:function(){return this.valueMethod.apply(this.element,arguments)},_keyEvent:function(e,t){(!this.isMultiLine||this.menu.element.is(":visible"))&&(this._move(e,t),t.preventDefault())}}),e.extend(e.ui.autocomplete,{escapeRegex:function(e){return e.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&")},filter:function(t,i){var s=RegExp(e.ui.autocomplete.escapeRegex(i),"i");return e.grep(t,function(e){return s.test(e.label||e.value||e)})}}),e.widget("ui.autocomplete",e.ui.autocomplete,{options:{messages:{noResults:"No search results.",results:function(e){return e+(e>1?" results are":" result is")+" available, use up and down arrow keys to navigate."}}},__response:function(t){var i;this._superApply(arguments),this.options.disabled||this.cancelSearch||(i=t&&t.length?this.options.messages.results(t.length):this.options.messages.noResults,this.liveRegion.children().hide(),e("<div>").text(i).appendTo(this.liveRegion))}}),e.ui.autocomplete;var c,p="ui-button ui-widget ui-state-default ui-corner-all",f="ui-button-icons-only ui-button-icon-only ui-button-text-icons ui-button-text-icon-primary ui-button-text-icon-secondary ui-button-text-only",m=function(){var t=e(this);setTimeout(function(){t.find(":ui-button").button("refresh")},1)},g=function(t){var i=t.name,s=t.form,n=e([]);return i&&(i=i.replace(/'/g,"\\'"),n=s?e(s).find("[name='"+i+"'][type=radio]"):e("[name='"+i+"'][type=radio]",t.ownerDocument).filter(function(){return!this.form})),n};e.widget("ui.button",{version:"1.11.4",defaultElement:"<button>",options:{disabled:null,text:!0,label:null,icons:{primary:null,secondary:null}},_create:function(){this.element.closest("form").unbind("reset"+this.eventNamespace).bind("reset"+this.eventNamespace,m),"boolean"!=typeof this.options.disabled?this.options.disabled=!!this.element.prop("disabled"):this.element.prop("disabled",this.options.disabled),this._determineButtonType(),this.hasTitle=!!this.buttonElement.attr("title");var t=this,i=this.options,s="checkbox"===this.type||"radio"===this.type,n=s?"":"ui-state-active";null===i.label&&(i.label="input"===this.type?this.buttonElement.val():this.buttonElement.html()),this._hoverable(this.buttonElement),this.buttonElement.addClass(p).attr("role","button").bind("mouseenter"+this.eventNamespace,function(){i.disabled||this===c&&e(this).addClass("ui-state-active")}).bind("mouseleave"+this.eventNamespace,function(){i.disabled||e(this).removeClass(n)}).bind("click"+this.eventNamespace,function(e){i.disabled&&(e.preventDefault(),e.stopImmediatePropagation())}),this._on({focus:function(){this.buttonElement.addClass("ui-state-focus")},blur:function(){this.buttonElement.removeClass("ui-state-focus")}}),s&&this.element.bind("change"+this.eventNamespace,function(){t.refresh()}),"checkbox"===this.type?this.buttonElement.bind("click"+this.eventNamespace,function(){return i.disabled?!1:void 0}):"radio"===this.type?this.buttonElement.bind("click"+this.eventNamespace,function(){if(i.disabled)return!1;e(this).addClass("ui-state-active"),t.buttonElement.attr("aria-pressed","true");var s=t.element[0];g(s).not(s).map(function(){return e(this).button("widget")[0]}).removeClass("ui-state-active").attr("aria-pressed","false")}):(this.buttonElement.bind("mousedown"+this.eventNamespace,function(){return i.disabled?!1:(e(this).addClass("ui-state-active"),c=this,t.document.one("mouseup",function(){c=null}),void 0)}).bind("mouseup"+this.eventNamespace,function(){return i.disabled?!1:(e(this).removeClass("ui-state-active"),void 0)}).bind("keydown"+this.eventNamespace,function(t){return i.disabled?!1:((t.keyCode===e.ui.keyCode.SPACE||t.keyCode===e.ui.keyCode.ENTER)&&e(this).addClass("ui-state-active"),void 0)}).bind("keyup"+this.eventNamespace+" blur"+this.eventNamespace,function(){e(this).removeClass("ui-state-active")}),this.buttonElement.is("a")&&this.buttonElement.keyup(function(t){t.keyCode===e.ui.keyCode.SPACE&&e(this).click()})),this._setOption("disabled",i.disabled),this._resetButton()},_determineButtonType:function(){var e,t,i;this.type=this.element.is("[type=checkbox]")?"checkbox":this.element.is("[type=radio]")?"radio":this.element.is("input")?"input":"button","checkbox"===this.type||"radio"===this.type?(e=this.element.parents().last(),t="label[for='"+this.element.attr("id")+"']",this.buttonElement=e.find(t),this.buttonElement.length||(e=e.length?e.siblings():this.element.siblings(),this.buttonElement=e.filter(t),this.buttonElement.length||(this.buttonElement=e.find(t))),this.element.addClass("ui-helper-hidden-accessible"),i=this.element.is(":checked"),i&&this.buttonElement.addClass("ui-state-active"),this.buttonElement.prop("aria-pressed",i)):this.buttonElement=this.element},widget:function(){return this.buttonElement},_destroy:function(){this.element.removeClass("ui-helper-hidden-accessible"),this.buttonElement.removeClass(p+" ui-state-active "+f).removeAttr("role").removeAttr("aria-pressed").html(this.buttonElement.find(".ui-button-text").html()),this.hasTitle||this.buttonElement.removeAttr("title")},_setOption:function(e,t){return this._super(e,t),"disabled"===e?(this.widget().toggleClass("ui-state-disabled",!!t),this.element.prop("disabled",!!t),t&&("checkbox"===this.type||"radio"===this.type?this.buttonElement.removeClass("ui-state-focus"):this.buttonElement.removeClass("ui-state-focus ui-state-active")),void 0):(this._resetButton(),void 0)},refresh:function(){var t=this.element.is("input, button")?this.element.is(":disabled"):this.element.hasClass("ui-button-disabled");t!==this.options.disabled&&this._setOption("disabled",t),"radio"===this.type?g(this.element[0]).each(function(){e(this).is(":checked")?e(this).button("widget").addClass("ui-state-active").attr("aria-pressed","true"):e(this).button("widget").removeClass("ui-state-active").attr("aria-pressed","false")}):"checkbox"===this.type&&(this.element.is(":checked")?this.buttonElement.addClass("ui-state-active").attr("aria-pressed","true"):this.buttonElement.removeClass("ui-state-active").attr("aria-pressed","false"))},_resetButton:function(){if("input"===this.type)return this.options.label&&this.element.val(this.options.label),void 0;var t=this.buttonElement.removeClass(f),i=e("<span></span>",this.document[0]).addClass("ui-button-text").html(this.options.label).appendTo(t.empty()).text(),s=this.options.icons,n=s.primary&&s.secondary,a=[];s.primary||s.secondary?(this.options.text&&a.push("ui-button-text-icon"+(n?"s":s.primary?"-primary":"-secondary")),s.primary&&t.prepend("<span class='ui-button-icon-primary ui-icon "+s.primary+"'></span>"),s.secondary&&t.append("<span class='ui-button-icon-secondary ui-icon "+s.secondary+"'></span>"),this.options.text||(a.push(n?"ui-button-icons-only":"ui-button-icon-only"),this.hasTitle||t.attr("title",e.trim(i)))):a.push("ui-button-text-only"),t.addClass(a.join(" "))}}),e.widget("ui.buttonset",{version:"1.11.4",options:{items:"button, input[type=button], input[type=submit], input[type=reset], input[type=checkbox], input[type=radio], a, :data(ui-button)"},_create:function(){this.element.addClass("ui-buttonset")},_init:function(){this.refresh()},_setOption:function(e,t){"disabled"===e&&this.buttons.button("option",e,t),this._super(e,t)},refresh:function(){var t="rtl"===this.element.css("direction"),i=this.element.find(this.options.items),s=i.filter(":ui-button");i.not(":ui-button").button(),s.button("refresh"),this.buttons=i.map(function(){return e(this).button("widget")[0]}).removeClass("ui-corner-all ui-corner-left ui-corner-right").filter(":first").addClass(t?"ui-corner-right":"ui-corner-left").end().filter(":last").addClass(t?"ui-corner-left":"ui-corner-right").end().end()},_destroy:function(){this.element.removeClass("ui-buttonset"),this.buttons.map(function(){return e(this).button("widget")[0]}).removeClass("ui-corner-left ui-corner-right").end().button("destroy")}}),e.ui.button,e.extend(e.ui,{datepicker:{version:"1.11.4"}});var v;e.extend(n.prototype,{markerClassName:"hasDatepicker",maxRows:4,_widgetDatepicker:function(){return this.dpDiv},setDefaults:function(e){return r(this._defaults,e||{}),this},_attachDatepicker:function(t,i){var s,n,a;s=t.nodeName.toLowerCase(),n="div"===s||"span"===s,t.id||(this.uuid+=1,t.id="dp"+this.uuid),a=this._newInst(e(t),n),a.settings=e.extend({},i||{}),"input"===s?this._connectDatepicker(t,a):n&&this._inlineDatepicker(t,a)},_newInst:function(t,i){var s=t[0].id.replace(/([^A-Za-z0-9_\-])/g,"\\\\$1");return{id:s,input:t,selectedDay:0,selectedMonth:0,selectedYear:0,drawMonth:0,drawYear:0,inline:i,dpDiv:i?a(e("<div class='"+this._inlineClass+" ui-datepicker ui-widget ui-widget-content ui-helper-clearfix ui-corner-all'></div>")):this.dpDiv}},_connectDatepicker:function(t,i){var s=e(t);i.append=e([]),i.trigger=e([]),s.hasClass(this.markerClassName)||(this._attachments(s,i),s.addClass(this.markerClassName).keydown(this._doKeyDown).keypress(this._doKeyPress).keyup(this._doKeyUp),this._autoSize(i),e.data(t,"datepicker",i),i.settings.disabled&&this._disableDatepicker(t))},_attachments:function(t,i){var s,n,a,o=this._get(i,"appendText"),r=this._get(i,"isRTL");i.append&&i.append.remove(),o&&(i.append=e("<span class='"+this._appendClass+"'>"+o+"</span>"),t[r?"before":"after"](i.append)),t.unbind("focus",this._showDatepicker),i.trigger&&i.trigger.remove(),s=this._get(i,"showOn"),("focus"===s||"both"===s)&&t.focus(this._showDatepicker),("button"===s||"both"===s)&&(n=this._get(i,"buttonText"),a=this._get(i,"buttonImage"),i.trigger=e(this._get(i,"buttonImageOnly")?e("<img/>").addClass(this._triggerClass).attr({src:a,alt:n,title:n}):e("<button type='button'></button>").addClass(this._triggerClass).html(a?e("<img/>").attr({src:a,alt:n,title:n}):n)),t[r?"before":"after"](i.trigger),i.trigger.click(function(){return e.datepicker._datepickerShowing&&e.datepicker._lastInput===t[0]?e.datepicker._hideDatepicker():e.datepicker._datepickerShowing&&e.datepicker._lastInput!==t[0]?(e.datepicker._hideDatepicker(),e.datepicker._showDatepicker(t[0])):e.datepicker._showDatepicker(t[0]),!1}))},_autoSize:function(e){if(this._get(e,"autoSize")&&!e.inline){var t,i,s,n,a=new Date(2009,11,20),o=this._get(e,"dateFormat");o.match(/[DM]/)&&(t=function(e){for(i=0,s=0,n=0;e.length>n;n++)e[n].length>i&&(i=e[n].length,s=n);return s},a.setMonth(t(this._get(e,o.match(/MM/)?"monthNames":"monthNamesShort"))),a.setDate(t(this._get(e,o.match(/DD/)?"dayNames":"dayNamesShort"))+20-a.getDay())),e.input.attr("size",this._formatDate(e,a).length)}},_inlineDatepicker:function(t,i){var s=e(t);s.hasClass(this.markerClassName)||(s.addClass(this.markerClassName).append(i.dpDiv),e.data(t,"datepicker",i),this._setDate(i,this._getDefaultDate(i),!0),this._updateDatepicker(i),this._updateAlternate(i),i.settings.disabled&&this._disableDatepicker(t),i.dpDiv.css("display","block"))},_dialogDatepicker:function(t,i,s,n,a){var o,h,l,u,d,c=this._dialogInst;return c||(this.uuid+=1,o="dp"+this.uuid,this._dialogInput=e("<input type='text' id='"+o+"' style='position: absolute; top: -100px; width: 0px;'/>"),this._dialogInput.keydown(this._doKeyDown),e("body").append(this._dialogInput),c=this._dialogInst=this._newInst(this._dialogInput,!1),c.settings={},e.data(this._dialogInput[0],"datepicker",c)),r(c.settings,n||{}),i=i&&i.constructor===Date?this._formatDate(c,i):i,this._dialogInput.val(i),this._pos=a?a.length?a:[a.pageX,a.pageY]:null,this._pos||(h=document.documentElement.clientWidth,l=document.documentElement.clientHeight,u=document.documentElement.scrollLeft||document.body.scrollLeft,d=document.documentElement.scrollTop||document.body.scrollTop,this._pos=[h/2-100+u,l/2-150+d]),this._dialogInput.css("left",this._pos[0]+20+"px").css("top",this._pos[1]+"px"),c.settings.onSelect=s,this._inDialog=!0,this.dpDiv.addClass(this._dialogClass),this._showDatepicker(this._dialogInput[0]),e.blockUI&&e.blockUI(this.dpDiv),e.data(this._dialogInput[0],"datepicker",c),this},_destroyDatepicker:function(t){var i,s=e(t),n=e.data(t,"datepicker");s.hasClass(this.markerClassName)&&(i=t.nodeName.toLowerCase(),e.removeData(t,"datepicker"),"input"===i?(n.append.remove(),n.trigger.remove(),s.removeClass(this.markerClassName).unbind("focus",this._showDatepicker).unbind("keydown",this._doKeyDown).unbind("keypress",this._doKeyPress).unbind("keyup",this._doKeyUp)):("div"===i||"span"===i)&&s.removeClass(this.markerClassName).empty(),v===n&&(v=null))},_enableDatepicker:function(t){var i,s,n=e(t),a=e.data(t,"datepicker");n.hasClass(this.markerClassName)&&(i=t.nodeName.toLowerCase(),"input"===i?(t.disabled=!1,a.trigger.filter("button").each(function(){this.disabled=!1}).end().filter("img").css({opacity:"1.0",cursor:""})):("div"===i||"span"===i)&&(s=n.children("."+this._inlineClass),s.children().removeClass("ui-state-disabled"),s.find("select.ui-datepicker-month, select.ui-datepicker-year").prop("disabled",!1)),this._disabledInputs=e.map(this._disabledInputs,function(e){return e===t?null:e}))},_disableDatepicker:function(t){var i,s,n=e(t),a=e.data(t,"datepicker");n.hasClass(this.markerClassName)&&(i=t.nodeName.toLowerCase(),"input"===i?(t.disabled=!0,a.trigger.filter("button").each(function(){this.disabled=!0}).end().filter("img").css({opacity:"0.5",cursor:"default"})):("div"===i||"span"===i)&&(s=n.children("."+this._inlineClass),s.children().addClass("ui-state-disabled"),s.find("select.ui-datepicker-month, select.ui-datepicker-year").prop("disabled",!0)),this._disabledInputs=e.map(this._disabledInputs,function(e){return e===t?null:e}),this._disabledInputs[this._disabledInputs.length]=t)},_isDisabledDatepicker:function(e){if(!e)return!1;for(var t=0;this._disabledInputs.length>t;t++)if(this._disabledInputs[t]===e)return!0;return!1},_getInst:function(t){try{return e.data(t,"datepicker")}catch(i){throw"Missing instance data for this datepicker"}},_optionDatepicker:function(t,i,s){var n,a,o,h,l=this._getInst(t);return 2===arguments.length&&"string"==typeof i?"defaults"===i?e.extend({},e.datepicker._defaults):l?"all"===i?e.extend({},l.settings):this._get(l,i):null:(n=i||{},"string"==typeof i&&(n={},n[i]=s),l&&(this._curInst===l&&this._hideDatepicker(),a=this._getDateDatepicker(t,!0),o=this._getMinMaxDate(l,"min"),h=this._getMinMaxDate(l,"max"),r(l.settings,n),null!==o&&void 0!==n.dateFormat&&void 0===n.minDate&&(l.settings.minDate=this._formatDate(l,o)),null!==h&&void 0!==n.dateFormat&&void 0===n.maxDate&&(l.settings.maxDate=this._formatDate(l,h)),"disabled"in n&&(n.disabled?this._disableDatepicker(t):this._enableDatepicker(t)),this._attachments(e(t),l),this._autoSize(l),this._setDate(l,a),this._updateAlternate(l),this._updateDatepicker(l)),void 0)},_changeDatepicker:function(e,t,i){this._optionDatepicker(e,t,i)},_refreshDatepicker:function(e){var t=this._getInst(e);t&&this._updateDatepicker(t)},_setDateDatepicker:function(e,t){var i=this._getInst(e);i&&(this._setDate(i,t),this._updateDatepicker(i),this._updateAlternate(i))},_getDateDatepicker:function(e,t){var i=this._getInst(e);return i&&!i.inline&&this._setDateFromField(i,t),i?this._getDate(i):null},_doKeyDown:function(t){var i,s,n,a=e.datepicker._getInst(t.target),o=!0,r=a.dpDiv.is(".ui-datepicker-rtl");if(a._keyEvent=!0,e.datepicker._datepickerShowing)switch(t.keyCode){case 9:e.datepicker._hideDatepicker(),o=!1;break;case 13:return n=e("td."+e.datepicker._dayOverClass+":not(."+e.datepicker._currentClass+")",a.dpDiv),n[0]&&e.datepicker._selectDay(t.target,a.selectedMonth,a.selectedYear,n[0]),i=e.datepicker._get(a,"onSelect"),i?(s=e.datepicker._formatDate(a),i.apply(a.input?a.input[0]:null,[s,a])):e.datepicker._hideDatepicker(),!1;case 27:e.datepicker._hideDatepicker();break;case 33:e.datepicker._adjustDate(t.target,t.ctrlKey?-e.datepicker._get(a,"stepBigMonths"):-e.datepicker._get(a,"stepMonths"),"M");break;case 34:e.datepicker._adjustDate(t.target,t.ctrlKey?+e.datepicker._get(a,"stepBigMonths"):+e.datepicker._get(a,"stepMonths"),"M");break;case 35:(t.ctrlKey||t.metaKey)&&e.datepicker._clearDate(t.target),o=t.ctrlKey||t.metaKey;break;case 36:(t.ctrlKey||t.metaKey)&&e.datepicker._gotoToday(t.target),o=t.ctrlKey||t.metaKey;break;case 37:(t.ctrlKey||t.metaKey)&&e.datepicker._adjustDate(t.target,r?1:-1,"D"),o=t.ctrlKey||t.metaKey,t.originalEvent.altKey&&e.datepicker._adjustDate(t.target,t.ctrlKey?-e.datepicker._get(a,"stepBigMonths"):-e.datepicker._get(a,"stepMonths"),"M");break;case 38:(t.ctrlKey||t.metaKey)&&e.datepicker._adjustDate(t.target,-7,"D"),o=t.ctrlKey||t.metaKey;break;case 39:(t.ctrlKey||t.metaKey)&&e.datepicker._adjustDate(t.target,r?-1:1,"D"),o=t.ctrlKey||t.metaKey,t.originalEvent.altKey&&e.datepicker._adjustDate(t.target,t.ctrlKey?+e.datepicker._get(a,"stepBigMonths"):+e.datepicker._get(a,"stepMonths"),"M");break;case 40:(t.ctrlKey||t.metaKey)&&e.datepicker._adjustDate(t.target,7,"D"),o=t.ctrlKey||t.metaKey;break;default:o=!1}else 36===t.keyCode&&t.ctrlKey?e.datepicker._showDatepicker(this):o=!1;o&&(t.preventDefault(),t.stopPropagation())},_doKeyPress:function(t){var i,s,n=e.datepicker._getInst(t.target);
return e.datepicker._get(n,"constrainInput")?(i=e.datepicker._possibleChars(e.datepicker._get(n,"dateFormat")),s=String.fromCharCode(null==t.charCode?t.keyCode:t.charCode),t.ctrlKey||t.metaKey||" ">s||!i||i.indexOf(s)>-1):void 0},_doKeyUp:function(t){var i,s=e.datepicker._getInst(t.target);if(s.input.val()!==s.lastVal)try{i=e.datepicker.parseDate(e.datepicker._get(s,"dateFormat"),s.input?s.input.val():null,e.datepicker._getFormatConfig(s)),i&&(e.datepicker._setDateFromField(s),e.datepicker._updateAlternate(s),e.datepicker._updateDatepicker(s))}catch(n){}return!0},_showDatepicker:function(t){if(t=t.target||t,"input"!==t.nodeName.toLowerCase()&&(t=e("input",t.parentNode)[0]),!e.datepicker._isDisabledDatepicker(t)&&e.datepicker._lastInput!==t){var i,n,a,o,h,l,u;i=e.datepicker._getInst(t),e.datepicker._curInst&&e.datepicker._curInst!==i&&(e.datepicker._curInst.dpDiv.stop(!0,!0),i&&e.datepicker._datepickerShowing&&e.datepicker._hideDatepicker(e.datepicker._curInst.input[0])),n=e.datepicker._get(i,"beforeShow"),a=n?n.apply(t,[t,i]):{},a!==!1&&(r(i.settings,a),i.lastVal=null,e.datepicker._lastInput=t,e.datepicker._setDateFromField(i),e.datepicker._inDialog&&(t.value=""),e.datepicker._pos||(e.datepicker._pos=e.datepicker._findPos(t),e.datepicker._pos[1]+=t.offsetHeight),o=!1,e(t).parents().each(function(){return o|="fixed"===e(this).css("position"),!o}),h={left:e.datepicker._pos[0],top:e.datepicker._pos[1]},e.datepicker._pos=null,i.dpDiv.empty(),i.dpDiv.css({position:"absolute",display:"block",top:"-1000px"}),e.datepicker._updateDatepicker(i),h=e.datepicker._checkOffset(i,h,o),i.dpDiv.css({position:e.datepicker._inDialog&&e.blockUI?"static":o?"fixed":"absolute",display:"none",left:h.left+"px",top:h.top+"px"}),i.inline||(l=e.datepicker._get(i,"showAnim"),u=e.datepicker._get(i,"duration"),i.dpDiv.css("z-index",s(e(t))+1),e.datepicker._datepickerShowing=!0,e.effects&&e.effects.effect[l]?i.dpDiv.show(l,e.datepicker._get(i,"showOptions"),u):i.dpDiv[l||"show"](l?u:null),e.datepicker._shouldFocusInput(i)&&i.input.focus(),e.datepicker._curInst=i))}},_updateDatepicker:function(t){this.maxRows=4,v=t,t.dpDiv.empty().append(this._generateHTML(t)),this._attachHandlers(t);var i,s=this._getNumberOfMonths(t),n=s[1],a=17,r=t.dpDiv.find("."+this._dayOverClass+" a");r.length>0&&o.apply(r.get(0)),t.dpDiv.removeClass("ui-datepicker-multi-2 ui-datepicker-multi-3 ui-datepicker-multi-4").width(""),n>1&&t.dpDiv.addClass("ui-datepicker-multi-"+n).css("width",a*n+"em"),t.dpDiv[(1!==s[0]||1!==s[1]?"add":"remove")+"Class"]("ui-datepicker-multi"),t.dpDiv[(this._get(t,"isRTL")?"add":"remove")+"Class"]("ui-datepicker-rtl"),t===e.datepicker._curInst&&e.datepicker._datepickerShowing&&e.datepicker._shouldFocusInput(t)&&t.input.focus(),t.yearshtml&&(i=t.yearshtml,setTimeout(function(){i===t.yearshtml&&t.yearshtml&&t.dpDiv.find("select.ui-datepicker-year:first").replaceWith(t.yearshtml),i=t.yearshtml=null},0))},_shouldFocusInput:function(e){return e.input&&e.input.is(":visible")&&!e.input.is(":disabled")&&!e.input.is(":focus")},_checkOffset:function(t,i,s){var n=t.dpDiv.outerWidth(),a=t.dpDiv.outerHeight(),o=t.input?t.input.outerWidth():0,r=t.input?t.input.outerHeight():0,h=document.documentElement.clientWidth+(s?0:e(document).scrollLeft()),l=document.documentElement.clientHeight+(s?0:e(document).scrollTop());return i.left-=this._get(t,"isRTL")?n-o:0,i.left-=s&&i.left===t.input.offset().left?e(document).scrollLeft():0,i.top-=s&&i.top===t.input.offset().top+r?e(document).scrollTop():0,i.left-=Math.min(i.left,i.left+n>h&&h>n?Math.abs(i.left+n-h):0),i.top-=Math.min(i.top,i.top+a>l&&l>a?Math.abs(a+r):0),i},_findPos:function(t){for(var i,s=this._getInst(t),n=this._get(s,"isRTL");t&&("hidden"===t.type||1!==t.nodeType||e.expr.filters.hidden(t));)t=t[n?"previousSibling":"nextSibling"];return i=e(t).offset(),[i.left,i.top]},_hideDatepicker:function(t){var i,s,n,a,o=this._curInst;!o||t&&o!==e.data(t,"datepicker")||this._datepickerShowing&&(i=this._get(o,"showAnim"),s=this._get(o,"duration"),n=function(){e.datepicker._tidyDialog(o)},e.effects&&(e.effects.effect[i]||e.effects[i])?o.dpDiv.hide(i,e.datepicker._get(o,"showOptions"),s,n):o.dpDiv["slideDown"===i?"slideUp":"fadeIn"===i?"fadeOut":"hide"](i?s:null,n),i||n(),this._datepickerShowing=!1,a=this._get(o,"onClose"),a&&a.apply(o.input?o.input[0]:null,[o.input?o.input.val():"",o]),this._lastInput=null,this._inDialog&&(this._dialogInput.css({position:"absolute",left:"0",top:"-100px"}),e.blockUI&&(e.unblockUI(),e("body").append(this.dpDiv))),this._inDialog=!1)},_tidyDialog:function(e){e.dpDiv.removeClass(this._dialogClass).unbind(".ui-datepicker-calendar")},_checkExternalClick:function(t){if(e.datepicker._curInst){var i=e(t.target),s=e.datepicker._getInst(i[0]);(i[0].id!==e.datepicker._mainDivId&&0===i.parents("#"+e.datepicker._mainDivId).length&&!i.hasClass(e.datepicker.markerClassName)&&!i.closest("."+e.datepicker._triggerClass).length&&e.datepicker._datepickerShowing&&(!e.datepicker._inDialog||!e.blockUI)||i.hasClass(e.datepicker.markerClassName)&&e.datepicker._curInst!==s)&&e.datepicker._hideDatepicker()}},_adjustDate:function(t,i,s){var n=e(t),a=this._getInst(n[0]);this._isDisabledDatepicker(n[0])||(this._adjustInstDate(a,i+("M"===s?this._get(a,"showCurrentAtPos"):0),s),this._updateDatepicker(a))},_gotoToday:function(t){var i,s=e(t),n=this._getInst(s[0]);this._get(n,"gotoCurrent")&&n.currentDay?(n.selectedDay=n.currentDay,n.drawMonth=n.selectedMonth=n.currentMonth,n.drawYear=n.selectedYear=n.currentYear):(i=new Date,n.selectedDay=i.getDate(),n.drawMonth=n.selectedMonth=i.getMonth(),n.drawYear=n.selectedYear=i.getFullYear()),this._notifyChange(n),this._adjustDate(s)},_selectMonthYear:function(t,i,s){var n=e(t),a=this._getInst(n[0]);a["selected"+("M"===s?"Month":"Year")]=a["draw"+("M"===s?"Month":"Year")]=parseInt(i.options[i.selectedIndex].value,10),this._notifyChange(a),this._adjustDate(n)},_selectDay:function(t,i,s,n){var a,o=e(t);e(n).hasClass(this._unselectableClass)||this._isDisabledDatepicker(o[0])||(a=this._getInst(o[0]),a.selectedDay=a.currentDay=e("a",n).html(),a.selectedMonth=a.currentMonth=i,a.selectedYear=a.currentYear=s,this._selectDate(t,this._formatDate(a,a.currentDay,a.currentMonth,a.currentYear)))},_clearDate:function(t){var i=e(t);this._selectDate(i,"")},_selectDate:function(t,i){var s,n=e(t),a=this._getInst(n[0]);i=null!=i?i:this._formatDate(a),a.input&&a.input.val(i),this._updateAlternate(a),s=this._get(a,"onSelect"),s?s.apply(a.input?a.input[0]:null,[i,a]):a.input&&a.input.trigger("change"),a.inline?this._updateDatepicker(a):(this._hideDatepicker(),this._lastInput=a.input[0],"object"!=typeof a.input[0]&&a.input.focus(),this._lastInput=null)},_updateAlternate:function(t){var i,s,n,a=this._get(t,"altField");a&&(i=this._get(t,"altFormat")||this._get(t,"dateFormat"),s=this._getDate(t),n=this.formatDate(i,s,this._getFormatConfig(t)),e(a).each(function(){e(this).val(n)}))},noWeekends:function(e){var t=e.getDay();return[t>0&&6>t,""]},iso8601Week:function(e){var t,i=new Date(e.getTime());return i.setDate(i.getDate()+4-(i.getDay()||7)),t=i.getTime(),i.setMonth(0),i.setDate(1),Math.floor(Math.round((t-i)/864e5)/7)+1},parseDate:function(t,i,s){if(null==t||null==i)throw"Invalid arguments";if(i="object"==typeof i?""+i:i+"",""===i)return null;var n,a,o,r,h=0,l=(s?s.shortYearCutoff:null)||this._defaults.shortYearCutoff,u="string"!=typeof l?l:(new Date).getFullYear()%100+parseInt(l,10),d=(s?s.dayNamesShort:null)||this._defaults.dayNamesShort,c=(s?s.dayNames:null)||this._defaults.dayNames,p=(s?s.monthNamesShort:null)||this._defaults.monthNamesShort,f=(s?s.monthNames:null)||this._defaults.monthNames,m=-1,g=-1,v=-1,y=-1,b=!1,_=function(e){var i=t.length>n+1&&t.charAt(n+1)===e;return i&&n++,i},x=function(e){var t=_(e),s="@"===e?14:"!"===e?20:"y"===e&&t?4:"o"===e?3:2,n="y"===e?s:1,a=RegExp("^\\d{"+n+","+s+"}"),o=i.substring(h).match(a);if(!o)throw"Missing number at position "+h;return h+=o[0].length,parseInt(o[0],10)},w=function(t,s,n){var a=-1,o=e.map(_(t)?n:s,function(e,t){return[[t,e]]}).sort(function(e,t){return-(e[1].length-t[1].length)});if(e.each(o,function(e,t){var s=t[1];return i.substr(h,s.length).toLowerCase()===s.toLowerCase()?(a=t[0],h+=s.length,!1):void 0}),-1!==a)return a+1;throw"Unknown name at position "+h},k=function(){if(i.charAt(h)!==t.charAt(n))throw"Unexpected literal at position "+h;h++};for(n=0;t.length>n;n++)if(b)"'"!==t.charAt(n)||_("'")?k():b=!1;else switch(t.charAt(n)){case"d":v=x("d");break;case"D":w("D",d,c);break;case"o":y=x("o");break;case"m":g=x("m");break;case"M":g=w("M",p,f);break;case"y":m=x("y");break;case"@":r=new Date(x("@")),m=r.getFullYear(),g=r.getMonth()+1,v=r.getDate();break;case"!":r=new Date((x("!")-this._ticksTo1970)/1e4),m=r.getFullYear(),g=r.getMonth()+1,v=r.getDate();break;case"'":_("'")?k():b=!0;break;default:k()}if(i.length>h&&(o=i.substr(h),!/^\s+/.test(o)))throw"Extra/unparsed characters found in date: "+o;if(-1===m?m=(new Date).getFullYear():100>m&&(m+=(new Date).getFullYear()-(new Date).getFullYear()%100+(u>=m?0:-100)),y>-1)for(g=1,v=y;;){if(a=this._getDaysInMonth(m,g-1),a>=v)break;g++,v-=a}if(r=this._daylightSavingAdjust(new Date(m,g-1,v)),r.getFullYear()!==m||r.getMonth()+1!==g||r.getDate()!==v)throw"Invalid date";return r},ATOM:"yy-mm-dd",COOKIE:"D, dd M yy",ISO_8601:"yy-mm-dd",RFC_822:"D, d M y",RFC_850:"DD, dd-M-y",RFC_1036:"D, d M y",RFC_1123:"D, d M yy",RFC_2822:"D, d M yy",RSS:"D, d M y",TICKS:"!",TIMESTAMP:"@",W3C:"yy-mm-dd",_ticksTo1970:1e7*60*60*24*(718685+Math.floor(492.5)-Math.floor(19.7)+Math.floor(4.925)),formatDate:function(e,t,i){if(!t)return"";var s,n=(i?i.dayNamesShort:null)||this._defaults.dayNamesShort,a=(i?i.dayNames:null)||this._defaults.dayNames,o=(i?i.monthNamesShort:null)||this._defaults.monthNamesShort,r=(i?i.monthNames:null)||this._defaults.monthNames,h=function(t){var i=e.length>s+1&&e.charAt(s+1)===t;return i&&s++,i},l=function(e,t,i){var s=""+t;if(h(e))for(;i>s.length;)s="0"+s;return s},u=function(e,t,i,s){return h(e)?s[t]:i[t]},d="",c=!1;if(t)for(s=0;e.length>s;s++)if(c)"'"!==e.charAt(s)||h("'")?d+=e.charAt(s):c=!1;else switch(e.charAt(s)){case"d":d+=l("d",t.getDate(),2);break;case"D":d+=u("D",t.getDay(),n,a);break;case"o":d+=l("o",Math.round((new Date(t.getFullYear(),t.getMonth(),t.getDate()).getTime()-new Date(t.getFullYear(),0,0).getTime())/864e5),3);break;case"m":d+=l("m",t.getMonth()+1,2);break;case"M":d+=u("M",t.getMonth(),o,r);break;case"y":d+=h("y")?t.getFullYear():(10>t.getYear()%100?"0":"")+t.getYear()%100;break;case"@":d+=t.getTime();break;case"!":d+=1e4*t.getTime()+this._ticksTo1970;break;case"'":h("'")?d+="'":c=!0;break;default:d+=e.charAt(s)}return d},_possibleChars:function(e){var t,i="",s=!1,n=function(i){var s=e.length>t+1&&e.charAt(t+1)===i;return s&&t++,s};for(t=0;e.length>t;t++)if(s)"'"!==e.charAt(t)||n("'")?i+=e.charAt(t):s=!1;else switch(e.charAt(t)){case"d":case"m":case"y":case"@":i+="0123456789";break;case"D":case"M":return null;case"'":n("'")?i+="'":s=!0;break;default:i+=e.charAt(t)}return i},_get:function(e,t){return void 0!==e.settings[t]?e.settings[t]:this._defaults[t]},_setDateFromField:function(e,t){if(e.input.val()!==e.lastVal){var i=this._get(e,"dateFormat"),s=e.lastVal=e.input?e.input.val():null,n=this._getDefaultDate(e),a=n,o=this._getFormatConfig(e);try{a=this.parseDate(i,s,o)||n}catch(r){s=t?"":s}e.selectedDay=a.getDate(),e.drawMonth=e.selectedMonth=a.getMonth(),e.drawYear=e.selectedYear=a.getFullYear(),e.currentDay=s?a.getDate():0,e.currentMonth=s?a.getMonth():0,e.currentYear=s?a.getFullYear():0,this._adjustInstDate(e)}},_getDefaultDate:function(e){return this._restrictMinMax(e,this._determineDate(e,this._get(e,"defaultDate"),new Date))},_determineDate:function(t,i,s){var n=function(e){var t=new Date;return t.setDate(t.getDate()+e),t},a=function(i){try{return e.datepicker.parseDate(e.datepicker._get(t,"dateFormat"),i,e.datepicker._getFormatConfig(t))}catch(s){}for(var n=(i.toLowerCase().match(/^c/)?e.datepicker._getDate(t):null)||new Date,a=n.getFullYear(),o=n.getMonth(),r=n.getDate(),h=/([+\-]?[0-9]+)\s*(d|D|w|W|m|M|y|Y)?/g,l=h.exec(i);l;){switch(l[2]||"d"){case"d":case"D":r+=parseInt(l[1],10);break;case"w":case"W":r+=7*parseInt(l[1],10);break;case"m":case"M":o+=parseInt(l[1],10),r=Math.min(r,e.datepicker._getDaysInMonth(a,o));break;case"y":case"Y":a+=parseInt(l[1],10),r=Math.min(r,e.datepicker._getDaysInMonth(a,o))}l=h.exec(i)}return new Date(a,o,r)},o=null==i||""===i?s:"string"==typeof i?a(i):"number"==typeof i?isNaN(i)?s:n(i):new Date(i.getTime());return o=o&&"Invalid Date"==""+o?s:o,o&&(o.setHours(0),o.setMinutes(0),o.setSeconds(0),o.setMilliseconds(0)),this._daylightSavingAdjust(o)},_daylightSavingAdjust:function(e){return e?(e.setHours(e.getHours()>12?e.getHours()+2:0),e):null},_setDate:function(e,t,i){var s=!t,n=e.selectedMonth,a=e.selectedYear,o=this._restrictMinMax(e,this._determineDate(e,t,new Date));e.selectedDay=e.currentDay=o.getDate(),e.drawMonth=e.selectedMonth=e.currentMonth=o.getMonth(),e.drawYear=e.selectedYear=e.currentYear=o.getFullYear(),n===e.selectedMonth&&a===e.selectedYear||i||this._notifyChange(e),this._adjustInstDate(e),e.input&&e.input.val(s?"":this._formatDate(e))},_getDate:function(e){var t=!e.currentYear||e.input&&""===e.input.val()?null:this._daylightSavingAdjust(new Date(e.currentYear,e.currentMonth,e.currentDay));return t},_attachHandlers:function(t){var i=this._get(t,"stepMonths"),s="#"+t.id.replace(/\\\\/g,"\\");t.dpDiv.find("[data-handler]").map(function(){var t={prev:function(){e.datepicker._adjustDate(s,-i,"M")},next:function(){e.datepicker._adjustDate(s,+i,"M")},hide:function(){e.datepicker._hideDatepicker()},today:function(){e.datepicker._gotoToday(s)},selectDay:function(){return e.datepicker._selectDay(s,+this.getAttribute("data-month"),+this.getAttribute("data-year"),this),!1},selectMonth:function(){return e.datepicker._selectMonthYear(s,this,"M"),!1},selectYear:function(){return e.datepicker._selectMonthYear(s,this,"Y"),!1}};e(this).bind(this.getAttribute("data-event"),t[this.getAttribute("data-handler")])})},_generateHTML:function(e){var t,i,s,n,a,o,r,h,l,u,d,c,p,f,m,g,v,y,b,_,x,w,k,T,D,S,M,C,N,A,P,I,H,z,F,E,O,j,W,L=new Date,R=this._daylightSavingAdjust(new Date(L.getFullYear(),L.getMonth(),L.getDate())),Y=this._get(e,"isRTL"),B=this._get(e,"showButtonPanel"),J=this._get(e,"hideIfNoPrevNext"),q=this._get(e,"navigationAsDateFormat"),K=this._getNumberOfMonths(e),V=this._get(e,"showCurrentAtPos"),U=this._get(e,"stepMonths"),Q=1!==K[0]||1!==K[1],G=this._daylightSavingAdjust(e.currentDay?new Date(e.currentYear,e.currentMonth,e.currentDay):new Date(9999,9,9)),X=this._getMinMaxDate(e,"min"),$=this._getMinMaxDate(e,"max"),Z=e.drawMonth-V,et=e.drawYear;if(0>Z&&(Z+=12,et--),$)for(t=this._daylightSavingAdjust(new Date($.getFullYear(),$.getMonth()-K[0]*K[1]+1,$.getDate())),t=X&&X>t?X:t;this._daylightSavingAdjust(new Date(et,Z,1))>t;)Z--,0>Z&&(Z=11,et--);for(e.drawMonth=Z,e.drawYear=et,i=this._get(e,"prevText"),i=q?this.formatDate(i,this._daylightSavingAdjust(new Date(et,Z-U,1)),this._getFormatConfig(e)):i,s=this._canAdjustMonth(e,-1,et,Z)?"<a class='ui-datepicker-prev ui-corner-all' data-handler='prev' data-event='click' title='"+i+"'><span class='ui-icon ui-icon-circle-triangle-"+(Y?"e":"w")+"'>"+i+"</span></a>":J?"":"<a class='ui-datepicker-prev ui-corner-all ui-state-disabled' title='"+i+"'><span class='ui-icon ui-icon-circle-triangle-"+(Y?"e":"w")+"'>"+i+"</span></a>",n=this._get(e,"nextText"),n=q?this.formatDate(n,this._daylightSavingAdjust(new Date(et,Z+U,1)),this._getFormatConfig(e)):n,a=this._canAdjustMonth(e,1,et,Z)?"<a class='ui-datepicker-next ui-corner-all' data-handler='next' data-event='click' title='"+n+"'><span class='ui-icon ui-icon-circle-triangle-"+(Y?"w":"e")+"'>"+n+"</span></a>":J?"":"<a class='ui-datepicker-next ui-corner-all ui-state-disabled' title='"+n+"'><span class='ui-icon ui-icon-circle-triangle-"+(Y?"w":"e")+"'>"+n+"</span></a>",o=this._get(e,"currentText"),r=this._get(e,"gotoCurrent")&&e.currentDay?G:R,o=q?this.formatDate(o,r,this._getFormatConfig(e)):o,h=e.inline?"":"<button type='button' class='ui-datepicker-close ui-state-default ui-priority-primary ui-corner-all' data-handler='hide' data-event='click'>"+this._get(e,"closeText")+"</button>",l=B?"<div class='ui-datepicker-buttonpane ui-widget-content'>"+(Y?h:"")+(this._isInRange(e,r)?"<button type='button' class='ui-datepicker-current ui-state-default ui-priority-secondary ui-corner-all' data-handler='today' data-event='click'>"+o+"</button>":"")+(Y?"":h)+"</div>":"",u=parseInt(this._get(e,"firstDay"),10),u=isNaN(u)?0:u,d=this._get(e,"showWeek"),c=this._get(e,"dayNames"),p=this._get(e,"dayNamesMin"),f=this._get(e,"monthNames"),m=this._get(e,"monthNamesShort"),g=this._get(e,"beforeShowDay"),v=this._get(e,"showOtherMonths"),y=this._get(e,"selectOtherMonths"),b=this._getDefaultDate(e),_="",w=0;K[0]>w;w++){for(k="",this.maxRows=4,T=0;K[1]>T;T++){if(D=this._daylightSavingAdjust(new Date(et,Z,e.selectedDay)),S=" ui-corner-all",M="",Q){if(M+="<div class='ui-datepicker-group",K[1]>1)switch(T){case 0:M+=" ui-datepicker-group-first",S=" ui-corner-"+(Y?"right":"left");break;case K[1]-1:M+=" ui-datepicker-group-last",S=" ui-corner-"+(Y?"left":"right");break;default:M+=" ui-datepicker-group-middle",S=""}M+="'>"}for(M+="<div class='ui-datepicker-header ui-widget-header ui-helper-clearfix"+S+"'>"+(/all|left/.test(S)&&0===w?Y?a:s:"")+(/all|right/.test(S)&&0===w?Y?s:a:"")+this._generateMonthYearHeader(e,Z,et,X,$,w>0||T>0,f,m)+"</div><table class='ui-datepicker-calendar'><thead>"+"<tr>",C=d?"<th class='ui-datepicker-week-col'>"+this._get(e,"weekHeader")+"</th>":"",x=0;7>x;x++)N=(x+u)%7,C+="<th scope='col'"+((x+u+6)%7>=5?" class='ui-datepicker-week-end'":"")+">"+"<span title='"+c[N]+"'>"+p[N]+"</span></th>";for(M+=C+"</tr></thead><tbody>",A=this._getDaysInMonth(et,Z),et===e.selectedYear&&Z===e.selectedMonth&&(e.selectedDay=Math.min(e.selectedDay,A)),P=(this._getFirstDayOfMonth(et,Z)-u+7)%7,I=Math.ceil((P+A)/7),H=Q?this.maxRows>I?this.maxRows:I:I,this.maxRows=H,z=this._daylightSavingAdjust(new Date(et,Z,1-P)),F=0;H>F;F++){for(M+="<tr>",E=d?"<td class='ui-datepicker-week-col'>"+this._get(e,"calculateWeek")(z)+"</td>":"",x=0;7>x;x++)O=g?g.apply(e.input?e.input[0]:null,[z]):[!0,""],j=z.getMonth()!==Z,W=j&&!y||!O[0]||X&&X>z||$&&z>$,E+="<td class='"+((x+u+6)%7>=5?" ui-datepicker-week-end":"")+(j?" ui-datepicker-other-month":"")+(z.getTime()===D.getTime()&&Z===e.selectedMonth&&e._keyEvent||b.getTime()===z.getTime()&&b.getTime()===D.getTime()?" "+this._dayOverClass:"")+(W?" "+this._unselectableClass+" ui-state-disabled":"")+(j&&!v?"":" "+O[1]+(z.getTime()===G.getTime()?" "+this._currentClass:"")+(z.getTime()===R.getTime()?" ui-datepicker-today":""))+"'"+(j&&!v||!O[2]?"":" title='"+O[2].replace(/'/g,"&#39;")+"'")+(W?"":" data-handler='selectDay' data-event='click' data-month='"+z.getMonth()+"' data-year='"+z.getFullYear()+"'")+">"+(j&&!v?"&#xa0;":W?"<span class='ui-state-default'>"+z.getDate()+"</span>":"<a class='ui-state-default"+(z.getTime()===R.getTime()?" ui-state-highlight":"")+(z.getTime()===G.getTime()?" ui-state-active":"")+(j?" ui-priority-secondary":"")+"' href='#'>"+z.getDate()+"</a>")+"</td>",z.setDate(z.getDate()+1),z=this._daylightSavingAdjust(z);M+=E+"</tr>"}Z++,Z>11&&(Z=0,et++),M+="</tbody></table>"+(Q?"</div>"+(K[0]>0&&T===K[1]-1?"<div class='ui-datepicker-row-break'></div>":""):""),k+=M}_+=k}return _+=l,e._keyEvent=!1,_},_generateMonthYearHeader:function(e,t,i,s,n,a,o,r){var h,l,u,d,c,p,f,m,g=this._get(e,"changeMonth"),v=this._get(e,"changeYear"),y=this._get(e,"showMonthAfterYear"),b="<div class='ui-datepicker-title'>",_="";if(a||!g)_+="<span class='ui-datepicker-month'>"+o[t]+"</span>";else{for(h=s&&s.getFullYear()===i,l=n&&n.getFullYear()===i,_+="<select class='ui-datepicker-month' data-handler='selectMonth' data-event='change'>",u=0;12>u;u++)(!h||u>=s.getMonth())&&(!l||n.getMonth()>=u)&&(_+="<option value='"+u+"'"+(u===t?" selected='selected'":"")+">"+r[u]+"</option>");_+="</select>"}if(y||(b+=_+(!a&&g&&v?"":"&#xa0;")),!e.yearshtml)if(e.yearshtml="",a||!v)b+="<span class='ui-datepicker-year'>"+i+"</span>";else{for(d=this._get(e,"yearRange").split(":"),c=(new Date).getFullYear(),p=function(e){var t=e.match(/c[+\-].*/)?i+parseInt(e.substring(1),10):e.match(/[+\-].*/)?c+parseInt(e,10):parseInt(e,10);return isNaN(t)?c:t},f=p(d[0]),m=Math.max(f,p(d[1]||"")),f=s?Math.max(f,s.getFullYear()):f,m=n?Math.min(m,n.getFullYear()):m,e.yearshtml+="<select class='ui-datepicker-year' data-handler='selectYear' data-event='change'>";m>=f;f++)e.yearshtml+="<option value='"+f+"'"+(f===i?" selected='selected'":"")+">"+f+"</option>";e.yearshtml+="</select>",b+=e.yearshtml,e.yearshtml=null}return b+=this._get(e,"yearSuffix"),y&&(b+=(!a&&g&&v?"":"&#xa0;")+_),b+="</div>"},_adjustInstDate:function(e,t,i){var s=e.drawYear+("Y"===i?t:0),n=e.drawMonth+("M"===i?t:0),a=Math.min(e.selectedDay,this._getDaysInMonth(s,n))+("D"===i?t:0),o=this._restrictMinMax(e,this._daylightSavingAdjust(new Date(s,n,a)));e.selectedDay=o.getDate(),e.drawMonth=e.selectedMonth=o.getMonth(),e.drawYear=e.selectedYear=o.getFullYear(),("M"===i||"Y"===i)&&this._notifyChange(e)},_restrictMinMax:function(e,t){var i=this._getMinMaxDate(e,"min"),s=this._getMinMaxDate(e,"max"),n=i&&i>t?i:t;return s&&n>s?s:n},_notifyChange:function(e){var t=this._get(e,"onChangeMonthYear");t&&t.apply(e.input?e.input[0]:null,[e.selectedYear,e.selectedMonth+1,e])},_getNumberOfMonths:function(e){var t=this._get(e,"numberOfMonths");return null==t?[1,1]:"number"==typeof t?[1,t]:t},_getMinMaxDate:function(e,t){return this._determineDate(e,this._get(e,t+"Date"),null)},_getDaysInMonth:function(e,t){return 32-this._daylightSavingAdjust(new Date(e,t,32)).getDate()},_getFirstDayOfMonth:function(e,t){return new Date(e,t,1).getDay()},_canAdjustMonth:function(e,t,i,s){var n=this._getNumberOfMonths(e),a=this._daylightSavingAdjust(new Date(i,s+(0>t?t:n[0]*n[1]),1));return 0>t&&a.setDate(this._getDaysInMonth(a.getFullYear(),a.getMonth())),this._isInRange(e,a)},_isInRange:function(e,t){var i,s,n=this._getMinMaxDate(e,"min"),a=this._getMinMaxDate(e,"max"),o=null,r=null,h=this._get(e,"yearRange");return h&&(i=h.split(":"),s=(new Date).getFullYear(),o=parseInt(i[0],10),r=parseInt(i[1],10),i[0].match(/[+\-].*/)&&(o+=s),i[1].match(/[+\-].*/)&&(r+=s)),(!n||t.getTime()>=n.getTime())&&(!a||t.getTime()<=a.getTime())&&(!o||t.getFullYear()>=o)&&(!r||r>=t.getFullYear())},_getFormatConfig:function(e){var t=this._get(e,"shortYearCutoff");return t="string"!=typeof t?t:(new Date).getFullYear()%100+parseInt(t,10),{shortYearCutoff:t,dayNamesShort:this._get(e,"dayNamesShort"),dayNames:this._get(e,"dayNames"),monthNamesShort:this._get(e,"monthNamesShort"),monthNames:this._get(e,"monthNames")}},_formatDate:function(e,t,i,s){t||(e.currentDay=e.selectedDay,e.currentMonth=e.selectedMonth,e.currentYear=e.selectedYear);var n=t?"object"==typeof t?t:this._daylightSavingAdjust(new Date(s,i,t)):this._daylightSavingAdjust(new Date(e.currentYear,e.currentMonth,e.currentDay));return this.formatDate(this._get(e,"dateFormat"),n,this._getFormatConfig(e))}}),e.fn.datepicker=function(t){if(!this.length)return this;e.datepicker.initialized||(e(document).mousedown(e.datepicker._checkExternalClick),e.datepicker.initialized=!0),0===e("#"+e.datepicker._mainDivId).length&&e("body").append(e.datepicker.dpDiv);var i=Array.prototype.slice.call(arguments,1);return"string"!=typeof t||"isDisabled"!==t&&"getDate"!==t&&"widget"!==t?"option"===t&&2===arguments.length&&"string"==typeof arguments[1]?e.datepicker["_"+t+"Datepicker"].apply(e.datepicker,[this[0]].concat(i)):this.each(function(){"string"==typeof t?e.datepicker["_"+t+"Datepicker"].apply(e.datepicker,[this].concat(i)):e.datepicker._attachDatepicker(this,t)}):e.datepicker["_"+t+"Datepicker"].apply(e.datepicker,[this[0]].concat(i))},e.datepicker=new n,e.datepicker.initialized=!1,e.datepicker.uuid=(new Date).getTime(),e.datepicker.version="1.11.4",e.datepicker,e.widget("ui.draggable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"drag",options:{addClasses:!0,appendTo:"parent",axis:!1,connectToSortable:!1,containment:!1,cursor:"auto",cursorAt:!1,grid:!1,handle:!1,helper:"original",iframeFix:!1,opacity:!1,refreshPositions:!1,revert:!1,revertDuration:500,scope:"default",scroll:!0,scrollSensitivity:20,scrollSpeed:20,snap:!1,snapMode:"both",snapTolerance:20,stack:!1,zIndex:!1,drag:null,start:null,stop:null},_create:function(){"original"===this.options.helper&&this._setPositionRelative(),this.options.addClasses&&this.element.addClass("ui-draggable"),this.options.disabled&&this.element.addClass("ui-draggable-disabled"),this._setHandleClassName(),this._mouseInit()},_setOption:function(e,t){this._super(e,t),"handle"===e&&(this._removeHandleClassName(),this._setHandleClassName())},_destroy:function(){return(this.helper||this.element).is(".ui-draggable-dragging")?(this.destroyOnClear=!0,void 0):(this.element.removeClass("ui-draggable ui-draggable-dragging ui-draggable-disabled"),this._removeHandleClassName(),this._mouseDestroy(),void 0)},_mouseCapture:function(t){var i=this.options;return this._blurActiveElement(t),this.helper||i.disabled||e(t.target).closest(".ui-resizable-handle").length>0?!1:(this.handle=this._getHandle(t),this.handle?(this._blockFrames(i.iframeFix===!0?"iframe":i.iframeFix),!0):!1)},_blockFrames:function(t){this.iframeBlocks=this.document.find(t).map(function(){var t=e(this);return e("<div>").css("position","absolute").appendTo(t.parent()).outerWidth(t.outerWidth()).outerHeight(t.outerHeight()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_blurActiveElement:function(t){var i=this.document[0];if(this.handleElement.is(t.target))try{i.activeElement&&"body"!==i.activeElement.nodeName.toLowerCase()&&e(i.activeElement).blur()}catch(s){}},_mouseStart:function(t){var i=this.options;return this.helper=this._createHelper(t),this.helper.addClass("ui-draggable-dragging"),this._cacheHelperProportions(),e.ui.ddmanager&&(e.ui.ddmanager.current=this),this._cacheMargins(),this.cssPosition=this.helper.css("position"),this.scrollParent=this.helper.scrollParent(!0),this.offsetParent=this.helper.offsetParent(),this.hasFixedAncestor=this.helper.parents().filter(function(){return"fixed"===e(this).css("position")}).length>0,this.positionAbs=this.element.offset(),this._refreshOffsets(t),this.originalPosition=this.position=this._generatePosition(t,!1),this.originalPageX=t.pageX,this.originalPageY=t.pageY,i.cursorAt&&this._adjustOffsetFromHelper(i.cursorAt),this._setContainment(),this._trigger("start",t)===!1?(this._clear(),!1):(this._cacheHelperProportions(),e.ui.ddmanager&&!i.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this._normalizeRightBottom(),this._mouseDrag(t,!0),e.ui.ddmanager&&e.ui.ddmanager.dragStart(this,t),!0)},_refreshOffsets:function(e){this.offset={top:this.positionAbs.top-this.margins.top,left:this.positionAbs.left-this.margins.left,scroll:!1,parent:this._getParentOffset(),relative:this._getRelativeOffset()},this.offset.click={left:e.pageX-this.offset.left,top:e.pageY-this.offset.top}},_mouseDrag:function(t,i){if(this.hasFixedAncestor&&(this.offset.parent=this._getParentOffset()),this.position=this._generatePosition(t,!0),this.positionAbs=this._convertPositionTo("absolute"),!i){var s=this._uiHash();if(this._trigger("drag",t,s)===!1)return this._mouseUp({}),!1;this.position=s.position}return this.helper[0].style.left=this.position.left+"px",this.helper[0].style.top=this.position.top+"px",e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),!1},_mouseStop:function(t){var i=this,s=!1;return e.ui.ddmanager&&!this.options.dropBehaviour&&(s=e.ui.ddmanager.drop(this,t)),this.dropped&&(s=this.dropped,this.dropped=!1),"invalid"===this.options.revert&&!s||"valid"===this.options.revert&&s||this.options.revert===!0||e.isFunction(this.options.revert)&&this.options.revert.call(this.element,s)?e(this.helper).animate(this.originalPosition,parseInt(this.options.revertDuration,10),function(){i._trigger("stop",t)!==!1&&i._clear()}):this._trigger("stop",t)!==!1&&this._clear(),!1},_mouseUp:function(t){return this._unblockFrames(),e.ui.ddmanager&&e.ui.ddmanager.dragStop(this,t),this.handleElement.is(t.target)&&this.element.focus(),e.ui.mouse.prototype._mouseUp.call(this,t)},cancel:function(){return this.helper.is(".ui-draggable-dragging")?this._mouseUp({}):this._clear(),this},_getHandle:function(t){return this.options.handle?!!e(t.target).closest(this.element.find(this.options.handle)).length:!0},_setHandleClassName:function(){this.handleElement=this.options.handle?this.element.find(this.options.handle):this.element,this.handleElement.addClass("ui-draggable-handle")},_removeHandleClassName:function(){this.handleElement.removeClass("ui-draggable-handle")},_createHelper:function(t){var i=this.options,s=e.isFunction(i.helper),n=s?e(i.helper.apply(this.element[0],[t])):"clone"===i.helper?this.element.clone().removeAttr("id"):this.element;return n.parents("body").length||n.appendTo("parent"===i.appendTo?this.element[0].parentNode:i.appendTo),s&&n[0]===this.element[0]&&this._setPositionRelative(),n[0]===this.element[0]||/(fixed|absolute)/.test(n.css("position"))||n.css("position","absolute"),n},_setPositionRelative:function(){/^(?:r|a|f)/.test(this.element.css("position"))||(this.element[0].style.position="relative")},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),"left"in t&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_isRootNode:function(e){return/(html|body)/i.test(e.tagName)||e===this.document[0]},_getParentOffset:function(){var t=this.offsetParent.offset(),i=this.document[0];return"absolute"===this.cssPosition&&this.scrollParent[0]!==i&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),this._isRootNode(this.offsetParent[0])&&(t={top:0,left:0}),{top:t.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"!==this.cssPosition)return{top:0,left:0};var e=this.element.position(),t=this._isRootNode(this.scrollParent[0]);return{top:e.top-(parseInt(this.helper.css("top"),10)||0)+(t?0:this.scrollParent.scrollTop()),left:e.left-(parseInt(this.helper.css("left"),10)||0)+(t?0:this.scrollParent.scrollLeft())}},_cacheMargins:function(){this.margins={left:parseInt(this.element.css("marginLeft"),10)||0,top:parseInt(this.element.css("marginTop"),10)||0,right:parseInt(this.element.css("marginRight"),10)||0,bottom:parseInt(this.element.css("marginBottom"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,i,s,n=this.options,a=this.document[0];return this.relativeContainer=null,n.containment?"window"===n.containment?(this.containment=[e(window).scrollLeft()-this.offset.relative.left-this.offset.parent.left,e(window).scrollTop()-this.offset.relative.top-this.offset.parent.top,e(window).scrollLeft()+e(window).width()-this.helperProportions.width-this.margins.left,e(window).scrollTop()+(e(window).height()||a.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):"document"===n.containment?(this.containment=[0,0,e(a).width()-this.helperProportions.width-this.margins.left,(e(a).height()||a.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):n.containment.constructor===Array?(this.containment=n.containment,void 0):("parent"===n.containment&&(n.containment=this.helper[0].parentNode),i=e(n.containment),s=i[0],s&&(t=/(scroll|auto)/.test(i.css("overflow")),this.containment=[(parseInt(i.css("borderLeftWidth"),10)||0)+(parseInt(i.css("paddingLeft"),10)||0),(parseInt(i.css("borderTopWidth"),10)||0)+(parseInt(i.css("paddingTop"),10)||0),(t?Math.max(s.scrollWidth,s.offsetWidth):s.offsetWidth)-(parseInt(i.css("borderRightWidth"),10)||0)-(parseInt(i.css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left-this.margins.right,(t?Math.max(s.scrollHeight,s.offsetHeight):s.offsetHeight)-(parseInt(i.css("borderBottomWidth"),10)||0)-(parseInt(i.css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top-this.margins.bottom],this.relativeContainer=i),void 0):(this.containment=null,void 0)
},_convertPositionTo:function(e,t){t||(t=this.position);var i="absolute"===e?1:-1,s=this._isRootNode(this.scrollParent[0]);return{top:t.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.offset.scroll.top:s?0:this.offset.scroll.top)*i,left:t.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.offset.scroll.left:s?0:this.offset.scroll.left)*i}},_generatePosition:function(e,t){var i,s,n,a,o=this.options,r=this._isRootNode(this.scrollParent[0]),h=e.pageX,l=e.pageY;return r&&this.offset.scroll||(this.offset.scroll={top:this.scrollParent.scrollTop(),left:this.scrollParent.scrollLeft()}),t&&(this.containment&&(this.relativeContainer?(s=this.relativeContainer.offset(),i=[this.containment[0]+s.left,this.containment[1]+s.top,this.containment[2]+s.left,this.containment[3]+s.top]):i=this.containment,e.pageX-this.offset.click.left<i[0]&&(h=i[0]+this.offset.click.left),e.pageY-this.offset.click.top<i[1]&&(l=i[1]+this.offset.click.top),e.pageX-this.offset.click.left>i[2]&&(h=i[2]+this.offset.click.left),e.pageY-this.offset.click.top>i[3]&&(l=i[3]+this.offset.click.top)),o.grid&&(n=o.grid[1]?this.originalPageY+Math.round((l-this.originalPageY)/o.grid[1])*o.grid[1]:this.originalPageY,l=i?n-this.offset.click.top>=i[1]||n-this.offset.click.top>i[3]?n:n-this.offset.click.top>=i[1]?n-o.grid[1]:n+o.grid[1]:n,a=o.grid[0]?this.originalPageX+Math.round((h-this.originalPageX)/o.grid[0])*o.grid[0]:this.originalPageX,h=i?a-this.offset.click.left>=i[0]||a-this.offset.click.left>i[2]?a:a-this.offset.click.left>=i[0]?a-o.grid[0]:a+o.grid[0]:a),"y"===o.axis&&(h=this.originalPageX),"x"===o.axis&&(l=this.originalPageY)),{top:l-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.offset.scroll.top:r?0:this.offset.scroll.top),left:h-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.offset.scroll.left:r?0:this.offset.scroll.left)}},_clear:function(){this.helper.removeClass("ui-draggable-dragging"),this.helper[0]===this.element[0]||this.cancelHelperRemoval||this.helper.remove(),this.helper=null,this.cancelHelperRemoval=!1,this.destroyOnClear&&this.destroy()},_normalizeRightBottom:function(){"y"!==this.options.axis&&"auto"!==this.helper.css("right")&&(this.helper.width(this.helper.width()),this.helper.css("right","auto")),"x"!==this.options.axis&&"auto"!==this.helper.css("bottom")&&(this.helper.height(this.helper.height()),this.helper.css("bottom","auto"))},_trigger:function(t,i,s){return s=s||this._uiHash(),e.ui.plugin.call(this,t,[i,s,this],!0),/^(drag|start|stop)/.test(t)&&(this.positionAbs=this._convertPositionTo("absolute"),s.offset=this.positionAbs),e.Widget.prototype._trigger.call(this,t,i,s)},plugins:{},_uiHash:function(){return{helper:this.helper,position:this.position,originalPosition:this.originalPosition,offset:this.positionAbs}}}),e.ui.plugin.add("draggable","connectToSortable",{start:function(t,i,s){var n=e.extend({},i,{item:s.element});s.sortables=[],e(s.options.connectToSortable).each(function(){var i=e(this).sortable("instance");i&&!i.options.disabled&&(s.sortables.push(i),i.refreshPositions(),i._trigger("activate",t,n))})},stop:function(t,i,s){var n=e.extend({},i,{item:s.element});s.cancelHelperRemoval=!1,e.each(s.sortables,function(){var e=this;e.isOver?(e.isOver=0,s.cancelHelperRemoval=!0,e.cancelHelperRemoval=!1,e._storedCSS={position:e.placeholder.css("position"),top:e.placeholder.css("top"),left:e.placeholder.css("left")},e._mouseStop(t),e.options.helper=e.options._helper):(e.cancelHelperRemoval=!0,e._trigger("deactivate",t,n))})},drag:function(t,i,s){e.each(s.sortables,function(){var n=!1,a=this;a.positionAbs=s.positionAbs,a.helperProportions=s.helperProportions,a.offset.click=s.offset.click,a._intersectsWith(a.containerCache)&&(n=!0,e.each(s.sortables,function(){return this.positionAbs=s.positionAbs,this.helperProportions=s.helperProportions,this.offset.click=s.offset.click,this!==a&&this._intersectsWith(this.containerCache)&&e.contains(a.element[0],this.element[0])&&(n=!1),n})),n?(a.isOver||(a.isOver=1,s._parent=i.helper.parent(),a.currentItem=i.helper.appendTo(a.element).data("ui-sortable-item",!0),a.options._helper=a.options.helper,a.options.helper=function(){return i.helper[0]},t.target=a.currentItem[0],a._mouseCapture(t,!0),a._mouseStart(t,!0,!0),a.offset.click.top=s.offset.click.top,a.offset.click.left=s.offset.click.left,a.offset.parent.left-=s.offset.parent.left-a.offset.parent.left,a.offset.parent.top-=s.offset.parent.top-a.offset.parent.top,s._trigger("toSortable",t),s.dropped=a.element,e.each(s.sortables,function(){this.refreshPositions()}),s.currentItem=s.element,a.fromOutside=s),a.currentItem&&(a._mouseDrag(t),i.position=a.position)):a.isOver&&(a.isOver=0,a.cancelHelperRemoval=!0,a.options._revert=a.options.revert,a.options.revert=!1,a._trigger("out",t,a._uiHash(a)),a._mouseStop(t,!0),a.options.revert=a.options._revert,a.options.helper=a.options._helper,a.placeholder&&a.placeholder.remove(),i.helper.appendTo(s._parent),s._refreshOffsets(t),i.position=s._generatePosition(t,!0),s._trigger("fromSortable",t),s.dropped=!1,e.each(s.sortables,function(){this.refreshPositions()}))})}}),e.ui.plugin.add("draggable","cursor",{start:function(t,i,s){var n=e("body"),a=s.options;n.css("cursor")&&(a._cursor=n.css("cursor")),n.css("cursor",a.cursor)},stop:function(t,i,s){var n=s.options;n._cursor&&e("body").css("cursor",n._cursor)}}),e.ui.plugin.add("draggable","opacity",{start:function(t,i,s){var n=e(i.helper),a=s.options;n.css("opacity")&&(a._opacity=n.css("opacity")),n.css("opacity",a.opacity)},stop:function(t,i,s){var n=s.options;n._opacity&&e(i.helper).css("opacity",n._opacity)}}),e.ui.plugin.add("draggable","scroll",{start:function(e,t,i){i.scrollParentNotHidden||(i.scrollParentNotHidden=i.helper.scrollParent(!1)),i.scrollParentNotHidden[0]!==i.document[0]&&"HTML"!==i.scrollParentNotHidden[0].tagName&&(i.overflowOffset=i.scrollParentNotHidden.offset())},drag:function(t,i,s){var n=s.options,a=!1,o=s.scrollParentNotHidden[0],r=s.document[0];o!==r&&"HTML"!==o.tagName?(n.axis&&"x"===n.axis||(s.overflowOffset.top+o.offsetHeight-t.pageY<n.scrollSensitivity?o.scrollTop=a=o.scrollTop+n.scrollSpeed:t.pageY-s.overflowOffset.top<n.scrollSensitivity&&(o.scrollTop=a=o.scrollTop-n.scrollSpeed)),n.axis&&"y"===n.axis||(s.overflowOffset.left+o.offsetWidth-t.pageX<n.scrollSensitivity?o.scrollLeft=a=o.scrollLeft+n.scrollSpeed:t.pageX-s.overflowOffset.left<n.scrollSensitivity&&(o.scrollLeft=a=o.scrollLeft-n.scrollSpeed))):(n.axis&&"x"===n.axis||(t.pageY-e(r).scrollTop()<n.scrollSensitivity?a=e(r).scrollTop(e(r).scrollTop()-n.scrollSpeed):e(window).height()-(t.pageY-e(r).scrollTop())<n.scrollSensitivity&&(a=e(r).scrollTop(e(r).scrollTop()+n.scrollSpeed))),n.axis&&"y"===n.axis||(t.pageX-e(r).scrollLeft()<n.scrollSensitivity?a=e(r).scrollLeft(e(r).scrollLeft()-n.scrollSpeed):e(window).width()-(t.pageX-e(r).scrollLeft())<n.scrollSensitivity&&(a=e(r).scrollLeft(e(r).scrollLeft()+n.scrollSpeed)))),a!==!1&&e.ui.ddmanager&&!n.dropBehaviour&&e.ui.ddmanager.prepareOffsets(s,t)}}),e.ui.plugin.add("draggable","snap",{start:function(t,i,s){var n=s.options;s.snapElements=[],e(n.snap.constructor!==String?n.snap.items||":data(ui-draggable)":n.snap).each(function(){var t=e(this),i=t.offset();this!==s.element[0]&&s.snapElements.push({item:this,width:t.outerWidth(),height:t.outerHeight(),top:i.top,left:i.left})})},drag:function(t,i,s){var n,a,o,r,h,l,u,d,c,p,f=s.options,m=f.snapTolerance,g=i.offset.left,v=g+s.helperProportions.width,y=i.offset.top,b=y+s.helperProportions.height;for(c=s.snapElements.length-1;c>=0;c--)h=s.snapElements[c].left-s.margins.left,l=h+s.snapElements[c].width,u=s.snapElements[c].top-s.margins.top,d=u+s.snapElements[c].height,h-m>v||g>l+m||u-m>b||y>d+m||!e.contains(s.snapElements[c].item.ownerDocument,s.snapElements[c].item)?(s.snapElements[c].snapping&&s.options.snap.release&&s.options.snap.release.call(s.element,t,e.extend(s._uiHash(),{snapItem:s.snapElements[c].item})),s.snapElements[c].snapping=!1):("inner"!==f.snapMode&&(n=m>=Math.abs(u-b),a=m>=Math.abs(d-y),o=m>=Math.abs(h-v),r=m>=Math.abs(l-g),n&&(i.position.top=s._convertPositionTo("relative",{top:u-s.helperProportions.height,left:0}).top),a&&(i.position.top=s._convertPositionTo("relative",{top:d,left:0}).top),o&&(i.position.left=s._convertPositionTo("relative",{top:0,left:h-s.helperProportions.width}).left),r&&(i.position.left=s._convertPositionTo("relative",{top:0,left:l}).left)),p=n||a||o||r,"outer"!==f.snapMode&&(n=m>=Math.abs(u-y),a=m>=Math.abs(d-b),o=m>=Math.abs(h-g),r=m>=Math.abs(l-v),n&&(i.position.top=s._convertPositionTo("relative",{top:u,left:0}).top),a&&(i.position.top=s._convertPositionTo("relative",{top:d-s.helperProportions.height,left:0}).top),o&&(i.position.left=s._convertPositionTo("relative",{top:0,left:h}).left),r&&(i.position.left=s._convertPositionTo("relative",{top:0,left:l-s.helperProportions.width}).left)),!s.snapElements[c].snapping&&(n||a||o||r||p)&&s.options.snap.snap&&s.options.snap.snap.call(s.element,t,e.extend(s._uiHash(),{snapItem:s.snapElements[c].item})),s.snapElements[c].snapping=n||a||o||r||p)}}),e.ui.plugin.add("draggable","stack",{start:function(t,i,s){var n,a=s.options,o=e.makeArray(e(a.stack)).sort(function(t,i){return(parseInt(e(t).css("zIndex"),10)||0)-(parseInt(e(i).css("zIndex"),10)||0)});o.length&&(n=parseInt(e(o[0]).css("zIndex"),10)||0,e(o).each(function(t){e(this).css("zIndex",n+t)}),this.css("zIndex",n+o.length))}}),e.ui.plugin.add("draggable","zIndex",{start:function(t,i,s){var n=e(i.helper),a=s.options;n.css("zIndex")&&(a._zIndex=n.css("zIndex")),n.css("zIndex",a.zIndex)},stop:function(t,i,s){var n=s.options;n._zIndex&&e(i.helper).css("zIndex",n._zIndex)}}),e.ui.draggable,e.widget("ui.resizable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"resize",options:{alsoResize:!1,animate:!1,animateDuration:"slow",animateEasing:"swing",aspectRatio:!1,autoHide:!1,containment:!1,ghost:!1,grid:!1,handles:"e,s,se",helper:!1,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:90,resize:null,start:null,stop:null},_num:function(e){return parseInt(e,10)||0},_isNumber:function(e){return!isNaN(parseInt(e,10))},_hasScroll:function(t,i){if("hidden"===e(t).css("overflow"))return!1;var s=i&&"left"===i?"scrollLeft":"scrollTop",n=!1;return t[s]>0?!0:(t[s]=1,n=t[s]>0,t[s]=0,n)},_create:function(){var t,i,s,n,a,o=this,r=this.options;if(this.element.addClass("ui-resizable"),e.extend(this,{_aspectRatio:!!r.aspectRatio,aspectRatio:r.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:r.helper||r.ghost||r.animate?r.helper||"ui-resizable-helper":null}),this.element[0].nodeName.match(/^(canvas|textarea|input|select|button|img)$/i)&&(this.element.wrap(e("<div class='ui-wrapper' style='overflow: hidden;'></div>").css({position:this.element.css("position"),width:this.element.outerWidth(),height:this.element.outerHeight(),top:this.element.css("top"),left:this.element.css("left")})),this.element=this.element.parent().data("ui-resizable",this.element.resizable("instance")),this.elementIsWrapper=!0,this.element.css({marginLeft:this.originalElement.css("marginLeft"),marginTop:this.originalElement.css("marginTop"),marginRight:this.originalElement.css("marginRight"),marginBottom:this.originalElement.css("marginBottom")}),this.originalElement.css({marginLeft:0,marginTop:0,marginRight:0,marginBottom:0}),this.originalResizeStyle=this.originalElement.css("resize"),this.originalElement.css("resize","none"),this._proportionallyResizeElements.push(this.originalElement.css({position:"static",zoom:1,display:"block"})),this.originalElement.css({margin:this.originalElement.css("margin")}),this._proportionallyResize()),this.handles=r.handles||(e(".ui-resizable-handle",this.element).length?{n:".ui-resizable-n",e:".ui-resizable-e",s:".ui-resizable-s",w:".ui-resizable-w",se:".ui-resizable-se",sw:".ui-resizable-sw",ne:".ui-resizable-ne",nw:".ui-resizable-nw"}:"e,s,se"),this._handles=e(),this.handles.constructor===String)for("all"===this.handles&&(this.handles="n,e,s,w,se,sw,ne,nw"),t=this.handles.split(","),this.handles={},i=0;t.length>i;i++)s=e.trim(t[i]),a="ui-resizable-"+s,n=e("<div class='ui-resizable-handle "+a+"'></div>"),n.css({zIndex:r.zIndex}),"se"===s&&n.addClass("ui-icon ui-icon-gripsmall-diagonal-se"),this.handles[s]=".ui-resizable-"+s,this.element.append(n);this._renderAxis=function(t){var i,s,n,a;t=t||this.element;for(i in this.handles)this.handles[i].constructor===String?this.handles[i]=this.element.children(this.handles[i]).first().show():(this.handles[i].jquery||this.handles[i].nodeType)&&(this.handles[i]=e(this.handles[i]),this._on(this.handles[i],{mousedown:o._mouseDown})),this.elementIsWrapper&&this.originalElement[0].nodeName.match(/^(textarea|input|select|button)$/i)&&(s=e(this.handles[i],this.element),a=/sw|ne|nw|se|n|s/.test(i)?s.outerHeight():s.outerWidth(),n=["padding",/ne|nw|n/.test(i)?"Top":/se|sw|s/.test(i)?"Bottom":/^e$/.test(i)?"Right":"Left"].join(""),t.css(n,a),this._proportionallyResize()),this._handles=this._handles.add(this.handles[i])},this._renderAxis(this.element),this._handles=this._handles.add(this.element.find(".ui-resizable-handle")),this._handles.disableSelection(),this._handles.mouseover(function(){o.resizing||(this.className&&(n=this.className.match(/ui-resizable-(se|sw|ne|nw|n|e|s|w)/i)),o.axis=n&&n[1]?n[1]:"se")}),r.autoHide&&(this._handles.hide(),e(this.element).addClass("ui-resizable-autohide").mouseenter(function(){r.disabled||(e(this).removeClass("ui-resizable-autohide"),o._handles.show())}).mouseleave(function(){r.disabled||o.resizing||(e(this).addClass("ui-resizable-autohide"),o._handles.hide())})),this._mouseInit()},_destroy:function(){this._mouseDestroy();var t,i=function(t){e(t).removeClass("ui-resizable ui-resizable-disabled ui-resizable-resizing").removeData("resizable").removeData("ui-resizable").unbind(".resizable").find(".ui-resizable-handle").remove()};return this.elementIsWrapper&&(i(this.element),t=this.element,this.originalElement.css({position:t.css("position"),width:t.outerWidth(),height:t.outerHeight(),top:t.css("top"),left:t.css("left")}).insertAfter(t),t.remove()),this.originalElement.css("resize",this.originalResizeStyle),i(this.originalElement),this},_mouseCapture:function(t){var i,s,n=!1;for(i in this.handles)s=e(this.handles[i])[0],(s===t.target||e.contains(s,t.target))&&(n=!0);return!this.options.disabled&&n},_mouseStart:function(t){var i,s,n,a=this.options,o=this.element;return this.resizing=!0,this._renderProxy(),i=this._num(this.helper.css("left")),s=this._num(this.helper.css("top")),a.containment&&(i+=e(a.containment).scrollLeft()||0,s+=e(a.containment).scrollTop()||0),this.offset=this.helper.offset(),this.position={left:i,top:s},this.size=this._helper?{width:this.helper.width(),height:this.helper.height()}:{width:o.width(),height:o.height()},this.originalSize=this._helper?{width:o.outerWidth(),height:o.outerHeight()}:{width:o.width(),height:o.height()},this.sizeDiff={width:o.outerWidth()-o.width(),height:o.outerHeight()-o.height()},this.originalPosition={left:i,top:s},this.originalMousePosition={left:t.pageX,top:t.pageY},this.aspectRatio="number"==typeof a.aspectRatio?a.aspectRatio:this.originalSize.width/this.originalSize.height||1,n=e(".ui-resizable-"+this.axis).css("cursor"),e("body").css("cursor","auto"===n?this.axis+"-resize":n),o.addClass("ui-resizable-resizing"),this._propagate("start",t),!0},_mouseDrag:function(t){var i,s,n=this.originalMousePosition,a=this.axis,o=t.pageX-n.left||0,r=t.pageY-n.top||0,h=this._change[a];return this._updatePrevProperties(),h?(i=h.apply(this,[t,o,r]),this._updateVirtualBoundaries(t.shiftKey),(this._aspectRatio||t.shiftKey)&&(i=this._updateRatio(i,t)),i=this._respectSize(i,t),this._updateCache(i),this._propagate("resize",t),s=this._applyChanges(),!this._helper&&this._proportionallyResizeElements.length&&this._proportionallyResize(),e.isEmptyObject(s)||(this._updatePrevProperties(),this._trigger("resize",t,this.ui()),this._applyChanges()),!1):!1},_mouseStop:function(t){this.resizing=!1;var i,s,n,a,o,r,h,l=this.options,u=this;return this._helper&&(i=this._proportionallyResizeElements,s=i.length&&/textarea/i.test(i[0].nodeName),n=s&&this._hasScroll(i[0],"left")?0:u.sizeDiff.height,a=s?0:u.sizeDiff.width,o={width:u.helper.width()-a,height:u.helper.height()-n},r=parseInt(u.element.css("left"),10)+(u.position.left-u.originalPosition.left)||null,h=parseInt(u.element.css("top"),10)+(u.position.top-u.originalPosition.top)||null,l.animate||this.element.css(e.extend(o,{top:h,left:r})),u.helper.height(u.size.height),u.helper.width(u.size.width),this._helper&&!l.animate&&this._proportionallyResize()),e("body").css("cursor","auto"),this.element.removeClass("ui-resizable-resizing"),this._propagate("stop",t),this._helper&&this.helper.remove(),!1},_updatePrevProperties:function(){this.prevPosition={top:this.position.top,left:this.position.left},this.prevSize={width:this.size.width,height:this.size.height}},_applyChanges:function(){var e={};return this.position.top!==this.prevPosition.top&&(e.top=this.position.top+"px"),this.position.left!==this.prevPosition.left&&(e.left=this.position.left+"px"),this.size.width!==this.prevSize.width&&(e.width=this.size.width+"px"),this.size.height!==this.prevSize.height&&(e.height=this.size.height+"px"),this.helper.css(e),e},_updateVirtualBoundaries:function(e){var t,i,s,n,a,o=this.options;a={minWidth:this._isNumber(o.minWidth)?o.minWidth:0,maxWidth:this._isNumber(o.maxWidth)?o.maxWidth:1/0,minHeight:this._isNumber(o.minHeight)?o.minHeight:0,maxHeight:this._isNumber(o.maxHeight)?o.maxHeight:1/0},(this._aspectRatio||e)&&(t=a.minHeight*this.aspectRatio,s=a.minWidth/this.aspectRatio,i=a.maxHeight*this.aspectRatio,n=a.maxWidth/this.aspectRatio,t>a.minWidth&&(a.minWidth=t),s>a.minHeight&&(a.minHeight=s),a.maxWidth>i&&(a.maxWidth=i),a.maxHeight>n&&(a.maxHeight=n)),this._vBoundaries=a},_updateCache:function(e){this.offset=this.helper.offset(),this._isNumber(e.left)&&(this.position.left=e.left),this._isNumber(e.top)&&(this.position.top=e.top),this._isNumber(e.height)&&(this.size.height=e.height),this._isNumber(e.width)&&(this.size.width=e.width)},_updateRatio:function(e){var t=this.position,i=this.size,s=this.axis;return this._isNumber(e.height)?e.width=e.height*this.aspectRatio:this._isNumber(e.width)&&(e.height=e.width/this.aspectRatio),"sw"===s&&(e.left=t.left+(i.width-e.width),e.top=null),"nw"===s&&(e.top=t.top+(i.height-e.height),e.left=t.left+(i.width-e.width)),e},_respectSize:function(e){var t=this._vBoundaries,i=this.axis,s=this._isNumber(e.width)&&t.maxWidth&&t.maxWidth<e.width,n=this._isNumber(e.height)&&t.maxHeight&&t.maxHeight<e.height,a=this._isNumber(e.width)&&t.minWidth&&t.minWidth>e.width,o=this._isNumber(e.height)&&t.minHeight&&t.minHeight>e.height,r=this.originalPosition.left+this.originalSize.width,h=this.position.top+this.size.height,l=/sw|nw|w/.test(i),u=/nw|ne|n/.test(i);return a&&(e.width=t.minWidth),o&&(e.height=t.minHeight),s&&(e.width=t.maxWidth),n&&(e.height=t.maxHeight),a&&l&&(e.left=r-t.minWidth),s&&l&&(e.left=r-t.maxWidth),o&&u&&(e.top=h-t.minHeight),n&&u&&(e.top=h-t.maxHeight),e.width||e.height||e.left||!e.top?e.width||e.height||e.top||!e.left||(e.left=null):e.top=null,e},_getPaddingPlusBorderDimensions:function(e){for(var t=0,i=[],s=[e.css("borderTopWidth"),e.css("borderRightWidth"),e.css("borderBottomWidth"),e.css("borderLeftWidth")],n=[e.css("paddingTop"),e.css("paddingRight"),e.css("paddingBottom"),e.css("paddingLeft")];4>t;t++)i[t]=parseInt(s[t],10)||0,i[t]+=parseInt(n[t],10)||0;return{height:i[0]+i[2],width:i[1]+i[3]}},_proportionallyResize:function(){if(this._proportionallyResizeElements.length)for(var e,t=0,i=this.helper||this.element;this._proportionallyResizeElements.length>t;t++)e=this._proportionallyResizeElements[t],this.outerDimensions||(this.outerDimensions=this._getPaddingPlusBorderDimensions(e)),e.css({height:i.height()-this.outerDimensions.height||0,width:i.width()-this.outerDimensions.width||0})},_renderProxy:function(){var t=this.element,i=this.options;this.elementOffset=t.offset(),this._helper?(this.helper=this.helper||e("<div style='overflow:hidden;'></div>"),this.helper.addClass(this._helper).css({width:this.element.outerWidth()-1,height:this.element.outerHeight()-1,position:"absolute",left:this.elementOffset.left+"px",top:this.elementOffset.top+"px",zIndex:++i.zIndex}),this.helper.appendTo("body").disableSelection()):this.helper=this.element},_change:{e:function(e,t){return{width:this.originalSize.width+t}},w:function(e,t){var i=this.originalSize,s=this.originalPosition;return{left:s.left+t,width:i.width-t}},n:function(e,t,i){var s=this.originalSize,n=this.originalPosition;return{top:n.top+i,height:s.height-i}},s:function(e,t,i){return{height:this.originalSize.height+i}},se:function(t,i,s){return e.extend(this._change.s.apply(this,arguments),this._change.e.apply(this,[t,i,s]))},sw:function(t,i,s){return e.extend(this._change.s.apply(this,arguments),this._change.w.apply(this,[t,i,s]))},ne:function(t,i,s){return e.extend(this._change.n.apply(this,arguments),this._change.e.apply(this,[t,i,s]))},nw:function(t,i,s){return e.extend(this._change.n.apply(this,arguments),this._change.w.apply(this,[t,i,s]))}},_propagate:function(t,i){e.ui.plugin.call(this,t,[i,this.ui()]),"resize"!==t&&this._trigger(t,i,this.ui())},plugins:{},ui:function(){return{originalElement:this.originalElement,element:this.element,helper:this.helper,position:this.position,size:this.size,originalSize:this.originalSize,originalPosition:this.originalPosition}}}),e.ui.plugin.add("resizable","animate",{stop:function(t){var i=e(this).resizable("instance"),s=i.options,n=i._proportionallyResizeElements,a=n.length&&/textarea/i.test(n[0].nodeName),o=a&&i._hasScroll(n[0],"left")?0:i.sizeDiff.height,r=a?0:i.sizeDiff.width,h={width:i.size.width-r,height:i.size.height-o},l=parseInt(i.element.css("left"),10)+(i.position.left-i.originalPosition.left)||null,u=parseInt(i.element.css("top"),10)+(i.position.top-i.originalPosition.top)||null;i.element.animate(e.extend(h,u&&l?{top:u,left:l}:{}),{duration:s.animateDuration,easing:s.animateEasing,step:function(){var s={width:parseInt(i.element.css("width"),10),height:parseInt(i.element.css("height"),10),top:parseInt(i.element.css("top"),10),left:parseInt(i.element.css("left"),10)};n&&n.length&&e(n[0]).css({width:s.width,height:s.height}),i._updateCache(s),i._propagate("resize",t)}})}}),e.ui.plugin.add("resizable","containment",{start:function(){var t,i,s,n,a,o,r,h=e(this).resizable("instance"),l=h.options,u=h.element,d=l.containment,c=d instanceof e?d.get(0):/parent/.test(d)?u.parent().get(0):d;c&&(h.containerElement=e(c),/document/.test(d)||d===document?(h.containerOffset={left:0,top:0},h.containerPosition={left:0,top:0},h.parentData={element:e(document),left:0,top:0,width:e(document).width(),height:e(document).height()||document.body.parentNode.scrollHeight}):(t=e(c),i=[],e(["Top","Right","Left","Bottom"]).each(function(e,s){i[e]=h._num(t.css("padding"+s))}),h.containerOffset=t.offset(),h.containerPosition=t.position(),h.containerSize={height:t.innerHeight()-i[3],width:t.innerWidth()-i[1]},s=h.containerOffset,n=h.containerSize.height,a=h.containerSize.width,o=h._hasScroll(c,"left")?c.scrollWidth:a,r=h._hasScroll(c)?c.scrollHeight:n,h.parentData={element:c,left:s.left,top:s.top,width:o,height:r}))},resize:function(t){var i,s,n,a,o=e(this).resizable("instance"),r=o.options,h=o.containerOffset,l=o.position,u=o._aspectRatio||t.shiftKey,d={top:0,left:0},c=o.containerElement,p=!0;c[0]!==document&&/static/.test(c.css("position"))&&(d=h),l.left<(o._helper?h.left:0)&&(o.size.width=o.size.width+(o._helper?o.position.left-h.left:o.position.left-d.left),u&&(o.size.height=o.size.width/o.aspectRatio,p=!1),o.position.left=r.helper?h.left:0),l.top<(o._helper?h.top:0)&&(o.size.height=o.size.height+(o._helper?o.position.top-h.top:o.position.top),u&&(o.size.width=o.size.height*o.aspectRatio,p=!1),o.position.top=o._helper?h.top:0),n=o.containerElement.get(0)===o.element.parent().get(0),a=/relative|absolute/.test(o.containerElement.css("position")),n&&a?(o.offset.left=o.parentData.left+o.position.left,o.offset.top=o.parentData.top+o.position.top):(o.offset.left=o.element.offset().left,o.offset.top=o.element.offset().top),i=Math.abs(o.sizeDiff.width+(o._helper?o.offset.left-d.left:o.offset.left-h.left)),s=Math.abs(o.sizeDiff.height+(o._helper?o.offset.top-d.top:o.offset.top-h.top)),i+o.size.width>=o.parentData.width&&(o.size.width=o.parentData.width-i,u&&(o.size.height=o.size.width/o.aspectRatio,p=!1)),s+o.size.height>=o.parentData.height&&(o.size.height=o.parentData.height-s,u&&(o.size.width=o.size.height*o.aspectRatio,p=!1)),p||(o.position.left=o.prevPosition.left,o.position.top=o.prevPosition.top,o.size.width=o.prevSize.width,o.size.height=o.prevSize.height)},stop:function(){var t=e(this).resizable("instance"),i=t.options,s=t.containerOffset,n=t.containerPosition,a=t.containerElement,o=e(t.helper),r=o.offset(),h=o.outerWidth()-t.sizeDiff.width,l=o.outerHeight()-t.sizeDiff.height;t._helper&&!i.animate&&/relative/.test(a.css("position"))&&e(this).css({left:r.left-n.left-s.left,width:h,height:l}),t._helper&&!i.animate&&/static/.test(a.css("position"))&&e(this).css({left:r.left-n.left-s.left,width:h,height:l})}}),e.ui.plugin.add("resizable","alsoResize",{start:function(){var t=e(this).resizable("instance"),i=t.options;e(i.alsoResize).each(function(){var t=e(this);t.data("ui-resizable-alsoresize",{width:parseInt(t.width(),10),height:parseInt(t.height(),10),left:parseInt(t.css("left"),10),top:parseInt(t.css("top"),10)})})},resize:function(t,i){var s=e(this).resizable("instance"),n=s.options,a=s.originalSize,o=s.originalPosition,r={height:s.size.height-a.height||0,width:s.size.width-a.width||0,top:s.position.top-o.top||0,left:s.position.left-o.left||0};e(n.alsoResize).each(function(){var t=e(this),s=e(this).data("ui-resizable-alsoresize"),n={},a=t.parents(i.originalElement[0]).length?["width","height"]:["width","height","top","left"];e.each(a,function(e,t){var i=(s[t]||0)+(r[t]||0);i&&i>=0&&(n[t]=i||null)}),t.css(n)})},stop:function(){e(this).removeData("resizable-alsoresize")}}),e.ui.plugin.add("resizable","ghost",{start:function(){var t=e(this).resizable("instance"),i=t.options,s=t.size;t.ghost=t.originalElement.clone(),t.ghost.css({opacity:.25,display:"block",position:"relative",height:s.height,width:s.width,margin:0,left:0,top:0}).addClass("ui-resizable-ghost").addClass("string"==typeof i.ghost?i.ghost:""),t.ghost.appendTo(t.helper)},resize:function(){var t=e(this).resizable("instance");t.ghost&&t.ghost.css({position:"relative",height:t.size.height,width:t.size.width})},stop:function(){var t=e(this).resizable("instance");t.ghost&&t.helper&&t.helper.get(0).removeChild(t.ghost.get(0))}}),e.ui.plugin.add("resizable","grid",{resize:function(){var t,i=e(this).resizable("instance"),s=i.options,n=i.size,a=i.originalSize,o=i.originalPosition,r=i.axis,h="number"==typeof s.grid?[s.grid,s.grid]:s.grid,l=h[0]||1,u=h[1]||1,d=Math.round((n.width-a.width)/l)*l,c=Math.round((n.height-a.height)/u)*u,p=a.width+d,f=a.height+c,m=s.maxWidth&&p>s.maxWidth,g=s.maxHeight&&f>s.maxHeight,v=s.minWidth&&s.minWidth>p,y=s.minHeight&&s.minHeight>f;s.grid=h,v&&(p+=l),y&&(f+=u),m&&(p-=l),g&&(f-=u),/^(se|s|e)$/.test(r)?(i.size.width=p,i.size.height=f):/^(ne)$/.test(r)?(i.size.width=p,i.size.height=f,i.position.top=o.top-c):/^(sw)$/.test(r)?(i.size.width=p,i.size.height=f,i.position.left=o.left-d):((0>=f-u||0>=p-l)&&(t=i._getPaddingPlusBorderDimensions(this)),f-u>0?(i.size.height=f,i.position.top=o.top-c):(f=u-t.height,i.size.height=f,i.position.top=o.top+a.height-f),p-l>0?(i.size.width=p,i.position.left=o.left-d):(p=l-t.width,i.size.width=p,i.position.left=o.left+a.width-p))}}),e.ui.resizable,e.widget("ui.dialog",{version:"1.11.4",options:{appendTo:"body",autoOpen:!0,buttons:[],closeOnEscape:!0,closeText:"Close",dialogClass:"",draggable:!0,hide:null,height:"auto",maxHeight:null,maxWidth:null,minHeight:150,minWidth:150,modal:!1,position:{my:"center",at:"center",of:window,collision:"fit",using:function(t){var i=e(this).css(t).offset().top;0>i&&e(this).css("top",t.top-i)}},resizable:!0,show:null,title:null,width:300,beforeClose:null,close:null,drag:null,dragStart:null,dragStop:null,focus:null,open:null,resize:null,resizeStart:null,resizeStop:null},sizeRelatedOptions:{buttons:!0,height:!0,maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0,width:!0},resizableRelatedOptions:{maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0},_create:function(){this.originalCss={display:this.element[0].style.display,width:this.element[0].style.width,minHeight:this.element[0].style.minHeight,maxHeight:this.element[0].style.maxHeight,height:this.element[0].style.height},this.originalPosition={parent:this.element.parent(),index:this.element.parent().children().index(this.element)},this.originalTitle=this.element.attr("title"),this.options.title=this.options.title||this.originalTitle,this._createWrapper(),this.element.show().removeAttr("title").addClass("ui-dialog-content ui-widget-content").appendTo(this.uiDialog),this._createTitlebar(),this._createButtonPane(),this.options.draggable&&e.fn.draggable&&this._makeDraggable(),this.options.resizable&&e.fn.resizable&&this._makeResizable(),this._isOpen=!1,this._trackFocus()},_init:function(){this.options.autoOpen&&this.open()},_appendTo:function(){var t=this.options.appendTo;return t&&(t.jquery||t.nodeType)?e(t):this.document.find(t||"body").eq(0)},_destroy:function(){var e,t=this.originalPosition;this._untrackInstance(),this._destroyOverlay(),this.element.removeUniqueId().removeClass("ui-dialog-content ui-widget-content").css(this.originalCss).detach(),this.uiDialog.stop(!0,!0).remove(),this.originalTitle&&this.element.attr("title",this.originalTitle),e=t.parent.children().eq(t.index),e.length&&e[0]!==this.element[0]?e.before(this.element):t.parent.append(this.element)},widget:function(){return this.uiDialog},disable:e.noop,enable:e.noop,close:function(t){var i,s=this;if(this._isOpen&&this._trigger("beforeClose",t)!==!1){if(this._isOpen=!1,this._focusedElement=null,this._destroyOverlay(),this._untrackInstance(),!this.opener.filter(":focusable").focus().length)try{i=this.document[0].activeElement,i&&"body"!==i.nodeName.toLowerCase()&&e(i).blur()}catch(n){}this._hide(this.uiDialog,this.options.hide,function(){s._trigger("close",t)})}},isOpen:function(){return this._isOpen},moveToTop:function(){this._moveToTop()},_moveToTop:function(t,i){var s=!1,n=this.uiDialog.siblings(".ui-front:visible").map(function(){return+e(this).css("z-index")}).get(),a=Math.max.apply(null,n);return a>=+this.uiDialog.css("z-index")&&(this.uiDialog.css("z-index",a+1),s=!0),s&&!i&&this._trigger("focus",t),s},open:function(){var t=this;return this._isOpen?(this._moveToTop()&&this._focusTabbable(),void 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"+this.options.dialogClass).hide().attr({tabIndex:-1,role:"dialog"}).appendTo(this._appendTo()),this._on(this.uiDialog,{keydown:function(t){if(this.options.closeOnEscape&&!t.isDefaultPrevented()&&t.keyCode&&t.keyCode===e.ui.keyCode.ESCAPE)return t.preventDefault(),this.close(t),void 0;
if(t.keyCode===e.ui.keyCode.TAB&&!t.isDefaultPrevented()){var i=this.uiDialog.find(":tabbable"),s=i.filter(":first"),n=i.filter(":last");t.target!==n[0]&&t.target!==this.uiDialog[0]||t.shiftKey?t.target!==s[0]&&t.target!==this.uiDialog[0]||!t.shiftKey||(this._delay(function(){n.focus()}),t.preventDefault()):(this._delay(function(){s.focus()}),t.preventDefault())}},mousedown:function(e){this._moveToTop(e)&&this._focusTabbable()}}),this.element.find("[aria-describedby]").length||this.uiDialog.attr({"aria-describedby":this.element.uniqueId().attr("id")})},_createTitlebar:function(){var t;this.uiDialogTitlebar=e("<div>").addClass("ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix").prependTo(this.uiDialog),this._on(this.uiDialogTitlebar,{mousedown:function(t){e(t.target).closest(".ui-dialog-titlebar-close")||this.uiDialog.focus()}}),this.uiDialogTitlebarClose=e("<button type='button'></button>").button({label:this.options.closeText,icons:{primary:"ui-icon-closethick"},text:!1}).addClass("ui-dialog-titlebar-close").appendTo(this.uiDialogTitlebar),this._on(this.uiDialogTitlebarClose,{click:function(e){e.preventDefault(),this.close(e)}}),t=e("<span>").uniqueId().addClass("ui-dialog-title").prependTo(this.uiDialogTitlebar),this._title(t),this.uiDialog.attr({"aria-labelledby":t.attr("id")})},_title:function(e){this.options.title||e.html("&#160;"),e.text(this.options.title)},_createButtonPane:function(){this.uiDialogButtonPane=e("<div>").addClass("ui-dialog-buttonpane ui-widget-content ui-helper-clearfix"),this.uiButtonSet=e("<div>").addClass("ui-dialog-buttonset").appendTo(this.uiDialogButtonPane),this._createButtons()},_createButtons:function(){var t=this,i=this.options.buttons;return this.uiDialogButtonPane.remove(),this.uiButtonSet.empty(),e.isEmptyObject(i)||e.isArray(i)&&!i.length?(this.uiDialog.removeClass("ui-dialog-buttons"),void 0):(e.each(i,function(i,s){var n,a;s=e.isFunction(s)?{click:s,text:i}:s,s=e.extend({type:"button"},s),n=s.click,s.click=function(){n.apply(t.element[0],arguments)},a={icons:s.icons,text:s.showText},delete s.icons,delete s.showText,e("<button></button>",s).button(a).appendTo(t.uiButtonSet)}),this.uiDialog.addClass("ui-dialog-buttons"),this.uiDialogButtonPane.appendTo(this.uiDialog),void 0)},_makeDraggable:function(){function t(e){return{position:e.position,offset:e.offset}}var i=this,s=this.options;this.uiDialog.draggable({cancel:".ui-dialog-content, .ui-dialog-titlebar-close",handle:".ui-dialog-titlebar",containment:"document",start:function(s,n){e(this).addClass("ui-dialog-dragging"),i._blockFrames(),i._trigger("dragStart",s,t(n))},drag:function(e,s){i._trigger("drag",e,t(s))},stop:function(n,a){var o=a.offset.left-i.document.scrollLeft(),r=a.offset.top-i.document.scrollTop();s.position={my:"left top",at:"left"+(o>=0?"+":"")+o+" "+"top"+(r>=0?"+":"")+r,of:i.window},e(this).removeClass("ui-dialog-dragging"),i._unblockFrames(),i._trigger("dragStop",n,t(a))}})},_makeResizable:function(){function t(e){return{originalPosition:e.originalPosition,originalSize:e.originalSize,position:e.position,size:e.size}}var i=this,s=this.options,n=s.resizable,a=this.uiDialog.css("position"),o="string"==typeof n?n:"n,e,s,w,se,sw,ne,nw";this.uiDialog.resizable({cancel:".ui-dialog-content",containment:"document",alsoResize:this.element,maxWidth:s.maxWidth,maxHeight:s.maxHeight,minWidth:s.minWidth,minHeight:this._minHeight(),handles:o,start:function(s,n){e(this).addClass("ui-dialog-resizing"),i._blockFrames(),i._trigger("resizeStart",s,t(n))},resize:function(e,s){i._trigger("resize",e,t(s))},stop:function(n,a){var o=i.uiDialog.offset(),r=o.left-i.document.scrollLeft(),h=o.top-i.document.scrollTop();s.height=i.uiDialog.height(),s.width=i.uiDialog.width(),s.position={my:"left top",at:"left"+(r>=0?"+":"")+r+" "+"top"+(h>=0?"+":"")+h,of:i.window},e(this).removeClass("ui-dialog-resizing"),i._unblockFrames(),i._trigger("resizeStop",n,t(a))}}).css("position",a)},_trackFocus:function(){this._on(this.widget(),{focusin:function(t){this._makeFocusTarget(),this._focusedElement=e(t.target)}})},_makeFocusTarget:function(){this._untrackInstance(),this._trackingInstances().unshift(this)},_untrackInstance:function(){var t=this._trackingInstances(),i=e.inArray(this,t);-1!==i&&t.splice(i,1)},_trackingInstances:function(){var e=this.document.data("ui-dialog-instances");return e||(e=[],this.document.data("ui-dialog-instances",e)),e},_minHeight:function(){var e=this.options;return"auto"===e.height?e.minHeight:Math.min(e.minHeight,e.height)},_position:function(){var e=this.uiDialog.is(":visible");e||this.uiDialog.show(),this.uiDialog.position(this.options.position),e||this.uiDialog.hide()},_setOptions:function(t){var i=this,s=!1,n={};e.each(t,function(e,t){i._setOption(e,t),e in i.sizeRelatedOptions&&(s=!0),e in i.resizableRelatedOptions&&(n[e]=t)}),s&&(this._size(),this._position()),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option",n)},_setOption:function(e,t){var i,s,n=this.uiDialog;"dialogClass"===e&&n.removeClass(this.options.dialogClass).addClass(t),"disabled"!==e&&(this._super(e,t),"appendTo"===e&&this.uiDialog.appendTo(this._appendTo()),"buttons"===e&&this._createButtons(),"closeText"===e&&this.uiDialogTitlebarClose.button({label:""+t}),"draggable"===e&&(i=n.is(":data(ui-draggable)"),i&&!t&&n.draggable("destroy"),!i&&t&&this._makeDraggable()),"position"===e&&this._position(),"resizable"===e&&(s=n.is(":data(ui-resizable)"),s&&!t&&n.resizable("destroy"),s&&"string"==typeof t&&n.resizable("option","handles",t),s||t===!1||this._makeResizable()),"title"===e&&this._title(this.uiDialogTitlebar.find(".ui-dialog-title")))},_size:function(){var e,t,i,s=this.options;this.element.show().css({width:"auto",minHeight:0,maxHeight:"none",height:0}),s.minWidth>s.width&&(s.width=s.minWidth),e=this.uiDialog.css({height:"auto",width:s.width}).outerHeight(),t=Math.max(0,s.minHeight-e),i="number"==typeof s.maxHeight?Math.max(0,s.maxHeight-e):"none","auto"===s.height?this.element.css({minHeight:t,maxHeight:i,height:"auto"}):this.element.height(Math.max(0,s.height-e)),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option","minHeight",this._minHeight())},_blockFrames:function(){this.iframeBlocks=this.document.find("iframe").map(function(){var t=e(this);return e("<div>").css({position:"absolute",width:t.outerWidth(),height:t.outerHeight()}).appendTo(t.parent()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_allowInteraction:function(t){return e(t.target).closest(".ui-dialog").length?!0:!!e(t.target).closest(".ui-datepicker").length},_createOverlay:function(){if(this.options.modal){var t=!0;this._delay(function(){t=!1}),this.document.data("ui-dialog-overlays")||this._on(this.document,{focusin:function(e){t||this._allowInteraction(e)||(e.preventDefault(),this._trackingInstances()[0]._focusTabbable())}}),this.overlay=e("<div>").addClass("ui-widget-overlay ui-front").appendTo(this._appendTo()),this._on(this.overlay,{mousedown:"_keepFocus"}),this.document.data("ui-dialog-overlays",(this.document.data("ui-dialog-overlays")||0)+1)}},_destroyOverlay:function(){if(this.options.modal&&this.overlay){var e=this.document.data("ui-dialog-overlays")-1;e?this.document.data("ui-dialog-overlays",e):this.document.unbind("focusin").removeData("ui-dialog-overlays"),this.overlay.remove(),this.overlay=null}}}),e.widget("ui.droppable",{version:"1.11.4",widgetEventPrefix:"drop",options:{accept:"*",activeClass:!1,addClasses:!0,greedy:!1,hoverClass:!1,scope:"default",tolerance:"intersect",activate:null,deactivate:null,drop:null,out:null,over:null},_create:function(){var t,i=this.options,s=i.accept;this.isover=!1,this.isout=!0,this.accept=e.isFunction(s)?s:function(e){return e.is(s)},this.proportions=function(){return arguments.length?(t=arguments[0],void 0):t?t:t={width:this.element[0].offsetWidth,height:this.element[0].offsetHeight}},this._addToManager(i.scope),i.addClasses&&this.element.addClass("ui-droppable")},_addToManager:function(t){e.ui.ddmanager.droppables[t]=e.ui.ddmanager.droppables[t]||[],e.ui.ddmanager.droppables[t].push(this)},_splice:function(e){for(var t=0;e.length>t;t++)e[t]===this&&e.splice(t,1)},_destroy:function(){var t=e.ui.ddmanager.droppables[this.options.scope];this._splice(t),this.element.removeClass("ui-droppable ui-droppable-disabled")},_setOption:function(t,i){if("accept"===t)this.accept=e.isFunction(i)?i:function(e){return e.is(i)};else if("scope"===t){var s=e.ui.ddmanager.droppables[this.options.scope];this._splice(s),this._addToManager(i)}this._super(t,i)},_activate:function(t){var i=e.ui.ddmanager.current;this.options.activeClass&&this.element.addClass(this.options.activeClass),i&&this._trigger("activate",t,this.ui(i))},_deactivate:function(t){var i=e.ui.ddmanager.current;this.options.activeClass&&this.element.removeClass(this.options.activeClass),i&&this._trigger("deactivate",t,this.ui(i))},_over:function(t){var i=e.ui.ddmanager.current;i&&(i.currentItem||i.element)[0]!==this.element[0]&&this.accept.call(this.element[0],i.currentItem||i.element)&&(this.options.hoverClass&&this.element.addClass(this.options.hoverClass),this._trigger("over",t,this.ui(i)))},_out:function(t){var i=e.ui.ddmanager.current;i&&(i.currentItem||i.element)[0]!==this.element[0]&&this.accept.call(this.element[0],i.currentItem||i.element)&&(this.options.hoverClass&&this.element.removeClass(this.options.hoverClass),this._trigger("out",t,this.ui(i)))},_drop:function(t,i){var s=i||e.ui.ddmanager.current,n=!1;return s&&(s.currentItem||s.element)[0]!==this.element[0]?(this.element.find(":data(ui-droppable)").not(".ui-draggable-dragging").each(function(){var i=e(this).droppable("instance");return i.options.greedy&&!i.options.disabled&&i.options.scope===s.options.scope&&i.accept.call(i.element[0],s.currentItem||s.element)&&e.ui.intersect(s,e.extend(i,{offset:i.element.offset()}),i.options.tolerance,t)?(n=!0,!1):void 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e(n.pageY,u,i.proportions().height)&&e(n.pageX,l,i.proportions().width);case"touch":return(o>=u&&c>=o||h>=u&&c>=h||u>o&&h>c)&&(a>=l&&d>=a||r>=l&&d>=r||l>a&&r>d);default:return!1}}}(),e.ui.ddmanager={current:null,droppables:{"default":[]},prepareOffsets:function(t,i){var s,n,a=e.ui.ddmanager.droppables[t.options.scope]||[],o=i?i.type:null,r=(t.currentItem||t.element).find(":data(ui-droppable)").addBack();e:for(s=0;a.length>s;s++)if(!(a[s].options.disabled||t&&!a[s].accept.call(a[s].element[0],t.currentItem||t.element))){for(n=0;r.length>n;n++)if(r[n]===a[s].element[0]){a[s].proportions().height=0;continue e}a[s].visible="none"!==a[s].element.css("display"),a[s].visible&&("mousedown"===o&&a[s]._activate.call(a[s],i),a[s].offset=a[s].element.offset(),a[s].proportions({width:a[s].element[0].offsetWidth,height:a[s].element[0].offsetHeight}))}},drop:function(t,i){var s=!1;return e.each((e.ui.ddmanager.droppables[t.options.scope]||[]).slice(),function(){this.options&&(!this.options.disabled&&this.visible&&e.ui.intersect(t,this,this.options.tolerance,i)&&(s=this._drop.call(this,i)||s),!this.options.disabled&&this.visible&&this.accept.call(this.element[0],t.currentItem||t.element)&&(this.isout=!0,this.isover=!1,this._deactivate.call(this,i)))}),s},dragStart:function(t,i){t.element.parentsUntil("body").bind("scroll.droppable",function(){t.options.refreshPositions||e.ui.ddmanager.prepareOffsets(t,i)})},drag:function(t,i){t.options.refreshPositions&&e.ui.ddmanager.prepareOffsets(t,i),e.each(e.ui.ddmanager.droppables[t.options.scope]||[],function(){if(!this.options.disabled&&!this.greedyChild&&this.visible){var s,n,a,o=e.ui.intersect(t,this,this.options.tolerance,i),r=!o&&this.isover?"isout":o&&!this.isover?"isover":null;r&&(this.options.greedy&&(n=this.options.scope,a=this.element.parents(":data(ui-droppable)").filter(function(){return 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this.menuItems.eq(this.element[0].selectedIndex)},_toggle:function(e){this[this.isOpen?"close":"open"](e)},_setSelection:function(){var e;this.range&&(window.getSelection?(e=window.getSelection(),e.removeAllRanges(),e.addRange(this.range)):this.range.select(),this.button.focus())},_documentClick:{mousedown:function(t){this.isOpen&&(e(t.target).closest(".ui-selectmenu-menu, #"+this.ids.button).length||this.close(t))}},_buttonEvents:{mousedown:function(){var e;window.getSelection?(e=window.getSelection(),e.rangeCount&&(this.range=e.getRangeAt(0))):this.range=document.selection.createRange()},click:function(e){this._setSelection(),this._toggle(e)},keydown:function(t){var i=!0;switch(t.keyCode){case e.ui.keyCode.TAB:case e.ui.keyCode.ESCAPE:this.close(t),i=!1;break;case e.ui.keyCode.ENTER:this.isOpen&&this._selectFocusedItem(t);break;case e.ui.keyCode.UP:t.altKey?this._toggle(t):this._move("prev",t);break;case e.ui.keyCode.DOWN:t.altKey?this._toggle(t):this._move("next",t);break;case e.ui.keyCode.SPACE:this.isOpen?this._selectFocusedItem(t):this._toggle(t);break;case e.ui.keyCode.LEFT:this._move("prev",t);break;case e.ui.keyCode.RIGHT:this._move("next",t);break;case e.ui.keyCode.HOME:case e.ui.keyCode.PAGE_UP:this._move("first",t);break;case e.ui.keyCode.END:case e.ui.keyCode.PAGE_DOWN:this._move("last",t);break;default:this.menu.trigger(t),i=!1}i&&t.preventDefault()}},_selectFocusedItem:function(e){var t=this.menuItems.eq(this.focusIndex);t.hasClass("ui-state-disabled")||this._select(t.data("ui-selectmenu-item"),e)},_select:function(e,t){var i=this.element[0].selectedIndex;this.element[0].selectedIndex=e.index,this._setText(this.buttonText,e.label),this._setAria(e),this._trigger("select",t,{item:e}),e.index!==i&&this._trigger("change",t,{item:e}),this.close(t)},_setAria:function(e){var t=this.menuItems.eq(e.index).attr("id");this.button.attr({"aria-labelledby":t,"aria-activedescendant":t}),this.menu.attr("aria-activedescendant",t)},_setOption:function(e,t){"icons"===e&&this.button.find("span.ui-icon").removeClass(this.options.icons.button).addClass(t.button),this._super(e,t),"appendTo"===e&&this.menuWrap.appendTo(this._appendTo()),"disabled"===e&&(this.menuInstance.option("disabled",t),this.button.toggleClass("ui-state-disabled",t).attr("aria-disabled",t),this.element.prop("disabled",t),t?(this.button.attr("tabindex",-1),this.close()):this.button.attr("tabindex",0)),"width"===e&&this._resizeButton()},_appendTo:function(){var t=this.options.appendTo;return t&&(t=t.jquery||t.nodeType?e(t):this.document.find(t).eq(0)),t&&t[0]||(t=this.element.closest(".ui-front")),t.length||(t=this.document[0].body),t},_toggleAttr:function(){this.button.toggleClass("ui-corner-top",this.isOpen).toggleClass("ui-corner-all",!this.isOpen).attr("aria-expanded",this.isOpen),this.menuWrap.toggleClass("ui-selectmenu-open",this.isOpen),this.menu.attr("aria-hidden",!this.isOpen)},_resizeButton:function(){var e=this.options.width;e||(e=this.element.show().outerWidth(),this.element.hide()),this.button.outerWidth(e)},_resizeMenu:function(){this.menu.outerWidth(Math.max(this.button.outerWidth(),this.menu.width("").outerWidth()+1))},_getCreateOptions:function(){return{disabled:this.element.prop("disabled")}},_parseOptions:function(t){var i=[];t.each(function(t,s){var n=e(s),a=n.parent("optgroup");i.push({element:n,index:t,value:n.val(),label:n.text(),optgroup:a.attr("label")||"",disabled:a.prop("disabled")||n.prop("disabled")})}),this.items=i},_destroy:function(){this.menuWrap.remove(),this.button.remove(),this.element.show(),this.element.removeUniqueId(),this.label.attr("for",this.ids.element)}}),e.widget("ui.slider",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"slide",options:{animate:!1,distance:0,max:100,min:0,orientation:"horizontal",range:!1,step:1,value:0,values:null,change:null,slide:null,start:null,stop:null},numPages:5,_create:function(){this._keySliding=!1,this._mouseSliding=!1,this._animateOff=!0,this._handleIndex=null,this._detectOrientation(),this._mouseInit(),this._calculateNewMax(),this.element.addClass("ui-slider ui-slider-"+this.orientation+" ui-widget"+" ui-widget-content"+" ui-corner-all"),this._refresh(),this._setOption("disabled",this.options.disabled),this._animateOff=!1},_refresh:function(){this._createRange(),this._createHandles(),this._setupEvents(),this._refreshValue()},_createHandles:function(){var t,i,s=this.options,n=this.element.find(".ui-slider-handle").addClass("ui-state-default ui-corner-all"),a="<span class='ui-slider-handle ui-state-default ui-corner-all' tabindex='0'></span>",o=[];for(i=s.values&&s.values.length||1,n.length>i&&(n.slice(i).remove(),n=n.slice(0,i)),t=n.length;i>t;t++)o.push(a);this.handles=n.add(e(o.join("")).appendTo(this.element)),this.handle=this.handles.eq(0),this.handles.each(function(t){e(this).data("ui-slider-handle-index",t)})},_createRange:function(){var t=this.options,i="";t.range?(t.range===!0&&(t.values?t.values.length&&2!==t.values.length?t.values=[t.values[0],t.values[0]]:e.isArray(t.values)&&(t.values=t.values.slice(0)):t.values=[this._valueMin(),this._valueMin()]),this.range&&this.range.length?this.range.removeClass("ui-slider-range-min ui-slider-range-max").css({left:"",bottom:""}):(this.range=e("<div></div>").appendTo(this.element),i="ui-slider-range ui-widget-header ui-corner-all"),this.range.addClass(i+("min"===t.range||"max"===t.range?" ui-slider-range-"+t.range:""))):(this.range&&this.range.remove(),this.range=null)},_setupEvents:function(){this._off(this.handles),this._on(this.handles,this._handleEvents),this._hoverable(this.handles),this._focusable(this.handles)},_destroy:function(){this.handles.remove(),this.range&&this.range.remove(),this.element.removeClass("ui-slider ui-slider-horizontal ui-slider-vertical ui-widget ui-widget-content ui-corner-all"),this._mouseDestroy()},_mouseCapture:function(t){var i,s,n,a,o,r,h,l,u=this,d=this.options;return d.disabled?!1:(this.elementSize={width:this.element.outerWidth(),height:this.element.outerHeight()},this.elementOffset=this.element.offset(),i={x:t.pageX,y:t.pageY},s=this._normValueFromMouse(i),n=this._valueMax()-this._valueMin()+1,this.handles.each(function(t){var i=Math.abs(s-u.values(t));(n>i||n===i&&(t===u._lastChangedValue||u.values(t)===d.min))&&(n=i,a=e(this),o=t)}),r=this._start(t,o),r===!1?!1:(this._mouseSliding=!0,this._handleIndex=o,a.addClass("ui-state-active").focus(),h=a.offset(),l=!e(t.target).parents().addBack().is(".ui-slider-handle"),this._clickOffset=l?{left:0,top:0}:{left:t.pageX-h.left-a.width()/2,top:t.pageY-h.top-a.height()/2-(parseInt(a.css("borderTopWidth"),10)||0)-(parseInt(a.css("borderBottomWidth"),10)||0)+(parseInt(a.css("marginTop"),10)||0)},this.handles.hasClass("ui-state-hover")||this._slide(t,o,s),this._animateOff=!0,!0))},_mouseStart:function(){return!0},_mouseDrag:function(e){var t={x:e.pageX,y:e.pageY},i=this._normValueFromMouse(t);return this._slide(e,this._handleIndex,i),!1},_mouseStop:function(e){return this.handles.removeClass("ui-state-active"),this._mouseSliding=!1,this._stop(e,this._handleIndex),this._change(e,this._handleIndex),this._handleIndex=null,this._clickOffset=null,this._animateOff=!1,!1},_detectOrientation:function(){this.orientation="vertical"===this.options.orientation?"vertical":"horizontal"},_normValueFromMouse:function(e){var t,i,s,n,a;return"horizontal"===this.orientation?(t=this.elementSize.width,i=e.x-this.elementOffset.left-(this._clickOffset?this._clickOffset.left:0)):(t=this.elementSize.height,i=e.y-this.elementOffset.top-(this._clickOffset?this._clickOffset.top:0)),s=i/t,s>1&&(s=1),0>s&&(s=0),"vertical"===this.orientation&&(s=1-s),n=this._valueMax()-this._valueMin(),a=this._valueMin()+s*n,this._trimAlignValue(a)},_start:function(e,t){var i={handle:this.handles[t],value:this.value()};return this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._trigger("start",e,i)},_slide:function(e,t,i){var s,n,a;this.options.values&&this.options.values.length?(s=this.values(t?0:1),2===this.options.values.length&&this.options.range===!0&&(0===t&&i>s||1===t&&s>i)&&(i=s),i!==this.values(t)&&(n=this.values(),n[t]=i,a=this._trigger("slide",e,{handle:this.handles[t],value:i,values:n}),s=this.values(t?0:1),a!==!1&&this.values(t,i))):i!==this.value()&&(a=this._trigger("slide",e,{handle:this.handles[t],value:i}),a!==!1&&this.value(i))},_stop:function(e,t){var i={handle:this.handles[t],value:this.value()};this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._trigger("stop",e,i)},_change:function(e,t){if(!this._keySliding&&!this._mouseSliding){var i={handle:this.handles[t],value:this.value()};this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._lastChangedValue=t,this._trigger("change",e,i)}},value:function(e){return arguments.length?(this.options.value=this._trimAlignValue(e),this._refreshValue(),this._change(null,0),void 0):this._value()},values:function(t,i){var s,n,a;if(arguments.length>1)return this.options.values[t]=this._trimAlignValue(i),this._refreshValue(),this._change(null,t),void 0;if(!arguments.length)return this._values();if(!e.isArray(arguments[0]))return this.options.values&&this.options.values.length?this._values(t):this.value();for(s=this.options.values,n=arguments[0],a=0;s.length>a;a+=1)s[a]=this._trimAlignValue(n[a]),this._change(null,a);this._refreshValue()},_setOption:function(t,i){var s,n=0;switch("range"===t&&this.options.range===!0&&("min"===i?(this.options.value=this._values(0),this.options.values=null):"max"===i&&(this.options.value=this._values(this.options.values.length-1),this.options.values=null)),e.isArray(this.options.values)&&(n=this.options.values.length),"disabled"===t&&this.element.toggleClass("ui-state-disabled",!!i),this._super(t,i),t){case"orientation":this._detectOrientation(),this.element.removeClass("ui-slider-horizontal ui-slider-vertical").addClass("ui-slider-"+this.orientation),this._refreshValue(),this.handles.css("horizontal"===i?"bottom":"left","");break;case"value":this._animateOff=!0,this._refreshValue(),this._change(null,0),this._animateOff=!1;break;case"values":for(this._animateOff=!0,this._refreshValue(),s=0;n>s;s+=1)this._change(null,s);this._animateOff=!1;break;case"step":case"min":case"max":this._animateOff=!0,this._calculateNewMax(),this._refreshValue(),this._animateOff=!1;break;case"range":this._animateOff=!0,this._refresh(),this._animateOff=!1}},_value:function(){var e=this.options.value;return e=this._trimAlignValue(e)},_values:function(e){var t,i,s;if(arguments.length)return t=this.options.values[e],t=this._trimAlignValue(t);if(this.options.values&&this.options.values.length){for(i=this.options.values.slice(),s=0;i.length>s;s+=1)i[s]=this._trimAlignValue(i[s]);return i}return[]},_trimAlignValue:function(e){if(this._valueMin()>=e)return this._valueMin();if(e>=this._valueMax())return this._valueMax();var t=this.options.step>0?this.options.step:1,i=(e-this._valueMin())%t,s=e-i;return 2*Math.abs(i)>=t&&(s+=i>0?t:-t),parseFloat(s.toFixed(5))},_calculateNewMax:function(){var e=this.options.max,t=this._valueMin(),i=this.options.step,s=Math.floor(+(e-t).toFixed(this._precision())/i)*i;e=s+t,this.max=parseFloat(e.toFixed(this._precision()))},_precision:function(){var e=this._precisionOf(this.options.step);return null!==this.options.min&&(e=Math.max(e,this._precisionOf(this.options.min))),e},_precisionOf:function(e){var t=""+e,i=t.indexOf(".");return-1===i?0:t.length-i-1},_valueMin:function(){return this.options.min},_valueMax:function(){return this.max},_refreshValue:function(){var t,i,s,n,a,o=this.options.range,r=this.options,h=this,l=this._animateOff?!1:r.animate,u={};this.options.values&&this.options.values.length?this.handles.each(function(s){i=100*((h.values(s)-h._valueMin())/(h._valueMax()-h._valueMin())),u["horizontal"===h.orientation?"left":"bottom"]=i+"%",e(this).stop(1,1)[l?"animate":"css"](u,r.animate),h.options.range===!0&&("horizontal"===h.orientation?(0===s&&h.range.stop(1,1)[l?"animate":"css"]({left:i+"%"},r.animate),1===s&&h.range[l?"animate":"css"]({width:i-t+"%"},{queue:!1,duration:r.animate})):(0===s&&h.range.stop(1,1)[l?"animate":"css"]({bottom:i+"%"},r.animate),1===s&&h.range[l?"animate":"css"]({height:i-t+"%"},{queue:!1,duration:r.animate}))),t=i}):(s=this.value(),n=this._valueMin(),a=this._valueMax(),i=a!==n?100*((s-n)/(a-n)):0,u["horizontal"===this.orientation?"left":"bottom"]=i+"%",this.handle.stop(1,1)[l?"animate":"css"](u,r.animate),"min"===o&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:i+"%"},r.animate),"max"===o&&"horizontal"===this.orientation&&this.range[l?"animate":"css"]({width:100-i+"%"},{queue:!1,duration:r.animate}),"min"===o&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:i+"%"},r.animate),"max"===o&&"vertical"===this.orientation&&this.range[l?"animate":"css"]({height:100-i+"%"},{queue:!1,duration:r.animate}))},_handleEvents:{keydown:function(t){var i,s,n,a,o=e(t.target).data("ui-slider-handle-index");switch(t.keyCode){case e.ui.keyCode.HOME:case e.ui.keyCode.END:case e.ui.keyCode.PAGE_UP:case e.ui.keyCode.PAGE_DOWN:case e.ui.keyCode.UP:case e.ui.keyCode.RIGHT:case e.ui.keyCode.DOWN:case e.ui.keyCode.LEFT:if(t.preventDefault(),!this._keySliding&&(this._keySliding=!0,e(t.target).addClass("ui-state-active"),i=this._start(t,o),i===!1))return}switch(a=this.options.step,s=n=this.options.values&&this.options.values.length?this.values(o):this.value(),t.keyCode){case e.ui.keyCode.HOME:n=this._valueMin();break;case e.ui.keyCode.END:n=this._valueMax();break;case e.ui.keyCode.PAGE_UP:n=this._trimAlignValue(s+(this._valueMax()-this._valueMin())/this.numPages);break;case e.ui.keyCode.PAGE_DOWN:n=this._trimAlignValue(s-(this._valueMax()-this._valueMin())/this.numPages);break;case e.ui.keyCode.UP:case e.ui.keyCode.RIGHT:if(s===this._valueMax())return;n=this._trimAlignValue(s+a);break;case e.ui.keyCode.DOWN:case e.ui.keyCode.LEFT:if(s===this._valueMin())return;n=this._trimAlignValue(s-a)}this._slide(t,o,n)},keyup:function(t){var i=e(t.target).data("ui-slider-handle-index");this._keySliding&&(this._keySliding=!1,this._stop(t,i),this._change(t,i),e(t.target).removeClass("ui-state-active"))}}}),e.widget("ui.sortable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"sort",ready:!1,options:{appendTo:"parent",axis:!1,connectWith:!1,containment:!1,cursor:"auto",cursorAt:!1,dropOnEmpty:!0,forcePlaceholderSize:!1,forceHelperSize:!1,grid:!1,handle:!1,helper:"original",items:"> *",opacity:!1,placeholder:!1,revert:!1,scroll:!0,scrollSensitivity:20,scrollSpeed:20,scope:"default",tolerance:"intersect",zIndex:1e3,activate:null,beforeStop:null,change:null,deactivate:null,out:null,over:null,receive:null,remove:null,sort:null,start:null,stop:null,update:null},_isOverAxis:function(e,t,i){return e>=t&&t+i>e},_isFloating:function(e){return/left|right/.test(e.css("float"))||/inline|table-cell/.test(e.css("display"))},_create:function(){this.containerCache={},this.element.addClass("ui-sortable"),this.refresh(),this.offset=this.element.offset(),this._mouseInit(),this._setHandleClassName(),this.ready=!0},_setOption:function(e,t){this._super(e,t),"handle"===e&&this._setHandleClassName()},_setHandleClassName:function(){this.element.find(".ui-sortable-handle").removeClass("ui-sortable-handle"),e.each(this.items,function(){(this.instance.options.handle?this.item.find(this.instance.options.handle):this.item).addClass("ui-sortable-handle")})},_destroy:function(){this.element.removeClass("ui-sortable ui-sortable-disabled").find(".ui-sortable-handle").removeClass("ui-sortable-handle"),this._mouseDestroy();for(var e=this.items.length-1;e>=0;e--)this.items[e].item.removeData(this.widgetName+"-item");return this},_mouseCapture:function(t,i){var s=null,n=!1,a=this;return this.reverting?!1:this.options.disabled||"static"===this.options.type?!1:(this._refreshItems(t),e(t.target).parents().each(function(){return e.data(this,a.widgetName+"-item")===a?(s=e(this),!1):void 0}),e.data(t.target,a.widgetName+"-item")===a&&(s=e(t.target)),s?!this.options.handle||i||(e(this.options.handle,s).find("*").addBack().each(function(){this===t.target&&(n=!0)}),n)?(this.currentItem=s,this._removeCurrentsFromItems(),!0):!1:!1)},_mouseStart:function(t,i,s){var n,a,o=this.options;if(this.currentContainer=this,this.refreshPositions(),this.helper=this._createHelper(t),this._cacheHelperProportions(),this._cacheMargins(),this.scrollParent=this.helper.scrollParent(),this.offset=this.currentItem.offset(),this.offset={top:this.offset.top-this.margins.top,left:this.offset.left-this.margins.left},e.extend(this.offset,{click:{left:t.pageX-this.offset.left,top:t.pageY-this.offset.top},parent:this._getParentOffset(),relative:this._getRelativeOffset()}),this.helper.css("position","absolute"),this.cssPosition=this.helper.css("position"),this.originalPosition=this._generatePosition(t),this.originalPageX=t.pageX,this.originalPageY=t.pageY,o.cursorAt&&this._adjustOffsetFromHelper(o.cursorAt),this.domPosition={prev:this.currentItem.prev()[0],parent:this.currentItem.parent()[0]},this.helper[0]!==this.currentItem[0]&&this.currentItem.hide(),this._createPlaceholder(),o.containment&&this._setContainment(),o.cursor&&"auto"!==o.cursor&&(a=this.document.find("body"),this.storedCursor=a.css("cursor"),a.css("cursor",o.cursor),this.storedStylesheet=e("<style>*{ cursor: "+o.cursor+" !important; }</style>").appendTo(a)),o.opacity&&(this.helper.css("opacity")&&(this._storedOpacity=this.helper.css("opacity")),this.helper.css("opacity",o.opacity)),o.zIndex&&(this.helper.css("zIndex")&&(this._storedZIndex=this.helper.css("zIndex")),this.helper.css("zIndex",o.zIndex)),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this._trigger("start",t,this._uiHash()),this._preserveHelperProportions||this._cacheHelperProportions(),!s)for(n=this.containers.length-1;n>=0;n--)this.containers[n]._trigger("activate",t,this._uiHash(this));
return e.ui.ddmanager&&(e.ui.ddmanager.current=this),e.ui.ddmanager&&!o.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this.dragging=!0,this.helper.addClass("ui-sortable-helper"),this._mouseDrag(t),!0},_mouseDrag:function(t){var i,s,n,a,o=this.options,r=!1;for(this.position=this._generatePosition(t),this.positionAbs=this._convertPositionTo("absolute"),this.lastPositionAbs||(this.lastPositionAbs=this.positionAbs),this.options.scroll&&(this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName?(this.overflowOffset.top+this.scrollParent[0].offsetHeight-t.pageY<o.scrollSensitivity?this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop+o.scrollSpeed:t.pageY-this.overflowOffset.top<o.scrollSensitivity&&(this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop-o.scrollSpeed),this.overflowOffset.left+this.scrollParent[0].offsetWidth-t.pageX<o.scrollSensitivity?this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft+o.scrollSpeed:t.pageX-this.overflowOffset.left<o.scrollSensitivity&&(this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft-o.scrollSpeed)):(t.pageY-this.document.scrollTop()<o.scrollSensitivity?r=this.document.scrollTop(this.document.scrollTop()-o.scrollSpeed):this.window.height()-(t.pageY-this.document.scrollTop())<o.scrollSensitivity&&(r=this.document.scrollTop(this.document.scrollTop()+o.scrollSpeed)),t.pageX-this.document.scrollLeft()<o.scrollSensitivity?r=this.document.scrollLeft(this.document.scrollLeft()-o.scrollSpeed):this.window.width()-(t.pageX-this.document.scrollLeft())<o.scrollSensitivity&&(r=this.document.scrollLeft(this.document.scrollLeft()+o.scrollSpeed))),r!==!1&&e.ui.ddmanager&&!o.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t)),this.positionAbs=this._convertPositionTo("absolute"),this.options.axis&&"y"===this.options.axis||(this.helper[0].style.left=this.position.left+"px"),this.options.axis&&"x"===this.options.axis||(this.helper[0].style.top=this.position.top+"px"),i=this.items.length-1;i>=0;i--)if(s=this.items[i],n=s.item[0],a=this._intersectsWithPointer(s),a&&s.instance===this.currentContainer&&n!==this.currentItem[0]&&this.placeholder[1===a?"next":"prev"]()[0]!==n&&!e.contains(this.placeholder[0],n)&&("semi-dynamic"===this.options.type?!e.contains(this.element[0],n):!0)){if(this.direction=1===a?"down":"up","pointer"!==this.options.tolerance&&!this._intersectsWithSides(s))break;this._rearrange(t,s),this._trigger("change",t,this._uiHash());break}return this._contactContainers(t),e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),this._trigger("sort",t,this._uiHash()),this.lastPositionAbs=this.positionAbs,!1},_mouseStop:function(t,i){if(t){if(e.ui.ddmanager&&!this.options.dropBehaviour&&e.ui.ddmanager.drop(this,t),this.options.revert){var s=this,n=this.placeholder.offset(),a=this.options.axis,o={};a&&"x"!==a||(o.left=n.left-this.offset.parent.left-this.margins.left+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollLeft)),a&&"y"!==a||(o.top=n.top-this.offset.parent.top-this.margins.top+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollTop)),this.reverting=!0,e(this.helper).animate(o,parseInt(this.options.revert,10)||500,function(){s._clear(t)})}else this._clear(t,i);return!1}},cancel:function(){if(this.dragging){this._mouseUp({target:null}),"original"===this.options.helper?this.currentItem.css(this._storedCSS).removeClass("ui-sortable-helper"):this.currentItem.show();for(var t=this.containers.length-1;t>=0;t--)this.containers[t]._trigger("deactivate",null,this._uiHash(this)),this.containers[t].containerCache.over&&(this.containers[t]._trigger("out",null,this._uiHash(this)),this.containers[t].containerCache.over=0)}return this.placeholder&&(this.placeholder[0].parentNode&&this.placeholder[0].parentNode.removeChild(this.placeholder[0]),"original"!==this.options.helper&&this.helper&&this.helper[0].parentNode&&this.helper.remove(),e.extend(this,{helper:null,dragging:!1,reverting:!1,_noFinalSort:null}),this.domPosition.prev?e(this.domPosition.prev).after(this.currentItem):e(this.domPosition.parent).prepend(this.currentItem)),this},serialize:function(t){var i=this._getItemsAsjQuery(t&&t.connected),s=[];return t=t||{},e(i).each(function(){var i=(e(t.item||this).attr(t.attribute||"id")||"").match(t.expression||/(.+)[\-=_](.+)/);i&&s.push((t.key||i[1]+"[]")+"="+(t.key&&t.expression?i[1]:i[2]))}),!s.length&&t.key&&s.push(t.key+"="),s.join("&")},toArray:function(t){var i=this._getItemsAsjQuery(t&&t.connected),s=[];return t=t||{},i.each(function(){s.push(e(t.item||this).attr(t.attribute||"id")||"")}),s},_intersectsWith:function(e){var t=this.positionAbs.left,i=t+this.helperProportions.width,s=this.positionAbs.top,n=s+this.helperProportions.height,a=e.left,o=a+e.width,r=e.top,h=r+e.height,l=this.offset.click.top,u=this.offset.click.left,d="x"===this.options.axis||s+l>r&&h>s+l,c="y"===this.options.axis||t+u>a&&o>t+u,p=d&&c;return"pointer"===this.options.tolerance||this.options.forcePointerForContainers||"pointer"!==this.options.tolerance&&this.helperProportions[this.floating?"width":"height"]>e[this.floating?"width":"height"]?p:t+this.helperProportions.width/2>a&&o>i-this.helperProportions.width/2&&s+this.helperProportions.height/2>r&&h>n-this.helperProportions.height/2},_intersectsWithPointer:function(e){var t="x"===this.options.axis||this._isOverAxis(this.positionAbs.top+this.offset.click.top,e.top,e.height),i="y"===this.options.axis||this._isOverAxis(this.positionAbs.left+this.offset.click.left,e.left,e.width),s=t&&i,n=this._getDragVerticalDirection(),a=this._getDragHorizontalDirection();return s?this.floating?a&&"right"===a||"down"===n?2:1:n&&("down"===n?2:1):!1},_intersectsWithSides:function(e){var t=this._isOverAxis(this.positionAbs.top+this.offset.click.top,e.top+e.height/2,e.height),i=this._isOverAxis(this.positionAbs.left+this.offset.click.left,e.left+e.width/2,e.width),s=this._getDragVerticalDirection(),n=this._getDragHorizontalDirection();return this.floating&&n?"right"===n&&i||"left"===n&&!i:s&&("down"===s&&t||"up"===s&&!t)},_getDragVerticalDirection:function(){var e=this.positionAbs.top-this.lastPositionAbs.top;return 0!==e&&(e>0?"down":"up")},_getDragHorizontalDirection:function(){var e=this.positionAbs.left-this.lastPositionAbs.left;return 0!==e&&(e>0?"right":"left")},refresh:function(e){return this._refreshItems(e),this._setHandleClassName(),this.refreshPositions(),this},_connectWith:function(){var e=this.options;return e.connectWith.constructor===String?[e.connectWith]:e.connectWith},_getItemsAsjQuery:function(t){function i(){r.push(this)}var s,n,a,o,r=[],h=[],l=this._connectWith();if(l&&t)for(s=l.length-1;s>=0;s--)for(a=e(l[s],this.document[0]),n=a.length-1;n>=0;n--)o=e.data(a[n],this.widgetFullName),o&&o!==this&&!o.options.disabled&&h.push([e.isFunction(o.options.items)?o.options.items.call(o.element):e(o.options.items,o.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),o]);for(h.push([e.isFunction(this.options.items)?this.options.items.call(this.element,null,{options:this.options,item:this.currentItem}):e(this.options.items,this.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),this]),s=h.length-1;s>=0;s--)h[s][0].each(i);return e(r)},_removeCurrentsFromItems:function(){var t=this.currentItem.find(":data("+this.widgetName+"-item)");this.items=e.grep(this.items,function(e){for(var i=0;t.length>i;i++)if(t[i]===e.item[0])return!1;return!0})},_refreshItems:function(t){this.items=[],this.containers=[this];var i,s,n,a,o,r,h,l,u=this.items,d=[[e.isFunction(this.options.items)?this.options.items.call(this.element[0],t,{item:this.currentItem}):e(this.options.items,this.element),this]],c=this._connectWith();if(c&&this.ready)for(i=c.length-1;i>=0;i--)for(n=e(c[i],this.document[0]),s=n.length-1;s>=0;s--)a=e.data(n[s],this.widgetFullName),a&&a!==this&&!a.options.disabled&&(d.push([e.isFunction(a.options.items)?a.options.items.call(a.element[0],t,{item:this.currentItem}):e(a.options.items,a.element),a]),this.containers.push(a));for(i=d.length-1;i>=0;i--)for(o=d[i][1],r=d[i][0],s=0,l=r.length;l>s;s++)h=e(r[s]),h.data(this.widgetName+"-item",o),u.push({item:h,instance:o,width:0,height:0,left:0,top:0})},refreshPositions:function(t){this.floating=this.items.length?"x"===this.options.axis||this._isFloating(this.items[0].item):!1,this.offsetParent&&this.helper&&(this.offset.parent=this._getParentOffset());var i,s,n,a;for(i=this.items.length-1;i>=0;i--)s=this.items[i],s.instance!==this.currentContainer&&this.currentContainer&&s.item[0]!==this.currentItem[0]||(n=this.options.toleranceElement?e(this.options.toleranceElement,s.item):s.item,t||(s.width=n.outerWidth(),s.height=n.outerHeight()),a=n.offset(),s.left=a.left,s.top=a.top);if(this.options.custom&&this.options.custom.refreshContainers)this.options.custom.refreshContainers.call(this);else for(i=this.containers.length-1;i>=0;i--)a=this.containers[i].element.offset(),this.containers[i].containerCache.left=a.left,this.containers[i].containerCache.top=a.top,this.containers[i].containerCache.width=this.containers[i].element.outerWidth(),this.containers[i].containerCache.height=this.containers[i].element.outerHeight();return this},_createPlaceholder:function(t){t=t||this;var i,s=t.options;s.placeholder&&s.placeholder.constructor!==String||(i=s.placeholder,s.placeholder={element:function(){var s=t.currentItem[0].nodeName.toLowerCase(),n=e("<"+s+">",t.document[0]).addClass(i||t.currentItem[0].className+" ui-sortable-placeholder").removeClass("ui-sortable-helper");return"tbody"===s?t._createTrPlaceholder(t.currentItem.find("tr").eq(0),e("<tr>",t.document[0]).appendTo(n)):"tr"===s?t._createTrPlaceholder(t.currentItem,n):"img"===s&&n.attr("src",t.currentItem.attr("src")),i||n.css("visibility","hidden"),n},update:function(e,n){(!i||s.forcePlaceholderSize)&&(n.height()||n.height(t.currentItem.innerHeight()-parseInt(t.currentItem.css("paddingTop")||0,10)-parseInt(t.currentItem.css("paddingBottom")||0,10)),n.width()||n.width(t.currentItem.innerWidth()-parseInt(t.currentItem.css("paddingLeft")||0,10)-parseInt(t.currentItem.css("paddingRight")||0,10)))}}),t.placeholder=e(s.placeholder.element.call(t.element,t.currentItem)),t.currentItem.after(t.placeholder),s.placeholder.update(t,t.placeholder)},_createTrPlaceholder:function(t,i){var s=this;t.children().each(function(){e("<td>&#160;</td>",s.document[0]).attr("colspan",e(this).attr("colspan")||1).appendTo(i)})},_contactContainers:function(t){var i,s,n,a,o,r,h,l,u,d,c=null,p=null;for(i=this.containers.length-1;i>=0;i--)if(!e.contains(this.currentItem[0],this.containers[i].element[0]))if(this._intersectsWith(this.containers[i].containerCache)){if(c&&e.contains(this.containers[i].element[0],c.element[0]))continue;c=this.containers[i],p=i}else this.containers[i].containerCache.over&&(this.containers[i]._trigger("out",t,this._uiHash(this)),this.containers[i].containerCache.over=0);if(c)if(1===this.containers.length)this.containers[p].containerCache.over||(this.containers[p]._trigger("over",t,this._uiHash(this)),this.containers[p].containerCache.over=1);else{for(n=1e4,a=null,u=c.floating||this._isFloating(this.currentItem),o=u?"left":"top",r=u?"width":"height",d=u?"clientX":"clientY",s=this.items.length-1;s>=0;s--)e.contains(this.containers[p].element[0],this.items[s].item[0])&&this.items[s].item[0]!==this.currentItem[0]&&(h=this.items[s].item.offset()[o],l=!1,t[d]-h>this.items[s][r]/2&&(l=!0),n>Math.abs(t[d]-h)&&(n=Math.abs(t[d]-h),a=this.items[s],this.direction=l?"up":"down"));if(!a&&!this.options.dropOnEmpty)return;if(this.currentContainer===this.containers[p])return this.currentContainer.containerCache.over||(this.containers[p]._trigger("over",t,this._uiHash()),this.currentContainer.containerCache.over=1),void 0;a?this._rearrange(t,a,null,!0):this._rearrange(t,null,this.containers[p].element,!0),this._trigger("change",t,this._uiHash()),this.containers[p]._trigger("change",t,this._uiHash(this)),this.currentContainer=this.containers[p],this.options.placeholder.update(this.currentContainer,this.placeholder),this.containers[p]._trigger("over",t,this._uiHash(this)),this.containers[p].containerCache.over=1}},_createHelper:function(t){var i=this.options,s=e.isFunction(i.helper)?e(i.helper.apply(this.element[0],[t,this.currentItem])):"clone"===i.helper?this.currentItem.clone():this.currentItem;return s.parents("body").length||e("parent"!==i.appendTo?i.appendTo:this.currentItem[0].parentNode)[0].appendChild(s[0]),s[0]===this.currentItem[0]&&(this._storedCSS={width:this.currentItem[0].style.width,height:this.currentItem[0].style.height,position:this.currentItem.css("position"),top:this.currentItem.css("top"),left:this.currentItem.css("left")}),(!s[0].style.width||i.forceHelperSize)&&s.width(this.currentItem.width()),(!s[0].style.height||i.forceHelperSize)&&s.height(this.currentItem.height()),s},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),"left"in t&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var t=this.offsetParent.offset();return"absolute"===this.cssPosition&&this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),(this.offsetParent[0]===this.document[0].body||this.offsetParent[0].tagName&&"html"===this.offsetParent[0].tagName.toLowerCase()&&e.ui.ie)&&(t={top:0,left:0}),{top:t.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"===this.cssPosition){var e=this.currentItem.position();return{top:e.top-(parseInt(this.helper.css("top"),10)||0)+this.scrollParent.scrollTop(),left:e.left-(parseInt(this.helper.css("left"),10)||0)+this.scrollParent.scrollLeft()}}return{top:0,left:0}},_cacheMargins:function(){this.margins={left:parseInt(this.currentItem.css("marginLeft"),10)||0,top:parseInt(this.currentItem.css("marginTop"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,i,s,n=this.options;"parent"===n.containment&&(n.containment=this.helper[0].parentNode),("document"===n.containment||"window"===n.containment)&&(this.containment=[0-this.offset.relative.left-this.offset.parent.left,0-this.offset.relative.top-this.offset.parent.top,"document"===n.containment?this.document.width():this.window.width()-this.helperProportions.width-this.margins.left,("document"===n.containment?this.document.width():this.window.height()||this.document[0].body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]),/^(document|window|parent)$/.test(n.containment)||(t=e(n.containment)[0],i=e(n.containment).offset(),s="hidden"!==e(t).css("overflow"),this.containment=[i.left+(parseInt(e(t).css("borderLeftWidth"),10)||0)+(parseInt(e(t).css("paddingLeft"),10)||0)-this.margins.left,i.top+(parseInt(e(t).css("borderTopWidth"),10)||0)+(parseInt(e(t).css("paddingTop"),10)||0)-this.margins.top,i.left+(s?Math.max(t.scrollWidth,t.offsetWidth):t.offsetWidth)-(parseInt(e(t).css("borderLeftWidth"),10)||0)-(parseInt(e(t).css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left,i.top+(s?Math.max(t.scrollHeight,t.offsetHeight):t.offsetHeight)-(parseInt(e(t).css("borderTopWidth"),10)||0)-(parseInt(e(t).css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top])},_convertPositionTo:function(t,i){i||(i=this.position);var s="absolute"===t?1:-1,n="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,a=/(html|body)/i.test(n[0].tagName);return{top:i.top+this.offset.relative.top*s+this.offset.parent.top*s-("fixed"===this.cssPosition?-this.scrollParent.scrollTop():a?0:n.scrollTop())*s,left:i.left+this.offset.relative.left*s+this.offset.parent.left*s-("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():a?0:n.scrollLeft())*s}},_generatePosition:function(t){var i,s,n=this.options,a=t.pageX,o=t.pageY,r="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,h=/(html|body)/i.test(r[0].tagName);return"relative"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&this.scrollParent[0]!==this.offsetParent[0]||(this.offset.relative=this._getRelativeOffset()),this.originalPosition&&(this.containment&&(t.pageX-this.offset.click.left<this.containment[0]&&(a=this.containment[0]+this.offset.click.left),t.pageY-this.offset.click.top<this.containment[1]&&(o=this.containment[1]+this.offset.click.top),t.pageX-this.offset.click.left>this.containment[2]&&(a=this.containment[2]+this.offset.click.left),t.pageY-this.offset.click.top>this.containment[3]&&(o=this.containment[3]+this.offset.click.top)),n.grid&&(i=this.originalPageY+Math.round((o-this.originalPageY)/n.grid[1])*n.grid[1],o=this.containment?i-this.offset.click.top>=this.containment[1]&&i-this.offset.click.top<=this.containment[3]?i:i-this.offset.click.top>=this.containment[1]?i-n.grid[1]:i+n.grid[1]:i,s=this.originalPageX+Math.round((a-this.originalPageX)/n.grid[0])*n.grid[0],a=this.containment?s-this.offset.click.left>=this.containment[0]&&s-this.offset.click.left<=this.containment[2]?s:s-this.offset.click.left>=this.containment[0]?s-n.grid[0]:s+n.grid[0]:s)),{top:o-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.scrollParent.scrollTop():h?0:r.scrollTop()),left:a-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():h?0:r.scrollLeft())}},_rearrange:function(e,t,i,s){i?i[0].appendChild(this.placeholder[0]):t.item[0].parentNode.insertBefore(this.placeholder[0],"down"===this.direction?t.item[0]:t.item[0].nextSibling),this.counter=this.counter?++this.counter:1;var n=this.counter;this._delay(function(){n===this.counter&&this.refreshPositions(!s)})},_clear:function(e,t){function i(e,t,i){return function(s){i._trigger(e,s,t._uiHash(t))}}this.reverting=!1;var s,n=[];if(!this._noFinalSort&&this.currentItem.parent().length&&this.placeholder.before(this.currentItem),this._noFinalSort=null,this.helper[0]===this.currentItem[0]){for(s in this._storedCSS)("auto"===this._storedCSS[s]||"static"===this._storedCSS[s])&&(this._storedCSS[s]="");this.currentItem.css(this._storedCSS).removeClass("ui-sortable-helper")}else this.currentItem.show();for(this.fromOutside&&!t&&n.push(function(e){this._trigger("receive",e,this._uiHash(this.fromOutside))}),!this.fromOutside&&this.domPosition.prev===this.currentItem.prev().not(".ui-sortable-helper")[0]&&this.domPosition.parent===this.currentItem.parent()[0]||t||n.push(function(e){this._trigger("update",e,this._uiHash())}),this!==this.currentContainer&&(t||(n.push(function(e){this._trigger("remove",e,this._uiHash())}),n.push(function(e){return function(t){e._trigger("receive",t,this._uiHash(this))}}.call(this,this.currentContainer)),n.push(function(e){return function(t){e._trigger("update",t,this._uiHash(this))}}.call(this,this.currentContainer)))),s=this.containers.length-1;s>=0;s--)t||n.push(i("deactivate",this,this.containers[s])),this.containers[s].containerCache.over&&(n.push(i("out",this,this.containers[s])),this.containers[s].containerCache.over=0);if(this.storedCursor&&(this.document.find("body").css("cursor",this.storedCursor),this.storedStylesheet.remove()),this._storedOpacity&&this.helper.css("opacity",this._storedOpacity),this._storedZIndex&&this.helper.css("zIndex","auto"===this._storedZIndex?"":this._storedZIndex),this.dragging=!1,t||this._trigger("beforeStop",e,this._uiHash()),this.placeholder[0].parentNode.removeChild(this.placeholder[0]),this.cancelHelperRemoval||(this.helper[0]!==this.currentItem[0]&&this.helper.remove(),this.helper=null),!t){for(s=0;n.length>s;s++)n[s].call(this,e);this._trigger("stop",e,this._uiHash())}return this.fromOutside=!1,!this.cancelHelperRemoval},_trigger:function(){e.Widget.prototype._trigger.apply(this,arguments)===!1&&this.cancel()},_uiHash:function(t){var i=t||this;return{helper:i.helper,placeholder:i.placeholder||e([]),position:i.position,originalPosition:i.originalPosition,offset:i.positionAbs,item:i.currentItem,sender:t?t.element:null}}}),e.widget("ui.spinner",{version:"1.11.4",defaultElement:"<input>",widgetEventPrefix:"spin",options:{culture:null,icons:{down:"ui-icon-triangle-1-s",up:"ui-icon-triangle-1-n"},incremental:!0,max:null,min:null,numberFormat:null,page:10,step:1,change:null,spin:null,start:null,stop:null},_create:function(){this._setOption("max",this.options.max),this._setOption("min",this.options.min),this._setOption("step",this.options.step),""!==this.value()&&this._value(this.element.val(),!0),this._draw(),this._on(this._events),this._refresh(),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_getCreateOptions:function(){var t={},i=this.element;return e.each(["min","max","step"],function(e,s){var n=i.attr(s);void 0!==n&&n.length&&(t[s]=n)}),t},_events:{keydown:function(e){this._start(e)&&this._keydown(e)&&e.preventDefault()},keyup:"_stop",focus:function(){this.previous=this.element.val()},blur:function(e){return this.cancelBlur?(delete this.cancelBlur,void 0):(this._stop(),this._refresh(),this.previous!==this.element.val()&&this._trigger("change",e),void 0)},mousewheel:function(e,t){if(t){if(!this.spinning&&!this._start(e))return!1;this._spin((t>0?1:-1)*this.options.step,e),clearTimeout(this.mousewheelTimer),this.mousewheelTimer=this._delay(function(){this.spinning&&this._stop(e)},100),e.preventDefault()}},"mousedown .ui-spinner-button":function(t){function i(){var e=this.element[0]===this.document[0].activeElement;e||(this.element.focus(),this.previous=s,this._delay(function(){this.previous=s}))}var s;s=this.element[0]===this.document[0].activeElement?this.previous:this.element.val(),t.preventDefault(),i.call(this),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur,i.call(this)}),this._start(t)!==!1&&this._repeat(null,e(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t)},"mouseup .ui-spinner-button":"_stop","mouseenter .ui-spinner-button":function(t){return e(t.currentTarget).hasClass("ui-state-active")?this._start(t)===!1?!1:(this._repeat(null,e(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t),void 0):void 0},"mouseleave .ui-spinner-button":"_stop"},_draw:function(){var e=this.uiSpinner=this.element.addClass("ui-spinner-input").attr("autocomplete","off").wrap(this._uiSpinnerHtml()).parent().append(this._buttonHtml());this.element.attr("role","spinbutton"),this.buttons=e.find(".ui-spinner-button").attr("tabIndex",-1).button().removeClass("ui-corner-all"),this.buttons.height()>Math.ceil(.5*e.height())&&e.height()>0&&e.height(e.height()),this.options.disabled&&this.disable()},_keydown:function(t){var i=this.options,s=e.ui.keyCode;switch(t.keyCode){case s.UP:return this._repeat(null,1,t),!0;case s.DOWN:return this._repeat(null,-1,t),!0;case s.PAGE_UP:return this._repeat(null,i.page,t),!0;case s.PAGE_DOWN:return this._repeat(null,-i.page,t),!0}return!1},_uiSpinnerHtml:function(){return"<span class='ui-spinner ui-widget ui-widget-content ui-corner-all'></span>"},_buttonHtml:function(){return"<a class='ui-spinner-button ui-spinner-up ui-corner-tr'><span class='ui-icon "+this.options.icons.up+"'>&#9650;</span>"+"</a>"+"<a class='ui-spinner-button ui-spinner-down ui-corner-br'>"+"<span class='ui-icon "+this.options.icons.down+"'>&#9660;</span>"+"</a>"},_start:function(e){return this.spinning||this._trigger("start",e)!==!1?(this.counter||(this.counter=1),this.spinning=!0,!0):!1},_repeat:function(e,t,i){e=e||500,clearTimeout(this.timer),this.timer=this._delay(function(){this._repeat(40,t,i)},e),this._spin(t*this.options.step,i)},_spin:function(e,t){var i=this.value()||0;this.counter||(this.counter=1),i=this._adjustValue(i+e*this._increment(this.counter)),this.spinning&&this._trigger("spin",t,{value:i})===!1||(this._value(i),this.counter++)},_increment:function(t){var i=this.options.incremental;return i?e.isFunction(i)?i(t):Math.floor(t*t*t/5e4-t*t/500+17*t/200+1):1},_precision:function(){var e=this._precisionOf(this.options.step);return null!==this.options.min&&(e=Math.max(e,this._precisionOf(this.options.min))),e},_precisionOf:function(e){var t=""+e,i=t.indexOf(".");return-1===i?0:t.length-i-1},_adjustValue:function(e){var t,i,s=this.options;return t=null!==s.min?s.min:0,i=e-t,i=Math.round(i/s.step)*s.step,e=t+i,e=parseFloat(e.toFixed(this._precision())),null!==s.max&&e>s.max?s.max:null!==s.min&&s.min>e?s.min:e},_stop:function(e){this.spinning&&(clearTimeout(this.timer),clearTimeout(this.mousewheelTimer),this.counter=0,this.spinning=!1,this._trigger("stop",e))},_setOption:function(e,t){if("culture"===e||"numberFormat"===e){var i=this._parse(this.element.val());return this.options[e]=t,this.element.val(this._format(i)),void 0}("max"===e||"min"===e||"step"===e)&&"string"==typeof t&&(t=this._parse(t)),"icons"===e&&(this.buttons.first().find(".ui-icon").removeClass(this.options.icons.up).addClass(t.up),this.buttons.last().find(".ui-icon").removeClass(this.options.icons.down).addClass(t.down)),this._super(e,t),"disabled"===e&&(this.widget().toggleClass("ui-state-disabled",!!t),this.element.prop("disabled",!!t),this.buttons.button(t?"disable":"enable"))},_setOptions:h(function(e){this._super(e)}),_parse:function(e){return"string"==typeof e&&""!==e&&(e=window.Globalize&&this.options.numberFormat?Globalize.parseFloat(e,10,this.options.culture):+e),""===e||isNaN(e)?null:e},_format:function(e){return""===e?"":window.Globalize&&this.options.numberFormat?Globalize.format(e,this.options.numberFormat,this.options.culture):e},_refresh:function(){this.element.attr({"aria-valuemin":this.options.min,"aria-valuemax":this.options.max,"aria-valuenow":this._parse(this.element.val())})},isValid:function(){var e=this.value();return null===e?!1:e===this._adjustValue(e)},_value:function(e,t){var i;""!==e&&(i=this._parse(e),null!==i&&(t||(i=this._adjustValue(i)),e=this._format(i))),this.element.val(e),this._refresh()},_destroy:function(){this.element.removeClass("ui-spinner-input").prop("disabled",!1).removeAttr("autocomplete").removeAttr("role").removeAttr("aria-valuemin").removeAttr("aria-valuemax").removeAttr("aria-valuenow"),this.uiSpinner.replaceWith(this.element)},stepUp:h(function(e){this._stepUp(e)}),_stepUp:function(e){this._start()&&(this._spin((e||1)*this.options.step),this._stop())},stepDown:h(function(e){this._stepDown(e)}),_stepDown:function(e){this._start()&&(this._spin((e||1)*-this.options.step),this._stop())},pageUp:h(function(e){this._stepUp((e||1)*this.options.page)}),pageDown:h(function(e){this._stepDown((e||1)*this.options.page)}),value:function(e){return arguments.length?(h(this._value).call(this,e),void 0):this._parse(this.element.val())},widget:function(){return this.uiSpinner}}),e.widget("ui.tabs",{version:"1.11.4",delay:300,options:{active:null,collapsible:!1,event:"click",heightStyle:"content",hide:null,show:null,activate:null,beforeActivate:null,beforeLoad:null,load:null},_isLocal:function(){var e=/#.*$/;return function(t){var i,s;t=t.cloneNode(!1),i=t.href.replace(e,""),s=location.href.replace(e,"");try{i=decodeURIComponent(i)}catch(n){}try{s=decodeURIComponent(s)}catch(n){}return t.hash.length>1&&i===s}}(),_create:function(){var t=this,i=this.options;this.running=!1,this.element.addClass("ui-tabs ui-widget ui-widget-content ui-corner-all").toggleClass("ui-tabs-collapsible",i.collapsible),this._processTabs(),i.active=this._initialActive(),e.isArray(i.disabled)&&(i.disabled=e.unique(i.disabled.concat(e.map(this.tabs.filter(".ui-state-disabled"),function(e){return t.tabs.index(e)}))).sort()),this.active=this.options.active!==!1&&this.anchors.length?this._findActive(i.active):e(),this._refresh(),this.active.length&&this.load(i.active)},_initialActive:function(){var t=this.options.active,i=this.options.collapsible,s=location.hash.substring(1);return null===t&&(s&&this.tabs.each(function(i,n){return e(n).attr("aria-controls")===s?(t=i,!1):void 0}),null===t&&(t=this.tabs.index(this.tabs.filter(".ui-tabs-active"))),(null===t||-1===t)&&(t=this.tabs.length?0:!1)),t!==!1&&(t=this.tabs.index(this.tabs.eq(t)),-1===t&&(t=i?!1:0)),!i&&t===!1&&this.anchors.length&&(t=0),t},_getCreateEventData:function(){return{tab:this.active,panel:this.active.length?this._getPanelForTab(this.active):e()}},_tabKeydown:function(t){var i=e(this.document[0].activeElement).closest("li"),s=this.tabs.index(i),n=!0;if(!this._handlePageNav(t)){switch(t.keyCode){case e.ui.keyCode.RIGHT:case e.ui.keyCode.DOWN:s++;break;case e.ui.keyCode.UP:case e.ui.keyCode.LEFT:n=!1,s--;break;case e.ui.keyCode.END:s=this.anchors.length-1;break;case e.ui.keyCode.HOME:s=0;break;case e.ui.keyCode.SPACE:return t.preventDefault(),clearTimeout(this.activating),this._activate(s),void 0;case e.ui.keyCode.ENTER:return t.preventDefault(),clearTimeout(this.activating),this._activate(s===this.options.active?!1:s),void 0;default:return}t.preventDefault(),clearTimeout(this.activating),s=this._focusNextTab(s,n),t.ctrlKey||t.metaKey||(i.attr("aria-selected","false"),this.tabs.eq(s).attr("aria-selected","true"),this.activating=this._delay(function(){this.option("active",s)},this.delay))}},_panelKeydown:function(t){this._handlePageNav(t)||t.ctrlKey&&t.keyCode===e.ui.keyCode.UP&&(t.preventDefault(),this.active.focus())},_handlePageNav:function(t){return t.altKey&&t.keyCode===e.ui.keyCode.PAGE_UP?(this._activate(this._focusNextTab(this.options.active-1,!1)),!0):t.altKey&&t.keyCode===e.ui.keyCode.PAGE_DOWN?(this._activate(this._focusNextTab(this.options.active+1,!0)),!0):void 0},_findNextTab:function(t,i){function s(){return t>n&&(t=0),0>t&&(t=n),t}for(var n=this.tabs.length-1;-1!==e.inArray(s(),this.options.disabled);)t=i?t+1:t-1;return t},_focusNextTab:function(e,t){return e=this._findNextTab(e,t),this.tabs.eq(e).focus(),e},_setOption:function(e,t){return"active"===e?(this._activate(t),void 0):"disabled"===e?(this._setupDisabled(t),void 0):(this._super(e,t),"collapsible"===e&&(this.element.toggleClass("ui-tabs-collapsible",t),t||this.options.active!==!1||this._activate(0)),"event"===e&&this._setupEvents(t),"heightStyle"===e&&this._setupHeightStyle(t),void 0)},_sanitizeSelector:function(e){return e?e.replace(/[!"$%&'()*+,.\/:;<=>?@\[\]\^`{|}~]/g,"\\$&"):""},refresh:function(){var t=this.options,i=this.tablist.children(":has(a[href])");t.disabled=e.map(i.filter(".ui-state-disabled"),function(e){return i.index(e)}),this._processTabs(),t.active!==!1&&this.anchors.length?this.active.length&&!e.contains(this.tablist[0],this.active[0])?this.tabs.length===t.disabled.length?(t.active=!1,this.active=e()):this._activate(this._findNextTab(Math.max(0,t.active-1),!1)):t.active=this.tabs.index(this.active):(t.active=!1,this.active=e()),this._refresh()},_refresh:function(){this._setupDisabled(this.options.disabled),this._setupEvents(this.options.event),this._setupHeightStyle(this.options.heightStyle),this.tabs.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}),this.panels.not(this._getPanelForTab(this.active)).hide().attr({"aria-hidden":"true"}),this.active.length?(this.active.addClass("ui-tabs-active ui-state-active").attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}),this._getPanelForTab(this.active).show().attr({"aria-hidden":"false"})):this.tabs.eq(0).attr("tabIndex",0)},_processTabs:function(){var t=this,i=this.tabs,s=this.anchors,n=this.panels;
this.tablist=this._getList().addClass("ui-tabs-nav ui-helper-reset ui-helper-clearfix ui-widget-header ui-corner-all").attr("role","tablist").delegate("> li","mousedown"+this.eventNamespace,function(t){e(this).is(".ui-state-disabled")&&t.preventDefault()}).delegate(".ui-tabs-anchor","focus"+this.eventNamespace,function(){e(this).closest("li").is(".ui-state-disabled")&&this.blur()}),this.tabs=this.tablist.find("> li:has(a[href])").addClass("ui-state-default ui-corner-top").attr({role:"tab",tabIndex:-1}),this.anchors=this.tabs.map(function(){return e("a",this)[0]}).addClass("ui-tabs-anchor").attr({role:"presentation",tabIndex:-1}),this.panels=e(),this.anchors.each(function(i,s){var n,a,o,r=e(s).uniqueId().attr("id"),h=e(s).closest("li"),l=h.attr("aria-controls");t._isLocal(s)?(n=s.hash,o=n.substring(1),a=t.element.find(t._sanitizeSelector(n))):(o=h.attr("aria-controls")||e({}).uniqueId()[0].id,n="#"+o,a=t.element.find(n),a.length||(a=t._createPanel(o),a.insertAfter(t.panels[i-1]||t.tablist)),a.attr("aria-live","polite")),a.length&&(t.panels=t.panels.add(a)),l&&h.data("ui-tabs-aria-controls",l),h.attr({"aria-controls":o,"aria-labelledby":r}),a.attr("aria-labelledby",r)}),this.panels.addClass("ui-tabs-panel ui-widget-content ui-corner-bottom").attr("role","tabpanel"),i&&(this._off(i.not(this.tabs)),this._off(s.not(this.anchors)),this._off(n.not(this.panels)))},_getList:function(){return this.tablist||this.element.find("ol,ul").eq(0)},_createPanel:function(t){return e("<div>").attr("id",t).addClass("ui-tabs-panel ui-widget-content ui-corner-bottom").data("ui-tabs-destroy",!0)},_setupDisabled:function(t){e.isArray(t)&&(t.length?t.length===this.anchors.length&&(t=!0):t=!1);for(var i,s=0;i=this.tabs[s];s++)t===!0||-1!==e.inArray(s,t)?e(i).addClass("ui-state-disabled").attr("aria-disabled","true"):e(i).removeClass("ui-state-disabled").removeAttr("aria-disabled");this.options.disabled=t},_setupEvents:function(t){var i={};t&&e.each(t.split(" "),function(e,t){i[t]="_eventHandler"}),this._off(this.anchors.add(this.tabs).add(this.panels)),this._on(!0,this.anchors,{click:function(e){e.preventDefault()}}),this._on(this.anchors,i),this._on(this.tabs,{keydown:"_tabKeydown"}),this._on(this.panels,{keydown:"_panelKeydown"}),this._focusable(this.tabs),this._hoverable(this.tabs)},_setupHeightStyle:function(t){var i,s=this.element.parent();"fill"===t?(i=s.height(),i-=this.element.outerHeight()-this.element.height(),this.element.siblings(":visible").each(function(){var t=e(this),s=t.css("position");"absolute"!==s&&"fixed"!==s&&(i-=t.outerHeight(!0))}),this.element.children().not(this.panels).each(function(){i-=e(this).outerHeight(!0)}),this.panels.each(function(){e(this).height(Math.max(0,i-e(this).innerHeight()+e(this).height()))}).css("overflow","auto")):"auto"===t&&(i=0,this.panels.each(function(){i=Math.max(i,e(this).height("").height())}).height(i))},_eventHandler:function(t){var i=this.options,s=this.active,n=e(t.currentTarget),a=n.closest("li"),o=a[0]===s[0],r=o&&i.collapsible,h=r?e():this._getPanelForTab(a),l=s.length?this._getPanelForTab(s):e(),u={oldTab:s,oldPanel:l,newTab:r?e():a,newPanel:h};t.preventDefault(),a.hasClass("ui-state-disabled")||a.hasClass("ui-tabs-loading")||this.running||o&&!i.collapsible||this._trigger("beforeActivate",t,u)===!1||(i.active=r?!1:this.tabs.index(a),this.active=o?e():a,this.xhr&&this.xhr.abort(),l.length||h.length||e.error("jQuery UI Tabs: Mismatching fragment identifier."),h.length&&this.load(this.tabs.index(a),t),this._toggle(t,u))},_toggle:function(t,i){function s(){a.running=!1,a._trigger("activate",t,i)}function n(){i.newTab.closest("li").addClass("ui-tabs-active ui-state-active"),o.length&&a.options.show?a._show(o,a.options.show,s):(o.show(),s())}var a=this,o=i.newPanel,r=i.oldPanel;this.running=!0,r.length&&this.options.hide?this._hide(r,this.options.hide,function(){i.oldTab.closest("li").removeClass("ui-tabs-active ui-state-active"),n()}):(i.oldTab.closest("li").removeClass("ui-tabs-active ui-state-active"),r.hide(),n()),r.attr("aria-hidden","true"),i.oldTab.attr({"aria-selected":"false","aria-expanded":"false"}),o.length&&r.length?i.oldTab.attr("tabIndex",-1):o.length&&this.tabs.filter(function(){return 0===e(this).attr("tabIndex")}).attr("tabIndex",-1),o.attr("aria-hidden","false"),i.newTab.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_activate:function(t){var i,s=this._findActive(t);s[0]!==this.active[0]&&(s.length||(s=this.active),i=s.find(".ui-tabs-anchor")[0],this._eventHandler({target:i,currentTarget:i,preventDefault:e.noop}))},_findActive:function(t){return t===!1?e():this.tabs.eq(t)},_getIndex:function(e){return"string"==typeof e&&(e=this.anchors.index(this.anchors.filter("[href$='"+e+"']"))),e},_destroy:function(){this.xhr&&this.xhr.abort(),this.element.removeClass("ui-tabs ui-widget ui-widget-content ui-corner-all ui-tabs-collapsible"),this.tablist.removeClass("ui-tabs-nav ui-helper-reset ui-helper-clearfix ui-widget-header ui-corner-all").removeAttr("role"),this.anchors.removeClass("ui-tabs-anchor").removeAttr("role").removeAttr("tabIndex").removeUniqueId(),this.tablist.unbind(this.eventNamespace),this.tabs.add(this.panels).each(function(){e.data(this,"ui-tabs-destroy")?e(this).remove():e(this).removeClass("ui-state-default ui-state-active ui-state-disabled ui-corner-top ui-corner-bottom ui-widget-content ui-tabs-active ui-tabs-panel").removeAttr("tabIndex").removeAttr("aria-live").removeAttr("aria-busy").removeAttr("aria-selected").removeAttr("aria-labelledby").removeAttr("aria-hidden").removeAttr("aria-expanded").removeAttr("role")}),this.tabs.each(function(){var t=e(this),i=t.data("ui-tabs-aria-controls");i?t.attr("aria-controls",i).removeData("ui-tabs-aria-controls"):t.removeAttr("aria-controls")}),this.panels.show(),"content"!==this.options.heightStyle&&this.panels.css("height","")},enable:function(t){var i=this.options.disabled;i!==!1&&(void 0===t?i=!1:(t=this._getIndex(t),i=e.isArray(i)?e.map(i,function(e){return e!==t?e:null}):e.map(this.tabs,function(e,i){return i!==t?i:null})),this._setupDisabled(i))},disable:function(t){var i=this.options.disabled;if(i!==!0){if(void 0===t)i=!0;else{if(t=this._getIndex(t),-1!==e.inArray(t,i))return;i=e.isArray(i)?e.merge([t],i).sort():[t]}this._setupDisabled(i)}},load:function(t,i){t=this._getIndex(t);var 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idx=0,min=f(data[0]),v=min;for(var i=1,n=data.length;i<n;++i){v=f(data[i]);if(v<min){min=v;idx=i}}return idx};vg.maxIndex=function(data,f){if(!vg.isArray(data)||data.length==0)return-1;f=f||vg.identity;var idx=0,max=f(data[0]),v=max;for(var i=1,n=data.length;i<n;++i){v=f(data[i]);if(v>max){max=v;idx=i}}return idx};vg.truncate=function(s,length,pos,word,ellipsis){var len=s.length;if(len<=length)return s;ellipsis=ellipsis||"...";var l=Math.max(0,length-ellipsis.length);switch(pos){case"left":return ellipsis+(word?vg_truncateOnWord(s,l,1):s.slice(len-l));case"middle":case"center":var l1=Math.ceil(l/2),l2=Math.floor(l/2);return(word?vg_truncateOnWord(s,l1):s.slice(0,l1))+ellipsis+(word?vg_truncateOnWord(s,l2,1):s.slice(len-l2));default:return(word?vg_truncateOnWord(s,l):s.slice(0,l))+ellipsis}};function vg_truncateOnWord(s,len,rev){var cnt=0,tok=s.split(vg_truncate_word_re);if(rev){s=(tok=tok.reverse()).filter(function(w){cnt+=w.length;return cnt<=len}).reverse()}else{s=tok.filter(function(w){cnt+=w.length;return cnt<=len})}return s.length?s.join("").trim():tok[0].slice(0,len)}var vg_truncate_word_re=/([\u0009\u000A\u000B\u000C\u000D\u0020\u00A0\u1680\u180E\u2000\u2001\u2002\u2003\u2004\u2005\u2006\u2007\u2008\u2009\u200A\u202F\u205F\u2028\u2029\u3000\uFEFF])/;function vg_write(msg){vg.config.isNode?process.stderr.write(msg+"\n"):console.log(msg)}vg.log=function(msg){vg_write("[Vega Log] "+msg)};vg.error=function(msg){msg="[Vega Err] "+msg;vg_write(msg);if(typeof alert!=="undefined")alert(msg)};vg.config={};vg.config.isNode=typeof exports!=="undefined"&&this.exports!==exports;vg.config.domainWhiteList=false;vg.config.safeMode=false;vg.config.baseURL="";vg.config.svgNamespace='version="1.1" xmlns="http://www.w3.org/2000/svg" '+'xmlns:xlink="http://www.w3.org/1999/xlink"';vg.config.autopadInset=5;vg.config.scale={time:d3.time.scale,utc:d3.time.scale.utc};vg.config.render={lineWidth:1,lineCap:"butt",font:"sans-serif",fontSize:11};vg.config.axis={orient:"bottom",ticks:10,padding:3,axisColor:"#000",gridColor:"#d8d8d8",tickColor:"#000",tickLabelColor:"#000",axisWidth:1,tickWidth:1,tickSize:6,tickLabelFontSize:11,tickLabelFont:"sans-serif",titleColor:"#000",titleFont:"sans-serif",titleFontSize:11,titleFontWeight:"bold",titleOffset:35};vg.config.legend={orient:"right",offset:10,padding:3,gradientStrokeColor:"#888",gradientStrokeWidth:1,gradientHeight:16,gradientWidth:100,labelColor:"#000",labelFontSize:10,labelFont:"sans-serif",labelAlign:"left",labelBaseline:"middle",labelOffset:8,symbolShape:"circle",symbolSize:50,symbolColor:"#888",symbolStrokeWidth:1,titleColor:"#000",titleFont:"sans-serif",titleFontSize:11,titleFontWeight:"bold"};vg.config.color={rgb:[128,128,128],lab:[50,0,0],hcl:[0,0,50],hsl:[0,0,.5]};vg.config.range={category10:["#1f77b4","#ff7f0e","#2ca02c","#d62728","#9467bd","#8c564b","#e377c2","#7f7f7f","#bcbd22","#17becf"],category20:["#1f77b4","#aec7e8","#ff7f0e","#ffbb78","#2ca02c","#98df8a","#d62728","#ff9896","#9467bd","#c5b0d5","#8c564b","#c49c94","#e377c2","#f7b6d2","#7f7f7f","#c7c7c7","#bcbd22","#dbdb8d","#17becf","#9edae5"],shapes:["circle","cross","diamond","square","triangle-down","triangle-up"]};vg.Bounds=function(){var bounds=function(b){this.clear();if(b)this.union(b)};var prototype=bounds.prototype;prototype.clear=function(){this.x1=+Number.MAX_VALUE;this.y1=+Number.MAX_VALUE;this.x2=-Number.MAX_VALUE;this.y2=-Number.MAX_VALUE;return this};prototype.set=function(x1,y1,x2,y2){this.x1=x1;this.y1=y1;this.x2=x2;this.y2=y2;return this};prototype.add=function(x,y){if(x<this.x1)this.x1=x;if(y<this.y1)this.y1=y;if(x>this.x2)this.x2=x;if(y>this.y2)this.y2=y;return this};prototype.expand=function(d){this.x1-=d;this.y1-=d;this.x2+=d;this.y2+=d;return this};prototype.round=function(){this.x1=Math.floor(this.x1);this.y1=Math.floor(this.y1);this.x2=Math.ceil(this.x2);this.y2=Math.ceil(this.y2);return this};prototype.translate=function(dx,dy){this.x1+=dx;this.x2+=dx;this.y1+=dy;this.y2+=dy;return this};prototype.rotate=function(angle,x,y){var cos=Math.cos(angle),sin=Math.sin(angle),cx=x-x*cos+y*sin,cy=y-x*sin-y*cos,x1=this.x1,x2=this.x2,y1=this.y1,y2=this.y2;return this.clear().add(cos*x1-sin*y1+cx,sin*x1+cos*y1+cy).add(cos*x1-sin*y2+cx,sin*x1+cos*y2+cy).add(cos*x2-sin*y1+cx,sin*x2+cos*y1+cy).add(cos*x2-sin*y2+cx,sin*x2+cos*y2+cy)};prototype.union=function(b){if(b.x1<this.x1)this.x1=b.x1;if(b.y1<this.y1)this.y1=b.y1;if(b.x2>this.x2)this.x2=b.x2;if(b.y2>this.y2)this.y2=b.y2;return this};prototype.encloses=function(b){return b&&(this.x1<=b.x1&&this.x2>=b.x2&&this.y1<=b.y1&&this.y2>=b.y2)};prototype.intersects=function(b){return b&&!(this.x2<b.x1||this.x1>b.x2||this.y2<b.y1||this.y1>b.y2)};prototype.contains=function(x,y){return!(x<this.x1||x>this.x2||y<this.y1||y>this.y2)};prototype.width=function(){return this.x2-this.x1};prototype.height=function(){return this.y2-this.y1};return bounds}();vg.Gradient=function(){function gradient(type){this.id="grad_"+vg_gradient_id++;this.type=type||"linear";this.stops=[];this.x1=0;this.x2=1;this.y1=0;this.y2=0}var prototype=gradient.prototype;prototype.stop=function(offset,color){this.stops.push({offset:offset,color:color});return this};return gradient}();var vg_gradient_id=0;vg.canvas={};vg.canvas.path=function(){var cmdLength={m:2,l:2,h:1,v:1,c:6,s:4,q:4,t:2,a:7},re=[/([MLHVCSQTAZmlhvcsqtaz])/g,/###/,/(\d)-/g,/\s|,|###/];function parse(path){var result=[],currentPath,chunks,parsed;path=path.slice().replace(re[0],"###$1").split(re[1]).slice(1);for(var i=0,j,chunksParsed,len=path.length;i<len;i++){currentPath=path[i];chunks=currentPath.slice(1).trim().replace(re[2],"$1###-").split(re[3]);chunksParsed=[currentPath.charAt(0)];for(var j=0,jlen=chunks.length;j<jlen;j++){parsed=parseFloat(chunks[j]);if(!isNaN(parsed)){chunksParsed.push(parsed)}}var command=chunksParsed[0].toLowerCase(),commandLength=cmdLength[command];if(chunksParsed.length-1>commandLength){for(var k=1,klen=chunksParsed.length;k<klen;k+=commandLength){result.push([chunksParsed[0]].concat(chunksParsed.slice(k,k+commandLength)))}}else{result.push(chunksParsed)}}return result}function drawArc(g,x,y,coords,bounds,l,t){var rx=coords[0];var ry=coords[1];var rot=coords[2];var large=coords[3];var sweep=coords[4];var ex=coords[5];var ey=coords[6];var segs=arcToSegments(ex,ey,rx,ry,large,sweep,rot,x,y);for(var i=0;i<segs.length;i++){var bez=segmentToBezier.apply(null,segs[i]);g.bezierCurveTo.apply(g,bez);bounds.add(bez[0]-l,bez[1]-t);bounds.add(bez[2]-l,bez[3]-t);bounds.add(bez[4]-l,bez[5]-t)}}function boundArc(x,y,coords,bounds){var rx=coords[0];var ry=coords[1];var rot=coords[2];var large=coords[3];var sweep=coords[4];var ex=coords[5];var ey=coords[6];var segs=arcToSegments(ex,ey,rx,ry,large,sweep,rot,x,y);for(var i=0;i<segs.length;i++){var bez=segmentToBezier.apply(null,segs[i]);bounds.add(bez[0],bez[1]);bounds.add(bez[2],bez[3]);bounds.add(bez[4],bez[5])}}var arcToSegmentsCache={},segmentToBezierCache={},join=Array.prototype.join,argsStr;function arcToSegments(x,y,rx,ry,large,sweep,rotateX,ox,oy){argsStr=join.call(arguments);if(arcToSegmentsCache[argsStr]){return arcToSegmentsCache[argsStr]}var th=rotateX*(Math.PI/180);var sin_th=Math.sin(th);var cos_th=Math.cos(th);rx=Math.abs(rx);ry=Math.abs(ry);var px=cos_th*(ox-x)*.5+sin_th*(oy-y)*.5;var py=cos_th*(oy-y)*.5-sin_th*(ox-x)*.5;var pl=px*px/(rx*rx)+py*py/(ry*ry);if(pl>1){pl=Math.sqrt(pl);rx*=pl;ry*=pl}var a00=cos_th/rx;var a01=sin_th/rx;var a10=-sin_th/ry;var a11=cos_th/ry;var x0=a00*ox+a01*oy;var y0=a10*ox+a11*oy;var x1=a00*x+a01*y;var y1=a10*x+a11*y;var d=(x1-x0)*(x1-x0)+(y1-y0)*(y1-y0);var sfactor_sq=1/d-.25;if(sfactor_sq<0)sfactor_sq=0;var sfactor=Math.sqrt(sfactor_sq);if(sweep==large)sfactor=-sfactor;var xc=.5*(x0+x1)-sfactor*(y1-y0);var yc=.5*(y0+y1)+sfactor*(x1-x0);var th0=Math.atan2(y0-yc,x0-xc);var th1=Math.atan2(y1-yc,x1-xc);var th_arc=th1-th0;if(th_arc<0&&sweep==1){th_arc+=2*Math.PI}else if(th_arc>0&&sweep==0){th_arc-=2*Math.PI}var segments=Math.ceil(Math.abs(th_arc/(Math.PI*.5+.001)));var result=[];for(var i=0;i<segments;i++){var th2=th0+i*th_arc/segments;var th3=th0+(i+1)*th_arc/segments;result[i]=[xc,yc,th2,th3,rx,ry,sin_th,cos_th]}return arcToSegmentsCache[argsStr]=result}function segmentToBezier(cx,cy,th0,th1,rx,ry,sin_th,cos_th){argsStr=join.call(arguments);if(segmentToBezierCache[argsStr]){return segmentToBezierCache[argsStr]}var a00=cos_th*rx;var a01=-sin_th*ry;var a10=sin_th*rx;var a11=cos_th*ry;var cos_th0=Math.cos(th0);var sin_th0=Math.sin(th0);var cos_th1=Math.cos(th1);var sin_th1=Math.sin(th1);var th_half=.5*(th1-th0);var sin_th_h2=Math.sin(th_half*.5);var t=8/3*sin_th_h2*sin_th_h2/Math.sin(th_half);var x1=cx+cos_th0-t*sin_th0;var y1=cy+sin_th0+t*cos_th0;var x3=cx+cos_th1;var y3=cy+sin_th1;var x2=x3+t*sin_th1;var y2=y3-t*cos_th1;return segmentToBezierCache[argsStr]=[a00*x1+a01*y1,a10*x1+a11*y1,a00*x2+a01*y2,a10*x2+a11*y2,a00*x3+a01*y3,a10*x3+a11*y3]}function render(g,path,l,t){var current,previous=null,x=0,y=0,controlX=0,controlY=0,tempX,tempY,tempControlX,tempControlY,bounds=new vg.Bounds;if(l==undefined)l=0;if(t==undefined)t=0;g.beginPath();for(var i=0,len=path.length;i<len;++i){current=path[i];switch(current[0]){case"l":x+=current[1];y+=current[2];g.lineTo(x+l,y+t);bounds.add(x,y);break;case"L":x=current[1];y=current[2];g.lineTo(x+l,y+t);bounds.add(x,y);break;case"h":x+=current[1];g.lineTo(x+l,y+t);bounds.add(x,y);break;case"H":x=current[1];g.lineTo(x+l,y+t);bounds.add(x,y);break;case"v":y+=current[1];g.lineTo(x+l,y+t);bounds.add(x,y);break;case"V":y=current[1];g.lineTo(x+l,y+t);bounds.add(x,y);break;case"m":x+=current[1];y+=current[2];g.moveTo(x+l,y+t);bounds.add(x,y);break;case"M":x=current[1];y=current[2];g.moveTo(x+l,y+t);bounds.add(x,y);break;case"c":tempX=x+current[5];tempY=y+current[6];controlX=x+current[3];controlY=y+current[4];g.bezierCurveTo(x+current[1]+l,y+current[2]+t,controlX+l,controlY+t,tempX+l,tempY+t);bounds.add(x+current[1],y+current[2]);bounds.add(controlX,controlY);bounds.add(tempX,tempY);x=tempX;y=tempY;break;case"C":x=current[5];y=current[6];controlX=current[3];controlY=current[4];g.bezierCurveTo(current[1]+l,current[2]+t,controlX+l,controlY+t,x+l,y+t);bounds.add(current[1],current[2]);bounds.add(controlX,controlY);bounds.add(x,y);break;case"s":tempX=x+current[3];tempY=y+current[4];controlX=2*x-controlX;controlY=2*y-controlY;g.bezierCurveTo(controlX+l,controlY+t,x+current[1]+l,y+current[2]+t,tempX+l,tempY+t);bounds.add(controlX,controlY);bounds.add(x+current[1],y+current[2]);bounds.add(tempX,tempY);controlX=x+current[1];controlY=y+current[2];x=tempX;y=tempY;break;case"S":tempX=current[3];tempY=current[4];controlX=2*x-controlX;controlY=2*y-controlY;g.bezierCurveTo(controlX+l,controlY+t,current[1]+l,current[2]+t,tempX+l,tempY+t);x=tempX;y=tempY;bounds.add(current[1],current[2]);bounds.add(controlX,controlY);bounds.add(tempX,tempY);controlX=current[1];controlY=current[2];break;case"q":tempX=x+current[3];tempY=y+current[4];controlX=x+current[1];controlY=y+current[2];g.quadraticCurveTo(controlX+l,controlY+t,tempX+l,tempY+t);x=tempX;y=tempY;bounds.add(controlX,controlY);bounds.add(tempX,tempY);break;case"Q":tempX=current[3];tempY=current[4];g.quadraticCurveTo(current[1]+l,current[2]+t,tempX+l,tempY+t);x=tempX;y=tempY;controlX=current[1];controlY=current[2];bounds.add(controlX,controlY);bounds.add(tempX,tempY);break;case"t":tempX=x+current[1];tempY=y+current[2];if(previous[0].match(/[QqTt]/)===null){controlX=x;controlY=y}else if(previous[0]==="t"){controlX=2*x-tempControlX;controlY=2*y-tempControlY}else if(previous[0]==="q"){controlX=2*x-controlX;controlY=2*y-controlY}tempControlX=controlX;tempControlY=controlY;g.quadraticCurveTo(controlX+l,controlY+t,tempX+l,tempY+t);x=tempX;y=tempY;controlX=x+current[1];controlY=y+current[2];bounds.add(controlX,controlY);bounds.add(tempX,tempY);break;case"T":tempX=current[1];tempY=current[2];controlX=2*x-controlX;controlY=2*y-controlY;g.quadraticCurveTo(controlX+l,controlY+t,tempX+l,tempY+t);x=tempX;y=tempY;bounds.add(controlX,controlY);bounds.add(tempX,tempY);break;case"a":drawArc(g,x+l,y+t,[current[1],current[2],current[3],current[4],current[5],current[6]+x+l,current[7]+y+t],bounds,l,t);x+=current[6];y+=current[7];break;case"A":drawArc(g,x+l,y+t,[current[1],current[2],current[3],current[4],current[5],current[6]+l,current[7]+t],bounds,l,t);x=current[6];y=current[7];break;case"z":case"Z":g.closePath();break}previous=current}return bounds.translate(l,t)}function bounds(path,bounds){var current,previous=null,x=0,y=0,controlX=0,controlY=0,tempX,tempY,tempControlX,tempControlY;for(var i=0,len=path.length;i<len;++i){current=path[i];switch(current[0]){case"l":x+=current[1];y+=current[2];bounds.add(x,y);break;case"L":x=current[1];y=current[2];bounds.add(x,y);break;case"h":x+=current[1];bounds.add(x,y);break;case"H":x=current[1];bounds.add(x,y);break;case"v":y+=current[1];bounds.add(x,y);break;case"V":y=current[1];bounds.add(x,y);break;case"m":x+=current[1];y+=current[2];bounds.add(x,y);break;case"M":x=current[1];y=current[2];bounds.add(x,y);break;case"c":tempX=x+current[5];tempY=y+current[6];controlX=x+current[3];controlY=y+current[4];bounds.add(x+current[1],y+current[2]);bounds.add(controlX,controlY);bounds.add(tempX,tempY);x=tempX;y=tempY;break;case"C":x=current[5];y=current[6];controlX=current[3];controlY=current[4];bounds.add(current[1],current[2]);bounds.add(controlX,controlY);bounds.add(x,y);break;case"s":tempX=x+current[3];tempY=y+current[4];controlX=2*x-controlX;controlY=2*y-controlY;bounds.add(controlX,controlY);bounds.add(x+current[1],y+current[2]);bounds.add(tempX,tempY);controlX=x+current[1];controlY=y+current[2];x=tempX;y=tempY;break;case"S":tempX=current[3];tempY=current[4];controlX=2*x-controlX;controlY=2*y-controlY;x=tempX;y=tempY;bounds.add(current[1],current[2]);bounds.add(controlX,controlY);bounds.add(tempX,tempY);controlX=current[1];controlY=current[2];break;case"q":tempX=x+current[3];tempY=y+current[4];controlX=x+current[1];controlY=y+current[2];x=tempX;y=tempY;bounds.add(controlX,controlY);bounds.add(tempX,tempY);break;case"Q":tempX=current[3];tempY=current[4];x=tempX;y=tempY;controlX=current[1];controlY=current[2];bounds.add(controlX,controlY);bounds.add(tempX,tempY);break;case"t":tempX=x+current[1];tempY=y+current[2];if(previous[0].match(/[QqTt]/)===null){controlX=x;controlY=y}else if(previous[0]==="t"){controlX=2*x-tempControlX;controlY=2*y-tempControlY}else if(previous[0]==="q"){controlX=2*x-controlX;controlY=2*y-controlY}tempControlX=controlX;tempControlY=controlY;x=tempX;y=tempY;controlX=x+current[1];controlY=y+current[2];bounds.add(controlX,controlY);bounds.add(tempX,tempY);break;case"T":tempX=current[1];tempY=current[2];controlX=2*x-controlX;controlY=2*y-controlY;x=tempX;y=tempY;bounds.add(controlX,controlY);bounds.add(tempX,tempY);break;case"a":boundArc(x,y,[current[1],current[2],current[3],current[4],current[5],current[6]+x,current[7]+y],bounds);x+=current[6];y+=current[7];break;case"A":boundArc(x,y,[current[1],current[2],current[3],current[4],current[5],current[6],current[7]],bounds);x=current[6];y=current[7];break;case"z":case"Z":break}previous=current}return bounds}function area(items){var o=items[0];var area;if(o.orient==="horizontal"){area=d3.svg.area().y(function(d){return d.y}).x0(function(d){return d.x}).x1(function(d){return d.x+d.width})}else{area=d3.svg.area().x(function(d){return d.x}).y1(function(d){return d.y}).y0(function(d){return d.y+d.height})}if(o.interpolate)area.interpolate(o.interpolate);if(o.tension!=null)area.tension(o.tension);return area(items)}function line(items){var o=items[0];var line=d3.svg.line().x(function(d){return d.x}).y(function(d){return d.y});if(o.interpolate)line.interpolate(o.interpolate);if(o.tension!=null)line.tension(o.tension);return line(items)}return{parse:parse,render:render,bounds:bounds,area:area,line:line}}();vg.canvas.marks=function(){var parsePath=vg.canvas.path.parse,renderPath=vg.canvas.path.render,halfpi=Math.PI/2,sqrt3=Math.sqrt(3),tan30=Math.tan(30*Math.PI/180),tmpBounds=new vg.Bounds;function arcPath(g,o){var x=o.x||0,y=o.y||0,ir=o.innerRadius||0,or=o.outerRadius||0,sa=(o.startAngle||0)-Math.PI/2,ea=(o.endAngle||0)-Math.PI/2;g.beginPath();if(ir===0)g.moveTo(x,y);else g.arc(x,y,ir,sa,ea,0);g.arc(x,y,or,ea,sa,1);g.closePath()}function areaPath(g,items){var o=items[0],m=o.mark,p=m.pathCache||(m.pathCache=parsePath(vg.canvas.path.area(items)));renderPath(g,p)}function linePath(g,items){var o=items[0],m=o.mark,p=m.pathCache||(m.pathCache=parsePath(vg.canvas.path.line(items)));renderPath(g,p)}function pathPath(g,o){if(o.path==null)return;var p=o.pathCache||(o.pathCache=parsePath(o.path));return renderPath(g,p,o.x,o.y)}function symbolPath(g,o){g.beginPath();var size=o.size!=null?o.size:100,x=o.x,y=o.y,r,t,rx,ry;if(o.shape==null||o.shape==="circle"){r=Math.sqrt(size/Math.PI);g.arc(x,y,r,0,2*Math.PI,0);g.closePath();return}switch(o.shape){case"cross":r=Math.sqrt(size/5)/2;t=3*r;g.moveTo(x-t,y-r);g.lineTo(x-r,y-r);g.lineTo(x-r,y-t);g.lineTo(x+r,y-t);g.lineTo(x+r,y-r);g.lineTo(x+t,y-r);g.lineTo(x+t,y+r);g.lineTo(x+r,y+r);g.lineTo(x+r,y+t);g.lineTo(x-r,y+t);g.lineTo(x-r,y+r);g.lineTo(x-t,y+r);break;case"diamond":ry=Math.sqrt(size/(2*tan30));rx=ry*tan30;g.moveTo(x,y-ry);g.lineTo(x+rx,y);g.lineTo(x,y+ry);g.lineTo(x-rx,y);break;case"square":t=Math.sqrt(size);r=t/2;g.rect(x-r,y-r,t,t);break;case"triangle-down":rx=Math.sqrt(size/sqrt3);ry=rx*sqrt3/2;g.moveTo(x,y+ry);g.lineTo(x+rx,y-ry);g.lineTo(x-rx,y-ry);break;case"triangle-up":rx=Math.sqrt(size/sqrt3);ry=rx*sqrt3/2;g.moveTo(x,y-ry);g.lineTo(x+rx,y+ry);g.lineTo(x-rx,y+ry)}g.closePath()}function lineStroke(g,items){var o=items[0],lw=o.strokeWidth,lc=o.strokeCap;g.lineWidth=lw!=null?lw:vg.config.render.lineWidth;g.lineCap=lc!=null?lc:vg.config.render.lineCap;linePath(g,items)}function ruleStroke(g,o){var x1=o.x||0,y1=o.y||0,x2=o.x2!=null?o.x2:x1,y2=o.y2!=null?o.y2:y1,lw=o.strokeWidth,lc=o.strokeCap;g.lineWidth=lw!=null?lw:vg.config.render.lineWidth;g.lineCap=lc!=null?lc:vg.config.render.lineCap;g.beginPath();g.moveTo(x1,y1);g.lineTo(x2,y2)}function drawPathOne(path,g,o,items){var fill=o.fill,stroke=o.stroke,opac,lc,lw;path(g,items);opac=o.opacity==null?1:o.opacity;if(opac==0||!fill&&!stroke)return;if(fill){g.globalAlpha=opac*(o.fillOpacity==null?1:o.fillOpacity);g.fillStyle=color(g,o,fill);g.fill()}if(stroke){lw=(lw=o.strokeWidth)!=null?lw:vg.config.render.lineWidth;if(lw>0){g.globalAlpha=opac*(o.strokeOpacity==null?1:o.strokeOpacity);g.strokeStyle=color(g,o,stroke);g.lineWidth=lw;g.lineCap=(lc=o.strokeCap)!=null?lc:vg.config.render.lineCap;g.vgLineDash(o.strokeDash||null);g.vgLineDashOffset(o.strokeDashOffset||0);g.stroke()}}}function drawPathAll(path,g,scene,bounds){var i,len,item;for(i=0,len=scene.items.length;i<len;++i){item=scene.items[i];if(bounds&&!bounds.intersects(item.bounds))continue;drawPathOne(path,g,item,item)}}function drawRect(g,scene,bounds){if(!scene.items.length)return;var items=scene.items,o,fill,stroke,opac,lc,lw,x,y,w,h;for(var i=0,len=items.length;i<len;++i){o=items[i];if(bounds&&!bounds.intersects(o.bounds))continue;x=o.x||0;y=o.y||0;w=o.width||0;h=o.height||0;opac=o.opacity==null?1:o.opacity;if(opac==0)continue;if(fill=o.fill){g.globalAlpha=opac*(o.fillOpacity==null?1:o.fillOpacity);g.fillStyle=color(g,o,fill);g.fillRect(x,y,w,h)}if(stroke=o.stroke){lw=(lw=o.strokeWidth)!=null?lw:vg.config.render.lineWidth;if(lw>0){g.globalAlpha=opac*(o.strokeOpacity==null?1:o.strokeOpacity);g.strokeStyle=color(g,o,stroke);g.lineWidth=lw;g.lineCap=(lc=o.strokeCap)!=null?lc:vg.config.render.lineCap;g.vgLineDash(o.strokeDash||null);g.vgLineDashOffset(o.strokeDashOffset||0);g.strokeRect(x,y,w,h)}}}}function drawRule(g,scene,bounds){if(!scene.items.length)return;var items=scene.items,o,stroke,opac,lc,lw,x1,y1,x2,y2;for(var i=0,len=items.length;i<len;++i){o=items[i];if(bounds&&!bounds.intersects(o.bounds))continue;x1=o.x||0;y1=o.y||0;x2=o.x2!=null?o.x2:x1;y2=o.y2!=null?o.y2:y1;opac=o.opacity==null?1:o.opacity;if(opac==0)continue;if(stroke=o.stroke){lw=(lw=o.strokeWidth)!=null?lw:vg.config.render.lineWidth;if(lw>0){g.globalAlpha=opac*(o.strokeOpacity==null?1:o.strokeOpacity);g.strokeStyle=color(g,o,stroke);g.lineWidth=lw;g.lineCap=(lc=o.strokeCap)!=null?lc:vg.config.render.lineCap;g.vgLineDash(o.strokeDash||null);g.vgLineDashOffset(o.strokeDashOffset||0);g.beginPath();g.moveTo(x1,y1);g.lineTo(x2,y2);g.stroke()}}}}function drawImage(g,scene,bounds){if(!scene.items.length)return;var renderer=this,items=scene.items,o;for(var i=0,len=items.length;i<len;++i){o=items[i];if(bounds&&!bounds.intersects(o.bounds))continue;if(!(o.image&&o.image.url===o.url)){o.image=renderer.loadImage(o.url);o.image.url=o.url}var x,y,w,h,opac;w=o.width||o.image&&o.image.width||0;h=o.height||o.image&&o.image.height||0;x=(o.x||0)-(o.align==="center"?w/2:o.align==="right"?w:0);y=(o.y||0)-(o.baseline==="middle"?h/2:o.baseline==="bottom"?h:0);if(o.image.loaded){g.globalAlpha=(opac=o.opacity)!=null?opac:1;g.drawImage(o.image,x,y,w,h)}}}function drawText(g,scene,bounds){if(!scene.items.length)return;var items=scene.items,o,fill,stroke,opac,lw,x,y,r,t;for(var i=0,len=items.length;i<len;++i){o=items[i];if(bounds&&!bounds.intersects(o.bounds))continue;g.font=vg.scene.fontString(o);g.textAlign=o.align||"left";g.textBaseline=o.baseline||"alphabetic";opac=o.opacity==null?1:o.opacity;if(opac==0)continue;x=o.x||0;y=o.y||0;if(r=o.radius){t=(o.theta||0)-Math.PI/2;x+=r*Math.cos(t);y+=r*Math.sin(t)}if(o.angle){g.save();g.translate(x,y);g.rotate(o.angle*Math.PI/180);x=o.dx||0;y=o.dy||0}else{x+=o.dx||0;y+=o.dy||0}if(fill=o.fill){g.globalAlpha=opac*(o.fillOpacity==null?1:o.fillOpacity);g.fillStyle=color(g,o,fill);g.fillText(o.text,x,y)}if(stroke=o.stroke){lw=(lw=o.strokeWidth)!=null?lw:1;if(lw>0){g.globalAlpha=opac*(o.strokeOpacity==null?1:o.strokeOpacity);g.strokeStyle=color(o,stroke);g.lineWidth=lw;g.strokeText(o.text,x,y)}}if(o.angle)g.restore()}}function drawAll(pathFunc){return function(g,scene,bounds){drawPathAll(pathFunc,g,scene,bounds)}}function drawOne(pathFunc){return function(g,scene,bounds){if(!scene.items.length)return;if(bounds&&!bounds.intersects(scene.items[0].bounds))return;drawPathOne(pathFunc,g,scene.items[0],scene.items)}}function drawGroup(g,scene,bounds){if(!scene.items.length)return;var items=scene.items,group,axes,legends,renderer=this,gx,gy,gb,i,n,j,m;drawRect(g,scene,bounds);for(i=0,n=items.length;i<n;++i){group=items[i];axes=group.axisItems||[];legends=group.legendItems||[];gx=group.x||0;gy=group.y||0;g.save();g.translate(gx,gy);if(group.clip){g.beginPath();g.rect(0,0,group.width||0,group.height||0);g.clip()}if(bounds)bounds.translate(-gx,-gy);for(j=0,m=axes.length;j<m;++j){if(axes[j].def.layer==="back"){renderer.draw(g,axes[j],bounds)}}for(j=0,m=group.items.length;j<m;++j){renderer.draw(g,group.items[j],bounds)}for(j=0,m=axes.length;j<m;++j){if(axes[j].def.layer!=="back"){renderer.draw(g,axes[j],bounds)}}for(j=0,m=legends.length;j<m;++j){renderer.draw(g,legends[j],bounds)}if(bounds)bounds.translate(gx,gy);g.restore()}}function color(g,o,value){return value.id?gradient(g,value,o.bounds):value}function gradient(g,p,b){var w=b.width(),h=b.height(),x1=b.x1+p.x1*w,y1=b.y1+p.y1*h,x2=b.x1+p.x2*w,y2=b.y1+p.y2*h,grad=g.createLinearGradient(x1,y1,x2,y2),stop=p.stops,i,n;for(i=0,n=stop.length;i<n;++i){grad.addColorStop(stop[i].offset,stop[i].color)}return grad}function pickGroup(g,scene,x,y,gx,gy){if(scene.items.length===0||scene.bounds&&!scene.bounds.contains(gx,gy)){return false}var items=scene.items,subscene,group,hit,dx,dy,handler=this,i,j;for(i=items.length;--i>=0;){group=items[i];dx=group.x||0;dy=group.y||0;g.save();g.translate(dx,dy);for(j=group.items.length;--j>=0;){subscene=group.items[j];if(subscene.interactive===false)continue;hit=handler.pick(subscene,x,y,gx-dx,gy-dy);if(hit){g.restore();return hit}}g.restore()}return scene.interactive?pickAll(hitTests.group,g,scene,x,y,gx,gy):false}function pickAll(test,g,scene,x,y,gx,gy){if(!scene.items.length)return false;var o,b,i;if(g._ratio!==1){x*=g._ratio;y*=g._ratio}for(i=scene.items.length;--i>=0;){o=scene.items[i];b=o.bounds;if(b&&!b.contains(gx,gy)||!b)continue;if(test(g,o,x,y,gx,gy))return o}return false}function pickArea(g,scene,x,y,gx,gy){if(!scene.items.length)return false;var items=scene.items,o,b,i,di,dd,od,dx,dy;b=items[0].bounds;if(b&&!b.contains(gx,gy))return false;if(g._ratio!==1){x*=g._ratio;y*=g._ratio}if(!hitTests.area(g,items,x,y))return false;return items[0]}function pickLine(g,scene,x,y,gx,gy){if(!scene.items.length)return false;var items=scene.items,o,b,i,di,dd,od,dx,dy;b=items[0].bounds;if(b&&!b.contains(gx,gy))return false;if(g._ratio!==1){x*=g._ratio;y*=g._ratio}if(!hitTests.line(g,items,x,y))return false;return items[0]}function pick(test){return function(g,scene,x,y,gx,gy){return pickAll(test,g,scene,x,y,gx,gy)}}function textHit(g,o,x,y,gx,gy){if(!o.fontSize)return false;if(!o.angle)return true;var b=vg.scene.bounds.text(o,tmpBounds,true),a=-o.angle*Math.PI/180,cos=Math.cos(a),sin=Math.sin(a),x=o.x,y=o.y,px=cos*gx-sin*gy+(x-x*cos+y*sin),py=sin*gx+cos*gy+(y-x*sin-y*cos);return b.contains(px,py)}var hitTests={text:textHit,rect:function(g,o,x,y){return true},image:function(g,o,x,y){return true},group:function(g,o,x,y){return o.fill||o.stroke},rule:function(g,o,x,y){if(!g.isPointInStroke)return false;ruleStroke(g,o);return g.isPointInStroke(x,y)},line:function(g,s,x,y){if(!g.isPointInStroke)return false;lineStroke(g,s);return g.isPointInStroke(x,y)},arc:function(g,o,x,y){arcPath(g,o);return g.isPointInPath(x,y)},area:function(g,s,x,y){areaPath(g,s);return g.isPointInPath(x,y)},path:function(g,o,x,y){pathPath(g,o);return g.isPointInPath(x,y)},symbol:function(g,o,x,y){symbolPath(g,o);return g.isPointInPath(x,y)}};return{draw:{group:drawGroup,area:drawOne(areaPath),line:drawOne(linePath),arc:drawAll(arcPath),path:drawAll(pathPath),symbol:drawAll(symbolPath),rect:drawRect,rule:drawRule,text:drawText,image:drawImage,drawOne:drawOne,drawAll:drawAll},pick:{group:pickGroup,area:pickArea,line:pickLine,arc:pick(hitTests.arc),path:pick(hitTests.path),symbol:pick(hitTests.symbol),rect:pick(hitTests.rect),rule:pick(hitTests.rule),text:pick(hitTests.text),image:pick(hitTests.image),pickAll:pickAll}}}();vg.canvas.Renderer=function(){var renderer=function(){this._ctx=null;this._el=null;this._imgload=0};var prototype=renderer.prototype;prototype.initialize=function(el,width,height,pad){this._el=el;if(!el)return this;var canvas=d3.select(el).selectAll("canvas.marks").data([1]);canvas.enter().append("canvas").attr("class","marks");canvas.exit().remove();return this.resize(width,height,pad)};prototype.resize=function(width,height,pad){this._width=width;this._height=height;this._padding=pad;if(this._el){var canvas=d3.select(this._el).select("canvas.marks");canvas.attr("width",width+pad.left+pad.right).attr("height",height+pad.top+pad.bottom);var s;this._ctx=canvas.node().getContext("2d");this._ctx._ratio=s=scaleCanvas(canvas.node(),this._ctx)||1;this._ctx.setTransform(s,0,0,s,s*pad.left,s*pad.top)}initializeLineDash(this._ctx);return this};function scaleCanvas(canvas,ctx){var devicePixelRatio=window.devicePixelRatio||1,backingStoreRatio=ctx.webkitBackingStorePixelRatio||ctx.mozBackingStorePixelRatio||ctx.msBackingStorePixelRatio||ctx.oBackingStorePixelRatio||ctx.backingStorePixelRatio||1,ratio=devicePixelRatio/backingStoreRatio;if(devicePixelRatio!==backingStoreRatio){var w=canvas.width,h=canvas.height;canvas.setAttribute("width",w*ratio);canvas.setAttribute("height",h*ratio);canvas.style.width=w+"px";canvas.style.height=h+"px"}return ratio}function initializeLineDash(ctx){if(ctx.vgLineDash)return;var NODASH=[];if(ctx.setLineDash){ctx.vgLineDash=function(dash){this.setLineDash(dash||NODASH)};ctx.vgLineDashOffset=function(off){this.lineDashOffset=off}}else if(ctx.webkitLineDash!==undefined){ctx.vgLineDash=function(dash){this.webkitLineDash=dash||NODASH};ctx.vgLineDashOffset=function(off){this.webkitLineDashOffset=off}}else if(ctx.mozDash!==undefined){ctx.vgLineDash=function(dash){this.mozDash=dash};ctx.vgLineDashOffset=function(off){}}else{ctx.vgLineDash=function(dash){};ctx.vgLineDashOffset=function(off){}}}prototype.context=function(ctx){if(ctx){this._ctx=ctx;return this}else return this._ctx};prototype.element=function(){return this._el};prototype.pendingImages=function(){return this._imgload};function translatedBounds(item,bounds){var b=new vg.Bounds(bounds);while((item=item.mark.group)!=null){b.translate(item.x||0,item.y||0)}return b}function getBounds(items){return!items?null:vg.array(items).reduce(function(b,item){return b.union(translatedBounds(item,item.bounds)).union(translatedBounds(item,item["bounds:prev"]))},new vg.Bounds)}function setBounds(g,bounds){var bbox=null;if(bounds){bbox=new vg.Bounds(bounds).round();g.beginPath();g.rect(bbox.x1,bbox.y1,bbox.width(),bbox.height());g.clip()}return bbox}prototype.render=function(scene,items){var g=this._ctx,pad=this._padding,w=this._width+pad.left+pad.right,h=this._height+pad.top+pad.bottom,bb=null,bb2;this._scene=scene;g.save();bb=setBounds(g,getBounds(items));g.clearRect(-pad.left,-pad.top,w,h);
this.draw(g,scene,bb);if(items){g.restore();g.save();bb2=setBounds(g,getBounds(items));if(!bb.encloses(bb2)){g.clearRect(-pad.left,-pad.top,w,h);this.draw(g,scene,bb2)}}g.restore();this._scene=null};prototype.draw=function(ctx,scene,bounds){var marktype=scene.marktype,renderer=vg.canvas.marks.draw[marktype];renderer.call(this,ctx,scene,bounds)};prototype.renderAsync=function(scene){var renderer=this;if(renderer._async_id){clearTimeout(renderer._async_id)}renderer._async_id=setTimeout(function(){renderer.render(scene);delete renderer._async_id},50)};prototype.loadImage=function(uri){var renderer=this,scene=renderer._scene,image=null,url;renderer._imgload+=1;if(vg.config.isNode){image=new(require("canvas").Image);vg.data.load(uri,function(err,data){if(err){vg.error(err);return}image.src=data;image.loaded=true;renderer._imgload-=1})}else{image=new Image;url=vg.config.baseURL+uri;image.onload=function(){vg.log("LOAD IMAGE: "+url);image.loaded=true;renderer._imgload-=1;renderer.renderAsync(scene)};image.src=url}return image};return renderer}();vg.canvas.Handler=function(){var handler=function(el,model){this._active=null;this._handlers={};if(el)this.initialize(el);if(model)this.model(model)};var prototype=handler.prototype;prototype.initialize=function(el,pad,obj){this._el=d3.select(el).node();this._canvas=d3.select(el).select("canvas.marks").node();this._padding=pad;this._obj=obj||null;var canvas=this._canvas,that=this;events.forEach(function(type){canvas.addEventListener(type,function(evt){prototype[type].call(that,evt)})});return this};prototype.padding=function(pad){this._padding=pad;return this};prototype.model=function(model){if(!arguments.length)return this._model;this._model=model;return this};prototype.handlers=function(){var h=this._handlers;return vg.keys(h).reduce(function(a,k){return h[k].reduce(function(a,x){return a.push(x),a},a)},[])};var events=["mousedown","mouseup","click","dblclick","wheel","keydown","keypress","keyup","mousewheel"];events.forEach(function(type){prototype[type]=function(evt){this.fire(type,evt)}});events.push("mousemove");events.push("mouseout");function eventName(name){var i=name.indexOf(".");return i<0?name:name.slice(0,i)}prototype.mousemove=function(evt){var pad=this._padding,b=evt.target.getBoundingClientRect(),x=evt.clientX-b.left,y=evt.clientY-b.top,a=this._active,p=this.pick(this._model.scene(),x,y,x-pad.left,y-pad.top);if(p===a){this.fire("mousemove",evt);return}else if(a){this.fire("mouseout",evt)}this._active=p;if(p){this.fire("mouseover",evt)}};prototype.mouseout=function(evt){if(this._active){this.fire("mouseout",evt)}this._active=null};prototype.DOMMouseScroll=function(evt){this.fire("mousewheel",evt)};prototype.fire=function(type,evt){var a=this._active,h=this._handlers[type];if(a&&h){for(var i=0,len=h.length;i<len;++i){h[i].handler.call(this._obj,evt,a)}}};prototype.on=function(type,handler){var name=eventName(type),h=this._handlers;h=h[name]||(h[name]=[]);h.push({type:type,handler:handler});return this};prototype.off=function(type,handler){var name=eventName(type),h=this._handlers[name];if(!h)return;for(var i=h.length;--i>=0;){if(h[i].type!==type)continue;if(!handler||h[i].handler===handler)h.splice(i,1)}return this};prototype.context=function(){return this._canvas.getContext("2d")};prototype.pick=function(scene,x,y,gx,gy){var g=this.context(),marktype=scene.marktype,picker=vg.canvas.marks.pick[marktype];return picker.call(this,g,scene,x,y,gx,gy)};return handler}();vg.svg={};vg.svg.marks=function(){function x(o){return o.x||0}function y(o){return o.y||0}function xw(o){return o.x+o.width||0}function yh(o){return o.y+o.height||0}function key(o){return o.key}function size(o){return o.size==null?100:o.size}function shape(o){return o.shape||"circle"}var arc_path=d3.svg.arc(),area_path_v=d3.svg.area().x(x).y1(y).y0(yh),area_path_h=d3.svg.area().y(y).x0(xw).x1(x),line_path=d3.svg.line().x(x).y(y),symbol_path=d3.svg.symbol().type(shape).size(size);var mark_id=0,clip_id=0;var textAlign={left:"start",center:"middle",right:"end"};var styles={fill:"fill",fillOpacity:"fill-opacity",stroke:"stroke",strokeWidth:"stroke-width",strokeOpacity:"stroke-opacity",strokeCap:"stroke-linecap",strokeDash:"stroke-dasharray",strokeDashOffset:"stroke-dashoffset",opacity:"opacity"};var styleProps=vg.keys(styles);function style(d){var i,n,prop,name,value,o=d.mark?d:d.length?d[0]:null;if(o===null)return;for(i=0,n=styleProps.length;i<n;++i){prop=styleProps[i];name=styles[prop];value=o[prop];if(value==null){if(name==="fill")this.style.setProperty(name,"none",null);else this.style.removeProperty(name)}else{if(value.id){vg.svg._cur._defs.gradient[value.id]=value;value="url("+window.location.href+"#"+value.id+")"}this.style.setProperty(name,value+"",null)}}}function arc(o){var x=o.x||0,y=o.y||0;this.setAttribute("transform","translate("+x+","+y+")");this.setAttribute("d",arc_path(o))}function area(items){if(!items.length)return;var o=items[0],path=o.orient==="horizontal"?area_path_h:area_path_v;path.interpolate(o.interpolate||"linear").tension(o.tension==null?.7:o.tension);this.setAttribute("d",path(items))}function line(items){if(!items.length)return;var o=items[0];line_path.interpolate(o.interpolate||"linear").tension(o.tension==null?.7:o.tension);this.setAttribute("d",line_path(items))}function path(o){var x=o.x||0,y=o.y||0;this.setAttribute("transform","translate("+x+","+y+")");if(o.path!=null)this.setAttribute("d",o.path)}function rect(o){this.setAttribute("x",o.x||0);this.setAttribute("y",o.y||0);this.setAttribute("width",o.width||0);this.setAttribute("height",o.height||0)}function rule(o){var x1=o.x||0,y1=o.y||0;this.setAttribute("x1",x1);this.setAttribute("y1",y1);this.setAttribute("x2",o.x2!=null?o.x2:x1);this.setAttribute("y2",o.y2!=null?o.y2:y1)}function symbol(o){var x=o.x||0,y=o.y||0;this.setAttribute("transform","translate("+x+","+y+")");this.setAttribute("d",symbol_path(o))}function image(o){var w=o.width||o.image&&o.image.width||0,h=o.height||o.image&&o.image.height||0,x=o.x-(o.align==="center"?w/2:o.align==="right"?w:0),y=o.y-(o.baseline==="middle"?h/2:o.baseline==="bottom"?h:0),url=vg.config.baseURL+o.url;this.setAttributeNS("http://www.w3.org/1999/xlink","href",url);this.setAttribute("x",x);this.setAttribute("y",y);this.setAttribute("width",w);this.setAttribute("height",h)}function fontString(o){return(o.fontStyle?o.fontStyle+" ":"")+(o.fontVariant?o.fontVariant+" ":"")+(o.fontWeight?o.fontWeight+" ":"")+(o.fontSize!=null?o.fontSize:vg.config.render.fontSize)+"px "+(o.font||vg.config.render.font)}function text(o){var x=o.x||0,y=o.y||0,dx=o.dx||0,dy=o.dy||0,a=o.angle||0,r=o.radius||0,align=textAlign[o.align||"left"],base=o.baseline==="top"?".9em":o.baseline==="middle"?".35em":0;if(r){var t=(o.theta||0)-Math.PI/2;x+=r*Math.cos(t);y+=r*Math.sin(t)}this.setAttribute("x",x+dx);this.setAttribute("y",y+dy);this.setAttribute("text-anchor",align);if(a)this.setAttribute("transform","rotate("+a+" "+x+","+y+")");else this.removeAttribute("transform");if(base)this.setAttribute("dy",base);else this.removeAttribute("dy");this.textContent=o.text;this.style.setProperty("font",fontString(o),null)}function group(o){var x=o.x||0,y=o.y||0;this.setAttribute("transform","translate("+x+","+y+")");if(o.clip){var c={width:o.width||0,height:o.height||0},id=o.clip_id||(o.clip_id="clip"+clip_id++);vg.svg._cur._defs.clipping[id]=c;this.setAttribute("clip-path","url(#"+id+")")}}function group_bg(o){var w=o.width||0,h=o.height||0;this.setAttribute("width",w);this.setAttribute("height",h)}function cssClass(def){var cls="type-"+def.type;if(def.name)cls+=" "+def.name;return cls}function draw(tag,attr,nest){return function(g,scene,index){drawMark(g,scene,index,"mark_",tag,attr,nest)}}function drawMark(g,scene,index,prefix,tag,attr,nest){var data=nest?[scene.items]:scene.items,evts=scene.interactive===false?"none":null,grps=g.node().childNodes,notG=tag!=="g",p=(p=grps[index+1])?d3.select(p):g.append("g").attr("id","g"+ ++mark_id).attr("class",cssClass(scene.def));var id=p.attr("id"),s="#"+id+" > "+tag,m=p.selectAll(s).data(data),e=m.enter().append(tag);if(notG){p.style("pointer-events",evts);e.each(function(d){if(d.mark)d._svg=this;else if(d.length)d[0]._svg=this})}else{e.append("rect").attr("class","background").style("pointer-events",evts)}m.exit().remove();m.each(attr);if(notG)m.each(style);else p.selectAll(s+" > rect.background").each(group_bg).each(style);return p}function drawGroup(g,scene,index,prefix){var p=drawMark(g,scene,index,prefix||"group_","g",group),c=p.node().childNodes,n=c.length,i,j,m;for(i=0;i<n;++i){var items=c[i].__data__.items,legends=c[i].__data__.legendItems||[],axes=c[i].__data__.axisItems||[],sel=d3.select(c[i]),idx=0;for(j=0,m=axes.length;j<m;++j){if(axes[j].def.layer==="back"){drawGroup.call(this,sel,axes[j],idx++,"axis_")}}for(j=0,m=items.length;j<m;++j){this.draw(sel,items[j],idx++)}for(j=0,m=axes.length;j<m;++j){if(axes[j].def.layer!=="back"){drawGroup.call(this,sel,axes[j],idx++,"axis_")}}for(j=0,m=legends.length;j<m;++j){drawGroup.call(this,sel,legends[j],idx++,"legend_")}}}return{update:{group:rect,area:area,line:line,arc:arc,path:path,symbol:symbol,rect:rect,rule:rule,text:text,image:image},nested:{area:true,line:true},style:style,draw:{group:drawGroup,area:draw("path",area,true),line:draw("path",line,true),arc:draw("path",arc),path:draw("path",path),symbol:draw("path",symbol),rect:draw("rect",rect),rule:draw("line",rule),text:draw("text",text),image:draw("image",image),draw:draw}}}();vg.svg.Renderer=function(){var renderer=function(){this._svg=null;this._ctx=null;this._el=null;this._defs={gradient:{},clipping:{}}};var prototype=renderer.prototype;prototype.initialize=function(el,width,height,pad){this._el=el;d3.select(el).select("svg.marks").remove();this._svg=d3.select(el).append("svg").attr("class","marks");this._ctx=this._svg.append("g");return this.resize(width,height,pad)};prototype.resize=function(width,height,pad){this._width=width;this._height=height;this._padding=pad;this._svg.attr("width",width+pad.left+pad.right).attr("height",height+pad.top+pad.bottom);this._ctx.attr("transform","translate("+pad.left+","+pad.top+")");return this};prototype.context=function(){return this._ctx};prototype.element=function(){return this._el};prototype.updateDefs=function(){var svg=this._svg,all=this._defs,dgrad=vg.keys(all.gradient),dclip=vg.keys(all.clipping),defs=svg.select("defs"),grad,clip;if(dgrad.length===0&&dclip.length==0){defs.remove();return}if(defs.empty())defs=svg.insert("defs",":first-child");grad=defs.selectAll("linearGradient").data(dgrad,vg.identity);grad.enter().append("linearGradient").attr("id",vg.identity);grad.exit().remove();grad.each(function(id){var def=all.gradient[id],grd=d3.select(this);grd.attr({x1:def.x1,x2:def.x2,y1:def.y1,y2:def.y2});stop=grd.selectAll("stop").data(def.stops);stop.enter().append("stop");stop.exit().remove();stop.attr("offset",function(d){return d.offset}).attr("stop-color",function(d){return d.color})});clip=defs.selectAll("clipPath").data(dclip,vg.identity);clip.enter().append("clipPath").attr("id",vg.identity);clip.exit().remove();clip.each(function(id){var def=all.clipping[id],cr=d3.select(this).selectAll("rect").data([1]);cr.enter().append("rect");cr.attr("x",0).attr("y",0).attr("width",def.width).attr("height",def.height)})};prototype.render=function(scene,items){vg.svg._cur=this;if(items){this.renderItems(vg.array(items))}else{this.draw(this._ctx,scene,-1)}this.updateDefs();delete vg.svg._cur};prototype.renderItems=function(items){var item,node,type,nest,i,n,marks=vg.svg.marks;for(i=0,n=items.length;i<n;++i){item=items[i];node=item._svg;type=item.mark.marktype;item=marks.nested[type]?item.mark.items:item;marks.update[type].call(node,item);marks.style.call(node,item)}};prototype.draw=function(ctx,scene,index){var marktype=scene.marktype,renderer=vg.svg.marks.draw[marktype];renderer.call(this,ctx,scene,index)};return renderer}();vg.svg.Handler=function(){var handler=function(el,model){this._active=null;this._handlers={};if(el)this.initialize(el);if(model)this.model(model)};function svgHandler(handler){var that=this;return function(evt){var target=evt.target,item=target.__data__;if(item){item=item.mark?item:item[0];handler.call(that._obj,evt,item)}}}function eventName(name){var i=name.indexOf(".");return i<0?name:name.slice(0,i)}var prototype=handler.prototype;prototype.initialize=function(el,pad,obj){this._el=d3.select(el).node();this._svg=d3.select(el).select("svg.marks").node();this._padding=pad;this._obj=obj||null;return this};prototype.padding=function(pad){this._padding=pad;return this};prototype.model=function(model){if(!arguments.length)return this._model;this._model=model;return this};prototype.handlers=function(){var h=this._handlers;return vg.keys(h).reduce(function(a,k){return h[k].reduce(function(a,x){return a.push(x),a},a)},[])};prototype.on=function(type,handler){var name=eventName(type),h=this._handlers,dom=d3.select(this._svg).node();var x={type:type,handler:handler,svg:svgHandler.call(this,handler)};h=h[name]||(h[name]=[]);h.push(x);dom.addEventListener(name,x.svg);return this};prototype.off=function(type,handler){var name=eventName(type),h=this._handlers[name],dom=d3.select(this._svg).node();if(!h)return;for(var i=h.length;--i>=0;){if(h[i].type!==type)continue;if(!handler||h[i].handler===handler){dom.removeEventListener(name,h[i].svg);h.splice(i,1)}}return this};return handler}();vg.data={};vg.data.ingestAll=function(data){return vg.isTree(data)?vg_make_tree(vg.data.ingestTree(data[0],data.children)):data.map(vg.data.ingest)};vg.data.ingest=function(datum,index){return{data:datum,index:index}};vg.data.ingestTree=function(node,children,index){var d=vg.data.ingest(node,index||0),c=node[children],n,i;if(c&&(n=c.length)){d.values=Array(n);for(i=0;i<n;++i){d.values[i]=vg.data.ingestTree(c[i],children,i)}}return d};function vg_make_tree(d){d.__vgtree__=true;d.nodes=function(){return vg_tree_nodes(this,[])};return d}function vg_tree_nodes(root,nodes){var c=root.values,n=c?c.length:0,i;nodes.push(root);for(i=0;i<n;++i){vg_tree_nodes(c[i],nodes)}return nodes}function vg_data_duplicate(d){var x=d,i,n;if(vg.isArray(d)){x=[];for(i=0,n=d.length;i<n;++i){x.push(vg_data_duplicate(d[i]))}}else if(vg.isObject(d)){x={};for(i in d){x[i]=vg_data_duplicate(d[i])}}return x}vg.data.mapper=function(func){return function(data){data.forEach(func);return data}};vg.data.size=function(size,group){size=vg.isArray(size)?size:[0,size];size=size.map(function(d){return typeof d==="string"?group[d]:d});return size};vg.data.load=function(uri,callback){var url=vg_load_hasProtocol(uri)?uri:vg.config.baseURL+uri;if(vg.config.isNode){var get=vg_load_isFile(url)?vg_load_file:vg_load_http;get(url,callback)}else{vg_load_xhr(url,callback)}};var vg_load_protocolRE=/^[A-Za-z]+\:\/\//;var vg_load_fileProtocol="file://";function vg_load_hasProtocol(url){return vg_load_protocolRE.test(url)}function vg_load_isFile(url){return url.indexOf(vg_load_fileProtocol)===0}function vg_load_xhr(url,callback){vg.log("LOAD: "+url);if(!vg_url_check(url)){vg.error("URL is not whitelisted: "+url);return}d3.xhr(url,function(err,resp){if(resp)resp=resp.responseText;callback(err,resp)})}function vg_url_check(url){if(!vg.config.domainWhiteList)return true;var a=document.createElement("a");a.href=url;var domain=a.hostname.toLowerCase();return window.location.hostname===domain||vg.config.domainWhiteList.some(function(d){var ind=domain.length-d.length;return d===domain||ind>1&&domain[ind-1]==="."&&domain.lastIndexOf(d)===ind})}function vg_load_file(file,callback){vg.log("LOAD FILE: "+file);var idx=file.indexOf(vg_load_fileProtocol);if(idx>=0)file=file.slice(vg_load_fileProtocol.length);require("fs").readFile(file,callback)}function vg_load_http(url,callback){vg.log("LOAD HTTP: "+url);var req=require("http").request(url,function(res){var pos=0,data=new Buffer(parseInt(res.headers["content-length"],10));res.on("error",function(err){callback(err,null)});res.on("data",function(x){x.copy(data,pos);pos+=x.length});res.on("end",function(){callback(null,data)})});req.on("error",function(err){callback(err)});req.end()}vg.data.read=function(){var formats={},parsers={number:vg.number,"boolean":vg.boolean,date:Date.parse};function read(data,format){var type=format&&format.type||"json";data=formats[type](data,format);if(format&&format.parse)parseValues(data,format.parse);return data}formats.json=function(data,format){var d=vg.isObject(data)?data:JSON.parse(data);if(format&&format.property){d=vg.accessor(format.property)(d)}return d};formats.csv=function(data,format){var d=d3.csv.parse(data);return d};formats.tsv=function(data,format){var d=d3.tsv.parse(data);return d};formats.topojson=function(data,format){if(topojson==null){vg.error("TopoJSON library not loaded.");return[]}var t=vg.isObject(data)?data:JSON.parse(data),obj=[];if(format&&format.feature){obj=(obj=t.objects[format.feature])?topojson.feature(t,obj).features:(vg.error("Invalid TopoJSON object: "+format.feature),[])}else if(format&&format.mesh){obj=(obj=t.objects[format.mesh])?[topojson.mesh(t,t.objects[format.mesh])]:(vg.error("Invalid TopoJSON object: "+format.mesh),[])}else{vg.error("Missing TopoJSON feature or mesh parameter.")}return obj};formats.treejson=function(data,format){data=vg.isObject(data)?data:JSON.parse(data);return vg.tree(data,format.children)};function parseValues(data,types){var cols=vg.keys(types),p=cols.map(function(col){return parsers[types[col]]}),tree=vg.isTree(data);vg_parseArray(tree?[data]:data,cols,p,tree)}function vg_parseArray(data,cols,p,tree){var d,i,j,len,clen;for(i=0,len=data.length;i<len;++i){d=data[i];for(j=0,clen=cols.length;j<clen;++j){d[cols[j]]=p[j](d[cols[j]])}if(tree&&d.values)parseValues(d,cols,p,true)}}read.formats=formats;read.parse=parseValues;return read}();vg.data.aggregate=function(){var groupby=[],fields=[],gaccess,faccess;var OPS={count:function(){},sum:function(c,s,x){return s+x},avg:function(c,s,x){return s+(x-s)/c.count},min:function(c,s,x){return x<s?x:s},max:function(c,s,x){return x>s?x:s}};OPS.min.init=function(){return+Infinity};OPS.max.init=function(){return-Infinity};function fkey(x){return x.op+"_"+x.field}var cells={};function cell(x){var k=gaccess.reduce(function(v,f){return v.push(f(x)),v},[]).join("|");return cells[k]||(cells[k]=new_cell(x))}function new_cell(x){var o={};for(var i=0,f;i<groupby.length;++i){o[groupby[i]]=gaccess[i](x)}o.count=0;for(i=0;i<fields.length;++i){if(fields[i].op==="count")continue;var op=OPS[fields[i].op];o[fkey(fields[i])]=op.init?op.init():0}return o}function aggregate(input){var output=[],k;var keys=fields.map(fkey);var ops=fields.map(function(x){return OPS[x.op]});input.forEach(function(x){var c=cell(x);c.count+=1;for(var i=0;i<fields.length;++i){c[keys[i]]=ops[i](c,c[keys[i]],faccess[i](x))}});var index=0;for(k in cells){output.push({index:index++,data:cells[k]})}cells={};return output}aggregate.fields=function(f){fields=vg.array(f);faccess=fields.map(function(x,i){var xf=x.field;if(xf.indexOf("data.")===0){fields[i]={op:x.op,field:xf.slice(5)}}return vg.accessor(xf)});return aggregate};aggregate.groupby=function(f){groupby=vg.array(f);gaccess=groupby.map(function(x,i){if(x.indexOf("data.")===0){groupby[i]=x.slice(5)}return vg.accessor(x)});return aggregate};return aggregate};vg.data.array=function(){var fields=[];function array(data){return data.map(function(d){var list=[];for(var i=0,len=fields.length;i<len;++i){list.push(fields[i](d))}return list})}array.fields=function(fieldList){fields=vg.array(fieldList).map(vg.accessor);return array};return array};vg.data.bin=function(){var field,accessor,setter,min=undefined,max=undefined,step=undefined,maxbins=20,output="bin";function compare(a,b){return a<b?-1:a>b?1:a>=b?0:NaN}function bisectLeft(a,x,lo,hi){if(arguments.length<3){lo=0}if(arguments.length<4){hi=a.length}while(lo<hi){var mid=lo+hi>>>1;if(compare(a[mid],x)<0){lo=mid+1}else{hi=mid}}return lo}function bins(opt){opt=opt||{};var maxb=opt.maxbins||1024,base=opt.base||10,div=opt.div||[5,2],mins=opt.minstep||0,logb=Math.log(base),level=Math.ceil(Math.log(maxb)/logb),min=opt.min,max=opt.max,span=max-min,step=Math.max(mins,Math.pow(base,Math.round(Math.log(span)/logb)-level)),nbins=Math.ceil(span/step),precision,v,i,eps;if(opt.step!=null){step=opt.step}else if(opt.steps){step=opt.steps[Math.min(opt.steps.length-1,bisectLeft(opt.steps,span/maxb))]}else{do{step*=base;nbins=Math.ceil(span/step)}while(nbins>maxb);for(i=0;i<div.length;++i){v=step/div[i];if(v>=mins&&span/v<=maxb){step=v;nbins=Math.ceil(span/step)}}}v=Math.log(step);precision=v>=0?0:~~(-v/logb)+1;eps=Math.pow(base,-precision-1);min=Math.min(min,Math.floor(min/step+eps)*step);max=Math.ceil(max/step)*step;return{start:min,stop:max,step:step,unit:precision}}function bin(input){var opt={min:min!=null?min:+Infinity,max:max!=null?max:-Infinity,step:step!=null?step:null,maxbins:maxbins};if(min==null||max==null){input.forEach(function(d){var v=accessor(d);if(min==null&&v>opt.max)opt.max=v;if(max==null&&v<opt.min)opt.min=v})}var b=bins(opt);input.forEach(function(d){var v=accessor(d);setter(d,b.start+b.step*~~((v-b.start)/b.step))});return input}bin.min=function(x){min=x;return bin};bin.max=function(x){max=x;return bin};bin.step=function(x){step=x;return bin};bin.maxbins=function(x){maxbins=x;return bin};bin.field=function(f){field=f;accessor=vg.accessor(f);return bin};bin.output=function(f){output=f;setter=vg.mutator(f);return bin};return bin};vg.data.copy=function(){var from=vg.accessor("data"),fields=[],as=null;var copy=vg.data.mapper(function(d){var src=from(d),i,len,source=fields,target=as||fields;for(i=0,len=fields.length;i<len;++i){d[target[i]]=src[fields[i]]}return d});copy.from=function(field){from=vg.accessor(field);return copy};copy.fields=function(fieldList){fields=vg.array(fieldList);return copy};copy.as=function(fieldList){as=vg.array(fieldList);return copy};return copy};vg.data.cross=function(){var other=null,nodiag=false,output={left:"a",right:"b"};function cross(data){var result=[],data2=other||data,o,i,j,n=data.length;for(i=0;i<n;++i){for(j=0;j<n;++j){if(nodiag&&i===j)continue;o={};o[output.left]=data[i];o[output.right]=data2[j];result.push(o)}}return result}cross["with"]=function(d){other=d;return cross};cross.diagonal=function(x){nodiag=!x;return cross};cross.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return cross};return cross};vg.data.facet=function(){var keys=[],sort=null;function facet(data){var result={key:"",keys:[],values:[]},map={},vals=result.values,obj,klist,kstr,len,i,j,k,kv,cmp;if(keys.length===0){vals.push(obj={key:"",keys:[],index:0,values:sort?data.slice():data});if(sort)sort(obj.values);return result}for(i=0,len=data.length;i<len;++i){for(k=0,klist=[],kstr="";k<keys.length;++k){kv=keys[k](data[i]);klist.push(kv);kstr+=(k>0?"|":"")+String(kv)}obj=map[kstr];if(obj===undefined){vals.push(obj=map[kstr]={key:kstr,keys:klist,index:vals.length,values:[]})}obj.values.push(data[i])}if(sort){for(i=0,len=vals.length;i<len;++i){sort(vals[i].values)}}return result}facet.keys=function(k){keys=vg.array(k).map(vg.accessor);return facet};facet.sort=function(s){sort=vg.data.sort().by(s);return facet};return facet};vg.data.filter=function(){var test=null;function filter(data){return test?data.filter(test):data}filter.test=function(func){test=vg.isFunction(func)?func:vg.parse.expr(func);return filter};return filter};vg.data.flatten=function(){function flatten(data){return flat(data,[])}function flat(data,list){if(data.values){for(var i=0,n=data.values.length;i<n;++i){flat(data.values[i],list)}}else{list.push(data)}return list}return flatten};vg.data.fold=function(){var fields=[],accessors=[],output={key:"key",value:"value"};function fold(data){var values=[],item,i,j,n,m=fields.length;for(i=0,n=data.length;i<n;++i){item=data[i];for(j=0;j<m;++j){var o={index:values.length,data:item.data};o[output.key]=fields[j];o[output.value]=accessors[j](item);values.push(o)}}return values}fold.fields=function(f){fields=vg.array(f);accessors=fields.map(vg.accessor);return fold};fold.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return fold};return fold};vg.data.force=function(){var layout=d3.layout.force(),links=null,linkDistance=20,linkStrength=1,charge=-30,iterations=500,size=["width","height"],params=["friction","theta","gravity","alpha"];function force(data,db,group){layout.size(vg.data.size(size,group)).nodes(data);if(links&&db[links]){layout.links(db[links])}layout.start();for(var i=0;i<iterations;++i){layout.tick()}layout.stop();return data}force.links=function(dataSetName){links=dataSetName;return force};force.size=function(sz){size=sz;return force};force.linkDistance=function(field){linkDistance=typeof field==="number"?field:vg.accessor(field);layout.linkDistance(linkDistance);return force};force.linkStrength=function(field){linkStrength=typeof field==="number"?field:vg.accessor(field);layout.linkStrength(linkStrength);return force};force.charge=function(field){charge=typeof field==="number"?field:vg.accessor(field);layout.charge(charge);return force};force.iterations=function(iter){iterations=iter;return force};params.forEach(function(name){force[name]=function(x){layout[name](x);return force}});return force};vg.data.force.dependencies=["links"];vg.data.formula=function(){return function(){var field=null,expr=vg.identity;var formula=vg.data.mapper(function(d,i,list){if(field)d[field]=expr.call(null,d,i,list);return d});formula.field=function(name){field=name;return formula};formula.expr=function(func){expr=vg.isFunction(func)?func:vg.parse.expr(func);return formula};return formula}}();vg.data.geo=function(){var params=["center","scale","translate","rotate","precision","clipAngle"];function geo(){var opt={},projection="mercator",func=d3.geo[projection](),lat=vg.identity,lon=vg.identity,output={x:"x",y:"y"};var map=vg.data.mapper(function(d){var ll=[lon(d),lat(d)],xy=func(ll);d[output.x]=xy[0];d[output.y]=xy[1];return d});map.func=function(){return func};map.projection=function(p){if(projection!==p){projection=p;func=d3.geo[projection]();for(var name in opt){func[name](opt[name])}}return map};params.forEach(function(name){map[name]=function(x){opt[name]=x;func[name](x);return map}});map.lon=function(field){lon=vg.accessor(field);return map};map.lat=function(field){lat=vg.accessor(field);return map};map.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return map};return map}geo.params=params;return geo}();vg.data.geopath=function(){var geopath=d3.geo.path().projection(d3.geo.mercator()),projection="mercator",geojson=vg.identity,opt={},output={path:"path"};var map=vg.data.mapper(function(d){d[output.path]=geopath(geojson(d));return d});map.projection=function(proj){if(projection!==proj){projection=proj;var p=d3.geo[projection]();for(var name in opt){p[name](opt[name])}geopath.projection(p)}return map};vg.data.geo.params.forEach(function(name){map[name]=function(x){opt[name]=x;geopath.projection()[name](x);return map}});map.value=function(field){geojson=vg.accessor(field);return map};map.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return map};return map};vg.data.link=function(){var shape="line",source=vg.accessor("source"),target=vg.accessor("target"),tension=.2,output={path:"path"};function line(d){var s=source(d),t=target(d);return"M"+s.x+","+s.y+"L"+t.x+","+t.y}function curve(d){var s=source(d),t=target(d),dx=t.x-s.x,dy=t.y-s.y,ix=tension*(dx+dy),iy=tension*(dy-dx);return"M"+s.x+","+s.y+"C"+(s.x+ix)+","+(s.y+iy)+" "+(t.x+iy)+","+(t.y-ix)+" "+t.x+","+t.y}function diagonalX(d){var s=source(d),t=target(d),m=(s.x+t.x)/2;return"M"+s.x+","+s.y+"C"+m+","+s.y+" "+m+","+t.y+" "+t.x+","+t.y}function diagonalY(d){var s=source(d),t=target(d),m=(s.y+t.y)/2;return"M"+s.x+","+s.y+"C"+s.x+","+m+" "+t.x+","+m+" "+t.x+","+t.y}var shapes={line:line,curve:curve,diagonal:diagonalX,diagonalX:diagonalX,diagonalY:diagonalY};function link(data){var path=shapes[shape];data.forEach(function(d){d[output.path]=path(d)});return data}link.shape=function(val){shape=val;return link};link.tension=function(val){tension=val;return link};link.source=function(field){source=vg.accessor(field);return link};link.target=function(field){target=vg.accessor(field);return link};link.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return link};return link};vg.data.pie=function(){var one=function(){return 1},value=one,start=0,end=2*Math.PI,sort=false,output={startAngle:"startAngle",endAngle:"endAngle",midAngle:"midAngle"};function pie(data){var values=data.map(function(d,i){return+value(d)}),a=start,k=(end-start)/d3.sum(values),index=d3.range(data.length);if(sort){index.sort(function(a,b){return values[a]-values[b]})}index.forEach(function(i){var d;data[i].value=d=values[i];data[i][output.startAngle]=a;data[i][output.midAngle]=a+.5*d*k;data[i][output.endAngle]=a+=d*k});return data}pie.sort=function(b){sort=b;return pie};pie.value=function(field){value=field?vg.accessor(field):one;return pie};pie.startAngle=function(startAngle){start=Math.PI*startAngle/180;return pie};pie.endAngle=function(endAngle){end=Math.PI*endAngle/180;return pie};pie.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return pie};return pie};vg.data.slice=function(){var by=null,field=vg.accessor("data");function slice(data){data=vg.values(data);if(by==="min"){data=[data[vg.minIndex(data,field)]]}else if(by==="max"){data=[data[vg.maxIndex(data,field)]]}else if(by==="median"){var list=data.slice().sort(function(a,b){a=field(a);b=field(b);return a<b?-1:a>b?1:0});data=[data[~~(list.length/2)]]}else{var idx=vg.array(by);data=data.slice(idx[0],idx[1])}return data}slice.by=function(x){by=x;return slice};slice.field=function(f){field=vg.accessor(f);return slice};return slice};vg.data.sort=function(){var by=null;function sort(data){data=vg.isArray(data)?data:data.values||[];data.sort(by);for(var i=0,n=data.length;i<n;++i)data[i].index=i;return data}sort.by=function(s){by=vg.comparator(s);return sort};return sort};vg.data.stack=function(){var layout=d3.layout.stack(),point=vg.accessor("index"),height=vg.accessor("data"),params=["offset","order"],output={y0:"y2",y1:"y",cy:"cy"};function stack(data){var out_y0=output["y0"],out_y1=output["y1"],out_cy=output["cy"];var series=stacks(data);if(series.length===0)return data;layout.out(function(d,y0,y){if(d.datum){d.datum[out_y0]=y0;d.datum[out_y1]=y+y0;d.datum[out_cy]=y0+y/2}})(series);return data}function stacks(data){var values=vg.values(data),points=[],series=[],a,i,n,j,m,k,p,v,x;if(values.length===0)return series;for(i=0,n=values.length;i<n;++i){a=vg.values(values[i]);for(j=0,m=a.length;j<m;++j){points.push({x:point(a[j]),y:height(a[j]),z:i,datum:a[j]})}series.push([])}points.sort(function(a,b){return a.x<b.x?-1:a.x>b.x?1:a.z<b.z?-1:a.z>b.z?1:0});for(x=points[0].x,i=0,j=0,k=0,n=points.length;k<n;++k){p=points[k];if(p.x!==x){while(i<series.length)series[i++].push({x:j,y:0});x=p.x;i=0;j+=1}while(p.z>i)series[i++].push({x:j,y:0});p.x=j;series[i++].push(p)}while(i<series.length)series[i++].push({x:j,y:0});return series}stack.point=function(field){point=vg.accessor(field);return stack};stack.height=function(field){height=vg.accessor(field);return stack};params.forEach(function(name){stack[name]=function(x){layout[name](x);return stack}});stack.output=function(map){d3.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return stack};return stack};vg.data.stats=function(){var value=vg.accessor("data"),assign=false,median=false,output={count:"count",min:"min",max:"max",sum:"sum",mean:"mean",variance:"variance",stdev:"stdev",median:"median"};function reduce(data){var min=+Infinity,max=-Infinity,sum=0,mean=0,M2=0,i,len,v,delta;var list=(vg.isArray(data)?data:data.values||[]).map(value);
for(i=0,len=list.length;i<len;++i){v=list[i];if(v<min)min=v;if(v>max)max=v;sum+=v;delta=v-mean;mean=mean+delta/(i+1);M2=M2+delta*(v-mean)}M2=M2/(len-1);var o=vg.isArray(data)?{}:data;if(median){list.sort(vg.numcmp);i=list.length>>1;o[output.median]=list.length%2?list[i]:(list[i-1]+list[i])/2}o[output.count]=len;o[output.min]=min;o[output.max]=max;o[output.sum]=sum;o[output.mean]=mean;o[output.variance]=M2;o[output.stdev]=Math.sqrt(M2);if(assign){list=vg.isArray(data)?data:data.values;v={};v[output.count]=len;v[output.min]=min;v[output.max]=max;v[output.sum]=sum;v[output.mean]=mean;v[output.variance]=M2;v[output.stdev]=Math.sqrt(M2);if(median)v[output.median]=o[output.median];for(i=0,len=list.length;i<len;++i){list[i].stats=v}if(vg.isArray(data))o=list}return o}function stats(data){if(vg.isArray(data)){return reduce(data)}else{return(data.values||[]).map(reduce)}}stats.median=function(bool){median=bool||false;return stats};stats.value=function(field){value=vg.accessor(field);return stats};stats.assign=function(b){assign=b;return stats};stats.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return stats};return stats};vg.data.treemap=function(){var layout=d3.layout.treemap().children(function(d){return d.values}),value=vg.accessor("data"),size=["width","height"],params=["round","sticky","ratio","padding"],output={x:"x",y:"y",dx:"width",dy:"height"};function treemap(data,db,group){data=layout.size(vg.data.size(size,group)).value(value).nodes(vg.isTree(data)?data:{values:data});var keys=vg.keys(output),len=keys.length;data.forEach(function(d){var key,val;for(var i=0;i<len;++i){key=keys[i];if(key!==output[key]){val=d[key];delete d[key];d[output[key]]=val}}});return data}treemap.size=function(sz){size=sz;return treemap};treemap.value=function(field){value=vg.accessor(field);return treemap};params.forEach(function(name){treemap[name]=function(x){layout[name](x);return treemap}});treemap.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return treemap};return treemap};vg.data.truncate=function(){var value=vg.accessor("data"),as="truncate",position="right",ellipsis="...",wordBreak=true,limit=100;var truncate=vg.data.mapper(function(d){var text=vg.truncate(value(d),limit,position,wordBreak,ellipsis);return d[as]=text,d});truncate.value=function(field){value=vg.accessor(field);return truncate};truncate.output=function(field){as=field;return truncate};truncate.limit=function(len){limit=+len;return truncate};truncate.position=function(pos){position=pos;return truncate};truncate.ellipsis=function(str){ellipsis=str+"";return truncate};truncate.wordbreak=function(b){wordBreak=!!b;return truncate};return truncate};vg.data.unique=function(){var field=null,as="field";function unique(data){return vg.unique(data,field).map(function(x){var o={};o[as]=x;return o})}unique.field=function(f){field=vg.accessor(f);return unique};unique.as=function(x){as=x;return unique};return unique};vg.data.window=function(){var size=2,step=1;function win(data){data=vg.isArray(data)?data:data.values||[];var runs=[],i,j,n=data.length-size,curr;for(i=0;i<=n;i+=step){for(j=0,curr=[];j<size;++j)curr.push(data[i+j]);runs.push({key:i,values:curr})}return{values:runs}}win.size=function(n){size=n;return win};win.step=function(n){step=n;return win};return win};vg.data.wordcloud=function(){var layout=d3.layout.cloud().size([900,500]),text=vg.accessor("data"),size=["width","height"],fontSize=function(){return 14},rotate=function(){return 0},params=["font","fontStyle","fontWeight","padding"];var output={x:"x",y:"y",size:"fontSize",font:"font",rotate:"angle"};function cloud(data,db,group){function finish(tags,bounds){var size=layout.size(),dx=size[0]/2,dy=size[1]/2,keys=vg.keys(output),key,d,i,n,k,m=keys.length;data.sort(function(a,b){return fontSize(b)-fontSize(a)});for(i=0,n=tags.length;i<n;++i){d=data[i];for(k=0;k<m;++k){key=keys[k];d[output[key]]=tags[i][key];if(key==="x")d[output.x]+=dx;if(key==="y")d[output.y]+=dy}}}layout.size(vg.data.size(size,group)).text(text).fontSize(fontSize).rotate(rotate).words(data).on("end",finish).start();return data}cloud.text=function(field){text=vg.accessor(field);return cloud};cloud.size=function(sz){size=sz;return cloud};cloud.fontSize=function(field){fontSize=vg.accessor(field);return cloud};cloud.rotate=function(x){var v;if(vg.isObject(x)&&!Array.isArray(x)){if(x.random!==undefined){v=(v=x.random)?vg.array(v):[0];rotate=function(){return v[~~(Math.random()*v.length-1e-5)]}}else if(x.alternate!==undefined){v=(v=x.alternate)?vg.array(v):[0];rotate=function(d,i){return v[i%v.length]}}}else{rotate=vg.accessor(field)}return cloud};params.forEach(function(name){cloud[name]=function(x){layout[name](x);return cloud}});cloud.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return cloud};return cloud};vg.data.zip=function(){var z=null,as="zip",key=vg.accessor("data"),defaultValue=undefined,withKey=null;function zip(data,db){var zdata=db[z],zlen=zdata.length,v,d,i,len,map;if(withKey){map={};zdata.forEach(function(s){map[withKey(s)]=s})}for(i=0,len=data.length;i<len;++i){d=data[i];d[as]=map?(v=map[key(d)])!=null?v:defaultValue:zdata[i%zlen]}return data}zip["with"]=function(d){z=d;return zip};zip["default"]=function(d){defaultValue=d;return zip};zip.as=function(name){as=name;return zip};zip.key=function(k){key=vg.accessor(k);return zip};zip.withKey=function(k){withKey=vg.accessor(k);return zip};return zip};vg.data.zip.dependencies=["with"];vg.parse={};vg.parse.axes=function(){var ORIENT={x:"bottom",y:"left",top:"top",bottom:"bottom",left:"left",right:"right"};function axes(spec,axes,scales){(spec||[]).forEach(function(def,index){axes[index]=axes[index]||vg.scene.axis();axis(def,index,axes[index],scales)})}function axis(def,index,axis,scales){if(def.scale!==undefined){axis.scale(scales[def.scale])}axis.orient(def.orient||ORIENT[def.type]);axis.offset(def.offset||0);axis.layer(def.layer||"front");axis.grid(def.grid||false);axis.title(def.title||null);axis.titleOffset(def.titleOffset!=null?def.titleOffset:vg.config.axis.titleOffset);axis.tickValues(def.values||null);axis.tickFormat(def.format||null);axis.tickSubdivide(def.subdivide||0);axis.tickPadding(def.tickPadding||vg.config.axis.padding);var size=[];if(def.tickSize!==undefined){for(var i=0;i<3;++i)size.push(def.tickSize)}else{var ts=vg.config.axis.tickSize;size=[ts,ts,ts]}if(def.tickSizeMajor!=null)size[0]=def.tickSizeMajor;if(def.tickSizeMinor!=null)size[1]=def.tickSizeMinor;if(def.tickSizeEnd!=null)size[2]=def.tickSizeEnd;if(size.length){axis.tickSize.apply(axis,size)}if(def.ticks!=null){var ticks=vg.isArray(def.ticks)?def.ticks:[def.ticks];axis.ticks.apply(axis,ticks)}else{axis.ticks(vg.config.axis.ticks)}var p=def.properties;if(p&&p.ticks){axis.majorTickProperties(p.majorTicks?vg.extend({},p.ticks,p.majorTicks):p.ticks);axis.minorTickProperties(p.minorTicks?vg.extend({},p.ticks,p.minorTicks):p.ticks)}else{axis.majorTickProperties(p&&p.majorTicks||{});axis.minorTickProperties(p&&p.minorTicks||{})}axis.tickLabelProperties(p&&p.labels||{});axis.titleProperties(p&&p.title||{});axis.gridLineProperties(p&&p.grid||{});axis.domainProperties(p&&p.axis||{})}return axes}();vg.parse.data=function(spec,callback){var model={defs:spec,load:{},flow:{},deps:{},source:{},sorted:null};var count=0;function load(d){return function(error,data){if(error){vg.error("LOADING FAILED: "+d.url)}else{model.load[d.name]=vg.data.read(data.toString(),d.format)}if(--count===0)callback()}}(spec||[]).forEach(function(d){if(d.url){count+=1;vg.data.load(d.url,load(d))}else if(d.values){model.load[d.name]=vg.data.read(d.values,d.format)}else if(d.source){(model.source[d.source]||(model.source[d.source]=[])).push(d.name)}if(d.transform){var flow=vg.parse.dataflow(d);model.flow[d.name]=flow;flow.dependencies.forEach(function(dep){(model.deps[dep]||(model.deps[dep]=[])).push(d.name)})}});var names=(spec||[]).map(vg.accessor("name")),order=[],v={},n;function visit(n){if(v[n]===1)return;if(!v[n]){v[n]=1;(model.source[n]||[]).forEach(visit);(model.deps[n]||[]).forEach(visit);v[n]=2;order.push(n)}}while(names.length){if(v[n=names.pop()]!==2)visit(n)}model.sorted=order.reverse();if(count===0)setTimeout(callback,1);return model};vg.parse.dataflow=function(def){var tx=(def.transform||[]).map(vg.parse.transform),df=tx.length?function(data,db,group){return tx.reduce(function(d,t){return t(d,db,group)},data)}:vg.identity;df.transforms=tx;df.dependencies=vg.keys((def.transform||[]).reduce(function(map,tdef){var deps=vg.data[tdef.type].dependencies;if(deps)deps.forEach(function(d){if(tdef[d])map[tdef[d]]=1});return map},{}));return df};vg.parse.expr=function(){var CONSTANT={E:"Math.E",LN2:"Math.LN2",LN10:"Math.LN10",LOG2E:"Math.LOG2E",LOG10E:"Math.LOG10E",PI:"Math.PI",SQRT1_2:"Math.SQRT1_2",SQRT2:"Math.SQRT2"};var FUNCTION={abs:"Math.abs",acos:"Math.acos",asin:"Math.asin",atan:"Math.atan",atan2:"Math.atan2",ceil:"Math.ceil",cos:"Math.cos",exp:"Math.exp",floor:"Math.floor",log:"Math.log",max:"Math.max",min:"Math.min",pow:"Math.pow",random:"Math.random",round:"Math.round",sin:"Math.sin",sqrt:"Math.sqrt",tan:"Math.tan"};var lexer=/([\"\']|[\=\<\>\~\&\|\?\:\+\-\/\*\%\!\^\,\;\[\]\{\}\(\) ]+)/;return function(x){if(vg.config.safeMode){vg.error("Safe mode: Expression parsing disabled.");return vg.true}var tokens=x.split(lexer),t,v,i,n,sq,dq;for(sq=0,dq=0,i=0,n=tokens.length;i<n;++i){var t=tokens[i];if(t==="'"){if(!dq)sq=!sq;continue}if(t==='"'){if(!sq)dq=!dq;continue}if(dq||sq)continue;if(CONSTANT[t]){tokens[i]=CONSTANT[t]}if(FUNCTION[t]&&(v=tokens[i+1])&&v[0]==="("){tokens[i]=FUNCTION[t]}}return Function("d","index","data","return ("+tokens.join("")+");")}}();vg.parse.legends=function(){function legends(spec,legends,scales){(spec||[]).forEach(function(def,index){legends[index]=legends[index]||vg.scene.legend();legend(def,index,legends[index],scales)})}function legend(def,index,legend,scales){legend.size(def.size?scales[def.size]:null);legend.shape(def.shape?scales[def.shape]:null);legend.fill(def.fill?scales[def.fill]:null);legend.stroke(def.stroke?scales[def.stroke]:null);if(def.orient)legend.orient(def.orient);if(def.offset!=null)legend.offset(def.offset);legend.title(def.title||null);legend.values(def.values||null);legend.format(def.format!==undefined?def.format:null);var p=def.properties;legend.titleProperties(p&&p.title||{});legend.labelProperties(p&&p.labels||{});legend.legendProperties(p&&p.legend||{});legend.symbolProperties(p&&p.symbols||{});legend.gradientProperties(p&&p.gradient||{})}return legends}();vg.parse.mark=function(mark){var props=mark.properties,group=mark.marks;vg.keys(props).forEach(function(k){props[k]=vg.parse.properties(mark.type,props[k])});if(mark.delay){mark.delay=vg.parse.properties(mark.type,{delay:mark.delay})}if(mark.from){var name=mark.from.data,tx=vg.parse.dataflow(mark.from);mark.from=function(db,group,parentData){var data=vg.scene.data(name?db[name]:null,parentData);return tx(data,db,group)}}if(group){mark.marks=group.map(vg.parse.mark)}return mark};vg.parse.marks=function(spec,width,height){return{type:"group",width:width,height:height,scales:spec.scales||[],axes:spec.axes||[],legends:spec.legends||[],marks:(spec.marks||[]).map(vg.parse.mark)}};vg.parse.padding=function(pad){if(pad==null)return"auto";else if(vg.isString(pad))return pad==="strict"?"strict":"auto";else if(vg.isObject(pad))return pad;var p=vg.isNumber(pad)?pad:20;return{top:p,left:p,right:p,bottom:p}};vg.parse.properties=function(){function compile(mark,spec){var code="",names=vg.keys(spec),i,len,name,ref,vars={};code+="var o = trans ? {} : item;\n";for(i=0,len=names.length;i<len;++i){ref=spec[name=names[i]];code+=i>0?"\n  ":"  ";code+="o."+name+" = "+valueRef(name,ref)+";";vars[name]=true}if(vars.x2){if(vars.x){code+="\n  if (o.x > o.x2) { "+"var t = o.x; o.x = o.x2; o.x2 = t; };";code+="\n  o.width = (o.x2 - o.x);"}else if(vars.width){code+="\n  o.x = (o.x2 - o.width);"}else{code+="\n  o.x = o.x2;"}}if(vars.xc){if(vars.width){code+="\n  o.x = (o.xc - o.width/2);"}else{code+="\n  o.x = o.xc;"}}if(vars.y2){if(vars.y){code+="\n  if (o.y > o.y2) { "+"var t = o.y; o.y = o.y2; o.y2 = t; };";code+="\n  o.height = (o.y2 - o.y);"}else if(vars.height){code+="\n  o.y = (o.y2 - o.height);"}else{code+="\n  o.y = o.y2;"}}if(vars.yc){if(vars.height){code+="\n  o.y = (o.yc - o.height/2);"}else{code+="\n  o.y = o.yc;"}}if(hasPath(mark,vars))code+="\n  item.touch();";code+="\n  if (trans) trans.interpolate(item, o);";try{return Function("item","group","trans",code)}catch(e){vg.error(e);vg.log(code)}}function hasPath(mark,vars){return vars.path||(mark==="area"||mark==="line")&&(vars.x||vars.x2||vars.width||vars.y||vars.y2||vars.height||vars.tension||vars.interpolate)}var GROUP_VARS={width:1,height:1,"mark.group.width":1,"mark.group.height":1};function valueRef(name,ref){if(ref==null)return null;var isColor=name==="fill"||name==="stroke";if(isColor){if(ref.c){return colorRef("hcl",ref.h,ref.c,ref.l)}else if(ref.h||ref.s){return colorRef("hsl",ref.h,ref.s,ref.l)}else if(ref.l||ref.a){return colorRef("lab",ref.l,ref.a,ref.b)}else if(ref.r||ref.g||ref.b){return colorRef("rgb",ref.r,ref.g,ref.b)}}var val="item.datum.data";if(ref.value!==undefined){val=vg.str(ref.value)}if(ref.group!=null){var grp="group.datum";if(vg.isString(ref.group)){grp=GROUP_VARS[ref.group]?"group."+ref.group:"group.datum["+vg.field(ref.group).map(vg.str).join("][")+"]"}}if(ref.field!=null){if(vg.isString(ref.field)){val="item.datum["+vg.field(ref.field).map(vg.str).join("][")+"]";if(ref.group!=null){val="this.accessor("+val+")("+grp+")"}}else{val="this.accessor(group.datum["+vg.field(ref.field.group).map(vg.str).join("][")+"])(item.datum.data)"}}else if(ref.group!=null){val=grp}if(ref.scale!=null){var scale=vg.isString(ref.scale)?vg.str(ref.scale):(ref.scale.group?"group":"item")+".datum["+vg.str(ref.scale.group||ref.scale.field)+"]";scale="group.scales["+scale+"]";val=scale+(ref.band?".rangeBand()":"("+val+")")}val="("+(ref.mult?vg.number(ref.mult)+" * ":"")+val+")"+(ref.offset?" + "+vg.number(ref.offset):"");return val}function colorRef(type,x,y,z){var xx=x?valueRef("",x):vg.config.color[type][0],yy=y?valueRef("",y):vg.config.color[type][1],zz=z?valueRef("",z):vg.config.color[type][2];return"(this.d3."+type+"("+[xx,yy,zz].join(",")+') + "")'}return compile}();vg.parse.scales=function(){var LINEAR="linear",ORDINAL="ordinal",LOG="log",POWER="pow",TIME="time",QUANTILE="quantile",GROUP_PROPERTY={width:1,height:1};function scales(spec,scales,db,group){return(spec||[]).reduce(function(o,def){var name=def.name,prev=name+":prev";o[name]=scale(def,o[name],db,group);o[prev]=o[prev]||o[name];return o},scales||{})}function scale(def,scale,db,group){var s=instance(def,scale),m=s.type===ORDINAL?ordinal:quantitative,rng=range(def,group),data=vg.values(group.datum);m(def,s,rng,db,data);return s}function instance(def,scale){var type=def.type||LINEAR;if(!scale||type!==scale.type){var ctor=vg.config.scale[type]||d3.scale[type];if(!ctor)vg.error("Unrecognized scale type: "+type);(scale=ctor()).type=scale.type||type;scale.scaleName=def.name}return scale}function ordinal(def,scale,rng,db,data){var dataDrivenRange=false,pad=def.padding||0,outer=def.outerPadding||0,domain,sort,str,refs;if(vg.isObject(def.range)&&!vg.isArray(def.range)){dataDrivenRange=true;refs=def.range.fields||vg.array(def.range);rng=extract(refs,db,data)}sort=def.sort&&!dataDrivenRange;domain=domainValues(def,db,data,sort);if(domain)scale.domain(domain);if(def.bandWidth){var bw=def.bandWidth,len=domain.length,start=rng[0]||0,space=def.points?pad*bw:pad*bw*(len-1)+2*outer;rng=[start,start+(bw*len+space)]}str=typeof rng[0]==="string";if(str||rng.length>2||rng.length===1||dataDrivenRange){scale.range(rng)}else if(def.points){scale.rangePoints(rng,pad)}else if(def.round||def.round===undefined){scale.rangeRoundBands(rng,pad,outer)}else{scale.rangeBands(rng,pad,outer)}}function quantitative(def,scale,rng,db,data){var domain,interval;domain=def.type===QUANTILE?domainValues(def,db,data,false):domainMinMax(def,db,data);scale.domain(domain);if(def.range==="height")rng=rng.reverse();scale[def.round&&scale.rangeRound?"rangeRound":"range"](rng);if(def.exponent&&def.type===POWER)scale.exponent(def.exponent);if(def.clamp)scale.clamp(true);if(def.nice){if(def.type===TIME){interval=d3.time[def.nice];if(!interval)vg.error("Unrecognized interval: "+interval);scale.nice(interval)}else{scale.nice()}}}function extract(refs,db,data){return refs.reduce(function(values,r){var dat=vg.values(db[r.data]||data),get=vg.accessor(vg.isString(r.field)?r.field:"data."+vg.accessor(r.field.group)(data));return vg.unique(dat,get,values)},[])}function domainValues(def,db,data,sort){var domain=def.domain,values,refs;if(vg.isArray(domain)){values=sort?domain.slice():domain}else if(vg.isObject(domain)){refs=domain.fields||vg.array(domain);values=extract(refs,db,data)}if(values&&sort)values.sort(vg.cmp);return values}function domainMinMax(def,db,data){var domain=[null,null],refs,z;function extract(ref,min,max,z){var dat=vg.values(db[ref.data]||data);var fields=vg.array(ref.field).map(function(f){return vg.isString(f)?f:"data."+vg.accessor(f.group)(data)});fields.forEach(function(f,i){f=vg.accessor(f);if(min)domain[0]=d3.min([domain[0],d3.min(dat,f)]);if(max)domain[z]=d3.max([domain[z],d3.max(dat,f)])})}if(def.domain!==undefined){if(vg.isArray(def.domain)){domain=def.domain.slice()}else if(vg.isObject(def.domain)){refs=def.domain.fields||vg.array(def.domain);refs.forEach(function(r){extract(r,1,1,1)})}else{domain=def.domain}}z=domain.length-1;if(def.domainMin!==undefined){if(vg.isObject(def.domainMin)){domain[0]=null;refs=def.domainMin.fields||vg.array(def.domainMin);refs.forEach(function(r){extract(r,1,0,z)})}else{domain[0]=def.domainMin}}if(def.domainMax!==undefined){if(vg.isObject(def.domainMax)){domain[z]=null;refs=def.domainMax.fields||vg.array(def.domainMax);refs.forEach(function(r){extract(r,0,1,z)})}else{domain[z]=def.domainMax}}if(def.type!==LOG&&def.type!==TIME&&(def.zero||def.zero===undefined)){domain[0]=Math.min(0,domain[0]);domain[z]=Math.max(0,domain[z])}return domain}function range(def,group){var rng=[null,null];if(def.range!==undefined){if(typeof def.range==="string"){if(GROUP_PROPERTY[def.range]){rng=[0,group[def.range]]}else if(vg.config.range[def.range]){rng=vg.config.range[def.range]}else{vg.error("Unrecogized range: "+def.range);return rng}}else if(vg.isArray(def.range)){rng=def.range}else if(vg.isObject(def.range)){return null}else{rng=[0,def.range]}}if(def.rangeMin!==undefined){rng[0]=def.rangeMin}if(def.rangeMax!==undefined){rng[rng.length-1]=def.rangeMax}if(def.reverse!==undefined){var rev=def.reverse;if(vg.isObject(rev)){rev=vg.accessor(rev.field)(group.datum)}if(rev)rng=rng.reverse()}return rng}return scales}();vg.parse.spec=function(spec,callback,viewFactory){viewFactory=viewFactory||vg.ViewFactory;function parse(spec){spec=vg.duplicate(spec);var width=spec.width||500,height=spec.height||500,viewport=spec.viewport||null;var defs={width:width,height:height,viewport:viewport,padding:vg.parse.padding(spec.padding),marks:vg.parse.marks(spec,width,height),data:vg.parse.data(spec.data,function(){callback(viewConstructor)})};var viewConstructor=viewFactory(defs)}vg.isObject(spec)?parse(spec):d3.json(spec,function(error,json){error?vg.error(error):parse(json)})};vg.parse.transform=function(def){var tx=vg.data[def.type]();vg.keys(def).forEach(function(k){if(k==="type")return;tx[k](def[k])});return tx};vg.scene={};vg.scene.GROUP="group",vg.scene.ENTER=0,vg.scene.UPDATE=1,vg.scene.EXIT=2;vg.scene.DEFAULT_DATA={sentinel:1};vg.scene.data=function(data,parentData){var DEFAULT=vg.scene.DEFAULT_DATA;data=vg.values(data||parentData||[DEFAULT]);if(data===DEFAULT)data=[DEFAULT];return data};vg.scene.fontString=function(o){return(o.fontStyle?o.fontStyle+" ":"")+(o.fontVariant?o.fontVariant+" ":"")+(o.fontWeight?o.fontWeight+" ":"")+(o.fontSize!=null?o.fontSize:vg.config.render.fontSize)+"px "+(o.font||vg.config.render.font)};vg.scene.Item=function(){function item(mark){this.mark=mark}var prototype=item.prototype;prototype.hasPropertySet=function(name){var props=this.mark.def.properties;return props&&props[name]!=null};prototype.cousin=function(offset,index){if(offset===0)return this;offset=offset||-1;var mark=this.mark,group=mark.group,iidx=index==null?mark.items.indexOf(this):index,midx=group.items.indexOf(mark)+offset;return group.items[midx].items[iidx]};prototype.sibling=function(offset){if(offset===0)return this;offset=offset||-1;var mark=this.mark,iidx=mark.items.indexOf(this)+offset;return mark.items[iidx]};prototype.remove=function(){var item=this,list=item.mark.items,i=list.indexOf(item);if(i>=0)i===list.length-1?list.pop():list.splice(i,1);return item};prototype.touch=function(){if(this.pathCache)this.pathCache=null;if(this.mark.pathCache)this.mark.pathCache=null};return item}();vg.scene.item=function(mark){return new vg.scene.Item(mark)};vg.scene.visit=function(node,func){var i,n,s,m,items;if(func(node))return true;var sets=["items","axisItems","legendItems"];for(s=0,m=sets.length;s<m;++s){if(items=node[sets[s]]){for(i=0,n=items.length;i<n;++i){if(vg.scene.visit(items[i],func))return true}}}};vg.scene.build=function(){var GROUP=vg.scene.GROUP,ENTER=vg.scene.ENTER,UPDATE=vg.scene.UPDATE,EXIT=vg.scene.EXIT,DEFAULT={sentinel:1};function build(def,db,node,parentData,reentrant){var data=vg.scene.data(def.from?def.from(db,node,parentData):null,parentData);node=buildNode(def,node);node.items=buildItems(def,data,node);buildTrans(def,node);if(def.type===GROUP){buildGroup(def,db,node,reentrant)}return node}function buildNode(def,node){node=node||{};node.def=def;node.marktype=def.type;node.interactive=!(def.interactive===false);return node}function buildItems(def,data,node){var keyf=keyFunction(def.key),prev=node.items||[],next=[],map={},i,key,len,item,datum,enter;for(i=0,len=prev.length;i<len;++i){item=prev[i];item.status=EXIT;if(keyf)map[item.key]=item}for(i=0,len=data.length;i<len;++i){datum=data[i];key=i;item=keyf?map[key=keyf(datum)]:prev[i];enter=item?false:(item=vg.scene.item(node),true);item.status=enter?ENTER:UPDATE;item.datum=datum;item.key=key;next.push(item)}for(i=0,len=prev.length;i<len;++i){item=prev[i];if(item.status===EXIT){item.key=keyf?item.key:next.length;next.splice(item.index,0,item)}}return next}function buildGroup(def,db,node,reentrant){var groups=node.items,marks=def.marks,i,len,m,mlen,name,group;for(i=0,len=groups.length;i<len;++i){group=groups[i];if(!reentrant&&group.scales)for(name in group.scales){if(name.indexOf(":prev")<0){group.scales[name+":prev"]=group.scales[name].copy()}}group.items=group.items||[];for(m=0,mlen=marks.length;m<mlen;++m){group.items[m]=build(marks[m],db,group.items[m],group.datum);group.items[m].group=group}}}function buildTrans(def,node){if(def.duration)node.duration=def.duration;if(def.ease)node.ease=d3.ease(def.ease);if(def.delay){var items=node.items,group=node.group,n=items.length,i;for(i=0;i<n;++i)def.delay.call(this,items[i],group)}}function keyFunction(key){if(key==null)return null;var f=vg.array(key).map(vg.accessor);return function(d){for(var s="",i=0,n=f.length;i<n;++i){if(i>0)s+="|";s+=String(f[i](d))}return s}}return build}();vg.scene.bounds=function(){var parse=vg.canvas.path.parse,boundPath=vg.canvas.path.bounds,areaPath=vg.canvas.path.area,linePath=vg.canvas.path.line,halfpi=Math.PI/2,sqrt3=Math.sqrt(3),tan30=Math.tan(30*Math.PI/180),gfx=null;function context(){return gfx||(gfx=(vg.config.isNode?new(require("canvas"))(1,1):d3.select("body").append("canvas").attr("class","vega_hidden").attr("width",1).attr("height",1).style("display","none").node()).getContext("2d"))}function pathBounds(o,path,bounds){if(path==null){bounds.set(0,0,0,0)}else{boundPath(path,bounds);if(o.stroke&&o.opacity!==0&&o.strokeWidth>0){bounds.expand(o.strokeWidth)}}return bounds}function path(o,bounds){var p=o.path?o.pathCache||(o.pathCache=parse(o.path)):null;return pathBounds(o,p,bounds)}function area(o,bounds){var items=o.mark.items,o=items[0];var p=o.pathCache||(o.pathCache=parse(areaPath(items)));return pathBounds(items[0],p,bounds)}function line(o,bounds){var items=o.mark.items,o=items[0];var p=o.pathCache||(o.pathCache=parse(linePath(items)));return pathBounds(items[0],p,bounds)}function rect(o,bounds){var x=o.x||0,y=o.y||0,w=x+o.width||0,h=y+o.height||0;bounds.set(x,y,w,h);if(o.stroke&&o.opacity!==0&&o.strokeWidth>0){bounds.expand(o.strokeWidth)}return bounds}function image(o,bounds){var w=o.width||0,h=o.height||0,x=(o.x||0)-(o.align==="center"?w/2:o.align==="right"?w:0),y=(o.y||0)-(o.baseline==="middle"?h/2:o.baseline==="bottom"?h:0);return bounds.set(x,y,x+w,y+h)}function rule(o,bounds){var x1,y1;bounds.set(x1=o.x||0,y1=o.y||0,o.x2!=null?o.x2:x1,o.y2!=null?o.y2:y1);if(o.stroke&&o.opacity!==0&&o.strokeWidth>0){bounds.expand(o.strokeWidth)}return bounds}function arc(o,bounds){var cx=o.x||0,cy=o.y||0,ir=o.innerRadius||0,or=o.outerRadius||0,sa=(o.startAngle||0)-halfpi,ea=(o.endAngle||0)-halfpi,xmin=Infinity,xmax=-Infinity,ymin=Infinity,ymax=-Infinity,a,i,n,x,y,ix,iy,ox,oy;var angles=[sa,ea],s=sa-sa%halfpi;for(i=0;i<4&&s<ea;++i,s+=halfpi){angles.push(s)}for(i=0,n=angles.length;i<n;++i){a=angles[i];x=Math.cos(a);ix=ir*x;ox=or*x;y=Math.sin(a);iy=ir*y;oy=or*y;xmin=Math.min(xmin,ix,ox);xmax=Math.max(xmax,ix,ox);ymin=Math.min(ymin,iy,oy);ymax=Math.max(ymax,iy,oy)}bounds.set(cx+xmin,cy+ymin,cx+xmax,cy+ymax);if(o.stroke&&o.opacity!==0&&o.strokeWidth>0){bounds.expand(o.strokeWidth)}return bounds}function symbol(o,bounds){var size=o.size!=null?o.size:100,x=o.x||0,y=o.y||0,r,t,rx,ry;switch(o.shape){case"cross":r=Math.sqrt(size/5)/2;t=3*r;bounds.set(x-t,y-r,x+t,y+r);break;case"diamond":ry=Math.sqrt(size/(2*tan30));rx=ry*tan30;bounds.set(x-rx,y-ry,x+rx,y+ry);break;case"square":t=Math.sqrt(size);r=t/2;bounds.set(x-r,y-r,x+r,y+r);break;case"triangle-down":rx=Math.sqrt(size/sqrt3);ry=rx*sqrt3/2;bounds.set(x-rx,y-ry,x+rx,y+ry);break;case"triangle-up":rx=Math.sqrt(size/sqrt3);ry=rx*sqrt3/2;bounds.set(x-rx,y-ry,x+rx,y+ry);break;default:r=Math.sqrt(size/Math.PI);bounds.set(x-r,y-r,x+r,y+r)}if(o.stroke&&o.opacity!==0&&o.strokeWidth>0){bounds.expand(o.strokeWidth)}return bounds}function text(o,bounds,noRotate){var x=(o.x||0)+(o.dx||0),y=(o.y||0)+(o.dy||0),h=o.fontSize||vg.config.render.fontSize,a=o.align,b=o.baseline,r=o.radius||0,g=context(),w,t;g.font=vg.scene.fontString(o);g.textAlign=a||"left";g.textBaseline=b||"alphabetic";w=g.measureText(o.text||"").width;if(r){t=(o.theta||0)-Math.PI/2;x+=r*Math.cos(t);y+=r*Math.sin(t)}if(a==="center"){x=x-w/2}else if(a==="right"){x=x-w}else{}if(b==="top"){y=y+h/5}else if(b==="bottom"){y=y-h}else if(b==="middle"){y=y-h/2+h/10}else{y=y-4*h/5}bounds.set(x,y,x+w,y+h);if(o.angle&&!noRotate){bounds.rotate(o.angle*Math.PI/180,o.x||0,o.y||0)}return bounds.expand(noRotate?0:1)}function group(g,bounds,includeLegends){var axes=g.axisItems||[],legends=g.legendItems||[],j,m;for(j=0,m=axes.length;j<m;++j){bounds.union(axes[j].bounds)}for(j=0,m=g.items.length;j<m;++j){bounds.union(g.items[j].bounds)}if(includeLegends){for(j=0,m=legends.length;j<m;++j){bounds.union(legends[j].bounds)}if(g.width!=null&&g.height!=null){bounds.add(g.width,g.height)}if(g.x!=null&&g.y!=null){bounds.add(0,0)}}bounds.translate(g.x||0,g.y||0);return bounds}var methods={group:group,symbol:symbol,image:image,rect:rect,rule:rule,arc:arc,text:text,path:path,area:area,line:line};function itemBounds(item,func,opt){func=func||methods[item.mark.marktype];if(!item.bounds_prev)item["bounds:prev"]=new vg.Bounds;var b=item.bounds,pb=item["bounds:prev"];if(b)pb.clear().union(b);item.bounds=func(item,b?b.clear():new vg.Bounds,opt);if(!b)pb.clear().union(item.bounds);return item.bounds}function markBounds(mark,bounds,opt){bounds=bounds||mark.bounds&&mark.bounds.clear()||new vg.Bounds;var type=mark.marktype,func=methods[type],items=mark.items,item,i,len;if(type==="area"||type==="line"){if(items.length){items[0].bounds=func(items[0],bounds)}}else{for(i=0,len=items.length;i<len;++i){bounds.union(itemBounds(items[i],func,opt))}}mark.bounds=bounds}return{mark:markBounds,item:itemBounds,text:text,group:group}}();vg.scene.encode=function(){var GROUP=vg.scene.GROUP,ENTER=vg.scene.ENTER,UPDATE=vg.scene.UPDATE,EXIT=vg.scene.EXIT,EMPTY={};function main(scene,def,trans,request,items){request&&items?update.call(this,scene,def,trans,request,items):encode.call(this,scene,scene,def,trans,request);return scene}function update(scene,def,trans,request,items){items=vg.array(items);var i,len,item,group,props,prop;for(i=0,len=items.length;i<len;++i){item=items[i];group=item.mark.group||null;props=item.mark.def.properties;prop=props&&props[request];if(prop){prop.call(vg,item,group,trans);vg.scene.bounds.item(item)}}}function encode(group,scene,def,trans,request){encodeItems.call(this,group,scene.items,def,trans,request);if(scene.marktype===GROUP){encodeGroup.call(this,scene,def,group,trans,request)}else{vg.scene.bounds.mark(scene)}}function encodeLegend(group,scene,def,trans,request){encodeGroup.call(this,scene,def,group,trans,request);encodeItems.call(this,group,scene.items,def,trans,request);vg.scene.bounds.mark(scene,null,true)}function encodeGroup(scene,def,parent,trans,request){var i,len,m,mlen,group,scales,axes,axisItems,axisDef,leg,legItems,legDef;for(i=0,len=scene.items.length;i<len;++i){group=scene.items[i];scales=group.scales||(group.scales=def.scales?vg.extend({},parent.scales):parent.scales);if(def.scales){vg.parse.scales(def.scales,scales,this._data,group)}if(def.axes){axes=group.axes||(group.axes=[]);axisItems=group.axisItems||(group.axisItems=[]);vg.parse.axes(def.axes,axes,group.scales);axes.forEach(function(a,i){axisDef=a.def();axisItems[i]=vg.scene.build(axisDef,this._data,axisItems[i],null,1);axisItems[i].group=group;encode.call(this,group,group.axisItems[i],axisDef,trans)})}for(m=0,mlen=group.items.length;m<mlen;++m){encode.call(this,group,group.items[m],def.marks[m],trans,request)}}vg.scene.bounds.mark(scene,null,!def.legends);if(def.legends){for(i=0,len=scene.items.length;i<len;++i){group=scene.items[i];leg=group.legends||(group.legends=[]);legItems=group.legendItems||(group.legendItems=[]);vg.parse.legends(def.legends,leg,group.scales);leg.forEach(function(l,i){legDef=l.def();legItems[i]=vg.scene.build(legDef,this._data,legItems[i],null,1);legItems[i].group=group;encodeLegend.call(this,group,group.legendItems[i],legDef,trans)})}vg.scene.bounds.mark(scene,null,true)}}function encodeItems(group,items,def,trans,request){var props=def.properties||EMPTY,enter=props.enter,update=props.update,exit=props.exit,i,len,item,prop;if(request){if(prop=props[request]){for(i=0,len=items.length;i<len;++i){prop.call(vg,items[i],group,trans)}}return}for(i=0;i<items.length;++i){item=items[i];if(item.status===ENTER){if(enter)enter.call(vg,item,group);item.status=UPDATE}if(item.status!==EXIT&&update){update.call(vg,item,group,trans)}if(item.status===EXIT){if(exit)exit.call(vg,item,group,trans);if(trans&&!exit)trans.interpolate(item,EMPTY);else if(!trans)items[i--].remove()}}}return main}();vg.scene.Transition=function(){function trans(duration,ease){this.duration=duration||500;this.ease=ease&&d3.ease(ease)||d3.ease("cubic-in-out");this.updates={next:null}}var prototype=trans.prototype;var skip={text:1,url:1};prototype.interpolate=function(item,values){var key,curr,next,interp,list=null;for(key in values){curr=item[key];next=values[key];if(curr!==next){if(skip[key]||curr===undefined){item[key]=next}else if(typeof curr==="number"&&!isFinite(curr)){item[key]=next}else{interp=d3.interpolate(curr,next);interp.property=key;(list||(list=[])).push(interp)}}}if(list===null&&item.status===vg.scene.EXIT){list=[]}if(list!=null){list.item=item;list.ease=item.mark.ease||this.ease;list.next=this.updates.next;this.updates.next=list}return this};prototype.start=function(callback){var t=this,prev=t.updates,curr=prev.next;for(;curr!=null;prev=curr,curr=prev.next){if(curr.item.status===vg.scene.EXIT)curr.remove=true
}t.callback=callback;d3.timer(function(elapsed){return step.call(t,elapsed)})};function step(elapsed){var list=this.updates,prev=list,curr=prev.next,duration=this.duration,item,delay,f,e,i,n,stop=true;for(;curr!=null;prev=curr,curr=prev.next){item=curr.item;delay=item.delay||0;f=(elapsed-delay)/duration;if(f<0){stop=false;continue}if(f>1)f=1;e=curr.ease(f);for(i=0,n=curr.length;i<n;++i){item[curr[i].property]=curr[i](e)}item.touch();vg.scene.bounds.item(item);if(f===1){if(curr.remove)item.remove();prev.next=curr.next;curr=prev}else{stop=false}}this.callback();return stop}return trans}();vg.scene.transition=function(dur,ease){return new vg.scene.Transition(dur,ease)};vg.scene.axis=function(){var scale,orient=vg.config.axis.orient,offset=0,titleOffset=vg.config.axis.titleOffset,axisDef=null,layer="front",grid=false,title=null,tickMajorSize=vg.config.axis.tickSize,tickMinorSize=vg.config.axis.tickSize,tickEndSize=vg.config.axis.tickSize,tickPadding=vg.config.axis.padding,tickValues=null,tickFormatString=null,tickFormat=null,tickSubdivide=0,tickArguments=[vg.config.axis.ticks],gridLineStyle={},tickLabelStyle={},majorTickStyle={},minorTickStyle={},titleStyle={},domainStyle={};var axis={};function reset(){axisDef=null}axis.def=function(){var def=axisDef?axisDef:axisDef=axis_def(scale);tickFormat=!tickFormatString?null:scale.type==="time"?d3.time.format(tickFormatString):d3.format(tickFormatString);var major=tickValues==null?scale.ticks?scale.ticks.apply(scale,tickArguments):scale.domain():tickValues;var minor=vg_axisSubdivide(scale,major,tickSubdivide).map(vg.data.ingest);major=major.map(vg.data.ingest);var fmt=tickFormat==null?scale.tickFormat?scale.tickFormat.apply(scale,tickArguments):String:tickFormat;major.forEach(function(d){d.label=fmt(d.data)});var tdata=title?[title].map(vg.data.ingest):[];def.marks[0].from=function(){return grid?major:[]};def.marks[1].from=function(){return major};def.marks[2].from=function(){return minor};def.marks[3].from=def.marks[1].from;def.marks[4].from=function(){return[1]};def.marks[5].from=function(){return tdata};def.offset=offset;def.orient=orient;def.layer=layer;return def};function axis_def(scale){var newScale,oldScale,range;if(scale.type==="ordinal"){newScale={scale:scale.scaleName,offset:.5+scale.rangeBand()/2};oldScale=newScale}else{newScale={scale:scale.scaleName,offset:.5};oldScale={scale:scale.scaleName+":prev",offset:.5}}range=vg_axisScaleRange(scale);var gridLines=vg_axisTicks();var majorTicks=vg_axisTicks();var minorTicks=vg_axisTicks();var tickLabels=vg_axisTickLabels();var domain=vg_axisDomain();var title=vg_axisTitle();gridLines.properties.enter.stroke={value:vg.config.axis.gridColor};vg_axisTicksExtend(orient,gridLines,oldScale,newScale,Infinity);vg_axisTicksExtend(orient,majorTicks,oldScale,newScale,tickMajorSize);vg_axisTicksExtend(orient,minorTicks,oldScale,newScale,tickMinorSize);vg_axisLabelExtend(orient,tickLabels,oldScale,newScale,tickMajorSize,tickPadding);vg_axisDomainExtend(orient,domain,range,tickEndSize);vg_axisTitleExtend(orient,title,range,titleOffset);vg.extend(gridLines.properties.update,gridLineStyle);vg.extend(majorTicks.properties.update,majorTickStyle);vg.extend(minorTicks.properties.update,minorTickStyle);vg.extend(tickLabels.properties.update,tickLabelStyle);vg.extend(domain.properties.update,domainStyle);vg.extend(title.properties.update,titleStyle);var marks=[gridLines,majorTicks,minorTicks,tickLabels,domain,title];return{type:"group",interactive:false,properties:{enter:vg_axisUpdate,update:vg_axisUpdate},marks:marks.map(vg.parse.mark)}}axis.scale=function(x){if(!arguments.length)return scale;if(scale!==x){scale=x;reset()}return axis};axis.orient=function(x){if(!arguments.length)return orient;if(orient!==x){orient=x in vg_axisOrients?x+"":vg.config.axis.orient;reset()}return axis};axis.title=function(x){if(!arguments.length)return title;if(title!==x){title=x;reset()}return axis};axis.ticks=function(){if(!arguments.length)return tickArguments;tickArguments=arguments;return axis};axis.tickValues=function(x){if(!arguments.length)return tickValues;tickValues=x;return axis};axis.tickFormat=function(x){if(!arguments.length)return tickFormatString;if(tickFormatString!==x){tickFormatString=x;reset()}return axis};axis.tickSize=function(x,y){if(!arguments.length)return tickMajorSize;var n=arguments.length-1,major=+x,minor=n>1?+y:tickMajorSize,end=n>0?+arguments[n]:tickMajorSize;if(tickMajorSize!==major||tickMinorSize!==minor||tickEndSize!==end){reset()}tickMajorSize=major;tickMinorSize=minor;tickEndSize=end;return axis};axis.tickSubdivide=function(x){if(!arguments.length)return tickSubdivide;tickSubdivide=+x;return axis};axis.offset=function(x){if(!arguments.length)return offset;offset=vg.isObject(x)?x:+x;return axis};axis.tickPadding=function(x){if(!arguments.length)return tickPadding;if(tickPadding!==+x){tickPadding=+x;reset()}return axis};axis.titleOffset=function(x){if(!arguments.length)return titleOffset;if(titleOffset!==+x){titleOffset=+x;reset()}return axis};axis.layer=function(x){if(!arguments.length)return layer;if(layer!==x){layer=x;reset()}return axis};axis.grid=function(x){if(!arguments.length)return grid;if(grid!==x){grid=x;reset()}return axis};axis.gridLineProperties=function(x){if(!arguments.length)return gridLineStyle;if(gridLineStyle!==x){gridLineStyle=x}return axis};axis.majorTickProperties=function(x){if(!arguments.length)return majorTickStyle;if(majorTickStyle!==x){majorTickStyle=x}return axis};axis.minorTickProperties=function(x){if(!arguments.length)return minorTickStyle;if(minorTickStyle!==x){minorTickStyle=x}return axis};axis.tickLabelProperties=function(x){if(!arguments.length)return tickLabelStyle;if(tickLabelStyle!==x){tickLabelStyle=x}return axis};axis.titleProperties=function(x){if(!arguments.length)return titleStyle;if(titleStyle!==x){titleStyle=x}return axis};axis.domainProperties=function(x){if(!arguments.length)return domainStyle;if(domainStyle!==x){domainStyle=x}return axis};axis.reset=function(){reset()};return axis};var vg_axisOrients={top:1,right:1,bottom:1,left:1};function vg_axisSubdivide(scale,ticks,m){subticks=[];if(m&&ticks.length>1){var extent=vg_axisScaleExtent(scale.domain()),subticks,i=-1,n=ticks.length,d=(ticks[1]-ticks[0])/++m,j,v;while(++i<n){for(j=m;--j>0;){if((v=+ticks[i]-j*d)>=extent[0]){subticks.push(v)}}}for(--i,j=0;++j<m&&(v=+ticks[i]+j*d)<extent[1];){subticks.push(v)}}return subticks}function vg_axisScaleExtent(domain){var start=domain[0],stop=domain[domain.length-1];return start<stop?[start,stop]:[stop,start]}function vg_axisScaleRange(scale){return scale.rangeExtent?scale.rangeExtent():vg_axisScaleExtent(scale.range())}var vg_axisAlign={bottom:"center",top:"center",left:"right",right:"left"};var vg_axisBaseline={bottom:"top",top:"bottom",left:"middle",right:"middle"};function vg_axisLabelExtend(orient,labels,oldScale,newScale,size,pad){size=Math.max(size,0)+pad;if(orient==="left"||orient==="top"){size*=-1}if(orient==="top"||orient==="bottom"){vg.extend(labels.properties.enter,{x:oldScale,y:{value:size}});vg.extend(labels.properties.update,{x:newScale,y:{value:size},align:{value:"center"},baseline:{value:vg_axisBaseline[orient]}})}else{vg.extend(labels.properties.enter,{x:{value:size},y:oldScale});vg.extend(labels.properties.update,{x:{value:size},y:newScale,align:{value:vg_axisAlign[orient]},baseline:{value:"middle"}})}}function vg_axisTicksExtend(orient,ticks,oldScale,newScale,size){var sign=orient==="left"||orient==="top"?-1:1;if(size===Infinity){size=orient==="top"||orient==="bottom"?{group:"mark.group.height",mult:-sign}:{group:"mark.group.width",mult:-sign}}else{size={value:sign*size}}if(orient==="top"||orient==="bottom"){vg.extend(ticks.properties.enter,{x:oldScale,y:{value:0},y2:size});vg.extend(ticks.properties.update,{x:newScale,y:{value:0},y2:size});vg.extend(ticks.properties.exit,{x:newScale})}else{vg.extend(ticks.properties.enter,{x:{value:0},x2:size,y:oldScale});vg.extend(ticks.properties.update,{x:{value:0},x2:size,y:newScale});vg.extend(ticks.properties.exit,{y:newScale})}}function vg_axisTitleExtend(orient,title,range,offset){var mid=~~((range[0]+range[1])/2),sign=orient==="top"||orient==="left"?-1:1;if(orient==="bottom"||orient==="top"){vg.extend(title.properties.update,{x:{value:mid},y:{value:sign*offset},angle:{value:0}})}else{vg.extend(title.properties.update,{x:{value:sign*offset},y:{value:mid},angle:{value:-90}})}}function vg_axisDomainExtend(orient,domain,range,size){var path;if(orient==="top"||orient==="left"){size=-1*size}if(orient==="bottom"||orient==="top"){path="M"+range[0]+","+size+"V0H"+range[1]+"V"+size}else{path="M"+size+","+range[0]+"H0V"+range[1]+"H"+size}domain.properties.update.path={value:path}}function vg_axisUpdate(item,group,trans){var o=trans?{}:item,offset=item.mark.def.offset,orient=item.mark.def.orient,width=group.width,height=group.height;if(vg.isArray(offset)){var ofx=offset[0],ofy=offset[1];switch(orient){case"left":{o.x=-ofx;o.y=ofy;break}case"right":{o.x=width+ofx;o.y=ofy;break}case"bottom":{o.x=ofx;o.y=height+ofy;break}case"top":{o.x=ofx;o.y=-ofy;break}default:{o.x=ofx;o.y=ofy}}}else{if(vg.isObject(offset)){offset=-group.scales[offset.scale](offset.value)}switch(orient){case"left":{o.x=-offset;o.y=0;break}case"right":{o.x=width+offset;o.y=0;break}case"bottom":{o.x=0;o.y=height+offset;break}case"top":{o.x=0;o.y=-offset;break}default:{o.x=0;o.y=0}}}if(trans)trans.interpolate(item,o)}function vg_axisTicks(){return{type:"rule",interactive:false,key:"data",properties:{enter:{stroke:{value:vg.config.axis.tickColor},strokeWidth:{value:vg.config.axis.tickWidth},opacity:{value:1e-6}},exit:{opacity:{value:1e-6}},update:{opacity:{value:1}}}}}function vg_axisTickLabels(){return{type:"text",interactive:true,key:"data",properties:{enter:{fill:{value:vg.config.axis.tickLabelColor},font:{value:vg.config.axis.tickLabelFont},fontSize:{value:vg.config.axis.tickLabelFontSize},opacity:{value:1e-6},text:{field:"label"}},exit:{opacity:{value:1e-6}},update:{opacity:{value:1}}}}}function vg_axisTitle(){return{type:"text",interactive:true,properties:{enter:{font:{value:vg.config.axis.titleFont},fontSize:{value:vg.config.axis.titleFontSize},fontWeight:{value:vg.config.axis.titleFontWeight},fill:{value:vg.config.axis.titleColor},align:{value:"center"},baseline:{value:"middle"},text:{field:"data"}},update:{}}}}function vg_axisDomain(){return{type:"path",interactive:false,properties:{enter:{x:{value:.5},y:{value:.5},stroke:{value:vg.config.axis.axisColor},strokeWidth:{value:vg.config.axis.axisWidth}},update:{}}}}vg.scene.legend=function(){var size=null,shape=null,fill=null,stroke=null,spacing=null,values=null,format=null,formatString=null,title=undefined,orient="right",offset=vg.config.legend.offset,padding=vg.config.legend.padding,legendDef,tickArguments=[5],legendStyle={},symbolStyle={},gradientStyle={},titleStyle={},labelStyle={};var legend={},legendDef=null;function reset(){legendDef=null}legend.def=function(){var scale=size||shape||fill||stroke;format=!formatString?null:scale.type==="time"?d3.time.format(formatString):d3.format(formatString);if(!legendDef){legendDef=(scale===fill||scale===stroke)&&!discrete(scale.type)?quantDef(scale):ordinalDef(scale)}legendDef.orient=orient;legendDef.offset=offset;legendDef.padding=padding;return legendDef};function discrete(type){return type==="ordinal"||type==="quantize"||type==="quantile"||type==="threshold"}function ordinalDef(scale){var def=o_legend_def(size,shape,fill,stroke);var data=(values==null?scale.ticks?scale.ticks.apply(scale,tickArguments):scale.domain():values).map(vg.data.ingest);var fmt=format==null?scale.tickFormat?scale.tickFormat.apply(scale,tickArguments):String:format;var fs,range,offset,pad=5,domain=d3.range(data.length);if(size){range=data.map(function(x){return Math.sqrt(size(x.data))});offset=d3.max(range);range=range.reduce(function(a,b,i,z){if(i>0)a[i]=a[i-1]+z[i-1]/2+pad;return a[i]+=b/2,a},[0]).map(Math.round)}else{offset=Math.round(Math.sqrt(vg.config.legend.symbolSize));range=spacing||(fs=labelStyle.fontSize)&&fs.value+pad||vg.config.legend.labelFontSize+pad;range=domain.map(function(d,i){return Math.round(offset/2+i*range)})}var sz=padding,ts;if(title){ts=titleStyle.fontSize;sz+=5+(ts&&ts.value||vg.config.legend.titleFontSize)}for(var i=0,n=range.length;i<n;++i)range[i]+=sz;var scale={name:"legend",type:"ordinal",points:true,domain:domain,range:range};var tdata=(title?[title]:[]).map(vg.data.ingest);data.forEach(function(d){d.label=fmt(d.data);d.offset=offset});def.scales=[scale];def.marks[0].from=function(){return tdata};def.marks[1].from=function(){return data};def.marks[2].from=def.marks[1].from;return def}function o_legend_def(size,shape,fill,stroke){var titles=vg_legendTitle(),symbols=vg_legendSymbols(),labels=vg_vLegendLabels();vg_legendSymbolExtend(symbols,size,shape,fill,stroke);vg.extend(titles.properties.update,titleStyle);vg.extend(symbols.properties.update,symbolStyle);vg.extend(labels.properties.update,labelStyle);titles.properties.enter.x.value+=padding;titles.properties.enter.y.value+=padding;labels.properties.enter.x.offset+=padding+1;symbols.properties.enter.x.offset=padding+1;labels.properties.update.x.offset+=padding+1;symbols.properties.update.x.offset=padding+1;return{type:"group",interactive:false,properties:{enter:vg.parse.properties("group",legendStyle),update:vg_legendUpdate},marks:[titles,symbols,labels].map(vg.parse.mark)}}function quantDef(scale){var def=q_legend_def(scale),dom=scale.domain(),data=dom.map(vg.data.ingest),width=gradientStyle.width&&gradientStyle.width.value||vg.config.legend.gradientWidth,fmt=format==null?scale.tickFormat?scale.tickFormat.apply(scale,tickArguments):String:format;var layout={name:"legend",type:scale.type,round:true,zero:false,domain:[dom[0],dom[dom.length-1]],range:[padding,width+padding]};if(scale.type==="pow")layout.exponent=scale.exponent();var tdata=(title?[title]:[]).map(vg.data.ingest);data.forEach(function(d,i){d.label=fmt(d.data);d.align=i==data.length-1?"right":i==0?"left":"center"});def.scales=[layout];def.marks[0].from=function(){return tdata};def.marks[1].from=function(){return[1]};def.marks[2].from=function(){return data};return def}function q_legend_def(scale){var titles=vg_legendTitle(),gradient=vg_legendGradient(),labels=vg_hLegendLabels(),grad=new vg.Gradient;var dom=scale.domain(),min=dom[0],max=dom[dom.length-1],f=scale.copy().domain([min,max]).range([0,1]);var stops=scale.type!=="linear"&&scale.ticks?scale.ticks.call(scale,15):dom;if(min!==stops[0])stops.unshift(min);if(max!==stops[stops.length-1])stops.push(max);for(var i=0,n=stops.length;i<n;++i){grad.stop(f(stops[i]),scale(stops[i]))}gradient.properties.enter.fill={value:grad};vg.extend(titles.properties.update,titleStyle);vg.extend(gradient.properties.update,gradientStyle);vg.extend(labels.properties.update,labelStyle);var gp=gradient.properties,gh=gradientStyle.height,hh=gh&&gh.value||gp.enter.height.value;labels.properties.enter.y.value=hh;labels.properties.update.y.value=hh;if(title){var tp=titles.properties,fs=titleStyle.fontSize,sz=4+(fs&&fs.value||tp.enter.fontSize.value);gradient.properties.enter.y.value+=sz;labels.properties.enter.y.value+=sz;gradient.properties.update.y.value+=sz;labels.properties.update.y.value+=sz}titles.properties.enter.x.value+=padding;titles.properties.enter.y.value+=padding;gradient.properties.enter.x.value+=padding;gradient.properties.enter.y.value+=padding;labels.properties.enter.y.value+=padding;gradient.properties.update.x.value+=padding;gradient.properties.update.y.value+=padding;labels.properties.update.y.value+=padding;return{type:"group",interactive:false,properties:{enter:vg.parse.properties("group",legendStyle),update:vg_legendUpdate},marks:[titles,gradient,labels].map(vg.parse.mark)}}legend.size=function(x){if(!arguments.length)return size;if(size!==x){size=x;reset()}return legend};legend.shape=function(x){if(!arguments.length)return shape;if(shape!==x){shape=x;reset()}return legend};legend.fill=function(x){if(!arguments.length)return fill;if(fill!==x){fill=x;reset()}return legend};legend.stroke=function(x){if(!arguments.length)return stroke;if(stroke!==x){stroke=x;reset()}return legend};legend.title=function(x){if(!arguments.length)return title;if(title!==x){title=x;reset()}return legend};legend.format=function(x){if(!arguments.length)return formatString;if(formatString!==x){formatString=x;reset()}return legend};legend.spacing=function(x){if(!arguments.length)return spacing;if(spacing!==+x){spacing=+x;reset()}return legend};legend.orient=function(x){if(!arguments.length)return orient;orient=x in vg_legendOrients?x+"":vg.config.legend.orient;return legend};legend.offset=function(x){if(!arguments.length)return offset;offset=+x;return legend};legend.values=function(x){if(!arguments.length)return values;values=x;return legend};legend.legendProperties=function(x){if(!arguments.length)return legendStyle;legendStyle=x;return legend};legend.symbolProperties=function(x){if(!arguments.length)return symbolStyle;symbolStyle=x;return legend};legend.gradientProperties=function(x){if(!arguments.length)return gradientStyle;gradientStyle=x;return legend};legend.labelProperties=function(x){if(!arguments.length)return labelStyle;labelStyle=x;return legend};legend.titleProperties=function(x){if(!arguments.length)return titleStyle;titleStyle=x;return legend};legend.reset=function(){reset()};return legend};var vg_legendOrients={right:1,left:1};function vg_legendUpdate(item,group,trans){var o=trans?{}:item,gx,offset=item.mark.def.offset,orient=item.mark.def.orient,pad=item.mark.def.padding*2,lw=~~item.bounds.width()+(item.width?0:pad),lh=~~item.bounds.height()+(item.height?0:pad);o.x=.5;o.y=.5;o.width=lw;o.height=lh;if(!trans&&group.bounds){group.bounds.delta=group.bounds.x2-group.width}switch(orient){case"left":{gx=group.bounds?group.bounds.x1:0;o.x+=gx-offset-lw;break};case"right":{gx=group.width;if(group.bounds)gx=trans?group.width+group.bounds.delta:group.bounds.x2;o.x+=gx+offset;break}}if(trans)trans.interpolate(item,o);item.mark.def.properties.enter(item,group,trans)}function vg_legendSymbolExtend(mark,size,shape,fill,stroke){var e=mark.properties.enter,u=mark.properties.update;if(size)e.size=u.size={scale:size.scaleName,field:"data"};if(shape)e.shape=u.shape={scale:shape.scaleName,field:"data"};if(fill)e.fill=u.fill={scale:fill.scaleName,field:"data"};if(stroke)e.stroke=u.stroke={scale:stroke.scaleName,field:"data"}}function vg_legendTitle(){var cfg=vg.config.legend;return{type:"text",interactive:false,key:"data",properties:{enter:{x:{value:0},y:{value:0},fill:{value:cfg.titleColor},font:{value:cfg.titleFont},fontSize:{value:cfg.titleFontSize},fontWeight:{value:cfg.titleFontWeight},baseline:{value:"top"},text:{field:"data"},opacity:{value:1e-6}},exit:{opacity:{value:1e-6}},update:{opacity:{value:1}}}}}function vg_legendSymbols(){var cfg=vg.config.legend;return{type:"symbol",interactive:false,key:"data",properties:{enter:{x:{field:"offset",mult:.5},y:{scale:"legend",field:"index"},shape:{value:cfg.symbolShape},size:{value:cfg.symbolSize},stroke:{value:cfg.symbolColor},strokeWidth:{value:cfg.symbolStrokeWidth},opacity:{value:1e-6}},exit:{opacity:{value:1e-6}},update:{x:{field:"offset",mult:.5},y:{scale:"legend",field:"index"},opacity:{value:1}}}}}function vg_vLegendLabels(){var cfg=vg.config.legend;return{type:"text",interactive:false,key:"data",properties:{enter:{x:{field:"offset",offset:5},y:{scale:"legend",field:"index"},fill:{value:cfg.labelColor},font:{value:cfg.labelFont},fontSize:{value:cfg.labelFontSize},align:{value:cfg.labelAlign},baseline:{value:cfg.labelBaseline},text:{field:"label"},opacity:{value:1e-6}},exit:{opacity:{value:1e-6}},update:{opacity:{value:1},x:{field:"offset",offset:5},y:{scale:"legend",field:"index"}}}}}function vg_legendGradient(){var cfg=vg.config.legend;return{type:"rect",interactive:false,properties:{enter:{x:{value:0},y:{value:0},width:{value:cfg.gradientWidth},height:{value:cfg.gradientHeight},stroke:{value:cfg.gradientStrokeColor},strokeWidth:{value:cfg.gradientStrokeWidth},opacity:{value:1e-6}},exit:{opacity:{value:1e-6}},update:{x:{value:0},y:{value:0},opacity:{value:1}}}}}function vg_hLegendLabels(){var cfg=vg.config.legend;return{type:"text",interactive:false,key:"data",properties:{enter:{x:{scale:"legend",field:"data"},y:{value:20},dy:{value:2},fill:{value:cfg.labelColor},font:{value:cfg.labelFont},fontSize:{value:cfg.labelFontSize},align:{field:"align"},baseline:{value:"top"},text:{field:"label"},opacity:{value:1e-6}},exit:{opacity:{value:1e-6}},update:{x:{scale:"legend",field:"data"},y:{value:20},opacity:{value:1}}}}}vg.Model=function(){function model(){this._defs=null;this._data={};this._scene=null;this._reset={axes:false,legends:false}}var prototype=model.prototype;prototype.defs=function(defs){if(!arguments.length)return this._defs;this._defs=defs;return this};prototype.data=function(data){if(!arguments.length)return this._data;var deps={},defs=this._defs,src=defs.data.source,tx=defs.data.flow||{},keys=defs.data.sorted,len=keys.length,i,k,x;function sources(k){(src[k]||[]).forEach(function(s){deps[s]=k;sources(s)})}vg.keys(data).forEach(sources);for(i=0;i<len;++i){if(data[k=keys[i]]){x=data[k]}else if(deps[k]){x=vg_data_duplicate(this._data[deps[k]]);if(vg.isTree(data))vg_make_tree(x)}else continue;this._data[k]=tx[k]?tx[k](x,this._data,defs.marks):x}this._reset.legends=true;return this};prototype.width=function(width){if(this._defs)this._defs.width=width;if(this._defs&&this._defs.marks)this._defs.marks.width=width;if(this._scene)this._scene.items[0].width=width;this._reset.axes=true;return this};prototype.height=function(height){if(this._defs)this._defs.height=height;if(this._defs&&this._defs.marks)this._defs.marks.height=height;if(this._scene)this._scene.items[0].height=height;this._reset.axes=true;return this};prototype.scene=function(node){if(!arguments.length)return this._scene;this._scene=node;return this};prototype.build=function(){var m=this,data=m._data,marks=m._defs.marks;m._scene=vg.scene.build.call(m,marks,data,m._scene);m._scene.items[0].width=marks.width;m._scene.items[0].height=marks.height;m._scene.interactive=false;return this};prototype.encode=function(trans,request,item){this.reset();var m=this,scene=m._scene,defs=m._defs;vg.scene.encode.call(m,scene,defs.marks,trans,request,item);return this};prototype.reset=function(){if(this._scene&&this._reset.axes){vg.scene.visit(this._scene,function(item){if(item.axes)item.axes.forEach(function(axis){axis.reset()})});this._reset.axes=false}if(this._scene&&this._reset.legends){vg.scene.visit(this._scene,function(item){if(item.legends)item.legends.forEach(function(l){l.reset()})});this._reset.legends=false}return this};return model}();vg.View=function(){var view=function(el,width,height){this._el=null;this._build=false;this._model=new vg.Model;this._width=this.__width=width||500;this._height=this.__height=height||500;this._autopad=1;this._padding={top:0,left:0,bottom:0,right:0};this._viewport=null;this._renderer=null;this._handler=null;this._io=vg.canvas;if(el)this.initialize(el)};var prototype=view.prototype;prototype.width=function(width){if(!arguments.length)return this.__width;if(this.__width!==width){this._width=this.__width=width;if(this._el)this.initialize(this._el.parentNode);this._model.width(width);if(this._strict)this._autopad=1}return this};prototype.height=function(height){if(!arguments.length)return this.__height;if(this.__height!==height){this._height=this.__height=height;if(this._el)this.initialize(this._el.parentNode);this._model.height(this._height);if(this._strict)this._autopad=1}return this};prototype.padding=function(pad){if(!arguments.length)return this._padding;if(this._padding!==pad){if(vg.isString(pad)){this._autopad=1;this._padding={top:0,left:0,bottom:0,right:0};this._strict=pad==="strict"}else{this._autopad=0;this._padding=pad;this._strict=false}if(this._el){this._renderer.resize(this._width,this._height,pad);this._handler.padding(pad)}}return this};prototype.autopad=function(opt){if(this._autopad<1)return this;else this._autopad=0;var pad=this._padding,b=this.model().scene().bounds,inset=vg.config.autopadInset,l=b.x1<0?Math.ceil(-b.x1)+inset:0,t=b.y1<0?Math.ceil(-b.y1)+inset:0,r=b.x2>this._width?Math.ceil(+b.x2-this._width)+inset:0,b=b.y2>this._height?Math.ceil(+b.y2-this._height)+inset:0;pad={left:l,top:t,right:r,bottom:b};if(this._strict){this._autopad=0;this._padding=pad;this._width=Math.max(0,this.__width-(l+r));this._height=Math.max(0,this.__height-(t+b));this._model.width(this._width);this._model.height(this._height);if(this._el)this.initialize(this._el.parentNode);this.update({props:"enter"}).update({props:"update"})}else{this.padding(pad).update(opt)}return this};prototype.viewport=function(size){if(!arguments.length)return this._viewport;if(this._viewport!==size){this._viewport=size;if(this._el)this.initialize(this._el.parentNode)}return this};prototype.renderer=function(type){if(!arguments.length)return this._io;if(type==="canvas")type=vg.canvas;if(type==="svg")type=vg.svg;if(this._io!==type){this._io=type;this._renderer=null;if(this._el)this.initialize(this._el.parentNode);if(this._build)this.render()}return this};prototype.defs=function(defs){if(!arguments.length)return this._model.defs();this._model.defs(defs);return this};prototype.data=function(data){if(!arguments.length)return this._model.data();var ingest=vg.keys(data).reduce(function(d,k){return d[k]=vg.data.ingestAll(data[k]),d},{});this._model.data(ingest);this._build=false;return this};prototype.model=function(model){if(!arguments.length)return this._model;if(this._model!==model){this._model=model;if(this._handler)this._handler.model(model)}return this};prototype.initialize=function(el){var v=this,prevHandler,w=v._width,h=v._height,pad=v._padding;d3.select(el).select("div.vega").remove();this._el=el=d3.select(el).append("div").attr("class","vega").style("position","relative").node();if(v._viewport){d3.select(el).style("width",(v._viewport[0]||w)+"px").style("height",(v._viewport[1]||h)+"px").style("overflow","auto")}v._renderer=(v._renderer||new this._io.Renderer).initialize(el,w,h,pad);prevHandler=v._handler;v._handler=(new this._io.Handler).initialize(el,pad,v).model(v._model);if(prevHandler){prevHandler.handlers().forEach(function(h){v._handler.on(h.type,h.handler)})}return this};prototype.render=function(items){this._renderer.render(this._model.scene(),items);return this};prototype.on=function(){this._handler.on.apply(this._handler,arguments);return this};prototype.off=function(){this._handler.off.apply(this._handler,arguments);return this};prototype.update=function(opt){opt=opt||{};var view=this,trans=opt.duration?vg.scene.transition(opt.duration,opt.ease):null;view._build=view._build||(view._model.build(),true);view._model.encode(trans,opt.props,opt.items);if(trans){trans.start(function(items){view._renderer.render(view._model.scene(),items)})}else view.render(opt.items);return view.autopad(opt)};return view}();vg.ViewFactory=function(defs){return function(opt){opt=opt||{};var v=(new vg.View).width(defs.width).height(defs.height).padding(defs.padding).viewport(defs.viewport).renderer(opt.renderer||"canvas").defs(defs);if(defs.data.load)v.data(defs.data.load);if(opt.data)v.data(opt.data);if(opt.el)v.initialize(opt.el);if(opt.hover!==false){v.on("mouseover",function(evt,item){if(item.hasPropertySet("hover")){this.update({props:"hover",items:item})}}).on("mouseout",function(evt,item){if(item.hasPropertySet("hover")){this.update({props:"update",items:item})}})}return v}};vg.Spec=function(){var spec=function(s){this.spec={width:500,height:500,padding:0,data:[],scales:[],axes:[],marks:[]};if(s)vg.extend(this.spec,s)};var prototype=spec.prototype;prototype.width=function(w){this.spec.width=w;return this};prototype.height=function(h){this.spec.height=h;return this};prototype.padding=function(p){this.spec.padding=p;return this};prototype.viewport=function(v){this.spec.viewport=v;return this};prototype.data=function(name,params){if(!params)params=vg.isString(name)?{name:name}:name;else params.name=name;this.spec.data.push(params);return this};prototype.scale=function(name,params){if(!params)params=vg.isString(name)?{name:name}:name;else params.name=name;this.spec.scales.push(params);return this};prototype.axis=function(params){this.spec.axes.push(params);return this};prototype.mark=function(type,mark){if(!mark)mark={type:type};else mark.type=type;mark.properties={};this.spec.marks.push(mark);var that=this;return{from:function(name,obj){mark.from=obj?(obj.data=name,obj):vg.isString(name)?{data:name}:name;return this},prop:function(name,obj){mark.properties[name]=vg.keys(obj).reduce(function(o,k){var v=obj[k];return o[k]=vg.isObject(v)?v:{value:v},o},{});return this},done:function(){return that}}};prototype.parse=function(callback){vg.parse.spec(this.spec,callback)};prototype.json=function(){return this.spec};return spec}();vg.spec=function(s){return new vg.Spec(s)};vg.headless={};vg.headless.View=function(){var view=function(width,height,pad,type,vp){this._canvas=null;this._type=type;this._el="body";this._build=false;this._model=new vg.Model;this._width=this.__width=width||500;this._height=this.__height=height||500;this._padding=pad||{top:0,left:0,bottom:0,right:0};this._autopad=vg.isString(this._padding)?1:0;this._renderer=new vg[type].Renderer;this._viewport=vp||null;this.initialize()};var prototype=view.prototype;prototype.el=function(el){if(!arguments.length)return this._el;if(this._el!==el){this._el=el;this.initialize()}return this};prototype.width=function(width){if(!arguments.length)return this._width;if(this._width!==width){this._width=width;this.initialize();this._model.width(width)}return this};prototype.height=function(height){if(!arguments.length)return this._height;if(this._height!==height){this._height=height;this.initialize();this._model.height(this._height)}return this};prototype.padding=function(pad){if(!arguments.length)return this._padding;if(this._padding!==pad){if(vg.isString(pad)){this._autopad=1;this._padding={top:0,left:0,bottom:0,right:0};this._strict=pad==="strict"}else{this._autopad=0;this._padding=pad;this._strict=false}this.initialize()}return this};prototype.autopad=function(opt){if(this._autopad<1)return this;else this._autopad=0;var pad=this._padding,b=this._model.scene().bounds,inset=vg.config.autopadInset,l=b.x1<0?Math.ceil(-b.x1)+inset:0,t=b.y1<0?Math.ceil(-b.y1)+inset:0,r=b.x2>this._width?Math.ceil(+b.x2-this._width)+inset:0,b=b.y2>this._height?Math.ceil(+b.y2-this._height)+inset:0;pad={left:l,top:t,right:r,bottom:b};if(this._strict){this._autopad=0;this._padding=pad;this._width=Math.max(0,this.__width-(l+r));this._height=Math.max(0,this.__height-(t+b));this._model.width(this._width);this._model.height(this._height);if(this._el)this.initialize();this.update({props:"enter"}).update({props:"update"})}else{this.padding(pad).update(opt)}return this};prototype.viewport=function(vp){if(!arguments.length)return _viewport;this._viewport=vp;this.initialize();return this};prototype.defs=function(defs){if(!arguments.length)return this._model.defs();this._model.defs(defs);return this};prototype.data=function(data){if(!arguments.length)return this._model.data();var ingest=vg.keys(data).reduce(function(d,k){return d[k]=vg.data.ingestAll(data[k]),d},{});this._model.data(ingest);this._build=false;return this};prototype.renderer=function(){return this._renderer};prototype.canvas=function(){return this._canvas};prototype.canvasAsync=function(callback){var r=this._renderer,view=this;function wait(){if(r.pendingImages()===0){view.render();callback(view._canvas)}else{setTimeout(wait,10)}}r.pendingImages()>0?wait():callback(this._canvas)};prototype.svg=function(){if(this._type!=="svg")return null;var p=this._padding,w=this._width+(p?p.left+p.right:0),h=this._height+(p?p.top+p.bottom:0);if(this._viewport){w=this._viewport[0]-(p?p.left+p.right:0);h=this._viewport[1]-(p?p.top+p.bottom:0)}var svg=d3.select(this._el).select("svg").node().innerHTML.replace(/ href=/g," xlink:href=");
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<style type="text/css">.vega {
-webkit-user-select: none;
}
.plots>div {
float: left;
}

.ggvis-output-container {
overflow: hidden;

position: relative;
}

.ui-resizable-helper {
border: 1px dotted #999;
}

.ggvis-tooltip {
position: absolute;
float: left;
border-radius: 3px;
border: 1px solid #999;
padding: 5px;
opacity: 0.85;
background-color: #fff;
box-shadow: 2px 2px 6px #888888;
}

.sidebar-bottom {
float:left;
margin-left:0;
}

.main-top {
float:right !important;
margin-left:auto;
}
@media (max-width: 767px) {

body {
padding-right: 0px;
padding-left: 0px;
}
.sidebar-bottom {
display: inline-block;
width: 100%;
}
.main-top {
float:none !important;
margin-left:0;
}
.ggvis-input-container {
width: 45%;
padding-left: 2.5%;
padding-right: 2.5%;
float: left;
}
}

.ggvis-dropdown-menu .ggvis-dropdown-item {
display: block;
padding: 3px 20px;
clear: both;
font-weight: normal;
line-height: 20px;
color: #333;
white-space: nowrap;
}

.plot-gear-icon {
position: absolute;
width: 15px;
height: 15px;
top: 0px;
right: 5px;
}
nav.ggvis-control {
position: relative;
float: right;
}
a.ggvis-dropdown-toggle {
background: url(data:image/png;base64,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) no-repeat;
background-size: 14px;
width: 14px;
height: 14px;
opacity: 0.3;
padding-left: 14px;
cursor: pointer;
}
ul.ggvis-dropdown {
display: none;
text-align: right;
position: absolute;
margin-top: .5em;
border-radius: 3px;
border: 1px solid #999;
padding: 4px;
opacity: 0.85;
background-color: #fff;
box-shadow: 2px 2px 6px #888888;
width: 200px;
margin-left: -200px;
}
ul.ggvis-dropdown li {
list-style-type: none;
padding: 5px 2px;
}
ul.ggvis-dropdown li a {
color: #00f;
text-decoration: none;
padding: 2px 2px;
cursor: pointer;
}
ul.ggvis-dropdown li a.inactive {
color: black;
cursor: initial;
}
</style>
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/*global vg, ggvis:true, lodash*/

ggvis = (function(_) {
  var ggvis = {
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  // Get a ggvis.Plot object with a particular name, creating it if needed
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    }
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  };

  // Internal functions --------------------------------------------------

  // Returns the value of a GET variable
  function queryVar (name) {
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  function pixelRatio() {
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      return window.devicePixelRatio;
    } else {
      return 1;
    }
  }

  // Are we in a panel - an iframe or RStudio viewer pane?
  function inPanel() {
    return (queryVar("viewer_pane") === "1" || queryVar("__subapp__") === "1");
  }

  // Are we in a window for exporting the image?
  function inExportPanel() {
    return queryVar("viewer_export") === "1";
  }

  // ggvis.CallbackRegistry class ----------------------------------------------
  ggvis.CallbackRegistry = (function() {
    // obj is an object to use as the context for the callbacks
    var callbackregistry = function(obj) {
      this._registry = {};
      this._obj = obj || null;
    };
    // this._registry has a structure like:
    // {
    //   "resize": [
    //     { "type": "resize", "fn": fn1 },
    //     { "type": "resize.foo", "fn": fn2 },
    //     { "type": "resize.foo", "fn": fn3 }
    //   ]
    //   "click": [
    //     { "type": "click", "fn": fn1 }
    //   ]
    // }

    var prototype = callbackregistry.prototype;

    // Register a callback for an event.
    // type can be a string like "resize" or "resize.foo". If there is a period,
    // the part after the period is an id which can be used for for targeted
    // removal.
    prototype.on = function(type, fn) {
      var event = eventName(type);
      var reg = this._registry;

      reg[event] = reg[event] || [];
      reg[event].push({
        type: type,
        fn: fn
      });
    };

    // Clear callbacks for a given event and (optional) id
    prototype.off = function(type) {
      var event = eventName(type);
      // If there's no . in the type, remove all callbacks for this event
      if (event === type) {
        delete this._registry[event];
        return;
      }

      var callbacks = this._registry[event];
      if (!callbacks) return;

      // Remove individual callbacks that match type
      for (var i = callbacks.length-1; i >= 0; i--) {
        if (callbacks[i].type === type) callbacks.splice(i, 1);
      }
    };

    // Trigger callbacks for a named event. Any extra arguments are passed to
    // the function.
    prototype.trigger = function(event) {
      var args = Array.prototype.slice.call(arguments, 1);
      var callbacks = this._registry[event];
      if (!callbacks) return;

      for (var i = 0; i < callbacks.length; i++) {
        callbacks[i].fn.apply(this._obj, args);
      }
    };

    // Given a string, return the part before "."
    function eventName(name) {
      var i = name.indexOf(".");
      if (i < 0) return name;
      return name.substring(0, i);
    }

    return callbackregistry;
  })(); // ggvis.CallbackRegistry


  // ggvis.Plot class ----------------------------------------------------------
  ggvis.Plot = (function() {

    var Plot = function(plotId) {
      this.plotId = plotId;
      this.pendingData = {}; // Data objects that have been received but not yet used
      this.chart = null;     // Vega chart object on the page
      this.spec = null;      // Vega spec for this plot
      this.initialized = false; // Has update() or enter() been run?
      this.opts = {};
      this.brush = new Plot.Brush(this);
      this.handlers = [];    // Interaction input handlers
      this._callbacks = new ggvis.CallbackRegistry(this);
    };

    var prototype = Plot.prototype;

    // Wrappers for CallbackRegistry methods
    prototype.on = function(type, fn) { this._callbacks.on(type, fn); };
    prototype.off = function(type) { this._callbacks.off(type); };
    prototype.trigger = function() {
      this._callbacks.trigger.apply(this._callbacks, arguments);
    };

    // opts is an optional object which can have any entries that are in spec.opts
    // (they get merged on top of spec.opts), and additionally:
    // * handlers: An object where the keys are event names and the values are
    //     functions. They are passed to Vega's view.on(key, value), which
    //     registers the handler functions for the named event.
    prototype.parseSpec = function(spec, opts) {
      var self = this;
      self.spec = spec;
      // Merge options passed to this function into options from the spec
      self.opts = $.extend(true, self.spec.ggvis_opts, opts);

      vg.parse.spec(spec, function(chart) {
        var opts = self.opts;

        // If hover_duration is supplied, use that later in a custom callback,
        // instead of the default hover behavior.
        var default_hover = true;
        if (opts.hover_duration && opts.hover_duration !== 0) {
          default_hover = false;
        }

        self.initialized = false;

        chart = chart({
          el: "#" + self.plotId,
          renderer: opts.renderer || "svg",
          hover: default_hover
        });
        // Save the chart object
        self.chart = chart;

        // Set the renderer (update buttons and download link)
        self.setRenderer(opts.renderer, false);

        // If hover_duration is specified, set callbacks for hover behavior
        if (opts.hover_duration && opts.hover_duration !== 0) {
          chart.on("mouseover", function(event, item) {
            this.update({ props:"hover", items:item, duration:opts.hover_duration });
          });
          chart.on("mouseout", function(event, item) {
            this.update({ props:"update", items:item, duration:opts.hover_duration });
          });
        }

        // If handlers are specified (typically for mouseover and out), add them.
        if (opts.handlers) {
          $.each(opts.handlers, function(eventname, fn) {
            chart.on(eventname, fn);
          });
        }

        self.removeHandlers();
        self.addHandlers(self.spec.handlers);

        self.brush.enable();

        if (inExportPanel()) {
          $('.plot-gear-icon').hide();
        }

        self.removeResizeListeners();

        if (inPanel()) {
          self.getWrapper().width("auto");
          self.enableAutoResizeToWindow();

        } else {

          var $wrap = self.getWrapper();
          // If auto-width, simply set CSS width to auto. This doesn't work for
          // height.
          if (opts.width)  $wrap.width(opts.width);
          if (opts.height) $wrap.height(opts.height);

          if (opts.width === "auto" || opts.height === "auto") {
            self.enableAutoResizeToWrapperParent();
          } else if (opts.resizable) {
            self.enableResizable();
          }
        }

        // If the data arrived earlier, use it.
        if (this.pendingData) self.loadPendingData();

        if (self.dataReady()) self.initialUpdate();
      });
    };

    // Get the div which wraps the svg or canvas object (created by vega).
    prototype.getVegaDiv = function() {
      // This is also known is this.getDiv().children(".vega")
      return $(this.chart._el);
    };

    // Get the div which wraps the .vega div
    prototype.getDiv = function() {
      return $("#" + this.plotId);
    };

    // Wrapper div, which includes sizing handle and gear
    prototype.getWrapper = function() {
      return this.getDiv().parent();
    };

    // Get the marks object (the Canvas or SVG object, which is rendered too)
    prototype.getMarks = function() {
      // Can't do this.getVegaDiv().children(".marks") because it doesn't work
      // for SVG DOM objects. So we'll just grab any svg or canvas object.
      return this.getVegaDiv().children("svg, canvas");
    };

    // Get the width of the marks object
    prototype.marksWidth = function() {
      var $marks = this.getMarks();
      // Can't use width() because it returns 0 for hidden DOM elements, and
      // sometimes the ggvis objects can start hidden.
      // getAttribute does work for hidden DOM elements, but for canvas, it
      // needs to be scaled down by the pixel ratio.
      var width = parseFloat($marks[0].getAttribute('width'));
      if (this.renderer === 'canvas') {
        width = width / pixelRatio();
      }
      return width;
    };

    prototype.marksHeight = function() {
      var $marks = this.getMarks();
      var height = parseFloat($marks[0].getAttribute('height'));
      if (this.renderer === 'canvas') {
        height = height / pixelRatio();
      }
      return height;
    };

    prototype.resizeEventNamespace = function() {
      return this.plotId + '_ggvis_resize';
    };

    // Remove any existing resize event listeners
    prototype.removeResizeListeners = function() {
      this.disableResizable();
      this.disableAutoResizeToWindow();
      this.disableAutoResizeToWrapperParent();
    };

    // Set the width of the chart to the wrapper div. If keep_aspect is true,
    // also set the height to maintain the aspect ratio.
    prototype.resizeToWrapper = function(duration, keep_aspect) {
      if (this.chart === null) return;
      if (duration === undefined) duration = this.opts.duration;
      if (duration === undefined) duration = 0;
      if (keep_aspect === undefined) keep_aspect = this.opts.keep_aspect;
      if (keep_aspect === undefined) keep_aspect = false;

      var $wrap = this.getWrapper(),
          $gear = $wrap.find(".plot-gear-icon"),
          chart = this.chart,
          padding = chart.padding(),
          ratio = this.opts.width/this.opts.height;

      var newWidth = $wrap.width() - $gear.width() - padding.left - padding.right,
          newHeight = $wrap.height() - padding.top - padding.bottom;

      if (keep_aspect) {
        if (newHeight > newWidth / ratio) {
          newHeight = Math.floor(newWidth / ratio);
        } else if (newHeight < newWidth / ratio) {
          newWidth = Math.floor(newHeight * ratio);
        }
      }
      // Chart height ends up 5 pixels too large, so compensate for it
      newHeight -= 5;

      chart.width(newWidth);
      chart.height(newHeight);
      chart.update({ duration: duration });
    };

    // Set height to fill window. Don't need to set width, because the wrapper
    // div should already have width="auto".
    prototype.resizeWrapperToWindow = function() {
      var $body = $('body');
      var $wrap = this.getWrapper();

      // Use $body.height() here because dynamic plots may have controls that
      // take some vertical space, which we need to take into account.
      var extra_height = $body.outerHeight(true) - $body.height();

      // Resize the wrapper div to the window, inside of scrollbars if present
      // The wrapper has overflow:hidden so that objects inside of it won't
      // scrollbars to appear while it's being resized.
      var docEl = document.documentElement;
      $wrap.height(docEl.clientHeight - extra_height);

      // Now if there are any other elements in the body that cause the page to
      // be larger than the window (like controls), we need to shrink the
      // plot so that they end up inside the window.
      $wrap.height(2 * (docEl.clientHeight - extra_height) - $body.height());
    };

    // Resize wrapper to fit its parent
    prototype.resizeWrapperToParent = function() {
      // We don't need to set the width, because when this.opts.width==="auto",
      // the div gets width="auto", and the browser automatically sizes it.
      // But that strategy doesn't work for height.
      if (this.opts.height === 'auto') {
        var $wrap = this.getWrapper();
        $wrap.height($wrap.parent().height());
      }
    };

    // Run an update on the chart for the first time
    prototype.initialUpdate = function() {
      // If chart hasn't been run yet, we need to run it once so that
      // resizeToWrapper will work properly (it needs the spec to have been run
      // before it can figure out what the padding will be).
      if (!this.initialized) this.chart.update({ duration: 0 });

      this.initialized = true;

      // Resizing to fit has to happen after the initial update
      if (inPanel()) {
        this.resizeWrapperToWindow();
      } else {
        this.resizeWrapperToParent();
      }
      this.resizeToWrapper(0);
    };

    // Make manually resizable (by dragging corner)
    prototype.enableResizable = function() {
      var self = this;

      // When done resizing, update chart with new width and height
      this.getWrapper().resizable({
        helper: "ui-resizable-helper",
        grid: [10, 10],
        handles: "se",
        stop: function() { self.resizeToWrapper(); }
      });
    };

    prototype.disableResizable = function() {
      var $wrap = this.getWrapper();
      if ($wrap.resizable('instance') !== undefined)
        $wrap.resizable('destroy');
    };

    // Make the plot auto-resize to fit window, if in viewer panel
    prototype.enableAutoResizeToWindow = function() {
      var self = this;
      var debounce_id = null;

      $(window).on('resize.' + this.resizeEventNamespace(), function() {
        clearTimeout(debounce_id);
        // Debounce to 100ms
        debounce_id = setTimeout(function() {
          self.resizeWrapperToWindow();
          self.resizeToWrapper(0);
        }, 100);
      });
    };

    prototype.disableAutoResizeToWindow = function() {
      $(window).off('.' + this.resizeEventNamespace());
    };

    // Make the wrapper auto-resize to its parent
    prototype.enableAutoResizeToWrapperParent = function() {
      var self = this;
      var debounce_id = null;

      this.getWrapper().parent().resize(function() {
        clearTimeout(debounce_id);
        // Debounce to 100ms
        debounce_id = setTimeout(function() {
          self.resizeWrapperToParent();
          self.resizeToWrapper(0);
        }, 100);
      });
    };

    prototype.disableAutoResizeToWrapperParent = function() {
      // Unfortunately, with the javascript resize listener, there's no way to
      // test if a resize listener has already been added, and attempting to
      // remove when none is present results in an error.
      try { this.getWrapper().parent().removeResize(); }
      catch(e) {}
    };

    // This is called when control outputs for a plot are updated
    prototype.onControlOutput = function() {
      if (inPanel()) {
        this.resizeWrapperToWindow();
        this.resizeToWrapper(0);
      }
    };

    prototype.loadPendingData = function() {
      this.chart.data(this.pendingData);
      delete this.pendingData;
    };

    // Returns true if all data objects for a spec have been registered, using
    // this.chart.data(dataset)
    prototype.dataReady = function() {
      var existing_data = Object.keys(this.chart.data());
      var expected_data = this.spec.data.map(function (x) {
        return x.name ;
      });

      return arraysEqual(existing_data, expected_data);
    };

    // Set the renderer, and update the renderer button and download link text if
    // present. Also update the chart (unless update is false).
    // renderer is either "canvas" or "svg".
    prototype.setRenderer = function(renderer, update) {
      // Don't set renderer if no change
      if (renderer === this.renderer) return;
      if (update === undefined) update = true;

      this.renderer = renderer;
      if (update) {
        this.chart.renderer(renderer).update();
      }
      this.setRendererButton(renderer);
      this.updateDownloadButtonText(renderer);
    };

    // Set the value of the renderer button, if present
    prototype.setRendererButton = function(renderer) {
      var $el = $("#" + this.plotId + "_renderer_" + renderer);

      // Toggle the renderer buttons when clicked
      $el.addClass('inactive');
      $el.siblings().removeClass('inactive');
    };

    // Given an <a> element, set the href of that element to the canvas content
    // of the plot converted to SVG or PNG. This will set the href when the link
    // is clicked; the download happens when it is released.
    prototype.updateDownloadLink = function(el) {
      var plot = $("#" + this.plotId + ".ggvis-output .marks")[0];
      var imageUrl;

      if (this.renderer === "svg") {
        // Extract the svg code and add needed xmlns attribute
        var svg = $(plot).clone().attr("xmlns", "http://www.w3.org/2000/svg");
        // Convert to string
        svg = $('<div>').append(svg).html();
        imageUrl = "data:image/octet-stream;base64,\n" + btoa(svg);

      } else if (this.renderer === "canvas") {
        imageUrl = plot.toDataURL("image/png").replace("image/png", "image/octet-stream");
      }

      // Set download filename and data URL
      var ext = "";
      if      (this.renderer === "svg")    ext = ".svg";
      else if (this.renderer === "canvas") ext = ".png";
      el.setAttribute("download", this.plotId + ext);
      el.setAttribute("href", imageUrl);
    };

    prototype.updateDownloadButtonText = function(renderer) {
      var $el = $("#" + this.plotId + "_download");
      if ($el[0]) {
        var filetype = "";
        if      (renderer === "svg")    filetype = "SVG";
        else if (renderer === "canvas") filetype = "PNG";

        $el.text("Download " + filetype);
      }
    };

    prototype.removeHandlers = function() {
      for (var i=0; i<this.handlers.length; i++) {
        this.handlers[i].remove();
      }

      this.handlers = [];
    };

    // h_spec is an array of handler specifications (typically taken from spec)
    prototype.addHandlers = function(h_spec) {
      if (h_spec === [] || h_spec === undefined || h_spec === null)
        return;

      var h, HandlerClass;

      // Call the appropriate handlers
      for (var i=0; i<h_spec.length; i++) {
        h = h_spec[i];
        // Grab the appropriate handler class and instantiate it
        HandlerClass = ggvis.handlers[h.type];
        this.handlers[i] = new HandlerClass(this, h);
      }
    };


    // Private methods ------------------------------------------------

    // Returns all top-level mark definitions in the scene graph.
    // These are available as soon as the spec is parsed.
    prototype._allMarkDefs = function() {
      return this.chart.model().defs().marks.marks;
    };

    // Returns all top-level marks in the scene graph.
    // These are available after the first update.
    prototype._allMarks = function() {
      return this.chart.model().scene().items[0].items;
    };

    prototype._getSceneBounds = function() {
      return this.chart.model().scene().items[0].bounds;
    };


    // Internal functions----------------------------------------------

    // Returns true if arrays have same contents (in any order), false otherwise.
    function arraysEqual(a, b) {
      return $(a).not(b).length === 0 && $(b).not(a).length === 0;
    }

    // Given a mark definition, property name, return an object with the
    // properties. If key is provided, then pull out that key.
    function getMarkProp(markdef, propname) {
      if (propname === undefined || propname === null ||
          markdef.properties === undefined) {
        return {};
      }
      var property = markdef.properties[propname];

      if (property === undefined || property === null) {
        return {};
      }

      // Call the property function on a dummy object
      var temp = {};
      property(temp);

      return temp;
    }


    // ggvis.Plot.Brush class --------------------------------------------------
    Plot.Brush = (function() {
      // Constructor
      // plot: The ggvis.Plot object which uses this brush.
      var brush = function(plot) {
        this.plot = plot;

        this._enabled = false;
        this._brushBounds = new vg.Bounds();
        this._clickPoint = null;      // Coordinates where mouse was clicked
        this._lastPoint = null;       // Previous mouse coordinate
        this._lastMatchingItems = [];
        this._callbacks = new ggvis.CallbackRegistry(this);

        this._brushing = false;
        this._dragging = false;
      };

      var prototype = brush.prototype;

      // Wrappers for CallbackRegistry methods
      prototype.on = function(type, fn) { this._callbacks.on(type, fn); };
      prototype.off = function(type) { this._callbacks.off(type); };
      prototype.trigger = function() {
        this._callbacks.trigger.apply(this._callbacks, arguments);
      };

      // Returns true if the plot has a brush mark object, false otherwise.
      prototype.hasBrushMark = function() {
        if (this._getBrushMarkDef()) return true;
        else return false;
      };

      // Enable the brush, if a brush mark is present.
      prototype.enable = function() {
        if (!this.hasBrushMark()) return;

        var self = this;
        var $div = this.plot.getDiv();

        if (self._enabled) {
          // Clear any existing mouse event handlers from the div
          $div.off("mousedown.ggvis_brush");
          $div.off("mouseup.ggvis_brush");
          $div.off("mousemove.ggvis_brush");

          // Clear any existing brush event handlers
          self.off("brushMove");
          self.off("updateItems");
        }


        // Hook up mouse event handlers
        $div.on("mousedown.ggvis_brush", "div.vega", function (event) {
          var point = self._removePadding(mouseOffset(event));

          if (self._brushBounds.contains(point.x, point.y)) {
            self._startDragging(point);
          } else {
            self._startBrushing(point);
          }
        });
        $div.on("mouseup.ggvis_brush", "div.vega", function (event) {
          /* jshint unused: false */
          if (self._dragging) self._stopDragging();
          if (self._brushing) self._stopBrushing();
        });
        $div.on("mousemove.ggvis_brush", "div.vega", function (event) {
          var point = self._removePadding(mouseOffset(event));
          if (self._dragging) self._dragTo(point);
          if (self._brushing) self._brushTo(point);
        });

        // Register functions to be called each time brush is dragged or resized.
        self.on("brushMove", self._updateBrush);

        // It's not uncommong for mouse events to occur at up to 120 Hz, but
        // throttling brush updates to 20 Hz still gives a responsive feel, while
        // allowing the CPU to spend more time doing other stuff.
        var updateThrottled = _.throttle(self._updateBrushedItems, 50);
        self.on("brushMove", updateThrottled);

        self._enabled = true;
      };

      // Dragging functions
      prototype._startDragging = function(point) {
        this._dragging = true;
        this._lastPoint = point;
        this._clickPoint = point;
        this.trigger("brushMove");
      };
      prototype._dragTo = function(point) {
        if (!this._dragging) return;

        var dx = point.x - this._lastPoint.x;
        var dy = point.y - this._lastPoint.y;

        this._brushBounds.translate(dx, dy);
        this._lastPoint = point;
        this.trigger("brushMove");
      };
      prototype._stopDragging = function() {
        this._dragging = false;
        this._clickPoint = null;
        this.trigger("brushMove");
      };

      // Brushing functions
      prototype._startBrushing = function(point) {
        // Reset brush
        this._brushBounds.set(0, 0, 0, 0);
        this._brushing = true;
        this._clickPoint = point;
        this.trigger("brushMove");
      };
      prototype._brushTo = function(point) {
        if (!this._brushing) return; // We're not brushing right now

        var limits = this.plot._getSceneBounds();

        // Calculate the bounds based on start and end points
        var end = point;
        var maxX = Math.min(Math.max(this._clickPoint.x, end.x), limits.x2);
        var minX = Math.max(Math.min(this._clickPoint.x, end.x), limits.x1);
        var maxY = Math.min(Math.max(this._clickPoint.y, end.y), limits.y2);
        var minY = Math.max(Math.min(this._clickPoint.y, end.y), limits.y1);

        this._brushBounds.set(minX, minY, maxX, maxY);
        this.trigger("brushMove");
      };
      prototype._stopBrushing = function() {
        this._brushing = false;
        this._clickPoint = null;
        this.trigger("brushMove");
      };

      // Update the brush with new coordinates stored in brushBounds variable
      // and call update on plot.
      prototype._updateBrush = function() {
        this.plot.chart.data({
          ggvis_brush: [{
            x:      this._brushBounds.x1,
            y:      this._brushBounds.y1,
            width:  this._brushBounds.width(),
            height: this._brushBounds.height()
          }]
        });

        this.plot.chart.update({
          props: "update",
          items: this._getBrushItem()
        });
      };

      // Find items that are and aren't under the brush, then call update on
      // each set, with the "brush" or "update" property set, as appropriate.
      prototype._updateBrushedItems = function() {
        // TODO: This function is a performance bottleneck.
        //   Could use a faster method for finding array differences, but it'll
        //   probably be even better to track brushed and unbrushed items from
        //   the previous run.

        // Find the items in the current scene that match
        var items = this._getBrushableItems();
        var matchingItems = [];
        for (var i = 0; i < items.length; i++) {
          if (this._brushBounds.intersects(items[i].bounds)) {
            matchingItems.push(items[i]);
          }
        }

        var newBrushItems = _.difference(matchingItems, this._lastMatchingItems);
        var unBrushItems  = _.difference(this._lastMatchingItems, matchingItems);

        this._lastMatchingItems = matchingItems;

        this.plot.chart.update({ props: "brush", items: newBrushItems });
        this.plot.chart.update({ props: "update", items: unBrushItems });

        // Collect information run updateItems callbacks
        var bounds = this._brushBounds;
        var info = {
          plot_id: this.plot.plotId,
          x1: bounds.x1,
          x2: bounds.x2,
          y1: bounds.y1,
          y2: bounds.y2,
          items: matchingItems
        };
        this.trigger("updateItems", info);
      };


      // Return the definition of the brush mark
      prototype._getBrushMarkDef = function() {
        var def = _.find(this.plot._allMarkDefs(), function(markdef) {
          var data = getMarkProp(markdef, "ggvis").data || null;
          return data === "ggvis_brush";
        });

        if (def === undefined) return null;

        return def;
      };

      // Return the brush mark; if not present, return null.
      prototype._getBrushMark = function() {
        // We can identify the brush mark because it draws data from ggvis_brush.
        var brushMark = _.find(this.plot._allMarks(), function(mark) {
          var data = getMarkProp(mark.def, "ggvis").data || null;
          return data === "ggvis_brush";
        });

        if (brushMark === undefined) return null;

        return brushMark;
      };

      prototype._getBrushItem = function() {
        var brushMark = this._getBrushMark();
        if (brushMark === null || brushMark.items === null) return null;

        return brushMark.items[0];
      };

      // Return all brushable items
      prototype._getBrushableItems = function() {
        // Get the brush mark, so we can make sure not to include it as a
        // brushable item.
        var brushMark = this._getBrushMark();

        var brushableMarks = this.plot._allMarks().filter(function(mark) {
          if (_.isEmpty(getMarkProp(mark.def, "brush")) || mark === brushMark)
            return false;
          else
            return true;
        });

        var items = _.pluck(brushableMarks, "items");
        return _.flatten(items);
      };

      // x/y coords are relative to the containing div. We need to account for the
      // padding that surrounds the data area by removing the padding before we
      // compare it to any scene item bounds.
      prototype._removePadding = function(point) {
        return {
          x: point.x - this.plot.chart.padding().left,
          y: point.y - this.plot.chart.padding().top
        };
      };

      // Internal functions --------------------------------------------
      function mouseOffset(e) {
        // A workaround for Firefox, which doesn't provide offsetX/Y
        // for mouse event.
        if (typeof(e.offsetX) === "undefined") {
          var offset = $(e.currentTarget).offset();
          return {
            x: e.pageX - offset.left,
            y: e.pageY - offset.top
          };
        }
        else {
          return {
            x: e.offsetX,
            y: e.offsetY
          };
        }
      }

      return brush;
    })(); // ggvis.Plot.Brush

    return Plot;
  })(); // ggvis.Plot


  // ---------------------------------------------------------------------------
  // Handlers for interaction events
  // Some of these are defined in shiny-ggvis.js
  ggvis.handlers = {};


  return ggvis;

})(lodash);

// This is like facet, but includes the key values in a  at each level.
vg.data.treefacet = function() {

  var keys = [], key_funs = [];

  function treefacet(data) {
    var result = {
          key: "",
          keys: [],
          values: []
        },
        map = {},
        vals = result.values,
        obj, klist, kstr, len, i, k, kv;

    if (keys.length === 0) {
      throw "Need at least one key";
    }

    for (i=0, len=data.length; i<len; ++i) {
      // Create single key value to index hash
      for (k=0, klist=[], kstr=""; k<keys.length; ++k) {
        kv = key_funs[k](data[i]);
        klist.push(kv);
        kstr += (k>0 ? "|" : "") + String(kv);
      }

      obj = map[kstr];
      if (obj === undefined) {
        // Not found, initialise object with empty value for children and keys
        obj = map[kstr] = {
          key: kstr,
          keys: klist,
          index: vals.length,
          data: {},
          values: []
        };
        // Keys look like data.xyz, this adds into a data element
        // so you can refer to like usual.
        for (var key in keys) obj.data[vg.field(keys[key])[1]] = klist[key];
        vals.push(obj);
      }
      obj.values.push(data[i]);
    }

    return result;
  }

  treefacet.keys = function(k) {
    keys = k;
    key_funs = vg.array(k).map(vg.accessor);
    return treefacet;
  };

  return treefacet;
};


$(function(){ //DOM Ready

  // Gear dropdown menu
  $("body").on("click", ".ggvis-dropdown-toggle", function(){
    $(this).next(".ggvis-dropdown").toggle();
  });

  $("body").on("click", ".ggvis-download", function() {
    var plot = ggvis.plots[$(this).data("plot-id")];
    plot.updateDownloadLink(this);
  });

  $("body").on("click", ".ggvis-renderer-button", function(e) {
    var $el = $(this);
    var plot = ggvis.plots[$el.data("plot-id")];

    plot.setRenderer($el.data("renderer"));

    // Don't close the dropdown
    e.stopPropagation();
  });

});
</script>


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a.sourceLine {
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}

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<script>
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<body>




<section class="page-header">
<h1 class="title toc-ignore project-name">ggvis包</h1>
<h4 class="author project-author">庄闪闪</h4>
<h4 class="date project-date">2020-12-30</h4>
</section>



<section class="main-content">
<div id="简介" class="section level2">
<h2>简介</h2>
<p><a href="http://ggvis.rstudio.com">ggvis</a>是R的一个数据可视化包，它可以：</p>
<ul>
<li><p>使用与ggplot2类似的语法描述数据图形；</p></li>
<li><p>创建丰富的交互式图形，在本地Rstudio或浏览器中使用这些图形；</p></li>
<li><p>利用shiny的基础结构发布交互式图形。</p></li>
</ul>
<p><strong>ggvis 与 ggplot2主要区别</strong>：</p>
<ul>
<li>基本命名转换：</li>
</ul>
<p>ggplot→ggvis</p>
<p>geom→layer function</p>
<p>stat→compute function</p>
<p>aes→props</p>
<p>+→%&gt;%</p>
<ul>
<li><p>ggvis目前不支持分面；</p></li>
<li><p>使用ggvis而不添加任何层类似于qplot</p></li>
</ul>
<blockquote>
<p>更详细的区别可见：<a href="http://ggvis.rstudio.com/ggplot2.html">ggvis vs ggplot2</a></p>
</blockquote>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="kw">library</span>(ggvis)</span>
<span id="cb1-2"><a href="#cb1-2"></a><span class="kw">library</span>(dplyr)</span></code></pre></div>
</div>
<div id="静态图" class="section level2">
<h2>静态图</h2>
<div id="散点图" class="section level3">
<h3>散点图</h3>
<p>使用<code>layer_points()</code>绘制，其中内部参数都用默认值。注意这里<code>ggvis(~wt, ~mpg)</code>比ggplot多了一个波浪线。</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a>mtcars <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb2-2"><a href="#cb2-2"></a><span class="st">  </span><span class="kw">ggvis</span>(<span class="op">~</span>wt, <span class="op">~</span>mpg) <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb2-3"><a href="#cb2-3"></a><span class="st">  </span><span class="kw">layer_points</span>()</span></code></pre></div>
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</script> 如果要加拟合线，和ggplot语法很类似，再加一层<code>layer_smooths()</code>。</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a>mtcars <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb3-2"><a href="#cb3-2"></a><span class="st">  </span><span class="kw">ggvis</span>(<span class="op">~</span>wt, <span class="op">~</span>mpg) <span class="op">%&gt;%</span></span>
<span id="cb3-3"><a href="#cb3-3"></a><span class="st">  </span><span class="kw">layer_points</span>() <span class="op">%&gt;%</span></span>
<span id="cb3-4"><a href="#cb3-4"></a><span class="st">  </span><span class="kw">layer_smooths</span>()</span></code></pre></div>
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</script> 内部参数也很类似（<code>se = TRUE</code>加入拟合区间），拟合方式使用“lm”方法。</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a>mtcars <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb4-2"><a href="#cb4-2"></a><span class="st">  </span><span class="kw">ggvis</span>(<span class="op">~</span>wt, <span class="op">~</span>mpg) <span class="op">%&gt;%</span></span>
<span id="cb4-3"><a href="#cb4-3"></a><span class="st">  </span><span class="kw">layer_points</span>() <span class="op">%&gt;%</span></span>
<span id="cb4-4"><a href="#cb4-4"></a><span class="st">  </span><span class="kw">layer_model_predictions</span>(<span class="dt">model =</span> <span class="st">&quot;lm&quot;</span>, <span class="dt">se =</span> <span class="ot">TRUE</span>)</span></code></pre></div>
<pre><code>## Guessing formula = mpg ~ wt</code></pre>
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<div id="分组的散点图" class="section level3">
<h3>分组的散点图</h3>
<p>如果想要使用分组说明散点图，可以加入<code>fill = ~factor(cyl)</code>或者<code>group_by(cyl)</code>进行分布。</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a>mtcars <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb6-2"><a href="#cb6-2"></a><span class="st">  </span><span class="kw">ggvis</span>(<span class="op">~</span>wt, <span class="op">~</span>mpg) <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb6-3"><a href="#cb6-3"></a><span class="st">  </span><span class="kw">layer_points</span>(<span class="dt">fill =</span> <span class="op">~</span><span class="kw">factor</span>(cyl))</span></code></pre></div>
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</script> 如果想要预测每组数据拟合情况，可以使用<code>ayer_model_predictions()</code>。</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a>mtcars <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb7-2"><a href="#cb7-2"></a><span class="st">  </span><span class="kw">ggvis</span>(<span class="op">~</span>wt, <span class="op">~</span>mpg, <span class="dt">fill =</span> <span class="op">~</span><span class="kw">factor</span>(cyl)) <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb7-3"><a href="#cb7-3"></a><span class="st">  </span><span class="kw">layer_points</span>() <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb7-4"><a href="#cb7-4"></a><span class="st">  </span><span class="kw">group_by</span>(cyl) <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb7-5"><a href="#cb7-5"></a><span class="st">  </span><span class="kw">layer_model_predictions</span>(<span class="dt">model =</span> <span class="st">&quot;lm&quot;</span>)</span></code></pre></div>
<pre><code>## Guessing formula = mpg ~ wt</code></pre>
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<div id="柱状图" class="section level3">
<h3>柱状图</h3>
<p>柱状图是使用<code>layer_bars()</code>函数，内部参数包括width（设置柱子宽度）等。</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a><span class="kw">head</span>(pressure)</span></code></pre></div>
<pre><code>##   temperature pressure
## 1           0   0.0002
## 2          20   0.0012
## 3          40   0.0060
## 4          60   0.0300
## 5          80   0.0900
## 6         100   0.2700</code></pre>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a>pressure <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb11-2"><a href="#cb11-2"></a><span class="st">  </span><span class="kw">ggvis</span>(<span class="op">~</span>temperature, <span class="op">~</span>pressure) <span class="op">%&gt;%</span></span>
<span id="cb11-3"><a href="#cb11-3"></a><span class="st">  </span><span class="kw">layer_bars</span>(<span class="dt">fill :=</span> <span class="st">&quot;#ff8080&quot;</span>)</span></code></pre></div>
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<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1"></a>pressure <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb12-2"><a href="#cb12-2"></a><span class="st">  </span><span class="kw">ggvis</span>(<span class="op">~</span>temperature, <span class="op">~</span>pressure) <span class="op">%&gt;%</span></span>
<span id="cb12-3"><a href="#cb12-3"></a><span class="st">  </span><span class="kw">layer_bars</span>(<span class="dt">width =</span> <span class="dv">15</span>,<span class="dt">fill :=</span> <span class="st">&quot;#ff8080&quot;</span>)</span></code></pre></div>
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<div id="曲线图" class="section level3">
<h3>曲线图</h3>
<p>使用<code>layer_lines()</code>绘制曲线图，当然你可以和散点图合并，效果更好。</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1"></a>pressure <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">ggvis</span>(<span class="op">~</span>temperature, <span class="op">~</span>pressure) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">layer_lines</span>()</span></code></pre></div>
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<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1"></a>pressure <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">ggvis</span>(<span class="op">~</span>temperature, <span class="op">~</span>pressure) <span class="op">%&gt;%</span></span>
<span id="cb14-2"><a href="#cb14-2"></a><span class="st">  </span><span class="kw">layer_points</span>(<span class="dt">size :=</span> <span class="dv">50</span>) <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb14-3"><a href="#cb14-3"></a><span class="st">  </span><span class="kw">layer_lines</span>()</span></code></pre></div>
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<div id="直方图" class="section level3">
<h3>直方图</h3>
<p>使用<code>layer_histograms()</code>绘制直方图，内部参数包括width（柱子宽度），boundary（两个箱子之间的边界），center（柱子中央为中心）等。</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1"></a><span class="kw">head</span>(faithful)</span></code></pre></div>
<pre><code>##   eruptions waiting
## 1     3.600      79
## 2     1.800      54
## 3     3.333      74
## 4     2.283      62
## 5     4.533      85
## 6     2.883      55</code></pre>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1"></a>faithful <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">ggvis</span>(<span class="op">~</span>eruptions, <span class="dt">fill :=</span> <span class="st">&quot;#ff8080&quot;</span>) <span class="op">%&gt;%</span></span>
<span id="cb17-2"><a href="#cb17-2"></a><span class="st">  </span><span class="kw">layer_histograms</span>(<span class="dt">width=</span><span class="fl">0.25</span>, <span class="dt">boundary=</span><span class="dv">0</span>) <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb17-3"><a href="#cb17-3"></a><span class="st">  </span><span class="kw">add_axis</span>(<span class="st">&quot;x&quot;</span>, <span class="dt">title =</span> <span class="st">&quot;month&quot;</span>) <span class="op">%&gt;%</span></span>
<span id="cb17-4"><a href="#cb17-4"></a><span class="st">  </span><span class="kw">add_axis</span>(<span class="st">&quot;y&quot;</span>, <span class="dt">title =</span> <span class="st">&quot;count&quot;</span>)</span></code></pre></div>
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<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1"></a>faithful <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">ggvis</span>(<span class="op">~</span>eruptions, <span class="dt">fill :=</span> <span class="st">&quot;#90bff9&quot;</span>) <span class="op">%&gt;%</span></span>
<span id="cb18-2"><a href="#cb18-2"></a><span class="st">  </span><span class="kw">layer_histograms</span>(<span class="dt">width=</span><span class="fl">0.25</span>, <span class="dt">center=</span><span class="dv">0</span>) <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb18-3"><a href="#cb18-3"></a><span class="st">  </span><span class="kw">add_axis</span>(<span class="st">&quot;x&quot;</span>, <span class="dt">title =</span> <span class="st">&quot;month&quot;</span>) <span class="op">%&gt;%</span></span>
<span id="cb18-4"><a href="#cb18-4"></a><span class="st">  </span><span class="kw">add_axis</span>(<span class="st">&quot;y&quot;</span>, <span class="dt">title =</span> <span class="st">&quot;count&quot;</span>)</span></code></pre></div>
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<div id="箱型图" class="section level3">
<h3>箱型图</h3>
<p>使用<code>layer_boxplots()</code>绘制箱型图，具体内部参数再次不做具体陈述。</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1"></a>mtcars <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">ggvis</span>(<span class="op">~</span><span class="kw">factor</span>(cyl), <span class="op">~</span>mpg) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">layer_boxplots</span>(<span class="dt">fill :=</span> <span class="st">&quot;#90bff9&quot;</span>) </span></code></pre></div>
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</div>
<div id="小编有话说" class="section level2">
<h2>小编有话说</h2>
<p>本篇推送参考<a href="http://ggvis.rstudio.com/cookbook.html">ggvis cookbook</a>，小编也只是一个搬运工。这篇主要是对该包中的常见图形进行静态展示，但是其实这个包更强大的功能在于交互式。鉴于本文内容较多，将在下次对这个包的交互使用进行详细解释。</p>
</div>
<div id="交互性" class="section level2">
<h2>交互性</h2>
<p><a href="https://ggvis.rstudio.com/interactivity.html">Interactivity</a></p>
</div>
<div id="基本的交互式控件" class="section level2">
<h2>基本的交互式控件</h2>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1"></a>mtcars <span class="op">%&gt;%</span></span>
<span id="cb20-2"><a href="#cb20-2"></a><span class="st">  </span><span class="kw">ggvis</span>(<span class="op">~</span>wt, <span class="op">~</span>mpg) <span class="op">%&gt;%</span></span>
<span id="cb20-3"><a href="#cb20-3"></a><span class="st">  </span><span class="kw">layer_smooths</span>(<span class="dt">span =</span> <span class="kw">input_slider</span>(<span class="fl">0.5</span>, <span class="dv">1</span>, <span class="dt">value =</span> <span class="dv">1</span>)) <span class="op">%&gt;%</span></span>
<span id="cb20-4"><a href="#cb20-4"></a><span class="st">  </span><span class="kw">layer_points</span>(<span class="dt">size :=</span> <span class="kw">input_slider</span>(<span class="dv">100</span>, <span class="dv">1000</span>, <span class="dt">value =</span> <span class="dv">100</span>))</span></code></pre></div>
<pre><code>## Warning: Can&#39;t output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.</code></pre>
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      "values": "\"pred_\",\"resp_\"\n1.513,32.3559076033652\n1.56250632911392,31.8753059433938\n1.61201265822785,31.4002837186303\n1.66151898734177,30.9307786143783\n1.7110253164557,30.4667283159411\n1.76053164556962,30.0080705086221\n1.81003797468354,29.5547428777247\n1.85954430379747,29.1066831085523\n1.90905063291139,28.6638288864083\n1.95855696202532,28.2261593300757\n2.00806329113924,27.7939085521993\n2.05756962025316,27.3671227416083\n2.10707594936709,26.9458006046978\n2.15658227848101,26.5299408478625\n2.20608860759494,26.1195421774975\n2.25559493670886,25.7146032999975\n2.30510126582278,25.3151229217576\n2.35460759493671,24.9210997491726\n2.40411392405063,24.5325324886374\n2.45362025316456,24.1494198465469\n2.50312658227848,23.7704013112288\n2.55263291139241,23.3930410310992\n2.60213924050633,23.0184854507692\n2.65164556962025,22.648013375053\n2.70115189873418,22.2829036087647\n2.7506582278481,21.9244349567186\n2.80016455696203,21.5738862237289\n2.84967088607595,21.2325362146096\n2.89917721518987,20.8994648774453\n2.9486835443038,20.563314278298\n2.99818987341772,20.2260322794124\n3.04769620253165,19.8921207313051\n3.09720253164557,19.5660814844932\n3.14670886075949,19.2524163894935\n3.19621518987342,18.9556272968227\n3.24572151898734,18.6776819657173\n3.29522784810127,18.4109241123963\n3.34473417721519,18.1533810871694\n3.39424050632911,17.9038917888661\n3.44374683544304,17.6613801039195\n3.49325316455696,17.4367412601373\n3.54275949367089,17.2198928657746\n3.59226582278481,16.9885817927012\n3.64177215189873,16.7560619903523\n3.69127848101266,16.5274875375278\n3.74078481012658,16.3032371340526\n3.79029113924051,16.0836894797512\n3.83979746835443,15.8692232744485\n3.88930379746835,15.6600927173047\n3.93881012658228,15.4562638006676\n3.9883164556962,15.2577332064667\n4.03782278481013,15.064498632161\n4.08732911392405,14.8765577752094\n4.13683544303798,14.6939083330709\n4.1863417721519,14.5165480032044\n4.23584810126582,14.344474483069\n4.28535443037975,14.1776854701235\n4.33486075949367,14.016178661827\n4.3843670886076,13.8599517556385\n4.43387341772152,13.7090024490167\n4.48337974683544,13.5633284394209\n4.53288607594937,13.4229274243098\n4.58239240506329,13.2877971011425\n4.63189873417722,13.157935167378\n4.68140506329114,13.0333393204751\n4.73091139240506,12.9140072578929\n4.78041772151899,12.7999366770903\n4.82992405063291,12.6911252755264\n4.87943037974684,12.58757075066\n4.92893670886076,12.4892707999501\n4.97844303797468,12.3962231208557\n5.02794936708861,12.3084254108357\n5.07745569620253,12.2258753673492\n5.12696202531646,12.148570687855\n5.17646835443038,12.0765090698122\n5.2259746835443,12.0096882106797\n5.27548101265823,11.9481058079165\n5.32498734177215,11.8917595589815\n5.37449367088608,11.8406471613337\n5.424,11.7947663124321"
    },
    {
      "name": "scale/x",
      "format": {
        "type": "csv",
        "parse": {
          "domain": "number"
        }
      },
      "values": "\"domain\"\n1.31745\n5.61955"
    },
    {
      "name": "scale/y",
      "format": {
        "type": "csv",
        "parse": {
          "domain": "number"
        }
      },
      "values": "\"domain\"\n9.225\n35.075"
    }
  ],
  "scales": [
    {
      "name": "x",
      "domain": {
        "data": "scale/x",
        "field": "data.domain"
      },
      "zero": false,
      "nice": false,
      "clamp": false,
      "range": "width"
    },
    {
      "name": "y",
      "domain": {
        "data": "scale/y",
        "field": "data.domain"
      },
      "zero": false,
      "nice": false,
      "clamp": false,
      "range": "height"
    }
  ],
  "marks": [
    {
      "type": "line",
      "properties": {
        "update": {
          "stroke": {
            "value": "#000000"
          },
          "strokeWidth": {
            "value": 2
          },
          "x": {
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            "field": "data.pred_"
          },
          "y": {
            "scale": "y",
            "field": "data.resp_"
          },
          "fill": {
            "value": "transparent"
          }
        },
        "ggvis": {
          "data": {
            "value": ".0/model_prediction1"
          }
        }
      },
      "from": {
        "data": ".0/model_prediction1"
      }
    },
    {
      "type": "symbol",
      "properties": {
        "update": {
          "fill": {
            "value": "#000000"
          },
          "x": {
            "scale": "x",
            "field": "data.wt"
          },
          "y": {
            "scale": "y",
            "field": "data.mpg"
          },
          "size": {
            "field": "data.reactive_229839864"
          }
        },
        "ggvis": {
          "data": {
            "value": ".0"
          }
        }
      },
      "from": {
        "data": ".0"
      }
    }
  ],
  "legends": [],
  "axes": [
    {
      "type": "x",
      "scale": "x",
      "orient": "bottom",
      "layer": "back",
      "grid": true,
      "title": "wt"
    },
    {
      "type": "y",
      "scale": "y",
      "orient": "left",
      "layer": "back",
      "grid": true,
      "title": "mpg"
    }
  ],
  "padding": null,
  "ggvis_opts": {
    "keep_aspect": false,
    "resizable": true,
    "padding": {},
    "duration": 250,
    "renderer": "svg",
    "hover_duration": 0,
    "width": 672,
    "height": 480
  },
  "handlers": null
};
ggvis.getPlot("plot_id236241828").parseSpec(plot_id236241828_spec);
</script>
</div>
</section>



<!-- code folding -->


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</body>
</html>


================================================
FILE: 2020年/2020.12.30ggvis/ggvis.md
================================================
## 简介
[ggvis](http://ggvis.rstudio.com "ggvis github")是R的一个数据可视化包，它可以：

- 使用与ggplot2类似的语法描述数据图形；

- 创建丰富的交互式图形，在本地Rstudio或浏览器中使用这些图形；

- 利用shiny的基础结构发布交互式图形。

**ggvis 与 ggplot2主要区别**：

- 基本命名转换：

|ggplot|ggvis|
|-|-|
|geom|layer function|
|stat|compute function|
|aes|props|
|+|%>%|

- ggvis目前不支持分面；

- 使用ggvis而不添加任何层类似于qplot

> 更详细的区别可见：<http://ggvis.rstudio.com/ggplot2.html>

这里先对包进行加载（可以直接使用instll.packages("")下载）
```{r message=FALSE, warning=FALSE}
library(ggvis)
library(dplyr)
```

## 静态图

### 1 散点图

使用`layer_points()`绘制，其中内部参数都用默认值。注意这里`ggvis(~wt, ~mpg) `比ggplot多了一个波浪线。
```{r}
mtcars %>% 
  ggvis(~wt, ~mpg) %>% 
  layer_points()
```
![](https://imgkr2.cn-bj.ufileos.com/e05e6ea6-945d-49eb-bf21-eb530501cb53.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=LXs5vhSIWoeH%252BPk02VPMmv3o4us%253D&Expires=1609416269)



如果要加拟合线，和ggplot语法很类似，再加一层`layer_smooths()`。
```{r}
mtcars %>% 
  ggvis(~wt, ~mpg) %>%
  layer_points() %>%
  layer_smooths()
```

![](https://imgkr2.cn-bj.ufileos.com/a63e61ca-6a9c-4799-9d42-79dfc8eed04b.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=7HcrOMNKMPjazM9ggUWg0x7w8hI%253D&Expires=1609416281)




内部参数也很类似（`se = TRUE`加入拟合区间），拟合方式使用"lm"方法。
```{r}
mtcars %>% 
  ggvis(~wt, ~mpg) %>%
  layer_points() %>%
  layer_model_predictions(model = "lm", se = TRUE)
```
![](https://imgkr2.cn-bj.ufileos.com/2c4cf0e9-942d-40d2-badc-62badaa1cfe3.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=RTssV26FflgDOGONU21qxGqIk%252BM%253D&Expires=1609416292)


### 2 分组的散点图

如果想要使用分组说明散点图，可以加入`fill = ~factor(cyl)`或者`group_by(cyl)`进行分布。
```{r}
mtcars %>% 
  ggvis(~wt, ~mpg) %>% 
  layer_points(fill = ~factor(cyl))
```
![](https://imgkr2.cn-bj.ufileos.com/6c6425a9-1eab-4807-8c42-252c0e230820.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=fs0d49JC85yxV8PF3rfmpE0ghmw%253D&Expires=1609415742)

如果想要预测每组数据拟合情况，可以使用`ayer_model_predictions()`。
```{r}
mtcars %>% 
  ggvis(~wt, ~mpg, fill = ~factor(cyl)) %>% 
  layer_points() %>% 
  group_by(cyl) %>% 
  layer_model_predictions(model = "lm")
```

![](https://imgkr2.cn-bj.ufileos.com/736edb8b-4921-40e7-a29b-81e9f7b738bf.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=pLrTlICigwIqryUu%252F8g4rT7sqI8%253D&Expires=1609415748)

### 3 柱状图

柱状图是使用`layer_bars()`函数，内部参数包括width（设置柱子宽度）等。

```{r}
head(pressure)

pressure %>% 
  ggvis(~temperature, ~pressure) %>%
  layer_bars(fill := "#ff8080")
```
![](https://imgkr2.cn-bj.ufileos.com/9be6d335-8709-416c-8f15-bfd60895602e.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=sOXRDgS2IAgJRChrA%252FQunzIBaNg%253D&Expires=1609415753)
```{r}
pressure %>% 
  ggvis(~temperature, ~pressure) %>%
  layer_bars(width = 15,fill := "#ff8080")
```
![](https://imgkr2.cn-bj.ufileos.com/ea13652f-f47f-420b-9290-b26f6acfc6d9.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=zs%252BCjmsAMfs6y8J6SiXRrQvdNHg%253D&Expires=1609415758)
### 4 曲线图

使用`layer_lines()`绘制曲线图，当然你可以和散点图合并，效果更好。
```{r}
pressure %>% ggvis(~temperature, ~pressure) %>% layer_lines()
```
![](https://imgkr2.cn-bj.ufileos.com/618bc22c-12d7-4d3a-be90-3369f086c589.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=gC7M9I7zFB5mlE2DtswbIWI1pFM%253D&Expires=1609415765)


```{r}
pressure %>% ggvis(~temperature, ~pressure) %>%
  layer_points(size := 50) %>% 
  layer_lines()
```
![](https://imgkr2.cn-bj.ufileos.com/07d3bb9d-8610-45e7-a1e4-33cb85ec3349.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=wWoV7E%252F8mLO%252F6MP0bynE3nTRglY%253D&Expires=1609415771)



### 5 直方图
使用`layer_histograms()`绘制直方图，内部参数包括width（柱子宽度），boundary（两个箱子之间的边界），center（柱子中央为中心）等。

```{r}
head(faithful)

faithful %>% ggvis(~eruptions, fill := "#ff8080") %>%
  layer_histograms(width=0.25, boundary=0) %>% 
  add_axis("x", title = "month") %>%
  add_axis("y", title = "count")

```

![](https://imgkr2.cn-bj.ufileos.com/9d792a91-74c5-4b68-9488-a5ed9821a5b6.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=LIfmPNVBe0FVYDGuU%252FvyK%252BmUiUA%253D&Expires=1609415818)



```{r}
faithful %>% ggvis(~eruptions, fill := "#90bff9") %>%
  layer_histograms(width=0.25, center=0) %>% 
  add_axis("x", title = "month") %>%
  add_axis("y", title = "count")
```

![](https://imgkr2.cn-bj.ufileos.com/09cb5684-f255-43fc-91ac-c5171157e4a2.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=HkPrRKYfaZWYtRUnisPymCIEsFQ%253D&Expires=1609415823)


> add_axis()可以设置坐标轴的名称等其他参数。

### 6 箱型图

使用`layer_boxplots()`绘制箱型图，具体内部参数再次不做具体陈述。

```{r}
mtcars %>% 
  ggvis(~factor(cyl), ~mpg) %>% 
  layer_boxplots(fill := "#90bff9") 
```

![](https://imgkr2.cn-bj.ufileos.com/35f114a1-6a53-4b5d-a8d8-8086b891d524.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=v9oowcqbxPwcZ3i%252FKA%252BnY0cXj%252FE%253D&Expires=1609415827)


## 小编有话说

本篇推送参考[ggvis cookbook](http://ggvis.rstudio.com/cookbook.html "ggvis cookbook")，小编也只是一个搬运工。这篇主要是对该包中的常见图形进行静态展示，但是其实这个包更强大的功能在于交互式。鉴于本文内容较多，将在下次对这个包的交互使用进行详细解释。




================================================
FILE: 2020年/2020.12.30ggvis/ggvis.rmd
================================================
---
title: "ggvis包"
author: "庄闪闪"
date: "`r Sys.Date()`"
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
vignette: >
  %\VignetteIndexEntry{Vignette Title}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---



## 简介
[ggvis](http://ggvis.rstudio.com)是R的一个数据可视化包，它可以：

- 使用与ggplot2类似的语法描述数据图形；

- 创建丰富的交互式图形，在本地Rstudio或浏览器中使用这些图形；

- 利用shiny的基础结构发布交互式图形。

**ggvis 与 ggplot2主要区别**：

- 基本命名转换：

ggplot→ggvis

geom→layer function

stat→compute function

aes→props

+→%>%

- ggvis目前不支持分面；

- 使用ggvis而不添加任何层类似于qplot

> 更详细的区别可见：[ggvis vs ggplot2](http://ggvis.rstudio.com/ggplot2.html)

```{r message=FALSE, warning=FALSE}
library(ggvis)
library(dplyr)
```

## 静态图

### 散点图

使用`layer_points()`绘制，其中内部参数都用默认值。注意这里`ggvis(~wt, ~mpg) `比ggplot多了一个波浪线。
```{r}
mtcars %>% 
  ggvis(~wt, ~mpg) %>% 
  layer_points()
```
如果要加拟合线，和ggplot语法很类似，再加一层`layer_smooths()`。
```{r}
mtcars %>% 
  ggvis(~wt, ~mpg) %>%
  layer_points() %>%
  layer_smooths()
```
内部参数也很类似（`se = TRUE`加入拟合区间），拟合方式使用"lm"方法。
```{r}
mtcars %>% 
  ggvis(~wt, ~mpg) %>%
  layer_points() %>%
  layer_model_predictions(model = "lm", se = TRUE)
```

### 分组的散点图

如果想要使用分组说明散点图，可以加入`fill = ~factor(cyl)`或者`group_by(cyl)`进行分布。
```{r}
mtcars %>% 
  ggvis(~wt, ~mpg) %>% 
  layer_points(fill = ~factor(cyl))
```
如果想要预测每组数据拟合情况，可以使用`ayer_model_predictions()`。
```{r}
mtcars %>% 
  ggvis(~wt, ~mpg, fill = ~factor(cyl)) %>% 
  layer_points() %>% 
  group_by(cyl) %>% 
  layer_model_predictions(model = "lm")
```

### 柱状图

柱状图是使用`layer_bars()`函数，内部参数包括width（设置柱子宽度）等。

```{r}
head(pressure)

pressure %>% 
  ggvis(~temperature, ~pressure) %>%
  layer_bars(fill := "#ff8080")
```

```{r}
pressure %>% 
  ggvis(~temperature, ~pressure) %>%
  layer_bars(width = 15,fill := "#ff8080")
```

### 曲线图

使用`layer_lines()`绘制曲线图，当然你可以和散点图合并，效果更好。
```{r}
pressure %>% ggvis(~temperature, ~pressure) %>% layer_lines()
```

```{r}
pressure %>% ggvis(~temperature, ~pressure) %>%
  layer_points(size := 50) %>% 
  layer_lines()
```


### 直方图
使用`layer_histograms()`绘制直方图，内部参数包括width（柱子宽度），boundary（两个箱子之间的边界），center（柱子中央为中心）等。
```{r}
head(faithful)

faithful %>% ggvis(~eruptions, fill := "#ff8080") %>%
  layer_histograms(width=0.25, boundary=0) %>% 
  add_axis("x", title = "month") %>%
  add_axis("y", title = "count")

```

```{r}
faithful %>% ggvis(~eruptions, fill := "#90bff9") %>%
  layer_histograms(width=0.25, center=0) %>% 
  add_axis("x", title = "month") %>%
  add_axis("y", title = "count")
```


### 箱型图

使用`layer_boxplots()`绘制箱型图，具体内部参数再次不做具体陈述。

```{r}
mtcars %>% ggvis(~factor(cyl), ~mpg) %>% layer_boxplots(fill := "#90bff9") 
```


## 小编有话说

本篇推送参考[ggvis cookbook](http://ggvis.rstudio.com/cookbook.html)，小编也只是一个搬运工。这篇主要是对该包中的常见图形进行静态展示，但是其实这个包更强大的功能在于交互式。鉴于本文内容较多，将在下次对这个包的交互使用进行详细解释。

## 交互性

[Interactivity](https://ggvis.rstudio.com/interactivity.html)

## 基本的交互式控件
```{r}
mtcars %>%
  ggvis(~wt, ~mpg) %>%
  layer_smooths(span = input_slider(0.5, 1, value = 1)) %>%
  layer_points(size := input_slider(100, 1000, value = 100))
```





================================================
FILE: 2020年/2020年R数据科学系列/2020.08.23几何对象/几何对象.md
================================================
## 前言

本最近打算把《R数据科学》过一遍，并且把课后习题都做一下。先从第一章开始吧，快速把ggplot过一下。第一章目录如下：![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjenWvrnBtdFRLwcPLeN3Peia1xnHicVnaCa1YeoVGaBu7F1WtRTS3ic1YYUg/640?wx_fmt=png)前面几节的内容比较少，第1.5节我对其做了补充，可见[R可视乎|分面一页多图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484186&idx=1&sn=c913a65f88132b3611e580b0318404d9&chksm=ea24fcfedd5375e87adfc3028850ee4034a0a0d34dd3855cf155b28eea9c71bfedb381d2c9e9&scene=21#wechat_redirect)，课后练习题也可在该篇文章中找到。1.6节主要讲几何对象：表示数据的几何图形对象，比如条形图，折线图，箱线图等。要想改变图中的几何对象，需要修改添加在ggplot\(\) 函数中的几何对象函数。1.6节的内容不是很多，我们主要通过写本节的练习来回顾知识点。

## 练习

### \(1\) 在绘制折线图、箱线图、直方图和分区图时，应该分别使用哪种几何对象？

**答**：geom\_line\(\),   geom\_boxplot\(\),  geom\_histogram\(\),

### \(2\) 在脑海中运行以下代码，并预测会有何种输出。接着在 R 中运行代码，并检查你的预测是否正确。

`ggplot(data = mpg,  
  mapping = aes(x = displ, y = hwy, color = drv)) +  
  geom_point() +  
  geom_smooth(se = FALSE)  
`

想象下：x轴是displ，y轴是hwy，颜色使用drv填充，然后加了一个散点图（中间没有参数，用默认），再加上一个拟合曲线，没有绘制出区间。由于两个集合对象都没有对mapping进行设置，所以会使用原始涂层的aes\(x = displ, y = hwy, color = drv\)\)。所以出来的结果，散点图和拟合曲线都是三种颜色。下面是出来的颜色：

![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjenC3nb0Q0tBfeLsWqKLM9xMybnHTY9laoezVgBtJhAQtCuNSWbAbWhibg/640?wx_fmt=png)

### \(3\)  show.legend = FALSE 的作用是什么？删除它会发生什么情况？

**答**：是把图例隐藏了，默认参数是show.legend = TRUE.

### \(4\)  geom\_smooth\(\) 函数中的 se 参数的作用是什么？

**答**：可以绘制出区间，当se = TRUE（默认）则会出现区间。

### \(5\) 以下代码生成的两张图有什么区别吗？为什么？

`#第一幅图  
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +  
geom_point() +  
geom_smooth()  
#第二幅图  
ggplot() +  
geom_point(data = mpg,  
mapping = aes(x = displ, y = hwy)  
) +  
geom_smooth(data = mpg,  
mapping = aes(x = displ, y = hwy)  
)  
`

没有什么区别，第一个图在原始上就设定了x，y。后面两个集合对象就可以默认使用前面的设置了。而第二个图则是原始没有设置，而是在集合对象中一一设置了。  
第一种方法：简便；第二种方法：灵活，可以设置不同的x，y。

### \(6\) 自己编写 R 代码来生成以下各图

![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjenGuoerDicLZuE7qdOZE4oeX5Q98ZtLiaplXQp8bAOBPmp5oGtAnpHiahew/640?wx_fmt=png)

**第一个图：** x为displ，y为hwy。画了散点图（geom\_point）并绘制了拟合曲线（geom\_smooth），没加置信区间（se =FALSE）

`ggplot(data = mpg,aes(x = displ,y = hwy))+  
	geom_point(size=3)+  
	geom_smooth(se=F,size=2)  
`

![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjenPBfZBjDqY9sr4IOVM5wKbRemDGhX1pGxImKQZzzpYKqb60E1tJlMibw/640?wx_fmt=png)

**第二个图：** 在第一个基础上根据drv变量绘制了三条拟合曲线，并且没有绘制区间。并且把图例删除了（show.legend = FALSE）

```
ggplot(data = mpg,aes(x = displ,y = hwy))+	geom_point(size=3)+	geom_smooth(aes(fill=drv),se=F,size=2,show.legend = FALSE)
```

![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjenWCtTf9TjquM9ubJF9ibibC6CM0CWfiaicjPfich5RfRAMUEa0tyYnWABqCA/640?wx_fmt=png)

**第三个图：** 散点图颜色的颜色根据drv变量进行变化，并且拟合曲线也是和散点图相同颜色（所以可以在最原始图层中加入color=drv），没有拟合曲线的区间，但是有图例（默认就是有的）。

`ggplot(data = mpg,aes(x = displ,y = hwy,color=drv))+  
	geom_point(size=3)+  
	geom_smooth(se=F,size=2)  
`

![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjenN9BC3yu7cB9cRyvbwROMia6zYxSuRyQBS07Zea58dOYtTm9pyZ2c7Mg/640?wx_fmt=png)

**第四个图：** 根据frv变量给散点图填充，但是只绘制了一条拟合线。所以这里不可以直接放在原始图层里，得放在geom\_point\(\)中。

`ggplot(data = mpg,aes(x = displ,y = hwy))+  
	geom_point(aes(color=drv),size=3)+  
	geom_smooth(se=F,size=2)  
`

![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjenaWcJd6T5EDU7bvZ3TVpibbPb2SJBP9ibbgiaOZG8Iicloya46yL3icsTIdQ/640?wx_fmt=png)

**第五幅图：**在第三幅图基础上改变了拟合曲线的线的类型\(linetype\)。

`ggplot(data = mpg,aes(x = displ,y = hwy,color=drv))+  
	geom_point(size=3)+  
	geom_smooth(aes(linetype=drv),se=F,size=2)  
`

![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjen6AoK8icH7MTiaQrnW8xicQbrzkVpNmHCEzWFWdwgTghLf7z3WoqhhwxLg/640?wx_fmt=png)

**第六幅图：** 没有拟合曲线，直接将散点图的颜色区分开了，有点像第三幅图的简化版。

`ggplot(data = mpg,aes(x = displ,y = hwy,color=drv))+  
	geom_point(size=3)  
`

![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjen59Lak7t1buKRSiacyTLTDWciaD0ThruzghrU0fF9KNNwAU2V3dqsyQlw/640?wx_fmt=png)

## 参考

1.  R for Data Science \[https://r4ds.had.co.nz/\]

  

![](https://mmbiz.qpic.cn/mmbiz_jpg/MIcgkkEyTHgfkvXafZE9scXp4icvdcNFyic0z7THajQBAyLNRiau3CKnZ3L9Y9K2YXObhaiblBm0Jbnicaq9lW3pz4g/640?wx_fmt=jpeg)  
欢迎**关注**我的**公众号**，**点赞，在看，收藏\~\~\~**

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.CtxtMenu_MenuRadioCheck.CtxtMenu_RTL {  right: .7em; left:auto}
.CtxtMenu_MenuInputBox {  padding-left: 1em; right:.5em; color:#666666;  font-family: null;}
.CtxtMenu_MenuInputBox.CtxtMenu_RTL {  left: .1em;}
.CtxtMenu_MenuComboBox {  left:.1em; padding-bottom:.5em;}
.CtxtMenu_MenuLabel {  padding: 1px 2em 3px 1.33em;  font-style:italic}
.CtxtMenu_MenuRule {  border-top: 1px solid #DDDDDD;  margin: 4px 3px;}
.CtxtMenu_MenuDisabled {  color:GrayText}
.CtxtMenu_MenuActive {  background-color: #606872;  color: white;}
.CtxtMenu_MenuDisabled:focus {  background-color: #E8E8E8}
.CtxtMenu_MenuLabel:focus {  background-color: #E8E8E8}
.CtxtMenu_ContextMenu:focus {  outline:none}
.CtxtMenu_ContextMenu .CtxtMenu_MenuItem:focus {  outline:none}
.CtxtMenu_Menu .CtxtMenu_MenuClose {  top:-10px; left:-10px}
</style><style id="MJX-SVG-styles">
mjx-container[jax="SVG"] {
  direction: ltr;
}

mjx-container[jax="SVG"] > svg {
  overflow: visible;
}

mjx-container[jax="SVG"] > svg a {
  fill: blue;
  stroke: blue;
}

mjx-assistive-mml {
  position: absolute !important;
  top: 0px;
  left: 0px;
  clip: rect(1px, 1px, 1px, 1px);
  padding: 1px 0px 0px 0px !important;
  border: 0px !important;
  display: block !important;
  width: auto !important;
  overflow: hidden !important;
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

mjx-assistive-mml[display="block"] {
  width: 100% !important;
}

mjx-container[jax="SVG"][display="true"] {
  display: block;
  text-align: center;
  margin: 1em 0;
}

mjx-container[jax="SVG"][justify="left"] {
  text-align: left;
}

mjx-container[jax="SVG"][justify="right"] {
  text-align: right;
}

g[data-mml-node="merror"] > g {
  fill: red;
  stroke: red;
}

g[data-mml-node="merror"] > rect[data-background] {
  fill: yellow;
  stroke: none;
}

g[data-mml-node="mtable"] > line[data-line] {
  stroke-width: 70px;
  fill: none;
}

g[data-mml-node="mtable"] > rect[data-frame] {
  stroke-width: 70px;
  fill: none;
}

g[data-mml-node="mtable"] > .mjx-dashed {
  stroke-dasharray: 140;
}

g[data-mml-node="mtable"] > .mjx-dotted {
  stroke-linecap: round;
  stroke-dasharray: 0,140;
}

g[data-mml-node="mtable"] > svg {
  overflow: visible;
}

[jax="SVG"] mjx-tool {
  display: inline-block;
  position: relative;
  width: 0;
  height: 0;
}

[jax="SVG"] mjx-tool > mjx-tip {
  position: absolute;
  top: 0;
  left: 0;
}

mjx-tool > mjx-tip {
  display: inline-block;
  padding: .2em;
  border: 1px solid #888;
  font-size: 70%;
  background-color: #F8F8F8;
  color: black;
  box-shadow: 2px 2px 5px #AAAAAA;
}

g[data-mml-node="maction"][data-toggle] {
  cursor: pointer;
}

mjx-status {
  display: block;
  position: fixed;
  left: 1em;
  bottom: 1em;
  min-width: 25%;
  padding: .2em .4em;
  border: 1px solid #888;
  font-size: 90%;
  background-color: #F8F8F8;
  color: black;
}

foreignObject[data-mjx-xml] {
  font-family: initial;
  line-height: normal;
  overflow: visible;
}

.MathJax path {
  stroke-width: 3;
}
</style><style type="text/css"></style><style type="text/css">.CodeMirror-line, .cm-header.cm-header-2, .cm-strong, .cm-image.cm-image-marker, .cm-image.cm-image-alt-text.cm-link, .cm-string.cm-url, .cm-image.cm-image-alt-text.cm-link.CodeMirror-matchingbracket, .CodeMirror-lines, .ant-input, .cc_cursor, body, .ogdlpmhglpejoiomcodnpjnfgcpmgale_default, body, html, input[type="date"], input[type="time"], input[type="datetime-local"], input[type="month"], input::-webkit-contacts-auto-fill-button, input:read-only {cursor: url("data:image/png;base64,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") 1 1 , auto !important; }.nice-themeselect-theme-item-name, .nice-themeselect-theme-item, .nice-codetheme-item, .nice-codetheme-item-name, .nice-menu-item, .nice-menu-name, .ant-select-selection-selected-value, .ant-select-selection__rendered, .ant-select-selection
...........ant-select-selection--single, .ant-btn.ant-btn-primary, .ant-btn.ant-btn-primary.cc_pointer, .ant-btn, .ant-btn.cc_pointer, .ant-select-selection__rendered.cc_pointer, .ant-select-selection-selected-value.cc_pointer, .nice-menu-link.ant-dropdown-trigger, .nice-menu-link.ant-dropdown-trigger.cc_pointer, .nice-menu-link.ant-dropdown-trigger.ant-dropdown-open, .nice-menu-link.ant-dropdown-trigger.ant-dropdown-open.cc_pointer, .nice-btn-zhihu, .ant-modal-close-x, .ant-modal-close-x.cc_pointer, .nice-btn-previewtype.ant-tooltip-open, .nice-themeselect-theme-item-author, .nice-themeselect-theme-item-author.cc_pointer, .nice-themeselect-theme-item-name.nice-menu-subscribe-more, .nice-themeselect-theme-item-name.nice-menu-subscribe-more.cc_pointer, .nice-menu-shortcut, .nice-menu-shortcut.cc_pointer, .nice-footer-message, .nice-footer-engine.ant-dropdown-trigger, .nice-footer-engine.ant-dropdown-trigger.cc_pointer, .nice-footer-message.cc_pointer, .ant-btn.nice-btn-login.ant-btn-circle, .ant-btn.nice-btn-login.ant-btn-circle.cc_pointer, .cc_pointer,[type="search"]::-webkit-search-cancel-button, [type="search"]::-webkit-search-decoration, .paper-button, .ytp-progress-bar-container, input[type=submit], :link, :visited, a > *, img, button, ::-webkit-scrollbar-button, .ogdlpmhglpejoiomcodnpjnfgcpmgale_pointer, ::-webkit-file-upload-button, button, .ytp-volume-panel, #myogdlpmhglpejoiomcodnpjnfgcpmgale .icon { cursor: url("data:image/png;base64,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22 3, auto !important; } </style><style id="markdown-theme">/*自定义样式，实时生效*/
#nice {
}

#nice p {
	margin: 0 0 20px;
	padding: 0;
	line-height: 1.8em;
	color: #3a3a3a;
}

/* 一级标题 */
#nice h1 {
  font-size: 2.1em;
	line-height: 1.1em;
	padding-top: 16px;
  padding-bottom: 10px;
  margin-bottom: 4px;
  border-bottom: 1px solid #c99833;
}
/* 一级标题内容 */
#nice h1 .content {
  color: #515151;
  font-weight: 700;
}

#nice h2, h3, h4, h5, h6 {
 line-height: 1.5em;
 margin-top: 2.2em;
 margin-bottom: 4px;
}

/* 一级标题修饰 请参考有实例的主题 */
#nice h1:after {}

/* 二级标题 */
#nice h2 {
 margin-bottom: 35px;
}

/* 二级标题内容 */

#nice h2 .content {
  display: inline-block;
  font-weight: bold;
  background: linear-gradient(#fff 60%, #ffb11b 40%);
  color: #515151;
  padding: 2px 13px 2px;
  margin-right: 3px;
  height: 50%;
}

/* 二级标题修饰 请参考有实例的主题 */
#nice h2:after {}

/* 三级标题 */
#nice h3 {
  line-height: 1.4;
  padding-top: 10px;
  margin: 10px 0 5px;
}

/* 三级标题内容 */
#nice h3 .content {
  color: #515151;
  font-weight: 700;
 font-size: 1.0em;
  padding-left: 20px;
  border-left: 3px solid #f9bf45;
}

/* 三级标题修饰 请参考有实例的主题 */
#nice h3:after {}

/* 引用
* 左边缘颜色 border-left-color: black;
* 背景色 background: gray;
*/
#nice blockquote {
  border-left-color: #ffb11b;
 background: #fff5e3;
}

/* 引用文字 */
#nice blockquote p {
  color: #595959;
}

/* 链接 */
#nice a {
  border: none;
  text-decoration: none;
  color: #dda52d;
}

#nice a:hover {
  color: #f9bf45;
  text-decoration: underline;
}

/* 无序列表整体样式
 * list-style-type: square|circle|disc;
 */
#nice ul {
}

/* 有序列表整体样式
 * list-style-type: upper-roman|lower-greek|lower-alpha;
 */
#nice ol {
}

/* 列表内容，不要设置li
 */
#nice li section {
}

/* 加粗 */
#nice strong {}

/* 斜体 */
#nice em {}

/* 加粗斜体 */
#nice em strong {}

/* 删除线 */
#nice del {
  color: #d19826;
}

/* 分隔线
* 粗细、样式和颜色
* border-top: 1px solid #3e3e3e;
*/
#nice hr {
  border-top: 1px solid #f9bf45;
  margin: 20px 0px;
}

/* 图片
* 宽度 width: 80%;
* 居中 margin: 0 auto;
* 居左 margin: 0 0;
*/
#nice img {
  width: 100%;
 border-radius: 5px;
 display: block;
 margin-bottom: 15px;
 height: auto;
}

/* 图片描述文字 */
#nice figcaption {
  color: #dda52d;
  font-size: 14px;
}

/* 行内代码 */
#nice p code, #nice li code {
  color: #9b6e23;
  background-color: #fff5e3;
  padding: 3px;
  margin: 3px;
}

/* 非微信代码块
 * 代码块不换行 display: -webkit-box !important;
 * 代码块换行 display: block;
 */
#nice pre code {}

/*
 * 表格内的单元格
 * 字体大小 font-size: 16px;
 * 边框 border: 1px solid #ccc;
 * 内边距 padding: 5px 10px;
 */
#nice table tr th,
#nice table tr td {
  text-align: center;
}

/* 脚注文字 */
#nice .footnote-word {
  color: #ffb11b;
  padding: 3px;
}

/* 脚注上标 */
#nice .footnote-ref {
  color: #dda52d;
  margin: 2px;
  padding: 3px;
}

/* "参考资料"四个字 
 * 内容 content: "参考资料";
 */
#nice .footnotes-sep:before {
  margin: 30px 0px 15px 0px;
  font-weight: 800;
}


/* 参考资料编号 */
#nice .footnote-num {
}

/* 参考资料文字 */
#nice .footnote-item p { 
}

/* 参考资料解释 */
#nice .footnote-item p em {
}

/* 行间公式
 * 最大宽度 max-width: 300% !important;
 */
#nice .block-equation svg {
}

/* 行内公式
 */
#nice .inline-equation svg {  
}

/* 滑动图片
 */
#nice .imageflow-img {
  display: inline-block;
  width:100%;
  margin-bottom: 0;
}</style><style id="code-theme">/*

Atom One Light by Daniel Gamage
Original One Light Syntax theme from https://github.com/atom/one-light-syntax

base:    #fafafa
mono-1:  #383a42
mono-2:  #686b77
mono-3:  #a0a1a7
hue-1:   #0184bb
hue-2:   #4078f2
hue-3:   #a626a4
hue-4:   #50a14f
hue-5:   #e45649
hue-5-2: #c91243
hue-6:   #986801
hue-6-2: #c18401

*/

.hljs {
  display: block;
  overflow-x: auto;
  padding: 16px;
  color: #383a42;
  background: #fafafa;
}

.hljs-comment,
.hljs-quote {
  color: #a0a1a7;
  font-style: italic;
}

.hljs-doctag,
.hljs-keyword,
.hljs-formula {
  color: #a626a4;
}

.hljs-section,
.hljs-name,
.hljs-selector-tag,
.hljs-deletion,
.hljs-subst {
  color: #e45649;
}

.hljs-literal {
  color: #0184bb;
}

.hljs-string,
.hljs-regexp,
.hljs-addition,
.hljs-attribute,
.hljs-meta-string {
  color: #50a14f;
}

.hljs-built_in,
.hljs-class .hljs-title {
  color: #c18401;
}

.hljs-attr,
.hljs-variable,
.hljs-template-variable,
.hljs-type,
.hljs-selector-class,
.hljs-selector-attr,
.hljs-selector-pseudo,
.hljs-number {
  color: #986801;
}

.hljs-symbol,
.hljs-bullet,
.hljs-link,
.hljs-meta,
.hljs-selector-id,
.hljs-title {
  color: #4078f2;
}

.hljs-emphasis {
  font-style: italic;
}

.hljs-strong {
  font-weight: bold;
}

.hljs-link {
  text-decoration: underline;
}

#nice .custom code {
  padding-top: 15px;
  background: #fafafa;
  border-radius: 5px;
}

#nice .custom:before {
  content: '';
  display:block;
  background: url(https://my-wechat.mdnice.com/point.png);
  height: 30px;
  width: 100%;
  background-size:40px;
  background-repeat: no-repeat;
  background-color: #fafafa;
  margin-bottom: -7px;
  border-radius: 5px;
  background-position: 10px 10px;
}

#nice .custom {
  border-radius: 5px;
  box-shadow: rgba(0, 0, 0, 0.55) 0px 2px 10px;
}</style></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"><div style="display: flex;"><div class="nice-app"><div class="nice-navbar"><div class="nice-left-nav"><section id="nice-title" class="nice-title">Markdown Nice</section><a id="nice-menu-file" class="nice-menu-link ant-dropdown-trigger" href="https://www.mdnice.com/#">文件</a><a id="nice-menu-pattern" class="nice-menu-link ant-dropdown-trigger" href="https://www.mdnice.com/#">格式</a><a id="nice-menu-function" class="nice-menu-link ant-dropdown-trigger" href="https://www.mdnice.com/#">功能</a><a id="nice-menu-view" class="nice-menu-link ant-dropdown-trigger" href="https://www.mdnice.com/#">查看</a><a id="nice-menu-theme" class="nice-menu-link ant-dropdown-trigger" href="https://www.mdnice.com/#">主题</a><a id="nice-menu-codetheme" class="nice-menu-link ant-dropdown-trigger" href="https://www.mdnice.com/#">代码主题</a><a id="nice-menu-setting" class="nice-menu-link ant-dropdown-trigger" href="https://www.mdnice.com/#">设置</a><a id="nice-menu-help" class="nice-menu-link ant-dropdown-trigger" href="https://www.mdnice.com/#">帮助</a></div><div class="nice-right-nav"><button type="button" class="ant-btn nice-btn-login ant-btn-circle cc_pointer"><svg viewBox="0 0 1024 1024" class="nice-btn-login-icon" fill="rgba(0,0,0,0.65)" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><path d="M512 16C238 16 16 238 16 512s222 496 496 496 496-222 496-496S786 16 512 16z m256 843.2c-71.8 53-160.2 84.8-256 84.8s-184.2-31.8-256-84.8V832c0-70.6 57.4-128 128-128 22.2 0 55 22.8 128 22.8 73.2 0 105.6-22.8 128-22.8 70.6 0 128 57.4 128 128v27.2z m61.2-55c-13.6-92.8-92.6-164.2-189.2-164.2-41 0-60.8 22.8-128 22.8S425.2 640 384 640c-96.6 0-175.6 71.4-189.2 164.2C123.8 727.2 80 624.8 80 512c0-238.2 193.8-432 432-432s432 193.8 432 432c0 112.8-43.8 215.2-114.8 292.2zM512 240c-97.2 0-176 78.8-176 176s78.8 176 176 176 176-78.8 176-176-78.8-176-176-176z m0 288c-61.8 0-112-50.2-112-112s50.2-112 112-112 112 50.2 112 112-50.2 112-112 112z" p-id="5538"></path></svg></button></div></div><div class="nice-text-container"><div id="nice-md-editor" class="nice-md-editing"><textarea style="display: none;"></textarea><div class="CodeMirror cm-s-md-mirror CodeMirror-wrap CodeMirror-focused" style="width: 100%; height: 100%;"><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 98.5px; left: 72px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;" tabindex="0"></textarea></div><div class="CodeMirror-vscrollbar" tabindex="-1" cm-not-content="true" style="bottom: 0px; display: block;"><div style="min-width: 1px; height: 3923px;"></div></div><div class="CodeMirror-hscrollbar" tabindex="-1" cm-not-content="true" style="right: 6px; left: 0px;"><div style="height: 100%; min-height: 1px; width: 0px;"></div></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true" style="height: 6px; width: 6px;"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1" draggable="false"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: -6px; border-right-width: 24px; min-height: 3883px; padding-right: 6px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre class="CodeMirror-line-like"><span>xxxxxxxxxx</span></pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-cursors" style="visibility: hidden;"><div class="CodeMirror-cursor" style="left: 52px; top: 74.5px; height: 25.5px;">&nbsp;</div></div><div class="CodeMirror-code" role="presentation" style=""><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-link">[R数据科学]</span> 1.6几何对象</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">本最近打算把《R数据科学》过一遍，并且把课后习题都做一下。先从第一章开始吧，快速把ggplot过一下。前面几节的内容比较少，第1.5节</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-image cm-image-marker">!</span><span class="cm-image cm-image-alt-text cm-link">[]</span><span class="cm-string cm-url">(https://imgkr2.cn-bj.ufileos.com/68657890-31e3-4050-a1b0-e3d9dd369b08.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&amp;Signature=xxhAwDgNHHoS6%252B5pdJ2mlIkVK6c%253D&amp;Expires=1598255421)</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">1.6节的内容不是很多，我们通过写本节的练习来回顾知识点：</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-header cm-header-2">## (1) 在绘制折线图、箱线图、直方图和分区图时，应该分别使用哪种几何对象？</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-strong">**答**</span>： geom_line(), &nbsp; geom_boxplot(),  geom_histogram(),</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-header cm-header-2">## (2) 在脑海中运行以下代码，并预测会有何种输出。接着在 R 中运行代码，并检查你的预测是否正确。</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-comment">```{r}</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-comment">ggplot(data = mpg,</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> &nbsp;<span class="cm-comment">mapping = aes(x = displ, y = hwy, color = drv)) +</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> &nbsp;<span class="cm-comment">geom_point() +</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> &nbsp;<span class="cm-comment">geom_smooth(se = FALSE)</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-comment">```</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">想象下：x轴是displ，y轴是hwy，颜色使用drv填充，然后加了一个散点图（中间没有参数，用默认），再加上一个拟合曲线，没有绘制出区间。由于两个集合对象都没有对mapping进行设置，所以会使用原始涂层的aes(x = displ, y = hwy, color = drv))。所以出来的结果，散点图和拟合曲线都是三种颜色。下面是出来的颜色：</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-image cm-image-marker">!</span><span class="cm-image cm-image-alt-text cm-link">[]</span><span class="cm-string cm-url">(https://imgkr2.cn-bj.ufileos.com/548a3c56-9776-459c-adc4-5014f0d8e5f0.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&amp;Signature=MzTb0eRJfMrwlO9rxcvr3%252FLvqjM%253D&amp;Expires=1598254653)</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-header cm-header-2">## (3)  show.legend = FALSE 的作用是什么？删除它会发生什么情况？</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-strong">**答**</span>：是把图例隐藏了，默认参数是show.legend = TRUE.</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-header cm-header-2">## (4)  geom_smooth() 函数中的 se 参数的作用是什么？</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-strong">**答**</span>：可以绘制出区间，当se = TRUE（默认）则会出现区间。</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre></div></div></div></div></div><div style="position: absolute; 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<p>本最近打算把《R数据科学》过一遍，并且把课后习题都做一下。先从第一章开始吧，快速把ggplot过一下。前面几节的内容比较少，第1.5节
<img src="./让微信排版变 Nice_files/68657890-31e3-4050-a1b0-e3d9dd369b08.png" alt="">
1.6节的内容不是很多，我们通过写本节的练习来回顾知识点：</p>
<h2><span class="prefix"></span><span class="content">(1) 在绘制折线图、箱线图、直方图和分区图时，应该分别使用哪种几何对象？</span><span class="suffix"></span></h2>
<p><strong>答</strong>： geom_line(),   geom_boxplot(),  geom_histogram(),</p>
<h2><span class="prefix"></span><span class="content">(2) 在脑海中运行以下代码，并预测会有何种输出。接着在 R 中运行代码，并检查你的预测是否正确。</span><span class="suffix"></span></h2>
<pre class="custom"><code class="hljs">ggplot(data = mpg,
  mapping = aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  geom_smooth(se = FALSE)
</code></pre>
<p>想象下：x轴是displ，y轴是hwy，颜色使用drv填充，然后加了一个散点图（中间没有参数，用默认），再加上一个拟合曲线，没有绘制出区间。由于两个集合对象都没有对mapping进行设置，所以会使用原始涂层的aes(x = displ, y = hwy, color = drv))。所以出来的结果，散点图和拟合曲线都是三种颜色。下面是出来的颜色：</p>
<figure><img src="./让微信排版变 Nice_files/548a3c56-9776-459c-adc4-5014f0d8e5f0.png" alt=""></figure>
<h2><span class="prefix"></span><span class="content">(3)  show.legend = FALSE 的作用是什么？删除它会发生什么情况？</span><span class="suffix"></span></h2>
<p><strong>答</strong>：是把图例隐藏了，默认参数是show.legend = TRUE.</p>
<h2><span class="prefix"></span><span class="content">(4)  geom_smooth() 函数中的 se 参数的作用是什么？</span><span class="suffix"></span></h2>
<p><strong>答</strong>：可以绘制出区间，当se = TRUE（默认）则会出现区间。</p>
<h2><span class="prefix"></span><span class="content">(5) 以下代码生成的两张图有什么区别吗？为什么？</span><span class="suffix"></span></h2>
<pre class="custom"><code class="hljs">#第一幅图
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point() +
geom_smooth()
#第二幅图
ggplot() +
geom_point(data = mpg,
mapping = aes(x = displ, y = hwy)
) +
geom_smooth(data = mpg,
mapping = aes(x = displ, y = hwy)
)
</code></pre>
<p>没有什么区别，第一个图在原始上就设定了x，y。后面两个集合对象就可以默认使用前面的设置了。而第二个图则是原始没有设置，而是在集合对象中一一设置了。</p>
<p>第一种方法：简便；是第二种方法灵活，可以设置不同的x，y。</p>
<h2><span class="prefix"></span><span class="content">(6) 自己编写 R 代码来生成以下各图。</span><span class="suffix"></span></h2>
<figure><img src="./让微信排版变 Nice_files/895d147f-bbd7-4e9c-b315-92ef71266684.png" alt=""></figure>
<p>第一个图，x为displ，y为hwy。画了散点图（geom_point）并绘制了拟合曲线（geom_smooth），没加置信区间（se =FALSE）</p>
<pre class="custom"><code class="hljs">ggplot(data = mpg,aes(x = displ,y = hwy))+
	geom_point(size=3)+
	geom_smooth(se=F,size=2)
</code></pre>
<figure><img src="./让微信排版变 Nice_files/1688792f-a05a-46c7-bd43-8b3072842fe2.png" alt=""></figure>
<p>第二个图在第一个基础上根据drv变量绘制了三条拟合曲线，并且没有绘制区间。并且把图例删除了（show.legend = FALSE）</p>
<pre class="custom"><code class="hljs">ggplot(data = mpg,aes(x = displ,y = hwy))+
	geom_point(size=3)+
	geom_smooth(aes(fill=drv),se=F,size=2,show.legend = FALSE)
</code></pre>
<figure><img src="./让微信排版变 Nice_files/cdcfe212-6ff5-4f56-8b9d-a0667cf189bc.png" alt=""></figure>
<p>第三个图散点图颜色的颜色根据drv变量进行变化，并且拟合曲线也是和散点图相同颜色（所以可以在最原始图层中加入color=drv），没有拟合曲线的区间，但是有图例（默认就是有的）。</p>
<pre class="custom"><code class="hljs">ggplot(data = mpg,aes(x = displ,y = hwy,color=drv))+
	geom_point(size=3)+
	geom_smooth(se=F,size=2)
</code></pre>
<figure><img src="./让微信排版变 Nice_files/feb40f9d-6846-4c9f-93ec-7acfbd62b13b.png" alt=""></figure>
<p>第四个图根据frv变量给散点图填充，但是只绘制了一条拟合线。所以这里不可以直接放在原始图层里，得放在geom_point()中。</p>
<pre class="custom"><code class="hljs">ggplot(data = mpg,aes(x = displ,y = hwy))+
	geom_point(aes(color=drv),size=3)+
	geom_smooth(se=F,size=2)
</code></pre>
<figure><img src="./让微信排版变 Nice_files/e0193630-2caf-4269-931a-d54231c820f0.png" alt=""></figure>
<p>第五幅图在第三幅图基础上改变了拟合曲线的线的类型(linetype)。</p>
<pre class="custom"><code class="hljs">ggplot(data = mpg,aes(x = displ,y = hwy,color=drv))+
	geom_point(size=3)+
	geom_smooth(aes(linetype=drv),se=F,size=2)
</code></pre>
<figure><img src="./让微信排版变 Nice_files/d859c9de-89a1-420c-ab6d-1a2ab492956b.png" alt=""></figure>
<p>最后一幅没有拟合曲线，直接将散点图的颜色区分开了，有点像第三幅图的简化版</p>
<pre class="custom"><code class="hljs">ggplot(data = mpg,aes(x = displ,y = hwy,color=drv))+
	geom_point(size=3)
</code></pre>
<figure><img src="./让微信排版变 Nice_files/1c33c089-f147-4913-a2b0-3e9b967ae5d2.png" alt=""></figure>
<h2><span class="prefix"></span><span class="content">参考</span><span class="suffix"></span></h2>
<ol>
<li><section><a href="https://r4ds.had.co.nz/data-visualisation.html">R for Data Science</a></section></li></ol>
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left: -1000px; z-index: -1000;"><div style="position: absolute; top: 0px; left: 0px; width: 100%;"><div><div class="ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden" style="left: 386px; top: -995px;"><ul class="ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical" role="menu" tabindex="0"><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-codetheme-wechat" class="nice-codetheme-item"><span><span class="nice-codetheme-item-flag"></span><span class="nice-codetheme-item-name">微信代码主题</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-codetheme-atomOneDark" class="nice-codetheme-item"><span><span class="nice-codetheme-item-flag"></span><span class="nice-codetheme-item-name">atom-one-dark</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-codetheme-atomOneLight" class="nice-codetheme-item"><span><span class="nice-codetheme-item-flag"><span>✔️</span></span><span class="nice-codetheme-item-name">atom-one-light</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-codetheme-monokai" class="nice-codetheme-item"><span><span class="nice-codetheme-item-flag"></span><span class="nice-codetheme-item-name">monokai</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-codetheme-github" class="nice-codetheme-item"><span><span class="nice-codetheme-item-flag"></span><span class="nice-codetheme-item-name">github</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-codetheme-vs2015" class="nice-codetheme-item"><span><span class="nice-codetheme-item-flag"></span><span class="nice-codetheme-item-name">vs2015</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-codetheme-xcode" class="nice-codetheme-item"><span><span class="nice-codetheme-item-flag"></span><span class="nice-codetheme-item-name">xcode</span></span></div></li><li class=" ant-dropdown-menu-item-divider"></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-codetheme-apple" class="nice-codetheme-item"><span><span class="nice-codetheme-item-flag"><span>✔️</span></span><span class="nice-codetheme-item-name">Mac 风格</span></span></div></li></ul></div></div></div><div style="position: absolute; top: 0px; left: 0px; width: 100%;"><div><div class="ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden" style="left: 462px; top: -995px;"><ul class="ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical" role="menu" tabindex="0"><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-sync-scroll" class="nice-menu-item"><span><span class="nice-menu-flag"><span>✔️</span></span><span class="nice-menu-name">同步滚动</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-contain-img-name" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">上传图片时包含名称</span></span></div></li></ul></div></div></div><div style="position: absolute; top: 0px; left: 0px; width: 100%;"><div><div class="ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden" style="left: 194px; top: -995px;"><ul class="ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical" role="menu" tabindex="0"><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-del" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">删除线</span></span><span class="nice-menu-shortcut">Ctrl+U</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-bold" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">加粗</span></span><span class="nice-menu-shortcut">Ctrl+B</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-italic" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">倾斜</span></span><span class="nice-menu-shortcut">Ctrl+I</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-code" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">代码</span></span><span class="nice-menu-shortcut">Ctrl+Alt+C</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-inline-code" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">行内代码</span></span><span class="nice-menu-shortcut">Ctrl+Alt+V</span></div></li><li class=" ant-dropdown-menu-item-divider"></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-link" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">链接</span></span><span class="nice-menu-shortcut">Ctrl+K</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-form" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">表格</span></span><span class="nice-menu-shortcut">Ctrl+Alt+T</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-image" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">图片</span></span><span class="nice-menu-shortcut">Ctrl+Alt+I</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-font" class="nice-menu-item"><span><span class="nice-menu-flag"><span>✔️</span></span><span class="nice-menu-name">衬线字体</span></span></div></li><li class=" ant-dropdown-menu-item-divider"></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-link-to-foot" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">微信外链转脚注</span></span><span class="nice-menu-shortcut">Ctrl+Alt+L</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-format" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">格式化文档</span></span><span class="nice-menu-shortcut cc_pointer">Ctrl+Alt+F</span></div></li></ul></div></div></div><div style="position: absolute; top: 0px; left: 0px; width: 100%;"><div><div class="ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden" style="left: 146px; top: -995px;"><ul class="ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical" role="menu" tabindex="0"><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-export-markdown" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">导出 Markdown</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-export-pdf" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">导出 PDF</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><label id="nice-menu-import-file" class="nice-menu-item" for="importFile"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">导入</span><input type="file" id="importFile" accept=".txt,.md" style="display: none;"></span></label></li></ul></div></div></div><div style="position: absolute; top: 0px; left: 0px; width: 100%;"><div><div class="ant-tooltip  ant-tooltip-placement-left  ant-tooltip-hidden" style="left: 1763px; top: 50px; transform-origin: 112px 50%;"><div class="ant-tooltip-content"><div class="ant-tooltip-arrow"></div><div class="ant-tooltip-inner" role="tooltip">复制到公众号</div></div></div></div></div><div><div class="ant-message"><span></span></div></div><div class="xl-chrome-ext-bar" id="xl_chrome_ext_{4DB361DE-01F7-4376-B494-639E489D19ED}" style="display: none;">
      <div class="xl-chrome-ext-bar__logo"></div>

      <a id="xl_chrome_ext_download" href="javascript:;" class="xl-chrome-ext-bar__option">下载视频</a>
      <a id="xl_chrome_ext_close" href="javascript:;" class="xl-chrome-ext-bar__close"></a>
    </div></body></html>

================================================
FILE: 2020年/2020年R数据科学系列/2020.08.23速查表/速查表.html
================================================
<!DOCTYPE html>
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<html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><link rel="icon" href="https://www.mdnice.com/favicon.svg"><meta name="viewport" content="width=device-width,initial-scale=1"><meta name="theme-color" content="#000000"><link rel="manifest" href="https://www.mdnice.com/manifest.json"><script src="./速查表_files/hm.js.下载"></script><script>var _hmt=_hmt||[];!function(){var e=document.createElement("script");e.src="https://hm.baidu.com/hm.js?f857e607ecc7cde02ed32a561f10efb2";var t=document.getElementsByTagName("script")[0];t.parentNode.insertBefore(e,t)}()</script><title>让微信排版变 Nice</title><meta name="keywords" content="mdnice 编辑器,Markdown 编辑器,Markdown 微信文章排版,Markdown 微信图文美化,Markdown 公众号内容编辑,Markdown 新媒体编辑,Markdown 微信编辑器, Markdown 公众号在线编辑, Markdown 微信图文排版"><meta name="description" content="mdnice 微信 Markdown 编辑器是一款 Markdown 微信编辑器,拥有良好的兼容性、海量主题样式、免费的图床、强大的技术团队，提供文章一键排版，同时支持知乎、掘金、微信订阅号等多个平台。"><link href="./速查表_files/7.bd9d6b76.chunk.css" rel="stylesheet"><link href="./速查表_files/main.9fc69ba1.chunk.css" rel="stylesheet"><style id="font-theme"></style><style id="basic-theme">/*默认样式，最佳实践*/

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/*图片*/
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/*图片*/
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/*图片描述文字*/
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/*表格容器 */
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/*表格*/
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/* 图片链接嵌套，图片解释 */
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</style><script charset="utf-8" src="./速查表_files/3.e00a7e4b.chunk.js.下载"></script><style type="text/css">.CtxtMenu_InfoClose {  top:.2em; right:.2em;}
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.CtxtMenu_MenuClose:hover span {  background-color:#CCC!important}
.CtxtMenu_MenuClose:hover:focus {  outline:none}
</style><style type="text/css">.CtxtMenu_Menu {  position:absolute;  background-color:white;  color:black;  width:auto; padding:5px 0px;  border:1px solid #CCCCCC; margin:0; cursor:default;  font: menu; text-align:left; text-indent:0; text-transform:none;  line-height:normal; letter-spacing:normal; word-spacing:normal;  word-wrap:normal; white-space:nowrap; float:none; z-index:201;  border-radius: 5px;                     /* Opera 10.5 and IE9 */  -webkit-border-radius: 5px;             /* Safari and Chrome */  -moz-border-radius: 5px;                /* Firefox */  -khtml-border-radius: 5px;              /* Konqueror */  box-shadow:0px 10px 20px #808080;         /* Opera 10.5 and IE9 */  -webkit-box-shadow:0px 10px 20px #808080; /* Safari 3 & Chrome */  -moz-box-shadow:0px 10px 20px #808080;    /* Forefox 3.5 */  -khtml-box-shadow:0px 10px 20px #808080;  /* Konqueror */}
.CtxtMenu_MenuItem {  padding: 1px 2em;  background:transparent;}
.CtxtMenu_MenuArrow {  position:absolute; right:.5em; padding-top:.25em; color:#666666;  font-family: null; font-size: .75em}
.CtxtMenu_MenuActive .CtxtMenu_MenuArrow {color:white}
.CtxtMenu_MenuArrow.CtxtMenu_RTL {left:.5em; right:auto}
.CtxtMenu_MenuCheck {  position:absolute; left:.7em;  font-family: null}
.CtxtMenu_MenuCheck.CtxtMenu_RTL { right:.7em; left:auto }
.CtxtMenu_MenuRadioCheck {  position:absolute; left: .7em;}
.CtxtMenu_MenuRadioCheck.CtxtMenu_RTL {  right: .7em; left:auto}
.CtxtMenu_MenuInputBox {  padding-left: 1em; right:.5em; color:#666666;  font-family: null;}
.CtxtMenu_MenuInputBox.CtxtMenu_RTL {  left: .1em;}
.CtxtMenu_MenuComboBox {  left:.1em; padding-bottom:.5em;}
.CtxtMenu_MenuLabel {  padding: 1px 2em 3px 1.33em;  font-style:italic}
.CtxtMenu_MenuRule {  border-top: 1px solid #DDDDDD;  margin: 4px 3px;}
.CtxtMenu_MenuDisabled {  color:GrayText}
.CtxtMenu_MenuActive {  background-color: #606872;  color: white;}
.CtxtMenu_MenuDisabled:focus {  background-color: #E8E8E8}
.CtxtMenu_MenuLabel:focus {  background-color: #E8E8E8}
.CtxtMenu_ContextMenu:focus {  outline:none}
.CtxtMenu_ContextMenu .CtxtMenu_MenuItem:focus {  outline:none}
.CtxtMenu_Menu .CtxtMenu_MenuClose {  top:-10px; left:-10px}
</style><style id="MJX-SVG-styles">
mjx-container[jax="SVG"] {
  direction: ltr;
}

mjx-container[jax="SVG"] > svg {
  overflow: visible;
}

mjx-container[jax="SVG"] > svg a {
  fill: blue;
  stroke: blue;
}

mjx-assistive-mml {
  position: absolute !important;
  top: 0px;
  left: 0px;
  clip: rect(1px, 1px, 1px, 1px);
  padding: 1px 0px 0px 0px !important;
  border: 0px !important;
  display: block !important;
  width: auto !important;
  overflow: hidden !important;
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

mjx-assistive-mml[display="block"] {
  width: 100% !important;
}

mjx-container[jax="SVG"][display="true"] {
  display: block;
  text-align: center;
  margin: 1em 0;
}

mjx-container[jax="SVG"][justify="left"] {
  text-align: left;
}

mjx-container[jax="SVG"][justify="right"] {
  text-align: right;
}

g[data-mml-node="merror"] > g {
  fill: red;
  stroke: red;
}

g[data-mml-node="merror"] > rect[data-background] {
  fill: yellow;
  stroke: none;
}

g[data-mml-node="mtable"] > line[data-line] {
  stroke-width: 70px;
  fill: none;
}

g[data-mml-node="mtable"] > rect[data-frame] {
  stroke-width: 70px;
  fill: none;
}

g[data-mml-node="mtable"] > .mjx-dashed {
  stroke-dasharray: 140;
}

g[data-mml-node="mtable"] > .mjx-dotted {
  stroke-linecap: round;
  stroke-dasharray: 0,140;
}

g[data-mml-node="mtable"] > svg {
  overflow: visible;
}

[jax="SVG"] mjx-tool {
  display: inline-block;
  position: relative;
  width: 0;
  height: 0;
}

[jax="SVG"] mjx-tool > mjx-tip {
  position: absolute;
  top: 0;
  left: 0;
}

mjx-tool > mjx-tip {
  display: inline-block;
  padding: .2em;
  border: 1px solid #888;
  font-size: 70%;
  background-color: #F8F8F8;
  color: black;
  box-shadow: 2px 2px 5px #AAAAAA;
}

g[data-mml-node="maction"][data-toggle] {
  cursor: pointer;
}

mjx-status {
  display: block;
  position: fixed;
  left: 1em;
  bottom: 1em;
  min-width: 25%;
  padding: .2em .4em;
  border: 1px solid #888;
  font-size: 90%;
  background-color: #F8F8F8;
  color: black;
}

foreignObject[data-mjx-xml] {
  font-family: initial;
  line-height: normal;
  overflow: visible;
}

.MathJax path {
  stroke-width: 3;
}
</style><style type="text/css"></style><style type="text/css">.CodeMirror-line, .cm-header.cm-header-2, .cm-strong, .cm-image.cm-image-marker, .cm-image.cm-image-alt-text.cm-link, .cm-string.cm-url, .cm-image.cm-image-alt-text.cm-link.CodeMirror-matchingbracket, .CodeMirror-lines, .ant-input, .cc_cursor, body, .ogdlpmhglpejoiomcodnpjnfgcpmgale_default, body, html, input[type="date"], input[type="time"], input[type="datetime-local"], input[type="month"], input::-webkit-contacts-auto-fill-button, input:read-only {cursor: url("data:image/png;base64,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") 1 1 , auto !important; }.nice-themeselect-theme-item-name, .nice-themeselect-theme-item, .nice-codetheme-item, .nice-codetheme-item-name, .nice-menu-item, .nice-menu-name, .ant-select-selection-selected-value, .ant-select-selection__rendered, .ant-select-selection
...........ant-select-selection--single, .ant-btn.ant-btn-primary, .ant-btn.ant-btn-primary.cc_pointer, .ant-btn, .ant-btn.cc_pointer, .ant-select-selection__rendered.cc_pointer, .ant-select-selection-selected-value.cc_pointer, .nice-menu-link.ant-dropdown-trigger, .nice-menu-link.ant-dropdown-trigger.cc_pointer, .nice-menu-link.ant-dropdown-trigger.ant-dropdown-open, .nice-menu-link.ant-dropdown-trigger.ant-dropdown-open.cc_pointer, .nice-btn-zhihu, .ant-modal-close-x, .ant-modal-close-x.cc_pointer, .nice-btn-previewtype.ant-tooltip-open, .nice-themeselect-theme-item-author, .nice-themeselect-theme-item-author.cc_pointer, .nice-themeselect-theme-item-name.nice-menu-subscribe-more, .nice-themeselect-theme-item-name.nice-menu-subscribe-more.cc_pointer, .nice-menu-shortcut, .nice-menu-shortcut.cc_pointer, .nice-footer-message, .nice-footer-engine.ant-dropdown-trigger, .nice-footer-engine.ant-dropdown-trigger.cc_pointer, .nice-footer-message.cc_pointer, .ant-btn.nice-btn-login.ant-btn-circle, .ant-btn.nice-btn-login.ant-btn-circle.cc_pointer, .cc_pointer,[type="search"]::-webkit-search-cancel-button, [type="search"]::-webkit-search-decoration, .paper-button, .ytp-progress-bar-container, input[type=submit], :link, :visited, a > *, img, button, ::-webkit-scrollbar-button, .ogdlpmhglpejoiomcodnpjnfgcpmgale_pointer, ::-webkit-file-upload-button, button, .ytp-volume-panel, #myogdlpmhglpejoiomcodnpjnfgcpmgale .icon { cursor: url("data:image/png;base64,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22 3, auto !important; } </style><style id="markdown-theme">/*自定义样式，实时生效*/
#nice {
}

#nice p {
	margin: 0 0 20px;
	padding: 0;
	line-height: 1.8em;
	color: #3a3a3a;
}

/* 一级标题 */
#nice h1 {
  font-size: 2.1em;
	line-height: 1.1em;
	padding-top: 16px;
  padding-bottom: 10px;
  margin-bottom: 4px;
  border-bottom: 1px solid #c99833;
}
/* 一级标题内容 */
#nice h1 .content {
  color: #515151;
  font-weight: 700;
}

#nice h2, h3, h4, h5, h6 {
 line-height: 1.5em;
 margin-top: 2.2em;
 margin-bottom: 4px;
}

/* 一级标题修饰 请参考有实例的主题 */
#nice h1:after {}

/* 二级标题 */
#nice h2 {
 margin-bottom: 35px;
}

/* 二级标题内容 */

#nice h2 .content {
  display: inline-block;
  font-weight: bold;
  background: linear-gradient(#fff 60%, #ffb11b 40%);
  color: #515151;
  padding: 2px 13px 2px;
  margin-right: 3px;
  height: 50%;
}

/* 二级标题修饰 请参考有实例的主题 */
#nice h2:after {}

/* 三级标题 */
#nice h3 {
  line-height: 1.4;
  padding-top: 10px;
  margin: 10px 0 5px;
}

/* 三级标题内容 */
#nice h3 .content {
  color: #515151;
  font-weight: 700;
 font-size: 1.0em;
  padding-left: 20px;
  border-left: 3px solid #f9bf45;
}

/* 三级标题修饰 请参考有实例的主题 */
#nice h3:after {}

/* 引用
* 左边缘颜色 border-left-color: black;
* 背景色 background: gray;
*/
#nice blockquote {
  border-left-color: #ffb11b;
 background: #fff5e3;
}

/* 引用文字 */
#nice blockquote p {
  color: #595959;
}

/* 链接 */
#nice a {
  border: none;
  text-decoration: none;
  color: #dda52d;
}

#nice a:hover {
  color: #f9bf45;
  text-decoration: underline;
}

/* 无序列表整体样式
 * list-style-type: square|circle|disc;
 */
#nice ul {
}

/* 有序列表整体样式
 * list-style-type: upper-roman|lower-greek|lower-alpha;
 */
#nice ol {
}

/* 列表内容，不要设置li
 */
#nice li section {
}

/* 加粗 */
#nice strong {}

/* 斜体 */
#nice em {}

/* 加粗斜体 */
#nice em strong {}

/* 删除线 */
#nice del {
  color: #d19826;
}

/* 分隔线
* 粗细、样式和颜色
* border-top: 1px solid #3e3e3e;
*/
#nice hr {
  border-top: 1px solid #f9bf45;
  margin: 20px 0px;
}

/* 图片
* 宽度 width: 80%;
* 居中 margin: 0 auto;
* 居左 margin: 0 0;
*/
#nice img {
  width: 100%;
 border-radius: 5px;
 display: block;
 margin-bottom: 15px;
 height: auto;
}

/* 图片描述文字 */
#nice figcaption {
  color: #dda52d;
  font-size: 14px;
}

/* 行内代码 */
#nice p code, #nice li code {
  color: #9b6e23;
  background-color: #fff5e3;
  padding: 3px;
  margin: 3px;
}

/* 非微信代码块
 * 代码块不换行 display: -webkit-box !important;
 * 代码块换行 display: block;
 */
#nice pre code {}

/*
 * 表格内的单元格
 * 字体大小 font-size: 16px;
 * 边框 border: 1px solid #ccc;
 * 内边距 padding: 5px 10px;
 */
#nice table tr th,
#nice table tr td {
  text-align: center;
}

/* 脚注文字 */
#nice .footnote-word {
  color: #ffb11b;
  padding: 3px;
}

/* 脚注上标 */
#nice .footnote-ref {
  color: #dda52d;
  margin: 2px;
  padding: 3px;
}

/* "参考资料"四个字 
 * 内容 content: "参考资料";
 */
#nice .footnotes-sep:before {
  margin: 30px 0px 15px 0px;
  font-weight: 800;
}


/* 参考资料编号 */
#nice .footnote-num {
}

/* 参考资料文字 */
#nice .footnote-item p { 
}

/* 参考资料解释 */
#nice .footnote-item p em {
}

/* 行间公式
 * 最大宽度 max-width: 300% !important;
 */
#nice .block-equation svg {
}

/* 行内公式
 */
#nice .inline-equation svg {  
}

/* 滑动图片
 */
#nice .imageflow-img {
  display: inline-block;
  width:100%;
  margin-bottom: 0;
}</style><style id="code-theme">/*

Atom One Light by Daniel Gamage
Original One Light Syntax theme from https://github.com/atom/one-light-syntax

base:    #fafafa
mono-1:  #383a42
mono-2:  #686b77
mono-3:  #a0a1a7
hue-1:   #0184bb
hue-2:   #4078f2
hue-3:   #a626a4
hue-4:   #50a14f
hue-5:   #e45649
hue-5-2: #c91243
hue-6:   #986801
hue-6-2: #c18401

*/

.hljs {
  display: block;
  overflow-x: auto;
  padding: 16px;
  color: #383a42;
  background: #fafafa;
}

.hljs-comment,
.hljs-quote {
  color: #a0a1a7;
  font-style: italic;
}

.hljs-doctag,
.hljs-keyword,
.hljs-formula {
  color: #a626a4;
}

.hljs-section,
.hljs-name,
.hljs-selector-tag,
.hljs-deletion,
.hljs-subst {
  color: #e45649;
}

.hljs-literal {
  color: #0184bb;
}

.hljs-string,
.hljs-regexp,
.hljs-addition,
.hljs-attribute,
.hljs-meta-string {
  color: #50a14f;
}

.hljs-built_in,
.hljs-class .hljs-title {
  color: #c18401;
}

.hljs-attr,
.hljs-variable,
.hljs-template-variable,
.hljs-type,
.hljs-selector-class,
.hljs-selector-attr,
.hljs-selector-pseudo,
.hljs-number {
  color: #986801;
}

.hljs-symbol,
.hljs-bullet,
.hljs-link,
.hljs-meta,
.hljs-selector-id,
.hljs-title {
  color: #4078f2;
}

.hljs-emphasis {
  font-style: italic;
}

.hljs-strong {
  font-weight: bold;
}

.hljs-link {
  text-decoration: underline;
}

#nice .custom code {
  padding-top: 15px;
  background: #fafafa;
  border-radius: 5px;
}

#nice .custom:before {
  content: '';
  display:block;
  background: url(https://my-wechat.mdnice.com/point.png);
  height: 30px;
  width: 100%;
  background-size:40px;
  background-repeat: no-repeat;
  background-color: #fafafa;
  margin-bottom: -7px;
  border-radius: 5px;
  background-position: 10px 10px;
}

#nice .custom {
  border-radius: 5px;
  box-shadow: rgba(0, 0, 0, 0.55) 0px 2px 10px;
}</style></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"><div style="display: flex;"><div class="nice-app"><div class="nice-navbar"><div class="nice-left-nav"><section id="nice-title" class="nice-title">Markdown Nice</section><a id="nice-menu-file" class="nice-menu-link ant-dropdown-trigger" href="https://www.mdnice.com/#">文件</a><a id="nice-menu-pattern" class="nice-menu-link ant-dropdown-trigger" href="https://www.mdnice.com/#">格式</a><a id="nice-menu-function" class="nice-menu-link ant-dropdown-trigger" href="https://www.mdnice.com/#">功能</a><a id="nice-menu-view" class="nice-menu-link ant-dropdown-trigger" href="https://www.mdnice.com/#">查看</a><a id="nice-menu-theme" class="nice-menu-link ant-dropdown-trigger" href="https://www.mdnice.com/#">主题</a><a id="nice-menu-codetheme" class="nice-menu-link ant-dropdown-trigger" href="https://www.mdnice.com/#">代码主题</a><a id="nice-menu-setting" class="nice-menu-link ant-dropdown-trigger" href="https://www.mdnice.com/#">设置</a><a id="nice-menu-help" class="nice-menu-link ant-dropdown-trigger" href="https://www.mdnice.com/#">帮助</a></div><div class="nice-right-nav"><button type="button" class="ant-btn nice-btn-login ant-btn-circle cc_pointer"><svg viewBox="0 0 1024 1024" class="nice-btn-login-icon" fill="rgba(0,0,0,0.65)" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><path d="M512 16C238 16 16 238 16 512s222 496 496 496 496-222 496-496S786 16 512 16z m256 843.2c-71.8 53-160.2 84.8-256 84.8s-184.2-31.8-256-84.8V832c0-70.6 57.4-128 128-128 22.2 0 55 22.8 128 22.8 73.2 0 105.6-22.8 128-22.8 70.6 0 128 57.4 128 128v27.2z m61.2-55c-13.6-92.8-92.6-164.2-189.2-164.2-41 0-60.8 22.8-128 22.8S425.2 640 384 640c-96.6 0-175.6 71.4-189.2 164.2C123.8 727.2 80 624.8 80 512c0-238.2 193.8-432 432-432s432 193.8 432 432c0 112.8-43.8 215.2-114.8 292.2zM512 240c-97.2 0-176 78.8-176 176s78.8 176 176 176 176-78.8 176-176-78.8-176-176-176z m0 288c-61.8 0-112-50.2-112-112s50.2-112 112-112 112 50.2 112 112-50.2 112-112 112z" p-id="5538"></path></svg></button></div></div><div class="nice-text-container"><div id="nice-md-editor" class="nice-md-editing"><textarea style="display: none;"></textarea><div class="CodeMirror cm-s-md-mirror CodeMirror-wrap CodeMirror-focused" style="width: 100%; height: 100%;"><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 272px; left: 187.225px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;" tabindex="0"></textarea></div><div class="CodeMirror-vscrollbar" tabindex="-1" cm-not-content="true" style="bottom: 0px; display: block;"><div style="min-width: 1px; height: 2033px;"></div></div><div class="CodeMirror-hscrollbar" tabindex="-1" cm-not-content="true" style="right: 6px; left: 0px;"><div style="height: 100%; min-height: 1px; width: 0px;"></div></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true" style="height: 6px; width: 6px;"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1" draggable="false"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: -5px; border-right-width: 25px; min-height: 1992px; padding-right: 5px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-cursors" style=""><div class="CodeMirror-cursor" style="left: 167.225px; top: 248px; height: 24.8px;">&nbsp;</div></div><div class="CodeMirror-code" role="presentation" style=""><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-link">[R数据科学]</span> R速查表</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-header cm-header-2">## 前言</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">R相关包的速查表如下：</span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-header cm-header-3">### 基础</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-image cm-image-marker">!</span><span class="cm-image cm-image-alt-text cm-link">[]</span><span class="cm-string cm-url">(https://imgkr2.cn-bj.ufileos.com/31cff8bc-c3f7-41d9-8d4a-b791c947cc26.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&amp;Signature=l9R5bULa866eIzEw%252Feq7uRtLs4c%253D&amp;Expires=1598259449)</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-header cm-header-3">### ggplot2（版本一）</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-image cm-image-marker">!</span><span class="cm-image cm-image-alt-text cm-link">[]</span><span class="cm-string cm-url">(https://imgkr2.cn-bj.ufileos.com/3097a1b4-5470-4f8e-a958-bb09c83b9bf8.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&amp;Signature=VfqQ%252B7KMM8kwu9%252BP4HfshShmxwY%253D&amp;Expires=1598259307)</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-image cm-image-marker">!</span><span class="cm-image cm-image-alt-text cm-link">[]</span><span class="cm-string cm-url">(https://imgkr2.cn-bj.ufileos.com/b94499a4-0d73-411e-a950-ac8658f7dcb2.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&amp;Signature=8tZbJIdpBOnhp5cRXHbq6BTcosE%253D&amp;Expires=1598259317)</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-header cm-header-3">### ggplot2（版本二）</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-image cm-image-marker">!</span><span class="cm-image cm-image-alt-text cm-link">[]</span><span class="cm-string cm-url">(https://imgkr2.cn-bj.ufileos.com/0bca34e0-f859-44f4-b3f4-2b668a47f598.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&amp;Signature=DFBtf9o6b1wqv7KB5rRvatMgFlk%253D&amp;Expires=1598259405)</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-image cm-image-marker">!</span><span class="cm-image cm-image-alt-text cm-link">[]</span><span class="cm-string cm-url">(https://imgkr2.cn-bj.ufileos.com/f80dd042-071f-4054-aef8-d9becf5c9618.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&amp;Signature=kKZOVm2SflLAPB8pFVDuUVIlFF4%253D&amp;Expires=1598259417)</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-header cm-header-3">### Rmarkdown</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span class="cm-image cm-image-marker">!</span><span class="cm-image cm-image-alt-text cm-link">[]</span><span class="cm-string cm-url">(https://imgkr2.cn-bj.ufileos.com/a7632e1e-1be2-4a65-94dd-bd4098fc6e01.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&amp;Signature=B2fwf%252Fsq27ifq1WEHBjN%252BSKrdJQ%253D&amp;Expires=1598259187)</span></span></pre><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"><span cm-text="">​</span></span></pre></div></div></div></div></div><div style="position: absolute; height: 25px; width: 1px; border-bottom: 0px solid transparent; top: 1992px;"></div><div class="CodeMirror-gutters" style="display: none; height: 2017px;"></div></div></div></div><div id="nice-rich-text" class="nice-marked-text nice-marked-text-pc"><div class="nice-sidebar"><a id="nice-sidebar-wechat" class="nice-btn-wechat"><svg viewBox="0 0 40 40" class="nice-btn-wechat-icon" fill="rgba(0,0,0,0.65)" xmlns="http://www.w3.org/2000/svg" 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0-64-28.654-64-64s28.654-64 64-64 64 28.654 64 64-28.654 64-64 64zM768 768l-512 0 0-640 512 0 0 640z"></path></svg></a></div><div id="nice-rich-text-box" class="nice-wx-box nice-wx-box-pc"><section id="nice" data-tool="mdnice编辑器" data-website="https://www.mdnice.com"><p>[R数据科学] R速查表</p>
<h2><span class="prefix"></span><span class="content">前言</span><span class="suffix"></span></h2>
<p>R相关包的速查表如下：</p>
<h3><span class="prefix"></span><span class="content">基础</span><span class="suffix"></span></h3>
<figure><img src="./速查表_files/31cff8bc-c3f7-41d9-8d4a-b791c947cc26.png" alt=""></figure>
<h3><span class="prefix"></span><span class="content">ggplot2（版本一）</span><span class="suffix"></span></h3>
<figure><img src="./速查表_files/3097a1b4-5470-4f8e-a958-bb09c83b9bf8.png" alt=""></figure>
<figure><img src="./速查表_files/b94499a4-0d73-411e-a950-ac8658f7dcb2.png" alt=""></figure>
<h3><span class="prefix"></span><span class="content">ggplot2（版本二）</span><span class="suffix"></span></h3>
<figure><img src="./速查表_files/0bca34e0-f859-44f4-b3f4-2b668a47f598.png" alt=""></figure>
<figure><img src="./速查表_files/f80dd042-071f-4054-aef8-d9becf5c9618.png" alt=""></figure>
<h3><span class="prefix"></span><span class="content">Rmarkdown</span><span class="suffix"></span></h3>
<figure><img src="./速查表_files/a7632e1e-1be2-4a65-94dd-bd4098fc6e01.png" alt=""></figure>
<figure><img src="./速查表_files/ff197626-b340-43e2-8a6b-608d77aaca1b.png" alt=""></figure>
<h3><span class="prefix"></span><span class="content">dplyr</span><span class="suffix"></span></h3>
<figure><img src="./速查表_files/ef71736f-0b28-45c0-9f69-2ea54677f267.png" alt=""></figure>
<figure><img src="./速查表_files/76caf006-e560-436b-b5bf-95181ad4ee83.png" alt=""></figure>
<h3><span class="prefix"></span><span class="content">stringr</span><span class="suffix"></span></h3>
<figure><img src="./速查表_files/2cf04768-102e-4482-a772-49c2009bbb8f.png" alt=""></figure>
<figure><img src="./速查表_files/7d82200e-78d0-44e4-8955-c525055a03f4.png" alt=""></figure>
<h3><span class="prefix"></span><span class="content">forcats</span><span class="suffix"></span></h3>
<figure><img src="./速查表_files/88fc1f2f-dba2-4d11-b200-9ac94c6d172f.png" alt=""></figure>
</section></div></div><div></div><ul id="nice-editor-menu" class="ant-menu nice-editor-menu ant-menu-light ant-menu-root ant-menu-vertical" role="menu"><li class="ant-menu-item ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-link-to-foot" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">微信外链转脚注</span></span><span class="nice-menu-shortcut">Ctrl+Alt+L</span></div></li><li class="ant-menu-item ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-format" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">格式化文档</span></span><span class="nice-menu-shortcut">Ctrl+Alt+F</span></div></li><li class="ant-menu-item ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-image" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">图片</span></span><span class="nice-menu-shortcut">Ctrl+Alt+I</span></div></li></ul></div><div 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t},l.n=function(e){var r=e&&e.__esModule?function(){return e.default}:function(){return e};return l.d(r,"a",r),r},l.o=function(e,r){return Object.prototype.hasOwnProperty.call(e,r)},l.p="/",l.oe=function(e){throw console.error(e),e};var r=this["webpackJsonpmarkdown-nice-editor"]=this["webpackJsonpmarkdown-nice-editor"]||[],f=r.push.bind(r);r.push=e,r=r.slice();for(var c=0;c<r.length;c++)e(r[c]);var s=f;o()}([])</script><script src="./速查表_files/7.53360f9c.chunk.js.下载"></script><script src="./速查表_files/main.29adf6a5.chunk.js.下载"></script><div style="position: absolute; top: 0px; left: 0px; width: 100%;"><div><div class="ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden" style="left: 338px; top: -995px;"><ul class="ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical" role="menu" tabindex="0"><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-normal" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">默认主题</span></span><span class="nice-themeselect-theme-item-author"></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-1" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">橙心</span></span><span class="nice-themeselect-theme-item-author">zhning12</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-3" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">姹紫</span></span><span class="nice-themeselect-theme-item-author">djmaxwow</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-4" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">嫩青</span></span><span class="nice-themeselect-theme-item-author">画手</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-5" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">绿意</span></span><span class="nice-themeselect-theme-item-author">夜尽天明</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-6" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">红绯</span></span><span class="nice-themeselect-theme-item-author">HeyRain</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-8" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">蓝莹</span></span><span class="nice-themeselect-theme-item-author">谭淞宸</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-10" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">兰青</span></span><span class="nice-themeselect-theme-item-author">Krahets</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-11" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"><span>✔️</span></span><span class="nice-themeselect-theme-item-name">山吹</span></span><span class="nice-themeselect-theme-item-author">ElyhG</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-12" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">前端之巅同款</span></span><span class="nice-themeselect-theme-item-author cc_pointer">HeyRain</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-13" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">极客黑

</span></span><span class="nice-themeselect-theme-item-author">hyper-xx</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-15" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">蔷薇紫</span></span><span class="nice-themeselect-theme-item-author">HeyRain</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-16" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">萌绿</span></span><span class="nice-themeselect-theme-item-author">koala</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-17" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">全栈蓝</span></span><span class="nice-themeselect-theme-item-author">Nealyang</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-18" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">极简黑</span></span><span class="nice-themeselect-theme-item-author">小鱼</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-19" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">橙蓝风</span></span><span class="nice-themeselect-theme-item-author">axuebin</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-custom" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">自定义</span></span><span class="nice-themeselect-theme-item-author"></span></div></li><li class=" ant-dropdown-menu-item-divider"></li><li class="nice-themeselect-menu-item"><div id="nice-menu-subscribe-more" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name nice-menu-subscribe-more cc_pointer">订阅更多主题</span></span></div></li><li class=" ant-dropdown-menu-item-divider"></li><li class="nice-themeselect-menu-item"><div id="nice-menu-view-css" class="nice-themeselect-theme-item"><span><span class="nice-themeselect-theme-item-flag"></span><span class="nice-themeselect-theme-item-name">查看主题 CSS</span></span></div></li></ul></div></div></div><div style="position: absolute; top: 0px; left: 0px; width: 100%;"><div><div class="ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden" style="left: 290px; top: -995px;"><ul class="ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical" role="menu" tabindex="0"><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-full-screen" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">全屏</span></span></div></li><li class=" ant-dropdown-menu-item-divider"></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-edit-area" class="nice-menu-item"><span><span class="nice-menu-flag"><span>✔️</span></span><span class="nice-menu-name">编辑区域</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-preview-area" class="nice-menu-item"><span><span class="nice-menu-flag"><span>✔️</span></span><span class="nice-menu-name">预览区域</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-theme-area" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">主题CSS区域</span></span></div></li></ul></div></div></div><input id="copy-input" style="position: absolute; left: -1000px; z-index: -1000;"><div style="position: absolute; top: 0px; left: 0px; width: 100%;"><div><div class="ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden" style="left: 386px; top: -995px;"><ul class="ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical" role="menu" tabindex="0"><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-codetheme-wechat" class="nice-codetheme-item"><span><span class="nice-codetheme-item-flag"></span><span class="nice-codetheme-item-name">微信代码主题</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-codetheme-atomOneDark" class="nice-codetheme-item"><span><span class="nice-codetheme-item-flag"></span><span class="nice-codetheme-item-name">atom-one-dark</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-codetheme-atomOneLight" class="nice-codetheme-item"><span><span class="nice-codetheme-item-flag"><span>✔️</span></span><span class="nice-codetheme-item-name">atom-one-light</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-codetheme-monokai" class="nice-codetheme-item"><span><span class="nice-codetheme-item-flag"></span><span class="nice-codetheme-item-name">monokai</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-codetheme-github" class="nice-codetheme-item"><span><span class="nice-codetheme-item-flag"></span><span class="nice-codetheme-item-name">github</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-codetheme-vs2015" class="nice-codetheme-item"><span><span class="nice-codetheme-item-flag"></span><span class="nice-codetheme-item-name">vs2015</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-codetheme-xcode" class="nice-codetheme-item"><span><span class="nice-codetheme-item-flag"></span><span class="nice-codetheme-item-name">xcode</span></span></div></li><li class=" ant-dropdown-menu-item-divider"></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-codetheme-apple" class="nice-codetheme-item"><span><span class="nice-codetheme-item-flag"><span>✔️</span></span><span class="nice-codetheme-item-name">Mac 风格</span></span></div></li></ul></div></div></div><div style="position: absolute; top: 0px; left: 0px; width: 100%;"><div><div class="ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden" style="left: 462px; top: -995px;"><ul class="ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical" role="menu" tabindex="0"><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-sync-scroll" class="nice-menu-item"><span><span class="nice-menu-flag"><span>✔️</span></span><span class="nice-menu-name">同步滚动</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-contain-img-name" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">上传图片时包含名称</span></span></div></li></ul></div></div></div><div style="position: absolute; top: 0px; left: 0px; width: 100%;"><div><div class="ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden" style="left: 194px; top: -995px;"><ul class="ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical" role="menu" tabindex="0"><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-del" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">删除线</span></span><span class="nice-menu-shortcut">Ctrl+U</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-bold" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">加粗</span></span><span class="nice-menu-shortcut">Ctrl+B</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-italic" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">倾斜</span></span><span class="nice-menu-shortcut">Ctrl+I</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-code" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">代码</span></span><span class="nice-menu-shortcut">Ctrl+Alt+C</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-inline-code" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">行内代码</span></span><span class="nice-menu-shortcut">Ctrl+Alt+V</span></div></li><li class=" ant-dropdown-menu-item-divider"></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-link" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">链接</span></span><span class="nice-menu-shortcut">Ctrl+K</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-form" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">表格</span></span><span class="nice-menu-shortcut">Ctrl+Alt+T</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-image" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">图片</span></span><span class="nice-menu-shortcut">Ctrl+Alt+I</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-font" class="nice-menu-item"><span><span class="nice-menu-flag"><span>✔️</span></span><span class="nice-menu-name">衬线字体</span></span></div></li><li class=" ant-dropdown-menu-item-divider"></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-link-to-foot" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">微信外链转脚注</span></span><span class="nice-menu-shortcut">Ctrl+Alt+L</span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-format" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">格式化文档</span></span><span class="nice-menu-shortcut cc_pointer">Ctrl+Alt+F</span></div></li></ul></div></div></div><div style="position: absolute; top: 0px; left: 0px; width: 100%;"><div><div class="ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden" style="left: 146px; top: -995px;"><ul class="ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical" role="menu" tabindex="0"><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-export-markdown" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">导出 Markdown</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><div id="nice-menu-export-pdf" class="nice-menu-item"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">导出 PDF</span></span></div></li><li class="ant-dropdown-menu-item" role="menuitem"><label id="nice-menu-import-file" class="nice-menu-item" for="importFile"><span><span class="nice-menu-flag"></span><span class="nice-menu-name">导入</span><input type="file" id="importFile" accept=".txt,.md" style="display: none;"></span></label></li></ul></div></div></div><div style="position: absolute; top: 0px; left: 0px; width: 100%;"><div><div class="ant-tooltip  ant-tooltip-placement-left  ant-tooltip-hidden" style="left: 1763px; top: 50px; transform-origin: 112px 50%;"><div class="ant-tooltip-content"><div class="ant-tooltip-arrow"></div><div class="ant-tooltip-inner" role="tooltip">复制到公众号</div></div></div></div></div><div class="xl-chrome-ext-bar" id="xl_chrome_ext_{4DB361DE-01F7-4376-B494-639E489D19ED}" style="display: none;">
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================================================
FILE: 2020年/2020年R数据科学系列/2020.09.27tidy data/Tidy_data.rmd
================================================
---
title: "[R数据科学]Tidy data案例"
author:
  - 庄亮亮
documentclass: ctexart
always_allow_html: true
output:
  rticles::ctex:
    fig_caption: yes
    number_sections: yes
    toc: yes
    toc_depth: 3
classoption: "hyperref,"
editor_options: 
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---

# Tidy data介绍

在本章中，你将学习在R中组织数据的一种一致的方法，这种组织被称为tidy data。将数据转换为这种格式需要一些前期工作，但这些工作从长远来看是值得的。一旦你有了整洁的数据和一些包提供的整洁工具，您将花费很少时间将数据从一种表示转换到另一种，从而可以将更多的时间花在分析问题上。

本文将为您提供整理数据的实用介绍以及tidyr包中附带的工具。如果你想了解更多的基本理论，你可能会喜欢发表在《统计软件杂志》上的整洁数据论文<http://www.jstatsoft.org/v59/i10/paper>


# 数据清洗案例

我们主要通过一个案例,来了解如何整洁数据,并将案例中的各个有用函数进行详细解读.该案例数据来自`tidyr::who`,其包含按年份，国家，年龄，性别和诊断方法细分的结核病（TB）病例。数据来自2014年世界卫生组织《全球结核病报告》，网址为<http://www.who.int/tb/country/data/download/en/>。

```{r}
library(tidyverse) 
who
```


这是一个非常典型的现实示例数据集。它包含**冗余列，奇数变量代码和许多缺失值**。我们需要采取多个步骤来对其进行整理。

## 不是变量的列汇集在一起

首先将不是变量的列聚集在一起。所包含的列包括：

- country，iso2和iso3是三个指定国家/地区的变量。

- year是一个变量。

- 变量名中给出的结构（例如new_sp_m014，new_ep_m014，new_ep_f014）可能是值，而不是变量。

因此，我们需要将从`new_sp_m014`到`newrel_f65`的所有列汇总在一起。我们用通用名称"key"来表示他们。我们知道单元格代表案件数，因此我们将变量数存储在`cases`中,并用`na.rm`去除含有缺失值的行。

这里使用`pivot_longer()`将数据变长,具体见后面函数详情.


```{r}
who1 <- who %>% 
	pivot_longer(
		cols = new_sp_m014:newrel_f65,
		names_to = 'key',
		values_to = 'cases',
		values_drop_na = T
)

who1
```

对key进行计数，我们可以得到一些有关值结构的提示：

```{r}
who1 %>% count(key)
```

其中key的具体含义，查阅可得：

每列的前三个字母：新、旧病例。

之后两个字母：结核的类型。

- rel 代表复发病例

- ep 代表肺外结核病例

- sn 代表无法通过肺部涂片诊断（涂片阴性）的肺结核病例

- sp 代表可被诊断为肺部涂片（涂片阳性）的肺结核病例

第六字母：结核病患者的性别。男性（m）和女性（f）

其余数字给出了年龄段。数据集将案例分为七个年龄组：

- 014 = 0 – 14岁

- 1524 = 15 – 24岁

- 2534 = 25 – 34岁

- 3544 = 35 – 44岁

- 4554 = 45 – 54岁

- 5564 = 55 – 64岁

- 65 = 65岁或以上

## 替换数据

我们需要对列名称的格式进行较小的修正：将new_rel替换为newrel（很难在这里找到它，但是如果您不修正它，我们将在后续步骤中出错）。这里使用了stringr包中的str_replace(),将`newrel`替换`new_rel`.

```{r}
who2 <- who1 %>% 
	mutate( names_from = stringr::str_replace(key,'newrel','new_rel')
	)
who2
```

## 字符分割

接下来就是将`key`中的字符进行分割,我们使用`separate()`对字符进行两次分割.

1.将在每个下划线处拆分代码。

```{r}
who3 <- who2 %>% 
	separate(key,c('new','type','sexage'),sep = '_')
who3
```

利用select()删除没用的列:new,iso2，iso3.

```{r}
who3 %>% count(new)
who4 <- who3 %>% select(-new,-iso2,-iso3)
who4
```

2. 将分离sexage到sex和age通过的第一个字符后拆分：

```{r}
who5 <- who4 %>% 
	separate(sexage,c('sex','age'),sep=1)
who5
```


这时,who数据集是目前整洁！

## 可视化

数据清洗完毕,就可以做一些初步的可视化,探索性分析.这里简单绘制了
前几个国家不同年份,不同性别的结核病病例总数。

```{r}
who5 %>% group_by(country,year,sex)  %>% filter(year<2003) %>% 
	count() %>% 
	head(100) %>%
	ggplot(aes(x=as.factor(year),y=n,fill=country))+geom_col() +facet_wrap(~sex,nrow = 1)+
	  scale_fill_brewer(palette = "Paired")

```

## 复杂的管道函数

事实上你可以直接只用管道函数构建一个复杂的函数：

```{r eval=FALSE, include=T}
who %>%
  pivot_longer(
    cols = new_sp_m014:newrel_f65, 
    names_to = "key", 
    values_to = "cases", 
    values_drop_na = TRUE
  ) %>% 
  mutate(
    key = stringr::str_replace(key, "newrel", "new_rel")
  ) %>%
  separate(key, c("new", "var", "sexage")) %>% 
  select(-new, -iso2, -iso3) %>% 
  separate(sexage, c("sex", "age"), sep = 1)
```

# 所用函数详细解释

## pivot_longer()、poivot_wider()

`pivot_longer()` 将在列中列名（数值）转换到一列上。具体可见下图,将列变量转化为数据存在year列名中,相当于把数据变长(longer).


![pivot_longer，从左到右](36.jpg)

函数主要参数:

> cols选取的列
> names_to 字符串，指定要从数据的列名中存储的数据创建的列的名称。
> values_to 字符串，指定要从存储在单元格值中的数据创建的列的名称。
> values_drop_na 如果为真，将删除value_to列中只包含NAs的行。这有效地将显式缺失值转换为隐式缺失值，通常只在由其结构创建的数据中缺失值时使用.

例子如上面例子:将new_sp_m014到newrel_f65之间的列选取,汇总到`key`列名中,值存在`cases`列名中,并将含有缺失值的行进行删除.

```{r}
who1 <- who %>% 
	pivot_longer(
		cols = new_sp_m014:newrel_f65,
		names_to = 'key',
		values_to = 'cases',
		values_drop_na = T
)
```


当然还有一个和他相反功能的函数`poivot_wider()`。具体见下图,相当于把key中的值变为列名,对应的values数据转化到population中.下面是简单的例子.


![pivot_wider，从右到左](37.jpg)

```{r}
library(tidyverse)
stocks <- tibble(
  year   = c(2015, 2015, 2016, 2016),
  half  = c(   1,    2,     1,    2),
  return = c(1.88, 0.59, 0.92, 0.17)
)
stocks
```

我们将数据变宽,将year变为列名,对应在return中的数据进行填充.

```{r}
stocks %>%
	pivot_wider(names_from = year,values_from = return) 
```


## separate()

该函数可将字符进行分割,具体案例如上.

默认情况下，当separate()看到非字母数字字符(即不是数字或字母的字符)时，它将分割值。可以用里面的参数`sep`。比如：`sep='_'`。他还有一个功能,当`sep=2`时,可通过第二个位置进行分割,使用在省份市级,等数据上.例如以下函数,其中into = c("century", "year")将原始分割后的数据导入两个新列上,分别叫century和year.


```
table3 %>% 
  separate(year, into = c("century", "year"), sep = 2)
```

**注意**:默认情况下，会转化成字符形式，你可以用参数`convert=T`，将数据转化最佳结构.

![](38.jpg)

## unite

是`separate()`的反函数,这里做个补充

![unite](39.jpg)

默认情况下，`sep='_'`如果我们不需要任何分隔符可以使用`sep=''`.


# 缺失值处理

两种情况会出现缺失值: 


1. **显式**: 标记为`NA`.

2. **隐式**: 没出在书中的.

以下为例子

```{r}
stocks <- tibble(
  year   = c(2015, 2015, 2015, 2015, 2016, 2016, 2016),
  qtr    = c(   1,    2,    3,    4,    2,    3,    4),
  return = c(1.88, 0.59, 0.35,   NA, 0.92, 0.17, 2.66)
)
```

在这个数据集中有两个缺失的值:

1. 2015年第四季度出现显式丢失，因为它的值应该在的单元格中包含NA。

2. 2016年第一季度的收益是隐式缺失的，因为它根本没有出现在数据集中。

对于隐式缺失值,我们可以将数据进行变化,显示隐性缺失.

```{r}
stocks %>% 
	pivot_wider(names_from = year,values_from = return)
```
这样就可以看到原来隐型的缺失值了.

这些显式缺失的值在数据的其他表示中可能并不重要，你可以在pivot_longer()中设置values_drop_na = TRUE来将显式缺失的值变成隐式缺失值:


```{r}
stocks %>% 
  pivot_wider(names_from = year, values_from = return) %>% 
  pivot_longer(
    cols = c(`2015`, `2016`), 
    names_to = "year", 
    values_to = "return", 
    values_drop_na = TRUE
  )
```

#### complete()

在整洁数据中显式显示缺失值的另一个重要工具是complete().

complete()获取一组列，并查找所有唯一的组合。然后，它确保原始数据集包含所有这些值，并在必要时显式填充NAs。

```{r}
stocks %>% 
	complete(year,qtr)
```

有时，当一个数据源主要用于数据输入时，丢失的值表明以前的值应该结转:

```{r}
treatment <- tribble(
	~person,~treatment,~response,
	'A',1,7,
	NA,2,10,
	NA,3,9,
	'B',1,4
)
```


#### fill()

您可以使用fill()填充这些缺失的值。它采用一组列，在这些列中，您希望用最近的非缺失值(有时称为最后一次观察结转)替换缺失值。

```{r}
treatment %>% fill(person)
```
直接将缺失值填写为前面的字符('A').


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/DESCRIPTION
================================================
Package: r4ds
Title: R for data science.
Version: 0.1
Authors@R: c(
  person("Hadley", "Wickham", , "hadley@rstudio.com", c("aut", "cre")),
  person("Garrett", "Grolemund", , "garrett@rstudio.com", "aut")
  )
Depends: R (>= 3.1.0)
URL: https://github.com/hadley/r4ds
Imports:
  bookdown,
  condvis,
  gapminder,
  ggrepel,
  hexbin,
  htmltools,
  htmlwidgets,
  jpeg,
  knitr,
  Lahman,
  leaflet,
  maps,
  microbenchmark,
  nycflights13,
  png,
  pryr,
  tidyverse,
  viridis
Remotes:
  hadley/ggplot2,
  slowkow/ggrepel,
  rstudio/bookdown,
  rstudio/rmarkdown


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/EDA.Rmd
================================================
# Exploratory Data Analysis

## Introduction

This chapter will show you how to use visualisation and transformation to explore your data in a systematic way, a task that statisticians call exploratory data analysis, or EDA for short. EDA is an iterative cycle. You:

1. Generate questions about your data.

1. Search for answers by visualising, transforming, and modelling your data.

1. Use what you learn to refine your questions and/or generate new questions.

EDA is not a formal process with a strict set of rules. More than anything, EDA is a state of mind. During the initial phases of EDA you should feel free to investigate every idea that occurs to you. Some of these ideas will pan out, and some will be dead ends. As your exploration continues, you will home in on a few particularly productive areas that you'll eventually write up and communicate to others.

EDA is an important part of any data analysis, even if the questions are handed to you on a platter, because you always need to investigate the quality of your data. Data cleaning is just one application of EDA: you ask questions about whether your data meets your expectations or not. To do data cleaning, you'll need to deploy all the tools of EDA: visualisation, transformation, and modelling.

### Prerequisites

In this chapter we'll combine what you've learned about dplyr and ggplot2 to interactively ask questions, answer them with data, and then ask new questions.

```{r setup, message = FALSE}
library(tidyverse)
```

## Questions

> "There are no routine statistical questions, only questionable statistical
> routines." --- Sir David Cox

> "Far better an approximate answer to the right question, which is often
> vague, than an exact answer to the wrong question, which can always be made
> precise." --- John Tukey

Your goal during EDA is to develop an understanding of your data. The easiest way to do this is to use questions as tools to guide your investigation. When you ask a question, the question focuses your attention on a specific part of your dataset and helps you decide which graphs, models, or transformations to make.

EDA is fundamentally a creative process. And like most creative processes, the key to asking _quality_ questions is to generate a large _quantity_ of questions. It is difficult to ask revealing questions at the start of your analysis because you do not know what insights are contained in your dataset. On the other hand, each new question that you ask will expose you to a new aspect of your data and increase your chance of making a discovery. You can quickly drill down into the most interesting parts of your data---and develop a set of thought-provoking questions---if you follow up each question with a new question based on what you find.

There is no rule about which questions you should ask to guide your research. However, two types of questions will always be useful for making discoveries within your data. You can loosely word these questions as:

1. What type of variation occurs within my variables?

1. What type of covariation occurs between my variables?

The rest of this chapter will look at these two questions. I'll explain what variation and covariation are, and I'll show you several ways to answer each question. To make the discussion easier, let's define some terms: 

*   A __variable__ is a quantity, quality, or property that you can measure. 

*   A __value__ is the state of a variable when you measure it. The value of a
    variable may change from measurement to measurement.
  
*   An __observation__ is a set of measurements made under similar conditions
    (you usually make all of the measurements in an observation at the same 
    time and on the same object). An observation will contain several values, 
    each associated with a different variable. I'll sometimes refer to 
    an observation as a data point.

*   __Tabular data__ is a set of values, each associated with a variable and an
    observation. Tabular data is _tidy_ if each value is placed in its own
    "cell", each variable in its own column, and each observation in its own 
    row. 

So far, all of the data that you've seen has been tidy. In real-life, most data isn't tidy, so we'll come back to these ideas again in [tidy data].

## Variation

**Variation** is the tendency of the values of a variable to change from measurement to measurement. You can see variation easily in real life; if you measure any continuous variable twice, you will get two different results. This is true even if you measure quantities that are constant, like the speed of light. Each of your measurements will include a small amount of error that varies from measurement to measurement. Categorical variables can also vary if you measure across different subjects (e.g. the eye colors of different people), or different times (e.g. the energy levels of an electron at different moments). 
Every variable has its own pattern of variation, which can reveal interesting information. The best way to understand that pattern is to visualise the distribution of the variable's values.

### Visualising distributions

How you visualise the distribution of a variable will depend on whether the variable is categorical or continuous. A variable is **categorical** if it can only take one of a small set of values. In R, categorical variables are usually saved as factors or character vectors. To examine the distribution of a categorical variable, use a bar chart:

```{r}
ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut))
```

The height of the bars displays how many observations occurred with each x value. You can compute these values manually with `dplyr::count()`:

```{r}
diamonds %>% 
  count(cut)
```

A variable is **continuous** if it can take any of an infinite set of ordered values. Numbers and date-times are two examples of continuous variables. To examine the distribution of a continuous variable, use a histogram:

```{r}
ggplot(data = diamonds) +
  geom_histogram(mapping = aes(x = carat), binwidth = 0.5)
```

You can compute this by hand by combining `dplyr::count()` and `ggplot2::cut_width()`:

```{r}
diamonds %>% 
  count(cut_width(carat, 0.5))
```

A histogram divides the x-axis into equally spaced bins and then uses the height of a bar to display the number of observations that fall in each bin. In the graph above, the tallest bar shows that almost 30,000 observations have a `carat` value between 0.25 and 0.75, which are the left and right edges of the bar. 

You can set the width of the intervals in a histogram with the `binwidth` argument, which is measured in the units of the `x` variable. You should always explore a variety of binwidths when working with histograms, as different binwidths can reveal different patterns. For example, here is how the graph above looks when we zoom into just the diamonds with a size of less than three carats and choose a smaller binwidth.

```{r}
smaller <- diamonds %>% 
  filter(carat < 3)
  
ggplot(data = smaller, mapping = aes(x = carat)) +
  geom_histogram(binwidth = 0.1)
```

If you wish to overlay multiple histograms in the same plot, I recommend using `geom_freqpoly()` instead of `geom_histogram()`. `geom_freqpoly()` performs the same calculation as `geom_histogram()`, but instead of displaying the counts with bars, uses lines instead. It's much easier to understand overlapping lines than bars.

```{r}
ggplot(data = smaller, mapping = aes(x = carat, colour = cut)) +
  geom_freqpoly(binwidth = 0.1)
```

There are a few challenges with this type of plot, which we will come back to in [visualising a categorical and a continuous variable](#cat-cont).

Now that you can visualise variation, what should you look for in your plots? And what type of follow-up questions should you ask? I've put together a list below of the most useful types of information that you will find in your graphs, along with some follow-up questions for each type of information. The key to asking good follow-up questions will be to rely on your curiosity (What do you want to learn more about?) as well as your skepticism (How could this be misleading?).

### Typical values

In both bar charts and histograms, tall bars show the common values of a variable, and shorter bars show less-common values. Places that do not have bars reveal values that were not seen in your data. To turn this information into useful questions, look for anything unexpected:

* Which values are the most common? Why?

* Which values are rare? Why? Does that match your expectations?

* Can you see any unusual patterns? What might explain them?

As an example, the histogram below suggests several interesting questions: 

* Why are there more diamonds at whole carats and common fractions of carats?

* Why are there more diamonds slightly to the right of each peak than there 
  are slightly to the left of each peak?
  
* Why are there no diamonds bigger than 3 carats?

```{r}
ggplot(data = smaller, mapping = aes(x = carat)) +
  geom_histogram(binwidth = 0.01)
```

Clusters of similar values suggest that subgroups exist in your data. To understand the subgroups, ask:

* How are the observations within each cluster similar to each other?

* How are the observations in separate clusters different from each other?

* How can you explain or describe the clusters?

* Why might the appearance of clusters be misleading?

The histogram below shows the length (in minutes) of 272 eruptions of the Old Faithful Geyser in Yellowstone National Park. Eruption times appear to be clustered into two groups: there are short eruptions (of around 2 minutes) and long eruptions (4-5 minutes), but little in between.

```{r}
ggplot(data = faithful, mapping = aes(x = eruptions)) + 
  geom_histogram(binwidth = 0.25)
```  

Many of the questions above will prompt you to explore a relationship *between* variables, for example, to see if the values of one variable can explain the behavior of another variable. We'll get to that shortly.

### Unusual values

Outliers are observations that are unusual; data points that don't seem to fit the pattern. Sometimes outliers are data entry errors; other times outliers suggest important new science. When you have a lot of data, outliers are sometimes difficult to see in a histogram.  For example, take the distribution of the `y` variable from the diamonds dataset. The only evidence of outliers is the unusually wide limits on the x-axis.

```{r}
ggplot(diamonds) + 
  geom_histogram(mapping = aes(x = y), binwidth = 0.5)
```   

There are so many observations in the common bins that the rare bins are so short that you can't see them (although maybe if you stare intently at 0 you'll spot something). To make it easy to see the unusual values, we need to zoom to small values of the y-axis with `coord_cartesian()`:

```{r}
ggplot(diamonds) + 
  geom_histogram(mapping = aes(x = y), binwidth = 0.5) +
  coord_cartesian(ylim = c(0, 50))
```   

(`coord_cartesian()` also has an `xlim()` argument for when you need to zoom into the x-axis. ggplot2 also has `xlim()` and `ylim()` functions that work slightly differently: they throw away the data outside the limits.)

This allows us to see that there are three unusual values: 0, ~30, and ~60. We pluck them out with dplyr: 

```{r, include = FALSE}
old <- options(tibble.print_max = 10, tibble.print_min = 10)
```

```{r}
unusual <- diamonds %>% 
  filter(y < 3 | y > 20) %>% 
  select(price, x, y, z) %>%
  arrange(y)
unusual
```

```{r, include = FALSE}
options(old)
```

The `y` variable measures one of the three dimensions of these diamonds, in mm. We know that diamonds can't have a width of 0mm, so these values must be incorrect. We might also suspect that measurements of 32mm and 59mm are implausible: those diamonds are over an inch long, but don't cost hundreds of thousands of dollars!

It's good practice to repeat your analysis with and without the outliers. If they have minimal effect on the results, and you can't figure out why they're there, it's reasonable to replace them with missing values, and move on. However, if they have a substantial effect on your results, you shouldn't drop them without justification. You'll need to figure out what caused them (e.g. a data entry error) and disclose that you removed them in your write-up.


### Exercises

1.  Explore the distribution of each of the `x`, `y`, and `z` variables 
    in `diamonds`. What do you learn? Think about a diamond and how you
    might decide which dimension is the length, width, and depth.

1.  Explore the distribution of `price`. Do you discover anything unusual
    or surprising? (Hint: Carefully think about the `binwidth` and make sure
    you try a wide range of values.)

1.  How many diamonds are 0.99 carat? How many are 1 carat? What
    do you think is the cause of the difference?
    
1.  Compare and contrast `coord_cartesian()` vs `xlim()` or `ylim()` when
    zooming in on a histogram. What happens if you leave `binwidth` unset?
    What happens if you try and zoom so only half a bar shows?
    
## Missing values

If you've encountered unusual values in your dataset, and simply want to move on to the rest of your analysis, you have two options.

1.  Drop the entire row with the strange values:

    ```{r, eval = FALSE}
    diamonds2 <- diamonds %>% 
      filter(between(y, 3, 20))
    ```
    
    I don't recommend this option because just because one measurement
    is invalid, doesn't mean all the measurements are. Additionally, if you
    have low quality data, by time that you've applied this approach to every
    variable you might find that you don't have any data left!

1.  Instead, I recommend replacing the unusual values with missing values.
    The easiest way to do this is to use `mutate()` to replace the variable
    with a modified copy. You can use the `ifelse()` function to replace
    unusual values with `NA`:

    ```{r}
    diamonds2 <- diamonds %>% 
      mutate(y = ifelse(y < 3 | y > 20, NA, y))
    ```

`ifelse()` has three arguments. The first argument `test` should be a logical vector. The result will contain the value of the second argument, `yes`, when `test` is `TRUE`, and the value of the third argument, `no`, when it is false. Alternatively to ifelse, use `dplyr::case_when()`. `case_when()` is particularly useful inside mutate when you want to create a new variable that relies on a complex combination of existing variables.

Like R, ggplot2 subscribes to the philosophy that missing values should never silently go missing. It's not obvious where you should plot missing values, so ggplot2 doesn't include them in the plot, but it does warn that they've been removed:

```{r, dev = "png"}
ggplot(data = diamonds2, mapping = aes(x = x, y = y)) + 
  geom_point()
```

To suppress that warning, set `na.rm = TRUE`:

```{r, eval = FALSE}
ggplot(data = diamonds2, mapping = aes(x = x, y = y)) + 
  geom_point(na.rm = TRUE)
```

Other times you want to understand what makes observations with missing values different to observations with recorded values. For example, in `nycflights13::flights`, missing values in the `dep_time` variable indicate that the flight was cancelled. So you might want to compare the scheduled departure times for cancelled and non-cancelled times. You can do this by making a new variable with `is.na()`.

```{r}
nycflights13::flights %>% 
  mutate(
    cancelled = is.na(dep_time),
    sched_hour = sched_dep_time %/% 100,
    sched_min = sched_dep_time %% 100,
    sched_dep_time = sched_hour + sched_min / 60
  ) %>% 
  ggplot(mapping = aes(sched_dep_time)) + 
    geom_freqpoly(mapping = aes(colour = cancelled), binwidth = 1/4)
```

However this plot isn't great because there are many more non-cancelled flights than cancelled flights. In the next section we'll explore some techniques for improving this comparison.

### Exercises

1.  What happens to missing values in a histogram?  What happens to missing
    values in a bar chart? Why is there a difference?

1.  What does `na.rm = TRUE` do in `mean()` and `sum()`?

## Covariation

If variation describes the behavior _within_ a variable, covariation describes the behavior _between_ variables. **Covariation** is the tendency for the values of two or more variables to vary together in a related way. The best way to spot covariation is to visualise the relationship between two or more variables. How you do that should again depend on the type of variables involved.

### A categorical and continuous variable {#cat-cont}

It's common to want to explore the distribution of a continuous variable broken down by a categorical variable, as in the previous frequency polygon. The default appearance of `geom_freqpoly()` is not that useful for that sort of comparison because the height is given by the count. That means if one of the groups is much smaller than the others, it's hard to see the differences in shape. For example, let's explore how the price of a diamond varies with its quality:

```{r}
ggplot(data = diamonds, mapping = aes(x = price)) + 
  geom_freqpoly(mapping = aes(colour = cut), binwidth = 500)
```

It's hard to see the difference in distribution because the overall counts differ so much:

```{r, fig.width = "50%", fig.width = 4}
ggplot(diamonds) + 
  geom_bar(mapping = aes(x = cut))
```

To make the comparison easier we need to swap what is displayed on the y-axis. Instead of displaying count, we'll display __density__, which is the count standardised so that the area under each frequency polygon is one.

```{r}
ggplot(data = diamonds, mapping = aes(x = price, y = ..density..)) + 
  geom_freqpoly(mapping = aes(colour = cut), binwidth = 500)
```

There's something rather surprising about this plot - it appears that fair diamonds (the lowest quality) have the highest average price!  But maybe that's because frequency polygons are a little hard to interpret - there's a lot going on in this plot.

Another alternative to display the distribution of a continuous variable broken down by a categorical variable is the boxplot. A **boxplot** is a type of visual shorthand for a distribution of values that is popular among statisticians. Each boxplot consists of:

* A box that stretches from the 25th percentile of the distribution to the 
  75th percentile, a distance known as the interquartile range (IQR). In the
  middle of the box is a line that displays the median, i.e. 50th percentile,
  of the distribution. These three lines give you a sense of the spread of the
  distribution and whether or not the distribution is symmetric about the
  median or skewed to one side. 

* Visual points that display observations that fall more than 1.5 times the 
  IQR from either edge of the box. These outlying points are unusual
  so are plotted individually.

* A line (or whisker) that extends from each end of the box and goes to the   
  farthest non-outlier point in the distribution.

```{r, echo = FALSE, out.width = "100%"}
knitr::include_graphics("images/EDA-boxplot.png")
```

Let's take a look at the distribution of price by cut using `geom_boxplot()`:

```{r fig.height = 3}
ggplot(data = diamonds, mapping = aes(x = cut, y = price)) +
  geom_boxplot()
```

We see much less information about the distribution, but the boxplots are much more compact so we can more easily compare them (and fit more on one plot). It supports the counterintuitive finding that better quality diamonds are cheaper on average! In the exercises, you'll be challenged to figure out why.

`cut` is an ordered factor: fair is worse than good, which is worse than very good and so on. Many categorical variables don't have such an intrinsic order, so you might want to reorder them to make a more informative display. One way to do that is with the `reorder()` function.

For example, take the `class` variable in the `mpg` dataset. You might be interested to know how highway mileage varies across classes:

```{r}
ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
  geom_boxplot()
```

To make the trend easier to see, we can reorder `class` based on the median value of `hwy`:

```{r fig.height = 3}
ggplot(data = mpg) +
  geom_boxplot(mapping = aes(x = reorder(class, hwy, FUN = median), y = hwy))
```

If you have long variable names, `geom_boxplot()` will work better if you flip it 90°. You can do that with `coord_flip()`.

```{r}
ggplot(data = mpg) +
  geom_boxplot(mapping = aes(x = reorder(class, hwy, FUN = median), y = hwy)) +
  coord_flip()
```

#### Exercises

1.  Use what you've learned to improve the visualisation of the departure times
    of cancelled vs. non-cancelled flights.

1.  What variable in the diamonds dataset is most important for predicting
    the price of a diamond? How is that variable correlated with cut?
    Why does the combination of those two relationships lead to lower quality
    diamonds being more expensive?

1.  Install the ggstance package, and create a horizontal boxplot.
    How does this compare to using `coord_flip()`?

1.  One problem with boxplots is that they were developed in an era of 
    much smaller datasets and tend to display a prohibitively large
    number of "outlying values". One approach to remedy this problem is
    the letter value plot. Install the lvplot package, and try using
    `geom_lv()` to display the distribution of price vs cut. What
    do you learn? How do you interpret the plots?

1.  Compare and contrast `geom_violin()` with a facetted `geom_histogram()`,
    or a coloured `geom_freqpoly()`. What are the pros and cons of each 
    method?

1.  If you have a small dataset, it's sometimes useful to use `geom_jitter()`
    to see the relationship between a continuous and categorical variable.
    The ggbeeswarm package provides a number of methods similar to 
    `geom_jitter()`. List them and briefly describe what each one does.

### Two categorical variables

To visualise the covariation between categorical variables, you'll need to count the number of observations for each combination. One way to do that is to rely on the built-in `geom_count()`:

```{r}
ggplot(data = diamonds) +
  geom_count(mapping = aes(x = cut, y = color))
```

The size of each circle in the plot displays how many observations occurred at each combination of values. Covariation will appear as a strong correlation between specific x values and specific y values. 

Another approach is to compute the count with dplyr:

```{r}
diamonds %>% 
  count(color, cut)
```

Then visualise with `geom_tile()` and the fill aesthetic:

```{r}
diamonds %>% 
  count(color, cut) %>%  
  ggplot(mapping = aes(x = color, y = cut)) +
    geom_tile(mapping = aes(fill = n))
```

If the categorical variables are unordered, you might want to use the seriation package to simultaneously reorder the rows and columns in order to more clearly reveal interesting patterns. For larger plots, you might want to try the d3heatmap or heatmaply packages, which create interactive plots.

#### Exercises

1.  How could you rescale the count dataset above to more clearly show
    the distribution of cut within colour, or colour within cut?

1.  Use `geom_tile()` together with dplyr to explore how average flight
    delays vary by destination and month of year.  What makes the 
    plot difficult to read? How could you improve it?

1.  Why is it slightly better to use `aes(x = color, y = cut)` rather
    than `aes(x = cut, y = color)` in the example above?

### Two continuous variables

You've already seen one great way to visualise the covariation between two continuous variables: draw a scatterplot with `geom_point()`. You can see covariation as a pattern in the points. For example, you can see an exponential relationship between the carat size and price of a diamond.

```{r, dev = "png"}
ggplot(data = diamonds) +
  geom_point(mapping = aes(x = carat, y = price))
```

Scatterplots become less useful as the size of your dataset grows, because points begin to overplot, and pile up into areas of uniform black (as above).
You've already seen one way to fix the problem: using the `alpha` aesthetic to add transparency.

```{r, dev = "png"}
ggplot(data = diamonds) + 
  geom_point(mapping = aes(x = carat, y = price), alpha = 1 / 100)
```

But using transparency can be challenging for very large datasets. Another solution is to use bin. Previously you used `geom_histogram()` and `geom_freqpoly()` to bin in one dimension. Now you'll learn how to use `geom_bin2d()` and `geom_hex()` to bin in two dimensions.

`geom_bin2d()` and `geom_hex()` divide the coordinate plane into 2d bins and then use a fill color to display how many points fall into each bin. `geom_bin2d()` creates rectangular bins. `geom_hex()` creates hexagonal bins. You will need to install the hexbin package to use `geom_hex()`.

```{r, fig.asp = 1, out.width = "50%", fig.align = "default", message = FALSE}
ggplot(data = smaller) +
  geom_bin2d(mapping = aes(x = carat, y = price))

# install.packages("hexbin")
ggplot(data = smaller) +
  geom_hex(mapping = aes(x = carat, y = price))
```

Another option is to bin one continuous variable so it acts like a categorical variable. Then you can use one of the techniques for visualising the combination of a categorical and a continuous variable that you learned about. For example, you could bin `carat` and then for each group, display a boxplot:

```{r}
ggplot(data = smaller, mapping = aes(x = carat, y = price)) + 
  geom_boxplot(mapping = aes(group = cut_width(carat, 0.1)))
```

`cut_width(x, width)`, as used above, divides `x` into bins of width `width`. By default, boxplots look roughly the same (apart from number of outliers) regardless of how many observations there are, so it's difficult to tell that each boxplot summarises a different number of points. One way to show that is to make the width of the boxplot proportional to the number of points with `varwidth = TRUE`.

Another approach is to display approximately the same number of points in each bin. That's the job of `cut_number()`:

```{r}
ggplot(data = smaller, mapping = aes(x = carat, y = price)) + 
  geom_boxplot(mapping = aes(group = cut_number(carat, 20)))
```

#### Exercises

1.  Instead of summarising the conditional distribution with a boxplot, you
    could use a frequency polygon. What do you need to consider when using
    `cut_width()` vs `cut_number()`? How does that impact a visualisation of
    the 2d distribution of `carat` and `price`?

1.  Visualise the distribution of carat, partitioned by price.

1.  How does the price distribution of very large diamonds compare to small 
    diamonds? Is it as you expect, or does it surprise you?
    
1.  Combine two of the techniques you've learned to visualise the 
    combined distribution of cut, carat, and price.

1. Two dimensional plots reveal outliers that are not visible in one 
   dimensional plots. For example, some points in the plot below have an 
   unusual combination of `x` and `y` values, which makes the points outliers 
   even though their `x` and `y` values appear normal when examined separately.
  
    ```{r, dev = "png"}
    ggplot(data = diamonds) +
      geom_point(mapping = aes(x = x, y = y)) +
      coord_cartesian(xlim = c(4, 11), ylim = c(4, 11))
    ```
    
    Why is a scatterplot a better display than a binned plot for this case?

## Patterns and models

Patterns in your data provide clues about relationships. If a systematic relationship exists between two variables it will appear as a pattern in the data. If you spot a pattern, ask yourself:

+ Could this pattern be due to coincidence (i.e. random chance)?

+ How can you describe the relationship implied by the pattern?

+ How strong is the relationship implied by the pattern?

+ What other variables might affect the relationship?

+ Does the relationship change if you look at individual subgroups of the data?

A scatterplot of Old Faithful eruption lengths versus the wait time between eruptions shows a pattern: longer wait times are associated with longer eruptions. The scatterplot also displays the two clusters that we noticed above.

```{r fig.height = 2}
ggplot(data = faithful) + 
  geom_point(mapping = aes(x = eruptions, y = waiting))
``` 

Patterns provide one of the most useful tools for data scientists because they reveal covariation. If you think of variation as a phenomenon that creates uncertainty, covariation is a phenomenon that reduces it. If two variables covary, you can use the values of one variable to make better predictions about the values of the second. If the covariation is due to a causal relationship (a special case), then you can use the value of one variable to control the value of the second.

Models are a tool for extracting patterns out of data. For example, consider the diamonds data. It's hard to understand the relationship between cut and price, because cut and carat, and carat and price are tightly related. It's possible to use a model to remove the very strong relationship between price and carat so we can explore the subtleties that remain. The following code fits a model that predicts `price` from `carat` and then computes the residuals (the difference between the predicted value and the actual value). The residuals give us a view of the price of the diamond, once the effect of carat has been removed. 

```{r, dev = "png"}
library(modelr)

mod <- lm(log(price) ~ log(carat), data = diamonds)

diamonds2 <- diamonds %>% 
  add_residuals(mod) %>% 
  mutate(resid = exp(resid))

ggplot(data = diamonds2) + 
  geom_point(mapping = aes(x = carat, y = resid))
```

Once you've removed the strong relationship between carat and price, you can see what you expect in the relationship between cut and price: relative to their size, better quality diamonds are more expensive. 

```{r}
ggplot(data = diamonds2) + 
  geom_boxplot(mapping = aes(x = cut, y = resid))
```

You'll learn how models, and the modelr package, work in the final part of the book, [model](#model-intro). We're saving modelling for later because understanding what models are and how they work is easiest once you have tools of data wrangling and programming in hand.

## ggplot2 calls

As we move on from these introductory chapters, we'll transition to a more concise expression of ggplot2 code. So far we've been very explicit, which is helpful when you are learning:

```{r, eval = FALSE}
ggplot(data = faithful, mapping = aes(x = eruptions)) + 
  geom_freqpoly(binwidth = 0.25)
```

Typically, the first one or two arguments to a function are so important that you should know them by heart. The first two arguments to `ggplot()` are `data` and `mapping`, and the first two arguments to `aes()` are `x` and `y`. In the remainder of the book, we won't supply those names. That saves typing, and, by reducing the amount of boilerplate, makes it easier to see what's different between plots. That's a really important programming concern that we'll come back in [functions].

Rewriting the previous plot more concisely yields:

```{r, eval = FALSE}
ggplot(faithful, aes(eruptions)) + 
  geom_freqpoly(binwidth = 0.25)
```

Sometimes we'll turn the end of a pipeline of data transformation into a plot. Watch for the transition from `%>%` to `+`. I wish this transition wasn't necessary but unfortunately ggplot2 was created before the pipe was discovered.

```{r, eval = FALSE}
diamonds %>% 
  count(cut, clarity) %>% 
  ggplot(aes(clarity, cut, fill = n)) + 
    geom_tile()
```

## Learning more

If you want to learn more about the mechanics of ggplot2, I'd highly recommend grabbing a copy of the ggplot2 book: <https://amzn.com/331924275X>. It's been recently updated, so it includes dplyr and tidyr code, and has much more space to explore all the facets of visualisation. Unfortunately the book isn't generally available for free, but if you have a connection to a university you can probably get an electronic version for free through SpringerLink.

Another useful resource is the [_R Graphics Cookbook_](https://amzn.com/1449316956) by Winston Chang. Much of the contents are available online at <http://www.cookbook-r.com/Graphs/>.

I also recommend [_Graphical Data Analysis with R_](https://amzn.com/1498715230), by Antony Unwin. This is a book-length treatment similar to the material covered in this chapter, but has the space to go into much greater depth. 


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/LICENSE
================================================
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/us/ or send a letter to Creative Commons, 444 Castro Street, Suite 900, Mountain View, California, 94041, USA.


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/README.md
================================================
# R for Data Science

This is code and text behind the [R for Data Science](http://r4ds.had.co.nz)
book. 

The R packages used in this book can be installed via

```{r}
devtools::install_github("hadley/r4ds")
```
The site is built using [bookdown package](https://github.com/rstudio/bookdown).
To create the site, you also need:

* [pandoc](http://johnmacfarlane.net/pandoc/)


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/_bookdown.yml
================================================
new_session: yes

rmd_files: [
  "index.rmd",
  "intro.Rmd",

  "explore.Rmd",
  "visualize.Rmd",
  "workflow-basics.Rmd",
  "transform.Rmd",
  "workflow-scripts.Rmd",
  "EDA.Rmd",
  "workflow-projects.Rmd",

  "wrangle.Rmd",
  "tibble.Rmd",
  "import.Rmd",
  "tidy.Rmd",
  "relational-data.Rmd",
  "strings.Rmd",
  "factors.Rmd",
  "datetimes.Rmd",

  "program.Rmd",
  "pipes.Rmd",
  "functions.Rmd",
  "vectors.Rmd",
  "iteration.Rmd",

  "model.Rmd",
  "model-basics.Rmd",
  "model-building.Rmd",
  "model-many.Rmd",

  "communicate.Rmd",
  "rmarkdown.Rmd",
  "communicate-plots.Rmd",
  "rmarkdown-formats.Rmd",
  "rmarkdown-workflow.Rmd",
]

before_chapter_script: "_common.R"


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FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/_common.R
================================================
set.seed(1014)
options(digits = 3)

knitr::opts_chunk$set(
  comment = "#>",
  collapse = TRUE,
  cache = TRUE,
  out.width = "70%",
  fig.align = 'center',
  fig.width = 6,
  fig.asp = 0.618,  # 1 / phi
  fig.show = "hold"
)

options(dplyr.print_min = 6, dplyr.print_max = 6)


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/_output.yaml
================================================
bookdown::gitbook:
  config:
    toc:
      collapse: section
      before: |
        <li><strong><a href="./">R for Data Science</a></strong></li>
    edit:
      link: https://github.com/hadley/r4ds/edit/master/%s
      text: "Edit"
    sharing: no
  css: r4ds.css

bookdown::pdf_book:
  latex_engine: "xelatex"



================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/communicate-plots.Rmd
================================================
# Graphics for communication

## Introduction

In [exploratory data analysis], you learned how to use plots as tools for _exploration_. When you make exploratory plots, you know---even before looking---which variables the plot will display. You made each plot for a purpose, could quickly look at it, and then move on to the next plot. In the course of most analyses, you'll produce tens or hundreds of plots, most of which are immediately thrown away.

Now that you understand your data, you need to _communicate_ your understanding to others. Your audience will likely not share your background knowledge and will not be deeply invested in the data. To help others quickly build up a good mental model of the data, you will need to invest considerable effort in making your plots as self-explanatory as possible. In this chapter, you'll learn some of the tools that ggplot2 provides to do so.

This chapter focuses on the tools you need to create good graphics. I assume that you know what you want, and just need to know how to do it. For that reason, I highly recommend pairing this chapter with a good general visualisation book. I particularly like [_The Truthful Art_](https://amzn.com/0321934075), by Albert Cairo. It doesn't teach the mechanics of creating visualisations, but instead focuses on what you need to think about in order to create effective graphics.

### Prerequisites

In this chapter, we'll focus once again on ggplot2. We'll also use a little dplyr for data manipulation, and a few ggplot2 extension packages, including __ggrepel__ and __viridis__. Rather than loading those extensions here, we'll refer to their functions explicitly, using the `::` notation. This will help make it clear which functions are built into ggplot2, and which come from other packages. Don't forget you'll need to install those packages with `install.packages()` if you don't already have them.

```{r, message = FALSE}
library(tidyverse)
```

## Label

The easiest place to start when turning an exploratory graphic into an expository graphic is with good labels. You add labels with the `labs()` function. This example adds a plot title:

```{r, message = FALSE}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  labs(title = "Fuel efficiency generally decreases with engine size")
```

The purpose of a plot title is to summarise the main finding. Avoid titles that just describe what the plot is, e.g. "A scatterplot of engine displacement vs. fuel economy". 

If you need to add more text, there are two other useful labels that you can use in ggplot2 2.2.0 and above (which should be available by the time you're reading this book):

*   `subtitle` adds additional detail in a smaller font beneath the title.

*   `caption` adds text at the bottom right of the plot, often used to describe
    the source of the data.

```{r, message = FALSE}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  labs(
    title = "Fuel efficiency generally decreases with engine size",
    subtitle = "Two seaters (sports cars) are an exception because of their light weight",
    caption = "Data from fueleconomy.gov"
  )
```

You can also use `labs()` to replace the axis and legend titles. It's usually a good idea to replace short variable names with more detailed descriptions, and to include the units.

```{r, message = FALSE}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) +
  labs(
    x = "Engine displacement (L)",
    y = "Highway fuel economy (mpg)",
    colour = "Car type"
  )
```

It's possible to use mathematical equations instead of text strings. Just switch `""` out for `quote()` and read about the available options in `?plotmath`:

```{r, fig.asp = 1, out.width = "50%", fig.width = 3}
df <- tibble(
  x = runif(10),
  y = runif(10)
)
ggplot(df, aes(x, y)) +
  geom_point() +
  labs(
    x = quote(sum(x[i] ^ 2, i == 1, n)),
    y = quote(alpha + beta + frac(delta, theta))
  )
```

### Exercises

1.  Create one plot on the fuel economy data with customised `title`,
    `subtitle`, `caption`, `x`, `y`, and `colour` labels.

1.  The `geom_smooth()` is somewhat misleading because the `hwy` for
    large engines is skewed upwards due to the inclusion of lightweight
    sports cars with big engines. Use your modelling tools to fit and display
    a better model.

1.  Take an exploratory graphic that you've created in the last month, and add
    informative titles to make it easier for others to understand.

## Annotations

In addition to labelling major components of your plot, it's often useful to label individual observations or groups of observations. The first tool you have at your disposal is `geom_text()`. `geom_text()` is similar to `geom_point()`, but it has an additional aesthetic: `label`. This makes it possible to add textual labels to your plots.

There are two possible sources of labels. First, you might have a tibble that provides labels. The plot below isn't terribly useful, but it illustrates a useful approach: pull out the most efficient car in each class with dplyr, and then label it on the plot:

```{r}
best_in_class <- mpg %>%
  group_by(class) %>%
  filter(row_number(desc(hwy)) == 1)

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_text(aes(label = model), data = best_in_class)
```

This is hard to read because the labels overlap with each other, and with the points. We can make things a little better by switching to `geom_label()` which draws a rectangle behind the text. We also use the `nudge_y` parameter to move the labels slightly above the corresponding points:

```{r}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_label(aes(label = model), data = best_in_class, nudge_y = 2, alpha = 0.5)
```

That helps a bit, but if you look closely in the top-left hand corner, you'll notice that there are two labels practically on top of each other. This happens because the highway mileage and displacement for the best cars in the compact and subcompact categories are exactly the same. There's no way that we can fix these by applying the same transformation for every label. Instead, we can use the __ggrepel__ package by Kamil Slowikowski. This useful package will automatically adjust labels so that they don't overlap:

```{r}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_point(size = 3, shape = 1, data = best_in_class) +
  ggrepel::geom_label_repel(aes(label = model), data = best_in_class)
```

Note another handy technique used here: I added a second layer of large, hollow points to highlight the points that I've labelled.

You can sometimes use the same idea to replace the legend with labels placed directly on the plot. It's not wonderful for this plot, but it isn't too bad. (`theme(legend.position = "none"`) turns the legend off --- we'll talk about it more shortly.)

```{r}
class_avg <- mpg %>%
  group_by(class) %>%
  summarise(
    displ = median(displ),
    hwy = median(hwy)
  )

ggplot(mpg, aes(displ, hwy, colour = class)) +
  ggrepel::geom_label_repel(aes(label = class),
    data = class_avg,
    size = 6,
    label.size = 0,
    segment.color = NA
  ) +
  geom_point() +
  theme(legend.position = "none")
```

Alternatively, you might just want to add a single label to the plot, but you'll still need to create a data frame. Often, you want the label in the corner of the plot, so it's convenient to create a new data frame using `summarise()` to compute the maximum values of x and y.

```{r}
label <- mpg %>%
  summarise(
    displ = max(displ),
    hwy = max(hwy),
    label = "Increasing engine size is \nrelated to decreasing fuel economy."
  )

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_text(aes(label = label), data = label, vjust = "top", hjust = "right")
```

If you want to place the text exactly on the borders of the plot, you can use `+Inf` and `-Inf`. Since we're no longer computing the positions from `mpg`, we can use `tibble()` to create the data frame:

```{r}
label <- tibble(
  displ = Inf,
  hwy = Inf,
  label = "Increasing engine size is \nrelated to decreasing fuel economy."
)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_text(aes(label = label), data = label, vjust = "top", hjust = "right")
```

In these examples, I manually broke the label up into lines using `"\n"`. Another approach is to use `stringr::str_wrap()` to automatically add line breaks, given the number of characters you want per line:

```{r}
"Increasing engine size is related to decreasing fuel economy." %>%
  stringr::str_wrap(width = 40) %>%
  writeLines()
```

Note the use of `hjust` and `vjust` to control the alignment of the label. Figure \@ref(fig:just) shows all nine possible combinations.

```{r just, echo = FALSE, fig.cap = "All nine combinations of `hjust` and `vjust`.", fig.asp = 0.5, fig.width = 4.5, out.width = "60%"}
vjust <- c(bottom = 0, center = 0.5, top = 1)
hjust <- c(left = 0, center = 0.5, right = 1)

df <- tidyr::crossing(hj = names(hjust), vj = names(vjust)) %>%
  mutate(
    y = vjust[vj],
    x = hjust[hj],
    label = paste0("hjust = '", hj, "'\n", "vjust = '", vj, "'")
  )

ggplot(df, aes(x, y)) +
  geom_point(colour = "grey70", size = 5) +
  geom_point(size = 0.5, colour = "red") +
  geom_text(aes(label = label, hjust = hj, vjust = vj), size = 4) +
  labs(x = NULL, y = NULL) 
```

Remember, in addition to `geom_text()`, you have many other geoms in ggplot2 available to help annotate your plot. A few ideas:

*   Use `geom_hline()` and `geom_vline()` to add reference lines. I often make
    them thick (`size = 2`) and white (`colour = white`), and draw them
    underneath the primary data layer. That makes them easy to see, without
    drawing attention away from the data.

*   Use `geom_rect()` to draw a rectangle around points of interest. The
    boundaries of the rectangle are defined by aesthetics `xmin`, `xmax`,
    `ymin`, `ymax`.

*   Use `geom_segment()` with the `arrow` argument to draw attention
    to a point with an arrow. Use aesthetics `x` and `y` to define the
    starting location, and `xend` and `yend` to define the end location.

The only limit is your imagination (and your patience with positioning annotations to be aesthetically pleasing)!

### Exercises

1.  Use `geom_text()` with infinite positions to place text at the
    four corners of the plot.

1.  Read the documentation for `annotate()`. How can you use it to add a text
    label to a plot without having to create a tibble?

1.  How do labels with `geom_text()` interact with faceting? How can you
    add a label to a single facet? How can you put a different label in
    each facet? (Hint: think about the underlying data.)

1.  What arguments to `geom_label()` control the appearance of the background
    box?

1.  What are the four arguments to `arrow()`? How do they work? Create a series
    of plots that demonstrate the most important options.

## Scales

The third way you can make your plot better for communication is to adjust the scales. Scales control the mapping from data values to things that you can perceive. Normally, ggplot2 automatically adds scales for you. For example, when you type:

```{r default-scales, fig.show = "hide"}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class))
```

ggplot2 automatically adds default scales behind the scenes:

```{r, fig.show = "hide"}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  scale_x_continuous() +
  scale_y_continuous() +
  scale_colour_discrete()
```

Note the naming scheme for scales: `scale_` followed by the name of the aesthetic, then `_`, then the name of the scale. The default scales are named according to the type of variable they align with: continuous, discrete, datetime, or date. There are lots of non-default scales which you'll learn about below.

The default scales have been carefully chosen to do a good job for a wide range of inputs. Nevertheless, you might want to override the defaults for two reasons:

*   You might want to tweak some of the parameters of the default scale.
    This allows you to do things like change the breaks on the axes, or the
    key labels on the legend.

*   You might want to replace the scale altogether, and use a completely
    different algorithm. Often you can do better than the default because
    you know more about the data.

### Axis ticks and legend keys

There are two primary arguments that affect the appearance of the ticks on the axes and the keys on the legend: `breaks` and `labels`. Breaks controls the position of the ticks, or the values associated with the keys. Labels controls the text label associated with each tick/key. The most common use of `breaks` is to override the default choice:

```{r}
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  scale_y_continuous(breaks = seq(15, 40, by = 5))
```

You can use `labels` in the same way (a character vector the same length as `breaks`), but you can also set it to `NULL` to suppress the labels altogether. This is useful for maps, or for publishing plots where you can't share the absolute numbers.

```{r}
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  scale_x_continuous(labels = NULL) +
  scale_y_continuous(labels = NULL)
```

You can also use `breaks` and `labels` to control the appearance of legends. Collectively axes and legends are called __guides__. Axes are used for x and y aesthetics; legends are used for everything else.

Another use of `breaks` is when you have relatively few data points and want to highlight exactly where the observations occur. For example, take this plot that shows when each US president started and ended their term.

```{r}
presidential %>%
  mutate(id = 33 + row_number()) %>%
  ggplot(aes(start, id)) +
    geom_point() +
    geom_segment(aes(xend = end, yend = id)) +
    scale_x_date(NULL, breaks = presidential$start, date_labels = "'%y")
```

Note that the specification of breaks and labels for date and datetime scales is a little different:

* `date_labels` takes a format specification, in the same form as
  `parse_datetime()`.

* `date_breaks` (not shown here), takes a string like "2 days" or "1 month".

### Legend layout

You will most often use `breaks` and `labels` to tweak the axes. While they both also work for legends, there are a few other techniques you are more likely to use.

To control the overall position of the legend, you need to use a `theme()` setting. We'll come back to themes at the end of the chapter, but in brief, they control the non-data parts of the plot. The theme setting `legend.position` controls where the legend is drawn:

```{r fig.asp = 1, fig.align = "default", out.width = "50%", fig.width = 4}
base <- ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class))

base + theme(legend.position = "left")
base + theme(legend.position = "top")
base + theme(legend.position = "bottom")
base + theme(legend.position = "right") # the default
```

You can also use `legend.position = "none"` to suppress the display of the legend altogether.

To control the display of individual legends, use `guides()` along with `guide_legend()` or `guide_colourbar()`. The following example shows two important settings: controlling the number of rows the legend uses with `nrow`, and overriding one of the aesthetics to make the points bigger. This is particularly useful if you have used a low `alpha` to display many points on a plot.

```{r}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) +
  theme(legend.position = "bottom") +
  guides(colour = guide_legend(nrow = 1, override.aes = list(size = 4)))
```

### Replacing a scale

Instead of just tweaking the details a little, you can instead replace the scale altogether. There are two types of scales you're mostly likely to want to switch out: continuous position scales and colour scales. Fortunately, the same principles apply to all the other aesthetics, so once you've mastered position and colour, you'll be able to quickly pick up other scale replacements.

It's very useful to plot transformations of your variable. For example, as we've seen in [diamond prices](diamond-prices) it's easier to see the precise relationship between `carat` and `price` if we log transform them:

```{r, fig.align = "default", out.width = "50%"}
ggplot(diamonds, aes(carat, price)) +
  geom_bin2d()

ggplot(diamonds, aes(log10(carat), log10(price))) +
  geom_bin2d()
```

However, the disadvantage of this transformation is that the axes are now labelled with the transformed values, making it hard to interpret the plot. Instead of doing the transformation in the aesthetic mapping, we can instead do it with the scale. This is visually identical, except the axes are labelled on the original data scale.

```{r}
ggplot(diamonds, aes(carat, price)) +
  geom_bin2d() + 
  scale_x_log10() + 
  scale_y_log10()
```

Another scale that is frequently customised is colour.The default categorical scale picks colours that are evenly spaced around the colour wheel. Useful alternatives are the ColorBrewer scales which have been hand tuned to work better for people with common types of colour blindness. The two plots below look similar, but there is enough difference in the shades of red and green that the dots on the right can be distinguished even by people with red-green colour blindness.

```{r, fig.align = "default", out.width = "50%"}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = drv))

ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = drv)) +
  scale_colour_brewer(palette = "Set1")
```

Don't forget simpler techniques. If there are just a few colours, you can add a redundant shape mapping. This will also help ensure your plot is interpretable in black and white.

```{r}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = drv, shape = drv)) +
  scale_colour_brewer(palette = "Set1")
```

The ColorBrewer scales are documented online at <http://colorbrewer2.org/> and made available in R via the __RColorBrewer__ package, by Erich Neuwirth. Figure \@ref(fig:brewer) shows the complete list of all palettes. The sequential (top) and diverging (bottom) palettes are particularly useful if your categorical values are ordered, or have a "middle". This often arises if you've used `cut()` to make a continuous variable into a categorical variable.

```{r brewer, fig.asp = 2.5, echo = FALSE, fig.cap = "All ColourBrewer scales."}
par(mar = c(0, 3, 0, 0))
RColorBrewer::display.brewer.all()
```

When you have a predefined mapping between values and colours, use `scale_colour_manual()`. For example, if we map presidential party to colour, we want to use the standard mapping of red for Republicans and blue for Democrats:

```{r}
presidential %>%
  mutate(id = 33 + row_number()) %>%
  ggplot(aes(start, id, colour = party)) +
    geom_point() +
    geom_segment(aes(xend = end, yend = id)) +
    scale_colour_manual(values = c(Republican = "red", Democratic = "blue"))
```

For continuous colour, you can use the built-in `scale_colour_gradient()` or `scale_fill_gradient()`. If you have a diverging scale, you can use `scale_colour_gradient2()`. That allows you to give, for example, positive and negative values different colours. That's sometimes also useful if you want to distinguish points above or below the mean.

Another option is `scale_colour_viridis()` provided by the __viridis__ package. It's a continuous analog of the categorical ColorBrewer scales. The designers, Nathaniel Smith and Stéfan van der Walt, carefully tailored a continuous colour scheme that has good perceptual properties. Here's an example from the viridis vignette.

```{r, fig.align = "default", fig.asp = 1, out.width = "50%", fig.width = 4}
df <- tibble(
  x = rnorm(10000),
  y = rnorm(10000)
)
ggplot(df, aes(x, y)) +
  geom_hex() +
  coord_fixed()

ggplot(df, aes(x, y)) +
  geom_hex() +
  viridis::scale_fill_viridis() +
  coord_fixed()
```

Note that all colour scales come in two variety: `scale_colour_x()` and `scale_fill_x()` for the `colour` and `fill` aesthetics respectively (the colour scales are available in both UK and US spellings).

### Exercises

1.  Why doesn't the following code override the default scale?

    ```{r fig.show = "hide"}
    ggplot(df, aes(x, y)) +
      geom_hex() +
      scale_colour_gradient(low = "white", high = "red") +
      coord_fixed()
    ```

1.  What is the first argument to every scale? How does it compare to `labs()`?

1.  Change the display of the presidential terms by:

    1. Combining the two variants shown above.
    1. Improving the display of the y axis.
    1. Labelling each term with the name of the president.
    1. Adding informative plot labels.
    1. Placing breaks every 4 years (this is trickier than it seems!).

1.  Use `override.aes` to make the legend on the following plot easier to see.

    ```{r, dev = "png", out.width = "50%"}
    ggplot(diamonds, aes(carat, price)) +
      geom_point(aes(colour = cut), alpha = 1/20)
    ```

## Zooming

There are three ways to control the plot limits:

1. Adjusting what data are plotted
1. Setting the limits in each scale
1. Setting `xlim` and `ylim` in `coord_cartesian()`

To zoom in on a region of the plot, it's generally best to use `coord_cartesian()`. Compare the following two plots:

```{r out.width = "50%", fig.align = "default", message = FALSE}
ggplot(mpg, mapping = aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth() +
  coord_cartesian(xlim = c(5, 7), ylim = c(10, 30))

mpg %>%
  filter(displ >= 5, displ <= 7, hwy >= 10, hwy <= 30) %>%
  ggplot(aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth()
```

You can also set the `limits` on individual scales. Reducing the limits is basically equivalent to subsetting the data. It is generally more useful if you want _expand_ the limits, for example, to match scales across different plots. For example, if we extract two classes of cars and plot them separately, it's difficult to compare the plots because all three scales (the x-axis, the y-axis, and the colour aesthetic) have different ranges.

```{r out.width = "50%", fig.align = "default", fig.width = 4}
suv <- mpg %>% filter(class == "suv")
compact <- mpg %>% filter(class == "compact")

ggplot(suv, aes(displ, hwy, colour = drv)) +
  geom_point()

ggplot(compact, aes(displ, hwy, colour = drv)) +
  geom_point()
```

One way to overcome this problem is to share scales across multiple plots, training the scales with the `limits` of the full data.

```{r out.width = "50%", fig.align = "default", fig.width = 4}
x_scale <- scale_x_continuous(limits = range(mpg$displ))
y_scale <- scale_y_continuous(limits = range(mpg$hwy))
col_scale <- scale_colour_discrete(limits = unique(mpg$drv))

ggplot(suv, aes(displ, hwy, colour = drv)) +
  geom_point() +
  x_scale +
  y_scale +
  col_scale

ggplot(compact, aes(displ, hwy, colour = drv)) +
  geom_point() +
  x_scale +
  y_scale +
  col_scale
```

In this particular case, you could have simply used faceting, but this technique is useful more generally, if for instance, you want spread plots over multiple pages of a report.

## Themes

Finally, you can customise the non-data elements of your plot with a theme:

```{r, message = FALSE}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(color = class)) +
  geom_smooth(se = FALSE) +
  theme_bw()
```

ggplot2 includes eight themes by default, as shown in Figure \@ref(fig:themes). Many more are included in add-on packages like __ggthemes__ (<https://github.com/jrnold/ggthemes>), by Jeffrey Arnold.

```{r themes, echo = FALSE, fig.cap = "The eight themes built-in to ggplot2."}
knitr::include_graphics("images/visualization-themes.png")
```

Many people wonder why the default theme has a grey background. This was a deliberate choice because it puts the data forward while still making the grid lines visible. The white grid lines are visible (which is important because they significantly aid position judgements), but they have little visual impact and we can easily tune them out. The grey background gives the plot a similar typographic colour to the text, ensuring that the graphics fit in with the flow of a document without jumping out with a bright white background. Finally, the grey background creates a continuous field of colour which ensures that the plot is perceived as a single visual entity.

It's also possible to control individual components of each theme, like the size and colour of the font used for the y axis. Unfortunately, this level of detail is outside the scope of this book, so you'll need to read the [ggplot2 book](https://amzn.com/331924275X) for the full details. You can also create your own themes, if you are trying to match a particular corporate or journal style.

## Saving your plots

There are two main ways to get your plots out of R and into your final write-up: `ggsave()` and knitr. `ggsave()` will save the most recent plot to disk:

```{r, fig.show = "none"}
ggplot(mpg, aes(displ, hwy)) + geom_point()
ggsave("my-plot.pdf")
```
```{r, include = FALSE}
file.remove("my-plot.pdf")
```

If you don't specify the `width` and `height` they will be taken from the dimensions of the current plotting device. For reproducible code, you'll want to specify them.

Generally, however, I think you should be assembling your final reports using R Markdown, so I want to focus on the important code chunk options that you should know about for graphics. You can learn more about `ggsave()` in the documentation.

### Figure sizing

The biggest challenge of graphics in R Markdown is getting your figures the right size and shape. There are five main options that control figure sizing: `fig.width`, `fig.height`, `fig.asp`, `out.width` and `out.height`. Image sizing is challenging because there are two sizes (the size of the figure created by R and the size at which it is inserted in the output document), and multiple ways of specifying the size (i.e., height, width, and aspect ratio: pick two of three).

I only ever use three of the five options:

* I find it most aesthetically pleasing for plots to have a consistent
  width. To enforce this, I set `fig.width = 6` (6") and `fig.asp = 0.618`
  (the golden ratio) in the defaults. Then in individual chunks, I only
  adjust `fig.asp`.

* I control the output size with `out.width` and set it to a percentage
  of the line width). I default to `out.width = "70%"`
  and `fig.align = "center"`. That give plots room to breathe, without taking
  up too much space.

* To put multiple plots in a single row I set the `out.width` to
  `50%` for two plots, `33%` for 3 plots, or `25%` to 4 plots, and set
  `fig.align = "default"`. Depending on what I'm trying to illustrate (e.g.
  show data or show plot variations), I'll also tweak `fig.width`, as
  discussed below.

If you find that you're having to squint to read the text in your plot, you need to tweak `fig.width`. If `fig.width` is larger than the size the figure is rendered in the final doc, the text will be too small; if `fig.width` is smaller, the text will be too big. You'll often need to do a little experimentation to figure out the right ratio between the `fig.width` and the eventual width in your document. To illustrate the principle, the following three plots have `fig.width` of 4, 6, and 8 respectively:

```{r, include = FALSE}
plot <- ggplot(mpg, aes(displ, hwy)) + geom_point()
```
```{r, fig.width = 4, echo = FALSE}
plot
```
```{r, fig.width = 6, echo = FALSE}
plot
```
```{r, fig.width = 8, echo = FALSE}
plot
```

If you want to make sure the font size is consistent across all your figures, whenever you set `out.width`, you'll also need to adjust `fig.width` to maintain the same ratio with your default `out.width`. For example, if your default `fig.width` is 6 and `out.width` is 0.7, when you set `out.width = "50%"` you'll need to set `fig.width` to 4.3 (6 * 0.5 / 0.7).

### Other important options

When mingling code and text, like I do in this book, I recommend setting `fig.show = "hold"` so that plots are shown after the code. This has the pleasant side effect of forcing you to break up large blocks of code with their explanations.

To add a caption to the plot, use `fig.cap`. In R Markdown this will change the figure from inline to "floating".

If you're producing PDF output, the default graphics type is PDF. This is a good default because PDFs are high quality vector graphics. However, they can produce very large and slow plots if you are displaying thousands of points. In that case, set `dev = "png"` to force the use of PNGs. They are slightly lower quality, but will be much more compact.

It's a good idea to name code chunks that produce figures, even if you don't routinely label other chunks. The chunk label is used to generate the file name of the graphic on disk, so naming your chunks makes it much easier to pick out plots and reuse in other circumstances (i.e. if you want to quickly drop a single plot into an email or a tweet).

## Learning more

The absolute best place to learn more is the ggplot2 book: [_ggplot2: Elegant graphics for data analysis_](https://amzn.com/331924275X). It goes into much more depth about the underlying theory, and has many more examples of how to combine the individual pieces to solve practical problems. Unfortunately, the book is not available online for free, although you can find the source code at <https://github.com/hadley/ggplot2-book>.

Another great resource is the ggplot2 extensions guide <http://www.ggplot2-exts.org/>. This site lists many of the packages that extend ggplot2 with new geoms and scales. It's a great place to start if you're trying to do something that seems hard with ggplot2.


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/communicate.Rmd
================================================
# (PART) Communicate {-}

# Introduction {#communicate-intro}

So far, you've learned the tools to get your data into R, tidy it into a form convenient for analysis, and then understand your data through transformation, visualisation and modelling. However, it doesn't matter how great your analysis is unless you can explain it to others: you need to __communicate__ your results.

```{r echo = FALSE, out.width = "75%"}
knitr::include_graphics("diagrams/data-science-communicate.png")
```

Communication is the theme of the following four chapters:

* In [R Markdown], you will learn about R Markdown, a tool for integrating
  prose, code, and results. You can use R Markdown in notebook mode for 
  analyst-to-analyst communication, and in report mode for 
  analyst-to-decision-maker communication. Thanks to the power of R Markdown
  formats, you can even use the same document for both purposes.
  
* In [Graphics for communication], you will learn how to take your exploratory
  graphics and turn them into expository graphics, graphics that help the
  newcomer to your analysis understand what's going on as quickly and 
  easily as possible.
  
* In [R Markdown formats], you'll learn a little about the many other varieties
  of outputs you can produce using R Markdown, including dashboards, websites,
  and books.
  
* We'll finish up with [R Markdown workflow], where you'll learn about the
  "analysis notebook" and how to systematically record your successes and 
  failures so that you can learn from them.

Unfortunately, these chapters focus mostly on the technical mechanics of communication, not the really hard problems of communicating your thoughts to other humans. However, there are lot of other great books about communication, which we'll point you to at the end of each chapter.


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/contribs.txt
================================================
   625	hadley
    93	Garrett
    77	Hadley Wickham
    50	S'busiso Mkhondwane
    21	behrman
    11	Brett Klamer
    10	Radu Grosu
     9	Brandon Greenwell
     8	Bill Behrman
     7	Garrett Grolemund
     7	Rademeyer Vermaak
     7	Colin Gillespie
     7	harrismcgehee
     6	jjchern
     6	Jakub Nowosad
     6	OaCantona
     5	kdpsingh
     5	Julian During
     4	Thomas Klebel
     4	Mine Cetinkaya-Rundel
     4	Jennifer (Jenny) Bryan
     4	Terence Teo
     4	Patrick Kennedy
     3	Jonathan Page
     3	Jose Roberto Ayala Solares
     3	yahwes
     3	seamus-mckinsey
     3	Ian Lyttle
     3	Ian Sealy
     3	Yihui Xie
     2	Cooper Morris
     2	Christian G. Warden
     2	Daniel Gromer
     2	Devin Pastoor
     2	Etienne B. Racine
     2	Jim Hester
     2	Joanne Jang
     2	Kirill Sevastyanenko
     2	MJMarshall
     2	Nirmal Patel
     2	Paul
     2	Robert Schuessler
     2	Will Beasley
     2	rlzijdeman
     2	robinlovelace
     2	sibusiso16
     2	spirgel
     1	robinsones
     1	Jeroen Janssens
     1	Mustafa Ascha
     1	Nelson Areal
     1	Nick Clark
     1	Alex
     1	Hengni Cai
     1	Gregory Jefferis
     1	seanpwilliams
     1	Peter Hurford
     1	Flemming Villalona
     1	Eric Watt
     1	shoili
     1	Earl Brown
     1	Shannon Ellis
     1	Steve Mortimer
     1	TJ Mahr
     1	Dylan Cashman
     1	Derwin McGeary
     1	Tom Prior
     1	Ajay Deonarine
     1	David Clark
     1	adi pradhan
     1	bahadir cankardes
     1	batpigandme
     1	Curtis Alexander
     1	Ahmed ElGabbas
     1	Christian Mongeau
     1	jennybc
     1	Ben Marwick
     1	jonathanflint
     1	Andrew Landgraf
     1	koalabearski
     1	nate-d-olson
     1	nickelas
     1	nwaff
     1	zeal626
     1	Jon Calder
     1	Julia Stewart Lowndes
     1	John Sears
     1	Justinas Petuchovas
     1	Kara Woo
     1	Kenny Darrell
     1	svenski
     1	KyleHumphrey
     1	Lawrence Wu
     1	Matthew Sedaghatfar


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/contribute.rmd
================================================
# Contributing

This book has been developed in the open, and it wouldn't be nearly as good 
without your contributions. There are a number of ways you can help make the
book even better:

* If you don't understand something, please 
  [let me know](mailto:h.wickham@gmail.com). Your feedback on what is confusing
  or hard to understand is valuable.

* If you spot a typo, feel free to edit the underlying page and send a pull 
  request. If you've never done this before, the process is very easy: 
  
    * Click the edit this page on the sidebar.
  
    * Make the changes using github's in-page editor and save.
    
    * Submit a pull request and include a brief description of your changes.
      "Fixing typos" is perfectly adequate.
      
    * If you make significant changes, include the phrase "I assign the 
      copyright of this contribution to Hadley Wickham" - I need this so I can 
      publish the printed book.


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/datetimes.Rmd
================================================
# Dates and times

## Introduction

This chapter will show you how to work with dates and times in R. At first glance, dates and times seem simple. You use them all the time in your regular life, and they don't seem to cause much confusion. However, the more you learn about dates and times, the more complicated they seem to get. To warm up, try these three seemingly simple questions:

* Does every year have 365 days?
* Does every day have 24 hours?
* Does every minute have 60 seconds?

I'm sure you know that not every year has 365 days, but do you know the full rule for determining if a year is a leap year? (It has three parts.) You might have remembered that many parts of the world use daylight savings time (DST), so that some days have 23 hours, and others have 25. You might not have known that some minutes have 61 seconds because every now and then leap seconds are added because the Earth's rotation is gradually slowing down.

Dates and times are hard because they have to reconcile two physical phenomena (the rotation of the Earth and its orbit around the sun) with a whole raft of geopolitical phenomena including months, time zones, and DST. This chapter won't teach you every last detail about dates and times, but it will give you a solid grounding of practical skills that will help you with common data analysis challenges.

### Prerequisites

This chapter will focus on the __lubridate__ package, which makes it easier to work with dates and times in R. lubridate is not part of core tidyverse because you only need it when you're working with dates/times. We will also need nycflights13 for practice data.

```{r setup, message = FALSE}
library(tidyverse)

library(lubridate)
library(nycflights13)
```

## Creating date/times

There are three types of date/time data that refer to an instant in time:

* A __date__. Tibbles print this as `<date>`.

* A __time__ within a day. Tibbles print this as `<time>`.

* A __date-time__ is a date plus a time: it uniquely identifies an
  instant in time (typically to the nearest second). Tibbles print this
  as `<dttm>`. Elsewhere in R these are called POSIXct, but I don't think
  that's a very useful name.
  
In this chapter we are only going to focus on dates and date-times as R doesn't have a native class for storing times. If you need one, you can use the __hms__ package.

You should always use the simplest possible data type that works for your needs. That means if you can use a date instead of a date-time, you should. Date-times are substantially more complicated because of the need to handle time zones, which we'll come back to at the end of the chapter.

To get the current date or date-time you can use `today()` or `now()`:

```{r}
today()
now()
```

Otherwise, there are three ways you're likely to create a date/time:

* From a string.
* From individual date-time components.
* From an existing date/time object.

They work as follows.

### From strings

Date/time data often comes as strings. You've seen one approach to parsing strings into date-times in [date-times](#readr-datetimes). Another approach is to use the helpers provided by lubridate. They automatically work out the format once you specify the order of the component. To use them, identify the order in which year, month, and day appear in your dates, then arrange "y", "m", and "d" in the same order. That gives you the name of the lubridate function that will parse your date. For example:

```{r}
ymd("2017-01-31")
mdy("January 31st, 2017")
dmy("31-Jan-2017")
```

These functions also take unquoted numbers. This is the most concise way to create a single date/time object, as you might need when filtering date/time data. `ymd()` is short and unambiguous:

```{r}
ymd(20170131)
```

`ymd()` and friends create dates. To create a date-time, add an underscore and one or more of "h", "m", and "s" to the name of the parsing function:

```{r}
ymd_hms("2017-01-31 20:11:59")
mdy_hm("01/31/2017 08:01")
```

You can also force the creation of a date-time from a date by supplying a timezone:

```{r}
ymd(20170131, tz = "UTC")
```

### From individual components

Instead of a single string, sometimes you'll have the individual components of the date-time spread across multiple columns. This is what we have in the flights data:

```{r}
flights %>% 
  select(year, month, day, hour, minute)
```

To create a date/time from this sort of input, use `make_date()` for dates, or `make_datetime()` for date-times:

```{r}
flights %>% 
  select(year, month, day, hour, minute) %>% 
  mutate(departure = make_datetime(year, month, day, hour, minute))
```

Let's do the same thing for each of the four time columns in `flights`. The times are represented in a slightly odd format, so we use modulus arithmetic to pull out the hour and minute components. Once I've created the date-time variables, I focus in on the variables we'll explore in the rest of the chapter.

```{r}
make_datetime_100 <- function(year, month, day, time) {
  make_datetime(year, month, day, time %/% 100, time %% 100)
}

flights_dt <- flights %>% 
  filter(!is.na(dep_time), !is.na(arr_time)) %>% 
  mutate(
    dep_time = make_datetime_100(year, month, day, dep_time),
    arr_time = make_datetime_100(year, month, day, arr_time),
    sched_dep_time = make_datetime_100(year, month, day, sched_dep_time),
    sched_arr_time = make_datetime_100(year, month, day, sched_arr_time)
  ) %>% 
  select(origin, dest, ends_with("delay"), ends_with("time"))

flights_dt
```

With this data, I can visualise the distribution of departure times across the year:

```{r}
flights_dt %>% 
  ggplot(aes(dep_time)) + 
  geom_freqpoly(binwidth = 86400) # 86400 seconds = 1 day
```

Or within a single day:

```{r}
flights_dt %>% 
  filter(dep_time < ymd(20130102)) %>% 
  ggplot(aes(dep_time)) + 
  geom_freqpoly(binwidth = 600) # 600 s = 10 minutes
```

Note that when you use date-times in a numeric context (like in a histogram), 1 means 1 second, so a binwidth of 86400 means one day. For dates, 1 means 1 day.

### From other types

You may want to switch between a date-time and a date. That's the job of `as_datetime()` and `as_date()`:

```{r}
as_datetime(today())
as_date(now())
```

Sometimes you'll get date/times as numeric offsets from the "Unix Epoch", 1970-01-01. If the offset is in seconds, use `as_datetime()`; if it's in days, use `as_date()`.

```{r}
as_datetime(60 * 60 * 10)
as_date(365 * 10 + 2)
```

### Exercises

1.  What happens if you parse a string that contains invalid dates?

    ```{r, eval = FALSE}
    ymd(c("2010-10-10", "bananas"))
    ```

1.  What does the `tzone` argument to `today()` do? Why is it important?

1.  Use the appropriate lubridate function to parse each of the following dates:

    ```{r}
    d1 <- "January 1, 2010"
    d2 <- "2015-Mar-07"
    d3 <- "06-Jun-2017"
    d4 <- c("August 19 (2015)", "July 1 (2015)")
    d5 <- "12/30/14" # Dec 30, 2014
    ```

## Date-time components

Now that you know how to get date-time data into R's date-time data structures, let's explore what you can do with them. This section will focus on the accessor functions that let you get and set individual components. The next section will look at how arithmetic works with date-times.

### Getting components

You can pull out individual parts of the date with the accessor functions `year()`, `month()`, `mday()` (day of the month), `yday()` (day of the year), `wday()` (day of the week), `hour()`, `minute()`, and `second()`. 

```{r}
datetime <- ymd_hms("2016-07-08 12:34:56")

year(datetime)
month(datetime)
mday(datetime)

yday(datetime)
wday(datetime)
```

For `month()` and `wday()` you can set `label = TRUE` to return the abbreviated name of the month or day of the week. Set `abbr = FALSE` to return the full name.

```{r}
month(datetime, label = TRUE)
wday(datetime, label = TRUE, abbr = FALSE)
```

We can use `wday()` to see that more flights depart during the week than on the weekend:

```{r}
flights_dt %>% 
  mutate(wday = wday(dep_time, label = TRUE)) %>% 
  ggplot(aes(x = wday)) +
    geom_bar()
```

There's an interesting pattern if we look at the average departure delay by minute within the hour. It looks like flights leaving in minutes 20-30 and 50-60 have much lower delays than the rest of the hour!

```{r}
flights_dt %>% 
  mutate(minute = minute(dep_time)) %>% 
  group_by(minute) %>% 
  summarise(
    avg_delay = mean(arr_delay, na.rm = TRUE),
    n = n()) %>% 
  ggplot(aes(minute, avg_delay)) +
    geom_line()
```

Interestingly, if we look at the _scheduled_ departure time we don't see such a strong pattern:

```{r}
sched_dep <- flights_dt %>% 
  mutate(minute = minute(sched_dep_time)) %>% 
  group_by(minute) %>% 
  summarise(
    avg_delay = mean(arr_delay, na.rm = TRUE),
    n = n())

ggplot(sched_dep, aes(minute, avg_delay)) +
  geom_line()
```

So why do we see that pattern with the actual departure times? Well, like much data collected by humans, there's a strong bias towards flights leaving at "nice" departure times. Always be alert for this sort of pattern whenever you work with data that involves human judgement!

```{r}
ggplot(sched_dep, aes(minute, n)) +
  geom_line()
```

### Rounding

An alternative approach to plotting individual components is to round the date to a nearby unit of time, with `floor_date()`, `round_date()`, and `ceiling_date()`. Each function takes a vector of dates to adjust and then the name of the unit round down (floor), round up (ceiling), or round to. This, for example, allows us to plot the number of flights per week:

```{r}
flights_dt %>% 
  count(week = floor_date(dep_time, "week")) %>% 
  ggplot(aes(week, n)) +
    geom_line()
```

Computing the difference between a rounded and unrounded date can be particularly useful.

### Setting components

You can also use each accessor function to set the components of a date/time: 

```{r}
(datetime <- ymd_hms("2016-07-08 12:34:56"))

year(datetime) <- 2020
datetime
month(datetime) <- 01
datetime
hour(datetime) <- hour(datetime) + 1
datetime
```

Alternatively, rather than modifying in place, you can create a new date-time with `update()`. This also allows you to set multiple values at once.

```{r}
update(datetime, year = 2020, month = 2, mday = 2, hour = 2)
```

If values are too big, they will roll-over:

```{r}
ymd("2015-02-01") %>% 
  update(mday = 30)
ymd("2015-02-01") %>% 
  update(hour = 400)
```

You can use `update()` to show the distribution of flights across the course of the day for every day of the year: 

```{r}
flights_dt %>% 
  mutate(dep_hour = update(dep_time, yday = 1)) %>% 
  ggplot(aes(dep_hour)) +
    geom_freqpoly(binwidth = 300)
```

Setting larger components of a date to a constant is a powerful technique that allows you to explore patterns in the smaller components.

### Exercises

1.  How does the distribution of flight times within a day change over the 
    course of the year?
    
1.  Compare `dep_time`, `sched_dep_time` and `dep_delay`. Are they consistent?
    Explain your findings.

1.  Compare `air_time` with the duration between the departure and arrival.
    Explain your findings. (Hint: consider the location of the airport.)
    
1.  How does the average delay time change over the course of a day?
    Should you use `dep_time` or `sched_dep_time`? Why?

1.  On what day of the week should you leave if you want to minimise the
    chance of a delay?

1.  What makes the distribution of `diamonds$carat` and 
    `flights$sched_dep_time` similar?

1.  Confirm my hypothesis that the early departures of flights in minutes
    20-30 and 50-60 are caused by scheduled flights that leave early. 
    Hint: create a binary variable that tells you whether or not a flight 
    was delayed.

## Time spans

Next you'll learn about how arithmetic with dates works, including subtraction, addition, and division. Along the way, you'll learn about three important classes that represent time spans:

* __durations__, which represent an exact number of seconds.
* __periods__, which represent human units like weeks and months.
* __intervals__, which represent a starting and ending point.

### Durations

In R, when you subtract two dates, you get a difftime object:

```{r}
# How old is Hadley?
h_age <- today() - ymd(19791014)
h_age
```

A difftime class object records a time span of seconds, minutes, hours, days, or weeks. This ambiguity can make difftimes a little painful to work with, so lubridate provides an alternative which always uses seconds: the __duration__.

```{r}
as.duration(h_age)
```

Durations come with a bunch of convenient constructors:

```{r}
dseconds(15)
dminutes(10)
dhours(c(12, 24))
ddays(0:5)
dweeks(3)
dyears(1)
```

Durations always record the time span in seconds. Larger units are created by converting minutes, hours, days, weeks, and years to seconds at the standard rate (60 seconds in a minute, 60 minutes in an hour, 24 hours in day, 7 days in a week, 365 days in a year).

You can add and multiply durations:

```{r}
2 * dyears(1)
dyears(1) + dweeks(12) + dhours(15)
```

You can add and subtract durations to and from days:

```{r}
tomorrow <- today() + ddays(1)
last_year <- today() - dyears(1)
```

However, because durations represent an exact number of seconds, sometimes you might get an unexpected result:

```{r}
one_pm <- ymd_hms("2016-03-12 13:00:00", tz = "America/New_York")

one_pm
one_pm + ddays(1)
```

Why is one day after 1pm on March 12, 2pm on March 13?! If you look carefully at the date you might also notice that the time zones have changed. Because of DST, March 12 only has 23 hours, so if we add a full days worth of seconds we end up with a different time.

### Periods

To solve this problem, lubridate provides __periods__. Periods are time spans but don't have a fixed length in seconds, instead they work with "human" times, like days and months. That allows them work in a more intuitive way:

```{r}
one_pm
one_pm + days(1)
```

Like durations, periods can be created with a number of friendly constructor functions. 

```{r}
seconds(15)
minutes(10)
hours(c(12, 24))
days(7)
months(1:6)
weeks(3)
years(1)
```

You can add and multiply periods:

```{r}
10 * (months(6) + days(1))
days(50) + hours(25) + minutes(2)
```

And of course, add them to dates. Compared to durations, periods are more likely to do what you expect:

```{r}
# A leap year
ymd("2016-01-01") + dyears(1)
ymd("2016-01-01") + years(1)

# Daylight Savings Time
one_pm + ddays(1)
one_pm + days(1)
```

Let's use periods to fix an oddity related to our flight dates. Some planes appear to have arrived at their destination _before_ they departed from New York City.

```{r}
flights_dt %>% 
  filter(arr_time < dep_time) 
```

These are overnight flights. We used the same date information for both the departure and the arrival times, but these flights arrived on the following day. We can fix this by adding `days(1)` to the arrival time of each overnight flight.

```{r}
flights_dt <- flights_dt %>% 
  mutate(
    overnight = arr_time < dep_time,
    arr_time = arr_time + days(overnight * 1),
    sched_arr_time = sched_arr_time + days(overnight * 1)
  )
```

Now all of our flights obey the laws of physics.

```{r}
flights_dt %>% 
  filter(overnight, arr_time < dep_time) 
```

### Intervals

It's obvious what `dyears(1) / ddays(365)` should return: one, because durations are always represented by a number of seconds, and a duration of a year is defined as 365 days worth of seconds.

What should `years(1) / days(1)` return? Well, if the year was 2015 it should return 365, but if it was 2016, it should return 366! There's not quite enough information for lubridate to give a single clear answer. What it does instead is give an estimate, with a warning:

```{r}
years(1) / days(1)
```

If you want a more accurate measurement, you'll have to use an __interval__. An interval is a duration with a starting point: that makes it precise so you can determine exactly how long it is:

```{r}
next_year <- today() + years(1)
(today() %--% next_year) / ddays(1)
```

To find out how many periods fall into an interval, you need to use integer division:

```{r}
(today() %--% next_year) %/% days(1)
```

### Summary

How do you pick between duration, periods, and intervals? As always, pick the simplest data structure that solves your problem. If you only care about physical time, use a duration; if you need to add human times, use a period; if you need to figure out how long a span is in human units, use an interval.

Figure \@ref(fig:dt-algebra) summarises permitted arithmetic operations between the different data types.

```{r dt-algebra, echo = FALSE, fig.cap = "The allowed arithmetic operations between pairs of date/time classes."}
knitr::include_graphics("diagrams/datetimes-arithmetic.png")
```

### Exercises

1.  Why is there `months()` but no `dmonths()`?

1.  Explain `days(overnight * 1)` to someone who has just started 
    learning R. How does it work?

1.  Create a vector of dates giving the first day of every month in 2015.
    Create a vector of dates giving the first day of every month
    in the _current_ year.

1.  Write a function that given your birthday (as a date), returns 
    how old you are in years.

1.  Why can't `(today() %--% (today() + years(1)) / months(1)` work?

## Time zones 

Time zones are an enormously complicated topic because of their interaction with geopolitical entities. Fortunately we don't need to dig into all the details as they're not all important for data analysis, but there are a few challenges we'll need to tackle head on.

The first challenge is that everyday names of time zones tend to be ambiguous. For example, if you're American you're probably familiar with EST, or Eastern Standard Time. However, both Australia and Canada also have EST! To avoid confusion, R uses the international standard IANA time zones. These use a consistent naming scheme "<area>/<location>", typically in the form "\<continent\>/\<city\>" (there are a few exceptions because not every country lies on a continent). Examples include "America/New_York", "Europe/Paris", and "Pacific/Auckland".

You might wonder why the time zone uses a city, when typically you think of time zones as associated with a country or region within a country. This is because the IANA database has to record decades worth of time zone rules. In the course of decades, countries change names (or break apart) fairly frequently, but city names tend to stay the same. Another problem is that name needs to reflect not only to the current behaviour, but also the complete history. For example, there are time zones for both "America/New_York" and "America/Detroit". These cities both currently use Eastern Standard Time but in 1969-1972 Michigan (the state in which Detroit is located), did not follow DST, so it needs a different name. It's worth reading the raw time zone database (available at <http://www.iana.org/time-zones>) just to read some of these stories!

You can find out what R thinks your current time zone is with `Sys.timezone()`:

```{r}
Sys.timezone()
```

(If R doesn't know, you'll get an `NA`.)

And see the complete list of all time zone names with `OlsonNames()`:

```{r}
length(OlsonNames())
head(OlsonNames())
```

In R, the time zone is an attribute of the date-time that only controls printing. For example, these three objects represent the same instant in time:

```{r}
(x1 <- ymd_hms("2015-06-01 12:00:00", tz = "America/New_York"))
(x2 <- ymd_hms("2015-06-01 18:00:00", tz = "Europe/Copenhagen"))
(x3 <- ymd_hms("2015-06-02 04:00:00", tz = "Pacific/Auckland"))
```

You can verify that they're the same time using subtraction:

```{r}
x1 - x2
x1 - x3
```

Unless otherwise specified, lubridate always uses UTC. UTC (Coordinated Universal Time) is the standard time zone used by the scientific community and roughly equivalent to its predecessor GMT (Greenwich Mean Time). It does not have DST, which makes a convenient representation for computation. Operations that combine date-times, like `c()`, will often drop the time zone. In that case, the date-times will display in your local time zone:

```{r}
x4 <- c(x1, x2, x3)
x4
```

You can change the time zone in two ways:

*   Keep the instant in time the same, and change how it's displayed.
    Use this when the instant is correct, but you want a more natural
    display.
  
    ```{r}
    x4a <- with_tz(x4, tzone = "Australia/Lord_Howe")
    x4a
    x4a - x4
    ```
    
    (This also illustrates another challenge of times zones: they're not
    all integer hour offsets!)

*   Change the underlying instant in time. Use this when you have an
    instant that has been labelled with the incorrect time zone, and you
    need to fix it.

    ```{r}
    x4b <- force_tz(x4, tzone = "Australia/Lord_Howe")
    x4b
    x4b - x4
    ```


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/explore.Rmd
================================================
# (PART) Explore {-}

# Introduction {#explore-intro}

The goal of the first part of this book is to get you up to speed with the basic tools of __data exploration__ as quickly as possible. Data exploration is the art of looking at your data, rapidly generating hypotheses, quickly testing them, then repeating again and again and again. The goal of data exploration is to generate many promising leads that you can later explore in more depth.

```{r echo = FALSE, out.width = "75%"}
knitr::include_graphics("diagrams/data-science-explore.png")
```

In this part of the book you will learn some useful tools that have an immediate payoff: 

*   Visualisation is a great place to start with R programming, because the 
    payoff is so clear: you get to make elegant and informative plots that help 
    you understand data. In [data visualisation] you'll dive into visualisation, 
    learning the basic structure of a ggplot2 plot, and powerful techniques for 
    turning data into plots. 

*   Visualisation alone is typically not enough, so in [data transformation] 
    you'll learn the key verbs that allow you to select important variables, 
    filter out key observations, create new variables, and compute summaries.
  
*   Finally, in [exploratory data analysis], you'll combine visualisation and
    transformation with your curiosity and scepticism to ask and answer 
    interesting questions about data.

Modelling is an important part of the exploratory process, but you don't have the skills to effectively learn or apply it yet. We'll come back to it in [modelling](#model-intro), once you're better equipped with more data wrangling and programming tools.

Nestled among these three chapters that teach you the tools of exploration are three chapters that focus on your R workflow. In [workflow: basics], [workflow: scripts], and [workflow: projects] you'll learn good practices for writing and organising your R code. These will set you up for success in the long run, as they'll give you the tools to stay organised when you tackle real projects.


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/factors.Rmd
================================================
# Factors

## Introduction

In R, factors are used to work with categorical variables, variables that have a fixed and known set of possible values. They are also useful when you want to display character vectors in a non-alphabetical order.

Historically, factors were much easier to work with than characters. As a result, many of the functions in base R automatically convert characters to factors. This means that factors often crop up in places where they're not actually helpful. Fortunately, you don't need to worry about that in the tidyverse, and can focus on situations where factors are genuinely useful.

### Prerequisites

To work with factors, we'll use the __forcats__ package, which provides tools for dealing with **cat**egorical variables (and it's an anagram of factors!). It provides a wide range of helpers for working with factors. forcats is not part of the core tidyverse, so we need to load it explicitly.

```{r setup, message = FALSE}
library(tidyverse)
library(forcats)
```

### Learning more

If you want to learn more about factors, I recommend reading Amelia McNamara and Nicholas Horton’s paper, [_Wrangling categorical data in R_](https://peerj.com/preprints/3163/). This paper lays out some of the history discussed in [_stringsAsFactors: An unauthorized biography_](http://simplystatistics.org/2015/07/24/stringsasfactors-an-unauthorized-biography/) and [_stringsAsFactors = \<sigh\>_](http://notstatschat.tumblr.com/post/124987394001/stringsasfactors-sigh), and compares the tidy approaches to categorical data outlined in this book with base R methods. A early version of the paper help motivate and scope the forcats package; thanks Amelia & Nick!

## Creating factors

Imagine that you have a variable that records month:

```{r}
x1 <- c("Dec", "Apr", "Jan", "Mar")
```

Using a string to record this variable has two problems:

1.  There are only twelve possible months, and there's nothing saving you
    from typos:
     
    ```{r}
    x2 <- c("Dec", "Apr", "Jam", "Mar")
    ```
    
1.  It doesn't sort in a useful way:

    ```{r}
    sort(x1)
    ```

You can fix both of these problems with a factor. To create a factor you must start by creating a list of the valid __levels__:

```{r}
month_levels <- c(
  "Jan", "Feb", "Mar", "Apr", "May", "Jun", 
  "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"
)
```

Now you can create a factor:

```{r}
y1 <- factor(x1, levels = month_levels)
y1
sort(y1)
```

And any values not in the set will be silently converted to NA:

```{r}
y2 <- factor(x2, levels = month_levels)
y2
```

If you want a warning, you can use `readr::parse_factor()`:

```{r}
y2 <- parse_factor(x2, levels = month_levels)
```

If you omit the levels, they'll be taken from the data in alphabetical order:

```{r}
factor(x1)
```

Sometimes you'd prefer that the order of the levels match the order of the first appearance in the data. You can do that when creating the factor by setting levels to `unique(x)`, or after the fact, with `fct_inorder()`:

```{r}
f1 <- factor(x1, levels = unique(x1))
f1

f2 <- x1 %>% factor() %>% fct_inorder()
f2
```

If you ever need to access the set of valid levels directly, you can do so with `levels()`:

```{r}
levels(f2)
```

## General Social Survey

For the rest of this chapter, we're going to focus on `forcats::gss_cat`. It's a sample of data from the [General Social Survey](http://gss.norc.org), which is a long-running US survey conducted by the independent research organization NORC at the University of Chicago. The survey has thousands of questions, so in `gss_cat` I've selected a handful that will illustrate some common challenges you'll encounter when working with factors.

```{r}
gss_cat
```

(Remember, since this dataset is provided by a package, you can get more information about the variables with `?gss_cat`.)

When factors are stored in a tibble, you can't see their levels so easily. One way to see them is with `count()`:

```{r}
gss_cat %>%
  count(race)
```

Or with a bar chart:

```{r}
ggplot(gss_cat, aes(race)) +
  geom_bar()
```

By default, ggplot2 will drop levels that don't have any values. You can force them to display with:

```{r}
ggplot(gss_cat, aes(race)) +
  geom_bar() +
  scale_x_discrete(drop = FALSE)
```

These levels represent valid values that simply did not occur in this dataset. Unfortunately, dplyr doesn't yet have a `drop` option, but it will in the future.

When working with factors, the two most common operations are changing the order of the levels, and changing the values of the levels. Those operations are described in the sections below.


## Modifying factor levels

More powerful than changing the orders of the levels is changing their values. This allows you to clarify labels for publication, and collapse levels for high-level displays. The most general and powerful tool is `fct_recode()`. It allows you to recode, or change, the value of each level. For example, take the `gss_cat$partyid`:

```{r}
gss_cat %>% count(partyid)
```

The levels are terse and inconsistent. Let's tweak them to be longer and use a parallel construction.

```{r}
gss_cat %>%
  mutate(partyid = fct_recode(partyid,
    "Republican, strong"    = "Strong republican",
    "Republican, weak"      = "Not str republican",
    "Independent, near rep" = "Ind,near rep",
    "Independent, near dem" = "Ind,near dem",
    "Democrat, weak"        = "Not str democrat",
    "Democrat, strong"      = "Strong democrat"
  )) %>%
  count(partyid)
```

`fct_recode()` will leave levels that aren't explicitly mentioned as is, and will warn you if you accidentally refer to a level that doesn't exist.

To combine groups, you can assign multiple old levels to the same new level:

```{r}
gss_cat %>%
  mutate(partyid = fct_recode(partyid,
    "Republican, strong"    = "Strong republican",
    "Republican, weak"      = "Not str republican",
    "Independent, near rep" = "Ind,near rep",
    "Independent, near dem" = "Ind,near dem",
    "Democrat, weak"        = "Not str democrat",
    "Democrat, strong"      = "Strong democrat",
    "Other"                 = "No answer",
    "Other"                 = "Don't know",
    "Other"                 = "Other party"
  )) %>%
  count(partyid)
```

You must use this technique with care: if you group together categories that are truly different you will end up with misleading results.

If you want to collapse a lot of levels, `fct_collapse()` is a useful variant of `fct_recode()`. For each new variable, you can provide a vector of old levels:

```{r}
gss_cat %>%
  mutate(partyid = fct_collapse(partyid,
    other = c("No answer", "Don't know", "Other party"),
    rep = c("Strong republican", "Not str republican"),
    ind = c("Ind,near rep", "Independent", "Ind,near dem"),
    dem = c("Not str democrat", "Strong democrat")
  )) %>%
  count(partyid)
```

### Exercises

1.  How have the proportions of people identifying as Democrat, Republican, and
    Independent changed over time?


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/figures.R
================================================
library(stringr)
library(purrr)

chapters <- dir("_bookdown_files", full.names = TRUE, pattern = "_files$")

figures <- dir(chapters, full.names = TRUE, pattern = "-latex")

name <- figures %>%
  dirname() %>%
  basename() %>%
  str_replace("_files", "")

out_path <- file.path("figures", name)

dir.create("figures/")
out_path %>% walk(dir.create)

map2(figures, out_path, ~ file.copy(dir(.x, full.names = TRUE), .y))


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/functions.Rmd
================================================
# Functions

## Introduction 

One of the best ways to improve your reach as a data scientist is to write functions. Functions allow you to automate common tasks in a more powerful and general way than copy-and-pasting. Writing a function has three big advantages over using copy-and-paste:

1.  You can give a function an evocative name that makes your code easier to 
    understand.

1.  As requirements change, you only need to update code in one place, instead
    of many.

1.  You eliminate the chance of making incidental mistakes when you copy and 
    paste (i.e. updating a variable name in one place, but not in another).

Writing good functions is a lifetime journey. Even after using R for many years I still learn new techniques and better ways of approaching old problems. The goal of this chapter is not to teach you every esoteric detail of functions but to get you started with some pragmatic advice that you can apply immediately.

As well as practical advice for writing functions, this chapter also gives you some suggestions for how to style your code. Good code style is like correct punctuation. Youcanmanagewithoutit, but it sure makes things easier to read! As with styles of punctuation, there are many possible variations. Here we present the style we use in our code, but the most important thing is to be consistent.

### Prerequisites

The focus of this chapter is on writing functions in base R, so you won't need any extra packages.

## When should you write a function?

You should consider writing a function whenever you've copied and pasted a block of code more than twice (i.e. you now have three copies of the same code). For example, take a look at this code. What does it do?

```{r}
df <- tibble::tibble(
  a = rnorm(10),
  b = rnorm(10),
  c = rnorm(10),
  d = rnorm(10)
)

df$a <- (df$a - min(df$a, na.rm = TRUE)) / 
  (max(df$a, na.rm = TRUE) - min(df$a, na.rm = TRUE))
df$b <- (df$b - min(df$b, na.rm = TRUE)) / 
  (max(df$b, na.rm = TRUE) - min(df$a, na.rm = TRUE))
df$c <- (df$c - min(df$c, na.rm = TRUE)) / 
  (max(df$c, na.rm = TRUE) - min(df$c, na.rm = TRUE))
df$d <- (df$d - min(df$d, na.rm = TRUE)) / 
  (max(df$d, na.rm = TRUE) - min(df$d, na.rm = TRUE))
```

You might be able to puzzle out that this rescales each column to have a range from 0 to 1. But did you spot the mistake? I made an error when copying-and-pasting the code for `df$b`: I forgot to change an `a` to a `b`. Extracting repeated code out into a function is a good idea because it prevents you from making this type of mistake.

To write a function you need to first analyse the code. How many inputs does it have?

```{r, eval = FALSE}
(df$a - min(df$a, na.rm = TRUE)) /
  (max(df$a, na.rm = TRUE) - min(df$a, na.rm = TRUE))
```

This code only has one input: `df$a`. (If you're surprised that `TRUE` is not an input, you can explore why in the exercise below.) To make the inputs more clear, it's a good idea to rewrite the code using temporary variables with general names. Here this code only requires a single numeric vector, so I'll call it `x`:

```{r}
x <- df$a
(x - min(x, na.rm = TRUE)) / (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
```

There is some duplication in this code. We're computing the range of the data three times, so it makes sense to do it in one step:

```{r}
rng <- range(x, na.rm = TRUE)
(x - rng[1]) / (rng[2] - rng[1])
```

Pulling out intermediate calculations into named variables is a good practice because it makes it more clear what the code is doing. Now that I've simplified the code, and checked that it still works, I can turn it into a function:

```{r}
rescale01 <- function(x) {
  rng <- range(x, na.rm = TRUE)
  (x - rng[1]) / (rng[2] - rng[1])
}
rescale01(c(0, 5, 10))
```

There are three key steps to creating a new function:

1.  You need to pick a __name__ for the function. Here I've used `rescale01` 
    because this function rescales a vector to lie between 0 and 1.

1.  You list the inputs, or __arguments__, to the function inside `function`.
    Here we have just one argument. If we had more the call would look like
    `function(x, y, z)`.

1.  You place the code you have developed in __body__ of the function, a 
    `{` block that immediately follows `function(...)`.

Note the overall process: I only made the function after I'd figured out how to make it work with a simple input. It's easier to start with working code and turn it into a function; it's harder to create a function and then try to make it work.

At this point it's a good idea to check your function with a few different inputs:

```{r}
rescale01(c(-10, 0, 10))
rescale01(c(1, 2, 3, NA, 5))
```

As you write more and more functions you'll eventually want to convert these informal, interactive tests into formal, automated tests. That process is called unit testing. Unfortunately, it's beyond the scope of this book, but you can learn about it in <http://r-pkgs.had.co.nz/tests.html>.

We can simplify the original example now that we have a function:

```{r}
df$a <- rescale01(df$a)
df$b <- rescale01(df$b)
df$c <- rescale01(df$c)
df$d <- rescale01(df$d)
```

Compared to the original, this code is easier to understand and we've eliminated one class of copy-and-paste errors. There is still quite a bit of duplication since we're doing the same thing to multiple columns. We'll learn how to eliminate that duplication in [iteration], once you've learned more about R's data structures in [vectors].

Another advantage of functions is that if our requirements change, we only need to make the change in one place. For example, we might discover that some of our variables include infinite values, and `rescale01()` fails:

```{r}
x <- c(1:10, Inf)
rescale01(x)
```

Because we've extracted the code into a function, we only need to make the fix in one place:

```{r}
rescale01 <- function(x) {
  rng <- range(x, na.rm = TRUE, finite = TRUE)
  (x - rng[1]) / (rng[2] - rng[1])
}
rescale01(x)
```

This is an important part of the "do not repeat yourself" (or DRY) principle. The more repetition you have in your code, the more places you need to remember to update when things change (and they always do!), and the more likely you are to create bugs over time.

### Practice

1.  Why is `TRUE` not a parameter to `rescale01()`? What would happen if
    `x` contained a single missing value, and `na.rm` was `FALSE`?

1.  In the second variant of `rescale01()`, infinite values are left
    unchanged. Rewrite `rescale01()` so that `-Inf` is mapped to 0, and 
    `Inf` is mapped to 1.

1.  Practice turning the following code snippets into functions. Think about 
    what each function does. What would you call it? How many arguments does it
    need? Can you rewrite it to be more expressive or less duplicative?

    ```{r, eval = FALSE}
    mean(is.na(x))
    
    x / sum(x, na.rm = TRUE)
    
    sd(x, na.rm = TRUE) / mean(x, na.rm = TRUE)
    ```

1.  Follow <http://nicercode.github.io/intro/writing-functions.html> to 
    write your own functions to compute the variance and skew of a numeric 
    vector.

1.  Write `both_na()`, a function that takes two vectors of the same length 
    and returns the number of positions that have an `NA` in both vectors.

1.  What do the following functions do? Why are they useful even though they
    are so short?
    
    ```{r}
    is_directory <- function(x) file.info(x)$isdir
    is_readable <- function(x) file.access(x, 4) == 0
    ```

1.  Read the [complete lyrics](https://en.wikipedia.org/wiki/Little_Bunny_Foo_Foo) 
    to "Little Bunny Foo Foo". There's a lot of duplication in this song. 
    Extend the initial piping example to recreate the complete song, and use 
    functions to reduce the duplication.

## Functions are for humans and computers

It's important to remember that functions are not just for the computer, but are also for humans. R doesn't care what your function is called, or what comments it contains, but these are important for human readers. This section discusses some things that you should bear in mind when writing functions that humans can understand.

The name of a function is important. Ideally, the name of your function will be short, but clearly evoke what the function does. That's hard! But it's better to be clear than short, as RStudio's autocomplete makes it easy to type long names.

Generally, function names should be verbs, and arguments should be nouns. There are some exceptions: nouns are ok if the function computes a very well known noun (i.e. `mean()` is better than `compute_mean()`), or accessing some property of an object (i.e. `coef()` is better than `get_coefficients()`). A good sign that a noun might be a better choice is if you're using a very broad verb like "get", "compute", "calculate", or "determine". Use your best judgement and don't be afraid to rename a function if you figure out a better name later.

```{r, eval = FALSE}
# Too short
f()

# Not a verb, or descriptive
my_awesome_function()

# Long, but clear
impute_missing()
collapse_years()
```

If your function name is composed of multiple words, I recommend using "snake\_case", where each lowercase word is separated by an underscore. camelCase is a popular alternative. It doesn't really matter which one you pick, the important thing is to be consistent: pick one or the other and stick with it. R itself is not very consistent, but there's nothing you can do about that. Make sure you don't fall into the same trap by making your code as consistent as possible.

```{r, eval = FALSE}
# Never do this!
col_mins <- function(x, y) {}
rowMaxes <- function(y, x) {}
```

If you have a family of functions that do similar things, make sure they have consistent names and arguments. Use a common prefix to indicate that they are connected. That's better than a common suffix because autocomplete allows you to type the prefix and see all the members of the family. 

```{r, eval = FALSE}
# Good
input_select()
input_checkbox()
input_text()

# Not so good
select_input()
checkbox_input()
text_input()
```

A good example of this design is the stringr package: if you don't remember exactly which function you need, you can type `str_` and jog your memory.

Where possible, avoid overriding existing functions and variables. It's impossible to do in general because so many good names are already taken by other packages, but avoiding the most common names from base R will avoid confusion.

```{r, eval = FALSE}
# Don't do this!
T <- FALSE
c <- 10
mean <- function(x) sum(x)
```

Use comments, lines starting with `#`, to explain the "why" of your code. You generally should avoid comments that explain the "what" or the "how". If you can't understand what the code does from reading it, you should think about how to rewrite it to be more clear. Do you need to add some intermediate variables with useful names? Do you need to break out a subcomponent of a large function so you can name it? However, your code can never capture the reasoning behind your decisions: why did you choose this approach instead of an alternative? What else did you try that didn't work? It's a great idea to capture that sort of thinking in a comment.

Another important use of comments is to break up your file into easily readable chunks. Use long lines of `-` and `=` to make it easy to spot the breaks.

```{r, eval = FALSE}
# Load data --------------------------------------

# Plot data --------------------------------------
```

RStudio provides a keyboard shortcut to create these headers (Cmd/Ctrl + Shift + R), and will display them in the code navigation drop-down at the bottom-left of the editor:

```{r, echo = FALSE, out.width = NULL}
knitr::include_graphics("screenshots/rstudio-nav.png")
```

### Exercises

1.  Read the source code for each of the following three functions, puzzle out
    what they do, and then brainstorm better names.
    
    ```{r}
    f1 <- function(string, prefix) {
      substr(string, 1, nchar(prefix)) == prefix
    }
    f2 <- function(x) {
      if (length(x) <= 1) return(NULL)
      x[-length(x)]
    }
    f3 <- function(x, y) {
      rep(y, length.out = length(x))
    }
    ```
    
1.  Take a function that you've written recently and spend 5 minutes 
    brainstorming a better name for it and its arguments.

1.  Compare and contrast `rnorm()` and `MASS::mvrnorm()`. How could you make
    them more consistent? 
    
1.  Make a case for why `norm_r()`, `norm_d()` etc would be better than
    `rnorm()`, `dnorm()`. Make a case for the opposite.

## Conditional execution

An `if` statement allows you to conditionally execute code. It looks like this:

```{r, eval = FALSE}
if (condition) {
  # code executed when condition is TRUE
} else {
  # code executed when condition is FALSE
}
```

To get help on `if` you need to surround it in backticks: `` ?`if` ``. The help isn't particularly helpful if you're not already an experienced programmer, but at least you know how to get to it!

Here's a simple function that uses an `if` statement. The goal of this function is to return a logical vector describing whether or not each element of a vector is named.

```{r}
has_name <- function(x) {
  nms <- names(x)
  if (is.null(nms)) {
    rep(FALSE, length(x))
  } else {
    !is.na(nms) & nms != ""
  }
}
```

This function takes advantage of the standard return rule: a function returns the last value that it computed. Here that is either one of the two branches of the `if` statement.

### Conditions

The `condition` must evaluate to either `TRUE` or `FALSE`. If it's a vector, you'll get a warning message; if it's an `NA`, you'll get an error. Watch out for these messages in your own code:

```{r, error = TRUE}
if (c(TRUE, FALSE)) {}

if (NA) {}
```

You can use `||` (or) and `&&` (and) to combine multiple logical expressions. These operators are "short-circuiting": as soon as `||` sees the first `TRUE` it returns `TRUE` without computing anything else. As soon as `&&` sees the first `FALSE` it returns `FALSE`. You should never use `|` or `&` in an `if` statement: these are vectorised operations that apply to multiple values (that's why you use them in `filter()`). If you do have a logical vector, you can use `any()` or `all()` to collapse it to a single value.

Be careful when testing for equality. `==` is vectorised, which means that it's easy to get more than one output.  Either check the length is already 1, collapse with `all()` or `any()`, or use the non-vectorised `identical()`. `identical()` is very strict: it always returns either a single `TRUE` or a single `FALSE`, and doesn't coerce types. This means that you need to be careful when comparing integers and doubles:

```{r}
identical(0L, 0)
```

You also need to be wary of floating point numbers:

```{r}
x <- sqrt(2) ^ 2
x
x == 2
x - 2
```

Instead use `dplyr::near()` for comparisons, as described in [comparisons].

And remember, `x == NA` doesn't do anything useful!

### Multiple conditions

You can chain multiple if statements together:

```{r, eval = FALSE}
if (this) {
  # do that
} else if (that) {
  # do something else
} else {
  # 
}
```

But if you end up with a very long series of chained `if` statements, you should consider rewriting. One useful technique is the `switch()` function. It allows you to evaluate selected code based on position or name.

```{r, echo = FALSE}
function(x, y, op) {
  switch(op,
    plus = x + y,
    minus = x - y,
    times = x * y,
    divide = x / y,
    stop("Unknown op!")
  )
}
```

Another useful function that can often eliminate long chains of `if` statements is `cut()`. It's used to discretise continuous variables.

### Code style

Both `if` and `function` should (almost) always be followed by squiggly brackets (`{}`), and the contents should be indented by two spaces. This makes it easier to see the hierarchy in your code by skimming the left-hand margin.

An opening curly brace should never go on its own line and should always be followed by a new line. A closing curly brace should always go on its own line, unless it's followed by `else`. Always indent the code inside curly braces.

```{r, eval = FALSE}
# Good
if (y < 0 && debug) {
  message("Y is negative")
}

if (y == 0) {
  log(x)
} else {
  y ^ x
}

# Bad
if (y < 0 && debug)
message("Y is negative")

if (y == 0) {
  log(x)
} 
else {
  y ^ x
}
```

It's ok to drop the curly braces if you have a very short `if` statement that can fit on one line:

```{r}
y <- 10
x <- if (y < 20) "Too low" else "Too high"
```

I recommend this only for very brief `if` statements. Otherwise, the full form is easier to read:

```{r}
if (y < 20) {
  x <- "Too low" 
} else {
  x <- "Too high"
}
```

### Exercises

1.  What's the difference between `if` and `ifelse()`? Carefully read the help
    and construct three examples that illustrate the key differences.

1.  Write a greeting function that says "good morning", "good afternoon",
    or "good evening", depending on the time of day. (Hint: use a time
    argument that defaults to `lubridate::now()`. That will make it 
    easier to test your function.)

1.  Implement a `fizzbuzz` function. It takes a single number as input. If
    the number is divisible by three, it returns "fizz". If it's divisible by
    five it returns "buzz". If it's divisible by three and five, it returns
    "fizzbuzz". Otherwise, it returns the number. Make sure you first write 
    working code before you create the function.
    
1.  How could you use `cut()` to simplify this set of nested if-else statements?

    ```{r, eval = FALSE}
    if (temp <= 0) {
      "freezing"
    } else if (temp <= 10) {
      "cold"
    } else if (temp <= 20) {
      "cool"
    } else if (temp <= 30) {
      "warm"
    } else {
      "hot"
    }
    ```
    
    How would you change the call to `cut()` if I'd used `<` instead of `<=`?
    What is the other chief advantage of `cut()` for this problem? (Hint:
    what happens if you have many values in `temp`?)

1.  What happens if you use `switch()` with numeric values?

1.  What does this `switch()` call do? What happens if `x` is "e"?

    ```{r, eval = FALSE}
    switch(x, 
      a = ,
      b = "ab",
      c = ,
      d = "cd"
    )
    ```
    
    Experiment, then carefully read the documentation. 

## Function arguments

The arguments to a function typically fall into two broad sets: one set supplies the __data__ to compute on, and the other supplies arguments that control the __details__ of the computation. For example:

* In `log()`, the data is `x`, and the detail is the `base` of the logarithm.

* In `mean()`, the data is `x`, and the details are how much data to trim
  from the ends (`trim`) and how to handle missing values (`na.rm`).

* In `t.test()`, the data are `x` and `y`, and the details of the test are
  `alternative`, `mu`, `paired`, `var.equal`, and `conf.level`.
  
* In `str_c()` you can supply any number of strings to `...`, and the details
  of the concatenation are controlled by `sep` and `collapse`.
  
Generally, data arguments should come first. Detail arguments should go on the end, and usually should have default values. You specify a default value in the same way you call a function with a named argument:

```{r}
# Compute confidence interval around mean using normal approximation
mean_ci <- function(x, conf = 0.95) {
  se <- sd(x) / sqrt(length(x))
  alpha <- 1 - conf
  mean(x) + se * qnorm(c(alpha / 2, 1 - alpha / 2))
}

x <- runif(100)
mean_ci(x)
mean_ci(x, conf = 0.99)
```

The default value should almost always be the most common value. The few exceptions to this rule are to do with safety. For example, it makes sense for `na.rm` to default to `FALSE` because missing values are important. Even though `na.rm = TRUE` is what you usually put in your code, it's a bad idea to silently ignore missing values by default.

When you call a function, you typically omit the names of the data arguments, because they are used so commonly. If you override the default value of a detail argument, you should use the full name:

```{r, eval = FALSE}
# Good
mean(1:10, na.rm = TRUE)

# Bad
mean(x = 1:10, , FALSE)
mean(, TRUE, x = c(1:10, NA))
```

You can refer to an argument by its unique prefix (e.g. `mean(x, n = TRUE)`), but this is generally best avoided given the possibilities for confusion.

Notice that when you call a function, you should place a space around `=` in function calls, and always put a space after a comma, not before (just like in regular English). Using whitespace makes it easier to skim the function for the important components.

```{r, eval = FALSE}
# Good
average <- mean(feet / 12 + inches, na.rm = TRUE)

# Bad
average<-mean(feet/12+inches,na.rm=TRUE)
```

### Choosing names

The names of the arguments are also important. R doesn't care, but the readers of your code (including future-you!) will. Generally you should prefer longer, more descriptive names, but there are a handful of very common, very short names. It's worth memorising these:

* `x`, `y`, `z`: vectors.
* `w`: a vector of weights.
* `df`: a data frame.
* `i`, `j`: numeric indices (typically rows and columns).
* `n`: length, or number of rows.
* `p`: number of columns.

Otherwise, consider matching names of arguments in existing R functions. For example, use `na.rm` to determine if missing values should be removed.

### Checking values

As you start to write more functions, you'll eventually get to the point where you don't remember exactly how your function works. At this point it's easy to call your function with invalid inputs. To avoid this problem, it's often useful to make constraints explicit. For example, imagine you've written some functions for computing weighted summary statistics:

```{r}
wt_mean <- function(x, w) {
  sum(x * w) / sum(w)
}
wt_var <- function(x, w) {
  mu <- wt_mean(x, w)
  sum(w * (x - mu) ^ 2) / sum(w)
}
wt_sd <- function(x, w) {
  sqrt(wt_var(x, w))
}
```

What happens if `x` and `w` are not the same length?

```{r}
wt_mean(1:6, 1:3)
```

In this case, because of R's vector recycling rules, we don't get an error. 

It's good practice to check important preconditions, and throw an error (with `stop()`), if they are not true:

```{r}
wt_mean <- function(x, w) {
  if (length(x) != length(w)) {
    stop("`x` and `w` must be the same length", call. = FALSE)
  }
  sum(w * x) / sum(w)
}
```

Be careful not to take this too far. There's a tradeoff between how much time you spend making your function robust, versus how long you spend writing it. For example, if you also added a `na.rm` argument, I probably wouldn't check it carefully:

```{r}
wt_mean <- function(x, w, na.rm = FALSE) {
  if (!is.logical(na.rm)) {
    stop("`na.rm` must be logical")
  }
  if (length(na.rm) != 1) {
    stop("`na.rm` must be length 1")
  }
  if (length(x) != length(w)) {
    stop("`x` and `w` must be the same length", call. = FALSE)
  }
  
  if (na.rm) {
    miss <- is.na(x) | is.na(w)
    x <- x[!miss]
    w <- w[!miss]
  }
  sum(w * x) / sum(w)
}
```

This is a lot of extra work for little additional gain. A useful compromise is the built-in `stopifnot()`: it checks that each argument is `TRUE`, and produces a generic error message if not.

```{r, error = TRUE}
wt_mean <- function(x, w, na.rm = FALSE) {
  stopifnot(is.logical(na.rm), length(na.rm) == 1)
  stopifnot(length(x) == length(w))
  
  if (na.rm) {
    miss <- is.na(x) | is.na(w)
    x <- x[!miss]
    w <- w[!miss]
  }
  sum(w * x) / sum(w)
}
wt_mean(1:6, 6:1, na.rm = "foo")
```

Note that when using `stopifnot()` you assert what should be true rather than checking for what might be wrong.

### Dot-dot-dot (...)

Many functions in R take an arbitrary number of inputs:

```{r}
sum(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
stringr::str_c("a", "b", "c", "d", "e", "f")
```

How do these functions work? They rely on a special argument: `...` (pronounced dot-dot-dot). This special argument captures any number of arguments that aren't otherwise matched. 

It's useful because you can then send those `...` on to another function. This is a useful catch-all if your function primarily wraps another function. For example, I commonly create these helper functions that wrap around `str_c()`:

```{r}
commas <- function(...) stringr::str_c(..., collapse = ", ")
commas(letters[1:10])

rule <- function(..., pad = "-") {
  title <- paste0(...)
  width <- getOption("width") - nchar(title) - 5
  cat(title, " ", stringr::str_dup(pad, width), "\n", sep = "")
}
rule("Important output")
```

Here `...` lets me forward on any arguments that I don't want to deal with to `str_c()`. It's a very convenient technique. But it does come at a price: any misspelled arguments will not raise an error. This makes it easy for typos to go unnoticed:

```{r}
x <- c(1, 2)
sum(x, na.mr = TRUE)
```

If you just want to capture the values of the `...`, use `list(...)`.

### Lazy evaluation

Arguments in R are lazily evaluated: they're not computed until they're needed. That means if they're never used, they're never called. This is an important property of R as a programming language, but is generally not important when you're writing your own functions for data analysis. You can read more about lazy evaluation at <http://adv-r.had.co.nz/Functions.html#lazy-evaluation>.

### Exercises

1.  What does `commas(letters, collapse = "-")` do? Why?

1.  It'd be nice if you could supply multiple characters to the `pad` argument, 
    e.g. `rule("Title", pad = "-+")`. Why doesn't this currently work? How 
    could you fix it?
    
1.  What does the `trim` argument to `mean()` do? When might you use it?

1.  The default value for the `method` argument to `cor()` is 
    `c("pearson", "kendall", "spearman")`. What does that mean? What 
    value is used by default?

## Return values

Figuring out what your function should return is usually straightforward: it's why you created the function in the first place! There are two things you should consider when returning a value: 

1. Does returning early make your function easier to read? 

2. Can you make your function pipeable?

### Explicit return statements

The value returned by the function is usually the last statement it evaluates, but you can choose to return early by using `return()`. I think it's best to save the use of `return()` to signal that you can return early with a simpler solution. A common reason to do this is because the inputs are empty:

```{r}
complicated_function <- function(x, y, z) {
  if (length(x) == 0 || length(y) == 0) {
    return(0)
  }
    
  # Complicated code here
}

```

Another reason is because you have a `if` statement with one complex block and one simple block. For example, you might write an if statement like this:

```{r, eval = FALSE}
f <- function() {
  if (x) {
    # Do 
    # something
    # that
    # takes
    # many
    # lines
    # to
    # express
  } else {
    # return something short
  }
}
```

But if the first block is very long, by the time you get to the `else`, you've forgotten the `condition`. One way to rewrite it is to use an early return for the simple case:

```{r, eval = FALSE}

f <- function() {
  if (!x) {
    return(something_short)
  }

  # Do 
  # something
  # that
  # takes
  # many
  # lines
  # to
  # express
}
```

This tends to make the code easier to understand, because you don't need quite so much context to understand it.

### Writing pipeable functions

If you want to write your own pipeable functions, it's important to think about the return value. Knowing the return value's object type will mean that your pipeline will "just work". For example, with dplyr and tidyr the object type is the data frame. 

There are two basic types of pipeable functions: transformations and side-effects. With __transformations__, an object is passed to the function's first argument and a modified object is returned. With __side-effects__, the passed object is not transformed. Instead, the function performs an action on the object, like drawing a plot or saving a file. Side-effects functions should "invisibly" return the first argument, so that while they're not printed they can still be used in a pipeline. For example, this simple function prints the number of missing values in a data frame:

```{r}
show_missings <- function(df) {
  n <- sum(is.na(df))
  cat("Missing values: ", n, "\n", sep = "")
  
  invisible(df)
}
```

If we call it interactively, the `invisible()` means that the input `df` doesn't get printed out:

```{r}
show_missings(mtcars)
```

But it's still there, it's just not printed by default:

```{r}
x <- show_missings(mtcars) 
class(x)
dim(x)
```

And we can still use it in a pipe:

```{r, include = FALSE}
library(dplyr)
```
```{r}
mtcars %>% 
  show_missings() %>% 
  mutate(mpg = ifelse(mpg < 20, NA, mpg)) %>% 
  show_missings() 
```

## Environment

The last component of a function is its environment. This is not something you need to understand deeply when you first start writing functions. However, it's important to know a little bit about environments because they are crucial to how functions work. The environment of a function controls how R finds the value associated with a name. For example, take this function:

```{r}
f <- function(x) {
  x + y
} 
```

In many programming languages, this would be an error, because `y` is not defined inside the function. In R, this is valid code because R uses rules called __lexical scoping__ to find the value associated with a name. Since `y` is not defined inside the function, R will look in the __environment__ where the function was defined:

```{r}
y <- 100
f(10)

y <- 1000
f(10)
```

This behaviour seems like a recipe for bugs, and indeed you should avoid creating functions like this deliberately, but by and large it doesn't cause too many problems (especially if you regularly restart R to get to a clean slate). 

The advantage of this behaviour is that from a language standpoint it allows R to be very consistent. Every name is looked up using the same set of rules. For `f()` that includes the behaviour of two things that you might not expect: `{` and `+`. This allows you to do devious things like:

```{r}
`+` <- function(x, y) {
  if (runif(1) < 0.1) {
    sum(x, y)
  } else {
    sum(x, y) * 1.1
  }
}
table(replicate(1000, 1 + 2))
rm(`+`)
```

This is a common phenomenon in R. R places few limits on your power. You can do many things that you can't do in other programming languages. You can do many things that 99% of the time are extremely ill-advised (like overriding how addition works!). But this power and flexibility is what makes tools like ggplot2 and dplyr possible. Learning how to make best use of this flexibility is beyond the scope of this book, but you can read about in [_Advanced R_](http://adv-r.had.co.nz).


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/hierarchy.Rmd
================================================
# Hierarchical data {#hierarchy}

## Introduction

This chapter belongs in [wrangle](#wrangle-intro): it will give you a set of tools for working with hierarchical data, such as the deeply nested lists you often get when working with JSON. However, you can only learn it now because working with hierarchical structures requires some programming skills, particularly an understanding of data structures, functions, and iteration. Now you have those tools under your belt, you can learn how to work with hierarchical data.

The 

As well as tools to simplify iteration, purrr provides tools for handling deeply nested lists. There are three common sources of such data:

* JSON and XML
* 

The map functions apply a function to every element in a list. They are the most commonly used part of purrr, but not the only part. Since lists are often used to represent complex hierarchies, purrr also provides tools to work with hierarchy:

* You can extract deeply nested elements in a single call by supplying
  a character vector to the map functions.

* You can remove a level of the hierarchy with the flatten functions.

* You can flip levels of the hierarchy with the transpose function.

### Prerequisites

This chapter focusses mostly on purrr. As well as the tools for iteration that you've already learned about, purrr also provides a number of tools specifically designed to manipulate hierarchical data.

```{r setup}
library(purrr)
```

## Initial exploration

Sometimes you get data structures that are very deeply nested. A common source of such data is JSON from a web API. I've previously downloaded a list of GitHub issues related to this book and saved it as `issues.json`. Now I'm going to load it into a list with jsonlite. By default `fromJSON()` tries to be helpful and simplifies the structure a little for you. Here I'm going to show you how to do it with purrr, so I set `simplifyVector = FALSE`:

```{r}
# From https://api.github.com/repos/hadley/r4ds/issues
issues <- jsonlite::fromJSON("issues.json", simplifyVector = FALSE)
```

You might be tempted to use `str()` on this data. Unfortunately, however, `str()` is not designed for lists that are both deep and wide, and you'll tend to get overwhelmed by the output. A better strategy is to pull the list apart piece by piece.

First, figure out how many elements are in the list, take a look at one, and then check they're all the same structure. In this case there are eight elements, and the first element is another list.

```{r}
length(issues)
str(issues[[1]])
```

(In this case we got lucky and the structure is (just) simple enough to print out with `str()`. If you're unlucky, you may need to repeat this procedure.)

```{r}
tibble::tibble(
  i = seq_along(issues),
  names = issues %>% map(names) 
) %>% 
  tidyr::unnest(names) %>% 
  table() %>% 
  t()
```

Another alternative is the __listviewer__ package, <https://github.com/timelyportfolio/listviewer>. 

## Extracting deeply nested elements

To work with this sort of data, you typically want to turn it into a data frame by extracting the related vectors that you're most interested in:

```{r}

issues %>% map_int("id")
issues %>% map_lgl("locked")
issues %>% map_chr("state")
```

You can use the same technique to extract more deeply nested structure. For example, imagine you want to extract the name and id of the user. You could do that in two steps:

```{r}
users <- issues %>% map("user")
users %>% map_chr("login")
users %>% map_int("id")
```

But by supplying a character _vector_ to `map_*`, you can do it in one:

```{r}
issues %>% map_chr(c("user", "login"))
issues %>% map_int(c("user", "id"))
```

What happens if that path is missing in some of the elements? For example, lets try and extract the HTML url to the pull request:

```{r, error = TRUE}
issues %>% map_chr(c("pull_request", "html_url"))
```

Unfortunately that doesn't work. Whenever you see an error from purrr complaining about the "type" of the result, it's because it's trying to shove it into a simple vector (here a character). You can diagnose the problem more easily if you use `map()`:

```{r}
issues %>% map(c("pull_request", "html_url"))
```

To get the results into a character vector, we need to tell purrr what it should change `NULL` to. You can do that with the `.null` argument. The most common value to use is `NA`:

```{r}
issues %>% map_chr(c("pull_request", "html_url"), .null = NA)
```

(You might wonder why that isn't the default value since it's so useful. Well, if it was the default, you'd never get an error message if you had a typo in the names. You'd just get a vector of missing values. That would be annoying to debug because it's a silent failure.)

It's possible to mix position and named indexing by using a list

```{r}
issues %>% map_chr(list("pull_request", 1), .null = NA)
```


## Removing a level of hierarchy

As well as indexing deeply into hierarchy, it's sometimes useful to flatten it. That's the job of the flatten family of functions: `flatten()`, `flatten_lgl()`, `flatten_int()`, `flatten_dbl()`, and `flatten_chr()`. In the code below we take a list of lists of double vectors, then flatten it to a list of double vectors, then to a double vector.

```{r}
x <- list(list(a = 1, b = 2), list(c = 3, d = 4))
str(x)

y <- flatten(x) 
str(y)
flatten_dbl(y)
```

Graphically, that sequence of operations looks like:

```{r, echo = FALSE}
knitr::include_graphics("diagrams/lists-flatten.png")
```

Whenever I get confused about a sequence of flattening operations, I'll often draw a diagram like this to help me understand what's going on.

Base R has `unlist()`, but I recommend avoiding it for the same reason I recommend avoiding `sapply()`: it always succeeds. Even if your data structure accidentally changes, `unlist()` will continue to work silently the wrong type of output. This tends to create problems that are frustrating to debug.

## Switching levels in the hierarchy {#transpose}

Other times the hierarchy feels "inside out". You can use `transpose()` to flip the first and second levels of a list: 

```{r}
x <- list(
  x = list(a = 1, b = 3, c = 5),
  y = list(a = 2, b = 4, c = 6)
)
x %>% str()
x %>% transpose() %>% str()
```

Graphically, this looks like:

```{r, echo = FALSE}
knitr::include_graphics("diagrams/lists-transpose.png")
```

You'll see an example of this in the next section, as `transpose()` is particularly useful in conjunction with adverbs like `safely()` and `quietly()`.

It's called transpose by analogy to matrices. When you subset a transposed matrix, you switch indices: `x[i, j]` is the same as `t(x)[j, i]`. It's the same idea when transposing a list, but the subsetting looks a little different: `x[[i]][[j]]` is equivalent to `transpose(x)[[j]][[i]]`. Similarly, a transpose is its own inverse so `transpose(transpose(x))` is equal to `x`.

Transpose is also useful when working with JSON APIs. Many JSON APIs represent data frames in a row-based format, rather than R's column-based format. `transpose()` makes it easy to switch between the two:

```{r}
df <- tibble::tibble(x = 1:3, y = c("a", "b", "c"))
df %>% transpose() %>% str()
```

## Turning lists into data frames

* Have a deeply nested list with missing pieces
* Need a tidy data frame so you can visualise, transform, model etc.
* What do you do?
* By hand with purrr, talk about `fromJSON` and `tidyJSON`
* tidyjson

### Exercises

1.  Challenge: read all the CSV files in a directory. Which ones failed
    and why? 

    ```{r, eval = FALSE}
    files <- dir("data", pattern = "\\.csv$")
    files %>%
      set_names(., basename(.)) %>%
      map_df(safely(readr::read_csv), .id = "filename") %>%
    ```



================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/import.Rmd
================================================
# Data import

## Introduction

Working with data provided by R packages is a great way to learn the tools of data science, but at some point you want to stop learning and start working with your own data. In this chapter, you'll learn how to read plain-text rectangular files into R. Here, we'll only scratch the surface of data import, but many of the principles will translate to other forms of data. We'll finish with a few pointers to packages that are useful for other types of data.

### Prerequisites

In this chapter, you'll learn how to load flat files in R with the __readr__ package, which is part of the core tidyverse.

```{r setup, message = FALSE}
library(tidyverse)
```

## Getting started

Most of readr's functions are concerned with turning flat files into data frames:

* `read_csv()` reads comma delimited files, `read_csv2()` reads semicolon
  separated files (common in countries where `,` is used as the decimal place),
  `read_tsv()` reads tab delimited files, and `read_delim()` reads in files
  with any delimiter.

* `read_fwf()` reads fixed width files. You can specify fields either by their
  widths with `fwf_widths()` or their position with `fwf_positions()`.
  `read_table()` reads a common variation of fixed width files where columns
  are separated by white space.

* `read_log()` reads Apache style log files. (But also check out
  [webreadr](https://github.com/Ironholds/webreadr) which is built on top
  of `read_log()` and provides many more helpful tools.)

These functions all have similar syntax: once you've mastered one, you can use the others with ease. For the rest of this chapter we'll focus on `read_csv()`. Not only are csv files one of the most common forms of data storage, but once you understand `read_csv()`, you can easily apply your knowledge to all the other functions in readr.

The first argument to `read_csv()` is the most important: it's the path to the file to read.

```{r, message = TRUE}
heights <- read_csv("data/heights.csv")
```

When you run `read_csv()` it prints out a column specification that gives the name and type of each column. That's an important part of readr, which we'll come back to in [parsing a file].

You can also supply an inline csv file. This is useful for experimenting with readr and for creating reproducible examples to share with others:

```{r}
read_csv("a,b,c
1,2,3
4,5,6")
```

In both cases `read_csv()` uses the first line of the data for the column names, which is a very common convention. There are two cases where you might want to tweak this behaviour:

1.  Sometimes there are a few lines of metadata at the top of the file. You can
    use `skip = n` to skip the first `n` lines; or use `comment = "#"` to drop
    all lines that start with (e.g.) `#`.
    
    ```{r}
    read_csv("The first line of metadata
      The second line of metadata
      x,y,z
      1,2,3", skip = 2)
    
    read_csv("# A comment I want to skip
      x,y,z
      1,2,3", comment = "#")
    ```
    
1.  The data might not have column names. You can use `col_names = FALSE` to
    tell `read_csv()` not to treat the first row as headings, and instead
    label them sequentially from `X1` to `Xn`:
    
    ```{r}
    read_csv("1,2,3\n4,5,6", col_names = FALSE)
    ```
    
    (`"\n"` is a convenient shortcut for adding a new line. You'll learn more
    about it and other types of string escape in [string basics].)
    
    Alternatively you can pass `col_names` a character vector which will be
    used as the column names:
    
    ```{r}
    read_csv("1,2,3\n4,5,6", col_names = c("x", "y", "z"))
    ```

Another option that commonly needs tweaking is `na`: this specifies the value (or values) that are used to represent missing values in your file:

```{r}
read_csv("a,b,c\n1,2,.", na = ".")
```

This is all you need to know to read ~75% of CSV files that you'll encounter in practice. You can also easily adapt what you've learned to read tab separated files with `read_tsv()` and fixed width files with `read_fwf()`. To read in more challenging files, you'll need to learn more about how readr parses each column, turning them into R vectors.

### Compared to base R

If you've used R before, you might wonder why we're not using `read.csv()`. There are a few good reasons to favour readr functions over the base equivalents:

* They are typically much faster (~10x) than their base equivalents.
  Long running jobs have a progress bar, so you can see what's happening. 
  If you're looking for raw speed, try `data.table::fread()`. It doesn't fit 
  quite so well into the tidyverse, but it can be quite a bit faster.

* They produce tibbles, they don't convert character vectors to factors,
  use row names, or munge the column names. These are common sources of
  frustration with the base R functions.

* They are more reproducible. Base R functions inherit some behaviour from
  your operating system and environment variables, so import code that works 
  on your computer might not work on someone else's.

### Exercises

1.  What function would you use to read a file where fields were separated with  
    "|"?
    
1.  Apart from `file`, `skip`, and `comment`, what other arguments do
    `read_csv()` and `read_tsv()` have in common?
    
1.  What are the most important arguments to `read_fwf()`?
   
1.  Sometimes strings in a CSV file contain commas. To prevent them from
    causing problems they need to be surrounded by a quoting character, like
    `"` or `'`. By convention, `read_csv()` assumes that the quoting
    character will be `"`, and if you want to change it you'll need to
    use `read_delim()` instead. What arguments do you need to specify
    to read the following text into a data frame?
    
    ```{r, eval = FALSE}
    "x,y\n1,'a,b'"
    ```
    
1.  Identify what is wrong with each of the following inline CSV files. 
    What happens when you run the code?
    
    ```{r, eval = FALSE}
    read_csv("a,b\n1,2,3\n4,5,6")
    read_csv("a,b,c\n1,2\n1,2,3,4")
    read_csv("a,b\n\"1")
    read_csv("a,b\n1,2\na,b")
    read_csv("a;b\n1;3")
    ```

## Parsing a vector

Before we get into the details of how readr reads files from disk, we need to take a little detour to talk about the `parse_*()` functions. These functions take a character vector and return a more specialised vector like a logical, integer, or date:

```{r}
str(parse_logical(c("TRUE", "FALSE", "NA")))
str(parse_integer(c("1", "2", "3")))
str(parse_date(c("2010-01-01", "1979-10-14")))
```

These functions are useful in their own right, but are also an important building block for readr. Once you've learned how the individual parsers work in this section, we'll circle back and see how they fit together to parse a complete file in the next section.

Like all functions in the tidyverse, the `parse_*()` functions are uniform: the first argument is a character vector to parse, and the `na` argument specifies which strings should be treated as missing:

```{r}
parse_integer(c("1", "231", ".", "456"), na = ".")
```

If parsing fails, you'll get a warning:

```{r}
x <- parse_integer(c("123", "345", "abc", "123.45"))
```

And the failures will be missing in the output:

```{r}
x
```

If there are many parsing failures, you'll need to use `problems()` to get the complete set. This returns a tibble, which you can then manipulate with dplyr.

```{r}
problems(x)
```

Using parsers is mostly a matter of understanding what's available and how they deal with different types of input. There are eight particularly important parsers:

1.  `parse_logical()` and `parse_integer()` parse logicals and integers
    respectively. There's basically nothing that can go wrong with these
    parsers so I won't describe them here further.
    
1.  `parse_double()` is a strict numeric parser, and `parse_number()` 
    is a flexible numeric parser. These are more complicated than you might
    expect because different parts of the world write numbers in different
    ways.
    
1.  `parse_character()` seems so simple that it shouldn't be necessary. But
    one complication makes it quite important: character encodings.

1.  `parse_factor()` create factors, the data structure that R uses to represent
    categorical variables with fixed and known values.

1.  `parse_datetime()`, `parse_date()`, and `parse_time()` allow you to
    parse various date & time specifications. These are the most complicated
    because there are so many different ways of writing dates.

The following sections describe these parsers in more detail.

### Numbers

It seems like it should be straightforward to parse a number, but three problems make it tricky:

1. People write numbers differently in different parts of the world.
   For example, some countries use `.` in between the integer and fractional 
   parts of a real number, while others use `,`.
   
1. Numbers are often surrounded by other characters that provide some
   context, like "$1000" or "10%".

1. Numbers often contain "grouping" characters to make them easier to read, 
   like "1,000,000", and these grouping characters vary around the world.

To address the first problem, readr has the notion of a "locale", an object that specifies parsing options that differ from place to place. When parsing numbers, the most important option is the character you use for the decimal mark. You can override the default value of `.` by creating a new locale and setting the `decimal_mark` argument:

```{r}
parse_double("1.23")
parse_double("1,23", locale = locale(decimal_mark = ","))
```

readr's default locale is US-centric, because generally R is US-centric (i.e. the documentation of base R is written in American English). An alternative approach would be to try and guess the defaults from your operating system. This is hard to do well, and, more importantly, makes your code fragile: even if it works on your computer, it might fail when you email it to a colleague in another country.

`parse_number()` addresses the second problem: it ignores non-numeric characters before and after the number. This is particularly useful for currencies and percentages, but also works to extract numbers embedded in text.

```{r}
parse_number("$100")
parse_number("20%")
parse_number("It cost $123.45")
```

The final problem is addressed by the combination of `parse_number()` and the locale as `parse_number()` will ignore the "grouping mark":

```{r}
# Used in America
parse_number("$123,456,789")

# Used in many parts of Europe
parse_number("123.456.789", locale = locale(grouping_mark = "."))

# Used in Switzerland
parse_number("123'456'789", locale = locale(grouping_mark = "'"))
```

### Strings {#readr-strings}

It seems like `parse_character()` should be really simple --- it could just return its input. Unfortunately life isn't so simple, as there are multiple ways to represent the same string. To understand what's going on, we need to dive into the details of how computers represent strings. In R, we can get at the underlying representation of a string using `charToRaw()`:

```{r}
charToRaw("Hadley")
```

Each hexadecimal number represents a byte of information: `48` is H, `61` is a, and so on. The mapping from hexadecimal number to character is called the encoding, and in this case the encoding is called ASCII. ASCII does a great job of representing English characters, because it's the __American__ Standard Code for Information Interchange.

Things get more complicated for languages other than English. In the early days of computing there were many competing standards for encoding non-English characters, and to correctly interpret a string you needed to know both the values and the encoding. For example, two common encodings are Latin1 (aka ISO-8859-1, used for Western European languages) and Latin2 (aka ISO-8859-2, used for Eastern European languages). In Latin1, the byte `b1` is "±", but in Latin2, it's "ą"! Fortunately, today there is one standard that is supported almost everywhere: UTF-8. UTF-8 can encode just about every character used by humans today, as well as many extra symbols (like emoji!).

readr uses UTF-8 everywhere: it assumes your data is UTF-8 encoded when you read it, and always uses it when writing. This is a good default, but will fail for data produced by older systems that don't understand UTF-8. If this happens to you, your strings will look weird when you print them. Sometimes just one or two characters might be messed up; other times you'll get complete gibberish. For example:

```{r}
x1 <- "El Ni\xf1o was particularly bad this year"
x2 <- "\x82\xb1\x82\xf1\x82\xc9\x82\xbf\x82\xcd"

x1
x2
```

To fix the problem you need to specify the encoding in `parse_character()`:

```{r}
parse_character(x1, locale = locale(encoding = "Latin1"))
parse_character(x2, locale = locale(encoding = "Shift-JIS"))
```

How do you find the correct encoding? If you're lucky, it'll be included somewhere in the data documentation. Unfortunately, that's rarely the case, so readr provides  `guess_encoding()` to help you figure it out. It's not foolproof, and it works better when you have lots of text (unlike here), but it's a reasonable place to start. Expect to try a few different encodings before you find the right one.

```{r}
guess_encoding(charToRaw(x1))
guess_encoding(charToRaw(x2))
```

The first argument to `guess_encoding()` can either be a path to a file, or, as in this case, a raw vector (useful if the strings are already in R).

Encodings are a rich and complex topic, and I've only scratched the surface here. If you'd like to learn more I'd recommend reading the detailed explanation at <http://kunststube.net/encoding/>.

### Factors {#readr-factors}

R uses factors to represent categorical variables that have a known set of possible values. Give `parse_factor()` a vector of known `levels` to generate a warning whenever an unexpected value is present:

```{r}
fruit <- c("apple", "banana")
parse_factor(c("apple", "banana", "bananana"), levels = fruit)
```

But if you have many problematic entries, it's often easier to leave as character vectors and then use the tools you'll learn about in [strings] and [factors] to clean them up.

### Dates, date-times, and times {#readr-datetimes}

You pick between three parsers depending on whether you want a date (the number of days since 1970-01-01), a date-time (the number of seconds since midnight 1970-01-01), or a time (the number of seconds since midnight). When called without any additional arguments:

*   `parse_datetime()` expects an ISO8601 date-time. ISO8601 is an
    international standard in which the components of a date are
    organised from biggest to smallest: year, month, day, hour, minute, 
    second.
    
    ```{r}
    parse_datetime("2010-10-01T2010")
    # If time is omitted, it will be set to midnight
    parse_datetime("20101010")
    ```
    
    This is the most important date/time standard, and if you work with
    dates and times frequently, I recommend reading
    <https://en.wikipedia.org/wiki/ISO_8601>
    
*   `parse_date()` expects a four digit year, a `-` or `/`, the month, a `-` 
    or `/`, then the day:
    
    ```{r}
    parse_date("2010-10-01")
    ```

*   `parse_time()` expects the hour, `:`, minutes, optionally `:` and seconds, 
    and an optional am/pm specifier:
  
    ```{r}
    library(hms)
    parse_time("01:10 am")
    parse_time("20:10:01")
    ```
    
    Base R doesn't have a great built in class for time data, so we use 
    the one provided in the hms package.

If these defaults don't work for your data you can supply your own date-time `format`, built up of the following pieces:

Year
: `%Y` (4 digits). 
: `%y` (2 digits); 00-69 -> 2000-2069, 70-99 -> 1970-1999.

Month
: `%m` (2 digits).
: `%b` (abbreviated name, like "Jan").
: `%B` (full name, "January").

Day
: `%d` (2 digits).
: `%e` (optional leading space).

Time
: `%H` 0-23 hour.
: `%I` 0-12, must be used with `%p`.
: `%p` AM/PM indicator.
: `%M` minutes.
: `%S` integer seconds.
: `%OS` real seconds. 
: `%Z` Time zone (as name, e.g. `America/Chicago`). Beware of abbreviations:
  if you're American, note that "EST" is a Canadian time zone that does not
  have daylight savings time. It is _not_ Eastern Standard Time! We'll
  come back to this [time zones].
: `%z` (as offset from UTC, e.g. `+0800`). 

Non-digits
: `%.` skips one non-digit character.
: `%*` skips any number of non-digits.

The best way to figure out the correct format is to create a few examples in a character vector, and test with one of the parsing functions. For example:

```{r}
parse_date("01/02/15", "%m/%d/%y")
parse_date("01/02/15", "%d/%m/%y")
parse_date("01/02/15", "%y/%m/%d")
```

If you're using `%b` or `%B` with non-English month names, you'll need to set the  `lang` argument to `locale()`. See the list of built-in languages in `date_names_langs()`, or if your language is not already included, create your own with `date_names()`.

```{r}
parse_date("1 janvier 2015", "%d %B %Y", locale = locale("fr"))
```

### Exercises

1.  What are the most important arguments to `locale()`? 

1.  What happens if you try and set `decimal_mark` and `grouping_mark` 
    to the same character? What happens to the default value of 
    `grouping_mark` when you set `decimal_mark` to ","? What happens
    to the default value of `decimal_mark` when you set the `grouping_mark`
    to "."?

1.  I didn't discuss the `date_format` and `time_format` options to
    `locale()`. What do they do? Construct an example that shows when 
    they might be useful.

1.  If you live outside the US, create a new locale object that encapsulates 
    the settings for the types of file you read most commonly.
    
1.  What's the difference between `read_csv()` and `read_csv2()`?
    
1.  What are the most common encodings used in Europe? What are the
    most common encodings used in Asia? Do some googling to find out.

1.  Generate the correct format string to parse each of the following 
    dates and times:
    
    ```{r}
    d1 <- "January 1, 2010"
    d2 <- "2015-Mar-07"
    d3 <- "06-Jun-2017"
    d4 <- c("August 19 (2015)", "July 1 (2015)")
    d5 <- "12/30/14" # Dec 30, 2014
    t1 <- "1705"
    t2 <- "11:15:10.12 PM"
    ```

## Parsing a file

Now that you've learned how to parse an individual vector, it's time to return to the beginning and explore how readr parses a file. There are two new things that you'll learn about in this section:

1. How readr automatically guesses the type of each column.
1. How to override the default specification.

### Strategy

readr uses a heuristic to figure out the type of each column: it reads the first 1000 rows and uses some (moderately conservative) heuristics to figure out the type of each column. You can emulate this process with a character vector using `guess_parser()`, which returns readr's best guess, and `parse_guess()` which uses that guess to parse the column:

```{r}
guess_parser("2010-10-01")
guess_parser("15:01")
guess_parser(c("TRUE", "FALSE"))
guess_parser(c("1", "5", "9"))
guess_parser(c("12,352,561"))

str(parse_guess("2010-10-10"))
```

The heuristic tries each of the following types, stopping when it finds a match:

* logical: contains only "F", "T", "FALSE", or "TRUE".
* integer: contains only numeric characters (and `-`).
* double: contains only valid doubles (including numbers like `4.5e-5`).
* number: contains valid doubles with the grouping mark inside.
* time: matches the default `time_format`.
* date: matches the default `date_format`.
* date-time: any ISO8601 date.

If none of these rules apply, then the column will stay as a vector of strings.

### Problems

These defaults don't always work for larger files. There are two basic problems:

1.  The first thousand rows might be a special case, and readr guesses
    a type that is not sufficiently general. For example, you might have 
    a column of doubles that only contains integers in the first 1000 rows. 

1.  The column might contain a lot of missing values. If the first 1000
    rows contain only `NA`s, readr will guess that it's a character 
    vector, whereas you probably want to parse it as something more
    specific.

readr contains a challenging CSV that illustrates both of these problems:

```{r}
challenge <- read_csv(readr_example("challenge.csv"))
```

(Note the use of `readr_example()` which finds the path to one of the files included with the package)

There are two printed outputs: the column specification generated by looking at the first 1000 rows, and the first five parsing failures. It's always a good idea to explicitly pull out the `problems()`, so you can explore them in more depth:

```{r}
problems(challenge)
```

A good strategy is to work column by column until there are no problems remaining. Here we can see that there are a lot of parsing problems with the `x` column - there are trailing characters after the integer value. That suggests we need to use a double parser instead.

To fix the call, start by copying and pasting the column specification into your original call:

```{r, eval = FALSE}
challenge <- read_csv(
  readr_example("challenge.csv"), 
  col_types = cols(
    x = col_integer(),
    y = col_character()
  )
)
```

Then you can tweak the type of the `x` column:

```{r}
challenge <- read_csv(
  readr_example("challenge.csv"), 
  col_types = cols(
    x = col_double(),
    y = col_character()
  )
)
```

That fixes the first problem, but if we look at the last few rows, you'll see that they're dates stored in a character vector:

```{r}
tail(challenge)
```

You can fix that by specifying that `y` is a date column:

```{r}
challenge <- read_csv(
  readr_example("challenge.csv"), 
  col_types = cols(
    x = col_double(),
    y = col_date()
  )
)
tail(challenge)
```

Every `parse_xyz()` function has a corresponding `col_xyz()` function. You use `parse_xyz()` when the data is in a character vector in R already; you use `col_xyz()` when you want to tell readr how to load the data.

I highly recommend always supplying `col_types`, building up from the print-out provided by readr. This ensures that you have a consistent and reproducible data import script. If you rely on the default guesses and your data changes, readr will continue to read it in. If you want to be really strict, use `stop_for_problems()`: that will throw an error and stop your script if there are any parsing problems.

### Other strategies

There are a few other general strategies to help you parse files:

*   In the previous example, we just got unlucky: if we look at just
    one more row than the default, we can correctly parse in one shot:
   
    ```{r}
    challenge2 <- read_csv(readr_example("challenge.csv"), guess_max = 1001)
    challenge2
    ```

*   Sometimes it's easier to diagnose problems if you just read in all
    the columns as character vectors:
   
    ```{r}
    challenge2 <- read_csv(readr_example("challenge.csv"), 
      col_types = cols(.default = col_character())
    )
    ```
    
    This is particularly useful in conjunction with `type_convert()`,
    which applies the parsing heuristics to the character columns in a data
    frame.

    ```{r}
    df <- tribble(
      ~x,  ~y,
      "1", "1.21",
      "2", "2.32",
      "3", "4.56"
    )
    df
    
    # Note the column types
    type_convert(df)
    ```
    
*   If you're reading a very large file, you might want to set `n_max` to
    a smallish number like 10,000 or 100,000. That will accelerate your 
    iterations while you eliminate common problems.

*   If you're having major parsing problems, sometimes it's easier
    to just read into a character vector of lines with `read_lines()`,
    or even a character vector of length 1 with `read_file()`. Then you
    can use the string parsing skills you'll learn later to parse
    more exotic formats.

## Writing to a file

readr also comes with two useful functions for writing data back to disk: `write_csv()` and `write_tsv()`. Both functions increase the chances of the output file being read back in correctly by:

* Always encoding strings in UTF-8.
  
* Saving dates and date-times in ISO8601 format so they are easily
  parsed elsewhere.

If you want to export a csv file to Excel, use `write_excel_csv()` --- this writes a special character (a "byte order mark") at the start of the file which tells Excel that you're using the UTF-8 encoding.

The most important arguments are `x` (the data frame to save), and `path` (the location to save it). You can also specify how missing values are written with `na`, and if you want to `append` to an existing file.

```{r, eval = FALSE}
write_csv(challenge, "challenge.csv")
```

Note that the type information is lost when you save to csv:

```{r, warning = FALSE}
challenge
write_csv(challenge, "challenge-2.csv")
read_csv("challenge-2.csv")
```

This makes CSVs a little unreliable for caching interim results---you need to recreate the column specification every time you load in. There are two alternatives:

1.  `write_rds()` and `read_rds()` are uniform wrappers around the base 
    functions `readRDS()` and `saveRDS()`. These store data in R's custom 
    binary format called RDS:
    
    ```{r}
    write_rds(challenge, "challenge.rds")
    read_rds("challenge.rds")
    ```
  
1.  The feather package implements a fast binary file format that can
    be shared across programming languages:
    
    ```{r, eval = FALSE}
    library(feather)
    write_feather(challenge, "challenge.feather")
    read_feather("challenge.feather")
    #> # A tibble: 2,000 x 2
    #>       x      y
    #>   <dbl> <date>
    #> 1   404   <NA>
    #> 2  4172   <NA>
    #> 3  3004   <NA>
    #> 4   787   <NA>
    #> 5    37   <NA>
    #> 6  2332   <NA>
    #> # ... with 1,994 more rows
    ```

Feather tends to be faster than RDS and is usable outside of R. RDS supports list-columns (which you'll learn about in [many models]); feather currently does not.

```{r, include = FALSE}
file.remove("challenge-2.csv")
file.remove("challenge.rds")
```

## Other types of data

To get other types of data into R, we recommend starting with the tidyverse packages listed below. They're certainly not perfect, but they are a good place to start. For rectangular data:

* __haven__ reads SPSS, Stata, and SAS files.

* __readxl__ reads excel files (both `.xls` and `.xlsx`).

* __DBI__, along with a database specific backend (e.g. __RMySQL__, 
  __RSQLite__, __RPostgreSQL__ etc) allows you to run SQL queries against a 
  database and return a data frame.

For hierarchical data: use __jsonlite__ (by Jeroen Ooms) for json, and __xml2__ for XML. Jenny Bryan has some excellent worked examples at <https://jennybc.github.io/purrr-tutorial/>.

For other file types, try the [R data import/export manual](https://cran.r-project.org/doc/manuals/r-release/R-data.html) and the [__rio__](https://github.com/leeper/rio) package.


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/index.rmd
================================================
---
knit: "bookdown::render_book"
title: "R for Data Science"
author: ["Garrett Grolemund", "Hadley Wickham"]
description: "This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots---and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data."
url: 'http\://r4ds.had.co.nz/'
github-repo: hadley/r4ds
twitter-handle: hadley
cover-image: cover.png
site: bookdown::bookdown_site
documentclass: book
---

# Welcome {-}

This is the website for __"R for Data Science"__. This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots---and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data. 

Want a physical copy of this material?

Published by O'Reilly January 2017 First Edition. Order from [amazon](http://amzn.to/2aHLAQ1).

<img src="cover.png" width="250" height="375" alt="Cover image" />

(R for Data Science was formerly called Data Science with R in Hands-On Programming with R)

This work is licensed under the [Creative Commons Attribution-NonCommercial-NoDerivs 3.0](http://creativecommons.org/licenses/by-nc-nd/3.0/us/) United States License. 


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/intro.Rmd
================================================
# Introduction

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of "R for Data Science" is to help you learn the most important tools in R that will allow you to do data science. After reading this book, you'll have the tools to tackle a wide variety of data science challenges, using the best parts of R. 

## What you will learn

Data science is a huge field, and there's no way you can master it by reading a single book. The goal of this book is to give you a solid foundation in the most important tools. Our model of the tools needed in a typical data science project looks something like this:

```{r echo = FALSE, out.width = "75%"}
knitr::include_graphics("diagrams/data-science.png")
```

First you must __import__ your data into R. This typically means that you take data stored in a file, database, or web API, and load it into a data frame in R. If you can't get your data into R, you can't do data science on it!

Once you've imported your data, it is a good idea to __tidy__ it. Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored. In brief, when your data is tidy, each column is a variable, and each row is an observation. Tidy data is important because the consistent structure lets you focus your struggle on questions about the data, not fighting to get the data into the right form for different functions.

Once you have tidy data, a common first step is to __transform__ it. Transformation includes narrowing in on observations of interest (like all people in one city, or all data from the last year), creating new variables that are functions of existing variables (like computing velocity from speed and time), and calculating a set of summary statistics (like counts or means). Together, tidying and transforming are called __wrangling__, because getting your data in a form that's natural to work with often feels like a fight!

Once you have tidy data with the variables you need, there are two main engines of knowledge generation: visualisation and modelling. These have complementary strengths and weaknesses so any real analysis will iterate between them many times.

__Visualisation__ is a fundamentally human activity. A good visualisation will show you things that you did not expect, or raise new questions about the data. A good visualisation might also hint that you're asking the wrong question, or you need to collect different data. Visualisations can surprise you, but don't scale particularly well because they require a human to interpret them.

__Models__ are complementary tools to visualisation. Once you have made your questions sufficiently precise, you can use a model to answer them. Models are a fundamentally mathematical or computational tool, so they generally scale well. Even when they don't, it's usually cheaper to buy more computers than it is to buy more brains! But every model makes assumptions, and by its very nature a model cannot question its own assumptions. That means a model cannot fundamentally surprise you.

The last step of data science is __communication__, an absolutely critical part of any data analysis project. It doesn't matter how well your models and visualisation have led you to understand the data unless you can also communicate your results to others.

Surrounding all these tools is __programming__. Programming is a cross-cutting tool that you use in every part of the project. You don't need to be an expert programmer to be a data scientist, but learning more about programming pays off because becoming a better programmer allows you to automate common tasks, and solve new problems with greater ease.

You'll use these tools in every data science project, but for most projects they're not enough. There's a rough 80-20 rule at play; you can tackle about 80% of every project using the tools that you'll learn in this book, but you'll need other tools to tackle the remaining 20%. Throughout this book we'll point you to resources where you can learn more.

## How this book is organised

The previous description of the tools of data science is organised roughly according to the order in which you use them in an analysis (although of course you'll iterate through them multiple times). In our experience, however, this is not the best way to learn them:

* Starting with data ingest and tidying is sub-optimal because 80% of the time 
  it's routine and boring, and the other 20% of the time it's weird and
  frustrating. That's a bad place to start learning a new subject! Instead, 
  we'll start with visualisation and transformation of data that's already been
  imported and tidied. That way, when you ingest and tidy your own data, your
  motivation will stay high because you know the pain is worth it.
  
* Some topics are best explained with other tools. For example, we believe that
  it's easier to understand how models work if you already know about 
  visualisation, tidy data, and programming.
  
* Programming tools are not necessarily interesting in their own right, 
  but do allow you to tackle considerably more challenging problems. We'll
  give you a selection of programming tools in the middle of the book, and 
  then you'll see how they can combine with the data science tools to tackle 
  interesting modelling problems.

Within each chapter, we try and stick to a similar pattern: start with some motivating examples so you can see the bigger picture, and then dive into the details. Each section of the book is paired with exercises to help you practice what you've learned. While it's tempting to skip the exercises, there's no better way to learn than practicing on real problems.

## What you won't learn

There are some important topics that this book doesn't cover. We believe it's important to stay ruthlessly focused on the essentials so you can get up and running as quickly as possible. That means this book can't cover every important topic.

### Big data

This book proudly focuses on small, in-memory datasets. This is the right place to start because you can't tackle big data unless you have experience with small data. The tools you learn in this book will easily handle hundreds of megabytes of data, and with a little care you can typically use them to work with 1-2 Gb of data. If you're routinely working with larger data (10-100 Gb, say), you should learn more about [data.table](https://github.com/Rdatatable/data.table). This book doesn't teach data.table because it has a very concise interface which makes it harder to learn since it offers fewer linguistic cues. But if you're working with large data, the performance payoff is worth the extra effort required to learn it.

If your data is bigger than this, carefully consider if your big data problem might actually be a small data problem in disguise. While the complete data might be big, often the data needed to answer a specific question is small. You might be able to find a subset, subsample, or summary that fits in memory and still allows you to answer the question that you're interested in. The challenge here is finding the right small data, which often requires a lot of iteration.

Another possibility is that your big data problem is actually a large number of small data problems. Each individual problem might fit in memory, but you have millions of them. For example, you might want to fit a model to each person in your dataset. That would be trivial if you had just 10 or 100 people, but instead you have a million. Fortunately each problem is independent of the others (a setup that is sometimes called embarrassingly parallel), so you just need a system (like Hadoop or Spark) that allows you to send different datasets to different computers for processing. Once you've figured out how to answer the question for a single subset using the tools described in this book, you learn new tools like sparklyr, rhipe, and ddr to solve it for the full dataset.

### Python, Julia, and friends

In this book, you won't learn anything about Python, Julia, or any other programming language useful for data science. This isn't because we think these tools are bad. They're not! And in practice, most data science teams use a mix of languages, often at least R and Python.

However, we strongly believe that it's best to master one tool at a time. You will get better faster if you dive deep, rather than spreading yourself thinly over many topics. This doesn't mean you should only know one thing, just that you'll generally learn faster if you stick to one thing at a time. You should strive to learn new things throughout your career, but make sure your understanding is solid before you move on to the next interesting thing.

We think R is a great place to start your data science journey because it is an environment designed from the ground up to support data science. R is not just a programming language, but it is also an interactive environment for doing data science. To support interaction, R is a much more flexible language than many of its peers. This flexibility comes with its downsides, but the big upside is how easy it is to evolve tailored grammars for specific parts of the data science process. These mini languages help you think about problems as a data scientist, while supporting fluent interaction between your brain and the computer.

### Non-rectangular data

This book focuses exclusively on rectangular data: collections of values that are each associated with a variable and an observation. There are lots of datasets that do not naturally fit in this paradigm: including images, sounds, trees, and text. But rectangular data frames are extremely common in science and industry, and we believe that they are a great place to start your data science journey.

### Hypothesis confirmation

It's possible to divide data analysis into two camps: hypothesis generation and hypothesis confirmation (sometimes called confirmatory analysis). The focus of this book is unabashedly on hypothesis generation, or data exploration. Here you'll look deeply at the data and, in combination with your subject knowledge, generate many interesting hypotheses to help explain why the data behaves the way it does. You evaluate the hypotheses informally, using your scepticism to challenge the data in multiple ways.

The complement of hypothesis generation is hypothesis confirmation. Hypothesis confirmation is hard for two reasons:

1.  You need a precise mathematical model in order to generate falsifiable
    predictions. This often requires considerable statistical sophistication.

1.  You can only use an observation once to confirm a hypothesis. As soon as
    you use it more than once you're back to doing exploratory analysis. 
    This means to do hypothesis confirmation you need to "preregister" 
    (write out in advance) your analysis plan, and not deviate from it
    even when you have seen the data. We'll talk a little about some 
    strategies you can use to make this easier in [modelling](#model-intro).

It's common to think about modelling as a tool for hypothesis confirmation, and visualisation as a tool for hypothesis generation. But that's a false dichotomy: models are often used for exploration, and with a little care you can use visualisation for confirmation. The key difference is how often do you look at each observation: if you look only once, it's confirmation; if you look more than once, it's exploration.

## Prerequisites

We've made a few assumptions about what you already know in order to get the most out of this book. You should be generally numerically literate, and it's helpful if you have some programming experience already. If you've never programmed before, you might find [Hands on Programming with R](http://amzn.com/1449359019) by Garrett to be a useful adjunct to this book.

There are four things you need to run the code in this book: R, RStudio, a collection of R packages called the __tidyverse__, and a handful of other packages. Packages are the fundamental units of reproducible R code. They include reusable functions, the documentation that describes how to use them, and sample data. 

### R 

To download R, go to CRAN, the **c**omprehensive **R** **a**rchive **n**etwork. CRAN is composed of a set of mirror servers distributed around the world and is used to distribute R and R packages. Don't try and pick a mirror that's close to you: instead use the cloud mirror, <https://cloud.r-project.org>, which automatically figures it out for you.

A new major version of R comes out once a year, and there are 2-3 minor releases each year. It's a good idea to update regularly. Upgrading can be a bit of a hassle, especially for major versions, which require you to reinstall all your packages, but putting it off only makes it worse.

### RStudio

RStudio is an integrated development environment, or IDE, for R programming. Download and install it from <http://www.rstudio.com/download>. RStudio is updated a couple of times a year. When a new version is available, RStudio will let you know. It's a good idea to upgrade regularly so you can take advantage of the latest and greatest features. For this book, make sure you have RStudio 1.0.0.

When you start RStudio, you'll see two key regions in the interface:

```{r echo = FALSE, out.width = "75%"}
knitr::include_graphics("diagrams/rstudio-console.png")
```

For now, all you need to know is that you type R code in the console pane, and press enter to run it. You'll learn more as we go along!

### The tidyverse

You'll also need to install some R packages. An R __package__ is a collection of functions, data, and documentation that extends the capabilities of base R. Using packages is key to the successful use of R. The majority of the packages that you will learn in this book are part of the so-called tidyverse. The packages in the tidyverse share a common philosophy of data and R programming, and are designed to work together naturally. 

You can install the complete tidyverse with a single line of code:

```{r, eval = FALSE}
install.packages("tidyverse")
```

On your own computer, type that line of code in the console, and then press enter to run it. R will download the packages from CRAN and install them on to your computer. If you have problems installing, make sure that you are connected to the internet, and that <https://cloud.r-project.org/> isn't blocked by your firewall or proxy. 

You will not be able to use the functions, objects, and help files in a package until you load it with `library()`. Once you have installed a package, you can load it with the `library()` function:

```{r}
library(tidyverse)
```

This tells you that tidyverse is loading the ggplot2, tibble, readr, purrr, and dplyr packages. These are considered to be the __core__ of the tidyverse because you'll use them in almost every analysis. 

Packages in the tidyverse change fairly frequently. You can see if updates are available, and optionally install them, by running `tidyverse_update()`.

### Other packages

There are many other excellent packages that are not part of the tidyverse, because they solve problems in a different domain, or are designed with a different set of underlying principles. This doesn't make them better or worse, just different. In other words, the complement to the tidyverse is not the messyverse, but many other universes of interrelated packages. As you tackle more data science projects with R, you'll learn new packages and new ways of thinking about data. 


## Running R code

The previous section showed you a couple of examples of running R code. Code in the book looks like this:

```{r, eval = TRUE}
1 + 2
#> [1] 3
```

If you run the same code in your local console, it will look like this:

```
> 1 + 2
[1] 3
```

There are two main differences. In your console, you type after the `>`, called the __prompt__; we don't show the prompt in the book. In the book, output is commented out with `#>`; in your console it appears directly after your code. These two differences mean that if you're working with an electronic version of the book, you can easily copy code out of the book and into the console.

Throughout the book we use a consistent set of conventions to refer to code:

* Functions are in a code font and followed by parentheses, like `sum()`, 
  or `mean()`.

* Other R objects (like data or function arguments) are in a code font,
  without parentheses, like `flights` or `x`.
  
* If we want to make it clear what package an object comes from, we'll use
  the package name followed by two colons, like `dplyr::mutate()`, or   
  `nycflights13::flights`. This is also valid R code.

## Getting help and learning more

This book is not an island; there is no single resource that will allow you to master R. As you start to apply the techniques described in this book to your own data you will soon find questions that I do not answer. This section describes a few tips on how to get help, and to help you keep learning.

If you get stuck, start with Google. Typically adding "R" to a query is enough to restrict it to relevant results: if the search isn't useful, it often means that there aren't any R-specific results available. Google is particularly useful for error messages. If you get an error message and you have no idea what it means, try googling it! Chances are that someone else has been confused by it in the past, and there will be help somewhere on the web. (If the error message isn't in English, run `Sys.setenv(LANGUAGE = "en")` and re-run the code; you're more likely to find help for English error messages.)

If Google doesn't help, try [stackoverflow](http://stackoverflow.com). Start by spending a little time searching for an existing answer, including `[R]` to restrict your search to questions and answers that use R. If you don't find anything useful, prepare a minimal reproducible example or __reprex__.  A good reprex makes it easier for other people to help you, and often you'll figure out the problem yourself in the course of making it.

There are three things you need to include to make your example reproducible: required packages, data, and code.

1.  **Packages** should be loaded at the top of the script, so it's easy to
    see which ones the example needs. This is a good time to check that you're
    using the latest version of each package; it's possible you've discovered
    a bug that's been fixed since you installed the package. For packages
    in the tidyverse, the easiest way to check is to run `tidyverse_update()`.

1.  The easiest way to include **data** in a question is to use `dput()` to 
    generate the R code to recreate it. For example, to recreate the `mtcars` 
    dataset in R, I'd perform the following steps:
  
    1. Run `dput(mtcars)` in R
    2. Copy the output
    3. In my reproducible script, type `mtcars <- ` then paste.
    
    Try and find the smallest subset of your data that still reveals
    the problem.

1.  Spend a little bit of time ensuring that your **code** is easy for others to
    read:

    * Make sure you've used spaces and your variable names are concise, yet
      informative.
    
    * Use comments to indicate where your problem lies.
    
    * Do your best to remove everything that is not related to the problem.  
      The shorter your code is, the easier it is to understand, and the 
      easier it is to fix.

Finish by checking that you have actually made a reproducible example by starting a fresh R session and copying and pasting your script in. 

You should also spend some time preparing yourself to solve problems before they occur. Investing a little time in learning R each day will pay off handsomely in the long run. One way is to follow what Hadley, Garrett, and everyone else at RStudio are doing on the [RStudio blog](https://blog.rstudio.org). This is where we post announcements about new packages, new IDE features, and in-person courses. You might also want to follow Hadley ([\@hadleywickham](https://twitter.com/hadleywickham)) or Garrett ([\@statgarrett](https://twitter.com/statgarrett)) on Twitter, or follow [\@rstudiotips](https://twitter.com/rstudiotips) to keep up with new features in the IDE.

To keep up with the R community more broadly, we recommend reading <http://www.r-bloggers.com>: it aggregates over 500 blogs about R from around the world. If you're an active Twitter user, follow the `#rstats` hashtag. Twitter is one of the key tools that Hadley uses to keep up with new developments in the community.

## Acknowledgements

This book isn't just the product of Hadley and Garrett, but is the result of many conversations (in person and online) that we've had with the many people in the R community. There are a few people we'd like to thank in particular, because they have spent many hours answering our dumb questions and helping us to better think about data science:

* Jenny Bryan and Lionel Henry for many helpful discussions around working
  with lists and list-columns.
  
* The three chapters on workflow were adapted (with permission), from
  <http://stat545.com/block002_hello-r-workspace-wd-project.html> by 
  Jenny Bryan.

* Genevera Allen for discussions about models, modelling, the statistical
  learning perspective, and the difference between hypothesis generation and 
  hypothesis confirmation.

* Yihui Xie for his work on the [bookdown](https://github.com/rstudio/bookdown) 
  package, and for tirelessly responding to my feature requests.

* Bill Behrman for his thoughtful reading of the entire book, and for trying 
  it out with his data science class at Stanford.

* The \#rstats twitter community who reviewed all of the draft chapters
  and provided tons of useful feedback.

* Tal Galili for augmenting his dendextend package to support a section on clustering that did not make it into the final draft.

This book was written in the open, and many people contributed pull requests to fix minor problems. Special thanks goes to everyone who contributed via GitHub: 

```{r, results = "asis", echo = FALSE, message = FALSE}
library(dplyr)
# git --no-pager shortlog -ns > contribs.txt
contribs <- readr::read_tsv("contribs.txt", col_names = c("n", "name"))

contribs <- contribs %>% 
  filter(!name %in% c("hadley", "Garrett", "Hadley Wickham",
                      "Garrett Grolemund")) %>% 
  arrange(name) %>% 
  mutate(uname = ifelse(!grepl(" ", name), paste0("@", name), name))

cat("Thanks go to all contributers in alphabetical order: ")
cat(paste0(contribs$uname, collapse = ", "))
cat(".\n")
```

## Colophon

This book was built with:

```{r}
devtools::session_info(c("tidyverse"))
```


================================================
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================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/iteration.Rmd
================================================
# Iteration

## Introduction

In [functions], we talked about how important it is to reduce duplication in your code by creating functions instead of copying-and-pasting. Reducing code duplication has three main benefits:

1.  It's easier to see the intent of your code, because your eyes are
    drawn to what's different, not what stays the same.
    
1.  It's easier to respond to changes in requirements. As your needs 
    change, you only need to make changes in one place, rather than
    remembering to change every place that you copied-and-pasted the 
    code.
    
1.  You're likely to have fewer bugs because each line of code is 
    used in more places.

One tool for reducing duplication is functions, which reduce duplication by identifying repeated patterns of code and extract them out into independent pieces that can be easily reused and updated. Another tool for reducing duplication is __iteration__, which helps you when you need to do the same thing to multiple inputs: repeating the same operation on different columns, or on different datasets. 
In this chapter you'll learn about two important iteration paradigms: imperative programming and functional programming. On the imperative side you have tools like for loops and while loops, which are a great place to start because they make iteration very explicit, so it's obvious what's happening. However, for loops are quite verbose, and require quite a bit of bookkeeping code that is duplicated for every for loop. Functional programming (FP) offers tools to extract out this duplicated code, so each common for loop pattern gets its own function. Once you master the vocabulary of FP, you can solve many common iteration problems with less code, more ease, and fewer errors.

### Prerequisites

Once you've mastered the for loops provided by base R, you'll learn some of the powerful programming tools provided by purrr, one of the tidyverse core packages.

```{r setup, message = FALSE}
library(tidyverse)
```

## For loops

Imagine we have this simple tibble:

```{r}
df <- tibble(
  a = rnorm(10),
  b = rnorm(10),
  c = rnorm(10),
  d = rnorm(10)
)
```

We want to compute the median of each column. You _could_ do with copy-and-paste:

```{r}
median(df$a)
median(df$b)
median(df$c)
median(df$d)
```

But that breaks our rule of thumb: never copy and paste more than twice. Instead, we could use a for loop:

```{r}
output <- vector("double", ncol(df))  # 1. output
for (i in seq_along(df)) {            # 2. sequence
  output[[i]] <- median(df[[i]])      # 3. body
}
output
```

Every for loop has three components:

1.  The __output__: `output <- vector("double", length(x))`. 
    Before you start the loop, you must always allocate sufficient space 
    for the output. This is very important for efficiency: if you grow
    the for loop at each iteration using `c()` (for example), your for loop 
    will be very slow. 
    
    A general way of creating an empty vector of given length is the `vector()`
    function. It has two arguments: the type of the vector ("logical", 
    "integer", "double", "character", etc) and the length of the vector. 

1.  The __sequence__: `i in seq_along(df)`. This determines what to loop over:
    each run of the for loop will assign `i` to a different value from 
    `seq_along(df)`. It's useful to think of `i` as a pronoun, like "it".
    
    You might not have seen `seq_along()` before. It's a safe version of the 
    familiar `1:length(l)`, with an important difference: if you have a
    zero-length vector, `seq_along()` does the right thing:

    ```{r}
    y <- vector("double", 0)
    seq_along(y)
    1:length(y)
    ```
    
    You probably won't create a zero-length vector deliberately, but
    it's easy to create them accidentally. If you use `1:length(x)` instead
    of `seq_along(x)`, you're likely to get a confusing error message.
    
1.  The __body__: `output[[i]] <- median(df[[i]])`. This is the code that does
    the work. It's run repeatedly, each time with a different value for `i`.
    The first iteration will run `output[[1]] <- median(df[[1]])`, 
    the second will run `output[[2]] <- median(df[[2]])`, and so on.

That's all there is to the for loop! Now is a good time to practice creating some basic (and not so basic) for loops using the exercises below. Then we'll move on some variations of the for loop that help you solve other problems that will crop up in practice. 

### Exercises

1.  Write for loops to:

    1. Compute the mean of every column in `mtcars`.
    1. Determine the type of each column in `nycflights13::flights`.
    1. Compute the number of unique values in each column of `iris`.
    1. Generate 10 random normals for each of $\mu = -10$, $0$, $10$, and $100$.
    
    Think about the output, sequence, and body __before__ you start writing
    the loop.

1.  Eliminate the for loop in each of the following examples by taking 
    advantage of an existing function that works with vectors:
    
    ```{r, eval = FALSE}
    out <- ""
    for (x in letters) {
      out <- stringr::str_c(out, x)
    }
    
    x <- sample(100)
    sd <- 0
    for (i in seq_along(x)) {
      sd <- sd + (x[i] - mean(x)) ^ 2
    }
    sd <- sqrt(sd / (length(x) - 1))
    
    x <- runif(100)
    out <- vector("numeric", length(x))
    out[1] <- x[1]
    for (i in 2:length(x)) {
      out[i] <- out[i - 1] + x[i]
    }
    ```

1.  Combine your function writing and for loop skills:

    1. Write a for loop that `prints()` the lyrics to the children's song 
       "Alice the camel".

    1. Convert the nursery rhyme "ten in the bed" to a function. Generalise 
       it to any number of people in any sleeping structure.

    1. Convert the song "99 bottles of beer on the wall" to a function.
       Generalise to any number of any vessel containing any liquid on 
       any surface.

1.  It's common to see for loops that don't preallocate the output and instead
    increase the length of a vector at each step:
    
    ```{r, eval = FALSE}
    output <- vector("integer", 0)
    for (i in seq_along(x)) {
      output <- c(output, lengths(x[[i]]))
    }
    output
    ```
    
    How does this affect performance? Design and execute an experiment.

## For loop variations

Once you have the basic for loop under your belt, there are some variations that you should be aware of. These variations are important regardless of how you do iteration, so don't forget about them once you've mastered the FP techniques you'll learn about in the next section.

There are four variations on the basic theme of the for loop:

1.  Modifying an existing object, instead of creating a new object.
1.  Looping over names or values, instead of indices.
1.  Handling outputs of unknown length.
1.  Handling sequences of unknown length.

### Modifying an existing object

Sometimes you want to use a for loop to modify an existing object. For example, remember our challenge from [functions]. We wanted to rescale every column in a data frame:

```{r}
df <- tibble(
  a = rnorm(10),
  b = rnorm(10),
  c = rnorm(10),
  d = rnorm(10)
)
rescale01 <- function(x) {
  rng <- range(x, na.rm = TRUE)
  (x - rng[1]) / (rng[2] - rng[1])
}

df$a <- rescale01(df$a)
df$b <- rescale01(df$b)
df$c <- rescale01(df$c)
df$d <- rescale01(df$d)
```

To solve this with a for loop we again think about the three components:

1.  __Output__: we already have the output --- it's the same as the input!

1.  __Sequence__: we can think about a data frame as a list of columns, so 
    we can iterate over each column with `seq_along(df)`.

1.  __Body__: apply `rescale01()`.

This gives us:

```{r}
for (i in seq_along(df)) {
  df[[i]] <- rescale01(df[[i]])
}
```

Typically you'll be modifying a list or data frame with this sort of loop, so remember to use `[[`, not `[`. You might have spotted that I used `[[` in all my for loops: I think it's better to use `[[` even for atomic vectors because it makes it clear that I want to work with a single element.

### Looping patterns

There are three basic ways to loop over a vector. So far I've shown you the most general: looping over the numeric indices with `for (i in seq_along(xs))`, and extracting the value with `x[[i]]`. There are two other forms:

1.  Loop over the elements: `for (x in xs)`. This is most useful if you only
    care about side-effects, like plotting or saving a file, because it's
    difficult to save the output efficiently.

1.  Loop over the names: `for (nm in names(xs))`. This gives you name, which
    you can use to access the value with `x[[nm]]`. This is useful if you want 
    to use the name in a plot title or a file name. If you're creating
    named output, make sure to name the results vector like so:
    
    ```{r, eval = FALSE}
    results <- vector("list", length(x))
    names(results) <- names(x)
    ```

Iteration over the numeric indices is the most general form, because given the position you can extract both the name and the value:

```{r, eval = FALSE}
for (i in seq_along(x)) {
  name <- names(x)[[i]]
  value <- x[[i]]
}
```

### Unknown output length

Sometimes you might not know how long the output will be. For example, imagine you want to simulate some random vectors of random lengths. You might be tempted to solve this problem by progressively growing the vector:

```{r}
means <- c(0, 1, 2)

output <- double()
for (i in seq_along(means)) {
  n <- sample(100, 1)
  output <- c(output, rnorm(n, means[[i]]))
}
str(output)
```

But this is not very efficient because in each iteration, R has to copy all the data from the previous iterations. In technical terms you get "quadratic" ($O(n^2)$) behaviour which means that a loop with three times as many elements would take nine ($3^2$) times as long to run.

A better solution to save the results in a list, and then combine into a single vector after the loop is done:

```{r}
out <- vector("list", length(means))
for (i in seq_along(means)) {
  n <- sample(100, 1)
  out[[i]] <- rnorm(n, means[[i]])
}
str(out)
str(unlist(out))
```

Here I've used `unlist()` to flatten a list of vectors into a single vector. A stricter option is to use `purrr::flatten_dbl()` --- it will throw an error if the input isn't a list of doubles.

This pattern occurs in other places too:

1.  You might be generating a long string. Instead of `paste()`ing together 
    each iteration with the previous, save the output in a character vector and
    then combine that vector into a single string with 
    `paste(output, collapse = "")`.
   
1.  You might be generating a big data frame. Instead of sequentially
    `rbind()`ing in each iteration, save the output in a list, then use 
    `dplyr::bind_rows(output)` to combine the output into a single
    data frame.

Watch out for this pattern. Whenever you see it, switch to a more complex result object, and then combine in one step at the end.

### Unknown sequence length

Sometimes you don't even know how long the input sequence should run for. This is common when doing simulations. For example, you might want to loop until you get three heads in a row. You can't do that sort of iteration with the for loop. Instead, you can use a while loop. A while loop is simpler than for loop because it only has two components, a condition and a body:

```{r, eval = FALSE}
while (condition) {
  # body
}
```

A while loop is also more general than a for loop, because you can rewrite any for loop as a while loop, but you can't rewrite every while loop as a for loop:

```{r, eval = FALSE}
for (i in seq_along(x)) {
  # body
}

# Equivalent to
i <- 1
while (i <= length(x)) {
  # body
  i <- i + 1 
}
```

Here's how we could use a while loop to find how many tries it takes to get three heads in a row:

```{r}
flip <- function() sample(c("T", "H"), 1)

flips <- 0
nheads <- 0

while (nheads < 3) {
  if (flip() == "H") {
    nheads <- nheads + 1
  } else {
    nheads <- 0
  }
  flips <- flips + 1
}
flips
```

I mention while loops only briefly, because I hardly ever use them. They're most often used for simulation, which is outside the scope of this book. However, it is good to know they exist so that you're prepared for problems where the number of iterations is not known in advance.

### Exercises

1.  Imagine you have a directory full of CSV files that you want to read in.
    You have their paths in a vector, 
    `files <- dir("data/", pattern = "\\.csv$", full.names = TRUE)`, and now
    want to read each one with `read_csv()`. Write the for loop that will 
    load them into a single data frame. 

1.  What happens if you use `for (nm in names(x))` and `x` has no names?
    What if only some of the elements are named? What if the names are
    not unique?

1.  Write a function that prints the mean of each numeric column in a data 
    frame, along with its name. For example, `show_mean(iris)` would print:
    
    ```{r, eval = FALSE}
    show_mean(iris)
    #> Sepal.Length: 5.84
    #> Sepal.Width:  3.06
    #> Petal.Length: 3.76
    #> Petal.Width:  1.20
    ```
    
    (Extra challenge: what function did I use to make sure that the numbers
    lined up nicely, even though the variable names had different lengths?)

1.  What does this code do? How does it work?

    ```{r, eval = FALSE}
    trans <- list( 
      disp = function(x) x * 0.0163871,
      am = function(x) {
        factor(x, labels = c("auto", "manual"))
      }
    )
    for (var in names(trans)) {
      mtcars[[var]] <- trans[[var]](mtcars[[var]])
    }
    ```

## For loops vs. functionals

For loops are not as important in R as they are in other languages because R is a functional programming language. This means that it's possible to wrap up for loops in a function, and call that function instead of using the for loop directly.

To see why this is important, consider (again) this simple data frame:

```{r}
df <- tibble(
  a = rnorm(10),
  b = rnorm(10),
  c = rnorm(10),
  d = rnorm(10)
)
```

Imagine you want to compute the mean of every column. You could do that with a for loop:

```{r}
output <- vector("double", length(df))
for (i in seq_along(df)) {
  output[[i]] <- mean(df[[i]])
}
output
```

You realise that you're going to want to compute the means of every column pretty frequently, so you extract it out into a function:

```{r}
col_mean <- function(df) {
  output <- vector("double", length(df))
  for (i in seq_along(df)) {
    output[i] <- mean(df[[i]])
  }
  output
}
```

But then you think it'd also be helpful to be able to compute the median, and the standard deviation, so you copy and paste your `col_mean()` function and replace the `mean()` with `median()` and `sd()`:

```{r}
col_median <- function(df) {
  output <- vector("double", length(df))
  for (i in seq_along(df)) {
    output[i] <- median(df[[i]])
  }
  output
}
col_sd <- function(df) {
  output <- vector("double", length(df))
  for (i in seq_along(df)) {
    output[i] <- sd(df[[i]])
  }
  output
}
```

Uh oh! You've copied-and-pasted this code twice, so it's time to think about how to generalise it. Notice that most of this code is for-loop boilerplate and it's hard to see the one thing (`mean()`, `median()`, `sd()`) that is different between the functions.

What would you do if you saw a set of functions like this:

```{r}
f1 <- function(x) abs(x - mean(x)) ^ 1
f2 <- function(x) abs(x - mean(x)) ^ 2
f3 <- function(x) abs(x - mean(x)) ^ 3
```

Hopefully, you'd notice that there's a lot of duplication, and extract it out into an additional argument:

```{r}
f <- function(x, i) abs(x - mean(x)) ^ i
```

You've reduced the chance of bugs (because you now have 1/3 less code), and made it easy to generalise to new situations. 

We can do exactly the same thing with `col_mean()`, `col_median()` and `col_sd()` by adding an argument that supplies the function to apply to each column:

```{r}
col_summary <- function(df, fun) {
  out <- vector("double", length(df))
  for (i in seq_along(df)) {
    out[i] <- fun(df[[i]])
  }
  out
}
col_summary(df, median)
col_summary(df, mean)
```

The idea of passing a function to another function is extremely powerful idea, and it's one of the behaviours that makes R a functional programming language. It might take you a while to wrap your head around the idea, but it's worth the investment. In the rest of the chapter, you'll learn about and use the __purrr__ package, which provides functions that eliminate the need for many common for loops. The apply family of functions in base R (`apply()`, `lapply()`, `tapply()`, etc) solve a similar problem, but purrr is more consistent and thus is easier to learn.

The goal of using purrr functions instead of for loops is to allow you break common list manipulation challenges into independent pieces: 

1. How can you solve the problem for a single element of the list? Once
   you've solved that problem, purrr takes care of generalising your
   solution to every element in the list.

1. If you're solving a complex problem, how can you break it down into
   bite-sized pieces that allow you to advance one small step towards a 
   solution? With purrr, you get lots of small pieces that you can
   compose together with the pipe.

This structure makes it easier to solve new problems. It also makes it easier to understand your solutions to old problems when you re-read your old code.

### Exercises

1.  Read the documentation for `apply()`. In the 2d case, what two for loops
    does it generalise?

1.  Adapt `col_summary()` so that it only applies to numeric columns
    You might want to start with an `is_numeric()` function that returns
    a logical vector that has a TRUE corresponding to each numeric column.

## The map functions

The pattern of looping over a vector, doing something to each element and saving the results is so common that the purrr package provides a family of functions to do it for you. There is one function for each type of output:

* `map()`     makes a list.
* `map_lgl()` makes a logical vector.
* `map_int()` makes an integer vector.
* `map_dbl()` makes a double vector.
* `map_chr()` makes a character vector.

Each function takes a vector as input, applies a function to each piece, and then returns a new vector that's the same length (and has the same names) as the input. The type of the vector is determined by the suffix to the map function. 

Once you master these functions, you'll find it takes much less time to solve iteration problems. But you should never feel bad about using a for loop instead of a map function. The map functions are a step up a tower of abstraction, and it can take a long time to get your head around how they work. The important thing is that you solve the problem that you're working on, not write the most concise and elegant code (although that's definitely something you want to strive towards!).

Some people will tell you to avoid for loops because they are slow. They're wrong! (Well at least they're rather out of date, as for loops haven't been slow for many years). The chief benefits of using functions like `map()` is not speed, but clarity: they make your code easier to write and to read.

We can use these functions to perform the same computations as the last for loop. Those summary functions returned doubles, so we need to use `map_dbl()`:

```{r}
map_dbl(df, mean)
map_dbl(df, median)
map_dbl(df, sd)
```

Compared to using a for loop, focus is on the operation being performed (i.e. `mean()`, `median()`, `sd()`), not the bookkeeping required to loop over every element and store the output. This is even more apparent if we use the pipe:

```{r}
df %>% map_dbl(mean)
df %>% map_dbl(median)
df %>% map_dbl(sd)
```

There are a few differences between `map_*()` and `col_summary()`:

*   All purrr functions are implemented in C. This makes them a little faster
    at the expense of readability.
    
*   The second argument, `.f`, the function to apply, can be a formula, a 
    character vector, or an integer vector. You'll learn about those handy 
    shortcuts in the next section.
    
*   `map_*()` uses ... ([dot dot dot]) to pass along additional arguments 
    to `.f` each time it's called:

    ```{r}
    map_dbl(df, mean, trim = 0.5)
    ```

*   The map functions also preserve names:

    ```{r}
    z <- list(x = 1:3, y = 4:5)
    map_int(z, length)
    ```

### Shortcuts

There are a few shortcuts that you can use with `.f` in order to save a little typing. Imagine you want to fit a linear model to each group in a dataset. The following toy example splits the up the `mtcars` dataset in to three pieces (one for each value of cylinder) and fits the same linear model to each piece:  

```{r}
models <- mtcars %>% 
  split(.$cyl) %>% 
  map(function(df) lm(mpg ~ wt, data = df))
```

The syntax for creating an anonymous function in R is quite verbose so purrr provides a convenient shortcut: a one-sided formula.

```{r}
models <- mtcars %>% 
  split(.$cyl) %>% 
  map(~lm(mpg ~ wt, data = .))
```

Here I've used `.` as a pronoun: it refers to the current list element (in the same way that `i` referred to the current index in the for loop). 

When you're looking at many models, you might want to extract a summary statistic like the $R^2$. To do that we need to first run `summary()` and then extract the component called `r.squared`. We could do that using the shorthand for anonymous functions:

```{r}
models %>% 
  map(summary) %>% 
  map_dbl(~.$r.squared)
```

But extracting named components is a common operation, so purrr provides an even shorter shortcut: you can use a string.

```{r}
models %>% 
  map(summary) %>% 
  map_dbl("r.squared")
```

You can also use an integer to select elements by position: 

```{r}
x <- list(list(1, 2, 3), list(4, 5, 6), list(7, 8, 9))
x %>% map_dbl(2)
```

### Base R
  
If you're familiar with the apply family of functions in base R, you might have noticed some similarities with the purrr functions:

*   `lapply()` is basically identical to `map()`, except that `map()` is 
    consistent with all the other functions in purrr, and you can use the 
    shortcuts for `.f`.

*   Base `sapply()` is a wrapper around `lapply()` that automatically
    simplifies the output. This is useful for interactive work but is 
    problematic in a function because you never know what sort of output
    you'll get:
    
    ```{r}
    x1 <- list(
      c(0.27, 0.37, 0.57, 0.91, 0.20),
      c(0.90, 0.94, 0.66, 0.63, 0.06), 
      c(0.21, 0.18, 0.69, 0.38, 0.77)
    )
    x2 <- list(
      c(0.50, 0.72, 0.99, 0.38, 0.78), 
      c(0.93, 0.21, 0.65, 0.13, 0.27), 
      c(0.39, 0.01, 0.38, 0.87, 0.34)
    )
    
    threshold <- function(x, cutoff = 0.8) x[x > cutoff]
    x1 %>% sapply(threshold) %>% str()
    x2 %>% sapply(threshold) %>% str()
    ```

*   `vapply()` is a safe alternative to `sapply()` because you supply an
    additional argument that defines the type. The only problem with 
    `vapply()` is that it's a lot of typing: 
    `vapply(df, is.numeric, logical(1))` is equivalent to
    `map_lgl(df, is.numeric)`. One advantage of `vapply()` over purrr's map
    functions is that it can also produce matrices --- the map functions only 
    ever produce vectors.

I focus on purrr functions here because they have more consistent names and arguments, helpful shortcuts, and in the future will provide easy parallelism and progress bars.

### Exercises

1.  Write code that uses one of the map functions to:

    1. Compute the mean of every column in `mtcars`.
    1. Determine the type of each column in `nycflights13::flights`.
    1. Compute the number of unique values in each column of `iris`.
    1. Generate 10 random normals for each of $\mu = -10$, $0$, $10$, and $100$.

1.  How can you create a single vector that for each column in a data frame
    indicates whether or not it's a factor?

1.  What happens when you use the map functions on vectors that aren't lists?
    What does `map(1:5, runif)` do? Why?
    
1.  What does `map(-2:2, rnorm, n = 5)` do? Why?
    What does `map_dbl(-2:2, rnorm, n = 5)` do? Why?

1.  Rewrite `map(x, function(df) lm(mpg ~ wt, data = df))` to eliminate the 
    anonymous function. 

## Dealing with failure

When you use the map functions to repeat many operations, the chances are much higher that one of those operations will fail. When this happens, you'll get an error message, and no output. This is annoying: why does one failure prevent you from accessing all the other successes? How do you ensure that one bad apple doesn't ruin the whole barrel?

In this section you'll learn how to deal this situation with a new function: `safely()`. `safely()` is an adverb: it takes a function (a verb) and returns a modified version. In this case, the modified function will never throw an error. Instead, it always returns a list with two elements:

1. `result` is the original result. If there was an error, this will be `NULL`.

1. `error` is an error object. If the operation was successful, this will be 
   `NULL`.

(You might be familiar with the `try()` function in base R. It's similar, but because it sometimes returns the original result and it sometimes returns an error object it's more difficult to work with.)

Let's illustrate this with a simple example: `log()`:

```{r}
safe_log <- safely(log)
str(safe_log(10))
str(safe_log("a"))
```

When the function succeeds, the `result` element contains the result and the `error` element is `NULL`. When the function fails, the `result` element is `NULL` and the `error` element contains an error object.

`safely()` is designed to work with map:

```{r}
x <- list(1, 10, "a")
y <- x %>% map(safely(log))
str(y)
```

This would be easier to work with if we had two lists: one of all the errors and one of all the output. That's easy to get with `purrr::transpose()`:

```{r}
y <- y %>% transpose()
str(y)
```

It's up to you how to deal with the errors, but typically you'll either look at the values of `x` where `y` is an error, or work with the values of `y` that are ok:

```{r}
is_ok <- y$error %>% map_lgl(is_null)
x[!is_ok]
y$result[is_ok] %>% flatten_dbl()
```

Purrr provides two other useful adverbs:

*   Like `safely()`, `possibly()` always succeeds. It's simpler than `safely()`, 
    because you give it a default value to return when there is an error. 
    
    ```{r}
    x <- list(1, 10, "a")
    x %>% map_dbl(possibly(log, NA_real_))
    ```
    
*   `quietly()` performs a similar role to `safely()`, but instead of capturing
    errors, it captures printed output, messages, and warnings:
    
    ```{r}
    x <- list(1, -1)
    x %>% map(quietly(log)) %>% str()
    ```

## Mapping over multiple arguments

So far we've mapped along a single input. But often you have multiple related inputs that you need iterate along in parallel. That's the job of the `map2()` and `pmap()` functions. For example, imagine you want to simulate some random normals with different means. You know how to do that with `map()`:

```{r}
mu <- list(5, 10, -3)
mu %>% 
  map(rnorm, n = 5) %>% 
  str()
```

What if you also want to vary the standard deviation? One way to do that would be to iterate over the indices and index into vectors of means and sds:

```{r}
sigma <- list(1, 5, 10)
seq_along(mu) %>% 
  map(~rnorm(5, mu[[.]], sigma[[.]])) %>% 
  str()
```

But that obfuscates the intent of the code. Instead we could use `map2()` which iterates over two vectors in parallel:

```{r}
map2(mu, sigma, rnorm, n = 5) %>% str()
```

`map2()` generates this series of function calls:

```{r, echo = FALSE}
knitr::include_graphics("diagrams/lists-map2.png")
```

Note that the arguments that vary for each call come _before_ the function; arguments that are the same for every call come _after_.

Like `map()`, `map2()` is just a wrapper around a for loop:

```{r}
map2 <- function(x, y, f, ...) {
  out <- vector("list", length(x))
  for (i in seq_along(x)) {
    out[[i]] <- f(x[[i]], y[[i]], ...)
  }
  out
}
```

You could also imagine `map3()`, `map4()`, `map5()`, `map6()` etc, but that would get tedious quickly. Instead, purrr provides `pmap()` which takes a list of arguments. You might use that if you wanted to vary the mean, standard deviation, and number of samples:

```{r}
n <- list(1, 3, 5)
args1 <- list(n, mu, sigma)
args1 %>%
  pmap(rnorm) %>% 
  str()
```

That looks like:

```{r, echo = FALSE}
knitr::include_graphics("diagrams/lists-pmap-unnamed.png")
```

If you don't name the elements of list, `pmap()` will use positional matching when calling the function. That's a little fragile, and makes the code harder to read, so it's better to name the arguments:

```{r, eval = FALSE}
args2 <- list(mean = mu, sd = sigma, n = n)
args2 %>% 
  pmap(rnorm) %>% 
  str()
```

That generates longer, but safer, calls:

```{r, echo = FALSE}
knitr::include_graphics("diagrams/lists-pmap-named.png")
```

Since the arguments are all the same length, it makes sense to store them in a data frame:

```{r}
params <- tribble(
  ~mean, ~sd, ~n,
    5,     1,  1,
   10,     5,  3,
   -3,    10,  5
)
params %>% 
  pmap(rnorm)
```

As soon as your code gets complicated, I think a data frame is a good approach because it ensures that each column has a name and is the same length as all the other columns.

### Invoking different functions

There's one more step up in complexity - as well as varying the arguments to the function you might also vary the function itself:

```{r}
f <- c("runif", "rnorm", "rpois")
param <- list(
  list(min = -1, max = 1), 
  list(sd = 5), 
  list(lambda = 10)
)
```

To handle this case, you can use `invoke_map()`:

```{r}
invoke_map(f, param, n = 5) %>% str()
```

```{r, echo = FALSE, out.width = NULL}
knitr::include_graphics("diagrams/lists-invoke.png")
```

The first argument is a list of functions or character vector of function names. The second argument is a list of lists giving the arguments that vary for each function. The subsequent arguments are passed on to every function.

And again, you can use `tribble()` to make creating these matching pairs a little easier:

```{r, eval = FALSE}
sim <- tribble(
  ~f,      ~params,
  "runif", list(min = -1, max = 1),
  "rnorm", list(sd = 5),
  "rpois", list(lambda = 10)
)
sim %>% 
  mutate(sim = invoke_map(f, params, n = 10))
```

## Walk {#walk}

Walk is an alternative to map that you use when you want to call a function for its side effects, rather than for its return value. You typically do this because you want to render output to the screen or save files to disk - the important thing is the action, not the return value. Here's a very simple example:

```{r}
x <- list(1, "a", 3)

x %>% 
  walk(print)
```

`walk()` is generally not that useful compared to `walk2()` or `pwalk()`. For example, if you had a list of plots and a vector of file names, you could use `pwalk()` to save each file to the corresponding location on disk:

```{r, eval = FALSE}
library(ggplot2)
plots <- mtcars %>% 
  split(.$cyl) %>% 
  map(~ggplot(., aes(mpg, wt)) + geom_point())
paths <- stringr::str_c(names(plots), ".pdf")

pwalk(list(paths, plots), ggsave, path = tempdir())
```

`walk()`, `walk2()` and `pwalk()` all invisibly return `.x`, the first argument. This makes them suitable for use in the middle of pipelines. 

## Other patterns of for loops

Purrr provides a number of other functions that abstract over other types of for loops. You'll use them less frequently than the map functions, but they're useful to know about. The goal here is to briefly illustrate each function, so hopefully it will come to mind if you see a similar problem in the future. Then you can go look up the documentation for more details.

### Predicate functions

A number of functions work with __predicate__ functions that return either a single `TRUE` or `FALSE`.

`keep()` and `discard()` keep elements of the input where the predicate is `TRUE` or `FALSE` respectively:

```{r}
iris %>% 
  keep(is.factor) %>% 
  str()

iris %>% 
  discard(is.factor) %>% 
  str()
```

`some()` and `every()` determine if the predicate is true for any or for all of
the elements.

```{r}
x <- list(1:5, letters, list(10))

x %>% 
  some(is_character)

x %>% 
  every(is_vector)
```

`detect()` finds the first element where the predicate is true; `detect_index()` returns its position.

```{r}
x <- sample(10)
x

x %>% 
  detect(~ . > 5)

x %>% 
  detect_index(~ . > 5)
```

`head_while()` and `tail_while()` take elements from the start or end of a vector while a predicate is true:

```{r}
x %>% 
  head_while(~ . > 5)

x %>% 
  tail_while(~ . > 5)
```

### Reduce and accumulate

Sometimes you have a complex list that you want to reduce to a simple list by repeatedly applying a function that reduces a pair to a singleton. This is useful if you want to apply a two-table dplyr verb to multiple tables. For example, you might have a list of data frames, and you want to reduce to a single data frame by joining the elements together:

```{r}
dfs <- list(
  age = tibble(name = "John", age = 30),
  sex = tibble(name = c("John", "Mary"), sex = c("M", "F")),
  trt = tibble(name = "Mary", treatment = "A")
)

dfs %>% reduce(full_join)
```

Or maybe you have a list of vectors, and want to find the intersection:

```{r}
vs <- list(
  c(1, 3, 5, 6, 10),
  c(1, 2, 3, 7, 8, 10),
  c(1, 2, 3, 4, 8, 9, 10)
)

vs %>% reduce(intersect)
```

The reduce function takes a "binary" function (i.e. a function with two primary inputs), and applies it repeatedly to a list until there is only a single element left.

Accumulate is similar but it keeps all the interim results. You could use it to implement a cumulative sum:

```{r}
x <- sample(10)
x
x %>% accumulate(`+`)
```

### Exercises

1.  Implement your own version of `every()` using a for loop. Compare it with
    `purrr::every()`. What does purrr's version do that your version doesn't?

1.  Create an enhanced `col_sum()` that applies a summary function to every
    numeric column in a data frame.

1.  A possible base R equivalent of `col_sum()` is:

    ```{r}
    col_sum3 <- function(df, f) {
      is_num <- sapply(df, is.numeric)
      df_num <- df[, is_num]

      sapply(df_num, f)
    }
    ```
    
    But it has a number of bugs as illustrated with the following inputs:
    
    ```{r, eval = FALSE}
    df <- tibble(
      x = 1:3, 
      y = 3:1,
      z = c("a", "b", "c")
    )
    # OK
    col_sum3(df, mean)
    # Has problems: don't always return numeric vector
    col_sum3(df[1:2], mean)
    col_sum3(df[1], mean)
    col_sum3(df[0], mean)
    ```
    
    What causes the bugs?


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/model-assess.Rmd
================================================
# Model assessment

In this chapter, you'll turn the tools of multiple models towards model assessment: learning how the model performs when given new data. So far we've focussed on models as tools for description, using models to help us understand the patterns in the data we have collected so far. But ideally a model will do more than just describe what we have seen so far - it will also help predict what will come next.  

In other words, we want a model that doesn't just perform well on the sample, but also accurately summarises the underlying population.

In some industries this is primarily the use of models: you spend relatively little time fitting the model compared to how many times you use it.

There are two basic ways that a model can fail with new data:

* You can under- or over-fit the model.  Underfitting is where you fail
  to model and important trend: you leave too much in the residuals, and not 
  enough in the model. Overfitting is the opposite: you fit a trend to
  what is actually random noise: you've too put much model and not left
  enough in the residuals. Generally overfitting tends to be more of a 
  problem than underfitting.

* The process that generates the data might change. There's nothing the 
  model can do about this. You can protect yourself against this to some
  extent by creating models that you understand and applying your knowledge
  to the problem. Are these fundamentals likely to change? If you have 
  a model that you are going to use again and again for a long time, you
  need to plan to maintain the model, regularly checking that it still 
  makes sense. i.e. is the population the same?
  
    <http://research.google.com/pubs/pub43146.html>
    <http://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/>
 
The most common problem with a model that causes it to do poorly with new data is overfitting. 


Obviously, there's a bit of a problem here: we don't have new data with which to check the model, and even if we did, we'd presumably use it to make the model better in the first place. One powerful technique of approaches can help us get around this problem: resampling.

There are two main resampling techniques that we're going to cover.

* We will use __cross-validation__ to assess model quality. In 
  cross-validation, you split the data into test and training sets. You fit 
  the data to the training set, and evaluate it on the test set. This avoids
  intrinsic bias of using the same data to both fit the model and assess it's
  quality. However it introduces a new bias: you're not using all the data to
  fit the model so it's not going to be quite as good as it could be.
  
* We will use __boostrapping__ to understand how stable (or how variable)
  the model is. If you sample data from the same population multiple times, 
  how much does your model vary? Instead of going back to collect new data, 
  you can use the best estimate of the population data: the data you've
  collected so far. The amazing idea of the bootstrap is that you can resample
  from the data you already have.

There are lots of high-level helpers to do these resampling methods in R. We're going to use the tools provided by the modelr package because they are explicit - you'll see exactly what's going on at each step. 

<http://topepo.github.io/caret>. [Applied Predictive Modeling](https://amzn.com/1461468485), by Max Kuhn and Kjell Johnson.

If you're competing in competitions, like Kaggle, that are predominantly about creating good predictions, developing a good strategy for avoiding overfitting is very important. Otherwise you risk tricking yourself into thinking that you have a good model, when in reality you just have a model that does a good job of fitting your data.

There is a closely related family that uses a similar idea: model ensembles. However, instead of trying to find the best models, ensembles make use of all the models, acknowledging that even models that don't fit all the data particularly well can still model some subsets well. In general, you can think of model ensemble techniques as functions that take a list of models, and a return a single model that attempts to take the best part of each.


### Prerequisites

```{r setup, message = FALSE}
# Standard data manipulation and visulisation
library(dplyr)
library(ggplot2)

# Tools for working with models
library(broom)
library(modelr)
library(splines)

# Tools for working with lots of models
library(purrr)
library(tidyr)
```

```{r}
# Options that make your life easier
options(
  contrasts = c("contr.treatment", "contr.treatment"),
  na.option = na.exclude
)
```


## Overfitting

Both bootstrapping and cross-validation help us to spot and remedy the problem of __over fitting__, where the model fits the data we've seen so far extremely well, but does a bad job of generalising to new data.

A classic example of over-fitting is to using a polynomial with too many degrees of freedom.

Bias - variance tradeoff.  Simpler = more biased. Complex = more variable.  Occam's razor.

```{r}
true_model <- function(x) {
  1 + 2 * x + rnorm(length(x), sd = 0.25)
}

df <- tibble(
  x = seq(0, 1, length = 20),
  y = true_model(x)
)

df %>% 
  ggplot(aes(x, y)) +
  geom_point()
```

We can create a model that fits this data very well:

```{r, message = FALSE}
library(splines)
my_model <- function(df) {
  lm(y ~ poly(x, 7), data = df)
}

mod <- my_model(df)
rmse(mod, df)

grid <- df %>% 
  expand(x = seq_range(x, 50))
preds <- grid %>% 
  add_predictions(mod, var = "y")

df %>% 
  ggplot(aes(x, y)) +
  geom_line(data = preds) + 
  geom_point()
```

As we fit progressively more and more complicated models, the model error decreases:

```{r}
fs <- list(
  y ~ x,
  y ~ poly(x, 2),
  y ~ poly(x, 3),
  y ~ poly(x, 4),
  y ~ poly(x, 5),
  y ~ poly(x, 6),
  y ~ poly(x, 7)
)

models <- tibble(
  n = 1:7, 
  f = fs,
  mod = map(f, lm, data = df),
  rmse = map2_dbl(mod, list(df), rmse)
)

models %>% 
  ggplot(aes(n, rmse)) + 
  geom_line(colour = "grey70") + 
  geom_point(size = 3)
```

But do you think this model will do well if we apply it to new data from the same population? 

In real-life you can't easily go out and recollect your data. There are two approaches to help you get around this problem. I'll introduce them briefly here, and then we'll go into more depth in the following sections.

```{r}
boot <- bootstrap(df, 100) %>% 
  mutate(
    mod = map(strap, my_model),
    pred = map2(list(grid), mod, add_predictions)
  )

boot %>% 
  unnest(pred) %>% 
  ggplot(aes(x, pred, group = .id)) +
  geom_line(alpha = 1/3)
```

It's a little easier to see what's going on if we zoom on the y axis:

```{r}
last_plot() + 
  coord_cartesian(ylim = c(0, 5))
```

(You might notice that while each individual model varies a lot, the average of all the models seems like it might not be that bad. That gives rise to a model ensemble technique called model averaging.)

Bootstrapping is a useful tool to help us understand how the model might vary if we'd collected a different sample from the population. A related technique is cross-validation which allows us to explore the quality of the model. It works by repeatedly splitting the data into two pieces. One piece, the training set, is used to fit, and the other piece, the test set, is used to measure the model quality.

The following code generates 100 test-training splits, holding out 20% of the data for testing each time. We then fit a model to the training set, and evaluate the error on the test set:

```{r}
cv <- crossv_mc(df, 100) %>% 
  mutate(
    mod = map(train, my_model),
    rmse = map2_dbl(mod, test, rmse)
  )
cv
```

Obviously, a plot is going to help us see distribution more easily. I've added our original estimate of the model error as a white vertical line (where the same dataset is used for both training and testing), and you can see it's very optimistic.

```{r}
cv %>% 
  ggplot(aes(rmse)) +
  geom_ref_line(v = rmse(mod, df)) +
  geom_freqpoly(binwidth = 0.2) +
  geom_rug()
```

The distribution of errors is highly skewed: there are a few cases which have very high errors. These represent samples where we ended up with a few cases on all with low values or high values of x.  Let's take a look:

```{r}
filter(cv, rmse > 1.5) %>% 
  unnest(map(train, as.data.frame)) %>% 
  ggplot(aes(x, .id)) + 
    geom_point() + 
    xlim(0, 1)
```

All of the models that fit particularly poorly were fit to samples that either missed the first one or two or the last one or two observation. Because polynomials shoot off to positive and negative, they give very bad predictions for those values.

Now that we've given you a quick overview and intuition for these techniques, let's dive in more detail.

## Resamples

### Building blocks

Both the boostrap and cross-validation are built on top of a "resample" object. In modelr, you can access these low-level tools directly with the `resample_*` functions. 

These functions return an object of class "resample", which represents the resample in a memory efficient way. Instead of storing the resampled dataset itself, it instead stores the integer indices, and a "pointer" to the original dataset. This makes resamples take up much less memory.

```{r}
x <- resample_bootstrap(as_tibble(mtcars))
class(x)

x
```

Most modelling functions call `as.data.frame()` on the `data` argument. This generates a resampled data frame. Because it's called automatically you can just pass the object.

```{r}
lm(mpg ~ wt, data = x)
```

If you get a strange error, it's probably because the modelling function doesn't do this, and you need to do it yourself. You'll also need to do it yourself if you want to `unnest()` the data so you can visualise it.  If you want to just get the rows selected, you can use `as.integer()`.

### Dataframe API

`bootstrap()` and `crossv_mc()` are built on top of these simpler primitives. They are designed to work naturally in a model exploration environment by returning data frames. Each row of the data frame represents a single sample. They return slightly different columns:

*   `boostrap()` returns a data frame with two columns:

    ```{r}
    bootstrap(df, 3)
    ```
    
    `strap` gives the bootstrap sample dataset, and `.id` assigns a 
    unique identifier to each model (this is often useful for plotting)
    
*   `crossv_mc()` return a data frame with three columns:

    ```{r}
    crossv_mc(df, 3)
    ```
    `train` contains the data that you should use to fit (train) the model,
    and `test` contains the data you should use to validate the model. Together,
    the test and train columns form an exclusive partition of the full dataset.

## Numeric summaries of model quality

When you start dealing with many models, it's helpful to have some rough way of comparing them so you can spend your time looking at the models that do the best job of capturing important features in the data. 

One way to capture the quality of the model is to summarise the distribution of the residuals. For example, you could look at the quantiles of the absolute residuals. For this dataset, 25% of predictions are less than \$7,400 away, and 75% are less than \$25,800 away. That seems like quite a bit of error when predicting someone's income!

```{r}
heights <- tibble(readRDS("data/heights.RDS"))
h <- lm(income ~ height, data = heights)
h 

qae(h, heights)
range(heights$income)
```

You might be familiar with the $R^2$. That's a single number summary that rescales the variance of the residuals to between 0 (very bad) and 1 (very good):

```{r}
rsquare(h, heights)
```

$R^2$ can be interpreted as the amount of variation in the data explained by the model. Here we're explaining 3% of the total variation - not a lot! But I don't think worrying about the relative amount of variation explained is that useful; instead I think you need to consider whether the absolute amount of variation explained is useful for your project.

It's called the $R^2$ because for simple models like this, it's just the square of the correlation between the variables:

```{r}
cor(heights$income, heights$height) ^ 2
```

The $R^2$ is an ok single number summary, but I prefer to think about the unscaled residuals because it's easier to interpret in the context of the original data. As you'll also learn later, it's also a rather optimistic interpretation of the model. Because you're assessing the model using the same data that was used to fit it, it really gives more of an upper bound on the quality of the model, not a fair assessment. 



## Bootstrapping


## Cross-validation



================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/model-basics.Rmd
================================================
# Model basics

## Introduction

The goal of a model is to provide a simple low-dimensional summary of a dataset. In the context of this book we're going to use models to partition data into patterns and residuals. Strong patterns will hide subtler trends, so we'll use models to help peel back layers of structure as we explore a dataset.

However, before we can start using models on interesting, real, datasets, you need to understand the basics of how models work. For that reason, this chapter of the book is unique because it uses only simulated datasets. These datasets are very simple, and not at all interesting, but they will help you understand the essence of modelling before you apply the same techniques to real data in the next chapter.

There are two parts to a model:

1.  First, you define a __family of models__ that express a precise, but 
    generic, pattern that you want to capture. For example, the pattern 
    might be a straight line, or a quadratic curve. You will express
    the model family as an equation like `y = a_1 * x + a_2` or 
    `y = a_1 * x ^ a_2`. Here, `x` and `y` are known variables from your
    data, and `a_1` and `a_2` are parameters that can vary to capture 
    different patterns.

1.  Next, you generate a __fitted model__ by finding the model from the 
    family that is the closest to your data. This takes the generic model 
    family and makes it specific, like `y = 3 * x + 7` or `y = 9 * x ^ 2`.

It's important to understand that a fitted model is just the closest model from a family of models. That implies that you have the "best" model (according to some criteria); it doesn't imply that you have a good model and it certainly doesn't imply that the model is "true". George Box puts this well in his famous aphorism:

> All models are wrong, but some are useful.

It's worth reading the fuller context of the quote:

> Now it would be very remarkable if any system existing in the real world 
> could be exactly represented by any simple model. However, cunningly chosen 
> parsimonious models often do provide remarkably useful approximations. For 
> example, the law PV = RT relating pressure P, volume V and temperature T of 
> an "ideal" gas via a constant R is not exactly true for any real gas, but it 
> frequently provides a useful approximation and furthermore its structure is 
> informative since it springs from a physical view of the behavior of gas 
> molecules.
> 
> For such a model there is no need to ask the question "Is the model true?". 
> If "truth" is to be the "whole truth" the answer must be "No". The only 
> question of interest is "Is the model illuminating and useful?".

The goal of a model is not to uncover truth, but to discover a simple approximation that is still useful. 

### Prerequisites

In this chapter we'll use the modelr package which wraps around base R's modelling functions to make them work naturally in a pipe.

```{r setup, message = FALSE}
library(tidyverse)

library(modelr)
options(na.action = na.warn)
```

## A simple model

Lets take a look at the simulated dataset `sim1`, included with the modelr package. It contains two continuous variables, `x` and `y`. Let's plot them to see how they're related:

```{r}
ggplot(sim1, aes(x, y)) + 
  geom_point()
```

You can see a strong pattern in the data. Let's use a model to capture that pattern and make it explicit. It's our job to supply the basic form of the model. In this case, the relationship looks linear, i.e. `y = a_0 + a_1 * x`.  Let's start by getting a feel for what models from that family look like by randomly generating a few and overlaying them on the data. For this simple case, we can use `geom_abline()` which takes a slope and intercept as parameters. Later on we'll learn more general techniques that work with any model.

```{r}
models <- tibble(
  a1 = runif(250, -20, 40),
  a2 = runif(250, -5, 5)
)

ggplot(sim1, aes(x, y)) + 
  geom_abline(aes(intercept = a1, slope = a2), data = models, alpha = 1/4) +
  geom_point() 
```

There are 250 models on this plot, but a lot are really bad! We need to find the good models by making precise our intuition that a good model is "close" to the data. We need a way to quantify the distance between the data and a model. Then we can fit the model by finding the value of `a_0` and `a_1` that generate the model with the smallest distance from this data.

One easy place to start is to find the vertical distance between each point and the model, as in the following diagram. (Note that I've shifted the x values slightly so you can see the individual distances.)

```{r, echo = FALSE}
dist1 <- sim1 %>% 
  mutate(
    dodge = rep(c(-1, 0, 1) / 20, 10),
    x1 = x + dodge,
    pred = 7 + x1 * 1.5
  )

ggplot(dist1, aes(x1, y)) + 
  geom_abline(intercept = 7, slope = 1.5, colour = "grey40") +
  geom_point(colour = "grey40") +
  geom_linerange(aes(ymin = y, ymax = pred), colour = "#3366FF") 
```

This distance is just the difference between the y value given by the model (the __prediction__), and the actual y value in the data (the __response__).

To compute this distance, we first turn our model family into an R function. This takes the model parameters and the data as inputs, and gives values predicted by the model as output:

```{r}
model1 <- function(a, data) {
  a[1] + data$x * a[2]
}
model1(c(7, 1.5), sim1)
```

Next, we need some way to compute an overall distance between the predicted and actual values. In other words, the plot above shows 30 distances: how do we collapse that into a single number?

One common way to do this in statistics to use the "root-mean-squared deviation". We compute the difference between actual and predicted, square them, average them, and the take the square root. This distance has lots of appealing mathematical properties, which we're not going to talk about here. You'll just have to take my word for it!

```{r}
measure_distance <- function(mod, data) {
  diff <- data$y - model1(mod, data)
  sqrt(mean(diff ^ 2))
}
measure_distance(c(7, 1.5), sim1)
```

Now we can use purrr to compute the distance for all the models defined above. We need a helper function because our distance function expects the model as a numeric vector of length 2.

```{r}
sim1_dist <- function(a1, a2) {
  measure_distance(c(a1, a2), sim1)
}

models <- models %>% 
  mutate(dist = purrr::map2_dbl(a1, a2, sim1_dist))
models
```

Next, let's overlay the 10 best models on to the data. I've coloured the models by `-dist`: this is an easy way to make sure that the best models (i.e. the ones with the smallest distance) get the brighest colours.

```{r}
ggplot(sim1, aes(x, y)) + 
  geom_point(size = 2, colour = "grey30") + 
  geom_abline(
    aes(intercept = a1, slope = a2, colour = -dist), 
    data = filter(models, rank(dist) <= 10)
  )
```

We can also think about these models as observations, and visualising with a scatterplot of `a1` vs  `a2`, again coloured by `-dist`. We can no longer directly see how the model compares to the data, but we can see many models at once. Again, I've highlighted the 10 best models, this time by drawing red circles underneath them.

```{r}
ggplot(models, aes(a1, a2)) +
  geom_point(data = filter(models, rank(dist) <= 10), size = 4, colour = "red") +
  geom_point(aes(colour = -dist))
```

Instead of trying lots of random models, we could be more systematic and generate an evenly spaced grid of points (this is called a grid search). I picked the parameters of the grid roughly by looking at where the best models were in the plot above.

```{r}
grid <- expand.grid(
  a1 = seq(-5, 20, length = 25),
  a2 = seq(1, 3, length = 25)
  ) %>% 
  mutate(dist = purrr::map2_dbl(a1, a2, sim1_dist))

grid %>% 
  ggplot(aes(a1, a2)) +
  geom_point(data = filter(grid, rank(dist) <= 10), size = 4, colour = "red") +
  geom_point(aes(colour = -dist)) 
```

When you overlay the best 10 models back on the original data, they all look pretty good:

```{r}
ggplot(sim1, aes(x, y)) + 
  geom_point(size = 2, colour = "grey30") + 
  geom_abline(
    aes(intercept = a1, slope = a2, colour = -dist), 
    data = filter(grid, rank(dist) <= 10)
  )
```

You could imagine iteratively making the grid finer and finer until you narrowed in on the best model. But there's a better way to tackle that problem: a numerical minimisation tool called Newton-Raphson search. The intuition of Newton-Raphson is pretty simple: you pick a starting point and look around for the steepest slope. You then ski down that slope a little way, and then repeat again and again, until you can't go any lower. In R, we can do that with `optim()`:

```{r}
best <- optim(c(0, 0), measure_distance, data = sim1)
best$par

ggplot(sim1, aes(x, y)) + 
  geom_point(size = 2, colour = "grey30") + 
  geom_abline(intercept = best$par[1], slope = best$par[2])
```

Don't worry too much about the details of how `optim()` works. It's the intuition that's important here. If you have a function that defines the distance between a model and a dataset, an algorithm that can minimise that distance by modifying the parameters of the model, you can find the best model. The neat thing about this approach is that it will work for any family of models that you can write an equation for. 

There's one more approach that we can use for this model, because it's a special case of a broader family: linear models. A linear model has the general form `y = a_1 + a_2 * x_1 + a_3 * x_2 + ... + a_n * x_(n - 1)`. So this simple model is equivalent to a general linear model where n is 2 and `x_1` is `x`. R has a tool specifically designed for fitting linear models called `lm()`. `lm()` has a special way to specify the model family: formulas. Formulas look like `y ~ x`, which `lm()` will translate to a function like `y = a_1 + a_2 * x`. We can fit the model and look at the output:

```{r}
sim1_mod <- lm(y ~ x, data = sim1)
coef(sim1_mod)
```

These are exactly the same values we got with `optim()`! Behind the scenes `lm()` doesn't use `optim()` but instead takes advantage of the mathematical structure of linear models. Using some connections between geometry, calculus, and linear algebra, `lm()` actually finds the closest model in a single step, using a sophisticated algorithm. This approach is both faster, and guarantees that there is a global minimum.

### Exercises

1.  One downside of the linear model is that it is sensitive to unusual values
    because the distance incorporates a squared term. Fit a linear model to 
    the simulated data below, and visualise the results. Rerun a few times to
    generate different simulated datasets. What do you notice about the model? 
    
    ```{r}
    sim1a <- tibble(
      x = rep(1:10, each = 3),
      y = x * 1.5 + 6 + rt(length(x), df = 2)
    )
    ```

1.  One way to make linear models more robust is to use a different distance
    measure. For example, instead of root-mean-squared distance, you could use
    mean-absolute distance:
    
    ```{r}
    measure_distance <- function(mod, data) {
      diff <- data$y - model1(mod, data)
      mean(abs(diff))
    }
    ```
    
    Use `optim()` to fit this model to the simulated data above and compare it 
    to the linear model.

1.  One challenge with performing numerical optimisation is that it's only
    guaranteed to find one local optimum. What's the problem with optimising
    a three parameter model like this?
    
    ```{r}
    model1 <- function(a, data) {
      a[1] + data$x * a[2] + a[3]
    }
    ```

## Visualising models

For simple models, like the one above, you can figure out what pattern the model captures by carefully studying the model family and the fitted coefficients. And if you ever take a statistics course on modelling, you're likely to spend a lot of time doing just that. Here, however, we're going to take a different tack. We're going to focus on understanding a model by looking at its predictions. This has a big advantage: every type of predictive model makes predictions (otherwise what use would it be?) so we can use the same set of techniques to understand any type of predictive model.

It's also useful to see what the model doesn't capture, the so-called residuals which are left after subtracting the predictions from the data. Residuals are powerful because they allow us to use models to remove striking patterns so we can study the subtler trends that remain.

### Predictions

To visualise the predictions from a model, we start by generating an evenly spaced grid of values that covers the region where our data lies. The easiest way to do that is to use `modelr::data_grid()`. Its first argument is a data frame, and for each subsequent argument it finds the unique variables and then generates all combinations:

```{r}
grid <- sim1 %>% 
  data_grid(x) 
grid
```

(This will get more interesting when we start to add more variables to our model.)

Next we add predictions. We'll use `modelr::add_predictions()` which takes a data frame and a model. It adds the predictions from the model to a new column in the data frame:

```{r}
grid <- grid %>% 
  add_predictions(sim1_mod) 
grid
```

(You can also use this function to add predictions to your original dataset.)

Next, we plot the predictions. You might wonder about all this extra work compared to just using `geom_abline()`. But the advantage of this approach is that it will work with _any_ model in R, from the simplest to the most complex. You're only limited by your visualisation skills. For more ideas about how to visualise more complex model types, you might try <http://vita.had.co.nz/papers/model-vis.html>.

```{r}
ggplot(sim1, aes(x)) +
  geom_point(aes(y = y)) +
  geom_line(aes(y = pred), data = grid, colour = "red", size = 1)
```

### Residuals

The flip-side of predictions are __residuals__. The predictions tells you the pattern that the model has captured, and the residuals tell you what the model has missed. The residuals are just the distances between the observed and predicted values that we computed above. 

We add residuals to the data with `add_residuals()`, which works much like `add_predictions()`. Note, however, that we use the original dataset, not a manufactured grid. This is because to compute residuals we need actual y values.

```{r}
sim1 <- sim1 %>% 
  add_residuals(sim1_mod)
sim1
```

There are a few different ways to understand what the residuals tell us about the model. One way is to simply draw a frequency polygon to help us understand the spread of the residuals:

```{r}
ggplot(sim1, aes(resid)) + 
  geom_freqpoly(binwidth = 0.5)
```

This helps you calibrate the quality of the model: how far away are the predictions from the observed values?  Note that the average of the residual will always be 0.

You'll often want to recreate plots using the residuals instead of the original predictor. You'll see a lot of that in the next chapter.

```{r}
ggplot(sim1, aes(x, resid)) + 
  geom_ref_line(h = 0) +
  geom_point() 
```

This looks like random noise, suggesting that our model has done a good job of capturing the patterns in the dataset.

### Exercises

1.  Instead of using `lm()` to fit a straight line, you can use `loess()`
    to fit a smooth curve. Repeat the process of model fitting, 
    grid generation, predictions, and visualisation on `sim1` using 
    `loess()` instead of `lm()`. How does the result compare to 
    `geom_smooth()`?
    
1.  `add_predictions()` is paired with `gather_predictions()` and 
    `spread_predictions()`. How do these three functions differ?
    
1.  What does `geom_ref_line()` do? What package does it come from?
    Why is displaying a reference line in plots showing residuals
    useful and important?
    
1.  Why might you want to look at a frequency polygon of absolute residuals?
    What are the pros and cons compared to looking at the raw residuals?

## Formulas and model families

You've seen formulas before when using `facet_wrap()` and `facet_grid()`. In R, formulas provide a general way of getting "special behaviour". Rather than evaluating the values of the variables right away, they capture them so they can be interpreted by the function.

The majority of modelling functions in R use a standard conversion from formulas to functions. You've seen one simple conversion already: `y ~ x` is translated to `y = a_1 + a_2 * x`.  If you want to see what R actually does, you can use the `model_matrix()` function. It takes a data frame and a formula and returns a tibble that defines the model equation: each column in the output is associated with one coefficient in the model, the function is always `y = a_1 * out1 + a_2 * out_2`. For the simplest case of `y ~ x1` this shows us something interesting:

```{r}
df <- tribble(
  ~y, ~x1, ~x2,
  4, 2, 5,
  5, 1, 6
)
model_matrix(df, y ~ x1)
```

The way that R adds the intercept to the model is just by having a column that is full of ones.  By default, R will always add this column. If you don't want, you need to explicitly drop it with `-1`:

```{r}
model_matrix(df, y ~ x1 - 1)
```

The model matrix grows in an unsurprising way when you add more variables to the the model:

```{r}
model_matrix(df, y ~ x1 + x2)
```

This formula notation is sometimes called "Wilkinson-Rogers notation", and was initially described in _Symbolic Description of Factorial Models for Analysis of Variance_, by G. N. Wilkinson and C. E. Rogers <https://www.jstor.org/stable/2346786>. It's worth digging up and reading the original paper if you'd like to understand the full details of the modelling algebra.

The following sections expand on how this formula notation works for categorical variables, interactions, and transformation.

### Categorical variables

Generating a function from a formula is straight forward when the predictor is continuous, but things get a bit more complicated when the predictor is categorical. Imagine you have a formula like `y ~ sex`, where sex could either be male or female. It doesn't make sense to convert that to a formula like `y = x_0 + x_1 * sex` because `sex` isn't a number - you can't multiply it! Instead what R does is convert it to `y = x_0 + x_1 * sex_male` where `sex_male` is one if `sex` is male and zero otherwise:

```{r}
df <- tribble(
  ~ sex, ~ response,
  "male", 1,
  "female", 2,
  "male", 1
)
model_matrix(df, response ~ sex)
```

You might wonder why R also doesn't create a `sexfemale` column. The problem is that would create a column that is perfectly predictable based on the other columns (i.e. `sexfemale = 1 - sexmale`). Unfortunately the exact details of why this is a problem is beyond the scope of this book, but basically it creates a model family that is too flexible, and will have infinitely many models that are equally close to the data.

Fortunately, however, if you focus on visualising predictions you don't need to worry about the exact parameterisation. Let's look at some data and models to make that concrete. Here's the `sim2` dataset from modelr:

```{r}
ggplot(sim2) + 
  geom_point(aes(x, y))
```

We can fit a model to it, and generate predictions:

```{r}
mod2 <- lm(y ~ x, data = sim2)

grid <- sim2 %>% 
  data_grid(x) %>% 
  add_predictions(mod2)
grid
```

Effectively, a model with a categorical `x` will predict the mean value for each category. (Why? Because the mean minimises the root-mean-squared distance.) That's easy to see if we overlay the predictions on top of the original data:

```{r}
ggplot(sim2, aes(x)) + 
  geom_point(aes(y = y)) +
  geom_point(data = grid, aes(y = pred), colour = "red", size = 4)
```

You can't make predictions about levels that you didn't observe. Sometimes you'll do this by accident so it's good to recognise this error message:

```{r, error = TRUE}
tibble(x = "e") %>% 
  add_predictions(mod2)
```

### Interactions (continuous and categorical)

What happens when you combine a continuous and a categorical variable?  `sim3` contains a categorical predictor and a continuous predictor. We can visualise it with a simple plot:

```{r}
ggplot(sim3, aes(x1, y)) + 
  geom_point(aes(colour = x2))
```

There are two possible models you could fit to this data:

```{r}
mod1 <- lm(y ~ x1 + x2, data = sim3)
mod2 <- lm(y ~ x1 * x2, data = sim3)
```

When you add variables with `+`, the model will estimate each effect independent of all the others. It's possible to fit the so-called interaction by using `*`. For example, `y ~ x1 * x2` is translated to `y = a_0 + a_1 * x1 + a_2 * x2 + a_12 * x1 * x2`. Note that whenever you use `*`, both the interaction and the individual components are included in the model.

To visualise these models we need two new tricks:

1.  We have two predictors, so we need to give `data_grid()` both variables. 
    It finds all the unique values of `x1` and `x2` and then generates all
    combinations. 
   
1.  To generate predictions from both models simultaneously, we can use 
    `gather_predictions()` which adds each prediction as a row. The
    complement of `gather_predictions()` is `spread_predictions()` which adds 
    each prediction to a new column.
    
Together this gives us:

```{r}
grid <- sim3 %>% 
  data_grid(x1, x2) %>% 
  gather_predictions(mod1, mod2)
grid
```

We can visualise the results for both models on one plot using facetting:

```{r}
ggplot(sim3, aes(x1, y, colour = x2)) + 
  geom_point() + 
  geom_line(data = grid, aes(y = pred)) + 
  facet_wrap(~ model)
```

Note that the model that uses `+` has the same slope for each line, but different intercepts. The model that uses `*` has a different slope and intercept for each line.

Which model is better for this data? We can take look at the residuals. Here I've facetted by both model and `x2` because it makes it easier to see the pattern within each group.

```{r}
sim3 <- sim3 %>% 
  gather_residuals(mod1, mod2)

ggplot(sim3, aes(x1, resid, colour = x2)) + 
  geom_point() + 
  facet_grid(model ~ x2)
```

There is little obvious pattern in the residuals for `mod2`. The residuals for `mod1` show that the model has clearly missed some pattern in `b`, and less so, but still present is pattern in `c`, and `d`. You might wonder if there's a precise way to tell which of `mod1` or `mod2` is better. There is, but it requires a lot of mathematical background, and we don't really care. Here, we're interested in a qualitative assessment of whether or not the model has captured the pattern that we're interested in. 

### Interactions (two continuous)

Let's take a look at the equivalent model for two continuous variables. Initially things proceed almost identically to the previous example:

```{r}
mod1 <- lm(y ~ x1 + x2, data = sim4)
mod2 <- lm(y ~ x1 * x2, data = sim4)

grid <- sim4 %>% 
  data_grid(
    x1 = seq_range(x1, 5), 
    x2 = seq_range(x2, 5) 
  ) %>% 
  gather_predictions(mod1, mod2)
grid
```

Note my use of `seq_range()` inside `data_grid()`. Instead of using every unique value of `x`, I'm going to use a regularly spaced grid of five values between the minimum and maximum numbers. It's probably not super important here, but it's a useful technique in general. There are two other useful arguments to `seq_range()`:

*  `pretty = TRUE` will generate a "pretty" sequence, i.e. something that looks
    nice to the human eye. This is useful if you want to produce tables of 
    output:
    
    ```{r}
    seq_range(c(0.0123, 0.923423), n = 5)
    seq_range(c(0.0123, 0.923423), n = 5, pretty = TRUE)
    ```
    
*   `trim = 0.1` will trim off 10% of the tail values. This is useful if the 
    variables have a long tailed distribution and you want to focus on generating
    values near the center:
    
    ```{r}
    x1 <- rcauchy(100)
    seq_range(x1, n = 5)
    seq_range(x1, n = 5, trim = 0.10)
    seq_range(x1, n = 5, trim = 0.25)
    seq_range(x1, n = 5, trim = 0.50)
    ```
    
*   `expand = 0.1` is in some sense the opposite of `trim()` it expands the 
    range by 10%.
    
    ```{r}
    x2 <- c(0, 1)
    seq_range(x2, n = 5)
    seq_range(x2, n = 5, expand = 0.10)
    seq_range(x2, n = 5, expand = 0.25)
    seq_range(x2, n = 5, expand = 0.50)
    ```

Next let's try and visualise that model. We have two continuous predictors, so you can imagine the model like a 3d surface. We could display that using `geom_tile()`:

```{r}
ggplot(grid, aes(x1, x2)) + 
  geom_tile(aes(fill = pred)) + 
  facet_wrap(~ model)
```

That doesn't suggest that the models are very different! But that's partly an illusion: our eyes and brains are not very good at accurately comparing shades of colour. Instead of looking at the surface from the top, we could look at it from either side, showing multiple slices:

```{r, asp = 1/2}
ggplot(grid, aes(x1, pred, colour = x2, group = x2)) + 
  geom_line() +
  facet_wrap(~ model)
ggplot(grid, aes(x2, pred, colour = x1, group = x1)) + 
  geom_line() +
  facet_wrap(~ model)
```

This shows you that interaction between two continuous variables works basically the same way as for a categorical and continuous variable. An interaction says that there's not a fixed offset: you need to consider both values of `x1` and `x2` simultaneously in order to predict `y`.

You can see that even with just two continuous variables, coming up with good visualisations are hard. But that's reasonable: you shouldn't expect it will be easy to understand how three or more variables simultaneously interact! But again, we're saved a little because we're using models for exploration, and you can gradually build up your model over time. The model doesn't have to be perfect, it just has to help you reveal a little more about your data.

I spent some time looking at the residuals to see if I could figure if `mod2` did better than `mod1`. I think it does, but it's pretty subtle. You'll have a chance to work on it in the exercises.

### Transformations

You can also perform transformations inside the model formula. For example, `log(y) ~ sqrt(x1) + x2` is transformed to `log(y) = a_1 + a_2 * sqrt(x1) + a_3 * x2`. If your transformation involves `+`, `*`, `^`, or `-`, you'll need to wrap it in `I()` so R doesn't treat it like part of the model specification. For example, `y ~ x + I(x ^ 2)` is translated to `y = a_1 + a_2 * x + a_3 * x^2`. If you forget the `I()` and specify `y ~ x ^ 2 + x`, R will compute `y ~ x * x + x`. `x * x` means the interaction of `x` with itself, which is the same as `x`. R automatically drops redundant variables so `x + x` become `x`, meaning that `y ~ x ^ 2 + x` specifies the function `y = a_1 + a_2 * x`. That's probably not what you intended!

Again, if you get confused about what your model is doing, you can always use `model_matrix()` to see exactly what equation `lm()` is fitting:

```{r}
df <- tribble(
  ~y, ~x,
   1,  1,
   2,  2, 
   3,  3
)
model_matrix(df, y ~ x^2 + x)
model_matrix(df, y ~ I(x^2) + x)
```

Transformations are useful because you can use them to approximate non-linear functions. If you've taken a calculus class, you may have heard of Taylor's theorem which says you can approximate any smooth function with an infinite sum of polynomials. That means you can use a polynomial function to get arbitrarily close to a smooth function by fitting an equation like `y = a_1 + a_2 * x + a_3 * x^2 + a_4 * x ^ 3`. Typing that sequence by hand is tedious, so R provides a helper function: `poly()`:

```{r}
model_matrix(df, y ~ poly(x, 2))
```

However there's one major problem with using `poly()`: outside the range of the data, polynomials rapidly shoot off to positive or negative infinity. One safer alternative is to use the natural spline, `splines::ns()`.

```{r}
library(splines)
model_matrix(df, y ~ ns(x, 2))
```

Let's see what that looks like when we try and approximate a non-linear function:

```{r}
sim5 <- tibble(
  x = seq(0, 3.5 * pi, length = 50),
  y = 4 * sin(x) + rnorm(length(x))
)

ggplot(sim5, aes(x, y)) +
  geom_point()
```

I'm going to fit five models to this data.

```{r}
mod1 <- lm(y ~ ns(x, 1), data = sim5)
mod2 <- lm(y ~ ns(x, 2), data = sim5)
mod3 <- lm(y ~ ns(x, 3), data = sim5)
mod4 <- lm(y ~ ns(x, 4), data = sim5)
mod5 <- lm(y ~ ns(x, 5), data = sim5)

grid <- sim5 %>% 
  data_grid(x = seq_range(x, n = 50, expand = 0.1)) %>% 
  gather_predictions(mod1, mod2, mod3, mod4, mod5, .pred = "y")

ggplot(sim5, aes(x, y)) + 
  geom_point() +
  geom_line(data = grid, colour = "red") +
  facet_wrap(~ model)
```

Notice that the extrapolation outside the range of the data is clearly bad. This is the downside to approximating a function with a polynomial. But this is a very real problem with every model: the model can never tell you if the behaviour is true when you start extrapolating outside the range of the data that you have seen. You must rely on theory and science.

### Exercises

1.  What happens if you repeat the analysis of `sim2` using a model without
    an intercept. What happens to the model equation? What happens to the
    predictions?
    
1.  Use `model_matrix()` to explore the equations generated for the models
    I fit to `sim3` and `sim4`. Why is `*` a good shorthand for interaction?

1.  Using the basic principles, convert the formulas in the following two
    models into functions. (Hint: start by converting the categorical variable
    into 0-1 variables.)
    
    ```{r, eval = FALSE}
    mod1 <- lm(y ~ x1 + x2, data = sim3)
    mod2 <- lm(y ~ x1 * x2, data = sim3)
    ```

1.   For `sim4`,  which of `mod1` and `mod2` is better? I think `mod2` does a 
     slightly better job at removing patterns, but it's pretty subtle. Can you 
     come up with a plot to support my claim? 

## Missing values

Missing values obviously can not convey any information about the relationship between the variables, so modelling functions will drop any rows that contain missing values. R's default behaviour is to silently drop them, but `options(na.action = na.warn)` (run in the prerequisites), makes sure you get a warning.

```{r}
df <- tribble(
  ~x, ~y,
  1, 2.2,
  2, NA,
  3, 3.5,
  4, 8.3,
  NA, 10
)

mod <- lm(y ~ x, data = df)
```

To suppress the warning, set `na.action = na.exclude`:

```{r}
mod <- lm(y ~ x, data = df, na.action = na.exclude)
```

You can always see exactly how many observations were used with `nobs()`:

```{r}
nobs(mod)
```

## Other model families

This chapter has focussed exclusively on the class of linear models, which assume a relationship of the form `y = a_1 * x1 + a_2 * x2 + ... + a_n * xn`. Linear models additionally assume that the residuals have a normal distribution, which we haven't talked about. There are a large set of model classes that extend the linear model in various interesting ways. Some of them are:

* __Generalised linear models__, e.g. `stats::glm()`. Linear models assume that
  the response is continuous and the error has a normal distribution. 
  Generalised linear models extend linear models to include non-continuous
  responses (e.g. binary data or counts). They work by defining a distance
  metric based on the statistical idea of likelihood.
  
* __Generalised additive models__, e.g. `mgcv::gam()`, extend generalised
  linear models to incorporate arbitrary smooth functions. That means you can
  write a formula like `y ~ s(x)` which becomes an equation like 
  `y = f(x)` and let `gam()` estimate what that function is (subject to some
  smoothness constraints to make the problem tractable).
  
* __Penalised linear models__, e.g. `glmnet::glmnet()`, add a penalty term to
  the distance that penalises complex models (as defined by the distance 
  between the parameter vector and the origin). This tends to make
  models that generalise better to new datasets from the same population.

* __Robust linear models__, e.g. `MASS:rlm()`, tweak the distance to downweight 
  points that are very far away. This makes them less sensitive to the presence
  of outliers, at the cost of being not quite as good when there are no 
  outliers.
  
* __Trees__, e.g. `rpart::rpart()`, attack the problem in a completely different
  way than linear models. They fit a piece-wise constant model, splitting the
  data into progressively smaller and smaller pieces. Trees aren't terribly
  effective by themselves, but they are very powerful when used in aggregate
  by models like __random forests__ (e.g. `randomForest::randomForest()`) or 
  __gradient boosting machines__ (e.g. `xgboost::xgboost`.)

These models all work similarly from a programming perspective. Once you've mastered linear models, you should find it easy to master the mechanics of these other model classes. Being a skilled modeller is a mixture of some good general principles and having a big toolbox of techniques. Now that you've learned some general tools and one useful class of models, you can go on and learn more classes from other sources.


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/model-building.Rmd
================================================
# Model building

## Introduction

In the previous chapter you learned how linear models work, and learned some basic tools for understanding what a model is telling you about your data. The previous chapter focussed on simulated datasets. This chapter will focus on real data, showing you how you can progressively build up a model to aid your understanding of the data.

We will take advantage of the fact that you can think about a model partitioning your data into pattern and residuals. We'll find patterns with visualisation, then make them concrete and precise with a model. We'll then repeat the process, but replace the old response variable with the residuals from the model. The goal is to transition from implicit knowledge in the data and your head to explicit knowledge in a quantitative model. This makes it easier to apply to new domains, and easier for others to use. 

For very large and complex datasets this will be a lot of work. There are certainly alternative approaches - a more machine learning approach is simply to focus on the predictive ability of the model. These approaches tend to produce black boxes: the model does a really good job at generating predictions, but you don't know why. This is a totally reasonable approach, but it does make it hard to apply your real world knowledge to the model. That, in turn, makes it difficult to assess whether or not the model will continue to work in the long-term, as fundamentals change. For most real models, I'd expect you to use some combination of this approach and a more classic automated approach.

It's a challenge to know when to stop. You need to figure out when your model is good enough, and when additional investment is unlikely to pay off. I particularly like this quote from reddit user Broseidon241: 

> A long time ago in art class, my teacher told me "An artist needs to know 
> when a piece is done. You can't tweak something into perfection - wrap it up. 
> If you don't like it, do it over again. Otherwise begin something new". Later
> in life, I heard "A poor seamstress makes many mistakes. A good seamstress 
> works hard to correct those mistakes. A great seamstress isn't afraid to 
> throw out the garment and start over."

-- Broseidon241, <https://www.reddit.com/r/datascience/comments/4irajq>

### Prerequisites

We'll use the same tools as in the previous chapter, but add in some real datasets: `diamonds` from ggplot2, and `flights` from nycflights13.  We'll also need lubridate in order to work with the date/times in `flights`.

```{r setup, message = FALSE}
library(tidyverse)
library(modelr)
options(na.action = na.warn)

library(nycflights13)
library(lubridate)
```

## Why are low quality diamonds more expensive? {#diamond-prices}

In previous chapters we've seen a surprising relationship between the quality of diamonds and their price: low quality diamonds (poor cuts, bad colours, and inferior clarity) have higher prices.

```{r dev = "png"}
ggplot(diamonds, aes(cut, price)) + geom_boxplot()
ggplot(diamonds, aes(color, price)) + geom_boxplot()
ggplot(diamonds, aes(clarity, price)) + geom_boxplot()
```

Note that the worst diamond color is J (slightly yellow), and the worst clarity is I1 (inclusions visible to the naked eye).

### Price and carat

It looks like lower quality diamonds have higher prices because there is an important confounding variable: the weight (`carat`) of the diamond. The weight of the diamond is the single most important factor for determining the price of the diamond, and lower quality diamonds tend to be larger.

```{r}
ggplot(diamonds, aes(carat, price)) + 
  geom_hex(bins = 50)
```

We can make it easier to see how the other attributes of a diamond affect its relative `price` by fitting a model to separate out the effect of `carat`. But first, lets make a couple of tweaks to the diamonds dataset to make it easier to work with:

1. Focus on diamonds smaller than 2.5 carats (99.7% of the data)
1. Log-transform the carat and price variables.

```{r}
diamonds2 <- diamonds %>% 
  filter(carat <= 2.5) %>% 
  mutate(lprice = log2(price), lcarat = log2(carat))
```

Together, these changes make it easier to see the relationship between `carat` and `price`:

```{r}
ggplot(diamonds2, aes(lcarat, lprice)) + 
  geom_hex(bins = 50)
```

The log-transformation is particularly useful here because it makes the pattern linear, and linear patterns are the easiest to work with. Let's take the next step and remove that strong linear pattern. We first make the pattern explicit by fitting a model:

```{r}
mod_diamond <- lm(lprice ~ lcarat, data = diamonds2)
```

Then we look at what the model tells us about the data. Note that I back transform the predictions, undoing the log transformation, so I can overlay the predictions on the raw data:

```{r}
grid <- diamonds2 %>% 
  data_grid(carat = seq_range(carat, 20)) %>% 
  mutate(lcarat = log2(carat)) %>% 
  add_predictions(mod_diamond, "lprice") %>% 
  mutate(price = 2 ^ lprice)

ggplot(diamonds2, aes(carat, price)) + 
  geom_hex(bins = 50) + 
  geom_line(data = grid, colour = "red", size = 1)
```

That tells us something interesting about our data. If we believe our model, then the large diamonds are much cheaper than expected. This is probably because no diamond in this dataset costs more than $19,000.

Now we can look at the residuals, which verifies that we've successfully removed the strong linear pattern:

```{r}
diamonds2 <- diamonds2 %>% 
  add_residuals(mod_diamond, "lresid")

ggplot(diamonds2, aes(lcarat, lresid)) + 
  geom_hex(bins = 50)
```

Importantly, we can now re-do our motivating plots using those residuals instead of `price`. 

```{r dev = "png"}
ggplot(diamonds2, aes(cut, lresid)) + geom_boxplot()
ggplot(diamonds2, aes(color, lresid)) + geom_boxplot()
ggplot(diamonds2, aes(clarity, lresid)) + geom_boxplot()
```

Now we see the relationship we expect: as the quality of the diamond increases, so too does its relative price. To interpret the `y` axis, we need to think about what the residuals are telling us, and what scale they are on. A residual of -1 indicates that `lprice` was 1 unit lower than a prediction based solely on its weight. $2^{-1}$ is 1/2, points with a value of -1 are half the expected price, and residuals with value 1 are twice the predicted price.

### A more complicated model

If we wanted to, we could continue to build up our model, moving the effects we've observed into the model to make them explicit. For example, we could include `color`, `cut`, and `clarity` into the model so that we also make explicit the effect of these three categorical variables:

```{r}
mod_diamond2 <- lm(lprice ~ lcarat + color + cut + clarity, data = diamonds2)
```

This model now includes four predictors, so it's getting harder to visualise. Fortunately, they're currently all independent which means that we can plot them individually in four plots. To make the process a little easier, we're going to use the `.model` argument to `data_grid`:

```{r}
grid <- diamonds2 %>% 
  data_grid(cut, .model = mod_diamond2) %>% 
  add_predictions(mod_diamond2)
grid

ggplot(grid, aes(cut, pred)) + 
  geom_point()
```

If the model needs variables that you haven't explicitly supplied, `data_grid()` will automatically fill them in with "typical" value. For continuous variables, it uses the median, and categorical variables it uses the most common value (or values, if there's a tie).

```{r}
diamonds2 <- diamonds2 %>% 
  add_residuals(mod_diamond2, "lresid2")

ggplot(diamonds2, aes(lcarat, lresid2)) + 
  geom_hex(bins = 50)
```

This plot indicates that there are some diamonds with quite large residuals - remember a residual of 2 indicates that the diamond is 4x the price that we expected. It's often useful to look at unusual values individually:

```{r}
diamonds2 %>% 
  filter(abs(lresid2) > 1) %>% 
  add_predictions(mod_diamond2) %>% 
  mutate(pred = round(2 ^ pred)) %>% 
  select(price, pred, carat:table, x:z) %>% 
  arrange(price)
```

Nothing really jumps out at me here, but it's probably worth spending time considering if this indicates a problem with our model, or if there are errors in the data. If there are mistakes in the data, this could be an opportunity to buy diamonds that have been priced low incorrectly.

### Exercises

1.  In the plot of `lcarat` vs. `lprice`, there are some bright vertical
    strips. What do they represent?

1.  If `log(price) = a_0 + a_1 * log(carat)`, what does that say about 
    the relationship between `price` and `carat`?
    
1.  Extract the diamonds that have very high and very low residuals. 
    Is there anything unusual about these diamonds? Are they particularly bad
    or good, or do you think these are pricing errors?

1.  Does the final model, `mod_diamond2`, do a good job of predicting
    diamond prices? Would you trust it to tell you how much to spend
    if you were buying a diamond?

## What affects the number of daily flights?

Let's work through a similar process for a dataset that seems even simpler at first glance: the number of flights that leave NYC per day. This is a really small dataset --- only 365 rows and 2 columns --- and we're not going to end up with a fully realised model, but as you'll see, the steps along the way will help us better understand the data. Let's get started by counting the number of flights per day and visualising it with ggplot2.

```{r}
daily <- flights %>% 
  mutate(date = make_date(year, month, day)) %>% 
  group_by(date) %>% 
  summarise(n = n())
daily

ggplot(daily, aes(date, n)) + 
  geom_line()
```

### Day of week

Understanding the long-term trend is challenging because there's a very strong day-of-week effect that dominates the subtler patterns. Let's start by looking at the distribution of flight numbers by day-of-week:

```{r}
daily <- daily %>% 
  mutate(wday = wday(date, label = TRUE))
ggplot(daily, aes(wday, n)) + 
  geom_boxplot()
```

There are fewer flights on weekends because most travel is for business. The effect is particularly pronounced on Saturday: you might sometimes leave on Sunday for a Monday morning meeting, but it's very rare that you'd leave on Saturday as you'd much rather be at home with your family.

One way to remove this strong pattern is to use a model. First, we fit the model, and display its predictions overlaid on the original data:

```{r}
mod <- lm(n ~ wday, data = daily)

grid <- daily %>% 
  data_grid(wday) %>% 
  add_predictions(mod, "n")

ggplot(daily, aes(wday, n)) + 
  geom_boxplot() +
  geom_point(data = grid, colour = "red", size = 4)
```

Next we compute and visualise the residuals:

```{r}
daily <- daily %>% 
  add_residuals(mod)
daily %>% 
  ggplot(aes(date, resid)) + 
  geom_ref_line(h = 0) + 
  geom_line()
```

Note the change in the y-axis: now we are seeing the deviation from the expected number of flights, given the day of week. This plot is useful because now that we've removed much of the large day-of-week effect, we can see some of the subtler patterns that remain:

1.  Our model seems to fail starting in June: you can still see a strong 
    regular pattern that our model hasn't captured. Drawing a plot with one 
    line for each day of the week makes the cause easier to see:

    ```{r}
    ggplot(daily, aes(date, resid, colour = wday)) + 
      geom_ref_line(h = 0) + 
      geom_line()
    ```

    Our model fails to accurately predict the number of flights on Saturday:
    during summer there are more flights than we expect, and during Fall there 
    are fewer. We'll see how we can do better to capture this pattern in the
    next section.

1.  There are some days with far fewer flights than expected:

    ```{r}
    daily %>% 
      filter(resid < -100)
    ```

    If you're familiar with American public holidays, you might spot New Year's 
    day, July 4th, Thanksgiving and Christmas. There are some others that don't 
    seem to correspond to public holidays. You'll work on those in one 
    of the exercises.
    
1.  There seems to be some smoother long term trend over the course of a year.
    We can highlight that trend with `geom_smooth()`:

    ```{r}
    daily %>% 
      ggplot(aes(date, resid)) + 
      geom_ref_line(h = 0) + 
      geom_line(colour = "grey50") + 
      geom_smooth(se = FALSE, span = 0.20)
    ```

    There are fewer flights in January (and December), and more in summer 
    (May-Sep). We can't do much with this pattern quantitatively, because we 
    only have a single year of data. But we can use our domain knowledge to 
    brainstorm potential explanations.

### Seasonal Saturday effect

Let's first tackle our failure to accurately predict the number of flights on Saturday. A good place to start is to go back to the raw numbers, focussing on Saturdays:

```{r}
daily %>% 
  filter(wday == "Sat") %>% 
  ggplot(aes(date, n)) + 
    geom_point() + 
    geom_line() +
    scale_x_date(NULL, date_breaks = "1 month", date_labels = "%b")
```

(I've used both points and lines to make it more clear what is data and what is interpolation.)

I suspect this pattern is caused by summer holidays: many people go on holiday in the summer, and people don't mind travelling on Saturdays for vacation. Looking at this plot, we might guess that summer holidays are from early June to late August. That seems to line up fairly well with the [state's school terms](http://schools.nyc.gov/Calendar/2013-2014+School+Year+Calendars.htm): summer break in 2013 was Jun 26--Sep 9. 

Why are there more Saturday flights in the Spring than the Fall? I asked some American friends and they suggested that it's less common to plan family vacations during the Fall because of the big Thanksgiving and Christmas holidays. We don't have the data to know for sure, but it seems like a plausible working hypothesis.

Lets create a "term" variable that roughly captures the three school terms, and check our work with a plot:

```{r}
term <- function(date) {
  cut(date, 
    breaks = ymd(20130101, 20130605, 20130825, 20140101),
    labels = c("spring", "summer", "fall") 
  )
}

daily <- daily %>% 
  mutate(term = term(date)) 

daily %>% 
  filter(wday == "Sat") %>% 
  ggplot(aes(date, n, colour = term)) +
  geom_point(alpha = 1/3) + 
  geom_line() +
  scale_x_date(NULL, date_breaks = "1 month", date_labels = "%b")
```

(I manually tweaked the dates to get nice breaks in the plot. Using a visualisation to help you understand what your function is doing is a really powerful and general technique.)

It's useful to see how this new variable affects the other days of the week:

```{r}
daily %>% 
  ggplot(aes(wday, n, colour = term)) +
    geom_boxplot()
```

It looks like there is significant variation across the terms, so fitting a separate day of week effect for each term is reasonable. This improves our model, but not as much as we might hope:

```{r}
mod1 <- lm(n ~ wday, data = daily)
mod2 <- lm(n ~ wday * term, data = daily)

daily %>% 
  gather_residuals(without_term = mod1, with_term = mod2) %>% 
  ggplot(aes(date, resid, colour = model)) +
    geom_line(alpha = 0.75)
```

We can see the problem by overlaying the predictions from the model on to the raw data:

```{r}
grid <- daily %>% 
  data_grid(wday, term) %>% 
  add_predictions(mod2, "n")

ggplot(daily, aes(wday, n)) +
  geom_boxplot() + 
  geom_point(data = grid, colour = "red") + 
  facet_wrap(~ term)
```

Our model is finding the _mean_ effect, but we have a lot of big outliers, so mean tends to be far away from the typical value. We can alleviate this problem by using a model that is robust to the effect of outliers: `MASS::rlm()`. This greatly reduces the impact of the outliers on our estimates, and gives a model that does a good job of removing the day of week pattern:

```{r, warn = FALSE}
mod3 <- MASS::rlm(n ~ wday * term, data = daily)

daily %>% 
  add_residuals(mod3, "resid") %>% 
  ggplot(aes(date, resid)) + 
  geom_hline(yintercept = 0, size = 2, colour = "white") + 
  geom_line()
```

It's now much easier to see the long-term trend, and the positive and negative outliers.


### Computed variables

If you're experimenting with many models and many visualisations, it's a good idea to bundle the creation of variables up into a function so there's no chance of accidentally applying a different transformation in different places. For example, we could write:

```{r}
compute_vars <- function(data) {
  data %>% 
    mutate(
      term = term(date), 
      wday = wday(date, label = TRUE)
    )
}
```

Another option is to put the transformations directly in the model formula:

```{r}
wday2 <- function(x) wday(x, label = TRUE)
mod3 <- lm(n ~ wday2(date) * term(date), data = daily)
```

Either approach is reasonable. Making the transformed variable explicit is useful if you want to check your work, or use them in a visualisation. But you can't easily use transformations (like splines) that return multiple columns. Including the transformations in the model function makes life a little easier when you're working with many different datasets because the model is self contained.

### Time of year: an alternative approach

In the previous section we used our domain knowledge (how the US school term affects travel) to improve the model. An alternative to using our knowledge explicitly in the model is to give the data more room to speak. We could use a more flexible model and allow that to capture the pattern we're interested in. A simple linear trend isn't adequate, so we could try using a natural spline to fit a smooth curve across the year:

```{r}
library(splines)
mod <- MASS::rlm(n ~ wday * ns(date, 5), data = daily)

daily %>% 
  data_grid(wday, date = seq_range(date, n = 13)) %>% 
  add_predictions(mod) %>% 
  ggplot(aes(date, pred, colour = wday)) + 
    geom_line() +
    geom_point()
```

We see a strong pattern in the numbers of Saturday flights. This is reassuring, because we also saw that pattern in the raw data. It's a good sign when you get the same signal from different approaches.


### Exercises

1.  Use your Google sleuthing skills to brainstorm why there were fewer than
    expected flights on Jan 20, May 26, and Sep 1. (Hint: they all have the
    same explanation.) How would these days generalise to another year?

1.  What do the three days with high positive residuals represent?
    How would these days generalise to another year?

    ```{r}
    daily %>% 
      top_n(3, resid)
    ```

1.  Create a new variable that splits the `wday` variable into terms, but only
    for Saturdays, i.e. it should have `Thurs`, `Fri`, but `Sat-summer`, 
    `Sat-spring`, `Sat-fall`. How does this model compare with the model with 
    every combination of `wday` and `term`?
    
1.  Create a new `wday` variable that combines the day of week, term 
    (for Saturdays), and public holidays. What do the residuals of 
    that model look like?

1.  What happens if you fit a day of week effect that varies by month 
    (i.e. `n ~ wday * month`)? Why is this not very helpful? 

1.  What would you expect the model `n ~ wday + ns(date, 5)` to look like?
    Knowing what you know about the data, why would you expect it to be
    not particularly effective?

1.  We hypothesised that people leaving on Sundays are more likely to be 
    business travellers who need to be somewhere on Monday. Explore that 
    hypothesis by seeing how it breaks down based on distance and time: if 
    it's true, you'd expect to see more Sunday evening flights to places that 
    are far away.

1.  It's a little frustrating that Sunday and Saturday are on separate ends
    of the plot. Write a small function to set the levels of the 
    factor so that the week starts on Monday.

## Learning more about models

We have only scratched the absolute surface of modelling, but you have hopefully gained some simple, but general-purpose tools that you can use to improve your own data analyses. It's OK to start simple! As you've seen, even very simple models can make a dramatic difference in your ability to tease out interactions between variables.

These modelling chapters are even more opinionated than the rest of the book. I approach modelling from a somewhat different perspective to most others, and there is relatively little space devoted to it. Modelling really deserves a book on its own, so I'd highly recommend that you read at least one of these three books:

* *Statistical Modeling: A Fresh Approach* by Danny Kaplan,
  <http://www.mosaic-web.org/go/StatisticalModeling/>. This book provides 
  a gentle introduction to modelling, where you build your intuition,
  mathematical tools, and R skills in parallel. The book replaces a traditional
  "introduction to statistics" course, providing a curriculum that is up-to-date 
  and relevant to data science.

* *An Introduction to Statistical Learning* by Gareth James, Daniela Witten, 
  Trevor Hastie, and Robert Tibshirani, <http://www-bcf.usc.edu/~gareth/ISL/> 
  (available online for free). This book presents a family of modern modelling
  techniques collectively known as statistical learning.  For an even deeper
  understanding of the math behind the models, read the classic 
  *Elements of Statistical Learning* by Trevor Hastie, Robert Tibshirani, and
  Jerome Friedman, <http://statweb.stanford.edu/~tibs/ElemStatLearn/> (also
  available online for free).

* *Applied Predictive Modeling* by Max Kuhn and Kjell Johnson, 
  <http://appliedpredictivemodeling.com>. This book is a companion to the 
  __caret__ package and provides practical tools for dealing with real-life
  predictive modelling challenges.


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/model-many.Rmd
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# Many models

## Introduction

In this chapter you're going to learn three powerful ideas that help you to work with large numbers of models with ease:

1.  Using many simple models to better understand complex datasets.

1.  Using list-columns to store arbitrary data structures in a data frame.
    For example, this will allow you to have a column that contains linear 
    models.
   
1.  Using the __broom__ package, by David Robinson, to turn models into tidy 
    data. This is a powerful technique for working with large numbers of models
    because once you have tidy data, you can apply all of the techniques that 
    you've learned about earlier in the book.

We'll start by diving into a motivating example using data about life expectancy around the world. It's a small dataset but it illustrates how important modelling can be for improving your visualisations. We'll use a large number of simple models to partition out some of the strongest signals so we can see the subtler signals that remain. We'll also see how model summaries can help us pick out outliers and unusual trends.

The following sections will dive into more detail about the individual techniques:

1. In [list-columns], you'll learn more about the list-column data structure,
   and why it's valid to put lists in data frames.
   
1. In [creating list-columns], you'll learn the three main ways in which you'll
   create list-columns.
   
1. In [simplifying list-columns] you'll learn how to convert list-columns back
   to regular atomic vectors (or sets of atomic vectors) so you can work
   with them more easily.
   
1. In [making tidy data with broom], you'll learn about the full set of tools
   provided by broom, and see how they can be applied to other types of 
   data structure.

This chapter is somewhat aspirational: if this book is your first introduction to R, this chapter is likely to be a struggle. It requires you have to deeply internalised ideas about modelling, data structures, and iteration. So don't worry if you don't get it --- just put this chapter aside for a few months, and come back when you want to stretch your brain. 

### Prerequisites

Working with many models requires many of the packages of the tidyverse (for data exploration, wrangling, and programming) and modelr to facilitate modelling.

```{r setup, message = FALSE}
library(modelr)
library(tidyverse)
```

## List-columns

Now that you've seen a basic workflow for managing many models, let's dive back into some of the details. In this section, we'll explore the list-column data structure in a little more detail. It's only recently that I've really appreciated the idea of the list-column. List-columns are implicit in the definition of the data frame: a data frame is a named list of equal length vectors. A list is a vector, so it's always been legitimate to use a list as a column of a data frame. However, base R doesn't make it easy to create list-columns, and `data.frame()` treats a list as a list of columns:.

```{r}
data.frame(x = list(1:3, 3:5))
```

You can prevent `data.frame()` from doing this with `I()`, but the result doesn't print particularly well:

```{r}
data.frame(
  x = I(list(1:3, 3:5)), 
  y = c("1, 2", "3, 4, 5")
)
```

Tibble alleviates this problem by being lazier (`tibble()` doesn't modify its inputs) and by providing a better print method:

```{r}
tibble(
  x = list(1:3, 3:5), 
  y = c("1, 2", "3, 4, 5")
)
```

It's even easier with `tribble()` as it can automatically work out that you need a list:

```{r}
tribble(
   ~x, ~y,
  1:3, "1, 2",
  3:5, "3, 4, 5"
)
```

List-columns are often most useful as intermediate data structure. They're hard to work with directly, because most R functions work with atomic vectors or data frames, but the advantage of keeping related items together in a data frame is worth a little hassle.

Generally there are three parts of an effective list-column pipeline:

1.  You create the list-column using one of `nest()`, `summarise()` + `list()`,
    or `mutate()` + a map function, as described in [Creating list-columns].

1.  You create other intermediate list-columns by transforming existing
    list columns with `map()`, `map2()` or `pmap()`. For example, 
    in the case study above, we created a list-column of models by transforming
    a list-column of data frames.
    
1.  You simplify the list-column back down to a data frame or atomic vector,
    as described in [Simplifying list-columns].

## Creating list-columns

Typically, you won't create list-columns with `tibble()`. Instead, you'll create them from regular columns, using one of three methods: 

1.  With `tidyr::nest()` to convert a grouped data frame into a nested data 
    frame where you have list-column of data frames.
    
1.  With `mutate()` and vectorised functions that return a list.

1.  With `summarise()` and summary functions that return multiple results. 

Alternatively, you might create them from a named list, using `tibble::enframe()`.

Generally, when creating list-columns, you should make sure they're homogeneous: each element should contain the same type of thing. There are no checks to make sure this is true, but if you use purrr and remember what you've learned about type-stable functions, you should find it happens naturally.

### With nesting

`nest()` creates a nested data frame, which is a data frame with a list-column of data frames. In a nested data frame each row is a meta-observation: the other columns give variables that define the observation, and the list-column of data frames gives the individual observations that make up the meta-observation.

There are two ways to use `nest()`. When applied to a grouped data frame, `nest()` keeps the grouping columns as is, and bundles everything else into the list-column.

You can also use it on an ungrouped data frame, specifying which columns you want to nest.

### From vectorised functions

Some useful functions take an atomic vector and return a list. For example, in [strings] you learned about `stringr::str_split()` which takes a character vector and returns a list of character vectors. If you use that inside mutate, you'll get a list-column:

```{r}
df <- tribble(
  ~x1,
  "a,b,c", 
  "d,e,f,g"
) 

df %>% 
  mutate(x2 = stringr::str_split(x1, ","))
```

`unnest()` knows how to handle these lists of vectors:

```{r}
df %>% 
  mutate(x2 = stringr::str_split(x1, ",")) %>% 
  unnest()
```

(If you find yourself using this pattern a lot, make sure to check out `tidyr::separate_rows()` which is a wrapper around this common pattern).

Another example of this pattern is using the `map()`, `map2()`, `pmap()` from purrr. For example, we could take the final example from [Invoking different functions] and rewrite it to use `mutate()`:

```{r}
sim <- tribble(
  ~f,      ~params,
  "runif", list(min = -1, max = 1),
  "rnorm", list(sd = 5),
  "rpois", list(lambda = 10)
)

sim %>%
  mutate(sims = invoke_map(f, params, n = 10))
```

Note that technically `sim` isn't homogeneous because it contains both double and integer vectors. However, this is unlikely to cause many problems since integers and doubles are both numeric vectors.

### From multivalued summaries

One restriction of `summarise()` is that it only works with summary functions that return a single value. That means that you can't use it with functions like `quantile()` that return a vector of arbitrary length:

```{r, error = TRUE}
mtcars %>% 
  group_by(cyl) %>% 
  summarise(q = quantile(mpg))
```

You can however, wrap the result in a list! This obeys the contract of `summarise()`, because each summary is now a list (a vector) of length 1.

```{r}
mtcars %>% 
  group_by(cyl) %>% 
  summarise(q = list(quantile(mpg)))
```

To make useful results with unnest, you'll also need to capture the probabilities:

```{r}
probs <- c(0.01, 0.25, 0.5, 0.75, 0.99)
mtcars %>% 
  group_by(cyl) %>% 
  summarise(p = list(probs), q = list(quantile(mpg, probs))) %>% 
  unnest()
```

### From a named list

Data frames with list-columns provide a solution to a common problem: what do you do if you want to iterate over both the contents of a list and its elements? Instead of trying to jam everything into one object, it's often easier to make a data frame: one column can contain the elements, and one column can contain the list.  An easy way to create such a data frame from a list is `tibble::enframe()`.  

```{r}
x <- list(
  a = 1:5,
  b = 3:4, 
  c = 5:6
) 

df <- enframe(x)
df
```

The advantage of this structure is that it generalises in a straightforward way - names are useful if you have character vector of metadata, but don't help if you have other types of data, or multiple vectors.

Now if you want to iterate over names and values in parallel, you can use `map2()`:

```{r}
df %>% 
  mutate(
    smry = map2_chr(name, value, ~ stringr::str_c(.x, ": ", .y[1]))
  )
```

### Exercises

1.  List all the functions that you can think of that take a atomic vector and 
    return a list.
    
1.  Brainstorm useful summary functions that, like `quantile()`, return
    multiple values.
    
1.  What's missing in the following data frame? How does `quantile()` return
    that missing piece? Why isn't that helpful here?

    ```{r}
    mtcars %>% 
      group_by(cyl) %>% 
      summarise(q = list(quantile(mpg))) %>% 
      unnest()
    ```

1.  What does this code do? Why might might it be useful?

    ```{r, eval = FALSE}
    mtcars %>% 
      group_by(cyl) %>% 
      summarise_each(funs(list))
    ```

## Simplifying list-columns

To apply the techniques of data manipulation and visualisation you've learned in this book, you'll need to simplify the list-column back to a regular column (an atomic vector), or set of columns. The technique you'll use to collapse back down to a simpler structure depends on whether you want a single value per element, or multiple values:

1.  If you want a single value, use `mutate()` with `map_lgl()`, 
    `map_int()`, `map_dbl()`, and `map_chr()` to create an atomic vector.
    
1.  If you want many values, use `unnest()` to convert list-columns back
    to regular columns, repeating the rows as many times as necessary.

These are described in more detail below.

### List to vector

If you can reduce your list column to an atomic vector then it will be a regular column. For example, you can always summarise an object with its type and length, so this code will work regardless of what sort of list-column you have:

```{r}
df <- tribble(
  ~x,
  letters[1:5],
  1:3,
  runif(5)
)
  
df %>% mutate(
  type = map_chr(x, typeof),
  length = map_int(x, length)
)
```

This is the same basic information that you get from the default tbl print method, but now you can use it for filtering. This is a useful technique if you have a heterogeneous list, and want to filter out the parts aren't working for you.

Don't forget about the `map_*()` shortcuts - you can use `map_chr(x, "apple")` to extract the string stored in `apple` for each element of `x`. This is useful for pulling apart nested lists into regular columns. Use the `.null` argument to provide a value to use if the element is missing (instead of returning `NULL`):

```{r}
df <- tribble(
  ~x,
  list(a = 1, b = 2),
  list(a = 2, c = 4)
)
df %>% mutate(
  a = map_dbl(x, "a"),
  b = map_dbl(x, "b", .null = NA_real_)
)
```

### Unnesting

`unnest()` works by repeating the regular columns once for each element of the list-column. For example, in the following very simple example we repeat the first row 4 times (because there the first element of `y` has length four), and the second row once:

```{r}
tibble(x = 1:2, y = list(1:4, 1)) %>% unnest(y)
```

This means that you can't simultaneously unnest two columns that contain different number of elements:

```{r, error = TRUE}
# Ok, because y and z have the same number of elements in
# every row
df1 <- tribble(
  ~x, ~y,           ~z,
   1, c("a", "b"), 1:2,
   2, "c",           3
)
df1
df1 %>% unnest(y, z)

# Doesn't work because y and z have different number of elements
df2 <- tribble(
  ~x, ~y,           ~z,
   1, "a",         1:2,  
   2, c("b", "c"),   3
)
df2
df2 %>% unnest(y, z)
```

The same principle applies when unnesting list-columns of data frames. You can unnest multiple list-cols as long as all the data frames in each row have the same number of rows.

### Exercises

1.  Why might the `lengths()` function be useful for creating atomic
    vector columns from list-columns?
    
1.  List the most common types of vector found in a data frame. What makes
    lists different?

## Making tidy data with broom

The broom package provides three general tools for turning models into tidy data frames:

1.  `broom::glance(model)` returns a row for each model. Each column gives a 
    model summary: either a measure of model quality, or complexity, or a 
    combination of the two.
   
1.  `broom::tidy(model)` returns a row for each coefficient in the model. Each 
    column gives information about the estimate or its variability.
    
1.  `broom::augment(model, data)` returns a row for each row in `data`, adding
    extra values like residuals, and influence statistics.



================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/model.Rmd
================================================
# (PART) Model {-}

# Introduction {#model-intro}

Now that you are equipped with powerful programming tools we can finally return to modelling. You'll use your new tools of data wrangling and programming, to fit many models and understand how they work. The focus of this book is on exploration, not confirmation or formal inference. But you'll learn a few basic tools that help you understand the variation within your models.

```{r echo = FALSE, out.width = "75%"}
knitr::include_graphics("diagrams/data-science-model.png")
```

The goal of a model is to provide a simple low-dimensional summary of a dataset. Ideally, the model will capture true "signals" (i.e. patterns generated by the phenomenon of interest), and ignore "noise" (i.e. random variation that you're not interested in). Here we only cover "predictive" models, which, as the name suggests, generate predictions. There is another type of model that we're not going to discuss: "data discovery" models. These models don't make predictions, but instead help you discover interesting relationships within your data. (These two categories of models are sometimes called supervised and unsupervised, but I don't think that terminology is particularly illuminating.)

This book is not going to give you a deep understanding of the mathematical theory that underlies models. It will, however, build your intuition about how statistical models work, and give you a family of useful tools that allow you to use models to better understand your data:

* In [model basics], you'll learn how models work mechanistically, focussing on
  the important family of linear models. You'll learn general tools for gaining
  insight into what a predictive model tells you about your data, focussing on
  simple simulated datasets.

* In [model building], you'll learn how to use models to pull out known
  patterns in real data. Once you have recognised an important pattern
  it's useful to make it explicit in a model, because then you can
  more easily see the subtler signals that remain.

* In [many models], you'll learn how to use many simple models to help 
  understand complex datasets. This is a powerful technique, but to access
  it you'll need to combine modelling and programming tools.

These topics are notable because of what they don't include: any tools for quantitatively assessing models. That is deliberate: precisely quantifying a model requires a couple of big ideas that we just don't have the space to cover here. For now, you'll rely on qualitative assessment and your natural scepticism. In [Learning more about models], we'll point you to other resources where you can learn more.

## Hypothesis generation vs. hypothesis confirmation

In this book, we are going to use models as a tool for exploration, completing the trifecta of the tools for EDA that were introduced in Part 1. This is not how models are usually taught, but as you will see, models are an important tool for exploration. Traditionally, the focus of modelling is on inference, or for confirming that an hypothesis is true. Doing this correctly is not complicated, but it is hard. There is a pair of ideas that you must understand in order to do inference correctly:

1. Each observation can either be used for exploration or confirmation, 
   not both.

1. You can use an observation as many times as you like for exploration,
   but you can only use it once for confirmation. As soon as you use an 
   observation twice, you've switched from confirmation to exploration.
   
This is necessary because to confirm a hypothesis you must use data independent of the data that you used to generate the hypothesis. Otherwise you will be over optimistic. There is absolutely nothing wrong with exploration, but you should never sell an exploratory analysis as a confirmatory analysis because it is fundamentally misleading. 

If you are serious about doing an confirmatory analysis, one approach is to split your data into three pieces before you begin the analysis:

1.  60% of your data goes into a __training__ (or exploration) set. You're 
    allowed to do anything you like with this data: visualise it and fit tons 
    of models to it.
  
1.  20% goes into a __query__ set. You can use this data to compare models 
    or visualisations by hand, but you're not allowed to use it as part of
    an automated process.

1.  20% is held back for a __test__ set. You can only use this data ONCE, to 
    test your final model. 
    
This partitioning allows you to explore the training data, occasionally generating candidate hypotheses that you check with the query set. When you are confident you have the right model, you can check it once with the test data.

(Note that even when doing confirmatory modelling, you will still need to do EDA. If you don't do any EDA you will remain blind to the quality problems with your data.)


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/pipes.Rmd
================================================
# Pipes

## Introduction

Pipes are a powerful tool for clearly expressing a sequence of multiple operations. So far, you've been using them without knowing how they work, or what the alternatives are. Now, in this chapter, it's time to explore the pipe in more detail. You'll learn the alternatives to the pipe, when you shouldn't use the pipe, and some useful related tools.

### Prerequisites

The pipe, `%>%`, comes from the __magrittr__ package by Stefan Milton Bache. Packages in the tidyverse load `%>%` for you automatically, so you don't usually load magrittr explicitly.  Here, however, we're focussing on piping, and we aren't loading any other packages, so we will load it explicitly.

```{r setup, message = FALSE}
library(magrittr)
```

## Piping alternatives

The point of the pipe is to help you write code in a way that is easier to read and understand. To see why the pipe is so useful, we're going to explore a number of ways of writing the same code. Let's use code to tell a story about a little bunny named Foo Foo:

> Little bunny Foo Foo  
> Went hopping through the forest  
> Scooping up the field mice  
> And bopping them on the head  

This is a popular Children's poem that is accompanied by hand actions.

We'll start by defining an object to represent little bunny Foo Foo:

```{r, eval = FALSE}
foo_foo <- little_bunny()
```

And we'll use a function for each key verb: `hop()`, `scoop()`, and `bop()`. Using this object and these verbs, there are (at least) four ways we could retell the story in code:

1. Save each intermediate step as a new object.
1. Overwrite the original object many times.
1. Compose functions.
1. Use the pipe.

We'll work through each approach, showing you the code and talking about the advantages and disadvantages.

### Intermediate steps

The simplest approach is to save each step as a new object:

```{r, eval = FALSE}
foo_foo_1 <- hop(foo_foo, through = forest)
foo_foo_2 <- scoop(foo_foo_1, up = field_mice)
foo_foo_3 <- bop(foo_foo_2, on = head)
```

The main downside of this form is that it forces you to name each intermediate element. If there are natural names, this is a good idea, and you should do it. But many times, like this in this example, there aren't natural names, and you add numeric suffixes to make the names unique. That leads to two problems:

1. The code is cluttered with unimportant names

1. You have to carefully increment the suffix on each line. 

Whenever I write code like this, I invariably use the wrong number on one line and then spend 10 minutes scratching my head and trying to figure out what went wrong with my code.

You may also worry that this form creates many copies of your data and takes up a lot of memory. Surprisingly, that's not the case. First, note that proactively worrying about memory is not a useful way to spend your time: worry about it when it becomes a problem (i.e. you run out of memory), not before. Second, R isn't stupid, and it will share columns across data frames, where possible. Let's take a look at an actual data manipulation pipeline where we add a new column to `ggplot2::diamonds`:

```{r}
diamonds <- ggplot2::diamonds
diamonds2 <- diamonds %>% 
  dplyr::mutate(price_per_carat = price / carat)

pryr::object_size(diamonds)
pryr::object_size(diamonds2)
pryr::object_size(diamonds, diamonds2)
```

`pryr::object_size()` gives the memory occupied by all of its arguments. The results seem counterintuitive at first:

* `diamonds` takes up 3.46 MB,
* `diamonds2` takes up 3.89 MB,
* `diamonds` and `diamonds2` together take up 3.89 MB!

How can that work? Well, `diamonds2` has 10 columns in common with `diamonds`: there's no need to duplicate all that data, so the two data frames have variables in common. These variables will only get copied if you modify one of them. In the following example, we modify a single value in `diamonds$carat`. That means the `carat` variable can no longer be shared between the two data frames, and a copy must be made. The size of each data frame is unchanged, but the collective size increases:

```{r}
diamonds$carat[1] <- NA
pryr::object_size(diamonds)
pryr::object_size(diamonds2)
pryr::object_size(diamonds, diamonds2)
```

(Note that we use `pryr::object_size()` here, not the built-in `object.size()`. `object.size()` only takes a single object so it can't compute how data is shared across multiple objects.)

### Overwrite the original

Instead of creating intermediate objects at each step, we could overwrite the original object:

```{r, eval = FALSE}
foo_foo <- hop(foo_foo, through = forest)
foo_foo <- scoop(foo_foo, up = field_mice)
foo_foo <- bop(foo_foo, on = head)
```

This is less typing (and less thinking), so you're less likely to make mistakes. However, there are two problems:

1.  Debugging is painful: if you make a mistake you'll need to re-run the 
    complete pipeline from the beginning.
    
1.  The repetition of the object being transformed (we've written `foo_foo` six 
    times!) obscures what's changing on each line. 

### Function composition

Another approach is to abandon assignment and just string the function calls together:

```{r, eval = FALSE}
bop(
  scoop(
    hop(foo_foo, through = forest),
    up = field_mice
  ), 
  on = head
)
```

Here the disadvantage is that you have to read from inside-out, from right-to-left, and that the arguments end up spread far apart (evocatively called the 
[dagwood sandwhich](https://en.wikipedia.org/wiki/Dagwood_sandwich) problem). In short, this code is hard for a human to consume.

### Use the pipe 

Finally, we can use the pipe:

```{r, eval = FALSE}
foo_foo %>%
  hop(through = forest) %>%
  scoop(up = field_mice) %>%
  bop(on = head)
```

This is my favourite form, because it focusses on verbs, not nouns. You can read this series of function compositions like it's a set of imperative actions. Foo Foo hops, then scoops, then bops. The downside, of course, is that you need to be familiar with the pipe. If you've never seen `%>%` before, you'll have no idea what this code does. Fortunately, most people pick up the idea very quickly, so when you share your code with others who aren't familiar with the pipe, you can easily teach them.

The pipe works by performing a "lexical transformation": behind the scenes, magrittr reassembles the code in the pipe to a form that works by overwriting an intermediate object. When you run a pipe like the one above, magrittr does something like this:

```{r, eval = FALSE}
my_pipe <- function(.) {
  . <- hop(., through = forest)
  . <- scoop(., up = field_mice)
  bop(., on = head)
}
my_pipe(foo_foo)
```

This means that the pipe won't work for two classes of functions:

1.  Functions that use the current environment. For example, `assign()`
    will create a new variable with the given name in the current environment:
     
    ```{r}
    assign("x", 10)
    x
    
    "x" %>% assign(100)
    x
    ```
    
    The use of assign with the pipe does not work because it assigns it to 
    a temporary environment used by `%>%`. If you do want to use assign with the
    pipe, you must be explicit about the environment:
    
    ```{r}
    env <- environment()
    "x" %>% assign(100, envir = env)
    x
    ```
    
    Other functions with this problem include `get()` and `load()`.

1.  Functions that use lazy evaluation. In R, function arguments
    are only computed when the function uses them, not prior to calling the 
    function. The pipe computes each element in turn, so you can't 
    rely on this behaviour.
    
    One place that this is a problem is `tryCatch()`, which lets you capture
    and handle errors:
    
    ```{r, error = TRUE}
    tryCatch(stop("!"), error = function(e) "An error")
    
    stop("!") %>% 
      tryCatch(error = function(e) "An error")
    ```
    
    There are a relatively wide class of functions with this behaviour,
    including `try()`, `suppressMessages()`, and `suppressWarnings()`
    in base R.
  
## When not to use the pipe

The pipe is a powerful tool, but it's not the only tool at your disposal, and it doesn't solve every problem! Pipes are most useful for rewriting a fairly short linear sequence of operations. I think you should reach for another tool when:

* Your pipes are longer than (say) ten steps. In that case, create 
  intermediate objects with meaningful names. That will make debugging easier,
  because you can more easily check the intermediate results, and it makes
  it easier to understand your code, because the variable names can help 
  communicate intent.
  
* You have multiple inputs or outputs. If there isn't one primary object
  being transformed, but two or more objects being combined together,
  don't use the pipe.

* You are starting to think about a directed graph with a complex
  dependency structure. Pipes are fundamentally linear and expressing 
  complex relationships with them will typically yield confusing code.

## Other tools from magrittr

All packages in the tidyverse automatically make `%>%` available for you, so you don't normally load magrittr explicitly. However, there are some other useful tools inside magrittr that you might want to try out:

*   When working with more complex pipes, it's sometimes useful to call a 
    function for its side-effects. Maybe you want to print out the current 
    object, or plot it, or save it to disk. Many times, such functions don't 
    return anything, effectively terminating the pipe.
    
    To work around this problem, you can use the "tee" pipe. `%T>%` works like 
    `%>%` except that it returns the left-hand side instead of the right-hand 
    side. It's called "tee" because it's like a literal T-shaped pipe.

    ```{r}
    rnorm(100) %>%
      matrix(ncol = 2) %>%
      plot() %>%
      str()
    
    rnorm(100) %>%
      matrix(ncol = 2) %T>%
      plot() %>%
      str()
    ```

*   If you're working with functions that don't have a data frame based API  
    (i.e. you pass them individual vectors, not a data frame and expressions 
    to be evaluated in the context of that data frame), you might find `%$%` 
    useful. It "explodes" out the variables in a data frame so that you can 
    refer to them explicitly. This is useful when working with many functions 
    in base R:
    
    ```{r}
    mtcars %$%
      cor(disp, mpg)
    ```

*   For assignment magrittr provides the `%<>%` operator which allows you to
    replace code like:
  
    ```{r, eval = FALSE}
    mtcars <- mtcars %>% 
      transform(cyl = cyl * 2)
    ```
    
    with
     
    ```{r, eval = FALSE}
    mtcars %<>% transform(cyl = cyl * 2)
    ```
    
    I'm not a fan of this operator because I think assignment is such a 
    special operation that it should always be clear when it's occurring.
    In my opinion, a little bit of duplication (i.e. repeating the 
    name of the object twice) is fine in return for making assignment
    more explicit.


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/program.Rmd
================================================
# (PART) Program {-}

# Introduction {#program-intro}

In this part of the book, you'll improve your programming skills. Programming is a cross-cutting skill needed for all data science work: you must use a computer to do data science; you cannot do it in your head, or with pencil and paper. 

```{r echo = FALSE, out.width = "75%"}
knitr::include_graphics("diagrams/data-science-program.png")
```

Programming produces code, and code is a tool of communication. Obviously code tells the computer what you want it to do. But it also communicates meaning to other humans. Thinking about code as a vehicle for communication is important because every project you do is fundamentally collaborative. Even if you're not working with other people, you'll definitely be working with future-you! Writing clear code is important so that others (like future-you) can understand why you tackled an analysis in the way you did. That means getting better at programming also involves getting better at communicating. Over time, you want your code to become not just easier to write, but easier for others to read. 

Writing code is similar in many ways to writing prose. One parallel which I find particularly useful is that in both cases rewriting is the key to clarity. The first expression of your ideas is unlikely to be particularly clear, and you may need to rewrite multiple times. After solving a data analysis challenge, it's often worth looking at your code and thinking about whether or not it's obvious what you've done. If you spend a little time rewriting your code while the ideas are fresh, you can save a lot of time later trying to recreate what your code did. But this doesn't mean you should rewrite every function: you need to balance what you need to achieve now with saving time in the long run. (But the more you rewrite your functions the more likely your first attempt will be clear.)

In the following four chapters, you'll learn skills that will allow you to both tackle new programs and to solve existing problems with greater clarity and ease: 

1.  In [pipes], you will dive deep into the __pipe__, `%>%`, and learn more 
    about how it works, what the alternatives are, and when not to use it.

1.  Copy-and-paste is a powerful tool, but you should avoid doing it more than
    twice. Repeating yourself in code is dangerous because it can easily lead 
    to errors and inconsistencies. Instead, in [functions], you'll learn
    how to write __functions__ which let you extract out repeated code so that 
    it can be easily reused.

1.  As you start to write more powerful functions, you'll need a solid
    grounding in R's __data structures__, provided by [vectors]. You must master 
    the four common atomic vectors, the three important S3 classes built on 
    top of them, and understand the mysteries of the list and data frame. 

1.  Functions extract out repeated code, but you often need to repeat the
    same actions on different inputs. You need tools for __iteration__ that
    let you do similar things again and again. These tools include for loops 
    and functional programming, which you'll learn about in [iteration].

## Learning more

The goal of these chapters is to teach you the minimum about programming that you need to practice data science, which turns out to be a reasonable amount. Once you have mastered the material in this book, I strongly believe you should invest further in your programming skills. Learning more about programming is a long-term investment: it won't pay off immediately, but in the long term it will allow you to solve new problems more quickly, and let you reuse your insights from previous problems in new scenarios.

To learn more you need to study R as a programming language, not just an interactive environment for data science. We have written two books that will help you do so:

* [_Hands on Programming with R_](https://amzn.com/1449359019),
  by Garrett Grolemund. This is an introduction to R as a programming language 
  and is a great place to start if R is your first programming language. It 
  covers similar material to these chapters, but with a different style and
  different motivation examples (based in the casino). It's a useful complement 
  if you find that these four chapters go by too quickly.
  
* [_Advanced R_](https://amzn.com/1466586966) by Hadley Wickham. This dives into the
  details of R the programming language. This is a great place to start if you
  have existing programming experience. It's also a great next step once you've 
  internalised the ideas in these chapters. You can read it online at
  <http://adv-r.had.co.nz>.


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# Relational data

## Introduction

It's rare that a data analysis involves only a single table of data. Typically you have many tables of data, and you must combine them to answer the questions that you're interested in. Collectively, multiple tables of data are called __relational data__ because it is the relations, not just the individual datasets, that are important.

Relations are always defined between a pair of tables. All other relations are built up from this simple idea: the relations of three or more tables are always a property of the relations between each pair. Sometimes both elements of a pair can be the same table! This is needed if, for example, you have a table of people, and each person has a reference to their parents.

To work with relational data you need verbs that work with pairs of tables. There are three families of verbs designed to work with relational data:

* __Mutating joins__, which add new variables to one data frame from matching
  observations in another.

* __Filtering joins__, which filter observations from one data frame based on
  whether or not they match an observation in the other table.

* __Set operations__, which treat observations as if they were set elements.

The most common place to find relational data is in a _relational_ database management system (or RDBMS), a term that encompasses almost all modern databases. If you've used a database before, you've almost certainly used SQL. If so, you should find the concepts in this chapter familiar, although their expression in dplyr is a little different. Generally, dplyr is a little easier to use than SQL because dplyr is specialised to do data analysis: it makes common data analysis operations easier, at the expense of making it more difficult to do other things that aren't commonly needed for data analysis.

### Prerequisites

We will explore relational data from `nycflights13` using the two-table verbs from dplyr.

```{r setup, message = FALSE}
library(tidyverse)
library(nycflights13)
```

## nycflights13 {#nycflights13-relational}

We will use the nycflights13 package to learn about relational data. nycflights13 contains four tibbles that are related to the `flights` table that you used in [data transformation]:

*   `airlines` lets you look up the full carrier name from its abbreviated
    code:

    ```{r}
    airlines
    ```

*   `airports` gives information about each airport, identified by the `faa`
    airport code:

    ```{r}
    airports
    ```

*   `planes` gives information about each plane, identified by its `tailnum`:

    ```{r}
    planes
    ```

*   `weather` gives the weather at each NYC airport for each hour:

    ```{r}
    weather
    ```

One way to show the relationships between the different tables is with a drawing:

```{r, echo = FALSE}
knitr::include_graphics("diagrams/relational-nycflights.png")
```

This diagram is a little overwhelming, but it's simple compared to some you'll see in the wild! The key to understanding diagrams like this is to remember each relation always concerns a pair of tables. You don't need to understand the whole thing; you just need to understand the chain of relations between the tables that you are interested in.

For nycflights13:

* `flights` connects to `planes` via a single variable, `tailnum`. 

* `flights` connects to `airlines` through the `carrier` variable.

* `flights` connects to `airports` in two ways: via the `origin` and
  `dest` variables.

* `flights` connects to `weather` via `origin` (the location), and
  `year`, `month`, `day` and `hour` (the time).

### Exercises

1.  Imagine you wanted to draw (approximately) the route each plane flies from
    its origin to its destination. What variables would you need? What tables
    would you need to combine?

1.  I forgot to draw the relationship between `weather` and `airports`.
    What is the relationship and how should it appear in the diagram?

1.  `weather` only contains information for the origin (NYC) airports. If
    it contained weather records for all airports in the USA, what additional
    relation would it define with `flights`?

1.  We know that some days of the year are "special", and fewer people than
    usual fly on them. How might you represent that data as a data frame?
    What would be the primary keys of that table? How would it connect to the
    existing tables?

## Keys

The variables used to connect each pair of tables are called __keys__. A key is a variable (or set of variables) that uniquely identifies an observation. In simple cases, a single variable is sufficient to identify an observation. For example, each plane is uniquely identified by its `tailnum`. In other cases, multiple variables may be needed. For example, to identify an observation in `weather` you need five variables: `year`, `month`, `day`, `hour`, and `origin`.

There are two types of keys:

* A __primary key__ uniquely identifies an observation in its own table.
  For example, `planes$tailnum` is a primary key because it uniquely identifies
  each plane in the `planes` table.

* A __foreign key__ uniquely identifies an observation in another table.
  For example, the `flights$tailnum` is a foreign key because it appears in the 
  `flights` table where it matches each flight to a unique plane.

A variable can be both a primary key _and_ a foreign key. For example, `origin` is part of the `weather` primary key, and is also a foreign key for the `airport` table.

Once you've identified the primary keys in your tables, it's good practice to verify that they do indeed uniquely identify each observation. One way to do that is to `count()` the primary keys and look for entries where `n` is greater than one:

```{r}
planes %>% 
  count(tailnum) %>% 
  filter(n > 1)

weather %>% 
  count(year, month, day, hour, origin) %>% 
  filter(n > 1)
```

Sometimes a table doesn't have an explicit primary key: each row is an observation, but no combination of variables reliably identifies it. For example, what's the primary key in the `flights` table? You might think it would be the date plus the flight or tail number, but neither of those are unique:

```{r}
flights %>% 
  count(year, month, day, flight) %>% 
  filter(n > 1)

flights %>% 
  count(year, month, day, tailnum) %>% 
  filter(n > 1)
```

When starting to work with this data, I had naively assumed that each flight number would be only used once per day: that would make it much easier to communicate problems with a specific flight. Unfortunately that is not the case! If a table lacks a primary key, it's sometimes useful to add one with `mutate()` and `row_number()`. That makes it easier to match observations if you've done some filtering and want to check back in with the original data. This is called a __surrogate key__.

A primary key and the corresponding foreign key in another table form a __relation__. Relations are typically one-to-many. For example, each flight has one plane, but each plane has many flights. In other data, you'll occasionally see a 1-to-1 relationship. You can think of this as a special case of 1-to-many. You can model many-to-many relations with a many-to-1 relation plus a 1-to-many relation. For example, in this data there's a many-to-many relationship between airlines and airports: each airline flies to many airports; each airport hosts many airlines.

### Exercises

1.  Add a surrogate key to `flights`.

1.  Identify the keys in the following datasets

    1.  `Lahman::Batting`,
    1.  `babynames::babynames`
    1.  `nasaweather::atmos`
    1.  `fueleconomy::vehicles`
    1.  `ggplot2::diamonds`
    
    (You might need to install some packages and read some documentation.)

1.  Draw a diagram illustrating the connections between the `Batting`,
    `Master`, and `Salaries` tables in the Lahman package. Draw another diagram
    that shows the relationship between `Master`, `Managers`, `AwardsManagers`.

    How would you characterise the relationship between the `Batting`,
    `Pitching`, and `Fielding` tables?

## Mutating joins {#mutating-joins}

The first tool we'll look at for combining a pair of tables is the __mutating join__. A mutating join allows you to combine variables from two tables. It first matches observations by their keys, then copies across variables from one table to the other.

Like `mutate()`, the join functions add variables to the right, so if you have a lot of variables already, the new variables won't get printed out. For these examples, we'll make it easier to see what's going on in the examples by creating a narrower dataset:

```{r}
flights2 <- flights %>% 
  select(year:day, hour, origin, dest, tailnum, carrier)
flights2
```

(Remember, when you're in RStudio, you can also use `View()` to avoid this problem.)

Imagine you want to add the full airline name to the `flights2` data. You can combine the `airlines` and `flights2` data frames with `left_join()`:

```{r}
flights2 %>%
  select(-origin, -dest) %>% 
  left_join(airlines, by = "carrier")
```

The result of joining airlines to flights2 is an additional variable: `name`. This is why I call this type of join a mutating join. In this case, you could have got to the same place using `mutate()` and R's base subsetting:

```{r}
flights2 %>%
  select(-origin, -dest) %>% 
  mutate(name = airlines$name[match(carrier, airlines$carrier)])
```

But this is hard to generalise when you need to match multiple variables, and takes close reading to figure out the overall intent.

The following sections explain, in detail, how mutating joins work. You'll start by learning a useful visual representation of joins. We'll then use that to explain the four mutating join functions: the inner join, and the three outer joins. When working with real data, keys don't always uniquely identify observations, so next we'll talk about what happens when there isn't a unique match. Finally, you'll learn how to tell dplyr which variables are the keys for a given join.

### Understanding joins

To help you learn how joins work, I'm going to use a visual representation:

```{r, echo = FALSE, out.width = NULL}
knitr::include_graphics("diagrams/join-setup.png")
```
```{r}
x <- tribble(
  ~key, ~val_x,
     1, "x1",
     2, "x2",
     3, "x3"
)
y <- tribble(
  ~key, ~val_y,
     1, "y1",
     2, "y2",
     4, "y3"
)
```

The coloured column represents the "key" variable: these are used to match the rows between the tables. The grey column represents the "value" column that is carried along for the ride. In these examples I'll show a single key variable, but the idea generalises in a straightforward way to multiple keys and multiple values.

A join is a way of connecting each row in `x` to zero, one, or more rows in `y`. The following diagram shows each potential match as an intersection of a pair of lines.

```{r, echo = FALSE, out.width = NULL}
knitr::include_graphics("diagrams/join-setup2.png")
```

(If you look closely, you might notice that we've switched the order of the key and value columns in `x`. This is to emphasise that joins match based on the key; the value is just carried along for the ride.)

In an actual join, matches will be indicated with dots. The number of dots = the number of matches = the number of rows in the output.

```{r, echo = FALSE, out.width = NULL}
knitr::include_graphics("diagrams/join-inner.png")
```

### Inner join {#inner-join}

The simplest type of join is the __inner join__. An inner join matches pairs of observations whenever their keys are equal:

```{r, echo = FALSE, out.width = NULL}
knitr::include_graphics("diagrams/join-inner.png")
```

(To be precise, this is an inner __equijoin__ because the keys are matched using the equality operator. Since most joins are equijoins we usually drop that specification.)

The output of an inner join is a new data frame that contains the key, the x values, and the y values. We use `by` to tell dplyr which variable is the key:

```{r}
x %>% 
  inner_join(y, by = "key")
```

The most important property of an inner join is that unmatched rows are not included in the result. This means that generally inner joins are usually not appropriate for use in analysis because it's too easy to lose observations.

### Outer joins {#outer-join}

An inner join keeps observations that appear in both tables. An __outer join__ keeps observations that appear in at least one of the tables. There are three types of outer joins:

* A __left join__ keeps all observations in `x`.
* A __right join__ keeps all observations in `y`.
* A __full join__ keeps all observations in `x` and `y`.

These joins work by adding an additional "virtual" observation to each table. This observation has a key that always matches (if no other key matches), and a value filled with `NA`.

Graphically, that looks like:

```{r, echo = FALSE, out.width = NULL}
knitr::include_graphics("diagrams/join-outer.png")
```

The most commonly used join is the left join: you use this whenever you look up additional data from another table, because it preserves the original observations even when there isn't a match. The left join should be your default join: use it unless you have a strong reason to prefer one of the others.

Another way to depict the different types of joins is with a Venn diagram:

```{r, echo = FALSE, out.width = NULL}
knitr::include_graphics("diagrams/join-venn.png")
```

However, this is not a great representation. It might jog your memory about which join preserves the observations in which table, but it suffers from a major limitation: a Venn diagram can't show what happens when keys don't uniquely identify an observation.

### Duplicate keys {#join-matches}

So far all the diagrams have assumed that the keys are unique. But that's not always the case. This section explains what happens when the keys are not unique. There are two possibilities:

1.  One table has duplicate keys. This is useful when you want to
    add in additional information as there is typically a one-to-many
    relationship.

    ```{r, echo = FALSE, out.width = NULL}
    knitr::include_graphics("diagrams/join-one-to-many.png")
    ```

    Note that I've put the key column in a slightly different position
    in the output. This reflects that the key is a primary key in `y`
    and a foreign key in `x`.

    ```{r}
    x <- tribble(
      ~key, ~val_x,
         1, "x1",
         2, "x2",
         2, "x3",
         1, "x4"
    )
    y <- tribble(
      ~key, ~val_y,
         1, "y1",
         2, "y2"
    )
    left_join(x, y, by = "key")
    ```

1.  Both tables have duplicate keys. This is usually an error because in
    neither table do the keys uniquely identify an observation. When you join
    duplicated keys, you get all possible combinations, the Cartesian product:

    ```{r, echo = FALSE, out.width = NULL}
    knitr::include_graphics("diagrams/join-many-to-many.png")
    ```

    ```{r}
    x <- tribble(
      ~key, ~val_x,
         1, "x1",
         2, "x2",
         2, "x3",
         3, "x4"
    )
    y <- tribble(
      ~key, ~val_y,
         1, "y1",
         2, "y2",
         2, "y3",
         3, "y4"
    )
    left_join(x, y, by = "key")
    ```

### Defining the key columns {#join-by}

So far, the pairs of tables have always been joined by a single variable, and that variable has the same name in both tables. That constraint was encoded by `by = "key"`. You can use other values for `by` to connect the tables in other ways:

  * The default, `by = NULL`, uses all variables that appear in both tables,
    the so called __natural__ join. For example, the flights and weather tables
    match on their common variables: `year`, `month`, `day`, `hour` and
    `origin`.

    ```{r}
    flights2 %>% 
      left_join(weather)
    ```

  * A character vector, `by = "x"`. This is like a natural join, but uses only
    some of the common variables. For example, `flights` and `planes` have
    `year` variables, but they mean different things so we only want to join by
    `tailnum`.

    ```{r}
    flights2 %>% 
      left_join(planes, by = "tailnum")
    ```

    Note that the `year` variables (which appear in both input data frames,
    but are not constrained to be equal) are disambiguated in the output with
    a suffix.

  * A named character vector: `by = c("a" = "b")`. This will
    match variable `a` in table `x` to variable `b` in table `y`. The
    variables from `x` will be used in the output.

    For example, if we want to draw a map we need to combine the flights data
    with the airports data which contains the location (`lat` and `lon`) of
    each airport. Each flight has an origin and destination `airport`, so we
    need to specify which one we want to join to:

    ```{r}
    flights2 %>% 
      left_join(airports, c("dest" = "faa"))
    
    flights2 %>% 
      left_join(airports, c("origin" = "faa"))
    ```

### Exercises

1.  Compute the average delay by destination, then join on the `airports`
    data frame so you can show the spatial distribution of delays. Here's an
    easy way to draw a map of the United States:

    ```{r, eval = FALSE}
    airports %>%
      semi_join(flights, c("faa" = "dest")) %>%
      ggplot(aes(lon, lat)) +
        borders("state") +
        geom_point() +
        coord_quickmap()
    ```

    (Don't worry if you don't understand what `semi_join()` does --- you'll
    learn about it next.)

    You might want to use the `size` or `colour` of the points to display
    the average delay for each airport.

1.  Add the location of the origin _and_ destination (i.e. the `lat` and `lon`)
    to `flights`.

1.  Is there a relationship between the age of a plane and its delays?

1.  What weather conditions make it more likely to see a delay?

1.  What happened on June 13 2013? Display the spatial pattern of delays,
    and then use Google to cross-reference with the weather.

    ```{r, eval = FALSE, include = FALSE}
    worst <- filter(flights, !is.na(dep_time), month == 6, day == 13)
    worst %>%
      group_by(dest) %>%
      summarise(delay = mean(arr_delay), n = n()) %>%
      filter(n > 5) %>%
      inner_join(airports, by = c("dest" = "faa")) %>%
      ggplot(aes(lon, lat)) +
        borders("state") +
        geom_point(aes(size = n, colour = delay)) +
        coord_quickmap()
    ```

### Other implementations

`base::merge()` can perform all four types of mutating join:

dplyr              | merge
-------------------|-------------------------------------------
`inner_join(x, y)` | `merge(x, y)`
`left_join(x, y)`  | `merge(x, y, all.x = TRUE)`
`right_join(x, y)` | `merge(x, y, all.y = TRUE)`,
`full_join(x, y)`  | `merge(x, y, all.x = TRUE, all.y = TRUE)`

The advantages of the specific dplyr verbs is that they more clearly convey the intent of your code: the difference between the joins is really important but concealed in the arguments of `merge()`. dplyr's joins are considerably faster and don't mess with the order of the rows.

SQL is the inspiration for dplyr's conventions, so the translation is straightforward:

dplyr                        | SQL
-----------------------------|-------------------------------------------
`inner_join(x, y, by = "z")` | `SELECT * FROM x INNER JOIN y USING (z)`
`left_join(x, y, by = "z")`  | `SELECT * FROM x LEFT OUTER JOIN y USING (z)`
`right_join(x, y, by = "z")` | `SELECT * FROM x RIGHT OUTER JOIN y USING (z)`
`full_join(x, y, by = "z")`  | `SELECT * FROM x FULL OUTER JOIN y USING (z)`

Note that "INNER" and "OUTER" are optional, and often omitted.

Joining different variables between the tables, e.g. `inner_join(x, y, by = c("a" = "b"))` uses a slightly different syntax in SQL: `SELECT * FROM x INNER JOIN y ON x.a = y.b`. As this syntax suggests, SQL supports a wider  range of join types than dplyr because you can connect the tables using constraints other than equality (sometimes called non-equijoins).

## Filtering joins {#filtering-joins}

Filtering joins match observations in the same way as mutating joins, but affect the observations, not the variables. There are two types:

* `semi_join(x, y)` __keeps__ all observations in `x` that have a match in `y`.
* `anti_join(x, y)` __drops__ all observations in `x` that have a match in `y`.

Semi-joins are useful for matching filtered summary tables back to the original rows. For example, imagine you've found the top ten most popular destinations:

```{r}
top_dest <- flights %>%
  count(dest, sort = TRUE) %>%
  head(10)
top_dest
```

Now you want to find each flight that went to one of those destinations. You could construct a filter yourself:

```{r}
flights %>% 
  filter(dest %in% top_dest$dest)
```

But it's difficult to extend that approach to multiple variables. For example, imagine that you'd found the 10 days with highest average delays. How would you construct the filter statement that used `year`, `month`, and `day` to match it back to `flights`?

Instead you can use a semi-join, which connects the two tables like a mutating join, but instead of adding new columns, only keeps the rows in `x` that have a match in `y`:

```{r}
flights %>% 
  semi_join(top_dest)
```

Graphically, a semi-join looks like this:

```{r, echo = FALSE, out.width = NULL}
knitr::include_graphics("diagrams/join-semi.png")
```

Only the existence of a match is important; it doesn't matter which observation is matched. This means that filtering joins never duplicate rows like mutating joins do:

```{r, echo = FALSE, out.width = NULL}
knitr::include_graphics("diagrams/join-semi-many.png")
```

The inverse of a semi-join is an anti-join. An anti-join keeps the rows that _don't_ have a match:

```{r, echo = FALSE, out.width = NULL}
knitr::include_graphics("diagrams/join-anti.png")
```

Anti-joins are useful for diagnosing join mismatches. For example, when connecting `flights` and `planes`, you might be interested to know that there are many `flights` that don't have a match in `planes`:

```{r}
flights %>%
  anti_join(planes, by = "tailnum") %>%
  count(tailnum, sort = TRUE)
```

### Exercises

1.  What does it mean for a flight to have a missing `tailnum`? What do the
    tail numbers that don't have a matching record in `planes` have in common?
    (Hint: one variable explains ~90% of the problems.)

1.  Filter flights to only show flights with planes that have flown at least 100
    flights.

1.  Combine `fueleconomy::vehicles` and `fueleconomy::common` to find only the
    records for the most common models.

1.  Find the 48 hours (over the course of the whole year) that have the worst
    delays. Cross-reference it with the `weather` data. Can you see any
    patterns?

1.  What does `anti_join(flights, airports, by = c("dest" = "faa"))` tell you?
    What does `anti_join(airports, flights, by = c("faa" = "dest"))` tell you?

1.  You might expect that there's an implicit relationship between plane
    and airline, because each plane is flown by a single airline. Confirm
    or reject this hypothesis using the tools you've learned above.

## Join problems

The data you've been working with in this chapter has been cleaned up so that you'll have as few problems as possible. Your own data is unlikely to be so nice, so there are a few things that you should do with your own data to make your joins go smoothly.

1.  Start by identifying the variables that form the primary key in each table.
    You should usually do this based on your understanding of the data, not
    empirically by looking for a combination of variables that give a
    unique identifier. If you just look for variables without thinking about
    what they mean, you might get (un)lucky and find a combination that's
    unique in your current data but the relationship might not be true in
    general.

    For example, the altitude and longitude uniquely identify each airport,
    but they are not good identifiers!

    ```{r}
    airports %>% count(alt, lon) %>% filter(n > 1)
    ```

1.  Check that none of the variables in the primary key are missing. If
    a value is missing then it can't identify an observation!

1.  Check that your foreign keys match primary keys in another table. The
    best way to do this is with an `anti_join()`. It's common for keys
    not to match because of data entry errors. Fixing these is often a lot of
    work.

    If you do have missing keys, you'll need to be thoughtful about your
    use of inner vs. outer joins, carefully considering whether or not you
    want to drop rows that don't have a match.

Be aware that simply checking the number of rows before and after the join is not sufficient to ensure that your join has gone smoothly. If you have an inner join with duplicate keys in both tables, you might get unlucky as the number of dropped rows might exactly equal the number of duplicated rows!

## Set operations {#set-operations}

The final type of two-table verb are the set operations. Generally, I use these the least frequently, but they are occasionally useful when you want to break a single complex filter into simpler pieces. All these operations work with a complete row, comparing the values of every variable. These expect the `x` and `y` inputs to have the same variables, and treat the observations like sets:

* `intersect(x, y)`: return only observations in both `x` and `y`.
* `union(x, y)`: return unique observations in `x` and `y`.
* `setdiff(x, y)`: return observations in `x`, but not in `y`.

Given this simple data:

```{r}
df1 <- tribble(
  ~x, ~y,
   1,  1,
   2,  1
)
df2 <- tribble(
  ~x, ~y,
   1,  1,
   1,  2
)
```

The four possibilities are:

```{r}
intersect(df1, df2)

# Note that we get 3 rows, not 4
union(df1, df2)

setdiff(df1, df2)

setdiff(df2, df1)
```


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/rmarkdown-formats.Rmd
================================================
# R Markdown formats

## Introduction

So far you've seen R Markdown used to produce HTML documents. This chapter gives a brief overview of some of the many other types of output you can produce with R Markdown. There are two ways to set the output of a document:

1.  Permanently, by modifying the YAML header: 
    
    ```yaml
    title: "Viridis Demo"
    output: html_document
    ```
    
1.  Transiently, by calling `rmarkdown::render()` by hand:
    
    ```{r eval = FALSE}
    rmarkdown::render("diamond-sizes.Rmd", output_format = "word_document")
    ```
    
    This is useful if you want to programmatically produce multiple types of
    output.

RStudio's knit button renders a file to the first format listed in its `output` field. You can render to additional formats by clicking the dropdown menu beside the knit button.

```{r, echo = FALSE, out.width = NULL}
knitr::include_graphics("screenshots/rmarkdown-knit.png")
```

## Output options

Each output format is associated with an R function. You can either write `foo` or `pkg::foo`. If you omit `pkg`, the default is assumed to be rmarkdown. It's important to know the name of the function that makes the output because that's where you get help. For example, to figure out what parameters you can set with `html_document`, look at `?rmarkdown::html_document`.

To override the default parameter values, you need to use an expanded `output` field. For example, if you wanted to render an `html_document` with a floating table of contents, you'd use:

```yaml
output:
  html_document:
    toc: true
    toc_float: true
```

You can even render to multiple outputs by supplying a list of formats:

```yaml
output:
  html_document:
    toc: true
    toc_float: true
  pdf_document: default
```

Note the special syntax if you don't want to override any of the default options.

## Documents

The previous chapter focused on the default `html_document` output. There are a number of basic variations on that theme, generating different types of documents:

*   `pdf_document` makes a PDF with LaTeX (an open source document layout 
    system), which you'll need to install. RStudio will prompt you if you 
    don't already have it.
  
*   `word_document` for Microsoft Word documents (`.docx`).
  
*   `odt_document` for OpenDocument Text documents (`.odt`).
  
*   `rtf_document` for Rich Text Format (`.rtf`) documents.
  
*   `md_document` for a Markdown document. This isn't typically useful by 
    itself, but you might use it if, for example, your corporate CMS or
    lab wiki uses markdown.
    
*   `github_document`: this is a tailored version of `md_document` 
    designed for sharing on GitHub. 

Remember, when generating a document to share with decision makers, you can turn off the default display of code by setting global options in the setup chunk:

```{r, eval = FALSE}
knitr::opts_chunk$set(echo = FALSE)
```

For `html_document`s another option is to make the code chunks hidden by default, but visible with a click:

```yaml
output:
  html_document:
    code_folding: hide
```

## Notebooks

A notebook, `html_notebook`, is a variation on a `html_document`. The rendered outputs are very similar, but the purpose is different. A `html_document` is focused on communicating with decision makers, while a notebook is focused on collaborating with other data scientists. These different purposes lead to using the HTML output in different ways. Both HTML outputs will contain the fully rendered output, but the notebook also contains the full source code. That means you can use the `.nb.html` generated by the notebook in two ways:

1. You can view it in a web browser, and see the rendered output. Unlike
   `html_document`, this rendering always includes an embedded copy of 
   the source code that generated it.

1. You can edit it in RStudio. When you open an `.nb.html` file, RStudio will
   automatically recreate the `.Rmd` file that generated it. In the future, you 
   will also be able to include supporting files (e.g. `.csv` data files), which 
   will be automatically extracted when needed. 

Emailing `.nb.html` files is a simple way to share analyses with your colleagues. But things will get painful as soon as they want to make changes. If this starts to happen, it's a good time to learn Git and GitHub. Learning Git and GitHub is definitely painful at first, but the collaboration payoff is huge. As mentioned earlier, Git and GitHub are outside the scope of the book, but there's one tip that's useful if you're already using them: use both `html_notebook` and `github_document` outputs:

```yaml
output:
  html_notebook: default
  github_document: default
```

`html_notebook` gives you a local preview, and a file that you can share via email. `github_document` creates a minimal md file that you can check into git. You can easily see how the results of your analysis (not just the code) change over time, and GitHub will render it for you nicely online.

## Presentations

You can also use R Markdown to produce presentations. You get less visual control than with a tool like Keynote or PowerPoint, but automatically inserting the results of your R code into a presentation can save a huge amount of time. Presentations work by dividing your content into slides, with a new slide beginning at each first (`#`) or second (`##`) level header. You can also insert a horizontal rule (`***`) to create a new slide without a header. 

R Markdown comes with three presentation formats built-in:

1.  `ioslides_presentation` - HTML presentation with ioslides

1.  `slidy_presentation` - HTML presentation with W3C Slidy

1.  `beamer_presentation` - PDF presentation with LaTeX Beamer.

Two other popular formats are provided by packages:

1.  `revealjs::revealjs_presentation` - HTML presentation with reveal.js. 
    Requires the __revealjs__ package.

1.  __rmdshower__, <https://github.com/MangoTheCat/rmdshower>, provides a 
    wrapper around the __shower__, <https://github.com/shower/shower>, 
    presentation engine

## Dashboards

Dashboards are a useful way to communicate large amounts of information visually and quickly. Flexdashboard makes it particularly easy to create dashboards using R Markdown and a convention for how the headers affect the layout:

* Each level 1 header (`#`) begins a new page in the dashboard.
* Each level 2 header (`##`) begins a new column.
* Each level 3 header (`###`) begins a new row.

For example, you can produce this dashboard:

```{r, echo = FALSE, out.width = "75%"}
knitr::include_graphics("screenshots/rmarkdown-flexdashboard.png")
```

Using this code:

```{r comment = "", echo = FALSE}
cat(readr::read_file("rmarkdown/dashboard.Rmd"))
```

Flexdashboard also provides simple tools for creating sidebars, tabsets, value boxes, and gauges. To learn more about flexdashboard visit <http://rmarkdown.rstudio.com/flexdashboard/>.

## Interactivity

Any HTML format (document, notebook, presentation, or dashboard) can contain interactive components.

### htmlwidgets

HTML is an interactive format, and you can take advantage of that interactivity with __htmlwidgets__, R functions that produce interactive HTML visualisations. For example, take the __leaflet__ map below. If you're viewing this page on the web, you can drag the map around, zoom in and out, etc. You obviously can't do that in a book, so rmarkdown automatically inserts a static screenshot for you.

```{r}
library(leaflet)
leaflet() %>%
  setView(174.764, -36.877, zoom = 16) %>% 
  addTiles() %>%
  addMarkers(174.764, -36.877, popup = "Maungawhau") 
```

The great thing about htmlwidgets is that you don't need to know anything about HTML or JavaScript to use them. All the details are wrapped inside the package, so you don't need to worry about it.  

There are many packages that provide htmlwidgets, including:

* __dygraphs__, <http://rstudio.github.io/dygraphs/>, for interactive time 
  series visualisations.

* __DT__, <http://rstudio.github.io/DT/>, for interactive tables.

* __threejs__, <https://github.com/bwlewis/rthreejs> for interactive 3d plots.

* __DiagrammeR__, <http://rich-iannone.github.io/DiagrammeR/> for diagrams
  (like flow charts and simple node-link diagrams).

To learn more about htmlwidgets and see a more complete list of packages that provide them visit <http://www.htmlwidgets.org/>.

### Shiny

htmlwidgets provide __client-side__ interactivity --- all the interactivity happens in the browser, independently of R. On one hand, that's great because you can distribute the HTML file without any connection to R. However, that fundamentally limits what you can do to things that have been implemented in HTML and JavaScript.  An alternative approach is to use __shiny__, a package that allows you to create interactivity using R code, not JavaScript.

To call Shiny code from an R Markdown document, add `runtime: shiny` to the header:

```yaml
title: "Shiny Web App"
output: html_document
runtime: shiny
```

Then you can use the "input" functions to add interactive components to the document:

```{r, eval = FALSE}
library(shiny)

textInput("name", "What is your name?")
numericInput("age", "How old are you?", NA, min = 0, max = 150)
```
```{r, echo = FALSE, out.width = NULL}
knitr::include_graphics("screenshots/rmarkdown-shiny.png")
```
You can then refer to the values with `input$name` and `input$age`, and the code that uses them will be automatically re-run whenever they change. 

I can't show you a live shiny app here because shiny interactions occur on the __server-side__. This means that you can write interactive apps without knowing JavaScript, but you need a server to run them on. This introduces a logistical issue: Shiny apps need a Shiny server to be run online. When you run shiny apps on your own computer, shiny automatically sets up a shiny server for you, but you need a public facing shiny server if you want to publish this sort of interactivity online. That's the fundamental trade-off of shiny: you can do anything in a shiny document that you can do in R, but it requires someone to be running R.

Learn more about Shiny at <http://shiny.rstudio.com/>.

## Websites

With a little additional infrastructure you can use R Markdown to generate a complete website:

*   Put your `.Rmd` files in a single directory. `index.Rmd` will become 
    the home page.

*   Add a YAML file named `_site.yml` provides the navigation for the site.
    For example:

    ```{r echo = FALSE, comment = ""}
    cat(readr::read_file("rmarkdown/example-site.yml"))
    ```

Execute `rmarkdown::render_site()` to build `_site`, a directory of files ready to deploy as a standalone static website, or if you use an RStudio Project for your website directory. RStudio will add a Build tab to the IDE that you can use to build and preview your site. 

Read more at <http://rmarkdown.rstudio.com/rmarkdown_websites.html>.

## Other formats

Other packages provide even more output formats:

*   The __bookdown__ package, <https://github.com/rstudio/bookdown>, 
    makes it easy to write books, like this one. To learn more, read 
    [_Authoring Books with R Markdown_](https://bookdown.org/yihui/bookdown/),
    by Yihui Xie, which is, of course, written in bookdown. Visit
    <http://www.bookdown.org> to see other bookdown books written by the 
    wider R community.

*   The __prettydoc__ package, <https://github.com/yixuan/prettydoc/>, 
    provides lightweight document formats with a range of attractive
    themes.

*   The __rticles__ package, <https://github.com/rstudio/rticles>, compiles a
    selection of formats tailored for specific scientific journals.

See <http://rmarkdown.rstudio.com/formats.html> for a list of even more formats.  You can also create your own by following the instructions at <http://rmarkdown.rstudio.com/developer_custom_formats.html>.

## Learning more

To learn more about effective communication in these different formats I recommend the following resources:

* To improve your presentation skills, I recommend 
  [_Presentation Patterns_](https://amzn.com/0321820800), by Neal Ford,
  Matthew McCollough, and Nathaniel Schutta. It provides a set of effective
  patterns (both low- and high-level) that you can apply to improve your 
  presentations.
  
* If you give academic talks, I recommend reading the [_Leek group guide
  to giving talks_](https://github.com/jtleek/talkguide).
  
* I haven't taken it myself, but I've heard good things about Matt 
  McGarrity's online course on public speaking: 
  <https://www.coursera.org/learn/public-speaking>.

* If you are creating a lot of dashboards, make sure to read Stephen Few's
  [Information Dashboard Design: The Effective Visual Communication 
  of Data](https://amzn.com/0596100167). It will help you create dashboards
  that are truly useful, not just pretty to look at.

* Effectively communicating your ideas often benefits from some
  knowledge of graphic design. [_The Non-Designer's Design
  Book_](http://amzn.com/0133966151) is a great place to start.



================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/rmarkdown-workflow.Rmd
================================================
# R Markdown workflow

Earlier, we discussed a basic workflow for capturing your R code where you work  interactively in the _console_, then capture what works in the _script editor_. R Markdown brings together the console and the script editor, blurring the lines between interactive exploration and long-term code capture. You can rapidly iterate within a chunk, editing and re-executing with Cmd/Ctrl + Shift + Enter. When you're happy, you move on and start a new chunk.

R Markdown is also important because it so tightly integrates prose and code. This makes it a great __analysis notebook__ because it lets you develop code and record your thoughts. An analysis notebook shares many of the same goals as a classic lab notebook in the physical sciences. It:

*   Records what you did and why you did it. Regardless of how great your
    memory is, if you don't record what you do, there will come a time when
    you have forgotten important details. Write them down so you don't forget!

*   Supports rigorous thinking. You are more likely to come up with a strong
    analysis if you record your thoughts as you go, and continue to reflect
    on them. This also saves you time when you eventually write up your
    analysis to share with others.

*   Helps others understand your work. It is rare to do data analysis by
    yourself, and you'll often be working as part of a team. A lab notebook
    helps you share not only what you've done, but why you did it with your
    colleagues or lab mates.

Much of the good advice about using lab notebooks effectively can also be translated to analysis notebooks. I've drawn on my own experiences and Colin Purrington's advice on lab notebooks  (<http://colinpurrington.com/tips/lab-notebooks>) to come up with the following tips:

*   Ensure each notebook has a descriptive title, an evocative filename, and a
    first paragraph that briefly describes the aims of the analysis.

*   Use the YAML header date field to record the date you started working on the
    notebook:

    ```yaml
    date: 2016-08-23
    ```

    Use ISO8601 YYYY-MM-DD format so that's there no ambiguity. Use it
    even if you don't normally write dates that way!

*   If you spend a lot of time on an analysis idea and it turns out to be a
    dead end, don't delete it! Write up a brief note about why it failed and
    leave it in the notebook. That will help you avoid going down the same
    dead end when you come back to the analysis in the future.

*   Generally, you're better off doing data entry outside of R. But if you 
    do need to record a small snippet of data, clearly lay it out using
    `tibble::tribble()`.

*   If you discover an error in a data file, never modify it directly, but
    instead write code to correct the value. Explain why you made the fix.

*   Before you finish for the day, make sure you can knit the notebook
    (if you're using caching, make sure to clear the caches). That will
    let you fix any problems while the code is still fresh in your mind.

*   If you want your code to be reproducible in the long-run (i.e. so you can
    come back to run it next month or next year), you'll need to track the
    versions of the packages that your code uses. A rigorous approach is to use
    __packrat__, <http://rstudio.github.io/packrat/>, which stores packages 
    in your project directory, or __checkpoint__,
    <https://github.com/RevolutionAnalytics/checkpoint>, which will reinstall
    packages available on a specified date. A quick and dirty hack is to include
    a chunk that runs `sessionInfo()` --- that won't let you easily recreate 
    your packages as they are today, but at least you'll know what they were.

*   You are going to create many, many, many analysis notebooks over the course
    of your career. How are you going to organise them so you can find them
    again in the future? I recommend storing them in individual projects,
    and coming up with a good naming scheme.


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/rmarkdown.Rmd
================================================
# R Markdown

## Introduction

R Markdown provides an unified authoring framework for data science, combining your code, its results, and your prose commentary. R Markdown documents are fully reproducible and support dozens of output formats, like PDFs, Word files, slideshows, and more. 

R Markdown files are designed to be used in three ways:

1.  For communicating to decision makers, who want to focus on the conclusions,
    not the code behind the analysis.

1.  For collaborating with other data scientists (including future you!), who
    are interested in both your conclusions, and how you reached them (i.e.
    the code).
    
1.  As an environment in which to _do_ data science, as a modern day lab 
    notebook where you can capture not only what you did, but also what you
    were thinking.

R Markdown integrates a number of R packages and external tools. This means that help is, by-and-large, not available through `?`. Instead, as you work through this chapter, and use R Markdown in the future, keep these resources close to hand:

*   R Markdown Cheat Sheet: _Help > Cheatsheets > R Markdown Cheat Sheet_,

*   R Markdown Reference Guide: _Help > Cheatsheets > R Markdown Reference 
    Guide_.

Both cheatsheets are also available at <http://rstudio.com/cheatsheets>.

### Prerequisites

You need the __rmarkdown__ package, but you don't need to explicitly install it or load it, as RStudio automatically does both when needed.

```{r setup, include = FALSE}
chunk <- "```"
inline <- function(x = "") paste0("`` `r ", x, "` ``")
library(tidyverse)
```

## R Markdown basics

This is an R Markdown file, a plain text file that has the extension `.Rmd`:

```{r echo = FALSE, comment = ""}
cat(htmltools::includeText("rmarkdown/diamond-sizes.Rmd"))
```

It contains three important types of content:

1.  An (optional) __YAML header__ surrounded by `---`s.
1.  __Chunks__ of R code surrounded by ```` ``` ````.
1.  Text mixed with simple text formatting like `# heading` and `_italics_`.

When you open an `.Rmd`, you get a notebook interface where code and output are interleaved. You can run each code chunk by clicking the Run icon (it looks like a play button at the top of the chunk), or by pressing Cmd/Ctrl + Shift + Enter. RStudio executes the code and displays the results inline with the code:

```{r, echo = FALSE, out.width = "75%"}
knitr::include_graphics("rmarkdown/diamond-sizes-notebook.png")
```

To produce a complete report containing all text, code, and results, click "Knit" or press Cmd/Ctrl + Shift + K.  You can also do this programmatically with `rmarkdown::render("1-example.Rmd")`. This will display the report in the viewer pane, and create a self-contained HTML file that you can share with others.

```{r, echo = FALSE, out.width = "75%"}
knitr::include_graphics("rmarkdown/diamond-sizes-report.png")
```

When you __knit__ the document, R Markdown sends the .Rmd file to __knitr__, http://yihui.name/knitr/, which executes all of the code chunks and creates a new markdown (.md) document which includes the code and its output. The markdown file generated by knitr is then processed by __pandoc__, <http://pandoc.org/>, which is responsible for creating the finished file. The advantage of this two step workflow is that you can create a very wide range of output formats, as you'll learn about in [R markdown formats].

```{r, echo = FALSE, out.width = "75%"}
knitr::include_graphics("images/RMarkdownFlow.png")
```

To get started with your own `.Rmd` file, select *File > New File > R Markdown...* in the menubar. RStudio will launch a wizard that you can use to pre-populate your file with useful content that reminds you how the key features of R Markdown work. 

The following sections dive into the three components of an R Markdown document in more details: the markdown text, the code chunks, and the YAML header.

### Exercises

1.  Create a new notebook using _File > New File > R Notebook_. Read the 
    instructions. Practice running the chunks. Verify that you can modify
    the code, re-run it, and see modified output.
    
1.  Create a new R Markdown document with _File > New File > R Markdown..._
    Knit it by clicking the appropriate button. Knit it by using the 
    appropriate keyboard short cut. Verify that you can modify the
    input and see the output update.
    
1.  Compare and contrast the R notebook and R markdown files you created
    above. How are the outputs similar? How are they different? How are
    the inputs similar? How are they different? What happens if you
    copy the YAML header from one to the other?

1.  Create one new R Markdown document for each of the three built-in
    formats: HTML, PDF and Word. Knit each of the three documents.
    How does the output differ? How does the input differ? (You may need
    to install LaTeX in order to build the PDF output --- RStudio will
    prompt you if this is necessary.)

## Text formatting with Markdown

Prose in `.Rmd` files is written in Markdown, a lightweight set of conventions for formatting plain text files. Markdown is designed to be easy to read and easy to write. It is also very easy to learn. The guide below shows how to use Pandoc's Markdown, a slightly extended version of Markdown that R Markdown understands.

```{r, echo = FALSE, comment = ""}
cat(readr::read_file("rmarkdown/markdown.Rmd"))
```

The best way to learn these is simply to try them out. It will take a few days, but soon they will become second nature, and you won't need to think about them. If you forget, you can get to a handy reference sheet with *Help > Markdown Quick Reference*.

### Exercises

1.  Practice what you've learned by creating a brief CV. The title should be
    your name, and you should include headings for (at least) education or
    employment. Each of the sections should include a bulleted list of
    jobs/degrees. Highlight the year in bold.
    
1.  Using the R Markdown quick reference, figure out how to:

    1.  Add a footnote.
    1.  Add a horizontal rule.
    1.  Add a block quote.
    
1.  Copy and paste the contents of `diamond-sizes.Rmd` from
    <https://github.com/hadley/r4ds/tree/master/rmarkdown> in to a local
    R markdown document. Check that you can run it, then add text after the 
    frequency polygon that describes its most striking features.

## Code chunks

To run code inside an R Markdown document, you need to insert a chunk. There are three ways to do so:

1. The keyboard shortcut Cmd/Ctrl + Alt + I

1. The "Insert" button icon in the editor toolbar.

1. By manually typing the chunk delimiters ` ```{r} ` and ` ``` `.

Obviously, I'd recommend you learn the keyboard shortcut. It will save you a lot of time in the long run!

You can continue to run the code using the keyboard shortcut that by now (I hope!) you know and love: Cmd/Ctrl + Enter. However, chunks get a new keyboard shortcut: Cmd/Ctrl + Shift + Enter, which runs all the code in the chunk. Think of a chunk like a function. A chunk should be relatively self-contained, and focussed around a single task. 

The following sections describe the chunk header which consists of ```` ```{r ````, followed by an optional chunk name, followed by comma separated options, followed by `}`. Next comes your R code and the chunk end is indicated by a final ```` ``` ````.

### Chunk name

Chunks can be given an optional name: ```` ```{r by-name} ````. This has three advantages:

1.  You can more easily navigate to specific chunks using the drop-down
    code navigator in the bottom-left of the script editor:

    ```{r, echo = FALSE, out.width = "30%"}
    knitr::include_graphics("screenshots/rmarkdown-chunk-nav.png")
    ```

1.  Graphics produced by the chunks will have useful names that make
    them easier to use elsewhere. More on that in [other important options].
    
1.  You can set up networks of cached chunks to avoid re-performing expensive
    computations on every run. More on that below.

There is one chunk name that imbues special behaviour: `setup`. When you're in a notebook mode, the chunk named setup will be run automatically once, before any other code is run.

### Chunk options

Chunk output can be customised with __options__, arguments supplied to chunk header. Knitr provides almost 60 options that you can use to customize your code chunks. Here we'll cover the most important chunk options that you'll use frequently. You can see the full list at <http://yihui.name/knitr/options/>. 

The most important set of options controls if your code block is executed and what results are inserted in the finished report:
  
*   `eval = FALSE` prevents code from being evaluated. (And obviously if the
    code is not run, no results will be generated). This is useful for 
    displaying example code, or for disabling a large block of code without 
    commenting each line.

*   `include = FALSE` runs the code, but doesn't show the code or results 
    in the final document. Use this for setup code that you don't want
    cluttering your report.

*   `echo = FALSE` prevents code, but not the results from appearing in the 
    finished file. Use this when writing reports aimed at people who don't
    want to see the underlying R code.
    
*   `message = FALSE` or `warning = FALSE` prevents messages or warnings 
    from appearing in the finished file.

*   `results = 'hide'` hides printed output; `fig.show = 'hide'` hides
    plots.

*   `error = TRUE` causes the render to continue even if code returns an error.
    This is rarely something you'll want to include in the final version
    of your report, but can be very useful if you need to debug exactly
    what is going on inside your `.Rmd`. It's also useful if you're teaching R
    and want to deliberately include an error. The default, `error = FALSE` causes 
    knitting to fail if there is a single error in the document.
    
The following table summarises which types of output each option supressess:

Option             | Run code | Show code | Output | Plots | Messages | Warnings 
-------------------|----------|-----------|--------|-------|----------|---------
`eval = FALSE`     | -        |           | -      | -     | -        | -
`include = FALSE`  |          | -         | -      | -     | -        | -
`echo = FALSE`     |          | -         |        |       |          |
`results = "hide"` |          |           | -      |       |          | 
`fig.show = "hide"`|          |           |        | -     |          |
`message = FALSE`  |          |           |        |       | -        |
`warning = FALSE`  |          |           |        |       |          | -

### Table

By default, R Markdown prints data frames and matrices as you'd see them in the console:

```{r}
mtcars[1:5, ]
```

If you prefer that data be displayed with additional formatting you can use the `knitr::kable` function. The code below generates Table \@ref(tab:kable).

```{r kable}
knitr::kable(
  mtcars[1:5, ], 
  caption = "A knitr kable."
)
```

Read the documentation for `?knitr::kable` to see the other ways in which you can customise the table. For even deeper customisation, consider the __xtable__, __stargazer__, __pander__, __tables__, and __ascii__ packages. Each provides a set of tools for returning formatted tables from R code.

There is also a rich set of options for controlling how figures are embedded. You'll learn about these in [saving your plots].

### Caching

Normally, each knit of a document starts from a completely clean slate. This is great for reproducibility, because it ensures that you've captured every important computation in code. However, it can be painful if you have some computations that take a long time. The solution is `cache = TRUE`. When set, this will save the output of the chunk to a specially named file on disk. On subsequent runs, knitr will check to see if the code has changed, and if it hasn't, it will reuse the cached results.

The caching system must be used with care, because by default it is based on the code only, not its dependencies. For example, here the `processed_data` chunk depends on the `raw_data` chunk:

    `r chunk`{r raw_data}
    rawdata <- readr::read_csv("a_very_large_file.csv")
    `r chunk`
    
    `r chunk`{r processed_data, cache = TRUE}
    processed_data <- rawdata %>% 
      filter(!is.na(import_var)) %>% 
      mutate(new_variable = complicated_transformation(x, y, z))
    `r chunk`

Caching the `processed_data` chunk means that it will get re-run if the dplyr pipeline is changed, but it won't get rerun if the `read_csv()` call changes. You can avoid that problem with the `dependson` chunk option:

    `r chunk`{r processed_data, cache = TRUE, dependson = "raw_data"}
    processed_data <- rawdata %>% 
      filter(!is.na(import_var)) %>% 
      mutate(new_variable = complicated_transformation(x, y, z))
    `r chunk`

`dependson` should contain a character vector of *every* chunk that the cached chunk depends on. Knitr will update the results for the cached chunk whenever it detects that one of its dependencies have changed.

Note that the chunks won't update if `a_very_large_file.csv` changes, because knitr caching only tracks changes within the `.Rmd` file. If you want to also track changes to that file you can use the `cache.extra` option. This is an arbitrary R expression that will invalidate the cache whenever it changes. A good function to use is `file.info()`: it returns a bunch of information about the file including when it was last modified. Then you can write:

    `r chunk`{r raw_data, cache.extra = file.info("a_very_large_file.csv")}
    rawdata <- readr::read_csv("a_very_large_file.csv")
    `r chunk`

As your caching strategies get progressively more complicated, it's a good idea to regularly clear out all your caches with `knitr::clean_cache()`.

I've used the advice of [David Robinson](https://twitter.com/drob/status/738786604731490304) to name these chunks: each chunk is named after the primary object that it creates. This makes it easier to understand the `dependson` specification.

### Global options

As you work more with knitr, you will discover that some of the default chunk options don't fit your needs and you want to change them. You can do this by calling `knitr::opts_chunk$set()` in a code chunk. For example, when writing books and tutorials I set:

```{r, eval = FALSE}
knitr::opts_chunk$set(
  comment = "#>",
  collapse = TRUE
)
```

This uses my preferred comment formatting, and ensures that the code and output are kept closely entwined. On the other hand, if you were preparing a report, you might set:

```{r eval = FALSE}
knitr::opts_chunk$set(
  echo = FALSE
)
```

That will hide the code by default, so only showing the chunks you deliberately choose to show (with `echo = TRUE`). You might consider setting `message = FALSE` and `warning = FALSE`, but that would make it harder to debug problems because you wouldn't see any messages in the final document.

### Inline code

There is one other way to embed R code into an R Markdown document: directly into the text, with:  `r inline()`. This can be very useful if you mention properties of your data in the text. For example, in the example document I used at the start of the chapter I had:

> We have data about `r inline('nrow(diamonds)')` diamonds. 
> Only `r inline('nrow(diamonds) - nrow(smaller)')` are larger 
> than 2.5 carats. The distribution of the remainder is shown below:

When the report is knit, the results of these computations are inserted into the text:

> We have data about 53940 diamonds. Only 126 are larger than 
> 2.5 carats. The distribution of the remainder is shown below:

When inserting numbers into text, `format()` is your friend. It allows you to set the number of `digits` so you don't print to a ridiculous degree of accuracy, and a `big.mark` to make numbers easier to read. I'll often combine these into a helper function:

```{r}
comma <- function(x) format(x, digits = 2, big.mark = ",")
comma(3452345)
comma(.12358124331)
```

### Exercises

1.  Add a section that explores how diamond sizes vary by cut, colour,
    and clarity. Assume you're writing a report for someone who doesn't know
    R, and instead of setting `echo = FALSE` on each chunk, set a global 
    option.

1.  Download `diamond-sizes.Rmd` from
    <https://github.com/hadley/r4ds/tree/master/rmarkdown>. Add a section
    that describes the largest 20 diamonds, including a table that displays
    their most important attributes.

1.  Modify `diamonds-sizes.Rmd` to use `comma()` to produce nicely
    formatted output. Also include the percentage of diamonds that are
    larger than 2.5 carats.

1.  Set up a network of chunks where `d` depends on `c` and `b`, and
    both `b` and `c` depend on `a`. Have each chunk print `lubridate::now()`,
    set `cache = TRUE`, then verify your understanding of caching.

## Troubleshooting

Troubleshooting R Markdown documents can be challenging because you are no longer in an interactive R environment, and you will need to learn some new tricks. The first thing you should always try is to recreate the problem in an interactive session. Restart R, then "Run all chunks" (either from Code menu, under Run region), or with the keyboard shortcut Ctrl + Alt + R. If you're lucky, that will recreate the problem, and you can figure out what's going on interactively.

If that doesn't help, there must be something different between your interactive environment and the R markdown environment. You're going to need to systematically explore the options. The most common difference is the working directory: the working directory of an R Markdown is the directory in which it lives. Check the working directory is what you expect by including `getwd()` in a chunk.

Next, brainstorm all the things that might cause the bug. You'll need to systematically check that they're the same in your R session and your R markdown session. The easiest way to do that is to set `error = TRUE` on the chunk causing the problem, then use `print()` and `str()` to check that settings are as you expect.

## YAML header

You can control many other "whole document" settings by tweaking the parameters of the YAML header.  You might wonder what YAML stands for: it's "yet another markup language", which is designed for representing hierarchical data in a way that's easy for humans to read and write. R Markdown uses it to control many details of the output. Here we'll discuss two: document parameters and bibliographies.

### Parameters

R Markdown documents can include one or more parameters whose values can be set when you render the report. Parameters are useful when you want to re-render the same report with distinct values for various key inputs. For example, you might be producing sales reports per branch, exam results by student, or demographic summaries by country. To declare one or more parameters, use the `params` field. 

This example uses a `my_class` parameter to determine which class of cars to display:

```{r, echo = FALSE, out.width = "100%", comment = ""}
cat(readr::read_file("rmarkdown/fuel-economy.Rmd"))
```

As you can see, parameters are available within the code chunks as a read-only list named `params`.

You can write atomic vectors directly into the YAML header. You can also run arbitrary R expressions by prefacing the parameter value with `!r`. This is a good way to specify date/time parameters.

```yaml
params:
  start: !r lubridate::ymd("2015-01-01")
  snapshot: !r lubridate::ymd_hms("2015-01-01 12:30:00")
```

In RStudio, you can click the "Knit with Parameters" option in the Knit dropdown menu to set parameters, render, and preview the report in a single user friendly step. You can customise the dialog by setting other options in the header. See <http://rmarkdown.rstudio.com/developer_parameterized_reports.html#parameter_user_interfaces> for more details.

Alternatively, if you need to produce many such paramterised reports, you can call `rmarkdown::render()` with a list of `params`:

```{r eval = FALSE}
rmarkdown::render("fuel-economy.Rmd", params = list(my_class = "suv"))
```

This is particularly powerful in conjunction with `purrr:pwalk()`. The following example creates a report for each value of `class` found in `mpg`. First we create a data frame that has one row for each class, giving the `filename` of the report and the `params`:

```{r}
reports <- tibble(
  class = unique(mpg$class),
  filename = stringr::str_c("fuel-economy-", class, ".html"),
  params = purrr::map(class, ~ list(my_class = .))
)
reports
```

Then we match the column names to the argument names of `render()`, and use purrr's **parallel** walk to call `render()` once for each row:

```{r, eval = FALSE}
reports %>% 
  select(output_file = filename, params) %>% 
  purrr::pwalk(rmarkdown::render, input = "fuel-economy.Rmd")
```

### Bibliographies and Citations

Pandoc can automatically generate citations and a bibliography in a number of styles. To use this feature, specify a bibliography file using the `bibliography` field in your file's header. The field should contain a path from the directory that contains your .Rmd file to the file that contains the bibliography file:

```yaml
bibliography: rmarkdown.bib
```

You can use many common bibliography formats including BibLaTeX, BibTeX, endnote, medline.

To create a citation within your .Rmd file, use a key composed of ‘@’ + the citation identifier from the bibliography file. Then place the citation in square brackets. Here are some examples:

```markdown
Separate multiple citations with a `;`: Blah blah [@smith04; @doe99].

You can add arbitrary comments inside the square brackets: 
Blah blah [see @doe99, pp. 33-35; also @smith04, ch. 1].

Remove the square brackets to create an in-text citation: @smith04 
says blah, or @smith04 [p. 33] says blah.

Add a `-` before the citation to suppress the author's name: 
Smith says blah [-@smith04].
```

When R Markdown renders your file, it will build and append a bibliography to the end of your document. The bibliography will contain each of the cited references from your bibliography file, but it will not contain a section heading. As a result it is common practice to end your file with a section header for the bibliography, such as `# References` or `# Bibliography`.

You can change the style of your citations and bibliography by referencing a CSL (citation style language) file in the `csl` field:

```yaml
bibliography: rmarkdown.bib
csl: apa.csl
```

As with the bibliography field, your csl file should contain a path to the file. Here I assume that the csl file is in the same directory as the .Rmd file. A good place to find CSL style files for common bibliography styles is  <http://github.com/citation-style-language/styles>.

## Learning more

R Markdown is still relatively young, and is still growing rapidly. The best place to stay on top of innovations is the official R Markdown website: <http://rmarkdown.rstudio.com>.

There are two important topics that we haven't covered here: collaboration, and the details of accurately communicating your ideas to other humans. Collaboration is a vital part of modern data science, and you can make your life much easier by using version control tools, like Git and GitHub. We recommend two free resources that will teach you about Git:

1.  "Happy Git with R": a user friendly introduction to Git and GitHub from 
    R users, by Jenny Bryan. The book is freely available online:
    <http://happygitwithr.com>
    
1.  The "Git and GitHub" chapter of _R Packages_, by Hadley. You can also 
    read it for free online: <http://r-pkgs.had.co.nz/git.html>.

I have also not touched on what you should actually write in order to clearly communicate the results of your analysis. To improve your writing, I highly recommend reading either [_Style: Lessons in Clarity and Grace_](https://amzn.com/0134080416) by Joseph M. Williams & Joseph Bizup, or [_The Sense of Structure: Writing from the Reader's Perspective_](https://amzn.com/0205296327) by George Gopen. Both books will help you understand the structure of sentences and paragraphs, and give you the tools to make your writing more clear. (These books are rather expensive if purchased new, but they're used by many English classes so there are plenty of cheap second-hand copies). George Gopen also has a number of short articles on writing at <https://www.georgegopen.com/the-litigation-articles.html>. They are aimed at lawyers, but almost everything applies to data scientists too. 
  


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/strings.Rmd
================================================
# Strings

## Introduction

This chapter introduces you to string manipulation in R. You'll learn the basics of how strings work and how to create them by hand, but the focus of this chapter will be on regular expressions, or regexps for short. Regular expressions are useful because strings usually contain unstructured or semi-structured data, and regexps are a concise language for describing patterns in strings. When you first look at a regexp, you'll think a cat walked across your keyboard, but as your understanding improves they will soon start to make sense.

### Prerequisites

This chapter will focus on the __stringr__ package for string manipulation. stringr is not part of the core tidyverse because you don't always have textual data, so we need to load it explicitly.

```{r setup, message = FALSE}
library(tidyverse)
library(stringr)
```

## String basics

You can create strings with either single quotes or double quotes. Unlike other languages, there is no difference in behaviour. I recommend always using `"`, unless you want to create a string that contains multiple `"`.

```{r}
string1 <- "This is a string"
string2 <- 'If I want to include a "quote" inside a string, I use single quotes'
```

If you forget to close a quote, you'll see `+`, the continuation character:

```
> "This is a string without a closing quote
+ 
+ 
+ HELP I'M STUCK
```

If this happen to you, press Escape and try again!

To include a literal single or double quote in a string you can use `\` to "escape" it:

```{r}
double_quote <- "\"" # or '"'
single_quote <- '\'' # or "'"
```

That means if you want to include a literal backslash, you'll need to double it up: `"\\"`.

Beware that the printed representation of a string is not the same as string itself, because the printed representation shows the escapes. To see the raw contents of the string, use `writeLines()`:

```{r}
x <- c("\"", "\\")
x
writeLines(x)
```

There are a handful of other special characters. The most common are `"\n"`, newline, and `"\t"`, tab, but you can see the complete list by requesting help on `"`: `?'"'`, or `?"'"`. You'll also sometimes see strings like `"\u00b5"`, this is a way of writing non-English characters that works on all platforms:

```{r}
x <- "\u00b5"
x
```

Multiple strings are often stored in a character vector, which you can create with `c()`:

```{r}
c("one", "two", "three")
```

### String length

Base R contains many functions to work with strings but we'll avoid them because they can be inconsistent, which makes them hard to remember. Instead we'll use functions from stringr. These have more intuitive names, and all start with `str_`. For example, `str_length()` tells you the number of characters in a string:

```{r}
str_length(c("a", "R for data science", NA))
```

The common `str_` prefix is particularly useful if you use RStudio, because typing `str_` will trigger autocomplete, allowing you to see all stringr functions:

```{r, echo = FALSE}
knitr::include_graphics("screenshots/stringr-autocomplete.png")
```

### Combining strings

To combine two or more strings, use `str_c()`:

```{r}
str_c("x", "y")
str_c("x", "y", "z")
```

Use the `sep` argument to control how they're separated:

```{r}
str_c("x", "y", sep = ", ")
```

Like most other functions in R, missing values are contagious. If you want them to print as `"NA"`, use `str_replace_na()`:

```{r}
x <- c("abc", NA)
str_c("|-", x, "-|")
str_c("|-", str_replace_na(x), "-|")
```

As shown above, `str_c()` is vectorised, and it automatically recycles shorter vectors to the same length as the longest:

```{r}
str_c("prefix-", c("a", "b", "c"), "-suffix")
```

Objects of length 0 are silently dropped. This is particularly useful in conjunction with `if`:

```{r}
name <- "Hadley"
time_of_day <- "morning"
birthday <- FALSE

str_c(
  "Good ", time_of_day, " ", name,
  if (birthday) " and HAPPY BIRTHDAY",
  "."
)
```

To collapse a vector of strings into a single string, use `collapse`:

```{r}
str_c(c("x", "y", "z"), collapse = ", ")
```

### Subsetting strings

You can extract parts of a string using `str_sub()`. As well as the string, `str_sub()` takes `start` and `end` arguments which give the (inclusive) position of the substring:

```{r}
x <- c("Apple", "Banana", "Pear")
str_sub(x, 1, 3)
# negative numbers count backwards from end
str_sub(x, -3, -1)
```

Note that `str_sub()` won't fail if the string is too short: it will just return as much as possible:

```{r}
str_sub("a", 1, 5)
```

You can also use the assignment form of `str_sub()` to modify strings:

```{r}
str_sub(x, 1, 1) <- str_to_lower(str_sub(x, 1, 1))
x
```

### Locales

Above I used `str_to_lower()` to change the text to lower case. You can also use `str_to_upper()` or `str_to_title()`. However, changing case is more complicated than it might at first appear because different languages have different rules for changing case. You can pick which set of rules to use by specifying a locale:

```{r}
# Turkish has two i's: with and without a dot, and it
# has a different rule for capitalising them:
str_to_upper(c("i", "ı"))
str_to_upper(c("i", "ı"), locale = "tr")
```

The locale is specified as a ISO 639 language code, which is a two or three letter abbreviation. If you don't already know the code for your language, [Wikipedia](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) has a good list. If you leave the locale blank, it will use the current locale, as provided by your operating system.

Another important operation that's affected by the locale is sorting. The base R `order()` and `sort()` functions sort strings using the current locale. If you want robust behaviour across different computers, you may want to use `str_sort()` and `str_order()` which take an additional `locale` argument:

```{r}
x <- c("apple", "eggplant", "banana")

str_sort(x, locale = "en")  # English

str_sort(x, locale = "haw") # Hawaiian
```

### Exercises

1.  In code that doesn't use stringr, you'll often see `paste()` and `paste0()`.
    What's the difference between the two functions? What stringr function are
    they equivalent to? How do the functions differ in their handling of 
    `NA`?
    
1.  In your own words, describe the difference between the `sep` and `collapse`
    arguments to `str_c()`.

1.  Use `str_length()` and `str_sub()` to extract the middle character from 
    a string. What will you do if the string has an even number of characters?

1.  What does `str_wrap()` do? When might you want to use it?

1.  What does `str_trim()` do? What's the opposite of `str_trim()`?

1.  Write a function that turns (e.g.) a vector `c("a", "b", "c")` into 
    the string `a, b, and c`. Think carefully about what it should do if
    given a vector of length 0, 1, or 2.

## Matching patterns with regular expressions

Regexps are a very terse language that allow you to describe patterns in strings. They take a little while to get your head around, but once you understand them, you'll find them extremely useful. 

To learn regular expressions, we'll use `str_view()` and `str_view_all()`. These functions take a character vector and a regular expression, and show you how they match. We'll start with very simple regular expressions and then gradually get more and more complicated. Once you've mastered pattern matching, you'll learn how to apply those ideas with various stringr functions.

### Basic matches

The simplest patterns match exact strings:

```{r}
x <- c("apple", "banana", "pear")
str_view(x, "an")
```

The next step up in complexity is `.`, which matches any character (except a newline):

```{r}
str_view(x, ".a.")
```

But if "`.`" matches any character, how do you match the character "`.`"? You need to use an "escape" to tell the regular expression you want to match it exactly, not use its special behaviour. Like strings, regexps use the backslash, `\`, to escape special behaviour. So to match an `.`, you need the regexp `\.`. Unfortunately this creates a problem. We use strings to represent regular expressions, and `\` is also used as an escape symbol in strings. So to create the regular expression `\.` we need the string `"\\."`. 

```{r}
# To create the regular expression, we need \\
dot <- "\\."

# But the expression itself only contains one:
writeLines(dot)

# And this tells R to look for an explicit .
str_view(c("abc", "a.c", "bef"), "a\\.c")
```

If `\` is used as an escape character in regular expressions, how do you match a literal `\`? Well you need to escape it, creating the regular expression `\\`. To create that regular expression, you need to use a string, which also needs to escape `\`. That means to match a literal `\` you need to write `"\\\\"` --- you need four backslashes to match one!

```{r}
x <- "a\\b"
writeLines(x)

str_view(x, "\\\\")
```

In this book, I'll write regular expression as `\.` and strings that represent the regular expression as `"\\."`.

#### Exercises

1.  Explain why each of these strings don't match a `\`: `"\"`, `"\\"`, `"\\\"`.

1.  How would you match the sequence `"'\`?

1.  What patterns will the regular expression `\..\..\..` match? 
    How would you represent it as a string?

### Anchors

By default, regular expressions will match any part of a string. It's often useful to _anchor_ the regular expression so that it matches from the start or end of the string. You can use:

* `^` to match the start of the string.
* `$` to match the end of the string.

```{r}
x <- c("apple", "banana", "pear")
str_view(x, "^a")
str_view(x, "a$")
```

To remember which is which, try this mnemonic which I learned from [Evan Misshula](https://twitter.com/emisshula/status/323863393167613953): if you begin with power (`^`), you end up with money (`$`).

To force a regular expression to only match a complete string, anchor it with both `^` and `$`:

```{r}
x <- c("apple pie", "apple", "apple cake")
str_view(x, "apple")
str_view(x, "^apple$")
```

You can also match the boundary between words with `\b`. I don't often use this in R, but I will sometimes use it when I'm doing a search in RStudio when I want to find the name of a function that's a component of other functions. For example, I'll search for `\bsum\b` to avoid matching `summarise`, `summary`, `rowsum` and so on.

#### Exercises

1.  How would you match the literal string `"$^$"`?

1.  Given the corpus of common words in `stringr::words`, create regular
    expressions that find all words that:
    
    1. Start with "y".
    1. End with "x"
    1. Are exactly three letters long. (Don't cheat by using `str_length()`!)
    1. Have seven letters or more.

    Since this list is long, you might want to use the `match` argument to
    `str_view()` to show only the matching or non-matching words.

### Character classes and alternatives

There are a number of special patterns that match more than one character. You've already seen `.`, which matches any character apart from a newline. There are four other useful tools:

* `\d`: matches any digit.
* `\s`: matches any whitespace (e.g. space, tab, newline).
* `[abc]`: matches a, b, or c.
* `[^abc]`: matches anything except a, b, or c.

Remember, to create a regular expression containing `\d` or `\s`, you'll need to escape the `\` for the string, so you'll type `"\\d"` or `"\\s"`.

A character class containing a single character is a nice alternative to backslash escapes when you want to include a single metacharacter in a regex. Many people find this more readable.

```{r}
# Look for a literal character that normally has special meaning in a regex
str_view(c("abc", "a.c", "a*c", "a c"), "a[.]c")
str_view(c("abc", "a.c", "a*c", "a c"), ".[*]c")
str_view(c("abc", "a.c", "a*c", "a c"), "a[ ]")
```

This works for most (but not all) regex metacharacters: `$` `.` `|` `?` `*` `+` `(` `)` `[` `{`. Unfortunately, a few characters have special meaning even inside a character class and must be handled with backslash escapes: `]` `\` `^` and `-`.

You can use _alternation_ to pick between one or more alternative patterns. For example, `abc|d..f` will match either '"abc"', or `"deaf"`. Note that the precedence for `|` is low, so that `abc|xyz` matches `abc` or `xyz` not `abcyz` or `abxyz`. Like with mathematical expressions, if precedence ever gets confusing, use parentheses to make it clear what you want:

```{r}
str_view(c("grey", "gray"), "gr(e|a)y")
```

#### Exercises

1.  Create regular expressions to find all words that:

    1. Start with a vowel.

    1. That only contain consonants. (Hint: thinking about matching 
       "not"-vowels.)

    1. End with `ed`, but not with `eed`.
    
    1. End with `ing` or `ise`.
    
1.  Empirically verify the rule "i before e except after c".

1.  Is "q" always followed by a "u"?

1.  Write a regular expression that matches a word if it's probably written
    in British English, not American English.

1.  Create a regular expression that will match telephone numbers as commonly
    written in your country.

### Repetition

The next step up in power involves controlling how many times a pattern matches:

* `?`: 0 or 1
* `+`: 1 or more
* `*`: 0 or more

```{r}
x <- "1888 is the longest year in Roman numerals: MDCCCLXXXVIII"
str_view(x, "CC?")
str_view(x, "CC+")
str_view(x, 'C[LX]+')
```

Note that the precedence of these operators is high, so you can write: `colou?r` to match either American or British spellings. That means most uses will need parentheses, like `bana(na)+`.

You can also specify the number of matches precisely:

* `{n}`: exactly n
* `{n,}`: n or more
* `{,m}`: at most m
* `{n,m}`: between n and m

```{r}
str_view(x, "C{2}")
str_view(x, "C{2,}")
str_view(x, "C{2,3}")
```

By default these matches are "greedy": they will match the longest string possible. You can make them "lazy", matching the shortest string possible by putting a `?` after them. This is an advanced feature of regular expressions, but it's useful to know that it exists:

```{r}
str_view(x, 'C{2,3}?')
str_view(x, 'C[LX]+?')
```

#### Exercises

1.  Describe the equivalents of `?`, `+`, `*` in `{m,n}` form.

1.  Describe in words what these regular expressions match:
    (read carefully to see if I'm using a regular expression or a string
    that defines a regular expression.)

    1. `^.*$`
    1. `"\\{.+\\}"`
    1. `\d{4}-\d{2}-\d{2}`
    1. `"\\\\{4}"`

1.  Create regular expressions to find all words that:

    1. Start with three consonants.
    1. Have three or more vowels in a row.
    1. Have two or more vowel-consonant pairs in a row.

1.  Solve the beginner regexp crosswords at
    <https://regexcrossword.com/challenges/beginner>.

### Grouping and backreferences

Earlier, you learned about parentheses as a way to disambiguate complex expressions. Parentheses also create a _numbered_ capturing group (number 1, 2 etc.). A capturing group stores _the part of the string_ matched by the part of the regular expression inside the parentheses. You can refer to the same text as previously matched by a capturing group with _backreferences_, like `\1`, `\2` etc. For example, the following regular expression finds all fruits that have a repeated pair of letters.

```{r}
str_view(fruit, "(..)\\1", match = TRUE)
```

(Shortly, you'll also see how they're useful in conjunction with `str_match()`.)

#### Exercises

1.  Describe, in words, what these expressions will match:

    1. `(.)\1\1`
    1. `"(.)(.)\\2\\1"`
    1. `(..)\1`
    1. `"(.).\\1.\\1"`
    1. `"(.)(.)(.).*\\3\\2\\1"`

1.  Construct regular expressions to match words that:

    1. Start and end with the same character.
    
    1. Contain a repeated pair of letters
       (e.g. "church" contains "ch" repeated twice.)
    
    1. Contain one letter repeated in at least three places
       (e.g. "eleven" contains three "e"s.)

## Tools

Now that you've learned the basics of regular expressions, it's time to learn how to apply them to real problems. In this section you'll learn a wide array of stringr functions that let you:

* Determine which strings match a pattern.
* Find the positions of matches.
* Extract the content of matches.
* Replace matches with new values.
* Split a string based on a match.

A word of caution before we continue: because regular expressions are so powerful, it's easy to try and solve every problem with a single regular expression. In the words of Jamie Zawinski:

> Some people, when confronted with a problem, think “I know, I’ll use regular
> expressions.” Now they have two problems. 

As a cautionary tale, check out this regular expression that checks if a email address is valid:

```
(?:(?:\r\n)?[ \t])*(?:(?:(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t]
)+|\Z|(?=[\["()<>@,;:\\".\[\]]))|"(?:[^\"\r\\]|\\.|(?:(?:\r\n)?[ \t]))*"(?:(?:
\r\n)?[ \t])*)(?:\.(?:(?:\r\n)?[ \t])*(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(
?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|"(?:[^\"\r\\]|\\.|(?:(?:\r\n)?[ 
\t]))*"(?:(?:\r\n)?[ \t])*))*@(?:(?:\r\n)?[ \t])*(?:[^()<>@,;:\\".\[\] \000-\0
31]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|\[([^\[\]\r\\]|\\.)*\
](?:(?:\r\n)?[ \t])*)(?:\.(?:(?:\r\n)?[ \t])*(?:[^()<>@,;:\\".\[\] \000-\031]+
(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|\[([^\[\]\r\\]|\\.)*\](?:
(?:\r\n)?[ \t])*))*|(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z
|(?=[\["()<>@,;:\\".\[\]]))|"(?:[^\"\r\\]|\\.|(?:(?:\r\n)?[ \t]))*"(?:(?:\r\n)
?[ \t])*)*\<(?:(?:\r\n)?[ \t])*(?:@(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\
r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|\[([^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[
 \t])*)(?:\.(?:(?:\r\n)?[ \t])*(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)
?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|\[([^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[ \t]
)*))*(?:,@(?:(?:\r\n)?[ \t])*(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[
 \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|\[([^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[ \t])*
)(?:\.(?:(?:\r\n)?[ \t])*(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t]
)+|\Z|(?=[\["()<>@,;:\\".\[\]]))|\[([^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[ \t])*))*)
*:(?:(?:\r\n)?[ \t])*)?(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+
|\Z|(?=[\["()<>@,;:\\".\[\]]))|"(?:[^\"\r\\]|\\.|(?:(?:\r\n)?[ \t]))*"(?:(?:\r
\n)?[ \t])*)(?:\.(?:(?:\r\n)?[ \t])*(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:
\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|"(?:[^\"\r\\]|\\.|(?:(?:\r\n)?[ \t
]))*"(?:(?:\r\n)?[ \t])*))*@(?:(?:\r\n)?[ \t])*(?:[^()<>@,;:\\".\[\] \000-\031
]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|\[([^\[\]\r\\]|\\.)*\](
?:(?:\r\n)?[ \t])*)(?:\.(?:(?:\r\n)?[ \t])*(?:[^()<>@,;:\\".\[\] \000-\031]+(?
:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|\[([^\[\]\r\\]|\\.)*\](?:(?
:\r\n)?[ \t])*))*\>(?:(?:\r\n)?[ \t])*)|(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?
:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|"(?:[^\"\r\\]|\\.|(?:(?:\r\n)?
[ \t]))*"(?:(?:\r\n)?[ \t])*)*:(?:(?:\r\n)?[ \t])*(?:(?:(?:[^()<>@,;:\\".\[\] 
\000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|"(?:[^\"\r\\]|
\\.|(?:(?:\r\n)?[ \t]))*"(?:(?:\r\n)?[ \t])*)(?:\.(?:(?:\r\n)?[ \t])*(?:[^()<>
@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|"
(?:[^\"\r\\]|\\.|(?:(?:\r\n)?[ \t]))*"(?:(?:\r\n)?[ \t])*))*@(?:(?:\r\n)?[ \t]
)*(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\
".\[\]]))|\[([^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[ \t])*)(?:\.(?:(?:\r\n)?[ \t])*(?
:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[
\]]))|\[([^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[ \t])*))*|(?:[^()<>@,;:\\".\[\] \000-
\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|"(?:[^\"\r\\]|\\.|(
?:(?:\r\n)?[ \t]))*"(?:(?:\r\n)?[ \t])*)*\<(?:(?:\r\n)?[ \t])*(?:@(?:[^()<>@,;
:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|\[([
^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[ \t])*)(?:\.(?:(?:\r\n)?[ \t])*(?:[^()<>@,;:\\"
.\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|\[([^\[\
]\r\\]|\\.)*\](?:(?:\r\n)?[ \t])*))*(?:,@(?:(?:\r\n)?[ \t])*(?:[^()<>@,;:\\".\
[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|\[([^\[\]\
r\\]|\\.)*\](?:(?:\r\n)?[ \t])*)(?:\.(?:(?:\r\n)?[ \t])*(?:[^()<>@,;:\\".\[\] 
\000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|\[([^\[\]\r\\]
|\\.)*\](?:(?:\r\n)?[ \t])*))*)*:(?:(?:\r\n)?[ \t])*)?(?:[^()<>@,;:\\".\[\] \0
00-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|"(?:[^\"\r\\]|\\
.|(?:(?:\r\n)?[ \t]))*"(?:(?:\r\n)?[ \t])*)(?:\.(?:(?:\r\n)?[ \t])*(?:[^()<>@,
;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|"(?
:[^\"\r\\]|\\.|(?:(?:\r\n)?[ \t]))*"(?:(?:\r\n)?[ \t])*))*@(?:(?:\r\n)?[ \t])*
(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".
\[\]]))|\[([^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[ \t])*)(?:\.(?:(?:\r\n)?[ \t])*(?:[
^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\]
]))|\[([^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[ \t])*))*\>(?:(?:\r\n)?[ \t])*)(?:,\s*(
?:(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\
".\[\]]))|"(?:[^\"\r\\]|\\.|(?:(?:\r\n)?[ \t]))*"(?:(?:\r\n)?[ \t])*)(?:\.(?:(
?:\r\n)?[ \t])*(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[
\["()<>@,;:\\".\[\]]))|"(?:[^\"\r\\]|\\.|(?:(?:\r\n)?[ \t]))*"(?:(?:\r\n)?[ \t
])*))*@(?:(?:\r\n)?[ \t])*(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t
])+|\Z|(?=[\["()<>@,;:\\".\[\]]))|\[([^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[ \t])*)(?
:\.(?:(?:\r\n)?[ \t])*(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|
\Z|(?=[\["()<>@,;:\\".\[\]]))|\[([^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[ \t])*))*|(?:
[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".\[\
]]))|"(?:[^\"\r\\]|\\.|(?:(?:\r\n)?[ \t]))*"(?:(?:\r\n)?[ \t])*)*\<(?:(?:\r\n)
?[ \t])*(?:@(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["
()<>@,;:\\".\[\]]))|\[([^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[ \t])*)(?:\.(?:(?:\r\n)
?[ \t])*(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>
@,;:\\".\[\]]))|\[([^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[ \t])*))*(?:,@(?:(?:\r\n)?[
 \t])*(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,
;:\\".\[\]]))|\[([^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[ \t])*)(?:\.(?:(?:\r\n)?[ \t]
)*(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\
".\[\]]))|\[([^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[ \t])*))*)*:(?:(?:\r\n)?[ \t])*)?
(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\["()<>@,;:\\".
\[\]]))|"(?:[^\"\r\\]|\\.|(?:(?:\r\n)?[ \t]))*"(?:(?:\r\n)?[ \t])*)(?:\.(?:(?:
\r\n)?[ \t])*(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z|(?=[\[
"()<>@,;:\\".\[\]]))|"(?:[^\"\r\\]|\\.|(?:(?:\r\n)?[ \t]))*"(?:(?:\r\n)?[ \t])
*))*@(?:(?:\r\n)?[ \t])*(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])
+|\Z|(?=[\["()<>@,;:\\".\[\]]))|\[([^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[ \t])*)(?:\
.(?:(?:\r\n)?[ \t])*(?:[^()<>@,;:\\".\[\] \000-\031]+(?:(?:(?:\r\n)?[ \t])+|\Z
|(?=[\["()<>@,;:\\".\[\]]))|\[([^\[\]\r\\]|\\.)*\](?:(?:\r\n)?[ \t])*))*\>(?:(
?:\r\n)?[ \t])*))*)?;\s*)
```

This is a somewhat pathological example (because email addresses are actually surprisingly complex), but is used in real code. See the stackoverflow discussion at <http://stackoverflow.com/a/201378> for more details. 

Don't forget that you're in a programming language and you have other tools at your disposal. Instead of creating one complex regular expression, it's often easier to write a series of simpler regexps. If you get stuck trying to create a single regexp that solves your problem, take a step back and think if you could break the problem down into smaller pieces, solving each challenge before moving onto the next one.

### Detect matches

To determine if a character vector matches a pattern, use `str_detect()`. It returns a logical vector the same length as the input:

```{r}
x <- c("apple", "banana", "pear")
str_detect(x, "e")
```

Remember that when you use a logical vector in a numeric context, `FALSE` becomes 0 and `TRUE` becomes 1. That makes `sum()` and `mean()` useful if you want to answer questions about matches across a larger vector:

```{r}
# How many common words start with t?
sum(str_detect(words, "^t"))
# What proportion of common words end with a vowel?
mean(str_detect(words, "[aeiou]$"))
```

When you have complex logical conditions (e.g. match a or b but not c unless d) it's often easier to combine multiple `str_detect()` calls with logical operators, rather than trying to create a single regular expression. For example, here are two ways to find all words that don't contain any vowels:

```{r}
# Find all words containing at least one vowel, and negate
no_vowels_1 <- !str_detect(words, "[aeiou]")
# Find all words consisting only of consonants (non-vowels)
no_vowels_2 <- str_detect(words, "^[^aeiou]+$")
identical(no_vowels_1, no_vowels_2)
```

The results are identical, but I think the first approach is significantly easier to understand. If your regular expression gets overly complicated, try breaking it up into smaller pieces, giving each piece a name, and then combining the pieces with logical operations.

A common use of `str_detect()` is to select the elements that match a pattern. You can do this with logical subsetting, or the convenient `str_subset()` wrapper:

```{r}
words[str_detect(words, "x$")]
str_subset(words, "x$")
```

Typically, however, your strings will be one column of a data frame, and you'll want to use filter instead:

```{r}
df <- tibble(
  word = words, 
  i = seq_along(word)
)
df %>% 
  filter(str_detect(words, "x$"))
```


A variation on `str_detect()` is `str_count()`: rather than a simple yes or no, it tells you how many matches there are in a string:

```{r}
x <- c("apple", "banana", "pear")
str_count(x, "a")

# On average, how many vowels per word?
mean(str_count(words, "[aeiou]"))
```

It's natural to use `str_count()` with `mutate()`:

```{r}
df %>% 
  mutate(
    vowels = str_count(word, "[aeiou]"),
    consonants = str_count(word, "[^aeiou]")
  )
```

Note that matches never overlap. For example, in `"abababa"`, how many times will the pattern `"aba"` match? Regular expressions say two, not three:

```{r}
str_count("abababa", "aba")
str_view_all("abababa", "aba")
```

Note the use of `str_view_all()`. As you'll shortly learn, many stringr functions come in pairs: one function works with a single match, and the other works with all matches. The second function will have the suffix `_all`.

### Exercises

1.  For each of the following challenges, try solving it by using both a single
    regular expression, and a combination of multiple `str_detect()` calls.
    
    1.  Find all words that start or end with `x`.
    
    1.  Find all words that start with a vowel and end with a consonant.
    
    1.  Are there any words that contain at least one of each different
        vowel?

1.  What word has the highest number of vowels? What word has the highest
    proportion of vowels? (Hint: what is the denominator?)

### Extract matches

To extract the actual text of a match, use `str_extract()`. To show that off, we're going to need a more complicated example. I'm going to use the [Harvard sentences](https://en.wikipedia.org/wiki/Harvard_sentences), which were designed to test VOIP systems, but are also useful for practicing regexps. These are provided in `stringr::sentences`:

```{r}
length(sentences)
head(sentences)
```

Imagine we want to find all sentences that contain a colour. We first create a vector of colour names, and then turn it into a single regular expression:

```{r}
colours <- c("red", "orange", "yellow", "green", "blue", "purple")
colour_match <- str_c(colours, collapse = "|")
colour_match
```

Now we can select the sentences that contain a colour, and then extract the colour to figure out which one it is:

```{r}
has_colour <- str_subset(sentences, colour_match)
matches <- str_extract(has_colour, colour_match)
head(matches)
```

Note that `str_extract()` only extracts the first match. We can see that most easily by first selecting all the sentences that have more than 1 match:

```{r}
more <- sentences[str_count(sentences, colour_match) > 1]
str_view_all(more, colour_match)

str_extract(more, colour_match)
```

This is a common pattern for stringr functions, because working with a single match allows you to use much simpler data structures. To get all matches, use `str_extract_all()`. It returns a list:

```{r}
str_extract_all(more, colour_match)
```

You'll learn more about lists in [lists](#lists) and [iteration].

If you use `simplify = TRUE`, `str_extract_all()` will return a matrix with short matches expanded to the same length as the longest:

```{r}
str_extract_all(more, colour_match, simplify = TRUE)

x <- c("a", "a b", "a b c")
str_extract_all(x, "[a-z]", simplify = TRUE)
```

#### Exercises

1.  In the previous example, you might have noticed that the regular
    expression matched "flickered", which is not a colour. Modify the 
    regex to fix the problem.

1.  From the Harvard sentences data, extract:

    1. The first word from each sentence.
    1. All words ending in `ing`.
    1. All plurals.

### Grouped matches

Earlier in this chapter we talked about the use of parentheses for clarifying precedence and for backreferences when matching. You can also use parentheses to extract parts of a complex match. For example, imagine we want to extract nouns from the sentences. As a heuristic, we'll look for any word that comes after "a" or "the". Defining a "word" in a regular expression is a little tricky, so here I use a simple approximation: a sequence of at least one character that isn't a space.

```{r}
noun <- "(a|the) ([^ ]+)"

has_noun <- sentences %>%
  str_subset(noun) %>%
  head(10)
has_noun %>% 
  str_extract(noun)
```

`str_extract()` gives us the complete match; `str_match()` gives each individual component. Instead of a character vector, it returns a matrix, with one column for the complete match followed by one column for each group:

```{r}
has_noun %>% 
  str_match(noun)
```

(Unsurprisingly, our heuristic for detecting nouns is poor, and also picks up adjectives like smooth and parked.)

If your data is in a tibble, it's often easier to use `tidyr::extract()`. It works like `str_match()` but requires you to name the matches, which are then placed in new columns:

```{r}
tibble(sentence = sentences) %>% 
  tidyr::extract(
    sentence, c("article", "noun"), "(a|the) ([^ ]+)", 
    remove = FALSE
  )
```

Like `str_extract()`, if you want all matches for each string, you'll need `str_match_all()`.

#### Exercises

1. Find all words that come after a "number" like "one", "two", "three" etc.
   Pull out both the number and the word.

1. Find all contractions. Separate out the pieces before and after the 
   apostrophe.

### Replacing matches

`str_replace()` and `str_replace_all()` allow you to replace matches with new strings. The simplest use is to replace a pattern with a fixed string:

```{r}
x <- c("apple", "pear", "banana")
str_replace(x, "[aeiou]", "-")
str_replace_all(x, "[aeiou]", "-")
```

With `str_replace_all()` you can perform multiple replacements by supplying a named vector:

```{r}
x <- c("1 house", "2 cars", "3 people")
str_replace_all(x, c("1" = "one", "2" = "two", "3" = "three"))
```

Instead of replacing with a fixed string you can use backreferences to insert components of the match. In the following code, I flip the order of the second and third words.

```{r}
sentences %>% 
  str_replace("([^ ]+) ([^ ]+) ([^ ]+)", "\\1 \\3 \\2") %>% 
  head(5)
```

#### Exercises

1.   Replace all forward slashes in a string with backslashes.

1.   Implement a simple version of `str_to_lower()` using `replace_all()`.

1.   Switch the first and last letters in `words`. Which of those strings
     are still words?

### Splitting

Use `str_split()` to split a string up into pieces. For example, we could split sentences into words:

```{r}
sentences %>%
  head(5) %>% 
  str_split(" ")
```

Because each component might contain a different number of pieces, this returns a list. If you're working with a length-1 vector, the easiest thing is to just extract the first element of the list:

```{r}
"a|b|c|d" %>% 
  str_split("\\|") %>% 
  .[[1]]
```

Otherwise, like the other stringr functions that return a list, you can use `simplify = TRUE` to return a matrix:

```{r}
sentences %>%
  head(5) %>% 
  str_split(" ", simplify = TRUE)
```

You can also request a maximum number of pieces:

```{r}
fields <- c("Name: Hadley", "Country: NZ", "Age: 35")
fields %>% str_split(": ", n = 2, simplify = TRUE)
```

Instead of splitting up strings by patterns, you can also split up by character, line, sentence and word `boundary()`s:

```{r}
x <- "This is a sentence.  This is another sentence."
str_view_all(x, boundary("word"))

str_split(x, " ")[[1]]
str_split(x, boundary("word"))[[1]]
```

#### Exercises

1.  Split up a string like `"apples, pears, and bananas"` into individual
    components.
    
1.  Why is it better to split up by `boundary("word")` than `" "`?

1.  What does splitting with an empty string (`""`) do? Experiment, and
    then read the documentation.

### Find matches

`str_locate()` and `str_locate_all()` give you the starting and ending positions of each match. These are particularly useful when none of the other functions does exactly what you want. You can use `str_locate()` to find the matching pattern, `str_sub()` to extract and/or modify them.

## Other types of pattern

When you use a pattern that's a string, it's automatically wrapped into a call to `regex()`:

```{r, eval = FALSE}
# The regular call:
str_view(fruit, "nana")
# Is shorthand for
str_view(fruit, regex("nana"))
```

You can use the other arguments of `regex()` to control details of the match:

*   `ignore_case = TRUE` allows characters to match either their uppercase or 
    lowercase forms. This always uses the current locale.
    
    ```{r}
    bananas <- c("banana", "Banana", "BANANA")
    str_view(bananas, "banana")
    str_view(bananas, regex("banana", ignore_case = TRUE))
    ```
    
*   `multiline = TRUE` allows `^` and `$` to match the start and end of each
    line rather than the start and end of the complete string.
    
    ```{r}
    x <- "Line 1\nLine 2\nLine 3"
    str_extract_all(x, "^Line")[[1]]
    str_extract_all(x, regex("^Line", multiline = TRUE))[[1]]
    ```
    
*   `comments = TRUE` allows you to use comments and white space to make 
    complex regular expressions more understandable. Spaces are ignored, as is 
    everything after `#`. To match a literal space, you'll need to escape it: 
    `"\\ "`.
    
    ```{r}
    phone <- regex("
      \\(?     # optional opening parens
      (\\d{3}) # area code
      [) -]?   # optional closing parens, space, or dash
      (\\d{3}) # another three numbers
      [ -]?    # optional space or dash
      (\\d{3}) # three more numbers
      ", comments = TRUE)
    
    str_match("514-791-8141", phone)
    ```

*   `dotall = TRUE` allows `.` to match everything, including `\n`.

There are three other functions you can use instead of `regex()`:

*   `fixed()`: matches exactly the specified sequence of bytes. It ignores
    all special regular expressions and operates at a very low level. 
    This allows you to avoid complex escaping and can be much faster than 
    regular expressions. The following microbenchmark shows that it's about
    3x faster for a simple example.
  
    ```{r}
    microbenchmark::microbenchmark(
      fixed = str_detect(sentences, fixed("the")),
      regex = str_detect(sentences, "the"),
      times = 20
    )
    ```
    
    Beware using `fixed()` with non-English data. It is problematic because 
    there are often multiple ways of representing the same character. For 
    example, there are two ways to define "á": either as a single character or 
    as an "a" plus an accent:
    
    ```{r}
    a1 <- "\u00e1"
    a2 <- "a\u0301"
    c(a1, a2)
    a1 == a2
    ```

    They render identically, but because they're defined differently, 
    `fixed()` doesn't find a match. Instead, you can use `coll()`, defined
    next, to respect human character comparison rules:

    ```{r}
    str_detect(a1, fixed(a2))
    str_detect(a1, coll(a2))
    ```
    
*   `coll()`: compare strings using standard **coll**ation rules. This is 
    useful for doing case insensitive matching. Note that `coll()` takes a
    `locale` parameter that controls which rules are used for comparing
    characters. Unfortunately different parts of the world use different rules!

    ```{r}
    # That means you also need to be aware of the difference
    # when doing case insensitive matches:
    i <- c("I", "İ", "i", "ı")
    i
    
    str_subset(i, coll("i", ignore_case = TRUE))
    str_subset(i, coll("i", ignore_case = TRUE, locale = "tr"))
    ```
    
    Both `fixed()` and `regex()` have `ignore_case` arguments, but they
    do not allow you to pick the locale: they always use the default locale.
    You can see what that is with the following code; more on stringi
    later.
    
    ```{r}
    stringi::stri_locale_info()
    ```
    
    The downside of `coll()` is speed; because the rules for recognising which
    characters are the same are complicated, `coll()` is relatively slow
    compared to `regex()` and `fixed()`.

*   As you saw with `str_split()` you can use `boundary()` to match boundaries.
    You can also use it with the other functions: 
    
    ```{r}
    x <- "This is a sentence."
    str_view_all(x, boundary("word"))
    str_extract_all(x, boundary("word"))
    ```

### Exercises

1.  How would you find all strings containing `\` with `regex()` vs.
    with `fixed()`?

1.  What are the five most common words in `sentences`?

## Other uses of regular expressions

There are two useful function in base R that also use regular expressions:

*   `apropos()` searches all objects available from the global environment. This
    is useful if you can't quite remember the name of the function.
    
    ```{r}
    apropos("replace")
    ```
    
*   `dir()` lists all the files in a directory. The `pattern` argument takes
    a regular expression and only returns file names that match the pattern.
    For example, you can find all the R Markdown files in the current
    directory with:
    
    ```{r}
    head(dir(pattern = "\\.Rmd$"))
    ```
    
    (If you're more comfortable with "globs" like `*.Rmd`, you can convert
    them to regular expressions with `glob2rx()`):

## stringi

stringr is built on top of the __stringi__ package. stringr is useful when you're learning because it exposes a minimal set of functions, which have been carefully picked to handle the most common string manipulation functions. stringi, on the other hand, is designed to be comprehensive. It contains almost every function you might ever need: stringi has `r length(getNamespaceExports("stringi"))` functions to stringr's `r length(getNamespaceExports("stringr"))`.

If you find yourself struggling to do something in stringr, it's worth taking a look at stringi. The packages work very similarly, so you should be able to translate your stringr knowledge in a natural way. The main difference is the prefix: `str_` vs. `stri_`.

### Exercises

1.  Find the stringi functions that:

    1. Count the number of words.
    1. Find duplicated strings.
    1. Generate random text.

1.  How do you control the language that `stri_sort()` uses for 
    sorting?


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/tibble.Rmd
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# Tibbles

## Introduction

Throughout this book we work with "tibbles" instead of R's traditional `data.frame`. Tibbles _are_ data frames, but they tweak some older behaviours to make life a little easier. R is an old language, and some things that were useful 10 or 20 years ago now get in your way. It's difficult to change base R without breaking existing code, so most innovation occurs in packages. Here we will describe the __tibble__ package, which provides opinionated data frames that make working in the tidyverse a little easier. In most places, I'll use the term tibble and data frame interchangeably; when I want to draw particular attention to R's built-in data frame, I'll call them `data.frame`s. 

If this chapter leaves you wanting to learn more about tibbles, you might enjoy `vignette("tibble")`.

### Prerequisites

In this chapter we'll explore the __tibble__ package, part of the core tidyverse.

```{r setup, message = FALSE}
library(tidyverse)
```

## Creating tibbles {#tibbles}

Almost all of the functions that you'll use in this book produce tibbles, as tibbles are one of the unifying features of the tidyverse. Most other R packages use regular data frames, so you might want to coerce a data frame to a tibble. You can do that with `as_tibble()`:

```{r}
as_tibble(iris)
```

You can create a new tibble from individual vectors with `tibble()`. `tibble()` will automatically recycle inputs of length 1, and allows you to refer to variables that you just created, as shown below.

```{r}
tibble(
  x = 1:5, 
  y = 1, 
  z = x ^ 2 + y
)
```

If you're already familiar with `data.frame()`, note that `tibble()` does much less: it never changes the type of the inputs (e.g. it never converts strings to factors!), it never changes the names of variables, and it never creates row names.

It's possible for a tibble to have column names that are not valid R variable names, aka __non-syntactic__ names. For example, they might not start with a letter, or they might contain unusual characters like a space. To refer to these variables, you need to surround them with backticks, `` ` ``:

```{r}
tb <- tibble(
  `:)` = "smile", 
  ` ` = "space",
  `2000` = "number"
)
tb
```

You'll also need the backticks when working with these variables in other packages, like ggplot2, dplyr, and tidyr.

Another way to create a tibble is with `tribble()`, short for **tr**ansposed tibble.  `tribble()` is customised for data entry in code: column headings are defined by formulas (i.e. they start with `~`), and entries are separated by commas. This makes it possible to lay out small amounts of data in easy to read form.

```{r}
tribble(
  ~x, ~y, ~z,
  #--|--|----
  "a", 2, 3.6,
  "b", 1, 8.5
)
```

I often add a comment (the line starting with `#`), to make it really clear where the header is.

## Tibbles vs. data.frame

There are two main differences in the usage of a tibble vs. a classic `data.frame`: printing and subsetting.

### Printing

Tibbles have a refined print method that shows only the first 10 rows, and all the columns that fit on screen. This makes it much easier to work with large data. In addition to its name, each column reports its type, a nice feature borrowed from `str()`:

```{r}
tibble(
  a = lubridate::now() + runif(1e3) * 86400,
  b = lubridate::today() + runif(1e3) * 30,
  c = 1:1e3,
  d = runif(1e3),
  e = sample(letters, 1e3, replace = TRUE)
)
```

Tibbles are designed so that you don't accidentally overwhelm your console when you print large data frames. But sometimes you need more output than the default display. There are a few options that can help.

First, you can explicitly `print()` the data frame and control the number of rows (`n`) and the `width` of the display. `width = Inf` will display all columns:

```{r, eval = FALSE}
nycflights13::flights %>% 
  print(n = 10, width = Inf)
```

You can also control the default print behaviour by setting options:

* `options(tibble.print_max = n, tibble.print_min = m)`: if more than `n`
  rows, print only `m` rows. Use `options(tibble.print_min = Inf)` to always
  show all rows.

* Use `options(tibble.width = Inf)` to always print all columns, regardless
  of the width of the screen.

You can see a complete list of options by looking at the package help with `package?tibble`.

A final option is to use RStudio's built-in data viewer to get a scrollable view of the complete dataset. This is also often useful at the end of a long chain of manipulations.

```{r, eval = FALSE}
nycflights13::flights %>% 
  View()
```

### Subsetting

So far all the tools you've learned have worked with complete data frames. If you want to pull out a single variable, you need some new tools, `$` and `[[`. `[[` can extract by name or position; `$` only extracts by name but is a little less typing.

```{r}
df <- tibble(
  x = runif(5),
  y = rnorm(5)
)

# Extract by name
df$x
df[["x"]]

# Extract by position
df[[1]]
```

To use these in a pipe, you'll need to use the special placeholder `.`:

```{r}
df %>% .$x
df %>% .[["x"]]
```

Compared to a `data.frame`, tibbles are more strict: they never do partial matching, and they will generate a warning if the column you are trying to access does not exist.

## Interacting with older code

Some older functions don't work with tibbles. If you encounter one of these functions, use `as.data.frame()` to turn a tibble back to a `data.frame`:

```{r}
class(as.data.frame(tb))
```

The main reason that some older functions don't work with tibble is the `[` function.  We don't use `[` much in this book because `dplyr::filter()` and `dplyr::select()` allow you to solve the same problems with clearer code (but you will learn a little about it in [vector subsetting](#vector-subsetting)). With base R data frames, `[` sometimes returns a data frame, and sometimes returns a vector. With tibbles, `[` always returns another tibble.

## Exercises

1.  How can you tell if an object is a tibble? (Hint: try printing `mtcars`,
    which is a regular data frame). 

1.  Compare and contrast the following operations on a `data.frame` and 
    equivalent tibble. What is different? Why might the default data frame
    behaviours cause you frustration?
    
    ```{r, eval = FALSE}
    df <- data.frame(abc = 1, xyz = "a")
    df$x
    df[, "xyz"]
    df[, c("abc", "xyz")]
    ```

1.  If you have the name of a variable stored in an object, e.g. `var <- "mpg"`,
    how can you extract the reference variable from a tibble?

1.  Practice referring to non-syntactic names in the following data frame by:

    1.  Extracting the variable called `1`.

    1.  Plotting a scatterplot of `1` vs `2`.

    1.  Creating a new column called `3` which is `2` divided by `1`.
        
    1.  Renaming the columns to `one`, `two` and `three`. 
    
    ```{r}
    annoying <- tibble(
      `1` = 1:10,
      `2` = `1` * 2 + rnorm(length(`1`))
    )
    ```

1.  What does `tibble::enframe()` do? When might you use it?

1.  What option controls how many additional column names are printed
    at the footer of a tibble?


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/transform.Rmd
================================================
# Data transformation {#transform}

## Introduction

Visualisation is an important tool for insight generation, but it is rare that you get the data in exactly the right form you need. Often you'll need to create some new variables or summaries, or maybe you just want to rename the variables or reorder the observations in order to make the data a little easier to work with. You'll learn how to do all that (and more!) in this chapter, which will teach you how to transform your data using the dplyr package and a new dataset on flights departing New York City in 2013.

### Prerequisites

In this chapter we're going to focus on how to use the dplyr package, another core member of the tidyverse. We'll illustrate the key ideas using data from the nycflights13 package, and use ggplot2 to help us understand the data. 

```{r setup, message = FALSE}
library(nycflights13)
library(tidyverse)
```

Take careful note of the conflicts message that's printed when you load the tidyverse. It tells you that dplyr overwrites some functions in base R. If you want to use the base version of these functions after loading dplyr, you'll need to use their full names: `stats::filter()` and `stats::lag()`.

### nycflights13

To explore the basic data manipulation verbs of dplyr, we'll use `nycflights13::flights`. This data frame contains all `r format(nrow(nycflights13::flights), big.mark = ",")` flights that departed from New York City in 2013. The data comes from the US [Bureau of Transportation Statistics](http://www.transtats.bts.gov/DatabaseInfo.asp?DB_ID=120&Link=0), and is documented in `?flights`.

```{r}
flights
```

You might notice that this data frame prints a little differently from other data frames you might have used in the past: it only shows the first few rows and all the columns that fit on one screen. (To see the whole dataset, you can run `View(flights)` which will open the dataset in the RStudio viewer). It prints differently because it's a __tibble__. Tibbles are data frames, but slightly tweaked to work better in the tidyverse. For now, you don't need to worry about the differences; we'll come back to tibbles in more detail in [wrangle](#wrangle-intro).
 
You might also have noticed the row of three (or four) letter abbreviations under the column names. These describe the type of each variable:

* `int` stands for integers.

* `dbl` stands for doubles, or real numbers.

* `chr` stands for character vectors, or strings.

* `dttm` stands for date-times (a date + a time).

There are three other common types of variables that aren't used in this dataset but you'll encounter later in the book:

* `lgl` stands for logical, vectors that contain only `TRUE` or `FALSE`.

* `fctr` stands for factors, which R uses to represent categorical variables
  with fixed possible values.

* `date` stands for dates.

### dplyr basics

In this chapter you are going to learn the five key dplyr functions that allow you to solve the vast majority of your data manipulation challenges:

* Pick observations by their values (`filter()`).
* Reorder the rows (`arrange()`).
* Pick variables by their names (`select()`).
* Create new variables with functions of existing variables (`mutate()`).
* Collapse many values down to a single summary (`summarise()`).

These can all be used in conjunction with `group_by()` which changes the scope of each function from operating on the entire dataset to operating on it group-by-group. These six functions provide the verbs for a language of data manipulation.

All verbs work similarly: 

1.  The first argument is a data frame.

1.  The subsequent arguments describe what to do with the data frame,
    using the variable names (without quotes).
    
1.  The result is a new data frame.

Together these properties make it easy to chain together multiple simple steps to achieve a complex result. Let's dive in and see how these verbs work.

## Filter rows with `filter()`

`filter()` allows you to subset observations based on their values. The first argument is the name of the data frame. The second and subsequent arguments are the expressions that filter the data frame. For example, we can select all flights on January 1st with:

```{r}
filter(flights, month == 1, day == 1)
```

When you run that line of code, dplyr executes the filtering operation and returns a new data frame. dplyr functions never modify their inputs, so if you want to save the result, you'll need to use the assignment operator, `<-`:

```{r}
jan1 <- filter(flights, month == 1, day == 1)
```

R either prints out the results, or saves them to a variable. If you want to do both, you can wrap the assignment in parentheses:

```{r}
(dec25 <- filter(flights, month == 12, day == 25))
```

### Comparisons

To use filtering effectively, you have to know how to select the observations that you want using the comparison operators. R provides the standard suite: `>`, `>=`, `<`, `<=`, `!=` (not equal), and `==` (equal). 

When you're starting out with R, the easiest mistake to make is to use `=` instead of `==` when testing for equality. When this happens you'll get an informative error:

```{r, error = TRUE}
filter(flights, month = 1)
```

There's another common problem you might encounter when using `==`: floating point numbers. These results might surprise you!

```{r}
sqrt(2) ^ 2 == 2
1 / 49 * 49 == 1
```

Computers use finite precision arithmetic (they obviously can't store an infinite number of digits!) so remember that every number you see is an approximation. Instead of relying on `==`, use `near()`:

```{r}
near(sqrt(2) ^ 2,  2)
near(1 / 49 * 49, 1)
```

### Logical operators

Multiple arguments to `filter()` are combined with "and": every expression must be true in order for a row to be included in the output. For other types of combinations, you'll need to use Boolean operators yourself: `&` is "and", `|` is "or", and `!` is "not". Figure \@ref(fig:bool-ops) shows the complete set of Boolean operations.

```{r bool-ops, echo = FALSE, fig.cap = "Complete set of boolean operations. `x` is the left-hand circle, `y` is the right-hand circle, and the shaded region show which parts each operator selects."}
knitr::include_graphics("diagrams/transform-logical.png")
```

The following code finds all flights that departed in November or December:

```{r, eval = FALSE}
filter(flights, month == 11 | month == 12)
```

The order of operations doesn't work like English. You can't write `filter(flights, month == 11 | 12)`, which you might literally translate into  "finds all flights that departed in November or December". Instead it finds all months that equal `11 | 12`, an expression that evaluates to `TRUE`. In a numeric context (like here), `TRUE` becomes one, so this finds all flights in January, not November or December. This is quite confusing!

A useful short-hand for this problem is `x %in% y`. This will select every row where `x` is one of the values in `y`. We could use it to rewrite the code above:

```{r, eval = FALSE}
nov_dec <- filter(flights, month %in% c(11, 12))
```

Sometimes you can simplify complicated subsetting by remembering De Morgan's law: `!(x & y)` is the same as `!x | !y`, and `!(x | y)` is the same as `!x & !y`. For example, if you wanted to find flights that weren't delayed (on arrival or departure) by more than two hours, you could use either of the following two filters:

```{r, eval = FALSE}
filter(flights, !(arr_delay > 120 | dep_delay > 120))
filter(flights, arr_delay <= 120, dep_delay <= 120)
```

As well as `&` and `|`, R also has `&&` and `||`. Don't use them here! You'll learn when you should use them in [conditional execution].

Whenever you start using complicated, multipart expressions in `filter()`, consider making them explicit variables instead. That makes it much easier to check your work. You'll learn how to create new variables shortly.

### Missing values

One important feature of R that can make comparison tricky are missing values, or `NA`s ("not availables"). `NA` represents an unknown value so missing values are "contagious": almost any operation involving an unknown value will also be unknown.

```{r}
NA > 5
10 == NA
NA + 10
NA / 2
```

The most confusing result is this one:

```{r}
NA == NA
```

It's easiest to understand why this is true with a bit more context:

```{r}
# Let x be Mary's age. We don't know how old she is.
x <- NA

# Let y be John's age. We don't know how old he is.
y <- NA

# Are John and Mary the same age?
x == y
# We don't know!
```

If you want to determine if a value is missing, use `is.na()`:

```{r}
is.na(x)
```

`filter()` only includes rows where the condition is `TRUE`; it excludes both `FALSE` and `NA` values. If you want to preserve missing values, ask for them explicitly:

```{r}
df <- tibble(x = c(1, NA, 3))
filter(df, x > 1)
filter(df, is.na(x) | x > 1)
```

### Exercises

1.  Find all flights that

    1. Had an arrival delay of two or more hours
    1. Flew to Houston (`IAH` or `HOU`)
    1. Were operated by United, American, or Delta
    1. Departed in summer (July, August, and September)
    1. Arrived more than two hours late, but didn't leave late
    1. Were delayed by at least an hour, but made up over 30 minutes in flight
    1. Departed between midnight and 6am (inclusive)

1.  Another useful dplyr filtering helper is `between()`. What does it do?
    Can you use it to simplify the code needed to answer the previous 
    challenges?

1.  How many flights have a missing `dep_time`? What other variables are 
    missing? What might these rows represent?

1.  Why is `NA ^ 0` not missing? Why is `NA | TRUE` not missing?
    Why is `FALSE & NA` not missing? Can you figure out the general
    rule?  (`NA * 0` is a tricky counterexample!)

## Arrange rows with `arrange()`

`arrange()` works similarly to `filter()` except that instead of selecting rows, it changes their order. It takes a data frame and a set of column names (or more complicated expressions) to order by. If you provide more than one column name, each additional column will be used to break ties in the values of preceding columns:

```{r}
arrange(flights, year, month, day)
```

Use `desc()` to re-order by a column in descending order:

```{r}
arrange(flights, desc(dep_delay))
```

Missing values are always sorted at the end:

```{r}
df <- tibble(x = c(5, 2, NA))
arrange(df, x)
arrange(df, desc(x))
```

### Exercises

1.  How could you use `arrange()` to sort all missing values to the start?
    (Hint: use `is.na()`).
    
1.  Sort `flights` to find the most delayed flights. Find the flights that
    left earliest.

1.  Sort `flights` to find the fastest flights.

1.  Which flights travelled the longest? Which travelled the shortest?

## Select columns with `select()` {#select}

It's not uncommon to get datasets with hundreds or even thousands of variables. In this case, the first challenge is often narrowing in on the variables you're actually interested in. `select()` allows you to rapidly zoom in on a useful subset using operations based on the names of the variables.

`select()` is not terribly useful with the flights data because we only have 19 variables, but you can still get the general idea:

```{r}
# Select columns by name
select(flights, year, month, day)
# Select all columns between year and day (inclusive)
select(flights, year:day)
# Select all columns except those from year to day (inclusive)
select(flights, -(year:day))
```

There are a number of helper functions you can use within `select()`:

* `starts_with("abc")`: matches names that begin with "abc".

* `ends_with("xyz")`: matches names that end with "xyz".

* `contains("ijk")`: matches names that contain "ijk".

* `matches("(.)\\1")`: selects variables that match a regular expression.
   This one matches any variables that contain repeated characters. You'll 
   learn more about regular expressions in [strings].
   
*  `num_range("x", 1:3)`: matches `x1`, `x2` and `x3`.
   
See `?select` for more details.

`select()` can be used to rename variables, but it's rarely useful because it drops all of the variables not explicitly mentioned. Instead, use `rename()`, which is a variant of `select()` that keeps all the variables that aren't explicitly mentioned:

```{r}
rename(flights, tail_num = tailnum)
```

Another option is to use `select()` in conjunction with the `everything()` helper. This is useful if you have a handful of variables you'd like to move to the start of the data frame.

```{r}
select(flights, time_hour, air_time, everything())
```

### Exercises

1.  Brainstorm as many ways as possible to select `dep_time`, `dep_delay`,
    `arr_time`, and `arr_delay` from `flights`.
    
1.  What happens if you include the name of a variable multiple times in
    a `select()` call?
  
1.  What does the `one_of()` function do? Why might it be helpful in conjunction
    with this vector?
    
    ```{r}
    vars <- c("year", "month", "day", "dep_delay", "arr_delay")
    ```
    
1.  Does the result of running the following code surprise you?  How do the
    select helpers deal with case by default? How can you change that default?

    ```{r, eval = FALSE}
    select(flights, contains("TIME"))
    ```

## Add new variables with `mutate()`

Besides selecting sets of existing columns, it's often useful to add new columns that are functions of existing columns. That's the job of `mutate()`. 

`mutate()` always adds new columns at the end of your dataset so we'll start by creating a narrower dataset so we can see the new variables. Remember that when you're in RStudio, the easiest way to see all the columns is `View()`.

```{r}
flights_sml <- select(flights, 
  year:day, 
  ends_with("delay"), 
  distance, 
  air_time
)
mutate(flights_sml,
  gain = dep_delay - arr_delay,
  speed = distance / air_time * 60
)
```

Note that you can refer to columns that you've just created:

```{r}
mutate(flights_sml,
  gain = dep_delay - arr_delay,
  hours = air_time / 60,
  gain_per_hour = gain / hours
)
```

If you only want to keep the new variables, use `transmute()`:

```{r}
transmute(flights,
  gain = dep_delay - arr_delay,
  hours = air_time / 60,
  gain_per_hour = gain / hours
)
```

### Useful creation functions {#mutate-funs}

There are many functions for creating new variables that you can use with `mutate()`. The key property is that the function must be vectorised: it must take a vector of values as input, return a vector with the same number of values as output. There's no way to list every possible function that you might use, but here's a selection of functions that are frequently useful:

*   Arithmetic operators: `+`, `-`, `*`, `/`, `^`. These are all vectorised,
    using the so called "recycling rules". If one parameter is shorter than 
    the other, it will be automatically extended to be the same length. This 
    is most useful when one of the arguments is a single number: `air_time / 60`,
    `hours * 60 + minute`, etc.
    
    Arithmetic operators are also useful in conjunction with the aggregate
    functions you'll learn about later. For example, `x / sum(x)` calculates 
    the proportion of a total, and `y - mean(y)` computes the difference from 
    the mean.
    
*   Modular arithmetic: `%/%` (integer division) and `%%` (remainder), where
    `x == y * (x %/% y) + (x %% y)`. Modular arithmetic is a handy tool because 
    it allows you to break integers up into pieces. For example, in the 
    flights dataset, you can compute `hour` and `minute` from `dep_time` with:
    
    ```{r}
    transmute(flights,
      dep_time,
      hour = dep_time %/% 100,
      minute = dep_time %% 100
    )
    ```
  
*   Logs: `log()`, `log2()`, `log10()`. Logarithms are an incredibly useful
    transformation for dealing with data that ranges across multiple orders of
    magnitude. They also convert multiplicative relationships to additive, a
    feature we'll come back to in modelling.
    
    All else being equal, I recommend using `log2()` because it's easy to
    interpret: a difference of 1 on the log scale corresponds to doubling on
    the original scale and a difference of -1 corresponds to halving.

*   Offsets: `lead()` and `lag()` allow you to refer to leading or lagging 
    values. This allows you to compute running differences (e.g. `x - lag(x)`) 
    or find when values change (`x != lag(x)`). They are most useful in 
    conjunction with `group_by()`, which you'll learn about shortly.
    
    ```{r}
    (x <- 1:10)
    lag(x)
    lead(x)
    ```
  
*   Cumulative and rolling aggregates: R provides functions for running sums,
    products, mins and maxes: `cumsum()`, `cumprod()`, `cummin()`, `cummax()`; 
    and dplyr provides `cummean()` for cumulative means. If you need rolling
    aggregates (i.e. a sum computed over a rolling window), try the RcppRoll
    package.
    
    ```{r}
    x
    cumsum(x)
    cummean(x)
    ```

*   Logical comparisons, `<`, `<=`, `>`, `>=`, `!=`, which you learned about
    earlier. If you're doing a complex sequence of logical operations it's 
    often a good idea to store the interim values in new variables so you can
    check that each step is working as expected.

*   Ranking: there are a number of ranking functions, but you should 
    start with `min_rank()`. It does the most usual type of ranking 
    (e.g. 1st, 2nd, 2nd, 4th). The default gives smallest values the small
    ranks; use `desc(x)` to give the largest values the smallest ranks. 
    
    ```{r}
    y <- c(1, 2, 2, NA, 3, 4)
    min_rank(y)
    min_rank(desc(y))
    ```
    
    If `min_rank()` doesn't do what you need, look at the variants
    `row_number()`, `dense_rank()`, `percent_rank()`, `cume_dist()`,
    `ntile()`.  See their help pages for more details.
    
    ```{r}
    row_number(y)
    dense_rank(y)
    percent_rank(y)
    cume_dist(y)
    ```

### Exercises

```{r, eval = FALSE, echo = FALSE}
flights <- flights %>% mutate(
  dep_time = hour * 60 + minute,
  arr_time = (arr_time %/% 100) * 60 + (arr_time %% 100),
  airtime2 = arr_time - dep_time,
  dep_sched = dep_time + dep_delay
)

ggplot(flights, aes(dep_sched)) + geom_histogram(binwidth = 60)
ggplot(flights, aes(dep_sched %% 60)) + geom_histogram(binwidth = 1)
ggplot(flights, aes(air_time - airtime2)) + geom_histogram()
```

1.  Currently `dep_time` and `sched_dep_time` are convenient to look at, but
    hard to compute with because they're not really continuous numbers. 
    Convert them to a more convenient representation of number of minutes
    since midnight.
    
1.  Compare `air_time` with `arr_time - dep_time`. What do you expect to see?
    What do you see? What do you need to do to fix it?
    
1.  Compare `dep_time`, `sched_dep_time`, and `dep_delay`. How would you
    expect those three numbers to be related?

1.  Find the 10 most delayed flights using a ranking function. How do you want 
    to handle ties? Carefully read the documentation for `min_rank()`.

1.  What does `1:3 + 1:10` return? Why?

1.  What trigonometric functions does R provide?

## Grouped summaries with `summarise()`

The last key verb is `summarise()`. It collapses a data frame to a single row:

```{r}
summarise(flights, delay = mean(dep_delay, na.rm = TRUE))
```

(We'll come back to what that `na.rm = TRUE` means very shortly.)

`summarise()` is not terribly useful unless we pair it with `group_by()`. This changes the unit of analysis from the complete dataset to individual groups. Then, when you use the dplyr verbs on a grouped data frame they'll be automatically applied "by group". For example, if we applied exactly the same code to a data frame grouped by date, we get the average delay per date:

```{r}
by_day <- group_by(flights, year, month, day)
summarise(by_day, delay = mean(dep_delay, na.rm = TRUE))
```

Together `group_by()` and `summarise()` provide one of the tools that you'll use most commonly when working with dplyr: grouped summaries. But before we go any further with this, we need to introduce a powerful new idea: the pipe.

### Combining multiple operations with the pipe

Imagine that we want to explore the relationship between the distance and average delay for each location. Using what you know about dplyr, you might write code like this:

```{r, fig.width = 6}
by_dest <- group_by(flights, dest)
delay <- summarise(by_dest,
  count = n(),
  dist = mean(distance, na.rm = TRUE),
  delay = mean(arr_delay, na.rm = TRUE)
)
delay <- filter(delay, count > 20, dest != "HNL")

# It looks like delays increase with distance up to ~750 miles 
# and then decrease. Maybe as flights get longer there's more 
# ability to make up delays in the air?
ggplot(data = delay, mapping = aes(x = dist, y = delay)) +
  geom_point(aes(size = count), alpha = 1/3) +
  geom_smooth(se = FALSE)
```

There are three steps to prepare this data:

1.  Group flights by destination.

1.  Summarise to compute distance, average delay, and number of flights.

1.  Filter to remove noisy points and Honolulu airport, which is almost
    twice as far away as the next closest airport.

This code is a little frustrating to write because we have to give each intermediate data frame a name, even though we don't care about it. Naming things is hard, so this slows down our analysis. 

There's another way to tackle the same problem with the pipe, `%>%`:

```{r}
delays <- flights %>% 
  group_by(dest) %>% 
  summarise(
    count = n(),
    dist = mean(distance, na.rm = TRUE),
    delay = mean(arr_delay, na.rm = TRUE)
  ) %>% 
  filter(count > 20, dest != "HNL")
```

This focuses on the transformations, not what's being transformed, which makes the code easier to read. You can read it as a series of imperative statements: group, then summarise, then filter. As suggested by this reading, a good way to pronounce `%>%` when reading code is "then".

Behind the scenes, `x %>% f(y)` turns into `f(x, y)`, and `x %>% f(y) %>% g(z)` turns into `g(f(x, y), z)` and so on. You can use the pipe to rewrite multiple operations in a way that you can read left-to-right, top-to-bottom. We'll use piping frequently from now on because it considerably improves the readability of code, and we'll come back to it in more detail in [pipes].

Working with the pipe is one of the key criteria for belonging to the tidyverse. The only exception is ggplot2: it was written before the pipe was discovered. Unfortunately, the next iteration of ggplot2, ggvis, which does use the pipe, isn't quite ready for prime time yet. 

### Missing values

You may have wondered about the `na.rm` argument we used above. What happens if we don't set it?

```{r}
flights %>% 
  group_by(year, month, day) %>% 
  summarise(mean = mean(dep_delay))
```

We get a lot of missing values! That's because aggregation functions obey the usual rule of missing values: if there's any missing value in the input, the output will be a missing value. Fortunately, all aggregation functions have an `na.rm` argument which removes the missing values prior to computation:

```{r}
flights %>% 
  group_by(year, month, day) %>% 
  summarise(mean = mean(dep_delay, na.rm = TRUE))
```

In this case, where missing values represent cancelled flights, we could also tackle the problem by first removing the cancelled flights. We'll save this dataset so we can reuse in the next few examples.

```{r}
not_cancelled <- flights %>% 
  filter(!is.na(dep_delay), !is.na(arr_delay))

not_cancelled %>% 
  group_by(year, month, day) %>% 
  summarise(mean = mean(dep_delay))
```

### Counts

Whenever you do any aggregation, it's always a good idea to include either a count (`n()`), or a count of non-missing values (`sum(!is.na(x))`). That way you can check that you're not drawing conclusions based on very small amounts of data. For example, let's look at the planes (identified by their tail number) that have the highest average delays:

```{r}
delays <- not_cancelled %>% 
  group_by(tailnum) %>% 
  summarise(
    delay = mean(arr_delay)
  )

ggplot(data = delays, mapping = aes(x = delay)) + 
  geom_freqpoly(binwidth = 10)
```

Wow, there are some planes that have an _average_ delay of 5 hours (300 minutes)!

The story is actually a little more nuanced. We can get more insight if we draw a scatterplot of number of flights vs. average delay:

```{r}
delays <- not_cancelled %>% 
  group_by(tailnum) %>% 
  summarise(
    delay = mean(arr_delay, na.rm = TRUE),
    n = n()
  )

ggplot(data = delays, mapping = aes(x = n, y = delay)) + 
  geom_point(alpha = 1/10)
```

Not surprisingly, there is much greater variation in the average delay when there are few flights. The shape of this plot is very characteristic: whenever you plot a mean (or other summary) vs. group size, you'll see that the variation decreases as the sample size increases.

When looking at this sort of plot, it's often useful to filter out the groups with the smallest numbers of observations, so you can see more of the pattern and less of the extreme variation in the smallest groups. This is what the following code does, as well as showing you a handy pattern for integrating ggplot2 into dplyr flows. It's a bit painful that you have to switch from `%>%` to `+`, but once you get the hang of it, it's quite convenient.

```{r}
delays %>% 
  filter(n > 25) %>% 
  ggplot(mapping = aes(x = n, y = delay)) + 
    geom_point(alpha = 1/10)
```

--------------------------------------------------------------------------------

RStudio tip: a useful keyboard shortcut is Cmd/Ctrl + Shift + P. This resends the previously sent chunk from the editor to the console. This is very convenient when you're (e.g.) exploring the value of `n` in the example above. You send the whole block once with Cmd/Ctrl + Enter, then you modify the value of `n` and press Cmd/Ctrl + Shift + P to resend the complete block.

--------------------------------------------------------------------------------

There's another common variation of this type of pattern. Let's look at how the average performance of batters in baseball is related to the number of times they're at bat. Here I use data from the __Lahman__ package to compute the batting average (number of hits / number of attempts) of every major league baseball player.  

When I plot the skill of the batter (measured by the batting average, `ba`) against the number of opportunities to hit the ball (measured by at bat, `ab`), you see two patterns:

1.  As above, the variation in our aggregate decreases as we get more 
    data points.
    
2.  There's a positive correlation between skill (`ba`) and opportunities to 
    hit the ball (`ab`). This is because teams control who gets to play, 
    and obviously they'll pick their best players.

```{r}
# Convert to a tibble so it prints nicely
batting <- as_tibble(Lahman::Batting)

batters <- batting %>% 
  group_by(playerID) %>% 
  summarise(
    ba = sum(H, na.rm = TRUE) / sum(AB, na.rm = TRUE),
    ab = sum(AB, na.rm = TRUE)
  )

batters %>% 
  filter(ab > 100) %>% 
  ggplot(mapping = aes(x = ab, y = ba)) +
    geom_point() + 
    geom_smooth(se = FALSE)
```

This also has important implications for ranking. If you naively sort on `desc(ba)`, the people with the best batting averages are clearly lucky, not skilled:

```{r}
batters %>% 
  arrange(desc(ba))
```

You can find a good explanation of this problem at <http://varianceexplained.org/r/empirical_bayes_baseball/> and <http://www.evanmiller.org/how-not-to-sort-by-average-rating.html>.

### Useful summary functions {#summarise-funs}

Just using means, counts, and sum can get you a long way, but R provides many other useful summary functions:

*   Measures of location: we've used `mean(x)`, but `median(x)` is also
    useful. The mean is the sum divided by the length; the median is a value 
    where 50% of `x` is above it, and 50% is below it.
    
    It's sometimes useful to combine aggregation with logical subsetting. 
    We haven't talked about this sort of subsetting yet, but you'll learn more
    about it in [subsetting].
    
    ```{r}
    not_cancelled %>% 
      group_by(year, month, day) %>% 
      summarise(
        avg_delay1 = mean(arr_delay),
        avg_delay2 = mean(arr_delay[arr_delay > 0]) # the average positive delay
      )
    ```

*   Measures of spread: `sd(x)`, `IQR(x)`, `mad(x)`. The root mean squared deviation,
    or standard deviation or sd for short, is the standard measure of spread.
    The interquartile range `IQR()` and median absolute deviation `mad(x)`
    are robust equivalents that may be more useful if you have outliers.
    
    ```{r}
    # Why is distance to some destinations more variable than to others?
    not_cancelled %>% 
      group_by(dest) %>% 
      summarise(distance_sd = sd(distance)) %>% 
      arrange(desc(distance_sd))
    ```
  
*   Measures of rank: `min(x)`, `quantile(x, 0.25)`, `max(x)`. Quantiles
    are a generalisation of the median. For example, `quantile(x, 0.25)`
    will find a value of `x` that is greater than 25% of the values,
    and less than the remaining 75%.

    ```{r}
    # When do the first and last flights leave each day?
    not_cancelled %>% 
      group_by(year, month, day) %>% 
      summarise(
        first = min(dep_time),
        last = max(dep_time)
      )
    ```
  
*   Measures of position: `first(x)`, `nth(x, 2)`, `last(x)`. These work 
    similarly to `x[1]`, `x[2]`, and `x[length(x)]` but let you set a default 
    value if that position does not exist (i.e. you're trying to get the 3rd
    element from a group that only has two elements). For example, we can
    find the first and last departure for each day:
    
    ```{r}
    not_cancelled %>% 
      group_by(year, month, day) %>% 
      summarise(
        first_dep = first(dep_time), 
        last_dep = last(dep_time)
      )
    ```
    
    These functions are complementary to filtering on ranks. Filtering gives
    you all variables, with each observation in a separate row:
    
    ```{r}
    not_cancelled %>% 
      group_by(year, month, day) %>% 
      mutate(r = min_rank(desc(dep_time))) %>% 
      filter(r %in% range(r))
    ```

*   Counts: You've seen `n()`, which takes no arguments, and returns the 
    size of the current group. To count the number of non-missing values, use
    `sum(!is.na(x))`. To count the number of distinct (unique) values, use
    `n_distinct(x)`.
    
    ```{r}
    # Which destinations have the most carriers?
    not_cancelled %>% 
      group_by(dest) %>% 
      summarise(carriers = n_distinct(carrier)) %>% 
      arrange(desc(carriers))
    ```
    
    Counts are so useful that dplyr provides a simple helper if all you want is 
    a count:
    
    ```{r}
    not_cancelled %>% 
      count(dest)
    ```
    
    You can optionally provide a weight variable. For example, you could use 
    this to "count" (sum) the total number of miles a plane flew:
    
    ```{r}
    not_cancelled %>% 
      count(tailnum, wt = distance)
    ```
  
*   Counts and proportions of logical values: `sum(x > 10)`, `mean(y == 0)`.
    When used with numeric functions, `TRUE` is converted to 1 and `FALSE` to 0. 
    This makes `sum()` and `mean()` very useful: `sum(x)` gives the number of 
    `TRUE`s in `x`, and `mean(x)` gives the proportion.
    
    ```{r}
    # How many flights left before 5am? (these usually indicate delayed
    # flights from the previous day)
    not_cancelled %>% 
      group_by(year, month, day) %>% 
      summarise(n_early = sum(dep_time < 500))
    
    # What proportion of flights are delayed by more than an hour?
    not_cancelled %>% 
      group_by(year, month, day) %>% 
      summarise(hour_perc = mean(arr_delay > 60))
    ```

### Grouping by multiple variables

When you group by multiple variables, each summary peels off one level of the grouping. That makes it easy to progressively roll up a dataset:

```{r}
daily <- group_by(flights, year, month, day)
(per_day   <- summarise(daily, flights = n()))
(per_month <- summarise(per_day, flights = sum(flights)))
(per_year  <- summarise(per_month, flights = sum(flights)))
```

Be careful when progressively rolling up summaries: it's OK for sums and counts, but you need to think about weighting means and variances, and it's not possible to do it exactly for rank-based statistics like the median. In other words, the sum of groupwise sums is the overall sum, but the median of groupwise medians is not the overall median.

### Ungrouping

If you need to remove grouping, and return to operations on ungrouped data, use `ungroup()`. 

```{r}
daily %>% 
  ungroup() %>%             # no longer grouped by date
  summarise(flights = n())  # all flights
```

### Exercises

1.  Brainstorm at least 5 different ways to assess the typical delay 
    characteristics of a group of flights. Consider the following scenarios:
    
    * A flight is 15 minutes early 50% of the time, and 15 minutes late 50% of 
      the time.
      
    * A flight is always 10 minutes late.

    * A flight is 30 minutes early 50% of the time, and 30 minutes late 50% of 
      the time.
      
    * 99% of the time a flight is on time. 1% of the time it's 2 hours late.
    
    Which is more important: arrival delay or departure delay?

1.  Come up with another approach that will give you the same output as 
    `not_cancelled %>% count(dest)` and 
    `not_cancelled %>% count(tailnum, wt = distance)` (without using 
    `count()`).

1.  Our definition of cancelled flights (`is.na(dep_delay) | is.na(arr_delay)`
    ) is slightly suboptimal. Why? Which is the most important column?

1.  Look at the number of cancelled flights per day. Is there a pattern?
    Is the proportion of cancelled flights related to the average delay?

1.  Which carrier has the worst delays? Challenge: can you disentangle the
    effects of bad airports vs. bad carriers? Why/why not? (Hint: think about
    `flights %>% group_by(carrier, dest) %>% summarise(n())`)

1.  What does the `sort` argument to `count()` do. When might you use it?

## Grouped mutates (and filters)

Grouping is most useful in conjunction with `summarise()`, but you can also do convenient operations with `mutate()` and `filter()`:

*   Find the worst members of each group:

    ```{r}
    flights_sml %>% 
      group_by(year, month, day) %>%
      filter(rank(desc(arr_delay)) < 10)
    ```

*   Find all groups bigger than a threshold:

    ```{r}
    popular_dests <- flights %>% 
      group_by(dest) %>% 
      filter(n() > 365)
    popular_dests
    ```

*   Standardise to compute per group metrics:

    ```{r}
    popular_dests %>% 
      filter(arr_delay > 0) %>% 
      mutate(prop_delay = arr_delay / sum(arr_delay)) %>% 
      select(year:day, dest, arr_delay, prop_delay)
    ```

A grouped filter is a grouped mutate followed by an ungrouped filter. I generally avoid them except for quick and dirty manipulations: otherwise it's hard to check that you've done the manipulation correctly.

Functions that work most naturally in grouped mutates and filters are known as  window functions (vs. the summary functions used for summaries). You can learn more about useful window functions in the corresponding vignette: `vignette("window-functions")`.

### Exercises

1.  Refer back to the lists of useful mutate and filtering functions. 
    Describe how each operation changes when you combine it with grouping.

1.  Which plane (`tailnum`) has the worst on-time record?

1.  What time of day should you fly if you want to avoid delays as much
    as possible?
    
1.  For each destination, compute the total minutes of delay. For each 
    flight, compute the proportion of the total delay for its destination.
    
1.  Delays are typically temporally correlated: even once the problem that
    caused the initial delay has been resolved, later flights are delayed 
    to allow earlier flights to leave. Using `lag()`, explore how the delay
    of a flight is related to the delay of the immediately preceding flight.
    
1.  Look at each destination. Can you find flights that are suspiciously
    fast? (i.e. flights that represent a potential data entry error). Compute
    the air time a flight relative to the shortest flight to that destination.
    Which flights were most delayed in the air?
    
1.  Find all destinations that are flown by at least two carriers. Use that
    information to rank the carriers.

1.  For each plane, count the number of flights before the first delay 
    of greater than 1 hour.


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/vectors.Rmd
================================================
# Vectors

## Introduction

So far this book has focussed on tibbles and packages that work with them. But as you start to write your own functions, and dig deeper into R, you need to learn about vectors, the objects that underlie tibbles. If you've learned R in a more traditional way, you're probably already familiar with vectors, as most R resources start with vectors and work their way up to tibbles. I think it's better to start with tibbles because they're immediately useful, and then work your way down to the underlying components.

Vectors are particularly important as most of the functions you will write will work with vectors. It is possible to write functions that work with tibbles (like ggplot2, dplyr, and tidyr), but the tools you need to write such functions are currently idiosyncratic and immature. I am working on a better approach, <https://github.com/hadley/lazyeval>, but it will not be ready in time for the publication of the book. Even when complete, you'll still need to understand vectors, it'll just make it easier to write a user-friendly layer on top.

### Prerequisites

The focus of this chapter is on base R data structures, so it isn't essential to load any packages. We will, however, use a handful of functions from the __purrr__ package to avoid some inconsistencies in base R.

```{r setup, message = FALSE}
library(tidyverse)
```

## Vector basics

There are two types of vectors:

1. __Atomic__ vectors, of which there are six types:
  __logical__, __integer__, __double__,  __character__, __complex__, and 
  __raw__. Integer and double vectors are collectively known as
  __numeric__ vectors. 

1. __Lists__,  which are sometimes called recursive vectors because lists can 
  contain other lists. 

The chief difference between atomic vectors and lists is that atomic vectors are __homogeneous__, while lists can be __heterogeneous__. There's one other related object: `NULL`. `NULL` is often used to represent the absence of a vector (as opposed to `NA` which is used to represent the absence of a value in a vector). `NULL` typically behaves like a vector of length 0. Figure \@ref(fig:datatypes) summarises the interrelationships. 

```{r datatypes, echo = FALSE, out.width = "50%", fig.cap = "The hierarchy of R's vector types"}
knitr::include_graphics("diagrams/data-structures-overview.png")
```

Every vector has two key properties: 

1.  Its __type__, which you can determine with `typeof()`.

    ```{r}
    typeof(letters)
    typeof(1:10)
    ```

1. Its __length__, which you can determine with `length()`.

    ```{r}
    x <- list("a", "b", 1:10)
    length(x)
    ```

Vectors can also contain arbitrary additional metadata in the form of attributes. These attributes are used to create __augmented vectors__ which build on additional behaviour. There are three important types of augmented vector:

* Factors are built on top of integer vectors.
* Dates and date-times are built on top of numeric vectors.
* Data frames and tibbles are built on top of lists.

This chapter will introduce you to these important vectors from simplest to most complicated. You'll start with atomic vectors, then build up to lists, and finish off with augmented vectors.

## Important types of atomic vector

The four most important types of atomic vector are logical, integer, double, and character. Raw and complex are rarely used during a data analysis, so I won't discuss them here.

### Logical

Logical vectors are the simplest type of atomic vector because they can take only three possible values: `FALSE`, `TRUE`, and `NA`. Logical vectors are usually constructed with comparison operators, as described in [comparisons]. You can also create them by hand with `c()`:

```{r}
1:10 %% 3 == 0

c(TRUE, TRUE, FALSE, NA)
```

### Numeric

Integer and double vectors are known collectively as numeric vectors. In R, numbers are doubles by default. To make an integer, place an `L` after the number:

```{r}
typeof(1)
typeof(1L)
1.5L
```

The distinction between integers and doubles is not usually important, but there are two important differences that you should be aware of:

1.   Doubles are approximations. Doubles represent floating point numbers that 
     can not always be precisely represented with a fixed amount of memory. 
     This means that you should consider all doubles to be approximations. 
     For example, what is square of the square root of two?

    ```{r}
    x <- sqrt(2) ^ 2
    x
    x - 2
    ```

    This behaviour is common when working with floating point numbers: most
    calculations include some approximation error. Instead of comparing floating
    point numbers using `==`, you should use `dplyr::near()` which allows for 
    some numerical tolerance.

1.  Integers have one special value: `NA`, while doubles have four:
    `NA`, `NaN`, `Inf` and `-Inf`. All three special values `NaN`, `Inf` and `-Inf` can arise during division:
   
    ```{r}
    c(-1, 0, 1) / 0
    ```

    Avoid using `==` to check for these other special values. Instead use the 
    helper functions `is.finite()`, `is.infinite()`, and `is.nan()`:
    
    |                  |  0  | Inf | NA  | NaN |
    |------------------|-----|-----|-----|-----|
    | `is.finite()`    |  x  |     |     |     |
    | `is.infinite()`  |     |  x  |     |     |
    | `is.na()`        |     |     |  x  |  x  |
    | `is.nan()`       |     |     |     |  x  |

### Character

Character vectors are the most complex type of atomic vector, because each element of a character vector is a string, and a string can contain an arbitrary amount of data. 

You've already learned a lot about working with strings in [strings]. Here I wanted to mention one important feature of the underlying string implementation: R uses a global string pool. This means that each unique string is only stored in memory once, and every use of the string points to that representation. This reduces the amount of memory needed by duplicated strings. You can see this behaviour in practice with `pryr::object_size()`:

```{r}
x <- "This is a reasonably long string."
pryr::object_size(x)

y <- rep(x, 1000)
pryr::object_size(y)
```

`y` doesn't take up 1,000x as much memory as `x`, because each element of `y` is just a pointer to that same string. A pointer is 8 bytes, so 1000 pointers to a 136 B string is 8 * 1000 + 136 = 8.13 kB.

### Missing values

Note that each type of atomic vector has its own missing value:

```{r}
NA            # logical
NA_integer_   # integer
NA_real_      # double
NA_character_ # character
```

Normally you don't need to know about these different types because you can always use `NA` and it will be converted to the correct type using the implicit coercion rules described next. However, there are some functions that are strict about their inputs, so it's useful to have this knowledge sitting in your back pocket so you can be specific when needed.

### Exercises

1.  Describe the difference between `is.finite(x)` and  `!is.infinite(x)`.

1.  Read the source code for `dplyr::near()` (Hint: to see the source code,
    drop the `()`). How does it work? 

1.  A logical vector can take 3 possible values. How many possible
    values can an integer vector take? How many possible values can
    a double take? Use google to do some research.

1.  Brainstorm at least four functions that allow you to convert a double to an
    integer. How do they differ? Be precise.
    
1.  What functions from the readr package allow you to turn a string
    into logical, integer, and double vector?

## Using atomic vectors

Now that you understand the different types of atomic vector, it's useful to review some of the important tools for working with them. These include:

1.  How to convert from one type to another, and when that happens
    automatically.

1.  How to tell if an object is a specific type of vector.

1.  What happens when you work with vectors of different lengths.

1.  How to name the elements of a vector.

1.  How to pull out elements of interest.

### Coercion

There are two ways to convert, or coerce, one type of vector to another:

1.  Explicit coercion happens when you call a function like `as.logical()`,
    `as.integer()`, `as.double()`, or `as.character()`. Whenever you find
    yourself using explicit coercion, you should always check whether you can
    make the fix upstream, so that the vector never had the wrong type in 
    the first place. For example, you may need to tweak your readr 
    `col_types` specification.

1.  Implicit coercion happens when you use a vector in a specific context
    that expects a certain type of vector. For example, when you use a logical
    vector with a numeric summary function, or when you use a double vector
    where an integer vector is expected.
    
Because explicit coercion is used relatively rarely, and is largely easy to understand, I'll focus on implicit coercion here. 

You've already seen the most important type of implicit coercion: using a logical vector in a numeric context. In this case `TRUE` is converted to `1` and `FALSE` converted to `0`. That means the sum of a logical vector is the number of trues, and the mean of a logical vector is the proportion of trues:

```{r}
x <- sample(20, 100, replace = TRUE)
y <- x > 10
sum(y)  # how many are greater than 10?
mean(y) # what proportion are greater than 10?
```

You may see some code (typically older) that relies on implicit coercion in the opposite direction, from integer to logical:

```{r, eval = FALSE}
if (length(x)) {
  # do something
}
```

In this case, 0 is converted to `FALSE` and everything else is converted to `TRUE`. I think this makes it harder to understand your code, and I don't recommend it. Instead be explicit: `length(x) > 0`.

It's also important to understand what happens when you try and create a vector containing multiple types with `c()`: the most complex type always wins.

```{r}
typeof(c(TRUE, 1L))
typeof(c(1L, 1.5))
typeof(c(1.5, "a"))
```

An atomic vector can not have a mix of different types because the type is a property of the complete vector, not the individual elements. If you need to mix multiple types in the same vector, you should use a list, which you'll learn about shortly.

### Test functions

Sometimes you want to do different things based on the type of vector. One option is to use `typeof()`. Another is to use a test function which returns a `TRUE` or `FALSE`. Base R provides many functions like `is.vector()` and `is.atomic()`, but they often returns surprising results. Instead, it's safer to use the `is_*` functions provided by purrr, which are summarised in the table below.

|                  | lgl | int | dbl | chr | list |
|------------------|-----|-----|-----|-----|------|
| `is_logical()`   |  x  |     |     |     |      |
| `is_integer()`   |     |  x  |     |     |      |
| `is_double()`    |     |     |  x  |     |      |
| `is_numeric()`   |     |  x  |  x  |     |      |
| `is_character()` |     |     |     |  x  |      |
| `is_atomic()`    |  x  |  x  |  x  |  x  |      |
| `is_list()`      |     |     |     |     |  x   |
| `is_vector()`    |  x  |  x  |  x  |  x  |  x   |

Each predicate also comes with a "scalar" version, like `is_scalar_atomic()`, which checks that the length is 1. This is useful, for example, if you want to check that an argument to your function is a single logical value.

### Scalars and recycling rules

As well as implicitly coercing the types of vectors to be compatible, R will also implicitly coerce the length of vectors. This is called vector __recycling__, because the shorter vector is repeated, or recycled, to the same length as the longer vector. 

This is generally most useful when you are mixing vectors and "scalars". I put scalars in quotes because R doesn't actually have scalars: instead, a single number is a vector of length 1. Because there are no scalars, most built-in functions are __vectorised__, meaning that they will operate on a vector of numbers. That's why, for example, this code works:

```{r}
sample(10) + 100
runif(10) > 0.5
```

In R, basic mathematical operations work with vectors. That means that you should never need to perform explicit iteration when performing simple mathematical computations.

It's intuitive what should happen if you add two vectors of the same length, or a vector and a "scalar", but what happens if you add two vectors of different lengths?

```{r}
1:10 + 1:2
```

Here, R will expand the shortest vector to the same length as the longest, so called recycling. This is silent except when the length of the longer is not an integer multiple of the length of the shorter:

```{r}
1:10 + 1:3
```

While vector recycling can be used to create very succinct, clever code, it can also silently conceal problems. For this reason, the vectorised functions in tidyverse will throw errors when you recycle anything other than a scalar. If you do want to recycle, you'll need to do it yourself with `rep()`:

```{r, error = TRUE}
tibble(x = 1:4, y = 1:2)

tibble(x = 1:4, y = rep(1:2, 2))

tibble(x = 1:4, y = rep(1:2, each = 2))
```

### Naming vectors

All types of vectors can be named. You can name them during creation with `c()`:

```{r}
c(x = 1, y = 2, z = 4)
```

Or after the fact with `purrr::set_names()`:

```{r}
set_names(1:3, c("a", "b", "c"))
```

Named vectors are most useful for subsetting, described next.

### Subsetting {#vector-subsetting}

So far we've used `dplyr::filter()` to filter the rows in a tibble. `filter()` only works with tibble, so we'll need new tool for vectors: `[`. `[` is the subsetting function, and is called like `x[a]`. There are four types of things that you can subset a vector with:

1.  A numeric vector containing only integers. The integers must either be all 
    positive, all negative, or zero.
    
    Subsetting with positive integers keeps the elements at those positions:
    
    ```{r}
    x <- c("one", "two", "three", "four", "five")
    x[c(3, 2, 5)]
    ```
    
    By repeating a position, you can actually make a longer output than 
    input:
    
    ```{r}
    x[c(1, 1, 5, 5, 5, 2)]
    ```
    
    Negative values drop the elements at the specified positions:
    
    ```{r}
    x[c(-1, -3, -5)]
    ```
    
    It's an error to mix positive and negative values:
    
    ```{r, error = TRUE}
    x[c(1, -1)]
    ```

    The error message mentions subsetting with zero, which returns no values:
    
    ```{r}
    x[0]
    ```
    
    This is not useful very often, but it can be helpful if you want to create 
    unusual data structures to test your functions with.
  
1.  Subsetting with a logical vector keeps all values corresponding to a
    `TRUE` value. This is most often useful in conjunction with the 
    comparison functions.
    
    ```{r}
    x <- c(10, 3, NA, 5, 8, 1, NA)
    
    # All non-missing values of x
    x[!is.na(x)]
    
    # All even (or missing!) values of x
    x[x %% 2 == 0]
    ```

1.  If you have a named vector, you can subset it with a character vector:
    
    ```{r}
    x <- c(abc = 1, def = 2, xyz = 5)
    x[c("xyz", "def")]
    ```
    
    Like with positive integers, you can also use a character vector to 
    duplicate individual entries.

1.  The simplest type of subsetting is nothing, `x[]`, which returns the 
    complete `x`. This is not useful for subsetting vectors, but it is useful
    when subsetting matrices (and other high dimensional structures) because
    it lets you select all the rows or all the columns, by leaving that
    index blank. For example, if `x` is 2d, `x[1, ]` selects the first row and 
    all the columns, and `x[, -1]` selects all rows and all columns except
    the first.
    
To learn more about the applications of subsetting, reading the "Subsetting" chapter of _Advanced R_: <http://adv-r.had.co.nz/Subsetting.html#applications>.

There is an important variation of `[` called `[[`. `[[` only ever extracts a single element, and always drops names. It's a good idea to use it whenever you want to make it clear that you're extracting a single item, as in a for loop. The distinction between `[` and `[[` is most important for lists, as we'll see shortly.

### Exercises

1.  What does `mean(is.na(x))` tell you about a vector `x`? What about
    `sum(!is.finite(x))`?

1.  Carefully read the documentation of `is.vector()`. What does it actually
    test for? Why does `is.atomic()` not agree with the definition of 
    atomic vectors above?
    
1.  Compare and contrast `setNames()` with `purrr::set_names()`.

1.  Create functions that take a vector as input and returns:
    
    1. The last value.  Should you use `[` or `[[`?

    1. The elements at even numbered positions.
    
    1. Every element except the last value.
    
    1. Only even numbers (and no missing values).

1.  Why is `x[-which(x > 0)]` not the same as `x[x <= 0]`? 

1.  What happens when you subset with a positive integer that's bigger
    than the length of the vector? What happens when you subset with a 
    name that doesn't exist?

## Recursive vectors (lists) {#lists}

Lists are a step up in complexity from atomic vectors, because lists can contain other lists. This makes them suitable for representing hierarchical or tree-like structures. You create a list with `list()`:

```{r}
x <- list(1, 2, 3)
x
```

A very useful tool for working with lists is `str()` because it focusses on the **str**ucture, not the contents.

```{r}
str(x)

x_named <- list(a = 1, b = 2, c = 3)
str(x_named)
```

Unlike atomic vectors, `list()` can contain a mix of objects:

```{r}
y <- list("a", 1L, 1.5, TRUE)
str(y)
```

Lists can even contain other lists!

```{r}
z <- list(list(1, 2), list(3, 4))
str(z)
```

### Visualising lists

To explain more complicated list manipulation functions, it's helpful to have a visual representation of lists. For example, take these three lists:

```{r}
x1 <- list(c(1, 2), c(3, 4))
x2 <- list(list(1, 2), list(3, 4))
x3 <- list(1, list(2, list(3)))
```

I'll draw them as follows:

```{r, echo = FALSE, out.width = "75%"}
knitr::include_graphics("diagrams/lists-structure.png")
```

There are three principles:

1.  Lists have rounded corners. Atomic vectors have square corners.
  
1.  Children are drawn inside their parent, and have a slightly darker
    background to make it easier to see  the hierarchy.
  
1.  The orientation of the children (i.e. rows or columns) isn't important, 
    so I'll pick a row or column orientation to either save space or illustrate 
    an important property in the example.

### Subsetting

There are three ways to subset a list, which I'll illustrate with a list named `a`:

```{r}
a <- list(a = 1:3, b = "a string", c = pi, d = list(-1, -5))
```

*   `[` extracts a sub-list. The result will always be a list.

    ```{r}
    str(a[1:2])
    str(a[4])
    ```
    
    Like with vectors, you can subset with a logical, integer, or character
    vector.
    
*   `[[` extracts a single component from a list. It removes a level of 
    hierarchy from the list.

    ```{r}
    str(a[[1]])
    str(a[[4]])
    ```

*   `$` is a shorthand for extracting named elements of a list. It works
    similarly to `[[` except that you don't need to use quotes.
    
    ```{r}
    a$a
    a[["a"]]
    ```

The distinction between `[` and `[[` is really important for lists, because `[[` drills down into the list while `[` returns a new, smaller list. Compare the code and output above with the visual representation in Figure \@ref(fig:lists-subsetting).

```{r lists-subsetting, echo = FALSE, out.width = "75%", fig.cap = "Subsetting a list, visually."}
knitr::include_graphics("diagrams/lists-subsetting.png")
```

### Lists of condiments

The difference between `[` and `[[` is very important, but it's easy to get confused. To help you remember, let me show you an unusual pepper shaker.

```{r, echo = FALSE, out.width = "25%"} 
knitr::include_graphics("images/pepper.jpg")
```

If this pepper shaker is your list `x`, then, `x[1]` is a pepper shaker containing a single pepper packet:

```{r, echo = FALSE, out.width = "25%"} 
knitr::include_graphics("images/pepper-1.jpg")
```

`x[2]` would look the same, but would contain the second packet. `x[1:2]` would be a pepper shaker containing two pepper packets. 

`x[[1]]` is:

```{r, echo = FALSE, out.width = "25%"} 
knitr::include_graphics("images/pepper-2.jpg")
```

If you wanted to get the content of the pepper package, you'd need `x[[1]][[1]]`:

```{r, echo = FALSE, out.width = "25%"} 
knitr::include_graphics("images/pepper-3.jpg")
```

### Exercises

1.  Draw the following lists as nested sets:

    1.  `list(a, b, list(c, d), list(e, f))`
    1.  `list(list(list(list(list(list(a))))))`

1.  What happens if you subset a tibble as if you're subsetting a list?
    What are the key differences between a list and a tibble?

## Attributes

Any vector can contain arbitrary additional metadata through its __attributes__. You can think of attributes as named list of vectors that can be attached to any object. 
You can get and set individual attribute values with `attr()` or see them all at once with `attributes()`.

```{r}
x <- 1:10
attr(x, "greeting")
attr(x, "greeting") <- "Hi!"
attr(x, "farewell") <- "Bye!"
attributes(x)
```

There are three very important attributes that are used to implement fundamental parts of R:

1. __Names__ are used to name the elements of a vector.
1. __Dimensions__ (dims, for short) make a vector behave like a matrix or array.
1. __Class__ is used to implement the S3 object oriented system.

You've seen names above, and we won't cover dimensions because we don't use matrices in this book. It remains to describe the class, which controls how __generic functions__ work. Generic functions are key to object oriented programming in R, because they make functions behave differently for different classes of input. A detailed discussion of object oriented programming is beyond the scope of this book, but you can read more about it in _Advanced R_ at <http://adv-r.had.co.nz/OO-essentials.html#s3>.

Here's what a typical generic function looks like:

```{r}
as.Date
```

The call to "UseMethod" means that this is a generic function, and it will call a specific __method__, a function, based on the class of the first argument. (All methods are functions; not all functions are methods). You can list all the methods for a generic with `methods()`:

```{r}
methods("as.Date")
```

For example, if `x` is a character vector, `as.Date()` will call `as.Date.character()`; if it's a factor, it'll call `as.Date.factor()`.

You can see the specific implementation of a method with `getS3method()`:

```{r}
getS3method("as.Date", "default")
getS3method("as.Date", "numeric")
```

The most important S3 generic is `print()`: it controls how the object is printed when you type its name at the console. Other important generics are the subsetting functions `[`, `[[`, and `$`. 

## Augmented vectors

Atomic vectors and lists are the building blocks for other important vector types like factors and dates. I call these __augmented vectors__, because they are vectors with additional __attributes__, including class. Because augmented vectors have a class, they behave differently to the atomic vector on which they are built. In this book, we make use of four important augmented vectors:

* Factors
* Dates 
* Date-times
* Tibbles

These are described below.

### Factors

Factors are designed to represent categorical data that can take a fixed set of possible values. Factors are built on top of integers, and have a levels attribute:

```{r}
x <- factor(c("ab", "cd", "ab"), levels = c("ab", "cd", "ef"))
typeof(x)
attributes(x)
```

### Dates and date-times

Dates in R are numeric vectors that represent the number of days since 1 January 1970.

```{r}
x <- as.Date("1971-01-01")
unclass(x)

typeof(x)
attributes(x)
```

Date-times are numeric vectors with class `POSIXct` that represent the number of seconds since 1 January 1970. (In case you were wondering, "POSIXct" stands for "Portable Operating System Interface", calendar time.)

```{r}
x <- lubridate::ymd_hm("1970-01-01 01:00")
unclass(x)

typeof(x)
attributes(x)
```

The `tzone` attribute is optional. It controls how the time is printed, not what absolute time it refers to.

```{r}
attr(x, "tzone") <- "US/Pacific"
x

attr(x, "tzone") <- "US/Eastern"
x
```

There is another type of date-times called POSIXlt. These are built on top of named lists:

```{r}
y <- as.POSIXlt(x)
typeof(y)
attributes(y)
```

POSIXlts are rare inside the tidyverse. They do crop up in base R, because they are needed to extract specific components of a date, like the year or month. Since lubridate provides helpers for you to do this instead, you don't need them. POSIXct's are always easier to work with, so if you find you have a POSIXlt, you should always convert it to a regular data time `lubridate::as_date_time()`.

### Tibbles

Tibbles are augmented lists: they have class "tbl_df" + "tbl" + "data.frame", and `names` (column) and `row.names` attributes:

```{r}
tb <- tibble::tibble(x = 1:5, y = 5:1)
typeof(tb)
attributes(tb)
```

The difference between a tibble and a list is that all the elements of a data frame must be vectors with the same length. All functions that work with tibbles enforce this constraint.

Traditional data.frames have a very similar structure:

```{r}
df <- data.frame(x = 1:5, y = 5:1)
typeof(df)
attributes(df)
```

The main difference is the class. The class of tibble includes "data.frame" which means tibbles inherit the regular data frame behaviour by default.

### Exercises

1.  What does `hms::hms(3600)` return? How does it print? What primitive
    type is the augmented vector built on top of? What attributes does it 
    use?
    
1.  Try and make a tibble that has columns with different lengths. What
    happens?

1.  Based on the definition above, is it ok to have a list as a
    column of a tibble?


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/visualize.Rmd
================================================
# Data visualisation

## Introduction

> "The simple graph has brought more information to the data analyst’s mind 
> than any other device." --- John Tukey

This chapter will teach you how to visualise your data using ggplot2. R has several systems for making graphs, but ggplot2 is one of the most elegant and most versatile. ggplot2 implements the __grammar of graphics__, a coherent system for describing and building graphs. With ggplot2, you can do more faster by learning one system and applying it in many places.

If you'd like to learn more about the theoretical underpinnings of ggplot2 before you start, I'd recommend reading "The Layered Grammar of Graphics", <http://vita.had.co.nz/papers/layered-grammar.pdf>.

### Prerequisites

This chapter focusses on ggplot2, one of the core members of the tidyverse. To access the datasets, help pages, and functions that we will use in this chapter, load the tidyverse by running this code:

```{r setup}
library(tidyverse)
```

That one line of code loads the core tidyverse; packages which you will use in almost every data analysis. It also tells you which functions from the tidyverse conflict with functions in base R (or from other packages you might have loaded). 

If you run this code and get the error message "there is no package called ‘tidyverse’", you'll need to first install it, then run `library()` once again.

```{r eval = FALSE}
install.packages("tidyverse")
library(tidyverse)
```

You only need to install a package once, but you need to reload it every time you start a new session.

If we need to be explicit about where a function (or dataset) comes from, we'll use the special form `package::function()`. For example, `ggplot2::ggplot()` tells you explicitly that we're using the `ggplot()` function from the ggplot2 package.

## First steps

Let's use our first graph to answer a question: Do cars with big engines use more fuel than cars with small engines? You probably already have an answer, but try to make your answer precise. What does the relationship between engine size and fuel efficiency look like? Is it positive? Negative? Linear? Nonlinear?

### The `mpg` data frame

You can test your answer with the `mpg` __data frame__ found in ggplot2 (aka  `ggplot2::mpg`). A data frame is a rectangular collection of variables (in the columns) and observations (in the rows). `mpg` contains observations collected by the US Environment Protection Agency on 38 models of car. 

```{r}
mpg
```

Among the variables in `mpg` are:

1. `displ`, a car's engine size, in litres.

1. `hwy`, a car's fuel efficiency on the highway, in miles per gallon (mpg). 
  A car with a low fuel efficiency consumes more fuel than a car with a high 
  fuel efficiency when they travel the same distance. 

To learn more about `mpg`, open its help page by running `?mpg`.

### Creating a ggplot

To plot `mpg`, run this code to put `displ` on the x-axis and `hwy` on the y-axis:

```{r}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))
```

The plot shows a negative relationship between engine size (`displ`) and fuel efficiency (`hwy`). In other words, cars with big engines use more fuel. Does this confirm or refute your hypothesis about fuel efficiency and engine size?

With ggplot2, you begin a plot with the function `ggplot()`. `ggplot()` creates a coordinate system that you can add layers to. The first argument of `ggplot()` is the dataset to use in the graph. So `ggplot(data = mpg)` creates an empty graph, but it's not very interesting so I'm not going to show it here.

You complete your graph by adding one or more layers to `ggplot()`. The function `geom_point()` adds a layer of points to your plot, which creates a scatterplot. ggplot2 comes with many geom functions that each add a different type of layer to a plot. You'll learn a whole bunch of them throughout this chapter.

Each geom function in ggplot2 takes a `mapping` argument. This defines how variables in your dataset are mapped to visual properties. The `mapping` argument is always paired with `aes()`, and the `x` and `y` arguments of `aes()` specify which variables to map to the x and y axes. ggplot2 looks for the mapped variable in the `data` argument, in this case, `mpg`.

### A graphing template

Let's turn this code into a reusable template for making graphs with ggplot2. To make a graph, replace the bracketed sections in the code below with a dataset, a geom function, or a collection of mappings.

```{r eval = FALSE}
ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))
```

The rest of this chapter will show you how to complete and extend this template to make different types of graphs. We will begin with the `<MAPPINGS>` component.

### Exercises

1.  Run `ggplot(data = mpg)`. What do you see?

1.  How many rows are in `mpg`? How many columns?

1.  What does the `drv` variable describe?  Read the help for `?mpg` to find
    out.
     
1.  Make a scatterplot of `hwy` vs `cyl`.

1.  What happens if you make a scatterplot of `class` vs `drv`? Why is
    the plot not useful?

## Aesthetic mappings

> "The greatest value of a picture is when it forces us to notice what we
> never expected to see." --- John Tukey

In the plot below, one group of points (highlighted in red) seems to fall outside of the linear trend. These cars have a higher mileage than you might expect. How can you explain these cars? 

```{r, echo = FALSE}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point() + 
  geom_point(data = dplyr::filter(mpg, displ > 5, hwy > 20), colour = "red", size = 2.2)
```

Let's hypothesize that the cars are hybrids. One way to test this hypothesis is to look at the `class` value for each car. The `class` variable of the `mpg` dataset classifies cars into groups such as compact, midsize, and SUV. If the outlying points are hybrids, they should be classified as compact cars or, perhaps, subcompact cars (keep in mind that this data was collected before hybrid trucks and SUVs became popular).

You can add a third variable, like `class`, to a two dimensional scatterplot by mapping it to an __aesthetic__. An aesthetic is a visual property of the objects in your plot. Aesthetics include things like the size, the shape, or the color of your points. You can display a point (like the one below) in different ways by changing the values of its aesthetic properties. Since we already use the word "value" to describe data, let's use the word "level" to describe aesthetic properties. Here we change the levels of a point's size, shape, and color to make the point small, triangular, or blue:

```{r, echo = FALSE, asp = 1/4}
ggplot() +
  geom_point(aes(1, 1), size = 20) +
  geom_point(aes(2, 1), size = 10) + 
  geom_point(aes(3, 1), size = 20, shape = 17) + 
  geom_point(aes(4, 1), size = 20, colour = "blue") + 
  scale_x_continuous(NULL, limits = c(0.5, 4.5), labels = NULL) + 
  scale_y_continuous(NULL, limits = c(0.9, 1.1), labels = NULL) + 
  theme(aspect.ratio = 1/3)
```

You can convey information about your data by mapping the aesthetics in your plot to the variables in your dataset. For example, you can map the colors of your points to the `class` variable to reveal the class of each car.

```{r}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))
```

(If you prefer British English, like Hadley, you can use `colour` instead of `color`.)

To map an aesthetic to a variable, associate the name of the aesthetic to the name of the variable inside `aes()`. ggplot2 will automatically assign a unique level of the aesthetic (here a unique color) to each unique value of the variable, a process known as __scaling__. ggplot2 will also add a legend that explains which levels correspond to which values.

The colors reveal that many of the unusual points are two-seater cars. These cars don't seem like hybrids, and are, in fact, sports cars! Sports cars have large engines like SUVs and pickup trucks, but small bodies like midsize and compact cars, which improves their gas mileage. In hindsight, these cars were unlikely to be hybrids since they have large engines.

In the above example, we mapped `class` to the color aesthetic, but we could have mapped `class` to the size aesthetic in the same way. In this case, the exact size of each point would reveal its class affiliation. We get a _warning_ here, because mapping an unordered variable (`class`) to an ordered aesthetic (`size`) is not a good idea.

```{r}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = class))
```

Or we could have mapped `class` to the _alpha_ aesthetic, which controls the transparency of the points, or the shape of the points.

```{r out.width = "50%", fig.align = 'default', warning = FALSE, fig.asp = 1/2, fig.cap =""}
# Left
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, alpha = class))

# Right
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class))
```

What happened to the SUVs? ggplot2 will only use six shapes at a time. By default, additional groups will go unplotted when you use the shape aesthetic.

For each aesthetic, you use `aes()` to associate the name of the aesthetic with a variable to display. The `aes()` function gathers together each of the aesthetic mappings used by a layer and passes them to the layer's mapping argument. The syntax highlights a useful insight about `x` and `y`: the x and y locations of a point are themselves aesthetics, visual properties that you can map to variables to display information about the data. 

Once you map an aesthetic, ggplot2 takes care of the rest. It selects a reasonable scale to use with the aesthetic, and it constructs a legend that explains the mapping between levels and values. For x and y aesthetics, ggplot2 does not create a legend, but it creates an axis line with tick marks and a label. The axis line acts as a legend; it explains the mapping between locations and values.

You can also _set_ the aesthetic properties of your geom manually. For example, we can make all of the points in our plot blue:

```{r}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")
```

Here, the color doesn't convey information about a variable, but only changes the appearance of the plot. To set an aesthetic manually, set the aesthetic by name as an argument of your geom function; i.e. it goes _outside_ of `aes()`. You'll need to pick a level that makes sense for that aesthetic:

* The name of a color as a character string.

* The size of a point in mm.

* The shape of a point as a number, as shown in Figure \@ref(fig:shapes).

```{r shapes, echo = FALSE, out.width = "75%", fig.asp = 1/3, fig.cap="R has 25 built in shapes that are identified by numbers. There are some seeming duplicates: for example, 0, 15, and 22 are all squares. The difference comes from the interaction of the `colour` and `fill` aesthetics. The hollow shapes (0--14) have a border determined by `colour`; the solid shapes (15--18) are filled with `colour`; the filled shapes (21--24) have a border of `colour` and are filled with `fill`.", warning = FALSE}
shapes <- tibble(
  shape = c(0, 1, 2, 5, 3, 4, 6:19, 22, 21, 24, 23, 20),
  x = (0:24 %/% 5) / 2,
  y = (-(0:24 %% 5)) / 4
)
ggplot(shapes, aes(x, y)) + 
  geom_point(aes(shape = shape), size = 5, fill = "red") +
  geom_text(aes(label = shape), hjust = 0, nudge_x = 0.15) +
  scale_shape_identity() +
  expand_limits(x = 4.1) +
  scale_x_continuous(NULL, breaks = NULL) + 
  scale_y_continuous(NULL, breaks = NULL, limits = c(-1.2, 0.2)) + 
  theme_minimal() +
  theme(aspect.ratio = 1/2.75)
```

### Exercises

1.  What's gone wrong with this code? Why are the points not blue?

    ```{r}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))
    ```
    
1.  Which variables in `mpg` are categorical? Which variables are continuous? 
    (Hint: type `?mpg` to read the documentation for the dataset). How
    can you see this information when you run `mpg`?

1.  Map a continuous variable to `color`, `size`, and `shape`. How do
    these aesthetics behave differently for categorical vs. continuous
    variables? 
    
1.  What happens if you map the same variable to multiple aesthetics? 

1.  What does the `stroke` aesthetic do? What shapes does it work with?
    (Hint: use `?geom_point`)
    
1.  What happens if you map an aesthetic to something other than a variable 
    name, like `aes(colour = displ < 5)`?  

## Common problems

As you start to run R code, you're likely to run into problems. Don't worry --- it happens to everyone. I have been writing R code for years, and every day I still write code that doesn't work! 

Start by carefully comparing the code that you're running to the code in the book. R is extremely picky, and a misplaced character can make all the difference. Make sure that every `(` is matched with a `)` and every `"` is paired with another `"`. Sometimes you'll run the code and nothing happens. Check the left-hand of your console: if it's a `+`, it means that R doesn't think you've typed a complete expression and it's waiting for you to finish it. In this case, it's usually easy to start from scratch again by pressing ESCAPE to abort processing the current command.

One common problem when creating ggplot2 graphics is to put the `+` in the wrong place: it has to come at the end of the line, not the start. In other words, make sure you haven't accidentally written code like this:

```R
ggplot(data = mpg) 
+ geom_point(mapping = aes(x = displ, y = hwy))
```

If you're still stuck, try the help. You can get help about any R function by running `?function_name` in the console, or selecting the function name and pressing F1 in RStudio. Don't worry if the help doesn't seem that helpful - instead skip down to the examples and look for code that matches what you're trying to do.

If that doesn't help, carefully read the error message. Sometimes the answer will be buried there! But when you're new to R, the answer might be in the error message but you don't yet know how to understand it. Another great tool is Google: try googling the error message, as it's likely someone else has had the same problem, and has gotten help online.

## Facets

One way to add additional variables is with aesthetics. Another way, particularly useful for categorical variables, is to split your plot into __facets__, subplots that each display one subset of the data. 

To facet your plot by a single variable, use `facet_wrap()`. The first argument of `facet_wrap()` should be a formula, which you create with `~` followed by a variable name (here "formula" is the name of a data structure in R, not a synonym for "equation"). The variable that you pass to `facet_wrap()` should be discrete. 

```{r}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)
```

To facet your plot on the combination of two variables, add `facet_grid()` to your plot call. The first argument of `facet_grid()` is also a formula. This time the formula should contain two variable names separated by a `~`. 

```{r}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ cyl)
```

If you prefer to not facet in the rows or columns dimension, use a `.` instead of a variable name, e.g. `+ facet_grid(. ~ cyl)`.

### Exercises

1.  What happens if you facet on a continuous variable?

1.  What do the empty cells in plot with `facet_grid(drv ~ cyl)` mean?
    How do they relate to this plot?
    
    ```{r, eval = FALSE}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = drv, y = cyl))
    ```

1.  What plots does the following code make? What does `.` do?

    ```{r eval = FALSE}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy)) +
      facet_grid(drv ~ .)
    
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy)) +
      facet_grid(. ~ cyl)
    ```

1.  Take the first faceted plot in this section:

    ```{r, eval = FALSE}
    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy)) + 
      facet_wrap(~ class, nrow = 2)
    ```
    
    What are the advantages to using faceting instead of the colour aesthetic?
    What are the disadvantages? How might the balance change if you had a 
    larger dataset?
    
1.  Read `?facet_wrap`. What does `nrow` do? What does `ncol` do? What other
    options control the layout of the individual panels? Why doesn't
    `facet_grid()` have `nrow` and `ncol` arguments?

1.  When using `facet_grid()` you should usually put the variable with more
    unique levels in the columns. Why?

## Geometric objects

How are these two plots similar? 

```{r echo = FALSE, out.width = "50%", fig.align="default", message = FALSE}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy))
```

Both plots contain the same x variable, the same y variable, and both describe the same data. But the plots are not identical. Each plot uses a different visual object to represent the data. In ggplot2 syntax, we say that they use different __geoms__.

A __geom__ is the geometrical object that a plot uses to represent data. People often describe plots by the type of geom that the plot uses. For example, bar charts use bar geoms, line charts use line geoms, boxplots use boxplot geoms, and so on. Scatterplots break the trend; they use the point geom. As we see above, you can use different geoms to plot the same data. The plot on the left uses the point geom, and the plot on the right uses the smooth geom, a smooth line fitted to the data. 

To change the geom in your plot, change the geom function that you add to `ggplot()`. For instance, to make the plots above, you can use this code:

```{r eval = FALSE}
# left
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

# right
ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy))
```

Every geom function in ggplot2 takes a `mapping` argument. However, not every aesthetic works with every geom. You could set the shape of a point, but you couldn't set the "shape" of a line. On the other hand, you _could_ set the linetype of a line. `geom_smooth()` will draw a different line, with a different linetype, for each unique value of the variable that you map to linetype.

```{r message = FALSE}
ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy, linetype = drv))
```

Here `geom_smooth()` separates the cars into three lines based on their `drv` value, which describes a car's drivetrain. One line describes all of the points with a `4` value, one line describes all of the points with an `f` value, and one line describes all of the points with an `r` value. Here, `4` stands for four-wheel drive, `f` for front-wheel drive, and `r` for rear-wheel drive.

If this sounds strange, we can make it more clear by overlaying the lines on top of the raw data and then coloring everything according to `drv`. 

```{r echo = FALSE, message = FALSE}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
  geom_point() +
  geom_smooth(mapping = aes(linetype = drv))
```

Notice that this plot contains two geoms in the same graph! If this makes you excited, buckle up. In the next section, we will learn how to place multiple geoms in the same plot.

ggplot2 provides over 30 geoms, and extension packages provide even more (see <https://www.ggplot2-exts.org> for a sampling). The best way to get a comprehensive overview is the ggplot2 cheatsheet, which you can find at <http://rstudio.com/cheatsheets>. To learn more about any single geom, use help: `?geom_smooth`.

Many geoms, like `geom_smooth()`, use a single geometric object to display multiple rows of data. For these geoms, you can set the `group` aesthetic to a categorical variable to draw multiple objects. ggplot2 will draw a separate object for each unique value of the grouping variable. In practice, ggplot2 will automatically group the data for these geoms whenever you map an aesthetic to a discrete variable (as in the `linetype` example). It is convenient to rely on this feature because the group aesthetic by itself does not add a legend or distinguishing features to the geoms.

```{r, fig.width = 3, fig.align = 'default', out.width = "33%", message = FALSE}
ggplot(data = mpg) +
  geom_smooth(mapping = aes(x = displ, y = hwy))
              
ggplot(data = mpg) +
  geom_smooth(mapping = aes(x = displ, y = hwy, group = drv))
    
ggplot(data = mpg) +
  geom_smooth(
    mapping = aes(x = displ, y = hwy, color = drv),
    show.legend = FALSE
  )
```

To display multiple geoms in the same plot, add multiple geom functions to `ggplot()`:

```{r, message = FALSE}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  geom_smooth(mapping = aes(x = displ, y = hwy))
```

This, however, introduces some duplication in our code. Imagine if you wanted to change the y-axis to display `cty` instead of `hwy`. You'd need to change the variable in two places, and you might forget to update one. You can avoid this type of repetition by passing a set of mappings to `ggplot()`. ggplot2 will treat these mappings as global mappings that apply to each geom in the graph.  In other words, this code will produce the same plot as the previous code:

```{r, eval = FALSE}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth()
```

If you place mappings in a geom function, ggplot2 will treat them as local mappings for the layer. It will use these mappings to extend or overwrite the global mappings _for that layer only_. This makes it possible to display different aesthetics in different layers.

```{r, message = FALSE}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth()
```

You can use the same idea to specify different `data` for each layer. Here, our smooth line displays just a subset of the `mpg` dataset, the subcompact cars. The local data argument in `geom_smooth()` overrides the global data argument in `ggplot()` for that layer only.

```{r, message = FALSE}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth(data = filter(mpg, class == "subcompact"), se = FALSE)
```

(You'll learn how `filter()` works in the next chapter: for now, just know that this command selects only the subcompact cars.)

### Exercises

1.  What geom would you use to draw a line chart? A boxplot? 
    A histogram? An area chart?

1.  Run this code in your head and predict what the output will look like.
    Then, run the code in R and check your predictions.
    
    ```{r, eval = FALSE}
    ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
      geom_point() + 
      geom_smooth(se = FALSE)
    ```

1.  What does `show.legend = FALSE` do?  What happens if you remove it?  
    Why do you think I used it earlier in the chapter?

1.  What does the `se` argument to `geom_smooth()` do?


1.  Will these two graphs look different? Why/why not?

    ```{r, eval = FALSE}
    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
      geom_point() + 
      geom_smooth()
    
    ggplot() + 
      geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) + 
      geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))
    ```

1.  Recreate the R code necessary to generate the following graphs.
    
    ```{r echo = FALSE, fig.width = 3, out.width = "50%", fig.align = "default", message = FALSE}
    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
      geom_point() + 
      geom_smooth(se = FALSE)
    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
      geom_smooth(aes(group = drv), se = FALSE) +
      geom_point()
    ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
      geom_point() + 
      geom_smooth(se = FALSE)
    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
      geom_point(aes(color = drv)) + 
      geom_smooth(se = FALSE)
    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
      geom_point(aes(color = drv)) +
      geom_smooth(aes(linetype = drv), se = FALSE)
    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
      geom_point(size = 4, colour = "white") + 
      geom_point(aes(colour = drv))
    ```

## Statistical transformations

Next, let's take a look at a bar chart. Bar charts seem simple, but they are interesting because they reveal something subtle about plots. Consider a basic bar chart, as drawn with `geom_bar()`. The following chart displays the total number of diamonds in the `diamonds` dataset, grouped by `cut`. The `diamonds` dataset comes in ggplot2 and contains information about ~54,000 diamonds, including the `price`, `carat`, `color`, `clarity`, and `cut` of each diamond. The chart shows that more diamonds are available with high quality cuts than with low quality cuts. 

```{r}
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut))
```

On the x-axis, the chart displays `cut`, a variable from `diamonds`. On the y-axis, it displays count, but count is not a variable in `diamonds`! Where does count come from? Many graphs, like scatterplots, plot the raw values of your dataset. Other graphs, like bar charts, calculate new values to plot:

* bar charts, histograms, and frequency polygons bin your data 
  and then plot bin counts, the number of points that fall in each bin.

* smoothers fit a model to your data and then plot predictions from the
  model.

* boxplots compute a robust summary of the distribution and then display a 
  specially formatted box.

The algorithm used to calculate new values for a graph is called a __stat__, short for statistical transformation. The figure below describes how this process works with `geom_bar()`.

```{r, echo = FALSE, out.width = "100%"}
knitr::include_graphics("images/visualization-stat-bar.png")
```

You can learn which stat a geom uses by inspecting the default value for the `stat` argument. For example, `?geom_bar` shows that the default value for `stat` is "count", which means that `geom_bar()` uses `stat_count()`. `stat_count()` is documented on the same page as `geom_bar()`, and if you scroll down you can find a section called "Computed variables". That describes how it computes two new variables: `count` and `prop`.

You can generally use geoms and stats interchangeably. For example, you can recreate the previous plot using `stat_count()` instead of `geom_bar()`:

```{r}
ggplot(data = diamonds) + 
  stat_count(mapping = aes(x = cut))
```

This works because every geom has a default stat; and every stat has a default geom. This means that you can typically use geoms without worrying about the underlying statistical transformation. There are three reasons you might need to use a stat explicitly:

1.  You might want to override the default stat. In the code below, I change 
    the stat of `geom_bar()` from count (the default) to identity. This lets 
    me map the height of the bars to the raw values of a $y$ variable. 
    Unfortunately when people talk about bar charts casually, they might be
    referring to this type of bar chart, where the height of the bar is already
    present in the data, or the previous bar chart where the height of the bar
    is generated by counting rows.
    
    ```{r, warning = FALSE}
    demo <- tribble(
      ~cut,         ~freq,
      "Fair",       1610,
      "Good",       4906,
      "Very Good",  12082,
      "Premium",    13791,
      "Ideal",      21551
    )
    
    ggplot(data = demo) +
      geom_bar(mapping = aes(x = cut, y = freq), stat = "identity")
    ```
    
    (Don't worry that you haven't seen `<-` or `tribble()` before. You might be
    able to guess at their meaning from the context, and you'll learn exactly
    what they do soon!)

1.  You might want to override the default mapping from transformed variables
    to aesthetics. For example, you might want to display a bar chart of
    proportion, rather than count:
    
    ```{r}
    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1))
    ```

    To find the variables computed by the stat, look for the help section
    titled "computed variables".
    
1.  You might want to draw greater attention to the statistical transformation
    in your code. For example, you might use `stat_summary()`, which
    summarises the y values for each unique x value, to draw 
    attention to the summary that you're computing:
    
    ```{r}
    ggplot(data = diamonds) + 
      stat_summary(
        mapping = aes(x = cut, y = depth),
        fun.ymin = min,
        fun.ymax = max,
        fun.y = median
      )
    ```
    
ggplot2 provides over 20 stats for you to use. Each stat is a function, so you can get help in the usual way, e.g. `?stat_bin`. To see a complete list of stats, try the ggplot2 cheatsheet.

### Exercises

1.  What is the default geom associated with `stat_summary()`? How could
    you rewrite the previous plot to use that geom function instead of the 
    stat function?

1.  What does `geom_col()` do? How is it different to `geom_bar()`?

1.  Most geoms and stats come in pairs that are almost always used in 
    concert. Read through the documentation and make a list of all the 
    pairs. What do they have in common?

1.  What variables does `stat_smooth()` compute? What parameters control
    its behaviour?

1.  In our proportion bar chart, we need to set `group = 1`. Why? In other
    words what is the problem with these two graphs?
    
    ```{r, eval = FALSE}
    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut, y = ..prop..))
    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut, fill = color, y = ..prop..))
    ```
  

## Position adjustments

There's one more piece of magic associated with bar charts. You can colour a bar chart using either the `colour` aesthetic, or, more usefully, `fill`:

```{r out.width = "50%", fig.align = "default"}
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, colour = cut))
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = cut))
```

Note what happens if you map the fill aesthetic to another variable, like `clarity`: the bars are automatically stacked. Each colored rectangle represents a combination of `cut` and `clarity`.

```{r}
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity))
```

The stacking is performed automatically by the __position adjustment__ specified by the `position` argument. If you don't want a stacked bar chart, you can use one of three other options: `"identity"`, `"dodge"` or `"fill"`.

*   `position = "identity"` will place each object exactly where it falls in 
    the context of the graph. This is not very useful for bars, because it
    overlaps them. To see that overlapping we either need to make the bars
    slightly transparent by setting `alpha` to a small value, or completely
    transparent by setting `fill = NA`.
    
    ```{r out.width = "50%", fig.align = "default"}
    ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + 
      geom_bar(alpha = 1/5, position = "identity")
    ggplot(data = diamonds, mapping = aes(x = cut, colour = clarity)) + 
      geom_bar(fill = NA, position = "identity")
    ```
    
    The identity position adjustment is more useful for 2d geoms, like points,
    where it is the default.
    
*   `position = "fill"` works like stacking, but makes each set of stacked bars
    the same height. This makes it easier to compare proportions across 
    groups.

    ```{r}
    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut, fill = clarity), position = "fill")
    ```

*   `position = "dodge"` places overlapping objects directly _beside_ one 
    another. This makes it easier to compare individual values.

    ```{r}
    ggplot(data = diamonds) + 
      geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge")
    ```

There's one other type of adjustment that's not useful for bar charts, but it can be very useful for scatterplots. Recall our first scatterplot. Did you notice that the plot displays only 126 points, even though there are 234 observations in the dataset?

```{r echo = FALSE}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))
```

The values of `hwy` and `displ` are rounded so the points appear on a grid and many points overlap each other. This problem is known as __overplotting__. This arrangement makes it hard to see where the mass of the data is. Are the data points spread equally throughout the graph, or is there one special combination of `hwy` and `displ` that contains 109 values? 

You can avoid this gridding by setting the position adjustment to "jitter".  `position = "jitter"` adds a small amount of random noise to each point. This spreads the points out because no two points are likely to receive the same amount of random noise.

```{r}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), position = "jitter")
```

Adding randomness seems like a strange way to improve your plot, but while it makes your graph less accurate at small scales, it makes your graph _more_ revealing at large scales. Because this is such a useful operation, ggplot2 comes with a shorthand for `geom_point(position = "jitter")`: `geom_jitter()`.

To learn more about a position adjustment, look up the help page associated with each adjustment: `?position_dodge`, `?position_fill`, `?position_identity`, `?position_jitter`, and `?position_stack`.

### Exercises

1.  What is the problem with this plot? How could you improve it?

    ```{r}
    ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) + 
      geom_point()
    ```

1.  What parameters to `geom_jitter()` control the amount of jittering?

1.  Compare and contrast `geom_jitter()` with `geom_count()`.

1.  What's the default position adjustment for `geom_boxplot()`? Create
    a visualisation of the `mpg` dataset that demonstrates it.

## Coordinate systems

Coordinate systems are probably the most complicated part of ggplot2. The default coordinate system is the Cartesian coordinate system where the x and y positions act independently to determine the location of each point. There are a number of other coordinate systems that are occasionally helpful.

*   `coord_flip()` switches the x and y axes. This is useful (for example),
    if you want horizontal boxplots. It's also useful for long labels: it's
    hard to get them to fit without overlapping on the x-axis.
    
    ```{r fig.width = 3, out.width = "50%", fig.align = "default"}
    ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
      geom_boxplot()
    ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
      geom_boxplot() +
      coord_flip()
    ```

*   `coord_quickmap()` sets the aspect ratio correctly for maps. This is very
    important if you're plotting spatial data with ggplot2 (which unfortunately
    we don't have the space to cover in this book).

    ```{r fig.width = 3, out.width = "50%", fig.align = "default", message = FALSE}
    nz <- map_data("nz")

    ggplot(nz, aes(long, lat, group = group)) +
      geom_polygon(fill = "white", colour = "black")

    ggplot(nz, aes(long, lat, group = group)) +
      geom_polygon(fill = "white", colour = "black") +
      coord_quickmap()
    ```

*   `coord_polar()` uses polar coordinates. Polar coordinates reveal an 
    interesting connection between a bar chart and a Coxcomb chart.
    
    ```{r fig.width = 3, out.width = "50%", fig.align = "default", fig.asp = 1}
    bar <- ggplot(data = diamonds) + 
      geom_bar(
        mapping = aes(x = cut, fill = cut), 
        show.legend = FALSE,
        width = 1
      ) + 
      theme(aspect.ratio = 1) +
      labs(x = NULL, y = NULL)
    
    bar + coord_flip()
    bar + coord_polar()
    ```

### Exercises

1.  Turn a stacked bar chart into a pie chart using `coord_polar()`.

1.  What does `labs()` do? Read the documentation.

1.  What's the difference between `coord_quickmap()` and `coord_map()`?

1.  What does the plot below tell you about the relationship between city
    and highway mpg? Why is `coord_fixed()` important? What does 
    `geom_abline()` do?
    
    ```{r, fig.asp = 1, out.width = "50%"}
    ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
      geom_point() + 
      geom_abline() +
      coord_fixed()
    ```

## The layered grammar of graphics

In the previous sections, you learned much more than how to make scatterplots, bar charts, and boxplots. You learned a foundation that you can use to make _any_ type of plot with ggplot2. To see this, let's add position adjustments, stats, coordinate systems, and faceting to our code template:

```
ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(
     mapping = aes(<MAPPINGS>),
     stat = <STAT>, 
     position = <POSITION>
  ) +
  <COORDINATE_FUNCTION> +
  <FACET_FUNCTION>
```

Our new template takes seven parameters, the bracketed words that appear in the template. In practice, you rarely need to supply all seven parameters to make a graph because ggplot2 will provide useful defaults for everything except the data, the mappings, and the geom function.

The seven parameters in the template compose the grammar of graphics, a formal system for building plots. The grammar of graphics is based on the insight that you can uniquely describe _any_ plot as a combination of a dataset, a geom, a set of mappings, a stat, a position adjustment, a coordinate system, and a faceting scheme. 

To see how this works, consider how you could build a basic plot from scratch: you could start with a dataset and then transform it into the information that you want to display (with a stat).

```{r, echo = FALSE, out.width = "100%"}
knitr::include_graphics("images/visualization-grammar-1.png")
```

Next, you could choose a geometric object to represent each observation in the transformed data. You could then use the aesthetic properties of the geoms to represent variables in the data. You would map the values of each variable to the levels of an aesthetic.

```{r, echo = FALSE, out.width = "100%"}
knitr::include_graphics("images/visualization-grammar-2.png")
```

You'd then select a coordinate system to place the geoms into. You'd use the location of the objects (which is itself an aesthetic property) to display the values of the x and y variables. At that point, you would have a complete graph, but you could further adjust the positions of the geoms within the coordinate system (a position adjustment) or split the graph into subplots (faceting). You could also extend the plot by adding one or more additional layers, where each additional layer uses a dataset, a geom, a set of mappings, a stat, and a position adjustment.

```{r, echo = FALSE, out.width = "100%"}
knitr::include_graphics("images/visualization-grammar-3.png")
```

You could use this method to build _any_ plot that you imagine. In other words, you can use the code template that you've learned in this chapter to build hundreds of thousands of unique plots.


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/workflow-basics.Rmd
================================================
# Workflow: basics

You now have some experience running R code. I didn't give you many details, but you've obviously figured out the basics, or you would've thrown this book away in frustration! Frustration is natural when you start programming in R, because it is such a stickler for punctuation, and even one character out of place will cause it to complain. But while you should expect to be a little frustrated, take comfort in that it's both typical and temporary: it happens to everyone, and the only way to get over it is to keep trying.

Before we go any further, let's make sure you've got a solid foundation in running R code, and that you know about some of the most helpful RStudio features.

## Coding basics

Let's review some basics we've so far omitted in the interests of getting you plotting as quickly as possible. You can use R as a calculator:

```{r}
1 / 200 * 30
(59 + 73 + 2) / 3
sin(pi / 2)
```

You can create new objects with `<-`:

```{r}
x <- 3 * 4
```

All R statements where you create objects, __assignment__ statements, have the same form:

```{r eval = FALSE}
object_name <- value
```

When reading that code say "object name gets value" in your head.

You will make lots of assignments and `<-` is a pain to type. Don't be lazy and use `=`: it will work, but it will cause confusion later. Instead, use RStudio's keyboard shortcut: Alt + - (the minus sign). Notice that RStudio automagically surrounds `<-` with spaces, which is a good code formatting practice. Code is miserable to read on a good day, so giveyoureyesabreak and use spaces.

## What's in a name?

Object names must start with a letter, and can only contain letters, numbers, `_` and `.`. You want your object names to be descriptive, so you'll need a convention for multiple words. I recommend __snake_case__ where you separate lowercase words with `_`. 

```{r, eval = FALSE}
i_use_snake_case
otherPeopleUseCamelCase
some.people.use.periods
And_aFew.People_RENOUNCEconvention
```

We'll come back to code style later, in [functions].

You can inspect an object by typing its name:

```{r}
x
```

Make another assignment:

```{r}
this_is_a_really_long_name <- 2.5
```

To inspect this object, try out RStudio's completion facility: type "this", press TAB, add characters until you have a unique prefix, then press return.

Ooops, you made a mistake! `this_is_a_really_long_name` should have value 3.5 not 2.5. Use another keyboard shortcut to help you fix it.  Type "this" then press Cmd/Ctrl + ↑. That will list all the commands you've typed that start those letters. Use the arrow keys to navigate, then press enter to retype the command. Change 2.5 to 3.5 and rerun.

Make yet another assignment:

```{r}
r_rocks <- 2 ^ 3
```

Let's try to inspect it:

```{r, eval = FALSE}
r_rock
#> Error: object 'r_rock' not found
R_rocks
#> Error: object 'R_rocks' not found
```

There's an implied contract between you and R: it will do the tedious computation for you, but in return, you must be completely precise in your instructions. Typos matter. Case matters.

## Calling functions

R has a large collection of built-in functions that are called like this:

```{r eval = FALSE}
function_name(arg1 = val1, arg2 = val2, ...)
```

Let's try using `seq()` which makes regular **seq**uences of numbers and, while we're at it, learn more helpful features of RStudio. Type `se` and hit TAB. A popup shows you possible completions. Specify `seq()` by typing more (a "q") to disambiguate, or by using ↑/↓ arrows to select. Notice the floating tooltip that pops up, reminding you of the function's arguments and purpose. If you want more help, press F1 to get all the details in the help tab in the lower right pane. 

Press TAB once more when you've selected the function you want. RStudio will add matching opening (`(`) and closing (`)`) parentheses for you. Type the arguments `1, 10` and hit return.

```{r}
seq(1, 10)
```

Type this code and notice you get similar assistance with the paired quotation marks:

```{r}
x <- "hello world"
```

Quotation marks and parentheses must always come in a pair. RStudio does its best to help you, but it's still possible to mess up and end up with a mismatch. If this happens, R will show you the continuation character "+":

```
> x <- "hello
+
```

The `+` tells you that R is waiting for more input; it doesn't think you're done yet. Usually that means you've forgotten either a `"` or a `)`. Either add the missing pair, or press ESCAPE to abort the expression and try again.

If you make an assignment, you don't get to see the value. You're then tempted to immediately double-check the result:

```{r}
y <- seq(1, 10, length.out = 5)
y
```

This common action can be shortened by surrounding the assignment with parentheses, which causes assignment and "print to screen" to happen.

```{r}
(y <- seq(1, 10, length.out = 5))
```

Now look at your environment in the upper right pane:

```{r, echo = FALSE, out.width = NULL}
knitr::include_graphics("screenshots/rstudio-env.png")
```

Here you can see all of the objects that you've created.

## Practice

1.  Why does this code not work?

    ```{r, error = TRUE}
    my_variable <- 10
    my_varıable
    ```
    
    Look carefully! (This may seem like an exercise in pointlessness, but
    training your brain to notice even the tiniest difference will pay off
    when programming.)
    
1.  Tweak each of the following R commands so that they run correctly:

    ```{r, eval = FALSE}
    library(tidyverse)

    ggplot(data = mpg) + 
      geom_point(mapping = aes(x = displ, y = hwy))
    
    fliter(mpg, cyl = 8)
    filter(diamond, carat > 3)
    ```
    
1.  Press Alt + Shift + K. What happens? How can you get to the same place
    using the menus?



================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/workflow-projects.Rmd
================================================
# Workflow: projects

One day you will need to quit R, go do something else and return to your analysis the next day. One day you will be working on multiple analyses simultaneously that all use R and you want to keep them separate. One day you will need to bring data from the outside world into R and send numerical results and figures from R back out into the world. To handle these real life situations, you need to make two decisions:

1.  What about your analysis is "real", i.e. what will you save as your 
    lasting record of what happened?

1.  Where does your analysis "live"?

## What is real?

As a beginning R user, it's OK to consider your environment (i.e. the objects listed in the environment pane) "real". However, in the long run, you'll be much better off if you consider your R scripts as "real". 

With your R scripts (and your data files), you can recreate the environment. It's much harder to recreate your R scripts from your environment! You'll either have to retype a lot of code from memory (making mistakes all the way) or you'll have to carefully mine your R history.

To foster this behaviour, I highly recommend that you instruct RStudio not to preserve your workspace between sessions:

```{r, echo = FALSE, out.width = "75%"}
knitr::include_graphics("screenshots/rstudio-workspace.png")
```

This will cause you some short-term pain, because now when you restart RStudio it will not remember the results of the code that you ran last time. But this short-term pain will save you long-term agony because it forces you to capture all important interactions in your code. There's nothing worse than discovering three months after the fact that you've only stored the results of an important calculation in your workspace, not the calculation itself in your code. 

There is a great pair of keyboard shortcuts that will work together to make sure you've captured the important parts of your code in the editor:

1. Press Cmd/Ctrl + Shift + F10 to restart RStudio.
2. Press Cmd/Ctrl + Shift + S to rerun the current script.

I use this pattern hundreds of times a week.

## Where does your analysis live?

R has a powerful notion of the __working directory__. This is where R looks for files that you ask it to load, and where it will put any files that you ask it to save. RStudio shows your current working directory at the top of the console:

```{r, echo = FALSE, out.width = "50%"}
knitr::include_graphics("screenshots/rstudio-wd.png")
```

And you can print this out in R code by running `getwd()`:

```{r eval = FALSE}
getwd()
#> [1] "/Users/hadley/Documents/r4ds/r4ds"
```

As a beginning R user, it's OK to let your home directory, documents directory, or any other weird directory on your computer be R's working directory. But you're six chapters into this book, and you're no longer a rank beginner. Very soon now you should evolve to organising your analytical projects into directories and, when working on a project, setting R's working directory to the associated directory.

__I do not recommend it__, but you can also set the working directory from within R:

```{r eval = FALSE}
setwd("/path/to/my/CoolProject")
```

But you should never do this because there's a better way; a way that also puts you on the path to managing your R work like an expert.

## Paths and directories

Paths and directories are a little complicated because there are two basic styles of paths: Mac/Linux and Windows. There are three chief ways in which they differ:

1.  The most important difference is how you separate the components of the
    path. Mac and Linux uses slashes (e.g. `plots/diamonds.pdf`) and Windows
    uses backslashes (e.g. `plots\diamonds.pdf`). R can work with either type
    (no matter what platform you're currently using), but unfortunately, 
    backslashes mean something special to R, and to get a single backslash 
    in the path, you need to type two backslashes! That makes life frustrating, 
    so I recommend always using the Linux/Mac style with forward slashes.

1.  Absolute paths (i.e. paths that point to the same place regardless of 
    your working directory) look different. In Windows they start with a drive
    letter (e.g. `C:`) or two backslashes (e.g. `\\servername`) and in
    Mac/Linux they start with a slash "/" (e.g. `/users/hadley`). You should
    __never__ use absolute paths in your scripts, because they hinder sharing: 
    no one else will have exactly the same directory configuration as you.

1.  The last minor difference is the place that `~` points to. `~` is a
    convenient shortcut to your home directory. Windows doesn't really have 
    the notion of a home directory, so it instead points to your documents
    directory.

## RStudio projects

R experts keep all the files associated with a project together --- input data, R scripts, analytical results, figures. This is such a wise and common practice that RStudio has built-in support for this via __projects__.

Let's make a project for you to use while you're working through the rest of this book. Click File > New Project, then:

```{r, echo = FALSE, out.width = "50%"}
knitr::include_graphics("screenshots/rstudio-project-1.png")
knitr::include_graphics("screenshots/rstudio-project-2.png")
knitr::include_graphics("screenshots/rstudio-project-3.png")
```

Call your project `r4ds` and think carefully about which _subdirectory_ you put the project in. If you don't store it somewhere sensible, it will be hard to find it in the future!

Once this process is complete, you'll get a new RStudio project just for this book. Check that the "home" directory of your project is the current working directory:

```{r eval = FALSE}
getwd()
#> [1] /Users/hadley/Documents/r4ds/r4ds
```

Whenever you refer to a file with a relative path it will look for it here. 

Now enter the following commands in the script editor, and save the file, calling it "diamonds.R". Next, run the complete script which will save a PDF and CSV file into your project directory. Don't worry about the details, you'll learn them later in the book.

```{r toy-line, eval = FALSE}
library(tidyverse)

ggplot(diamonds, aes(carat, price)) + 
  geom_hex()
ggsave("diamonds.pdf")

write_csv(diamonds, "diamonds.csv")
```

Quit RStudio. Inspect the folder associated with your project --- notice the `.Rproj` file. Double-click that file to re-open the project. Notice you get back to where you left off: it's the same working directory and command history, and all the files you were working on are still open. Because you followed my instructions above, you will, however, have a completely fresh environment, guaranteeing that you're starting with a clean slate.

In your favorite OS-specific way, search your computer for `diamonds.pdf` and you will find the PDF (no surprise) but _also the script that created it_ (`diamonds.R`). This is huge win! One day you will want to remake a figure or just understand where it came from. If you rigorously save figures to files __with R code__ and never with the mouse or the clipboard, you will be able to reproduce old work with ease!

## Summary

In summary, RStudio projects give you a solid workflow that will serve you well in the future:

* Create an RStudio project for each data analysis project. 

* Keep data files there; we'll talk about loading them into R in 
  [data import].

* Keep scripts there; edit them, run them in bits or as a whole.

* Save your outputs (plots and cleaned data) there.

* Only ever use relative paths, not absolute paths.

Everything you need is in one place, and cleanly separated from all the other projects that you are working on.


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/workflow-scripts.Rmd
================================================
# Workflow: scripts

So far you've been using the console to run code. That's a great place to start, but you'll find it gets cramped pretty quickly as you create more complex ggplot2 graphics and dplyr pipes. To give yourself more room to work, it's a great idea to use the script editor. Open it up either by clicking the File menu, and selecting New File, then R script, or using the keyboard shortcut Cmd/Ctrl + Shift + N. Now you'll see four panes:

```{r echo = FALSE, out.width = "75%"}
knitr::include_graphics("diagrams/rstudio-editor.png")
```

The script editor is a great place to put code you care about. Keep experimenting in the console, but once you have written code that works and does what you want, put it in the script editor. RStudio will automatically save the contents of the editor when you quit RStudio, and will automatically load it when you re-open. Nevertheless, it's a good idea to save your scripts regularly and to back them up.

## Running code

The script editor is also a great place to build up complex ggplot2 plots or long sequences of dplyr manipulations. The key to using the script editor effectively is to memorise one of the most important keyboard shortcuts: Cmd/Ctrl + Enter. This executes the current R expression in the console. For example, take the code below. If your cursor is at █, pressing Cmd/Ctrl + Enter will run the complete command that generates `not_cancelled`. It will also move the cursor to the next statement (beginning with `not_cancelled %>%`). That makes it easy to run your complete script by repeatedly pressing Cmd/Ctrl + Enter.

```{r, eval = FALSE}
library(dplyr)
library(nycflights13)

not_cancelled <- flights %>% 
  filter(!is.na(dep_delay)█, !is.na(arr_delay))

not_cancelled %>% 
  group_by(year, month, day) %>% 
  summarise(mean = mean(dep_delay))
```

Instead of running expression-by-expression, you can also execute the complete script in one step: Cmd/Ctrl + Shift + S. Doing this regularly is a great way to check that you've captured all the important parts of your code in the script. 

I recommend that you always start your script with the packages that you need. That way, if you share your code with others, they can easily see what packages they need to install. Note, however, that you should never include `install.packages()` or `setwd()` in a script that you share. It's very antisocial to change settings on someone else's computer!

When working through future chapters, I highly recommend starting in the editor and practicing your keyboard shortcuts. Over time, sending code to the console in this way will become so natural that you won't even think about it.

## RStudio diagnostics

The script editor will also highlight syntax errors with a red squiggly line and a cross in the sidebar:

```{r echo = FALSE, out.width = NULL}
knitr::include_graphics("screenshots/rstudio-diagnostic.png")
```

Hover over the cross to see what the problem is:

```{r echo = FALSE, out.width = NULL}
knitr::include_graphics("screenshots/rstudio-diagnostic-tip.png")
```

RStudio will also let you know about potential problems:

```{r echo = FALSE, out.width = NULL}
knitr::include_graphics("screenshots/rstudio-diagnostic-warn.png")
```

## Practice

1.  Go to the RStudio Tips twitter account, <https://twitter.com/rstudiotips>
    and find one tip that looks interesting. Practice using it!

1.  What other common mistakes will RStudio diagnostics report?  Read
    <https://support.rstudio.com/hc/en-us/articles/205753617-Code-Diagnostics> to 
    find out.
    


================================================
FILE: 2020年/2020年R数据科学系列/《R数据科学》源代码/wrangle.Rmd
================================================
# (PART) Wrangle {-}

# Introduction {#wrangle-intro}

In this part of the book, you'll learn about data wrangling, the art of getting your data into R in a useful form for visualisation and modelling. Data wrangling is very important: without it you can't work with your own data! There are three main parts to data wrangling:

```{r echo = FALSE, out.width = "75%"}
knitr::include_graphics("diagrams/data-science-wrangle.png")
```

This part of the book proceeds as follows:

*   In [tibbles], you'll learn about the variant of the data frame that we use
    in this book: the __tibble__.  You'll learn what makes them different
    from regular data frames, and how you can construct them "by hand".

*   In [data import], you'll learn how to get your data from disk and into R.
    We'll focus on plain-text rectangular formats, but will give you pointers 
    to packages that help with other types of data.

	
Data wrangling also encompasses data transformation, which you've already learned a little about. Now we'll focus on new skills for three specific types of data you will frequently encounter in practice:

*   [Relational data] will give you tools for working with multiple
    interrelated datasets.
    
*   [Strings] will introduce regular expressions, a powerful tool for
    manipulating strings.

*   [Factors] are how R stores categorical data. They are used when a variable
    has a fixed set of possible values, or when you want to use a non-alphabetical
    ordering of a string.
    
*   [Dates and times] will give you the key tools for working with 
    dates and date-times.


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Output written on pac_visual.pdf (9 pages).


================================================
FILE: 2021年/2021.01.31主成分结果可视化/pac_visual.rmd
================================================
---
title: "主成分分析结果可视化"
author:
  - 庄闪闪
documentclass: ctexart
keywords:
  - 中文
  - R Markdown
output:
  rticles::ctex:
    fig_caption: yes
    number_sections: yes
    toc: yes
editor_options: 
  chunk_output_type: console
---

```{r message=FALSE, warning=FALSE, include=FALSE}
showtext::showtext_auto()
```

# 简介

主成分分析法，也被称为主分量分析法，是很常用的一种[数据降维方法](https://github.com/EasyChart/Beautiful-Visualization-with-R)。采用一个线性变换将数据变换到一个新的坐标系统，使得任何数据点投影到第一个坐标（成为第一主成
分）的方差最大，在第二个坐标（第二主成分）的方差为第二大，以此类推。因此，主成分分析可以减少数据的维数，并保持对方差贡献最大的特征，相当于保留低阶主成分，忽略高阶主成分。

关于主成分的理论介绍和R语言代码实现可见前段时间赵西西写的推文：

但是后面留了一个小尾巴，如果想对主成分结果进行可视化，那得怎么实现？有没有简便的方法呢？

正好这几天有读者问起，那今天就来说说这个问题吧。


# 方法一

使用ggbiplot包中的ggbiplot()函数，该函数 使用ggplot2对主成分进行可视化。函数内部参数如下
```
  ggbiplot(pcobj, choices = 1:2, scale = 1, pc.biplot =
  TRUE, obs.scale = 1 - scale, var.scale = scale, groups =
  NULL, ellipse = FALSE, ellipse.prob = 0.68, labels =
  NULL, labels.size = 3, alpha = 1, var.axes = TRUE, circle
  = FALSE, circle.prob = 0.69, varname.size = 3,
  varname.adjust = 1.5, varname.abbrev = FALSE, ...)
```
内部参数过多，就不做详细解释。如果对内部参数有兴趣可以通过帮助文档进行查询（?ggbiplot）。

这里使用鸢尾花数据，给出一个简单的例子。大家可以将自己的数据进行导入（如何导入？可见推文：xxx），替换鸢尾花数据。

> 注意：检查自己数据集的数据结构是否和鸢尾花数据结构一致

这个包在github中，官方说可以使用以下参数进行下载（但是小编下载不了，只能通过强暴的方法进行，具体可见推文：。该压缩包已经处理成tar.gz放到公众号内了，如有需要，后台回复[ggbiplot]即可获得）。

之后使用`prcomp()`进行主成分分析，然后将结果保存到res.pca变量中。之后使用`ggbiplot()`进行可视化。其中观测的尺度因子为1（`obs.scale = 1`），变量的尺度因子为1（`var.scale = 1`），每组绘制一个椭圆（`ellipse = TRUE`）并添加相关系数的圆。

```{r message=FALSE, warning=FALSE}
# install_github("vqv/ggbiplot")
library(ggbiplot)
res.pca <- prcomp(iris[, -5],  scale = TRUE)
ggbiplot(res.pca, obs.scale = 1, var.scale = 1, ellipse = TRUE, circle = TRUE)
```

如果想给不同组别添加分别显示不同颜色，则可以使用参数groups，然后设定为原始数据对应的组别向量（如果原始数据没有，可以自行构造一个向量。）

```{r}
# 添加组别颜色
ggbiplot(res.pca, obs.scale = 1, var.scale = 1, ellipse = TRUE,groups = iris$Species, circle = TRUE)
```

当然你可以在此基础上加入ggplot内部的参数，比如更改主题，更改颜色，添加标题等一系列操作。
```{r}
# 更改主题
ggbiplot(res.pca, obs.scale = 1, var.scale = 1, ellipse = TRUE,groups = iris$Species, circle = TRUE) +
  theme_bw() +
  theme(panel.grid = element_blank()) +
  scale_color_brewer(palette = "Set2") +
  labs(title = "庄闪闪的R语言手册",subtitle = "快来关注这个宝藏公众号呀！",caption ="绘于：洞头岛")
```


小编最近有幸上了两节线上的R语言数据可视化公益课，把R语言base包以及ggplot语法系统的过了一遍，如果有需要补补可视化基础的朋友，可移步我的b站[账号名：庄闪闪]，视频回放已等候多时了。


# 方法二

使用`FactoMineR`包的`PCA()`函数或者使用基础包的
`prcomp()`函数进行数据降维处理，然后使用`factoextra`包的`fviz_pca_ind()`函数对结果进行可视化。

这里还是以鸢尾花的数据作为例子，沿用方法一的主成分分析结果res.pca。

这个包内部有四个主要绘制主成分结果的函数。

- fviz_pca_ind(): 各个样本图

- fviz_pca_var(): 变量图

- fviz_pca_biplot(): 各个样本和变量的联合图

- fviz_pca(): fviz_pca_biplot()的别名

内部参数不做过多介绍，有兴趣的读者请看帮助文档。这里只对下面的代码中出现的参数进行解释。

使用散点图进行绘制（`geom = "point"`），颜色使用"cos2"（`col.ind="cos2"`），使用3阶梯度颜色（`gradient.cols = c("white", "#2E9FDF", "#FC4E07" )`）。

```{r message=FALSE, warning=FALSE}
library(ggplot2)
library(factoextra) #可以直接通过install.packages()进行下载
# 各个样本图
fviz_pca_ind(res.pca, col.ind="cos2", geom = "point",
             gradient.cols = c("white", "#2E9FDF", "#FC4E07" ))
```

如果想展示分组变量信息，可以通过habillage参数设定，和第一种方法类似，这里还加入了一些细节：各组添加椭圆（`addEllipses=TRUE`），图的版式使用"Dark2"（`palette = "Dark2"`）。

```{r}
fviz_pca_ind(res.pca, label="none", habillage=iris$Species,
             addEllipses=TRUE, ellipse.level=0.95, palette = "Dark2")
```
当然可以使用`palette = c("#999999", "#E69F00", "#56B4E9")`，根据论文全文配色，进行手动调整。
```{r}
fviz_pca_ind(res.pca, label="none", habillage=iris$Species,
             addEllipses=TRUE, ellipse.level=0.95,
             palette = c("#999999", "#E69F00", "#56B4E9"))
```

如果想绘制个体和变量的双图，可以使用`fviz_pca_biplot()`，内部其他参数构造相同，然后可以添加各种其他ggplot的函数。

```{r}
# 个体和变量的双图
# 只保留变量的标签 #按组改变颜色，添加省略号
fviz_pca_biplot(res.pca, label = "var", habillage=iris$Species,
                addEllipses=TRUE, ellipse.level=0.95,
                ggtheme = theme_bw()) +
  theme(panel.grid = element_blank()) +
  scale_color_brewer(palette = "Set1") +
  labs(title = "庄闪闪的R语言手册",subtitle = "快来关注这个宝藏公众号呀！",caption ="绘于：洞头岛")
```






================================================
FILE: 2021年/2021.02.21克利夫兰点图系列/DotPlots_Data.csv
================================================
City,Female,Male
Acapulco,2565.51,2595.8
Bellingham,453.36,539.27
Beverly Hills,5050.46,5269.71
Bremerton,5269.89,5705
Camacho,3643.3,2154.15
Guadalajara,290.99,232.33
Hidalgo,7361.04,3951.73
Los Angeles,6014.64,6281.53
Merida,4770.14,3970.31
Mexico City,1255.17,1233.14
Orizaba,3531.85,2713.37
Portland,5612.43,6314.17
Salem,9416.67,8795.17
San Andres,3458.54,4747.51
San Diego,5800.65,5569.12
San Francisco,392.54,351.76
Seattle,6561.9,5590.49
Spokane,5835.68,5398.36
Tacoma,8467.8,8363.35
Vancouver,3973.95,4691.05
Victoria,1151.91,1250.12
Walla Walla,488.42,676.18
Yakima,2158.89,2911.08


================================================
FILE: 2021年/2021.02.21克利夫兰点图系列/cleveland's .Rmd
================================================
---
title: "R可视乎｜克利夫兰点图系列"
author:
  - 庄亮亮
documentclass: ctexart
keywords:
  - 中文
  - R Markdown
output:
  rticles::ctex:
    fig_caption: yes
    number_sections: yes
    toc: yes
editor_options: 
  chunk_output_type: console
---

# 克利夫兰点图系列


## 数据结构

为了方便起见，我这里直接模拟产生数据进行实验。大家根据自己数据进行变化即可。test_data包含两列，产品名称（因子类型），产品失效时间。

> 注：随机种子的设定，方便大家可以运行出和我一样的结果。

```{r}
set.seed(1) #设置随机种子
test_data = data.frame("Id" = LETTERS[1:20], "Time" = rnorm(20,10,10)+20)
test_data$Id = as.factor(test_data$Id) #将Id转换为因子型数据
```
```{r}
head(test_data)
```


## 棒棒糖图

**棒棒糖图（lollipop chart）**：棒棒糖图传达了与柱形图或者条形图相同的信息，只是将矩形转变成线条，这样可减少展示空间，重点放在数据点上，从而看起来更加简洁、美观。相对柱形图与条形图，棒棒糖图更加适合数据量比较多的情况。图3-4-1(a)为横向棒棒糖图，对应条形图；而如果是纵向棒棒糖图则对应柱形图。


使用 ggplot2 包中的散点绘制函数`geom_point()`及连接线函数 `geom_segment()`来绘制棒棒图。其中`geom_segment()`函数根据起点坐标`（x,y）`和终点坐标`（xend,yend）`绘制两者之间的连接线，棒棒糖图的连接线为平行于X轴水平绘制，其长度（length）对应 X 轴变量的数值。


```{r}
library(ggplot2)
ggplot(test_data,aes(y = Id,x = Time))+
  geom_segment(aes(x=0,xend=Time,y=Id,yend=reorder(Id,Time)),col="gray60") + #添加连接线
  geom_point(shape=21,size=4,colour="gray60",fill="skyblue")
```

如果想按照产品失效时间进行绘制，则可以使用`y = reorder(Id,Time)`对`y = Id`进行替换。

```{r}
ggplot(test_data,aes(y = reorder(Id,Time),x = Time))+
  geom_segment(aes(x=0,xend=Time,y=reorder(Id,Time),yend=reorder(Id,Time)),col="gray60") + #添加连接线
  geom_point(shape=21,size=4,colour="gray60",fill="skyblue") + ylab("Product")
```


当然也可以实际使用中，尤其是在生存分析，可靠性分析中。我们可以数据包含产品测试开始时间和终点时间。这时只需将segement中的x参数进行变化即可。

```{r}
test_data$start_Time = rnorm(20,5,3) #模拟产生开始时间数据
ggplot(test_data,aes(y = reorder(Id,Time),x = Time))+
  geom_segment(aes(x=start_Time,xend=Time,y=reorder(Id,Time),yend=reorder(Id,Time)),col="gray60") + #添加连接线
  geom_point(shape=21,size=4,colour="gray60",fill="skyblue") + ylab("Product")
```


## 克利夫兰点图

**克利夫兰点图（Cleveland's dot plot）**：也就是我们常用的滑珠散点图，非常类似棒棒糖图，只是没有连接的线条，重点强调数据的排序展示及互相之间的差距。克利夫兰点图一般都横向展示，所以 Y 轴变量一般为类别型变量。

只需使用`geom_point()`即可绘制克利夫兰点图。
```{r}
ggplot(test_data,aes(y = reorder(Id,Time),x = Time))+
  geom_point(shape=21,size=4,colour="gray60",fill="skyblue") + ylab("Product")
```


## 哑铃图

**哑铃图（dumbbell plot）**：可以看成多数据系列的克利夫兰点图，只是使用直线连接了两个数据系列的数据点。哑铃图主要用于：

①展示在同一时间段两个数据点的相对位置（增加或者减少）；

②比较两个类别之间的数据值差别。

这里，我们的模拟数据就不大适合了，为了绘制该图我将数据进行变化。实际背景如下：两个工厂对不同产品类型进行相同实验测试，得到数据如下：

```{r}
set.seed(2)
test_data1 = data.frame("Id" = LETTERS[1:20], "Time" = rnorm(20,10,10)+20)
test_data1$Id = as.factor(test_data1$Id) 
test_data1$start_Time = rnorm(20,5,3) #模拟产生开始时间数据
test_data_dum = rbind(test_data,test_data1)

test_data_dum$Group = c(rep(1,20),rep(2,20))
test_data_dum$Group = as.factor(test_data_dum$Group)
```

此时前6行数据如下：
```{r}
head(test_data_dum)
```

```{r}
ggplot(test_data_dum,aes(y = reorder(Id,Time),x = Time,fill=Group)) +
  geom_line(aes(group = reorder(Id,Time))) +
  geom_point(shape=21,size=4,colour="gray60") + ylab("Product") +
  scale_fill_manual(values=c( "#FC4E07","#36BED9"))+
  theme(
    legend.background = element_blank(),
    legend.position = c(0.85,0.12)
  )
```




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================================================
FILE: 2021年/2021.02.28常用主题风格/R可视乎｜ggplot常用主题风格汇总.md
================================================


借助`theme()`函数，可以自定义ggplot2图表的任何部分。 幸运的是，可以使用大量的预构建主题，仅用一行代码即可获得良好的样式。小编汇总了常用几个包的主题风格以供参考，以后可以根据论文的风格选择内置的一些主题。

## 1.具体操作

这里使用iris数据集，给出绘制散点图的例子，这里没有对主题风格进行设置，使用了默认主题。
```r
library(ggplot2)
ggplot(iris,aes(x = Sepal.Length,y = Sepal.Width,col = Species,shape = Species)) +
  geom_point()
```

如果你想更换主题，可以直接在之后加入对应参数即可，例如

```r
library(ggplot2)
ggplot(iris,aes(x = Sepal.Length,y = Sepal.Width,col = Species,shape = Species)) +
  geom_point() +
  theme_bw()
```

接下来，我们对常用的主题风格进行汇总。

## 2.ggplot2包

ggplot2包内部就有一些内置主题样式。

### default

![](https://static01.imgkr.com/temp/12bc9ce4738f4eb58f4f3b92063247fd.png)

### theme_bw()

![](https://static01.imgkr.com/temp/5e6e4b3ea8f74a4c9d1d9f3c48b176ad.png)

### theme_minimal()

![](https://imgkr2.cn-bj.ufileos.com/d60b293c-7cb7-4870-a561-9518455d60ed.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=f66%252FptGu2ii4wKaNV5VZpUzZZQg%253D&Expires=1614591717)

### theme_classic()

![](https://imgkr2.cn-bj.ufileos.com/875f9127-458a-42b1-891e-f0785391ec9a.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=z3ioiQ2RqTsicIp6SsyfbF7KapY%253D&Expires=1614591721)

### theme_gray()

![](https://static01.imgkr.com/temp/6f0dc53f3d7c461f8fd2b2de356d6b60.png)

## ggthemes包

> **注**： 使用该包内部的函数，记得提前安装和加载该包

>该包的github网站为：https://github.com/jrnold/ggthemes。jornld给出了很多主题风格的例子可见：https://github.com/BTJ01/ggthemes/tree/master/inst/examples

这个包算是ggplot拓展包最热门的包之一了，这里罗列一些常用的主题风格函数。想继续研究的伙伴可以看上面的两个网站。

### theme_excel()


![](https://imgkr2.cn-bj.ufileos.com/5941f66a-2826-4907-bce2-0010cf8f9860.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=XFmfeDCgWixVtXlPqWGMrzkdnAE%253D&Expires=1614591989)


### theme_economist()


![](https://imgkr2.cn-bj.ufileos.com/6be40e75-3fe1-45b7-8e93-3c04ca7f3be5.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=Ry4tY9ZvkGm3PHzqlIGYRPAEEDk%253D&Expires=1614592030)


### theme_fivethirtyeight()

![](https://static01.imgkr.com/temp/3b6e7b6132594662a98c1a84512efc54.png)




### theme_tufte()

![](https://static01.imgkr.com/temp/67078048f3564990a5272b8302ede352.png)

### theme_geocs()
![](https://static01.imgkr.com/temp/5092ca26a5794278adc171720929baee.png)

### theme_wsj()
![](https://imgkr2.cn-bj.ufileos.com/cdc7ed4a-7e76-4558-8e79-b87c245a1362.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=gRzvU1c1NwNWIzRWLp%252FamMUsgys%253D&Expires=1614592045)

### theme_calc()
![](https://imgkr2.cn-bj.ufileos.com/032dbaa9-594d-4e51-8dbb-2be49b3ed8d8.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=Uq7K0LZE1I3DDXxPtVo%252BDSsgp1o%253D&Expires=1614592045)

### theme_hc()

![](https://imgkr2.cn-bj.ufileos.com/6eb78ba2-414a-431f-8be1-7550ad33a7be.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=pf8zkArhfXq0RdKZ00C4PIhSAPM%253D&Expires=1614592067)

## 其他包

### egg包中的theme_article()

![](https://static01.imgkr.com/temp/f6256958de4c4e97806944ec73815898.png)

### ggpubr包中的theme_pubr() 


![](https://static01.imgkr.com/temp/7076cb88f033425cb96d406ae0bc97f6.png)











================================================
FILE: 2022年/2022.01.14 如何使用 ggplot2 绘制双轴分离图？/双轴分离.R
================================================
# 代码解释见推文：https://mp.weixin.qq.com/s/QiMHA10X8nGtK5iOH4armQ
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》

library(ggplot2)
library(dplyr)
library(ggthemes)
library(viridis)
# Build the demo data set.
df1 <- data.frame(
  term = paste0('term', 1:4),
  p.val = runif(4, 1, 5)
)
df2 <- data.frame(x = c(0,2,4,6), y = df1$term)
# （初级版本）============
ggplot(df1, aes(p.val, term)) +
  geom_col(width = 0.6) +
  labs(x = '-log(BH p value)', 
       y = 'Terms')
# （高级版本）============
ggplot(df1, aes(p.val, term)) +
  geom_col(aes(fill = term), width = 0.6) +
  geom_rangeframe(data = df2, aes(x = x, y = y), sides = 'bl') +
  scale_fill_viridis(discrete = T)+
  theme_tufte() +
  theme(
    legend.position = 'none',
    panel.grid = element_blank(),
    axis.text = element_text(size = rel(1.1))
  ) +
  labs(x = '-log(BH p value)', 
       y = 'Terms')
#================

df1 <- data.frame(
  term = paste0('term', 1:8),
  p.val = runif(8, 1, 5),
  score = rnorm(8, 0, 1)
)
df2 <- data.frame(x = c(0:6,6), y = df1$term)

ggplot(df1, aes(p.val, term)) +
  geom_col(aes(fill = score), width = 0.6) +
  geom_rangeframe(data = df2, aes(x = x, y = y), sides = 'bl') +
  scale_fill_gradient2(
    low = 'cyan',
    mid = '#fbffff',
    high = 'chocolate1',
    midpoint = 0)+
  theme_tufte() +
  theme(
    legend.position = c(0.9,0.75),
    panel.grid = element_blank(),
    axis.text = element_text(size = rel(1.1))
  ) +
  labs(x = '-log(BH p value)', 
       y = 'Terms')


================================================
FILE: 2022年/2022.01.28 如何绘制省市级地图？/df_China4.csv
================================================
[File too large to display: 29.0 MB]

================================================
FILE: 2022年/2022.01.28 如何绘制省市级地图？/各区县经营效率平均值.csv
================================================
,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020
龙湾区,0.726381633,0.721543426,0.753039566,0.728987862,0.717086546,0.701303469,0.697723125,0.709898114,0.709184237,0.650927818,0.728344797,0.70534892
鹿城区,0.620662164,0.592147605,0.644306726,0.580623772,0.550632408,0.528825852,0.544839502,0.565875985,0.563623198,0.391130443,0.422182473,0.386727617
龙港市,0.353384772,0.371474523,0.401951999,0.397120003,0.420499522,0.392652937,0.375298437,0.358311457,0.390752188,0.345212808,0.329515816,0.277278401
苍南县,0.174382638,0.18029899,0.194481119,0.190436193,0.210348171,0.193244431,0.194091057,0.1762433,0.187351086,0.1590393,0.156389091,0.164463369
瓯海区,0.502000673,0.467288971,0.574963688,0.544259758,0.486134852,0.499343913,0.508989627,0.55334276,0.539849982,0.394010518,0.430174909,0.392200534
瑞安市,0.41951156,0.422906122,0.424430702,0.402121258,0.406506067,0.431667346,0.412837307,0.402695101,0.430972464,0.518576519,0.545342852,0.54953396
洞头县,0.32535264,0.319407835,0.332293072,0.316885715,0.323690662,0.339532771,0.33917024,0.297769657,0.311690382,0.349087931,0.378338345,0.407340944
泰顺县,0.115587064,0.114197982,0.126368864,0.124187238,0.121185888,0.116080305,0.10429468,0.092062868,0.095106192,0.072609564,0.069480051,0.079565197
永嘉县,0.134189854,0.13104519,0.153101248,0.142955503,0.138648165,0.142197666,0.13018194,0.140898589,0.142483923,0.111985898,0.112345301,0.12088453
文成县,0.170379994,0.171080432,0.20234863,0.184824087,0.203160858,0.198642651,0.180153264,0.162254801,0.170313981,0.122547619,0.123558338,0.130797695
平阳县,0.220193661,0.209816842,0.217141214,0.213597191,0.221699962,0.213311602,0.210978055,0.210844849,0.234571794,0.183145765,0.187311718,0.196576114
乐清市,0.274827744,0.292215059,0.317271895,0.296091351,0.278735652,0.289949025,0.269916085,0.286228804,0.305679949,0.213311931,0.206464669,0.201076792


================================================
FILE: 2022年/2022.01.28 如何绘制省市级地图？/温州地图绘制.r
================================================
#===============================================================
#======================== 温州市地图绘制 =======================
#===============================================================


library(leafletCN)
library(dplyr)
library(leaflet)
#demomap("温州")


#主函数——geojsonmap：地图标色函数  
data = read.csv("各区县经营效率平均值.csv",header=T)
data$mean = apply(data[,2:13],1,mean)
colnames(data) = c('region',as.character(2009:2020),"mean")
dim(data)
region = regionNames("温州市")
dat = data.frame(region,runif(length(region)))
data1 = full_join(dat,data[,c(1,8)])
data2 = data1[,-2]
map = leafletGeo("温州市", data2)

#涂色环节heat.colors(6,rev = T)
# c("#FFFF00FF","#FF0000FF","#00FF00FF","#00FFFFFF","#FF00FFFF","#0000FFFF")
pal <- colorNumeric(
palette = c("purple","blue","lightblue","green","yellow","orange",'red'),
domain = map$value)

#载入高德地图amap
leaflet(map) %>% amap() %>%
#加入框边界及颜色
    addPolygons(stroke = TRUE,
                smoothFactor = 1,
                fillOpacity = 1,
                weight = 1,
                color = ~pal(value),
                popup = ~htmltools::htmlEscape(popup)
    ) %>%
    #加入右下角边框
    addLegend("bottomright", pal = pal, values = ~value,
              title = "效率值",
              labFormat = leaflet::labelFormat(prefix = ""),
              opacity = 2) 






================================================
FILE: 2022年/2022.01.28 如何绘制省市级地图？/温州市.json
================================================
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================================================
FILE: 2022年/2022.01.28 如何绘制省市级地图？/绘制浙江省地图.R
================================================
# 代码解释见推文:https://mp.weixin.qq.com/s/qCNTvFCgaNBH_HJQ_U7uPg
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》

# 绘制浙江省地图 ===========
library(leaflet)
library(leafletCN)
region = regionNames("浙江")
dem_data = c(30,52,20,27,16,37,38,42,39,76,45) # 这里转换成自己的数据
dat = data.frame(region,dem_data)
map = leafletGeo("浙江", dat)

#涂色环节
pal <- colorNumeric(
  palette = "Greens",
  domain = map$value)

#载入高德地图amap
leaflet(map) %>% amap() %>%
  #加入框边界及颜色
  addPolygons(stroke = TRUE,
              smoothFactor = 2,
              fillOpacity = 1.7,
              weight = 4,
              color = ~pal(value),
              popup = ~htmltools::htmlEscape(popup)
  ) %>%
  #加入右下角边框
  addLegend("bottomright", pal = pal, values = ~value,
            title = "legendTitle",
            labFormat = leaflet::labelFormat(prefix = ""),
            opacity = 1)


================================================
FILE: 2022年/2022.02.08读者投稿｜绘制一系列黑白印刷风格图表/acchist.r
================================================
# 本篇推文由西南财经大学经济学院杜云晗投稿，由闪闪排版整理。以下是正文内容：
# 代码解释见推文：https://mp.weixin.qq.com/s/f47_ujT2diu5CO0yUxKTbw
# 公众号：《庄闪闪的R语言手册》

# 2.1 安装说明
rm(list = ls())
if (is.element("chip", installed.packages()[, 1]) == FALSE) {
  if (is.element("devtools", installed.packages()[, 1]) == FALSE) {
    install.packages("devtools")
    library(devtools)
  } else if (is.element("devtools", installed.packages()[, 1]) == TRUE) {
    library(devtools)
  }
  install_github("youngyaoguai/Rdraw/chip0.1/chip")
  library(chip)
} else if (is.element("chip", installed.packages()[, 1]) == TRUE) {
  library(chip)
}

## 2.2 堆积柱状图黑白印刷风格绘制
help(acchist)  #查看函数说明
data("acchist_test_data",package = "chip")  #加载包内置数据集
View(acchist_test_data)  #查看内置数据集结构
axis.x.title <- c("region")   #x轴标题
axis.y.title <- c("ratio")    #y轴标题
individual.name <- c("B","C","D","E","F")  #区域名称
fill.name <- c("academic","vocational","college","high","further","primary")  #要填充的名称
bar_width <- 0.3   #柱子宽度
axis.title.size <- 30  #坐标轴标题大小
axis.text.x.size <- 30  #x轴的文本显示大小
axis.text.y.size <- 30  #y轴的文本显示大小
legend.text.size <- 30  #图例的文本显示大小
legend.title.size <- 30  #图例标题大小
#调用绘图函数
acchist(acchist_test_data,acchist_test_data$region,acchist_test_data$edu,acchist_test_data$ratio)


# 2.3 棒棒图黑白印刷风格绘制
help(bonbon)  #查看函数说明
data("bonbon_test_data",package = "chip")  #load integration sample data  #载入内置数据集
View(bonbon_test_data) #查看数据结构
#要着重显示的个体（如果没有要着重显示的个体，请设置为outstanding <- c(“ ”)）
outstanding <- c("wholesaleretail","Leasing and business services","realestate") 
outstanding.label <- c("24.75%","28.51%","5.16%") #要着重显示的个体的数据标签，需要和outstanding一并调整
outstanding.label.size <- 7  #要着重显示的个体的数据标签大小
outstanding.size <- 4    #要着重显示的个体的棒状物大小
not.outstanding.size <- 2.3  #不需要着重显示的个体的棒状物大小
outstanding.head.size <- 7  #要着重显示的个体的棒状物圆头大小
not.outstanding.head.size <- 5   #不需要重显示的个体的棒状物圆头大小
axis.title.x.size <- 30     #x轴标题大小
axis.text.y.size <- 30     #y轴的文本大小
value.max <- 100   #样本最大值
value.break <- c(0,value.max%/%3,78,100)   #要在x轴上显示的样本数值点
xlab.name <- c("ratio of service industry(%)")  #x轴标题
#所有个体名称排序，会按照从左往右的顺序绘制，绘制结果为从下到上
individual.name <- c("Leasing and business services","wholesaleretail","scientificresearch","Accommodation and Catering","Informationtransmission","culture","socialwork","publicfacilities","residentsservice","financial","education","transportation","publicmanagement","realestate")
bonbon(bonbon_test_data,bonbon_test_data$industry,bonbon_test_data$index)

# 2.4 箱线图黑白印刷风格绘制
help(boxeasy)  #查看绘图函数说明
data("box_test_data",package = "chip")  #载入内置数据集
View((box_test_data)) #查看数据结构
ylab.name <- c("index")       #y轴标题
xlab.name <- c("year")  #x轴标题
axis.title.x.size <- 15  #x轴标题大小
axis.title.y.size <- 15  #y轴标题大小
axis.text.x.size <- 15  #x轴文本大小
axis.text.y.size <- 15  #y轴文本大小
#调用函数
boxeasy(box_test_data,box_test_data$year,box_test_data$index)


# 2.5 简单折线图黑白印刷风格绘制
help(easyline)  #查看函数说明
data("easyline_test_data",package = "chip")  #载入内置数据集
View(easyline_test_data) #查看数据结构
ylab.name <- "population" #y轴标题
xlab.name <- "age" #x轴标题
axis.title.x.size <- 25 #x轴标题大小
axis.title.y.size <- 25#y轴标题大小
axis.line.x.thickness <- 0.5 #x轴粗细
axis.line.y.thickness <- 0.5 #y轴粗细
axis.text.x.size <- 20  #x轴文本大小
axis.text.y.size <- 20 #y轴文本大小
x.start <- 0  #x轴开始数值
x.end <- 100 #x轴结束数值
x.interval <- 10 #x轴数值间隔
y.start <- 0 #y轴开始数值
y.end <- 21571 #y轴结束数值
y.interval <- 5000 #y轴数值间隔
#调用函数
easyline(easyline_test_data,easyline_test_data$population,easyline_test_data$age)

# 2.6 核密度图黑白印刷风格绘制

help(densline)  #查看说明
data("densline_test_data",package = "chip")  #载入内置数据集
View(densline_test_data)  #查看数据结构
yscope <- c()  #第一次使用无法知道具体的范围，填空
xscope <- c() #第一次使用无法知道具体的范围，填空
#根据数据集从左往右填写
legend.name <- c("line1","line2","line3","line4","line5")
#调用函数
densline(test_data)
#根据图示大概知道数值范围，进行微调
yscope <- c(0.0004,0.00155) 
xscope <- c(0,1000)  
legend.name <- c("line1","line2","line3","line4","line5")
densline(densline_test_data)


# 2.7 柱状图黑白印刷风格绘制
help(histeasy)  # 查看帮助
data("hist_test_data ",package = "chip")  #载入内置数据集
View(hist_test_data)   #查看数据结构
x.size <- 2  #x轴文本大小
y.size <- 2  #y轴文本大小
lab.size <- 2  #坐标轴标题大小
legend.x <- 30   #图例水平位置
legend.y <- 0.3   #图例垂直位置
legend.x.distance <- 0.1  #图例中图片和文本的距离
axisname.x <- c("region")  #x轴标题
axisname.y <- c("ratio")  #y轴标题
type.name = c("0-14","15-64","65+","immortality")  #不同类别
individual.name=c("A","B","C","D","E")   #不同个体
histeasy(hist_test_data,hist_test_data$region,hist_test_data$age,hist_test_data$ratio)


# 2.8 加点折线图黑白印刷风格绘制
help(linepoint)
data("line_test_data",package = "chip")  
View(line_test_data)  #查看内置数据集数据结构
start <- 2009  #x轴开始数值
end <- 2019  #x轴结束数值
interval <- 2  #x轴数值间隔
axis.text.x.size <- 30  #x轴文本大小
axis.text.y.size <- 30  #y轴文本大小
legend.title.size <- 30  #图例标题大小
legend.text.size <- 30   #图例文本大小
axis.title.x.size <- 30  #x轴标题大小
axis.title.y.size <- 30  #y轴标题大小
axis.line.x.size <- 0.5  #x轴粗细
axis.line.y.size <- 0.5  #y轴粗细
geom.point.size <- 7  #折线点大小
ylab.name <- c("index")  #y轴标题
xlab.name <- c("year")  #x轴标题
individual.name <- c("A","B","C","D","E")  #个体名称
legend.name <- c("region")  #图例标题
linepoint(line_test_data,line_test_data$region,line_test_data$index,line_test_data$year)

# 2.9 金字塔图黑白印刷风格绘制

help(pyramid)
View(pyramid_test_data)  #查看内置数据集数据结构
data("pyramid_test_data",package = "chip")  #load integration sample data
v_max <- 4400  #样本中的最大数值
middle.size <- 6.6  #中间文本大小
left.axis.title.x.size <- 30  #左边x轴标题大小
left.axis.text.x.size <- 30  #左边x轴文本大小
right.axis.title.x.size <- 30  #右边x轴标题大小
right.axis.text.x.size <- 30  #右边x轴文本大小
right.title <- c("女")  #右边x轴标题
left.title <- c("male")  #左边x轴标题
pyramid(pyramid_test_data,pyramid_test_data$male,pyramid_test_data$female,pyramid_test_data$group,pyramid_test_data$group_name)




================================================
FILE: 2022年/2022.03.14绘制混合密度函数图以及添加分位数线/mix-quantile.r
================================================
# 代码解释见推文：https://mp.weixin.qq.com/s/bQ_aefTxk32x30Avfzv5kw
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》



# =====绘制混合密度函数图以及添加分位数线=====

# 加载包
library(ggplot2)
library(ggridges)

# 产生数据集
item <- 10000
inds <- rbinom(1, item, 0.5)
x <- c(rnorm(inds, 1, 1), rnorm(item - inds, 8, 1))
data <- data.frame("value" = x, "class" = rep(1, length(x)))

# 绘制密度函数图并添加分位数线

# 绘图
p1 <- ggplot(data, aes(x = value, y = class, fill = factor(stat(quantile)))) +
  stat_density_ridges(
    geom = "density_ridges_gradient",
    calc_ecdf = TRUE,
    quantiles = c(0.025, 0.975) #添加分为数线
  ) +
  scale_fill_manual(
    name = "Probability", values = c("#E2EAF6", "#436FB0", "#E2EAF6")
  ) +
  theme_bw() +
  theme(legend.position = "none", panel.grid = element_blank()) +
  labs(x = "x", y = "Density")
p1


p2 <- ggplot(data, aes(x = value, y = class, fill = factor(stat(quantile)))) +
  stat_density_ridges(
    geom = "density_ridges_gradient",
    calc_ecdf = TRUE,
    quantiles = c(0.005, 0.495, 0.51, 0.99)
  ) +
  scale_fill_manual(
    name = "Probability", values = c("#E2EAF6", "#436FB0", "#E2EAF6", "#436FB0", "#E2EAF6"),
  ) +
  theme_bw() +
  theme(legend.position = "none", panel.grid = element_blank()) +
  labs(x = "x", y = "Density")

p2


# 合并两图
library(cowplot)
# pdf("plot_cow.pdf", width = 8, height = 4)
plot_grid(p1, p2, ncol = 1, nrow = 2)
# dev.off()


================================================
FILE: 2022年/2022.04.08R 案例｜绘制不同分布的 QQ 图/qqplot.r
================================================
# 代码解释见推文：https://mp.weixin.qq.com/s/ncorK41sntA4iBJWBCdVkA
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》
# 参考博客：An Introduction to qqplotr: https://cran.r-project.org/web/packages/qqplotr/vignettes/introduction.html

# 1. 加载包
library(qqplotr)
library(ggplot2)

# 2. 随机产生数据
set.seed(0)
smp <- data.frame(norm = rnorm(100))

# 3. 绘制正态分布的 QQ 图
gg <- ggplot(data = smp, mapping = aes(sample = norm)) +
  stat_qq_band() +
  stat_qq_line() +
  stat_qq_point() +
  labs(x = "Theoretical Quantiles", y = "Sample Quantiles")
gg

# 4.拓展
library(viridis)
gg <- ggplot(data = smp, mapping = aes(sample = norm)) +
  geom_qq_band(bandType = "ks", mapping = aes(fill = "KS"), alpha = 0.9) +
  geom_qq_band(bandType = "ts", mapping = aes(fill = "TS"), alpha = 0.9) +
  geom_qq_band(bandType = "pointwise", mapping = aes(fill = "Normal"), alpha = 0.9) +
  geom_qq_band(bandType = "boot", mapping = aes(fill = "Bootstrap"), alpha = 0.9) +
  stat_qq_line() +
  stat_qq_point() +
  labs(x = "Theoretical Quantiles", y = "Sample Quantiles") +
  scale_fill_viridis(discrete = T,direction = -1)

gg

# 进阶版本

data = data.frame('y' = c(1.339, 1.434, 1.549, 1.574 ,1.589, 1.613, 1.746 ,1.753, 1.764 ,1.807, 1.812, 1.84, 1.852, 1.852, 1.862, 1.864, 1.931, 1.952, 1.974, 2.019, 2.051, 2.055, 2.058 ,2.088, 2.125, 2.162, 2.171, 2.172 ,2.18 ,2.194 ,2.211 ,2.27, 2.272, 2.28, 2.299, 2.308, 2.335 ,2.349 ,2.356 ,2.386, 2.39, 2.41, 2.43, 2.431, 2.458, 2.471, 2.497, 2.514 ,2.558, 2.577, 2.593, 2.601, 2.604, 2.62 ,2.633, 2.67, 2.682, 2.699, 2.705, 2.735, 2.785, 2.785,3.02, 3.042, 3.116, 3.174))

## 绘制指数分布的 QQ 图
dp <- list(rate = 2.2867)
di <- "exp"
p1 = ggplot(data = data, mapping = aes(sample = y)) +
  stat_qq_band(distribution = di, dparams = dp) +
  stat_qq_line(distribution = di, dparams = dp) +
  stat_qq_point(distribution = di, dparams = dp) +
  labs(x = "Theoretical Quantiles", y = "Sample Quantiles") +
  theme_bw() + 
  theme(panel.grid = element_blank())
p1

## 绘制威布尔分布的 QQ 图
# weibull distribution
di <- "weibull" # exponential distribution
dp <- list(shape=5.4766,scale=2.4113)
p2 = ggplot(data = data, mapping = aes(sample = y)) +
  stat_qq_band(distribution = di, dparams = dp) +
  stat_qq_line(distribution = di, dparams = dp) +
  stat_qq_point(distribution = di, dparams = dp) +
  labs(x = "Theoretical Quantiles", y = "Sample Quantiles") +
  theme_bw() + 
  theme(panel.grid = element_blank())
p2

## 合并两幅 QQ 图
library(cowplot)
plot_grid(p1, p2, ncol = 2, nrow = 1)







================================================
FILE: 2022年/2022.05.15老板让你复现一个图片，你会使用什么软件？/example.r
================================================
# 代码解释见推文：https://mp.weixin.qq.com/s/V8H-U719PjCgsn1lVY_PFg
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》


# 数据产生
set.seed(1) #确保不同机子产生的随机数相同
mu = c(2,5)
std = c(1,1)
num = 1000
r1 = rnorm(num,mu[1],std[1]) #正态分布
r2 = rnorm(num,mu[2],std[2]) #正态分布
data = data.frame('value' = c(r1,rep(NA,num),r2,rep(NA,5*num)),
                  'class' = factor(rep(c(1:8),each= num)))
knitr::kable(head(data))

# 定义主题
theme_manual = function(){ 
  theme(panel.grid = element_blank(),
        panel.border = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        legend.position="none") 
}

# 画图

ggplot(data, aes(x = value, y = class,fill = factor(stat(quantile)))) + 
  # 添加密度函数图
  stat_density_ridges(
    geom = "density_ridges_gradient",
    calc_ecdf = TRUE,rel_min_height = 0.02,
    quantiles = c(0.025, 0.975),
    alpha = 1,scale = 0.6,bandwidth = 1
  ) +
  scale_fill_manual(
    name = "Probability", values = c("#FF0000A0", "white", "#FF0000A0"),
    labels = c("(0, 0.025]", "(0.025, 0.975]", "(0.975, 1]")
  ) + 
  # 手动一条条添加各种线段，文字
  annotate("segment", x = mu[1], xend = mu[1], y = 1, yend = 7,colour = "black") +
  annotate("segment", x = mu[2]-1, xend = mu[2]-1, y = 1, yend = 7,colour = "#0000FFA0",lty = "dashed") +
  annotate("segment", x = mu[1]-2.15, xend = mu[1]-2, y = 1, yend = 7,colour = "#0000FFA0",lty = "dashed") +
  annotate("segment", x = mu[2], xend = mu[2], y = 3-0.02, yend = 4.18,colour = "black") +
  annotate("segment", x = 0, xend = 10, y = 3, yend = 3,colour = "black") +
  annotate("segment", x = -3, xend = 7, y = 1, yend = 1,colour = "black") +
  annotate("text", x = mu[1], y = 0.7, label = expression(mu[T])) +
  annotate("text", x = mu[1]-2.15, y = 0.7, label = expression(alpha[1])) + 
  annotate("text", x = mu[1]+2, y = 0.7, label = expression(alpha[2])) +
  annotate("text", x = mu[2], y = 2.8, label = expression(mu[T])) +
  annotate("text", x = 0.5, y = 3.9, label = expression(beta)) + 
  annotate(geom = "line",x = c(0.8, 3.1),
           y = c(3.8, 3.2),
           arrow = arrow(angle = 20, length = unit(4, "mm"))) + # 添加线段并且包含箭头
  annotate("text", x = mu[1], y = 7.3, label = "CL") +
  annotate("text", x = mu[2]-1, y = 7.4, label = "LCL") +
  annotate("text", x = mu[1]-2, y = 7.4, label = "UCL") +
  annotate("text", x = -1.3, y = 2.3, label = expression(alpha[1] + alpha[2] == alpha)) +
  coord_flip() + # 转换横纵坐标
  theme_bw() + # 主题设置
  theme_manual() + xlab('') + ylab('')



================================================
FILE: 2022年/2022.08.08 ggplot 分面的细节调整汇总/ggplot_facet.r
================================================
# 代码解释见推文:https://mp.weixin.qq.com/s/fzcGJugs0VxJpM6nb25hLA
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》

# 生成数据

# 导入包
library(ggplot2)

set.seed(1)
dat = data.frame("time" = rep(2013:2019,6),
                 "method" = rep(LETTERS[1:6],each = 7),
                 "value" = rnorm(7*6,5,1),
                 "upper" = rnorm(7*6,5,1) + abs(rnorm(7*6,2,0.1)),
                 "lower" = rnorm(7*6,5,1) - abs(rnorm(7*6,2,0.1))
)
head(dat)

# 基础版本绘图
cols <- c("#85BA8F", "#A3C8DC","#349839","#EA5D2D","#EABB77","#F09594") #设置颜色

p = ggplot(dat,aes(x = time, y = value,fill = method)) +                                                       
  geom_line(aes(color = method)) + #添加线
  geom_point(aes(color = method)) + #添加散点
  geom_ribbon(aes(ymin=lower, ymax=upper), alpha=0.3) + #添加区间
  scale_x_continuous(breaks = 2013:2019) +
  facet_wrap(vars(method),nrow = 4) + 
  theme_bw() + ylab("Value") + xlab("Time") + #主题设置
  theme(panel.grid = element_blank())
p

# 刻度尺修改
## x
p + facet_wrap(vars(method),nrow = 4,scales = "free_x")
## y 
p + facet_wrap(vars(method),nrow = 4,scales = "free_y")
## 双轴
p + facet_wrap(vars(method),nrow = 4,scales = "free")

## 标题框调整
p + facet_wrap(vars(method),nrow = 3,strip.position = "left")

## 去除标题框背景
p + theme(
  strip.background = element_blank() #去除标题框背景
)

## 修改标题框背景颜色
p + theme(strip.background=element_rect(colour="black",
                                        fill="#2072A8"))

## 修改标题框文字颜色
p +  theme(strip.text.x=element_text(colour="white"))


## 删除标题框
p +  theme(strip.text.x = element_blank())




================================================
FILE: 2022年/2022.09.09 如何在分面中添加数学表达式标签？ /add_math_label.r
================================================
# 代码解释见推文：https://mp.weixin.qq.com/s/Dbx9J_flkTtyi7LtRAdRPQ
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》

# 构建模拟数据
library(ggplot2)
library(grid)
mydf <- data.frame(letter = factor(rep(c("A", "B", "C", "D"), each = 20)), x = rnorm(80), y = rnorm(80))
head(mydf)

# 基本绘图
ggplot(mydf, aes(x = x, y = y)) + 
  geom_smooth(method = "lm") + 
  geom_point() + 
  facet_wrap(~ letter)

# 设置表达式标签
f_names <- list('A' = expression(paste(alpha[2])), 'B' = expression(Gamma(3,4)), 'C' = expression(paste(y = beta[0] + beta[1]*x[1])), 'D' = expression(delta))
f_labeller <- function(variable, value){return(f_names[value])}
ggplot(mydf, aes(x = x, y = y)) + 
  geom_smooth(method = "lm") + 
  geom_point() + 
  facet_wrap(~ letter, labeller = f_labeller)


# 修改主题
ggplot(mydf, aes(x = x, y = y)) + 
  geom_smooth(method = "lm",color = "#e99e9c",fill = "#98c0d7") + 
  geom_point(color = "gray60") + 
  facet_wrap(~ letter, labeller = f_labeller,scales = "free") +
  theme_bw() + ylab("Value") + xlab("Time") + #主题设置
  theme(panel.grid = element_blank(), 
        strip.background = element_blank()) 


# 添加中文标签

f_names <- list('A' = "庄闪闪", 'B' = "庄亮亮", 
                'C' = "庄晶晶", 'D' = "庄暗暗") # 设置标签

library(showtext)
showtext.auto()

f_labeller <- function(variable, value){return(f_names[value])}
ggplot(mydf, aes(x = x, y = y)) + 
  geom_smooth(method = "lm",color = "#e99e9c",fill = "#98c0d7") + 
  geom_point(color = "gray60") + 
  facet_wrap(~ letter, labeller = f_labeller,scales = "free") +
  theme_bw() + ylab("Value") + xlab("Time") + #主题设置
  theme(panel.grid = element_blank(), 
        strip.background = element_blank()) 


================================================
FILE: 2022年/2022.09.24 中国地图绘制/.RData
================================================
[File too large to display: 35.9 MB]

================================================
FILE: 2022年/2022.09.24 中国地图绘制/.Rhistory
================================================
"",
values = c("#116E13", "#9CE69B", "#DCEEE2"),
breaks = c("Target site", "Spatial correlated site", "Other site"),
labels = c("Target site", "Spatial correlated site", "Other site")
)+
theme(legend.position = "top",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
ggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +
geom_text(data = label_city, aes(x = x, y = y, label = city), color = 'White',parse = T,size=2) +
#ggtitle("河北省GDP") +
xlab("Long (°E)") + ylab("Lat (°N)") +
scale_fill_manual(
"",
values = c("#116E13", "#9CE69B", "#DCEEE2"),
breaks = c("Target site", "Spatial correlated site", "Other site"),
labels = c("Target site", "Spatial correlated site", "Other site")
)+
theme(legend.position = "top",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
ggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +
geom_text(data = label_city, aes(x = x, y = y, label = city), color = '#116E13',parse = T,nudge_x = 122) +
#ggtitle("河北省GDP") +
xlab("Long (°E)") + ylab("Lat (°N)") +
scale_fill_manual(
"",
values = c("#116E13", "#9CE69B", "#DCEEE2"),
breaks = c("Target site", "Spatial correlated site", "Other site"),
labels = c("Target site", "Spatial correlated site", "Other site")
)+
theme(legend.position = "top",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
ggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +
geom_text(data = label_city, aes(x = x, y = y, label = city), color = '#116E13',parse = T,nudge_x = 0.7) +
#ggtitle("河北省GDP") +
xlab("Long (°E)") + ylab("Lat (°N)") +
scale_fill_manual(
"",
values = c("#116E13", "#9CE69B", "#DCEEE2"),
breaks = c("Target site", "Spatial correlated site", "Other site"),
labels = c("Target site", "Spatial correlated site", "Other site")
)+
theme(legend.position = "top",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
ggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +
geom_text(data = label_city, aes(x = x, y = y, label = city), color = '#116E13',parse = T,nudge_x = 0.8) +
#ggtitle("河北省GDP") +
xlab("Long (°E)") + ylab("Lat (°N)") +
scale_fill_manual(
"",
values = c("#116E13", "#9CE69B", "#DCEEE2"),
breaks = c("Target site", "Spatial correlated site", "Other site"),
labels = c("Target site", "Spatial correlated site", "Other site")
)+
theme(legend.position = "top",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
ggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +
geom_text(data = label_city, aes(x = x, y = y, label = city), color = '#116E13',parse = T,nudge_x = 0.9) +
#ggtitle("河北省GDP") +
xlab("Long (°E)") + ylab("Lat (°N)") +
scale_fill_manual(
"",
values = c("#116E13", "#9CE69B", "#DCEEE2"),
breaks = c("Target site", "Spatial correlated site", "Other site"),
labels = c("Target site", "Spatial correlated site", "Other site")
)+
theme(legend.position = "top",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
ggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +
geom_text(data = label_city, aes(x = x, y = y, label = city), color = '#116E13',parse = T,nudge_x = 0.96) +
#ggtitle("河北省GDP") +
xlab("Long (°E)") + ylab("Lat (°N)") +
scale_fill_manual(
"",
values = c("#116E13", "#9CE69B", "#DCEEE2"),
breaks = c("Target site", "Spatial correlated site", "Other site"),
labels = c("Target site", "Spatial correlated site", "Other site")
)+
theme(legend.position = "top",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
ggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +
geom_text(data = label_city, aes(x = x, y = y, label = city), color = '#116E13',parse = T,nudge_x = 1) +
#ggtitle("河北省GDP") +
xlab("Long (°E)") + ylab("Lat (°N)") +
scale_fill_manual(
"",
values = c("#116E13", "#9CE69B", "#DCEEE2"),
breaks = c("Target site", "Spatial correlated site", "Other site"),
labels = c("Target site", "Spatial correlated site", "Other site")
)+
theme(legend.position = "top",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
ggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +
geom_text(data = label_city, aes(x = x, y = y, label = city), color = '#116E13',parse = T,nudge_x = 1) +
#ggtitle("河北省GDP") +
xlab("Long (°E)") + ylab("Lat (°N)") +
scale_fill_manual(
"",
values = c("#116E13", "#9CE69B", "#DCEEE2"),
breaks = c("Target site", "Spatial correlated site", "Other site"),
labels = c("Target site", "Spatial correlated site", "Other site")
)+
theme(legend.position = "none",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
show_data <- read_xlsx("河北省各市GDP增长率.xlsx","hubei")
shp_data <- st_read("CHN_adm/CHN_adm2.shp")
index <- which(shp_data$NAME_1=="Shanghai")
shp_data$NL_NAME_2[index] <- "上海"
shp_data$NL_NAME_2[index]
shp_data$NL_NAME_2 <- as.character(shp_data$NL_NAME_2)
my_data <- dplyr::left_join(show_data, shp_data,by = c("city" = "NL_NAME_2"))
head(my_data)
label_city <- data.frame(x = 121.48,y = 31.22)
label_city['city'] <- "Wuhan"
ggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +
geom_text(data = label_city, aes(x = x, y = y, label = city), color = '#116E13',parse = T,nudge_x = 1) +
#ggtitle("河北省GDP") +
xlab("Long (°E)") + ylab("Lat (°N)") +
scale_fill_manual(
"",
values = c("#116E13", "#9CE69B", "#DCEEE2"),
breaks = c("Target site", "Spatial correlated site", "Other site"),
labels = c("Target site", "Spatial correlated site", "Other site")
)+
theme(legend.position = "none",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
ggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +
geom_text(data = label_city, aes(x = x, y = y, label = city), color = 'white',parse = T) +
#ggtitle("河北省GDP") +
xlab("Long (°E)") + ylab("Lat (°N)") +
scale_fill_manual(
"",
values = c("#116E13", "#9CE69B", "#DCEEE2"),
breaks = c("Target site", "Spatial correlated site", "Other site"),
labels = c("Target site", "Spatial correlated site", "Other site")
)+
theme(legend.position = "top",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
ggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +
geom_text(data = label_city, aes(x = x, y = y, label = city), color = 'white',parse = T) +
#ggtitle("河北省GDP") +
xlab("Long (°E)") + ylab("Lat (°N)") +
scale_fill_manual(
"",
values = c("#116E13", "#9CE69B", "#DCEEE2"),
breaks = c("Target site", "Spatial correlated site", "Other site"),
labels = c("Target site", "Spatial correlated site", "Other site")
)+
theme(legend.position = "top",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
label_city <- data.frame(x = 114.31,y = 30.52)
label_city['city'] <- "Wuhan"
ggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +
geom_text(data = label_city, aes(x = x, y = y, label = city), color = 'white',parse = T) +
#ggtitle("河北省GDP") +
xlab("Long (°E)") + ylab("Lat (°N)") +
scale_fill_manual(
"",
values = c("#116E13", "#9CE69B", "#DCEEE2"),
breaks = c("Target site", "Spatial correlated site", "Other site"),
labels = c("Target site", "Spatial correlated site", "Other site")
)+
theme(legend.position = "top",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
ggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +
geom_text(data = label_city, aes(x = x, y = y, label = city), color = 'white',parse = T) +
#ggtitle("河北省GDP") +
xlab("Long (°E)") + ylab("Lat (°N)") +
scale_fill_manual(
"",
values = c("#116E13", "#9CE69B", "#DCEEE2"),
breaks = c("Target site", "Spatial correlated site", "Other site"),
labels = c("Target site", "Spatial correlated site", "Other site")
)+
theme(legend.position = "none",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
ggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +
geom_text(data = label_city, aes(x = x, y = y, label = city), color = 'white',parse = T) +
#ggtitle("河北省GDP") +
xlab("Long (°E)") + ylab("Lat (°N)") +
scale_fill_manual(
"",
values = c("#116E13", "#9CE69B", "#DCEEE2"),
breaks = c("Target site", "Spatial correlated site", "Other site"),
labels = c("Target site", "Spatial correlated site", "Other site")
)+
theme(legend.position = "top",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
library(rgdal)
library(ggplot2)
library(maptools)
library(mapproj)
library(ggsn)
library(readxl)
library(sf)
show_data <- read_xlsx("测试数据.xlsx","hubei")
shp_data <- st_read("china_shp/CHN_adm2.shp")
shp_data$NL_NAME_2 <- as.character(shp_data$NL_NAME_2)
my_data <- dplyr::left_join(show_data, shp_data,by = c("city" = "NL_NAME_2"))
head(my_data)
ggplot(data = my_data) + geom_sf(aes(fill = as.factor(value), geometry = `geometry`)) +
geom_sf_text(aes(label = `city`,geometry = `geometry`), color = 'Black',size=2)+
xlab("Long (°E)") + ylab("Lat (°N)") +
##更改图形颜色，不加这个语句使用随机配色
scale_fill_manual(
"value",
values = c("#DFD3F2", "#00C2F9", "#FFF7D0"),
breaks = c("A", "B", "C"),
labels = c("A", "B", "C")
)+
theme(legend.position = "top",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
hubei <- read_xlsx("测试数据.xlsx","hubei")
jiangxi <- read_xlsx("测试数据.xlsx","jiangxi")
all_province <- rbind(hubei,jiangxi)
shp_data <- st_read("china_shp/CHN_adm2.shp")
shp_data$NL_NAME_2 <- as.character(shp_data$NL_NAME_2)
data2 <- dplyr::left_join(all_province, shp_data,by = c("city" = "NL_NAME_2"))
ggplot(data = data2) + geom_sf(aes(fill = as.factor(value), geometry = `geometry`)) +
geom_sf_text(aes(label = `city`,geometry = `geometry`), color = 'Black',size=2)+
xlab("Long (°E)") + ylab("Lat (°N)") +
##更改图形颜色，不加这个语句使用随机配色
scale_fill_manual(
"value",
values = c("#DFD3F2", "#00C2F9", "#FFF7D0"),
breaks = c("A", "B", "C"),
labels = c("A", "B", "C")
)+
theme(legend.position = "top",panel.background = element_rect(fill = "white",color = "black"),
panel.grid = element_line(color = "grey"))
library(geojsonsf)
library(sf)
library(ggplot2)
library(RColorBrewer)
API_pre = "http://xzqh.mca.gov.cn/data/"
China = st_read(dsn = paste0(API_pre, "quanguo.json"),
stringsAsFactors=FALSE)
st_crs(China) = 4326
# 2.国境线
China_line = st_read(dsn = paste0(API_pre, "quanguo_Line.geojson"),
stringsAsFactors=FALSE)
st_crs(China_line) = 4326
gjx <- China_line[China_line$QUHUADAIMA == "guojiexian",]
province <- read.csv("province.csv")
# 4.着色数据+全国地图
colour <- read.csv("colour.csv")
colour$QUHUADAIMA <- as.character(colour$QUHUADAIMA)
CHINA <- dplyr::left_join(China,colour,by= "QUHUADAIMA")
head(colour)
###----全国地图完整（无右下角小地图）----------###
fig1 <-  ggplot() +
# 绘制主图
geom_sf(
data = CHINA,
aes(fill = factor(new_colour))) +
scale_fill_manual(
"class",
values = c("#FFFFFF", "#79CBC2", "#5EA9AC", "#4B8793", "#3C6777"),
breaks = c("0", "1", "2", "3", "4"),
labels = c("0", "1", "2", "3", "4")
) +
geom_sf(data = gjx) +
geom_text(
data = province,
aes(x = dili_Jd, y = dili_Wd, label = 省市),
position = "identity",
size = 3,
check_overlap = TRUE
) +
labs(title = "中国地图") +
theme(
plot.title = element_text(
color = "black",
size = 16,
face = "bold",
vjust = 0.1,
hjust = 0.5
),
legend.title = element_blank(),
legend.position = c(0.2, 0.2),
panel.grid = element_blank(),
panel.background = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank()
) +
annotation_north_arrow(
location = 'tl',
which_north = 'false',
style = north_arrow_orienteering()
)
fig1 <-  ggplot()+
# 绘制主图
geom_sf(data = CHINA,fill='NA') +
# 绘制国界线及十段线
geom_sf(data = gjx)+
##添加省份名称
geom_text(data = province,aes(x=dili_Jd,y=dili_Wd,label=省市),position = "identity",size=3,check_overlap = TRUE) +
labs(title="中国地图")+
theme(plot.title = element_text(color="black", size=16, face="bold",vjust = 0.1,hjust = 0.5),
legend.title=element_blank(),
legend.position = c(0.2,0.2),
panel.grid=element_blank(),
panel.background=element_blank(),
axis.text=element_blank(),
axis.ticks=element_blank(),
axis.title=element_blank()
)+
##添加指北针，“style”参数可以更改样式
annotation_north_arrow(location='tl', which_north='false',
style=north_arrow_orienteering())
API_pre = "http://xzqh.mca.gov.cn/data/"
China = st_read(dsn = paste0(API_pre, "quanguo.json"),
stringsAsFactors=FALSE)
st_crs(China) = 4326
# 2.国境线
China_line = st_read(dsn = paste0(API_pre, "quanguo_Line.geojson"),
stringsAsFactors=FALSE)
st_crs(China_line) = 4326
gjx <- China_line[China_line$QUHUADAIMA == "guojiexian",]
province <- read.csv("province.csv")
fig1 <-  ggplot()+
# 绘制主图
geom_sf(data = CHINA,fill='NA') +
# 绘制国界线及十段线
geom_sf(data = gjx)+
##添加省份名称
geom_text(data = province,aes(x=dili_Jd,y=dili_Wd,label=省市),position = "identity",size=3,check_overlap = TRUE) +
labs(title="中国地图")+
theme(plot.title = element_text(color="black", size=16, face="bold",vjust = 0.1,hjust = 0.5),
legend.title=element_blank(),
legend.position = c(0.2,0.2),
panel.grid=element_blank(),
panel.background=element_blank(),
axis.text=element_blank(),
axis.ticks=element_blank(),
axis.title=element_blank()
)+
##添加指北针，“style”参数可以更改样式
annotation_north_arrow(location='tl', which_north='false',
style=north_arrow_orienteering())
library(geojsonsf)
library(sf)
library(ggplot2)
library(RColorBrewer)
??annotation_north_arrow
library(ggspatial)
fig1 <-  ggplot()+
# 绘制主图
geom_sf(data = CHINA,fill='NA') +
# 绘制国界线及十段线
geom_sf(data = gjx)+
##添加省份名称
geom_text(data = province,aes(x=dili_Jd,y=dili_Wd,label=省市),position = "identity",size=3,check_overlap = TRUE) +
labs(title="中国地图")+
theme(plot.title = element_text(color="black", size=16, face="bold",vjust = 0.1,hjust = 0.5),
legend.title=element_blank(),
legend.position = c(0.2,0.2),
panel.grid=element_blank(),
panel.background=element_blank(),
axis.text=element_blank(),
axis.ticks=element_blank(),
axis.title=element_blank()
)+
##添加指北针，“style”参数可以更改样式
annotation_north_arrow(location='tl', which_north='false',
style=north_arrow_orienteering())
fig1
nine_lines = read_sf('南海.geojson')
# 绘制九段线小图
fig2 = ggplot() +
geom_sf(data = CHINA,fill='NA', size=0.5) +
geom_sf(data = nine_lines,color='black',size=0.5)+
##去掉主图的部分区域
coord_sf(ylim = c(-4028017,-1877844),xlim = c(117131.4,2115095),crs="+proj=laea +lat_0=40 +lon_0=104")+
theme(
aspect.ratio = 1.25, #调节长宽比
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
panel.background = element_blank(),
panel.border = element_rect(fill=NA,color="grey10",linetype=1,size=0.5),
plot.margin=unit(c(0,0,0,0),"mm"))
fig2
fig = ggdraw() +
draw_plot(fig1) +
draw_plot(fig2, x = 0.8, y = 0, width = 0.13, height = 0.39)
fig
??ggdraw
library(cowplot)
fig = ggdraw() +
draw_plot(fig1) +
draw_plot(fig2, x = 0.8, y = 0, width = 0.13, height = 0.39)
fig
colour <- read.csv("colour.csv")
head(colour)
colour$QUHUADAIMA <- as.character(colour$QUHUADAIMA)
CHINA <- dplyr::left_join(China,colour,by= "QUHUADAIMA")
fig1 <-  ggplot() +
geom_sf(
data = CHINA,
aes(fill = factor(colour))) +
## 填色
scale_fill_manual(
"class",
values = c("#9CEED3", "#79CBC2", "#5EA9AC", "#4B8793", "#3C6777"),
breaks = c("0~200", "200~400", "400~600", "600~1000", "1000+"),
labels = c("0~200", "200~400", "400~600", "600~1000", "1000+")
) +
geom_sf(data = gjx) +
geom_text(
data = province,
aes(x = dili_Jd, y = dili_Wd, label = 省市),
position = "identity",
size = 3,
check_overlap = TRUE
) +
labs(title = "中国地图") +
theme(
plot.title = element_text(
color = "black",
size = 16,
face = "bold",
vjust = 0.1,
hjust = 0.5
),
legend.title = element_blank(),
legend.position = c(0.2, 0.2),
panel.grid = element_blank(),
panel.background = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank()
) +
annotation_north_arrow(
location = 'tl',
which_north = 'false',
style = north_arrow_orienteering()
)
# 读入九段线数据
nine_lines = read_sf('南海.geojson')
# 绘制九段线小图
nine_map = ggplot() +
geom_sf(data = CHINA,fill='NA', size=0.5) +
geom_sf(data = nine_lines,color='black',size=0.5)+
coord_sf(ylim = c(-4028017,-1877844),xlim = c(117131.4,2115095),crs="+proj=laea +lat_0=40 +lon_0=104")+
theme(
aspect.ratio = 1.25, #调节长宽比
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
panel.background = element_blank(),
panel.border = element_rect(fill=NA,color="grey10",linetype=1,size=0.5),
plot.margin=unit(c(0,0,0,0),"mm"))
# 使用cowplot包将大图小图拼在一起
fig = ggdraw() +
draw_plot(fig1) +
draw_plot(nine_map, x = 0.8, y = 0, width = 0.13, height = 0.39)
fig
colour$new_colour <- rep(0,nrow(colour))
##给目标省份赋予不同的数值
colour$new_colour[which(colour$shengfen=="重庆")] <- 1
colour$new_colour[which(colour$shengfen=="山西")] <- 2
colour$new_colour[which(colour$shengfen=="新疆")] <- 3
colour$new_colour[which(colour$shengfen=="内蒙古")] <- 4
CHINA <- dplyr::left_join(China,colour,by= "QUHUADAIMA")
fig1 <-  ggplot() +
geom_sf(
data = CHINA,
aes(fill = factor(new_colour))) +
scale_fill_manual(
"class",
values = c("#FFFFFF", "#79CBC2", "#5EA9AC", "#4B8793", "#3C6777"),
breaks = c("0", "1", "2", "3", "4"),
labels = c("0", "1", "2", "3", "4")
)+
geom_sf(data = gjx) +
geom_text(
data = province,
aes(x = dili_Jd, y = dili_Wd, label = 省市),
position = "identity",
size = 3,
check_overlap = TRUE
) +
labs(title = "中国地图") +
theme(
plot.title = element_text(
color = "black",
size = 16,
face = "bold",
vjust = 0.1,
hjust = 0.5
),
legend.title = element_blank(),
legend.position = c(0.2, 0.2),
panel.grid = element_blank(),
panel.background = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank()
) +
annotation_north_arrow(
location = 'tl',
which_north = 'false',
style = north_arrow_orienteering()
)
fig1
fig = ggdraw() +
draw_plot(fig1) +
draw_plot(nine_map, x = 0.8, y = 0, width = 0.13, height = 0.39)
fig


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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm0.cpg
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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm0.csv
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"OBJECTID","ID_0","ISO","NAME_0","OBJECTID_1","ISO3","NAME_ENGLISH","NAME_ISO","NAME_FAO","NAME_LOCAL","NAME_OBSOLETE","NAME_VARIANTS","NAME_NONLATIN","NAME_FRENCH","NAME_SPANISH","NAME_RUSSIAN","NAME_ARABIC","NAME_CHINESE","WASPARTOF","CONTAINS","SOVEREIGN","ISO2","WWW","FIPS","ISON","VALIDFR","VALIDTO","POP2000","SQKM","POPSQKM","UNREGION1","UNREGION2","DEVELOPING","CIS","Transition","OECD","WBREGION","WBINCOME","WBDEBT","WBOTHER","CEEAC","CEMAC","CEPLG","COMESA","EAC","ECOWAS","IGAD","IOC","MRU","SACU","UEMOA","UMA","PALOP","PARTA","CACM","EurAsEC","Agadir","SAARC","ASEAN","NAFTA","GCC","CSN","CARICOM","EU","CAN","ACP","Landlocked","AOSIS","SIDS","Islands","LDC"
1,49,"CHN","China",45,"CHN","China","CHINA","China","Zhong Guo",NA,NA,"<U+4E2D><U+534E><U+4EBA><U+6C11><U+5171><U+548C><U+56FD>|<U+4E2D><U+83EF><U+4EBA><U+6C11><U+5171><U+548C><U+570B>","Chine ","China ","<U+041A><U+0438><U+0442><U+0430><U+0439>","<U+062C><U+0645><U+0647><U+0648><U+0631><U+064A><U+0629> <U+0627><U+0644><U+0635><U+064A><U+0646> <U+0627><U+0644><U+0634><U+0639><U+0628><U+064A><U+0629>","<U+4E2D><U+56FD>, <U+4E2D><U+534E> ",NA,"Tibet","China","CN",NA,"CH",156,"Unknown","Present",1275132866,9338902,136.539912936232,"Eastern Asia","Asia",1,NA,NA,NA,"East Asia & Pacific","Lower middle income","Less indebted",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA


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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm0.prj
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GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]

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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm0.shp
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[File too large to display: 11.3 MB]

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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm1.cpg
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UTF-8

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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm1.csv
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"OBJECTID","ID_0","ISO","NAME_0","ID_1","NAME_1","TYPE_1","ENGTYPE_1","NL_NAME_1","VARNAME_1"
1,49,"CHN","China",1,"Anhui","Sheng","Province","<U+5B89><U+5FBD>|<U+5B89><U+5FBD>","Anhui"
2,49,"CHN","China",2,"Beijing","Zhíxiáshì","Municipality","<U+5317><U+4EAC>|<U+5317><U+4EAC>","Beijing"
3,49,"CHN","China",3,"Chongqing","Zhíxiáshì","Municipality","<U+91CD><U+6176>|<U+91CD><U+5E86>","Chóngqìng"
4,49,"CHN","China",4,"Fujian","Sheng","Province","<U+798F><U+5EFA>","Fújiàn"
5,49,"CHN","China",5,"Gansu","Sheng","Province","<U+7518><U+8085>|<U+7518><U+8083>","Gansù"
6,49,"CHN","China",6,"Guangdong","Sheng","Province","<U+5EE3><U+6771>|<U+5E7F><U+4E1C>","Guangdong"
7,49,"CHN","China",7,"Guangxi","Zìzhìqu","Autonomous Region","<U+5EE3><U+897F><U+58EF><U+65CF><U+81EA><U+6CBB><U+5340>|<U+5E7F><U+897F><U+58EE><U+65CF><U+81EA><U+6CBB><U+533A>","Guangxi Zhuàngzú"
8,49,"CHN","China",8,"Guizhou","Sheng","Province","<U+8CB4><U+5DDE>|<U+8D35><U+5DDE>","Gùizhou"
9,49,"CHN","China",9,"Hainan","Sheng","Province","<U+6D77><U+5357>","Hainán"
10,49,"CHN","China",10,"Hebei","Sheng","Province","<U+6CB3><U+5317>","Hébei"
11,49,"CHN","China",11,"Heilongjiang","Sheng","Province","<U+9ED1><U+9F99><U+6C5F><U+7701>|<U+9ED1><U+9F8D><U+6C5F><U+7701>","Heilóngjiang"
12,49,"CHN","China",12,"Henan","Sheng","Province","<U+6CB3><U+5357>","Hénán"
13,49,"CHN","China",13,"Hubei","Sheng","Province","<U+6E56><U+5317>","Húbei"
14,49,"CHN","China",14,"Hunan","Sheng","Province","<U+6E56><U+5357>","Húnán"
15,49,"CHN","China",15,"Jiangsu","Sheng","Province","<U+6C5F><U+8607>|<U+6C5F><U+82CF>","Jiangsu"
16,49,"CHN","China",16,"Jiangxi","Sheng","Province","<U+6C5F><U+897F>","Jiangxi"
17,49,"CHN","China",17,"Jilin","Sheng","Province","<U+5409><U+6797>","Jílín"
18,49,"CHN","China",18,"Liaoning","Sheng","Province","<U+907C><U+5BE7>|<U+8FBD><U+5B81>","Liáoníng"
19,49,"CHN","China",19,"Nei Mongol","Zìzhìqu","Autonomous Region","<U+5167><U+8499><U+53E4><U+81EA><U+6CBB><U+5340>|<U+5185><U+8499><U+53E4><U+81EA><U+6CBB><U+533A>","Inner Mongol|Nèimenggu"
20,49,"CHN","China",20,"Ningxia Hui","Zìzhìqu","Autonomous Region","<U+5BE7><U+590F><U+56DE><U+65CF><U+81EA><U+6CBB><U+5340>|<U+5B81><U+590F><U+56DE><U+65CF><U+81EA><U+6CBB><U+533A>","Níngxià Húizú"
21,49,"CHN","China",21,"Qinghai","Sheng","Province","<U+9752><U+6D77>","Qinghai"
22,49,"CHN","China",22,"Shaanxi","Sheng","Province","<U+965D><U+897F>|<U+9655><U+897F>","Shanxi"
23,49,"CHN","China",23,"Shandong","Sheng","Province","<U+5C71><U+6771>|<U+5C71><U+4E1C>","Shandong"
24,49,"CHN","China",24,"Shanghai","Zhíxiáshì","Municipality","<U+4E0A><U+6D77>|<U+4E0A><U+6D77>","Shànghai"
25,49,"CHN","China",25,"Shanxi","Sheng","Province","<U+5C71><U+897F>","Shanxi"
26,49,"CHN","China",26,"Sichuan","Sheng","Province","<U+56DB><U+5DDD>","Sìchuan"
27,49,"CHN","China",27,"Tianjin","Zhíxiáshì","Municipality","<U+5929><U+6D25>|<U+5929><U+6D25>","Tianjin"
28,49,"CHN","China",28,"Xinjiang Uygur","Zìzhìqu","Autonomous Region","<U+65B0><U+7586><U+7DAD><U+543E><U+723E><U+81EA><U+6CBB><U+5340>|<U+65B0><U+7586><U+7EF4><U+543E><U+5C14><U+81EA><U+6CBB><U+533A>","Xinjiang Wéiwúer"
29,49,"CHN","China",29,"Xizang","Zìzhìqu","Autonomous Region","<U+897F><U+85CF><U+81EA><U+6CBB><U+5340>|<U+897F><U+85CF><U+81EA><U+6CBB><U+533A>","Tibet|Xizàng"
30,49,"CHN","China",30,"Yunnan","Sheng","Province","<U+96F2><U+5357>|<U+4E91><U+5357>","Yúnnán"
31,49,"CHN","China",31,"Zhejiang","Sheng","Province","<U+6D59><U+6C5F>","Zhèjiang"


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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm1.prj
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GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]

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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm1.shp
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[File too large to display: 13.8 MB]

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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm2.cpg
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UTF-8

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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm2.csv
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"OBJECTID","ID_0","ISO","NAME_0","ID_1","NAME_1","ID_2","NAME_2","TYPE_2","ENGTYPE_2","NL_NAME_2","VARNAME_2"
1,49,"CHN","China",1,"Anhui",1,"Anqing","Dìjíshì","Prefecture City","<U+5B89><U+5E86><U+5E02>","Anqìng"
2,49,"CHN","China",1,"Anhui",2,"Bengbu","Dìjíshì","Prefecture City","<U+868C><U+57E0><U+5E02>","Bèngbù"
3,49,"CHN","China",1,"Anhui",3,"Bozhou","Dìjíshì","Prefecture City","<U+4EB3><U+5DDE><U+5E02>","Bózhou"
4,49,"CHN","China",1,"Anhui",4,"Chaohu","Dìjíshì","Prefecture City","<U+5DE2><U+6E56><U+5E02>","Cháohú"
5,49,"CHN","China",1,"Anhui",5,"Chizhou","Dìjíshì","Prefecture City","<U+6C60><U+5DDE><U+5E02>","Chízhou"
6,49,"CHN","China",1,"Anhui",6,"Chuzhou","Dìjíshì","Prefecture City","<U+6EC1><U+5DDE><U+5E02>","Chúzhou"
7,49,"CHN","China",1,"Anhui",7,"Fuyang","Dìjíshì","Prefecture City","<U+961C><U+9633><U+5E02>","Fùyáng"
8,49,"CHN","China",1,"Anhui",8,"Hefei","Dìjíshì","Prefecture City","<U+5408><U+80A5><U+5E02>","Héféi"
9,49,"CHN","China",1,"Anhui",9,"Huaibei","Dìjíshì","Prefecture City","<U+6DEE><U+5317><U+5E02>","Huáibei"
10,49,"CHN","China",1,"Anhui",10,"Huainan","Dìjíshì","Prefecture City","<U+6DEE><U+5357><U+5E02>","Huáinán"
11,49,"CHN","China",1,"Anhui",11,"Huangshan","Dìjíshì","Prefecture City","<U+9EC4><U+5C71><U+5E02>","Huángshan"
12,49,"CHN","China",1,"Anhui",12,"Lu'an","Dìjíshì","Prefecture City","<U+516D><U+5B89><U+5E02>","Lù'an"
13,49,"CHN","China",1,"Anhui",13,"Ma'anshan","Dìjíshì","Prefecture City","<U+9A6C><U+978D><U+5C71><U+5E02>","Ma'anshan"
14,49,"CHN","China",1,"Anhui",14,"Suzhou","Dìjíshì","Prefecture City","<U+5BBF><U+5DDE><U+5E02>","Sùzhou"
15,49,"CHN","China",1,"Anhui",15,"Tongling","Dìjíshì","Prefecture City","<U+94DC><U+9675><U+5E02>","Tónglíng"
16,49,"CHN","China",1,"Anhui",16,"Wuhu","Dìjíshì","Prefecture City","<U+829C><U+6E56><U+5E02>","Wúhú"
17,49,"CHN","China",1,"Anhui",17,"Xuancheng","Dìjíshì","Prefecture City","<U+5BA3><U+57CE><U+5E02>","Xuanchéng"
18,49,"CHN","China",2,"Beijing",18,"Beijing","Zhíxiáshì","Municipality","<U+5317><U+4EAC>|<U+5317><U+4EAC>","Beijing"
19,49,"CHN","China",3,"Chongqing",19,"Chongqing","Zhíxiáshì","Municipality","<U+91CD><U+6176>|<U+91CD><U+5E86>","Chóngqìng"
20,49,"CHN","China",4,"Fujian",20,"Fuzhou","Dìjíshì","Prefecture City","<U+798F><U+5DDE><U+5E02>","Fúzhou"
21,49,"CHN","China",4,"Fujian",21,"Longyan","Dìjíshì","Prefecture City","<U+9F99><U+5CA9><U+5E02>","Lóngyán"
22,49,"CHN","China",4,"Fujian",22,"Nanping","Dìjíshì","Prefecture City","<U+5357><U+5E73><U+5E02>","Nánpíng"
23,49,"CHN","China",4,"Fujian",23,"Ningde","Dìjíshì","Prefecture City","<U+5B81><U+5FB7><U+5E02>","Níngdé"
24,49,"CHN","China",4,"Fujian",24,"Putian","Dìjíshì","Prefecture City","<U+8386><U+7530><U+5E02>","Pútián"
25,49,"CHN","China",4,"Fujian",25,"Quanzhou","Dìjíshì","Prefecture City","<U+6CC9><U+5DDE><U+5E02>","Quánzhou"
26,49,"CHN","China",4,"Fujian",26,"Sanming","Dìjíshì","Prefecture City","<U+4E09><U+660E><U+5E02>","Sanmíng"
27,49,"CHN","China",4,"Fujian",27,"Xiamen","Dìjíshì","Prefecture City","<U+53A6><U+95E8><U+5E02>","Xiàmén"
28,49,"CHN","China",4,"Fujian",28,"Zhangzhou","Dìjíshì","Prefecture City","<U+6F33><U+5DDE><U+5E02>","Zhangzhou"
29,49,"CHN","China",5,"Gansu",29,"Baiyin","Dìjíshì","Prefecture City","<U+767D><U+94F6><U+5E02>","Báiyín"
30,49,"CHN","China",5,"Gansu",30,"Dingxi","Dìjíshì","Prefecture City","<U+5B9A><U+897F><U+5E02>","Dìngxi"
31,49,"CHN","China",5,"Gansu",31,"Gannan Tibetan","Zìzhìzhou","Autonomous Prefecture","<U+7518><U+5357><U+85CF><U+65CF><U+81EA><U+6CBB><U+5DDE>","Gannán Zàngzú"
32,49,"CHN","China",5,"Gansu",32,"Jiayuguan","Dìjíshì","Prefecture City","<U+5609><U+5CEA><U+5173><U+5E02>","Jiayùguan"
33,49,"CHN","China",5,"Gansu",33,"Jinchang","Dìjíshì","Prefecture City","<U+91D1><U+660C><U+5E02>","Jinchang"
34,49,"CHN","China",5,"Gansu",34,"Jiuquan","Dìjíshì","Prefecture City","<U+9152><U+6CC9><U+5E02>","Jiuquán"
35,49,"CHN","China",5,"Gansu",35,"Lanzhou","Dìjíshì","Prefecture City","<U+5170><U+5DDE><U+5E02>","Lánzhou"
36,49,"CHN","China",5,"Gansu",36,"Linxia Hui","Zìzhìzhou","Autonomous Prefecture","<U+4E34><U+590F><U+56DE><U+65CF><U+81EA><U+6CBB><U+5DDE>","Línxià Huizù"
37,49,"CHN","China",5,"Gansu",37,"Longnan","Dìjíshì","Prefecture City","<U+9647><U+5357><U+5E02>","Longnán"
38,49,"CHN","China",5,"Gansu",38,"Pingliang","Dìjíshì","Prefecture City","<U+5E73><U+51C9><U+5E02>","Píngliáng"
39,49,"CHN","China",5,"Gansu",39,"Qingyang","Dìjíshì","Prefecture City","<U+5E86><U+9633><U+5E02>","Qìngyáng"
40,49,"CHN","China",5,"Gansu",40,"Tianshui","Dìjíshì","Prefecture City","<U+5929><U+6C34><U+5E02>","Tianshui"
41,49,"CHN","China",5,"Gansu",41,"Wuwei","Dìjíshì","Prefecture City","<U+6B66><U+5A01><U+5E02>","Wuwei"
42,49,"CHN","China",5,"Gansu",42,"Zhangye","Dìjíshì","Prefecture City","<U+5F20><U+6396><U+5E02>","Zhangyè"
43,49,"CHN","China",6,"Guangdong",43,"Chaozhou","Dìjíshì","Prefecture City","<U+6F6E><U+5DDE><U+5E02>","Cháozhou"
44,49,"CHN","China",6,"Guangdong",44,"Dongguan","Dìjíshì","Prefecture City","<U+4E1C><U+839E><U+5E02>","Dongguan"
45,49,"CHN","China",6,"Guangdong",45,"Foshan","Dìjíshì","Prefecture City","<U+4F5B><U+5C71><U+5E02>","Fóshan"
46,49,"CHN","China",6,"Guangdong",46,"Guangzhou","Dìjíshì","Prefecture City","<U+5E7F><U+5DDE><U+5E02>","Guangzhou"
47,49,"CHN","China",6,"Guangdong",47,"Heyuan","Dìjíshì","Prefecture City","<U+6CB3><U+6E90><U+5E02>","Héyuán"
48,49,"CHN","China",6,"Guangdong",48,"Huizhou","Dìjíshì","Prefecture City","<U+60E0><U+5DDE><U+5E02>","Huìzhou"
49,49,"CHN","China",6,"Guangdong",49,"Jiangmen","Dìjíshì","Prefecture City","<U+6C5F><U+95E8><U+5E02>","Jiangmén"
50,49,"CHN","China",6,"Guangdong",50,"Jieyang","Dìjíshì","Prefecture City","<U+63ED><U+9633><U+5E02>","Jieyáng"
51,49,"CHN","China",6,"Guangdong",51,"Maoming","Dìjíshì","Prefecture City","<U+8302><U+540D><U+5E02>","Màomíng"
52,49,"CHN","China",6,"Guangdong",52,"Meizhou","Dìjíshì","Prefecture City","<U+6885><U+5DDE><U+5E02>","Méizhou"
53,49,"CHN","China",6,"Guangdong",53,"Qingyuan","Dìjíshì","Prefecture City","<U+6E05><U+8FDC><U+5E02>","Qingyuan"
54,49,"CHN","China",6,"Guangdong",54,"Shantou","Dìjíshì","Prefecture City","<U+6C55><U+5934><U+5E02>","Shàntóu"
55,49,"CHN","China",6,"Guangdong",55,"Shanwei","Dìjíshì","Prefecture City","<U+6C55><U+5C3E><U+5E02>","Shànwei"
56,49,"CHN","China",6,"Guangdong",56,"Shaoguan","Dìjíshì","Prefecture City","<U+97F6><U+5173><U+5E02>","Sháoguan"
57,49,"CHN","China",6,"Guangdong",57,"Shenzhen","Dìjíshì","Prefecture City","<U+6DF1><U+5733><U+5E02>","Shenzhèn"
58,49,"CHN","China",6,"Guangdong",58,"Yangjiang","Dìjíshì","Prefecture City","<U+9633><U+6C5F><U+5E02>","Yángjiang"
59,49,"CHN","China",6,"Guangdong",59,"Yunfu","Dìjíshì","Prefecture City","<U+4E91><U+6D6E><U+5E02>","Yúnfú"
60,49,"CHN","China",6,"Guangdong",60,"Zhanjiang","Dìjíshì","Prefecture City","<U+6E5B><U+6C5F><U+5E02>","Zhànjiang"
61,49,"CHN","China",6,"Guangdong",61,"Zhaoqing","Dìjíshì","Prefecture City","<U+8087><U+5E86><U+5E02>","Zhàoqìng"
62,49,"CHN","China",6,"Guangdong",62,"Zhongshan","Dìjíshì","Prefecture City","<U+4E2D><U+5C71><U+5E02>","Zhongshan"
63,49,"CHN","China",6,"Guangdong",63,"Zhuhai","Dìjíshì","Prefecture City","<U+73E0><U+6D77><U+5E02>","Zhuhai"
64,49,"CHN","China",7,"Guangxi",64,"Baise","Dìjíshì","Prefecture City","<U+767E><U+8272><U+5E02>","Baisè"
65,49,"CHN","China",7,"Guangxi",65,"Beihai","Dìjíshì","Prefecture City","<U+5317><U+6D77><U+5E02>","Beihai"
66,49,"CHN","China",7,"Guangxi",66,"Chongzuo","Dìjíshì","Prefecture City","<U+5D07><U+5DE6><U+5E02>","Chóngzuo"
67,49,"CHN","China",7,"Guangxi",67,"Fangchenggang","Dìjíshì","Prefecture City","<U+9632><U+57CE><U+6E2F><U+5E02>","Fángchénggang"
68,49,"CHN","China",7,"Guangxi",68,"Guigang","Dìjíshì","Prefecture City","<U+8D35><U+6E2F><U+5E02>","Guìgang"
69,49,"CHN","China",7,"Guangxi",69,"Guilin","Dìjíshì","Prefecture City","<U+6842><U+6797><U+5E02>","Guìlín"
70,49,"CHN","China",7,"Guangxi",70,"Hechi","Dìjíshì","Prefecture City","<U+6CB3><U+6C60><U+5E02>","Héchí"
71,49,"CHN","China",7,"Guangxi",71,"Hezhou","Dìjíshì","Prefecture City","<U+8D3A><U+5DDE><U+5E02>","Hèzhou"
72,49,"CHN","China",7,"Guangxi",72,"Laibin","Dìjíshì","Prefecture City","<U+6765><U+5BBE><U+5E02>","Láibin"
73,49,"CHN","China",7,"Guangxi",73,"Liuzhou","Dìjíshì","Prefecture City","<U+67F3><U+5DDE><U+5E02>","Liuzhou"
74,49,"CHN","China",7,"Guangxi",74,"Nanning","Dìjíshì","Prefecture City","<U+5357><U+5B81><U+5E02>","Nánníng"
75,49,"CHN","China",7,"Guangxi",75,"Qinzhou","Dìjíshì","Prefecture City","<U+94A6><U+5DDE><U+5E02>","Qinzhou"
76,49,"CHN","China",7,"Guangxi",76,"Wuzhou","Dìjíshì","Prefecture City","<U+68A7><U+5DDE><U+5E02>","Wúzhou"
77,49,"CHN","China",7,"Guangxi",77,"Yulin","Dìjíshì","Prefecture City","<U+7389><U+6797><U+5E02>","Yùlín"
78,49,"CHN","China",8,"Guizhou",78,"Anshun","Dìjíshì","Prefecture City","<U+5B89><U+987A><U+5E02>","Anshùn"
79,49,"CHN","China",8,"Guizhou",79,"Bijie","Dìqu","Prefecture","<U+6BD5><U+8282><U+5730><U+533A>","Bìjié"
80,49,"CHN","China",8,"Guizhou",80,"Guiyang","Dìjíshì","Prefecture City","<U+8D35><U+9633><U+5E02>","Guìyáng"
81,49,"CHN","China",8,"Guizhou",81,"Liupanshui","Dìjíshì","Prefecture City","<U+516D><U+76D8><U+6C34><U+5E02>","Liùpánshui"
82,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong","Zìzhìzhou","Autonomous Prefecture","<U+9ED4><U+4E1C><U+5357><U+82D7><U+65CF><U+4F97><U+65CF><U+81EA><U+6CBB><U+5DDE>","Qiándongnán Miáozú|Dòngzú"
83,49,"CHN","China",8,"Guizhou",83,"Qiannan Buyei and Miao","Zìzhìzhou","Autonomous Prefecture","<U+9ED4><U+5357><U+5E03><U+4F9D><U+65CF><U+82D7><U+65CF><U+81EA><U+6CBB><U+5DDE>","Qiánnán Bùyizú|Miáozú"
84,49,"CHN","China",8,"Guizhou",84,"Qianxinan Buyei and Miao","Zìzhìzhou","Autonomous Prefecture","<U+9ED4><U+897F><U+5357><U+5E03><U+4F9D><U+65CF><U+82D7><U+65CF><U+81EA><U+6CBB><U+5DDE>","Qiánxinán Bùyizú|Miáozú"
85,49,"CHN","China",8,"Guizhou",85,"Tongren","Dìqu","Prefecture","<U+94DC><U+4EC1><U+5730><U+533A>","Tóngrén"
86,49,"CHN","China",8,"Guizhou",86,"Zunyi","Dìjíshì","Prefecture City","<U+9075><U+4E49><U+5E02>","Zunyì"
87,49,"CHN","China",9,"Hainan",87,"Haikou","Dìjíshì","Prefecture City","<U+6D77><U+53E3><U+5E02>","Haikou"
88,49,"CHN","China",9,"Hainan",88,"Hainan","Dìqu","Prefecture","<U+6D77><U+5357>","Hainán"
89,49,"CHN","China",9,"Hainan",89,"Sanya","Dìjíshì","Prefecture City","<U+4E09><U+4E9A><U+5E02>","Sanyà"
90,49,"CHN","China",10,"Hebei",90,"Baoding","Dìjíshì","Prefecture City","<U+4FDD><U+5B9A><U+5E02>","Baodìng"
91,49,"CHN","China",10,"Hebei",91,"Cangzhou","Dìjíshì","Prefecture City","<U+6CA7><U+5DDE><U+5E02>","Cangzhou"
92,49,"CHN","China",10,"Hebei",92,"Chengde","Dìjíshì","Prefecture City","<U+627F><U+5FB7><U+5E02>","Chéngdé"
93,49,"CHN","China",10,"Hebei",93,"Handan","Dìjíshì","Prefecture City","<U+90AF><U+90F8><U+5E02>","Hándan"
94,49,"CHN","China",10,"Hebei",94,"Hengshui","Dìjíshì","Prefecture City","<U+8861><U+6C34><U+5E02>","Héngshui"
95,49,"CHN","China",10,"Hebei",95,"Langfang","Dìjíshì","Prefecture City","<U+5ECA><U+574A><U+5E02>","Lángfáng"
96,49,"CHN","China",10,"Hebei",96,"Qinhuangdao","Dìjíshì","Prefecture City","<U+79E6><U+7687><U+5C9B><U+5E02>","Qínhuángdao"
97,49,"CHN","China",10,"Hebei",97,"Shijiazhuang","Dìjíshì","Prefecture City","<U+77F3><U+5BB6><U+5E84><U+5E02>","Shíjiazhuang"
98,49,"CHN","China",10,"Hebei",98,"Tangshan","Dìjíshì","Prefecture City","<U+5510><U+5C71><U+5E02>","Tángshan"
99,49,"CHN","China",10,"Hebei",99,"Xingtai","Dìjíshì","Prefecture City","<U+90A2><U+53F0><U+5E02>","Xíngtái"
100,49,"CHN","China",10,"Hebei",100,"Zhangjiakou","Dìjíshì","Prefecture City","<U+5F20><U+5BB6><U+53E3><U+5E02>","Zhangjiakou"
101,49,"CHN","China",11,"Heilongjiang",101,"Daqing","Dìjíshì","Prefecture City","<U+5927><U+5E86><U+5E02>","Dàqìng"
102,49,"CHN","China",11,"Heilongjiang",102,"Daxing'anling","Dìqu","Prefecture","<U+5927><U+5174><U+5B89><U+5CAD><U+5730><U+533A>","Dàxing'anling"
103,49,"CHN","China",11,"Heilongjiang",103,"Harbin","Dìjíshì","Prefecture City","<U+54C8><U+5C14><U+6EE8><U+5E02>","Ha'erbin"
104,49,"CHN","China",11,"Heilongjiang",104,"Hegang","Dìjíshì","Prefecture City","<U+9E64><U+5C97><U+5E02>","Hègang"
105,49,"CHN","China",11,"Heilongjiang",105,"Heihe","Dìjíshì","Prefecture City","<U+9ED1><U+6CB3><U+5E02>","Heihé"
106,49,"CHN","China",11,"Heilongjiang",106,"Jiamusi","Dìjíshì","Prefecture City","<U+4F73><U+6728><U+65AF><U+5E02>","Jiamùsi"
107,49,"CHN","China",11,"Heilongjiang",107,"Jixi","Dìjíshì","Prefecture City","<U+9E21><U+897F><U+5E02>","Jixi"
108,49,"CHN","China",11,"Heilongjiang",108,"Mudanjiang","Dìjíshì","Prefecture City","<U+7261><U+4E39><U+6C5F><U+5E02>","Mudanjiang"
109,49,"CHN","China",11,"Heilongjiang",109,"Qiqihar","Dìjíshì","Prefecture City","<U+9F50><U+9F50><U+54C8><U+5C14><U+5E02>","Qíqíha'er"
110,49,"CHN","China",11,"Heilongjiang",110,"Qitaihe","Dìjíshì","Prefecture City","<U+4E03><U+53F0><U+6CB3><U+5E02>","Qitáihé"
111,49,"CHN","China",11,"Heilongjiang",111,"Shuangyashan","Dìjíshì","Prefecture City","<U+53CC><U+9E2D><U+5C71><U+5E02>","Shuangyashan"
112,49,"CHN","China",11,"Heilongjiang",112,"Suihua","Dìjíshì","Prefecture City","<U+7EE5><U+5316><U+5E02>","Suíhuà"
113,49,"CHN","China",11,"Heilongjiang",113,"Yichun","Dìjíshì","Prefecture City","<U+4F0A><U+6625><U+5E02>","Yichun"
114,49,"CHN","China",12,"Henan",114,"Anyang","Dìjíshì","Prefecture City","<U+5B89><U+9633><U+5E02>","Anyáng"
115,49,"CHN","China",12,"Henan",115,"Hebi","Dìjíshì","Prefecture City","<U+9E64><U+58C1><U+5E02>","Hèbì"
116,49,"CHN","China",12,"Henan",116,"Jiaozuo","Dìjíshì","Prefecture City","<U+7126><U+4F5C><U+5E02>","Jiaozuò"
117,49,"CHN","China",12,"Henan",117,"Jiyuan shi","Dìjíshì","Prefecture City","<U+6D4E><U+6E90><U+5E02>","Jiyuán"
118,49,"CHN","China",12,"Henan",118,"Kaifeng","Dìjíshì","Prefecture City","<U+5F00><U+5C01><U+5E02>","Kaifeng"
119,49,"CHN","China",12,"Henan",119,"Luohe","Dìjíshì","Prefecture City","<U+6F2F><U+6CB3><U+5E02>","Luòhé"
120,49,"CHN","China",12,"Henan",120,"Luoyang","Dìjíshì","Prefecture City","<U+6D1B><U+9633><U+5E02>","Luòyáng"
121,49,"CHN","China",12,"Henan",121,"Nanyang","Dìjíshì","Prefecture City","<U+5357><U+9633><U+5E02>","Nányáng"
122,49,"CHN","China",12,"Henan",122,"Pingdingshan","Dìjíshì","Prefecture City","<U+5E73><U+9876><U+5C71><U+5E02>","Píngdingshan"
123,49,"CHN","China",12,"Henan",123,"Puyang","Dìjíshì","Prefecture City","<U+6FEE><U+9633><U+5E02>","Púyáng"
124,49,"CHN","China",12,"Henan",124,"Sanmenxia","Dìjíshì","Prefecture City","<U+4E09><U+95E8><U+5CE1><U+5E02>","Sanménxiá"
125,49,"CHN","China",12,"Henan",125,"Shangqiu","Dìqu","Prefecture","<U+5546><U+4E18><U+5E02>","Shangqiu Xiàn"
126,49,"CHN","China",12,"Henan",126,"Xinxiang","Dìjíshì","Prefecture City","<U+65B0><U+4E61><U+5E02>","Xinxiang"
127,49,"CHN","China",12,"Henan",127,"Xinyang","Dìjíshì","Prefecture City","<U+4FE1><U+9633><U+5E02>","Xìnyáng"
128,49,"CHN","China",12,"Henan",128,"Xuchang","Dìjíshì","Prefecture City","<U+8BB8><U+660C><U+5E02>","Xuchang"
129,49,"CHN","China",12,"Henan",129,"Zhengzhou","Dìjíshì","Prefecture City","<U+90D1><U+5DDE><U+5E02>","Zhèngzhou"
130,49,"CHN","China",12,"Henan",130,"Zhoukou","Dìjíshì","Prefecture City","<U+5468><U+53E3><U+5E02>","Zhoukou"
131,49,"CHN","China",12,"Henan",131,"Zhumadian","Dìjíshì","Prefecture City","<U+9A7B><U+9A6C><U+5E97><U+5E02>","Zhùmadiàn"
132,49,"CHN","China",13,"Hubei",132,"Enshi Tujia and Miao","Zìzhìzhou","Autonomous Prefecture","<U+6069><U+65BD><U+571F><U+5BB6><U+65CF><U+82D7><U+65CF><U+81EA><U+6CBB><U+5DDE>","Enshi Tujiazú|Miáozú"
133,49,"CHN","China",13,"Hubei",133,"Ezhou","Dìjíshì","Prefecture City","<U+9102><U+5DDE><U+5E02>","Èzhou"
134,49,"CHN","China",13,"Hubei",134,"Huanggang","Dìjíshì","Prefecture City","<U+9EC4><U+5188><U+5E02>","Huánggang"
135,49,"CHN","China",13,"Hubei",135,"Huangshi","Dìjíshì","Prefecture City","<U+9EC4><U+77F3><U+5E02>","Huángshí"
136,49,"CHN","China",13,"Hubei",136,"Jingmen","Dìjíshì","Prefecture City","<U+8346><U+95E8><U+5E02>","Jingmén"
137,49,"CHN","China",13,"Hubei",137,"Jingzhou","Dìjíshì","Prefecture City","<U+8346><U+5DDE><U+5E02>","Jingzhou"
138,49,"CHN","China",13,"Hubei",138,"Qianjiang","Dìjíshì","Prefecture City","<U+6F5C><U+6C5F><U+5E02>","Qiánjiang"
139,49,"CHN","China",13,"Hubei",139,"Shennongjia","Dìqu","Prefecture","<U+795E><U+519C><U+67B6><U+6797><U+533A>","Shénnóngjià Línqu"
140,49,"CHN","China",13,"Hubei",140,"Shiyan","Dìjíshì","Prefecture City","<U+5341><U+5830><U+5E02>","Shíyàn"
141,49,"CHN","China",13,"Hubei",141,"Suizhou Shi","Dìjíshì","Prefecture City","<U+968F><U+5DDE><U+5E02>","Suízhou"
142,49,"CHN","China",13,"Hubei",142,"Tianmen","Dìjíshì","Prefecture City","<U+5929><U+95E8><U+5E02>","Tianmén"
143,49,"CHN","China",13,"Hubei",143,"Wuhan","Dìjíshì","Prefecture City","<U+6B66><U+6C49><U+5E02>","Wuhàn"
144,49,"CHN","China",13,"Hubei",144,"Xiangfan","Dìjíshì","Prefecture City","<U+8944><U+6A0A><U+5E02>","Xiangfán"
145,49,"CHN","China",13,"Hubei",145,"Xianning","Dìjíshì","Prefecture City","<U+54B8><U+5B81><U+5E02>","Xiánníng"
146,49,"CHN","China",13,"Hubei",146,"Xiantao","Dìjíshì","Prefecture City","<U+4ED9><U+6843><U+5E02>","Xiantáo"
147,49,"CHN","China",13,"Hubei",147,"Xiaogan","Dìjíshì","Prefecture City","<U+5B5D><U+611F><U+5E02>","Xiàogan"
148,49,"CHN","China",13,"Hubei",148,"Yichang","Dìjíshì","Prefecture City","<U+5B9C><U+660C><U+5E02>","Yíchang"
149,49,"CHN","China",14,"Hunan",149,"Changde","Dìjíshì","Prefecture City","<U+5E38><U+5FB7><U+5E02>|<U+5E38><U+5FB7><U+5E02>","Chángdé"
150,49,"CHN","China",14,"Hunan",150,"Changsha","Dìjíshì","Prefecture City","<U+957F><U+6C99><U+5E02>|<U+9577><U+6C99><U+5E02>","Chángsha"
151,49,"CHN","China",14,"Hunan",151,"Chenzhou","Dìjíshì","Prefecture City","<U+90F4><U+5DDE><U+5E02>|<U+90F4><U+5DDE><U+5E02>","Chenzhou"
152,49,"CHN","China",14,"Hunan",152,"Hengyang","Dìjíshì","Prefecture City","<U+8861><U+9633><U+5E02>|<U+8861><U+967D><U+5E02>","Héngyáng"
153,49,"CHN","China",14,"Hunan",153,"Huaihua","Dìjíshì","Prefecture City","<U+6000><U+5316><U+5E02>|<U+61F7><U+5316><U+5E02>","Huáihuà"
154,49,"CHN","China",14,"Hunan",154,"Loudi","Dìjíshì","Prefecture City","<U+5A04><U+5E95><U+5E02>|<U+5A41><U+5E95><U+5E02>","Lóudi"
155,49,"CHN","China",14,"Hunan",155,"Shaoyang","Dìjíshì","Prefecture City","<U+90B5><U+9633><U+5E02>|<U+90B5><U+967D><U+5E02>","Shàoyáng"
156,49,"CHN","China",14,"Hunan",156,"Xiangtan","Dìjíshì","Prefecture City","<U+6E58><U+6F6D><U+5E02>|<U+6E58><U+6F6D><U+5E02>","Xiangtán"
157,49,"CHN","China",14,"Hunan",157,"Xiangxi Tujia and Miao","Zìzhìzhou","Autonomous Prefecture","<U+6E58><U+897F><U+571F><U+5BB6><U+65CF><U+82D7><U+65CF><U+81EA><U+6CBB><U+5DDE>|<U+6E58><U+897F><U+571F><U+5BB6><U+65CF><U+82D7><U+65CF><U+81EA><U+6CBB><U+5DDE>","Xiangxi Tujiazú|Miáozú"
158,49,"CHN","China",14,"Hunan",158,"Yiyang","Dìjíshì","Prefecture City","<U+76CA><U+9633><U+5E02>|<U+76CA><U+967D><U+5E02>","Yìyáng"
159,49,"CHN","China",14,"Hunan",159,"Yongzhou","Dìjíshì","Prefecture City","<U+6C38><U+5DDE><U+5E02>|<U+6C38><U+5DDE><U+5E02>","Yongzhou"
160,49,"CHN","China",14,"Hunan",160,"Yueyang","Dìjíshì","Prefecture City","<U+5CB3><U+9633><U+5E02>|<U+5CB3><U+967D><U+5E02>","Yuèyáng"
161,49,"CHN","China",14,"Hunan",161,"Zhangjiajie","Dìjíshì","Prefecture City","<U+5F20><U+5BB6><U+754C><U+5E02>|<U+5F35><U+5BB6><U+754C><U+5E02>","Zhangjiajiè"
162,49,"CHN","China",14,"Hunan",162,"Zhuzhou","Dìjíshì","Prefecture City","<U+682A><U+6D32><U+5E02>|<U+682A><U+6D32><U+5E02>","Zhuzhou"
163,49,"CHN","China",15,"Jiangsu",163,"Changzhou","Dìjíshì","Prefecture City","<U+5E38><U+5DDE><U+5E02>","Chángzhou"
164,49,"CHN","China",15,"Jiangsu",164,"Huai'an","Dìjíshì","Prefecture City","<U+6DEE><U+5B89><U+5E02>","Huái'an"
165,49,"CHN","China",15,"Jiangsu",165,"Lianyungang","Dìjíshì","Prefecture City","<U+8FDE><U+4E91><U+6E2F><U+5E02>","Liányúngang"
166,49,"CHN","China",15,"Jiangsu",166,"Nanjing","Dìjíshì","Prefecture City","<U+5357><U+4EAC><U+5E02>","Nánjing"
167,49,"CHN","China",15,"Jiangsu",167,"Nantong","Dìjíshì","Prefecture City","<U+5357><U+901A><U+5E02>","Nántong"
168,49,"CHN","China",15,"Jiangsu",168,"Suqian","Dìjíshì","Prefecture City","<U+5BBF><U+8FC1><U+5E02>","Sùqian"
169,49,"CHN","China",15,"Jiangsu",169,"Suzhou","Dìjíshì","Prefecture City","<U+82CF><U+5DDE><U+5E02>","Suzhou"
170,49,"CHN","China",15,"Jiangsu",170,"Taizhou","Dìjíshì","Prefecture City","<U+6CF0><U+5DDE><U+5E02>","Tàizhou"
171,49,"CHN","China",15,"Jiangsu",171,"Wuxi","Dìjíshì","Prefecture City","<U+65E0><U+9521><U+5E02>","Wúxi"
172,49,"CHN","China",15,"Jiangsu",172,"Xuzhou","Dìjíshì","Prefecture City","<U+5F90><U+5DDE><U+5E02>","Xúzhou"
173,49,"CHN","China",15,"Jiangsu",173,"Yancheng","Dìjíshì","Prefecture City","<U+76D0><U+57CE><U+5E02>","Yánchéng"
174,49,"CHN","China",15,"Jiangsu",174,"Yangzhou","Dìjíshì","Prefecture City","<U+626C><U+5DDE><U+5E02>","Yángzhou"
175,49,"CHN","China",15,"Jiangsu",175,"Zhenjiang","Dìjíshì","Prefecture City","<U+9547><U+6C5F><U+5E02>","Zhènjiang"
176,49,"CHN","China",16,"Jiangxi",176,"Fuzhou","Dìjíshì","Prefecture City","<U+629A><U+5DDE><U+5E02>","Fuzhou"
177,49,"CHN","China",16,"Jiangxi",177,"Ganzhou","Dìjíshì","Prefecture City","<U+8D63><U+5DDE><U+5E02>","Gànzhou"
178,49,"CHN","China",16,"Jiangxi",178,"Ji'an","Dìjíshì","Prefecture City","<U+5409><U+5B89><U+5E02>","Jí'an"
179,49,"CHN","China",16,"Jiangxi",179,"Jingdezhen","Dìjíshì","Prefecture City","<U+666F><U+5FB7><U+9547><U+5E02>","Jingdézhèn"
180,49,"CHN","China",16,"Jiangxi",180,"Jiujiang","Dìjíshì","Prefecture City","<U+4E5D><U+6C5F><U+5E02>","Jiujiang"
181,49,"CHN","China",16,"Jiangxi",181,"Nanchang","Dìjíshì","Prefecture City","<U+5357><U+660C><U+5E02>","Nánchang"
182,49,"CHN","China",16,"Jiangxi",182,"Pingxiang","Dìjíshì","Prefecture City","<U+840D><U+4E61><U+5E02>","Píngxiang"
183,49,"CHN","China",16,"Jiangxi",183,"Shangrao","Dìjíshì","Prefecture City","<U+4E0A><U+9976><U+5E02>","Shàngráo"
184,49,"CHN","China",16,"Jiangxi",184,"Xinyu","Dìjíshì","Prefecture City","<U+65B0><U+4F59><U+5E02>","Xinyú"
185,49,"CHN","China",16,"Jiangxi",185,"Yichun","Dìjíshì","Prefecture City","<U+5B9C><U+6625><U+5E02>","Yíchun"
186,49,"CHN","China",16,"Jiangxi",186,"Yingtan","Dìjíshì","Prefecture City","<U+9E70><U+6F6D><U+5E02>","Yingtán"
187,49,"CHN","China",17,"Jilin",187,"Baicheng","Dìjíshì","Prefecture City","<U+767D><U+57CE><U+5E02>","Báichéng"
188,49,"CHN","China",17,"Jilin",188,"Baishan","Dìjíshì","Prefecture City","<U+767D><U+5C71><U+5E02>","Báishan"
189,49,"CHN","China",17,"Jilin",189,"Changchun","Dìjíshì","Prefecture City","<U+957F><U+6625><U+5E02>","Chángchun"
190,49,"CHN","China",17,"Jilin",190,"Jilin","Dìjíshì","Prefecture City","<U+5409><U+6797><U+5E02>","Jílín"
191,49,"CHN","China",17,"Jilin",191,"Liaoyuan","Dìjíshì","Prefecture City","<U+8FBD><U+6E90><U+5E02>","Liáoyuán"
192,49,"CHN","China",17,"Jilin",192,"Siping","Dìjíshì","Prefecture City","<U+56DB><U+5E73><U+5E02>","Sìpíng"
193,49,"CHN","China",17,"Jilin",193,"Songyuan","Dìjíshì","Prefecture City","<U+677E><U+539F><U+5E02>","Songyuán"
194,49,"CHN","China",17,"Jilin",194,"Tonghua","Dìjíshì","Prefecture City","<U+901A><U+5316><U+5E02>","Tonghuà"
195,49,"CHN","China",17,"Jilin",195,"Yanbian Korean","Zìzhìzhou","Autonomous Prefecture","<U+5EF6><U+8FB9><U+671D><U+9C9C><U+65CF><U+81EA><U+6CBB><U+5DDE>","Yánbian Cháoxianzú"
196,49,"CHN","China",18,"Liaoning",196,"Anshan","Dìjíshì","Prefecture City","<U+978D><U+5C71><U+5E02>","Anshan"
197,49,"CHN","China",18,"Liaoning",197,"Benxi","Dìjíshì","Prefecture City","<U+672C><U+6EAA><U+5E02>","Benxi"
198,49,"CHN","China",18,"Liaoning",198,"Chaoyang","Dìjíshì","Prefecture City","<U+671D><U+9633><U+5E02>","Cháoyáng"
199,49,"CHN","China",18,"Liaoning",199,"Dalian","Dìjíshì","Prefecture City","<U+5927><U+8FDE><U+5E02>","Dàlián"
200,49,"CHN","China",18,"Liaoning",200,"Dandong","Dìjíshì","Prefecture City","<U+4E39><U+4E1C><U+5E02>","Dandong"
201,49,"CHN","China",18,"Liaoning",201,"Fushun","Dìjíshì","Prefecture City","<U+629A><U+987A><U+5E02>","Fushùn"
202,49,"CHN","China",18,"Liaoning",202,"Fuxin","Dìjíshì","Prefecture City","<U+961C><U+65B0><U+5E02>","Fùxin"
203,49,"CHN","China",18,"Liaoning",203,"Huludao","Dìjíshì","Prefecture City","<U+846B><U+82A6><U+5C9B><U+5E02>","Húludao"
204,49,"CHN","China",18,"Liaoning",204,"Jinzhou","Dìjíshì","Prefecture City","<U+9526><U+5DDE><U+5E02>","Jinzhou"
205,49,"CHN","China",18,"Liaoning",205,"Liaoyang","Dìjíshì","Prefecture City","<U+8FBD><U+9633><U+5E02>","Liáoyáng"
206,49,"CHN","China",18,"Liaoning",206,"Panjin","Dìjíshì","Prefecture City","<U+76D8><U+9526><U+5E02>","Pánjin"
207,49,"CHN","China",18,"Liaoning",207,"Shenyang","Dìjíshì","Prefecture City","<U+6C88><U+9633><U+5E02>","Shenyáng"
208,49,"CHN","China",18,"Liaoning",208,"Tieling","Dìjíshì","Prefecture City","<U+94C1><U+5CAD><U+5E02>","Tieling"
209,49,"CHN","China",19,"Nei Mongol",209,"Alxa","Méng","League","<U+963F><U+62C9><U+5584><U+76DF>","Alashàn"
210,49,"CHN","China",19,"Nei Mongol",210,"Baotou","Dìjíshì","Prefecture City","<U+5305><U+5934><U+5E02>","Baotóu"
211,49,"CHN","China",19,"Nei Mongol",211,"Baynnur","Dìjíshì","Prefecture City","<U+5DF4><U+5F66><U+6DD6><U+5C14><U+5E02>","Bayànnào'er"
212,49,"CHN","China",19,"Nei Mongol",212,"Chifeng","Dìjíshì","Prefecture City","<U+8D64><U+5CF0><U+5E02>","Chìfeng"
213,49,"CHN","China",19,"Nei Mongol",213,"Hohhot","Dìjíshì","Prefecture City","<U+547C><U+548C><U+6D69><U+7279><U+5E02>","Huhéhàotè"
214,49,"CHN","China",19,"Nei Mongol",214,"Hulunbuir","Dìjíshì","Prefecture City","<U+547C><U+4F26><U+8D1D><U+5C14><U+5E02>","Hulúnbèi'er"
215,49,"CHN","China",19,"Nei Mongol",215,"Ordos","Dìjíshì","Prefecture City","<U+9102><U+5C14><U+591A><U+65AF><U+5E02>","È'erduosi"
216,49,"CHN","China",19,"Nei Mongol",216,"Tongliao","Dìjíshì","Prefecture City","<U+901A><U+8FBD><U+5E02>","Tongliáo"
217,49,"CHN","China",19,"Nei Mongol",217,"Ulaan Chab","Dìjíshì","Prefecture City","<U+4E4C><U+5170><U+5BDF><U+5E03><U+5E02>","Wulánchábù"
218,49,"CHN","China",19,"Nei Mongol",218,"Wuhai","Dìjíshì","Prefecture City","<U+4E4C><U+6D77><U+5E02>","Wuhai"
219,49,"CHN","China",19,"Nei Mongol",219,"Xilin Gol","Méng","League","<U+9521><U+6797><U+90ED><U+52D2><U+76DF>","Xilínguolè"
220,49,"CHN","China",19,"Nei Mongol",220,"Xing'an","Méng","League","<U+5174><U+5B89><U+76DF>","Xing'an"
221,49,"CHN","China",20,"Ningxia Hui",221,"Guyuan","Dìjíshì","Prefecture City","<U+56FA><U+539F><U+5E02>","Gùyuán"
222,49,"CHN","China",20,"Ningxia Hui",222,"Shizuishan","Dìjíshì","Prefecture City","<U+77F3><U+5634><U+5C71><U+5E02>","Shízuishan"
223,49,"CHN","China",20,"Ningxia Hui",223,"Wuzhong","Dìjíshì","Prefecture City","<U+5434><U+5FE0><U+5E02>","Wúzhong"
224,49,"CHN","China",20,"Ningxia Hui",224,"Yinchuan","Dìjíshì","Prefecture City","<U+94F6><U+5DDD><U+5E02>","Yínchuan"
225,49,"CHN","China",20,"Ningxia Hui",225,"Zhongwei","Dìjíshì","Prefecture City","<U+4E2D><U+536B><U+5E02>","Zhongwèi"
226,49,"CHN","China",21,"Qinghai",226,"Golog Tibetan","Zìzhìzhou","Autonomous Prefecture","<U+679C><U+6D1B><U+85CF><U+65CF><U+81EA><U+6CBB><U+5DDE>","Guoluò Zàngzú"
227,49,"CHN","China",21,"Qinghai",227,"Gyêgu Tibetan","Zìzhìzhou","Autonomous Prefecture","<U+7389><U+6811><U+85CF><U+65CF><U+81EA><U+6CBB><U+5DDE>","Yùshù Zàngzú"
228,49,"CHN","China",21,"Qinghai",228,"Haibei Tibetan","Zìzhìzhou","Autonomous Prefecture","<U+6D77><U+5317><U+85CF><U+65CF><U+81EA><U+6CBB><U+5DDE>","Haibei Zàngzú"
229,49,"CHN","China",21,"Qinghai",229,"Haidong","Dìqu","Prefecture","<U+6D77><U+4E1C><U+5730><U+533A>","Haidong"
230,49,"CHN","China",21,"Qinghai",230,"Hainan Tibetan","Zìzhìzhou","Autonomous Prefecture","<U+6D77><U+5357><U+85CF><U+65CF><U+81EA><U+6CBB><U+5DDE>","Hainán Zàngzú"
231,49,"CHN","China",21,"Qinghai",231,"Haixi Mongol and Tibetan","Zìzhìzhou","Autonomous Prefecture","<U+6D77><U+897F><U+8499><U+53E4><U+65CF><U+85CF><U+65CF><U+81EA><U+6CBB><U+5DDE>","Haixi Mengguzú Zàngzú"
232,49,"CHN","China",21,"Qinghai",232,"Huangnan Tibetan","Zìzhìzhou","Autonomous Prefecture","<U+9EC4><U+5357><U+85CF><U+65CF><U+81EA><U+6CBB><U+5DDE>","Huángnán Zàngzú"
233,49,"CHN","China",21,"Qinghai",233,"Xining","Dìjíshì","Prefecture City","<U+897F><U+5B81><U+5E02>","Xiníng"
234,49,"CHN","China",22,"Shaanxi",234,"Ankang","Dìjíshì","Prefecture City","<U+5B89><U+5EB7><U+5E02>","Ankang"
235,49,"CHN","China",22,"Shaanxi",235,"Baoji","Dìjíshì","Prefecture City","<U+5B9D><U+9E21><U+5E02>","Baoji"
236,49,"CHN","China",22,"Shaanxi",236,"Hanzhong","Dìjíshì","Prefecture City","<U+6C49><U+4E2D><U+5E02>","Hànzhong"
237,49,"CHN","China",22,"Shaanxi",237,"Shangluo","Dìjíshì","Prefecture City","<U+5546><U+6D1B><U+5E02>","Shangluò"
238,49,"CHN","China",22,"Shaanxi",238,"Tongchuan","Dìjíshì","Prefecture City","<U+94DC><U+5DDD><U+5E02>","Tóngchuan"
239,49,"CHN","China",22,"Shaanxi",239,"Weinan","Dìjíshì","Prefecture City","<U+6E2D><U+5357><U+5E02>","Wèinán"
240,49,"CHN","China",22,"Shaanxi",240,"Xi'an","Dìjíshì","Prefecture City","<U+897F><U+5B89><U+5E02>","Xi'an"
241,49,"CHN","China",22,"Shaanxi",241,"Xianyang","Dìjíshì","Prefecture City","<U+54B8><U+9633><U+5E02>","Xiányáng"
242,49,"CHN","China",22,"Shaanxi",242,"Yan'an","Dìjíshì","Prefecture City","<U+5EF6><U+5B89><U+5E02>","Yán'an"
243,49,"CHN","China",22,"Shaanxi",243,"Yulin","Dìjíshì","Prefecture City","<U+6986><U+6797><U+5E02>","Yúlín"
244,49,"CHN","China",23,"Shandong",244,"Binzhou","Dìqu","Prefecture","<U+6EE8><U+5DDE>","Binzhou"
245,49,"CHN","China",23,"Shandong",245,"Dezhou","Dìjíshì","Prefecture City","<U+5FB7><U+5DDE><U+5E02>","Dézhou"
246,49,"CHN","China",23,"Shandong",246,"Dongying","Dìjíshì","Prefecture City","<U+4E1C><U+8425><U+5E02>","Dongyíng"
247,49,"CHN","China",23,"Shandong",247,"Heze","Dìjíshì","Prefecture City","<U+83CF><U+6CFD><U+5E02>","Hézé"
248,49,"CHN","China",23,"Shandong",248,"Jinan","Dìjíshì","Prefecture City","<U+6D4E><U+5357><U+5E02>","Jinán"
249,49,"CHN","China",23,"Shandong",249,"Jining","Dìjíshì","Prefecture City","<U+6D4E><U+5B81><U+5E02>","Jiníng"
250,49,"CHN","China",23,"Shandong",250,"Laiwu","Dìjíshì","Prefecture City","<U+83B1><U+829C><U+5E02>","Láiwú"
251,49,"CHN","China",23,"Shandong",251,"Liaocheng","Dìjíshì","Prefecture City","<U+804A><U+57CE><U+5E02>","Liáochéng"
252,49,"CHN","China",23,"Shandong",252,"Linyi","Dìjíshì","Prefecture City","<U+4E34><U+6C82><U+5E02>","Línyí"
253,49,"CHN","China",23,"Shandong",253,"Qingdao","Dìjíshì","Prefecture City","<U+9752><U+5C9B><U+5E02>","Qingdao"
254,49,"CHN","China",23,"Shandong",254,"Rizhao","Dìjíshì","Prefecture City","<U+65E5><U+7167><U+5E02>","Rìzhào"
255,49,"CHN","China",23,"Shandong",255,"Tai'an","Dìjíshì","Prefecture City","<U+6CF0><U+5B89><U+5E02>","Tài'an"
256,49,"CHN","China",23,"Shandong",256,"Weifang","Dìjíshì","Prefecture City","<U+6F4D><U+574A><U+5E02>","Wéifang"
257,49,"CHN","China",23,"Shandong",257,"Weihai","Dìjíshì","Prefecture City","<U+5A01><U+6D77><U+5E02>","Weihai"
258,49,"CHN","China",23,"Shandong",258,"Yantai","Dìjíshì","Prefecture City","<U+70DF><U+53F0><U+5E02>","Yantái"
259,49,"CHN","China",23,"Shandong",259,"Zaozhuang","Dìjíshì","Prefecture City","<U+67A3><U+5E84><U+5E02>","Zaozhuang"
260,49,"CHN","China",23,"Shandong",260,"Zibo","Dìjíshì","Prefecture City","<U+6DC4><U+535A><U+5E02>","Zibó"
261,49,"CHN","China",24,"Shanghai",261,"Shanghai","Zhíxiáshì","Municipality","<U+4E0A><U+6D77>|<U+4E0A><U+6D77>","Shànghai"
262,49,"CHN","China",25,"Shanxi",262,"Changzhi","Dìjíshì","Prefecture City","<U+957F><U+6CBB><U+5E02>","Chángzhì"
263,49,"CHN","China",25,"Shanxi",263,"Datong","Dìjíshì","Prefecture City","<U+5927><U+540C><U+5E02>","Dàtóng"
264,49,"CHN","China",25,"Shanxi",264,"Jincheng","Dìjíshì","Prefecture City","<U+664B><U+57CE><U+5E02>","Jìnchéng"
265,49,"CHN","China",25,"Shanxi",265,"Jinzhong","Dìjíshì","Prefecture City","<U+664B><U+4E2D><U+5E02>","Jìnzhong"
266,49,"CHN","China",25,"Shanxi",266,"Linfen","Dìjíshì","Prefecture City","<U+4E34><U+6C7E><U+5E02>","Línfén"
267,49,"CHN","China",25,"Shanxi",267,"Luliang","Dìjíshì","Prefecture City","<U+5415><U+6881><U+5E02>","Lulíang"
268,49,"CHN","China",25,"Shanxi",268,"Shuozhou","Dìjíshì","Prefecture City","<U+6714><U+5DDE><U+5E02>","Shuòzhou"
269,49,"CHN","China",25,"Shanxi",269,"Taiyuan","Dìjíshì","Prefecture City","<U+592A><U+539F><U+5E02>","Tàiyuán"
270,49,"CHN","China",25,"Shanxi",270,"Xinzhou","Dìjíshì","Prefecture City","<U+5FFB><U+5DDE><U+5E02>","Xinzhou"
271,49,"CHN","China",25,"Shanxi",271,"Yangquan","Dìjíshì","Prefecture City","<U+9633><U+6CC9><U+5E02>","Yángquán"
272,49,"CHN","China",25,"Shanxi",272,"Yuncheng","Dìjíshì","Prefecture City","<U+8FD0><U+57CE><U+53BF>","Yùnchéng"
273,49,"CHN","China",26,"Sichuan",273,"Bazhong","Dìjíshì","Prefecture City","<U+5DF4><U+4E2D><U+5E02>","Bazhong"
274,49,"CHN","China",26,"Sichuan",274,"Chengdu","Dìjíshì","Prefecture City","<U+6210><U+90FD><U+5E02>","Chéngdu"
275,49,"CHN","China",26,"Sichuan",275,"Dazhou","Dìjíshì","Prefecture City","<U+8FBE><U+5DDE><U+5E02>","Dázhou"
276,49,"CHN","China",26,"Sichuan",276,"Deyang","Dìjíshì","Prefecture City","<U+5FB7><U+9633><U+5E02>","Déyáng"
277,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan","Zìzhìzhou","Autonomous Prefecture","<U+7518><U+5B5C><U+85CF><U+65CF><U+81EA><U+6CBB><U+5DDE>","Ganzi Zàngzú"
278,49,"CHN","China",26,"Sichuan",278,"Guang'an","Dìjíshì","Prefecture City","<U+5E7F><U+5B89><U+5E02>","Guang'an"
279,49,"CHN","China",26,"Sichuan",279,"Guangyuan","Dìjíshì","Prefecture City","<U+5E7F><U+5143><U+5E02>","Guangyuán"
280,49,"CHN","China",26,"Sichuan",280,"Leshan","Dìjíshì","Prefecture City","<U+4E50><U+5C71><U+5E02>","Lèshan"
281,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi","Zìzhìzhou","Autonomous Prefecture","<U+51C9><U+5C71><U+5F5D><U+65CF><U+81EA><U+6CBB><U+5DDE>","Liángshan Yízú"
282,49,"CHN","China",26,"Sichuan",282,"Luzhou","Dìjíshì","Prefecture City","<U+6CF8><U+5DDE><U+5E02>","Lúzhou"
283,49,"CHN","China",26,"Sichuan",283,"Meishan","Dìjíshì","Prefecture City","<U+7709><U+5C71><U+5E02>","Méishan"
284,49,"CHN","China",26,"Sichuan",284,"Mianyang","Dìjíshì","Prefecture City","<U+7EF5><U+9633><U+5E02>","Míanyáng"
285,49,"CHN","China",26,"Sichuan",285,"Nanchong","Dìjíshì","Prefecture City","<U+5357><U+5145><U+5E02>","Nánchong"
286,49,"CHN","China",26,"Sichuan",286,"Neijiang]]","Dìjíshì","Prefecture City","<U+5185><U+6C5F><U+5E02>","Nèijiang"
287,49,"CHN","China",26,"Sichuan",287,"Neijiang","Dìjíshì","Prefecture City","<U+5185><U+6C5F><U+5E02>","Nèijiang"
288,49,"CHN","China",26,"Sichuan",288,"Ngawa Tibetan and Qiang","Zìzhìzhou","Autonomous Prefecture","<U+963F><U+575D><U+85CF><U+65CF><U+7F8C><U+65CF><U+81EA><U+6CBB><U+5DDE>","Abà Zàngzú|Qiangzú"
289,49,"CHN","China",26,"Sichuan",289,"Panzhihua","Dìjíshì","Prefecture City","<U+6500><U+679D><U+82B1><U+5E02>","Panzhihua"
290,49,"CHN","China",26,"Sichuan",290,"Suining","Dìjíshì","Prefecture City","<U+9042><U+5B81><U+5E02>","Sùiníng"
291,49,"CHN","China",26,"Sichuan",291,"Ya'an","Dìjíshì","Prefecture City","<U+96C5><U+5B89><U+5E02>","Ya'an"
292,49,"CHN","China",26,"Sichuan",292,"Yibin","Dìjíshì","Prefecture City","<U+5B9C><U+5BBE><U+5E02>","Yíbin"
293,49,"CHN","China",26,"Sichuan",293,"Zigong","Dìjíshì","Prefecture City","<U+81EA><U+8D21><U+5E02>","Zìgòng"
294,49,"CHN","China",26,"Sichuan",294,"Ziyang","Dìjíshì","Prefecture City","<U+8D44><U+9633><U+5E02>","Ziyáng"
295,49,"CHN","China",27,"Tianjin",295,"Tianjin","Zhíxiáshì","Municipality","<U+5929><U+6D25>|<U+5929><U+6D25>","Tianjin"
296,49,"CHN","China",28,"Xinjiang Uygur",296,"Aksu","Dìqu","Prefecture","<U+963F><U+514B><U+82CF><U+5730><U+533A>","Akèsu"
297,49,"CHN","China",28,"Xinjiang Uygur",297,"Altay","Dìqu","Prefecture","<U+963F><U+52D2><U+6CF0><U+5730><U+533A>","Alètài"
298,49,"CHN","China",28,"Xinjiang Uygur",298,"Ürümqi","Dìjíshì","Prefecture City","<U+4E4C><U+9C81><U+6728><U+9F50><U+5E02>","Wulumùqí"
299,49,"CHN","China",28,"Xinjiang Uygur",299,"Börtala Mongol","Zìzhìzhou","Autonomous Prefecture","<U+535A><U+5C14><U+5854><U+62C9><U+8499><U+53E4><U+81EA><U+6CBB><U+5DDE>","Bó'ertala Menggu"
300,49,"CHN","China",28,"Xinjiang Uygur",300,"Bayin'gholin Mongol","Zìzhìzhou","Autonomous Prefecture","<U+5DF4><U+97F3><U+90ED><U+6123><U+8499><U+53E4><U+81EA><U+6CBB><U+5DDE>","Bayinguolèng Menggu"
301,49,"CHN","China",28,"Xinjiang Uygur",301,"Changji Hui","Zìzhìzhou","Autonomous Prefecture","<U+660C><U+5409><U+56DE><U+65CF><U+81EA><U+6CBB><U+5DDE>","Changjí Huízú"
302,49,"CHN","China",28,"Xinjiang Uygur",302,"Hami","Dìqu","Prefecture","<U+54C8><U+5BC6><U+5730><U+533A>","Hamì"
303,49,"CHN","China",28,"Xinjiang Uygur",303,"Ili Kazakh","Zìzhìzhou","Autonomous Prefecture","<U+4F0A><U+7281><U+54C8><U+8428><U+514B><U+81EA><U+6CBB><U+5DDE>","Yilí Hasàkè"
304,49,"CHN","China",28,"Xinjiang Uygur",304,"Karamay","Dìjíshì","Prefecture City","<U+514B><U+62C9><U+739B><U+4F9D><U+5E02>","Kèlamayi"
305,49,"CHN","China",28,"Xinjiang Uygur",305,"Kashgar","Dìqu","Prefecture","<U+5580><U+4EC0><U+5730><U+533A>","Kashí"
306,49,"CHN","China",28,"Xinjiang Uygur",306,"Khotan","Dìqu","Prefecture","<U+548C><U+7530><U+5730><U+533A>","Hétián"
307,49,"CHN","China",28,"Xinjiang Uygur",307,"Kizilsu Kirghiz","Zìzhìzhou","Autonomous Prefecture","<U+514B><U+5B5C><U+52D2><U+82CF><U+67EF><U+5C14><U+514B><U+5B5C><U+81EA><U+6CBB><U+5DDE>","Kèzilèsu Ke'erkèzi"
308,49,"CHN","China",28,"Xinjiang Uygur",308,"Shihezi","Dìjíshì","Prefecture City","<U+77F3><U+6CB3><U+5B50><U+5E02>","Shíhézi"
309,49,"CHN","China",28,"Xinjiang Uygur",309,"Tacheng","Dìqu","Prefecture","<U+5854><U+57CE><U+5730><U+533A>","Tachéng"
310,49,"CHN","China",28,"Xinjiang Uygur",310,"Turfan","Dìqu","Prefecture","<U+5410><U+9C81><U+756A><U+5730><U+533A>","Tulufan"
311,49,"CHN","China",29,"Xizang",311,"Chamdo","Dìqu","Prefecture","<U+660C><U+90FD><U+5730><U+533A>","Changdu"
312,49,"CHN","China",29,"Xizang",312,"Lhasa","Dìjíshì","Prefecture City","<U+62C9><U+8428><U+5E02>","Lasà"
313,49,"CHN","China",29,"Xizang",313,"Nagchu","Dìqu","Prefecture","<U+90A3><U+66F2><U+5730><U+533A>","Nàqu"
314,49,"CHN","China",29,"Xizang",314,"Ngari","Dìqu","Prefecture","<U+963F><U+91CC><U+5730><U+533A>","Ali"
315,49,"CHN","China",29,"Xizang",315,"Nyingtri","Dìqu","Prefecture","<U+6797><U+829D><U+5730><U+533A>","Línzhi"
316,49,"CHN","China",29,"Xizang",316,"Shannan","Dìqu","Prefecture","<U+5C71><U+5357><U+5730><U+533A>","Shannán"
317,49,"CHN","China",29,"Xizang",317,"Shigatse","Dìqu","Prefecture","<U+65E5><U+5580><U+5219><U+5730><U+533A>","Rìkazé"
318,49,"CHN","China",30,"Yunnan",318,"Baoshan","Dìjíshì","Prefecture City","<U+4FDD><U+5C71><U+5E02>","Baoshan"
319,49,"CHN","China",30,"Yunnan",319,"Chuxiong Yi","Zìzhìzhou","Autonomous Prefecture","<U+695A><U+96C4><U+5F5D><U+65CF><U+81EA><U+6CBB><U+5DDE>","Chuxióng Yízú"
320,49,"CHN","China",30,"Yunnan",320,"Dêqên Tibetan","Zìzhìzhou","Autonomous Prefecture","<U+8FEA><U+5E86><U+85CF><U+65CF><U+81EA><U+6CBB><U+5DDE>","Díqìng Zàngzú"
321,49,"CHN","China",30,"Yunnan",321,"Dali Bai","Zìzhìzhou","Autonomous Prefecture","<U+5927><U+7406><U+767D><U+65CF><U+81EA><U+6CBB><U+5DDE>","Dàli Báizú"
322,49,"CHN","China",30,"Yunnan",322,"Dehong Dai and Jingpo","Zìzhìzhou","Autonomous Prefecture","<U+5FB7><U+5B8F><U+50A3><U+65CF><U+666F><U+9887><U+65CF><U+81EA><U+6CBB><U+5DDE>","Déhóng Daizú|Jingpozú"
323,49,"CHN","China",30,"Yunnan",323,"Honghe Hani and Yi","Zìzhìzhou","Autonomous Prefecture","<U+7EA2><U+6CB3><U+54C8><U+5C3C><U+65CF><U+5F5D><U+65CF><U+81EA><U+6CBB><U+5DDE>","Hónghé Hanízú|Yízú"
324,49,"CHN","China",30,"Yunnan",324,"Kunming","Dìjíshì","Prefecture City","<U+6606><U+660E><U+5E02>","Kunmíng"
325,49,"CHN","China",30,"Yunnan",325,"Lijiang","Dìjíshì","Prefecture City","<U+4E3D><U+6C5F><U+5E02>","Lìjiang"
326,49,"CHN","China",30,"Yunnan",326,"Lincang","Dìjíshì","Prefecture City","<U+4E34><U+6CA7><U+5E02>","Líncang"
327,49,"CHN","China",30,"Yunnan",327,"Nujiang Lisu","Zìzhìzhou","Autonomous Prefecture","<U+6012><U+6C5F><U+5088><U+50F3><U+65CF><U+81EA><U+6CBB><U+5DDE>","Nùjiang Lìsùzú"
328,49,"CHN","China",30,"Yunnan",328,"Pu'er","Dìjíshì","Prefecture City","<U+666E><U+6D31><U+5E02>","Pu'er"
329,49,"CHN","China",30,"Yunnan",329,"Qujing","Dìjíshì","Prefecture City","<U+66F2><U+9756><U+5E02>","Qujìng"
330,49,"CHN","China",30,"Yunnan",330,"Wenshan Zhuang and Miao","Zìzhìzhou","Autonomous Prefecture","<U+6587><U+5C71><U+58EE><U+65CF><U+82D7><U+65CF><U+81EA><U+6CBB><U+5DDE>","Wénshan Zhuàngzú|Miáozú"
331,49,"CHN","China",30,"Yunnan",331,"Xishuangbanna Dai","Zìzhìzhou","Autonomous Prefecture","<U+897F><U+53CC><U+7248><U+7EB3><U+50A3><U+65CF><U+81EA><U+6CBB><U+5DDE>","Xishuangbannà|Daizú"
332,49,"CHN","China",30,"Yunnan",332,"Yuxi","Dìjíshì","Prefecture City","<U+7389><U+6EAA><U+5E02>","Yùxi"
333,49,"CHN","China",30,"Yunnan",333,"Zhaotong","Dìjíshì","Prefecture City","<U+662D><U+901A><U+5E02>","Zhaotong"
334,49,"CHN","China",31,"Zhejiang",334,"Hangzhou","Dìjíshì","Prefecture City","<U+676D><U+5DDE><U+5E02>","Hángzhou"
335,49,"CHN","China",31,"Zhejiang",335,"Huzhou","Dìjíshì","Prefecture City","<U+6E56><U+5DDE><U+5E02>","Húzhou"
336,49,"CHN","China",31,"Zhejiang",336,"Jiaxing","Dìjíshì","Prefecture City","<U+5609><U+5174><U+5E02>","Jiaxing"
337,49,"CHN","China",31,"Zhejiang",337,"Jinhua","Dìjíshì","Prefecture City","<U+91D1><U+534E><U+5E02>","Jinhuá"
338,49,"CHN","China",31,"Zhejiang",338,"Lishui","Dìjíshì","Prefecture City","<U+4E3D><U+6C34><U+5E02>","Líshui"
339,49,"CHN","China",31,"Zhejiang",339,"Ningbo","Dìjíshì","Prefecture City","<U+5B81><U+6CE2><U+5E02>","Níngbo"
340,49,"CHN","China",31,"Zhejiang",340,"Quzhou","Dìjíshì","Prefecture City","<U+8862><U+5DDE><U+5E02>","Qúzhou"
341,49,"CHN","China",31,"Zhejiang",341,"Shaoxing","Dìjíshì","Prefecture City","<U+7ECD><U+5174><U+5E02>","Shàoxing"
342,49,"CHN","China",31,"Zhejiang",342,"Taizhou","Dìjíshì","Prefecture City","<U+53F0><U+5DDE><U+5E02>","Taizhou"
343,49,"CHN","China",31,"Zhejiang",343,"Wenzhou","Dìjíshì","Prefecture City","<U+6E29><U+5DDE><U+5E02>","Wenzhou"
344,49,"CHN","China",31,"Zhejiang",344,"Zhoushan","Dìjíshì","Prefecture City","<U+821F><U+5C71><U+5E02>","Zhoushan"


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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm2.prj
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GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]

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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm2.shp
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[File too large to display: 18.2 MB]

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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm3.cpg
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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm3.csv
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"OBJECTID","ID_0","ISO","NAME_0","ID_1","NAME_1","ID_2","NAME_2","ID_3","NAME_3","TYPE_3","ENGTYPE_3","NL_NAME_3","VARNAME_3"
1,49,"CHN","China",1,"Anhui",1,"Anqing",1,"Anqing","Xiànjíshì","County City",NA,NA
2,49,"CHN","China",1,"Anhui",1,"Anqing",2,"Huaining","Xiàn","County","<U+6000><U+5B81><U+53BF>","Huáiníng"
3,49,"CHN","China",1,"Anhui",1,"Anqing",3,"Qianshan","Xiàn","County","<U+6F5C><U+5C71><U+53BF>","Qiánshan"
4,49,"CHN","China",1,"Anhui",1,"Anqing",4,"Susong","Xiàn","County","<U+5BBF><U+677E><U+53BF>","Sùsong"
5,49,"CHN","China",1,"Anhui",1,"Anqing",5,"Taihu","Xiàn","County","<U+592A><U+6E56><U+53BF>","Tàihú"
6,49,"CHN","China",1,"Anhui",1,"Anqing",6,"Tongcheng","Xiànjíshì","County City","<U+6850><U+57CE><U+5E02>","Tóngchéng"
7,49,"CHN","China",1,"Anhui",1,"Anqing",7,"Wangjiang","Xiàn","County","<U+671B><U+6C5F><U+53BF>","Wàngjiang"
8,49,"CHN","China",1,"Anhui",1,"Anqing",8,"Yuexi","Xiàn","County","<U+5CB3><U+897F><U+53BF>","Yuèxi"
9,49,"CHN","China",1,"Anhui",1,"Anqing",9,"Zongyang","Xiàn","County","<U+679E><U+9633><U+53BF>","Zongyáng"
10,49,"CHN","China",1,"Anhui",2,"Bengbu",10,"Bengbu","Xiànjíshì","County City",NA,NA
11,49,"CHN","China",1,"Anhui",2,"Bengbu",11,"Guzhen","Xiàn","County","<U+56FA><U+9547><U+53BF>","Gùzhèn"
12,49,"CHN","China",1,"Anhui",2,"Bengbu",12,"Huaiyuan","Xiàn","County","<U+6000><U+8FDC><U+53BF>","Huáiyuan"
13,49,"CHN","China",1,"Anhui",2,"Bengbu",13,"Wuhe","Xiàn","County","<U+4E94><U+6CB3><U+53BF>","Wuhé"
14,49,"CHN","China",1,"Anhui",3,"Bozhou",14,"Boxian","Xiàn","County",NA,NA
15,49,"CHN","China",1,"Anhui",3,"Bozhou",15,"Guoyang","Xiàn","County",NA,NA
16,49,"CHN","China",1,"Anhui",3,"Bozhou",16,"Lixin","Xiàn","County","<U+5229><U+8F9B><U+53BF>","Lìxin"
17,49,"CHN","China",1,"Anhui",3,"Bozhou",17,"Mengcheng","Xiàn","County","<U+8499><U+57CE><U+53BF>","Méngchéng"
18,49,"CHN","China",1,"Anhui",4,"Chaohu",18,"Chao","Xiàn","County",NA,NA
19,49,"CHN","China",1,"Anhui",4,"Chaohu",19,"Hanshan","Xiàn","County","<U+542B><U+5C71><U+53BF>","Hánshan"
20,49,"CHN","China",1,"Anhui",4,"Chaohu",20,"He","Xiàn","County","<U+548C><U+53BF>","Hé"
21,49,"CHN","China",1,"Anhui",4,"Chaohu",21,"Lujiang","Xiàn","County","<U+5E90><U+6C5F><U+53BF>","Lújiang"
22,49,"CHN","China",1,"Anhui",4,"Chaohu",22,"Wuwei","Xiàn","County","<U+65E0><U+4E3A><U+53BF>","Wúwéi"
23,49,"CHN","China",1,"Anhui",5,"Chizhou",23,"Dongzhi","Xiàn","County","<U+4E1C><U+81F3><U+53BF>","Dongzhì"
24,49,"CHN","China",1,"Anhui",5,"Chizhou",24,"Guichi","Shìxiáqu","District","<U+8D35><U+6C60><U+533A>","Guìchí"
25,49,"CHN","China",1,"Anhui",5,"Chizhou",25,"Qingyang","Xiàn","County","<U+9752><U+9633><U+53BF>","Qingyáng"
26,49,"CHN","China",1,"Anhui",5,"Chizhou",26,"Shitai","Xiàn","County","<U+77F3><U+53F0><U+53BF>","Shítái"
27,49,"CHN","China",1,"Anhui",6,"Chuzhou",27,"Chuxian","Xiàn","County",NA,NA
28,49,"CHN","China",1,"Anhui",6,"Chuzhou",28,"Dingyuan","Xiàn","County","<U+5B9A><U+8FDC><U+53BF>","Dìngyuan"
29,49,"CHN","China",1,"Anhui",6,"Chuzhou",29,"Fengyang","Xiàn","County","<U+51E4><U+9633><U+53BF>","Fèngyáng"
30,49,"CHN","China",1,"Anhui",6,"Chuzhou",30,"Jiashan","Xiàn","County",NA,NA
31,49,"CHN","China",1,"Anhui",6,"Chuzhou",31,"Lai'an","Xiàn","County","<U+6765><U+5B89><U+53BF>","Lái'an"
32,49,"CHN","China",1,"Anhui",6,"Chuzhou",32,"Quanjiao","Xiàn","County","<U+5168><U+6912><U+53BF>","Quánjiao"
33,49,"CHN","China",1,"Anhui",6,"Chuzhou",33,"Tianchang","Xiànjíshì","County City","<U+5929><U+957F><U+5E02>","Tiancháng"
34,49,"CHN","China",1,"Anhui",7,"Fuyang",34,"Funan","Xiàn","County","<U+961C><U+5357><U+53BF>","Fùnán"
35,49,"CHN","China",1,"Anhui",7,"Fuyang",35,"Fuyang","Xiànjíshì","County City","<U+961C><U+9633><U+5E02>","Fùyáng"
36,49,"CHN","China",1,"Anhui",7,"Fuyang",36,"Jieshou","Xiànjíshì","County City","<U+754C><U+9996><U+5E02>","Jièshou"
37,49,"CHN","China",1,"Anhui",7,"Fuyang",37,"Linquan","Xiàn","County","<U+4E34><U+6CC9><U+53BF>","Línquán"
38,49,"CHN","China",1,"Anhui",7,"Fuyang",38,"Taihe","Xiàn","County","<U+592A><U+548C><U+53BF>","Tàihé"
39,49,"CHN","China",1,"Anhui",7,"Fuyang",39,"Yaohai","Shìxiáqu","District","<U+7476><U+6D77><U+533A>","Yáohai"
40,49,"CHN","China",1,"Anhui",8,"Hefei",40,"Changfeng","Xiàn","County","<U+957F><U+4E30><U+53BF>","Chángfeng"
41,49,"CHN","China",1,"Anhui",8,"Hefei",41,"Feidong","Xiàn","County","<U+80A5><U+4E1C><U+53BF>","Féidong"
42,49,"CHN","China",1,"Anhui",8,"Hefei",42,"Feixi","Xiàn","County","<U+80A5><U+897F><U+53BF>","Féixi"
43,49,"CHN","China",1,"Anhui",8,"Hefei",43,"Hefei","Shìxiáqu","District",NA,NA
44,49,"CHN","China",1,"Anhui",9,"Huaibei",44,"Huaibei","Shìxiáqu","District",NA,NA
45,49,"CHN","China",1,"Anhui",9,"Huaibei",45,"Suixi","Xiàn","County","<U+6FC9><U+6EAA><U+53BF>","Suixi"
46,49,"CHN","China",1,"Anhui",10,"Huainan",46,"Fengtai","Xiàn","County","<U+51E4><U+53F0><U+53BF>","Fèngtái"
47,49,"CHN","China",1,"Anhui",10,"Huainan",47,"Huainan","Shìxiáqu","District",NA,NA
48,49,"CHN","China",1,"Anhui",11,"Huangshan",48,"Qimen","Xiàn","County","<U+7941><U+95E8><U+53BF>","Qímén"
49,49,"CHN","China",1,"Anhui",11,"Huangshan",49,"She","Xiàn","County","<U+6B59><U+53BF>","Shè"
50,49,"CHN","China",1,"Anhui",11,"Huangshan",50,"Taiping","Xiàn","County",NA,NA
51,49,"CHN","China",1,"Anhui",11,"Huangshan",51,"Tunxi","Shìxiáqu","District","<U+5C6F><U+6EAA><U+533A>","Túnxi"
52,49,"CHN","China",1,"Anhui",11,"Huangshan",52,"Xiuning","Xiàn","County","<U+4F11><U+5B81><U+53BF>","Xiuníng"
53,49,"CHN","China",1,"Anhui",11,"Huangshan",53,"Yi","Xiàn","County","<U+9EDF><U+53BF>","Yi"
54,49,"CHN","China",1,"Anhui",12,"Lu'an",54,"Huoqiu","Xiàn","County","<U+970D><U+90B1><U+53BF>","Huòqiu"
55,49,"CHN","China",1,"Anhui",12,"Lu'an",55,"Huoshan","Xiàn","County","<U+970D><U+5C71><U+53BF>","Huòshan"
56,49,"CHN","China",1,"Anhui",12,"Lu'an",56,"Jinzhai","Xiàn","County","<U+91D1><U+5BE8><U+53BF>","Jinzhài"
57,49,"CHN","China",1,"Anhui",12,"Lu'an",57,"Lu An","Xiànjíshì","County City",NA,NA
58,49,"CHN","China",1,"Anhui",12,"Lu'an",58,"Shou","Xiàn","County","<U+5BFF><U+53BF>","Shòu"
59,49,"CHN","China",1,"Anhui",12,"Lu'an",59,"Shucheng","Xiàn","County","<U+8212><U+57CE><U+53BF>","Shuchéng"
60,49,"CHN","China",1,"Anhui",13,"Ma'anshan",60,"Dangtu","Xiàn","County","<U+5F53><U+6D82><U+53BF>","Dangtú"
61,49,"CHN","China",1,"Anhui",13,"Ma'anshan",61,"Maanshan","Xiànjíshì","County City",NA,NA
62,49,"CHN","China",1,"Anhui",14,"Suzhou",62,"Dangshan","Shìxiáqu","District","<U+7800><U+5C71><U+533A>","Dàngshan"
63,49,"CHN","China",1,"Anhui",14,"Suzhou",63,"Lingbi","Xiàn","County","<U+7075><U+74A7><U+53BF>","Língbì"
64,49,"CHN","China",1,"Anhui",14,"Suzhou",64,"Si","Xiàn","County","<U+6CD7><U+53BF>","Sì"
65,49,"CHN","China",1,"Anhui",14,"Suzhou",65,"Xiao","Xiàn","County","<U+8427><U+53BF>","Xiao"
66,49,"CHN","China",1,"Anhui",14,"Suzhou",66,"Yongqiao","Shìxiáqu","District","<U+57C7><U+6865><U+533A>",NA
67,49,"CHN","China",1,"Anhui",15,"Tongling",67,"Tongling","Xiàn","County","<U+94DC><U+9675><U+53BF>","Tónglíng"
68,49,"CHN","China",1,"Anhui",16,"Wuhu",68,"Fanchang","Xiàn","County","<U+7E41><U+660C><U+53BF>","Fánchang"
69,49,"CHN","China",1,"Anhui",16,"Wuhu",69,"Nanling","Xiàn","County","<U+5357><U+9675><U+53BF>","Nánlíng"
70,49,"CHN","China",1,"Anhui",16,"Wuhu",70,"Wuhu","Xiàn","County",NA,"Wúhú"
71,49,"CHN","China",1,"Anhui",17,"Xuancheng",71,"Guangde","Xiàn","County","<U+5E7F><U+5FB7><U+53BF>","Guangdé"
72,49,"CHN","China",1,"Anhui",17,"Xuancheng",72,"Jingde","Xiàn","County","<U+65CC><U+5FB7><U+53BF>","Jingdé"
73,49,"CHN","China",1,"Anhui",17,"Xuancheng",73,"Jing","Xiàn","County","<U+6CFE><U+53BF>","Jing"
74,49,"CHN","China",1,"Anhui",17,"Xuancheng",74,"Jixi","Xiàn","County","<U+7EE9><U+6EAA><U+53BF>","Jìxi"
75,49,"CHN","China",1,"Anhui",17,"Xuancheng",75,"Langxi","Xiàn","County","<U+90CE><U+6EAA><U+53BF>","Lángxi"
76,49,"CHN","China",1,"Anhui",17,"Xuancheng",76,"Ningguo","Xiànjíshì","County City","<U+5B81><U+56FD><U+5E02>","Níngguó"
77,49,"CHN","China",1,"Anhui",17,"Xuancheng",77,"Xuanzhou","Shìxiáqu","District","<U+5BA3><U+5DDE><U+533A>","Xuanzhou"
78,49,"CHN","China",2,"Beijing",18,"Beijing",78,"Beijing","Zhíxiáshì","Municipality","<U+5317><U+4EAC>|<U+5317><U+4EAC>","Beijing"
79,49,"CHN","China",2,"Beijing",18,"Beijing",79,"Changping","Shìxiáqu","District","<U+660C><U+5E73><U+533A>","Changpíng"
80,49,"CHN","China",2,"Beijing",18,"Beijing",80,"Daxing","Shìxiáqu","District","<U+5927><U+5174><U+533A>","Dàxing"
81,49,"CHN","China",2,"Beijing",18,"Beijing",81,"Fangshan","Shìxiáqu","District","<U+623F><U+5C71><U+533A>","Fángshan"
82,49,"CHN","China",2,"Beijing",18,"Beijing",82,"Huairou","Shìxiáqu","District","<U+6000><U+67D4><U+533A>","Huáiróu"
83,49,"CHN","China",2,"Beijing",18,"Beijing",83,"Mentougou","Shìxiáqu","District","<U+95E8><U+5934><U+6C9F><U+533A>","Méntóugou"
84,49,"CHN","China",2,"Beijing",18,"Beijing",84,"Miyun","Xiàn","County","<U+5BC6><U+4E91><U+53BF>","Mìyún"
85,49,"CHN","China",2,"Beijing",18,"Beijing",85,"Pinggu","Shìxiáqu","District","<U+5E73><U+8C37><U+533A>","Pínggu"
86,49,"CHN","China",2,"Beijing",18,"Beijing",86,"Shunyi","Shìxiáqu","District","<U+987A><U+4E49><U+533A>","Shùnyì"
87,49,"CHN","China",2,"Beijing",18,"Beijing",87,"Tongzhou","Shìxiáqu","District","<U+901A><U+5DDE><U+533A>","Tongzhou"
88,49,"CHN","China",2,"Beijing",18,"Beijing",88,"Yanqing","Xiàn","County","<U+5EF6><U+5E86><U+53BF>","Yánqìng"
89,49,"CHN","China",3,"Chongqing",19,"Chongqing",89,"Ba","Xiàn","County",NA,NA
90,49,"CHN","China",3,"Chongqing",19,"Chongqing",90,"Bishan","Xiàn","County",NA,NA
91,49,"CHN","China",3,"Chongqing",19,"Chongqing",91,"Changshou","Xiàn","County",NA,NA
92,49,"CHN","China",3,"Chongqing",19,"Chongqing",92,"Chengkou","Xiàn","County",NA,NA
93,49,"CHN","China",3,"Chongqing",19,"Chongqing",93,"Chongqing","Shìxiáqu","District",NA,NA
94,49,"CHN","China",3,"Chongqing",19,"Chongqing",94,"Dazu","Xiàn","County",NA,NA
95,49,"CHN","China",3,"Chongqing",19,"Chongqing",95,"Dianjiang","Xiàn","County",NA,NA
96,49,"CHN","China",3,"Chongqing",19,"Chongqing",96,"Fengdu","Xiàn","County",NA,NA
97,49,"CHN","China",3,"Chongqing",19,"Chongqing",97,"Fengjie","Xiàn","County",NA,NA
98,49,"CHN","China",3,"Chongqing",19,"Chongqing",98,"Fuling","Xiàn","County",NA,NA
99,49,"CHN","China",3,"Chongqing",19,"Chongqing",99,"Hechuan","Xiàn","County",NA,NA
100,49,"CHN","China",3,"Chongqing",19,"Chongqing",100,"Jiangbei","Xiàn","County",NA,NA
101,49,"CHN","China",3,"Chongqing",19,"Chongqing",101,"Jiangjin","Xiàn","County",NA,NA
102,49,"CHN","China",3,"Chongqing",19,"Chongqing",102,"Kai","Xiàn","County",NA,NA
103,49,"CHN","China",3,"Chongqing",19,"Chongqing",103,"Liangping","Xiàn","County",NA,NA
104,49,"CHN","China",3,"Chongqing",19,"Chongqing",104,"Nanchuan","Xiàn","County",NA,NA
105,49,"CHN","China",3,"Chongqing",19,"Chongqing",105,"Pengshui","Xiàn","County",NA,NA
106,49,"CHN","China",3,"Chongqing",19,"Chongqing",106,"Qianjiang","Xiàn","County",NA,NA
107,49,"CHN","China",3,"Chongqing",19,"Chongqing",107,"Qijiang","Xiàn","County",NA,NA
108,49,"CHN","China",3,"Chongqing",19,"Chongqing",108,"Rongchang","Xiàn","County",NA,NA
109,49,"CHN","China",3,"Chongqing",19,"Chongqing",109,"Shizhu","Xiàn","County",NA,NA
110,49,"CHN","China",3,"Chongqing",19,"Chongqing",110,"Tongliang","Xiàn","County",NA,NA
111,49,"CHN","China",3,"Chongqing",19,"Chongqing",111,"Tongnan","Xiàn","County",NA,NA
112,49,"CHN","China",3,"Chongqing",19,"Chongqing",112,"Wan","Xiàn","County",NA,NA
113,49,"CHN","China",3,"Chongqing",19,"Chongqing",113,"Wulong","Xiàn","County",NA,NA
114,49,"CHN","China",3,"Chongqing",19,"Chongqing",114,"Wushan","Xiàn","County",NA,NA
115,49,"CHN","China",3,"Chongqing",19,"Chongqing",115,"Wuxi","Xiàn","County",NA,NA
116,49,"CHN","China",3,"Chongqing",19,"Chongqing",116,"Xiushan","Xiàn","County",NA,NA
117,49,"CHN","China",3,"Chongqing",19,"Chongqing",117,"Yongchuan","Xiàn","County",NA,NA
118,49,"CHN","China",3,"Chongqing",19,"Chongqing",118,"Youyang","Xiàn","County",NA,NA
119,49,"CHN","China",3,"Chongqing",19,"Chongqing",119,"Yunyang","Xiàn","County",NA,NA
120,49,"CHN","China",3,"Chongqing",19,"Chongqing",120,"Zhong","Xiàn","County",NA,NA
121,49,"CHN","China",4,"Fujian",20,"Fuzhou",121,"Changle","Xiànjíshì","County City","<U+957F><U+4E50><U+5E02>","Chánglè"
122,49,"CHN","China",4,"Fujian",20,"Fuzhou",122,"Fuqing","Xiànjíshì","County City","<U+798F><U+6E05><U+5E02>","Fúqing"
123,49,"CHN","China",4,"Fujian",20,"Fuzhou",123,"Fuzhou","Shìxiáqu","District",NA,NA
124,49,"CHN","China",4,"Fujian",20,"Fuzhou",124,"Lianjiang","Xiàn","County","<U+8FDE><U+6C5F><U+53BF>","Liánjiang"
125,49,"CHN","China",4,"Fujian",20,"Fuzhou",125,"Luoyuan","Xiàn","County","<U+7F57><U+6E90><U+53BF>","Luóyuán"
126,49,"CHN","China",4,"Fujian",20,"Fuzhou",126,"Minhou","Xiàn","County","<U+95FD><U+4FAF><U+53BF>","Minhòu"
127,49,"CHN","China",4,"Fujian",20,"Fuzhou",127,"Minqing","Xiàn","County","<U+95FD><U+6E05><U+53BF>","Minqing"
128,49,"CHN","China",4,"Fujian",20,"Fuzhou",128,"Yongtai","Xiàn","County","<U+6C38><U+6CF0><U+53BF>","Yongtài"
129,49,"CHN","China",4,"Fujian",21,"Longyan",129,"Changting","Xiàn","County","<U+957F><U+6C40><U+53BF>","Chángting"
130,49,"CHN","China",4,"Fujian",21,"Longyan",130,"Liancheng","Xiàn","County","<U+8FDE><U+57CE><U+53BF>","Liánchéng"
131,49,"CHN","China",4,"Fujian",21,"Longyan",131,"Luojiang","Shìxiáqu","District","<U+6D1B><U+6C5F><U+533A>","Luòjiang"
132,49,"CHN","China",4,"Fujian",21,"Longyan",132,"Shanghang","Xiàn","County","<U+4E0A><U+676D><U+53BF>","Shàngháng"
133,49,"CHN","China",4,"Fujian",21,"Longyan",133,"Wuping","Xiàn","County","<U+6B66><U+5E73><U+53BF>","Wupíng"
134,49,"CHN","China",4,"Fujian",21,"Longyan",134,"Yongding","Xiàn","County","<U+6C38><U+5B9A><U+53BF>","Yongdìng"
135,49,"CHN","China",4,"Fujian",21,"Longyan",135,"Zhangping","Xiànjíshì","County City","<U+6F33><U+5E73><U+5E02>","Zhangpíng"
136,49,"CHN","China",4,"Fujian",22,"Nanping",136,"Chongan","Xiàn","County",NA,NA
137,49,"CHN","China",4,"Fujian",22,"Nanping",137,"Guangze","Xiàn","County","<U+5149><U+6CFD><U+53BF>","Guangzé"
138,49,"CHN","China",4,"Fujian",22,"Nanping",138,"Jian'ou","Xiànjíshì","County City","<U+5EFA><U+74EF><U+5E02>","Jiàn'ou"
139,49,"CHN","China",4,"Fujian",22,"Nanping",139,"Jianyang","Xiànjíshì","County City","<U+5EFA><U+9633><U+5E02>","Jiànyáng"
140,49,"CHN","China",4,"Fujian",22,"Nanping",140,"Nanping","Xiànjíshì","County City",NA,NA
141,49,"CHN","China",4,"Fujian",22,"Nanping",141,"Pucheng","Xiàn","County","<U+6D66><U+57CE><U+53BF>","Puchéng"
142,49,"CHN","China",4,"Fujian",22,"Nanping",142,"Shaowu","Xiànjíshì","County City","<U+90B5><U+6B66><U+5E02>","Shàowu"
143,49,"CHN","China",4,"Fujian",22,"Nanping",143,"Shunchang","Xiàn","County","<U+987A><U+660C><U+53BF>","Shùnchang"
144,49,"CHN","China",4,"Fujian",22,"Nanping",144,"Songxi","Xiàn","County","<U+677E><U+6EAA><U+53BF>","Songxi"
145,49,"CHN","China",4,"Fujian",22,"Nanping",145,"Zhenghe","Xiàn","County","<U+653F><U+548C><U+53BF>","Zhènghé"
146,49,"CHN","China",4,"Fujian",23,"Ningde",146,"Fu'an","Xiànjíshì","County City","<U+798F><U+5B89><U+5E02>","Fú'an"
147,49,"CHN","China",4,"Fujian",23,"Ningde",147,"Fuding","Xiànjíshì","County City","<U+798F><U+9F0E><U+5E02>","Fúding"
148,49,"CHN","China",4,"Fujian",23,"Ningde",148,"Gutian","Xiàn","County","<U+53E4><U+7530><U+53BF>","Gutián"
149,49,"CHN","China",4,"Fujian",23,"Ningde",149,"Jiaocheng","Shìxiáqu","District","<U+8549><U+57CE><U+533A>",NA
150,49,"CHN","China",4,"Fujian",23,"Ningde",150,"Pingnan","Xiàn","County","<U+5C4F><U+5357><U+53BF>","Píngnán"
151,49,"CHN","China",4,"Fujian",23,"Ningde",151,"Shouning","Xiàn","County","<U+5BFF><U+5B81><U+53BF>","Shòuníng"
152,49,"CHN","China",4,"Fujian",23,"Ningde",152,"Xiapu","Xiàn","County","<U+971E><U+6D66><U+53BF>","Xiápu"
153,49,"CHN","China",4,"Fujian",23,"Ningde",153,"Zherong","Xiàn","County","<U+67D8><U+8363><U+53BF>","Zhèróng"
154,49,"CHN","China",4,"Fujian",23,"Ningde",154,"Zhouning","Xiàn","County","<U+5468><U+5B81><U+53BF>","Zhouníng"
155,49,"CHN","China",4,"Fujian",24,"Putian",155,"Putian County","Xiàn","County","<U+8386><U+7530><U+5E02>","Pútián"
156,49,"CHN","China",4,"Fujian",24,"Putian",156,"Putian","Shìxiáqu","District","<U+8386><U+7530><U+5E02>","Pútián"
157,49,"CHN","China",4,"Fujian",24,"Putian",157,"Xianyou","Xiàn","County","<U+4ED9><U+6E38><U+53BF>","Xianyóu"
158,49,"CHN","China",4,"Fujian",25,"Quanzhou",158,"Anxi","Xiàn","County","<U+5B89><U+6EAA><U+53BF>","Anxi"
159,49,"CHN","China",4,"Fujian",25,"Quanzhou",159,"Dehua","Xiàn","County","<U+5FB7><U+5316><U+53BF>","Déhuà"
160,49,"CHN","China",4,"Fujian",25,"Quanzhou",160,"Hui'an","Xiàn","County","<U+60E0><U+5B89><U+53BF>","Huì'an"
161,49,"CHN","China",4,"Fujian",25,"Quanzhou",161,"Jinjiang","Xiànjíshì","County City","<U+664B><U+6C5F><U+5E02>","Jìnjiang"
162,49,"CHN","China",4,"Fujian",25,"Quanzhou",162,"Jinmen","Xiàn","County","<U+91D1><U+9580><U+7E23>","Jinmén"
163,49,"CHN","China",4,"Fujian",25,"Quanzhou",163,"Nan'an","Xiànjíshì","County City","<U+5357><U+5B89><U+5E02>","Nán'an"
164,49,"CHN","China",4,"Fujian",25,"Quanzhou",164,"Sanyuan","Shìxiáqu","District","<U+4E09><U+5143><U+533A>","Sanyuán"
165,49,"CHN","China",4,"Fujian",25,"Quanzhou",165,"Shishi","Xiànjíshì","County City","<U+77F3><U+72EE><U+5E02>","Shíshi"
166,49,"CHN","China",4,"Fujian",25,"Quanzhou",166,"Yongchun","Xiàn","County","<U+6C38><U+6625><U+53BF>","Yongchun"
167,49,"CHN","China",4,"Fujian",26,"Sanming",167,"Datian","Xiàn","County","<U+5927><U+7530><U+53BF>","Dàtián"
168,49,"CHN","China",4,"Fujian",26,"Sanming",168,"Jiangle","Xiàn","County","<U+5C06><U+4E50><U+53BF>","Jianglè"
169,49,"CHN","China",4,"Fujian",26,"Sanming",169,"Jianning","Xiàn","County","<U+5EFA><U+5B81><U+53BF>","Jiànníng"
170,49,"CHN","China",4,"Fujian",26,"Sanming",170,"Mingxi","Xiàn","County","<U+660E><U+6EAA><U+53BF>","Míngxi"
171,49,"CHN","China",4,"Fujian",26,"Sanming",171,"Ninghua","Xiàn","County","<U+5B81><U+5316><U+53BF>","Nínghuà"
172,49,"CHN","China",4,"Fujian",26,"Sanming",172,"Qingliu","Xiàn","County","<U+6E05><U+6D41><U+53BF>","Qingliú"
173,49,"CHN","China",4,"Fujian",26,"Sanming",173,"Shanming","Xiànjíshì","County City",NA,NA
174,49,"CHN","China",4,"Fujian",26,"Sanming",174,"Sha","Xiàn","County","<U+6C99><U+53BF>","Sha"
175,49,"CHN","China",4,"Fujian",26,"Sanming",175,"Taining","Xiàn","County","<U+6CF0><U+5B81><U+53BF>","Tàiníng"
176,49,"CHN","China",4,"Fujian",26,"Sanming",176,"Yong'an","Xiànjíshì","County City","<U+6C38><U+5B89><U+5E02>","Yong'an"
177,49,"CHN","China",4,"Fujian",26,"Sanming",177,"Youxi","Xiàn","County","<U+5C24><U+6EAA><U+53BF>","Yóuxi"
178,49,"CHN","China",4,"Fujian",27,"Xiamen",178,"Tong'an","Shìxiáqu","District","<U+540C><U+5B89><U+533A>","Tóng'an"
179,49,"CHN","China",4,"Fujian",27,"Xiamen",179,"Xiamen","Shìxiáqu","District",NA,NA
180,49,"CHN","China",4,"Fujian",28,"Zhangzhou",180,"Changtai","Xiàn","County","<U+957F><U+6CF0><U+53BF>","Chángtài"
181,49,"CHN","China",4,"Fujian",28,"Zhangzhou",181,"Dongshan","Xiàn","County","<U+4E1C><U+5C71><U+53BF>","Dongshan"
182,49,"CHN","China",4,"Fujian",28,"Zhangzhou",182,"Hua'an","Xiàn","County","<U+534E><U+5B89><U+53BF>","Huá'an"
183,49,"CHN","China",4,"Fujian",28,"Zhangzhou",183,"Longhai","Xiànjíshì","County City","<U+9F99><U+6D77><U+5E02>","Lónghai"
184,49,"CHN","China",4,"Fujian",28,"Zhangzhou",184,"Nanjing","Xiàn","County","<U+5357><U+9756><U+53BF>","Nánjìng"
185,49,"CHN","China",4,"Fujian",28,"Zhangzhou",185,"Pinghe","Xiàn","County","<U+5E73><U+548C><U+53BF>","Pínghé"
186,49,"CHN","China",4,"Fujian",28,"Zhangzhou",186,"Zhangpu","Xiàn","County","<U+6F33><U+6D66><U+53BF>","Zhangpu"
187,49,"CHN","China",4,"Fujian",28,"Zhangzhou",187,"Zhangzhou","Xiànjíshì","County City",NA,NA
188,49,"CHN","China",4,"Fujian",28,"Zhangzhou",188,"Zhao'an","Xiàn","County","<U+8BCF><U+5B89><U+53BF>","Zhào'an"
189,49,"CHN","China",5,"Gansu",29,"Baiyin",189,"Baiyin","Shìxiáqu","District","<U+767D><U+94F6><U+533A>","Báiyín"
190,49,"CHN","China",5,"Gansu",29,"Baiyin",190,"Huining","Xiàn","County","<U+4F1A><U+5B81><U+53BF>","Huìníng"
191,49,"CHN","China",5,"Gansu",29,"Baiyin",191,"Jingtai","Xiàn","County","<U+666F><U+6CF0><U+53BF>","Jingtài"
192,49,"CHN","China",5,"Gansu",29,"Baiyin",192,"Jingyuan","Xiàn","County","<U+9756><U+8FDC><U+53BF>","Jìngyuan"
193,49,"CHN","China",5,"Gansu",29,"Baiyin",193,"Pingchuan","Shìxiáqu","District","<U+5E73><U+5DDD><U+533A>","Píngchuan"
194,49,"CHN","China",5,"Gansu",30,"Dingxi",194,"Dingxi","Xiàn","County",NA,NA
195,49,"CHN","China",5,"Gansu",30,"Dingxi",195,"Lintao","Xiàn","County","<U+4E34><U+6D2E><U+53BF>","Líntáo"
196,49,"CHN","China",5,"Gansu",30,"Dingxi",196,"Longxi","Xiàn","County","<U+9647><U+897F><U+53BF>","Longxi"
197,49,"CHN","China",5,"Gansu",30,"Dingxi",197,"Min","Xiàn","County","<U+5CB7><U+53BF>","Mín"
198,49,"CHN","China",5,"Gansu",30,"Dingxi",198,"Tongwei","Xiàn","County","<U+901A><U+6E2D><U+53BF>","Tongwèi"
199,49,"CHN","China",5,"Gansu",30,"Dingxi",199,"Weiyuan","Xiàn","County","<U+6E2D><U+6E90><U+53BF>","Wèiyuán"
200,49,"CHN","China",5,"Gansu",30,"Dingxi",200,"Zhang","Xiàn","County","<U+6F33><U+53BF>","Zhang"
201,49,"CHN","China",5,"Gansu",31,"Gannan Tibetan",201,"Jonê","Xiàn","County","<U+5353><U+5C3C><U+53BF>","Zhuóní"
202,49,"CHN","China",5,"Gansu",31,"Gannan Tibetan",202,"Lintan","Xiàn","County","<U+4E34><U+6F6D><U+53BF>","Líntán"
203,49,"CHN","China",5,"Gansu",31,"Gannan Tibetan",203,"Luqu","Xiàn","County","<U+788C><U+66F2><U+53BF>","Lùqu"
204,49,"CHN","China",5,"Gansu",31,"Gannan Tibetan",204,"Maqu","Xiàn","County","<U+739B><U+66F2><U+53BF>","Maqu"
205,49,"CHN","China",5,"Gansu",31,"Gannan Tibetan",205,"Têwo","Xiàn","County","<U+8FED><U+90E8><U+53BF>","Diébù"
206,49,"CHN","China",5,"Gansu",31,"Gannan Tibetan",206,"Xiahe","Xiàn","County","<U+590F><U+6CB3><U+53BF>","Xiàhé"
207,49,"CHN","China",5,"Gansu",31,"Gannan Tibetan",207,"Zhugqu","Xiàn","County","<U+821F><U+66F2><U+53BF>","Zhouqu"
208,49,"CHN","China",5,"Gansu",32,"Jiayuguan",208,"Jiayuguan","Xiànjíshì","County City",NA,NA
209,49,"CHN","China",5,"Gansu",33,"Jinchang",209,"Jinchuan","Shìxiáqu","District","<U+91D1><U+5DDD><U+533A>","Jinchuan"
210,49,"CHN","China",5,"Gansu",33,"Jinchang",210,"Yongchang","Xiàn","County","<U+6C38><U+660C><U+53BF>","Yongchang"
211,49,"CHN","China",5,"Gansu",34,"Jiuquan",211,"Aksai Kazakh","Zìzhìxiàn","Autonomous County","<U+963F><U+514B><U+585E><U+54C8><U+8428><U+514B><U+65CF><U+81EA><U+6CBB><U+53BF>","Akèsài Hasàkèzú"
212,49,"CHN","China",5,"Gansu",34,"Jiuquan",212,"Anxi","Xiàn","County",NA,NA
213,49,"CHN","China",5,"Gansu",34,"Jiuquan",213,"Dunhuang","Xiànjíshì","County City","<U+6566><U+714C><U+5E02>","Dunhuáng"
214,49,"CHN","China",5,"Gansu",34,"Jiuquan",214,"Jinta","Xiàn","County","<U+91D1><U+5854><U+53BF>","Jinta"
215,49,"CHN","China",5,"Gansu",34,"Jiuquan",215,"Subei Mongol","Zìzhìxiàn","Autonomous County","<U+8083><U+5317><U+8499><U+53E4><U+65CF><U+81EA><U+6CBB><U+53BF>","Sùbei Mengguzú"
216,49,"CHN","China",5,"Gansu",34,"Jiuquan",216,"Suzhou","Shìxiáqu","District","<U+8083><U+5DDE><U+533A>","Sùzhou"
217,49,"CHN","China",5,"Gansu",34,"Jiuquan",217,"Yumen","Xiànjíshì","County City",NA,NA
218,49,"CHN","China",5,"Gansu",35,"Lanzhou",218,"Gaolan","Xiàn","County","<U+768B><U+5170><U+53BF>","Gaolán"
219,49,"CHN","China",5,"Gansu",35,"Lanzhou",219,"Liangzhou","Shìxiáqu","District","<U+51C9><U+5DDE><U+533A>","Liángzhou"
220,49,"CHN","China",5,"Gansu",35,"Lanzhou",220,"Yongdeng","Xiàn","County","<U+6C38><U+767B><U+53BF>","Yongdeng"
221,49,"CHN","China",5,"Gansu",35,"Lanzhou",221,"Yuzhong","Xiàn","County","<U+6986><U+4E2D><U+53BF>","Yúzhong"
222,49,"CHN","China",5,"Gansu",36,"Linxia Hui",222,"Dongxiang","Zìzhìxiàn","Autonomous County","<U+4E1C><U+4E61><U+65CF><U+81EA><U+6CBB><U+53BF>","Dongxiangzú"
223,49,"CHN","China",5,"Gansu",36,"Linxia Hui",223,"Guanghe","Xiàn","County","<U+5E7F><U+6CB3><U+53BF>","Guanghé"
224,49,"CHN","China",5,"Gansu",36,"Linxia Hui",224,"Hezheng","Xiàn","County","<U+548C><U+653F><U+53BF>","Hézhèng"
225,49,"CHN","China",5,"Gansu",36,"Linxia Hui",225,"Jishishan Bonan, Dongxiang and Salar","Zìzhìxiàn","Autonomous County","<U+79EF><U+77F3><U+5C71><U+4FDD><U+5B89><U+65CF><U+4E1C><U+4E61><U+65CF><U+6492><U+62C9><U+65CF><U+81EA><U+6CBB><U+53BF>","Jishíshan Bao'anzú Dongxiangzú Salazú"
226,49,"CHN","China",5,"Gansu",36,"Linxia Hui",226,"Kangle","Xiàn","County","<U+5EB7><U+4E50><U+53BF>","Kanglè"
227,49,"CHN","China",5,"Gansu",36,"Linxia Hui",227,"Linxia Shi","Xiànjíshì","County City","<U+4E34><U+590F><U+5E02>","Línxià"
228,49,"CHN","China",5,"Gansu",36,"Linxia Hui",228,"Linxia","Xiàn","County","<U+4E34><U+590F><U+5E02>","Línxià"
229,49,"CHN","China",5,"Gansu",36,"Linxia Hui",229,"Yongjing","Xiàn","County","<U+6C38><U+9756><U+53BF>","Yongjìng"
230,49,"CHN","China",5,"Gansu",37,"Longnan",230,"Cheng","Xiàn","County","<U+6210><U+53BF>","Chéng"
231,49,"CHN","China",5,"Gansu",37,"Longnan",231,"Dangchang","Xiàn","County","<U+5B95><U+660C><U+53BF>","Dàngchang"
232,49,"CHN","China",5,"Gansu",37,"Longnan",232,"Hui","Xiàn","County","<U+5FBD><U+53BF>","Hui"
233,49,"CHN","China",5,"Gansu",37,"Longnan",233,"Kang","Xiàn","County","<U+5EB7><U+53BF>","Kang"
234,49,"CHN","China",5,"Gansu",37,"Longnan",234,"Liangdang","Xiàn","County","<U+4E24><U+5F53><U+53BF>","Liangdang"
235,49,"CHN","China",5,"Gansu",37,"Longnan",235,"Li","Xiàn","County","<U+793C><U+53BF>","Li"
236,49,"CHN","China",5,"Gansu",37,"Longnan",236,"Wen","Xiàn","County","<U+6587><U+53BF>","Wén"
237,49,"CHN","China",5,"Gansu",37,"Longnan",237,"Wudu","Shìxiáqu","District","<U+6B66><U+90FD><U+533A>","Wudu"
238,49,"CHN","China",5,"Gansu",37,"Longnan",238,"Xihe","Xiàn","County","<U+897F><U+548C><U+53BF>","Xihé"
239,49,"CHN","China",5,"Gansu",38,"Pingliang",239,"Chongxin","Xiàn","County","<U+5D07><U+4FE1><U+53BF>","Chóngxìn"
240,49,"CHN","China",5,"Gansu",38,"Pingliang",240,"Huating","Xiàn","County","<U+534E><U+4EAD><U+53BF>","Huátíng"
241,49,"CHN","China",5,"Gansu",38,"Pingliang",241,"Jingchuan","Xiàn","County","<U+6CFE><U+5DDD><U+53BF>","Jingchuan"
242,49,"CHN","China",5,"Gansu",38,"Pingliang",242,"Jingning","Xiàn","County","<U+9759><U+5B81><U+53BF>","Jìngníng"
243,49,"CHN","China",5,"Gansu",38,"Pingliang",243,"Lingtai","Xiàn","County","<U+7075><U+53F0><U+53BF>","Língtái"
244,49,"CHN","China",5,"Gansu",38,"Pingliang",244,"Pingliang","Xiàn","County",NA,NA
245,49,"CHN","China",5,"Gansu",38,"Pingliang",245,"Zhuanglang","Xiàn","County","<U+5E84><U+6D6A><U+53BF>","Zhuanglàng"
246,49,"CHN","China",5,"Gansu",39,"Qingyang",246,"Hezheng","Xiàn","County","<U+548C><U+653F><U+53BF>","Hézhèng"
247,49,"CHN","China",5,"Gansu",39,"Qingyang",247,"Huachi","Xiàn","County","<U+534E><U+6C60><U+53BF>","Huáchí"
248,49,"CHN","China",5,"Gansu",39,"Qingyang",248,"Huan","Xiàn","County","<U+73AF><U+53BF>","Huán"
249,49,"CHN","China",5,"Gansu",39,"Qingyang",249,"Ning","Xiàn","County","<U+5B81><U+53BF>","Níng"
250,49,"CHN","China",5,"Gansu",39,"Qingyang",250,"Qingyang","Xiàn","County",NA,NA
251,49,"CHN","China",5,"Gansu",39,"Qingyang",251,"Xifeng","Shìxiáqu","District","<U+897F><U+5CF0><U+533A>","Xifeng"
252,49,"CHN","China",5,"Gansu",39,"Qingyang",252,"Zhengning","Xiàn","County","<U+6B63><U+5B81><U+53BF>","Zhèngníng"
253,49,"CHN","China",5,"Gansu",39,"Qingyang",253,"Zhenyuan","Xiàn","County","<U+9547><U+539F><U+53BF>","Zhènyuán"
254,49,"CHN","China",5,"Gansu",40,"Tianshui",254,"Gangu","Xiàn","County","<U+7518><U+8C37><U+53BF>","Gangu"
255,49,"CHN","China",5,"Gansu",40,"Tianshui",255,"Qin'an","Xiàn","County","<U+79E6><U+5B89><U+53BF>","Qín'an"
256,49,"CHN","China",5,"Gansu",40,"Tianshui",256,"Qingcheng","Xiàn","County","<U+5E86><U+57CE><U+53BF>","Qìngchéng"
257,49,"CHN","China",5,"Gansu",40,"Tianshui",257,"Tianshui","Xiàn","County",NA,NA
258,49,"CHN","China",5,"Gansu",40,"Tianshui",258,"Wushan","Xiàn","County","<U+6B66><U+5C71><U+53BF>","Wushan"
259,49,"CHN","China",5,"Gansu",40,"Tianshui",259,"Zhangjiachuan Hui","Zìzhìxiàn","Autonomous County","<U+5F20><U+5BB6><U+5DDD><U+56DE><U+65CF><U+81EA><U+6CBB><U+53BF>","Zhangjiachuan Huízú"
260,49,"CHN","China",5,"Gansu",41,"Wuwei",260,"Heshui","Xiàn","County","<U+5408><U+6C34><U+53BF>","Héshui"
261,49,"CHN","China",5,"Gansu",41,"Wuwei",261,"Minqin","Xiàn","County","<U+6C11><U+52E4><U+53BF>","Mínqín"
262,49,"CHN","China",5,"Gansu",41,"Wuwei",262,"Tianzhu Tibetan","Zìzhìxiàn","Autonomous County","<U+5929><U+795D><U+85CF><U+65CF><U+81EA><U+6CBB><U+53BF>","Tianzhù Zàngzú"
263,49,"CHN","China",5,"Gansu",41,"Wuwei",263,"Wuwei","Xiàn","County",NA,NA
264,49,"CHN","China",5,"Gansu",42,"Zhangye",264,"Gaotai","Xiàn","County","<U+9AD8><U+53F0><U+53BF>","Gaotái"
265,49,"CHN","China",5,"Gansu",42,"Zhangye",265,"Linze","Xiàn","County","<U+4E34><U+6CFD><U+53BF>","Línzé"
266,49,"CHN","China",5,"Gansu",42,"Zhangye",266,"Minle","Xiàn","County","<U+6C11><U+4E50><U+53BF>","Mínlè"
267,49,"CHN","China",5,"Gansu",42,"Zhangye",267,"Shandan","Xiàn","County","<U+5C71><U+4E39><U+53BF>","Shandan"
268,49,"CHN","China",5,"Gansu",42,"Zhangye",268,"Sunan Yugur","Zìzhìxiàn","Autonomous County","<U+8083><U+5357><U+88D5><U+56FA><U+65CF><U+81EA><U+6CBB><U+53BF>","Sùnán Yùgùzú"
269,49,"CHN","China",5,"Gansu",42,"Zhangye",269,"Zhangye","Xiàn","County",NA,NA
270,49,"CHN","China",6,"Guangdong",43,"Chaozhou",270,"Chaonan","Shìxiáqu","District","<U+6F6E><U+5357><U+533A>","Cháonán"
271,49,"CHN","China",6,"Guangdong",43,"Chaozhou",271,"Chaozhou","Xiànjíshì","County City",NA,NA
272,49,"CHN","China",6,"Guangdong",43,"Chaozhou",272,"Raoping","Xiàn","County","<U+9976><U+5E73><U+53BF>","Ràopíng"
273,49,"CHN","China",6,"Guangdong",44,"Dongguan",273,"Dongyuan","Xiàn","County","<U+4E1C><U+6E90><U+53BF>","Dongyuán"
274,49,"CHN","China",6,"Guangdong",45,"Foshan",274,"Futian","Shìxiáqu","District","<U+798F><U+7530><U+533A>","Fútián"
275,49,"CHN","China",6,"Guangdong",45,"Foshan",275,"Gaoming","Shìxiáqu","District","<U+9AD8><U+660E><U+533A>","Gaomíng"
276,49,"CHN","China",6,"Guangdong",45,"Foshan",276,"Nanhai","Shìxiáqu","District","<U+5357><U+6D77><U+533A>","Nánhai"
277,49,"CHN","China",6,"Guangdong",45,"Foshan",277,"Sanshui","Shìxiáqu","District","<U+4E09><U+6C34><U+533A>","Sanshui"
278,49,"CHN","China",6,"Guangdong",45,"Foshan",278,"Shunde","Shìxiáqu","District","<U+987A><U+5FB7><U+533A>","Shùndé"
279,49,"CHN","China",6,"Guangdong",46,"Guangzhou",279,"Conghua","Xiànjíshì","County City","<U+4ECE><U+5316><U+5E02>","Cónghuà"
280,49,"CHN","China",6,"Guangdong",46,"Guangzhou",280,"Guangzhou","Shìxiáqu","District",NA,NA
281,49,"CHN","China",6,"Guangdong",46,"Guangzhou",281,"Huaxian","Xiàn","County",NA,NA
282,49,"CHN","China",6,"Guangdong",46,"Guangzhou",282,"Nansha","Shìxiáqu","District","<U+5357><U+6C99><U+533A>","Nánsha"
283,49,"CHN","China",6,"Guangdong",46,"Guangzhou",283,"Panyu","Shìxiáqu","District","<U+756A><U+79BA><U+533A>","Panyú"
284,49,"CHN","China",6,"Guangdong",46,"Guangzhou",284,"Zengcheng","Xiànjíshì","County City","<U+589E><U+57CE><U+5E02>","Zengchéng"
285,49,"CHN","China",6,"Guangdong",47,"Heyuan",285,"Dongyuan","Xiàn","County","<U+4E1C><U+6E90><U+53BF>","Dongyuán"
286,49,"CHN","China",6,"Guangdong",47,"Heyuan",286,"Heping","Xiàn","County","<U+548C><U+5E73><U+53BF>","Hépíng"
287,49,"CHN","China",6,"Guangdong",47,"Heyuan",287,"Lianping","Xiàn","County","<U+8FDE><U+5E73><U+53BF>","Liánpíng"
288,49,"CHN","China",6,"Guangdong",47,"Heyuan",288,"Lungchuan","Xiàn","County","<U+9F99><U+5DDD><U+53BF>","Lóngchuan"
289,49,"CHN","China",6,"Guangdong",47,"Heyuan",289,"Yuancheng","Shìxiáqu","District","<U+6E90><U+57CE><U+533A>","Yuánchéng"
290,49,"CHN","China",6,"Guangdong",47,"Heyuan",290,"Zijin","Xiàn","County","<U+7D2B><U+91D1><U+53BF>","Zijin"
291,49,"CHN","China",6,"Guangdong",48,"Huizhou",291,"Boluo","Xiàn","County","<U+535A><U+7F57><U+53BF>","Bóluó"
292,49,"CHN","China",6,"Guangdong",48,"Huizhou",292,"Huidong","Xiàn","County","<U+60E0><U+4E1C><U+53BF>","Huìdong"
293,49,"CHN","China",6,"Guangdong",48,"Huizhou",293,"Huiyang","Shìxiáqu","District","<U+60E0><U+9633><U+533A>","Huìyáng"
294,49,"CHN","China",6,"Guangdong",48,"Huizhou",294,"Huizhou","Xiànjíshì","County City",NA,NA
295,49,"CHN","China",6,"Guangdong",48,"Huizhou",295,"Longmen","Xiàn","County","<U+9F99><U+95E8><U+53BF>","Lóngmén"
296,49,"CHN","China",6,"Guangdong",49,"Jiangmen",296,"Enping","Xiànjíshì","County City","<U+6069><U+5E73><U+5E02>","Enpíng"
297,49,"CHN","China",6,"Guangdong",49,"Jiangmen",297,"Heshan","Xiànjíshì","County City","<U+9E64><U+5C71><U+5E02>","Hèshan"
298,49,"CHN","China",6,"Guangdong",49,"Jiangmen",298,"Jiangmen","Xiànjíshì","County City",NA,NA
299,49,"CHN","China",6,"Guangdong",49,"Jiangmen",299,"Kaiping","Xiànjíshì","County City","<U+5F00><U+5E73><U+5E02>","Kaipíng"
300,49,"CHN","China",6,"Guangdong",49,"Jiangmen",300,"Taishan","Xiànjíshì","County City","<U+53F0><U+5C71><U+5E02>","Táishan"
301,49,"CHN","China",6,"Guangdong",49,"Jiangmen",301,"Xinhui","Shìxiáqu","District","<U+65B0><U+4F1A><U+533A>","Xinhuì"
302,49,"CHN","China",6,"Guangdong",50,"Jieyang",302,"Huilai","Xiàn","County","<U+60E0><U+6765><U+53BF>","Huìlái"
303,49,"CHN","China",6,"Guangdong",50,"Jieyang",303,"Jiedong","Xiàn","County","<U+63ED><U+4E1C><U+53BF>","Jiedong"
304,49,"CHN","China",6,"Guangdong",50,"Jieyang",304,"Jiexi","Xiàn","County","<U+63ED><U+897F><U+53BF>","Jiexi"
305,49,"CHN","China",6,"Guangdong",50,"Jieyang",305,"Puning","Xiànjíshì","County City","<U+666E><U+5B81><U+5E02>","Puníng"
306,49,"CHN","China",6,"Guangdong",50,"Jieyang",306,"Rongcheng","Shìxiáqu","District","<U+6995><U+57CE><U+533A>","Róngchéng"
307,49,"CHN","China",6,"Guangdong",51,"Maoming",307,"Dianbai","Xiàn","County","<U+7535><U+767D><U+53BF>","Diànbái"
308,49,"CHN","China",6,"Guangdong",51,"Maoming",308,"Gaozhou","Xiànjíshì","County City","<U+9AD8><U+5DDE><U+5E02>","Gaozhou"
309,49,"CHN","China",6,"Guangdong",51,"Maoming",309,"Huazhou","Xiànjíshì","County City","<U+5316><U+5DDE><U+5E02>","Huàzhou"
310,49,"CHN","China",6,"Guangdong",51,"Maoming",310,"Maoming","Xiàn","County",NA,NA
311,49,"CHN","China",6,"Guangdong",51,"Maoming",311,"Xinyi","Xiànjíshì","County City","<U+4FE1><U+5B9C><U+5E02>","Xìnyí"
312,49,"CHN","China",6,"Guangdong",52,"Meizhou",312,"Dapu","Xiàn","County","<U+5927><U+57D4><U+53BF>","Dàpu"
313,49,"CHN","China",6,"Guangdong",52,"Meizhou",313,"Fengshun","Xiàn","County","<U+4E30><U+987A><U+53BF>","Fengshùn"
314,49,"CHN","China",6,"Guangdong",52,"Meizhou",314,"Jiaoling","Xiàn","County","<U+8549><U+5CAD><U+53BF>","Jiaoling"
315,49,"CHN","China",6,"Guangdong",52,"Meizhou",315,"Mei","Xiàn","County","<U+6885><U+53BF>","Méi"
316,49,"CHN","China",6,"Guangdong",52,"Meizhou",316,"Meizhou","Xiànjíshì","County City",NA,NA
317,49,"CHN","China",6,"Guangdong",52,"Meizhou",317,"Pingyuan","Xiàn","County","<U+5E73><U+8FDC><U+53BF>","Píngyuan"
318,49,"CHN","China",6,"Guangdong",52,"Meizhou",318,"Wuhua","Xiàn","County","<U+4E94><U+534E><U+53BF>","Wuhuá"
319,49,"CHN","China",6,"Guangdong",52,"Meizhou",319,"Xingning","Xiànjíshì","County City","<U+5174><U+5B81><U+5E02>","Xingníng"
320,49,"CHN","China",6,"Guangdong",53,"Qingyuan",320,"Fogang","Xiàn","County","<U+4F5B><U+5188><U+53BF>","Fógang"
321,49,"CHN","China",6,"Guangdong",53,"Qingyuan",321,"Liannan Yao","Zìzhìxiàn","Autonomous County","<U+8FDE><U+5357><U+7476><U+65CF><U+81EA><U+6CBB><U+53BF>","Liánnán Yáozú Zìzhìxiàn|Yáozú"
322,49,"CHN","China",6,"Guangdong",53,"Qingyuan",322,"Lianshan Zhuang and Yao","Zìzhìxiàn","Autonomous County","<U+8FDE><U+5C71><U+58EE><U+65CF>|<U+7476><U+65CF><U+81EA><U+6CBB><U+53BF>","Liánsha"
323,49,"CHN","China",6,"Guangdong",53,"Qingyuan",323,"Lianzhou","Xiànjíshì","County City","<U+8FDE><U+5DDE><U+5E02>","Liánzhou"
324,49,"CHN","China",6,"Guangdong",53,"Qingyuan",324,"Qingcheng","Shìxiáqu","District","<U+6E05><U+57CE><U+533A>","Qingchéng"
325,49,"CHN","China",6,"Guangdong",53,"Qingyuan",325,"Qingxin","Xiàn","County","<U+6E05><U+65B0><U+53BF>","Qingxin"
326,49,"CHN","China",6,"Guangdong",53,"Qingyuan",326,"Yangshan","Xiàn","County","<U+9633><U+5C71><U+53BF>","Yángshan"
327,49,"CHN","China",6,"Guangdong",53,"Qingyuan",327,"Yingde","Xiànjíshì","County City","<U+82F1><U+5FB7><U+5E02>","Yingdé"
328,49,"CHN","China",6,"Guangdong",54,"Shantou",328,"Chaoyang","Shìxiáqu","District","<U+6F6E><U+9633><U+533A>","Cháoyáng"
329,49,"CHN","China",6,"Guangdong",54,"Shantou",329,"Chenghai","Shìxiáqu","District","<U+6F84><U+6D77><U+533A>","Chénghai"
330,49,"CHN","China",6,"Guangdong",54,"Shantou",330,"Shantou","Xiànjíshì","County City",NA,NA
331,49,"CHN","China",6,"Guangdong",55,"Shanwei",331,"Haifeng","Xiàn","County","<U+6D77><U+4E30><U+53BF>","Haifeng"
332,49,"CHN","China",6,"Guangdong",55,"Shanwei",332,"Lufeng","Xiànjíshì","County City","<U+9646><U+4E30><U+5E02>","Lùfeng"
333,49,"CHN","China",6,"Guangdong",55,"Shanwei",333,"Luhe","Xiàn","County","<U+9646><U+6CB3><U+53BF>","	Lùhé"
334,49,"CHN","China",6,"Guangdong",55,"Shanwei",334,"Shanwei","Xiànjíshì","County City","<U+6C55><U+5C3E><U+5E02>","Shànwei"
335,49,"CHN","China",6,"Guangdong",56,"Shaoguan",335,"Lechang","Xiànjíshì","County City","<U+4E50><U+660C><U+5E02>","Lèchang"
336,49,"CHN","China",6,"Guangdong",56,"Shaoguan",336,"Nanxiong","Xiànjíshì","County City","<U+5357><U+96C4><U+5E02>","Nánxióng"
337,49,"CHN","China",6,"Guangdong",56,"Shaoguan",337,"Qujiang","Shìxiáqu","District","<U+66F2><U+6C5F><U+533A>","Qujiang"
338,49,"CHN","China",6,"Guangdong",56,"Shaoguan",338,"Renhua","Xiàn","County","<U+4EC1><U+5316><U+53BF>","Rénhuà"
339,49,"CHN","China",6,"Guangdong",56,"Shaoguan",339,"Ruyuan Yao","Zìzhìxiàn","Autonomous County","<U+4E73><U+6E90><U+7476><U+65CF><U+81EA><U+6CBB><U+53BF>","Ruyuán Yáozú"
340,49,"CHN","China",6,"Guangdong",56,"Shaoguan",340,"Shaoguan","Shìxiáqu","District",NA,NA
341,49,"CHN","China",6,"Guangdong",56,"Shaoguan",341,"Shixing","Xiàn","County","<U+59CB><U+5174><U+53BF>","Shixing"
342,49,"CHN","China",6,"Guangdong",56,"Shaoguan",342,"Wengyuan","Xiàn","County","<U+7FC1><U+6E90><U+53BF>","Wengyuán"
343,49,"CHN","China",6,"Guangdong",56,"Shaoguan",343,"Xinfeng","Xiàn","County","<U+65B0><U+4E30><U+53BF>","Xinfeng"
344,49,"CHN","China",6,"Guangdong",57,"Shenzhen",344,"Bao'an","Shìxiáqu","District","<U+5B9D><U+5B89><U+533A>","Bao'an"
345,49,"CHN","China",6,"Guangdong",57,"Shenzhen",345,"Shenzhen","Shìxiáqu","District","<U+6DF1><U+5733>","Shenzhèn"
346,49,"CHN","China",6,"Guangdong",58,"Yangjiang",346,"Yangchun","Xiànjíshì","County City","<U+9633><U+6625><U+5E02>","Yángchun"
347,49,"CHN","China",6,"Guangdong",58,"Yangjiang",347,"Yangdong","Xiàn","County","<U+9633><U+4E1C><U+53BF>","Yángdong"
348,49,"CHN","China",6,"Guangdong",58,"Yangjiang",348,"Yangjiang","Xiànjíshì","County City","<U+9633><U+6C5F><U+5E02>","Yángjiang"
349,49,"CHN","China",6,"Guangdong",58,"Yangjiang",349,"Yangxi","Xiàn","County","<U+9633><U+897F><U+53BF>","Yángxi"
350,49,"CHN","China",6,"Guangdong",59,"Yunfu",350,"Luoding","Xiànjíshì","County City","<U+7F57><U+5B9A><U+5E02>","Luódìng"
351,49,"CHN","China",6,"Guangdong",59,"Yunfu",351,"Xinxing","Xiàn","County","<U+65B0><U+5174><U+53BF>","Xinxing"
352,49,"CHN","China",6,"Guangdong",59,"Yunfu",352,"Yun'an","Xiàn","County","<U+4E91><U+5B89><U+53BF>","Yún'an"
353,49,"CHN","China",6,"Guangdong",59,"Yunfu",353,"Yunan","Xiàn","County","<U+90C1><U+5357><U+53BF>","Yùnán"
354,49,"CHN","China",6,"Guangdong",59,"Yunfu",354,"Yuncheng","Shìxiáqu","District","<U+4E91><U+57CE><U+533A>","Yúnchéng"
355,49,"CHN","China",6,"Guangdong",60,"Zhanjiang",355,"Leizhou","Xiànjíshì","County City","<U+96F7><U+5DDE><U+5E02>","Léizhou"
356,49,"CHN","China",6,"Guangdong",60,"Zhanjiang",356,"Lianjiang","Xiànjíshì","County City","<U+5EC9><U+6C5F><U+5E02>","Liánjiang"
357,49,"CHN","China",6,"Guangdong",60,"Zhanjiang",357,"Suixi","Xiàn","County","<U+9042><U+6EAA><U+53BF>","Suíxi"
358,49,"CHN","China",6,"Guangdong",60,"Zhanjiang",358,"Wuchuan","Xiànjíshì","County City","<U+5434><U+5DDD><U+5E02>","Wúchuan"
359,49,"CHN","China",6,"Guangdong",60,"Zhanjiang",359,"Xuwen","Xiàn","County","<U+5F90><U+95FB><U+53BF>","Xúwén"
360,49,"CHN","China",6,"Guangdong",60,"Zhanjiang",360,"Zhenjiang","Shìxiáqu","District","<U+6D48><U+6C5F><U+533A>","Zhenjiang"
361,49,"CHN","China",6,"Guangdong",61,"Zhaoqing",361,"Deqing","Xiàn","County","<U+5FB7><U+5E86><U+53BF>","Déqìng"
362,49,"CHN","China",6,"Guangdong",61,"Zhaoqing",362,"Fengkai","Xiàn","County","<U+5C01><U+5F00><U+53BF>","Fengkai"
363,49,"CHN","China",6,"Guangdong",61,"Zhaoqing",363,"Gaoyao","Xiànjíshì","County City","<U+9AD8><U+8981><U+5E02>","Gaoyào"
364,49,"CHN","China",6,"Guangdong",61,"Zhaoqing",364,"Guangning","Xiàn","County","<U+5E7F><U+5B81><U+53BF>","Guangníng"
365,49,"CHN","China",6,"Guangdong",61,"Zhaoqing",365,"Huaiji","Xiàn","County","<U+6000><U+96C6><U+53BF>","Huáijí"
366,49,"CHN","China",6,"Guangdong",61,"Zhaoqing",366,"Sihui","Xiànjíshì","County City","<U+56DB><U+4F1A><U+5E02>","Sìhuì"
367,49,"CHN","China",6,"Guangdong",61,"Zhaoqing",367,"Zhaoqing","Xiànjíshì","County City",NA,NA
368,49,"CHN","China",6,"Guangdong",62,"Zhongshan",368,"Zhongshan","Xiànjíshì","County City","<U+4E2D><U+5C71><U+5E02>","Zhongshan"
369,49,"CHN","China",6,"Guangdong",63,"Zhuhai",369,"Doumen","Shìxiáqu","District","<U+6597><U+95E8><U+533A>","Doumén"
370,49,"CHN","China",6,"Guangdong",63,"Zhuhai",370,"Zhuhai","Xiànjíshì","County City",NA,NA
371,49,"CHN","China",7,"Guangxi",64,"Baise",371,"Baise","Xiànjíshì","County City","<U+767E><U+8272><U+5E02>","Baisè"
372,49,"CHN","China",7,"Guangxi",64,"Baise",372,"Debao","Xiàn","County","<U+5FB7><U+4FDD><U+53BF>","Débao"
373,49,"CHN","China",7,"Guangxi",64,"Baise",373,"Jingxi","Xiàn","County","<U+9756><U+897F><U+53BF>","Jìngxi"
374,49,"CHN","China",7,"Guangxi",64,"Baise",374,"Leye","Xiàn","County","<U+4E50><U+4E1A><U+53BF>","Lèyè"
375,49,"CHN","China",7,"Guangxi",64,"Baise",375,"Lingyun","Xiàn","County","<U+51CC><U+4E91><U+53BF>","Língyún"
376,49,"CHN","China",7,"Guangxi",64,"Baise",376,"Longlin Various Nationalities","Zìzhìxiàn","Autonomous County","<U+9686><U+6797><U+5404><U+65CF><U+81EA><U+6CBB><U+53BF>","Lónglín Gèzú"
377,49,"CHN","China",7,"Guangxi",64,"Baise",377,"Napo","Xiàn","County","<U+90A3><U+5761><U+53BF>","Nàpo"
378,49,"CHN","China",7,"Guangxi",64,"Baise",378,"Pingguo","Xiàn","County","<U+5E73><U+679C><U+53BF>","Píngguo"
379,49,"CHN","China",7,"Guangxi",64,"Baise",379,"Tiandong","Xiàn","County","<U+7530><U+4E1C><U+53BF>","Tiándong"
380,49,"CHN","China",7,"Guangxi",64,"Baise",380,"Tianlin","Xiàn","County","<U+7530><U+6797><U+53BF>","Tiánlín"
381,49,"CHN","China",7,"Guangxi",64,"Baise",381,"Tianyang","Xiàn","County","<U+7530><U+9633><U+53BF>","Tiányáng"
382,49,"CHN","China",7,"Guangxi",64,"Baise",382,"Xilin","Xiàn","County","<U+897F><U+6797><U+53BF>","Xilín"
383,49,"CHN","China",7,"Guangxi",65,"Beihai",383,"Beihai","Xiànjíshì","County City",NA,NA
384,49,"CHN","China",7,"Guangxi",65,"Beihai",384,"Hepu","Xiàn","County","<U+5408><U+6D66><U+53BF>","Hépu"
385,49,"CHN","China",7,"Guangxi",66,"Chongzuo",385,"Changzhou","Shìxiáqu","District","<U+957F><U+6D32><U+533A>","Chángzhou"
386,49,"CHN","China",7,"Guangxi",66,"Chongzuo",386,"Daxin","Xiàn","County","<U+5927><U+65B0><U+53BF>","Dàxin"
387,49,"CHN","China",7,"Guangxi",66,"Chongzuo",387,"Fusui","Xiàn","County","<U+6276><U+7EE5><U+53BF>","Fúsuí"
388,49,"CHN","China",7,"Guangxi",66,"Chongzuo",388,"Longzhou","Xiàn","County","<U+9F99><U+5DDE><U+53BF>","Lóngzhou"
389,49,"CHN","China",7,"Guangxi",66,"Chongzuo",389,"Ningming","Xiàn","County","<U+5B81><U+660E><U+53BF>","Níngmíng"
390,49,"CHN","China",7,"Guangxi",66,"Chongzuo",390,"Pingxiang","Xiànjíshì","County City","<U+51ED><U+7965><U+5E02>","Píngxiáng"
391,49,"CHN","China",7,"Guangxi",66,"Chongzuo",391,"Tiandeng","Xiàn","County","<U+5929><U+7B49><U+53BF>","Tiandeng"
392,49,"CHN","China",7,"Guangxi",67,"Fangchenggang",392,"Fangcheng","Shìxiáqu","District","<U+9632><U+57CE><U+533A>","Fángchéng"
393,49,"CHN","China",7,"Guangxi",67,"Fangchenggang",393,"Shangsi","Xiàn","County","<U+4E0A><U+601D><U+53BF>","Shàngsi"
394,49,"CHN","China",7,"Guangxi",68,"Guigang",394,"Guiping","Xiànjíshì","County City","<U+6842><U+5E73><U+5E02>","Guìpíng"
395,49,"CHN","China",7,"Guangxi",68,"Guigang",395,"Gui","Xiàn","County",NA,NA
396,49,"CHN","China",7,"Guangxi",68,"Guigang",396,"Pingnan","Xiàn","County","<U+5E73><U+5357><U+53BF>","Píngnán"
397,49,"CHN","China",7,"Guangxi",69,"Guilin",397,"Gongcheng Yao","Zìzhìxiàn","Autonomous County","<U+606D><U+57CE><U+7476><U+65CF><U+81EA><U+6CBB><U+53BF>","Gongchéng Yáozú"
398,49,"CHN","China",7,"Guangxi",69,"Guilin",398,"Guanyang","Xiàn","County","<U+704C><U+9633><U+53BF>","Guànyáng"
399,49,"CHN","China",7,"Guangxi",69,"Guilin",399,"Guilin","Shìxiáqu","District",NA,NA
400,49,"CHN","China",7,"Guangxi",69,"Guilin",400,"Lingchuan","Xiàn","County","<U+7075><U+5DDD><U+53BF>","Língchuan"
401,49,"CHN","China",7,"Guangxi",69,"Guilin",401,"Lingui","Xiàn","County","<U+4E34><U+6842><U+53BF>","Línguì"
402,49,"CHN","China",7,"Guangxi",69,"Guilin",402,"Lipu","Xiàn","County","<U+8354><U+6D66><U+53BF>","Lìpu"
403,49,"CHN","China",7,"Guangxi",69,"Guilin",403,"Longsheng Various Nationalities","Zìzhìxiàn","Autonomous County","<U+9F99><U+80DC><U+5404><U+65CF><U+81EA><U+6CBB><U+53BF>","Lóngshèng Gèzú"
404,49,"CHN","China",7,"Guangxi",69,"Guilin",404,"Pingle","Xiàn","County","<U+5E73><U+4E50><U+53BF>","Pínglè"
405,49,"CHN","China",7,"Guangxi",69,"Guilin",405,"Quanzhou","Xiàn","County","<U+5168><U+5DDE><U+53BF>","Quánzhou"
406,49,"CHN","China",7,"Guangxi",69,"Guilin",406,"Xing’an","Xiàn","County","<U+5174><U+5B89><U+53BF>","Xing’an"
407,49,"CHN","China",7,"Guangxi",69,"Guilin",407,"Yangshuo","Xiàn","County","<U+9633><U+6714><U+53BF>","Yángshuò"
408,49,"CHN","China",7,"Guangxi",69,"Guilin",408,"Yongfu","Xiàn","County","<U+6C38><U+798F><U+53BF>","Yongfú"
409,49,"CHN","China",7,"Guangxi",69,"Guilin",409,"Ziyuan","Xiàn","County","<U+8D44><U+6E90><U+53BF>","Ziyuán"
410,49,"CHN","China",7,"Guangxi",70,"Hechi",410,"Bama Yao","Zìzhìxiàn","Autonomous County","<U+5DF4><U+9A6C><U+7476><U+65CF><U+81EA><U+6CBB><U+53BF>","Bama Yáozú"
411,49,"CHN","China",7,"Guangxi",70,"Hechi",411,"Dahua Yao 	","Zìzhìxiàn","Autonomous County","<U+5927><U+5316><U+7476><U+65CF><U+81EA><U+6CBB><U+53BF>","Dàhuà Yáozú"
412,49,"CHN","China",7,"Guangxi",70,"Hechi",412,"Donglan","Xiàn","County","<U+4E1C><U+5170><U+53BF>","Donglán"
413,49,"CHN","China",7,"Guangxi",70,"Hechi",413,"Du’an Yao","Zìzhìxiàn","Autonomous County","<U+90FD><U+5B89><U+7476><U+65CF><U+81EA><U+6CBB><U+53BF>","Du'an Yáozú"
414,49,"CHN","China",7,"Guangxi",70,"Hechi",414,"Fengshan","Xiàn","County","<U+51E4><U+5C71><U+53BF>","Fèngshan"
415,49,"CHN","China",7,"Guangxi",70,"Hechi",415,"Hechi","Zizhiqu","County",NA,NA
416,49,"CHN","China",7,"Guangxi",70,"Hechi",416,"Huanjiang Maonan","Zìzhìxiàn","Autonomous County","<U+73AF><U+6C5F><U+6BDB><U+5357><U+65CF><U+81EA><U+6CBB><U+53BF>","Huánjiang Máonánzú"
417,49,"CHN","China",7,"Guangxi",70,"Hechi",417,"Luocheng Mulao","Zìzhìxiàn","Autonomous County","<U+7F57><U+57CE><U+4EEB><U+4F6C><U+65CF><U+81EA><U+6CBB><U+53BF>","Luóchéng Mùlaozú"
418,49,"CHN","China",7,"Guangxi",70,"Hechi",418,"Nandan","Xiàn","County","<U+5357><U+4E39><U+53BF>","Nándan"
419,49,"CHN","China",7,"Guangxi",70,"Hechi",419,"Tian'e","Xiàn","County","<U+5929><U+5CE8><U+53BF>","Tian'é"
420,49,"CHN","China",7,"Guangxi",70,"Hechi",420,"Yanshan","Shìxiáqu","District","<U+96C1><U+5C71><U+533A>","Yànshan"
421,49,"CHN","China",7,"Guangxi",71,"Hezhou",421,"Fuchuan Yao","Zìzhìxiàn","Autonomous County","<U+5BCC><U+5DDD><U+7476><U+65CF><U+81EA><U+6CBB><U+53BF>","Fùchuan Yáozú"
422,49,"CHN","China",7,"Guangxi",71,"Hezhou",422,"He","Xiàn","County",NA,NA
423,49,"CHN","China",7,"Guangxi",71,"Hezhou",423,"Zhaoping","Xiàn","County","<U+662D><U+5E73><U+53BF>","Zhaopíng"
424,49,"CHN","China",7,"Guangxi",71,"Hezhou",424,"Zhongshan","Xiàn","County","<U+949F><U+5C71><U+53BF>","Zhongshan"
425,49,"CHN","China",7,"Guangxi",72,"Laibin",425,"Heshan","Xiànjíshì","County City","<U+5408><U+5C71><U+5E02>","Héshan"
426,49,"CHN","China",7,"Guangxi",72,"Laibin",426,"Jinxiu Yao","Zìzhìxiàn","Autonomous County","<U+91D1><U+79C0><U+7476><U+65CF><U+81EA><U+6CBB><U+53BF>","Jinxiù Yáozú"
427,49,"CHN","China",7,"Guangxi",72,"Laibin",427,"Laibin","Zizhiqu","County",NA,NA
428,49,"CHN","China",7,"Guangxi",72,"Laibin",428,"Wuxuan","Xiàn","County","<U+6B66><U+5BA3><U+53BF>","Wuxuan"
429,49,"CHN","China",7,"Guangxi",72,"Laibin",429,"Xiangzhou","Xiàn","County","<U+8C61><U+5DDE><U+53BF>","Xiàngzhou"
430,49,"CHN","China",7,"Guangxi",72,"Laibin",430,"Xincheng","Xiàn","County","<U+5FFB><U+57CE><U+53BF>","Xinchéng"
431,49,"CHN","China",7,"Guangxi",73,"Liuzhou",431,"Liucheng","Xiàn","County","<U+67F3><U+57CE><U+53BF>","Liuchéng"
432,49,"CHN","China",7,"Guangxi",73,"Liuzhou",432,"Liujiang","Xiàn","County","<U+67F3><U+6C5F><U+53BF>","Liujiang"
433,49,"CHN","China",7,"Guangxi",73,"Liuzhou",433,"Liuzhou","Xiànjíshì","County City",NA,NA
434,49,"CHN","China",7,"Guangxi",73,"Liuzhou",434,"Luzhai","Xiàn","County","<U+9E7F><U+5BE8><U+53BF>","Lùzhài"
435,49,"CHN","China",7,"Guangxi",73,"Liuzhou",435,"Rong'an","Xiàn","County","<U+878D><U+5B89><U+53BF>","Róng'an"
436,49,"CHN","China",7,"Guangxi",73,"Liuzhou",436,"Rongshui Miao","Zìzhìxiàn","Autonomous County","<U+878D><U+6C34><U+82D7><U+65CF><U+81EA><U+6CBB><U+53BF>","Róngshui Miáozú"
437,49,"CHN","China",7,"Guangxi",73,"Liuzhou",437,"Sanjiang Dong","Zìzhìxiàn","Autonomous County","<U+4E09><U+6C5F><U+4F97><U+65CF><U+81EA><U+6CBB><U+53BF>","Sanjiang Dòngzú"
438,49,"CHN","China",7,"Guangxi",74,"Nanning",438,"Binyang","Xiàn","County","<U+5BBE><U+9633><U+53BF>","Binyáng"
439,49,"CHN","China",7,"Guangxi",74,"Nanning",439,"Heng","Xiàn","County","<U+6A2A><U+53BF>","Héng"
440,49,"CHN","China",7,"Guangxi",74,"Nanning",440,"Long'an","Xiàn","County","<U+9686><U+5B89><U+53BF>","Lóng'an"
441,49,"CHN","China",7,"Guangxi",74,"Nanning",441,"Mashan","Xiàn","County","<U+9A6C><U+5C71><U+53BF>","Mashan"
442,49,"CHN","China",7,"Guangxi",74,"Nanning",442,"Nanning","Zizhiqu","County",NA,NA
443,49,"CHN","China",7,"Guangxi",74,"Nanning",443,"Shanglin","Xiàn","County","<U+4E0A><U+6797><U+53BF>","Shànglín"
444,49,"CHN","China",7,"Guangxi",74,"Nanning",444,"Wuming","Xiàn","County","<U+6B66><U+9E23><U+53BF>","Wumíng"
445,49,"CHN","China",7,"Guangxi",74,"Nanning",445,"Yongning","Shìxiáqu","District","<U+9095><U+5B81><U+533A>","Yongníng"
446,49,"CHN","China",7,"Guangxi",75,"Qinzhou",446,"Lingshan","Xiàn","County","<U+7075><U+5C71><U+53BF>","Língshan"
447,49,"CHN","China",7,"Guangxi",75,"Qinzhou",447,"Pubei","Xiàn","County","<U+6D66><U+5317><U+53BF>","Pubei"
448,49,"CHN","China",7,"Guangxi",75,"Qinzhou",448,"Qinzhou","Zizhiqu","County",NA,NA
449,49,"CHN","China",7,"Guangxi",76,"Wuzhou",449,"Cangwu","Xiàn","County","<U+82CD><U+68A7><U+53BF>","Cangwú"
450,49,"CHN","China",7,"Guangxi",76,"Wuzhou",450,"Cenxi","Xiànjíshì","County City","<U+5C91><U+6EAA><U+5E02>","Cénxi"
451,49,"CHN","China",7,"Guangxi",76,"Wuzhou",451,"Mengshan","Xiàn","County","<U+8499><U+5C71><U+53BF>","Méngshan"
452,49,"CHN","China",7,"Guangxi",76,"Wuzhou",452,"Teng","Xiàn","County","<U+85E4><U+53BF>","Téng"
453,49,"CHN","China",7,"Guangxi",76,"Wuzhou",453,"Wuzhou","Xiànjíshì","County City","<U+68A7><U+5DDE><U+5E02>","Wúzhou"
454,49,"CHN","China",7,"Guangxi",77,"Yulin",454,"Beiliu","Xiànjíshì","County City","<U+5317><U+6D41><U+5E02>","Beiliú"
455,49,"CHN","China",7,"Guangxi",77,"Yulin",455,"Bobai","Xiàn","County","<U+535A><U+767D><U+53BF>","Bóbái"
456,49,"CHN","China",7,"Guangxi",77,"Yulin",456,"Luchuan","Xiàn","County","<U+9646><U+5DDD><U+53BF>","Lùchuan"
457,49,"CHN","China",7,"Guangxi",77,"Yulin",457,"Rong","Xiàn","County","<U+5BB9><U+53BF>","Róng"
458,49,"CHN","China",7,"Guangxi",77,"Yulin",458,"Yufeng","Shìxiáqu","District","<U+9C7C><U+5CF0><U+533A>","Yúfeng"
459,49,"CHN","China",8,"Guizhou",78,"Anshun",459,"Anshun","Xiàn","County",NA,NA
460,49,"CHN","China",8,"Guizhou",78,"Anshun",460,"Guanling Buyei and Miao","Zìzhìxiàn","Autonomous County","<U+5173><U+5CAD><U+5E03><U+4F9D><U+65CF>|<U+82D7><U+65CF><U+81EA><U+6CBB><U+53BF>","Guanling Bùyizú|Miáozú"
461,49,"CHN","China",8,"Guizhou",78,"Anshun",461,"Pingba","Xiàn","County","<U+5E73><U+575D><U+53BF>","Píngbà"
462,49,"CHN","China",8,"Guizhou",78,"Anshun",462,"Puding","Xiàn","County","<U+666E><U+5B9A><U+53BF>","Pudìng"
463,49,"CHN","China",8,"Guizhou",78,"Anshun",463,"Zhenning Buyei and Miao","Zìzhìxiàn","Autonomous County","<U+9547><U+5B81><U+5E03><U+4F9D><U+65CF>|<U+82D7><U+65CF><U+81EA><U+6CBB><U+53BF>","Zhènníng Bùyizú|Miáozú"
464,49,"CHN","China",8,"Guizhou",78,"Anshun",464,"Ziyun Miao and Buyei","Zìzhìxiàn","Autonomous County","<U+7D2B><U+4E91><U+82D7><U+65CF>|<U+5E03><U+4F9D><U+65CF><U+81EA><U+6CBB><U+53BF>","Ziyún Miáozú|Bùyizú"
465,49,"CHN","China",8,"Guizhou",79,"Bijie",465,"Bijie","Xiànjíshì","County City","<U+6BD5><U+8282><U+5E02>","Bìjié"
466,49,"CHN","China",8,"Guizhou",79,"Bijie",466,"Dafang","Xiàn","County","<U+5927><U+65B9><U+53BF>","Dàfang"
467,49,"CHN","China",8,"Guizhou",79,"Bijie",467,"Hezhang","Xiàn","County","<U+8D6B><U+7AE0><U+53BF>","Hèzhang"
468,49,"CHN","China",8,"Guizhou",79,"Bijie",468,"Jinsha","Xiàn","County","<U+91D1><U+6C99><U+53BF>","Jinsha"
469,49,"CHN","China",8,"Guizhou",79,"Bijie",469,"Nayong","Xiàn","County","<U+7EB3><U+96CD><U+53BF>","Nàyong"
470,49,"CHN","China",8,"Guizhou",79,"Bijie",470,"Qianxi","Xiàn","County","<U+9ED4><U+897F><U+53BF>","Qiánxi"
471,49,"CHN","China",8,"Guizhou",79,"Bijie",471,"Weining Yi, Hui and Miao","Zìzhìxiàn","Autonomous County","<U+5A01><U+5B81><U+5F5D><U+65CF><U+56DE><U+65CF><U+82D7><U+65CF><U+81EA><U+6CBB><U+53BF>","Weiníng Yízú Huízú Miáozú"
472,49,"CHN","China",8,"Guizhou",79,"Bijie",472,"Zhijin","Xiàn","County","<U+7EC7><U+91D1><U+53BF>","Zhijin"
473,49,"CHN","China",8,"Guizhou",80,"Guiyang",473,"Guiyang","Xiànjíshì","County City",NA,NA
474,49,"CHN","China",8,"Guizhou",80,"Guiyang",474,"Kaiyang","Xiàn","County","<U+5F00><U+9633><U+53BF>","Kaiyáng"
475,49,"CHN","China",8,"Guizhou",80,"Guiyang",475,"Qingzhen","Xiànjíshì","County City","<U+6E05><U+9547><U+5E02>","Qingzhèn"
476,49,"CHN","China",8,"Guizhou",80,"Guiyang",476,"Xifeng","Xiàn","County","<U+606F><U+70FD><U+53BF>","Xifeng"
477,49,"CHN","China",8,"Guizhou",80,"Guiyang",477,"Xiuwen","Xiàn","County","<U+4FEE><U+6587><U+53BF>","Xiuwén"
478,49,"CHN","China",8,"Guizhou",81,"Liupanshui",478,"Liuzhi","Tèqu","Special District","<U+516D><U+679D><U+7279><U+533A>","Liùzhi"
479,49,"CHN","China",8,"Guizhou",81,"Liupanshui",479,"Pan","Xiàn","County","<U+76D8><U+53BF>","Pán"
480,49,"CHN","China",8,"Guizhou",81,"Liupanshui",480,"Shuicheng","Xiàn","County","<U+6C34><U+57CE><U+53BF>","Shuichéng"
481,49,"CHN","China",8,"Guizhou",81,"Liupanshui",481,"Zhongshan","Shìxiáqu","District","<U+949F><U+5C71><U+533A>","Zhongshan"
482,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong",482,"Cengong","Xiàn","County","<U+5C91><U+5DE9><U+53BF>","Céngong"
483,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong",483,"Congjiang","Xiàn","County","<U+4ECE><U+6C5F><U+53BF>","Cóngjiang"
484,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong",484,"Danzhai","Xiàn","County","<U+4E39><U+5BE8><U+53BF>","Danzhài"
485,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong",485,"Huangping","Xiàn","County","<U+9EC4><U+5E73><U+53BF>","Huángpíng"
486,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong",486,"Jianhe","Xiàn","County","<U+5251><U+6CB3><U+53BF>","Jiànhé"
487,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong",487,"Jinping","Xiàn","County","<U+9526><U+5C4F><U+53BF>","Jinpíng"
488,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong",488,"Kaili","Xiànjíshì","County City","<U+51EF><U+91CC><U+5E02>","Kaili"
489,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong",489,"Leishan","Xiàn","County","<U+96F7><U+5C71><U+53BF>","Léishan"
490,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong",490,"Liping","Xiàn","County","<U+9ECE><U+5E73><U+53BF>","Lípíng"
491,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong",491,"Majiang","Xiàn","County","<U+9EBB><U+6C5F><U+53BF>","Májiang"
492,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong",492,"Rongjiang","Xiàn","County","<U+6995><U+6C5F><U+53BF>","Róngjiang"
493,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong",493,"Sansui","Xiàn","County","<U+4E09><U+7A57><U+53BF>","Sansuì"
494,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong",494,"Shibing","Xiàn","County","<U+65BD><U+79C9><U+53BF>","Shibing"
495,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong",495,"Taijiang","Xiàn","County","<U+53F0><U+6C5F><U+53BF>","Táijiang"
496,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong",496,"Tianzhu","Xiàn","County","<U+5929><U+67F1><U+53BF>","Tianzhù"
497,49,"CHN","China",8,"Guizhou",82,"Qiandongnan Miao and Dong",497,"Zhenyuan","Xiàn","County","<U+9547><U+8FDC><U+53BF>","Zhènyuan"
498,49,"CHN","China",8,"Guizhou",83,"Qiannan Buyei and Miao",498,"Changshun","Xiàn","County","<U+957F><U+987A><U+53BF>","Chángshùn"
499,49,"CHN","China",8,"Guizhou",83,"Qiannan Buyei and Miao",499,"Dushan","Xiàn","County","<U+72EC><U+5C71><U+53BF>","Dúshan"
500,49,"CHN","China",8,"Guizhou",83,"Qiannan Buyei and Miao",500,"Duyun","Xiànjíshì","County City","<U+90FD><U+5300><U+5E02>","Duyún"
501,49,"CHN","China",8,"Guizhou",83,"Qiannan Buyei and Miao",501,"Fuquan","Xiànjíshì","County City","<U+798F><U+6CC9><U+5E02>","Fúquán"
502,49,"CHN","China",8,"Guizhou",83,"Qiannan Buyei and Miao",502,"Guiding","Xiàn","County","<U+8D35><U+5B9A><U+53BF>","Guìdìng"
503,49,"CHN","China",8,"Guizhou",83,"Qiannan Buyei and Miao",503,"Huaxi","Shìxiáqu","District","<U+82B1><U+6EAA><U+533A>","Huaxi"
504,49,"CHN","China",8,"Guizhou",83,"Qiannan Buyei and Miao",504,"Libo","Xiàn","County","<U+8354><U+6CE2><U+53BF>","Lìbo"
505,49,"CHN","China",8,"Guizhou",83,"Qiannan Buyei and Miao",505,"Longli","Xiàn","County","<U+9F99><U+91CC><U+53BF>","Lóngli"
506,49,"CHN","China",8,"Guizhou",83,"Qiannan Buyei and Miao",506,"Luodian","Xiàn","County","<U+7F57><U+7538><U+53BF>","Luódiàn"
507,49,"CHN","China",8,"Guizhou",83,"Qiannan Buyei and Miao",507,"Pingtang","Xiàn","County","<U+5E73><U+5858><U+53BF>","Píngtáng"
508,49,"CHN","China",8,"Guizhou",83,"Qiannan Buyei and Miao",508,"Sandu Shui","Zìzhìxiàn","Autonomous County","<U+4E09><U+90FD><U+6C34><U+65CF><U+81EA><U+6CBB><U+53BF>","Sandu Shuizú"
509,49,"CHN","China",8,"Guizhou",83,"Qiannan Buyei and Miao",509,"Weng'an","Xiàn","County","<U+74EE><U+5B89><U+53BF>","Wèng'an"
510,49,"CHN","China",8,"Guizhou",84,"Qianxinan Buyei and Miao",510,"Anlong","Xiàn","County","<U+5B89><U+9F99><U+53BF>","Anlóng"
511,49,"CHN","China",8,"Guizhou",84,"Qianxinan Buyei and Miao",511,"Ceheng","Xiàn","County","<U+518C><U+4EA8><U+53BF>","Cèheng"
512,49,"CHN","China",8,"Guizhou",84,"Qianxinan Buyei and Miao",512,"Pu'an","Xiàn","County","<U+666E><U+5B89><U+53BF>","Pu'an"
513,49,"CHN","China",8,"Guizhou",84,"Qianxinan Buyei and Miao",513,"Qinglong","Xiàn","County","<U+6674><U+9686><U+53BF>","Qínglóng"
514,49,"CHN","China",8,"Guizhou",84,"Qianxinan Buyei and Miao",514,"Wangmo","Xiàn","County","<U+671B><U+8C1F><U+53BF>","Wàngmó"
515,49,"CHN","China",8,"Guizhou",84,"Qianxinan Buyei and Miao",515,"Xingren","Xiàn","County","<U+5174><U+4EC1><U+53BF>","Xingrén"
516,49,"CHN","China",8,"Guizhou",84,"Qianxinan Buyei and Miao",516,"Xingyi","Xiànjíshì","County City","<U+5174><U+4E49><U+5E02>","Xingyì"
517,49,"CHN","China",8,"Guizhou",84,"Qianxinan Buyei and Miao",517,"Zhenfeng","Xiàn","County","<U+8D1E><U+4E30><U+53BF>","Zhenfeng"
518,49,"CHN","China",8,"Guizhou",85,"Tongren",518,"Dejiang","Xiàn","County","<U+5FB7><U+6C5F><U+53BF>","Déjiang"
519,49,"CHN","China",8,"Guizhou",85,"Tongren",519,"Jiangkou","Xiàn","County","<U+6C5F><U+53E3><U+53BF>","Jiangkou"
520,49,"CHN","China",8,"Guizhou",85,"Tongren",520,"Shiqian","Xiàn","County","<U+77F3><U+9621><U+53BF>","Shíqian"
521,49,"CHN","China",8,"Guizhou",85,"Tongren",521,"Sinan","Xiàn","County","<U+601D><U+5357><U+53BF>","Sinán"
522,49,"CHN","China",8,"Guizhou",85,"Tongren",522,"Songtao Miao","Zìzhìxiàn","Autonomous County","<U+677E><U+6843><U+82D7><U+65CF><U+81EA><U+6CBB><U+53BF>","Songtáo Miáozú"
523,49,"CHN","China",8,"Guizhou",85,"Tongren",523,"Tongren","Xiànjíshì","County City","<U+94DC><U+4EC1><U+5E02>","Tóngrén"
524,49,"CHN","China",8,"Guizhou",85,"Tongren",524,"Wanshan Special","Shìxiáqu","District","<U+4E07><U+5C71><U+7279><U+533A>","Wànshan T"
525,49,"CHN","China",8,"Guizhou",85,"Tongren",525,"Yanhe Tujia","Zìzhìxiàn","Autonomous County","<U+6CBF><U+6CB3><U+571F><U+5BB6><U+65CF><U+81EA><U+6CBB><U+53BF>","Yánhé Tujiazú"
526,49,"CHN","China",8,"Guizhou",85,"Tongren",526,"Yinjiang Tujia and Miao","Zìzhìxiàn","Autonomous County","<U+5370><U+6C5F><U+571F><U+5BB6><U+65CF>|<U+82D7><U+65CF><U+81EA><U+6CBB><U+53BF>","Yìnjiang Tujiazú|Miáozú"
527,49,"CHN","China",8,"Guizhou",85,"Tongren",527,"Yuping Dong","Zìzhìxiàn","Autonomous County","<U+7389><U+5C4F><U+4F97><U+65CF><U+81EA><U+6CBB><U+53BF>","Yùpíng Dòngzú"
528,49,"CHN","China",8,"Guizhou",86,"Zunyi",528,"Chishui","Xiànjíshì","County City","<U+8D64><U+6C34><U+5E02>","Chìshui"
529,49,"CHN","China",8,"Guizhou",86,"Zunyi",529,"Daozhen Gelao and Miao","Zìzhìxiàn","Autonomous County","<U+9053><U+771F><U+4EE1><U+4F6C><U+65CF>|<U+82D7><U+65CF><U+81EA><U+6CBB><U+53BF>","Dàozhen Gelaozú|Miáozú"
530,49,"CHN","China",8,"Guizhou",86,"Zunyi",530,"Fenggang","Xiàn","County","<U+51E4><U+5188><U+53BF>","Fènggang"
531,49,"CHN","China",8,"Guizhou",86,"Zunyi",531,"Meitan","Xiàn","County","<U+6E44><U+6F6D><U+53BF>","Méitán"
532,49,"CHN","China",8,"Guizhou",86,"Zunyi",532,"Renhuai","Xiànjíshì","County City","<U+4EC1><U+6000><U+5E02>","Rénhuái"
533,49,"CHN","China",8,"Guizhou",86,"Zunyi",533,"Suiyang","Xiàn","County","<U+7EE5><U+9633><U+53BF>","Suíyáng"
534,49,"CHN","China",8,"Guizhou",86,"Zunyi",534,"Tongzi","Xiàn","County","<U+6850><U+6893><U+53BF>","Tóngzi"
535,49,"CHN","China",8,"Guizhou",86,"Zunyi",535,"Wuchuan Gelao and Miao","Zìzhìxiàn","Autonomous County","<U+52A1><U+5DDD><U+4EE1><U+4F6C><U+65CF>|<U+82D7><U+65CF><U+81EA><U+6CBB><U+53BF>","Wùchuan Gelaozú|Miáozú"
536,49,"CHN","China",8,"Guizhou",86,"Zunyi",536,"Xishui","Xiàn","County","<U+4E60><U+6C34><U+53BF>","Xíshui"
537,49,"CHN","China",8,"Guizhou",86,"Zunyi",537,"Yuqing","Xiàn","County","<U+4F59><U+5E86><U+53BF>","Yúqìng"
538,49,"CHN","China",8,"Guizhou",86,"Zunyi",538,"Zheng'an","Xiàn","County","<U+6B63><U+5B89><U+53BF>","Zhèng'an"
539,49,"CHN","China",8,"Guizhou",86,"Zunyi",539,"Zunyi Shì","Xiànjíshì","County City","<U+9075><U+4E49><U+5E02>",NA
540,49,"CHN","China",8,"Guizhou",86,"Zunyi",540,"Zunyi Xiàn","Xiàn","County","<U+9075><U+4E49><U+53BF>","Zunyì"
541,49,"CHN","China",9,"Hainan",87,"Haikou",541,"Haikou","Xiànjíshì","County City","<U+6D77><U+53E3><U+5E02>",NA
542,49,"CHN","China",9,"Hainan",87,"Haikou",542,"Qiongshan","Shìxiáqu","District","<U+743C><U+5C71><U+533A>","Qióngshan"
543,49,"CHN","China",9,"Hainan",88,"Hainan",543,"Baisha Li","Zìzhìxiàn","Autonomous County","<U+767D><U+6C99><U+9ECE><U+65CF><U+81EA><U+6CBB><U+53BF>","Báisha Lízú"
544,49,"CHN","China",9,"Hainan",88,"Hainan",544,"Baoting Li","Zìzhìxiàn","Autonomous County","<U+4FDD><U+4EAD><U+9ECE><U+65CF><U+82D7><U+65CF><U+81EA><U+6CBB><U+53BF>","Baotíng Lízú Miáozú"
545,49,"CHN","China",9,"Hainan",88,"Hainan",545,"Changjiang Li","Zìzhìxiàn","Autonomous County","<U+660C><U+6C5F><U+9ECE><U+65CF><U+81EA><U+6CBB><U+53BF>","Changjiang Lízú"
546,49,"CHN","China",9,"Hainan",88,"Hainan",546,"Chengmai","Xiàn","County","<U+6F84><U+8FC8><U+53BF>","Chéngmài"
547,49,"CHN","China",9,"Hainan",88,"Hainan",547,"Danzhou","Xiànjíshì","County City","<U+510B><U+5DDE><U+5E02>","Danzhou"
548,49,"CHN","China",9,"Hainan",88,"Hainan",548,"Ding An","Xiàn","County","<U+5B9A><U+5B89><U+53BF>","Dìng'an"
549,49,"CHN","China",9,"Hainan",88,"Hainan",549,"Dongfang","Xiànjíshì","County City","<U+4E1C><U+65B9><U+5E02>","Dongfang"
550,49,"CHN","China",9,"Hainan",88,"Hainan",550,"Ledong Li","Zìzhìxiàn","Autonomous County","<U+4E50><U+4E1C><U+9ECE><U+65CF><U+81EA><U+6CBB><U+53BF>","Lèdong Lízú"
551,49,"CHN","China",9,"Hainan",88,"Hainan",551,"Lingao","Xiàn","County",NA,"Língao"
552,49,"CHN","China",9,"Hainan",88,"Hainan",552,"Lingshui Li","Zìzhìxiàn","Autonomous County","<U+9675><U+6C34><U+9ECE><U+65CF><U+81EA><U+6CBB><U+53BF>","Língshui Lízú"
553,49,"CHN","China",9,"Hainan",88,"Hainan",553,"Qionghai","Xiàn","County",NA,NA
554,49,"CHN","China",9,"Hainan",88,"Hainan",554,"Qiongzhong Li and Miao","Zìzhìxiàn","Autonomous County","<U+743C><U+4E2D><U+9ECE><U+65CF><U+82D7><U+65CF><U+81EA><U+6CBB><U+53BF>","Qióngzhong Lízú Miáozú"
555,49,"CHN","China",9,"Hainan",88,"Hainan",555,"Tunchang","Xiàn","County",NA,NA
556,49,"CHN","China",9,"Hainan",88,"Hainan",556,"Wanning","Xiànjíshì","County City","<U+4E07><U+5B81><U+5E02>","Wànníng"
557,49,"CHN","China",9,"Hainan",88,"Hainan",557,"Wenchang","Xiànjíshì","County City","<U+6587><U+660C><U+5E02>","Wénchang"
558,49,"CHN","China",9,"Hainan",89,"Sanya",558,"Sanya","Xiànjíshì","County City","<U+4E09><U+4E9A><U+5E02>","Sanyà"
559,49,"CHN","China",10,"Hebei",90,"Baoding",559,"Anguo","Xiànjíshì","County City","<U+5B89><U+56FD><U+5E02>","Anguó"
560,49,"CHN","China",10,"Hebei",90,"Baoding",560,"Anxin","Xiàn","County","<U+5B89><U+65B0><U+53BF>","Anxin"
561,49,"CHN","China",10,"Hebei",90,"Baoding",561,"Baoding","Xiànjíshì","County City",NA,NA
562,49,"CHN","China",10,"Hebei",90,"Baoding",562,"Boye","Xiàn","County","<U+535A><U+91CE><U+53BF>","Bóye"
563,49,"CHN","China",10,"Hebei",90,"Baoding",563,"Ding","Xiàn","County",NA,NA
564,49,"CHN","China",10,"Hebei",90,"Baoding",564,"Dingxing","Xiàn","County","<U+5B9A><U+5174><U+53BF>","Dìngxing"
565,49,"CHN","China",10,"Hebei",90,"Baoding",565,"Fuping","Xiàn","County","<U+961C><U+5E73><U+53BF>","Fùpíng"
566,49,"CHN","China",10,"Hebei",90,"Baoding",566,"Gaoyang","Xiàn","County","<U+9AD8><U+9633><U+53BF>","Gaoyáng"
567,49,"CHN","China",10,"Hebei",90,"Baoding",567,"Laishui","Xiàn","County","<U+6D9E><U+6C34><U+53BF>","Láishui"
568,49,"CHN","China",10,"Hebei",90,"Baoding",568,"Laiyuan","Xiàn","County","<U+6D9E><U+6E90><U+53BF>","Láiyuán"
569,49,"CHN","China",10,"Hebei",90,"Baoding",569,"Li","Xiàn","County","<U+8821><U+53BF>","Li"
570,49,"CHN","China",10,"Hebei",90,"Baoding",570,"Mancheng","Xiàn","County","<U+6EE1><U+57CE><U+53BF>","Manchéng"
571,49,"CHN","China",10,"Hebei",90,"Baoding",571,"Qing Yuan","Xiàn","County",NA,NA
572,49,"CHN","China",10,"Hebei",90,"Baoding",572,"Quyang","Xiàn","County","<U+66F2><U+9633><U+53BF>","Quyáng"
573,49,"CHN","China",10,"Hebei",90,"Baoding",573,"Rongcheng","Xiàn","County","<U+5BB9><U+57CE><U+53BF>","Róngchéng"
574,49,"CHN","China",10,"Hebei",90,"Baoding",574,"Tang","Xiàn","County","<U+5510><U+53BF>","Táng"
575,49,"CHN","China",10,"Hebei",90,"Baoding",575,"Wangdu","Xiàn","County","<U+671B><U+90FD><U+53BF>","Wàngdu"
576,49,"CHN","China",10,"Hebei",90,"Baoding",576,"Wan","Xiàn","County",NA,NA
577,49,"CHN","China",10,"Hebei",90,"Baoding",577,"Xincheng","Xiàn","County",NA,NA
578,49,"CHN","China",10,"Hebei",90,"Baoding",578,"Xiong","Xiàn","County","<U+96C4><U+53BF>","Xióng"
579,49,"CHN","China",10,"Hebei",90,"Baoding",579,"Xushui","Xiàn","County","<U+5F90><U+6C34><U+53BF>","Xúshui"
580,49,"CHN","China",10,"Hebei",90,"Baoding",580,"Yi","Xiàn","County","<U+6613><U+53BF>","Yì"
581,49,"CHN","China",10,"Hebei",90,"Baoding",581,"Zhuo","Xiàn","County",NA,NA
582,49,"CHN","China",10,"Hebei",91,"Cangzhou",582,"Cang","Xiàn","County","<U+6CA7><U+53BF>","Cang"
583,49,"CHN","China",10,"Hebei",91,"Cangzhou",583,"Cangzhou","Xiàn","County",NA,NA
584,49,"CHN","China",10,"Hebei",91,"Cangzhou",584,"Dongguang","Xiàn","County","<U+4E1C><U+5149><U+53BF>","Dongguang"
585,49,"CHN","China",10,"Hebei",91,"Cangzhou",585,"Haixing","Xiàn","County","<U+6D77><U+5174><U+53BF>","Haixing"
586,49,"CHN","China",10,"Hebei",91,"Cangzhou",586,"Hejian","Xiànjíshì","County City","<U+6CB3><U+95F4><U+5E02>","Héjian"
587,49,"CHN","China",10,"Hebei",91,"Cangzhou",587,"Huanghua","Xiànjíshì","County City","<U+9EC4><U+9A85><U+5E02>","Huánghuá"
588,49,"CHN","China",10,"Hebei",91,"Cangzhou",588,"Jiaohe","Xiàn","County",NA,NA
589,49,"CHN","China",10,"Hebei",91,"Cangzhou",589,"Mengcun Hui","Zìzhìxiàn","Autonomous County","<U+5B5F><U+6751><U+56DE><U+65CF><U+81EA><U+6CBB><U+53BF>","Mèngcun Huízú"
590,49,"CHN","China",10,"Hebei",91,"Cangzhou",590,"Nanpi","Xiàn","County","<U+5357><U+76AE><U+53BF>","Nánpí"
591,49,"CHN","China",10,"Hebei",91,"Cangzhou",591,"Qing","Xiàn","County",NA,NA
592,49,"CHN","China",10,"Hebei",91,"Cangzhou",592,"Renqiu","Xiànjíshì","County City","<U+4EFB><U+4E18><U+5E02>","Rénqiu Shì not Rèn"
593,49,"CHN","China",10,"Hebei",91,"Cangzhou",593,"Suning","Xiàn","County","<U+8083><U+5B81><U+53BF>","Sùníng"
594,49,"CHN","China",10,"Hebei",91,"Cangzhou",594,"Wuqiao","Xiàn","County","<U+5434><U+6865><U+53BF>","Wúqiáo"
595,49,"CHN","China",10,"Hebei",91,"Cangzhou",595,"Xian","Xiàn","County","<U+732E><U+53BF>","Xìàn"
596,49,"CHN","China",10,"Hebei",91,"Cangzhou",596,"Yanshan","Xiàn","County","<U+76D0><U+5C71><U+53BF>","Yánshan"
597,49,"CHN","China",10,"Hebei",92,"Chengde",597,"Chengde Shì","Xiànjíshì","County City","<U+627F><U+5FB7><U+5E02>","Chéngdé"
598,49,"CHN","China",10,"Hebei",92,"Chengde",598,"Chengde Xiàn","Xiàn","County","<U+627F><U+5FB7><U+53BF>","Chéngdé"
599,49,"CHN","China",10,"Hebei",92,"Chengde",599,"Fengning Manchu","Zìzhìxiàn","Autonomous County","<U+4E30><U+5B81><U+6EE1><U+65CF><U+81EA><U+6CBB><U+53BF>","Fengníng Manzú"
600,49,"CHN","China",10,"Hebei",92,"Chengde",600,"Kuancheng Manchu","Zìzhìxiàn","Autonomous County","<U+5BBD><U+57CE><U+6EE1><U+65CF><U+81EA><U+6CBB><U+53BF>","Kuanchéng Manzú"
601,49,"CHN","China",10,"Hebei",92,"Chengde",601,"Longhua","Xiàn","County","<U+9686><U+5316><U+53BF>","Lónghuà"
602,49,"CHN","China",10,"Hebei",92,"Chengde",602,"Luanping","Xiàn","County","<U+6EE6><U+5E73><U+53BF>","Luánpíng"
603,49,"CHN","China",10,"Hebei",92,"Chengde",603,"Pingquan","Xiàn","County","<U+5E73><U+6CC9><U+53BF>","Píngquán"
604,49,"CHN","China",10,"Hebei",92,"Chengde",604,"Weichang Manchu and Mongol","Zìzhìxiàn","Autonomous County","<U+56F4><U+573A><U+6EE1><U+65CF>|<U+8499><U+53E4><U+65CF><U+81EA><U+6CBB><U+53BF>","Wéichang Manzú|Mengguzú"
605,49,"CHN","China",10,"Hebei",92,"Chengde",605,"Xinglong","Xiàn","County","<U+5174><U+9686><U+53BF>","Xinglóng"
606,49,"CHN","China",10,"Hebei",92,"Chengde",606,"Yingshouyingzikuang","Shìxiáqu","District","<U+9E70><U+624B><U+8425><U+5B50><U+77FF><U+533A>","Yingshouyíngzi Kuàng"
607,49,"CHN","China",10,"Hebei",93,"Handan",607,"Cheng'an","Xiàn","County","<U+6210><U+5B89><U+53BF>","Chéng'an"
608,49,"CHN","China",10,"Hebei",93,"Handan",608,"Ci","Xiàn","County","<U+78C1><U+53BF>","Cí"
609,49,"CHN","China",10,"Hebei",93,"Handan",609,"Daming","Xiàn","County","<U+5927><U+540D><U+53BF>","Dàmíng"
610,49,"CHN","China",10,"Hebei",93,"Handan",610,"Feixiang","Xiàn","County","<U+80A5><U+4E61><U+53BF>","Féixiang"
611,49,"CHN","China",10,"Hebei",93,"Handan",611,"Fengfengkuang","Shìxiáqu","District","<U+5CF0><U+5CF0><U+77FF><U+533A>","Fengfeng Kuàng"
612,49,"CHN","China",10,"Hebei",93,"Handan",612,"Guangping","Xiàn","County","<U+5E7F><U+5E73><U+53BF>","Guangpíng"
613,49,"CHN","China",10,"Hebei",93,"Handan",613,"Guantao","Xiàn","County","<U+9986><U+9676><U+53BF>","Guantáo"
614,49,"CHN","China",10,"Hebei",93,"Handan",614,"Handan shì","Xiànjíshì","County City","<U+90AF><U+90F8><U+5E02>",NA
615,49,"CHN","China",10,"Hebei",93,"Handan",615,"Handan Xiàn","Xiàn","County","<U+90AF><U+90F8><U+53BF>",NA
616,49,"CHN","China",10,"Hebei",93,"Handan",616,"Jize","Xiàn","County","<U+9E21><U+6CFD><U+53BF>","Jizé"
617,49,"CHN","China",10,"Hebei",93,"Handan",617,"Linzhang","Xiàn","County","<U+4E34><U+6F33><U+53BF>","Línzhang"
618,49,"CHN","China",10,"Hebei",93,"Handan",618,"Qiu","Xiàn","County","<U+90B1><U+53BF>","Qiu"
619,49,"CHN","China",10,"Hebei",93,"Handan",619,"Quzhou","Xiàn","County","<U+66F2><U+5468><U+53BF>","Quzhou"
620,49,"CHN","China",10,"Hebei",93,"Handan",620,"She","Xiàn","County","<U+6D89><U+53BF>","Shè"
621,49,"CHN","China",10,"Hebei",93,"Handan",621,"Wei","Xiàn","County","<U+9B4F><U+53BF>","Wèi"
622,49,"CHN","China",10,"Hebei",93,"Handan",622,"Wu'an","Xiànjíshì","County City","<U+6B66><U+5B89><U+5E02>","Wu'an"
623,49,"CHN","China",10,"Hebei",93,"Handan",623,"Yongnian","Xiàn","County","<U+6C38><U+5E74><U+53BF>","Yongnián"
624,49,"CHN","China",10,"Hebei",94,"Hengshui",624,"Anping","Xiàn","County","<U+5B89><U+5E73><U+53BF>","Anpíng"
625,49,"CHN","China",10,"Hebei",94,"Hengshui",625,"Fucheng","Xiàn","County","<U+961C><U+57CE><U+53BF>","Fùchéng"
626,49,"CHN","China",10,"Hebei",94,"Hengshui",626,"Gucheng","Xiàn","County","<U+6545><U+57CE><U+53BF>","Gùchéng"
627,49,"CHN","China",10,"Hebei",94,"Hengshui",627,"Hengshui","Xiànjíshì","County City",NA,NA
628,49,"CHN","China",10,"Hebei",94,"Hengshui",628,"Jing","Xiàn","County","<U+666F><U+53BF>","Jing"
629,49,"CHN","China",10,"Hebei",94,"Hengshui",629,"Ji","Xiàn","County",NA,NA
630,49,"CHN","China",10,"Hebei",94,"Hengshui",630,"Raoyang","Xiàn","County","<U+9976><U+9633><U+53BF>","Ráoyáng"
631,49,"CHN","China",10,"Hebei",94,"Hengshui",631,"Shen","Xiàn","County",NA,NA
632,49,"CHN","China",10,"Hebei",94,"Hengshui",632,"Wuqiang","Xiàn","County","<U+6B66><U+5F3A><U+53BF>","Wuqiáng"
633,49,"CHN","China",10,"Hebei",94,"Hengshui",633,"Wuyi","Xiàn","County","<U+6B66><U+9091><U+53BF>","Wuyì"
634,49,"CHN","China",10,"Hebei",94,"Hengshui",634,"Zaoqiang","Xiàn","County","<U+67A3><U+5F3A><U+53BF>","Zaoqiáng"
635,49,"CHN","China",10,"Hebei",95,"Langfang",635,"Ba","Xiàn","County",NA,NA
636,49,"CHN","China",10,"Hebei",95,"Langfang",636,"Dachang Hui","Zìzhìxiàn","Autonomous County","<U+5927><U+5382><U+56DE><U+65CF><U+81EA><U+6CBB><U+53BF>","Dàchang Huízú"
637,49,"CHN","China",10,"Hebei",95,"Langfang",637,"Daicheng","Xiàn","County","<U+5927><U+57CE><U+53BF>","Dàichéng"
638,49,"CHN","China",10,"Hebei",95,"Langfang",638,"Gu'an","Xiàn","County","<U+56FA><U+5B89><U+53BF>","Gù'an"
639,49,"CHN","China",10,"Hebei",95,"Langfang",639,"Longfang","Xiànjíshì","County City",NA,NA
640,49,"CHN","China",10,"Hebei",95,"Langfang",640,"Sanhe","Xiànjíshì","County City","<U+4E09><U+6CB3><U+5E02>","Sanhé"
641,49,"CHN","China",10,"Hebei",95,"Langfang",641,"Wen'an","Xiàn","County","<U+6587><U+5B89><U+53BF>","Wén'an"
642,49,"CHN","China",10,"Hebei",95,"Langfang",642,"Xianghe","Xiàn","County","<U+9999><U+6CB3><U+53BF>","Xianghé"
643,49,"CHN","China",10,"Hebei",95,"Langfang",643,"Yongqing","Xiàn","County","<U+6C38><U+6E05><U+53BF>","Yongqing"
644,49,"CHN","China",10,"Hebei",96,"Qinhuangdao",644,"Changli","Xiàn","County","<U+660C><U+9ECE><U+53BF>","Changlí"
645,49,"CHN","China",10,"Hebei",96,"Qinhuangdao",645,"Funing","Xiàn","County","<U+629A><U+5B81><U+53BF>","Funíng"
646,49,"CHN","China",10,"Hebei",96,"Qinhuangdao",646,"Lulong","Xiàn","County","<U+5362><U+9F99><U+53BF>","Lúlóng"
647,49,"CHN","China",10,"Hebei",96,"Qinhuangdao",647,"Qinglong","Xiàn","County",NA,NA
648,49,"CHN","China",10,"Hebei",96,"Qinhuangdao",648,"Qinhuangdao","Xiànjíshì","County City",NA,NA
649,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",649,"Gaocheng","Xiànjíshì","County City","<U+85F3><U+57CE><U+5E02>","Gàochéng"
650,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",650,"Gaoyi","Xiàn","County","<U+9AD8><U+9091><U+53BF>","Gaoyì"
651,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",651,"Huelu","Xiàn","County",NA,NA
652,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",652,"Jingjing","Xiàn","County","<U+4E95><U+9649><U+53BF>","Jingjìng"
653,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",653,"Ji","Xiàn","County",NA,NA
654,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",654,"Lingshou","Xiàn","County","<U+7075><U+5BFF><U+53BF>","Língshòu"
655,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",655,"Luancheng","Xiàn","County","<U+683E><U+57CE><U+53BF>","Luánchéng"
656,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",656,"Pingshan","Xiàn","County","<U+5E73><U+5C71><U+53BF>","Píngshan"
657,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",657,"Shenze","Xiàn","County","<U+6DF1><U+6CFD><U+53BF>","Shenzé"
658,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",658,"Shijiazhuang","Xiànjíshì","County City",NA,NA
659,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",659,"Shulu","Xiàn","County",NA,NA
660,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",660,"Wuji","Xiàn","County","<U+65E0><U+6781><U+53BF>","Wújí"
661,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",661,"Xingtang","Xiàn","County","<U+884C><U+5510><U+53BF>","Xíngtáng"
662,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",662,"Xinle","Xiànjíshì","County City","<U+65B0><U+4E50><U+5E02>","Xinlè"
663,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",663,"Yuanshi","Xiàn","County","<U+5143><U+6C0F><U+53BF>","Yuánshì"
664,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",664,"Zanhuang","Xiàn","County","<U+8D5E><U+7687><U+53BF>","Zànhuáng"
665,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",665,"Zhao","Xiàn","County","<U+8D75><U+53BF>","Zhào"
666,49,"CHN","China",10,"Hebei",97,"Shijiazhuang",666,"Zhengding","Xiàn","County","<U+6B63><U+5B9A><U+53BF>","Zhèngdìng"
667,49,"CHN","China",10,"Hebei",98,"Tangshan",667,"Fengnan","Shìxiáqu","District","<U+4E30><U+5357><U+533A>","Fengnán"
668,49,"CHN","China",10,"Hebei",98,"Tangshan",668,"Fengrun","Shìxiáqu","District","<U+4E30><U+6DA6><U+533A>","Fengrùn"
669,49,"CHN","China",10,"Hebei",98,"Tangshan",669,"Leting","Xiàn","County","<U+4E50><U+4EAD><U+53BF>","Lètíng"
670,49,"CHN","China",10,"Hebei",98,"Tangshan",670,"Luannan","Xiàn","County","<U+6EE6><U+5357><U+53BF>","Luánnán"
671,49,"CHN","China",10,"Hebei",98,"Tangshan",671,"Luan","Xiàn","County","<U+6EE6><U+53BF>","Luán"
672,49,"CHN","China",10,"Hebei",98,"Tangshan",672,"Qian'an","Xiànjíshì","County City","<U+8FC1><U+5B89><U+5E02>","Qian'an"
673,49,"CHN","China",10,"Hebei",98,"Tangshan",673,"Qianxi","Xiàn","County","<U+8FC1><U+897F><U+53BF>","Qianxi"
674,49,"CHN","China",10,"Hebei",98,"Tangshan",674,"Tanghai","Xiàn","County","<U+5510><U+6D77><U+53BF>","Tánghai"
675,49,"CHN","China",10,"Hebei",98,"Tangshan",675,"Tangshan","Xiànjíshì","County City","<U+5510><U+5C71><U+5E02>","Tángshan"
676,49,"CHN","China",10,"Hebei",98,"Tangshan",676,"Yutian","Xiàn","County","<U+7389><U+7530><U+53BF>","Yùtián"
677,49,"CHN","China",10,"Hebei",98,"Tangshan",677,"Zunhua","Xiànjíshì","County City","<U+9075><U+5316><U+5E02>","Zunhuà"
678,49,"CHN","China",10,"Hebei",99,"Xingtai",678,"Baixiang","Xiàn","County","<U+67CF><U+4E61><U+53BF>","Baixiang"
679,49,"CHN","China",10,"Hebei",99,"Xingtai",679,"Guangzong","Xiàn","County","<U+5E7F><U+5B97><U+53BF>","Guangzong"
680,49,"CHN","China",10,"Hebei",99,"Xingtai",680,"Julu","Xiàn","County","<U+5DE8><U+9E7F><U+53BF>","Jùlù"
681,49,"CHN","China",10,"Hebei",99,"Xingtai",681,"Lincheng","Xiàn","County","<U+4E34><U+57CE><U+53BF>","Línchéng"
682,49,"CHN","China",10,"Hebei",99,"Xingtai",682,"Linxi","Xiàn","County","<U+4E34><U+897F><U+53BF>","Línxi"
683,49,"CHN","China",10,"Hebei",99,"Xingtai",683,"Longyao","Xiàn","County","<U+9686><U+5C27><U+53BF>","Lóngyáo"
684,49,"CHN","China",10,"Hebei",99,"Xingtai",684,"Nangong","Xiànjíshì","County City","<U+5357><U+5BAB><U+5E02>","Nángong"
685,49,"CHN","China",10,"Hebei",99,"Xingtai",685,"Nanhe","Xiàn","County","<U+5357><U+548C><U+53BF>","Nánhé"
686,49,"CHN","China",10,"Hebei",99,"Xingtai",686,"Neiqiu","Xiàn","County","<U+5185><U+4E18><U+53BF>","Nèiqiu"
687,49,"CHN","China",10,"Hebei",99,"Xingtai",687,"Ningjin","Xiàn","County","<U+5B81><U+664B><U+53BF>","Níngjìn"
688,49,"CHN","China",10,"Hebei",99,"Xingtai",688,"Pingxiang","Xiàn","County","<U+5E73><U+4E61><U+53BF>","Píngxiang"
689,49,"CHN","China",10,"Hebei",99,"Xingtai",689,"Qinghe","Xiàn","County",NA,NA
690,49,"CHN","China",10,"Hebei",99,"Xingtai",690,"Ren","Xiàn","County","<U+4EFB><U+53BF>","Rén"
691,49,"CHN","China",10,"Hebei",99,"Xingtai",691,"Shahe","Xiànjíshì","County City","<U+6C99><U+6CB3><U+5E02>","Shahé"
692,49,"CHN","China",10,"Hebei",99,"Xingtai",692,"Wei","Xiàn","County","<U+9B4F><U+53BF>","Wèi"
693,49,"CHN","China",10,"Hebei",99,"Xingtai",693,"Xingtai shì","Xiànjíshì","County City","<U+90A2><U+53F0><U+53BF>",NA
694,49,"CHN","China",10,"Hebei",99,"Xingtai",694,"Xingtai Xiàn","Xiàn","County","<U+90A2><U+53F0><U+53BF>","Xíngtái"
695,49,"CHN","China",10,"Hebei",99,"Xingtai",695,"Xinhe","Xiàn","County","<U+65B0><U+6CB3><U+53BF>","Xinhé"
696,49,"CHN","China",10,"Hebei",100,"Zhangjiakou",696,"Chicheng","Xiàn","County","<U+8D64><U+57CE><U+53BF>","Chìchéng"
697,49,"CHN","China",10,"Hebei",100,"Zhangjiakou",697,"Chongli","Xiàn","County","<U+5D07><U+793C><U+53BF>","Chóngli"
698,49,"CHN","China",10,"Hebei",100,"Zhangjiakou",698,"Guyuan","Xiàn","County","<U+6CBD><U+6E90><U+53BF>","Guyuán"
699,49,"CHN","China",10,"Hebei",100,"Zhangjiakou",699,"Huai'an","Xiàn","County","<U+6000><U+5B89><U+53BF>","Huái'an"
700,49,"CHN","China",10,"Hebei",100,"Zhangjiakou",700,"Huailai","Xiàn","County","<U+6000><U+6765><U+53BF>","Huáilái"
701,49,"CHN","China",10,"Hebei",100,"Zhangjiakou",701,"Kangbao","Xiàn","County","<U+5EB7><U+4FDD><U+53BF>","Kangbao"
702,49,"CHN","China",10,"Hebei",100,"Zhangjiakou",702,"Shangyi","Xiàn","County","<U+5C1A><U+4E49><U+53BF>","Shàngyì"
703,49,"CHN","China",10,"Hebei",100,"Zhangjiakou",703,"Wanquan","Xiàn","County","<U+4E07><U+5168><U+53BF>","Wànquán"
704,49,"CHN","China",10,"Hebei",100,"Zhangjiakou",704,"Xuanhua","Xiàn","County","<U+5BA3><U+5316><U+53BF>","Xuanhuà"
705,49,"CHN","China",10,"Hebei",100,"Zhangjiakou",705,"Yangyuan","Xiàn","County","<U+9633><U+539F><U+53BF>","Yángyuán"
706,49,"CHN","China",10,"Hebei",100,"Zhangjiakou",706,"Yu","Xiàn","County","<U+851A><U+53BF>","Yù"
707,49,"CHN","China",10,"Hebei",100,"Zhangjiakou",707,"Zhangbei","Xiàn","County","<U+5F20><U+5317><U+53BF>","Zhangbei"
708,49,"CHN","China",10,"Hebei",100,"Zhangjiakou",708,"Zhangjiakou","Xiànjíshì","County City",NA,NA
709,49,"CHN","China",10,"Hebei",100,"Zhangjiakou",709,"Zhuolu","Xiàn","County","<U+6DBF><U+9E7F><U+53BF>","Zhuolù"
710,49,"CHN","China",11,"Heilongjiang",101,"Daqing",710,"Daqina","Xiànjíshì","County City",NA,NA
711,49,"CHN","China",11,"Heilongjiang",101,"Daqing",711,"Dorbod Mongol","Zìzhìxiàn","Autonomous County","<U+675C><U+5C14><U+4F2F><U+7279>|<U+8499><U+53E4><U+65CF><U+81EA><U+6CBB><U+53BF>","Dù'erbótè Mengguzú"
712,49,"CHN","China",11,"Heilongjiang",101,"Daqing",712,"Lindian","Xiàn","County","<U+6797><U+7538><U+53BF>","Líndiàn"
713,49,"CHN","China",11,"Heilongjiang",101,"Daqing",713,"Zhaoyuan","Xiàn","County","<U+8087><U+6E90><U+53BF>","Zhàoyuán"
714,49,"CHN","China",11,"Heilongjiang",101,"Daqing",714,"Zhaozhou","Xiàn","County","<U+8087><U+5DDE><U+53BF>","Zhàozhou"
715,49,"CHN","China",11,"Heilongjiang",102,"Daxing'anling",715,"Huma","Xiàn","County","<U+547C><U+739B><U+53BF>","Huma"
716,49,"CHN","China",11,"Heilongjiang",102,"Daxing'anling",716,"Huzhong","Shìxiáqu","District","<U+547C><U+739B><U+53BF>","Huzhong"
717,49,"CHN","China",11,"Heilongjiang",102,"Daxing'anling",717,"Mohe","Xiàn","County","<U+6F20><U+6CB3><U+53BF>","Mòhé"
718,49,"CHN","China",11,"Heilongjiang",102,"Daxing'anling",718,"Tahe","Xiàn","County","<U+5854><U+6CB3><U+53BF>","Tahé"
719,49,"CHN","China",11,"Heilongjiang",102,"Daxing'anling",719,"Xinlin","Shìxiáqu","District","<U+65B0><U+6797><U+533A>","Xinlín"
720,49,"CHN","China",11,"Heilongjiang",103,"Harbin",720,"Acheng","Shìxiáqu","District","<U+963F><U+57CE><U+533A>","Achéng"
721,49,"CHN","China",11,"Heilongjiang",103,"Harbin",721,"Bayan","Xiàn","County","<U+5DF4><U+5F66><U+53BF>","Bayàn"
722,49,"CHN","China",11,"Heilongjiang",103,"Harbin",722,"Bin","Xiàn","County","<U+5BBE><U+53BF>","Bin"
723,49,"CHN","China",11,"Heilongjiang",103,"Harbin",723,"Fangzheng","Xiàn","County","<U+65B9><U+6B63><U+53BF>","Fangzhèng"
724,49,"CHN","China",11,"Heilongjiang",103,"Harbin",724,"Harbin","Xiànjíshì","County City",NA,NA
725,49,"CHN","China",11,"Heilongjiang",103,"Harbin",725,"Hulan","Shìxiáqu","District","<U+547C><U+5170><U+533A>","Hulán"
726,49,"CHN","China",11,"Heilongjiang",103,"Harbin",726,"Mulan","Xiàn","County","<U+6728><U+5170><U+53BF>","Mùlán"
727,49,"CHN","China",11,"Heilongjiang",103,"Harbin",727,"Shangzhi","Xiànjíshì","County City","<U+5C1A><U+5FD7><U+5E02>","Shàngzhì"
728,49,"CHN","China",11,"Heilongjiang",103,"Harbin",728,"Shuangcheng","Xiànjíshì","County City","<U+53CC><U+57CE><U+5E02>","Shuangchéng"
729,49,"CHN","China",11,"Heilongjiang",103,"Harbin",729,"Tonghe","Xiàn","County","<U+901A><U+6CB3><U+53BF>","Tonghé"
730,49,"CHN","China",11,"Heilongjiang",103,"Harbin",730,"Wuchang","Xiànjíshì","County City","<U+4E94><U+5E38><U+5E02>","Wucháng"
731,49,"CHN","China",11,"Heilongjiang",103,"Harbin",731,"Yanshou","Xiàn","County","<U+5EF6><U+5BFF><U+53BF>","Yánshòu"
732,49,"CHN","China",11,"Heilongjiang",103,"Harbin",732,"Yilan","Xiàn","County","<U+4F9D><U+5170><U+53BF>","Yilán"
733,49,"CHN","China",11,"Heilongjiang",104,"Hegang",733,"Hegang shì","Xiànjíshì","County City","<U+9E64><U+5C97><U+5E02>","Hègang"
734,49,"CHN","China",11,"Heilongjiang",104,"Hegang",734,"Hegang","Shìxiáqu","District","<U+9E64><U+5C97><U+533A>",NA
735,49,"CHN","China",11,"Heilongjiang",104,"Hegang",735,"Luobei","Xiàn","County","<U+841D><U+5317><U+53BF>","Luóbei"
736,49,"CHN","China",11,"Heilongjiang",104,"Hegang",736,"Suibin","Xiàn","County","<U+7EE5><U+6EE8><U+53BF>","Suíbin"
737,49,"CHN","China",11,"Heilongjiang",105,"Heihe",737,"Bei'an","Xiànjíshì","County City","<U+5317><U+5B89><U+5E02>","Bei'an"
738,49,"CHN","China",11,"Heilongjiang",105,"Heihe",738,"Dedu","Xiàn","County",NA,NA
739,49,"CHN","China",11,"Heilongjiang",105,"Heihe",739,"Heihe","Xiànjíshì","County City",NA,NA
740,49,"CHN","China",11,"Heilongjiang",105,"Heihe",740,"Nenjiang","Xiàn","County","<U+5AE9><U+6C5F><U+53BF>","Nènjiang"
741,49,"CHN","China",11,"Heilongjiang",105,"Heihe",741,"Sunwu","Xiàn","County","<U+5B59><U+5434><U+53BF>","Sunwú"
742,49,"CHN","China",11,"Heilongjiang",105,"Heihe",742,"Wudalianchi","Xiànjíshì","County City","<U+4E94><U+5927><U+8FDE><U+6C60><U+5E02>","Wudàliánchí"
743,49,"CHN","China",11,"Heilongjiang",105,"Heihe",743,"Xunke","Xiàn","County","<U+900A><U+514B><U+53BF>","Xùnkè"
744,49,"CHN","China",11,"Heilongjiang",106,"Jiamusi",744,"Fujin","Xiànjíshì","County City","<U+5BCC><U+9526><U+5E02>","Fùjin"
745,49,"CHN","China",11,"Heilongjiang",106,"Jiamusi",745,"Fuyuan","Xiàn","County","<U+629A><U+8FDC><U+53BF>","Fuyuan"
746,49,"CHN","China",11,"Heilongjiang",106,"Jiamusi",746,"Huachuan","Xiàn","County","<U+6866><U+5DDD><U+53BF>","Huàchuan"
747,49,"CHN","China",11,"Heilongjiang",106,"Jiamusi",747,"Huanan","Xiàn","County","<U+6866><U+5357><U+53BF>","Huànán"
748,49,"CHN","China",11,"Heilongjiang",106,"Jiamusi",748,"Jiamusi","Xiànjíshì","County City",NA,NA
749,49,"CHN","China",11,"Heilongjiang",106,"Jiamusi",749,"Tangyuan","Xiàn","County","<U+6C64><U+539F><U+53BF>","Tangyuán"
750,49,"CHN","China",11,"Heilongjiang",106,"Jiamusi",750,"Tongjiang","Xiànjíshì","County City","<U+540C><U+6C5F><U+5E02>","Tóngjiang"
751,49,"CHN","China",11,"Heilongjiang",107,"Jixi",751,"Hulin","Xiànjíshì","County City","<U+864E><U+6797><U+5E02>","Hulín"
752,49,"CHN","China",11,"Heilongjiang",107,"Jixi",752,"Jidong","Xiàn","County","<U+9E21><U+4E1C><U+53BF>","Jidong"
753,49,"CHN","China",11,"Heilongjiang",107,"Jixi",753,"Jixi","Xiànjíshì","County City",NA,NA
754,49,"CHN","China",11,"Heilongjiang",107,"Jixi",754,"Mishan","Xiànjíshì","County City","<U+5BC6><U+5C71><U+5E02>","Mìshan"
755,49,"CHN","China",11,"Heilongjiang",108,"Mudanjiang",755,"Dongning","Xiàn","County","<U+4E1C><U+5B81><U+53BF>","Dongníng"
756,49,"CHN","China",11,"Heilongjiang",108,"Mudanjiang",756,"Hailin","Xiànjíshì","County City","<U+6D77><U+6797><U+5E02>","Hailín"
757,49,"CHN","China",11,"Heilongjiang",108,"Mudanjiang",757,"Linkou","Xiàn","County",NA,NA
758,49,"CHN","China",11,"Heilongjiang",108,"Mudanjiang",758,"Mudanjiang","Xiànjíshì","County City",NA,NA
759,49,"CHN","China",11,"Heilongjiang",108,"Mudanjiang",759,"Muling","Xiànjíshì","County City","<U+7A46><U+68F1><U+5E02>","Mùlíng Shì not l"
760,49,"CHN","China",11,"Heilongjiang",108,"Mudanjiang",760,"Ning'an","Xiànjíshì","County City","<U+5B81><U+5B89><U+5E02>","Níng'an"
761,49,"CHN","China",11,"Heilongjiang",108,"Mudanjiang",761,"Suifenhe","Xiànjíshì","County City","<U+7EE5><U+82AC><U+6CB3><U+5E02>","Suífenhé"
762,49,"CHN","China",11,"Heilongjiang",109,"Qiqihar",762,"Baiquan","Xiàn","County","<U+62DC><U+6CC9><U+53BF>","Bàiquán"
763,49,"CHN","China",11,"Heilongjiang",109,"Qiqihar",763,"Fuyu","Xiàn","County","<U+5BCC><U+88D5><U+53BF>","Fùyù"
764,49,"CHN","China",11,"Heilongjiang",109,"Qiqihar",764,"Gannan","Xiàn","County","<U+7518><U+5357><U+53BF>","Gannán"
765,49,"CHN","China",11,"Heilongjiang",109,"Qiqihar",765,"Kedong","Xiàn","County","<U+514B><U+4E1C><U+53BF>","Kèdong"
766,49,"CHN","China",11,"Heilongjiang",109,"Qiqihar",766,"Keshan","Xiàn","County","<U+514B><U+5C71><U+53BF>","Kèshan"
767,49,"CHN","China",11,"Heilongjiang",109,"Qiqihar",767,"Longjiang","Xiàn","County",NA,NA
768,49,"CHN","China",11,"Heilongjiang",109,"Qiqihar",768,"Nehe","Xiànjíshì","County City","<U+8BB7><U+6CB3><U+5E02>","Nehé"
769,49,"CHN","China",11,"Heilongjiang",109,"Qiqihar",769,"Nianzishan","Shìxiáqu","District","<U+78BE><U+5B50><U+5C71><U+533A>","Nianzishan"
770,49,"CHN","China",11,"Heilongjiang",109,"Qiqihar",770,"Qiqihar","Xiànjíshì","County City",NA,NA
771,49,"CHN","China",11,"Heilongjiang",109,"Qiqihar",771,"Tailai","Xiàn","County","<U+6CF0><U+6765><U+53BF>","Tàilái"
772,49,"CHN","China",11,"Heilongjiang",109,"Qiqihar",772,"Yi'an","Xiàn","County","<U+4F9D><U+5B89><U+53BF>","Yi'an"
773,49,"CHN","China",11,"Heilongjiang",110,"Qitaihe",773,"Boli","Xiàn","County","<U+52C3><U+5229><U+53BF>","Bólì"
774,49,"CHN","China",11,"Heilongjiang",110,"Qitaihe",774,"Qitaihe","Xiànjíshì","County City",NA,NA
775,49,"CHN","China",11,"Heilongjiang",111,"Shuangyashan",775,"Baoqing","Xiàn","County","<U+5B9D><U+6E05><U+53BF>","Baoqing"
776,49,"CHN","China",11,"Heilongjiang",111,"Shuangyashan",776,"Jixian","Xiàn","County","<U+96C6><U+8D24><U+53BF>","Jíxián"
777,49,"CHN","China",11,"Heilongjiang",111,"Shuangyashan",777,"Raohe","Xiàn","County","<U+9976><U+6CB3><U+53BF>","Ráohé"
778,49,"CHN","China",11,"Heilongjiang",111,"Shuangyashan",778,"Shuangyashan","Xiànjíshì","County City",NA,NA
779,49,"CHN","China",11,"Heilongjiang",111,"Shuangyashan",779,"Youyi","Xiàn","County","<U+53CB><U+8C0A><U+53BF>","Youyì"
780,49,"CHN","China",11,"Heilongjiang",112,"Suihua",780,"Anda","Xiànjíshì","County City","<U+5B89><U+8FBE><U+5E02>","Andá"
781,49,"CHN","China",11,"Heilongjiang",112,"Suihua",781,"Hailun","Xiànjíshì","County City","<U+6D77><U+4F26><U+5E02>","Hailún"
782,49,"CHN","China",11,"Heilongjiang",112,"Suihua",782,"Lanxi","Xiàn","County","<U+5170><U+897F><U+53BF>","Lánxi"
783,49,"CHN","China",11,"Heilongjiang",112,"Suihua",783,"Mingshui","Xiàn","County","<U+660E><U+6C34><U+53BF>","Míngshui"
784,49,"CHN","China",11,"Heilongjiang",112,"Suihua",784,"Qing'an","Xiàn","County","<U+5E86><U+5B89><U+53BF>","Qìng'an"
785,49,"CHN","China",11,"Heilongjiang",112,"Suihua",785,"Qinggang","Xiàn","County","<U+9752><U+5188><U+53BF>","Qinggang"
786,49,"CHN","China",11,"Heilongjiang",112,"Suihua",786,"Suihua","Xiàn","County",NA,NA
787,49,"CHN","China",11,"Heilongjiang",112,"Suihua",787,"Suileng","Xiàn","County","<U+7EE5><U+68F1><U+53BF>","Suíléng"
788,49,"CHN","China",11,"Heilongjiang",112,"Suihua",788,"Wangkui","Xiàn","County","<U+671B><U+594E><U+53BF>","Wàngkuí"
789,49,"CHN","China",11,"Heilongjiang",112,"Suihua",789,"Zhaodong","Xiànjíshì","County City","<U+8087><U+4E1C><U+5E02>","Zhàodong"
790,49,"CHN","China",11,"Heilongjiang",113,"Yichun",790,"Cuiluan","Shìxiáqu","District","<U+7FE0><U+5CE6><U+533A>","Cuìluán"
791,49,"CHN","China",11,"Heilongjiang",113,"Yichun",791,"Jiayin","Xiàn","County",NA,NA
792,49,"CHN","China",11,"Heilongjiang",113,"Yichun",792,"Tieli","Xiànjíshì","County City","<U+94C1><U+529B><U+5E02>","Tielì"
793,49,"CHN","China",11,"Heilongjiang",113,"Yichun",793,"Wuyiling","Shìxiáqu","District","<U+4E4C><U+4F0A><U+5CAD><U+533A>","Wuyiling"
794,49,"CHN","China",11,"Heilongjiang",113,"Yichun",794,"Yichun","Shìxiáqu","District","<U+4F0A><U+6625><U+533A>","Yichun"
795,49,"CHN","China",12,"Henan",114,"Anyang",795,"Anyang Shì","Xiànjíshì","County City",NA,NA
796,49,"CHN","China",12,"Henan",114,"Anyang",796,"Anyang Xiàn","Xiàn","County",NA,"Anyáng"
797,49,"CHN","China",12,"Henan",114,"Anyang",797,"Hua","Xiàn","County","<U+6ED1><U+53BF>","Huá"
798,49,"CHN","China",12,"Henan",114,"Anyang",798,"Lin","Xiàn","County",NA,NA
799,49,"CHN","China",12,"Henan",114,"Anyang",799,"Neihuang","Xiàn","County","<U+5185><U+9EC4><U+53BF>","Nèihuáng"
800,49,"CHN","China",12,"Henan",114,"Anyang",800,"Tangyin","Xiàn","County","<U+6C64><U+9634><U+53BF>","Tangyin"
801,49,"CHN","China",12,"Henan",115,"Hebi",801,"Hebi","Xiànjíshì","County City",NA,NA
802,49,"CHN","China",12,"Henan",115,"Hebi",802,"Qi","Xiàn","County","<U+6DC7><U+53BF>","Qí"
803,49,"CHN","China",12,"Henan",115,"Hebi",803,"Xun","Xiàn","County",NA,NA
804,49,"CHN","China",12,"Henan",116,"Jiaozuo",804,"Bo'ai","Xiàn","County","<U+535A><U+7231><U+53BF>","Bó'ài"
805,49,"CHN","China",12,"Henan",116,"Jiaozuo",805,"Jiaozuo","Xiàn","County",NA,NA
806,49,"CHN","China",12,"Henan",116,"Jiaozuo",806,"Meng","Xiàn","County",NA,NA
807,49,"CHN","China",12,"Henan",116,"Jiaozuo",807,"Qinyang","Xiànjíshì","County City","<U+6C81><U+9633><U+5E02>","Qìnyáng"
808,49,"CHN","China",12,"Henan",116,"Jiaozuo",808,"Wen","Xiàn","County","<U+6E29><U+53BF>","Wen"
809,49,"CHN","China",12,"Henan",116,"Jiaozuo",809,"Wuzhi","Xiàn","County","<U+6B66><U+965F><U+53BF>","Wuzhì"
810,49,"CHN","China",12,"Henan",116,"Jiaozuo",810,"Xiuwu","Xiàn","County","<U+4FEE><U+6B66><U+53BF>","Xiuwu"
811,49,"CHN","China",12,"Henan",117,"Jiyuan shi",811,"Jiyuan","Xiàn","County",NA,NA
812,49,"CHN","China",12,"Henan",118,"Kaifeng",812,"Kaifeng Xiàn","Xiàn","County","<U+5F00><U+5C01><U+53BF>","Kaifeng"
813,49,"CHN","China",12,"Henan",118,"Kaifeng",813,"Kaifeng","Xiànjíshì","County City","<U+5F00><U+5C01><U+53BF>",NA
814,49,"CHN","China",12,"Henan",118,"Kaifeng",814,"Lankao","Xiàn","County","<U+5170><U+8003><U+53BF>","Lánkao"
815,49,"CHN","China",12,"Henan",118,"Kaifeng",815,"Qi","Xiàn","County","<U+6DC7><U+53BF>","Qí"
816,49,"CHN","China",12,"Henan",118,"Kaifeng",816,"Tongxu","Xiàn","County","<U+901A><U+8BB8><U+53BF>","Tongxu"
817,49,"CHN","China",12,"Henan",118,"Kaifeng",817,"Weishi","Xiàn","County","<U+5C09><U+6C0F><U+53BF>","Wèishì"
818,49,"CHN","China",12,"Henan",119,"Luohe",818,"Linying","Xiàn","County","<U+4E34><U+988D><U+53BF>","Línying"
819,49,"CHN","China",12,"Henan",119,"Luohe",819,"Luohe","Xiànjíshì","County City",NA,NA
820,49,"CHN","China",12,"Henan",119,"Luohe",820,"Wuyang","Xiàn","County","<U+821E><U+9633><U+53BF>","Wuyáng"
821,49,"CHN","China",12,"Henan",119,"Luohe",821,"Yancheng","Shìxiáqu","District","<U+90FE><U+57CE><U+533A>","Yanchéng"
822,49,"CHN","China",12,"Henan",120,"Luoyang",822,"Luanchuan","Xiàn","County","<U+683E><U+5DDD><U+53BF>","Luánchuan"
823,49,"CHN","China",12,"Henan",120,"Luoyang",823,"Luoning","Xiàn","County","<U+6D1B><U+5B81><U+53BF>","Luòníng"
824,49,"CHN","China",12,"Henan",120,"Luoyang",824,"Luoyang","Xiànjíshì","County City",NA,NA
825,49,"CHN","China",12,"Henan",120,"Luoyang",825,"Mengjin","Xiàn","County","<U+5B5F><U+6D25><U+53BF>","Mèngjin"
826,49,"CHN","China",12,"Henan",120,"Luoyang",826,"Ruyang","Xiàn","County","<U+6C5D><U+9633><U+53BF>","Ruyáng"
827,49,"CHN","China",12,"Henan",120,"Luoyang",827,"Song","Xiàn","County","<U+5D69><U+53BF>","Song"
828,49,"CHN","China",12,"Henan",120,"Luoyang",828,"Xin'an","Xiàn","County","<U+65B0><U+5B89><U+53BF>","Xin'an"
829,49,"CHN","China",12,"Henan",120,"Luoyang",829,"Yanshi","Xiànjíshì","County City","<U+5043><U+5E08><U+5E02>","Yanshi"
830,49,"CHN","China",12,"Henan",120,"Luoyang",830,"Yichuan","Xiàn","County","<U+4F0A><U+5DDD><U+53BF>","Yichuan"
831,49,"CHN","China",12,"Henan",120,"Luoyang",831,"Yiyang","Xiàn","County","<U+5B9C><U+9633><U+53BF>","Yíyáng"
832,49,"CHN","China",12,"Henan",121,"Nanyang",832,"Deng","Xiàn","County",NA,NA
833,49,"CHN","China",12,"Henan",121,"Nanyang",833,"Fangcheng","Xiàn","County","<U+65B9><U+57CE><U+53BF>","Fangchéng"
834,49,"CHN","China",12,"Henan",121,"Nanyang",834,"Nanyang Shì","Xiànjíshì","County City","<U+5357><U+9633><U+5E02>","Nányáng"
835,49,"CHN","China",12,"Henan",121,"Nanyang",835,"Nanyang Xiàn","Xiàn","County","<U+5357><U+9633><U+53BF>","Nányáng"
836,49,"CHN","China",12,"Henan",121,"Nanyang",836,"Nanzhao","Xiàn","County","<U+5357><U+53EC><U+53BF>","Nánzhào"
837,49,"CHN","China",12,"Henan",121,"Nanyang",837,"Neixiang","Xiàn","County","<U+5185><U+4E61><U+53BF>","Nèixiang"
838,49,"CHN","China",12,"Henan",121,"Nanyang",838,"Sheqi","Xiàn","County","<U+793E><U+65D7><U+53BF>","Shèqí"
839,49,"CHN","China",12,"Henan",121,"Nanyang",839,"Tanghe","Xiàn","County","<U+5510><U+6CB3><U+53BF>","Tánghé"
840,49,"CHN","China",12,"Henan",121,"Nanyang",840,"Tongbai","Xiàn","County","<U+6850><U+67CF><U+53BF>","Tóngbai"
841,49,"CHN","China",12,"Henan",121,"Nanyang",841,"Xichuan","Xiàn","County","<U+6DC5><U+5DDD><U+53BF>","Xichuan"
842,49,"CHN","China",12,"Henan",121,"Nanyang",842,"Xinye","Xiàn","County","<U+65B0><U+91CE><U+53BF>","Xinye"
843,49,"CHN","China",12,"Henan",121,"Nanyang",843,"Xixia","Xiàn","County","<U+897F><U+5CE1><U+53BF>","Xixiá"
844,49,"CHN","China",12,"Henan",121,"Nanyang",844,"Zhenping","Xiàn","County","<U+9547><U+5E73><U+53BF>","Zhènpíng"
845,49,"CHN","China",12,"Henan",122,"Pingdingshan",845,"Baofeng","Xiàn","County","<U+5B9D><U+4E30><U+53BF>","Baofeng"
846,49,"CHN","China",12,"Henan",122,"Pingdingshan",846,"Jia","Xiàn","County","<U+90CF><U+53BF>","Jiá"
847,49,"CHN","China",12,"Henan",122,"Pingdingshan",847,"Linru","Xiàn","County",NA,NA
848,49,"CHN","China",12,"Henan",122,"Pingdingshan",848,"Lushan","Xiàn","County","<U+9C81><U+5C71><U+53BF>","Lushan"
849,49,"CHN","China",12,"Henan",122,"Pingdingshan",849,"Pingdingshan","Xiànjíshì","County City",NA,NA
850,49,"CHN","China",12,"Henan",122,"Pingdingshan",850,"Wugang","Xiànjíshì","County City","<U+821E><U+94A2><U+5E02>","Wugang"
851,49,"CHN","China",12,"Henan",122,"Pingdingshan",851,"Ye","Xiàn","County","<U+53F6><U+53BF>","Yè"
852,49,"CHN","China",12,"Henan",123,"Puyang",852,"Fanxian","Xiàn","County",NA,NA
853,49,"CHN","China",12,"Henan",123,"Puyang",853,"Nanle","Xiàn","County","<U+5357><U+4E50><U+53BF>","Nánlè"
854,49,"CHN","China",12,"Henan",123,"Puyang",854,"Puyang Shì","Xiànjíshì","County City","<U+6FEE><U+9633><U+5E02>","Púyáng"
855,49,"CHN","China",12,"Henan",123,"Puyang",855,"Puyang Xiàn","Xiàn","County","<U+6FEE><U+9633><U+53BF>","Púyáng"
856,49,"CHN","China",12,"Henan",123,"Puyang",856,"Qingfeng","Xiàn","County","<U+6E05><U+4E30><U+53BF>","Qingfeng"
857,49,"CHN","China",12,"Henan",123,"Puyang",857,"Taiqian","Xiàn","County","<U+53F0><U+524D><U+53BF>","Táiqián"
858,49,"CHN","China",12,"Henan",124,"Sanmenxia",858,"Lingbao","Xiàn","County",NA,NA
859,49,"CHN","China",12,"Henan",124,"Sanmenxia",859,"Lushi","Xiàn","County","<U+5362><U+6C0F><U+53BF>","Lúshì"
860,49,"CHN","China",12,"Henan",124,"Sanmenxia",860,"Mianchi","Xiàn","County","<U+6E11><U+6C60><U+53BF>","Mianchí"
861,49,"CHN","China",12,"Henan",124,"Sanmenxia",861,"Sanmenxia","Xiànjíshì","County City",NA,NA
862,49,"CHN","China",12,"Henan",124,"Sanmenxia",862,"Shan","Xiàn","County","<U+9655><U+53BF>","Shan"
863,49,"CHN","China",12,"Henan",124,"Sanmenxia",863,"Yima","Xiànjíshì","County City","<U+4E49><U+9A6C><U+5E02>","Yìma"
864,49,"CHN","China",12,"Henan",125,"Shangqiu",864,"Minquan","Xiàn","County","<U+6C11><U+6743><U+53BF>","Mínquán"
865,49,"CHN","China",12,"Henan",125,"Shangqiu",865,"Ningling","Xiàn","County","<U+5B81><U+9675><U+53BF>","Nínglíng"
866,49,"CHN","China",12,"Henan",125,"Shangqiu",866,"Shangqiu Shì","Xiànjíshì","County City","<U+7762><U+9633><U+5E02>","Suiyáng"
867,49,"CHN","China",12,"Henan",125,"Shangqiu",867,"Sui","Xiàn","County","<U+7762><U+53BF>","Sui"
868,49,"CHN","China",12,"Henan",125,"Shangqiu",868,"Suiyang","Shìxiáqu","District","<U+7762><U+9633><U+533A>","Suiyáng"
869,49,"CHN","China",12,"Henan",125,"Shangqiu",869,"Xiayi","Xiàn","County","<U+590F><U+9091><U+53BF>","Xiàyì"
870,49,"CHN","China",12,"Henan",125,"Shangqiu",870,"Yongcheng","Xiànjíshì","County City","<U+6C38><U+57CE><U+5E02>","Yongchéng"
871,49,"CHN","China",12,"Henan",125,"Shangqiu",871,"Yucheng","Xiàn","County","<U+865E><U+57CE><U+53BF>","Yúchéng"
872,49,"CHN","China",12,"Henan",125,"Shangqiu",872,"Zhecheng","Xiàn","County",NA,NA
873,49,"CHN","China",12,"Henan",126,"Xinxiang",873,"Changyuan","Xiàn","County","<U+957F><U+57A3><U+53BF>","Chángyuán"
874,49,"CHN","China",12,"Henan",126,"Xinxiang",874,"Fengqiu","Xiàn","County","<U+5C01><U+4E18><U+53BF>","Fengqiu"
875,49,"CHN","China",12,"Henan",126,"Xinxiang",875,"Houjia","Xiàn","County",NA,NA
876,49,"CHN","China",12,"Henan",126,"Xinxiang",876,"Huixian","Xiànjíshì","County City","<U+8F89><U+53BF><U+5E02>","Huixiàn"
877,49,"CHN","China",12,"Henan",126,"Xinxiang",877,"Ji","Xiàn","County",NA,NA
878,49,"CHN","China",12,"Henan",126,"Xinxiang",878,"Xinxiang Shì","Xiànjíshì","County City",NA,NA
879,49,"CHN","China",12,"Henan",126,"Xinxiang",879,"Xinxiang Xiàn","Xiàn","County",NA,"Xinxiang"
880,49,"CHN","China",12,"Henan",126,"Xinxiang",880,"Yanjin","Xiàn","County","<U+5EF6><U+6D25><U+53BF>","Yánjin"
881,49,"CHN","China",12,"Henan",126,"Xinxiang",881,"Yuanyang","Xiàn","County","<U+539F><U+9633><U+53BF>","Yuányáng"
882,49,"CHN","China",12,"Henan",127,"Xinyang",882,"Guangshan","Xiàn","County","<U+5149><U+5C71><U+53BF>","Guangshan"
883,49,"CHN","China",12,"Henan",127,"Xinyang",883,"Gushi","Xiàn","County","<U+56FA><U+59CB><U+53BF>","Gùshi"
884,49,"CHN","China",12,"Henan",127,"Xinyang",884,"Huaibin","Xiàn","County","<U+6DEE><U+6EE8><U+53BF>","Huáibin"
885,49,"CHN","China",12,"Henan",127,"Xinyang",885,"Huangchuan","Xiàn","County","<U+6F62><U+5DDD><U+53BF>","Huángchuan"
886,49,"CHN","China",12,"Henan",127,"Xinyang",886,"Luoshan","Xiàn","County","<U+7F57><U+5C71><U+53BF>","Luóshan"
887,49,"CHN","China",12,"Henan",127,"Xinyang",887,"Shancheng","Shìxiáqu","District","<U+5C71><U+57CE><U+533A>","Shanchéng"
888,49,"CHN","China",12,"Henan",127,"Xinyang",888,"Xinxian","Xiàn","County",NA,NA
889,49,"CHN","China",12,"Henan",127,"Xinyang",889,"Xinyang Shì","Xiànjíshì","County City","<U+4FE1><U+9633><U+5E02>","Xìnyáng"
890,49,"CHN","China",12,"Henan",127,"Xinyang",890,"Xinyang Xiàn","Xiàn","County","<U+4FE1><U+9633><U+53BF>","Xìnyáng"
891,49,"CHN","China",12,"Henan",127,"Xinyang",891,"Xi","Xiàn","County","<U+606F><U+53BF>","Xi"
892,49,"CHN","China",12,"Henan",128,"Xuchang",892,"Changge","Xiànjíshì","County City","<U+957F><U+845B><U+5E02>","Chángge"
893,49,"CHN","China",12,"Henan",128,"Xuchang",893,"Xiangcheng","Xiànjíshì","County City","<U+9879><U+57CE><U+5E02>","Xiàngchéng"
894,49,"CHN","China",12,"Henan",128,"Xuchang",894,"Xuchang Shì","Xiànjíshì","County City","<U+8BB8><U+660C><U+5E02>","Xuchang"
895,49,"CHN","China",12,"Henan",128,"Xuchang",895,"Xuchang Xiàn","Xiàn","County","<U+8BB8><U+660C><U+53BF>","Xuchang"
896,49,"CHN","China",12,"Henan",128,"Xuchang",896,"Yanling","Xiàn","County","<U+9122><U+9675><U+53BF>","Yanlíng"
897,49,"CHN","China",12,"Henan",128,"Xuchang",897,"Yu","Xiàn","County",NA,NA
898,49,"CHN","China",12,"Henan",129,"Zhengzhou",898,"Dengfeng","Xiànjíshì","County City","<U+767B><U+5C01><U+5E02>","Dengfeng"
899,49,"CHN","China",12,"Henan",129,"Zhengzhou",899,"Gong","Xiàn","County",NA,NA
900,49,"CHN","China",12,"Henan",129,"Zhengzhou",900,"Mi","Xiàn","County",NA,NA
901,49,"CHN","China",12,"Henan",129,"Zhengzhou",901,"Xingyang","Xiànjíshì","County City","<U+8365><U+9633><U+5E02>","Xíngyáng"
902,49,"CHN","China",12,"Henan",129,"Zhengzhou",902,"Xinzheng","Xiànjíshì","County City","<U+65B0><U+90D1><U+5E02>","Xinzhèng"
903,49,"CHN","China",12,"Henan",129,"Zhengzhou",903,"Zhengzhou","Shìxiáqu","District",NA,NA
904,49,"CHN","China",12,"Henan",129,"Zhengzhou",904,"Zhongmou","Xiàn","County","<U+4E2D><U+725F><U+53BF>","Zhongmóu"
905,49,"CHN","China",12,"Henan",130,"Zhoukou",905,"Dancheng","Xiàn","County","<U+90F8><U+57CE><U+53BF>","Danchéng"
906,49,"CHN","China",12,"Henan",130,"Zhoukou",906,"Fugou","Xiàn","County","<U+6276><U+6C9F><U+53BF>","Fúgou"
907,49,"CHN","China",12,"Henan",130,"Zhoukou",907,"Huaiyang","Xiàn","County","<U+6DEE><U+9633><U+53BF>","Huáiyáng"
908,49,"CHN","China",12,"Henan",130,"Zhoukou",908,"Luyi","Xiàn","County","<U+9E7F><U+9091><U+53BF>","Lùyì"
909,49,"CHN","China",12,"Henan",130,"Zhoukou",909,"Shangshui","Xiàn","County","<U+5546><U+6C34><U+53BF>","Shangshui"
910,49,"CHN","China",12,"Henan",130,"Zhoukou",910,"Shenqiu","Xiàn","County","<U+6C88><U+4E18><U+53BF>","Shenqiu"
911,49,"CHN","China",12,"Henan",130,"Zhoukou",911,"Taikang","Xiàn","County","<U+592A><U+5EB7><U+53BF>","Tàikang"
912,49,"CHN","China",12,"Henan",130,"Zhoukou",912,"Xiangcheng","Xiànjíshì","County City","<U+9879><U+57CE><U+5E02>","Xiàngchéng"
913,49,"CHN","China",12,"Henan",130,"Zhoukou",913,"Xihua","Xiàn","County","<U+897F><U+534E><U+53BF>","Xihuá"
914,49,"CHN","China",12,"Henan",130,"Zhoukou",914,"Zhoukou","Xiànjíshì","County City",NA,NA
915,49,"CHN","China",12,"Henan",131,"Zhumadian",915,"Biyang","Xiàn","County",NA,NA
916,49,"CHN","China",12,"Henan",131,"Zhumadian",916,"Pingyu","Xiàn","County","<U+5E73><U+8206><U+53BF>","Píngyú"
917,49,"CHN","China",12,"Henan",131,"Zhumadian",917,"Queshan","Xiàn","County","<U+786E><U+5C71><U+53BF>","Quèshan"
918,49,"CHN","China",12,"Henan",131,"Zhumadian",918,"Runan","Xiàn","County","<U+6C5D><U+5357><U+53BF>","Runán"
919,49,"CHN","China",12,"Henan",131,"Zhumadian",919,"Shangcai","Xiàn","County","<U+4E0A><U+8521><U+53BF>","Shàngcài"
920,49,"CHN","China",12,"Henan",131,"Zhumadian",920,"Suiping","Xiàn","County","<U+9042><U+5E73><U+53BF>","Suípíng"
921,49,"CHN","China",12,"Henan",131,"Zhumadian",921,"Xincai","Xiàn","County","<U+65B0><U+8521><U+53BF>","Xincài"
922,49,"CHN","China",12,"Henan",131,"Zhumadian",922,"Xiping","Xiàn","County","<U+897F><U+5E73><U+53BF>","Xipíng"
923,49,"CHN","China",12,"Henan",131,"Zhumadian",923,"Zhengyang","Xiàn","County","<U+6B63><U+9633><U+53BF>","Zhèngyáng"
924,49,"CHN","China",12,"Henan",131,"Zhumadian",924,"Zhumadian","Xiànjíshì","County City",NA,NA
925,49,"CHN","China",13,"Hubei",132,"Enshi Tujia and Miao",925,"Badong","Xiàn","County","<U+5DF4><U+4E1C><U+53BF>","Badong"
926,49,"CHN","China",13,"Hubei",132,"Enshi Tujia and Miao",926,"Enshi Tujia and Miao","Zìzhìzhou","Autonomous Prefecture","<U+6069><U+65BD><U+571F><U+5BB6><U+65CF><U+82D7><U+65CF><U+81EA><U+6CBB><U+5DDE>","Enshi Tujiazú Miáozú"
927,49,"CHN","China",13,"Hubei",132,"Enshi Tujia and Miao",927,"Hefeng","Xiàn","County","<U+9E64><U+5CF0><U+53BF>","Hèfeng"
928,49,"CHN","China",13,"Hubei",132,"Enshi Tujia and Miao",928,"Jianshi","Xiàn","County","<U+5EFA><U+59CB><U+53BF>","Jiànshi"
929,49,"CHN","China",13,"Hubei",132,"Enshi Tujia and Miao",929,"Laifeng","Xiàn","County","<U+6765><U+51E4><U+53BF>","Láifèng"
930,49,"CHN","China",13,"Hubei",132,"Enshi Tujia and Miao",930,"Lichuan","Xiànjíshì","County City","<U+5229><U+5DDD><U+5E02>","Lìchuan"
931,49,"CHN","China",13,"Hubei",132,"Enshi Tujia and Miao",931,"Xianfeng","Xiàn","County","<U+54B8><U+4E30><U+53BF>","Xiánfeng"
932,49,"CHN","China",13,"Hubei",132,"Enshi Tujia and Miao",932,"Xuan En","Xiàn","County",NA,NA
933,49,"CHN","China",13,"Hubei",133,"Ezhou",933,"Echeng Qu","Shìxiáqu","District","<U+9102><U+57CE><U+533A>","Èchéng"
934,49,"CHN","China",13,"Hubei",133,"Ezhou",934,"Echeng Shì","Xiànjíshì","County City","<U+9102><U+57CE><U+5E02>","Èchéng"
935,49,"CHN","China",13,"Hubei",134,"Huanggang",935,"Guangji","Xiàn","County",NA,NA
936,49,"CHN","China",13,"Hubei",134,"Huanggang",936,"Hong'an","Xiàn","County","<U+7EA2><U+5B89><U+53BF>","Hóng'an"
937,49,"CHN","China",13,"Hubei",134,"Huanggang",937,"Huanggang","Xiàn","County",NA,NA
938,49,"CHN","China",13,"Hubei",134,"Huanggang",938,"Huangmei","Xiàn","County","<U+9EC4><U+6885><U+53BF>","Huángméi"
939,49,"CHN","China",13,"Hubei",134,"Huanggang",939,"Luotian","Xiàn","County","<U+7F57><U+7530><U+53BF>","Luótián"
940,49,"CHN","China",13,"Hubei",134,"Huanggang",940,"Macheng","Xiànjíshì","County City","<U+9EBB><U+57CE><U+5E02>","Máchéng"
941,49,"CHN","China",13,"Hubei",134,"Huanggang",941,"Qichun","Xiàn","County","<U+8572><U+6625><U+53BF>","Qíchun"
942,49,"CHN","China",13,"Hubei",134,"Huanggang",942,"Xishui","Xiàn","County","<U+6D60><U+6C34><U+53BF>","Xishui"
943,49,"CHN","China",13,"Hubei",134,"Huanggang",943,"Yingshan","Xiàn","County","<U+82F1><U+5C71><U+53BF>","Yingshan"
944,49,"CHN","China",13,"Hubei",135,"Huangshi",944,"Daye","Xiànjíshì","County City","<U+5927><U+51B6><U+5E02>","Dàye"
945,49,"CHN","China",13,"Hubei",135,"Huangshi",945,"Huangshi","Shìxiáqu","District",NA,NA
946,49,"CHN","China",13,"Hubei",135,"Huangshi",946,"Yangxin","Xiàn","County","<U+9633><U+65B0><U+53BF>","Yángxin"
947,49,"CHN","China",13,"Hubei",136,"Jingmen",947,"Jingmen","Xiàn","County",NA,NA
948,49,"CHN","China",13,"Hubei",136,"Jingmen",948,"Jingshan","Xiàn","County","<U+4EAC><U+5C71><U+53BF>","Jingshan"
949,49,"CHN","China",13,"Hubei",136,"Jingmen",949,"Shayang","Xiàn","County","<U+6C99><U+6D0B><U+53BF>","Shayáng"
950,49,"CHN","China",13,"Hubei",136,"Jingmen",950,"Zhongxiang","Xiànjíshì","County City","<U+949F><U+7965><U+5E02>","Zhongxiáng"
951,49,"CHN","China",13,"Hubei",137,"Jingzhou",951,"Gong'an","Xiàn","County","<U+516C><U+5B89><U+53BF>","Gong'an"
952,49,"CHN","China",13,"Hubei",137,"Jingzhou",952,"Honghu","Xiànjíshì","County City","<U+6D2A><U+6E56><U+5E02>","Hónghú"
953,49,"CHN","China",13,"Hubei",137,"Jingzhou",953,"Jiangling","Xiàn","County","<U+6C5F><U+9675><U+53BF>","Jianglíng"
954,49,"CHN","China",13,"Hubei",137,"Jingzhou",954,"Jianli","Xiàn","County","<U+76D1><U+5229><U+53BF>","Jianlì"
955,49,"CHN","China",13,"Hubei",137,"Jingzhou",955,"Jingzhou","Xiànjíshì","County City","<U+8346><U+5DDE><U+5E02>","Jingzhou"
956,49,"CHN","China",13,"Hubei",137,"Jingzhou",956,"Shashi","Shìxiáqu","District","<U+6C99><U+5E02><U+533A>","Shashì"
957,49,"CHN","China",13,"Hubei",137,"Jingzhou",957,"Shishou","Xiàn","County",NA,NA
958,49,"CHN","China",13,"Hubei",137,"Jingzhou",958,"Songzi","Xiànjíshì","County City","<U+677E><U+6ECB><U+5E02>","Songzi"
959,49,"CHN","China",13,"Hubei",138,"Qianjiang",959,"Qianjiang","Xiànjíshì","County City","<U+6F5C><U+6C5F><U+5E02>","Qiánjiang"
960,49,"CHN","China",13,"Hubei",139,"Shennongjia",960,"Shennongjia","Xiàn","County",NA,NA
961,49,"CHN","China",13,"Hubei",140,"Shiyan",961,"Fang","Xiàn","County","<U+623F><U+53BF>","Fáng"
962,49,"CHN","China",13,"Hubei",140,"Shiyan",962,"Junxian","Xiàn","County",NA,NA
963,49,"CHN","China",13,"Hubei",140,"Shiyan",963,"Shiyan","Xiànjíshì","County City",NA,NA
964,49,"CHN","China",13,"Hubei",140,"Shiyan",964,"Yun","Xiàn","County","<U+90E7><U+53BF>","Yún"
965,49,"CHN","China",13,"Hubei",140,"Shiyan",965,"Yunxi","Xiàn","County","<U+90E7><U+897F><U+53BF>","Yúnxi"
966,49,"CHN","China",13,"Hubei",140,"Shiyan",966,"Zhushan","Xiàn","County","<U+7AF9><U+5C71><U+53BF>","Zhúshan"
967,49,"CHN","China",13,"Hubei",140,"Shiyan",967,"Zhuxi","Xiàn","County","<U+7AF9><U+6EAA><U+53BF>","Zhúxi"
968,49,"CHN","China",13,"Hubei",141,"Suizhou Shi",968,"Suizhou","Xiànjíshì","County City",NA,NA
969,49,"CHN","China",13,"Hubei",141,"Suizhou Shi",969,"Yingshan","Xiàn","County","<U+82F1><U+5C71><U+53BF>","Yingshan"
970,49,"CHN","China",13,"Hubei",142,"Tianmen",970,"Tianmen","Xiànjíshì","County City","<U+5929><U+95E8><U+5E02>","Tianmén"
971,49,"CHN","China",13,"Hubei",143,"Wuhan",971,"Hanyang","Shìxiáqu","District","<U+6C49><U+9633><U+533A>","Hànyáng"
972,49,"CHN","China",13,"Hubei",143,"Wuhan",972,"Huangpi","Shìxiáqu","District","<U+9EC4><U+9642><U+533A>","Huángpí"
973,49,"CHN","China",13,"Hubei",143,"Wuhan",973,"Wuchang","Shìxiáqu","District","<U+6B66><U+660C><U+533A>","Wuchang"
974,49,"CHN","China",13,"Hubei",143,"Wuhan",974,"Wuhan","Shìxiáqu","District",NA,NA
975,49,"CHN","China",13,"Hubei",143,"Wuhan",975,"Xinzhou","Shìxiáqu","District","<U+65B0><U+6D32><U+533A>","Xinzhou"
976,49,"CHN","China",13,"Hubei",144,"Xiangfan",976,"Baokang","Xiàn","County","<U+4FDD><U+5EB7><U+53BF>","Baokang"
977,49,"CHN","China",13,"Hubei",144,"Xiangfan",977,"Gucheng","Xiàn","County","<U+8C37><U+57CE><U+53BF>","Guchéng"
978,49,"CHN","China",13,"Hubei",144,"Xiangfan",978,"Laohekou","Xiànjíshì","County City","<U+8001><U+6CB3><U+53E3><U+5E02>","Laohékou"
979,49,"CHN","China",13,"Hubei",144,"Xiangfan",979,"Nanzhang","Xiàn","County","<U+5357><U+6F33><U+53BF>","Nánzhang"
980,49,"CHN","China",13,"Hubei",144,"Xiangfan",980,"Xiangfan","Xiànjíshì","County City",NA,NA
981,49,"CHN","China",13,"Hubei",144,"Xiangfan",981,"Xiangyang","Shìxiáqu","District","<U+8944><U+9633><U+533A>","Xiangyáng"
982,49,"CHN","China",13,"Hubei",144,"Xiangfan",982,"Yicheng","Xiànjíshì","County City","<U+5B9C><U+57CE><U+5E02>","Yíchéng"
983,49,"CHN","China",13,"Hubei",144,"Xiangfan",983,"Zaoyang","Xiànjíshì","County City","<U+67A3><U+9633><U+5E02>","Zaoyáng"
984,49,"CHN","China",13,"Hubei",145,"Xianning",984,"Chongyang","Xiàn","County","<U+5D07><U+9633><U+53BF>","Chóngyáng"
985,49,"CHN","China",13,"Hubei",145,"Xianning",985,"Jiayu","Xiàn","County","<U+5609><U+9C7C><U+53BF>","Jiayú"
986,49,"CHN","China",13,"Hubei",145,"Xianning",986,"Puqi","Xiàn","County",NA,NA
987,49,"CHN","China",13,"Hubei",145,"Xianning",987,"Tongcheng","Xiàn","County","<U+901A><U+57CE><U+53BF>","Tongchéng"
988,49,"CHN","China",13,"Hubei",145,"Xianning",988,"Tongshan","Xiàn","County","<U+901A><U+5C71><U+53BF>","Tongshan"
989,49,"CHN","China",13,"Hubei",145,"Xianning",989,"Xianning","Xiàn","County",NA,NA
990,49,"CHN","China",13,"Hubei",146,"Xiantao",990,"Mianyang","Xiàn","County",NA,NA
991,49,"CHN","China",13,"Hubei",147,"Xiaogan",991,"Anlu","Xiànjíshì","County City","<U+5B89><U+9646><U+5E02>","Anlù"
992,49,"CHN","China",13,"Hubei",147,"Xiaogan",992,"Dawu","Xiàn","County","<U+5927><U+609F><U+53BF>","Dàwù"
993,49,"CHN","China",13,"Hubei",147,"Xiaogan",993,"Hanchuan","Xiànjíshì","County City","<U+6C49><U+5DDD><U+5E02>","Hànchuan"
994,49,"CHN","China",13,"Hubei",147,"Xiaogan",994,"Xiaochang","Xiàn","County","<U+5B5D><U+660C><U+53BF>","Xiàochang"
995,49,"CHN","China",13,"Hubei",147,"Xiaogan",995,"Xiaogan","Xiàn","County",NA,NA
996,49,"CHN","China",13,"Hubei",147,"Xiaogan",996,"Yingcheng","Shìxiáqu","District","<U+5E94><U+57CE><U+533A>","Yìngchéng"
997,49,"CHN","China",13,"Hubei",147,"Xiaogan",997,"Yunmeng","Xiàn","County","<U+4E91><U+68A6><U+53BF>","Yúnmèng"
998,49,"CHN","China",13,"Hubei",148,"Yichang",998,"Changyang Tujia","Zìzhìxiàn","Autonomous County","<U+957F><U+9633><U+571F><U+5BB6><U+65CF><U+81EA><U+6CBB><U+53BF>","Chángyáng Tujiazú"
999,49,"CHN","China",13,"Hubei",148,"Yichang",999,"Dangyang","Xiànjíshì","County City","<U+5F53><U+9633><U+5E02>","Dangyáng"
1000,49,"CHN","China",13,"Hubei",148,"Yichang",1000,"Wufeng Tujia","Zìzhìxiàn","Autonomous County","<U+4E94><U+5CF0><U+571F><U+5BB6><U+65CF><U+81EA><U+6CBB><U+53BF>","Wufeng Tujiazú"
1001,49,"CHN","China",13,"Hubei",148,"Yichang",1001,"Xingshan","Xiàn","County","<U+5174><U+5C71><U+53BF>","Xingshan"
1002,49,"CHN","China",13,"Hubei",148,"Yichang",1002,"Yichang","Xiànjíshì","County City","<U+5B9C><U+660C><U+5E02>","Yíchang"
1003,49,"CHN","China",13,"Hubei",148,"Yichang",1003,"Yidu","Xiànjíshì","County City",NA,"Yídu"
1004,49,"CHN","China",13,"Hubei",148,"Yichang",1004,"Yiling","Shìxiáqu","District","<U+5937><U+9675><U+533A>","Yílíng"
1005,49,"CHN","China",13,"Hubei",148,"Yichang",1005,"Yuan'an","Xiàn","County","<U+8FDC><U+5B89><U+53BF>","Yuan'an"
1006,49,"CHN","China",13,"Hubei",148,"Yichang",1006,"Zhijiang","Xiànjíshì","County City","<U+679D><U+6C5F><U+5E02>","Zhijiang"
1007,49,"CHN","China",13,"Hubei",148,"Yichang",1007,"Zigui","Xiàn","County","<U+79ED><U+5F52><U+53BF>","Zigui"
1008,49,"CHN","China",14,"Hunan",149,"Changde",1008,"Anxiang","Xiàn","County","<U+5B89><U+4E61><U+53BF>|<U+5B89><U+9109><U+7E23>","Anxiang"
1009,49,"CHN","China",14,"Hunan",149,"Changde",1009,"Dingcheng","Shìxiáqu","District","<U+9F0E><U+57CE><U+533A>","Dingcheng"
1010,49,"CHN","China",14,"Hunan",149,"Changde",1010,"Hanshou","Xiàn","County","<U+6C49><U+5BFF><U+53BF>|<U+6F22><U+58FD><U+7E23>","Hànshòu"
1011,49,"CHN","China",14,"Hunan",149,"Changde",1011,"Jinshi","Xiànjíshì","County City","<U+6D25><U+5E02><U+5E02>|<U+6D25><U+5E02><U+5E02>","Jinshì"
1012,49,"CHN","China",14,"Hunan",149,"Changde",1012,"Linli","Xiàn","County","<U+4E34><U+6FA7><U+53BF>|<U+81E8><U+6FA7><U+7E23>","Línli"
1013,49,"CHN","China",14,"Hunan",149,"Changde",1013,"Li","Xiàn","County",NA,NA
1014,49,"CHN","China",14,"Hunan",149,"Changde",1014,"Shimen","Xiàn","County","<U+77F3><U+95E8><U+53BF>|<U+77F3><U+9580><U+7E23>","Shímén"
1015,49,"CHN","China",14,"Hunan",149,"Changde",1015,"Taoyuan","Xiàn","County","<U+6843><U+6E90><U+53BF>|<U+6843><U+6E90><U+7E23>","Táoyuán"
1016,49,"CHN","China",14,"Hunan",149,"Changde",1016,"Wuling","Shìxiáqu","District","<U+6B66><U+9675><U+533A>","Wulíng"
1017,49,"CHN","China",14,"Hunan",150,"Changsha",1017,"Changsha Shì","Shìxiáqu","District","<U+957F><U+6C99><U+533A>","Chángsha"
1018,49,"CHN","China",14,"Hunan",150,"Changsha",1018,"Changsha Xiàn","Xiàn","County","<U+957F><U+6C99><U+53BF>","Chángsha"
1019,49,"CHN","China",14,"Hunan",150,"Changsha",1019,"Liuyang","Xiànjíshì","County City","<U+6D4F><U+9633><U+5E02>|<U+700F><U+967D><U+5E02>","Liúyáng"
1020,49,"CHN","China",14,"Hunan",150,"Changsha",1020,"Ningxiang","Xiàn","County","<U+5B81><U+4E61><U+53BF>|<U+5BE7><U+9109><U+7E23>","Níngxiang"
1021,49,"CHN","China",14,"Hunan",150,"Changsha",1021,"Wangcheng","Xiàn","County","<U+671B><U+57CE><U+53BF>|<U+671B><U+57CE><U+7E23>","Wàngchéng"
1022,49,"CHN","China",14,"Hunan",151,"Chenzhou",1022,"Anren","Xiàn","County","<U+5B89><U+4EC1><U+53BF>|<U+5B89><U+4EC1><U+7E23>","Anrén"
1023,49,"CHN","China",14,"Hunan",151,"Chenzhou",1023,"Chen","Xiàn","County",NA,NA
1024,49,"CHN","China",14,"Hunan",151,"Chenzhou",1024,"Chenzhou","Xiànjíshì","County City","<U+90F4><U+5DDE><U+5E02>","Chenzhou"
1025,49,"CHN","China",14,"Hunan",151,"Chenzhou",1025,"Guidong","Xiàn","County","<U+6842><U+4E1C><U+53BF>|<U+6842><U+6771><U+7E23>","Guìdong"
1026,49,"CHN","China",14,"Hunan",151,"Chenzhou",1026,"Guiyang","Xiàn","County","<U+6842><U+9633><U+53BF>|<U+6842><U+967D><U+7E23>","Guìyáng"
1027,49,"CHN","China",14,"Hunan",151,"Chenzhou",1027,"Jiahe","Xiàn","County","<U+5609><U+79BE><U+53BF>|<U+5609><U+79BE><U+7E23>","Jiahé"
1028,49,"CHN","China",14,"Hunan",151,"Chenzhou",1028,"Linwu","Xiàn","County","<U+4E34><U+6B66><U+53BF>|<U+81E8><U+6B66><U+7E23>","Línwu"
1029,49,"CHN","China",14,"Hunan",151,"Chenzhou",1029,"Rucheng","Xiàn","County","<U+6C5D><U+57CE><U+53BF>|<U+6C5D><U+57CE><U+7E23>","Ruchéng"
1030,49,"CHN","China",14,"Hunan",151,"Chenzhou",1030,"Yizhang","Xiàn","County","<U+5B9C><U+7AE0><U+53BF>|<U+5B9C><U+7AE0><U+7E23>","Yízhang"
1031,49,"CHN","China",14,"Hunan",151,"Chenzhou",1031,"Yongxing","Xiàn","County","<U+6C38><U+5174><U+53BF>|<U+6C38><U+8208><U+7E23>","Yongxing"
1032,49,"CHN","China",14,"Hunan",151,"Chenzhou",1032,"Zixing","Xiànjíshì","County City","<U+8D44><U+5174><U+5E02>|<U+8CC7><U+8208><U+5E02>","Zixing"
1033,49,"CHN","China",14,"Hunan",152,"Hengyang",1033,"Changning","Xiànjíshì","County City","<U+5E38><U+5B81><U+5E02>|<U+5E38><U+5BE7><U+5E02>","Chángníng"
1034,49,"CHN","China",14,"Hunan",152,"Hengyang",1034,"Hengdong","Xiàn","County","<U+8861><U+4E1C><U+53BF>|<U+8861><U+6771><U+7E23>","Héngdong"
1035,49,"CHN","China",14,"Hunan",152,"Hengyang",1035,"Hengnan","Xiàn","County","<U+8861><U+5357><U+53BF>|<U+8861><U+5357><U+7E23>","Héngnán"
1036,49,"CHN","China",14,"Hunan",152,"Hengyang",1036,"Hengshan","Xiàn","County","<U+8861><U+5C71><U+53BF>|<U+8861><U+5C71><U+7E23>","Héngshan"
1037,49,"CHN","China",14,"Hunan",152,"Hengyang",1037,"Hengyang Shì","Xiànjíshì","County City","<U+8861><U+9633><U+5E02>","Héngyáng"
1038,49,"CHN","China",14,"Hunan",152,"Hengyang",1038,"Hengyang Xiàn","Xiàn","County","<U+8861><U+9633><U+53BF>","Héngyáng"
1039,49,"CHN","China",14,"Hunan",152,"Hengyang",1039,"Laiyang","Xiàn","County",NA,NA
1040,49,"CHN","China",14,"Hunan",152,"Hengyang",1040,"Qidong","Xiàn","County","<U+7941><U+4E1C><U+53BF>|<U+7941><U+6771><U+7E23>","Qídóng"
1041,49,"CHN","China",14,"Hunan",153,"Huaihua",1041,"Chenxi","Xiàn","County","<U+8FB0><U+6EAA><U+53BF>|<U+8FB0><U+6EAA><U+7E23>","Chénxi"
1042,49,"CHN","China",14,"Hunan",153,"Huaihua",1042,"Hóngjiang Qu","Shìxiáqu","District","<U+6D2A><U+6C5F><U+533A>","Hóngjiang"
1043,49,"CHN","China",14,"Hunan",153,"Huaihua",1043,"Hóngjiang Shì","Xiànjíshì","County City","<U+6D2A><U+6C5F><U+5E02>","Hóngjiang"
1044,49,"CHN","China",14,"Hunan",153,"Huaihua",1044,"Huaihua","Xiàn","County",NA,NA
1045,49,"CHN","China",14,"Hunan",153,"Huaihua",1045,"Huitong","Xiàn","County","<U+4F1A><U+540C><U+53BF>|<U+6703><U+540C><U+7E23>","Huìtong"
1046,49,"CHN","China",14,"Hunan",153,"Huaihua",1046,"Jing","Xiàn","County",NA,NA
1047,49,"CHN","China",14,"Hunan",153,"Huaihua",1047,"Mayang Miao","Zìzhìxiàn","Autonomous County","<U+9EBB><U+9633><U+82D7><U+65CF><U+81EA><U+6CBB><U+53BF>","Mayang Miao Dòngzú"
1048,49,"CHN","China",14,"Hunan",153,"Huaihua",1048,"Tongdao Dong","Zìzhìxiàn","Autonomous County","<U+901A><U+9053><U+4F97><U+65CF><U+81EA><U+6CBB><U+53BF>|<U+901A><U+9053><U+4F97><U+65CF><U+81EA><U+6CBB><U+7E23>","Máyáng Dòngzú"
1049,49,"CHN","China",14,"Hunan",153,"Huaihua",1049,"Xinhuang Dong Dongzu","Zìzhìxiàn","Autonomous County","<U+65B0><U+6643><U+4F97><U+65CF><U+81EA><U+6CBB><U+53BF>","Xinhuang Dong Dòngzú"
1050,49,"CHN","China",14,"Hunan",153,"Huaihua",1050,"Xupu","Xiàn","County","<U+6E86><U+6D66><U+53BF>|<U+6F35><U+6D66><U+7E23>","Xùpu"
1051,49,"CHN","China",14,"Hunan",153,"Huaihua",1051,"Yuanling","Xiàn","County","<U+6C85><U+9675><U+53BF>|<U+6C85><U+9675><U+7E23>","Yuánlíng"
1052,49,"CHN","China",14,"Hunan",153,"Huaihua",1052,"Zhijiang Dong Dongzu","Zìzhìxiàn","Autonomous County","<U+82B7><U+6C5F><U+4F97><U+65CF><U+81EA><U+6CBB><U+53BF>|<U+82B7><U+6C5F><U+4F97><U+65CF><U+81EA><U+6CBB><U+7E23>","Zhijiang Dong Dòngzú"
1053,49,"CHN","China",14,"Hunan",154,"Loudi",1053,"Lengshuijang","Xiànjíshì","County City",NA,NA
1054,49,"CHN","China",14,"Hunan",154,"Loudi",1054,"Lianyuan","Xiànjíshì","County City","<U+6D9F><U+6E90><U+5E02>|<U+6F23><U+6E90><U+5E02>","Liányuán"
1055,49,"CHN","China",14,"Hunan",154,"Loudi",1055,"Loudi","Xiànjíshì","County City",NA,NA
1056,49,"CHN","China",14,"Hunan",154,"Loudi",1056,"Shuangfeng","Xiàn","County","<U+53CC><U+5CF0><U+53BF>|<U+96D9><U+5CF0><U+7E23>","Shuangfeng"
1057,49,"CHN","China",14,"Hunan",154,"Loudi",1057,"Xinhua","Xiàn","County","<U+65B0><U+5316><U+53BF>|<U+65B0><U+5316><U+7E23>","Xinhuà"
1058,49,"CHN","China",14,"Hunan",155,"Shaoyang",1058,"Chengbu Miao","Zìzhìxiàn","Autonomous County","<U+57CE><U+6B65><U+82D7><U+65CF><U+81EA><U+6CBB><U+53BF>|<U+57CE><U+6B65><U+82D7><U+65CF><U+81EA><U+6CBB><U+7E23>","Chéngbù Miáozú"
1059,49,"CHN","China",14,"Hunan",155,"Shaoyang",1059,"Daxiang","Shìxiáqu","District","<U+5927><U+7965><U+533A>|<U+5927><U+7965><U+5340>","Dàxiáng"
1060,49,"CHN","China",14,"Hunan",155,"Shaoyang",1060,"Dongkou","Xiàn","County","<U+6D1E><U+53E3><U+53BF>|<U+6D1E><U+53E3><U+7E23>","Dòngkou"
1061,49,"CHN","China",14,"Hunan",155,"Shaoyang",1061,"Longhui","Xiàn","County","<U+9686><U+56DE><U+53BF>|<U+9686><U+56DE><U+7E23>","Lónghuí"
1062,49,"CHN","China",14,"Hunan",155,"Shaoyang",1062,"Shaodong","Xiàn","County","<U+90B5><U+4E1C><U+53BF>|<U+90B5><U+6771><U+7E23>","Shoodong"
1063,49,"CHN","China",14,"Hunan",155,"Shaoyang",1063,"Shaoyang Shì","Xiànjíshì","County City","<U+90B5><U+9633><U+5E02>","Shàoyáng"
1064,49,"CHN","China",14,"Hunan",155,"Shaoyang",1064,"Shaoyang Xiàn","Xiàn","County","<U+90B5><U+9633><U+53BF>","Shàoyáng"
1065,49,"CHN","China",14,"Hunan",155,"Shaoyang",1065,"Suining","Xiàn","County","<U+7EE5><U+5B81><U+53BF>|<U+7D8F><U+5BE7><U+7E23>","Suíníng"
1066,49,"CHN","China",14,"Hunan",155,"Shaoyang",1066,"Wugang","Xiànjíshì","County City","<U+6B66><U+5188><U+5E02>|<U+6B66><U+5CA1><U+5E02>","Wugang"
1067,49,"CHN","China",14,"Hunan",155,"Shaoyang",1067,"Xinning","Xiàn","County","<U+65B0><U+5B81><U+53BF>|<U+65B0><U+5BE7><U+7E23>","Xinníng"
1068,49,"CHN","China",14,"Hunan",155,"Shaoyang",1068,"Xinshao","Xiàn","County","<U+65B0><U+90B5><U+53BF>|<U+65B0><U+90B5><U+7E23>","Xinshào"
1069,49,"CHN","China",14,"Hunan",156,"Xiangtan",1069,"Shaoshan","Xiànjíshì","County City","<U+97F6><U+5C71><U+5E02>","Sháoshan"
1070,49,"CHN","China",14,"Hunan",156,"Xiangtan",1070,"Xiangtan Shì","Xiànjíshì","County City","<U+6E58><U+6F6D><U+5E02>","Xiangtán"
1071,49,"CHN","China",14,"Hunan",156,"Xiangtan",1071,"Xiangtan Xiàn","Xiàn","County","<U+6E58><U+6F6D><U+53BF>","Xiangtán"
1072,49,"CHN","China",14,"Hunan",156,"Xiangtan",1072,"Xiangxiang","Xiànjíshì","County City","<U+6E58><U+4E61><U+5E02>|<U+6E58><U+9109><U+5E02>","Xiangxiang"
1073,49,"CHN","China",14,"Hunan",157,"Xiangxi Tujia and Miao",1073,"Baojing","Xiàn","County","<U+4FDD><U+9756><U+53BF>|<U+4FDD><U+9756><U+7E23>","Baojìng"
1074,49,"CHN","China",14,"Hunan",157,"Xiangxi Tujia and Miao",1074,"Fenghuang","Xiàn","County","<U+51E4><U+51F0><U+53BF>|<U+9CF3><U+51F0><U+7E23>","Fènghuáng"
1075,49,"CHN","China",14,"Hunan",157,"Xiangxi Tujia and Miao",1075,"Guzhang","Xiàn","County","<U+53E4><U+4E08><U+53BF>|<U+53E4><U+4E08><U+7E23>","Guzhàng"
1076,49,"CHN","China",14,"Hunan",157,"Xiangxi Tujia and Miao",1076,"Huayuan","Xiàn","County","<U+82B1><U+57A3><U+53BF>|<U+82B1><U+57A3><U+7E23>","Huayuán"
1077,49,"CHN","China",14,"Hunan",157,"Xiangxi Tujia and Miao",1077,"Jishou","Xiànjíshì","County City","<U+5409><U+9996><U+5E02>|<U+5409><U+9996><U+5E02>","Jíshou"
1078,49,"CHN","China",14,"Hunan",157,"Xiangxi Tujia and Miao",1078,"Longshan","Xiàn","County","<U+9F99><U+5C71><U+53BF>|<U+9F8D><U+5C71><U+7E23>","Lóngshan"
1079,49,"CHN","China",14,"Hunan",157,"Xiangxi Tujia and Miao",1079,"Luxi","Xiàn","County","<U+6CF8><U+6EAA><U+53BF>|<U+7018><U+6EAA><U+7E23>","Lúxi"
1080,49,"CHN","China",14,"Hunan",157,"Xiangxi Tujia and Miao",1080,"Yongshun","Xiàn","County","<U+6C38><U+987A><U+53BF>|<U+6C38><U+9806><U+7E23>","Yongshùn"
1081,49,"CHN","China",14,"Hunan",158,"Yiyang",1081,"Anhua","Xiàn","County","<U+5B89><U+5316><U+53BF>|<U+5B89><U+5316><U+7E23>","Anhuà"
1082,49,"CHN","China",14,"Hunan",158,"Yiyang",1082,"Nan","Xiàn","County","<U+5357><U+53BF>|<U+5357><U+7E23>","Nán"
1083,49,"CHN","China",14,"Hunan",158,"Yiyang",1083,"Taojiang","Xiàn","County","<U+6843><U+6C5F><U+53BF>|<U+6843><U+6C5F><U+7E23>","Táojiang"
1084,49,"CHN","China",14,"Hunan",158,"Yiyang",1084,"Yiyang Shì","Xiànjíshì","County City","<U+76CA><U+9633><U+5E02>","Yìyáng"
1085,49,"CHN","China",14,"Hunan",158,"Yiyang",1085,"Yiyang Xiàn","Xiàn","County","<U+76CA><U+9633><U+53BF>","Yìyáng"
1086,49,"CHN","China",14,"Hunan",158,"Yiyang",1086,"Yuanjiang","Xiànjíshì","County City","<U+6C85><U+6C5F><U+5E02>|<U+6C85><U+6C5F><U+5E02>","Yuánjiang"
1087,49,"CHN","China",14,"Hunan",159,"Yongzhou",1087,"Dao","Xiàn","County","<U+9053><U+53BF>|<U+9053><U+7E23>","Dào"
1088,49,"CHN","China",14,"Hunan",159,"Yongzhou",1088,"Jianghua Yao","Zìzhìxiàn","Autonomous County","<U+6C5F><U+534E><U+7476><U+65CF><U+81EA><U+6CBB><U+53BF>|<U+6C5F><U+83EF><U+7464><U+65CF><U+81EA><U+6CBB><U+7E23>","Jianghuá Yáozu"
1089,49,"CHN","China",14,"Hunan",159,"Yongzhou",1089,"Jiangyong","Xiàn","County","<U+6C5F><U+6C38><U+53BF>|<U+6C5F><U+6C38><U+7E23>","Jiangyong"
1090,49,"CHN","China",14,"Hunan",159,"Yongzhou",1090,"Lanshan","Xiàn","County","<U+84DD><U+5C71><U+53BF>|<U+85CD><U+5C71><U+7E23>","Lánshan"
1091,49,"CHN","China",14,"Hunan",159,"Yongzhou",1091,"Lengshuitan","Shìxiáqu","District","<U+51B7><U+6C34><U+6EE9><U+533A>","Lengshuitan"
1092,49,"CHN","China",14,"Hunan",159,"Yongzhou",1092,"Lingling","Shìxiáqu","District","<U+96F6><U+9675><U+533A>|<U+96F6><U+9675><U+5340>","Linglíng"
1093,49,"CHN","China",14,"Hunan",159,"Yongzhou",1093,"Ningyuan","Xiàn","County","<U+5B81><U+8FDC><U+53BF>|<U+5BE7><U+9060><U+7E23>","Ningyuan"
1094,49,"CHN","China",14,"Hunan",159,"Yongzhou",1094,"Qiyang","Xiàn","County","<U+7941><U+9633><U+53BF>|<U+7941><U+967D><U+7E23>","Qíyáng"
1095,49,"CHN","China",14,"Hunan",159,"Yongzhou",1095,"Shuangpai","Xiàn","County","<U+53CC><U+724C><U+53BF>|<U+96D9><U+724C><U+7E23>","Shuangpái"
1096,49,"CHN","China",14,"Hunan",159,"Yongzhou",1096,"Xintian","Xiàn","County","<U+65B0><U+7530><U+53BF>|<U+65B0><U+7530><U+7E23>","Xintián"
1097,49,"CHN","China",14,"Hunan",160,"Yueyang",1097,"Huarong","Xiàn","County","<U+534E><U+5BB9><U+53BF>|<U+83EF><U+5BB9><U+7E23>","Huáróng"
1098,49,"CHN","China",14,"Hunan",160,"Yueyang",1098,"Linxiang","Xiànjíshì","County City","<U+4E34><U+6E58><U+5E02>|<U+81E8><U+6E58><U+5E02>","Línxiang"
1099,49,"CHN","China",14,"Hunan",160,"Yueyang",1099,"Miluo","Xiànjíshì","County City","<U+6C68><U+7F57><U+5E02>|<U+6C68><U+7F85><U+5E02>","Mìluó"
1100,49,"CHN","China",14,"Hunan",160,"Yueyang",1100,"Pingjiang","Xiàn","County","<U+5E73><U+6C5F><U+53BF>|<U+5E73><U+6C5F><U+7E23>","Píngjiang"
1101,49,"CHN","China",14,"Hunan",160,"Yueyang",1101,"Xiangyin","Xiàn","County","<U+6E58><U+9634><U+53BF>|<U+6E58><U+9670><U+7E23>","Xiangyin"
1102,49,"CHN","China",14,"Hunan",160,"Yueyang",1102,"Yueyang Qu","Shìxiáqu","District","<U+5CB3><U+9633><U+533A>","Yuèyáng"
1103,49,"CHN","China",14,"Hunan",160,"Yueyang",1103,"Yueyang Xiàn","Xiàn","County","<U+5CB3><U+9633><U+53BF>","Yuèyáng"
1104,49,"CHN","China",14,"Hunan",161,"Zhangjiajie",1104,"Cili","Xiàn","County","<U+6148><U+5229><U+53BF>|<U+6148><U+5229><U+7E23>","Cílì"
1105,49,"CHN","China",14,"Hunan",161,"Zhangjiajie",1105,"Dayong","Xiàn","County",NA,NA
1106,49,"CHN","China",14,"Hunan",161,"Zhangjiajie",1106,"Sangzhi","Xiàn","County","<U+6851><U+690D><U+53BF>|<U+6851><U+690D><U+7E23>","Sangzhí"
1107,49,"CHN","China",14,"Hunan",162,"Zhuzhou",1107,"Chaling","Xiàn","County","<U+8336><U+9675><U+53BF>|<U+8336><U+9675><U+7E23>","Chálíng"
1108,49,"CHN","China",14,"Hunan",162,"Zhuzhou",1108,"Liling","Xiànjíshì","County City","<U+91B4><U+9675><U+5E02>|<U+91B4><U+9675><U+5E02>","Lilíng"
1109,49,"CHN","China",14,"Hunan",162,"Zhuzhou",1109,"Ling","Xiàn","County",NA,NA
1110,49,"CHN","China",14,"Hunan",162,"Zhuzhou",1110,"Youxian","Xiàn","County","<U+6538><U+53BF>|<U+6538><U+7E23>","You"
1111,49,"CHN","China",14,"Hunan",162,"Zhuzhou",1111,"Zhuzhou Qu","Shìxiáqu","District","<U+682A><U+6D32><U+5E02>","Zhuzhou"
1112,49,"CHN","China",14,"Hunan",162,"Zhuzhou",1112,"Zhuzhou Xiàn","Xiàn","County","<U+682A><U+6D32><U+53BF>","Zhuzhou"
1113,49,"CHN","China",15,"Jiangsu",163,"Changzhou",1113,"Changzhou","Xiànjíshì","County City",NA,NA
1114,49,"CHN","China",15,"Jiangsu",163,"Changzhou",1114,"Jintan","Xiànjíshì","County City","<U+91D1><U+575B><U+5E02>","Jintán"
1115,49,"CHN","China",15,"Jiangsu",163,"Changzhou",1115,"Liyang","Xiànjíshì","County City","<U+6EA7><U+9633><U+5E02>","Lìyáng"
1116,49,"CHN","China",15,"Jiangsu",163,"Changzhou",1116,"Wujin","Shìxiáqu","District","<U+6B66><U+8FDB><U+533A>","Wujìn"
1117,49,"CHN","China",15,"Jiangsu",164,"Huai'an",1117,"Hongze","Xiàn","County","<U+6D2A><U+6CFD><U+53BF>","Hóngzé"
1118,49,"CHN","China",15,"Jiangsu",164,"Huai'an",1118,"Huaian","Xiàn","County",NA,NA
1119,49,"CHN","China",15,"Jiangsu",164,"Huai'an",1119,"Huaiyin","Shìxiáqu","District","<U+6DEE><U+9634><U+533A>","Huáiyin"
1120,49,"CHN","China",15,"Jiangsu",164,"Huai'an",1120,"Jinhu","Xiàn","County","<U+91D1><U+6E56><U+53BF>","Jinhú"
1121,49,"CHN","China",15,"Jiangsu",164,"Huai'an",1121,"Lianshui","Xiàn","County","<U+6D9F><U+6C34><U+53BF>","Liánshui"
1122,49,"CHN","China",15,"Jiangsu",164,"Huai'an",1122,"Qingjiang","Xiànjíshì","County City",NA,NA
1123,49,"CHN","China",15,"Jiangsu",164,"Huai'an",1123,"Xuyi","Xiàn","County","<U+76F1><U+7719><U+53BF>","Xuyí"
1124,49,"CHN","China",15,"Jiangsu",165,"Lianyungang",1124,"Donghai","Xiàn","County","<U+4E1C><U+6D77><U+53BF>","Donghai"
1125,49,"CHN","China",15,"Jiangsu",165,"Lianyungang",1125,"Ganyu","Xiàn","County","<U+8D63><U+6986><U+53BF>","Gànyú"
1126,49,"CHN","China",15,"Jiangsu",165,"Lianyungang",1126,"Guannan","Xiàn","County","<U+704C><U+5357><U+53BF>","Guànnán"
1127,49,"CHN","China",15,"Jiangsu",165,"Lianyungang",1127,"Guanyun","Xiàn","County","<U+704C><U+4E91><U+53BF>","Guànyún"
1128,49,"CHN","China",15,"Jiangsu",165,"Lianyungang",1128,"Lianyungang","Xiànjíshì","County City",NA,NA
1129,49,"CHN","China",15,"Jiangsu",166,"Nanjing",1129,"Gaochun","Xiàn","County","<U+9AD8><U+6DF3><U+53BF>","Gaochún"
1130,49,"CHN","China",15,"Jiangsu",166,"Nanjing",1130,"Jiangning","Shìxiáqu","District","<U+6C5F><U+5B81><U+533A>","Jiangníng"
1131,49,"CHN","China",15,"Jiangsu",166,"Nanjing",1131,"Jiangpu","Xiàn","County",NA,NA
1132,49,"CHN","China",15,"Jiangsu",166,"Nanjing",1132,"Lishui","Xiàn","County","<U+6EA7><U+6C34><U+53BF>","Lìshui"
1133,49,"CHN","China",15,"Jiangsu",166,"Nanjing",1133,"Luhe","Shìxiáqu","District","<U+516D><U+5408><U+533A>","Lùhé Qu (not l"
1134,49,"CHN","China",15,"Jiangsu",166,"Nanjing",1134,"Nanjing","Shìxiáqu","District",NA,NA
1135,49,"CHN","China",15,"Jiangsu",167,"Nantong",1135,"Hai'an","Xiàn","County","<U+6D77><U+5B89><U+53BF>","Hai'an"
1136,49,"CHN","China",15,"Jiangsu",167,"Nantong",1136,"Haimen","Xiànjíshì","County City","<U+6D77><U+95E8><U+5E02>","Haimén"
1137,49,"CHN","China",15,"Jiangsu",167,"Nantong",1137,"Nantong","Xiànjíshì","County City","<U+5357><U+901A><U+5E02>","Nántong"
1138,49,"CHN","China",15,"Jiangsu",167,"Nantong",1138,"Qidong","Xiànjíshì","County City","<U+542F><U+4E1C><U+5E02>","Qidong"
1139,49,"CHN","China",15,"Jiangsu",167,"Nantong",1139,"Rudong","Xiàn","County","<U+5982><U+4E1C><U+53BF>","Rúdong"
1140,49,"CHN","China",15,"Jiangsu",167,"Nantong",1140,"Rugao","Xiànjíshì","County City","<U+5982><U+768B><U+5E02>","Rúgao"
1141,49,"CHN","China",15,"Jiangsu",167,"Nantong",1141,"Tongzhou","Xiànjíshì","County City","<U+901A><U+5DDE><U+5E02>","Tongzhou"
1142,49,"CHN","China",15,"Jiangsu",168,"Suqian",1142,"Shuyang","Xiàn","County","<U+6CAD><U+9633><U+53BF>","Shùyáng"
1143,49,"CHN","China",15,"Jiangsu",168,"Suqian",1143,"Sihong","Xiàn","County","<U+6CD7><U+6D2A><U+53BF>","Sìhóng"
1144,49,"CHN","China",15,"Jiangsu",168,"Suqian",1144,"Siyang","Xiàn","County","<U+6CD7><U+9633><U+53BF>","Sìyáng"
1145,49,"CHN","China",15,"Jiangsu",168,"Suqian",1145,"Suqian","Xiànjíshì","County City","<U+5BBF><U+8FC1><U+5E02>","Sùqian"
1146,49,"CHN","China",15,"Jiangsu",168,"Suqian",1146,"Suyu","Shìxiáqu","District","<U+5BBF><U+8C6B><U+533A>","Sùyù"
1147,49,"CHN","China",15,"Jiangsu",169,"Suzhou",1147,"Changshu","Xiànjíshì","County City","<U+5E38><U+719F><U+5E02>","Chángshú"
1148,49,"CHN","China",15,"Jiangsu",169,"Suzhou",1148,"Kunshan","Xiànjíshì","County City","<U+6606><U+5C71><U+5E02>","Kunshan"
1149,49,"CHN","China",15,"Jiangsu",169,"Suzhou",1149,"Shazhou","Xiàn","County",NA,NA
1150,49,"CHN","China",15,"Jiangsu",169,"Suzhou",1150,"Suzhou","Xiànjíshì","County City",NA,NA
1151,49,"CHN","China",15,"Jiangsu",169,"Suzhou",1151,"Taicang","Xiànjíshì","County City","<U+592A><U+4ED3><U+5E02>","Tàicang"
1152,49,"CHN","China",15,"Jiangsu",169,"Suzhou",1152,"Wujiang","Xiànjíshì","County City","<U+5434><U+6C5F><U+5E02>","Wújiang"
1153,49,"CHN","China",15,"Jiangsu",169,"Suzhou",1153,"Wuxian","Xiàn","County",NA,NA
1154,49,"CHN","China",15,"Jiangsu",170,"Taizhou",1154,"Jiangyan","Xiànjíshì","County City","<U+59DC><U+5830><U+5E02>","Jiangyàn"
1155,49,"CHN","China",15,"Jiangsu",170,"Taizhou",1155,"Jingjiang","Xiànjíshì","County City","<U+9756><U+6C5F><U+5E02>","Jìngjiang"
1156,49,"CHN","China",15,"Jiangsu",170,"Taizhou",1156,"Taixing","Xiànjíshì","County City","<U+6CF0><U+5174><U+5E02>","Tàixing"
1157,49,"CHN","China",15,"Jiangsu",170,"Taizhou",1157,"Taizhou","Xiànjíshì","County City","<U+6CF0><U+5DDE><U+5E02>","Tàizhou"
1158,49,"CHN","China",15,"Jiangsu",170,"Taizhou",1158,"Xinghua","Xiànjíshì","County City","<U+5174><U+5316><U+5E02>","Xinghuà"
1159,49,"CHN","China",15,"Jiangsu",171,"Wuxi",1159,"Jiangyin","Xiànjíshì","County City","<U+6C5F><U+9634><U+5E02>","Jiangyin"
1160,49,"CHN","China",15,"Jiangsu",171,"Wuxi",1160,"Wuxi New District","Xinqu","New District","<U+65E0><U+9521><U+65B0><U+533A>","Wúxi"
1161,49,"CHN","China",15,"Jiangsu",171,"Wuxi",1161,"Wuxi","Xiànjíshì","County City","<U+65E0><U+9521><U+5E02>","Wúxi"
1162,49,"CHN","China",15,"Jiangsu",171,"Wuxi",1162,"Xishan","Shìxiáqu","District","<U+9521><U+5C71><U+533A>","Xishan"
1163,49,"CHN","China",15,"Jiangsu",171,"Wuxi",1163,"Yixing","Xiànjíshì","County City","<U+5B9C><U+5174><U+5E02>","Yíxing"
1164,49,"CHN","China",15,"Jiangsu",172,"Xuzhou",1164,"Feng","Xiàn","County","<U+4E30><U+53BF>","Feng"
1165,49,"CHN","China",15,"Jiangsu",172,"Xuzhou",1165,"Pei","Xiàn","County","<U+6C9B><U+53BF>","Pèi"
1166,49,"CHN","China",15,"Jiangsu",172,"Xuzhou",1166,"Pizhou","Xiànjíshì","County City","<U+90B3><U+5DDE><U+5E02>","Pizhou"
1167,49,"CHN","China",15,"Jiangsu",172,"Xuzhou",1167,"Suining","Xiàn","County","<U+7762><U+5B81><U+53BF>","Suiníng"
1168,49,"CHN","China",15,"Jiangsu",172,"Xuzhou",1168,"Tongshan","Xiàn","County","<U+94DC><U+5C71><U+53BF>","Tóngshan"
1169,49,"CHN","China",15,"Jiangsu",172,"Xuzhou",1169,"Xinyi","Xiànjíshì","County City","<U+65B0><U+6C82><U+5E02>","Xinyí"
1170,49,"CHN","China",15,"Jiangsu",172,"Xuzhou",1170,"Xuzhou","Xiànjíshì","County City",NA,NA
1171,49,"CHN","China",15,"Jiangsu",173,"Yancheng",1171,"Binhai","Xiàn","County","<U+6EE8><U+6D77><U+53BF>","Binhai"
1172,49,"CHN","China",15,"Jiangsu",173,"Yancheng",1172,"Dafeng","Xiànjíshì","County City","<U+5927><U+4E30><U+5E02>","Dàfeng"
1173,49,"CHN","China",15,"Jiangsu",173,"Yancheng",1173,"Dongtai","Xiànjíshì","County City","<U+4E1C><U+53F0><U+5E02>","Dongtái"
1174,49,"CHN","China",15,"Jiangsu",173,"Yancheng",1174,"Funing","Xiàn","County","<U+961C><U+5B81><U+53BF>","Fùníng"
1175,49,"CHN","China",15,"Jiangsu",173,"Yancheng",1175,"Jianhu","Xiàn","County","<U+5EFA><U+6E56><U+53BF>","Jiànhú"
1176,49,"CHN","China",15,"Jiangsu",173,"Yancheng",1176,"Sheyang","Xiàn","County","<U+5C04><U+9633><U+53BF>","Shèyáng"
1177,49,"CHN","China",15,"Jiangsu",173,"Yancheng",1177,"Xiangshui","Xiàn","County","<U+54CD><U+6C34><U+53BF>","Xiangshui"
1178,49,"CHN","China",15,"Jiangsu",173,"Yancheng",1178,"Yandu","Shìxiáqu","District","<U+76D0><U+90FD><U+533A>","Yándu"
1179,49,"CHN","China",15,"Jiangsu",173,"Yancheng",1179,"Yiancheng","Xiàn","County",NA,NA
1180,49,"CHN","China",15,"Jiangsu",174,"Yangzhou",1180,"Baoying","Xiàn","County","<U+5B9D><U+5E94><U+53BF>","Baoyìng"
1181,49,"CHN","China",15,"Jiangsu",174,"Yangzhou",1181,"Gaoyou","Xiànjíshì","County City","<U+9AD8><U+90AE><U+5E02>","Gaoyóu"
1182,49,"CHN","China",15,"Jiangsu",174,"Yangzhou",1182,"Hanjiang","Shìxiáqu","District","<U+9097><U+6C5F><U+533A>","Hánjiang"
1183,49,"CHN","China",15,"Jiangsu",174,"Yangzhou",1183,"Jiangdu","Xiànjíshì","County City","<U+6C5F><U+90FD><U+5E02>","Jiangdu"
1184,49,"CHN","China",15,"Jiangsu",174,"Yangzhou",1184,"Yangzhou","Xiànjíshì","County City",NA,NA
1185,49,"CHN","China",15,"Jiangsu",174,"Yangzhou",1185,"Yizheng","Xiànjíshì","County City","<U+4EEA><U+5F81><U+5E02>","Yízheng"
1186,49,"CHN","China",15,"Jiangsu",175,"Zhenjiang",1186,"Dantu","Shìxiáqu","District","<U+4E39><U+5F92><U+533A>","Dantú"
1187,49,"CHN","China",15,"Jiangsu",175,"Zhenjiang",1187,"Danyang","Xiànjíshì","County City","<U+4E39><U+9633><U+5E02>","Danyáng"
1188,49,"CHN","China",15,"Jiangsu",175,"Zhenjiang",1188,"Jurong","Xiànjíshì","County City","<U+53E5><U+5BB9><U+5E02>","Jùróng"
1189,49,"CHN","China",15,"Jiangsu",175,"Zhenjiang",1189,"Yangzhong","Xiànjíshì","County City","<U+626C><U+4E2D><U+5E02>","Yángzhong"
1190,49,"CHN","China",15,"Jiangsu",175,"Zhenjiang",1190,"Zhenjiang","Xiànjíshì","County City",NA,NA
1191,49,"CHN","China",16,"Jiangxi",176,"Fuzhou",1191,"Chongren","Xiàn","County","<U+5D07><U+4EC1><U+53BF>","Chóngrén"
1192,49,"CHN","China",16,"Jiangxi",176,"Fuzhou",1192,"Dongxiang","Xiàn","County","<U+4E1C><U+4E61><U+53BF>","Dongxiang"
1193,49,"CHN","China",16,"Jiangxi",176,"Fuzhou",1193,"Guangchang","Xiàn","County","<U+5E7F><U+660C><U+53BF>","Guangchang"
1194,49,"CHN","China",16,"Jiangxi",176,"Fuzhou",1194,"Jinxi","Xiàn","County","<U+91D1><U+6EAA><U+53BF>","Jinxi"
1195,49,"CHN","China",16,"Jiangxi",176,"Fuzhou",1195,"Le'an","Xiàn","County","<U+4E50><U+5B89><U+53BF>","Lè'an"
1196,49,"CHN","China",16,"Jiangxi",176,"Fuzhou",1196,"Lichuan","Xiàn","County","<U+9ECE><U+5DDD><U+53BF>","Líchuan"
1197,49,"CHN","China",16,"Jiangxi",176,"Fuzhou",1197,"Linchuan","Shìxiáqu","District","<U+4E34><U+5DDD><U+533A>","Línchuan"
1198,49,"CHN","China",16,"Jiangxi",176,"Fuzhou",1198,"Nancheng","Xiàn","County","<U+5357><U+57CE><U+53BF>","Nánchéng"
1199,49,"CHN","China",16,"Jiangxi",176,"Fuzhou",1199,"Nanfeng","Xiàn","County","<U+5357><U+4E30><U+53BF>","Nánfeng"
1200,49,"CHN","China",16,"Jiangxi",176,"Fuzhou",1200,"Yihuang","Xiàn","County","<U+5B9C><U+9EC4><U+53BF>","Yíhuáng"
1201,49,"CHN","China",16,"Jiangxi",176,"Fuzhou",1201,"Zixi","Xiàn","County","<U+8D44><U+6EAA><U+53BF>","Zixi"
1202,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1202,"Anyuan","Xiàn","County","<U+5B89><U+8FDC><U+53BF>","Anyuan"
1203,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1203,"Chongyi","Xiàn","County","<U+5D07><U+4E49><U+53BF>","Chóngyì"
1204,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1204,"Dayu","Xiàn","County","<U+5927><U+4F59><U+53BF>","Dàyú"
1205,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1205,"Dingnan","Xiàn","County","<U+5B9A><U+5357><U+53BF>","Dìngnán"
1206,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1206,"Gan","Xiàn","County","<U+8D63><U+53BF>","Gàn"
1207,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1207,"Ganzhou","Xiànjíshì","County City",NA,NA
1208,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1208,"Huichang","Xiàn","County","<U+4F1A><U+660C><U+53BF>","Huìchang"
1209,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1209,"Longnan","Xiàn","County","<U+9F99><U+5357><U+53BF>","Lóngnán"
1210,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1210,"Nankang","Xiànjíshì","County City","<U+5357><U+5EB7><U+5E02>","Nánkang"
1211,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1211,"Ningdu","Xiàn","County","<U+5B81><U+90FD><U+53BF>","Níngdu"
1212,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1212,"Quannan","Xiàn","County",NA,NA
1213,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1213,"Ruijin","Xiànjíshì","County City","<U+745E><U+91D1><U+5E02>","Ruìjin"
1214,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1214,"Shangyou","Xiàn","County","<U+4E0A><U+72B9><U+53BF>","Shàngyóu"
1215,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1215,"Shicheng","Xiàn","County","<U+77F3><U+57CE><U+53BF>","Shíchéng"
1216,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1216,"Xinfeng","Xiàn","County","<U+4FE1><U+4E30><U+53BF>","Xìnfeng"
1217,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1217,"Xingguo","Xiàn","County","<U+5174><U+56FD><U+53BF>","Xingguó"
1218,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1218,"Xunwu","Xiàn","County","<U+5BFB><U+4E4C><U+53BF>","Xúnwu"
1219,49,"CHN","China",16,"Jiangxi",177,"Ganzhou",1219,"Yudu","Xiàn","County","<U+4E8E><U+90FD><U+53BF>","Yúdu"
1220,49,"CHN","China",16,"Jiangxi",178,"Ji'an",1220,"Anfu","Xiàn","County","<U+5B89><U+798F><U+53BF>","Anfú"
1221,49,"CHN","China",16,"Jiangxi",178,"Ji'an",1221,"Ji'an","Xiàn","County","<U+5409><U+5B89><U+53BF>","Jí'an"
1222,49,"CHN","China",16,"Jiangxi",178,"Ji'an",1222,"Ji An","Xiànjíshì","County City",NA,NA
1223,49,"CHN","China",16,"Jiangxi",178,"Ji'an",1223,"Jinggangshan","Xiànjíshì","County City","<U+4E95><U+5188><U+5C71><U+5E02>","Jinggangshan"
1224,49,"CHN","China",16,"Jiangxi",178,"Ji'an",1224,"Jishui","Xiàn","County","<U+5409><U+6C34><U+53BF>","Jíshui"
1225,49,"CHN","China",16,"Jiangxi",178,"Ji'an",1225,"Ninggang","Xiàn","County",NA,NA
1226,49,"CHN","China",16,"Jiangxi",178,"Ji'an",1226,"Suichuan","Xiàn","County","<U+9042><U+5DDD><U+53BF>","Suíchuan"
1227,49,"CHN","China",16,"Jiangxi",178,"Ji'an",1227,"Taihe","Xiàn","County","<U+592A><U+548C><U+53BF>","Tàihé"
1228,49,"CHN","China",16,"Jiangxi",178,"Ji'an",1228,"Wan’an","Xiàn","County","<U+4E07><U+5B89><U+53BF>","Wàn'an"
1229,49,"CHN","China",16,"Jiangxi",178,"Ji'an",1229,"Xiajiang","Xiàn","County","<U+5CE1><U+6C5F><U+53BF>","Xiájiang"
1230,49,"CHN","China",16,"Jiangxi",178,"Ji'an",1230,"Xingan","Xiàn","County","<U+65B0><U+5E72><U+53BF>","Xingan"
1231,49,"CHN","China",16,"Jiangxi",178,"Ji'an",1231,"Yongfeng","Xiàn","County","<U+6C38><U+4E30><U+53BF>","Yongfeng"
1232,49,"CHN","China",16,"Jiangxi",178,"Ji'an",1232,"Yongxin","Xiàn","County","<U+6C38><U+65B0><U+53BF>","Yongxin"
1233,49,"CHN","China",16,"Jiangxi",179,"Jingdezhen",1233,"Jingdezhen","Xiànjíshì","County City",NA,NA
1234,49,"CHN","China",16,"Jiangxi",179,"Jingdezhen",1234,"Leping","Xiànjíshì","County City","<U+4E50><U+5E73><U+5E02>","Lèpíng"
1235,49,"CHN","China",16,"Jiangxi",180,"Jiujiang",1235,"De'an","Xiàn","County","<U+5FB7><U+5B89><U+53BF>","Dé'an"
1236,49,"CHN","China",16,"Jiangxi",180,"Jiujiang",1236,"Duchang","Xiàn","County","<U+90FD><U+660C><U+53BF>","Duchang"
1237,49,"CHN","China",16,"Jiangxi",180,"Jiujiang",1237,"Hukou","Xiàn","County","<U+6E56><U+53E3><U+53BF>","Húkou"
1238,49,"CHN","China",16,"Jiangxi",180,"Jiujiang",1238,"Jiujiang Qu","Shìxiáqu","District","<U+4E5D><U+6C5F><U+533A>","Jiujiang"
1239,49,"CHN","China",16,"Jiangxi",180,"Jiujiang",1239,"Jiujiang Xiàn","Xiàn","County","<U+4E5D><U+6C5F><U+53BF>","Jiujiang"
1240,49,"CHN","China",16,"Jiangxi",180,"Jiujiang",1240,"Pengze","Xiàn","County","<U+5F6D><U+6CFD><U+53BF>","Péngzé"
1241,49,"CHN","China",16,"Jiangxi",180,"Jiujiang",1241,"Ruichang","Xiànjíshì","County City","<U+745E><U+660C><U+5E02>","Ruìchang"
1242,49,"CHN","China",16,"Jiangxi",180,"Jiujiang",1242,"Wuning","Xiàn","County","<U+6B66><U+5B81><U+53BF>","Wuníng"
1243,49,"CHN","China",16,"Jiangxi",180,"Jiujiang",1243,"Xingzi","Xiàn","County","<U+661F><U+5B50><U+53BF>","Xingzi"
1244,49,"CHN","China",16,"Jiangxi",180,"Jiujiang",1244,"Xiushui","Xiàn","County","<U+4FEE><U+6C34><U+53BF>","Xiushui"
1245,49,"CHN","China",16,"Jiangxi",180,"Jiujiang",1245,"Yongxiu","Xiàn","County","<U+6C38><U+4FEE><U+53BF>","Yongxiu"
1246,49,"CHN","China",16,"Jiangxi",181,"Nanchang",1246,"Anyi","Xiàn","County","<U+5B89><U+4E49><U+53BF>","Anyì"
1247,49,"CHN","China",16,"Jiangxi",181,"Nanchang",1247,"Jinxian","Xiàn","County","<U+8FDB><U+8D24><U+53BF>","Jìnxián"
1248,49,"CHN","China",16,"Jiangxi",181,"Nanchang",1248,"Nanchang Qu","Shìxiáqu","District","<U+5357><U+660C><U+533A>","Nánchang"
1249,49,"CHN","China",16,"Jiangxi",181,"Nanchang",1249,"Nanchang Xiàn","Xiàn","County","<U+5357><U+660C><U+53BF>","Nánchang"
1250,49,"CHN","China",16,"Jiangxi",181,"Nanchang",1250,"Xingan","Xiàn","County","<U+65B0><U+5E72><U+53BF>","Xingan"
1251,49,"CHN","China",16,"Jiangxi",181,"Nanchang",1251,"Xinjian","Xiàn","County","<U+65B0><U+5EFA><U+53BF>","Xinjiàn"
1252,49,"CHN","China",16,"Jiangxi",182,"Pingxiang",1252,"Lianhua","Xiàn","County","<U+83B2><U+82B1><U+53BF>","Liánhua"
1253,49,"CHN","China",16,"Jiangxi",182,"Pingxiang",1253,"Pingxiang","Xiànjíshì","County City",NA,NA
1254,49,"CHN","China",16,"Jiangxi",183,"Shangrao",1254,"Boyang","Xiàn","County",NA,NA
1255,49,"CHN","China",16,"Jiangxi",183,"Shangrao",1255,"Dexing","Xiànjíshì","County City","<U+5FB7><U+5174><U+5E02>","Déxing"
1256,49,"CHN","China",16,"Jiangxi",183,"Shangrao",1256,"Guangfeng","Xiàn","County","<U+5E7F><U+4E30><U+53BF>","Guangfeng"
1257,49,"CHN","China",16,"Jiangxi",183,"Shangrao",1257,"Hengfeng","Xiàn","County","<U+6A2A><U+5CF0><U+53BF>","Héngfeng"
1258,49,"CHN","China",16,"Jiangxi",183,"Shangrao",1258,"Qianshan","Xiàn","County",NA,NA
1259,49,"CHN","China",16,"Jiangxi",183,"Shangrao",1259,"Shangrao Shì","Xiànjíshì","County City","<U+4E0A><U+9976><U+5E02>","Shàngráo"
1260,49,"CHN","China",16,"Jiangxi",183,"Shangrao",1260,"Shangrao Xiàn","Xiàn","County","<U+4E0A><U+9976><U+53BF>","Shàngráo"
1261,49,"CHN","China",16,"Jiangxi",183,"Shangrao",1261,"Wannian","Xiàn","County","<U+4E07><U+5E74><U+53BF>","Wànnián"
1262,49,"CHN","China",16,"Jiangxi",183,"Shangrao",1262,"Wuyuan","Xiàn","County","<U+5A7A><U+6E90><U+53BF>","Wùyuán"
1263,49,"CHN","China",16,"Jiangxi",183,"Shangrao",1263,"Yiyang","Xiàn","County","<U+5F0B><U+9633><U+53BF>","Yìyáng"
1264,49,"CHN","China",16,"Jiangxi",183,"Shangrao",1264,"Yugan","Xiàn","County","<U+4F59><U+5E72><U+53BF>","Yúgan"
1265,49,"CHN","China",16,"Jiangxi",183,"Shangrao",1265,"Yushan","Xiàn","County","<U+7389><U+5C71><U+53BF>","Yùshan"
1266,49,"CHN","China",16,"Jiangxi",184,"Xinyu",1266,"Fenyi","Xiàn","County","<U+5206><U+5B9C><U+53BF>","Fenyí"
1267,49,"CHN","China",16,"Jiangxi",184,"Xinyu",1267,"Xinyu","Xiàn","County",NA,NA
1268,49,"CHN","China",16,"Jiangxi",185,"Yichun",1268,"Fengcheng","Xiànjíshì","County City","<U+4E30><U+57CE><U+5E02>","Fengchéng"
1269,49,"CHN","China",16,"Jiangxi",185,"Yichun",1269,"Fengxin","Xiàn","County","<U+5949><U+65B0><U+53BF>","Fèngxin"
1270,49,"CHN","China",16,"Jiangxi",185,"Yichun",1270,"Gao'an","Xiànjíshì","County City","<U+9AD8><U+5B89><U+5E02>","Gao'an"
1271,49,"CHN","China",16,"Jiangxi",185,"Yichun",1271,"Jing'an","Xiàn","County","<U+9756><U+5B89><U+53BF>","Jìng'an"
1272,49,"CHN","China",16,"Jiangxi",185,"Yichun",1272,"Shanggao","Xiàn","County","<U+4E0A><U+9AD8><U+53BF>","Shànggao"
1273,49,"CHN","China",16,"Jiangxi",185,"Yichun",1273,"Tonggu","Xiàn","County","<U+94DC><U+9F13><U+53BF>","Tónggu"
1274,49,"CHN","China",16,"Jiangxi",185,"Yichun",1274,"Wanzai","Xiàn","County","<U+4E07><U+8F7D><U+53BF>","Wànzai"
1275,49,"CHN","China",16,"Jiangxi",185,"Yichun",1275,"Yichun","Xiàn","County",NA,NA
1276,49,"CHN","China",16,"Jiangxi",185,"Yichun",1276,"Yifeng","Xiàn","County","<U+5B9C><U+4E30><U+53BF>","Yífeng"
1277,49,"CHN","China",16,"Jiangxi",185,"Yichun",1277,"Zhangshu","Xiànjíshì","County City","<U+6A1F><U+6811><U+5E02>","Zhangshù"
1278,49,"CHN","China",16,"Jiangxi",186,"Yingtan",1278,"Guixi","Xiànjíshì","County City","<U+8D35><U+6EAA><U+5E02>","Guìxi"
1279,49,"CHN","China",16,"Jiangxi",186,"Yingtan",1279,"Yingtan","Xiànjíshì","County City",NA,NA
1280,49,"CHN","China",16,"Jiangxi",186,"Yingtan",1280,"Yujiang","Xiàn","County","<U+4F59><U+6C5F><U+53BF>","Yújiang"
1281,49,"CHN","China",17,"Jilin",187,"Baicheng",1281,"Baicheng","Xiànjíshì","County City",NA,NA
1282,49,"CHN","China",17,"Jilin",187,"Baicheng",1282,"Da'an","Xiànjíshì","County City","<U+5927><U+5B89><U+5E02>","Dà'an"
1283,49,"CHN","China",17,"Jilin",187,"Baicheng",1283,"Taonan","Xiànjíshì","County City","<U+6D2E><U+5357><U+5E02>","Táonán"
1284,49,"CHN","China",17,"Jilin",187,"Baicheng",1284,"Tongyu","Xiàn","County","<U+901A><U+6986><U+53BF>","Tongyú"
1285,49,"CHN","China",17,"Jilin",187,"Baicheng",1285,"Zhenlai","Xiàn","County","<U+9547><U+8D49><U+53BF>","Zhènlài"
1286,49,"CHN","China",17,"Jilin",188,"Baishan",1286,"Badaojiang","Shìxiáqu","District","<U+516B><U+9053><U+6C5F><U+533A>","Badàojiang"
1287,49,"CHN","China",17,"Jilin",188,"Baishan",1287,"Changbai Korean","Zìzhìxiàn","Autonomous County","<U+957F><U+767D><U+671D><U+9C9C><U+65CF><U+81EA><U+6CBB><U+53BF>","Chángbái Cháoxianzú"
1288,49,"CHN","China",17,"Jilin",188,"Baishan",1288,"Fusong","Xiàn","County","<U+629A><U+677E><U+53BF>","Fusong"
1289,49,"CHN","China",17,"Jilin",188,"Baishan",1289,"Jiangyuan","Shìxiáqu","District","<U+6C5F><U+6E90><U+533A>","Jiangyuán"
1290,49,"CHN","China",17,"Jilin",188,"Baishan",1290,"Jingyu","Xiàn","County","<U+9756><U+5B87><U+53BF>","Jìngyu"
1291,49,"CHN","China",17,"Jilin",188,"Baishan",1291,"Linjiang","Xiànjíshì","County City","<U+4E34><U+6C5F><U+5E02>","Línjiang"
1292,49,"CHN","China",17,"Jilin",189,"Changchun",1292,"Changchun","Xiànjíshì","County City","<U+957F><U+6625><U+5E02>","Chángchun"
1293,49,"CHN","China",17,"Jilin",189,"Changchun",1293,"Dehui","Xiànjíshì","County City","<U+5FB7><U+60E0><U+5E02>","Déhuì"
1294,49,"CHN","China",17,"Jilin",189,"Changchun",1294,"Jiutai","Xiànjíshì","County City","<U+4E5D><U+53F0><U+5E02>","Jiutái"
1295,49,"CHN","China",17,"Jilin",189,"Changchun",1295,"Nong'an","Xiàn","County","<U+519C><U+5B89><U+53BF>","Nóng'an"
1296,49,"CHN","China",17,"Jilin",189,"Changchun",1296,"Shuangyang","Shìxiáqu","District","<U+53CC><U+9633><U+533A>","Shuangyáng"
1297,49,"CHN","China",17,"Jilin",189,"Changchun",1297,"Yushu","Xiànjíshì","County City","<U+6986><U+6811><U+5E02>","Yúshù"
1298,49,"CHN","China",17,"Jilin",190,"Jilin",1298,"Huadian","Xiànjíshì","County City","<U+6866><U+7538><U+5E02>","Huàdiàn"
1299,49,"CHN","China",17,"Jilin",190,"Jilin",1299,"Jiaohe","Xiànjíshì","County City","<U+86DF><U+6CB3><U+5E02>","Jiaohé"
1300,49,"CHN","China",17,"Jilin",190,"Jilin",1300,"Jilin","Shìxiáqu","District","<U+5409><U+6797><U+5E02>","Jílín"
1301,49,"CHN","China",17,"Jilin",190,"Jilin",1301,"Panshi","Xiànjíshì","County City","<U+78D0><U+77F3><U+5E02>","Pánshí"
1302,49,"CHN","China",17,"Jilin",190,"Jilin",1302,"Shulan","Xiànjíshì","County City","<U+8212><U+5170><U+5E02>","Shulán"
1303,49,"CHN","China",17,"Jilin",190,"Jilin",1303,"Yongji","Xiàn","County","<U+6C38><U+5409><U+53BF>","Yongjí"
1304,49,"CHN","China",17,"Jilin",191,"Liaoyuan",1304,"Dongfeng","Xiàn","County","<U+4E1C><U+98CE><U+53BF>","Dongfeng"
1305,49,"CHN","China",17,"Jilin",191,"Liaoyuan",1305,"Dongliao","Xiàn","County","<U+4E1C><U+8FBD><U+53BF>","Dongliáo"
1306,49,"CHN","China",17,"Jilin",191,"Liaoyuan",1306,"Liaoyuan","Xiànjíshì","County City",NA,NA
1307,49,"CHN","China",17,"Jilin",192,"Siping",1307,"Huaide","Xiàn","County",NA,NA
1308,49,"CHN","China",17,"Jilin",192,"Siping",1308,"Lishu","Xiàn","County","<U+68A8><U+6811><U+53BF>","Líshù"
1309,49,"CHN","China",17,"Jilin",192,"Siping",1309,"Shuangliao","Xiànjíshì","County City","<U+53CC><U+8FBD><U+5E02>","Shuangliáo"
1310,49,"CHN","China",17,"Jilin",192,"Siping",1310,"Siping","Xiànjíshì","County City",NA,NA
1311,49,"CHN","China",17,"Jilin",192,"Siping",1311,"Yitong Manchu","Zìzhìxiàn","Autonomous County","<U+4F0A><U+901A><U+6EE1><U+65CF><U+81EA><U+6CBB><U+53BF>","Yi"
1312,49,"CHN","China",17,"Jilin",193,"Songyuan",1312,"Changling","Xiàn","County","<U+957F><U+5CAD><U+53BF>","Chángling"
1313,49,"CHN","China",17,"Jilin",193,"Songyuan",1313,"Fuyu","Xiàn","County","<U+6276><U+4F59><U+53BF>","Fúyú"
1314,49,"CHN","China",17,"Jilin",193,"Songyuan",1314,"Qian'an","Xiàn","County","<U+4E7E><U+5B89><U+53BF>","Qián'an"
1315,49,"CHN","China",17,"Jilin",193,"Songyuan",1315,"Qian Gorlos Mongol","Zìzhìxiàn","Autonomous County","<U+524D><U+90ED><U+5C14><U+7F57><U+65AF><U+8499><U+53E4><U+65CF><U+81EA><U+6CBB><U+53BF>","Qiánguo'erluósi|Mengguzú"
1316,49,"CHN","China",17,"Jilin",194,"Tonghua",1316,"Dongchang","Shìxiáqu","District","<U+4E1C><U+660C><U+533A>","Dongchang"
1317,49,"CHN","China",17,"Jilin",194,"Tonghua",1317,"Erdaojiang","Shìxiáqu","District","<U+4E8C><U+9053><U+6C5F><U+533A>","Èrdàojiang"
1318,49,"CHN","China",17,"Jilin",194,"Tonghua",1318,"Huinan","Xiàn","County","<U+8F89><U+5357><U+53BF>","Huinán"
1319,49,"CHN","China",17,"Jilin",194,"Tonghua",1319,"Ji'an","Xiànjíshì","County City","<U+96C6><U+5B89><U+5E02>","Jí'an"
1320,49,"CHN","China",17,"Jilin",194,"Tonghua",1320,"Liuhe","Xiàn","County","<U+67F3><U+6CB3><U+53BF>","Liuhé"
1321,49,"CHN","China",17,"Jilin",194,"Tonghua",1321,"Meihekou","Xiànjíshì","County City","<U+6885><U+6CB3><U+53E3><U+5E02>","Méihékou"
1322,49,"CHN","China",17,"Jilin",194,"Tonghua",1322,"Tonghua","Xiàn","County","<U+901A><U+5316><U+53BF>","Tonghuà"
1323,49,"CHN","China",17,"Jilin",195,"Yanbian Korean",1323,"Antu","Xiàn","County","<U+5B89><U+56FE><U+53BF>","Antú"
1324,49,"CHN","China",17,"Jilin",195,"Yanbian Korean",1324,"Dunhua","Xiànjíshì","County City","<U+6566><U+5316><U+5E02>","Dunhuà"
1325,49,"CHN","China",17,"Jilin",195,"Yanbian Korean",1325,"Helong","Xiànjíshì","County City","<U+548C><U+9F99><U+5E02>","Hélóng"
1326,49,"CHN","China",17,"Jilin",195,"Yanbian Korean",1326,"Hunchun","Xiànjíshì","County City","<U+73F2><U+6625><U+5E02>","Húnchun"
1327,49,"CHN","China",17,"Jilin",195,"Yanbian Korean",1327,"Longjing","Xiànjíshì","County City","<U+9F99><U+4E95><U+5E02>","Lóngjing"
1328,49,"CHN","China",17,"Jilin",195,"Yanbian Korean",1328,"Tumen","Xiànjíshì","County City","<U+56FE><U+4EEC><U+5E02>","Túmén"
1329,49,"CHN","China",17,"Jilin",195,"Yanbian Korean",1329,"Wangqing","Xiàn","County","<U+6C6A><U+6E05><U+53BF>","Wangqing"
1330,49,"CHN","China",17,"Jilin",195,"Yanbian Korean",1330,"Yanji","Xiànjíshì","County City","<U+5EF6><U+5409><U+5E02>","Yánjí"
1331,49,"CHN","China",18,"Liaoning",196,"Anshan",1331,"Anshan","Shìxiáqu","District",NA,NA
1332,49,"CHN","China",18,"Liaoning",196,"Anshan",1332,"Haicheng","Xiànjíshì","County City","<U+6D77><U+57CE><U+5E02>","Haichéng"
1333,49,"CHN","China",18,"Liaoning",196,"Anshan",1333,"Tai'an","Xiàn","County","<U+53F0><U+5B89><U+53BF>","Tái'an"
1334,49,"CHN","China",18,"Liaoning",196,"Anshan",1334,"Xiuyan Manchu","Zìzhìxiàn","Autonomous County","<U+5CAB><U+5CA9><U+6EE1><U+65CF><U+81EA><U+6CBB><U+53BF>","Xiùyán Manzú Zì"
1335,49,"CHN","China",18,"Liaoning",197,"Benxi",1335,"Benxi Manchu","Zìzhìxiàn","Autonomous County","<U+672C><U+6EAA><U+6EE1><U+65CF><U+81EA><U+6CBB><U+53BF>","Benxi Manzú"
1336,49,"CHN","China",18,"Liaoning",197,"Benxi",1336,"Benxi","Shìxiáqu","District",NA,NA
1337,49,"CHN","China",18,"Liaoning",197,"Benxi",1337,"Huanren Manchu","Zìzhìxiàn","Autonomous County","<U+6853><U+4EC1><U+6EE1><U+65CF><U+81EA><U+6CBB><U+53BF>","Huánrén Manzú"
1338,49,"CHN","China",18,"Liaoning",198,"Chaoyang",1338,"Beipiao","Xiànjíshì","County City","<U+5317><U+7968><U+5E02>","Beipiào"
1339,49,"CHN","China",18,"Liaoning",198,"Chaoyang",1339,"Chaoyang Shì","Xiànjíshì","County City","<U+671D><U+9633><U+5E02>","Cháoyáng"
1340,49,"CHN","China",18,"Liaoning",198,"Chaoyang",1340,"Chaoyang Xiàn","Xiàn","County","<U+671D><U+9633><U+53BF>","Cháoyáng"
1341,49,"CHN","China",18,"Liaoning",198,"Chaoyang",1341,"Harqin Left Mongol","Zìzhìxiàn","Autonomous County","<U+5580><U+5587><U+6C81><U+5DE6><U+7FFC><U+8499><U+53E4><U+65CF><U+81EA><U+6CBB><U+53BF>","Kalaqìn Zuoyì Mengguzú"
1342,49,"CHN","China",18,"Liaoning",198,"Chaoyang",1342,"Jianping","Xiàn","County","<U+5EFA><U+5E73><U+53BF>","Jiànpíng"
1343,49,"CHN","China",18,"Liaoning",198,"Chaoyang",1343,"Lingyuan","Xiànjíshì","County City","<U+51CC><U+6E90><U+5E02>","Língyuán"
1344,49,"CHN","China",18,"Liaoning",199,"Dalian",1344,"Changhai","Xiàn","County","<U+957F><U+6D77><U+53BF>","Chánghai"
1345,49,"CHN","China",18,"Liaoning",199,"Dalian",1345,"Dalian","Shìxiáqu","District",NA,NA
1346,49,"CHN","China",18,"Liaoning",199,"Dalian",1346,"Jinzhou","Shìxiáqu","District","<U+91D1><U+5DDE><U+533A>","Jinzhou"
1347,49,"CHN","China",18,"Liaoning",199,"Dalian",1347,"Lushunkou","Shìxiáqu","District","<U+65C5><U+987A><U+53E3><U+533A>","Lushùnkou"
1348,49,"CHN","China",18,"Liaoning",199,"Dalian",1348,"Wafangdian","Xiàn","County","<U+74E6><U+623F><U+5E97><U+5E02>","Wafángdiàn"
1349,49,"CHN","China",18,"Liaoning",199,"Dalian",1349,"Xinjin","Xiàn","County",NA,NA
1350,49,"CHN","China",18,"Liaoning",199,"Dalian",1350,"Zhuanghe","Xiànjíshì","County City","<U+5E84><U+6CB3><U+5E02>","Zhuanghé"
1351,49,"CHN","China",18,"Liaoning",200,"Dandong",1351,"Dadong","Shìxiáqu","District","<U+5927><U+4E1C><U+533A>","Dàdong"
1352,49,"CHN","China",18,"Liaoning",200,"Dandong",1352,"Donggang","Xiànjíshì","County City","<U+4E1C><U+6E2F><U+5E02>","Donggang"
1353,49,"CHN","China",18,"Liaoning",200,"Dandong",1353,"Fengcheng","Xiànjíshì","County City","<U+51E4><U+57CE><U+5E02>","Fèngchéng"
1354,49,"CHN","China",18,"Liaoning",200,"Dandong",1354,"Kuandian Manchu","Zìzhìxiàn","Autonomous County","<U+5BBD><U+7538><U+6EE1><U+65CF><U+81EA><U+6CBB><U+53BF>","Kuandiàn Manzú"
1355,49,"CHN","China",18,"Liaoning",201,"Fushun",1355,"Fushun","Xiàn","County","<U+629A><U+987A><U+53BF>","Fushùn"
1356,49,"CHN","China",18,"Liaoning",201,"Fushun",1356,"Qingyuan Manchu","Zìzhìxiàn","Autonomous County","<U+6E05><U+539F><U+6EE1><U+65CF><U+81EA><U+6CBB><U+53BF>","Qingyuán Manzú"
1357,49,"CHN","China",18,"Liaoning",201,"Fushun",1357,"Shuncheng","Shìxiáqu","District","<U+987A><U+57CE><U+533A>","Shùnchéng"
1358,49,"CHN","China",18,"Liaoning",201,"Fushun",1358,"Xinbin Manchu","Zìzhìxiàn","Autonomous County","<U+65B0><U+5BBE><U+6EE1><U+65CF><U+81EA><U+6CBB><U+53BF>","Xinbin Manzú"
1359,49,"CHN","China",18,"Liaoning",202,"Fuxin",1359,"Fuxin Mongol","Zìzhìxiàn","Autonomous County","<U+961C><U+65B0><U+8499><U+53E4><U+65CF><U+81EA><U+6CBB><U+53BF>","Fùxin Mengguzú"
1360,49,"CHN","China",18,"Liaoning",202,"Fuxin",1360,"Fuxin","Shìxiáqu","District",NA,NA
1361,49,"CHN","China",18,"Liaoning",202,"Fuxin",1361,"Zhangwu","Xiàn","County","<U+5F70><U+6B66><U+53BF>","Zhangwu"
1362,49,"CHN","China",18,"Liaoning",203,"Huludao",1362,"Jianchang","Xiàn","County","<U+5EFA><U+660C><U+53BF>","Jiànchang"
1363,49,"CHN","China",18,"Liaoning",203,"Huludao",1363,"Jinxi","Xiàn","County",NA,NA
1364,49,"CHN","China",18,"Liaoning",203,"Huludao",1364,"Suizhong","Xiàn","County","<U+7EE5><U+4E2D><U+53BF>","Suízhong"
1365,49,"CHN","China",18,"Liaoning",203,"Huludao",1365,"Xingcheng","Xiànjíshì","County City","<U+5174><U+57CE><U+5E02>","Xingchéng"
1366,49,"CHN","China",18,"Liaoning",204,"Jinzhou",1366,"Beizhen","Xiànjíshì","County City","<U+5317><U+9547><U+5E02>","Beizhèn"
1367,49,"CHN","China",18,"Liaoning",204,"Jinzhou",1367,"Heishan","Xiàn","County","<U+9ED1><U+5C71><U+53BF>","Heishan"
1368,49,"CHN","China",18,"Liaoning",204,"Jinzhou",1368,"Jin","Xiàn","County",NA,NA
1369,49,"CHN","China",18,"Liaoning",204,"Jinzhou",1369,"Jinzhou","Shìxiáqu","District","<U+91D1><U+5DDE><U+533A>","Jinzhou"
1370,49,"CHN","China",18,"Liaoning",204,"Jinzhou",1370,"Yi","Xiàn","County","<U+4E49><U+53BF>","Yì"
1371,49,"CHN","China",18,"Liaoning",205,"Liaoyang",1371,"Dengta","Xiànjíshì","County City","<U+706F><U+5854><U+5E02>","Dengta"
1372,49,"CHN","China",18,"Liaoning",205,"Liaoyang",1372,"Liaoyang","Shìxiáqu","District","<U+8FBD><U+9633><U+533A>","Liáoyáng Qu"
1373,49,"CHN","China",18,"Liaoning",206,"Panjin",1373,"Dawa","Xiàn","County","<U+5927><U+6D3C><U+53BF>","Dàwa"
1374,49,"CHN","China",18,"Liaoning",206,"Panjin",1374,"Panjin","Xiànjíshì","County City","<U+76D8><U+9526><U+5E02>","Pánjin"
1375,49,"CHN","China",18,"Liaoning",206,"Panjin",1375,"Panshan","Xiàn","County","<U+76D8><U+5C71><U+53BF>","Pánshan"
1376,49,"CHN","China",18,"Liaoning",207,"Shenyang",1376,"Faku","Xiàn","County","<U+6CD5><U+5E93><U+53BF>","Fakù"
1377,49,"CHN","China",18,"Liaoning",207,"Shenyang",1377,"Kangping","Xiàn","County","<U+5EB7><U+5E73><U+53BF>","Kangpíng"
1378,49,"CHN","China",18,"Liaoning",207,"Shenyang",1378,"Liaozhong","Xiàn","County","<U+8FBD><U+4E2D><U+53BF>","Liáozhong"
1379,49,"CHN","China",18,"Liaoning",207,"Shenyang",1379,"Shenyang Shì","Xiànjíshì","County City","<U+6C88><U+9633><U+5E02>","Shenyáng"
1380,49,"CHN","China",18,"Liaoning",207,"Shenyang",1380,"Shenyang Xiàn","Xiàn","County","<U+6C88><U+9633><U+53BF>","Shenyáng"
1381,49,"CHN","China",18,"Liaoning",207,"Shenyang",1381,"Xinmin","Xiànjíshì","County City","<U+65B0><U+6C11><U+5E02>","Xinmín Shì"
1382,49,"CHN","China",18,"Liaoning",208,"Tieling",1382,"Changtu","Xiàn","County","<U+660C><U+56FE><U+53BF>","Changtú"
1383,49,"CHN","China",18,"Liaoning",208,"Tieling",1383,"Gai","Xiàn","County",NA,NA
1384,49,"CHN","China",18,"Liaoning",208,"Tieling",1384,"Kaiyuan","Xiànjíshì","County City","<U+5F00><U+539F><U+5E02>","Kaiyuán"
1385,49,"CHN","China",18,"Liaoning",208,"Tieling",1385,"Tiefa","Xiànjíshì","County City",NA,NA
1386,49,"CHN","China",18,"Liaoning",208,"Tieling",1386,"Tieling Shì","Xiànjíshì","County City","<U+94C1><U+5CAD><U+5E02>","Tieling"
1387,49,"CHN","China",18,"Liaoning",208,"Tieling",1387,"Tieling Xiàn","Xiàn","County","<U+94C1><U+5CAD><U+53BF>","Tieling"
1388,49,"CHN","China",18,"Liaoning",208,"Tieling",1388,"Xifeng","Xiàn","County","<U+897F><U+4E30><U+53BF>","Xifeng"
1389,49,"CHN","China",18,"Liaoning",208,"Tieling",1389,"Yingkou","Shìxiáqu","District",NA,NA
1390,49,"CHN","China",18,"Liaoning",208,"Tieling",1390,"Yinzhou","Shìxiáqu","District","<U+94F6><U+5DDE><U+533A>","Yínzhou"
1391,49,"CHN","China",19,"Nei Mongol",209,"Alxa",1391,"Alxa Left","Qí","Banner","<U+963F><U+62C9><U+5584><U+5DE6><U+65D7>","Alashàn Zuo"
1392,49,"CHN","China",19,"Nei Mongol",209,"Alxa",1392,"Alxa Right","Qí","Banner","<U+963F><U+62C9><U+5584><U+53F3><U+65D7>","Alashàn Yòu"
1393,49,"CHN","China",19,"Nei Mongol",209,"Alxa",1393,"Ejin","Qí","Banner","<U+989D><U+6D4E><U+7EB3><U+65D7>","Éjìnà"
1394,49,"CHN","China",19,"Nei Mongol",210,"Baotou",1394,"Baotou","Shìxiáqu","District",NA,NA
1395,49,"CHN","China",19,"Nei Mongol",210,"Baotou",1395,"Darhan Muminggan Lia","Xiàn","County","<U+8FBE><U+5C14><U+7F55><U+8302><U+660E><U+5B89>","Dá'erhan Màomíng'an"
1396,49,"CHN","China",19,"Nei Mongol",210,"Baotou",1396,"Guyang","Xiàn","County","<U+56FA><U+9633><U+53BF>","Gùyáng"
1397,49,"CHN","China",19,"Nei Mongol",211,"Baynnur",1397,"Dengkou","Xiàn","County","<U+78F4><U+53E3><U+53BF>","Dèngkou"
1398,49,"CHN","China",19,"Nei Mongol",211,"Baynnur",1398,"Haibowan","Shìxiáqu","District","<U+6D77><U+52C3><U+6E7E><U+533A>","Haibówan"
1399,49,"CHN","China",19,"Nei Mongol",211,"Baynnur",1399,"Linhe","Shìxiáqu","District","<U+4E34><U+6CB3><U+533A>","Línhé"
1400,49,"CHN","China",19,"Nei Mongol",211,"Baynnur",1400,"Urad Back","Qí","Banner","<U+4E4C><U+62C9><U+7279><U+540E><U+65D7>","Wulatè Hòu"
1401,49,"CHN","China",19,"Nei Mongol",211,"Baynnur",1401,"Urad Front","Qí","Banner","<U+4E4C><U+62C9><U+7279><U+524D><U+65D7>","Wulatè Qián"
1402,49,"CHN","China",19,"Nei Mongol",211,"Baynnur",1402,"Urad Middle","Qí","Banner","<U+4E4C><U+62C9><U+7279><U+4E2D><U+65D7>","Wulatè Zhong"
1403,49,"CHN","China",19,"Nei Mongol",211,"Baynnur",1403,"Wuyuan","Xiàn","County","<U+4E94><U+539F><U+53BF>","Wuyuán"
1404,49,"CHN","China",19,"Nei Mongol",212,"Chifeng",1404,"Aohan","Qí","Banner","<U+6556><U+6C49><U+65D7>","Áohàn"
1405,49,"CHN","China",19,"Nei Mongol",212,"Chifeng",1405,"Ar Horqin","Qí","Banner","<U+963F><U+9C81><U+79D1><U+5C14><U+6C81><U+65D7>","Aluke'erqìn"
1406,49,"CHN","China",19,"Nei Mongol",212,"Chifeng",1406,"Bairin Left","Qí","Banner","<U+5DF4><U+6797><U+5DE6><U+65D7>","Balín Zuo"
1407,49,"CHN","China",19,"Nei Mongol",212,"Chifeng",1407,"Bairin Right","Qí","Banner","<U+5DF4><U+6797><U+53F3><U+65D7>","Balín Yòu"
1408,49,"CHN","China",19,"Nei Mongol",212,"Chifeng",1408,"Chifeng","Zizhiqu","County",NA,NA
1409,49,"CHN","China",19,"Nei Mongol",212,"Chifeng",1409,"Harqin","Qí","Banner","<U+5580><U+5587><U+6C81><U+65D7>","Kalaqìn"
1410,49,"CHN","China",19,"Nei Mongol",212,"Chifeng",1410,"Hexigten","Qí","Banner","<U+514B><U+4EC0><U+514B><U+817E><U+65D7>","Kèshíkèténg"
1411,49,"CHN","China",19,"Nei Mongol",212,"Chifeng",1411,"Linxi","Xiàn","County","<U+6797><U+897F><U+53BF>","Línxi"
1412,49,"CHN","China",19,"Nei Mongol",212,"Chifeng",1412,"Ningcheng","Xiàn","County","<U+5B81><U+57CE><U+53BF>","Níngchéng"
1413,49,"CHN","China",19,"Nei Mongol",212,"Chifeng",1413,"Ongniud","Qí","Banner","<U+7FC1><U+725B><U+7279><U+65D7>","Wengniútè"
1414,49,"CHN","China",19,"Nei Mongol",213,"Hohhot",1414,"Hohhot","Shìxiáqu","District",NA,NA
1415,49,"CHN","China",19,"Nei Mongol",213,"Hohhot",1415,"Horinger","Xiàn","County","<U+548C><U+6797><U+683C><U+5C14><U+53BF>","Hélíngé'er"
1416,49,"CHN","China",19,"Nei Mongol",213,"Hohhot",1416,"Qingshuihe","Xiàn","County","<U+6E05><U+6C34><U+6CB3><U+53BF>","Qingshuihé"
1417,49,"CHN","China",19,"Nei Mongol",213,"Hohhot",1417,"Togtoh","Xiàn","County","<U+6258><U+514B><U+6258><U+53BF>","Tuokètuo"
1418,49,"CHN","China",19,"Nei Mongol",213,"Hohhot",1418,"Tumd Left","Qí","Banner","<U+571F><U+9ED8><U+7279><U+5DE6><U+65D7>","Tumòtè Zuo"
1419,49,"CHN","China",19,"Nei Mongol",213,"Hohhot",1419,"Tumd Right","Qí","Banner","<U+571F><U+9ED8><U+7279><U+53F3><U+65D7>","Tumòtè Yòu"
1420,49,"CHN","China",19,"Nei Mongol",213,"Hohhot",1420,"Wuchuan","Xiàn","County","<U+6B66><U+5DDD><U+53BF>","Wuchuan"
1421,49,"CHN","China",19,"Nei Mongol",214,"Hulunbuir",1421,"Arun","Qí","Banner","<U+963F><U+8363><U+65D7>","Aróng"
1422,49,"CHN","China",19,"Nei Mongol",214,"Hulunbuir",1422,"Butha Qi","Qí","Banner",NA,NA
1423,49,"CHN","China",19,"Nei Mongol",214,"Hulunbuir",1423,"Chenbarag Qi","Qí","Banner",NA,NA
1424,49,"CHN","China",19,"Nei Mongol",214,"Hulunbuir",1424,"Ergun","Xiànjíshì","County City","<U+989D><U+5C14><U+53E4><U+7EB3><U+5E02>","É'ergunà|Ergüne xot|<U+042D><U+0440><U+0433><U+04AF><U+043D><U+044D> <U+0445><U+043E><U+0442>"
1425,49,"CHN","China",19,"Nei Mongol",214,"Hulunbuir",1425,"Evenk","Zìzhìqí","Autonomous Banner","<U+9102><U+6E29><U+514B><U+65CF><U+81EA><U+6CBB><U+65D7>","Èwenkèzú Zìzhìq"
1426,49,"CHN","China",19,"Nei Mongol",214,"Hulunbuir",1426,"Genhe","Xiànjíshì","County City","<U+6839><U+6CB3><U+5E02>","Genhé|Gegeen-gol xot|<U+0413><U+044D><U+0433><U+044D><U+044D><U+043D>-<U+0433><U+043E><U+043B> <U+0445><U+043E><U+0442>"
1427,49,"CHN","China",19,"Nei Mongol",214,"Hulunbuir",1427,"Hailar","Shìxiáqu","District","<U+6D77><U+62C9><U+5C14><U+533A>","Haila'er"
1428,49,"CHN","China",19,"Nei Mongol",214,"Hulunbuir",1428,"Manzhouli","Xiànjíshì","County City","<U+6EE1><U+6D32><U+91CC><U+5E02>","Manzhouli"
1429,49,"CHN","China",19,"Nei Mongol",214,"Hulunbuir",1429,"Morin Dawa Daur","Zìzhìqí","Autonomous Banner","<U+83AB><U+5229><U+8FBE><U+74E6><U+8FBE><U+65A1><U+5C14><U+65CF><U+81EA><U+6CBB><U+65D7>","Mòlìdáwa Dáwò'erzú Zìzhìq"
1430,49,"CHN","China",19,"Nei Mongol",214,"Hulunbuir",1430,"Oroqin","Zìzhìqí","Autonomous Banner","<U+9102><U+4F26><U+6625><U+81EA><U+6CBB><U+65D7>","Èlúnchun Zìzhìqí"
1431,49,"CHN","China",19,"Nei Mongol",214,"Hulunbuir",1431,"Xinbarag Left","Qí","Banner",NA,"Xinbarag Zuoqi"
1432,49,"CHN","China",19,"Nei Mongol",214,"Hulunbuir",1432,"Xinbarag Right","Qí","Banner",NA,"Xinbarag Youqi"
1433,49,"CHN","China",19,"Nei Mongol",214,"Hulunbuir",1433,"Xuguit Qi","Qí","Banner",NA,NA
1434,49,"CHN","China",19,"Nei Mongol",215,"Ordos",1434,"Dalad","Qí","Banner","<U+8FBE><U+62C9><U+7279><U+65D7>","Dálatè"
1435,49,"CHN","China",19,"Nei Mongol",215,"Ordos",1435,"Dongsheng","Shìxiáqu","District","<U+4E1C><U+80DC><U+533A>","Dongshèng"
1436,49,"CHN","China",19,"Nei Mongol",215,"Ordos",1436,"Ejin Horo","Qí","Banner","<U+4F0A><U+91D1><U+970D><U+6D1B><U+65D7>","Yijinhuòluò"
1437,49,"CHN","China",19,"Nei Mongol",215,"Ordos",1437,"Hanggin","Qí","Banner","<U+676D><U+9526><U+65D7>","Hángjin"
1438,49,"CHN","China",19,"Nei Mongol",215,"Ordos",1438,"Jungar","Qí","Banner","<U+51C6><U+683C><U+5C14><U+65D7>","Zhungé'er"
1439,49,"CHN","China",19,"Nei Mongol",215,"Ordos",1439,"Otog Front","Qí","Banner","<U+9102><U+6258><U+514B><U+524D><U+65D7>","Ètuokè Qián"
1440,49,"CHN","China",19,"Nei Mongol",215,"Ordos",1440,"Otog","Qí","Banner","<U+9102><U+6258><U+514B><U+65D7>","Ètuokè"
1441,49,"CHN","China",19,"Nei Mongol",215,"Ordos",1441,"Uxin","Qí","Banner","<U+4E4C><U+5BA1><U+65D7>","Wushen"
1442,49,"CHN","China",19,"Nei Mongol",216,"Tongliao",1442,"Horqin Left Back","Qí","Banner","<U+79D1><U+5C14><U+6C81><U+5DE6><U+7FFC><U+540E><U+65D7>","Ke'erqìn Zuoyì Hòu"
1443,49,"CHN","China",19,"Nei Mongol",216,"Tongliao",1443,"Horqin Left Middle","Qí","Banner","<U+79D1><U+5C14><U+6C81><U+5DE6><U+7FFC><U+4E2D><U+65D7>","Ke'erqìn Zuoyì Zhong"
1444,49,"CHN","China",19,"Nei Mongol",216,"Tongliao",1444,"Huolin Gol","Xiànjíshì","County City","<U+970D><U+6797><U+90ED><U+52D2><U+5E02>","Huòlínguolè"
1445,49,"CHN","China",19,"Nei Mongol",216,"Tongliao",1445,"Hure","Qí","Banner","<U+5E93><U+4F26><U+65D7>","Kùlún"
1446,49,"CHN","China",19,"Nei Mongol",216,"Tongliao",1446,"Jarud","Qí","Banner","<U+624E><U+9C81><U+7279><U+65D7>","Zalutè"
1447,49,"CHN","China",19,"Nei Mongol",216,"Tongliao",1447,"Kailu","Xiàn","County","<U+5F00><U+9C81><U+53BF>","Kailu"
1448,49,"CHN","China",19,"Nei Mongol",216,"Tongliao",1448,"Naiman","Qí","Banner","<U+5948><U+66FC><U+65D7>","Nàimàn"
1449,49,"CHN","China",19,"Nei Mongol",216,"Tongliao",1449,"Tongliao","Zizhiqu","County",NA,NA
1450,49,"CHN","China",19,"Nei Mongol",217,"Ulaan Chab",1450,"Fengzhen","Xiànjíshì","County City","<U+4E30><U+9547><U+5E02>","Fengzhèn"
1451,49,"CHN","China",19,"Nei Mongol",217,"Ulaan Chab",1451,"Huade","Xiàn","County","<U+5316><U+5FB7><U+53BF>","Huàdé"
1452,49,"CHN","China",19,"Nei Mongol",217,"Ulaan Chab",1452,"Jining","Shìxiáqu","District","<U+96C6><U+5B81><U+533A>","Jíníng"
1453,49,"CHN","China",19,"Nei Mongol",217,"Ulaan Chab",1453,"Liangcheng","Xiàn","County","<U+51C9><U+57CE><U+53BF>","Liángchéng"
1454,49,"CHN","China",19,"Nei Mongol",217,"Ulaan Chab",1454,"Qahar Youyi Houqi","Zizhiqu","County",NA,NA
1455,49,"CHN","China",19,"Nei Mongol",217,"Ulaan Chab",1455,"Qahar Youyi Qianqi","Zizhiqu","County",NA,NA
1456,49,"CHN","China",19,"Nei Mongol",217,"Ulaan Chab",1456,"Qahar Youyi Zhongqi","Zizhiqu","County",NA,NA
1457,49,"CHN","China",19,"Nei Mongol",217,"Ulaan Chab",1457,"Shangdu","Xiàn","County","<U+5546><U+90FD><U+53BF>","Shangdu"
1458,49,"CHN","China",19,"Nei Mongol",217,"Ulaan Chab",1458,"Siziwang","Qí","Banner","<U+56DB><U+5B50><U+738B><U+65D7>","Sìziwáng"
1459,49,"CHN","China",19,"Nei Mongol",217,"Ulaan Chab",1459,"Xinghe","Xiàn","County","<U+5174><U+548C><U+53BF>","Xinghé"
1460,49,"CHN","China",19,"Nei Mongol",217,"Ulaan Chab",1460,"Zhuozi","Xiàn","County","<U+5353><U+8D44><U+53BF>","Zhuózi"
1461,49,"CHN","China",19,"Nei Mongol",218,"Wuhai",1461,"Wuhai","Xiànjíshì","County City",NA,NA
1462,49,"CHN","China",19,"Nei Mongol",219,"Xilin Gol",1462,"Abagnar Qi","Qí","Banner",NA,NA
1463,49,"CHN","China",19,"Nei Mongol",219,"Xilin Gol",1463,"Abag","Qí","Banner","<U+963F><U+5DF4><U+560E><U+65D7>","Abaga"
1464,49,"CHN","China",19,"Nei Mongol",219,"Xilin Gol",1464,"Bordered Yellow","Qí","Banner","<U+9576><U+9EC4><U+65D7>","Xianghuáng"
1465,49,"CHN","China",19,"Nei Mongol",219,"Xilin Gol",1465,"Dong Ujimqin","Qí","Banner",NA,NA
1466,49,"CHN","China",19,"Nei Mongol",219,"Xilin Gol",1466,"Duolun","Xiàn","County","<U+591A><U+4F26><U+53BF>","Duolún"
1467,49,"CHN","China",19,"Nei Mongol",219,"Xilin Gol",1467,"Erenhot","Xiànjíshì","County City","<U+4E8C><U+8FDE><U+6D69><U+7279><U+5E02>","Èrliánhàotè"
1468,49,"CHN","China",19,"Nei Mongol",219,"Xilin Gol",1468,"Plain and Bordered White","Qí","Banner","<U+6B63><U+9576><U+767D><U+65D7>","Zhèngxiangbái"
1469,49,"CHN","China",19,"Nei Mongol",219,"Xilin Gol",1469,"Plain Blue","Qí","Banner","<U+6B63><U+84DD><U+65D7>","Zhènglán"
1470,49,"CHN","China",19,"Nei Mongol",219,"Xilin Gol",1470,"Sonid Left","Qí","Banner","<U+82CF><U+5C3C><U+7279><U+5DE6><U+65D7>","Sunítè Zuo"
1471,49,"CHN","China",19,"Nei Mongol",219,"Xilin Gol",1471,"Sonid Right","Qí","Banner","<U+82CF><U+5C3C><U+7279><U+53F3><U+65D7>","Sunítè Yòu"
1472,49,"CHN","China",19,"Nei Mongol",219,"Xilin Gol",1472,"Taibus","Qí","Banner","<U+592A><U+4EC6><U+5BFA><U+65D7>","Tàipúsì"
1473,49,"CHN","China",19,"Nei Mongol",219,"Xilin Gol",1473,"West Ujimqin","Qí","Banner","<U+897F><U+4E4C><U+73E0><U+7A46><U+6C81><U+65D7>","Xiwuzhumùqìn"
1474,49,"CHN","China",19,"Nei Mongol",220,"Xing'an",1474,"Horqin Right Front","Qí","Banner","<U+79D1><U+5C14><U+6C81><U+53F3><U+7FFC><U+524D><U+65D7>","Ke'erqìn Yòuyì Qián"
1475,49,"CHN","China",19,"Nei Mongol",220,"Xing'an",1475,"Horqin Right Middle","Qí","Banner","<U+79D1><U+5C14><U+6C81><U+53F3><U+7FFC><U+4E2D><U+65D7>","Ke'erqìn Yòuyì Zhong"
1476,49,"CHN","China",19,"Nei Mongol",220,"Xing'an",1476,"Jalaid","Qí","Banner","<U+624E><U+8D49><U+7279><U+65D7>","Zalàitè"
1477,49,"CHN","China",19,"Nei Mongol",220,"Xing'an",1477,"Tuquan","Xiàn","County","<U+7A81><U+6CC9><U+53BF>","Tuquán"
1478,49,"CHN","China",19,"Nei Mongol",220,"Xing'an",1478,"Ulan Hot","Xiànjíshì","County City","<U+4E4C><U+5170><U+6D69><U+7279><U+5E02>","Wulánhàotè"
1479,49,"CHN","China",20,"Ningxia Hui",221,"Guyuan",1479,"Guyuan","Zizhiqu","County",NA,NA
1480,49,"CHN","China",20,"Ningxia Hui",221,"Guyuan",1480,"Jingyuan","Xiàn","County","<U+6CFE><U+6E90><U+53BF>","Jingyuán"
1481,49,"CHN","China",20,"Ningxia Hui",221,"Guyuan",1481,"Longde","Xiàn","County","<U+9686><U+5FB7><U+53BF>","Lóngdé"
1482,49,"CHN","China",20,"Ningxia Hui",221,"Guyuan",1482,"Pengyang","Xiàn","County","<U+5F6D><U+9633><U+53BF>","Péngyáng"
1483,49,"CHN","China",20,"Ningxia Hui",221,"Guyuan",1483,"Xiji","Xiàn","County","<U+897F><U+5409><U+53BF>","Xijí"
1484,49,"CHN","China",20,"Ningxia Hui",222,"Shizuishan",1484,"Huinong","Shìxiáqu","District","<U+60E0><U+519C><U+533A>","Hùinóng"
1485,49,"CHN","China",20,"Ningxia Hui",222,"Shizuishan",1485,"Pingluo","Xiàn","County","<U+5E73><U+7F57><U+53BF>","Píngluó"
1486,49,"CHN","China",20,"Ningxia Hui",222,"Shizuishan",1486,"Shizuishan Qu","Shìxiáqu","District","<U+77F3><U+5634><U+5C71><U+533A>","Shízuishan"
1487,49,"CHN","China",20,"Ningxia Hui",222,"Shizuishan",1487,"Shizuishan Shì","Xiànjíshì","County City","<U+77F3><U+5634><U+5C71><U+5E02>","Shízuishan"
1488,49,"CHN","China",20,"Ningxia Hui",222,"Shizuishan",1488,"Taole","Zizhiqu","County",NA,NA
1489,49,"CHN","China",20,"Ningxia Hui",223,"Wuzhong",1489,"Qingtongxia","Xiànjíshì","County City","<U+9752><U+94DC><U+5CE1><U+5E02>","Qingtóngxiá"
1490,49,"CHN","China",20,"Ningxia Hui",223,"Wuzhong",1490,"Tongxin","Xiàn","County","<U+540C><U+5FC3><U+53BF>","Tóngxin"
1491,49,"CHN","China",20,"Ningxia Hui",223,"Wuzhong",1491,"Wuzhong","Zizhiqu","County",NA,NA
1492,49,"CHN","China",20,"Ningxia Hui",223,"Wuzhong",1492,"Yanchi","Xiàn","County","<U+76D0><U+6C60><U+53BF>","Yánchí"
1493,49,"CHN","China",20,"Ningxia Hui",224,"Yinchuan",1493,"Helan","Xiàn","County","<U+8D3A><U+5170><U+53BF>","Hèlán"
1494,49,"CHN","China",20,"Ningxia Hui",224,"Yinchuan",1494,"Lingwu","Xiànjíshì","County City","<U+7075><U+6B66><U+5E02>","Língwu"
1495,49,"CHN","China",20,"Ningxia Hui",224,"Yinchuan",1495,"Yinchuan","Shìxiáqu","District",NA,NA
1496,49,"CHN","China",20,"Ningxia Hui",224,"Yinchuan",1496,"Yongning","Xiàn","County","<U+6C38><U+5B81><U+53BF>","Yongníng"
1497,49,"CHN","China",20,"Ningxia Hui",225,"Zhongwei",1497,"Haiyuan","Xiàn","County","<U+6D77><U+539F><U+53BF>","Haiyuán"
1498,49,"CHN","China",20,"Ningxia Hui",225,"Zhongwei",1498,"Zhongning","Xiàn","County","<U+4E2D><U+5B81><U+53BF>","Zhongníng"
1499,49,"CHN","China",20,"Ningxia Hui",225,"Zhongwei",1499,"Zhongwei","Zizhiqu","County",NA,NA
1500,49,"CHN","China",21,"Qinghai",226,"Golog Tibetan",1500,"Baima","Xiàn","County","<U+73ED><U+739B><U+53BF>","Banma"
1501,49,"CHN","China",21,"Qinghai",226,"Golog Tibetan",1501,"Darlag","Xiàn","County","<U+8FBE><U+65E5><U+53BF>","Dárì"
1502,49,"CHN","China",21,"Qinghai",226,"Golog Tibetan",1502,"Gadê","Xiàn","County","<U+7518><U+5FB7><U+53BF>","Gandé"
1503,49,"CHN","China",21,"Qinghai",226,"Golog Tibetan",1503,"Jigzhi","Xiàn","County","<U+4E45><U+6CBB><U+53BF>","Jiuzhì"
1504,49,"CHN","China",21,"Qinghai",226,"Golog Tibetan",1504,"Madoi","Xiàn","County","<U+739B><U+591A><U+53BF>","Maduo"
1505,49,"CHN","China",21,"Qinghai",226,"Golog Tibetan",1505,"Maqên","Xiàn","County","<U+739B><U+6C81><U+53BF>","Maqìn"
1506,49,"CHN","China",21,"Qinghai",227,"Gyêgu Tibetan",1506,"Chindu","Xiàn","County","<U+79F0><U+591A><U+53BF>","Chengduo"
1507,49,"CHN","China",21,"Qinghai",227,"Gyêgu Tibetan",1507,"Golmud","Xiànjíshì","County City","<U+683C><U+5C14><U+6728><U+5E02>","Gé'ermù"
1508,49,"CHN","China",21,"Qinghai",227,"Gyêgu Tibetan",1508,"Nangqên","Xiàn","County","<U+56CA><U+8C26><U+53BF>","Nángqian"
1509,49,"CHN","China",21,"Qinghai",227,"Gyêgu Tibetan",1509,"Qumarleb","Xiàn","County","<U+66F2><U+9EBB><U+83B1><U+53BF>","Qumálái"
1510,49,"CHN","China",21,"Qinghai",227,"Gyêgu Tibetan",1510,"Yushu","Xiàn","County",NA,NA
1511,49,"CHN","China",21,"Qinghai",227,"Gyêgu Tibetan",1511,"Zadoi","Xiàn","County","<U+6742><U+591A><U+53BF>","Záduo"
1512,49,"CHN","China",21,"Qinghai",227,"Gyêgu Tibetan",1512,"Zhidoi","Xiàn","County","<U+6CBB><U+591A><U+53BF>","Zhìduo"
1513,49,"CHN","China",21,"Qinghai",228,"Haibei Tibetan",1513,"Gangca","Xiàn","County","<U+521A><U+5BDF><U+53BF>","Gangchá"
1514,49,"CHN","China",21,"Qinghai",228,"Haibei Tibetan",1514,"Haiyan","Xiàn","County","<U+6D77><U+664F><U+53BF>","Haiyàn"
1515,49,"CHN","China",21,"Qinghai",228,"Haibei Tibetan",1515,"Menyuan Hui","Zìzhìxiàn","Autonomous County","<U+95E8><U+6E90><U+56DE><U+65CF><U+81EA><U+6CBB><U+53BF>","Ményuán Huízú"
1516,49,"CHN","China",21,"Qinghai",228,"Haibei Tibetan",1516,"Qilian","Xiàn","County","<U+7941><U+8FDE><U+53BF>","Qílián"
1517,49,"CHN","China",21,"Qinghai",229,"Haidong",1517,"Hualong Hui","Zìzhìxiàn","Autonomous County","<U+5316><U+9686><U+56DE><U+65CF><U+81EA><U+6CBB><U+53BF>","Huàlóng Huízú"
1518,49,"CHN","China",21,"Qinghai",229,"Haidong",1518,"Huzhu Tu","Zìzhìxiàn","Autonomous County","<U+4E92><U+52A9><U+571F><U+65CF><U+81EA><U+6CBB><U+53BF>","Hùzhù Tuzú"
1519,49,"CHN","China",21,"Qinghai",229,"Haidong",1519,"Ledu","Xiàn","County","<U+4E50><U+90FD><U+53BF>","Lèdu"
1520,49,"CHN","China",21,"Qinghai",229,"Haidong",1520,"Minhe Hui and Tu","Zìzhìxiàn","Autonomous County","<U+6C11><U+548C><U+56DE><U+65CF>|<U+571F><U+65CF><U+81EA><U+6CBB><U+53BF>","Mínhé Huízú|Tuzú"
1521,49,"CHN","China",21,"Qinghai",229,"Haidong",1521,"Ping'an","Xiàn","County","<U+5E73><U+5B89><U+53BF>","Píng'an"
1522,49,"CHN","China",21,"Qinghai",229,"Haidong",1522,"Xunhua Salar","Zìzhìxiàn","Autonomous County","<U+5FAA><U+5316><U+6492><U+62C9><U+65CF><U+81EA><U+6CBB><U+53BF>","Xúnhuà Salazú"
1523,49,"CHN","China",21,"Qinghai",230,"Hainan Tibetan",1523,"Gonghe","Xiàn","County","<U+5171><U+548C><U+53BF>","Gònghé"
1524,49,"CHN","China",21,"Qinghai",230,"Hainan Tibetan",1524,"Guide","Xiàn","County","<U+8D35><U+5FB7><U+53BF>","Guìdé"
1525,49,"CHN","China",21,"Qinghai",230,"Hainan Tibetan",1525,"Guinan","Xiàn","County","<U+8D35><U+5357><U+53BF>","Guìnán"
1526,49,"CHN","China",21,"Qinghai",230,"Hainan Tibetan",1526,"Tongde","Xiàn","County","<U+540C><U+5FB7><U+53BF>","Tóngdé"
1527,49,"CHN","China",21,"Qinghai",230,"Hainan Tibetan",1527,"Xinghai","Xiàn","County","<U+5174><U+6D77><U+53BF>","Xinghai"
1528,49,"CHN","China",21,"Qinghai",231,"Haixi Mongol and Tibetan",1528,"Delingha","Xiànjíshì","County","<U+5FB7><U+4EE4><U+54C8><U+5E02>","Délìngha"
1529,49,"CHN","China",21,"Qinghai",231,"Haixi Mongol and Tibetan",1529,"Dulan","Xiàn","County","<U+90FD><U+5170><U+53BF>","Dulán"
1530,49,"CHN","China",21,"Qinghai",231,"Haixi Mongol and Tibetan",1530,"Golmud","Xiànjíshì","County City","<U+683C><U+5C14><U+6728><U+5E02>","Gé'ermù"
1531,49,"CHN","China",21,"Qinghai",231,"Haixi Mongol and Tibetan",1531,"Lenghu","Xíngzhèng Weiyuánhuì","Administrative Committee","<U+51B7><U+6E56><U+884C><U+653F><U+59D4><U+5458><U+4F1A>","Lenghú"
1532,49,"CHN","China",21,"Qinghai",231,"Haixi Mongol and Tibetan",1532,"Tianjun","Xiàn","County","<U+5929><U+5CFB><U+53BF>","Tianjùn"
1533,49,"CHN","China",21,"Qinghai",231,"Haixi Mongol and Tibetan",1533,"Wulan","Xiàn","County","<U+4E4C><U+5170><U+53BF>","Wulán"
1534,49,"CHN","China",21,"Qinghai",232,"Huangnan Tibetan",1534,"Henan Mongol","Zìzhìxiàn","Autonomous County","<U+6CB3><U+5357><U+8499><U+53E4><U+65CF><U+81EA><U+6CBB><U+53BF>","Hénán Mengguzú"
1535,49,"CHN","China",21,"Qinghai",232,"Huangnan Tibetan",1535,"Jainca","Xiàn","County","<U+5C16><U+624E><U+53BF>","Jianzha"
1536,49,"CHN","China",21,"Qinghai",232,"Huangnan Tibetan",1536,"Tongren","Xiàn","County","<U+540C><U+4EC1><U+53BF>","Tóngrén"
1537,49,"CHN","China",21,"Qinghai",232,"Huangnan Tibetan",1537,"Zêkog","Xiàn","County","<U+6CFD><U+5E93><U+53BF>","Zékù"
1538,49,"CHN","China",21,"Qinghai",233,"Xining",1538,"Datong Hui and Tu","Zìzhìxiàn","Autonomous County","<U+5927><U+901A><U+56DE><U+65CF>|<U+571F><U+65CF><U+81EA><U+6CBB><U+53BF>","Dàtong Huízú|Tuzú"
1539,49,"CHN","China",21,"Qinghai",233,"Xining",1539,"Huangyuan","Xiàn","County","<U+6E5F><U+6E90><U+53BF>","Huángyuán"
1540,49,"CHN","China",21,"Qinghai",233,"Xining",1540,"Huangzhong","Xiàn","County","<U+6E5F><U+4E2D><U+53BF>","Huángzhong"
1541,49,"CHN","China",21,"Qinghai",233,"Xining",1541,"Xining","Shìxiáqu","District",NA,NA
1542,49,"CHN","China",22,"Shaanxi",234,"Ankang",1542,"Ankang","Xiàn","County",NA,NA
1543,49,"CHN","China",22,"Shaanxi",234,"Ankang",1543,"Baihe","Xiàn","County","<U+767D><U+6CB3><U+53BF>","Báihé"
1544,49,"CHN","China",22,"Shaanxi",234,"Ankang",1544,"Hanyin","Xiàn","County","<U+6C49><U+9634><U+53BF>","Hànyin"
1545,49,"CHN","China",22,"Shaanxi",234,"Ankang",1545,"Langao","Xiàn","County","<U+5C9A><U+768B><U+53BF>","Lángao"
1546,49,"CHN","China",22,"Shaanxi",234,"Ankang",1546,"Ningshan","Xiàn","County","<U+5B81><U+9655><U+53BF>","Níngshan"
1547,49,"CHN","China",22,"Shaanxi",234,"Ankang",1547,"Pingli","Xiàn","County","<U+5E73><U+5229><U+53BF>","Pínglì"
1548,49,"CHN","China",22,"Shaanxi",234,"Ankang",1548,"Shiquan","Xiàn","County","<U+77F3><U+6CC9><U+53BF>","Shíquán"
1549,49,"CHN","China",22,"Shaanxi",234,"Ankang",1549,"Xunyang","Xiàn","County","<U+65EC><U+9633><U+53BF>","Xúnyáng"
1550,49,"CHN","China",22,"Shaanxi",234,"Ankang",1550,"Zhenping","Xiàn","County","<U+9547><U+576A><U+53BF>","Zhènpíng"
1551,49,"CHN","China",22,"Shaanxi",234,"Ankang",1551,"Ziyang","Xiàn","County","<U+7D2B><U+9633><U+53BF>","Ziyáng"
1552,49,"CHN","China",22,"Shaanxi",235,"Baoji",1552,"Baoji Qu","Shìxiáqu","District","<U+5B9D><U+9E21><U+533A>","Baoji"
1553,49,"CHN","China",22,"Shaanxi",235,"Baoji",1553,"Baoji Xiàn","Xiàn","County","<U+5B9D><U+9E21><U+53BF>","Baoji"
1554,49,"CHN","China",22,"Shaanxi",235,"Baoji",1554,"Feng","Xiàn","County","<U+51E4><U+53BF>","Fèng"
1555,49,"CHN","China",22,"Shaanxi",235,"Baoji",1555,"Fengxiang","Xiàn","County","<U+51E4><U+7FD4><U+53BF>","Fèngxiáng"
1556,49,"CHN","China",22,"Shaanxi",235,"Baoji",1556,"Fufeng","Xiàn","County","<U+6276><U+98CE><U+53BF>","Fúfeng"
1557,49,"CHN","China",22,"Shaanxi",235,"Baoji",1557,"Linyou","Xiàn","County","<U+9E9F><U+6E38><U+53BF>","Línyóu"
1558,49,"CHN","China",22,"Shaanxi",235,"Baoji",1558,"Long","Xiàn","County","<U+9647><U+53BF>","Long"
1559,49,"CHN","China",22,"Shaanxi",235,"Baoji",1559,"Mei","Xiàn","County","<U+7709><U+53BF>","Méi"
1560,49,"CHN","China",22,"Shaanxi",235,"Baoji",1560,"Qianyang","Xiàn","County","<U+5343><U+9633><U+53BF>","Qianyáng"
1561,49,"CHN","China",22,"Shaanxi",235,"Baoji",1561,"Qishan","Xiàn","County","<U+5C90><U+5C71><U+53BF>","Qíshan"
1562,49,"CHN","China",22,"Shaanxi",235,"Baoji",1562,"Taibai","Xiàn","County","<U+592A><U+767D><U+53BF>","Tàibái"
1563,49,"CHN","China",22,"Shaanxi",236,"Hanzhong",1563,"Chenggu","Xiàn","County","<U+57CE><U+56FA><U+53BF>","Chénggù"
1564,49,"CHN","China",22,"Shaanxi",236,"Hanzhong",1564,"Foping","Xiàn","County","<U+4F5B><U+576A><U+53BF>","Fópíng"
1565,49,"CHN","China",22,"Shaanxi",236,"Hanzhong",1565,"Hanzhong","Xiànjíshì","County City",NA,NA
1566,49,"CHN","China",22,"Shaanxi",236,"Hanzhong",1566,"Liuba","Xiàn","County","<U+7559><U+575D><U+53BF>","Liúbà"
1567,49,"CHN","China",22,"Shaanxi",236,"Hanzhong",1567,"Lueyang","Xiàn","County","<U+7565><U+9633><U+53BF>","Lüèyáng"
1568,49,"CHN","China",22,"Shaanxi",236,"Hanzhong",1568,"Mian","Xiàn","County","<U+52C9><U+53BF>","Mian"
1569,49,"CHN","China",22,"Shaanxi",236,"Hanzhong",1569,"Nanzheng","Xiàn","County","<U+5357><U+90D1><U+53BF>","Nánzhèng"
1570,49,"CHN","China",22,"Shaanxi",236,"Hanzhong",1570,"Ningqiang","Xiàn","County","<U+5B81><U+5F3A><U+53BF>","Níngqiáng"
1571,49,"CHN","China",22,"Shaanxi",236,"Hanzhong",1571,"Xixiang","Xiàn","County","<U+897F><U+4E61><U+53BF>","Xixiang"
1572,49,"CHN","China",22,"Shaanxi",236,"Hanzhong",1572,"Yang","Xiàn","County","<U+6D0B><U+53BF>","Yáng"
1573,49,"CHN","China",22,"Shaanxi",236,"Hanzhong",1573,"Zhenba","Xiàn","County","<U+9547><U+5DF4><U+53BF>","Zhènba"
1574,49,"CHN","China",22,"Shaanxi",237,"Shangluo",1574,"Danfeng","Xiàn","County","<U+4E39><U+51E4><U+53BF>","Danfèng"
1575,49,"CHN","China",22,"Shaanxi",237,"Shangluo",1575,"Luonan","Xiàn","County","<U+6D1B><U+5357><U+53BF>","Luònán"
1576,49,"CHN","China",22,"Shaanxi",237,"Shangluo",1576,"Shangnan","Xiàn","County","<U+5546><U+5357><U+53BF>","Shangnán"
1577,49,"CHN","China",22,"Shaanxi",237,"Shangluo",1577,"Shang","Xiàn","County",NA,NA
1578,49,"CHN","China",22,"Shaanxi",237,"Shangluo",1578,"Shanyang","Xiàn","County","<U+5C71><U+9633><U+53BF>","Shanyáng"
1579,49,"CHN","China",22,"Shaanxi",237,"Shangluo",1579,"Zhashui","Xiàn","County","<U+67DE><U+6C34><U+53BF>","Zhàshui"
1580,49,"CHN","China",22,"Shaanxi",237,"Shangluo",1580,"Zhen'an","Xiàn","County","<U+9547><U+5B89><U+53BF>","Zhèn'an"
1581,49,"CHN","China",22,"Shaanxi",238,"Tongchuan",1581,"Tongchuan","Shìxiáqu","District",NA,NA
1582,49,"CHN","China",22,"Shaanxi",238,"Tongchuan",1582,"Yijun","Xiàn","County","<U+5B9C><U+541B><U+53BF>","Yíjun"
1583,49,"CHN","China",22,"Shaanxi",239,"Weinan",1583,"Baishui","Xiàn","County","<U+767D><U+6C34><U+53BF>","Báishui"
1584,49,"CHN","China",22,"Shaanxi",239,"Weinan",1584,"Chencang","Shìxiáqu","District","<U+9648><U+4ED3><U+533A>","Chéncang"
1585,49,"CHN","China",22,"Shaanxi",239,"Weinan",1585,"Dali","Xiàn","County","<U+5927><U+8354><U+53BF>","Dàlì"
1586,49,"CHN","China",22,"Shaanxi",239,"Weinan",1586,"Fuping","Xiàn","County","<U+5BCC><U+5E73><U+53BF>","Fùpíng"
1587,49,"CHN","China",22,"Shaanxi",239,"Weinan",1587,"Hancheng","Xiànjíshì","County City","<U+97E9><U+57CE><U+5E02>","Hánchéng"
1588,49,"CHN","China",22,"Shaanxi",239,"Weinan",1588,"Heyang","Xiàn","County","<U+5408><U+9633><U+53BF>","Héyáng"
1589,49,"CHN","China",22,"Shaanxi",239,"Weinan",1589,"Hua","Xiàn","County","<U+534E><U+53BF>","Huà"
1590,49,"CHN","China",22,"Shaanxi",239,"Weinan",1590,"Huayin","Xiànjíshì","County City","<U+534E><U+9634><U+5E02>","Huàyin"
1591,49,"CHN","China",22,"Shaanxi",239,"Weinan",1591,"Pucheng","Xiàn","County","<U+84B2><U+57CE><U+53BF>","Púchéng"
1592,49,"CHN","China",22,"Shaanxi",239,"Weinan",1592,"Tongguan","Xiàn","County",NA,NA
1593,49,"CHN","China",22,"Shaanxi",239,"Weinan",1593,"Weinan","Xiàn","County",NA,NA
1594,49,"CHN","China",22,"Shaanxi",240,"Xi'an",1594,"Chang'an","Shìxiáqu","District","<U+957F><U+5B89><U+533A>","Cháng'an"
1595,49,"CHN","China",22,"Shaanxi",240,"Xi'an",1595,"Gaoling","Xiàn","County","<U+9AD8><U+9675><U+53BF>","Gaolíng"
1596,49,"CHN","China",22,"Shaanxi",240,"Xi'an",1596,"Hu","Xiàn","County","<U+6237><U+53BF>","Hù"
1597,49,"CHN","China",22,"Shaanxi",240,"Xi'an",1597,"Lantian","Xiàn","County","<U+84DD><U+7530><U+53BF>","Lántián"
1598,49,"CHN","China",22,"Shaanxi",240,"Xi'an",1598,"Lintong","Shìxiáqu","District","<U+4E34><U+6F7C><U+533A>","Líntóng"
1599,49,"CHN","China",22,"Shaanxi",240,"Xi'an",1599,"Xincheng","Shìxiáqu","District","<U+65B0><U+57CE><U+533A>","Xinchéng"
1600,49,"CHN","China",22,"Shaanxi",240,"Xi'an",1600,"Zhouzhi","Xiàn","County","<U+5468><U+81F3><U+53BF>","Zhouzhì"
1601,49,"CHN","China",22,"Shaanxi",241,"Xianyang",1601,"Bin","Xiàn","County","<U+5F6C><U+53BF>","Bin"
1602,49,"CHN","China",22,"Shaanxi",241,"Xianyang",1602,"Changwu","Xiàn","County","<U+957F><U+6B66><U+53BF>","Chángwu"
1603,49,"CHN","China",22,"Shaanxi",241,"Xianyang",1603,"Chunhua","Xiàn","County","<U+6DF3><U+5316><U+53BF>","Chúnhuà"
1604,49,"CHN","China",22,"Shaanxi",241,"Xianyang",1604,"Jingyang","Xiàn","County","<U+6CFE><U+9633><U+53BF>","Jingyáng"
1605,49,"CHN","China",22,"Shaanxi",241,"Xianyang",1605,"Liquan","Xiàn","County","<U+793C><U+6CC9><U+53BF>","Liquán"
1606,49,"CHN","China",22,"Shaanxi",241,"Xianyang",1606,"Qian","Xiàn","County","<U+4E7E><U+53BF>","Qián"
1607,49,"CHN","China",22,"Shaanxi",241,"Xianyang",1607,"Sanyuan","Xiàn","County","<U+4E09><U+539F><U+53BF>","Sanyuán"
1608,49,"CHN","China",22,"Shaanxi",241,"Xianyang",1608,"Wugong","Xiàn","County",NA,NA
1609,49,"CHN","China",22,"Shaanxi",241,"Xianyang",1609,"Xianyang","Xiànjíshì","County City",NA,NA
1610,49,"CHN","China",22,"Shaanxi",241,"Xianyang",1610,"Xingping","Xiànjíshì","County City","<U+5174><U+5E73><U+5E02>","Xingpíng"
1611,49,"CHN","China",22,"Shaanxi",241,"Xianyang",1611,"Xunyi","Xiàn","County","<U+65EC><U+9091><U+53BF>","Xúnyì"
1612,49,"CHN","China",22,"Shaanxi",241,"Xianyang",1612,"Yangling","Shìxiáqu","District","<U+6768><U+9675><U+533A>","Yánglíng"
1613,49,"CHN","China",22,"Shaanxi",241,"Xianyang",1613,"Yongshou","Xiàn","County","<U+6C38><U+5BFF><U+53BF>","Yongshòu"
1614,49,"CHN","China",22,"Shaanxi",242,"Yan'an",1614,"Ansai","Xiàn","County","<U+5B89><U+585E><U+53BF>","Ansài"
1615,49,"CHN","China",22,"Shaanxi",242,"Yan'an",1615,"Fu","Xiàn","County","<U+5BCC><U+53BF>","Fù"
1616,49,"CHN","China",22,"Shaanxi",242,"Yan'an",1616,"Ganquan","Xiàn","County","<U+7518><U+6CC9><U+53BF>","Ganquán"
1617,49,"CHN","China",22,"Shaanxi",242,"Yan'an",1617,"Huangling","Xiàn","County","<U+9EC4><U+9675><U+53BF>","Huánglíng"
1618,49,"CHN","China",22,"Shaanxi",242,"Yan'an",1618,"Huanglong","Xiàn","County","<U+9EC4><U+9F99><U+53BF>","Huánglóng"
1619,49,"CHN","China",22,"Shaanxi",242,"Yan'an",1619,"Luochuan","Xiàn","County","<U+6D1B><U+5DDD><U+53BF>","Luòchuan"
1620,49,"CHN","China",22,"Shaanxi",242,"Yan'an",1620,"Wuqi","Xiàn","County","<U+5434><U+8D77><U+53BF>","Wúqi"
1621,49,"CHN","China",22,"Shaanxi",242,"Yan'an",1621,"Yan An","Xiànjíshì","County City",NA,NA
1622,49,"CHN","China",22,"Shaanxi",242,"Yan'an",1622,"Yanchang","Xiàn","County","<U+5EF6><U+957F><U+53BF>","Yáncháng"
1623,49,"CHN","China",22,"Shaanxi",242,"Yan'an",1623,"Yanchuan","Xiàn","County","<U+5EF6><U+5DDD><U+53BF>","Yánchuan"
1624,49,"CHN","China",22,"Shaanxi",242,"Yan'an",1624,"Yichuan","Xiàn","County","<U+5B9C><U+5DDD><U+53BF>","Yíchuan"
1625,49,"CHN","China",22,"Shaanxi",242,"Yan'an",1625,"Zhidan","Xiàn","County","<U+5FD7><U+4E39><U+53BF>","Zhìdan"
1626,49,"CHN","China",22,"Shaanxi",242,"Yan'an",1626,"Zichang","Xiàn","County","<U+5B50><U+957F><U+53BF>","Zicháng"
1627,49,"CHN","China",22,"Shaanxi",243,"Yulin",1627,"Dingbian","Xiàn","County","<U+5B9A><U+8FB9><U+53BF>","Dìngbian"
1628,49,"CHN","China",22,"Shaanxi",243,"Yulin",1628,"Fugu","Xiàn","County","<U+5E9C><U+8C37><U+53BF>","Fugu"
1629,49,"CHN","China",22,"Shaanxi",243,"Yulin",1629,"Hengshan","Xiàn","County","<U+6A2A><U+5C71><U+53BF>","Héngshan"
1630,49,"CHN","China",22,"Shaanxi",243,"Yulin",1630,"Jia","Xiàn","County","<U+4F73><U+53BF>","Jia"
1631,49,"CHN","China",22,"Shaanxi",243,"Yulin",1631,"Jingbian","Xiàn","County","<U+9756><U+8FB9><U+53BF>","Jìngbian"
1632,49,"CHN","China",22,"Shaanxi",243,"Yulin",1632,"Mizhi","Xiàn","County","<U+7C73><U+8102><U+53BF>","Mizhi"
1633,49,"CHN","China",22,"Shaanxi",243,"Yulin",1633,"Qingjian","Xiàn","County","<U+6E05><U+6DA7><U+53BF>","Qingjiàn"
1634,49,"CHN","China",22,"Shaanxi",243,"Yulin",1634,"Shenmu","Xiàn","County","<U+795E><U+6728><U+53BF>","Shénmù"
1635,49,"CHN","China",22,"Shaanxi",243,"Yulin",1635,"Suide","Xiàn","County","<U+7EE5><U+5FB7><U+53BF>","Suídé"
1636,49,"CHN","China",22,"Shaanxi",243,"Yulin",1636,"Wubu","Xiàn","County",NA,NA
1637,49,"CHN","China",22,"Shaanxi",243,"Yulin",1637,"Yulin","Xiàn","County",NA,NA
1638,49,"CHN","China",22,"Shaanxi",243,"Yulin",1638,"Zizhou","Xiàn","County","<U+5B50><U+6D32><U+53BF>","Zizhou"
1639,49,"CHN","China",23,"Shandong",244,"Binzhou",1639,"Binxian","Xiàn","County",NA,NA
1640,49,"CHN","China",23,"Shandong",244,"Binzhou",1640,"Boxing","Xiàn","County","<U+535A><U+5174><U+53BF>","Bóxing"
1641,49,"CHN","China",23,"Shandong",244,"Binzhou",1641,"Huimin","Xiàn","County","<U+60E0><U+6C11><U+53BF>","Huìmín"
1642,49,"CHN","China",23,"Shandong",244,"Binzhou",1642,"Wudi","Xiàn","County","<U+65E0><U+68E3><U+53BF>","Wúdì"
1643,49,"CHN","China",23,"Shandong",244,"Binzhou",1643,"Yangxin","Xiàn","County","<U+9633><U+4FE1><U+53BF>","Yángxìn"
1644,49,"CHN","China",23,"Shandong",244,"Binzhou",1644,"Zhanhua","Xiàn","County","<U+6CBE><U+5316><U+53BF>","Zhanhuà"
1645,49,"CHN","China",23,"Shandong",244,"Binzhou",1645,"Zouping","Xiàn","County","<U+90B9><U+5E73><U+53BF>","Zoupíng"
1646,49,"CHN","China",23,"Shandong",245,"Dezhou",1646,"Dezhou","Xiànjíshì","County City",NA,NA
1647,49,"CHN","China",23,"Shandong",245,"Dezhou",1647,"Leling","Xiànjíshì","County City","<U+4E50><U+9675><U+5E02>","Lèlíng"
1648,49,"CHN","China",23,"Shandong",245,"Dezhou",1648,"Ling","Xiàn","County","<U+9675><U+53BF>","Líng"
1649,49,"CHN","China",23,"Shandong",245,"Dezhou",1649,"Linyi","Xiàn","County","<U+4E34><U+9091><U+53BF>","Línyì"
1650,49,"CHN","China",23,"Shandong",245,"Dezhou",1650,"Ningjin","Xiàn","County","<U+5B81><U+6D25><U+53BF>","Níngjin"
1651,49,"CHN","China",23,"Shandong",245,"Dezhou",1651,"Pingyuan","Xiàn","County","<U+5E73><U+539F><U+53BF>","Píngyuán"
1652,49,"CHN","China",23,"Shandong",245,"Dezhou",1652,"Qihe","Xiàn","County","<U+9F50><U+6CB3><U+53BF>","Qíhé"
1653,49,"CHN","China",23,"Shandong",245,"Dezhou",1653,"Qingyun","Xiàn","County","<U+5E86><U+4E91><U+53BF>","Qìngyún"
1654,49,"CHN","China",23,"Shandong",245,"Dezhou",1654,"Wucheng","Xiàn","County","<U+6B66><U+57CE><U+53BF>","Wuchéng"
1655,49,"CHN","China",23,"Shandong",245,"Dezhou",1655,"Xiajin","Xiàn","County","<U+590F><U+6D25><U+53BF>","Xiàjin"
1656,49,"CHN","China",23,"Shandong",245,"Dezhou",1656,"Yuncheng","Xiàn","County","<U+90D3><U+57CE><U+53BF>","Yùnchéng"
1657,49,"CHN","China",23,"Shandong",246,"Dongying",1657,"Dongying","Shìxiáqu","District","<U+4E1C><U+8425><U+533A>","Dongyíng"
1658,49,"CHN","China",23,"Shandong",246,"Dongying",1658,"Guangrao","Xiàn","County","<U+5E7F><U+9976><U+53BF>","Guangráo"
1659,49,"CHN","China",23,"Shandong",246,"Dongying",1659,"Hekou","Shìxiáqu","District","<U+6CB3><U+53E3><U+533A>","Hékou"
1660,49,"CHN","China",23,"Shandong",246,"Dongying",1660,"Kenli","Xiàn","County","<U+57A6><U+5229><U+53BF>","Kenlì"
1661,49,"CHN","China",23,"Shandong",246,"Dongying",1661,"Lijin","Xiàn","County","<U+5229><U+6D25><U+53BF>","Lìjin"
1662,49,"CHN","China",23,"Shandong",247,"Heze",1662,"Cao","Xiàn","County","<U+66F9><U+53BF>","Cáo"
1663,49,"CHN","China",23,"Shandong",247,"Heze",1663,"Chengwu","Xiàn","County","<U+6210><U+6B66><U+53BF>","Chéngwu"
1664,49,"CHN","China",23,"Shandong",247,"Heze",1664,"Dingtao","Xiàn","County","<U+5B9A><U+9676><U+53BF>","Dìngtáo"
1665,49,"CHN","China",23,"Shandong",247,"Heze",1665,"Dongming","Xiàn","County","<U+4E1C><U+660E><U+53BF>","Dongmíng"
1666,49,"CHN","China",23,"Shandong",247,"Heze",1666,"Heze","Xiàn","County",NA,NA
1667,49,"CHN","China",23,"Shandong",247,"Heze",1667,"Juancheng","Xiàn","County",NA,NA
1668,49,"CHN","China",23,"Shandong",247,"Heze",1668,"Juye","Xiàn","County","<U+5DE8><U+91CE><U+53BF>","Jùye"
1669,49,"CHN","China",23,"Shandong",247,"Heze",1669,"Shan","Xiàn","County","<U+5355><U+53BF>","Shàn"
1670,49,"CHN","China",23,"Shandong",247,"Heze",1670,"Yucheng","Xiànjíshì","County City","<U+79B9><U+57CE><U+5E02>","Yuchéng"
1671,49,"CHN","China",23,"Shandong",248,"Jinan",1671,"Changqing","Shìxiáqu","District","<U+957F><U+6E05><U+533A>","Chángqing"
1672,49,"CHN","China",23,"Shandong",248,"Jinan",1672,"Jinan","Shìxiáqu","District","<U+6D4E><U+5357><U+533A>","Jinán"
1673,49,"CHN","China",23,"Shandong",248,"Jinan",1673,"Jiyang","Xiàn","County","<U+6D4E><U+9633><U+53BF>","Jiyáng"
1674,49,"CHN","China",23,"Shandong",248,"Jinan",1674,"Pingyin","Xiàn","County","<U+5E73><U+9634><U+53BF>","Píngyin"
1675,49,"CHN","China",23,"Shandong",248,"Jinan",1675,"Shanghe","Xiàn","County","<U+5546><U+6CB3><U+53BF>","Shanghé"
1676,49,"CHN","China",23,"Shandong",248,"Jinan",1676,"Zhangqiu","Xiànjíshì","County City","<U+7AE0><U+4E18><U+5E02>","Zhangqiu"
1677,49,"CHN","China",23,"Shandong",249,"Jining",1677,"Jiaxiang","Xiàn","County","<U+5609><U+7965><U+53BF>","Jiaxiáng"
1678,49,"CHN","China",23,"Shandong",249,"Jining",1678,"Jining","Xiàn","County",NA,NA
1679,49,"CHN","China",23,"Shandong",249,"Jining",1679,"Jinxiang","Xiàn","County","<U+91D1><U+4E61><U+53BF>","Jinxiang"
1680,49,"CHN","China",23,"Shandong",249,"Jining",1680,"Liangshan","Xiàn","County","<U+6881><U+5C71><U+53BF>","Liángshan"
1681,49,"CHN","China",23,"Shandong",249,"Jining",1681,"Qufu","Xiànjíshì","County City","<U+66F2><U+961C><U+5E02>","Qufù"
1682,49,"CHN","China",23,"Shandong",249,"Jining",1682,"Sishui","Xiàn","County","<U+6CD7><U+6C34><U+53BF>","Sìshui"
1683,49,"CHN","China",23,"Shandong",249,"Jining",1683,"Weishan","Xiàn","County",NA,NA
1684,49,"CHN","China",23,"Shandong",249,"Jining",1684,"Wenshang","Xiàn","County","<U+6C76><U+4E0A><U+53BF>","Wénshàng"
1685,49,"CHN","China",23,"Shandong",249,"Jining",1685,"Yanzhou","Xiànjíshì","County City","<U+5156><U+5DDE><U+5E02>","Yanzhou"
1686,49,"CHN","China",23,"Shandong",249,"Jining",1686,"Yutai","Xiàn","County","<U+9C7C><U+53F0><U+53BF>","Yútái"
1687,49,"CHN","China",23,"Shandong",250,"Laiwu",1687,"Laiwu","Xiànjíshì","County City","<U+83B1><U+829C><U+5E02>","Láiwú"
1688,49,"CHN","China",23,"Shandong",251,"Liaocheng",1688,"Chiping","Xiàn","County",NA,NA
1689,49,"CHN","China",23,"Shandong",251,"Liaocheng",1689,"Dong'e","Xiàn","County","<U+4E1C><U+963F><U+53BF>","Dong'e"
1690,49,"CHN","China",23,"Shandong",251,"Liaocheng",1690,"Gaotang","Xiàn","County","<U+9AD8><U+5510><U+53BF>","Gaotáng"
1691,49,"CHN","China",23,"Shandong",251,"Liaocheng",1691,"Guan","Xiàn","County","<U+51A0><U+53BF>","Guàn"
1692,49,"CHN","China",23,"Shandong",251,"Liaocheng",1692,"Liaocheng","Xiàn","County",NA,NA
1693,49,"CHN","China",23,"Shandong",251,"Liaocheng",1693,"Linqing","Xiànjíshì","County City","<U+4E34><U+6E05><U+5E02>","Línqing"
1694,49,"CHN","China",23,"Shandong",251,"Liaocheng",1694,"Shenxian","Xiàn","County",NA,NA
1695,49,"CHN","China",23,"Shandong",251,"Liaocheng",1695,"Yanggu","Xiàn","County","<U+9633><U+8C37><U+53BF>","Yánggu"
1696,49,"CHN","China",23,"Shandong",252,"Linyi",1696,"Cangshan","Xiàn","County","<U+82CD><U+5C71><U+53BF>","Cangshan"
1697,49,"CHN","China",23,"Shandong",252,"Linyi",1697,"Feixian","Xiàn","County",NA,NA
1698,49,"CHN","China",23,"Shandong",252,"Linyi",1698,"Junan","Xiàn","County","<U+8392><U+5357><U+53BF>","Junán"
1699,49,"CHN","China",23,"Shandong",252,"Linyi",1699,"Linshu","Xiàn","County","<U+4E34><U+6CAD><U+53BF>","Línshù"
1700,49,"CHN","China",23,"Shandong",252,"Linyi",1700,"Linyi","Xiàn","County","<U+4E34><U+9091><U+53BF>","Línyì"
1701,49,"CHN","China",23,"Shandong",252,"Linyi",1701,"Mengyin","Xiàn","County","<U+8499><U+9634><U+53BF>","Méngyin"
1702,49,"CHN","China",23,"Shandong",252,"Linyi",1702,"Pingyi","Xiàn","County","<U+5E73><U+9091><U+53BF>","Píngyì"
1703,49,"CHN","China",23,"Shandong",252,"Linyi",1703,"Tancheng","Xiàn","County",NA,NA
1704,49,"CHN","China",23,"Shandong",252,"Linyi",1704,"Yinan","Xiàn","County","<U+6C82><U+5357><U+53BF>","Yínán"
1705,49,"CHN","China",23,"Shandong",252,"Linyi",1705,"Yishui","Xiàn","County","<U+6C82><U+6C34><U+53BF>","Yíshui"
1706,49,"CHN","China",23,"Shandong",253,"Qingdao",1706,"Huangdao","Xiànjíshì","District","<U+9EC4><U+5C9B><U+533A>","Huángdao"
1707,49,"CHN","China",23,"Shandong",253,"Qingdao",1707,"Jiaonan","Xiànjíshì","County City","<U+80F6><U+5357><U+5E02>","Jiaonán"
1708,49,"CHN","China",23,"Shandong",253,"Qingdao",1708,"Jiaoxian","Xiàn","County",NA,NA
1709,49,"CHN","China",23,"Shandong",253,"Qingdao",1709,"Jimo","Xiànjíshì","County City","<U+5373><U+58A8><U+5E02>","Jìmò"
1710,49,"CHN","China",23,"Shandong",253,"Qingdao",1710,"Laixi","Xiànjíshì","County City","<U+83B1><U+897F><U+5E02>","Láixi"
1711,49,"CHN","China",23,"Shandong",253,"Qingdao",1711,"Laoshan","Shìxiáqu","District","<U+5D02><U+5C71><U+533A>","Láoshan"
1712,49,"CHN","China",23,"Shandong",253,"Qingdao",1712,"Pingdu","Xiànjíshì","County City","<U+5E73><U+5EA6><U+5E02>","Píngdù"
1713,49,"CHN","China",23,"Shandong",254,"Rizhao",1713,"Juxian","Xiàn","County",NA,NA
1714,49,"CHN","China",23,"Shandong",254,"Rizhao",1714,"Rizhao","Xiàn","County",NA,NA
1715,49,"CHN","China",23,"Shandong",254,"Rizhao",1715,"Wulian","Xiàn","County","<U+4E94><U+83B2><U+53BF>","Wulián"
1716,49,"CHN","China",23,"Shandong",255,"Tai'an",1716,"Dongping","Xiàn","County","<U+4E1C><U+5E73><U+53BF>","Dongpíng"
1717,49,"CHN","China",23,"Shandong",255,"Tai'an",1717,"Feicheng","Xiànjíshì","County City","<U+80A5><U+57CE><U+5E02>","Féichéng"
1718,49,"CHN","China",23,"Shandong",255,"Tai'an",1718,"Ningyang","Xiàn","County","<U+5B81><U+9633><U+53BF>","Níngyáng"
1719,49,"CHN","China",23,"Shandong",255,"Tai'an",1719,"Tai An","Xiànjíshì","County City",NA,NA
1720,49,"CHN","China",23,"Shandong",255,"Tai'an",1720,"Xinwen","Xiàn","County",NA,NA
1721,49,"CHN","China",23,"Shandong",256,"Weifang",1721,"Anqiu","Xiànjíshì","County City","<U+5B89><U+4E18><U+5E02>","Anqiu"
1722,49,"CHN","China",23,"Shandong",256,"Weifang",1722,"Changle","Xiàn","County","<U+660C><U+4E50><U+53BF>","Changlè"
1723,49,"CHN","China",23,"Shandong",256,"Weifang",1723,"Changyi","Xiànjíshì","County City","<U+660C><U+9091><U+5E02>","Changyì"
1724,49,"CHN","China",23,"Shandong",256,"Weifang",1724,"Gaomi","Xiànjíshì","County City","<U+9AD8><U+5BC6><U+5E02>","Gaomì"
1725,49,"CHN","China",23,"Shandong",256,"Weifang",1725,"Linqu","Xiàn","County","<U+4E34><U+6710><U+53BF>","Línqú"
1726,49,"CHN","China",23,"Shandong",256,"Weifang",1726,"Qingzhou","Xiànjíshì","County City","<U+9752><U+5DDE><U+5E02>","Qingzhou"
1727,49,"CHN","China",23,"Shandong",256,"Weifang",1727,"Shouguang","Xiànjíshì","County City","<U+5BFF><U+5149><U+5E02>","Shòuguang"
1728,49,"CHN","China",23,"Shandong",256,"Weifang",1728,"Weifang Shì","Xiànjíshì","County City","<U+6F4D><U+574A><U+5E02>","Wéifang"
1729,49,"CHN","China",23,"Shandong",256,"Weifang",1729,"Weifang Xiàn","Xiàn","County","<U+6F4D><U+574A><U+5E02>","Wéifang"
1730,49,"CHN","China",23,"Shandong",256,"Weifang",1730,"Zhucheng","Xiànjíshì","County City","<U+8BF8><U+57CE><U+5E02>","Zhuchéng"
1731,49,"CHN","China",23,"Shandong",257,"Weihai",1731,"Rencheng","Shìxiáqu","District","<U+4EFB><U+57CE><U+533A>","Rènchéng"
1732,49,"CHN","China",23,"Shandong",257,"Weihai",1732,"Rushan","Xiànjíshì","County City","<U+4E73><U+5C71><U+5E02>","Rushan"
1733,49,"CHN","China",23,"Shandong",257,"Weihai",1733,"Weihai","Xiànjíshì","County City",NA,NA
1734,49,"CHN","China",23,"Shandong",257,"Weihai",1734,"Wendeng","Xiàn","County",NA,NA
1735,49,"CHN","China",23,"Shandong",258,"Yantai",1735,"Changdao","Xiàn","County","<U+957F><U+5C9B><U+53BF>","Chángdao"
1736,49,"CHN","China",23,"Shandong",258,"Yantai",1736,"Fushan","Xiàn","County",NA,NA
1737,49,"CHN","China",23,"Shandong",258,"Yantai",1737,"Haiyang","Xiànjíshì","County City","<U+6D77><U+9633><U+5E02>","Haiyáng"
1738,49,"CHN","China",23,"Shandong",258,"Yantai",1738,"Laiyang","Xiànjíshì","County City","<U+83B1><U+9633><U+5E02>","Láiyáng"
1739,49,"CHN","China",23,"Shandong",258,"Yantai",1739,"Laizhou","Xiànjíshì","County City","<U+83B1><U+5DDE><U+5E02>","Láizhou"
1740,49,"CHN","China",23,"Shandong",258,"Yantai",1740,"Longkou","Xiànjíshì","County City","<U+9F99><U+53E3><U+5E02>","Lóngkou"
1741,49,"CHN","China",23,"Shandong",258,"Yantai",1741,"Muping","Shìxiáqu","District","<U+725F><U+5E73><U+533A>","Mùpíng"
1742,49,"CHN","China",23,"Shandong",258,"Yantai",1742,"Penglai","Xiànjíshì","County City","<U+84EC><U+83B1><U+5E02>","Pénglái"
1743,49,"CHN","China",23,"Shandong",258,"Yantai",1743,"Qixia","Xiànjíshì","County City","<U+6816><U+971E><U+5E02>","Qixiá"
1744,49,"CHN","China",23,"Shandong",258,"Yantai",1744,"Yantai","Xiànjíshì","County City",NA,NA
1745,49,"CHN","China",23,"Shandong",258,"Yantai",1745,"Zhaoyuan","Xiànjíshì","County City","<U+62DB><U+8FDC><U+5E02>","Zhaoyuan"
1746,49,"CHN","China",23,"Shandong",259,"Zaozhuang",1746,"Teng","Xiàn","County",NA,NA
1747,49,"CHN","China",23,"Shandong",259,"Zaozhuang",1747,"Zhaozhuang","Shìxiáqu","District",NA,NA
1748,49,"CHN","China",23,"Shandong",259,"Zaozhuang",1748,"Zouxian","Xiàn","County",NA,NA
1749,49,"CHN","China",23,"Shandong",260,"Zibo",1749,"Gaoqing","Xiàn","County","<U+9AD8><U+9752><U+53BF>","Gaoqing"
1750,49,"CHN","China",23,"Shandong",260,"Zibo",1750,"Huantai","Xiàn","County","<U+6853><U+53F0><U+53BF>","Huántái"
1751,49,"CHN","China",23,"Shandong",260,"Zibo",1751,"Yiyuan","Xiàn","County","<U+6C82><U+6E90><U+53BF>","Yíyuán"
1752,49,"CHN","China",23,"Shandong",260,"Zibo",1752,"Zibo","Xiànjíshì","County City",NA,NA
1753,49,"CHN","China",23,"Shandong",260,"Zibo",1753,"Zichuan","Shìxiáqu","District","<U+6DC4><U+5DDD><U+533A>","Zichuan"
1754,49,"CHN","China",24,"Shanghai",261,"Shanghai",1754,"Baoshan","Shìxiáqu","District","<U+5B9D><U+5C71><U+533A>","Baoshan"
1755,49,"CHN","China",24,"Shanghai",261,"Shanghai",1755,"Chongming","Xiàn","County","<U+5D07><U+660E><U+53BF>","Chóngmíng"
1756,49,"CHN","China",24,"Shanghai",261,"Shanghai",1756,"Fengxian","Shìxiáqu","District","<U+5949><U+8D24><U+533A>",NA
1757,49,"CHN","China",24,"Shanghai",261,"Shanghai",1757,"Jiading","Shìxiáqu","District","<U+5609><U+5B9A><U+533A>","Jiadìng"
1758,49,"CHN","China",24,"Shanghai",261,"Shanghai",1758,"Jinshan","Shìxiáqu","District","<U+91D1><U+5C71><U+533A>","Jinshan"
1759,49,"CHN","China",24,"Shanghai",261,"Shanghai",1759,"Minhang","Shìxiáqu","District","<U+95F5><U+884C><U+533A>","Minháng"
1760,49,"CHN","China",24,"Shanghai",261,"Shanghai",1760,"Nanhui","Shìxiáqu","District","<U+5357><U+6C47><U+533A>","Nánhuì"
1761,49,"CHN","China",24,"Shanghai",261,"Shanghai",1761,"Pudong","Shìxiáqu","District","<U+6D66><U+4E1C><U+65B0><U+533A>","Pudong Xin"
1762,49,"CHN","China",24,"Shanghai",261,"Shanghai",1762,"Qingpu","Shìxiáqu","District","<U+9752><U+6D66><U+533A>","Qingpu"
1763,49,"CHN","China",24,"Shanghai",261,"Shanghai",1763,"Shanghai","Shìxiáqu","District","<U+4E0A><U+6D77><U+5E02><U+533A>","Shanghai Proper"
1764,49,"CHN","China",24,"Shanghai",261,"Shanghai",1764,"Shengsi","Xiàn","County","<U+5D4A><U+6CD7><U+53BF>","Shèngsì"
1765,49,"CHN","China",24,"Shanghai",261,"Shanghai",1765,"Songjiang","Shìxiáqu","District","<U+677E><U+6C5F><U+533A>","Jinshan"
1766,49,"CHN","China",25,"Shanxi",262,"Changzhi",1766,"Changzhi Shì","Xiànjíshì","County City","<U+957F><U+6CBB><U+53BF>","Chángzhì"
1767,49,"CHN","China",25,"Shanxi",262,"Changzhi",1767,"Changzhi Xiàn","Xiàn","County","<U+957F><U+6CBB><U+53BF>","Chángzhì"
1768,49,"CHN","China",25,"Shanxi",262,"Changzhi",1768,"Huguan","Xiàn","County","<U+58F6><U+5173><U+53BF>","Húguan"
1769,49,"CHN","China",25,"Shanxi",262,"Changzhi",1769,"Licheng","Xiàn","County","<U+9ECE><U+57CE><U+53BF>","Líchéng"
1770,49,"CHN","China",25,"Shanxi",262,"Changzhi",1770,"Lucheng","Xiànjíshì","County City","<U+6F5E><U+57CE><U+5E02>","Lùchéng"
1771,49,"CHN","China",25,"Shanxi",262,"Changzhi",1771,"Pingshun","Xiàn","County","<U+5E73><U+987A><U+53BF>","Píngshùn"
1772,49,"CHN","China",25,"Shanxi",262,"Changzhi",1772,"Qin","Xiàn","County","<U+6C81><U+53BF>","Qìn"
1773,49,"CHN","China",25,"Shanxi",262,"Changzhi",1773,"Qinyuan","Xiàn","County","<U+6C81><U+6E90><U+53BF>","Qìnyuán"
1774,49,"CHN","China",25,"Shanxi",262,"Changzhi",1774,"Tunliu","Xiàn","County","<U+5C6F><U+7559><U+53BF>","Túnliú"
1775,49,"CHN","China",25,"Shanxi",262,"Changzhi",1775,"Wuxiang","Xiàn","County","<U+6B66><U+4E61><U+53BF>","Wuxiang"
1776,49,"CHN","China",25,"Shanxi",262,"Changzhi",1776,"Xiangyuan","Xiàn","County","<U+8944><U+57A3><U+53BF>","Xiangyuán"
1777,49,"CHN","China",25,"Shanxi",262,"Changzhi",1777,"Zhangzi","Xiàn","County","<U+957F><U+5B50><U+53BF>","Zhangzi"
1778,49,"CHN","China",25,"Shanxi",263,"Datong",1778,"Datong Shì","Xiànjíshì","County City","<U+5927><U+540C><U+5E02>","Dàtóng"
1779,49,"CHN","China",25,"Shanxi",263,"Datong",1779,"Datong Xiàn","Xiàn","County","<U+5927><U+540C><U+53BF>","Dàtóng"
1780,49,"CHN","China",25,"Shanxi",263,"Datong",1780,"Guangling","Xiàn","County","<U+5E7F><U+7075><U+53BF>","Guanglíng"
1781,49,"CHN","China",25,"Shanxi",263,"Datong",1781,"Hunyuan","Xiàn","County","<U+6D51><U+6E90><U+53BF>","Húnyuán"
1782,49,"CHN","China",25,"Shanxi",263,"Datong",1782,"Lingqiu","Xiàn","County","<U+7075><U+4E18><U+53BF>","Língqiu"
1783,49,"CHN","China",25,"Shanxi",263,"Datong",1783,"Tianzhen","Xiàn","County","<U+5929><U+9547><U+53BF>","Tianzhèn"
1784,49,"CHN","China",25,"Shanxi",263,"Datong",1784,"Yanggao","Xiàn","County","<U+9633><U+9AD8><U+53BF>","Yánggao"
1785,49,"CHN","China",25,"Shanxi",263,"Datong",1785,"Zuoyun","Xiàn","County","<U+5DE6><U+4E91><U+53BF>","Zuoyún"
1786,49,"CHN","China",25,"Shanxi",264,"Jincheng",1786,"Gaoping","Xiànjíshì","County City","<U+9AD8><U+5E73><U+5E02>","Gaopíng"
1787,49,"CHN","China",25,"Shanxi",264,"Jincheng",1787,"Jingcheng","Xiàn","County",NA,NA
1788,49,"CHN","China",25,"Shanxi",264,"Jincheng",1788,"Lingchuan","Xiàn","County","<U+9675><U+5DDD><U+53BF>","Língchuan"
1789,49,"CHN","China",25,"Shanxi",264,"Jincheng",1789,"Qinshui","Xiàn","County","<U+6C81><U+6C34><U+53BF>","Qìnshui"
1790,49,"CHN","China",25,"Shanxi",264,"Jincheng",1790,"Yangcheng","Xiàn","County","<U+9633><U+57CE><U+53BF>","Yángchéng"
1791,49,"CHN","China",25,"Shanxi",265,"Jinzhong",1791,"Heshun","Xiàn","County","<U+548C><U+987A><U+53BF>","Héshùn"
1792,49,"CHN","China",25,"Shanxi",265,"Jinzhong",1792,"Jiexiu","Xiànjíshì","County City","<U+4ECB><U+4F11><U+5E02>","Jièxiu"
1793,49,"CHN","China",25,"Shanxi",265,"Jinzhong",1793,"Lingshi","Xiàn","County","<U+7075><U+77F3><U+53BF>","Língshí"
1794,49,"CHN","China",25,"Shanxi",265,"Jinzhong",1794,"Pingyao","Xiàn","County","<U+5E73><U+9065><U+53BF>","Píngyáo"
1795,49,"CHN","China",25,"Shanxi",265,"Jinzhong",1795,"Qi","Xiàn","County","<U+7941><U+53BF>","Qí"
1796,49,"CHN","China",25,"Shanxi",265,"Jinzhong",1796,"Shouyang","Xiàn","County",NA,NA
1797,49,"CHN","China",25,"Shanxi",265,"Jinzhong",1797,"Taigu","Xiàn","County","<U+592A><U+8C37><U+53BF>","Tàigu"
1798,49,"CHN","China",25,"Shanxi",265,"Jinzhong",1798,"Xiyang","Xiàn","County","<U+6614><U+9633><U+53BF>","Xiyáng"
1799,49,"CHN","China",25,"Shanxi",265,"Jinzhong",1799,"Yuci","Shìxiáqu","District",NA,"Yúcì"
1800,49,"CHN","China",25,"Shanxi",265,"Jinzhong",1800,"Yushe","Xiàn","County","<U+6986><U+793E><U+53BF>","Yúshè"
1801,49,"CHN","China",25,"Shanxi",265,"Jinzhong",1801,"Zuoquan","Xiàn","County","<U+5DE6><U+6743><U+53BF>","Zuoquán"
1802,49,"CHN","China",25,"Shanxi",266,"Linfen",1802,"Anze","Xiàn","County","<U+5B89><U+6CFD><U+53BF>","Anzé"
1803,49,"CHN","China",25,"Shanxi",266,"Linfen",1803,"Daning","Xiàn","County","<U+5927><U+5B81><U+53BF>","Dàníng"
1804,49,"CHN","China",25,"Shanxi",266,"Linfen",1804,"Fenxi","Xiàn","County","<U+6C7E><U+897F><U+53BF>","Fénxi"
1805,49,"CHN","China",25,"Shanxi",266,"Linfen",1805,"Fushan","Xiàn","County","<U+6D6E><U+5C71><U+53BF>","Fúshan"
1806,49,"CHN","China",25,"Shanxi",266,"Linfen",1806,"Gu","Xiàn","County","<U+53E4><U+53BF>","Gu"
1807,49,"CHN","China",25,"Shanxi",266,"Linfen",1807,"Hongtong","Xiàn","County","<U+6D2A><U+6D1E><U+53BF>","Hóngtòng"
1808,49,"CHN","China",25,"Shanxi",266,"Linfen",1808,"Houma","Xiànjíshì","County City","<U+4FAF><U+9A6C><U+5E02>","Hóuma"
1809,49,"CHN","China",25,"Shanxi",266,"Linfen",1809,"Huo","Xiàn","County",NA,NA
1810,49,"CHN","China",25,"Shanxi",266,"Linfen",1810,"Ji","Xiàn","County","<U+5409><U+53BF>","Jí"
1811,49,"CHN","China",25,"Shanxi",266,"Linfen",1811,"Linfen","Xiàn","County",NA,NA
1812,49,"CHN","China",25,"Shanxi",266,"Linfen",1812,"Pu","Xiàn","County","<U+84B2><U+53BF>","Pú"
1813,49,"CHN","China",25,"Shanxi",266,"Linfen",1813,"Quwo","Xiàn","County","<U+66F2><U+6C83><U+53BF>","Quwò"
1814,49,"CHN","China",25,"Shanxi",266,"Linfen",1814,"Xiangfen","Xiàn","County","<U+8944><U+6C7E><U+53BF>","Xiangfén"
1815,49,"CHN","China",25,"Shanxi",266,"Linfen",1815,"Xiangning","Xiàn","County","<U+4E61><U+5B81><U+53BF>","Xiangníng"
1816,49,"CHN","China",25,"Shanxi",266,"Linfen",1816,"Xi","Xiàn","County","<U+96B0><U+53BF>","Xí"
1817,49,"CHN","China",25,"Shanxi",266,"Linfen",1817,"Yicheng","Xiàn","County","<U+7FFC><U+57CE><U+53BF>","Yìchéng"
1818,49,"CHN","China",25,"Shanxi",266,"Linfen",1818,"Yonghe","Xiàn","County",NA,NA
1819,49,"CHN","China",25,"Shanxi",267,"Luliang",1819,"Fangshan","Xiàn","County","<U+65B9><U+5C71><U+53BF>","Fangshan"
1820,49,"CHN","China",25,"Shanxi",267,"Luliang",1820,"Fenyang","Xiànjíshì","County City","<U+6C7E><U+9633><U+5E02>","Fényáng"
1821,49,"CHN","China",25,"Shanxi",267,"Luliang",1821,"Jiaocheng","Xiàn","County","<U+4EA4><U+57CE><U+53BF>","Jiaochéng"
1822,49,"CHN","China",25,"Shanxi",267,"Luliang",1822,"Jiaokou","Xiàn","County","<U+4EA4><U+53E3><U+53BF>","Jiaokou"
1823,49,"CHN","China",25,"Shanxi",267,"Luliang",1823,"Lan","Xiàn","County","<U+5C9A><U+53BF>","Lán"
1824,49,"CHN","China",25,"Shanxi",267,"Luliang",1824,"Lin","Xiàn","County","<U+4E34><U+53BF>","Lín"
1825,49,"CHN","China",25,"Shanxi",267,"Luliang",1825,"Lishi","Shìxiáqu","District","<U+79BB><U+77F3><U+533A>","Líshí"
1826,49,"CHN","China",25,"Shanxi",267,"Luliang",1826,"Liulin","Xiàn","County","<U+67F3><U+6797><U+53BF>","Liulín"
1827,49,"CHN","China",25,"Shanxi",267,"Luliang",1827,"Shilou","Xiàn","County","<U+77F3><U+697C><U+53BF>","Shílóu"
1828,49,"CHN","China",25,"Shanxi",267,"Luliang",1828,"Wenshui","Xiàn","County","<U+6587><U+6C34><U+53BF>","Wénshui"
1829,49,"CHN","China",25,"Shanxi",267,"Luliang",1829,"Xiaoyi","Xiànjíshì","County City","<U+5B5D><U+4E49><U+5E02>","Xiàoyì"
1830,49,"CHN","China",25,"Shanxi",267,"Luliang",1830,"Xing","Xiàn","County","<U+5174><U+53BF>","Xing"
1831,49,"CHN","China",25,"Shanxi",267,"Luliang",1831,"Zhongyang","Xiàn","County","<U+4E2D><U+9633><U+53BF>","Zhongyáng"
1832,49,"CHN","China",25,"Shanxi",268,"Shuozhou",1832,"Huairen","Xiàn","County","<U+6000><U+4EC1><U+53BF>","Huáirén"
1833,49,"CHN","China",25,"Shanxi",268,"Shuozhou",1833,"Pinglu","Xiàn","County","<U+5E73><U+9646><U+53BF>","Pínglù"
1834,49,"CHN","China",25,"Shanxi",268,"Shuozhou",1834,"Shanyin","Xiàn","County","<U+5C71><U+9634><U+53BF>","Shanyin"
1835,49,"CHN","China",25,"Shanxi",268,"Shuozhou",1835,"Shuo","Xiàn","County",NA,NA
1836,49,"CHN","China",25,"Shanxi",268,"Shuozhou",1836,"Ying","Xiàn","County","<U+5E94><U+53BF>","Yìng"
1837,49,"CHN","China",25,"Shanxi",268,"Shuozhou",1837,"Youyu","Xiàn","County","<U+53F3><U+7389><U+53BF>","Yòuyù"
1838,49,"CHN","China",25,"Shanxi",269,"Taiyuan",1838,"Gujiao","Xiànjíshì","County City","<U+53E4><U+4EA4><U+5E02>","Gujiao"
1839,49,"CHN","China",25,"Shanxi",269,"Taiyuan",1839,"Loufan","Xiàn","County","<U+5A04><U+70E6><U+53BF>","Lóufán"
1840,49,"CHN","China",25,"Shanxi",269,"Taiyuan",1840,"Qingxu","Xiàn","County","<U+6E05><U+5F90><U+53BF>","Qingxú"
1841,49,"CHN","China",25,"Shanxi",269,"Taiyuan",1841,"Taiyuan","Shìxiáqu","District",NA,NA
1842,49,"CHN","China",25,"Shanxi",269,"Taiyuan",1842,"Yangqu","Xiàn","County","<U+9633><U+66F2><U+53BF>","Yángqu"
1843,49,"CHN","China",25,"Shanxi",270,"Xinzhou",1843,"Baode","Xiàn","County","<U+4FDD><U+5FB7><U+53BF>","Baodé"
1844,49,"CHN","China",25,"Shanxi",270,"Xinzhou",1844,"Dai","Xiàn","County","<U+4EE3><U+53BF>","Dài"
1845,49,"CHN","China",25,"Shanxi",270,"Xinzhou",1845,"Dingxiang","Xiàn","County","<U+5B9A><U+8944><U+53BF>","Dìngxiang"
1846,49,"CHN","China",25,"Shanxi",270,"Xinzhou",1846,"Fanshi","Xiàn","County","<U+7E41><U+5CD9><U+53BF>","Fánshì"
1847,49,"CHN","China",25,"Shanxi",270,"Xinzhou",1847,"Hequ","Xiàn","County","<U+6CB3><U+66F2><U+53BF>","Héqu"
1848,49,"CHN","China",25,"Shanxi",270,"Xinzhou",1848,"Jingle","Xiàn","County","<U+9759><U+4E50><U+53BF>","Jìnglè"
1849,49,"CHN","China",25,"Shanxi",270,"Xinzhou",1849,"Kelan","Xiàn","County","<U+5CA2><U+5C9A><U+53BF>","Kelán"
1850,49,"CHN","China",25,"Shanxi",270,"Xinzhou",1850,"Ningwu","Xiàn","County","<U+5B81><U+6B66><U+53BF>","Níngwu"
1851,49,"CHN","China",25,"Shanxi",270,"Xinzhou",1851,"Pianguan","Xiàn","County","<U+504F><U+5173><U+53BF>","Pianguan"
1852,49,"CHN","China",25,"Shanxi",270,"Xinzhou",1852,"Shenchi","Xiàn","County","<U+795E><U+6C60><U+53BF>","Shénchí"
1853,49,"CHN","China",25,"Shanxi",270,"Xinzhou",1853,"Wutai","Xiàn","County","<U+4E94><U+53F0><U+53BF>","Wutái"
1854,49,"CHN","China",25,"Shanxi",270,"Xinzhou",1854,"Wuzhai","Xiàn","County","<U+4E94><U+5BE8><U+53BF>","Wuzhài"
1855,49,"CHN","China",25,"Shanxi",270,"Xinzhou",1855,"Xinfu","Shìxiáqu","District","<U+5FFB><U+5E9C><U+533A>","Xinfu"
1856,49,"CHN","China",25,"Shanxi",270,"Xinzhou",1856,"Yuanping","Xiànjíshì","County City","<U+539F><U+5E73><U+5E02>","Yuánpíng"
1857,49,"CHN","China",25,"Shanxi",271,"Yangquan",1857,"Pingding","Xiàn","County","<U+5E73><U+5B9A><U+53BF>","Píngdìng"
1858,49,"CHN","China",25,"Shanxi",271,"Yangquan",1858,"Yangquan","Xiànjíshì","County City",NA,NA
1859,49,"CHN","China",25,"Shanxi",271,"Yangquan",1859,"Yu","Xiàn","County","<U+76C2><U+53BF>","Yù"
1860,49,"CHN","China",25,"Shanxi",272,"Yuncheng",1860,"Hejin","Xiànjíshì","County City","<U+6CB3><U+6D25><U+5E02>","Héjin"
1861,49,"CHN","China",25,"Shanxi",272,"Yuncheng",1861,"Jiang","Xiàn","County","<U+7EDB><U+53BF>","Jiàng"
1862,49,"CHN","China",25,"Shanxi",272,"Yuncheng",1862,"Jishan","Xiàn","County","<U+7A37><U+5C71><U+53BF>","Jìshan"
1863,49,"CHN","China",25,"Shanxi",272,"Yuncheng",1863,"Linyi","Xiàn","County","<U+4E34><U+7317><U+53BF>","Línyi"
1864,49,"CHN","China",25,"Shanxi",272,"Yuncheng",1864,"Pinglu","Xiàn","County","<U+5E73><U+9646><U+53BF>","Pínglù"
1865,49,"CHN","China",25,"Shanxi",272,"Yuncheng",1865,"Ruicheng","Xiàn","County","<U+82AE><U+57CE><U+53BF>","Ruìchéng"
1866,49,"CHN","China",25,"Shanxi",272,"Yuncheng",1866,"Wanrong","Xiàn","County","<U+4E07><U+8363><U+53BF>","Wànróng"
1867,49,"CHN","China",25,"Shanxi",272,"Yuncheng",1867,"Wenxi","Xiàn","County","<U+95FB><U+559C><U+53BF>","Wénxi"
1868,49,"CHN","China",25,"Shanxi",272,"Yuncheng",1868,"Xia","Xiàn","County","<U+590F><U+53BF>","Xià"
1869,49,"CHN","China",25,"Shanxi",272,"Yuncheng",1869,"Xinjiang","Xiàn","County","<U+65B0><U+7EDB><U+53BF>","Xinjiàng"
1870,49,"CHN","China",25,"Shanxi",272,"Yuncheng",1870,"Yongji","Xiànjíshì","County City","<U+6C38><U+6D4E><U+5E02>","Yongjì"
1871,49,"CHN","China",25,"Shanxi",272,"Yuncheng",1871,"Yuanqu","Xiàn","County","<U+57A3><U+66F2><U+53BF>","Yuánqu"
1872,49,"CHN","China",25,"Shanxi",272,"Yuncheng",1872,"Yuncheng","Xiàn","County",NA,NA
1873,49,"CHN","China",26,"Sichuan",273,"Bazhong",1873,"Bazhong","Shìxiáqu","District","<U+5DF4><U+4E2D><U+533A>","Bazhong"
1874,49,"CHN","China",26,"Sichuan",273,"Bazhong",1874,"Nanjiang","Xiàn","County",NA,NA
1875,49,"CHN","China",26,"Sichuan",273,"Bazhong",1875,"Pingchang","Xiàn","County","<U+5E73><U+660C><U+53BF>","Píngchang"
1876,49,"CHN","China",26,"Sichuan",273,"Bazhong",1876,"Tongjiang","Xiàn","County","<U+901A><U+6C5F><U+53BF>","Tongjiang"
1877,49,"CHN","China",26,"Sichuan",274,"Chengdu",1877,"Chengdu Qu","Shìxiáqu","District","<U+6210><U+90FD><U+533A>","Chéngdu"
1878,49,"CHN","China",26,"Sichuan",274,"Chengdu",1878,"Chengdu Shì","Xiànjíshì","County City","<U+6210><U+90FD><U+5E02>","Chéngdu"
1879,49,"CHN","China",26,"Sichuan",274,"Chengdu",1879,"Chongqing","Xiàn","County",NA,NA
1880,49,"CHN","China",26,"Sichuan",274,"Chengdu",1880,"Dayi","Xiàn","County","<U+5927><U+9091><U+53BF>","Dàyì"
1881,49,"CHN","China",26,"Sichuan",274,"Chengdu",1881,"Jintang","Xiàn","County","<U+91D1><U+5802><U+53BF>","Jintáng"
1882,49,"CHN","China",26,"Sichuan",274,"Chengdu",1882,"Peng","Xiàn","County",NA,NA
1883,49,"CHN","China",26,"Sichuan",274,"Chengdu",1883,"Pi","Xiàn","County","<U+90EB><U+53BF>","Pí"
1884,49,"CHN","China",26,"Sichuan",274,"Chengdu",1884,"Pujiang","Xiàn","County","<U+84B2><U+6C5F><U+53BF>","Pújiang"
1885,49,"CHN","China",26,"Sichuan",274,"Chengdu",1885,"Qionglai","Xiànjíshì","County City","<U+909B><U+5D03><U+5E02>","Qiónglái"
1886,49,"CHN","China",26,"Sichuan",274,"Chengdu",1886,"Shuangliu","Xiàn","County","<U+53CC><U+6D41><U+53BF>","Shuangliú"
1887,49,"CHN","China",26,"Sichuan",274,"Chengdu",1887,"Wenjiang","Shìxiáqu","District","<U+6E29><U+6C5F><U+533A>","Wenjiang"
1888,49,"CHN","China",26,"Sichuan",274,"Chengdu",1888,"Xindu","Shìxiáqu","District","<U+65B0><U+90FD><U+533A>","Xindu"
1889,49,"CHN","China",26,"Sichuan",274,"Chengdu",1889,"Xinjin","Xiàn","County","<U+65B0><U+6D25><U+53BF>","Xinjin"
1890,49,"CHN","China",26,"Sichuan",275,"Dazhou",1890,"Da","Xiàn","County","<U+8FBE><U+53BF>","Dá"
1891,49,"CHN","China",26,"Sichuan",275,"Dazhou",1891,"Daxian","Xiànjíshì","County City",NA,NA
1892,49,"CHN","China",26,"Sichuan",275,"Dazhou",1892,"Dazhu","Xiàn","County","<U+5927><U+7AF9><U+53BF>","Dàzhú"
1893,49,"CHN","China",26,"Sichuan",275,"Dazhou",1893,"Kaijiang","Xiàn","County","<U+5F00><U+6C5F><U+53BF>","Kaijiang"
1894,49,"CHN","China",26,"Sichuan",275,"Dazhou",1894,"Qu","Xiàn","County","<U+6E20><U+53BF>","Qú"
1895,49,"CHN","China",26,"Sichuan",275,"Dazhou",1895,"Wanyuan","Xiànjíshì","County City","<U+4E07><U+6E90><U+5E02>","Wànyuán"
1896,49,"CHN","China",26,"Sichuan",275,"Dazhou",1896,"Xuanhan","Xiàn","County","<U+5BA3><U+6C49><U+53BF>","Xuanhàn"
1897,49,"CHN","China",26,"Sichuan",276,"Deyang",1897,"Deyang","Xiàn","County",NA,NA
1898,49,"CHN","China",26,"Sichuan",276,"Deyang",1898,"Guanghan","Xiànjíshì","County City","<U+5E7F><U+6C49><U+5E02>","Guanghàn"
1899,49,"CHN","China",26,"Sichuan",276,"Deyang",1899,"Mianzhu","Xiànjíshì","County City","<U+7EF5><U+7AF9><U+5E02>","Miánzhú"
1900,49,"CHN","China",26,"Sichuan",276,"Deyang",1900,"Shifang","Xiànjíshì","County City","<U+4EC0><U+90A1><U+5E02>","Shífang"
1901,49,"CHN","China",26,"Sichuan",276,"Deyang",1901,"Zhongjiang","Xiàn","County","<U+4E2D><U+6C5F><U+53BF>","Zhongjiang"
1902,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1902,"Baiyü","Xiàn","County","<U+767D><U+7389><U+53BF>","Báiyù"
1903,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1903,"Batang","Xiàn","County","<U+5DF4><U+5858><U+53BF>","Batáng"
1904,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1904,"Dêgê","Xiàn","County","<U+5FB7><U+683C><U+53BF>","Dégé"
1905,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1905,"Dêrong","Xiàn","County","<U+5F97><U+8363><U+53BF>","Déróng"
1906,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1906,"Danba","Xiàn","County","<U+4E39><U+5DF4><U+53BF>","Danba"
1907,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1907,"Daocheng","Xiàn","County","<U+7A3B><U+57CE><U+53BF>","Dàochéng"
1908,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1908,"Dawu","Xiàn","County","<U+9053><U+5B5A><U+53BF>","Dàofú"
1909,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1909,"Garzê","Xiàn","County","<U+7518><U+5B5C><U+53BF>","Ganzi"
1910,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1910,"Jiulong","Xiàn","County","<U+4E5D><U+9F99><U+53BF>","Jiulóng"
1911,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1911,"Kangding","Xiàn","County","<U+5EB7><U+5B9A><U+53BF>","Kangdìng"
1912,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1912,"Litang","Xiàn","County","<U+7406><U+5858><U+53BF>","Litáng"
1913,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1913,"Luding","Xiàn","County","<U+6CF8><U+5B9A><U+53BF>","Lúdìng"
1914,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1914,"Luhuo","Xiàn","County","<U+7089><U+970D><U+53BF>","Lúhuò"
1915,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1915,"Sêrtar","Xiàn","County","<U+8272><U+8FBE><U+53BF>","Sèdá"
1916,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1916,"Sêrxü","Xiàn","County","<U+77F3><U+6E20><U+53BF>","Shíqú"
1917,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1917,"Xiangcheng","Xiàn","County","<U+4E61><U+57CE><U+53BF>","Xiangchéng"
1918,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1918,"Xinlong","Xiàn","County","<U+65B0><U+9F99><U+53BF>","Xinlóng"
1919,49,"CHN","China",26,"Sichuan",277,"Garzê Tibetan",1919,"Yajiang","Xiàn","County","<U+96C5><U+6C5F><U+53BF>","Yajiang"
1920,49,"CHN","China",26,"Sichuan",278,"Guang'an",1920,"Guang'an","Shìxiáqu","District","<U+5E7F><U+5B89><U+533A>","Guang'an"
1921,49,"CHN","China",26,"Sichuan",278,"Guang'an",1921,"Huayun","Xiàn","County",NA,NA
1922,49,"CHN","China",26,"Sichuan",278,"Guang'an",1922,"Linshui","Xiàn","County","<U+90BB><U+6C34><U+53BF>","Línshui"
1923,49,"CHN","China",26,"Sichuan",278,"Guang'an",1923,"Wusheng","Xiàn","County","<U+6B66><U+80DC><U+53BF>","Wusheng"
1924,49,"CHN","China",26,"Sichuan",278,"Guang'an",1924,"Yuechi","Xiàn","County","<U+5CB3><U+6C60><U+53BF>","Yuèchí"
1925,49,"CHN","China",26,"Sichuan",279,"Guangyuan",1925,"Cangxi","Xiàn","County","<U+82CD><U+6EAA><U+53BF>","Cangxi"
1926,49,"CHN","China",26,"Sichuan",279,"Guangyuan",1926,"Chaotian","Shìxiáqu","District","<U+671D><U+5929><U+533A>","Cháotian"
1927,49,"CHN","China",26,"Sichuan",279,"Guangyuan",1927,"Jiange","Xiàn","County","<U+5251><U+9601><U+53BF>","Jiàngé"
1928,49,"CHN","China",26,"Sichuan",279,"Guangyuan",1928,"Qingchuan","Xiàn","County","<U+9752><U+5DDD><U+53BF>","Qingchuan"
1929,49,"CHN","China",26,"Sichuan",279,"Guangyuan",1929,"Shizhong","Shìxiáqu","District","<U+5E02><U+4E2D><U+533A>","Shìzhong"
1930,49,"CHN","China",26,"Sichuan",279,"Guangyuan",1930,"Wangcang","Xiàn","County","<U+65FA><U+82CD><U+53BF>","Wàngcang"
1931,49,"CHN","China",26,"Sichuan",279,"Guangyuan",1931,"Yuanba","Shìxiáqu","District","<U+5143><U+575D><U+533A>","Yuánbà"
1932,49,"CHN","China",26,"Sichuan",280,"Leshan",1932,"Ebian Yi","Zìzhìxiàn","Autonomous County","<U+5CE8><U+8FB9><U+5F5D><U+65CF><U+81EA><U+6CBB><U+53BF>","Ébian Yízú"
1933,49,"CHN","China",26,"Sichuan",280,"Leshan",1933,"Emeishan","Xiànjíshì","County City","<U+5CE8><U+7709><U+5C71><U+5E02>","Éméishan"
1934,49,"CHN","China",26,"Sichuan",280,"Leshan",1934,"Jiajiang","Xiàn","County","<U+5939><U+6C5F><U+53BF>","Jiajiang"
1935,49,"CHN","China",26,"Sichuan",280,"Leshan",1935,"Jinkouhe","Shìxiáqu","District","<U+91D1><U+53E3><U+6CB3><U+533A>","Jinkouhé"
1936,49,"CHN","China",26,"Sichuan",280,"Leshan",1936,"Mabian Yi","Zìzhìxiàn","Autonomous County","<U+9A6C><U+8FB9><U+5F5D><U+65CF><U+81EA><U+6CBB><U+53BF>","Mabian Yízú"
1937,49,"CHN","China",26,"Sichuan",280,"Leshan",1937,"Muchuan","Xiàn","County","<U+6C90><U+5DDD><U+53BF>","Mùchuan"
1938,49,"CHN","China",26,"Sichuan",280,"Leshan",1938,"Qianwei","Xiàn","County","<U+728D><U+4E3A><U+53BF>","Qiánwéi"
1939,49,"CHN","China",26,"Sichuan",280,"Leshan",1939,"Shawan","Shìxiáqu","District","<U+6C99><U+6E7E><U+533A>","Shawan"
1940,49,"CHN","China",26,"Sichuan",280,"Leshan",1940,"Shizhong","Shìxiáqu","District","<U+5E02><U+4E2D><U+533A>","Shìzhong"
1941,49,"CHN","China",26,"Sichuan",280,"Leshan",1941,"Wutongqiao","Shìxiáqu","District","<U+4E94><U+901A><U+6865><U+533A>","Wutongqiáo"
1942,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1942,"Butuo","Xiàn","County","<U+5E03><U+62D6><U+53BF>","Bùtuo"
1943,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1943,"Dechang","Xiàn","County","<U+5FB7><U+660C><U+53BF>","Déchang"
1944,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1944,"Ganluo","Xiàn","County","<U+7518><U+6D1B><U+53BF>","Ganluò"
1945,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1945,"Huidong","Xiàn","County","<U+4F1A><U+4E1C><U+53BF>","Huìdong"
1946,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1946,"Huili","Xiàn","County","<U+4F1A><U+7406><U+53BF>","Huìli"
1947,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1947,"Jingyang","Shìxiáqu","District","<U+65CC><U+9633><U+533A>","Jingyáng"
1948,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1948,"Leibo","Xiàn","County","<U+96F7><U+6CE2><U+53BF>","Léibo"
1949,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1949,"Meigu","Xiàn","County","<U+7F8E><U+59D1><U+53BF>","Meigu"
1950,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1950,"Mianning","Xiàn","County","<U+5195><U+5B81><U+53BF>","Mianníng"
1951,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1951,"Muli Tibetan","Zìzhìxiàn","Autonomous County","<U+6728><U+91CC><U+85CF><U+65CF><U+81EA><U+6CBB><U+53BF>","Mùli Zàngzú"
1952,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1952,"Ningnan","Xiàn","County","<U+5B81><U+5357><U+53BF>","Níngnán"
1953,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1953,"Puge","Xiàn","County","<U+666E><U+683C><U+53BF>","Pugé"
1954,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1954,"Xichang","Xiànjíshì","County City","<U+897F><U+660C><U+5E02>","Xichang"
1955,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1955,"Xide","Xiàn","County","<U+559C><U+5FB7><U+53BF>","Xidé"
1956,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1956,"Yanyuan","Xiàn","County","<U+76D0><U+6E90><U+53BF>","Yányuán"
1957,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1957,"Yuexi","Xiàn","County","<U+8D8A><U+897F><U+53BF>","Yuèxi"
1958,49,"CHN","China",26,"Sichuan",281,"Liangshan Yi",1958,"Zhaojue","Xiàn","County","<U+662D><U+89C9><U+53BF>","Zhaojué"
1959,49,"CHN","China",26,"Sichuan",282,"Luzhou",1959,"Gulin","Xiàn","County","<U+53E4><U+853A><U+53BF>","Gulìn"
1960,49,"CHN","China",26,"Sichuan",282,"Luzhou",1960,"Hejiang","Xiàn","County","<U+5408><U+6C5F><U+53BF>","Héjiang"
1961,49,"CHN","China",26,"Sichuan",282,"Luzhou",1961,"Lu","Xiàn","County","<U+6CF8><U+53BF>","Lú"
1962,49,"CHN","China",26,"Sichuan",282,"Luzhou",1962,"Luzhou","Xiànjíshì","County City","<U+6CF8><U+5DDE><U+5E02>","Lúzhou"
1963,49,"CHN","China",26,"Sichuan",282,"Luzhou",1963,"Nàxi","Shìxiáqu","District","<U+7EB3><U+6EAA><U+533A>",NA
1964,49,"CHN","China",26,"Sichuan",282,"Luzhou",1964,"Xuyong","Xiàn","County","<U+53D9><U+6C38><U+53BF>","Xùyong"
1965,49,"CHN","China",26,"Sichuan",283,"Meishan",1965,"Danleng","Xiàn","County","<U+4E39><U+68F1><U+53BF>","Danléng"
1966,49,"CHN","China",26,"Sichuan",283,"Meishan",1966,"Hongya","Xiàn","County","<U+6D2A><U+96C5><U+53BF>","Hóngya"
1967,49,"CHN","China",26,"Sichuan",283,"Meishan",1967,"Jingyan","Xiàn","County","<U+4E95><U+7814><U+53BF>","Jingyán"
1968,49,"CHN","China",26,"Sichuan",283,"Meishan",1968,"Meishan","Xiàn","County",NA,NA
1969,49,"CHN","China",26,"Sichuan",283,"Meishan",1969,"Pengshan","Xiàn","County","<U+5F6D><U+5C71><U+53BF>","Péngshan"
1970,49,"CHN","China",26,"Sichuan",283,"Meishan",1970,"Qingshen","Xiàn","County","<U+9752><U+795E><U+53BF>","Qingshén"
1971,49,"CHN","China",26,"Sichuan",283,"Meishan",1971,"Renshou","Xiàn","County","<U+4EC1><U+5BFF><U+53BF>","Rénshòu"
1972,49,"CHN","China",26,"Sichuan",284,"Mianyang",1972,"An","Xiàn","County","<U+5B89><U+53BF>","An"
1973,49,"CHN","China",26,"Sichuan",284,"Mianyang",1973,"Beichuan Qiang","Zìzhìxiàn","Autonomous County","<U+5317><U+5DDD><U+7F8C><U+65CF><U+81EA><U+6CBB><U+53BF>","Beichuan Qiangzú"
1974,49,"CHN","China",26,"Sichuan",284,"Mianyang",1974,"Jiangyou","Xiànjíshì","County City","<U+6C5F><U+6CB9><U+5E02>","Jiangyóu"
1975,49,"CHN","China",26,"Sichuan",284,"Mianyang",1975,"Mianyang","Xiànjíshì","County City",NA,NA
1976,49,"CHN","China",26,"Sichuan",284,"Mianyang",1976,"Pingwu","Xiàn","County","<U+5E73><U+6B66><U+53BF>","Píngwu"
1977,49,"CHN","China",26,"Sichuan",284,"Mianyang",1977,"Santai","Xiàn","County","<U+4E09><U+53F0><U+53BF>","Santái"
1978,49,"CHN","China",26,"Sichuan",284,"Mianyang",1978,"Yanting","Xiàn","County","<U+76D0><U+4EAD><U+53BF>","Yántíng"
1979,49,"CHN","China",26,"Sichuan",284,"Mianyang",1979,"Zitong","Xiàn","County","<U+6893><U+6F7C><U+53BF>","Zitóng"
1980,49,"CHN","China",26,"Sichuan",285,"Nanchong",1980,"Langzhong","Xiànjíshì","County City","<U+9606><U+4E2D><U+5E02>","Lángzhong"
1981,49,"CHN","China",26,"Sichuan",285,"Nanchong",1981,"Nanbu","Xiàn","County","<U+5357><U+90E8><U+53BF>","Nánbù"
1982,49,"CHN","China",26,"Sichuan",285,"Nanchong",1982,"Nanchong Shì","Xiànjíshì","County City","<U+5357><U+5145><U+5E02>","Nánchong"
1983,49,"CHN","China",26,"Sichuan",285,"Nanchong",1983,"Nanchong Xiàn","Xiàn","County","<U+5357><U+5145><U+5E02>","Nánchong"
1984,49,"CHN","China",26,"Sichuan",285,"Nanchong",1984,"Peng'an","Xiàn","County","<U+84EC><U+5B89><U+53BF>","Péng'an"
1985,49,"CHN","China",26,"Sichuan",285,"Nanchong",1985,"Xichong","Xiàn","County","<U+897F><U+5145><U+53BF>","Xichong"
1986,49,"CHN","China",26,"Sichuan",285,"Nanchong",1986,"Yilong","Xiàn","County","<U+4EEA><U+9647><U+53BF>","Yílong"
1987,49,"CHN","China",26,"Sichuan",285,"Nanchong",1987,"Yingshan","Xiàn","County","<U+8425><U+5C71><U+53BF>","Yíngshan"
1988,49,"CHN","China",26,"Sichuan",286,"Neijiang]]",1988,"Longchang","Xiàn","County","<U+9686><U+660C><U+53BF>","Lóngchang"
1989,49,"CHN","China",26,"Sichuan",286,"Neijiang]]",1989,"Zizhong","Xiàn","County","<U+8D44><U+4E2D><U+53BF>","Zizhong"
1990,49,"CHN","China",26,"Sichuan",287,"Neijiang",1990,"Dongxing","Shìxiáqu","District","<U+4E1C><U+5174><U+533A>","Dongxing"
1991,49,"CHN","China",26,"Sichuan",287,"Neijiang",1991,"Shizhong","Shìxiáqu","District","<U+5E02><U+4E2D><U+533A>","Shìzhong"
1992,49,"CHN","China",26,"Sichuan",287,"Neijiang",1992,"Weiyuan","Xiàn","County","<U+5A01><U+8FDC><U+53BF>","Weiyuan"
1993,49,"CHN","China",26,"Sichuan",288,"Ngawa Tibetan and Qiang",1993,"Aba","Xiàn","County",NA,NA
1994,49,"CHN","China",26,"Sichuan",288,"Ngawa Tibetan and Qiang",1994,"Barkam","Xiàn","County","<U+9A6C><U+5C14><U+5EB7><U+53BF>","Ma'erkang"
1995,49,"CHN","China",26,"Sichuan",288,"Ngawa Tibetan and Qiang",1995,"Guan","Xiàn","County",NA,NA
1996,49,"CHN","China",26,"Sichuan",288,"Ngawa Tibetan and Qiang",1996,"Heishui","Xiàn","County","<U+9ED1><U+6C34><U+53BF>","Heishui"
1997,49,"CHN","China",26,"Sichuan",288,"Ngawa Tibetan and Qiang",1997,"Hongyuan","Xiàn","County","<U+7EA2><U+539F><U+53BF>","Hóngyuán"
1998,49,"CHN","China",26,"Sichuan",288,"Ngawa Tibetan and Qiang",1998,"Jinchuan","Xiàn","County","<U+91D1><U+5DDD><U+53BF>","Jinchuan"
1999,49,"CHN","China",26,"Sichuan",288,"Ngawa Tibetan and Qiang",1999,"Li","Xiàn","County","<U+7406><U+53BF>","Li"
2000,49,"CHN","China",26,"Sichuan",288,"Ngawa Tibetan and Qiang",2000,"Mao","Xiàn","County","<U+8302><U+53BF>","Mào"
2001,49,"CHN","China",26,"Sichuan",288,"Ngawa Tibetan and Qiang",2001,"Nanping","Xiàn","County",NA,NA
2002,49,"CHN","China",26,"Sichuan",288,"Ngawa Tibetan and Qiang",2002,"Songpan","Xiàn","County","<U+677E><U+6F58><U+53BF>","Songpan"
2003,49,"CHN","China",26,"Sichuan",288,"Ngawa Tibetan and Qiang",2003,"Wenchuan","Xiàn","County","<U+6C76><U+5DDD><U+53BF>","Wènchuan"
2004,49,"CHN","China",26,"Sichuan",288,"Ngawa Tibetan and Qiang",2004,"Xiaojin","Xiàn","County","<U+5C0F><U+91D1><U+53BF>","Xiaojin"
2005,49,"CHN","China",26,"Sichuan",288,"Ngawa Tibetan and Qiang",2005,"Zamtang","Xiàn","County","<U+58E4><U+5858><U+53BF>","Rangtáng"
2006,49,"CHN","China",26,"Sichuan",288,"Ngawa Tibetan and Qiang",2006,"Zoigê","Xiàn","County","<U+82E5><U+5C14><U+76D6><U+53BF>","Ruò'ergài"
2007,49,"CHN","China",26,"Sichuan",289,"Panzhihua",2007,"East","Shìxiáqu","District","<U+4E1C><U+533A>","Dong"
2008,49,"CHN","China",26,"Sichuan",289,"Panzhihua",2008,"Miyi","Xiàn","County","<U+7C73><U+6613><U+53BF>","Miyì"
2009,49,"CHN","China",26,"Sichuan",289,"Panzhihua",2009,"Yanbian","Xiàn","County","<U+76D0><U+8FB9><U+53BF>","Yánbian"
2010,49,"CHN","China",26,"Sichuan",290,"Suining",2010,"Pengxi","Xiàn","County","<U+84EC><U+6EAA><U+53BF>","Péngxi"
2011,49,"CHN","China",26,"Sichuan",290,"Suining",2011,"Shehong","Xiàn","County","<U+5C04><U+6D2A><U+53BF>","Shèhóng"
2012,49,"CHN","China",26,"Sichuan",290,"Suining",2012,"Suining","Xiàn","County",NA,NA
2013,49,"CHN","China",26,"Sichuan",291,"Ya'an",2013,"Baoxing","Xiàn","County","<U+5B9D><U+5174><U+53BF>","Baoxing"
2014,49,"CHN","China",26,"Sichuan",291,"Ya'an",2014,"Hanyuan","Xiàn","County","<U+6C49><U+6E90><U+53BF>","Hànyuán"
2015,49,"CHN","China",26,"Sichuan",291,"Ya'an",2015,"Lushan","Xiàn","County","<U+82A6><U+5C71><U+53BF>","Lúshan"
2016,49,"CHN","China",26,"Sichuan",291,"Ya'an",2016,"Mingshan","Xiàn","County","<U+540D><U+5C71><U+53BF>","Míngshan"
2017,49,"CHN","China",26,"Sichuan",291,"Ya'an",2017,"Shimian","Xiàn","County","<U+77F3><U+68C9><U+53BF>","Shímián"
2018,49,"CHN","China",26,"Sichuan",291,"Ya'an",2018,"Tianquan","Xiàn","County","<U+5929><U+5168><U+53BF>","Tianquán"
2019,49,"CHN","China",26,"Sichuan",291,"Ya'an",2019,"Ya An","Xiànjíshì","County City","<U+96C5><U+5B89><U+5E02>","Ya'an"
2020,49,"CHN","China",26,"Sichuan",291,"Ya'an",2020,"Yingjing","Xiàn","County","<U+8365><U+7ECF><U+53BF>","Yíngjing"
2021,49,"CHN","China",26,"Sichuan",292,"Yibin",2021,"Changning","Xiàn","County","<U+957F><U+5B81><U+53BF>","Chángníng"
2022,49,"CHN","China",26,"Sichuan",292,"Yibin",2022,"Gao","Xiàn","County","<U+9AD8><U+53BF>","Gao"
2023,49,"CHN","China",26,"Sichuan",292,"Yibin",2023,"Gong","Xiàn","County","<U+73D9><U+53BF>","Gong"
2024,49,"CHN","China",26,"Sichuan",292,"Yibin",2024,"Jiang'an","Xiàn","County","<U+6C5F><U+5B89><U+53BF>","Jiang'an"
2025,49,"CHN","China",26,"Sichuan",292,"Yibin",2025,"Junlian","Xiàn","County","<U+7B60><U+8FDE><U+53BF>","Junlián"
2026,49,"CHN","China",26,"Sichuan",292,"Yibin",2026,"Nanxi","Xiàn","County","<U+5357><U+6EAA><U+53BF>","Nánxi"
2027,49,"CHN","China",26,"Sichuan",292,"Yibin",2027,"Pingshan","Xiàn","County","<U+5C4F><U+5C71><U+53BF>","Píngshan"
2028,49,"CHN","China",26,"Sichuan",292,"Yibin",2028,"Xingwen","Xiàn","County","<U+5174><U+6587><U+53BF>","Xingwén"
2029,49,"CHN","China",26,"Sichuan",292,"Yibin",2029,"Yibin Shì","Xiànjíshì","County City","<U+5B9C><U+5BBE><U+5E02>","Yíbin"
2030,49,"CHN","China",26,"Sichuan",292,"Yibin",2030,"Yibin Xiàn","Xiàn","County","<U+5B9C><U+5BBE><U+53BF>","Yíbin"
2031,49,"CHN","China",26,"Sichuan",293,"Zigong",2031,"Fushun","Xiàn","County","<U+5BCC><U+987A><U+53BF>","Fùshùn"
2032,49,"CHN","China",26,"Sichuan",293,"Zigong",2032,"Rong","Xiàn","County","<U+8363><U+53BF>","Róng"
2033,49,"CHN","China",26,"Sichuan",293,"Zigong",2033,"Zigong","Shìxiáqu","District",NA,NA
2034,49,"CHN","China",26,"Sichuan",294,"Ziyang",2034,"Anju","Shìxiáqu","District","<U+5B89><U+5C45><U+533A>","Anju"
2035,49,"CHN","China",26,"Sichuan",294,"Ziyang",2035,"Jiangyang","Shìxiáqu","District","<U+6C5F><U+9633><U+533A>","Jiangyáng"
2036,49,"CHN","China",26,"Sichuan",294,"Ziyang",2036,"Lezhi","Xiàn","County","<U+4E50><U+81F3><U+53BF>","Lèzhì"
2037,49,"CHN","China",26,"Sichuan",294,"Ziyang",2037,"Ziyang","Xiàn","County",NA,NA
2038,49,"CHN","China",27,"Tianjin",295,"Tianjin",2038,"Baodi","Shìxiáqu","District","<U+5B9D><U+577B><U+533A>","Baodi"
2039,49,"CHN","China",27,"Tianjin",295,"Tianjin",2039,"Beichen","Shìxiáqu","District","<U+5317><U+8FB0><U+533A>","Beichén"
2040,49,"CHN","China",27,"Tianjin",295,"Tianjin",2040,"Dagang","Shìxiáqu","District","<U+5927><U+6E2F><U+533A>","Dàgang"
2041,49,"CHN","China",27,"Tianjin",295,"Tianjin",2041,"Dongli","Shìxiáqu","District","<U+4E1C><U+4E3D><U+533A>","Donglì"
2042,49,"CHN","China",27,"Tianjin",295,"Tianjin",2042,"Hangu","Shìxiáqu","District","<U+6C49><U+6CBD><U+533A>","Hàngu"
2043,49,"CHN","China",27,"Tianjin",295,"Tianjin",2043,"Jinghai","Xiàn","County","<U+9759><U+6D77><U+53BF>","Jìnghai"
2044,49,"CHN","China",27,"Tianjin",295,"Tianjin",2044,"Ji","Xiàn","County","<U+84DF><U+53BF>","Jì"
2045,49,"CHN","China",27,"Tianjin",295,"Tianjin",2045,"Ninghe","Xiàn","County","<U+5B81><U+6CB3><U+53BF>","Nínghé"
2046,49,"CHN","China",27,"Tianjin",295,"Tianjin",2046,"Tanggu","Shìxiáqu","District","<U+5858><U+6CBD><U+533A>","Tánggu"
2047,49,"CHN","China",27,"Tianjin",295,"Tianjin",2047,"Tianjin","Shìxiáqu","District",NA,NA
2048,49,"CHN","China",27,"Tianjin",295,"Tianjin",2048,"Wuqing","Shìxiáqu","District","<U+6B66><U+6E05><U+533A>","Wuqing"
2049,49,"CHN","China",27,"Tianjin",295,"Tianjin",2049,"Xiqing","Shìxiáqu","District","<U+897F><U+9752><U+533A>","Xiqing"
2050,49,"CHN","China",28,"Xinjiang Uygur",296,"Aksu",2050,"Aksu","Xiànjíshì","County City","<U+963F><U+514B><U+82CF><U+5E02>","Akèsu"
2051,49,"CHN","China",28,"Xinjiang Uygur",296,"Aksu",2051,"Awat","Xiàn","County","<U+963F><U+74E6><U+63D0><U+53BF>","Awatí"
2052,49,"CHN","China",28,"Xinjiang Uygur",296,"Aksu",2052,"Baicheng","Xiàn","County","<U+62DC><U+57CE><U+53BF>","Bàichéng"
2053,49,"CHN","China",28,"Xinjiang Uygur",296,"Aksu",2053,"Kalpin","Xiàn","County","<U+67EF><U+576A><U+53BF>","Kepíng"
2054,49,"CHN","China",28,"Xinjiang Uygur",296,"Aksu",2054,"Kuqa","Zizhiqu","County",NA,NA
2055,49,"CHN","China",28,"Xinjiang Uygur",296,"Aksu",2055,"Shayar","Xiàn","County","<U+6C99><U+96C5><U+53BF>","Shaya"
2056,49,"CHN","China",28,"Xinjiang Uygur",296,"Aksu",2056,"Toksu","Xiàn","County","<U+65B0><U+548C><U+53BF>","Xinhé"
2057,49,"CHN","China",28,"Xinjiang Uygur",296,"Aksu",2057,"Wensu","Xiàn","County","<U+6E29><U+5BBF><U+53BF>","Wensù"
2058,49,"CHN","China",28,"Xinjiang Uygur",296,"Aksu",2058,"Wushi","Zizhiqu","County",NA,NA
2059,49,"CHN","China",28,"Xinjiang Uygur",297,"Altay",2059,"Altay","Xiànjíshì","County City","<U+963F><U+52D2><U+6CF0><U+5E02>","Alètài"
2060,49,"CHN","China",28,"Xinjiang Uygur",297,"Altay",2060,"Burqin","Xiàn","County","<U+5E03><U+5C14><U+6D25><U+53BF>","Bù'erjin"
2061,49,"CHN","China",28,"Xinjiang Uygur",297,"Altay",2061,"Fuhai","Xiàn","County","<U+798F><U+6D77><U+53BF>","Fúhai"
2062,49,"CHN","China",28,"Xinjiang Uygur",297,"Altay",2062,"Fuyun","Xiàn","County","<U+5BCC><U+8574><U+53BF>","Fùyùn"
2063,49,"CHN","China",28,"Xinjiang Uygur",297,"Altay",2063,"Habahe","Xiàn","County","<U+54C8><U+5DF4><U+6CB3><U+53BF>","Habahé"
2064,49,"CHN","China",28,"Xinjiang Uygur",297,"Altay",2064,"Jeminay","Xiàn","County","<U+5409><U+6728><U+4E43><U+53BF>","Jímùnai"
2065,49,"CHN","China",28,"Xinjiang Uygur",297,"Altay",2065,"Qinggil","Xiàn","County","<U+6E05><U+6CB3><U+53BF>","Qinghé"
2066,49,"CHN","China",28,"Xinjiang Uygur",298,"Ürümqi",2066,"Ürümqi Shì","Xiànjíshì","County City","<U+4E4C><U+9C81><U+6728><U+9F50><U+5E02>","Wulumùqí"
2067,49,"CHN","China",28,"Xinjiang Uygur",298,"Ürümqi",2067,"Ürümqi Xiàn","Xiàn","County","<U+4E4C><U+9C81><U+6728><U+9F50><U+53BF>","Wulumùqí"
2068,49,"CHN","China",28,"Xinjiang Uygur",299,"Börtala Mongol",2068,"Bole","Xiànjíshì","County City","<U+535A><U+4E50><U+5E02>","Bólè"
2069,49,"CHN","China",28,"Xinjiang Uygur",299,"Börtala Mongol",2069,"Jinghe","Xiàn","County","<U+7CBE><U+6CB3><U+53BF>","Jinghé"
2070,49,"CHN","China",28,"Xinjiang Uygur",299,"Börtala Mongol",2070,"Wenquan","Xiàn","County","<U+6E29><U+6CC9><U+53BF>","Wenquán"
2071,49,"CHN","China",28,"Xinjiang Uygur",300,"Bayin'gholin Mongol",2071,"Bohu","Xiàn","County","<U+535A><U+6E56><U+53BF>","Bóhú"
2072,49,"CHN","China",28,"Xinjiang Uygur",300,"Bayin'gholin Mongol",2072,"Hejing","Xiàn","County","<U+548C><U+9759><U+53BF>","Héjìng"
2073,49,"CHN","China",28,"Xinjiang Uygur",300,"Bayin'gholin Mongol",2073,"Hoxud","Xiàn","County","<U+548C><U+7855><U+53BF>","Héshuò"
2074,49,"CHN","China",28,"Xinjiang Uygur",300,"Bayin'gholin Mongol",2074,"Korla","Zizhiqu","County",NA,NA
2075,49,"CHN","China",28,"Xinjiang Uygur",300,"Bayin'gholin Mongol",2075,"Luntai","Xiàn","County","<U+8F6E><U+53F0><U+53BF>","Lúntái"
2076,49,"CHN","China",28,"Xinjiang Uygur",300,"Bayin'gholin Mongol",2076,"Qiemo","Xiàn","County","<U+4E14><U+672B><U+53BF>","Qiemò"
2077,49,"CHN","China",28,"Xinjiang Uygur",300,"Bayin'gholin Mongol",2077,"Ruoqiang","Xiàn","County","<U+82E5><U+7F8C><U+53BF>","Ruòqiang"
2078,49,"CHN","China",28,"Xinjiang Uygur",300,"Bayin'gholin Mongol",2078,"Yanqi Hui","Zìzhìxiàn","Autonomous County","<U+7109><U+8006><U+56DE><U+65CF><U+81EA><U+6CBB><U+53BF>","Yanqí Huízú"
2079,49,"CHN","China",28,"Xinjiang Uygur",300,"Bayin'gholin Mongol",2079,"Yuli","Xiàn","County","<U+5C09><U+7281><U+53BF>","Yùlí"
2080,49,"CHN","China",28,"Xinjiang Uygur",301,"Changji Hui",2080,"Changji","Xiànjíshì","County City","<U+660C><U+5409><U+5E02>","Changjí"
2081,49,"CHN","China",28,"Xinjiang Uygur",301,"Changji Hui",2081,"Fukang","Xiànjíshì","County City","<U+961C><U+5EB7><U+5E02>","Fùkang"
2082,49,"CHN","China",28,"Xinjiang Uygur",301,"Changji Hui",2082,"Hutubi","Xiàn","County","<U+547C><U+56FE><U+58C1><U+53BF>","Hutúbì"
2083,49,"CHN","China",28,"Xinjiang Uygur",301,"Changji Hui",2083,"Jimsar","Xiàn","County","<U+5409><U+6728><U+8428><U+5C14><U+53BF>","Jímùsà'er"
2084,49,"CHN","China",28,"Xinjiang Uygur",301,"Changji Hui",2084,"Manas","Xiàn","County","<U+739B><U+7EB3><U+65AF><U+53BF>","Manàsi"
2085,49,"CHN","China",28,"Xinjiang Uygur",301,"Changji Hui",2085,"Miquan","Xiànjíshì","County City","<U+7C73><U+6CC9><U+5E02>","Miquán"
2086,49,"CHN","China",28,"Xinjiang Uygur",301,"Changji Hui",2086,"Mori","Zìzhìxiàn","Autonomous County","<U+6728><U+5792><U+54C8><U+8428><U+514B><U+81EA><U+6CBB><U+53BF>","Mùlei Hasàkè"
2087,49,"CHN","China",28,"Xinjiang Uygur",301,"Changji Hui",2087,"Qitai","Xiàn","County","<U+5947><U+53F0><U+53BF>","Qítái"
2088,49,"CHN","China",28,"Xinjiang Uygur",302,"Hami",2088,"Barkol Kazakh","Zìzhìxiàn","Autonomous County","<U+5DF4><U+91CC><U+5764><U+54C8><U+8428><U+514B><U+81EA><U+6CBB><U+53BF><U+963F><U+56FE><U+4EC0><U+5E02>","Balikun Hasàkè"
2089,49,"CHN","China",28,"Xinjiang Uygur",302,"Hami",2089,"Hami","Xiànjíshì","County City",NA,"Hamì"
2090,49,"CHN","China",28,"Xinjiang Uygur",302,"Hami",2090,"Yiwu","Xiàn","County","<U+4F0A><U+543E><U+53BF>","Yiwú"
2091,49,"CHN","China",28,"Xinjiang Uygur",303,"Ili Kazakh",2091,"Gongliu","Xiàn","County","<U+5DE9><U+7559><U+53BF>","Gongliú"
2092,49,"CHN","China",28,"Xinjiang Uygur",303,"Ili Kazakh",2092,"Huocheng","Xiàn","County","<U+970D><U+57CE><U+53BF>","Huòchéng"
2093,49,"CHN","China",28,"Xinjiang Uygur",303,"Ili Kazakh",2093,"Kuitun","Xiànjíshì","County City","<U+594E><U+5C6F><U+5E02>","Kuítún"
2094,49,"CHN","China",28,"Xinjiang Uygur",303,"Ili Kazakh",2094,"Nilka","Xiàn","County","<U+5C3C><U+52D2><U+514B><U+53BF>","Nílèkè"
2095,49,"CHN","China",28,"Xinjiang Uygur",303,"Ili Kazakh",2095,"Qapqal Xibe","Zìzhìxiàn","Autonomous County","<U+5BDF><U+5E03><U+67E5><U+5C14><U+9521><U+4F2F><U+81EA><U+6CBB><U+53BF>","Chábùchá'er Xibó"
2096,49,"CHN","China",28,"Xinjiang Uygur",303,"Ili Kazakh",2096,"Tekes","Xiàn","County","<U+7279><U+514B><U+65AF><U+53BF>","Tèkèsi"
2097,49,"CHN","China",28,"Xinjiang Uygur",303,"Ili Kazakh",2097,"Xinyuan","Xiàn","County","<U+65B0><U+6E90><U+53BF>","Xinyuán"
2098,49,"CHN","China",28,"Xinjiang Uygur",303,"Ili Kazakh",2098,"Yining Shì","Xiànjíshì","County City","<U+4F0A><U+5B81><U+53BF>","Yiníng"
2099,49,"CHN","China",28,"Xinjiang Uygur",303,"Ili Kazakh",2099,"Yining","Xiàn","County","<U+4F0A><U+5B81><U+53BF>","Yiníng"
2100,49,"CHN","China",28,"Xinjiang Uygur",303,"Ili Kazakh",2100,"Zhaosu","Xiàn","County","<U+662D><U+82CF><U+53BF>","Zhaosu"
2101,49,"CHN","China",28,"Xinjiang Uygur",304,"Karamay",2101,"Dushanzi","Shìxiáqu","District","<U+72EC><U+5C71><U+5B50><U+533A>","Dúshanzi"
2102,49,"CHN","China",28,"Xinjiang Uygur",304,"Karamay",2102,"Karamay","Shìxiáqu","District","<U+514B><U+62C9><U+739B><U+4F9D><U+533A>","Kèlamayi"
2103,49,"CHN","China",28,"Xinjiang Uygur",305,"Kashgar",2103,"Bachu","Xiàn","County","<U+5DF4><U+695A><U+53BF>","Maralwexi"
2104,49,"CHN","China",28,"Xinjiang Uygur",305,"Kashgar",2104,"Jiashi","Xiàn","County","<U+4F3D><U+5E08><U+53BF>","Payzawat"
2105,49,"CHN","China",28,"Xinjiang Uygur",305,"Kashgar",2105,"Kashgar","Xiànjíshì","County City","<U+5580><U+4EC0><U+5E02>","Kashí"
2106,49,"CHN","China",28,"Xinjiang Uygur",305,"Kashgar",2106,"Makit","Xiàn","County","<U+9EA6><U+76D6><U+63D0><U+53BF>","Màigàití"
2107,49,"CHN","China",28,"Xinjiang Uygur",305,"Kashgar",2107,"Poskam","Xiàn","County","<U+6CFD><U+666E><U+53BF>","Zépu"
2108,49,"CHN","China",28,"Xinjiang Uygur",305,"Kashgar",2108,"Shufu","Xiàn","County","<U+758F><U+9644><U+53BF>","Shufù"
2109,49,"CHN","China",28,"Xinjiang Uygur",305,"Kashgar",2109,"Shule","Xiàn","County","<U+758F><U+52D2><U+53BF>","Shulè"
2110,49,"CHN","China",28,"Xinjiang Uygur",305,"Kashgar",2110,"Taxkorgan Tajik","Zìzhìxiàn","Autonomous County","<U+5854><U+4EC0><U+5E93><U+5C14><U+5E72><U+5854><U+5409><U+514B><U+81EA><U+6CBB><U+53BF>","Tashíkù'ergan Tajíkè"
2111,49,"CHN","China",28,"Xinjiang Uygur",305,"Kashgar",2111,"Yecheng","Xiàn","County","<U+53F6><U+57CE><U+53BF>","Yèchéng|Kargilik"
2112,49,"CHN","China",28,"Xinjiang Uygur",305,"Kashgar",2112,"Yengisar","Xiàn","County","<U+82F1><U+5409><U+6C99><U+53BF>","Yingjísha"
2113,49,"CHN","China",28,"Xinjiang Uygur",305,"Kashgar",2113,"Yopurga","Xiàn","County","<U+5CB3><U+666E><U+6E56><U+53BF>","Yuèpuhú"
2114,49,"CHN","China",28,"Xinjiang Uygur",305,"Kashgar",2114,"Zepu","Xiàn","County","<U+6CFD><U+666E><U+53BF>","Poskam"
2115,49,"CHN","China",28,"Xinjiang Uygur",306,"Khotan",2115,"Khotan","Xiànjíshì","County City","<U+548C><U+7530><U+5E02>","Hétián"
2116,49,"CHN","China",28,"Xinjiang Uygur",306,"Khotan",2116,"Lop","Xiàn","County","<U+6D1B><U+6D66><U+53BF>","Luòpu"
2117,49,"CHN","China",28,"Xinjiang Uygur",306,"Khotan",2117,"Minfeng","Xiàn","County","<U+6C11><U+4E30><U+53BF>","Mínfeng"
2118,49,"CHN","China",28,"Xinjiang Uygur",306,"Khotan",2118,"Moyu","Zizhiqu","County",NA,NA
2119,49,"CHN","China",28,"Xinjiang Uygur",306,"Khotan",2119,"Pishan","Xiàn","County","<U+76AE><U+5C71><U+53BF>","Píshan"
2120,49,"CHN","China",28,"Xinjiang Uygur",306,"Khotan",2120,"Qira","Zizhiqu","County",NA,NA
2121,49,"CHN","China",28,"Xinjiang Uygur",306,"Khotan",2121,"Yutian","Zizhiqu","County",NA,NA
2122,49,"CHN","China",28,"Xinjiang Uygur",307,"Kizilsu Kirghiz",2122,"Akqi","Xiàn","County","<U+963F><U+5408><U+5947><U+53BF>","Ahéqí"
2123,49,"CHN","China",28,"Xinjiang Uygur",307,"Kizilsu Kirghiz",2123,"Akto","Xiàn","County","<U+963F><U+514B><U+9676><U+53BF>","Akètáo"
2124,49,"CHN","China",28,"Xinjiang Uygur",307,"Kizilsu Kirghiz",2124,"Artux","Xiànjíshì","County City","<U+963F><U+56FE><U+4EC0><U+5E02>","Atúshí"
2125,49,"CHN","China",28,"Xinjiang Uygur",307,"Kizilsu Kirghiz",2125,"Wuqia","Zizhiqu","County",NA,NA
2126,49,"CHN","China",28,"Xinjiang Uygur",308,"Shihezi",2126,"Shihezi","Xiànjíshì","County City","<U+77F3><U+6CB3><U+5B50><U+5E02>","Shíhézi"
2127,49,"CHN","China",28,"Xinjiang Uygur",309,"Tacheng",2127,"Emin","Xiàn","County","<U+989D><U+654F><U+53BF>","Émin"
2128,49,"CHN","China",28,"Xinjiang Uygur",309,"Tacheng",2128,"Hoboksar Mongol","Zìzhìxiàn","Autonomous County","<U+548C><U+5E03><U+514B><U+8D5B><U+5C14><U+8499><U+53E4><U+81EA><U+6CBB><U+53BF>","Hébùkèsài'er Menggu"
2129,49,"CHN","China",28,"Xinjiang Uygur",309,"Tacheng",2129,"Shawan","Xiàn","County","<U+6C99><U+6E7E><U+53BF>","Shawan"
2130,49,"CHN","China",28,"Xinjiang Uygur",309,"Tacheng",2130,"Tacheng","Xiànjíshì","County City","<U+5854><U+57CE><U+5E02>","Tachéng"
2131,49,"CHN","China",28,"Xinjiang Uygur",309,"Tacheng",2131,"Toli","Xiàn","County","<U+6258><U+91CC><U+53BF>","Tuoli"
2132,49,"CHN","China",28,"Xinjiang Uygur",309,"Tacheng",2132,"Usu","Zizhiqu","County",NA,NA
2133,49,"CHN","China",28,"Xinjiang Uygur",309,"Tacheng",2133,"Yumin","Xiàn","County","<U+88D5><U+6C11><U+53BF>","Yùmín"
2134,49,"CHN","China",28,"Xinjiang Uygur",310,"Turfan",2134,"Shanshan","Zizhiqu","County",NA,NA
2135,49,"CHN","China",28,"Xinjiang Uygur",310,"Turfan",2135,"Toksun","Xiàn","County","<U+6258><U+514B><U+900A><U+53BF>","Tuokèxùn"
2136,49,"CHN","China",28,"Xinjiang Uygur",310,"Turfan",2136,"Turfan","Xiànjíshì","County City","<U+5410><U+9C81><U+756A><U+5E02>","Tulufan"
2137,49,"CHN","China",29,"Xizang",311,"Chamdo",2137,"Banbar","Xiàn","County","<U+8FB9><U+575D><U+53BF>","Bianbà"
2138,49,"CHN","China",29,"Xizang",311,"Chamdo",2138,"Baxoi","Xiàn","County","<U+516B><U+5BBF><U+53BF>","Basù"
2139,49,"CHN","China",29,"Xizang",311,"Chamdo",2139,"Dêngqên","Xiàn","County","<U+4E01><U+9752><U+53BF>","Dingqing"
2140,49,"CHN","China",29,"Xizang",311,"Chamdo",2140,"Gonjo","Xiàn","County","<U+8D21><U+89C9><U+53BF>","Gòngjué"
2141,49,"CHN","China",29,"Xizang",311,"Chamdo",2141,"Jomdo","Xiàn","County","<U+6C5F><U+8FBE><U+53BF>","Jiangdá"
2142,49,"CHN","China",29,"Xizang",311,"Chamdo",2142,"Lhorong","Xiàn","County","<U+6D1B><U+9686><U+53BF>","Luòlóng"
2143,49,"CHN","China",29,"Xizang",311,"Chamdo",2143,"Markam","Xiàn","County","<U+8292><U+5EB7><U+53BF>","Mángkang"
2144,49,"CHN","China",29,"Xizang",311,"Chamdo",2144,"Qamdo","Xiàn","County","<U+660C><U+90FD><U+53BF>","Changdu"
2145,49,"CHN","China",29,"Xizang",311,"Chamdo",2145,"Riwoqê","Xiàn","County","<U+7C7B><U+4E4C><U+9F50><U+53BF>","Lèiwuqí"
2146,49,"CHN","China",29,"Xizang",311,"Chamdo",2146,"Zhag'yab","Xiàn","County","<U+5BDF><U+96C5><U+53BF>","Cháya"
2147,49,"CHN","China",29,"Xizang",311,"Chamdo",2147,"Zogang","Xiàn","County","<U+5DE6><U+8D21><U+53BF>","Zuogòng"
2148,49,"CHN","China",29,"Xizang",312,"Lhasa",2148,"Dagzê","Xiàn","County","<U+8FBE><U+5B5C><U+53BF>","Dázi"
2149,49,"CHN","China",29,"Xizang",312,"Lhasa",2149,"Damxung","Xiàn","County","<U+5F53><U+96C4><U+53BF>","Dangxióng"
2150,49,"CHN","China",29,"Xizang",312,"Lhasa",2150,"Doilungdêqên","Xiàn","County","<U+5806><U+9F99><U+5FB7><U+5E86><U+53BF>","Duilóngdéqìng"
2151,49,"CHN","China",29,"Xizang",312,"Lhasa",2151,"Lhünzhub","Xiàn","County","<U+6797><U+5468><U+53BF>","Línzhou"
2152,49,"CHN","China",29,"Xizang",312,"Lhasa",2152,"Lhasa","Shìxiáqu","District",NA,NA
2153,49,"CHN","China",29,"Xizang",312,"Lhasa",2153,"Maizhokunggar","Xiàn","County","<U+58A8><U+7AF9><U+5DE5><U+5361><U+53BF>","Mòzhúgongka"
2154,49,"CHN","China",29,"Xizang",312,"Lhasa",2154,"Nyêmo","Xiàn","County","<U+5C3C><U+6728><U+53BF>","Nímù"
2155,49,"CHN","China",29,"Xizang",312,"Lhasa",2155,"Qüxü","Xiàn","County","<U+66F2><U+6C34><U+53BF>","Qushui"
2156,49,"CHN","China",29,"Xizang",313,"Nagchu",2156,"Amdo","Xiàn","County","<U+5B89><U+591A><U+53BF>","Anduo"
2157,49,"CHN","China",29,"Xizang",313,"Nagchu",2157,"Baqên","Xiàn","County","<U+5DF4><U+9752><U+53BF>","Baqing"
2158,49,"CHN","China",29,"Xizang",313,"Nagchu",2158,"Biru","Xiàn","County","<U+6BD4><U+5982><U+53BF>","Birú"
2159,49,"CHN","China",29,"Xizang",313,"Nagchu",2159,"Lhari","Xiàn","County","<U+5609><U+9ECE><U+53BF>","Jialí"
2160,49,"CHN","China",29,"Xizang",313,"Nagchu",2160,"Nagchu","Xiàn","County","<U+90A3><U+66F2><U+53BF>","Nàqu"
2161,49,"CHN","China",29,"Xizang",313,"Nagchu",2161,"Nyainrong","Xiàn","County","<U+8042><U+8363><U+53BF>","Nièróng"
2162,49,"CHN","China",29,"Xizang",313,"Nagchu",2162,"Nyima","Xiàn","County","<U+5C3C><U+739B><U+53BF>","Níma"
2163,49,"CHN","China",29,"Xizang",313,"Nagchu",2163,"Raingoin","Zizhiqu","County",NA,NA
2164,49,"CHN","China",29,"Xizang",313,"Nagchu",2164,"Sog","Xiàn","County","<U+7D22><U+53BF>","Suo"
2165,49,"CHN","China",29,"Xizang",313,"Nagchu",2165,"Xainza","Xiàn","County","<U+7533><U+624E><U+53BF>","Shenzha"
2166,49,"CHN","China",29,"Xizang",314,"Ngari",2166,"Burang","Xiàn","County","<U+666E><U+5170><U+53BF>","Pulán"
2167,49,"CHN","China",29,"Xizang",314,"Ngari",2167,"Coqên","Xiàn","County","<U+63AA><U+52E4><U+53BF>","Cuòqín"
2168,49,"CHN","China",29,"Xizang",314,"Ngari",2168,"Gê'gyai","Xiàn","County","<U+9769><U+5409><U+53BF>","Géjí"
2169,49,"CHN","China",29,"Xizang",314,"Ngari",2169,"Gêrzê","Xiàn","County","<U+6539><U+5219><U+53BF>","Gaizé"
2170,49,"CHN","China",29,"Xizang",314,"Ngari",2170,"Gar","Xiàn","County","<U+5676><U+5C14><U+53BF>","Gá'er"
2171,49,"CHN","China",29,"Xizang",314,"Ngari",2171,"Rutog","Xiàn","County","<U+65E5><U+571F><U+53BF>","Rìtu"
2172,49,"CHN","China",29,"Xizang",314,"Ngari",2172,"Zanda","Xiàn","County","<U+672D><U+8FBE><U+53BF>","Zhádá"
2173,49,"CHN","China",29,"Xizang",315,"Nyingtri",2173,"Bomi","Xiàn","County","<U+6CE2><U+5BC6><U+53BF>","Bomì"
2174,49,"CHN","China",29,"Xizang",315,"Nyingtri",2174,"Gongbo'gyamda","Xiàn","County","<U+5DE5><U+5E03><U+6C5F><U+8FBE><U+53BF>","Gongbùjiangdá"
2175,49,"CHN","China",29,"Xizang",315,"Nyingtri",2175,"Mainling","Xiàn","County","<U+7C73><U+6797><U+53BF>","Milín"
2176,49,"CHN","China",29,"Xizang",315,"Nyingtri",2176,"Nang","Xiàn","County","<U+6717><U+53BF>","Lang"
2177,49,"CHN","China",29,"Xizang",315,"Nyingtri",2177,"Nyingchi","Xiàn","County","<U+6797><U+829D><U+53BF>","Línzhi"
2178,49,"CHN","China",29,"Xizang",315,"Nyingtri",2178,"Zayü","Xiàn","County","<U+5BDF><U+9685><U+53BF>","Cháyú"
2179,49,"CHN","China",29,"Xizang",316,"Shannan",2179,"Comai","Xiàn","County","<U+63AA><U+7F8E><U+53BF>","Cuòmei"
2180,49,"CHN","China",29,"Xizang",316,"Shannan",2180,"Cona","Xiàn","County","<U+9519><U+90A3><U+53BF>","Cuònà"
2181,49,"CHN","China",29,"Xizang",316,"Shannan",2181,"Gonggar","Xiàn","County","<U+8D21><U+560E><U+53BF>","Gòngga"
2182,49,"CHN","China",29,"Xizang",316,"Shannan",2182,"Gyaca","Xiàn","County","<U+52A0><U+67E5><U+53BF>","Jiachá"
2183,49,"CHN","China",29,"Xizang",316,"Shannan",2183,"Lhünzê","Xiàn","County","<U+9686><U+5B50><U+53BF>","Lóngzi"
2184,49,"CHN","China",29,"Xizang",316,"Shannan",2184,"Lhozhag","Xiàn","County","<U+6D1B><U+624E><U+53BF>","Luòzha"
2185,49,"CHN","China",29,"Xizang",316,"Shannan",2185,"Nêdong","Xiàn","County","<U+4E43><U+4E1C><U+53BF>","Naidong"
2186,49,"CHN","China",29,"Xizang",316,"Shannan",2186,"Nagarzê","Xiàn","County","<U+6D6A><U+5361><U+5B50><U+53BF>","Làngkazi"
2187,49,"CHN","China",29,"Xizang",316,"Shannan",2187,"Qonggyai","Xiàn","County","<U+743C><U+7ED3><U+53BF>","Qióngjié"
2188,49,"CHN","China",29,"Xizang",316,"Shannan",2188,"Qusum","Xiàn","County","<U+66F2><U+677E><U+53BF>","Qusong"
2189,49,"CHN","China",29,"Xizang",316,"Shannan",2189,"Sangri","Xiàn","County","<U+6851><U+65E5><U+53BF>","Sangrì"
2190,49,"CHN","China",29,"Xizang",316,"Shannan",2190,"Zhanang","Xiàn","County","<U+624E><U+56CA><U+53BF>","Zhanáng"
2191,49,"CHN","China",29,"Xizang",317,"Shigatse",2191,"Bainang","Xiàn","County","<U+767D><U+6717><U+53BF>","Báilang"
2192,49,"CHN","China",29,"Xizang",317,"Shigatse",2192,"Dinggyê","Xiàn","County","<U+5B9A><U+7ED3><U+53BF>","Dìngjié"
2193,49,"CHN","China",29,"Xizang",317,"Shigatse",2193,"Gamba","Xiàn","County","<U+5C97><U+5DF4><U+53BF>","Gangba"
2194,49,"CHN","China",29,"Xizang",317,"Shigatse",2194,"Gyangzê","Xiàn","County","<U+6C5F><U+5B5C><U+53BF>","Jiangzi"
2195,49,"CHN","China",29,"Xizang",317,"Shigatse",2195,"Gyirong","Xiàn","County","<U+5409><U+9686><U+53BF>","Jílóng"
2196,49,"CHN","China",29,"Xizang",317,"Shigatse",2196,"Kangmar","Xiàn","County","<U+5EB7><U+9A6C><U+53BF>","Kangma"
2197,49,"CHN","China",29,"Xizang",317,"Shigatse",2197,"Lhazê","Xiàn","County","<U+62C9><U+5B5C><U+53BF>","Lazi"
2198,49,"CHN","China",29,"Xizang",317,"Shigatse",2198,"Namling","Xiàn","County","<U+5357><U+6728><U+6797><U+53BF>","Nánmùlín"
2199,49,"CHN","China",29,"Xizang",317,"Shigatse",2199,"Ngamring","Xiàn","County","<U+6602><U+4EC1><U+53BF>","Ángrén"
2200,49,"CHN","China",29,"Xizang",317,"Shigatse",2200,"Nyalam","Xiàn","County","<U+8042><U+62C9><U+6728><U+53BF>","Nièlamù"
2201,49,"CHN","China",29,"Xizang",317,"Shigatse",2201,"Rinbung","Xiàn","County","<U+4EC1><U+5E03><U+53BF>","Rénbù"
2202,49,"CHN","China",29,"Xizang",317,"Shigatse",2202,"Sa'gya","Xiàn","County","<U+8428><U+8FE6><U+53BF>","Sàjia"
2203,49,"CHN","China",29,"Xizang",317,"Shigatse",2203,"Saga","Xiàn","County","<U+8428><U+560E><U+53BF>","Sàga"
2204,49,"CHN","China",29,"Xizang",317,"Shigatse",2204,"Shigatse","Xiànjíshì","County City","<U+65E5><U+5580><U+5219><U+5E02>","Rìkazé"
2205,49,"CHN","China",29,"Xizang",317,"Shigatse",2205,"Tingri","Xiàn","County","<U+5B9A><U+65E5><U+53BF>","Dìngrì"
2206,49,"CHN","China",29,"Xizang",317,"Shigatse",2206,"Xaitongmoin","Xiàn","County","<U+8C22><U+901A><U+95E8><U+53BF>","Xiètongmén"
2207,49,"CHN","China",29,"Xizang",317,"Shigatse",2207,"Yadong","Xiàn","County","<U+4E9A><U+4E1C><U+53BF>","Yàdong"
2208,49,"CHN","China",29,"Xizang",317,"Shigatse",2208,"Zhongba","Xiàn","County","<U+4EF2><U+5DF4><U+53BF>","Zhòngba"
2209,49,"CHN","China",30,"Yunnan",318,"Baoshan",2209,"Baoshan","Xiàn","County",NA,NA
2210,49,"CHN","China",30,"Yunnan",318,"Baoshan",2210,"Changning","Xiàn","County","<U+660C><U+5B81><U+53BF>","Changníng"
2211,49,"CHN","China",30,"Yunnan",318,"Baoshan",2211,"Longling","Xiàn","County","<U+9F99><U+9675><U+53BF>","Lónglíng"
2212,49,"CHN","China",30,"Yunnan",318,"Baoshan",2212,"Shidian","Xiàn","County","<U+65BD><U+7538><U+53BF>","Shidiàn"
2213,49,"CHN","China",30,"Yunnan",318,"Baoshan",2213,"Tengchong","Xiàn","County","<U+817E><U+51B2><U+53BF>","Téngchong"
2214,49,"CHN","China",30,"Yunnan",319,"Chuxiong Yi",2214,"Chuxiong","Xiànjíshì","County City","<U+695A><U+96C4><U+5E02>","Chuxióng"
2215,49,"CHN","China",30,"Yunnan",319,"Chuxiong Yi",2215,"Dayao","Xiàn","County","<U+5927><U+59DA><U+53BF>","Dàyáo"
2216,49,"CHN","China",30,"Yunnan",319,"Chuxiong Yi",2216,"Lufeng","Xiàn","County","<U+7984><U+4E30><U+53BF>","Lùfeng"
2217,49,"CHN","China",30,"Yunnan",319,"Chuxiong Yi",2217,"Mouding","Xiàn","County","<U+725F><U+5B9A><U+53BF>","Móudìng"
2218,49,"CHN","China",30,"Yunnan",319,"Chuxiong Yi",2218,"Nanhua","Xiàn","County","<U+5357><U+534E><U+53BF>","Nánhuá"
2219,49,"CHN","China",30,"Yunnan",319,"Chuxiong Yi",2219,"Shuangbai","Xiàn","County","<U+53CC><U+67CF><U+53BF>","Shuangbai"
2220,49,"CHN","China",30,"Yunnan",319,"Chuxiong Yi",2220,"Wuding","Xiàn","County","<U+6B66><U+5B9A><U+53BF>","Wudìng"
2221,49,"CHN","China",30,"Yunnan",319,"Chuxiong Yi",2221,"Yao'an","Xiàn","County","<U+59DA><U+5B89><U+53BF>","Yáo'an"
2222,49,"CHN","China",30,"Yunnan",319,"Chuxiong Yi",2222,"Yongren","Xiàn","County","<U+6C38><U+4EC1><U+53BF>","Yongrén"
2223,49,"CHN","China",30,"Yunnan",319,"Chuxiong Yi",2223,"Yuanmou","Xiàn","County","<U+5143><U+8C0B><U+53BF>","Yuánmóu"
2224,49,"CHN","China",30,"Yunnan",320,"Dêqên Tibetan",2224,"Dêqên","Xiàn","County","<U+5FB7><U+94A6><U+53BF>","Déqin"
2225,49,"CHN","China",30,"Yunnan",320,"Dêqên Tibetan",2225,"Weixin","Xiàn","County","<U+5A01><U+4FE1><U+53BF>","Weixìn"
2226,49,"CHN","China",30,"Yunnan",320,"Dêqên Tibetan",2226,"Zhongdian","Xiàn","County",NA,NA
2227,49,"CHN","China",30,"Yunnan",321,"Dali Bai",2227,"Binchuan","Xiàn","County","<U+5BBE><U+5DDD><U+53BF>","Binchuan"
2228,49,"CHN","China",30,"Yunnan",321,"Dali Bai",2228,"Dali","Xiànjíshì","County City","<U+5927><U+7406><U+5E02>","Dàli"
2229,49,"CHN","China",30,"Yunnan",321,"Dali Bai",2229,"Eryuan","Xiàn","County","<U+6D31><U+6E90><U+53BF>","Eryuán"
2230,49,"CHN","China",30,"Yunnan",321,"Dali Bai",2230,"Heqing","Xiàn","County","<U+9E64><U+5E86><U+53BF>","Hèqìng"
2231,49,"CHN","China",30,"Yunnan",321,"Dali Bai",2231,"Jianchuan","Xiàn","County","<U+5251><U+5DDD><U+53BF>","Jiànchuan"
2232,49,"CHN","China",30,"Yunnan",321,"Dali Bai",2232,"Midu","Xiàn","County","<U+5F25><U+6E21><U+53BF>","Mídù"
2233,49,"CHN","China",30,"Yunnan",321,"Dali Bai",2233,"Nanjian Yi","Zìzhìxiàn","Autonomous County","<U+5357><U+6DA7><U+5F5D><U+65CF><U+81EA><U+6CBB><U+53BF>","Nánjiàn Yízú"
2234,49,"CHN","China",30,"Yunnan",321,"Dali Bai",2234,"Weishan Yi and Hui","Zìzhìxiàn","Autonomous County","<U+5DCD><U+5C71><U+5F5D><U+65CF><U+5DCD><U+5C71><U+5F5D><U+65CF>","Weishan Yízú Huízú"
2235,49,"CHN","China",30,"Yunnan",321,"Dali Bai",2235,"Xiangyun","Xiàn","County","<U+7965><U+4E91><U+53BF>","Xiángyún"
2236,49,"CHN","China",30,"Yunnan",321,"Dali Bai",2236,"Yangbi Yi","Zìzhìxiàn","Autonomous County","<U+6F3E><U+6FDE><U+5F5D><U+65CF><U+81EA><U+6CBB><U+53BF>","Yàngbì Yízú"
2237,49,"CHN","China",30,"Yunnan",321,"Dali Bai",2237,"Yongping","Xiàn","County","<U+6C38><U+5E73><U+53BF>","Yongpíng"
2238,49,"CHN","China",30,"Yunnan",321,"Dali Bai",2238,"Yulong Naxi","Zìzhìxiàn","Autonomous County","<U+7389><U+9F99><U+7EB3><U+897F><U+65CF><U+81EA><U+6CBB><U+53BF>","Yùlóng Nàxizú"
2239,49,"CHN","China",30,"Yunnan",322,"Dehong Dai and Jingpo",2239,"Lianghe","Xiàn","County","<U+6881><U+6CB3><U+53BF>","Liánghé"
2240,49,"CHN","China",30,"Yunnan",322,"Dehong Dai and Jingpo",2240,"Longchuan","Xiàn","County","<U+9647><U+5DDD><U+53BF>","Longchuan"
2241,49,"CHN","China",30,"Yunnan",322,"Dehong Dai and Jingpo",2241,"Luxi","Xiànjíshì","County City","<U+6F5E><U+897F><U+5E02>","Lùxi"
2242,49,"CHN","China",30,"Yunnan",322,"Dehong Dai and Jingpo",2242,"Ruili","Xiànjíshì","County City","<U+745E><U+4E3D><U+5E02>","Ruìlì"
2243,49,"CHN","China",30,"Yunnan",322,"Dehong Dai and Jingpo",2243,"Yingjiang","Xiàn","County","<U+76C8><U+6C5F><U+53BF>","Yíngjiang"
2244,49,"CHN","China",30,"Yunnan",323,"Honghe Hani and Yi",2244,"Gejiu","Xiànjíshì","County City","<U+4E2A><U+65E7><U+5E02>","Gèjiù"
2245,49,"CHN","China",30,"Yunnan",323,"Honghe Hani and Yi",2245,"Hekou Yao","Zìzhìxiàn","Autonomous County","<U+6CB3><U+53E3><U+7476><U+65CF><U+81EA><U+6CBB><U+53BF>","Hékou Yáozú"
2246,49,"CHN","China",30,"Yunnan",323,"Honghe Hani and Yi",2246,"Honghe","Xiàn","County","<U+7EA2><U+6CB3><U+53BF>","Hónghé"
2247,49,"CHN","China",30,"Yunnan",323,"Honghe Hani and Yi",2247,"Jianshui","Xiàn","County","<U+5EFA><U+6C34><U+53BF>","Jiànshui"
2248,49,"CHN","China",30,"Yunnan",323,"Honghe Hani and Yi",2248,"Jinping Miao, Yao and Dai","Zìzhìxiàn","Autonomous County","<U+91D1><U+5E73><U+82D7><U+65CF><U+7476><U+65CF><U+50A3><U+65CF><U+81EA><U+6CBB><U+53BF>","Jinpíng Miáozú Yáozú Daiz"
2249,49,"CHN","China",30,"Yunnan",323,"Honghe Hani and Yi",2249,"Kaiyuan","Xiàn","County","<U+5F00><U+8FDC><U+5E02>","Kaiyuan"
2250,49,"CHN","China",30,"Yunnan",323,"Honghe Hani and Yi",2250,"Lüchun","Xiàn","County","<U+7EFF><U+6625><U+53BF>","Luchun"
2251,49,"CHN","China",30,"Yunnan",323,"Honghe Hani and Yi",2251,"Luxi","Xiànjíshì","County City","<U+6F5E><U+897F><U+5E02>","Lùxi"
2252,49,"CHN","China",30,"Yunnan",323,"Honghe Hani and Yi",2252,"Mengzi","Xiàn","County","<U+8499><U+81EA><U+53BF>","Méngzì"
2253,49,"CHN","China",30,"Yunnan",323,"Honghe Hani and Yi",2253,"Mile","Xiàn","County","<U+5F25><U+52D2><U+53BF>","Mílè"
2254,49,"CHN","China",30,"Yunnan",323,"Honghe Hani and Yi",2254,"Pingbian Miao","Zìzhìxiàn","Autonomous County","<U+5C4F><U+8FB9><U+82D7><U+65CF><U+81EA><U+6CBB><U+53BF>","Píngbian Miáozú"
2255,49,"CHN","China",30,"Yunnan",323,"Honghe Hani and Yi",2255,"Shiping","Xiàn","County","<U+77F3><U+5C4F><U+53BF>","Shípíng"
2256,49,"CHN","China",30,"Yunnan",323,"Honghe Hani and Yi",2256,"Yuanyang","Xiàn","County","<U+5143><U+9633><U+53BF>","Yuányáng"
2257,49,"CHN","China",30,"Yunnan",324,"Kunming",2257,"Anning","Xiànjíshì","County City","<U+5B89><U+5B81><U+5E02>","Anníng"
2258,49,"CHN","China",30,"Yunnan",324,"Kunming",2258,"Chenggong","Xiàn","County","<U+5448><U+8D21><U+53BF>","Chénggòng"
2259,49,"CHN","China",30,"Yunnan",324,"Kunming",2259,"Dongchuan","Shìxiáqu","District","<U+4E1C><U+5DDD><U+533A>","Dongchuan"
2260,49,"CHN","China",30,"Yunnan",324,"Kunming",2260,"Fumin","Xiàn","County","<U+5BCC><U+6C11><U+53BF>","Fùmín"
2261,49,"CHN","China",30,"Yunnan",324,"Kunming",2261,"Jinning","Xiàn","County","<U+664B><U+5B81><U+53BF>","Jìnníng"
2262,49,"CHN","China",30,"Yunnan",324,"Kunming",2262,"Kunming","Xiànjíshì","County City","<U+6606><U+660E><U+5E02>","Kunmíng"
2263,49,"CHN","China",30,"Yunnan",324,"Kunming",2263,"Lunan","Xiàn","County",NA,NA
2264,49,"CHN","China",30,"Yunnan",324,"Kunming",2264,"Luquan Yi and Miao","Zìzhìxiàn","Autonomous County","<U+7984><U+529D><U+5F5D><U+65CF><U+82D7><U+65CF><U+81EA><U+6CBB><U+53BF>","Lùquàn Yízú Miáozú"
2265,49,"CHN","China",30,"Yunnan",324,"Kunming",2265,"Songming","Xiàn","County","<U+5D69><U+660E><U+53BF>","Songmíng"
2266,49,"CHN","China",30,"Yunnan",324,"Kunming",2266,"Xishan","Shìxiáqu","District","<U+897F><U+5C71><U+533A>","Xishan"
2267,49,"CHN","China",30,"Yunnan",324,"Kunming",2267,"Xundian Hui and Yi","Zìzhìxiàn","Autonomous County","<U+5BFB><U+7538><U+56DE><U+65CF><U+5F5D><U+65CF><U+81EA><U+6CBB><U+53BF>","Xúndiàn Huízú Yízú"
2268,49,"CHN","China",30,"Yunnan",324,"Kunming",2268,"Yiliang","Xiàn","County","<U+5B9C><U+826F><U+53BF>","Yíliáng"
2269,49,"CHN","China",30,"Yunnan",325,"Lijiang",2269,"Huaping","Xiàn","County","<U+534E><U+576A><U+53BF>","Huápíng"
2270,49,"CHN","China",30,"Yunnan",325,"Lijiang",2270,"Lijiang","Xiàn","County",NA,NA
2271,49,"CHN","China",30,"Yunnan",325,"Lijiang",2271,"Ninglang Yi","Zìzhìxiàn","Autonomous County","<U+5B81><U+8497><U+5F5D><U+65CF><U+81EA><U+6CBB><U+53BF>","Nínglàng Yízú"
2272,49,"CHN","China",30,"Yunnan",325,"Lijiang",2272,"Yongsheng","Xiàn","County","<U+6C38><U+80DC><U+53BF>","Yongshèng"
2273,49,"CHN","China",30,"Yunnan",326,"Lincang",2273,"Cangyuan Va","Zìzhìxiàn","Autonomous County","<U+6CA7><U+6E90><U+4F64><U+65CF><U+81EA><U+6CBB><U+53BF>","Cangyuán Wazú"
2274,49,"CHN","China",30,"Yunnan",326,"Lincang",2274,"Fengqing","Xiàn","County","<U+51E4><U+5E86><U+53BF>","Fèngqìng"
2275,49,"CHN","China",30,"Yunnan",326,"Lincang",2275,"Gengma Dai and Va","Zìzhìxiàn","Autonomous County","<U+803F><U+9A6C><U+50A3><U+65CF><U+4F64><U+65CF><U+81EA><U+6CBB><U+53BF>","Gengma Daizú Wazú"
2276,49,"CHN","China",30,"Yunnan",326,"Lincang",2276,"Lincang","Xiàn","County",NA,NA
2277,49,"CHN","China",30,"Yunnan",326,"Lincang",2277,"Shuangjiang Lahu, Va, Blang and Dai","Zìzhìxiàn","Autonomous County","<U+53CC><U+6C5F><U+62C9><U+795C><U+65CF><U+4F64><U+65CF><U+5E03><U+6717><U+65CF><U+50A3><U+65CF><U+81EA><U+6CBB><U+53BF>","Shuangjiang Lahùzú Wazú Bùlangzú aizú"
2278,49,"CHN","China",30,"Yunnan",326,"Lincang",2278,"Yongde","Xiàn","County","<U+6C38><U+5FB7><U+53BF>","Yongdé"
2279,49,"CHN","China",30,"Yunnan",326,"Lincang",2279,"Yun","Xiàn","County","<U+4E91><U+53BF>","Yún"
2280,49,"CHN","China",30,"Yunnan",326,"Lincang",2280,"Zhenkang","Xiàn","County","<U+9547><U+5EB7><U+53BF>","Zhènkang"
2281,49,"CHN","China",30,"Yunnan",327,"Nujiang Lisu",2281,"Bijiang","Xiàn","County",NA,NA
2282,49,"CHN","China",30,"Yunnan",327,"Nujiang Lisu",2282,"Gongshan Derung and Nu","Zìzhìxiàn","Autonomous County","<U+8D21><U+5C71><U+72EC><U+9F99><U+65CF><U+6012><U+65CF><U+81EA><U+6CBB><U+53BF>","Gòngshan Dúlóngzú Nùzú"
2283,49,"CHN","China",30,"Yunnan",327,"Nujiang Lisu",2283,"Lanping Bai and Pumi","Zìzhìxiàn","Autonomous County","<U+5170><U+576A><U+767D><U+65CF><U+666E><U+7C73><U+65CF><U+81EA><U+6CBB><U+53BF>","Lánpíng Báizú Pumizú"
2284,49,"CHN","China",30,"Yunnan",327,"Nujiang Lisu",2284,"Lushui","Xiàn","County","<U+6CF8><U+6C34><U+53BF>","Lúshui"
2285,49,"CHN","China",30,"Yunnan",328,"Pu'er",2285,"Jiangcheng Hani and Yi","Zìzhìxiàn","Autonomous County","<U+6C5F><U+57CE><U+54C8><U+5C3C><U+65CF><U+5F5D><U+65CF><U+81EA><U+6CBB><U+53BF>","Jiangchéng HanízúYízú"
2286,49,"CHN","China",30,"Yunnan",328,"Pu'er",2286,"Jingdong Yi","Zìzhìxiàn","Autonomous County","<U+666F><U+4E1C><U+5F5D><U+65CF><U+81EA><U+6CBB><U+53BF>","Jingdong Yízú"
2287,49,"CHN","China",30,"Yunnan",328,"Pu'er",2287,"Jinggu Dai and Yi","Zìzhìxiàn","Autonomous County","<U+666F><U+8C37><U+50A3><U+65CF><U+5F5D><U+65CF><U+81EA><U+6CBB><U+53BF>","Jinggu Daizú Yízú"
2288,49,"CHN","China",30,"Yunnan",328,"Pu'er",2288,"Lancang Lahu","Zìzhìxiàn","Autonomous County","<U+6F9C><U+6CA7><U+62C9><U+795C><U+65CF><U+81EA><U+6CBB><U+53BF>","Láncang Lahùzú"
2289,49,"CHN","China",30,"Yunnan",328,"Pu'er",2289,"Menglian Dai, Lahu and Va","Zìzhìxiàn","Autonomous County","<U+5B5F><U+8FDE><U+50A3><U+65CF><U+62C9><U+795C><U+65CF><U+4F64><U+65CF><U+81EA><U+6CBB><U+53BF>","Mènglián Daizú Lahùzú Wazú"
2290,49,"CHN","China",30,"Yunnan",328,"Pu'er",2290,"Mojiang Hani","Zìzhìxiàn","Autonomous County","<U+58A8><U+6C5F><U+54C8><U+5C3C><U+65CF><U+81EA><U+6CBB><U+53BF>","Mòjiang Hanízú"
2291,49,"CHN","China",30,"Yunnan",328,"Pu'er",2291,"Puer","Xiàn","County",NA,NA
2292,49,"CHN","China",30,"Yunnan",328,"Pu'er",2292,"Simao","Shìxiáqu","District","<U+601D><U+8305><U+533A>","Simáo"
2293,49,"CHN","China",30,"Yunnan",328,"Pu'er",2293,"Ximeng Va","Zìzhìxiàn","Autonomous County","<U+897F><U+76DF><U+4F64><U+65CF><U+81EA><U+6CBB><U+53BF>","Ximéng Wazú"
2294,49,"CHN","China",30,"Yunnan",328,"Pu'er",2294,"Zhenyuan Yi, Hani and Lahu","Zìzhìxiàn","Autonomous County","<U+9547><U+6C85><U+5F5D><U+65CF><U+54C8><U+5C3C><U+65CF><U+62C9><U+795C><U+65CF><U+81EA><U+6CBB><U+53BF>","Zhènyuán Yízú Hanízú Lahùzú"
2295,49,"CHN","China",30,"Yunnan",329,"Qujing",2295,"Fuyuan","Xiàn","County","<U+5BCC><U+6E90><U+53BF>","Fùyuán"
2296,49,"CHN","China",30,"Yunnan",329,"Qujing",2296,"Huize","Xiàn","County","<U+4F1A><U+6CFD><U+53BF>","Huìzé"
2297,49,"CHN","China",30,"Yunnan",329,"Qujing",2297,"Luliang","Xiàn","County","<U+9646><U+826F><U+53BF>","Lùliáng"
2298,49,"CHN","China",30,"Yunnan",329,"Qujing",2298,"Luoping","Xiàn","County","<U+7F57><U+5E73><U+53BF>","Luópíng"
2299,49,"CHN","China",30,"Yunnan",329,"Qujing",2299,"Malong","Xiàn","County","<U+9A6C><U+9F99><U+53BF>","Malóng"
2300,49,"CHN","China",30,"Yunnan",329,"Qujing",2300,"Shizong","Xiàn","County","<U+5E08><U+5B97><U+53BF>","Shizong"
2301,49,"CHN","China",30,"Yunnan",329,"Qujing",2301,"Xuanwei","Xiànjíshì","County City","<U+5BA3><U+5A01><U+5E02>","Xuanwei"
2302,49,"CHN","China",30,"Yunnan",329,"Qujing",2302,"Zhanyi","Xiàn","County","<U+6CBE><U+76CA><U+53BF>","Zhanyì"
2303,49,"CHN","China",30,"Yunnan",330,"Wenshan Zhuang and Miao",2303,"Funing","Xiàn","County","<U+5BCC><U+5B81><U+53BF>","Fùníng"
2304,49,"CHN","China",30,"Yunnan",330,"Wenshan Zhuang and Miao",2304,"Guangnan","Xiàn","County","<U+5E7F><U+5357><U+53BF>","Guangnán"
2305,49,"CHN","China",30,"Yunnan",330,"Wenshan Zhuang and Miao",2305,"Maguan","Xiàn","County","<U+9A6C><U+5173><U+53BF>","Maguan"
2306,49,"CHN","China",30,"Yunnan",330,"Wenshan Zhuang and Miao",2306,"Malipo","Xiàn","County","<U+9EBB><U+6817><U+5761><U+53BF>","Málìpo"
2307,49,"CHN","China",30,"Yunnan",330,"Wenshan Zhuang and Miao",2307,"Qiubei","Xiàn","County","<U+4E18><U+5317><U+53BF>","Qiubei"
2308,49,"CHN","China",30,"Yunnan",330,"Wenshan Zhuang and Miao",2308,"Wenshan","Xiàn","County","<U+6587><U+5C71><U+53BF>","Wénshan"
2309,49,"CHN","China",30,"Yunnan",330,"Wenshan Zhuang and Miao",2309,"Xichou","Xiàn","County","<U+897F><U+7574><U+53BF>","Xichóu"
2310,49,"CHN","China",30,"Yunnan",330,"Wenshan Zhuang and Miao",2310,"Yanshan","Xiàn","County","<U+781A><U+5C71><U+53BF>","Yànshan"
2311,49,"CHN","China",30,"Yunnan",331,"Xishuangbanna Dai",2311,"Jinghong","Xiànjíshì","County City","<U+666F><U+6D2A><U+5E02>","Jinghóng"
2312,49,"CHN","China",30,"Yunnan",331,"Xishuangbanna Dai",2312,"Menghai","Xiàn","County","<U+52D0><U+6D77><U+53BF>","Menghai"
2313,49,"CHN","China",30,"Yunnan",331,"Xishuangbanna Dai",2313,"Mengla","Xiàn","County","<U+52D0><U+814A><U+53BF>","Menglà"
2314,49,"CHN","China",30,"Yunnan",332,"Yuxi",2314,"Chengjiang","Xiàn","County","<U+6F84><U+6C5F><U+53BF>","Chéngjiang"
2315,49,"CHN","China",30,"Yunnan",332,"Yuxi",2315,"Eshan Yi","Zìzhìxiàn","Autonomous County","<U+5CE8><U+5C71><U+5F5D><U+65CF><U+81EA><U+6CBB><U+53BF>","Éshan Yízú"
2316,49,"CHN","China",30,"Yunnan",332,"Yuxi",2316,"Huaning","Xiàn","County","<U+534E><U+5B81><U+53BF>","Huáníng"
2317,49,"CHN","China",30,"Yunnan",332,"Yuxi",2317,"Jiangchuan","Xiàn","County","<U+6C5F><U+5DDD><U+53BF>","Jiangchuan"
2318,49,"CHN","China",30,"Yunnan",332,"Yuxi",2318,"Tonghai","Xiàn","County",NA,NA
2319,49,"CHN","China",30,"Yunnan",332,"Yuxi",2319,"Xinping Yi and Dai","Zìzhìxiàn","Autonomous County","<U+65B0><U+5E73><U+5F5D><U+65CF><U+50A3><U+65CF><U+81EA><U+6CBB><U+53BF>","Xinpíng Yízú Daizú"
2320,49,"CHN","China",30,"Yunnan",332,"Yuxi",2320,"Yimen","Xiàn","County","<U+6613><U+95E8><U+53BF>","Yìmén"
2321,49,"CHN","China",30,"Yunnan",332,"Yuxi",2321,"Yuanjiang Hani, Yi and Dai","Zìzhìxiàn","Autonomous County","<U+5143><U+6C5F><U+54C8><U+5C3C><U+65CF><U+5F5D><U+65CF><U+50A3><U+65CF><U+81EA><U+6CBB><U+53BF>","Yuánjiang Hanízú Yízú Daizú"
2322,49,"CHN","China",30,"Yunnan",332,"Yuxi",2322,"Yuxi","Xiàn","County",NA,NA
2323,49,"CHN","China",30,"Yunnan",333,"Zhaotong",2323,"Daguan","Xiàn","County","<U+5927><U+5173><U+53BF>","Dàguan"
2324,49,"CHN","China",30,"Yunnan",333,"Zhaotong",2324,"Ludian","Xiàn","County","<U+9C81><U+7538><U+53BF>","Ludiàn"
2325,49,"CHN","China",30,"Yunnan",333,"Zhaotong",2325,"Qiaojia","Xiàn","County","<U+5DE7><U+5BB6><U+53BF>","Qiaojia"
2326,49,"CHN","China",30,"Yunnan",333,"Zhaotong",2326,"Shuifu","Xiàn","County","<U+6C34><U+5BCC><U+53BF>","Shuifù"
2327,49,"CHN","China",30,"Yunnan",333,"Zhaotong",2327,"Suijiang","Xiàn","County","<U+7EE5><U+6C5F><U+53BF>","Suíjiang"
2328,49,"CHN","China",30,"Yunnan",333,"Zhaotong",2328,"Weixi Lisu","Zìzhìxiàn","Autonomous County","<U+7EF4><U+897F><U+5088><U+50F3><U+65CF><U+81EA><U+6CBB><U+53BF>","Wéixi Lìsùzú"
2329,49,"CHN","China",30,"Yunnan",333,"Zhaotong",2329,"Yanjin","Xiàn","County","<U+76D0><U+6D25><U+53BF>","Yánjin"
2330,49,"CHN","China",30,"Yunnan",333,"Zhaotong",2330,"Yiliang","Xiàn","County","<U+5B9C><U+826F><U+53BF>","Yíliáng"
2331,49,"CHN","China",30,"Yunnan",333,"Zhaotong",2331,"Yongshan","Xiàn","County","<U+6C38><U+5584><U+53BF>","Yongshàn"
2332,49,"CHN","China",30,"Yunnan",333,"Zhaotong",2332,"Zhaotong","Xiànjíshì","County City",NA,NA
2333,49,"CHN","China",30,"Yunnan",333,"Zhaotong",2333,"Zhenxiong","Xiàn","County","<U+9547><U+96C4><U+53BF>","Zhènxióng"
2334,49,"CHN","China",31,"Zhejiang",334,"Hangzhou",2334,"Chun'an","Xiàn","County","<U+6DF3><U+5B89><U+53BF>","Chún'an"
2335,49,"CHN","China",31,"Zhejiang",334,"Hangzhou",2335,"Fuyang","Xiànjíshì","County City","<U+5BCC><U+9633><U+5E02>","Fùyáng"
2336,49,"CHN","China",31,"Zhejiang",334,"Hangzhou",2336,"Hangzhou","Shìxiáqu","District",NA,NA
2337,49,"CHN","China",31,"Zhejiang",334,"Hangzhou",2337,"Jiande","Xiànjíshì","County City","<U+5EFA><U+5FB7><U+5E02>","Jiàndé"
2338,49,"CHN","China",31,"Zhejiang",334,"Hangzhou",2338,"Lin'an","Xiànjíshì","County City","<U+4E34><U+5B89><U+5E02>","Lín'an"
2339,49,"CHN","China",31,"Zhejiang",334,"Hangzhou",2339,"Tonglu","Xiàn","County","<U+6850><U+5E90><U+53BF>","Tónglú"
2340,49,"CHN","China",31,"Zhejiang",334,"Hangzhou",2340,"Xiaoshan","Shìxiáqu","District","<U+8427><U+5C71><U+533A>","Xiaoshan"
2341,49,"CHN","China",31,"Zhejiang",334,"Hangzhou",2341,"Yuhang","Shìxiáqu","District","<U+4F59><U+676D><U+533A>","Yúháng"
2342,49,"CHN","China",31,"Zhejiang",335,"Huzhou",2342,"Anji","Xiàn","County","<U+5B89><U+5409><U+53BF>","Anjí"
2343,49,"CHN","China",31,"Zhejiang",335,"Huzhou",2343,"Changxing","Xiàn","County","<U+957F><U+5174><U+53BF>","Chángxing"
2344,49,"CHN","China",31,"Zhejiang",335,"Huzhou",2344,"Deqing","Xiàn","County","<U+5FB7><U+6E05><U+53BF>","Déqing"
2345,49,"CHN","China",31,"Zhejiang",335,"Huzhou",2345,"Huzhou","Xiàn","County",NA,NA
2346,49,"CHN","China",31,"Zhejiang",336,"Jiaxing",2346,"Haining","Xiànjíshì","County City","<U+6D77><U+5B81><U+5E02>","Hainíng"
2347,49,"CHN","China",31,"Zhejiang",336,"Jiaxing",2347,"Haiyan","Xiàn","County","<U+6D77><U+76D0><U+53BF>","Haiyán"
2348,49,"CHN","China",31,"Zhejiang",336,"Jiaxing",2348,"Jiashan","Xiàn","County","<U+5609><U+5584><U+53BF>","Jiashàn"
2349,49,"CHN","China",31,"Zhejiang",336,"Jiaxing",2349,"Jiaxing","Xiànjíshì","County City","<U+5609><U+5174><U+5E02>","Jiaxing"
2350,49,"CHN","China",31,"Zhejiang",336,"Jiaxing",2350,"Pinghu","Xiànjíshì","County City","<U+5E73><U+6E56><U+5E02>","Pínghú"
2351,49,"CHN","China",31,"Zhejiang",336,"Jiaxing",2351,"Tongxiang","Xiànjíshì","County City","<U+6850><U+4E61><U+5E02>","Tóngxiang"
2352,49,"CHN","China",31,"Zhejiang",336,"Jiaxing",2352,"Xiuzhou","Shìxiáqu","District","<U+79C0><U+6D32><U+533A>","Xiùzhou"
2353,49,"CHN","China",31,"Zhejiang",337,"Jinhua",2353,"Dongyang","Xiànjíshì","County City","<U+4E1C><U+9633><U+5E02>","Dongyáng"
2354,49,"CHN","China",31,"Zhejiang",337,"Jinhua",2354,"Jinhua Shì","Xiànjíshì","County City","<U+91D1><U+534E><U+5E02>","Jinhuá"
2355,49,"CHN","China",31,"Zhejiang",337,"Jinhua",2355,"Jinhua Xiàn","Xiàn","County","<U+91D1><U+534E><U+53BF>","Jinhuá"
2356,49,"CHN","China",31,"Zhejiang",337,"Jinhua",2356,"Lanxi","Xiànjíshì","County City","<U+5170><U+6EAA><U+5E02>","Lánxi"
2357,49,"CHN","China",31,"Zhejiang",337,"Jinhua",2357,"Pan'an","Xiàn","County","<U+78D0><U+5B89><U+53BF>","Pán'an"
2358,49,"CHN","China",31,"Zhejiang",337,"Jinhua",2358,"Pujiang","Xiàn","County","<U+6D66><U+6C5F><U+53BF>","Pujiang"
2359,49,"CHN","China",31,"Zhejiang",337,"Jinhua",2359,"Wuyi","Xiàn","County","<U+6B66><U+4E49><U+53BF>","Wuyì"
2360,49,"CHN","China",31,"Zhejiang",337,"Jinhua",2360,"Yiwu","Xiànjíshì","County City","<U+4E49><U+4E4C><U+5E02>","Yìwu"
2361,49,"CHN","China",31,"Zhejiang",337,"Jinhua",2361,"Yongkang","Xiànjíshì","County City","<U+6C38><U+5EB7><U+5E02>","Yongkang"
2362,49,"CHN","China",31,"Zhejiang",338,"Lishui",2362,"Jingning She","Zìzhìxiàn","Autonomous County","<U+666F><U+5B81><U+7572><U+65CF><U+81EA><U+6CBB><U+53BF>","Jingníng Shezú"
2363,49,"CHN","China",31,"Zhejiang",338,"Lishui",2363,"Jinyun","Xiàn","County","<U+7F19><U+4E91><U+53BF>","Jìnyún"
2364,49,"CHN","China",31,"Zhejiang",338,"Lishui",2364,"Lishui","Xiàn","County",NA,NA
2365,49,"CHN","China",31,"Zhejiang",338,"Lishui",2365,"Longquan","Xiànjíshì","County City","<U+9F99><U+6CC9><U+5E02>","Lóngquán S"
2366,49,"CHN","China",31,"Zhejiang",338,"Lishui",2366,"Qingtian","Xiàn","County","<U+9752><U+7530><U+53BF>","Qingtián"
2367,49,"CHN","China",31,"Zhejiang",338,"Lishui",2367,"Qingyuan","Xiàn","County","<U+5E86><U+5143><U+53BF>","Qìngyuán"
2368,49,"CHN","China",31,"Zhejiang",338,"Lishui",2368,"Songyang","Xiàn","County","<U+677E><U+9633><U+53BF>","Songyáng"
2369,49,"CHN","China",31,"Zhejiang",338,"Lishui",2369,"Suichang","Xiàn","County","<U+9042><U+660C><U+53BF>","Suíchang"
2370,49,"CHN","China",31,"Zhejiang",338,"Lishui",2370,"Yunhe","Xiàn","County","<U+4E91><U+548C><U+53BF>","Yúnhé"
2371,49,"CHN","China",31,"Zhejiang",339,"Ningbo",2371,"Cixi","Xiànjíshì","County City","<U+6148><U+6EAA><U+5E02>","Cíxi"
2372,49,"CHN","China",31,"Zhejiang",339,"Ningbo",2372,"Fenghua","Xiànjíshì","County City","<U+5949><U+5316><U+5E02>","Fènghuà"
2373,49,"CHN","China",31,"Zhejiang",339,"Ningbo",2373,"Longwan","Shìxiáqu","District","<U+9F99><U+6E7E><U+533A>","Lóngwan"
2374,49,"CHN","China",31,"Zhejiang",339,"Ningbo",2374,"Ninghai","Xiàn","County","<U+5B81><U+6D77><U+53BF>","Nínghai"
2375,49,"CHN","China",31,"Zhejiang",339,"Ningbo",2375,"Xiangshan","Xiàn","County","<U+8C61><U+5C71><U+53BF>","Xiangshan"
2376,49,"CHN","China",31,"Zhejiang",339,"Ningbo",2376,"Xiuzhou","Shìxiáqu","District","<U+79C0><U+6D32><U+533A>","Xiùzhou"
2377,49,"CHN","China",31,"Zhejiang",339,"Ningbo",2377,"Yuyao","Xiànjíshì","County City","<U+4F59><U+59DA><U+5E02>","Yúyáo S"
2378,49,"CHN","China",31,"Zhejiang",339,"Ningbo",2378,"Zhenhai","Shìxiáqu","District","<U+9547><U+6D77><U+533A>","Zhènhai"
2379,49,"CHN","China",31,"Zhejiang",340,"Quzhou",2379,"Changshan","Xiàn","County","<U+5E38><U+5C71><U+53BF>","Chángshan"
2380,49,"CHN","China",31,"Zhejiang",340,"Quzhou",2380,"Jiangshan","Xiànjíshì","County City","<U+6C5F><U+5C71><U+5E02>","Jiangshan"
2381,49,"CHN","China",31,"Zhejiang",340,"Quzhou",2381,"Kaihua","Xiàn","County","<U+5F00><U+5316><U+53BF>","Kaihuà"
2382,49,"CHN","China",31,"Zhejiang",340,"Quzhou",2382,"Longyou","Xiàn","County","<U+9F99><U+6E38><U+53BF>","Lóngyóu"
2383,49,"CHN","China",31,"Zhejiang",340,"Quzhou",2383,"Quzhou Shì","Xiànjíshì","County City","<U+8862><U+5DDE><U+5E02>","Qúzhou"
2384,49,"CHN","China",31,"Zhejiang",340,"Quzhou",2384,"Quzhou Xiàn","Xiàn","County","<U+8862><U+5DDE><U+53BF>","Qúzhou"
2385,49,"CHN","China",31,"Zhejiang",341,"Shaoxing",2385,"Shangyu","Xiànjíshì","County City","<U+4E0A><U+865E><U+5E02>","Shàngyú"
2386,49,"CHN","China",31,"Zhejiang",341,"Shaoxing",2386,"Shaoxing Xiàn","Xiàn","County","<U+7ECD><U+5174><U+53BF>","Shàoxing"
2387,49,"CHN","China",31,"Zhejiang",341,"Shaoxing",2387,"Shaoxing","Xiànjíshì","County City","<U+7ECD><U+5174><U+5E02>","Shàoxing"
2388,49,"CHN","China",31,"Zhejiang",341,"Shaoxing",2388,"Shengsi","Xiàn","County","<U+5D4A><U+6CD7><U+53BF>","Shèngsì"
2389,49,"CHN","China",31,"Zhejiang",341,"Shaoxing",2389,"Xinchang","Xiàn","County","<U+65B0><U+660C><U+53BF>","Xinchang"
2390,49,"CHN","China",31,"Zhejiang",341,"Shaoxing",2390,"Zhuji","Xiànjíshì","County City","<U+8BF8><U+66A8><U+5E02>","Zhujì"
2391,49,"CHN","China",31,"Zhejiang",342,"Taizhou",2391,"Huangyan","Shìxiáqu","District","<U+9EC4><U+5CA9><U+533A>","Huángyán"
2392,49,"CHN","China",31,"Zhejiang",342,"Taizhou",2392,"Linhai","Xiànjíshì","County City","<U+4E34><U+6D77><U+5E02>","Línhai"
2393,49,"CHN","China",31,"Zhejiang",342,"Taizhou",2393,"Sanmen","Xiàn","County","<U+4E09><U+95E8><U+53BF>","Sanmén"
2394,49,"CHN","China",31,"Zhejiang",342,"Taizhou",2394,"Tiantai","Xiàn","County","<U+5929><U+53F0><U+53BF>","Tiantai"
2395,49,"CHN","China",31,"Zhejiang",342,"Taizhou",2395,"Wenling","Xiànjíshì","County City","<U+6E29><U+5CAD><U+5E02>","Wenling"
2396,49,"CHN","China",31,"Zhejiang",342,"Taizhou",2396,"Xianju","Xiàn","County","<U+4ED9><U+5C45><U+53BF>","Xianju"
2397,49,"CHN","China",31,"Zhejiang",342,"Taizhou",2397,"Yuhuan","Xiàn","County","<U+7389><U+73AF><U+53BF>","Yùhuán"
2398,49,"CHN","China",31,"Zhejiang",343,"Wenzhou",2398,"Cangnan","Xiàn","County","<U+82CD><U+5357><U+53BF>","Cangnán"
2399,49,"CHN","China",31,"Zhejiang",343,"Wenzhou",2399,"Ouhai","Shìxiáqu","District","<U+74EF><U+6D77><U+533A>","Ouhai"
2400,49,"CHN","China",31,"Zhejiang",343,"Wenzhou",2400,"Pingyang","Xiàn","County","<U+5E73><U+9633><U+53BF>","Píngyáng"
2401,49,"CHN","China",31,"Zhejiang",343,"Wenzhou",2401,"Ruian","Xiànjíshì","County City","<U+745E><U+5B89><U+5E02>","Ruì'an"
2402,49,"CHN","China",31,"Zhejiang",343,"Wenzhou",2402,"Taishun","Xiàn","County","<U+6CF0><U+987A><U+53BF>","Tàishùn"
2403,49,"CHN","China",31,"Zhejiang",343,"Wenzhou",2403,"Wencheng","Xiàn","County","<U+6587><U+6210><U+53BF>","Wénchéng"
2404,49,"CHN","China",31,"Zhejiang",343,"Wenzhou",2404,"Wenzhou","Xiànjíshì","County City","<U+6E29><U+5DDE><U+5E02>","Wenzhou"
2405,49,"CHN","China",31,"Zhejiang",343,"Wenzhou",2405,"Yongjia","Xiàn","County","<U+6C38><U+5609><U+53BF>","Yongjia"
2406,49,"CHN","China",31,"Zhejiang",343,"Wenzhou",2406,"Yueqing","Xiànjíshì","County City","<U+4E50><U+6E05><U+5E02>","	Yuèqing"
2407,49,"CHN","China",31,"Zhejiang",344,"Zhoushan",2407,"Daishan","Xiàn","County","<U+5CB1><U+5C71><U+53BF>","Dàishan"
2408,49,"CHN","China",31,"Zhejiang",344,"Zhoushan",2408,"Dinghai","Shìxiáqu","District","<U+5B9A><U+6D77><U+533A>","Dìnghai"
2409,49,"CHN","China",31,"Zhejiang",344,"Zhoushan",2409,"Putuo","Shìxiáqu","District","<U+666E><U+9640><U+533A>","Putuó"


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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm3.prj
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GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]

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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm3.shp
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[File too large to display: 27.1 MB]

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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/license.txt
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These data were extracted from the GADM database (www.gadm.org), version 2.5, July 2015. They can be used for non-commercial purposes only.  It is not allowed to redistribute these data, or use them for commercial purposes, without prior consent.

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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/my_file.txt
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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/colour.csv
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shengfen,colour,QUHUADAIMA
½,0~200,650000
,0~200,540000
ຣ,600~1000,630000
,0~200,620000
,200~400,640000
ɹ,400~600,150000
ɽ,0~200,140000
,0~200,230000
,0~200,220000
,200~400,530000
,400~600,520000
,0~200,450000
,0~200,500000
,0~200,360000
,0~200,460000
Ĵ,200~400,510000
,0~200,610000
,0~200,410000
ӱ,0~200,130000
,200~400,340000
,200~400,350000
,200~400,430000
,400~600,420000
,400~600,210000
,400~600,120000
㽭,600~1000,330000
Ϻ,600~1000,310000
,1000+,110000
ɽ,1000+,370000
,1000+,320000
㶫,600~1000,440000
̨,600~1000,710000
,600~1000,810000
,600~1000,820000


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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/map.Rproj
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Version: 1.0

RestoreWorkspace: Default
SaveWorkspace: Default
AlwaysSaveHistory: Default

EnableCodeIndexing: Yes
UseSpacesForTab: Yes
NumSpacesForTab: 2
Encoding: UTF-8

RnwWeave: knitr
LaTeX: pdfLaTeX


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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/province.csv
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ʡ,ʡ,shenghui_mid,sf_Jd,sf_Wd,dili_mid,dili_Jd,dili_Wd,beizhu
,,"c(116.4666667,39.9)",116.4666667,39.9,"c(116.533982575088, 40.315106188478)",116.5339826,40.31510619,
Ϻ,Ϻ,"c(121.4833333,31.23333333)",121.4833333,31.23333333,"c(121.540428873312, 31.2095383685209)",121.5404289,31.20953837,
,,"c(117.1833333,39.15)",117.1833333,39.15,"c(117.458023010432, 39.4371653956601)",117.458023,39.4371654,
,,"c(106.5333333,29.53333333)",106.5333333,29.53333333,"c(108.01001812709, 30.2218546606162)",108.0100181,30.22185466,
,,"c(126.6833333,45.75)",126.6833333,45.75,"c(127.893527270599, 47.9970969498844)",127.8935273,47.99709695,
,,"c(125.3166667,43.86666667)",125.3166667,43.86666667,"c(126.320389141051, 43.7944047371614)",126.3203891,43.79440474,
,,"c(123.4,41.83333333)",123.4,41.83333333,"c(122.743364828602, 41.427633679966)",122.7433648,41.42763368,
ɹ,ͺ,"c(111.8,40.81666667)",111.8,40.81666667,"c(114.058881423766, 44.2194391897076)",114.0588814,43.21943919,γ-1
ӱ,ʯׯ,"c(114.4666667,38.03333333)",114.4666667,38.03333333,,115.4666667,39.03333333,ʡ꾭+1γ+1
ɽ,̫ԭ,"c(112.5666667,37.86666667)",112.5666667,37.86666667,"c(112.425479766607, 37.6953486118982)",112.4254798,37.69534861,
ɽ,,"c(117,36.63333333)",117,36.63333333,"c(118.281712436475, 36.4808315698521)",118.2817124,36.48083157,
,֣,"c(113.7,34.8)",113.7,34.8,"c(113.732611968596, 34.0262791911931)",113.732612,34.02627919,
,,"c(108.9,34.26666667)",108.9,34.26666667,"c(109.000236165247, 35.3344425983614)",109.0002362,35.3344426,
,,"c(103.8166667,36.05)",103.8166667,36.05,"c(100.730361465946, 38.0176991236257)",100.7303615,38.51769912,γ+0.5
,,"c(106.2666667,38.33333333)",106.2666667,38.33333333,"c(106.295906065515, 37.4065563460928)",106.2959061,37.40655635,
ຣ,,"c(101.75,36.63333333)",101.75,36.63333333,"c(96.1677035508218, 35.7983962533039)",96.16770355,35.79839625,
½,³ľ,"c(87.6,43.8)",87.6,43.8,"c(85.3249856320736, 41.2501570887227)",85.32498563,41.25015709,
,Ϸ,"c(117.3,31.85)",117.3,31.85,"c(117.351643586328, 31.9537017702415)",117.3516436,31.95370177,
,Ͼ,"c(118.8333333,32.03333333)",118.8333333,32.03333333,"c(119.582729973854, 33.1019082410552)",119.58273,33.10190824,
㽭,,"c(120.15,30.23333333)",120.15,30.23333333,"c(120.184224978466, 29.2972448333294)",120.184225,29.29724483,
,ɳ,"c(113,28.18333333)",113,28.18333333,"c(111.842280603681, 27.7447628512016)",111.8422806,27.74476285,
,ϲ,"c(115.8666667,28.68333333)",115.8666667,28.68333333,"c(115.857549380971, 27.734680323998)",115.8575494,27.73468032,
,人,"c(114.35,30.61666667)",114.35,30.61666667,"c(112.405873792758, 31.1119538140425)",112.4058738,31.11195381,
Ĵ,ɶ,"c(104.0833333,30.65)",104.0833333,30.65,"c(102.820301877824, 30.7598119258279)",102.8203019,30.75981193,
,,"c(106.7,26.58333333)",106.7,26.58333333,"c(107.015315250832, 26.9522197194248)",107.0153153,26.95221972,
,,"c(119.3,26.08333333)",119.3,26.08333333,"c(118.113118831087, 26.2094758350264)",118.1131188,26.20947584,
̨,̨,"c(121.5166667,25.05)",121.5166667,25.05,,121.0166667,24.05,ʡ꾭-0.5γ-1
㶫,,"c(113.25,23.13333333)",113.25,23.13333333,"c(113.542568068397, 23.4656883053524)",113.5425681,23.46568831,
,,"c(110.3333333,20.03333333)",110.3333333,20.03333333,"c(109.877177580232, 19.3269620231367)",109.8771776,19.32696202,
,,"c(108.3333333,22.8)",108.3333333,22.8,"c(108.90840775646, 23.9644856706756)",108.9084078,23.96448567,
,,"c(102.6833333,25)",102.6833333,25,"c(101.619355216508, 25.1023431677425)",101.6193552,25.10234317,
,,"c(91.16666667,29.66666667)",91.16666667,29.66666667,"c(88.5701164584765, 31.6231104944092)",88.57011646,31.62311049,
,,"c(114.1666667,22.3)",114.1666667,22.3,,114.1666667,22.3,ʡ
,,"c(113.5,22.2)",113.5,22.2,,113.5,22.2,ʡ


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FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/南海.geojson
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================================================
FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/地图十段线.R
================================================
library(geojsonsf)
library(sf)
library(ggplot2)
library(RColorBrewer)
library(ggspatial)
library(cowplot)

API_pre = "http://xzqh.mca.gov.cn/data/"

China = st_read(dsn = paste0(API_pre, "quanguo.json"), 
                stringsAsFactors=FALSE) 
st_crs(China) = 4326

# 2.国境线
China_line = st_read(dsn = paste0(API_pre, "quanguo_Line.geojson"), 
                     stringsAsFactors=FALSE) 
st_crs(China_line) = 4326

gjx <- China_line[China_line$QUHUADAIMA == "guojiexian",]


province <- read.csv("province.csv")

fig1 <-  ggplot()+
  # 绘制主图
  geom_sf(data = CHINA,fill='NA') +
  # 绘制国界线及十段线
  geom_sf(data = gjx)+
  ##添加省份名称
  geom_text(data = province,aes(x=dili_Jd,y=dili_Wd,label=省市),position = "identity",size=3,check_overlap = TRUE) +
  labs(title="中国地图")+
  theme(plot.title = element_text(color="black", size=16, face="bold",vjust = 0.1,hjust = 0.5),
        legend.title=element_blank(),
        legend.position = c(0.2,0.2),
        panel.grid=element_blank(),
        panel.background=element_blank(),
        axis.text=element_blank(),
        axis.ticks=element_blank(),
        axis.title=element_blank()
  )+
  ##添加指北针，“style”参数可以更改样式
  annotation_north_arrow(location='tl', which_north='false',
                         style=north_arrow_orienteering())
fig1


# 读入数据
nine_lines = read_sf('南海.geojson') 

# 绘制九段线小图
fig2 = ggplot() +
  geom_sf(data = CHINA,fill='NA', size=0.5) + 
  geom_sf(data = nine_lines,color='black',size=0.5)+
  ##去掉主图的部分区域
  coord_sf(ylim = c(-4028017,-1877844),xlim = c(117131.4,2115095),crs="+proj=laea +lat_0=40 +lon_0=104")+
  theme(
    aspect.ratio = 1.25, #调节长宽比
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    axis.title = element_blank(),
    panel.grid = element_blank(),
    panel.background = element_blank(),
    panel.border = element_rect(fill=NA,color="grey10",linetype=1,size=0.5),
    plot.margin=unit(c(0,0,0,0),"mm"))
fig2

fig = ggdraw() +
  draw_plot(fig1) +
  draw_plot(fig2, x = 0.8, y = 0, width = 0.13, height = 0.39)
fig


colour <- read.csv("colour.csv")
head(colour)

colour$QUHUADAIMA <- as.character(colour$QUHUADAIMA)
CHINA <- dplyr::left_join(China,colour,by= "QUHUADAIMA")

fig1 <-  ggplot() +
  geom_sf(
    data = CHINA,
    aes(fill = factor(colour))) +
  ## 填色
  scale_fill_manual(
    "class",
    values = c("#9CEED3", "#79CBC2", "#5EA9AC", "#4B8793", "#3C6777"),
    breaks = c("0~200", "200~400", "400~600", "600~1000", "1000+"),
    labels = c("0~200", "200~400", "400~600", "600~1000", "1000+")
  ) +
  geom_sf(data = gjx) +
  geom_text(
    data = province,
    aes(x = dili_Jd, y = dili_Wd, label = 省市),
    position = "identity",
    size = 3,
    check_overlap = TRUE
  ) +
  labs(title = "中国地图") +
  theme(
    plot.title = element_text(
      color = "black",
      size = 16,
      face = "bold",
      vjust = 0.1,
      hjust = 0.5
    ),
    legend.title = element_blank(),
    legend.position = c(0.2, 0.2),
    panel.grid = element_blank(),
    panel.background = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    axis.title = element_blank()
  ) +
  annotation_north_arrow(
    location = 'tl',
    which_north = 'false',
    style = north_arrow_orienteering()
  )


# 读入九段线数据
nine_lines = read_sf('南海.geojson') 

# 绘制九段线小图
nine_map = ggplot() +
  geom_sf(data = CHINA,fill='NA', size=0.5) + 
  geom_sf(data = nine_lines,color='black',size=0.5)+
  coord_sf(ylim = c(-4028017,-1877844),xlim = c(117131.4,2115095),crs="+proj=laea +lat_0=40 +lon_0=104")+
  theme(
    aspect.ratio = 1.25, #调节长宽比
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    axis.title = element_blank(),
    panel.grid = element_blank(),
    panel.background = element_blank(),
    panel.border = element_rect(fill=NA,color="grey10",linetype=1,size=0.5),
    plot.margin=unit(c(0,0,0,0),"mm"))

# 使用cowplot包将大图小图拼在一起
fig = ggdraw() +
  draw_plot(fig1) +
  draw_plot(nine_map, x = 0.8, y = 0, width = 0.13, height = 0.39)
fig


colour$new_colour <- rep(0,nrow(colour))
##给目标省份赋予不同的数值
colour$new_colour[which(colour$shengfen=="重庆")] <- 1
colour$new_colour[which(colour$shengfen=="山西")] <- 2
colour$new_colour[which(colour$shengfen=="新疆")] <- 3
colour$new_colour[which(colour$shengfen=="内蒙古")] <- 4
CHINA <- dplyr::left_join(China,colour,by= "QUHUADAIMA")

fig1 <-  ggplot() +
  geom_sf(
    data = CHINA,
    aes(fill = factor(new_colour))) +
  scale_fill_manual(
    "class",
    values = c("#FFFFFF", "#79CBC2", "#5EA9AC", "#4B8793", "#3C6777"),
    breaks = c("0", "1", "2", "3", "4"),
    labels = c("0", "1", "2", "3", "4")
  )+
  geom_sf(data = gjx) +
  geom_text(
    data = province,
    aes(x = dili_Jd, y = dili_Wd, label = 省市),
    position = "identity",
    size = 3,
    check_overlap = TRUE
  ) +
  labs(title = "中国地图") +
  theme(
    plot.title = element_text(
      color = "black",
      size = 16,
      face = "bold",
      vjust = 0.1,
      hjust = 0.5
    ),
    legend.title = element_blank(),
    legend.position = c(0.2, 0.2),
    panel.grid = element_blank(),
    panel.background = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    axis.title = element_blank()
  ) +
  annotation_north_arrow(
    location = 'tl',
    which_north = 'false',
    style = north_arrow_orienteering()
  )


fig = ggdraw() +
  draw_plot(fig1) +
  draw_plot(nine_map, x = 0.8, y = 0, width = 0.13, height = 0.39)
fig



================================================
FILE: 2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/省份地图.R
================================================

library(rgdal)
library(ggplot2)
library(maptools)
library(mapproj)
library(ggsn)
library(readxl)
library(sf)
show_data <- read_xlsx("测试数据.xlsx","hubei")
shp_data <- st_read("china_shp/CHN_adm2.shp")


shp_data$NL_NAME_2 <- as.character(shp_data$NL_NAME_2)
my_data <- dplyr::left_join(show_data, shp_data,by = c("city" = "NL_NAME_2"))
head(my_data)

ggplot(data = my_data) + geom_sf(aes(fill = as.factor(value), geometry = `geometry`)) + 
  geom_sf_text(aes(label = `city`,geometry = `geometry`), color = 'Black',size=2)+
  xlab("Long (°E)") + ylab("Lat (°N)") + 
  ##更改图形颜色，不加这个语句使用随机配色
  scale_fill_manual(
    "value",
    values = c("#DFD3F2", "#00C2F9", "#FFF7D0"),
    breaks = c("A", "B", "C"),
    labels = c("A", "B", "C")
  )+
  theme(legend.position = "top",panel.background = element_rect(fill = "white",color = "black"),
        panel.grid = element_line(color = "grey"))

## 绘制多个省份
hubei <- read_xlsx("测试数据.xlsx","hubei")
jiangxi <- read_xlsx("测试数据.xlsx","jiangxi")
all_province <- rbind(hubei,jiangxi)

shp_data <- st_read("china_shp/CHN_adm2.shp")
shp_data$NL_NAME_2 <- as.character(shp_data$NL_NAME_2)
data2 <- dplyr::left_join(all_province, shp_data,by = c("city" = "NL_NAME_2"))

ggplot(data = data2) + geom_sf(aes(fill = as.factor(value), geometry = `geometry`)) + 
  geom_sf_text(aes(label = `city`,geometry = `geometry`), color = 'Black',size=2)+
  xlab("Long (°E)") + ylab("Lat (°N)") + 
  ##更改图形颜色，不加这个语句使用随机配色
  scale_fill_manual(
    "value",
    values = c("#DFD3F2", "#00C2F9", "#FFF7D0"),
    breaks = c("A", "B", "C"),
    labels = c("A", "B", "C")
  )+
  theme(legend.position = "top",panel.background = element_rect(fill = "white",color = "black"),
        panel.grid = element_line(color = "grey"))


================================================
FILE: 2022年/2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/.Rhistory
================================================
data(mtcars)
corr <- round(cor(mtcars), 1)
# Plot
ggcorrplot(corr, hc.order = TRUE,
type = "lower",
lab = TRUE,
lab_size = 3,
method="circle",
colors = c("tomato2", "white", "springgreen3"),
title="Correlogram of mtcars",
ggtheme=theme_bw)
data("mtcars")  # load data
mtcars$`car name` <- rownames(mtcars)  # create new column for car names
mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg
mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, "below", "above")  # above / below avg flag
mtcars <- mtcars[order(mtcars$mpg_z), ]  # sort
mtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`)
# Diverging Barcharts
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +
geom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +
scale_fill_manual(name="Mileage",
labels = c("Above Average", "Below Average"),
values = c("above"="#00ba38", "below"="#f8766d")) +
labs(subtitle="Normalised mileage from 'mtcars'",
title= "Diverging Bars") +
coord_flip()
ggplot(cty_mpg, aes(x=make, y=mileage)) +
geom_bar(stat="identity", width=.5, fill="tomato3") +
labs(title="Ordered Bar Chart",
subtitle="Make Vs Avg. Mileage",
caption="source: mpg") +
theme(axis.text.x = element_text(angle=65, vjust=0.6))
# Chunk 1: setup
knitr::opts_chunk$set(echo = TRUE,warning = F,message = F)
# Chunk 2
library(ggplot2)
library(plotrix)
options(scipen=999)  # 关掉像1e+48这样的科学符号
data("midwest", package = "ggplot2") #加载数据集
# Chunk 3
# 颜色设置（灰色系列）
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7")
# 颜色设置（黑色系列）
cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7")
ggplot <- function(...) ggplot2::ggplot(...) +
scale_color_manual(values = cbp1) +
scale_fill_manual(values = cbp1) + # 注意: 使用连续色阶时需要重写
theme_bw()
# Chunk 4
# Scatterplot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state, size=popdensity)) +
geom_smooth(method="loess", se=F) +
xlim(c(0, 0.1)) +
ylim(c(0, 500000)) +
labs(subtitle="Area Vs Population",
y="Population",
x="Area",
title="Scatterplot",
caption = "Source: midwest")
plot(gg)
# Chunk 5
# install.packages("ggalt")
library(ggalt)
options(scipen = 999)
library(ggplot2)
library(ggalt)
## 设定筛选条件
midwest_select <- midwest[midwest$poptotal > 350000 &
midwest$poptotal <= 500000 &
midwest$area > 0.01 &
midwest$area < 0.1, ]
# Plot
ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state, size=popdensity)) +   # draw points
geom_smooth(method="loess", se=F) +
xlim(c(0, 0.1)) +
ylim(c(0, 500000)) +   # draw smoothing line
geom_encircle(aes(x=area, y=poptotal),
data=midwest_select,
color="red",
size=2,
expand=0.08) +   # encircle
labs(subtitle="Area Vs Population",
y="Population",
x="Area",
title="Scatterplot + Encircle",
caption="Source: midwest")
# Chunk 6
g <- ggplot(mpg, aes(cty, hwy))
g + geom_jitter(width = .5, size=1) +
labs(subtitle="mpg: city vs highway mileage",
y="hwy",
x="cty",
title="Jittered Points")
# Chunk 7
g + geom_count(col="tomato3", show.legend=F) +
labs(subtitle="mpg: city vs highway mileage",
y="hwy",
x="cty",
title="Counts Plot")
# Chunk 8
data(mpg, package="ggplot2")
mpg_select <- mpg[mpg$manufacturer %in% c("audi", "ford", "honda", "hyundai"), ]
# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
g <- ggplot(mpg_select, aes(displ, cty)) +
labs(subtitle="mpg: Displacement vs City Mileage",
title="Bubble chart")
g + geom_jitter(aes(col=manufacturer, size=hwy)) +
geom_smooth(aes(col=manufacturer), method="lm", se=F)
# Chunk 9
library(ggExtra)
data(mpg, package="ggplot2")
# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
mpg_select <- mpg[mpg$hwy >= 35 & mpg$cty > 27, ]
g <- ggplot(mpg, aes(cty, hwy)) +
geom_count() +
geom_smooth(method="lm", se=F)
ggMarginal(g, type = "histogram", fill="transparent")
# Chunk 10
ggMarginal(g, type = "boxplot", fill="transparent")
# Chunk 11
ggMarginal(g, type = "density", fill="transparent")
# Chunk 12
library(ggcorrplot)
# Correlation matrix
data(mtcars)
corr <- round(cor(mtcars), 1)
# Plot
ggcorrplot(corr, hc.order = TRUE,
type = "lower",
lab = TRUE,
lab_size = 3,
method="circle",
colors = c("tomato2", "white", "springgreen3"),
title="Correlogram of mtcars",
ggtheme=theme_bw)
# Chunk 13
data("mtcars")  # load data
mtcars$`car name` <- rownames(mtcars)  # create new column for car names
mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg
mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, "below", "above")  # above / below avg flag
mtcars <- mtcars[order(mtcars$mpg_z), ]  # sort
mtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`)
# Diverging Barcharts
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +
geom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +
scale_fill_manual(name="Mileage",
labels = c("Above Average", "Below Average"),
values = c("above"="#00ba38", "below"="#f8766d")) +
labs(subtitle="Normalised mileage from 'mtcars'",
title= "Diverging Bars") +
coord_flip()
# Chunk 14
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +
geom_point(stat='identity', fill="black", size=6)  +
geom_segment(aes(y = 0,
x = `car name`,
yend = mpg_z,
xend = `car name`),
color = "black") +
geom_text(color="white", size=2) +
labs(title="Diverging Lollipop Chart",
subtitle="Normalized mileage from 'mtcars': Lollipop") +
ylim(-2.5, 2.5) +
coord_flip()
# Chunk 15
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +
geom_point(stat='identity', aes(col=mpg_type), size=6)  +
scale_color_manual(name="Mileage",
labels = c("Above Average", "Below Average"),
values = c("above"="#00ba38", "below"="#f8766d")) +
geom_text(color="white", size=2) +
labs(title="Diverging Dot Plot",
subtitle="Normalized mileage from 'mtcars': Dotplot") +
ylim(-2.5, 2.5) +
coord_flip()
# Chunk 16
library(quantmod)
data("economics", package = "ggplot2")
# 计算利润
economics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])
# 为轴刻度创建断点和标签
brks <- economics$date[seq(1, length(economics$date), 12)]
lbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])
# Plot
ggplot(economics[1:100, ], aes(date, returns_perc)) +
geom_area() +
scale_x_date(breaks=brks, labels=lbls) +
theme(axis.text.x = element_text(angle=90)) +
labs(title="Area Chart",
subtitle = "Perc Returns for Personal Savings",
y="% Returns for Personal savings",
caption="Source: economics")
# Chunk 17
# 准备数据：按厂商分组计算平均城市里程。
cty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate
colnames(cty_mpg) <- c("make", "mileage")  # change column names
cty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort
cty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.
head(cty_mpg, 4)
ggplot(cty_mpg, aes(x=make, y=mileage)) +
geom_bar(stat="identity", width=.5, fill="tomato3") +
labs(title="Ordered Bar Chart",
subtitle="Make Vs Avg. Mileage",
caption="source: mpg") +
theme(axis.text.x = element_text(angle=65, vjust=0.6))
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +
geom_point(stat='identity', aes(col=mpg_type), size=6)  +
scale_color_manual(name="Mileage",
labels = c("Above Average", "Below Average"),
values = c("above"="#00ba38", "below"="#f8766d")) +
geom_text(color="white", size=2) +
labs(title="Diverging Dot Plot",
subtitle="Normalized mileage from 'mtcars': Dotplot") +
ylim(-2.5, 2.5) +
coord_flip()
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +
geom_point(stat='identity', fill="black", size=6)  +
geom_segment(aes(y = 0,
x = `car name`,
yend = mpg_z,
xend = `car name`),
color = "black") +
geom_text(color="white", size=2) +
labs(title="Diverging Lollipop Chart",
subtitle="Normalized mileage from 'mtcars': Lollipop") +
ylim(-2.5, 2.5) +
coord_flip()
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +
geom_point(stat='identity', aes(col=mpg_type), size=6)  +
scale_color_manual(name="Mileage",
labels = c("Above Average", "Below Average"),
values = c("above"="#00ba38", "below"="#f8766d")) +
geom_text(color="white", size=2) +
labs(title="Diverging Dot Plot",
subtitle="Normalized mileage from 'mtcars': Dotplot") +
ylim(-2.5, 2.5) +
coord_flip()
library(quantmod)
data("economics", package = "ggplot2")
# 计算利润
economics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])
# 为轴刻度创建断点和标签
brks <- economics$date[seq(1, length(economics$date), 12)]
lbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])
# Plot
ggplot(economics[1:100, ], aes(date, returns_perc)) +
geom_area() +
scale_x_date(breaks=brks, labels=lbls) +
theme(axis.text.x = element_text(angle=90)) +
labs(title="Area Chart",
subtitle = "Perc Returns for Personal Savings",
y="% Returns for Personal savings",
caption="Source: economics")
# 准备数据：按厂商分组计算平均城市里程。
cty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate
colnames(cty_mpg) <- c("make", "mileage")  # change column names
cty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort
cty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.
head(cty_mpg, 4)
head(df)
library(scales)
# 数据准备
df <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv")
colnames(df) <- c("continent", "1952", "1957")
left_label <- paste(df$continent, round(df$'1952'),sep=", ")
right_label <- paste(df$continent, round(df$'1957'),sep=", ")
df$class <- ifelse((df$'1957' - df$'1952') < 0, "red", "green")
head(df)
df
library(ggplot2)
library(plyr)
library(scales)
library(zoo)
df <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv")
df$date <- as.Date(df$date)  # format date
df <- df[df$year >= 2012, ]  # filter reqd years
# Create Month Week
df$yearmonth <- as.yearmon(df$date)
df$yearmonthf <- factor(df$yearmonth)
df <- ddply(df,.(yearmonthf), transform, monthweek=1+week-min(week))  # compute week number of month
df <- df[, c("year", "yearmonthf", "monthf", "week", "monthweek", "weekdayf", "VIX.Close")]
head(df)
head(df)
# Chunk 1: setup
knitr::opts_chunk$set(echo = TRUE,warning = F,message = F)
# Chunk 2
library(ggplot2)
library(plotrix)
options(scipen=999)  # 关掉像1e+48这样的科学符号
data("midwest", package = "ggplot2") #加载数据集
# Chunk 3
# 颜色设置（灰色系列）
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7")
# 颜色设置（黑色系列）
cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7")
ggplot <- function(...) ggplot2::ggplot(...) +
scale_color_manual(values = cbp1) +
scale_fill_manual(values = cbp1) + # 注意: 使用连续色阶时需要重写
theme_bw()
# Chunk 4
# Scatterplot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state, size=popdensity)) +
geom_smooth(method="loess", se=F) +
xlim(c(0, 0.1)) +
ylim(c(0, 500000)) +
labs(subtitle="Area Vs Population",
y="Population",
x="Area",
title="Scatterplot",
caption = "Source: midwest")
plot(gg)
# Chunk 5
# install.packages("ggalt")
library(ggalt)
options(scipen = 999)
library(ggplot2)
library(ggalt)
## 设定筛选条件
midwest_select <- midwest[midwest$poptotal > 350000 &
midwest$poptotal <= 500000 &
midwest$area > 0.01 &
midwest$area < 0.1, ]
# Plot
ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state, size=popdensity)) +   # draw points
geom_smooth(method="loess", se=F) +
xlim(c(0, 0.1)) +
ylim(c(0, 500000)) +   # draw smoothing line
geom_encircle(aes(x=area, y=poptotal),
data=midwest_select,
color="red",
size=2,
expand=0.08) +   # encircle
labs(subtitle="Area Vs Population",
y="Population",
x="Area",
title="Scatterplot + Encircle",
caption="Source: midwest")
# Chunk 6
g <- ggplot(mpg, aes(cty, hwy))
g + geom_jitter(width = .5, size=1) +
labs(subtitle="mpg: city vs highway mileage",
y="hwy",
x="cty",
title="Jittered Points")
# Chunk 7
g + geom_count(col="tomato3", show.legend=F) +
labs(subtitle="mpg: city vs highway mileage",
y="hwy",
x="cty",
title="Counts Plot")
# Chunk 8
data(mpg, package="ggplot2")
mpg_select <- mpg[mpg$manufacturer %in% c("audi", "ford", "honda", "hyundai"), ]
# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
g <- ggplot(mpg_select, aes(displ, cty)) +
labs(subtitle="mpg: Displacement vs City Mileage",
title="Bubble chart")
g + geom_jitter(aes(col=manufacturer, size=hwy)) +
geom_smooth(aes(col=manufacturer), method="lm", se=F)
# Chunk 9
library(ggExtra)
data(mpg, package="ggplot2")
# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
mpg_select <- mpg[mpg$hwy >= 35 & mpg$cty > 27, ]
g <- ggplot(mpg, aes(cty, hwy)) +
geom_count() +
geom_smooth(method="lm", se=F)
ggMarginal(g, type = "histogram", fill="transparent")
# Chunk 10
ggMarginal(g, type = "boxplot", fill="transparent")
# Chunk 11
ggMarginal(g, type = "density", fill="transparent")
# Chunk 12
library(ggcorrplot)
# Correlation matrix
data(mtcars)
corr <- round(cor(mtcars), 1)
# Plot
ggcorrplot(corr, hc.order = TRUE,
type = "lower",
lab = TRUE,
lab_size = 3,
method="circle",
colors = c("tomato2", "white", "springgreen3"),
title="Correlogram of mtcars",
ggtheme=theme_bw)
# Chunk 13
data("mtcars")  # load data
mtcars$`car name` <- rownames(mtcars)  # create new column for car names
mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg
mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, "below", "above")  # above / below avg flag
mtcars <- mtcars[order(mtcars$mpg_z), ]  # sort
mtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`)
# Diverging Barcharts
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +
geom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +
scale_fill_manual(name="Mileage",
labels = c("Above Average", "Below Average"),
values = c("above"="#00ba38", "below"="#f8766d")) +
labs(subtitle="Normalised mileage from 'mtcars'",
title= "Diverging Bars") +
coord_flip()
# Chunk 14
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +
geom_point(stat='identity', fill="black", size=6)  +
geom_segment(aes(y = 0,
x = `car name`,
yend = mpg_z,
xend = `car name`),
color = "black") +
geom_text(color="white", size=2) +
labs(title="Diverging Lollipop Chart",
subtitle="Normalized mileage from 'mtcars': Lollipop") +
ylim(-2.5, 2.5) +
coord_flip()
# Chunk 15
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +
geom_point(stat='identity', aes(col=mpg_type), size=6)  +
scale_color_manual(name="Mileage",
labels = c("Above Average", "Below Average"),
values = c("above"="#00ba38", "below"="#f8766d")) +
geom_text(color="white", size=2) +
labs(title="Diverging Dot Plot",
subtitle="Normalized mileage from 'mtcars': Dotplot") +
ylim(-2.5, 2.5) +
coord_flip()
# Chunk 16
library(quantmod)
data("economics", package = "ggplot2")
# 计算利润
economics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])
# 为轴刻度创建断点和标签
brks <- economics$date[seq(1, length(economics$date), 12)]
lbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])
# Plot
ggplot(economics[1:100, ], aes(date, returns_perc)) +
geom_area() +
scale_x_date(breaks=brks, labels=lbls) +
theme(axis.text.x = element_text(angle=90)) +
labs(title="Area Chart",
subtitle = "Perc Returns for Personal Savings",
y="% Returns for Personal savings",
caption="Source: economics")
# Chunk 17
# 准备数据：按厂商分组计算平均城市里程。
cty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate
colnames(cty_mpg) <- c("make", "mileage")  # change column names
cty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort
cty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.
head(cty_mpg, 4)
# Chunk 18
ggplot(cty_mpg, aes(x=make, y=mileage)) +
geom_bar(stat="identity", width=.5, fill="tomato3") +
labs(title="Ordered Bar Chart",
subtitle="Make Vs Avg. Mileage",
caption="source: mpg") +
theme(axis.text.x = element_text(angle=65, vjust=0.6))
# Chunk 19
ggplot(cty_mpg, aes(x=make, y=mileage)) +
geom_point(size=3) +
geom_segment(aes(x=make,
xend=make,
y=0,
yend=mileage)) +
labs(title="Lollipop Chart",
subtitle="Make Vs Avg. Mileage",
caption="source: mpg") +
theme(axis.text.x = element_text(angle=65, vjust=0.6))
# Chunk 20
ggplot(cty_mpg, aes(x=make, y=mileage)) +
geom_point(col="tomato2", size=3) +   # Draw points
geom_segment(aes(x=make,
xend=make,
y=min(mileage),
yend=max(mileage)),
linetype="dashed",
size=0.1) +   # Draw dashed lines
labs(title="Dot Plot",
subtitle="Make Vs Avg. Mileage",
caption="source: mpg") +
coord_flip()
# Chunk 21
library(scales)
# 数据准备
df <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv")
data(economics_long, package = "ggplot2")
head(economics_long)
library(ggplot2)
library(plyr)
library(scales)
library(zoo)
df <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv")
df$date <- as.Date(df$date)  # format date
df <- df[df$year >= 2012, ]  # filter reqd years
# Create Month Week
df$yearmonth <- as.yearmon(df$date)
df$yearmonthf <- factor(df$yearmonth)
df <- ddply(df,.(yearmonthf), transform, monthweek=1+week-min(week))  # compute week number of month
df <- df[, c("year", "yearmonthf", "monthf", "week", "monthweek", "weekdayf", "VIX.Close")]
head(df)


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---
title: "ggplot绘图手册"
author: "赵子茜"
date: "`r Sys.Date()`"
output: 
  prettydoc::html_pretty:
    toc: true
    toc_depth: 4
    theme: cayman
    highlight: github
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,warning = F,message = F)
```

## 全局主题设置
```{r}
library(ggplot2)
library(plotrix)
options(scipen=999)  # turn-off scientific notation like 1e+48
# theme_set(theme_bw())  # pre-set the bw theme.
data("midwest", package = "ggplot2")
midwest <- read.csv("http://goo.gl/G1K41K")  # bkup data source
# write.csv(midwest,"midwest.csv")

```





```{r}
# The palette with grey:
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

# The palette with black:
cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")


ggplot <- function(...) ggplot2::ggplot(...) + 
  scale_color_manual(values = cbp1) +
  scale_fill_manual(values = cbp1) + # note: needs to be overridden when using continuous color scales
  theme_bw()

```





## 1. 散点图
### 1.1 两个变量
展示两个变量之间的相关关系，最常使用的是**散点图**。在 ggplot 中，使用`geom_point()`绘制。此外，默认情况下，`geom_smooth`会绘制一条平滑线（基于 losses ），可以通过设置`method='lm'`来调整以绘制最佳拟合的线。


```{r}
# Scatterplot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) + 
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.1)) + 
  ylim(c(0, 500000)) + 
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot", 
       caption = "Source: midwest")

plot(gg)

```


### 1.2 环绕式散点图
使用 ggalt 包中的`geom_encircle()`函数可实现在散点图中圈选特定区域：
```{r message=FALSE, warning=FALSE}
# install.packages("ggalt")
library(ggalt)
options(scipen = 999)
library(ggplot2)
library(ggalt)

## 设定筛选条件
midwest_select <- midwest[midwest$poptotal > 350000 & 
                            midwest$poptotal <= 500000 & 
                            midwest$area > 0.01 & 
                            midwest$area < 0.1, ]

# Plot
ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) +   # draw points
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.1)) + 
  ylim(c(0, 500000)) +   # draw smoothing line
  geom_encircle(aes(x=area, y=poptotal), 
                data=midwest_select, 
                color="red", 
                size=2, 
                expand=0.08) +   # encircle
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot + Encircle", 
       caption="Source: midwest")
```




### 1.3 抖动图

抖动图可以解决数据点重叠的问题。通过`geom_jitter`函数中的`width`参数设置抖动范围，重叠点在其原始位置周围随机抖动。
```{r}
g <- ggplot(mpg, aes(cty, hwy))
g + geom_jitter(width = .5, size=1) +
  labs(subtitle="mpg: city vs highway mileage", 
       y="hwy", 
       x="cty", 
       title="Jittered Points")

```

### 1.4 数值图

克服数据点重叠问题的第二个选择是使用计数图。重叠点越多，圆就越大。

```{r}
g + geom_count(col="tomato3", show.legend=F) +
  labs(subtitle="mpg: city vs highway mileage", 
       y="hwy", 
       x="cty", 
       title="Counts Plot")
```

### 1.5 气泡图
气泡图适合 4 维数据，其中两个是数值型（分别是 X 和 Y），另一个是分类变量（用`color`表示）和另一个数值变量（用`size`表示）。
```{r}
data(mpg, package="ggplot2")


mpg_select <- mpg[mpg$manufacturer %in% c("audi", "ford", "honda", "hyundai"), ]

# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
g <- ggplot(mpg_select, aes(displ, cty)) + 
  labs(subtitle="mpg: Displacement vs City Mileage",
       title="Bubble chart")

g + geom_jitter(aes(col=manufacturer, size=hwy)) + 
  geom_smooth(aes(col=manufacturer), method="lm", se=F)
```


<!-- ### 1.6 动态气泡图 -->

<!-- ```{r} -->
<!-- # install.packages("gganimate") -->
<!-- # install.packages("gapminder") -->
<!-- library(gganimate) -->
<!-- library(gapminder) -->

<!-- g <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, frame = year)) + -->
<!--   geom_point() + -->
<!--   geom_smooth(aes(group = year),  -->
<!--               method = "lm",  -->
<!--               show.legend = FALSE) + -->
<!--   facet_wrap(~continent, scales = "free") + -->
<!--   scale_x_log10()  # convert to log scale -->

<!-- gganimate(g, interval=0.2) -->
<!-- ``` -->


### 1.6 边际直方图/箱线图

如果您想在同一个图中显示变量的关系和分布，可以使用边际直方图。更改`ggMarginal()`函数中的`type`参数可以将边际直方图换成箱线图或者密度图。

```{r}
library(ggExtra)
data(mpg, package="ggplot2")


# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
mpg_select <- mpg[mpg$hwy >= 35 & mpg$cty > 27, ]
g <- ggplot(mpg, aes(cty, hwy)) + 
  geom_count() + 
  geom_smooth(method="lm", se=F)

ggMarginal(g, type = "histogram", fill="transparent")

```
```{r}
ggMarginal(g, type = "boxplot", fill="transparent")

```

```{r}
ggMarginal(g, type = "density", fill="transparent")
```

### 1.7 相关系数图

相关系数图可以查看同一组数据中多个连续变量的相关性。
```{r}
library(ggcorrplot)

# Correlation matrix
data(mtcars)
corr <- round(cor(mtcars), 1)

# Plot
ggcorrplot(corr, hc.order = TRUE, 
           type = "lower", 
           lab = TRUE, 
           lab_size = 3, 
           method="circle", 
           colors = c("tomato2", "white", "springgreen3"), 
           title="Correlogram of mtcars", 
           ggtheme=theme_bw)
```

## 2 偏差
### 2.1 发散条形图
发散条形图是一种可以同时处理负值和正值的条形图。这可以通过`geom_bar()`来实现。但是`geom_bar()`的用法可能会让人很困惑。这是因为，它既可以用来制作柱状图，也可以用来制作直方图。默认情况下，`geom_bar()`中`stat`参数的默认值为`count`。这意味着，当您只提供一个连续的 x 变量（而不提供 y 变量）时，它会尝试从数据中生成一个直方图。如果要制作条形图，需要做两件事：

- 设置`stat=identity`;
- 在`aes()`中同时输入 x 和 y，其中 x 是字符或因子型变量，y 是数值型。

为了确保得到的是发散条形图，数据需要满足：分类变量的两个类别在连续变量的某个阈值处改变其值。在下面的例子中，`mtcars`数据集的 mpg 通过计算 z 分数被规范化。那些 mpg 在 0 以上的车辆被标记为绿色，低于 0 的车辆被标记为红色。
```{r}
data("mtcars")  # load data
mtcars$`car name` <- rownames(mtcars)  # create new column for car names
mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg
mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, "below", "above")  # above / below avg flag
mtcars <- mtcars[order(mtcars$mpg_z), ]  # sort
mtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`)  # convert to factor to retain sorted order in plot.

# Diverging Barcharts
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +
  scale_fill_manual(name="Mileage", 
                    labels = c("Above Average", "Below Average"), 
                    values = c("above"="#00ba38", "below"="#f8766d")) + 
  labs(subtitle="Normalised mileage from 'mtcars'", 
       title= "Diverging Bars") + 
  coord_flip()
```


### 2.2 带标记的发散型棒棒糖图
这个图形是发散条形图的一个变体。
```{r}
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_point(stat='identity', fill="black", size=6)  +
  geom_segment(aes(y = 0, 
                   x = `car name`, 
                   yend = mpg_z, 
                   xend = `car name`), 
               color = "black") +
  geom_text(color="white", size=2) +
  labs(title="Diverging Lollipop Chart", 
       subtitle="Normalized mileage from 'mtcars': Lollipop") + 
  ylim(-2.5, 2.5) +
  coord_flip()
```


### 2.3 发散性点图

```{r}
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_point(stat='identity', aes(col=mpg_type), size=6)  +
  scale_color_manual(name="Mileage", 
                     labels = c("Above Average", "Below Average"), 
                     values = c("above"="#00ba38", "below"="#f8766d")) + 
  geom_text(color="white", size=2) +
  labs(title="Diverging Dot Plot", 
       subtitle="Normalized mileage from 'mtcars': Dotplot") + 
  ylim(-2.5, 2.5) +
  coord_flip()
```


### 2.4 面积图
面积图通常用于可视化特定指标（如股票回报率）与基线的对比情况。
```{r}
library(quantmod)
data("economics", package = "ggplot2")

# 计算利润
economics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])

# 为轴刻度创建断点和标签
brks <- economics$date[seq(1, length(economics$date), 12)]
lbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])

# Plot
ggplot(economics[1:100, ], aes(date, returns_perc)) + 
  geom_area() + 
  scale_x_date(breaks=brks, labels=lbls) + 
  theme(axis.text.x = element_text(angle=90)) + 
  labs(title="Area Chart", 
       subtitle = "Perc Returns for Personal Savings", 
       y="% Returns for Personal savings", 
       caption="Source: economics")

```

## 3 排序
### 3.1 有序条形图
有序条形图是按 Y 轴变量排序的条形图，X 轴变量必须转换为因子型。
```{r}
# 准备数据：按厂商分组计算平均城市里程。
cty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate
colnames(cty_mpg) <- c("make", "mileage")  # change column names
cty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort
cty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.
head(cty_mpg, 4)

```

```{r}
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_bar(stat="identity", width=.5, fill="tomato3") + 
  labs(title="Ordered Bar Chart", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

### 3.2 棒棒糖图
棒棒糖图传达的信息与柱状图相同。通过将粗条转变为细线，减少了杂乱，使图形看起来更美观。
```{r}
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_point(size=3) + 
  geom_segment(aes(x=make, 
                   xend=make, 
                   y=0, 
                   yend=mileage)) + 
  labs(title="Lollipop Chart", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

### 3.3 点图
点图非常类似于棒棒糖图，但没有线条，并且各标签都在水平位置上。它更强调项目的顺序与实际值有关。
```{r}
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_point(col="tomato2", size=3) +   # Draw points
  geom_segment(aes(x=make, 
                   xend=make, 
                   y=min(mileage), 
                   yend=max(mileage)), 
               linetype="dashed", 
               size=0.1) +   # Draw dashed lines
  labs(title="Dot Plot", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") +  
  coord_flip()
```

### 3.4 坡度图
坡度图是比较两点之间差异的绝佳方法。目前，还没有内置函数来构建这个图形。下面的代码可以作为一个示例框架，告诉您如何绘制这个图形。
```{r}
library(scales)

# 数据准备
df <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv")
colnames(df) <- c("continent", "1952", "1957")
left_label <- paste(df$continent, round(df$'1952'),sep=", ")
right_label <- paste(df$continent, round(df$'1957'),sep=", ")
df$class <- ifelse((df$'1957' - df$'1952') < 0, "red", "green")
```

```{r}
library(ggplot2)
p <- ggplot(df) + geom_segment(aes(x=1, xend=2, y=`1952`, yend=`1957`, col=class), size=.75, show.legend=F) + 
                  geom_vline(xintercept=1, linetype="dashed", size=.1) + 
                  geom_vline(xintercept=2, linetype="dashed", size=.1) +
                  scale_color_manual(labels = c("Up", "Down"), 
                                     values = c("green"="#00ba38", "red"="#f8766d")) +  # color of lines
                  labs(x="", y="Mean GdpPerCap") +  # Axis labels
                  xlim(.5, 2.5) + ylim(0,(1.1*(max(df$`1952`, df$`1957`))))  # X and Y axis limits

# Add texts
p <- p + geom_text(label=left_label, y=df$`1952`, x=rep(1, NROW(df)), hjust=1.1, size=3.5)
p <- p + geom_text(label=right_label, y=df$`1957`, x=rep(2, NROW(df)), hjust=-0.1, size=3.5)
p <- p + geom_text(label="Time 1", x=1, y=1.1*(max(df$`1952`, df$`1957`)), hjust=1.2, size=5)  # title
p <- p + geom_text(label="Time 2", x=2, y=1.1*(max(df$`1952`, df$`1957`)), hjust=-0.1, size=5)  # title


#设置主题
p + theme(panel.background = element_blank(),
           panel.grid = element_blank(),
           axis.ticks = element_blank(),
           axis.text.x = element_blank(),
           panel.border = element_blank(),
           plot.margin = unit(c(1,2,1,2), "cm"))


```

### 3.5 哑铃图
哑铃图可以：1)比较两个时间点之间的相对位置（比如增长和下降）；2) 比较两类之间的距离。为了得到哑铃的正确顺序，Y 变量应该是一个因子，因子变量的水平应该与它在图中出现的顺序相同。
```{r}
health <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/health.csv")
health$Area <- factor(health$Area, levels=as.character(health$Area))  # for right ordering of the dumbells

# health$Area <- factor(health$Area)
gg <- ggplot(health, aes(x=pct_2013, xend=pct_2014, y=Area, group=Area)) + 
        geom_dumbbell(color="#a3c4dc", 
                      size=0.75, 
                      point.colour.l="#0e668b") + 
        scale_x_continuous(label=percent) + 
        labs(x=NULL, 
             y=NULL, 
             title="Dumbbell Chart", 
             subtitle="Pct Change: 2013 vs 2014", 
             caption="Source: https://github.com/hrbrmstr/ggalt") +
        theme(plot.title = element_text(hjust=0.5, face="bold"),
              plot.background=element_rect(fill="#f7f7f7"),
              panel.background=element_rect(fill="#f7f7f7"),
              panel.grid.minor=element_blank(),
              panel.grid.major.y=element_blank(),
              panel.grid.major.x=element_line(),
              axis.ticks=element_blank(),
              legend.position="top",
              panel.border=element_blank())

plot(gg)

```

## 4 分布
### 4.1 直方图
#### 4.4.1 连续变量的直方图
连续变量的直方图可以使用`geom_bar()`或`geom_histogram()`来完成。当使用`geom_histogram()`时，可以使用`bins`参数来控制分箱的数量。也可以使用`binwidth`设置每个分箱覆盖的范围。`binwidth`的值与建立直方图的连续变量在同一个尺度上。
```{r}
# 连续（数值）变量的直方图
g <- ggplot(mpg, aes(displ)) + scale_fill_brewer(palette = "Spectral")

g + geom_histogram(aes(fill=class), 
                   binwidth = .1, 
                   col="black", 
                   size=.1) +  # change binwidth
  labs(title="Histogram with Auto Binning", 
       subtitle="Engine Displacement across Vehicle Classes")

 
```


```{r}

g + geom_histogram(aes(fill=class), 
                   bins=5, 
                   col="black", 
                   size=.1) +   # change number of bins
  labs(title="Histogram with Fixed Bins", 
       subtitle="Engine Displacement across Vehicle Classes") 

 
```

#### 4.1.2分类变量的直方图
分类变量的直方图实际上是根据每个类别的频率绘制的条形图。
```{r}
library(ggplot2)
theme_set(theme_classic())

# Histogram on a Categorical variable
g <- ggplot(mpg, aes(manufacturer))
g + geom_bar(aes(fill=class), width = 0.5) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Histogram on Categorical Variable", 
       subtitle="Manufacturer across Vehicle Classes")

 
```

### 4.2 密度图

```{r}
g <- ggplot(mpg, aes(cty))
g + geom_density(aes(fill=factor(cyl)), alpha=0.8) + 
    labs(title="Density plot", 
         subtitle="City Mileage Grouped by Number of cylinders",
         caption="Source: mpg",
         x="City Mileage",
         fill="# Cylinders")
 
```

### 4.3 箱线图
箱形图是研究数据分布的一个有用工具。它还可以显示多个组内的分布，以及中值、范围和异常值。箱子内的黑线表示中位数。箱顶是 75% 分位数，箱底是 25% 分位数。线的端点（又称晶须）距离为1.5*IQR，其中 IQR （四分位差）是 25% 到 75% 分位数之间距离。须外的点通常被认为是极值点。设置`varwidth=T`将调整盒子的宽度，使其与观察的数量成比例。
```{r}
g <- ggplot(mpg, aes(class, cty))
g + geom_boxplot(varwidth=T, fill="plum") + 
    labs(title="Box plot", 
         subtitle="City Mileage grouped by Class of vehicle",
         caption="Source: mpg",
         x="Class of Vehicle",
         y="City Mileage")
 
```

```{r}
g <- ggplot(mpg, aes(class, cty))
g + geom_boxplot(aes(fill=factor(cyl))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="City Mileage grouped by Class of vehicle",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
 
```

### 4.4 点图+箱线图
在箱线图的基础上，添加点图可以提供更清晰的信息。这些点交错排列，每个点代表一次观测。
```{r}
g <- ggplot(mpg, aes(manufacturer, cty))
g + geom_boxplot() + 
  geom_dotplot(binaxis='y', 
               stackdir='center', 
               dotsize = .5, 
               fill="red") +
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot + Dot plot", 
       subtitle="City Mileage vs Class: Each dot represents 1 row in source data",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
 
```

### 4.5 塔夫特箱线图
这是一个简化版的箱线图。
```{r}

library(ggthemes)
library(ggplot2)
theme_set(theme_tufte())  # from ggthemes

# plot
g <- ggplot(mpg, aes(manufacturer, cty))
g + geom_tufteboxplot() + 
      theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
      labs(title="Tufte Styled Boxplot", 
           subtitle="City Mileage grouped by Class of vehicle",
           caption="Source: mpg",
           x="Class of Vehicle",
           y="City Mileage")


```

### 4.6 小提琴图
小提琴图类似于箱线图，但显示了组内的密度。
```{r}
g <- ggplot(mpg, aes(class, cty))
g + geom_violin() + 
  labs(title="Violin plot", 
       subtitle="City Mileage vs Class of vehicle",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
 
```

### 4.7 金字塔图
金字塔图提供了一种独特的方式来可视化每个类别包含的样本比例。下面的金字塔是一个很好的例子，说明了在一个营销活动中每个阶段的用户留存率。
```{r}
library(ggplot2)
library(ggthemes)
options(scipen = 999)  # turns of scientific notations like 1e+40

# Read data
email_campaign_funnel <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv")

# X Axis Breaks and Labels 
brks <- seq(-15000000, 15000000, 5000000)
lbls = paste0(as.character(c(seq(15, 0, -5), seq(5, 15, 5))), "m")

# Plot
ggplot(email_campaign_funnel, aes(x = Stage, y = Users, fill = Gender)) +   # Fill column
                              geom_bar(stat = "identity", width = .6) +   # draw the bars
                              scale_y_continuous(breaks = brks,   # Breaks
                                                 labels = lbls) + # Labels
                              coord_flip() +  # Flip axes
                              labs(title="Email Campaign Funnel") +
                              theme_tufte() +  # Tufte theme from ggfortify
                              theme(plot.title = element_text(hjust = .5), 
                                    axis.ticks = element_blank()) +   # Centre plot title
                              scale_fill_brewer(palette = "Dark2")  # Color palette
```

## 5 组成
### 5.1 华夫饼图
华夫图可以显示总体的组成成分。
```{r}
var <- mpg$class  # the categorical data 

## Prep data (nothing to change here)
nrows <- 10
df <- expand.grid(y = 1:nrows, x = 1:nrows)
categ_table <- round(table(var) * ((nrows*nrows)/(length(var))))


df$category <- factor(rep(names(categ_table), categ_table))  
# NOTE: if sum(categ_table) is not 100 (i.e. nrows^2), it will need adjustment to make the sum to 100.

## Plot
ggplot(df, aes(x = x, y = y, fill = category)) + 
        geom_tile(color = "black", size = 0.5) +
        scale_x_continuous(expand = c(0, 0)) +
        scale_y_continuous(expand = c(0, 0), trans = 'reverse') +
        scale_fill_brewer(palette = "Set3") +
        labs(title="Waffle Chart", subtitle="'Class' of vehicles",
             caption="Source: mpg") + 
        theme(panel.border = element_rect(size = 2),
              plot.title = element_text(size = rel(1.2)),
              axis.text = element_blank(),
              axis.title = element_blank(),
              axis.ticks = element_blank(),
              legend.title = element_blank(),
              legend.position = "right")

```

### 5.2 饼图

```{r}
library(ggplot2)
theme_set(theme_classic())

# Source: Frequency table
df <- as.data.frame(table(mpg$class))
colnames(df) <- c("class", "freq")
pie <- ggplot(df, aes(x = "", y=freq, fill = factor(class))) + 
  geom_bar(width = 1, stat = "identity") +
  theme(axis.line = element_blank(), 
        plot.title = element_text(hjust=0.5)) + 
  labs(fill="class", 
       x=NULL, 
       y=NULL, 
       title="Pie Chart of class", 
       caption="Source: mpg")

pie + coord_polar(theta = "y", start=0)

# Source: Categorical variable.
# mpg$class
pie <- ggplot(mpg, aes(x = "", fill = factor(class))) + 
  geom_bar(width = 1) +
  theme(axis.line = element_blank(), 
        plot.title = element_text(hjust=0.5)) + 
  labs(fill="class", 
       x=NULL, 
       y=NULL, 
       title="Pie Chart of class", 
       caption="Source: mpg")
  
pie + coord_polar(theta = "y", start=0)
```



### 5.3 条形图
```{r}
 #prep frequency table
freqtable <- table(mpg$manufacturer)
df <- as.data.frame.table(freqtable)

```

```{r}
library(ggplot2)
theme_set(theme_classic())

# Plot
g <- ggplot(df, aes(Var1, Freq))
g + geom_bar(stat="identity", width = 0.5, fill="tomato2") + 
      labs(title="Bar Chart", 
           subtitle="Manufacturer of vehicles", 
           caption="Source: Frequency of Manufacturers from 'mpg' dataset") +
      theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

```{r}
# From on a categorical column variable
g <- ggplot(mpg, aes(manufacturer))
g + geom_bar(aes(fill=class), width = 0.5) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) +
  labs(title="Categorywise Bar Chart", 
       subtitle="Manufacturer of vehicles", 
       caption="Source: Manufacturers from 'mpg' dataset")
```



## 6 变化趋势
### 6.1 时间序列图：基于时间序列对象（ts）
`ggfortify`包可以对时间序列直接绘图。
```{r}
## From Timeseries object (ts)
library(ggplot2)
library(ggfortify)
theme_set(theme_classic())

# Plot 
autoplot(AirPassengers) + 
  labs(title="AirPassengers") + 
  theme(plot.title = element_text(hjust=0.5))
```

### 6.2 时间序列图：基于数据框
```{r}
library(ggplot2)
theme_set(theme_classic())

# 使用默认的时间跨度
ggplot(economics, aes(x=date)) + 
  geom_line(aes(y=returns_perc)) + 
  labs(title="Time Series Chart", 
       subtitle="Returns Percentage from 'Economics' Dataset", 
       caption="Source: Economics", 
       y="Returns %")

```

如果想设置特定的时间间隔，则需要使用`scale_x_date()`函数。
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

economics_m <- economics[1:24, ]

# 设定时间跨度为一个月
lbls <- paste0(month.abb[month(economics_m$date)], " ", lubridate::year(economics_m$date))
brks <- economics_m$date

# plot
ggplot(economics_m, aes(x=date)) + 
  geom_line(aes(y=returns_perc)) + 
  labs(title="Monthly Time Series", 
       subtitle="Returns Percentage from Economics Dataset", 
       caption="Source: Economics", 
       y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, 
               breaks = brks) +  # change to monthly ticks and labels
  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text
        panel.grid.minor = element_blank())  # turn off minor grid
```

设置时间跨度为 1 年：
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

economics_y <- economics[1:90, ]

# labels and breaks for X axis text
brks <- economics_y$date[seq(1, length(economics_y$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(economics_y, aes(x=date)) + 
  geom_line(aes(y=returns_perc)) + 
  labs(title="Yearly Time Series", 
       subtitle="Returns Percentage from Economics Dataset", 
       caption="Source: Economics", 
       y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, 
               breaks = brks) +  # change to monthly ticks and labels
  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text
        panel.grid.minor = element_blank())  # turn off minor grid
```

### 6.3 多个时间序列
在本例中，基于长数据格式进行可视化。这意味着，所有列的列名和各自的值被存放在两个变量中（分别是`variable`和`value`）。

```{r}
data(economics_long, package = "ggplot2")
head(economics_long)
```

在下面的代码中，在`geom_line()`函数中设置绘图对象为`value`，颜色匹配对象为`variable`。这样，只要调用一次geom_line，就会绘制多条彩色线，每条线代表`variable`列中的每个唯一`value`。`scale_x_date()`将更改 X 轴断点和标签，`scale_color_manual`将更改行颜色。
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

df <- economics_long[economics_long$variable %in% c("psavert", "uempmed"), ]
df <- df[lubridate::year(df$date) %in% c(1967:1981), ]

# labels and breaks for X axis text
brks <- df$date[seq(1, length(df$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(df, aes(x=date)) + 
  geom_line(aes(y=value, col=variable)) + 
  labs(title="Time Series of Returns Percentage", 
       subtitle="Drawn from Long Data format", 
       caption="Source: Economics", 
       y="Returns %", 
       color=NULL) +  # title and caption
  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels
  scale_color_manual(labels = c("psavert", "uempmed"), 
                     values = c("psavert"="#00ba38", "uempmed"="#f8766d")) +  # line color
  theme(axis.text.x = element_text(angle = 90, vjust=0.5, size = 8),  # rotate x axis text
        panel.grid.minor = element_blank())  # turn off minor grid
```

如果从一个宽格式创建一个时间序列，则必须通过对每条线调用一次`geom_line()`制。因此，默认情况下不会绘制图例，需要手动添加。
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

df <- economics[, c("date", "psavert", "uempmed")]
df <- df[lubridate::year(df$date) %in% c(1967:1981), ]

# labels and breaks for X axis text
brks <- df$date[seq(1, length(df$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(df, aes(x=date)) + 
  geom_line(aes(y=psavert, col="psavert")) + 
  geom_line(aes(y=uempmed, col="uempmed")) + 
  labs(title="Time Series of Returns Percentage", 
       subtitle="Drawn From Wide Data format", 
       caption="Source: Economics", y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels
  scale_color_manual(name="", 
                     values = c("psavert"="#00ba38", "uempmed"="#f8766d")) +  # line color
  theme(panel.grid.minor = element_blank())  # turn off minor grid
```



### 6.4 堆叠面积图
堆叠面积图与折线图类似，只是图下方的区域全部着色。应用场景有：

- 想要描述数量或体积（而不是价格之类的变量）随时间的变化；
- 有很多数据点。对于很少的数据点，可以考虑绘制柱状图。
- 希望展示各个类别的贡献。

```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

df <- economics[, c("date", "psavert", "uempmed")]
df <- df[lubridate::year(df$date) %in% c(1967:1981), ]

# labels and breaks for X axis text
brks <- df$date[seq(1, length(df$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(df, aes(x=date)) + 
  geom_area(aes(y=psavert+uempmed, fill="psavert")) + 
  geom_area(aes(y=uempmed, fill="uempmed")) + 
  labs(title="Area Chart of Returns Percentage", 
       subtitle="From Wide Data format", 
       caption="Source: Economics", 
       y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels
  scale_fill_manual(name="", 
                    values = c("psavert"="#00ba38", "uempmed"="#f8766d")) +  # line color
  theme(panel.grid.minor = element_blank())  # turn off minor grid
```


### 6.5 日历热力图

当您想要在实际的日历上看到像股票价格这类指标的变化，特别是高点和低点时，日历热力图是一个很好的工具。它强调随着时间的推移视觉上的变化，而不是实际数值的变化。这可以通过使用`geom_tile()`来实现。
```{r}
library(ggplot2)
library(plyr)
library(scales)
library(zoo)

df <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv")
df$date <- as.Date(df$date)  # format date
df <- df[df$year >= 2012, ]  # filter reqd years

# Create Month Week
df$yearmonth <- as.yearmon(df$date)
df$yearmonthf <- factor(df$yearmonth)
df <- ddply(df,.(yearmonthf), transform, monthweek=1+week-min(week))  # compute week number of month
df <- df[, c("year", "yearmonthf", "monthf", "week", "monthweek", "weekdayf", "VIX.Close")]
head(df)
```

```{r}
ggplot(df, aes(monthweek, weekdayf, fill = VIX.Close)) + 
  geom_tile(colour = "white") + 
  facet_grid(year~monthf) + 
  scale_fill_gradient(low="red", high="green") +
  labs(x="Week of Month",
       y="",
       title = "Time-Series Calendar Heatmap", 
       subtitle="Yahoo Closing Price", 
       fill="Close")
```

### 6.6 坡度图
坡度图可以可视化数值和类别排名之间的变化。这更适用于时间点很少的时间序列。
```{r}
library(dplyr)
theme_set(theme_classic())
source_df <- read.csv("https://raw.githubusercontent.com/jkeirstead/r-slopegraph/master/cancer_survival_rates.csv")

# Define functions. Source: https://github.com/jkeirstead/r-slopegraph
tufte_sort <- function(df, x="year", y="value", group="group", method="tufte", min.space=0.05) {
    ## First rename the columns for consistency
    ids <- match(c(x, y, group), names(df))
    df <- df[,ids]
    names(df) <- c("x", "y", "group")

    ## Expand grid to ensure every combination has a defined value
    tmp <- expand.grid(x=unique(df$x), group=unique(df$group))
    tmp <- merge(df, tmp, all.y=TRUE)
    df <- mutate(tmp, y=ifelse(is.na(y), 0, y))
  
    ## Cast into a matrix shape and arrange by first column
    require(reshape2)
    tmp <- dcast(df, group ~ x, value.var="y")
    ord <- order(tmp[,2])
    tmp <- tmp[ord,]
    
    min.space <- min.space*diff(range(tmp[,-1]))
    yshift <- numeric(nrow(tmp))
    ## Start at "bottom" row
    ## Repeat for rest of the rows until you hit the top
    for (i in 2:nrow(tmp)) {
        ## Shift subsequent row up by equal space so gap between
        ## two entries is >= minimum
        mat <- as.matrix(tmp[(i-1):i, -1])
        d.min <- min(diff(mat))
        yshift[i] <- ifelse(d.min < min.space, min.space - d.min, 0)
    }

    
    tmp <- cbind(tmp, yshift=cumsum(yshift))

    scale <- 1
    tmp <- melt(tmp, id=c("group", "yshift"), variable.name="x", value.name="y")
    ## Store these gaps in a separate variable so that they can be scaled ypos = a*yshift + y

    tmp <- transform(tmp, ypos=y + scale*yshift)
    return(tmp)
   
}

plot_slopegraph <- function(df) {
    ylabs <- subset(df, x==head(x,1))$group
    yvals <- subset(df, x==head(x,1))$ypos
    fontSize <- 3
    gg <- ggplot(df,aes(x=x,y=ypos)) +
        geom_line(aes(group=group),colour="grey80") +
        geom_point(colour="white",size=8) +
        geom_text(aes(label=y), size=fontSize, family="American Typewriter") +
        scale_y_continuous(name="", breaks=yvals, labels=ylabs)
    return(gg)
}    

## Prepare data    
df <- tufte_sort(source_df, 
                 x="year", 
                 y="value", 
                 group="group", 
                 method="tufte", 
                 min.space=0.05)

df <- transform(df, 
                x=factor(x, levels=c(5,10,15,20), 
                            labels=c("5 years","10 years","15 years","20 years")), 
                y=round(y))

## Plot
plot_slopegraph(df) + labs(title="Estimates of % survival rates") + 
                      theme(axis.title=element_blank(),
                            axis.ticks = element_blank(),
                            plot.title = element_text(hjust=0.5,
                                                      family = "American Typewriter",
                                                      face="bold"),
                            axis.text = element_text(family = "American Typewriter",
                                                     face="bold"))
```

### 6.7 季节图
如果您正在处理`ts`或`xts`类型的时间序列对象，您可以通过使用`forecast::ggseasonplot`绘制的季节图来查看季节波动。下面是一个使用`AirPassengers`和`nottem`数据集绘制的例子。
```{r}
library(ggplot2)
library(forecast)
theme_set(theme_classic())

# Subset data
nottem_small <- window(nottem, start=c(1920, 1), end=c(1925, 12))  # subset a smaller timewindow

# Plot
ggseasonplot(AirPassengers) + labs(title="Seasonal plot: International Airline Passengers")

```

```{r}
ggseasonplot(nottem_small) + labs(title="Seasonal plot: Air temperatures at Nottingham Castle")
```

## 7 群体
### 7.1 谱系图
```{r}

library(ggplot2)
library(ggdendro)
theme_set(theme_bw())

hc <- hclust(dist(USArrests), "ave")  # hierarchical clustering

# plot
ggdendrogram(hc, rotate = TRUE, size = 2)
```

### 7.2 聚类图
可以使用`geom_surround()`来显示不同的簇或组。如果数据集有多个特征，还可以计算主成分，并使用 PC1 和 PC2 作为 X 和 Y 轴绘制散点图。`geom_encircle()`可用于框选所需的组。
```{r}
# devtools::install_github("hrbrmstr/ggalt")
library(ggplot2)
library(ggalt)
library(ggfortify)
theme_set(theme_classic())

# Compute data with principal components ------------------
df <- iris[c(1, 2, 3, 4)]
pca_mod <- prcomp(df)  # compute principal components

# Data frame of principal components ----------------------
df_pc <- data.frame(pca_mod$x, Species=iris$Species)  # dataframe of principal components
df_pc_vir <- df_pc[df_pc$Species == "virginica", ]  # df for 'virginica'
df_pc_set <- df_pc[df_pc$Species == "setosa", ]  # df for 'setosa'
df_pc_ver <- df_pc[df_pc$Species == "versicolor", ]  # df for 'versicolor'
 
# Plot ----------------------------------------------------
ggplot(df_pc, aes(PC1, PC2, col=Species)) + 
  geom_point(aes(shape=Species), size=2) +   # draw points
  labs(title="Iris Clustering", 
       subtitle="With principal components PC1 and PC2 as X and Y axis",
       caption="Source: Iris") + 
  coord_cartesian(xlim = 1.2 * c(min(df_pc$PC1), max(df_pc$PC1)), 
                  ylim = 1.2 * c(min(df_pc$PC2), max(df_pc$PC2))) +   # change axis limits
  geom_encircle(data = df_pc_vir, aes(x=PC1, y=PC2)) +   # draw circles
  geom_encircle(data = df_pc_set, aes(x=PC1, y=PC2)) + 
  geom_encircle(data = df_pc_ver, aes(x=PC1, y=PC2))
```



================================================
FILE: 2022年/2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/.Rproj.user/161F88A0/sources/per/t/CD5422DD-contents 2
================================================
---
title: "ggplot绘图手册"
author: "赵子茜"
date: "`r Sys.Date()`"
output: 
  prettydoc::html_pretty:
    toc: true
    toc_depth: 4
    theme: cayman
    highlight: github
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,warning = F,message = F)
```

## 全局主题设置
```{r}
library(ggplot2)
library(plotrix)
options(scipen=999)  # turn-off scientific notation like 1e+48
# theme_set(theme_bw())  # pre-set the bw theme.
data("midwest", package = "ggplot2")
midwest <- read.csv("http://goo.gl/G1K41K")  # bkup data source
# write.csv(midwest,"midwest.csv")

```





```{r}
# The palette with grey:
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

# The palette with black:
cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")


ggplot <- function(...) ggplot2::ggplot(...) + 
  scale_color_manual(values = cbp1) +
  scale_fill_manual(values = cbp1) + # note: needs to be overridden when using continuous color scales
  theme_bw()

```





## 1. 散点图
### 1.1 两个变量
展示两个变量之间的相关关系，最常使用的是**散点图**。在 ggplot 中，使用`geom_point()`绘制。此外，默认情况下，`geom_smooth`会绘制一条平滑线（基于 losses ），可以通过设置`method='lm'`来调整以绘制最佳拟合的线。


```{r}
# Scatterplot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) + 
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.1)) + 
  ylim(c(0, 500000)) + 
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot", 
       caption = "Source: midwest")

plot(gg)

```


### 1.2 环绕式散点图
使用 ggalt 包中的`geom_encircle()`函数可实现在散点图中圈选特定区域：
```{r message=FALSE, warning=FALSE}
# install.packages("ggalt")
library(ggalt)
options(scipen = 999)
library(ggplot2)
library(ggalt)

## 设定筛选条件
midwest_select <- midwest[midwest$poptotal > 350000 & 
                            midwest$poptotal <= 500000 & 
                            midwest$area > 0.01 & 
                            midwest$area < 0.1, ]

# Plot
ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) +   # draw points
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.1)) + 
  ylim(c(0, 500000)) +   # draw smoothing line
  geom_encircle(aes(x=area, y=poptotal), 
                data=midwest_select, 
                color="red", 
                size=2, 
                expand=0.08) +   # encircle
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot + Encircle", 
       caption="Source: midwest")
```




### 1.3 抖动图

抖动图可以解决数据点重叠的问题。通过`geom_jitter`函数中的`width`参数设置抖动范围，重叠点在其原始位置周围随机抖动。
```{r}
g <- ggplot(mpg, aes(cty, hwy))
g + geom_jitter(width = .5, size=1) +
  labs(subtitle="mpg: city vs highway mileage", 
       y="hwy", 
       x="cty", 
       title="Jittered Points")

```

### 1.4 数值图

克服数据点重叠问题的第二个选择是使用计数图。重叠点越多，圆就越大。

```{r}
g + geom_count(col="tomato3", show.legend=F) +
  labs(subtitle="mpg: city vs highway mileage", 
       y="hwy", 
       x="cty", 
       title="Counts Plot")
```

### 1.5 气泡图
气泡图适合 4 维数据，其中两个是数值型（分别是 X 和 Y），另一个是分类变量（用`color`表示）和另一个数值变量（用`size`表示）。
```{r}
data(mpg, package="ggplot2")


mpg_select <- mpg[mpg$manufacturer %in% c("audi", "ford", "honda", "hyundai"), ]

# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
g <- ggplot(mpg_select, aes(displ, cty)) + 
  labs(subtitle="mpg: Displacement vs City Mileage",
       title="Bubble chart")

g + geom_jitter(aes(col=manufacturer, size=hwy)) + 
  geom_smooth(aes(col=manufacturer), method="lm", se=F)
```


<!-- ### 1.6 动态气泡图 -->

<!-- ```{r} -->
<!-- # install.packages("gganimate") -->
<!-- # install.packages("gapminder") -->
<!-- library(gganimate) -->
<!-- library(gapminder) -->

<!-- g <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, frame = year)) + -->
<!--   geom_point() + -->
<!--   geom_smooth(aes(group = year),  -->
<!--               method = "lm",  -->
<!--               show.legend = FALSE) + -->
<!--   facet_wrap(~continent, scales = "free") + -->
<!--   scale_x_log10()  # convert to log scale -->

<!-- gganimate(g, interval=0.2) -->
<!-- ``` -->


### 1.6 边际直方图/箱线图

如果您想在同一个图中显示变量的关系和分布，可以使用边际直方图。更改`ggMarginal()`函数中的`type`参数可以将边际直方图换成箱线图或者密度图。

```{r}
library(ggExtra)
data(mpg, package="ggplot2")


# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
mpg_select <- mpg[mpg$hwy >= 35 & mpg$cty > 27, ]
g <- ggplot(mpg, aes(cty, hwy)) + 
  geom_count() + 
  geom_smooth(method="lm", se=F)

ggMarginal(g, type = "histogram", fill="transparent")

```
```{r}
ggMarginal(g, type = "boxplot", fill="transparent")

```

```{r}
ggMarginal(g, type = "density", fill="transparent")
```

### 1.7 相关系数图

相关系数图可以查看同一组数据中多个连续变量的相关性。
```{r}
library(ggcorrplot)

# Correlation matrix
data(mtcars)
corr <- round(cor(mtcars), 1)

# Plot
ggcorrplot(corr, hc.order = TRUE, 
           type = "lower", 
           lab = TRUE, 
           lab_size = 3, 
           method="circle", 
           colors = c("tomato2", "white", "springgreen3"), 
           title="Correlogram of mtcars", 
           ggtheme=theme_bw)
```

## 2 偏差
### 2.1 发散条形图
发散条形图是一种可以同时处理负值和正值的条形图。这可以通过`geom_bar()`来实现。但是`geom_bar()`的用法可能会让人很困惑。这是因为，它既可以用来制作柱状图，也可以用来制作直方图。默认情况下，`geom_bar()`中`stat`参数的默认值为`count`。这意味着，当您只提供一个连续的 x 变量（而不提供 y 变量）时，它会尝试从数据中生成一个直方图。如果要制作条形图，需要做两件事：

- 设置`stat=identity`;
- 在`aes()`中同时输入 x 和 y，其中 x 是字符或因子型变量，y 是数值型。

为了确保得到的是发散条形图，数据需要满足：分类变量的两个类别在连续变量的某个阈值处改变其值。在下面的例子中，`mtcars`数据集的 mpg 通过计算 z 分数被规范化。那些 mpg 在 0 以上的车辆被标记为绿色，低于 0 的车辆被标记为红色。
```{r}
data("mtcars")  # load data
mtcars$`car name` <- rownames(mtcars)  # create new column for car names
mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg
mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, "below", "above")  # above / below avg flag
mtcars <- mtcars[order(mtcars$mpg_z), ]  # sort
mtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`)  # convert to factor to retain sorted order in plot.

# Diverging Barcharts
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +
  scale_fill_manual(name="Mileage", 
                    labels = c("Above Average", "Below Average"), 
                    values = c("above"="#00ba38", "below"="#f8766d")) + 
  labs(subtitle="Normalised mileage from 'mtcars'", 
       title= "Diverging Bars") + 
  coord_flip()
```


### 2.2 带标记的发散型棒棒糖图
这个图形是发散条形图的一个变体。
```{r}
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_point(stat='identity', fill="black", size=6)  +
  geom_segment(aes(y = 0, 
                   x = `car name`, 
                   yend = mpg_z, 
                   xend = `car name`), 
               color = "black") +
  geom_text(color="white", size=2) +
  labs(title="Diverging Lollipop Chart", 
       subtitle="Normalized mileage from 'mtcars': Lollipop") + 
  ylim(-2.5, 2.5) +
  coord_flip()
```


### 2.3 发散性点图

```{r}
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_point(stat='identity', aes(col=mpg_type), size=6)  +
  scale_color_manual(name="Mileage", 
                     labels = c("Above Average", "Below Average"), 
                     values = c("above"="#00ba38", "below"="#f8766d")) + 
  geom_text(color="white", size=2) +
  labs(title="Diverging Dot Plot", 
       subtitle="Normalized mileage from 'mtcars': Dotplot") + 
  ylim(-2.5, 2.5) +
  coord_flip()
```


### 2.4 面积图
面积图通常用于可视化特定指标（如股票回报率）与基线的对比情况。
```{r}
library(quantmod)
data("economics", package = "ggplot2")

# 计算利润
economics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])

# 为轴刻度创建断点和标签
brks <- economics$date[seq(1, length(economics$date), 12)]
lbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])

# Plot
ggplot(economics[1:100, ], aes(date, returns_perc)) + 
  geom_area() + 
  scale_x_date(breaks=brks, labels=lbls) + 
  theme(axis.text.x = element_text(angle=90)) + 
  labs(title="Area Chart", 
       subtitle = "Perc Returns for Personal Savings", 
       y="% Returns for Personal savings", 
       caption="Source: economics")

```

## 3 排序
### 3.1 有序条形图
有序条形图是按 Y 轴变量排序的条形图，X 轴变量必须转换为因子型。
```{r}
# 准备数据：按厂商分组计算平均城市里程。
cty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate
colnames(cty_mpg) <- c("make", "mileage")  # change column names
cty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort
cty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.
head(cty_mpg, 4)

```

```{r}
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_bar(stat="identity", width=.5, fill="tomato3") + 
  labs(title="Ordered Bar Chart", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

### 3.2 棒棒糖图
棒棒糖图传达的信息与柱状图相同。通过将粗条转变为细线，减少了杂乱，使图形看起来更美观。
```{r}
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_point(size=3) + 
  geom_segment(aes(x=make, 
                   xend=make, 
                   y=0, 
                   yend=mileage)) + 
  labs(title="Lollipop Chart", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

### 3.3 点图
点图非常类似于棒棒糖图，但没有线条，并且各标签都在水平位置上。它更强调项目的顺序与实际值有关。
```{r}
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_point(col="tomato2", size=3) +   # Draw points
  geom_segment(aes(x=make, 
                   xend=make, 
                   y=min(mileage), 
                   yend=max(mileage)), 
               linetype="dashed", 
               size=0.1) +   # Draw dashed lines
  labs(title="Dot Plot", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") +  
  coord_flip()
```

### 3.4 坡度图
坡度图是比较两点之间差异的绝佳方法。目前，还没有内置函数来构建这个图形。下面的代码可以作为一个示例框架，告诉您如何绘制这个图形。
```{r}
library(scales)

# 数据准备
df <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv")
colnames(df) <- c("continent", "1952", "1957")
left_label <- paste(df$continent, round(df$'1952'),sep=", ")
right_label <- paste(df$continent, round(df$'1957'),sep=", ")
df$class <- ifelse((df$'1957' - df$'1952') < 0, "red", "green")
```

```{r}
library(ggplot2)
p <- ggplot(df) + geom_segment(aes(x=1, xend=2, y=`1952`, yend=`1957`, col=class), size=.75, show.legend=F) + 
                  geom_vline(xintercept=1, linetype="dashed", size=.1) + 
                  geom_vline(xintercept=2, linetype="dashed", size=.1) +
                  scale_color_manual(labels = c("Up", "Down"), 
                                     values = c("green"="#00ba38", "red"="#f8766d")) +  # color of lines
                  labs(x="", y="Mean GdpPerCap") +  # Axis labels
                  xlim(.5, 2.5) + ylim(0,(1.1*(max(df$`1952`, df$`1957`))))  # X and Y axis limits

# Add texts
p <- p + geom_text(label=left_label, y=df$`1952`, x=rep(1, NROW(df)), hjust=1.1, size=3.5)
p <- p + geom_text(label=right_label, y=df$`1957`, x=rep(2, NROW(df)), hjust=-0.1, size=3.5)
p <- p + geom_text(label="Time 1", x=1, y=1.1*(max(df$`1952`, df$`1957`)), hjust=1.2, size=5)  # title
p <- p + geom_text(label="Time 2", x=2, y=1.1*(max(df$`1952`, df$`1957`)), hjust=-0.1, size=5)  # title


#设置主题
p + theme(panel.background = element_blank(),
           panel.grid = element_blank(),
           axis.ticks = element_blank(),
           axis.text.x = element_blank(),
           panel.border = element_blank(),
           plot.margin = unit(c(1,2,1,2), "cm"))


```

### 3.5 哑铃图
哑铃图可以：1)比较两个时间点之间的相对位置（比如增长和下降）；2) 比较两类之间的距离。为了得到哑铃的正确顺序，Y 变量应该是一个因子，因子变量的水平应该与它在图中出现的顺序相同。
```{r}
health <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/health.csv")
health$Area <- factor(health$Area, levels=as.character(health$Area))  # for right ordering of the dumbells

# health$Area <- factor(health$Area)
gg <- ggplot(health, aes(x=pct_2013, xend=pct_2014, y=Area, group=Area)) + 
        geom_dumbbell(color="#a3c4dc", 
                      size=0.75, 
                      point.colour.l="#0e668b") + 
        scale_x_continuous(label=percent) + 
        labs(x=NULL, 
             y=NULL, 
             title="Dumbbell Chart", 
             subtitle="Pct Change: 2013 vs 2014", 
             caption="Source: https://github.com/hrbrmstr/ggalt") +
        theme(plot.title = element_text(hjust=0.5, face="bold"),
              plot.background=element_rect(fill="#f7f7f7"),
              panel.background=element_rect(fill="#f7f7f7"),
              panel.grid.minor=element_blank(),
              panel.grid.major.y=element_blank(),
              panel.grid.major.x=element_line(),
              axis.ticks=element_blank(),
              legend.position="top",
              panel.border=element_blank())

plot(gg)

```

## 4 分布
### 4.1 直方图
#### 4.4.1 连续变量的直方图
连续变量的直方图可以使用`geom_bar()`或`geom_histogram()`来完成。当使用`geom_histogram()`时，可以使用`bins`参数来控制分箱的数量。也可以使用`binwidth`设置每个分箱覆盖的范围。`binwidth`的值与建立直方图的连续变量在同一个尺度上。
```{r}
# 连续（数值）变量的直方图
g <- ggplot(mpg, aes(displ)) + scale_fill_brewer(palette = "Spectral")

g + geom_histogram(aes(fill=class), 
                   binwidth = .1, 
                   col="black", 
                   size=.1) +  # change binwidth
  labs(title="Histogram with Auto Binning", 
       subtitle="Engine Displacement across Vehicle Classes")

 
```


```{r}

g + geom_histogram(aes(fill=class), 
                   bins=5, 
                   col="black", 
                   size=.1) +   # change number of bins
  labs(title="Histogram with Fixed Bins", 
       subtitle="Engine Displacement across Vehicle Classes") 

 
```

#### 4.1.2分类变量的直方图
分类变量的直方图实际上是根据每个类别的频率绘制的条形图。
```{r}
library(ggplot2)
theme_set(theme_classic())

# Histogram on a Categorical variable
g <- ggplot(mpg, aes(manufacturer))
g + geom_bar(aes(fill=class), width = 0.5) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Histogram on Categorical Variable", 
       subtitle="Manufacturer across Vehicle Classes")

 
```

### 4.2 密度图

```{r}
g <- ggplot(mpg, aes(cty))
g + geom_density(aes(fill=factor(cyl)), alpha=0.8) + 
    labs(title="Density plot", 
         subtitle="City Mileage Grouped by Number of cylinders",
         caption="Source: mpg",
         x="City Mileage",
         fill="# Cylinders")
 
```

### 4.3 箱线图
箱形图是研究数据分布的一个有用工具。它还可以显示多个组内的分布，以及中值、范围和异常值。箱子内的黑线表示中位数。箱顶是 75% 分位数，箱底是 25% 分位数。线的端点（又称晶须）距离为1.5*IQR，其中 IQR （四分位差）是 25% 到 75% 分位数之间距离。须外的点通常被认为是极值点。设置`varwidth=T`将调整盒子的宽度，使其与观察的数量成比例。
```{r}
g <- ggplot(mpg, aes(class, cty))
g + geom_boxplot(varwidth=T, fill="plum") + 
    labs(title="Box plot", 
         subtitle="City Mileage grouped by Class of vehicle",
         caption="Source: mpg",
         x="Class of Vehicle",
         y="City Mileage")
 
```

```{r}
g <- ggplot(mpg, aes(class, cty))
g + geom_boxplot(aes(fill=factor(cyl))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="City Mileage grouped by Class of vehicle",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
 
```

### 4.4 点图+箱线图
在箱线图的基础上，添加点图可以提供更清晰的信息。这些点交错排列，每个点代表一次观测。
```{r}
g <- ggplot(mpg, aes(manufacturer, cty))
g + geom_boxplot() + 
  geom_dotplot(binaxis='y', 
               stackdir='center', 
               dotsize = .5, 
               fill="red") +
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot + Dot plot", 
       subtitle="City Mileage vs Class: Each dot represents 1 row in source data",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
 
```

### 4.5 塔夫特箱线图
这是一个简化版的箱线图。
```{r}

library(ggthemes)
library(ggplot2)
theme_set(theme_tufte())  # from ggthemes

# plot
g <- ggplot(mpg, aes(manufacturer, cty))
g + geom_tufteboxplot() + 
      theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
      labs(title="Tufte Styled Boxplot", 
           subtitle="City Mileage grouped by Class of vehicle",
           caption="Source: mpg",
           x="Class of Vehicle",
           y="City Mileage")


```

### 4.6 小提琴图
小提琴图类似于箱线图，但显示了组内的密度。
```{r}
g <- ggplot(mpg, aes(class, cty))
g + geom_violin() + 
  labs(title="Violin plot", 
       subtitle="City Mileage vs Class of vehicle",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
 
```

### 4.7 金字塔图
金字塔图提供了一种独特的方式来可视化每个类别包含的样本比例。下面的金字塔是一个很好的例子，说明了在一个营销活动中每个阶段的用户留存率。
```{r}
library(ggplot2)
library(ggthemes)
options(scipen = 999)  # turns of scientific notations like 1e+40

# Read data
email_campaign_funnel <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv")

# X Axis Breaks and Labels 
brks <- seq(-15000000, 15000000, 5000000)
lbls = paste0(as.character(c(seq(15, 0, -5), seq(5, 15, 5))), "m")

# Plot
ggplot(email_campaign_funnel, aes(x = Stage, y = Users, fill = Gender)) +   # Fill column
                              geom_bar(stat = "identity", width = .6) +   # draw the bars
                              scale_y_continuous(breaks = brks,   # Breaks
                                                 labels = lbls) + # Labels
                              coord_flip() +  # Flip axes
                              labs(title="Email Campaign Funnel") +
                              theme_tufte() +  # Tufte theme from ggfortify
                              theme(plot.title = element_text(hjust = .5), 
                                    axis.ticks = element_blank()) +   # Centre plot title
                              scale_fill_brewer(palette = "Dark2")  # Color palette
```

## 5 组成
### 5.1 华夫饼图
华夫图可以显示总体的组成成分。
```{r}
var <- mpg$class  # the categorical data 

## Prep data (nothing to change here)
nrows <- 10
df <- expand.grid(y = 1:nrows, x = 1:nrows)
categ_table <- round(table(var) * ((nrows*nrows)/(length(var))))


df$category <- factor(rep(names(categ_table), categ_table))  
# NOTE: if sum(categ_table) is not 100 (i.e. nrows^2), it will need adjustment to make the sum to 100.

## Plot
ggplot(df, aes(x = x, y = y, fill = category)) + 
        geom_tile(color = "black", size = 0.5) +
        scale_x_continuous(expand = c(0, 0)) +
        scale_y_continuous(expand = c(0, 0), trans = 'reverse') +
        scale_fill_brewer(palette = "Set3") +
        labs(title="Waffle Chart", subtitle="'Class' of vehicles",
             caption="Source: mpg") + 
        theme(panel.border = element_rect(size = 2),
              plot.title = element_text(size = rel(1.2)),
              axis.text = element_blank(),
              axis.title = element_blank(),
              axis.ticks = element_blank(),
              legend.title = element_blank(),
              legend.position = "right")

```

### 5.2 饼图

```{r}
library(ggplot2)
theme_set(theme_classic())

# Source: Frequency table
df <- as.data.frame(table(mpg$class))
colnames(df) <- c("class", "freq")
pie <- ggplot(df, aes(x = "", y=freq, fill = factor(class))) + 
  geom_bar(width = 1, stat = "identity") +
  theme(axis.line = element_blank(), 
        plot.title = element_text(hjust=0.5)) + 
  labs(fill="class", 
       x=NULL, 
       y=NULL, 
       title="Pie Chart of class", 
       caption="Source: mpg")

pie + coord_polar(theta = "y", start=0)

# Source: Categorical variable.
# mpg$class
pie <- ggplot(mpg, aes(x = "", fill = factor(class))) + 
  geom_bar(width = 1) +
  theme(axis.line = element_blank(), 
        plot.title = element_text(hjust=0.5)) + 
  labs(fill="class", 
       x=NULL, 
       y=NULL, 
       title="Pie Chart of class", 
       caption="Source: mpg")
  
pie + coord_polar(theta = "y", start=0)
```



### 5.3 条形图
```{r}
 #prep frequency table
freqtable <- table(mpg$manufacturer)
df <- as.data.frame.table(freqtable)

```

```{r}
library(ggplot2)
theme_set(theme_classic())

# Plot
g <- ggplot(df, aes(Var1, Freq))
g + geom_bar(stat="identity", width = 0.5, fill="tomato2") + 
      labs(title="Bar Chart", 
           subtitle="Manufacturer of vehicles", 
           caption="Source: Frequency of Manufacturers from 'mpg' dataset") +
      theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

```{r}
# From on a categorical column variable
g <- ggplot(mpg, aes(manufacturer))
g + geom_bar(aes(fill=class), width = 0.5) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) +
  labs(title="Categorywise Bar Chart", 
       subtitle="Manufacturer of vehicles", 
       caption="Source: Manufacturers from 'mpg' dataset")
```



## 6 变化趋势
### 6.1 时间序列图：基于时间序列对象（ts）
`ggfortify`包可以对时间序列直接绘图。
```{r}
## From Timeseries object (ts)
library(ggplot2)
library(ggfortify)
theme_set(theme_classic())

# Plot 
autoplot(AirPassengers) + 
  labs(title="AirPassengers") + 
  theme(plot.title = element_text(hjust=0.5))
```

### 6.2 时间序列图：基于数据框
```{r}
library(ggplot2)
theme_set(theme_classic())

# 使用默认的时间跨度
ggplot(economics, aes(x=date)) + 
  geom_line(aes(y=returns_perc)) + 
  labs(title="Time Series Chart", 
       subtitle="Returns Percentage from 'Economics' Dataset", 
       caption="Source: Economics", 
       y="Returns %")

```

如果想设置特定的时间间隔，则需要使用`scale_x_date()`函数。
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

economics_m <- economics[1:24, ]

# 设定时间跨度为一个月
lbls <- paste0(month.abb[month(economics_m$date)], " ", lubridate::year(economics_m$date))
brks <- economics_m$date

# plot
ggplot(economics_m, aes(x=date)) + 
  geom_line(aes(y=returns_perc)) + 
  labs(title="Monthly Time Series", 
       subtitle="Returns Percentage from Economics Dataset", 
       caption="Source: Economics", 
       y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, 
               breaks = brks) +  # change to monthly ticks and labels
  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text
        panel.grid.minor = element_blank())  # turn off minor grid
```

设置时间跨度为 1 年：
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

economics_y <- economics[1:90, ]

# labels and breaks for X axis text
brks <- economics_y$date[seq(1, length(economics_y$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(economics_y, aes(x=date)) + 
  geom_line(aes(y=returns_perc)) + 
  labs(title="Yearly Time Series", 
       subtitle="Returns Percentage from Economics Dataset", 
       caption="Source: Economics", 
       y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, 
               breaks = brks) +  # change to monthly ticks and labels
  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text
        panel.grid.minor = element_blank())  # turn off minor grid
```

### 6.3 多个时间序列
在本例中，基于长数据格式进行可视化。这意味着，所有列的列名和各自的值被存放在两个变量中（分别是`variable`和`value`）。

```{r}
data(economics_long, package = "ggplot2")
head(economics_long)
```

在下面的代码中，在`geom_line()`函数中设置绘图对象为`value`，颜色匹配对象为`variable`。这样，只要调用一次geom_line，就会绘制多条彩色线，每条线代表`variable`列中的每个唯一`value`。`scale_x_date()`将更改 X 轴断点和标签，`scale_color_manual`将更改行颜色。
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

df <- economics_long[economics_long$variable %in% c("psavert", "uempmed"), ]
df <- df[lubridate::year(df$date) %in% c(1967:1981), ]

# labels and breaks for X axis text
brks <- df$date[seq(1, length(df$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(df, aes(x=date)) + 
  geom_line(aes(y=value, col=variable)) + 
  labs(title="Time Series of Returns Percentage", 
       subtitle="Drawn from Long Data format", 
       caption="Source: Economics", 
       y="Returns %", 
       color=NULL) +  # title and caption
  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels
  scale_color_manual(labels = c("psavert", "uempmed"), 
                     values = c("psavert"="#00ba38", "uempmed"="#f8766d")) +  # line color
  theme(axis.text.x = element_text(angle = 90, vjust=0.5, size = 8),  # rotate x axis text
        panel.grid.minor = element_blank())  # turn off minor grid
```

如果从一个宽格式创建一个时间序列，则必须通过对每条线调用一次`geom_line()`制。因此，默认情况下不会绘制图例，需要手动添加。
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

df <- economics[, c("date", "psavert", "uempmed")]
df <- df[lubridate::year(df$date) %in% c(1967:1981), ]

# labels and breaks for X axis text
brks <- df$date[seq(1, length(df$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(df, aes(x=date)) + 
  geom_line(aes(y=psavert, col="psavert")) + 
  geom_line(aes(y=uempmed, col="uempmed")) + 
  labs(title="Time Series of Returns Percentage", 
       subtitle="Drawn From Wide Data format", 
       caption="Source: Economics", y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels
  scale_color_manual(name="", 
                     values = c("psavert"="#00ba38", "uempmed"="#f8766d")) +  # line color
  theme(panel.grid.minor = element_blank())  # turn off minor grid
```



### 6.4 堆叠面积图
堆叠面积图与折线图类似，只是图下方的区域全部着色。应用场景有：

- 想要描述数量或体积（而不是价格之类的变量）随时间的变化；
- 有很多数据点。对于很少的数据点，可以考虑绘制柱状图。
- 希望展示各个类别的贡献。

```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

df <- economics[, c("date", "psavert", "uempmed")]
df <- df[lubridate::year(df$date) %in% c(1967:1981), ]

# labels and breaks for X axis text
brks <- df$date[seq(1, length(df$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(df, aes(x=date)) + 
  geom_area(aes(y=psavert+uempmed, fill="psavert")) + 
  geom_area(aes(y=uempmed, fill="uempmed")) + 
  labs(title="Area Chart of Returns Percentage", 
       subtitle="From Wide Data format", 
       caption="Source: Economics", 
       y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels
  scale_fill_manual(name="", 
                    values = c("psavert"="#00ba38", "uempmed"="#f8766d")) +  # line color
  theme(panel.grid.minor = element_blank())  # turn off minor grid
```


### 6.5 日历热力图

当您想要在实际的日历上看到像股票价格这类指标的变化，特别是高点和低点时，日历热力图是一个很好的工具。它强调随着时间的推移视觉上的变化，而不是实际数值的变化。这可以通过使用`geom_tile()`来实现。
```{r}
library(ggplot2)
library(plyr)
library(scales)
library(zoo)

df <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv")
df$date <- as.Date(df$date)  # format date
df <- df[df$year >= 2012, ]  # filter reqd years

# Create Month Week
df$yearmonth <- as.yearmon(df$date)
df$yearmonthf <- factor(df$yearmonth)
df <- ddply(df,.(yearmonthf), transform, monthweek=1+week-min(week))  # compute week number of month
df <- df[, c("year", "yearmonthf", "monthf", "week", "monthweek", "weekdayf", "VIX.Close")]
head(df)
```

```{r}
ggplot(df, aes(monthweek, weekdayf, fill = VIX.Close)) + 
  geom_tile(colour = "white") + 
  facet_grid(year~monthf) + 
  scale_fill_gradient(low="red", high="green") +
  labs(x="Week of Month",
       y="",
       title = "Time-Series Calendar Heatmap", 
       subtitle="Yahoo Closing Price", 
       fill="Close")
```

### 6.6 坡度图
坡度图可以可视化数值和类别排名之间的变化。这更适用于时间点很少的时间序列。
```{r}
library(dplyr)
theme_set(theme_classic())
source_df <- read.csv("https://raw.githubusercontent.com/jkeirstead/r-slopegraph/master/cancer_survival_rates.csv")

# Define functions. Source: https://github.com/jkeirstead/r-slopegraph
tufte_sort <- function(df, x="year", y="value", group="group", method="tufte", min.space=0.05) {
    ## First rename the columns for consistency
    ids <- match(c(x, y, group), names(df))
    df <- df[,ids]
    names(df) <- c("x", "y", "group")

    ## Expand grid to ensure every combination has a defined value
    tmp <- expand.grid(x=unique(df$x), group=unique(df$group))
    tmp <- merge(df, tmp, all.y=TRUE)
    df <- mutate(tmp, y=ifelse(is.na(y), 0, y))
  
    ## Cast into a matrix shape and arrange by first column
    require(reshape2)
    tmp <- dcast(df, group ~ x, value.var="y")
    ord <- order(tmp[,2])
    tmp <- tmp[ord,]
    
    min.space <- min.space*diff(range(tmp[,-1]))
    yshift <- numeric(nrow(tmp))
    ## Start at "bottom" row
    ## Repeat for rest of the rows until you hit the top
    for (i in 2:nrow(tmp)) {
        ## Shift subsequent row up by equal space so gap between
        ## two entries is >= minimum
        mat <- as.matrix(tmp[(i-1):i, -1])
        d.min <- min(diff(mat))
        yshift[i] <- ifelse(d.min < min.space, min.space - d.min, 0)
    }

    
    tmp <- cbind(tmp, yshift=cumsum(yshift))

    scale <- 1
    tmp <- melt(tmp, id=c("group", "yshift"), variable.name="x", value.name="y")
    ## Store these gaps in a separate variable so that they can be scaled ypos = a*yshift + y

    tmp <- transform(tmp, ypos=y + scale*yshift)
    return(tmp)
   
}

plot_slopegraph <- function(df) {
    ylabs <- subset(df, x==head(x,1))$group
    yvals <- subset(df, x==head(x,1))$ypos
    fontSize <- 3
    gg <- ggplot(df,aes(x=x,y=ypos)) +
        geom_line(aes(group=group),colour="grey80") +
        geom_point(colour="white",size=8) +
        geom_text(aes(label=y), size=fontSize, family="American Typewriter") +
        scale_y_continuous(name="", breaks=yvals, labels=ylabs)
    return(gg)
}    

## Prepare data    
df <- tufte_sort(source_df, 
                 x="year", 
                 y="value", 
                 group="group", 
                 method="tufte", 
                 min.space=0.05)

df <- transform(df, 
                x=factor(x, levels=c(5,10,15,20), 
                            labels=c("5 years","10 years","15 years","20 years")), 
                y=round(y))

## Plot
plot_slopegraph(df) + labs(title="Estimates of % survival rates") + 
                      theme(axis.title=element_blank(),
                            axis.ticks = element_blank(),
                            plot.title = element_text(hjust=0.5,
                                                      family = "American Typewriter",
                                                      face="bold"),
                            axis.text = element_text(family = "American Typewriter",
                                                     face="bold"))
```

### 6.7 季节图
如果您正在处理`ts`或`xts`类型的时间序列对象，您可以通过使用`forecast::ggseasonplot`绘制的季节图来查看季节波动。下面是一个使用`AirPassengers`和`nottem`数据集绘制的例子。
```{r}
library(ggplot2)
library(forecast)
theme_set(theme_classic())

# Subset data
nottem_small <- window(nottem, start=c(1920, 1), end=c(1925, 12))  # subset a smaller timewindow

# Plot
ggseasonplot(AirPassengers) + labs(title="Seasonal plot: International Airline Passengers")

```

```{r}
ggseasonplot(nottem_small) + labs(title="Seasonal plot: Air temperatures at Nottingham Castle")
```

## 7 群体
### 7.1 谱系图
```{r}

library(ggplot2)
library(ggdendro)
theme_set(theme_bw())

hc <- hclust(dist(USArrests), "ave")  # hierarchical clustering

# plot
ggdendrogram(hc, rotate = TRUE, size = 2)
```

### 7.2 聚类图
可以使用`geom_surround()`来显示不同的簇或组。如果数据集有多个特征，还可以计算主成分，并使用 PC1 和 PC2 作为 X 和 Y 轴绘制散点图。`geom_encircle()`可用于框选所需的组。
```{r}
# devtools::install_github("hrbrmstr/ggalt")
library(ggplot2)
library(ggalt)
library(ggfortify)
theme_set(theme_classic())

# Compute data with principal components ------------------
df <- iris[c(1, 2, 3, 4)]
pca_mod <- prcomp(df)  # compute principal components

# Data frame of principal components ----------------------
df_pc <- data.frame(pca_mod$x, Species=iris$Species)  # dataframe of principal components
df_pc_vir <- df_pc[df_pc$Species == "virginica", ]  # df for 'virginica'
df_pc_set <- df_pc[df_pc$Species == "setosa", ]  # df for 'setosa'
df_pc_ver <- df_pc[df_pc$Species == "versicolor", ]  # df for 'versicolor'
 
# Plot ----------------------------------------------------
ggplot(df_pc, aes(PC1, PC2, col=Species)) + 
  geom_point(aes(shape=Species), size=2) +   # draw points
  labs(title="Iris Clustering", 
       subtitle="With principal components PC1 and PC2 as X and Y axis",
       caption="Source: Iris") + 
  coord_cartesian(xlim = 1.2 * c(min(df_pc$PC1), max(df_pc$PC1)), 
                  ylim = 1.2 * c(min(df_pc$PC2), max(df_pc$PC2))) +   # change axis limits
  geom_encircle(data = df_pc_vir, aes(x=PC1, y=PC2)) +   # draw circles
  geom_encircle(data = df_pc_set, aes(x=PC1, y=PC2)) + 
  geom_encircle(data = df_pc_ver, aes(x=PC1, y=PC2))
```



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# install.packages("ggplot2")
library(ggplot2)
options(scipen=999)  # turn-off scientific notation like 1e+48
# theme_set(theme_bw())  # pre-set the bw theme.
data("midwest", package = "ggplot2")
midwest <- read.csv("http://goo.gl/G1K41K")  # bkup data source
write.csv(midwest,"midwest.csv")


# The palette with grey:
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

# The palette with black:
cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

library(plotrix)
sliceValues <- rep(10, 8) # each slice value=10 for proportionate slices
(
  p <- pie3D(sliceValues, 
             explode=0, 
             theta = 1.2, 
             col = cbp1, 
             labels = cbp1, 
             labelcex = 0.9,
             shade = 0.6,
             main = "Colorblind\nfriendly palette")
)

ggplot <- function(...) ggplot2::ggplot(...) + 
  scale_color_manual(values = cbp1) +
  scale_fill_manual(values = cbp1) + # note: needs to be overridden when using continuous color scales
  theme_bw()



# Scatterplot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) + 
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.1)) + 
  ylim(c(0, 500000)) + 
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot", 
       caption = "Source: midwest")




================================================
FILE: 2022年/2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/.Rproj.user/161F88A0/sources/per/u/86D463FB-contents 2
================================================
# install.packages("ggplot2")
library(ggplot2)
options(scipen=999)  # turn-off scientific notation like 1e+48
# theme_set(theme_bw())  # pre-set the bw theme.
data("midwest", package = "ggplot2")
midwest <- read.csv("http://goo.gl/G1K41K")  # bkup data source
write.csv(midwest,"midwest.csv")


# The palette with grey:
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

# The palette with black:
cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

library(plotrix)
sliceValues <- rep(10, 8) # each slice value=10 for proportionate slices
(
  p <- pie3D(sliceValues, 
             explode=0, 
             theta = 1.2, 
             col = cbp1, 
             labels = cbp1, 
             labelcex = 0.9,
             shade = 0.6,
             main = "Colorblind\nfriendly palette")
)

ggplot <- function(...) ggplot2::ggplot(...) + 
  scale_color_manual(values = cbp1) +
  scale_fill_manual(values = cbp1) + # note: needs to be overridden when using continuous color scales
  theme_bw()



# Scatterplot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) + 
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.1)) + 
  ylim(c(0, 500000)) + 
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot", 
       caption = "Source: midwest")




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FILE: 2022年/2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/.Rproj.user/C6560784/sources/per/t/C36CFFC0-contents
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---
title: "ggplot绘图手册"
author: "赵子茜"
date: "`r Sys.Date()`"
output: 
  prettydoc::html_pretty:
    toc: true
    toc_depth: 4
    theme: cayman
    highlight: github
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,warning = F,message = F)
```

## 加载数据集

```{r}
library(ggplot2)
library(plotrix)
options(scipen=999)  # 关掉像1e+48这样的科学符号
data("midwest", package = "ggplot2") #加载数据集
```

## 全局主题设置

```{r}
# 颜色设置（灰色系列）
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

# 颜色设置（黑色系列）
cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")


ggplot <- function(...) ggplot2::ggplot(...) + 
  scale_color_manual(values = cbp1) +
  scale_fill_manual(values = cbp1) + # 注意: 使用连续色阶时需要重写
  theme_bw()

```




## 1. 相关关系
### 1.1 两个变量
展示两个变量之间的相关关系，最常使用的是**散点图**。在 ggplot 中，使用`geom_point()`绘制。此外，默认情况下，`geom_smooth`会绘制一条平滑线（基于 losses ），可以通过设置`method='lm'`来调整以绘制最佳拟合的线。


```{r}
# Scatterplot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) + 
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.1)) + 
  ylim(c(0, 500000)) + 
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot", 
       caption = "Source: midwest")

plot(gg)

```


### 1.2 环绕式散点图
使用 ggalt 包中的`geom_encircle()`函数可实现在散点图中圈选特定区域：
```{r message=FALSE, warning=FALSE}
# install.packages("ggalt")
library(ggalt)
options(scipen = 999)
library(ggplot2)
library(ggalt)

## 设定筛选条件
midwest_select <- midwest[midwest$poptotal > 350000 & 
                            midwest$poptotal <= 500000 & 
                            midwest$area > 0.01 & 
                            midwest$area < 0.1, ]

# Plot
ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) +   # draw points
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.1)) + 
  ylim(c(0, 500000)) +   # draw smoothing line
  geom_encircle(aes(x=area, y=poptotal), 
                data=midwest_select, 
                color="red", 
                size=2, 
                expand=0.08) +   # encircle
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot + Encircle", 
       caption="Source: midwest")
```




### 1.3 抖动图

抖动图可以解决数据点重叠的问题。通过`geom_jitter`函数中的`width`参数设置抖动范围，重叠点在其原始位置周围随机抖动。
```{r}
g <- ggplot(mpg, aes(cty, hwy))
g + geom_jitter(width = .5, size=1) +
  labs(subtitle="mpg: city vs highway mileage", 
       y="hwy", 
       x="cty", 
       title="Jittered Points")

```

### 1.4 计数图

克服数据点重叠问题的第二个选择是使用计数图 `geom_count()`。重叠点越多，圆就越大。

```{r}
g + geom_count(col="tomato3", show.legend=F) +
  labs(subtitle="mpg: city vs highway mileage", 
       y="hwy", 
       x="cty", 
       title="Counts Plot")
```

### 1.5 气泡图
气泡图适合 4 维数据，其中两个是数值型（分别是 X 和 Y），另一个是分类变量（用`color`表示）和另一个数值变量（用`size`表示）。
```{r}
data(mpg, package="ggplot2")


mpg_select <- mpg[mpg$manufacturer %in% c("audi", "ford", "honda", "hyundai"), ]

# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
g <- ggplot(mpg_select, aes(displ, cty)) + 
  labs(subtitle="mpg: Displacement vs City Mileage",
       title="Bubble chart")

g + geom_jitter(aes(col=manufacturer, size=hwy)) + 
  geom_smooth(aes(col=manufacturer), method="lm", se=F)
```


<!-- ### 1.6 动态气泡图 -->

<!-- ```{r} -->
<!-- # install.packages("gganimate") -->
<!-- # install.packages("gapminder") -->
<!-- library(gganimate) -->
<!-- library(gapminder) -->

<!-- g <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, frame = year)) + -->
<!--   geom_point() + -->
<!--   geom_smooth(aes(group = year), -->
<!--               method = "lm", -->
<!--               show.legend = FALSE) + -->
<!--   facet_wrap(~continent, scales = "free") + -->
<!--   scale_x_log10()  # convert to log scale -->

<!-- gganimate(g, interval=0.2) -->
<!-- ``` -->


### 1.6 边际直方图/箱线图

如果您想在同一个图中显示变量的关系和分布，可以使用边际直方图。更改`ggMarginal()`函数中的`type`参数可以将边际直方图换成箱线图或者密度图。

```{r}
library(ggExtra)
data(mpg, package="ggplot2")


# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
mpg_select <- mpg[mpg$hwy >= 35 & mpg$cty > 27, ]
g <- ggplot(mpg, aes(cty, hwy)) + 
  geom_count() + 
  geom_smooth(method="lm", se=F)

ggMarginal(g, type = "histogram", fill="transparent")

```
```{r}
ggMarginal(g, type = "boxplot", fill="transparent")

```

```{r}
ggMarginal(g, type = "density", fill="transparent")
```

### 1.7 相关系数图

相关系数图可以查看同一组数据中多个连续变量的相关性。
```{r}
library(ggcorrplot)

# Correlation matrix
data(mtcars)
corr <- round(cor(mtcars), 1)

# Plot
ggcorrplot(corr, hc.order = TRUE, 
           type = "lower", 
           lab = TRUE, 
           lab_size = 3, 
           method="circle", 
           colors = c("tomato2", "white", "springgreen3"), 
           title="Correlogram of mtcars", 
           ggtheme=theme_bw)
```

## 2 偏差
### 2.1 发散条形图
发散条形图是一种可以同时处理负值和正值的条形图。这可以通过 `geom_bar()` 来实现。但是 `geom_bar()` 的用法可能会让人很困惑。这是因为，它既可以用来制作柱状图，也可以用来制作直方图。默认情况下，`geom_bar()` 中 `stat` 参数的默认值为 `count`。这意味着，当您只提供一个连续的 x 变量（而不提供 y 变量）时，它会尝试从数据中生成一个直方图。如果要制作条形图，需要做两件事：

- 设置 `stat=identity`;
- 在 `aes()` 中同时输入 x 和 y，其中 x 是字符或因子型变量，y 是数值型。

为了确保得到的是发散条形图，数据需要满足：分类变量的两个类别在连续变量的某个阈值处改变其值。在下面的例子中，`mtcars` 数据集的 mpg 通过计算 z 分数被规范化。那些 mpg 在 0 以上的车辆被标记为绿色，低于 0 的车辆被标记为红色。

```{r}
data("mtcars")  # load data
mtcars$`car name` <- rownames(mtcars)  # create new column for car names
mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg
mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, "below", "above")  # above / below avg flag
mtcars <- mtcars[order(mtcars$mpg_z), ]  # sort
mtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`) 

# Diverging Barcharts
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +
  scale_fill_manual(name="Mileage", 
                    labels = c("Above Average", "Below Average"), 
                    values = c("above"="#00ba38", "below"="#f8766d")) + 
  labs(subtitle="Normalised mileage from 'mtcars'", 
       title= "Diverging Bars") + 
  coord_flip()
```


### 2.2 带标记的发散型棒棒糖图
这个图形是发散条形图的一个变体。
```{r}
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_point(stat='identity', fill="black", size=6)  +
  geom_segment(aes(y = 0, 
                   x = `car name`, 
                   yend = mpg_z, 
                   xend = `car name`), 
               color = "black") +
  geom_text(color="white", size=2) +
  labs(title="Diverging Lollipop Chart", 
       subtitle="Normalized mileage from 'mtcars': Lollipop") + 
  ylim(-2.5, 2.5) +
  coord_flip()
```


### 2.3 发散性点图

```{r}
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_point(stat='identity', aes(col=mpg_type), size=6)  +
  scale_color_manual(name="Mileage", 
                     labels = c("Above Average", "Below Average"), 
                     values = c("above"="#00ba38", "below"="#f8766d")) + 
  geom_text(color="white", size=2) +
  labs(title="Diverging Dot Plot", 
       subtitle="Normalized mileage from 'mtcars': Dotplot") + 
  ylim(-2.5, 2.5) +
  coord_flip()
```


### 2.4 面积图
面积图通常用于可视化特定指标（如股票回报率）与基线的对比情况。
```{r}
library(quantmod)
data("economics", package = "ggplot2")

# 计算利润
economics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])

# 为轴刻度创建断点和标签
brks <- economics$date[seq(1, length(economics$date), 12)]
lbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])

# Plot
ggplot(economics[1:100, ], aes(date, returns_perc)) + 
  geom_area() + 
  scale_x_date(breaks=brks, labels=lbls) + 
  theme(axis.text.x = element_text(angle=90)) + 
  labs(title="Area Chart", 
       subtitle = "Perc Returns for Personal Savings", 
       y="% Returns for Personal savings", 
       caption="Source: economics")

```

## 3 排序
### 3.1 有序条形图
有序条形图是按 Y 轴变量排序的条形图，X 轴变量必须转换为因子型。
```{r}
# 准备数据：按厂商分组计算平均城市里程。
cty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate
colnames(cty_mpg) <- c("make", "mileage")  # change column names
cty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort
cty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.
head(cty_mpg, 4)

```

```{r}
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_bar(stat="identity", width=.5, fill="tomato3") + 
  labs(title="Ordered Bar Chart", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

### 3.2 棒棒糖图
棒棒糖图传达的信息与柱状图相同。通过将粗条转变为细线，减少了杂乱，使图形看起来更美观。
```{r}
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_point(size=3) + 
  geom_segment(aes(x=make, 
                   xend=make, 
                   y=0, 
                   yend=mileage)) + 
  labs(title="Lollipop Chart", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

### 3.3 点图
点图非常类似于棒棒糖图，但没有线条，并且各标签都在水平位置上。它更强调项目的顺序与实际值有关。
```{r}
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_point(col="tomato2", size=3) +   # Draw points
  geom_segment(aes(x=make, 
                   xend=make, 
                   y=min(mileage), 
                   yend=max(mileage)), 
               linetype="dashed", 
               size=0.1) +   # Draw dashed lines
  labs(title="Dot Plot", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") +  
  coord_flip()
```

### 3.4 坡度图
坡度图是比较两点之间差异的绝佳方法。目前，还没有内置函数来构建这个图形。下面的代码可以作为一个示例框架，告诉您如何绘制这个图形。
```{r}
library(scales)

# 数据准备
df <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv")
colnames(df) <- c("continent", "1952", "1957")
left_label <- paste(df$continent, round(df$'1952'),sep=", ")
right_label <- paste(df$continent, round(df$'1957'),sep=", ")
df$class <- ifelse((df$'1957' - df$'1952') < 0, "red", "green")
head(df)
```

```{r}
library(ggplot2)
p <- ggplot(df) + geom_segment(aes(x=1, xend=2, y=`1952`, yend=`1957`, col=class), size=.75, show.legend=F) + 
                  geom_vline(xintercept=1, linetype="dashed", size=.1) + 
                  geom_vline(xintercept=2, linetype="dashed", size=.1) +
                  scale_color_manual(labels = c("Up", "Down"), 
                                     values = c("green"="#00ba38", "red"="#f8766d")) +  # color of lines
                  labs(x="", y="Mean GdpPerCap") +  # Axis labels
                  xlim(.5, 2.5) + ylim(0,(1.1*(max(df$`1952`, df$`1957`))))  # X and Y axis limits

# Add texts
p <- p + geom_text(label=left_label, y=df$`1952`, x=rep(1, NROW(df)), hjust=1.1, size=3.5)
p <- p + geom_text(label=right_label, y=df$`1957`, x=rep(2, NROW(df)), hjust=-0.1, size=3.5)
p <- p + geom_text(label="Time 1", x=1, y=1.1*(max(df$`1952`, df$`1957`)), hjust=1.2, size=5)  # title
p <- p + geom_text(label="Time 2", x=2, y=1.1*(max(df$`1952`, df$`1957`)), hjust=-0.1, size=5)  # title


#设置主题
p + theme(panel.background = element_blank(),
           panel.grid = element_blank(),
           axis.ticks = element_blank(),
           axis.text.x = element_blank(),
           panel.border = element_blank(),
           plot.margin = unit(c(1,2,1,2), "cm"))


```

### 3.5 哑铃图
哑铃图可以：1)比较两个时间点之间的相对位置（比如增长和下降）；2) 比较两类之间的距离。为了得到哑铃的正确顺序，Y 变量应该是一个因子，因子变量的水平应该与它在图中出现的顺序相同。
```{r}
health <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/health.csv")
health$Area <- factor(health$Area, levels=as.character(health$Area))  # for right ordering of the dumbells

# health$Area <- factor(health$Area)
gg <- ggplot(health, aes(x=pct_2013, xend=pct_2014, y=Area, group=Area)) + 
        geom_dumbbell(color="#a3c4dc", 
                      size=0.75, 
                      point.colour.l="#0e668b") + 
        scale_x_continuous(label=percent) + 
        labs(x=NULL, 
             y=NULL, 
             title="Dumbbell Chart", 
             subtitle="Pct Change: 2013 vs 2014", 
             caption="Source: https://github.com/hrbrmstr/ggalt") +
        theme(plot.title = element_text(hjust=0.5, face="bold"),
              plot.background=element_rect(fill="#f7f7f7"),
              panel.background=element_rect(fill="#f7f7f7"),
              panel.grid.minor=element_blank(),
              panel.grid.major.y=element_blank(),
              panel.grid.major.x=element_line(),
              axis.ticks=element_blank(),
              legend.position="top",
              panel.border=element_blank())

plot(gg)

```

## 4 分布
### 4.1 直方图
#### 4.4.1 连续变量的直方图
连续变量的直方图可以使用`geom_bar()`或`geom_histogram()`来完成。当使用`geom_histogram()`时，可以使用`bins`参数来控制分箱的数量。也可以使用`binwidth`设置每个分箱覆盖的范围。`binwidth`的值与建立直方图的连续变量在同一个尺度上。
```{r}
# 连续（数值）变量的直方图
g <- ggplot(mpg, aes(displ)) + scale_fill_brewer(palette = "Spectral")

g + geom_histogram(aes(fill=class), 
                   binwidth = .1, 
                   col="black", 
                   size=.1) +  # change binwidth
  labs(title="Histogram with Auto Binning", 
       subtitle="Engine Displacement across Vehicle Classes")

 
```


```{r}

g + geom_histogram(aes(fill=class), 
                   bins=5, 
                   col="black", 
                   size=.1) +   # change number of bins
  labs(title="Histogram with Fixed Bins", 
       subtitle="Engine Displacement across Vehicle Classes") 

 
```

#### 4.1.2分类变量的直方图
分类变量的直方图实际上是根据每个类别的频率绘制的条形图。
```{r}
library(ggplot2)
theme_set(theme_classic())

# Histogram on a Categorical variable
g <- ggplot(mpg, aes(manufacturer))
g + geom_bar(aes(fill=class), width = 0.5) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Histogram on Categorical Variable", 
       subtitle="Manufacturer across Vehicle Classes")

 
```

### 4.2 密度图

```{r}
g <- ggplot(mpg, aes(cty))
g + geom_density(aes(fill=factor(cyl)), alpha=0.8) + 
    labs(title="Density plot", 
         subtitle="City Mileage Grouped by Number of cylinders",
         caption="Source: mpg",
         x="City Mileage",
         fill="# Cylinders")
 
```

### 4.3 箱线图
箱形图是研究数据分布的一个有用工具。它还可以显示多个组内的分布，以及中值、范围和异常值。箱子内的黑线表示中位数。箱顶是 75% 分位数，箱底是 25% 分位数。线的端点（又称晶须）距离为1.5*IQR，其中 IQR （四分位差）是 25% 到 75% 分位数之间距离。须外的点通常被认为是极值点。设置`varwidth=T`将调整盒子的宽度，使其与观察的数量成比例。
```{r}
g <- ggplot(mpg, aes(class, cty))
g + geom_boxplot(varwidth=T, fill="plum") + 
    labs(title="Box plot", 
         subtitle="City Mileage grouped by Class of vehicle",
         caption="Source: mpg",
         x="Class of Vehicle",
         y="City Mileage")
 
```

```{r}
g <- ggplot(mpg, aes(class, cty))
g + geom_boxplot(aes(fill=factor(cyl))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="City Mileage grouped by Class of vehicle",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
 
```

### 4.4 点图+箱线图
在箱线图的基础上，添加点图可以提供更清晰的信息。这些点交错排列，每个点代表一次观测。
```{r}
g <- ggplot(mpg, aes(manufacturer, cty))
g + geom_boxplot() + 
  geom_dotplot(binaxis='y', 
               stackdir='center', 
               dotsize = .5, 
               fill="red") +
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot + Dot plot", 
       subtitle="City Mileage vs Class: Each dot represents 1 row in source data",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
 
```

### 4.5 塔夫特箱线图
这是一个简化版的箱线图。
```{r}

library(ggthemes)
library(ggplot2)
theme_set(theme_tufte())  # from ggthemes

# plot
g <- ggplot(mpg, aes(manufacturer, cty))
g + geom_tufteboxplot() + 
      theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
      labs(title="Tufte Styled Boxplot", 
           subtitle="City Mileage grouped by Class of vehicle",
           caption="Source: mpg",
           x="Class of Vehicle",
           y="City Mileage")


```

### 4.6 小提琴图
小提琴图类似于箱线图，但显示了组内的密度。
```{r}
g <- ggplot(mpg, aes(class, cty))
g + geom_violin() + 
  labs(title="Violin plot", 
       subtitle="City Mileage vs Class of vehicle",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
 
```

### 4.7 金字塔图
金字塔图提供了一种独特的方式来可视化每个类别包含的样本比例。下面的金字塔是一个很好的例子，说明了在一个营销活动中每个阶段的用户留存率。
```{r}library(ggplot2)
library(ggthemes)
options(scipen = 999)  # turns of scientific notations like 1e+40

# Read data
email_campaign_funnel <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv")

# X Axis Breaks and Labels 
brks <- seq(-15000000, 15000000, 5000000)
lbls = paste0(as.character(c(seq(15, 0, -5), seq(5, 15, 5))), "m")

# Plot
ggplot(email_campaign_funnel, aes(x = Stage, y = Users, fill = Gender)) +   # Fill column
                              geom_bar(stat = "identity", width = .6) +   # draw the bars
                              scale_y_continuous(breaks = brks,   # Breaks
                                                 labels = lbls) + # Labels
                              coord_flip() +  # Flip axes
                              labs(title="Email Campaign Funnel") +
                              theme_tufte() +  # Tufte theme from ggfortify
                              theme(plot.title = element_text(hjust = .5), 
                                    axis.ticks = element_blank()) +   # Centre plot title
                              scale_fill_brewer(palette = "Dark2")  # Color palette
```

## 5 组成
### 5.1 华夫饼图
华夫图可以显示总体的组成成分。
```{r}
var <- mpg$class  # the categorical data 

## Prep data (nothing to change here)
nrows <- 10
df <- expand.grid(y = 1:nrows, x = 1:nrows)
categ_table <- round(table(var) * ((nrows*nrows)/(length(var))))


df$category <- factor(rep(names(categ_table), categ_table))  
# NOTE: if sum(categ_table) is not 100 (i.e. nrows^2), it will need adjustment to make the sum to 100.

## Plot
ggplot(df, aes(x = x, y = y, fill = category)) + 
        geom_tile(color = "black", size = 0.5) +
        scale_x_continuous(expand = c(0, 0)) +
        scale_y_continuous(expand = c(0, 0), trans = 'reverse') +
        scale_fill_brewer(palette = "Set3") +
        labs(title="Waffle Chart", subtitle="'Class' of vehicles",
             caption="Source: mpg") + 
        theme(panel.border = element_rect(size = 2),
              plot.title = element_text(size = rel(1.2)),
              axis.text = element_blank(),
              axis.title = element_blank(),
              axis.ticks = element_blank(),
              legend.title = element_blank(),
              legend.position = "right")

```

### 5.2 饼图

```{r}
library(ggplot2)
theme_set(theme_classic())

# Source: Frequency table
df <- as.data.frame(table(mpg$class))
colnames(df) <- c("class", "freq")
pie <- ggplot(df, aes(x = "", y=freq, fill = factor(class))) + 
  geom_bar(width = 1, stat = "identity") +
  theme(axis.line = element_blank(), 
        plot.title = element_text(hjust=0.5)) + 
  labs(fill="class", 
       x=NULL, 
       y=NULL, 
       title="Pie Chart of class", 
       caption="Source: mpg")

pie + coord_polar(theta = "y", start=0)

# Source: Categorical variable.
# mpg$class
pie <- ggplot(mpg, aes(x = "", fill = factor(class))) + 
  geom_bar(width = 1) +
  theme(axis.line = element_blank(), 
        plot.title = element_text(hjust=0.5)) + 
  labs(fill="class", 
       x=NULL, 
       y=NULL, 
       title="Pie Chart of class", 
       caption="Source: mpg")
  
pie + coord_polar(theta = "y", start=0)
```



### 5.3 条形图
```{r}
 #prep frequency table
freqtable <- table(mpg$manufacturer)
df <- as.data.frame.table(freqtable)

```

```{r}
library(ggplot2)
theme_set(theme_classic())

# Plot
g <- ggplot(df, aes(Var1, Freq))
g + geom_bar(stat="identity", width = 0.5, fill="tomato2") + 
      labs(title="Bar Chart", 
           subtitle="Manufacturer of vehicles", 
           caption="Source: Frequency of Manufacturers from 'mpg' dataset") +
      theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

```{r}
# From on a categorical column variable
g <- ggplot(mpg, aes(manufacturer))
g + geom_bar(aes(fill=class), width = 0.5) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) +
  labs(title="Categorywise Bar Chart", 
       subtitle="Manufacturer of vehicles", 
       caption="Source: Manufacturers from 'mpg' dataset")
```



## 6 变化趋势
### 6.1 时间序列图：基于时间序列对象（ts）
`ggfortify`包可以对时间序列直接绘图。
```{r}
## From Timeseries object (ts)
library(ggplot2)
library(ggfortify)
theme_set(theme_classic())

# Plot 
autoplot(AirPassengers) + 
  labs(title="AirPassengers") + 
  theme(plot.title = element_text(hjust=0.5))
```

### 6.2 时间序列图：基于数据框
```{r}
library(ggplot2)
theme_set(theme_classic())

# 使用默认的时间跨度
ggplot(economics, aes(x=date)) + 
  geom_line(aes(y=returns_perc)) + 
  labs(title="Time Series Chart", 
       subtitle="Returns Percentage from 'Economics' Dataset", 
       caption="Source: Economics", 
       y="Returns %")

```

如果想设置特定的时间间隔，则需要使用`scale_x_date()`函数。
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

economics_m <- economics[1:24, ]

# 设定时间跨度为一个月
lbls <- paste0(month.abb[month(economics_m$date)], " ", lubridate::year(economics_m$date))
brks <- economics_m$date

# plot
ggplot(economics_m, aes(x=date)) + 
  geom_line(aes(y=returns_perc)) + 
  labs(title="Monthly Time Series", 
       subtitle="Returns Percentage from Economics Dataset", 
       caption="Source: Economics", 
       y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, 
               breaks = brks) +  # change to monthly ticks and labels
  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text
        panel.grid.minor = element_blank())  # turn off minor grid
```

设置时间跨度为 1 年：
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

economics_y <- economics[1:90, ]

# labels and breaks for X axis text
brks <- economics_y$date[seq(1, length(economics_y$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(economics_y, aes(x=date)) + 
  geom_line(aes(y=returns_perc)) + 
  labs(title="Yearly Time Series", 
       subtitle="Returns Percentage from Economics Dataset", 
       caption="Source: Economics", 
       y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, 
               breaks = brks) +  # change to monthly ticks and labels
  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text
        panel.grid.minor = element_blank())  # turn off minor grid
```

### 6.3 多个时间序列
在本例中，基于长数据格式进行可视化。这意味着，所有列的列名和各自的值被存放在两个变量中（分别是`variable`和`value`）。

```{r}
data(economics_long, package = "ggplot2")
head(economics_long)
```

在下面的代码中，在`geom_line()`函数中设置绘图对象为`value`，颜色匹配对象为`variable`。这样，只要调用一次geom_line，就会绘制多条彩色线，每条线代表`variable`列中的每个唯一`value`。`scale_x_date()`将更改 X 轴断点和标签，`scale_color_manual`将更改行颜色。
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

df <- economics_long[economics_long$variable %in% c("psavert", "uempmed"), ]
df <- df[lubridate::year(df$date) %in% c(1967:1981), ]

# labels and breaks for X axis text
brks <- df$date[seq(1, length(df$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(df, aes(x=date)) + 
  geom_line(aes(y=value, col=variable)) + 
  labs(title="Time Series of Returns Percentage", 
       subtitle="Drawn from Long Data format", 
       caption="Source: Economics", 
       y="Returns %", 
       color=NULL) +  # title and caption
  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels
  scale_color_manual(labels = c("psavert", "uempmed"), 
                     values = c("psavert"="#00ba38", "uempmed"="#f8766d")) +  # line color
  theme(axis.text.x = element_text(angle = 90, vjust=0.5, size = 8),  # rotate x axis text
        panel.grid.minor = element_blank())  # turn off minor grid
```

如果从一个宽格式创建一个时间序列，则必须通过对每条线调用一次`geom_line()`制。因此，默认情况下不会绘制图例，需要手动添加。
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

df <- economics[, c("date", "psavert", "uempmed")]
df <- df[lubridate::year(df$date) %in% c(1967:1981), ]

# labels and breaks for X axis text
brks <- df$date[seq(1, length(df$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(df, aes(x=date)) + 
  geom_line(aes(y=psavert, col="psavert")) + 
  geom_line(aes(y=uempmed, col="uempmed")) + 
  labs(title="Time Series of Returns Percentage", 
       subtitle="Drawn From Wide Data format", 
       caption="Source: Economics", y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels
  scale_color_manual(name="", 
                     values = c("psavert"="#00ba38", "uempmed"="#f8766d")) +  # line color
  theme(panel.grid.minor = element_blank())  # turn off minor grid
```



### 6.4 堆叠面积图
堆叠面积图与折线图类似，只是图下方的区域全部着色。应用场景有：

- 想要描述数量或体积（而不是价格之类的变量）随时间的变化；
- 有很多数据点。对于很少的数据点，可以考虑绘制柱状图。
- 希望展示各个类别的贡献。

```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

df <- economics[, c("date", "psavert", "uempmed")]
df <- df[lubridate::year(df$date) %in% c(1967:1981), ]

# labels and breaks for X axis text
brks <- df$date[seq(1, length(df$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(df, aes(x=date)) + 
  geom_area(aes(y=psavert+uempmed, fill="psavert")) + 
  geom_area(aes(y=uempmed, fill="uempmed")) + 
  labs(title="Area Chart of Returns Percentage", 
       subtitle="From Wide Data format", 
       caption="Source: Economics", 
       y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels
  scale_fill_manual(name="", 
                    values = c("psavert"="#00ba38", "uempmed"="#f8766d")) +  # line color
  theme(panel.grid.minor = element_blank())  # turn off minor grid
```


### 6.5 日历热力图

当您想要在实际的日历上看到像股票价格这类指标的变化，特别是高点和低点时，日历热力图是一个很好的工具。它强调随着时间的推移视觉上的变化，而不是实际数值的变化。这可以通过使用`geom_tile()`来实现。
```{r}
library(ggplot2)
library(plyr)
library(scales)
library(zoo)

df <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv")
df$date <- as.Date(df$date)  # format date
df <- df[df$year >= 2012, ]  # filter reqd years

# Create Month Week
df$yearmonth <- as.yearmon(df$date)
df$yearmonthf <- factor(df$yearmonth)
df <- ddply(df,.(yearmonthf), transform, monthweek=1+week-min(week))  # compute week number of month
df <- df[, c("year", "yearmonthf", "monthf", "week", "monthweek", "weekdayf", "VIX.Close")]
head(df)
```

```{r}
ggplot(df, aes(monthweek, weekdayf, fill = VIX.Close)) + 
  geom_tile(colour = "white") + 
  facet_grid(year~monthf) + 
  scale_fill_gradient(low="red", high="green") +
  labs(x="Week of Month",
       y="",
       title = "Time-Series Calendar Heatmap", 
       subtitle="Yahoo Closing Price", 
       fill="Close")
```

### 6.6 坡度图
坡度图可以可视化数值和类别排名之间的变化。这更适用于时间点很少的时间序列。
```{r}
library(dplyr)
theme_set(theme_classic())
source_df <- read.csv("https://raw.githubusercontent.com/jkeirstead/r-slopegraph/master/cancer_survival_rates.csv")

# Define functions. Source: https://github.com/jkeirstead/r-slopegraph
tufte_sort <- function(df, x="year", y="value", group="group", method="tufte", min.space=0.05) {
    ## First rename the columns for consistency
    ids <- match(c(x, y, group), names(df))
    df <- df[,ids]
    names(df) <- c("x", "y", "group")

    ## Expand grid to ensure every combination has a defined value
    tmp <- expand.grid(x=unique(df$x), group=unique(df$group))
    tmp <- merge(df, tmp, all.y=TRUE)
    df <- mutate(tmp, y=ifelse(is.na(y), 0, y))
  
    ## Cast into a matrix shape and arrange by first column
    require(reshape2)
    tmp <- dcast(df, group ~ x, value.var="y")
    ord <- order(tmp[,2])
    tmp <- tmp[ord,]
    
    min.space <- min.space*diff(range(tmp[,-1]))
    yshift <- numeric(nrow(tmp))
    ## Start at "bottom" row
    ## Repeat for rest of the rows until you hit the top
    for (i in 2:nrow(tmp)) {
        ## Shift subsequent row up by equal space so gap between
        ## two entries is >= minimum
        mat <- as.matrix(tmp[(i-1):i, -1])
        d.min <- min(diff(mat))
        yshift[i] <- ifelse(d.min < min.space, min.space - d.min, 0)
    }

    
    tmp <- cbind(tmp, yshift=cumsum(yshift))

    scale <- 1
    tmp <- melt(tmp, id=c("group", "yshift"), variable.name="x", value.name="y")
    ## Store these gaps in a separate variable so that they can be scaled ypos = a*yshift + y

    tmp <- transform(tmp, ypos=y + scale*yshift)
    return(tmp)
   
}

plot_slopegraph <- function(df) {
    ylabs <- subset(df, x==head(x,1))$group
    yvals <- subset(df, x==head(x,1))$ypos
    fontSize <- 3
    gg <- ggplot(df,aes(x=x,y=ypos)) +
        geom_line(aes(group=group),colour="grey80") +
        geom_point(colour="white",size=8) +
        geom_text(aes(label=y), size=fontSize, family="American Typewriter") +
        scale_y_continuous(name="", breaks=yvals, labels=ylabs)
    return(gg)
}    

## Prepare data    
df <- tufte_sort(source_df, 
                 x="year", 
                 y="value", 
                 group="group", 
                 method="tufte", 
                 min.space=0.05)

df <- transform(df, 
                x=factor(x, levels=c(5,10,15,20), 
                            labels=c("5 years","10 years","15 years","20 years")), 
                y=round(y))

## Plot
plot_slopegraph(df) + labs(title="Estimates of % survival rates") + 
                      theme(axis.title=element_blank(),
                            axis.ticks = element_blank(),
                            plot.title = element_text(hjust=0.5,
                                                      family = "American Typewriter",
                                                      face="bold"),
                            axis.text = element_text(family = "American Typewriter",
                                                     face="bold"))
```

### 6.7 季节图
如果您正在处理`ts`或`xts`类型的时间序列对象，您可以通过使用`forecast::ggseasonplot`绘制的季节图来查看季节波动。下面是一个使用`AirPassengers`和`nottem`数据集绘制的例子。
```{r}
library(ggplot2)
library(forecast)
theme_set(theme_classic())

# Subset data
nottem_small <- window(nottem, start=c(1920, 1), end=c(1925, 12))  # subset a smaller timewindow

# Plot
ggseasonplot(AirPassengers) + labs(title="Seasonal plot: International Airline Passengers")

```

```{r}
ggseasonplot(nottem_small) + labs(title="Seasonal plot: Air temperatures at Nottingham Castle")
```

## 7 群体
### 7.1 谱系图
```{r}

library(ggplot2)
library(ggdendro)
theme_set(theme_bw())

hc <- hclust(dist(USArrests), "ave")  # hierarchical clustering

# plot
ggdendrogram(hc, rotate = TRUE, size = 2)
```

### 7.2 聚类图
可以使用`geom_surround()`来显示不同的簇或组。如果数据集有多个特征，还可以计算主成分，并使用 PC1 和 PC2 作为 X 和 Y 轴绘制散点图。`geom_encircle()`可用于框选所需的组。
```{r}
# devtools::install_github("hrbrmstr/ggalt")
library(ggplot2)
library(ggalt)
library(ggfortify)
theme_set(theme_classic())

# Compute data with principal components ------------------
df <- iris[c(1, 2, 3, 4)]
pca_mod <- prcomp(df)  # compute principal components

# Data frame of principal components ----------------------
df_pc <- data.frame(pca_mod$x, Species=iris$Species)  # dataframe of principal components
df_pc_vir <- df_pc[df_pc$Species == "virginica", ]  # df for 'virginica'
df_pc_set <- df_pc[df_pc$Species == "setosa", ]  # df for 'setosa'
df_pc_ver <- df_pc[df_pc$Species == "versicolor", ]  # df for 'versicolor'
 
# Plot ----------------------------------------------------
ggplot(df_pc, aes(PC1, PC2, col=Species)) + 
  geom_point(aes(shape=Species), size=2) +   # draw points
  labs(title="Iris Clustering", 
       subtitle="With principal components PC1 and PC2 as X and Y axis",
       caption="Source: Iris") + 
  coord_cartesian(xlim = 1.2 * c(min(df_pc$PC1), max(df_pc$PC1)), 
                  ylim = 1.2 * c(min(df_pc$PC2), max(df_pc$PC2))) +   # change axis limits
  geom_encircle(data = df_pc_vir, aes(x=PC1, y=PC2)) +   # draw circles
  geom_encircle(data = df_pc_set, aes(x=PC1, y=PC2)) + 
  geom_encircle(data = df_pc_ver, aes(x=PC1, y=PC2))
```



================================================
FILE: 2022年/2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/.Rproj.user/C6560784/sources/per/t/C36CFFC0-contents 2
================================================
---
title: "ggplot绘图手册"
author: "赵子茜"
date: "`r Sys.Date()`"
output: 
  prettydoc::html_pretty:
    toc: true
    toc_depth: 4
    theme: cayman
    highlight: github
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,warning = F,message = F)
```

## 加载数据集

```{r}
library(ggplot2)
library(plotrix)
options(scipen=999)  # 关掉像1e+48这样的科学符号
data("midwest", package = "ggplot2") #加载数据集
```

## 全局主题设置

```{r}
# 颜色设置（灰色系列）
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

# 颜色设置（黑色系列）
cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")


ggplot <- function(...) ggplot2::ggplot(...) + 
  scale_color_manual(values = cbp1) +
  scale_fill_manual(values = cbp1) + # 注意: 使用连续色阶时需要重写
  theme_bw()

```




## 1. 相关关系
### 1.1 两个变量
展示两个变量之间的相关关系，最常使用的是**散点图**。在 ggplot 中，使用`geom_point()`绘制。此外，默认情况下，`geom_smooth`会绘制一条平滑线（基于 losses ），可以通过设置`method='lm'`来调整以绘制最佳拟合的线。


```{r}
# Scatterplot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) + 
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.1)) + 
  ylim(c(0, 500000)) + 
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot", 
       caption = "Source: midwest")

plot(gg)

```


### 1.2 环绕式散点图
使用 ggalt 包中的`geom_encircle()`函数可实现在散点图中圈选特定区域：
```{r message=FALSE, warning=FALSE}
# install.packages("ggalt")
library(ggalt)
options(scipen = 999)
library(ggplot2)
library(ggalt)

## 设定筛选条件
midwest_select <- midwest[midwest$poptotal > 350000 & 
                            midwest$poptotal <= 500000 & 
                            midwest$area > 0.01 & 
                            midwest$area < 0.1, ]

# Plot
ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) +   # draw points
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.1)) + 
  ylim(c(0, 500000)) +   # draw smoothing line
  geom_encircle(aes(x=area, y=poptotal), 
                data=midwest_select, 
                color="red", 
                size=2, 
                expand=0.08) +   # encircle
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot + Encircle", 
       caption="Source: midwest")
```




### 1.3 抖动图

抖动图可以解决数据点重叠的问题。通过`geom_jitter`函数中的`width`参数设置抖动范围，重叠点在其原始位置周围随机抖动。
```{r}
g <- ggplot(mpg, aes(cty, hwy))
g + geom_jitter(width = .5, size=1) +
  labs(subtitle="mpg: city vs highway mileage", 
       y="hwy", 
       x="cty", 
       title="Jittered Points")

```

### 1.4 计数图

克服数据点重叠问题的第二个选择是使用计数图 `geom_count()`。重叠点越多，圆就越大。

```{r}
g + geom_count(col="tomato3", show.legend=F) +
  labs(subtitle="mpg: city vs highway mileage", 
       y="hwy", 
       x="cty", 
       title="Counts Plot")
```

### 1.5 气泡图
气泡图适合 4 维数据，其中两个是数值型（分别是 X 和 Y），另一个是分类变量（用`color`表示）和另一个数值变量（用`size`表示）。
```{r}
data(mpg, package="ggplot2")


mpg_select <- mpg[mpg$manufacturer %in% c("audi", "ford", "honda", "hyundai"), ]

# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
g <- ggplot(mpg_select, aes(displ, cty)) + 
  labs(subtitle="mpg: Displacement vs City Mileage",
       title="Bubble chart")

g + geom_jitter(aes(col=manufacturer, size=hwy)) + 
  geom_smooth(aes(col=manufacturer), method="lm", se=F)
```


<!-- ### 1.6 动态气泡图 -->

<!-- ```{r} -->
<!-- # install.packages("gganimate") -->
<!-- # install.packages("gapminder") -->
<!-- library(gganimate) -->
<!-- library(gapminder) -->

<!-- g <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, frame = year)) + -->
<!--   geom_point() + -->
<!--   geom_smooth(aes(group = year), -->
<!--               method = "lm", -->
<!--               show.legend = FALSE) + -->
<!--   facet_wrap(~continent, scales = "free") + -->
<!--   scale_x_log10()  # convert to log scale -->

<!-- gganimate(g, interval=0.2) -->
<!-- ``` -->


### 1.6 边际直方图/箱线图

如果您想在同一个图中显示变量的关系和分布，可以使用边际直方图。更改`ggMarginal()`函数中的`type`参数可以将边际直方图换成箱线图或者密度图。

```{r}
library(ggExtra)
data(mpg, package="ggplot2")


# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
mpg_select <- mpg[mpg$hwy >= 35 & mpg$cty > 27, ]
g <- ggplot(mpg, aes(cty, hwy)) + 
  geom_count() + 
  geom_smooth(method="lm", se=F)

ggMarginal(g, type = "histogram", fill="transparent")

```
```{r}
ggMarginal(g, type = "boxplot", fill="transparent")

```

```{r}
ggMarginal(g, type = "density", fill="transparent")
```

### 1.7 相关系数图

相关系数图可以查看同一组数据中多个连续变量的相关性。
```{r}
library(ggcorrplot)

# Correlation matrix
data(mtcars)
corr <- round(cor(mtcars), 1)

# Plot
ggcorrplot(corr, hc.order = TRUE, 
           type = "lower", 
           lab = TRUE, 
           lab_size = 3, 
           method="circle", 
           colors = c("tomato2", "white", "springgreen3"), 
           title="Correlogram of mtcars", 
           ggtheme=theme_bw)
```

## 2 偏差
### 2.1 发散条形图
发散条形图是一种可以同时处理负值和正值的条形图。这可以通过 `geom_bar()` 来实现。但是 `geom_bar()` 的用法可能会让人很困惑。这是因为，它既可以用来制作柱状图，也可以用来制作直方图。默认情况下，`geom_bar()` 中 `stat` 参数的默认值为 `count`。这意味着，当您只提供一个连续的 x 变量（而不提供 y 变量）时，它会尝试从数据中生成一个直方图。如果要制作条形图，需要做两件事：

- 设置 `stat=identity`;
- 在 `aes()` 中同时输入 x 和 y，其中 x 是字符或因子型变量，y 是数值型。

为了确保得到的是发散条形图，数据需要满足：分类变量的两个类别在连续变量的某个阈值处改变其值。在下面的例子中，`mtcars` 数据集的 mpg 通过计算 z 分数被规范化。那些 mpg 在 0 以上的车辆被标记为绿色，低于 0 的车辆被标记为红色。

```{r}
data("mtcars")  # load data
mtcars$`car name` <- rownames(mtcars)  # create new column for car names
mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg
mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, "below", "above")  # above / below avg flag
mtcars <- mtcars[order(mtcars$mpg_z), ]  # sort
mtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`) 

# Diverging Barcharts
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +
  scale_fill_manual(name="Mileage", 
                    labels = c("Above Average", "Below Average"), 
                    values = c("above"="#00ba38", "below"="#f8766d")) + 
  labs(subtitle="Normalised mileage from 'mtcars'", 
       title= "Diverging Bars") + 
  coord_flip()
```


### 2.2 带标记的发散型棒棒糖图
这个图形是发散条形图的一个变体。
```{r}
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_point(stat='identity', fill="black", size=6)  +
  geom_segment(aes(y = 0, 
                   x = `car name`, 
                   yend = mpg_z, 
                   xend = `car name`), 
               color = "black") +
  geom_text(color="white", size=2) +
  labs(title="Diverging Lollipop Chart", 
       subtitle="Normalized mileage from 'mtcars': Lollipop") + 
  ylim(-2.5, 2.5) +
  coord_flip()
```


### 2.3 发散性点图

```{r}
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_point(stat='identity', aes(col=mpg_type), size=6)  +
  scale_color_manual(name="Mileage", 
                     labels = c("Above Average", "Below Average"), 
                     values = c("above"="#00ba38", "below"="#f8766d")) + 
  geom_text(color="white", size=2) +
  labs(title="Diverging Dot Plot", 
       subtitle="Normalized mileage from 'mtcars': Dotplot") + 
  ylim(-2.5, 2.5) +
  coord_flip()
```


### 2.4 面积图
面积图通常用于可视化特定指标（如股票回报率）与基线的对比情况。
```{r}
library(quantmod)
data("economics", package = "ggplot2")

# 计算利润
economics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])

# 为轴刻度创建断点和标签
brks <- economics$date[seq(1, length(economics$date), 12)]
lbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])

# Plot
ggplot(economics[1:100, ], aes(date, returns_perc)) + 
  geom_area() + 
  scale_x_date(breaks=brks, labels=lbls) + 
  theme(axis.text.x = element_text(angle=90)) + 
  labs(title="Area Chart", 
       subtitle = "Perc Returns for Personal Savings", 
       y="% Returns for Personal savings", 
       caption="Source: economics")

```

## 3 排序
### 3.1 有序条形图
有序条形图是按 Y 轴变量排序的条形图，X 轴变量必须转换为因子型。
```{r}
# 准备数据：按厂商分组计算平均城市里程。
cty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate
colnames(cty_mpg) <- c("make", "mileage")  # change column names
cty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort
cty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.
head(cty_mpg, 4)

```

```{r}
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_bar(stat="identity", width=.5, fill="tomato3") + 
  labs(title="Ordered Bar Chart", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

### 3.2 棒棒糖图
棒棒糖图传达的信息与柱状图相同。通过将粗条转变为细线，减少了杂乱，使图形看起来更美观。
```{r}
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_point(size=3) + 
  geom_segment(aes(x=make, 
                   xend=make, 
                   y=0, 
                   yend=mileage)) + 
  labs(title="Lollipop Chart", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

### 3.3 点图
点图非常类似于棒棒糖图，但没有线条，并且各标签都在水平位置上。它更强调项目的顺序与实际值有关。
```{r}
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_point(col="tomato2", size=3) +   # Draw points
  geom_segment(aes(x=make, 
                   xend=make, 
                   y=min(mileage), 
                   yend=max(mileage)), 
               linetype="dashed", 
               size=0.1) +   # Draw dashed lines
  labs(title="Dot Plot", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") +  
  coord_flip()
```

### 3.4 坡度图
坡度图是比较两点之间差异的绝佳方法。目前，还没有内置函数来构建这个图形。下面的代码可以作为一个示例框架，告诉您如何绘制这个图形。
```{r}
library(scales)

# 数据准备
df <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv")
colnames(df) <- c("continent", "1952", "1957")
left_label <- paste(df$continent, round(df$'1952'),sep=", ")
right_label <- paste(df$continent, round(df$'1957'),sep=", ")
df$class <- ifelse((df$'1957' - df$'1952') < 0, "red", "green")
head(df)
```

```{r}
library(ggplot2)
p <- ggplot(df) + geom_segment(aes(x=1, xend=2, y=`1952`, yend=`1957`, col=class), size=.75, show.legend=F) + 
                  geom_vline(xintercept=1, linetype="dashed", size=.1) + 
                  geom_vline(xintercept=2, linetype="dashed", size=.1) +
                  scale_color_manual(labels = c("Up", "Down"), 
                                     values = c("green"="#00ba38", "red"="#f8766d")) +  # color of lines
                  labs(x="", y="Mean GdpPerCap") +  # Axis labels
                  xlim(.5, 2.5) + ylim(0,(1.1*(max(df$`1952`, df$`1957`))))  # X and Y axis limits

# Add texts
p <- p + geom_text(label=left_label, y=df$`1952`, x=rep(1, NROW(df)), hjust=1.1, size=3.5)
p <- p + geom_text(label=right_label, y=df$`1957`, x=rep(2, NROW(df)), hjust=-0.1, size=3.5)
p <- p + geom_text(label="Time 1", x=1, y=1.1*(max(df$`1952`, df$`1957`)), hjust=1.2, size=5)  # title
p <- p + geom_text(label="Time 2", x=2, y=1.1*(max(df$`1952`, df$`1957`)), hjust=-0.1, size=5)  # title


#设置主题
p + theme(panel.background = element_blank(),
           panel.grid = element_blank(),
           axis.ticks = element_blank(),
           axis.text.x = element_blank(),
           panel.border = element_blank(),
           plot.margin = unit(c(1,2,1,2), "cm"))


```

### 3.5 哑铃图
哑铃图可以：1)比较两个时间点之间的相对位置（比如增长和下降）；2) 比较两类之间的距离。为了得到哑铃的正确顺序，Y 变量应该是一个因子，因子变量的水平应该与它在图中出现的顺序相同。
```{r}
health <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/health.csv")
health$Area <- factor(health$Area, levels=as.character(health$Area))  # for right ordering of the dumbells

# health$Area <- factor(health$Area)
gg <- ggplot(health, aes(x=pct_2013, xend=pct_2014, y=Area, group=Area)) + 
        geom_dumbbell(color="#a3c4dc", 
                      size=0.75, 
                      point.colour.l="#0e668b") + 
        scale_x_continuous(label=percent) + 
        labs(x=NULL, 
             y=NULL, 
             title="Dumbbell Chart", 
             subtitle="Pct Change: 2013 vs 2014", 
             caption="Source: https://github.com/hrbrmstr/ggalt") +
        theme(plot.title = element_text(hjust=0.5, face="bold"),
              plot.background=element_rect(fill="#f7f7f7"),
              panel.background=element_rect(fill="#f7f7f7"),
              panel.grid.minor=element_blank(),
              panel.grid.major.y=element_blank(),
              panel.grid.major.x=element_line(),
              axis.ticks=element_blank(),
              legend.position="top",
              panel.border=element_blank())

plot(gg)

```

## 4 分布
### 4.1 直方图
#### 4.4.1 连续变量的直方图
连续变量的直方图可以使用`geom_bar()`或`geom_histogram()`来完成。当使用`geom_histogram()`时，可以使用`bins`参数来控制分箱的数量。也可以使用`binwidth`设置每个分箱覆盖的范围。`binwidth`的值与建立直方图的连续变量在同一个尺度上。
```{r}
# 连续（数值）变量的直方图
g <- ggplot(mpg, aes(displ)) + scale_fill_brewer(palette = "Spectral")

g + geom_histogram(aes(fill=class), 
                   binwidth = .1, 
                   col="black", 
                   size=.1) +  # change binwidth
  labs(title="Histogram with Auto Binning", 
       subtitle="Engine Displacement across Vehicle Classes")

 
```


```{r}

g + geom_histogram(aes(fill=class), 
                   bins=5, 
                   col="black", 
                   size=.1) +   # change number of bins
  labs(title="Histogram with Fixed Bins", 
       subtitle="Engine Displacement across Vehicle Classes") 

 
```

#### 4.1.2分类变量的直方图
分类变量的直方图实际上是根据每个类别的频率绘制的条形图。
```{r}
library(ggplot2)
theme_set(theme_classic())

# Histogram on a Categorical variable
g <- ggplot(mpg, aes(manufacturer))
g + geom_bar(aes(fill=class), width = 0.5) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Histogram on Categorical Variable", 
       subtitle="Manufacturer across Vehicle Classes")

 
```

### 4.2 密度图

```{r}
g <- ggplot(mpg, aes(cty))
g + geom_density(aes(fill=factor(cyl)), alpha=0.8) + 
    labs(title="Density plot", 
         subtitle="City Mileage Grouped by Number of cylinders",
         caption="Source: mpg",
         x="City Mileage",
         fill="# Cylinders")
 
```

### 4.3 箱线图
箱形图是研究数据分布的一个有用工具。它还可以显示多个组内的分布，以及中值、范围和异常值。箱子内的黑线表示中位数。箱顶是 75% 分位数，箱底是 25% 分位数。线的端点（又称晶须）距离为1.5*IQR，其中 IQR （四分位差）是 25% 到 75% 分位数之间距离。须外的点通常被认为是极值点。设置`varwidth=T`将调整盒子的宽度，使其与观察的数量成比例。
```{r}
g <- ggplot(mpg, aes(class, cty))
g + geom_boxplot(varwidth=T, fill="plum") + 
    labs(title="Box plot", 
         subtitle="City Mileage grouped by Class of vehicle",
         caption="Source: mpg",
         x="Class of Vehicle",
         y="City Mileage")
 
```

```{r}
g <- ggplot(mpg, aes(class, cty))
g + geom_boxplot(aes(fill=factor(cyl))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="City Mileage grouped by Class of vehicle",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
 
```

### 4.4 点图+箱线图
在箱线图的基础上，添加点图可以提供更清晰的信息。这些点交错排列，每个点代表一次观测。
```{r}
g <- ggplot(mpg, aes(manufacturer, cty))
g + geom_boxplot() + 
  geom_dotplot(binaxis='y', 
               stackdir='center', 
               dotsize = .5, 
               fill="red") +
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot + Dot plot", 
       subtitle="City Mileage vs Class: Each dot represents 1 row in source data",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
 
```

### 4.5 塔夫特箱线图
这是一个简化版的箱线图。
```{r}

library(ggthemes)
library(ggplot2)
theme_set(theme_tufte())  # from ggthemes

# plot
g <- ggplot(mpg, aes(manufacturer, cty))
g + geom_tufteboxplot() + 
      theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
      labs(title="Tufte Styled Boxplot", 
           subtitle="City Mileage grouped by Class of vehicle",
           caption="Source: mpg",
           x="Class of Vehicle",
           y="City Mileage")


```

### 4.6 小提琴图
小提琴图类似于箱线图，但显示了组内的密度。
```{r}
g <- ggplot(mpg, aes(class, cty))
g + geom_violin() + 
  labs(title="Violin plot", 
       subtitle="City Mileage vs Class of vehicle",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
 
```

### 4.7 金字塔图
金字塔图提供了一种独特的方式来可视化每个类别包含的样本比例。下面的金字塔是一个很好的例子，说明了在一个营销活动中每个阶段的用户留存率。
```{r}library(ggplot2)
library(ggthemes)
options(scipen = 999)  # turns of scientific notations like 1e+40

# Read data
email_campaign_funnel <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv")

# X Axis Breaks and Labels 
brks <- seq(-15000000, 15000000, 5000000)
lbls = paste0(as.character(c(seq(15, 0, -5), seq(5, 15, 5))), "m")

# Plot
ggplot(email_campaign_funnel, aes(x = Stage, y = Users, fill = Gender)) +   # Fill column
                              geom_bar(stat = "identity", width = .6) +   # draw the bars
                              scale_y_continuous(breaks = brks,   # Breaks
                                                 labels = lbls) + # Labels
                              coord_flip() +  # Flip axes
                              labs(title="Email Campaign Funnel") +
                              theme_tufte() +  # Tufte theme from ggfortify
                              theme(plot.title = element_text(hjust = .5), 
                                    axis.ticks = element_blank()) +   # Centre plot title
                              scale_fill_brewer(palette = "Dark2")  # Color palette
```

## 5 组成
### 5.1 华夫饼图
华夫图可以显示总体的组成成分。
```{r}
var <- mpg$class  # the categorical data 

## Prep data (nothing to change here)
nrows <- 10
df <- expand.grid(y = 1:nrows, x = 1:nrows)
categ_table <- round(table(var) * ((nrows*nrows)/(length(var))))


df$category <- factor(rep(names(categ_table), categ_table))  
# NOTE: if sum(categ_table) is not 100 (i.e. nrows^2), it will need adjustment to make the sum to 100.

## Plot
ggplot(df, aes(x = x, y = y, fill = category)) + 
        geom_tile(color = "black", size = 0.5) +
        scale_x_continuous(expand = c(0, 0)) +
        scale_y_continuous(expand = c(0, 0), trans = 'reverse') +
        scale_fill_brewer(palette = "Set3") +
        labs(title="Waffle Chart", subtitle="'Class' of vehicles",
             caption="Source: mpg") + 
        theme(panel.border = element_rect(size = 2),
              plot.title = element_text(size = rel(1.2)),
              axis.text = element_blank(),
              axis.title = element_blank(),
              axis.ticks = element_blank(),
              legend.title = element_blank(),
              legend.position = "right")

```

### 5.2 饼图

```{r}
library(ggplot2)
theme_set(theme_classic())

# Source: Frequency table
df <- as.data.frame(table(mpg$class))
colnames(df) <- c("class", "freq")
pie <- ggplot(df, aes(x = "", y=freq, fill = factor(class))) + 
  geom_bar(width = 1, stat = "identity") +
  theme(axis.line = element_blank(), 
        plot.title = element_text(hjust=0.5)) + 
  labs(fill="class", 
       x=NULL, 
       y=NULL, 
       title="Pie Chart of class", 
       caption="Source: mpg")

pie + coord_polar(theta = "y", start=0)

# Source: Categorical variable.
# mpg$class
pie <- ggplot(mpg, aes(x = "", fill = factor(class))) + 
  geom_bar(width = 1) +
  theme(axis.line = element_blank(), 
        plot.title = element_text(hjust=0.5)) + 
  labs(fill="class", 
       x=NULL, 
       y=NULL, 
       title="Pie Chart of class", 
       caption="Source: mpg")
  
pie + coord_polar(theta = "y", start=0)
```



### 5.3 条形图
```{r}
 #prep frequency table
freqtable <- table(mpg$manufacturer)
df <- as.data.frame.table(freqtable)

```

```{r}
library(ggplot2)
theme_set(theme_classic())

# Plot
g <- ggplot(df, aes(Var1, Freq))
g + geom_bar(stat="identity", width = 0.5, fill="tomato2") + 
      labs(title="Bar Chart", 
           subtitle="Manufacturer of vehicles", 
           caption="Source: Frequency of Manufacturers from 'mpg' dataset") +
      theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

```{r}
# From on a categorical column variable
g <- ggplot(mpg, aes(manufacturer))
g + geom_bar(aes(fill=class), width = 0.5) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) +
  labs(title="Categorywise Bar Chart", 
       subtitle="Manufacturer of vehicles", 
       caption="Source: Manufacturers from 'mpg' dataset")
```



## 6 变化趋势
### 6.1 时间序列图：基于时间序列对象（ts）
`ggfortify`包可以对时间序列直接绘图。
```{r}
## From Timeseries object (ts)
library(ggplot2)
library(ggfortify)
theme_set(theme_classic())

# Plot 
autoplot(AirPassengers) + 
  labs(title="AirPassengers") + 
  theme(plot.title = element_text(hjust=0.5))
```

### 6.2 时间序列图：基于数据框
```{r}
library(ggplot2)
theme_set(theme_classic())

# 使用默认的时间跨度
ggplot(economics, aes(x=date)) + 
  geom_line(aes(y=returns_perc)) + 
  labs(title="Time Series Chart", 
       subtitle="Returns Percentage from 'Economics' Dataset", 
       caption="Source: Economics", 
       y="Returns %")

```

如果想设置特定的时间间隔，则需要使用`scale_x_date()`函数。
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

economics_m <- economics[1:24, ]

# 设定时间跨度为一个月
lbls <- paste0(month.abb[month(economics_m$date)], " ", lubridate::year(economics_m$date))
brks <- economics_m$date

# plot
ggplot(economics_m, aes(x=date)) + 
  geom_line(aes(y=returns_perc)) + 
  labs(title="Monthly Time Series", 
       subtitle="Returns Percentage from Economics Dataset", 
       caption="Source: Economics", 
       y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, 
               breaks = brks) +  # change to monthly ticks and labels
  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text
        panel.grid.minor = element_blank())  # turn off minor grid
```

设置时间跨度为 1 年：
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

economics_y <- economics[1:90, ]

# labels and breaks for X axis text
brks <- economics_y$date[seq(1, length(economics_y$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(economics_y, aes(x=date)) + 
  geom_line(aes(y=returns_perc)) + 
  labs(title="Yearly Time Series", 
       subtitle="Returns Percentage from Economics Dataset", 
       caption="Source: Economics", 
       y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, 
               breaks = brks) +  # change to monthly ticks and labels
  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text
        panel.grid.minor = element_blank())  # turn off minor grid
```

### 6.3 多个时间序列
在本例中，基于长数据格式进行可视化。这意味着，所有列的列名和各自的值被存放在两个变量中（分别是`variable`和`value`）。

```{r}
data(economics_long, package = "ggplot2")
head(economics_long)
```

在下面的代码中，在`geom_line()`函数中设置绘图对象为`value`，颜色匹配对象为`variable`。这样，只要调用一次geom_line，就会绘制多条彩色线，每条线代表`variable`列中的每个唯一`value`。`scale_x_date()`将更改 X 轴断点和标签，`scale_color_manual`将更改行颜色。
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

df <- economics_long[economics_long$variable %in% c("psavert", "uempmed"), ]
df <- df[lubridate::year(df$date) %in% c(1967:1981), ]

# labels and breaks for X axis text
brks <- df$date[seq(1, length(df$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(df, aes(x=date)) + 
  geom_line(aes(y=value, col=variable)) + 
  labs(title="Time Series of Returns Percentage", 
       subtitle="Drawn from Long Data format", 
       caption="Source: Economics", 
       y="Returns %", 
       color=NULL) +  # title and caption
  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels
  scale_color_manual(labels = c("psavert", "uempmed"), 
                     values = c("psavert"="#00ba38", "uempmed"="#f8766d")) +  # line color
  theme(axis.text.x = element_text(angle = 90, vjust=0.5, size = 8),  # rotate x axis text
        panel.grid.minor = element_blank())  # turn off minor grid
```

如果从一个宽格式创建一个时间序列，则必须通过对每条线调用一次`geom_line()`制。因此，默认情况下不会绘制图例，需要手动添加。
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

df <- economics[, c("date", "psavert", "uempmed")]
df <- df[lubridate::year(df$date) %in% c(1967:1981), ]

# labels and breaks for X axis text
brks <- df$date[seq(1, length(df$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(df, aes(x=date)) + 
  geom_line(aes(y=psavert, col="psavert")) + 
  geom_line(aes(y=uempmed, col="uempmed")) + 
  labs(title="Time Series of Returns Percentage", 
       subtitle="Drawn From Wide Data format", 
       caption="Source: Economics", y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels
  scale_color_manual(name="", 
                     values = c("psavert"="#00ba38", "uempmed"="#f8766d")) +  # line color
  theme(panel.grid.minor = element_blank())  # turn off minor grid
```



### 6.4 堆叠面积图
堆叠面积图与折线图类似，只是图下方的区域全部着色。应用场景有：

- 想要描述数量或体积（而不是价格之类的变量）随时间的变化；
- 有很多数据点。对于很少的数据点，可以考虑绘制柱状图。
- 希望展示各个类别的贡献。

```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

df <- economics[, c("date", "psavert", "uempmed")]
df <- df[lubridate::year(df$date) %in% c(1967:1981), ]

# labels and breaks for X axis text
brks <- df$date[seq(1, length(df$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(df, aes(x=date)) + 
  geom_area(aes(y=psavert+uempmed, fill="psavert")) + 
  geom_area(aes(y=uempmed, fill="uempmed")) + 
  labs(title="Area Chart of Returns Percentage", 
       subtitle="From Wide Data format", 
       caption="Source: Economics", 
       y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels
  scale_fill_manual(name="", 
                    values = c("psavert"="#00ba38", "uempmed"="#f8766d")) +  # line color
  theme(panel.grid.minor = element_blank())  # turn off minor grid
```


### 6.5 日历热力图

当您想要在实际的日历上看到像股票价格这类指标的变化，特别是高点和低点时，日历热力图是一个很好的工具。它强调随着时间的推移视觉上的变化，而不是实际数值的变化。这可以通过使用`geom_tile()`来实现。
```{r}
library(ggplot2)
library(plyr)
library(scales)
library(zoo)

df <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv")
df$date <- as.Date(df$date)  # format date
df <- df[df$year >= 2012, ]  # filter reqd years

# Create Month Week
df$yearmonth <- as.yearmon(df$date)
df$yearmonthf <- factor(df$yearmonth)
df <- ddply(df,.(yearmonthf), transform, monthweek=1+week-min(week))  # compute week number of month
df <- df[, c("year", "yearmonthf", "monthf", "week", "monthweek", "weekdayf", "VIX.Close")]
head(df)
```

```{r}
ggplot(df, aes(monthweek, weekdayf, fill = VIX.Close)) + 
  geom_tile(colour = "white") + 
  facet_grid(year~monthf) + 
  scale_fill_gradient(low="red", high="green") +
  labs(x="Week of Month",
       y="",
       title = "Time-Series Calendar Heatmap", 
       subtitle="Yahoo Closing Price", 
       fill="Close")
```

### 6.6 坡度图
坡度图可以可视化数值和类别排名之间的变化。这更适用于时间点很少的时间序列。
```{r}
library(dplyr)
theme_set(theme_classic())
source_df <- read.csv("https://raw.githubusercontent.com/jkeirstead/r-slopegraph/master/cancer_survival_rates.csv")

# Define functions. Source: https://github.com/jkeirstead/r-slopegraph
tufte_sort <- function(df, x="year", y="value", group="group", method="tufte", min.space=0.05) {
    ## First rename the columns for consistency
    ids <- match(c(x, y, group), names(df))
    df <- df[,ids]
    names(df) <- c("x", "y", "group")

    ## Expand grid to ensure every combination has a defined value
    tmp <- expand.grid(x=unique(df$x), group=unique(df$group))
    tmp <- merge(df, tmp, all.y=TRUE)
    df <- mutate(tmp, y=ifelse(is.na(y), 0, y))
  
    ## Cast into a matrix shape and arrange by first column
    require(reshape2)
    tmp <- dcast(df, group ~ x, value.var="y")
    ord <- order(tmp[,2])
    tmp <- tmp[ord,]
    
    min.space <- min.space*diff(range(tmp[,-1]))
    yshift <- numeric(nrow(tmp))
    ## Start at "bottom" row
    ## Repeat for rest of the rows until you hit the top
    for (i in 2:nrow(tmp)) {
        ## Shift subsequent row up by equal space so gap between
        ## two entries is >= minimum
        mat <- as.matrix(tmp[(i-1):i, -1])
        d.min <- min(diff(mat))
        yshift[i] <- ifelse(d.min < min.space, min.space - d.min, 0)
    }

    
    tmp <- cbind(tmp, yshift=cumsum(yshift))

    scale <- 1
    tmp <- melt(tmp, id=c("group", "yshift"), variable.name="x", value.name="y")
    ## Store these gaps in a separate variable so that they can be scaled ypos = a*yshift + y

    tmp <- transform(tmp, ypos=y + scale*yshift)
    return(tmp)
   
}

plot_slopegraph <- function(df) {
    ylabs <- subset(df, x==head(x,1))$group
    yvals <- subset(df, x==head(x,1))$ypos
    fontSize <- 3
    gg <- ggplot(df,aes(x=x,y=ypos)) +
        geom_line(aes(group=group),colour="grey80") +
        geom_point(colour="white",size=8) +
        geom_text(aes(label=y), size=fontSize, family="American Typewriter") +
        scale_y_continuous(name="", breaks=yvals, labels=ylabs)
    return(gg)
}    

## Prepare data    
df <- tufte_sort(source_df, 
                 x="year", 
                 y="value", 
                 group="group", 
                 method="tufte", 
                 min.space=0.05)

df <- transform(df, 
                x=factor(x, levels=c(5,10,15,20), 
                            labels=c("5 years","10 years","15 years","20 years")), 
                y=round(y))

## Plot
plot_slopegraph(df) + labs(title="Estimates of % survival rates") + 
                      theme(axis.title=element_blank(),
                            axis.ticks = element_blank(),
                            plot.title = element_text(hjust=0.5,
                                                      family = "American Typewriter",
                                                      face="bold"),
                            axis.text = element_text(family = "American Typewriter",
                                                     face="bold"))
```

### 6.7 季节图
如果您正在处理`ts`或`xts`类型的时间序列对象，您可以通过使用`forecast::ggseasonplot`绘制的季节图来查看季节波动。下面是一个使用`AirPassengers`和`nottem`数据集绘制的例子。
```{r}
library(ggplot2)
library(forecast)
theme_set(theme_classic())

# Subset data
nottem_small <- window(nottem, start=c(1920, 1), end=c(1925, 12))  # subset a smaller timewindow

# Plot
ggseasonplot(AirPassengers) + labs(title="Seasonal plot: International Airline Passengers")

```

```{r}
ggseasonplot(nottem_small) + labs(title="Seasonal plot: Air temperatures at Nottingham Castle")
```

## 7 群体
### 7.1 谱系图
```{r}

library(ggplot2)
library(ggdendro)
theme_set(theme_bw())

hc <- hclust(dist(USArrests), "ave")  # hierarchical clustering

# plot
ggdendrogram(hc, rotate = TRUE, size = 2)
```

### 7.2 聚类图
可以使用`geom_surround()`来显示不同的簇或组。如果数据集有多个特征，还可以计算主成分，并使用 PC1 和 PC2 作为 X 和 Y 轴绘制散点图。`geom_encircle()`可用于框选所需的组。
```{r}
# devtools::install_github("hrbrmstr/ggalt")
library(ggplot2)
library(ggalt)
library(ggfortify)
theme_set(theme_classic())

# Compute data with principal components ------------------
df <- iris[c(1, 2, 3, 4)]
pca_mod <- prcomp(df)  # compute principal components

# Data frame of principal components ----------------------
df_pc <- data.frame(pca_mod$x, Species=iris$Species)  # dataframe of principal components
df_pc_vir <- df_pc[df_pc$Species == "virginica", ]  # df for 'virginica'
df_pc_set <- df_pc[df_pc$Species == "setosa", ]  # df for 'setosa'
df_pc_ver <- df_pc[df_pc$Species == "versicolor", ]  # df for 'versicolor'
 
# Plot ----------------------------------------------------
ggplot(df_pc, aes(PC1, PC2, col=Species)) + 
  geom_point(aes(shape=Species), size=2) +   # draw points
  labs(title="Iris Clustering", 
       subtitle="With principal components PC1 and PC2 as X and Y axis",
       caption="Source: Iris") + 
  coord_cartesian(xlim = 1.2 * c(min(df_pc$PC1), max(df_pc$PC1)), 
                  ylim = 1.2 * c(min(df_pc$PC2), max(df_pc$PC2))) +   # change axis limits
  geom_encircle(data = df_pc_vir, aes(x=PC1, y=PC2)) +   # draw circles
  geom_encircle(data = df_pc_set, aes(x=PC1, y=PC2)) + 
  geom_encircle(data = df_pc_ver, aes(x=PC1, y=PC2))
```



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<body>




<section class="page-header">
<h1 class="title toc-ignore project-name">ggplot绘图手册</h1>
<h4 class="author project-author">赵子茜</h4>
<h4 class="date project-date">2022-08-09</h4>
</section>


<div id="TOC" class="toc">
<div class="toc-box">
<ul>
<li><a href="#全局主题设置" id="toc-全局主题设置">全局主题设置</a></li>
<li><a href="#散点图" id="toc-散点图">1. 散点图</a>
<ul>
<li><a href="#两个变量" id="toc-两个变量">1.1 两个变量</a></li>
<li><a href="#环绕式散点图" id="toc-环绕式散点图">1.2
环绕式散点图</a></li>
<li><a href="#抖动图" id="toc-抖动图">1.3 抖动图</a></li>
<li><a href="#数值图" id="toc-数值图">1.4 数值图</a></li>
<li><a href="#气泡图" id="toc-气泡图">1.5 气泡图</a></li>
<li><a href="#边际直方图箱线图" id="toc-边际直方图箱线图">1.6
边际直方图/箱线图</a></li>
<li><a href="#相关系数图" id="toc-相关系数图">1.7 相关系数图</a></li>
</ul></li>
<li><a href="#偏差" id="toc-偏差">2 偏差</a>
<ul>
<li><a href="#发散条形图" id="toc-发散条形图">2.1 发散条形图</a></li>
<li><a href="#带标记的发散型棒棒糖图" id="toc-带标记的发散型棒棒糖图">2.2 带标记的发散型棒棒糖图</a></li>
<li><a href="#发散性点图" id="toc-发散性点图">2.3 发散性点图</a></li>
<li><a href="#面积图" id="toc-面积图">2.4 面积图</a></li>
</ul></li>
<li><a href="#排序" id="toc-排序">3 排序</a>
<ul>
<li><a href="#有序条形图" id="toc-有序条形图">3.1 有序条形图</a></li>
<li><a href="#棒棒糖图" id="toc-棒棒糖图">3.2 棒棒糖图</a></li>
<li><a href="#点图" id="toc-点图">3.3 点图</a></li>
<li><a href="#坡度图" id="toc-坡度图">3.4 坡度图</a></li>
<li><a href="#哑铃图" id="toc-哑铃图">3.5 哑铃图</a></li>
</ul></li>
<li><a href="#分布" id="toc-分布">4 分布</a>
<ul>
<li><a href="#直方图" id="toc-直方图">4.1 直方图</a>
<ul>
<li><a href="#连续变量的直方图" id="toc-连续变量的直方图">4.4.1
连续变量的直方图</a></li>
<li><a href="#分类变量的直方图" id="toc-分类变量的直方图">4.1.2分类变量的直方图</a></li>
</ul></li>
<li><a href="#密度图" id="toc-密度图">4.2 密度图</a></li>
<li><a href="#箱线图" id="toc-箱线图">4.3 箱线图</a></li>
<li><a href="#点图箱线图" id="toc-点图箱线图">4.4 点图+箱线图</a></li>
<li><a href="#塔夫特箱线图" id="toc-塔夫特箱线图">4.5
塔夫特箱线图</a></li>
<li><a href="#小提琴图" id="toc-小提琴图">4.6 小提琴图</a></li>
<li><a href="#金字塔图" id="toc-金字塔图">4.7 金字塔图</a></li>
</ul></li>
<li><a href="#组成" id="toc-组成">5 组成</a>
<ul>
<li><a href="#华夫饼图" id="toc-华夫饼图">5.1 华夫饼图</a></li>
<li><a href="#饼图" id="toc-饼图">5.2 饼图</a></li>
<li><a href="#条形图" id="toc-条形图">5.3 条形图</a></li>
</ul></li>
<li><a href="#变化趋势" id="toc-变化趋势">6 变化趋势</a>
<ul>
<li><a href="#时间序列图基于时间序列对象ts" id="toc-时间序列图基于时间序列对象ts">6.1
时间序列图：基于时间序列对象（ts）</a></li>
<li><a href="#时间序列图基于数据框" id="toc-时间序列图基于数据框">6.2
时间序列图：基于数据框</a></li>
<li><a href="#多个时间序列" id="toc-多个时间序列">6.3
多个时间序列</a></li>
<li><a href="#堆叠面积图" id="toc-堆叠面积图">6.4 堆叠面积图</a></li>
<li><a href="#日历热力图" id="toc-日历热力图">6.5 日历热力图</a></li>
<li><a href="#坡度图-1" id="toc-坡度图-1">6.6 坡度图</a></li>
<li><a href="#季节图" id="toc-季节图">6.7 季节图</a></li>
</ul></li>
<li><a href="#群体" id="toc-群体">7 群体</a>
<ul>
<li><a href="#谱系图" id="toc-谱系图">7.1 谱系图</a></li>
<li><a href="#聚类图" id="toc-聚类图">7.2 聚类图</a></li>
</ul></li>
</ul>
</div>
</div>

<section class="main-content">
<div id="全局主题设置" class="section level2">
<h2>全局主题设置</h2>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(plotrix)</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">options</span>(<span class="at">scipen=</span><span class="dv">999</span>)  <span class="co"># turn-off scientific notation like 1e+48</span></span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="co"># theme_set(theme_bw())  # pre-set the bw theme.</span></span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;midwest&quot;</span>, <span class="at">package =</span> <span class="st">&quot;ggplot2&quot;</span>)</span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>midwest <span class="ot">&lt;-</span> <span class="fu">read.csv</span>(<span class="st">&quot;midwest.csv&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># The palette with grey:</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>cbp1 <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;#999999&quot;</span>, <span class="st">&quot;#E69F00&quot;</span>, <span class="st">&quot;#56B4E9&quot;</span>, <span class="st">&quot;#009E73&quot;</span>,</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>          <span class="st">&quot;#F0E442&quot;</span>, <span class="st">&quot;#0072B2&quot;</span>, <span class="st">&quot;#D55E00&quot;</span>, <span class="st">&quot;#CC79A7&quot;</span>)</span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a><span class="co"># The palette with black:</span></span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a>cbp2 <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;#000000&quot;</span>, <span class="st">&quot;#E69F00&quot;</span>, <span class="st">&quot;#56B4E9&quot;</span>, <span class="st">&quot;#009E73&quot;</span>,</span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a>          <span class="st">&quot;#F0E442&quot;</span>, <span class="st">&quot;#0072B2&quot;</span>, <span class="st">&quot;#D55E00&quot;</span>, <span class="st">&quot;#CC79A7&quot;</span>)</span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a>ggplot <span class="ot">&lt;-</span> <span class="cf">function</span>(...) ggplot2<span class="sc">::</span><span class="fu">ggplot</span>(...) <span class="sc">+</span> </span>
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values =</span> cbp1) <span class="sc">+</span></span>
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values =</span> cbp1) <span class="sc">+</span> <span class="co"># note: needs to be overridden when using continuous color scales</span></span>
<span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_bw</span>()</span></code></pre></div>
</div>
<div id="散点图" class="section level2">
<h2>1. 散点图</h2>
<div id="两个变量" class="section level3">
<h3>1.1 两个变量</h3>
<p>展示两个变量之间的相关关系，最常使用的是<strong>散点图</strong>。在
ggplot
中，使用<code>geom_point()</code>绘制。此外，默认情况下，<code>geom_smooth</code>会绘制一条平滑线（基于
losses
），可以通过设置<code>method=&#39;lm&#39;</code>来调整以绘制最佳拟合的线。</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Scatterplot</span></span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>gg <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(midwest, <span class="fu">aes</span>(<span class="at">x=</span>area, <span class="at">y=</span>poptotal)) <span class="sc">+</span> </span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">col=</span>state, <span class="at">size=</span>popdensity)) <span class="sc">+</span> </span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method=</span><span class="st">&quot;loess&quot;</span>, <span class="at">se=</span>F) <span class="sc">+</span> </span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlim</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="fl">0.1</span>)) <span class="sc">+</span> </span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylim</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">500000</span>)) <span class="sc">+</span> </span>
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">subtitle=</span><span class="st">&quot;Area Vs Population&quot;</span>, </span>
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a>       <span class="at">y=</span><span class="st">&quot;Population&quot;</span>, </span>
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a>       <span class="at">x=</span><span class="st">&quot;Area&quot;</span>, </span>
<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a>       <span class="at">title=</span><span class="st">&quot;Scatterplot&quot;</span>, </span>
<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption =</span> <span class="st">&quot;Source: midwest&quot;</span>)</span>
<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(gg)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="环绕式散点图" class="section level3">
<h3>1.2 环绕式散点图</h3>
<p>使用 ggalt
包中的<code>geom_encircle()</code>函数可实现在散点图中圈选特定区域：</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># install.packages(&quot;ggalt&quot;)</span></span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggalt)</span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a><span class="fu">options</span>(<span class="at">scipen =</span> <span class="dv">999</span>)</span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggalt)</span>
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a><span class="do">## 设定筛选条件</span></span>
<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a>midwest_select <span class="ot">&lt;-</span> midwest[midwest<span class="sc">$</span>poptotal <span class="sc">&gt;</span> <span class="dv">350000</span> <span class="sc">&amp;</span> </span>
<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a>                            midwest<span class="sc">$</span>poptotal <span class="sc">&lt;=</span> <span class="dv">500000</span> <span class="sc">&amp;</span> </span>
<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a>                            midwest<span class="sc">$</span>area <span class="sc">&gt;</span> <span class="fl">0.01</span> <span class="sc">&amp;</span> </span>
<span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a>                            midwest<span class="sc">$</span>area <span class="sc">&lt;</span> <span class="fl">0.1</span>, ]</span>
<span id="cb4-12"><a href="#cb4-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-13"><a href="#cb4-13" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
<span id="cb4-14"><a href="#cb4-14" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(midwest, <span class="fu">aes</span>(<span class="at">x=</span>area, <span class="at">y=</span>poptotal)) <span class="sc">+</span> </span>
<span id="cb4-15"><a href="#cb4-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">col=</span>state, <span class="at">size=</span>popdensity)) <span class="sc">+</span>   <span class="co"># draw points</span></span>
<span id="cb4-16"><a href="#cb4-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method=</span><span class="st">&quot;loess&quot;</span>, <span class="at">se=</span>F) <span class="sc">+</span> </span>
<span id="cb4-17"><a href="#cb4-17" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlim</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="fl">0.1</span>)) <span class="sc">+</span> </span>
<span id="cb4-18"><a href="#cb4-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylim</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">500000</span>)) <span class="sc">+</span>   <span class="co"># draw smoothing line</span></span>
<span id="cb4-19"><a href="#cb4-19" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_encircle</span>(<span class="fu">aes</span>(<span class="at">x=</span>area, <span class="at">y=</span>poptotal), </span>
<span id="cb4-20"><a href="#cb4-20" aria-hidden="true" tabindex="-1"></a>                <span class="at">data=</span>midwest_select, </span>
<span id="cb4-21"><a href="#cb4-21" aria-hidden="true" tabindex="-1"></a>                <span class="at">color=</span><span class="st">&quot;red&quot;</span>, </span>
<span id="cb4-22"><a href="#cb4-22" aria-hidden="true" tabindex="-1"></a>                <span class="at">size=</span><span class="dv">2</span>, </span>
<span id="cb4-23"><a href="#cb4-23" aria-hidden="true" tabindex="-1"></a>                <span class="at">expand=</span><span class="fl">0.08</span>) <span class="sc">+</span>   <span class="co"># encircle</span></span>
<span id="cb4-24"><a href="#cb4-24" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">subtitle=</span><span class="st">&quot;Area Vs Population&quot;</span>, </span>
<span id="cb4-25"><a href="#cb4-25" aria-hidden="true" tabindex="-1"></a>       <span class="at">y=</span><span class="st">&quot;Population&quot;</span>, </span>
<span id="cb4-26"><a href="#cb4-26" aria-hidden="true" tabindex="-1"></a>       <span class="at">x=</span><span class="st">&quot;Area&quot;</span>, </span>
<span id="cb4-27"><a href="#cb4-27" aria-hidden="true" tabindex="-1"></a>       <span class="at">title=</span><span class="st">&quot;Scatterplot + Encircle&quot;</span>, </span>
<span id="cb4-28"><a href="#cb4-28" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;Source: midwest&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="抖动图" class="section level3">
<h3>1.3 抖动图</h3>
<p>抖动图可以解决数据点重叠的问题。通过<code>geom_jitter</code>函数中的<code>width</code>参数设置抖动范围，重叠点在其原始位置周围随机抖动。</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(cty, hwy))</span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>g <span class="sc">+</span> <span class="fu">geom_jitter</span>(<span class="at">width =</span> .<span class="dv">5</span>, <span class="at">size=</span><span class="dv">1</span>) <span class="sc">+</span></span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">subtitle=</span><span class="st">&quot;mpg: city vs highway mileage&quot;</span>, </span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a>       <span class="at">y=</span><span class="st">&quot;hwy&quot;</span>, </span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">x=</span><span class="st">&quot;cty&quot;</span>, </span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">title=</span><span class="st">&quot;Jittered Points&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="数值图" class="section level3">
<h3>1.4 数值图</h3>
<p>克服数据点重叠问题的第二个选择是使用计数图。重叠点越多，圆就越大。</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>g <span class="sc">+</span> <span class="fu">geom_count</span>(<span class="at">col=</span><span class="st">&quot;tomato3&quot;</span>, <span class="at">show.legend=</span>F) <span class="sc">+</span></span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">subtitle=</span><span class="st">&quot;mpg: city vs highway mileage&quot;</span>, </span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>       <span class="at">y=</span><span class="st">&quot;hwy&quot;</span>, </span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>       <span class="at">x=</span><span class="st">&quot;cty&quot;</span>, </span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">title=</span><span class="st">&quot;Counts Plot&quot;</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="气泡图" class="section level3">
<h3>1.5 气泡图</h3>
<p>气泡图适合 4 维数据，其中两个是数值型（分别是 X 和
Y），另一个是分类变量（用<code>color</code>表示）和另一个数值变量（用<code>size</code>表示）。</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(mpg, <span class="at">package=</span><span class="st">&quot;ggplot2&quot;</span>)</span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>mpg_select <span class="ot">&lt;-</span> mpg[mpg<span class="sc">$</span>manufacturer <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">&quot;audi&quot;</span>, <span class="st">&quot;ford&quot;</span>, <span class="st">&quot;honda&quot;</span>, <span class="st">&quot;hyundai&quot;</span>), ]</span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a><span class="co"># Scatterplot</span></span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_bw</span>())  <span class="co"># pre-set the bw theme.</span></span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mpg_select, <span class="fu">aes</span>(displ, cty)) <span class="sc">+</span> </span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">subtitle=</span><span class="st">&quot;mpg: Displacement vs City Mileage&quot;</span>,</span>
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a>       <span class="at">title=</span><span class="st">&quot;Bubble chart&quot;</span>)</span>
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a>g <span class="sc">+</span> <span class="fu">geom_jitter</span>(<span class="fu">aes</span>(<span class="at">col=</span>manufacturer, <span class="at">size=</span>hwy)) <span class="sc">+</span> </span>
<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="fu">aes</span>(<span class="at">col=</span>manufacturer), <span class="at">method=</span><span class="st">&quot;lm&quot;</span>, <span class="at">se=</span>F)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<!-- ### 1.6 动态气泡图 -->
<!-- ```{r} -->
<!-- # install.packages("gganimate") -->
<!-- # install.packages("gapminder") -->
<!-- library(gganimate) -->
<!-- library(gapminder) -->
<!-- g <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, frame = year)) + -->
<!--   geom_point() + -->
<!--   geom_smooth(aes(group = year),  -->
<!--               method = "lm",  -->
<!--               show.legend = FALSE) + -->
<!--   facet_wrap(~continent, scales = "free") + -->
<!--   scale_x_log10()  # convert to log scale -->
<!-- gganimate(g, interval=0.2) -->
<!-- ``` -->
</div>
<div id="边际直方图箱线图" class="section level3">
<h3>1.6 边际直方图/箱线图</h3>
<p>如果您想在同一个图中显示变量的关系和分布，可以使用边际直方图。更改<code>ggMarginal()</code>函数中的<code>type</code>参数可以将边际直方图换成箱线图或者密度图。</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggExtra)</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(mpg, <span class="at">package=</span><span class="st">&quot;ggplot2&quot;</span>)</span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Scatterplot</span></span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_bw</span>())  <span class="co"># pre-set the bw theme.</span></span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a>mpg_select <span class="ot">&lt;-</span> mpg[mpg<span class="sc">$</span>hwy <span class="sc">&gt;=</span> <span class="dv">35</span> <span class="sc">&amp;</span> mpg<span class="sc">$</span>cty <span class="sc">&gt;</span> <span class="dv">27</span>, ]</span>
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(cty, hwy)) <span class="sc">+</span> </span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_count</span>() <span class="sc">+</span> </span>
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_smooth</span>(<span class="at">method=</span><span class="st">&quot;lm&quot;</span>, <span class="at">se=</span>F)</span>
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a><span class="fu">ggMarginal</span>(g, <span class="at">type =</span> <span class="st">&quot;histogram&quot;</span>, <span class="at">fill=</span><span class="st">&quot;transparent&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggMarginal</span>(g, <span class="at">type =</span> <span class="st">&quot;boxplot&quot;</span>, <span class="at">fill=</span><span class="st">&quot;transparent&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggMarginal</span>(g, <span class="at">type =</span> <span class="st">&quot;density&quot;</span>, <span class="at">fill=</span><span class="st">&quot;transparent&quot;</span>)</span></code></pre></div>
<p><img 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9u7du2KESNGmOw0H374odIkhntQdP0UEzh8+DC+/fZbfP311ybgPWfya9asaRRR2o0WL148xXXrRBEQAREQAecQkALqnLEIqCXey+1Mn/ncc88hY8aMAdWjwiLgVAL79+/HxIkTMXbsWEyfPt140teqVcsoo02aNMEll1zi1KarXSIgAiIgAskQkAKaDCAnHqZ3e9u2bTF37lyzTNmnTx8UK1bMiU1Vm0QgKAToST9hwgSjjNJ2lCHF6FhHc5PGjRujYMGCQbmOKhEBERABEQgNASmgoeEclKvs3r3bLLd/8sknoHf7wIEDcdNNNwWlblUiApFCYNeuXcZxicv0f/zxhwnjVK9ePfMw1qhRI+TJkydSuqJ2ioAIiEDMEpACGgFDzyDeH330EV577TUw7SG929u3bw95t0fA4KmJaUpg27ZtGDdunLEZ/eeff5ApUybUr1/fhHW6++67kS1btjS9vioXAREQARFIGQEpoCnjFrKz/vrrLzz77LNYsGCBCdz93nvvyfYtZPR1oUgisHHjRowZM8Z40y9cuBA5cuQAsy4xxiizMOmBLZJGU20VARGIdgJSQB06wjt27ECnTp1M0O6qVauC3u033nijQ1urZomAswgsW7bMKKLMwLRu3ToUKlTI2IsyJ/21117rrMaqNSIgAiIQgwSkgDps0E+fPm1sO9944w2Tr/3NN99EmzZtkD59eoe1VM0RgcggwKX5UaNGGQcmOjOVL18eVESZnlYxRiNjDNVKERCB6CMgBdRBY/rLL78Y284VK1bg8ccfxzvvvCPvXgeNj5oS2QT4cPfjjz+aVYXJkyeDMUaZi75FixZgWKecOXNGdgfVehEQARGIIAJSQB0wWLRde+GFF4xnL5cHudxeo0YNB7RMTRCB6CRw4MABMyP6+eefg3bWdFaiB33Lli1NIod06dJFZ8fVKxEQARFwCAEpoGEcCKYcpFNRr169jMMEZzw58xkXFxfGVunSIhBbBNauXQsqonxt2LABJUqUMEv0VEYvv/zy2IKh3oqACIhAiAhIAQ0RaO/LjB8/Hi+++CK2bt1qgsrT5jN37tzexfRZBEQgRAQY3P63337DZ599Bn4/jxw5YtKAUhFlwPu8efOGqCW6jAiIgAhEPwEpoCEeY4aHYcpMZnO55ZZb0L9/fxNUPsTN0OVEQASSIHD06FFjEkNldMaMGSa+KOOK0l70jjvuUEinJNjpkAiIgAj4Q0AKqD+UglCGWYwYSH748OEoXbo03n//fROjMAhVqwoREIE0JLBp0ybjRc8l+pUrVxrHwAcffNB40ctWOw3Bq2oREIGoJiAFNI2Hl562AwYMQM+ePXHu3Dm8/PLL6NixIzJnzpzGV1b1IiACwSbw77//Gi96BrxnSKdy5cqZQPcMds/wThIREAEREAH/CEgB9Y9TikoxRWCXLl2MY8Njjz1mlNAiRYqkqC6dJAIi4BwCTIk7bdo0fPnll/j2229x7NgxVKtWzdiKNm3aFGXKlHFOY9USERABEXAgASmgaTAof//9t3EwYgDsevXqoW/fvrjyyivT4EqqUgREINwEaC/KuKKjR482SilXPa655hoTW7Rx48a47LLLwt1EXV8EREAEHEdACmgQh4QB5LnEPnHiRONYxBBLDRo0COIVVJUIiICTCRw8eNDMiHL14+eff8bJkydRqVIlY+99zz33mDSgCrPm5BFU20RABEJFQApoEEhv2bIFDKM0cuRIFC5cGN27dzfxPJU+MwhwVYUIRCiBw4cPY+rUqeaBlBmYDh06ZO4Pd955J/i69dZbFXotQsdWzRYBEUg9ASmgqWC4a9cuky5zyJAhyJo1Kzp37owOHTqY7VRUq1NFQASijACX5RljdMqUKUYpXbduHfiAWrNmTaOIMiTbddddp/BOUTbu6o4IiEDiBKSAJs4m0SMMqdS7d28MGjTIZC2i0vnSSy8hT548iZ6jAyIgAiJgE1i1apXJS//TTz+ZmMC0I82RIwduuOEG1K1b1+Sopx1pxowZ7VP0LgIiIAJRRUAKaADDuX37dvTp0wcfffSROatNmzZm1rNgwYIB1KKiIiACIuAmcPr0acyaNQu//vqrCXrPbdqOMj8944zWqlUL119/vZktzZcvn/tEbYmACIhABBOQAurH4K1Zs8bMeDIrCmckqHgyjWahQoX8OFtFREAERMB/AlQ+GW/0jz/+wJ9//glG0zhw4IBZbaFHPZfqqZjyxegaiinsP1uVFAERcA4BKaBJjMVff/1lQihNmjTJLK+3a9cO7du3h2YhkoCmQyIgAkElwBz1y5cvN4ooZ0epnC5duhRnz541D8SVK1dG9erVTegnLttXqVJFSmlQR0CViYAIpAUBKaBeVDn7MHbsWJO96L///jMBpZm7vVWrVsiePbtXaX0UAREQgdAToM3o/PnzMWfOHPA+NXfuXNCulMoqV2kqVqyIq6++2rwYIJ8zpbQxlYiACIiAUwhIAfUYiVdffRVDhw41KfZq165tPNrvvfde463qUUybIiACIuA4Agz7RKV03rx58S/GJuZMabp06VC/fn3kypXLZGyiUspXgQIFHNcPNUgERCA2CEgB9RhnznJmypQJzzzzjFnG8jikTREQARGIOALHjx/HokWLjGK6detW43m/ZMkSnDhxwvSlRIkSCRRSKqUlS5aMuH6qwSIgApFHQApo5I2ZWiwCIiACKSbAPPa0KeVsqT1jumDBAhMon5Xmz58fV111Vbxiyu3y5ctrJSjFxHWiCIiALwJSQH1R0T4REAERiCECtB1lcHxbKbXfd+zYYSgw0QadnaiM0p60atWqZpVIsY9j6J9EXRWBIBOQAhpkoKpOBERABKKFABVQzo7ar4ULF2L16tXGrpR95BI+ve6Z754vOj9VqFABOXPmjBYE6ocIiEAaEZACmkZgVa0IiIAIRCMB2pUyDBRtSxcvXgzalPJlz5ayz0WLFjXL9oxbWq5cOZQtW9ZEFCldujTy5s0bjVjUJxEQgQAJSAENEJiKi4AIiIAIXExg//79WLZsmbEvXblypQkLxdlSLu0zvJ0tnB3lzGmxYsWMolqkSBEULlwYzChH+1PGWebSfu7cuU3oKIa/i4uLs09P0fu5c+eM4xWVZ8/XsWPHzGfvd5aho5bn69SpU6YfzFxlvxhhgC/Wz5ctbC8jD6RPnx4ZMmQwL4bHYtIAvrJkyWJezHbFF0Nksc/cz0gFNHVg/yUiEM0EpIBG8+iqbyIgAiIQZgJUzOiBv379emzcuBGbNm3C5s2bsWXLFjC9MWdOd+/ebZQ6X02lMmcrbFTeqMhRqaNyRyWPQhtWXofKIJ2sbAWRii9f3BeIsG7avXpel9dmlBS+7DZ4toPttBVlWyHlu2d7bCWWii2VXCq+fLEMzReowFNmzJiBunXrmm39EYFoJSAFNFpHVv0SAREQgQghQAXy4MGDJgYzZ1KZepSfjxw5Yl72jCSVSSpxVNhsJY9d9J5xpILIF5VFzxlHKpRULPnizKOvd+7juaEU9ol9ZSxXvsqUKaPEJ6EcAF0rLASkgIYFuy4qAiIgAiIgAiIgArFLwLV+Ebv9V89FQAREQAREQAREQARCTEAKaIiB63IiIAIiIAIiIAIiEOsEpIDG+n+A+i8CIiACIiACIiACISYgBTTEwHU5ERABERABERABEYh1AhkiFcC+fftMGAuntJ9enBQ7DIdT2uXZDrbR6e1zMkOnj7HdPjH0/K8PbNtm6PTvidPbp//BwP7vPEun1f8gY60ybJREBJxCIGK94Nu2bYtbbrnFxIJzAkyG0WA8NwYRtmPTOaFdnm1gmI9gBHX2rDOY2ww/QnZsoxOFYV8Yv4/hW5wojIFIhgxq7dQfGobTYWgcxll0ovA7TI5OTiXJ7zHH2IlC5Ynhk/gd4Tg7UXivpji1fXbwewamD5ZMmzYN7dq1M2lSg1Wn6hGB1BKI6MehBg0aOOYmwh9Wxq4rVKiQY39cOWvMNHhOnT3Zs2ePYefUVH1UTPjj79QMJQy+TYbMJuPUH9dDhw6Z2IuhjrPo742S32FyZFYepwq/x8wW5EShAsrA8vyOOPVBjQ8ZvAcy3qcTxY4HeskllwSteUwCIBEBpxGQDajTRkTtEQEREAEREAEREIEoJyAFNMoHWN0TAREQAREQAREQAacRkALqtBFRe0RABERABERABEQgyglIAY3yAVb3REAEREAEREAERMBpBKSAOm1E1B4REAEREAEREAERiHICUkCjfIDVPREQAREQAREQARFwGgEpoE4bEbVHBERABERABERABKKcgBTQKB9gdU8EREAEREAEREAEnEZACqjTRkTtEQEREAEREAEREIEoJyAFNMoHWN0TAREQAREQAREQAacRkALqtBFRe0RABERABERABEQgyglIAY3yAVb3REAEREAEREAERMBpBKSAOm1E1B4REAEREAEREAERiHICUkCjfIDVPREQAREQgcggMGM20LlvFpw7FxntVStFIDUEpICmhp7OFQEREAEREIFUEjh7Fuj7KdDmDWD6rAwYOSlHKmvU6cEiMHjwYFSvXj1Y1akeDwIZPLa1KQIiIAIiIAIiEEICe/YDL/QCZi9yX3Tp2kygUpo+vXuftsJDIL01CPPmzcPx48eRNWvW8DQiSq+qGdAoHVh1SwREQAREwNkE/lsC3PdsQuXz0Yan0PuFfVI+HTJ05cuXR968ebF27VqHtCh6miEFNHrGUj0RAREQARGIEAIffwO07ALstmZAKbmsVfchrwPPPnQK6fXL7ILigL9ly5bFvn37sGrVKge0JrqaoCX46BpP9UYEREAERMDBBA4fBbr0tWw9/3E3slI5oP/LQPEiwJEj7v3aCj+B4sWLI1u2bFi5cmX4GxNlLZACGmUDqu6IgAiIgAg4k8CKdUC7nsDmHe72PXAH8MrTQKaM7n3acg6BuLg4XH755VixYoVzGhUlLZECGiUDqW6IgAiIgAg4l8D4n4Aeg4BTp11tzJIZeMOy/7z3Zue2WS1zEbjiiiuwfPly4QgyASmgQQaq6kRABERABETAJnDipKV4DgYm/GzvAcoUs5bcXwEuL+3epy3nEqhYsSKmTJmC8+fPgzOikuAQkKlzcDiqFhEQAREQARFIQGDjNuCB5xMqn/VvAMb1l/KZAJTDP1ABPWIZ527atMnhLY2s5mkGNLLGS60VAREQARGIAAI//w10tZyNjhxzNTaDFdPzpVaW53vDCGi8mpiAQOXKlc3npUuXolSpUgmO6UPKCWgGNOXsdKYIiIAIiIAIJCBwxgog32uEy9nIVj4L5we+eE/KZwJQEfSBoZiyZMmCJUuswK2SoBHQDGjQUKoiERABERCBWCawcy/Q8R1g3jI3heurAe93AvLmdu/TVmQRYDYkOiItXrw4shru8NZKAXX4AKl5IiACIiACzicwa6ErpebeA6620lelzYNWYPmHgXRaa3T+ACbTwipVqmDhQmuQJUEjoK9F0FCqIhEQAREQgVgjYDlG46MxwOOWV7utfObJCQzrAbRvLuUzWv4fqIAyFuiZM2eipUth74cU0LAPgRogAiIgAiIQiQQOHgae7g588Dlw7pyrB1XLAxM/BG68JhJ7pDYnRoAK6MmTJ5WSMzFAKdgvBTQF0HSKCIiACIhAbBNYshpo1A74bY6bw8N3A6MsZ6NLCrr3aSs6CFABpcgONHjjKQU0eCxVkwiIgAiIQAwQGD0VaPYCsHWXq7PZsgB9OwOvtVFKzWgd/qJFiyJ//vxSQIM4wHJCCiJMVSUCIiACIhC9BI6fALoNBKbMcPexbAlg4KvApda7JLoJcBZUM6DBG2PNgAaPpWoSAREQARGIUgLrtgD3P5dQ+by7riurkZTPKB10r24xIL0UUC8oqfgoBTQV8HSqCIiACIhA9BP44XdL+ewArNnk6mtGa+3w9bZAbyu+J5ffJbFBgArohg0bcPTo0djocBr3UgpoGgNW9SIgAiIgApFJ4LQVceetj6zg8u8CR4+7+lC0EPBVH8sGtEFk9kmtTjkBKqDnrbhby5Z5ZBpIeXUxf6YU0Jj/FxAAERABERABbwLbdwOPvGSl0JzsPlK7OjDBsgGtcrl7n7Zih0DFihVNZ5kTXpJ6AnJCSj1D1SACIiACIhBFBP6cB7xohVM6cMjVKWY1YlD5px8AuC2JTQJ58+ZFkSJFsHz58tgEEOReSwENMlBVJwIiIAIiEJkEGEx+8Ghg0Fewllpdfchn5XB/3wqx9L+rIrNPanVwCTAnvBTQ4DCNWAWUdhhMiRXnkMfRs2fPmhFhm87ZKTGCM0ZBq4XMTp8+7Rhm3h1j+8iObXSicIyd3D47RZyTvhfe40h+dju9jznhM9tnf0+c0B5fbXDy/yDZUfhdcfL3mL8b3u3bb812dumbHn/Pd1umXVXhHN7vdBaF8sMq72s0gr/P/i3xbl9qrmTXmZo6dK6LQIUKFfDLL78IRxAIRLQCeuLECXOjCwKHVFdh/6iyTenSuW9gqa44iBXwJsT2OUVp9+4af1gpbKMTxVaenNo++0fm1KlTjvleeI+j/T2x372Ph/szGVKJcuoYkw//D53aPlsBDabyFOz/CbaN90C7rax/yep06NIvK3budd+7H2pwCu0ePokM6XlPCnYrEq/P/m4Ec4zte0PiV9URfwmUL18ew4cPNw/SGTJErArlb3fTtFzE0qOSlyNHDmTKlClNAflb+fHjx82PAtuUPr11x3Kg8MabM2dOxyqgzLNLdmyjE4U38SNHjji2fRxf/h9my5bNMd8L73Hkj37WrFmRMWNG70OO+Mwxtr8njmiQj0Y4uX0cX4aoyZIli/k/9NH8sO86duyYuQfy/5BCJ6New4EzrkUsZLd2v/M8cFst/raE/veF9xg+RAbzPuiU30kDPML/XHbZZUb5XL9+PbgtSTmBiFVAU95lnSkCIiACIhDrBBhW6dX+AGN82nJ5aWDAK0DpYvYevYtAQgJly5Y1O9auXSsFNCGagD9JAQ0YmU4QAREQARGIZAJrN8XhpfeB9VvcvbjvFldw+SyZ3fu0JQLeBEqXLm3M7NatW+d9SJ8DJCAFNEBgKi4CIiACIhC5BL7/PT16Ds2EEyddfchkWYO81sbKdFQ/cvukloeOQObMmXHJJZeYjEihu2p0XkkKaHSOq3olAiIgAiLgQeCU5cX+9lBgzPfuKc7iRVxL7hVdq6oepbUpAokTKFmyJDZt2pR4AR3xi4AUUL8wqZAIiIAIiECkEtiyE3jubXq7u3tQrybwruVslCuHe5+2RMAfAiVKlMDmzZv9KaoySRBwx5xIopAOiYAIiIAIiEAkEpj5L9C4nVv5TJfuPDo8chqDXrtY+Rw/frxZXmWYpmHDhoW8u2vWrEGVKlVQtGhRNGzYMOTX1wX9I1CsWDFs27bNv8IqlSgBKaCJotEBERABERCBSCVgRdRCv8+s9JndgYNHXL0okBcY2v0kHr2PSUwS9mzhwoW4//77sWPHDnPgqaeewt9//52wUBp+YngohvXZsGGDucrkyZPRu3fvNLyiqk4pAdqAbt++PaWn67wLBKSA6l9BBERABEQgqgjsPQA8/qqlbH7t7ta1lYGJA4FrKroSXriPuLaWLVsG5vq2hXE4ly5dan9M83cqvrlzW3k/LwhjqoZSAbavq/fkCRQuXBiMW33w4MHkC6tEogSkgCaKRgdEQAREQAQijcBcS2e871lg9kJ3y59oAnz6DlAwn3uf91atWrWwf//++N2HDx9G/fqhc42nY0v27NnjE4UwmH/z5s3j26MN5xAoWLCgaczu3bud06gIbIkU0AgcNDVZBERABETgYgIjJwAtOgO79rmO5cwODH4dePFxWFnWLi7vuYcK4PLly1G8eHHceOON+O+//0Bnk1AJ0zquXLkSbAftQAcOHIhGjRqF6vK6TgAE8ufPb0rv3bs3gLNU1JuAvOC9ieizCIiACIhARBE4cgzo2hf42cNkk6GVmNWIoZb8lQoVKoTVu5mpnJcsWQLOvtLOUOJMAvnyuabSPWfMndlSZ7dKCqizx0etEwEREAERSILAyvVAu57AJg+fEAaVZ3B5BpmXiECwCdi2urIBTR1ZKaCp46ezRUAEREAEwkTgm5+AHoOBk6dcDWAaze5tgYa3hKlBumxMEKCDGoUz1ZKUE5ACmnJ2OlMEREAERCAMBKhwUvGkAmpL6WJA/5eB8mXsPXoXgbQhQAex9JZR8dGjR9PmAjFSqxTQGBlodVMEREAEooEAl9rbvwWsWOfuze21gLc6AjmyufdpSwTSkkDWrFnB2K2SlBOQAppydjpTBERABEQghASm/wN0fh+g0xElg+XZ/lIroKWSBrmA6G/ICHAW9MSJEyG7XjReSApoNI6q+iQCIiACUUTgDLMafQp8/I27U4WsSDgfdAWurujepy0RCBWBzJkz49SpC8bHobpolF1HCmiUDai6IwIiIALRRIAxPZ9/F/hvibtX/7sKeN+K95nPnTjIfVBbIhACAozbevr06RBcKXovIQU0esdWPRMBERCBiCYwe5FL+WRqTVvaNLPCLj0MpFMaFRuJ3sNAgAro2bPW1LwkxQSkgKYYnU4UAREQARFICwJWGnQMH2ctsX8OnLuQuj13DqD3S0Dta9PiiqpTBAIjkM56Ajpn/3MGdqpKXyAgBVT/CiIgAiIgAo4hcNAKrdjFcjSa8a+7SVUud4VYKlrIvU9bIhBOAnFxcTjPJyVJiglIAU0xOp0oAiIgAiIQTAJL17hCLG3d6a71obsshfRJZTVyE9GWCEQHASmg0TGO6oUIiIAIRDSBr38Aeg4BTp9xdSOrldXozQ7AXXUjultqfJQS4OwnZ0ElKScgBTTl7HSmCIiACIhAKgkct0Ipvv4hMPlXd0WXlgAGvgKULenepy0RcBIB2n/SDlSScgJSQFPOTmeKgAiIgAikgsD6La4l99Ub3ZU0qOOa+cyWxb1PWyLgNAJSQFM/IlJAU89QNYiACIiACARI4Mc/gVf6AUePu07MaP0a0dbz4bsDrEjFRSAMBBiCSTOgqQMvBTR1/HS2CIiACIhAAARo49n7Y+Dzb90nXVLQ5eVetbx7n7ZEwMkEOAOaPr2VC1aSYgJSQFOMTieKgAiIgAgEQmDHHuC5t4EFK9xn3VgdeO9FIG8u9z5tiYDTCXAGVApo6kZJCmjq+OlsERABERABPwj8PR94oRew/5CrMB2ImdGImY3kTOwHQBVxFAEtwad+OKSApp6hahABERABEUiEAGN1DxltebV/CStwt6sQZzuZy/36aomcpN0i4HACckJK/QBJAU09Q9UgAiIgAiLggwBnO1/qDfw5132w2hVWis2uQOEC7n3aEoFII8A4oHJCSt2oSQFNHT+dLQIiIAIi4IPAopVAB8vec/tu98EW91oKaSuAHu8SEYhkAkrDmfrR020g9QxVgwiIgAhEBYGDBw9i0aJFqFChAgoWtFzTUyhf/5ABfT9zZzXKnhV4qyNQ/4YUVqjTRMBhBJQFKfUDIgU09QxVgwiIgAhEPIGtW7eiePHiyJ8/P/bu3Yt58+ahWrXAjDQZ07PH0DyY8W+meB6Xl3aFWCpTPH6XNkQg4glQAaUdqCTlBJRHKuXsdKYIiIAIRA2BihUrmr5Q+aTcf//95t3fP2s2AU2tWc4Z/1rTnRfk3puBr/sCUj5tInqPFgIMwURPeEnKCUgBTTk7nSkCIiACUUOgVKlSCfqydu3aBJ+T+jBlhqWwdgDWbbZiK1mSMcN59GgH9HoByKqUmkmh07EIJZAxY0acOWNlVZCkmIAU0BSj04kiIAIiED0Euna1XNMtsT17X3vttWQ7d+o08MYgl6f78ZOu4kUKnMGnb59A0zuSPV0FRCBiCWTKlAknT174p4/YXoS34bIBDS9/XV0EREAEHEGgWbNmuPTSSzFlyhRUrVoVTZs2TbJdW3e6vNyXrHYXq1vjPF5ovgfFLlFaIzcVbUUjgaxZs+LEiRPR2LWQ9UkKaMhQ60IiIAIi4GwC1113HfhKTn6f45r1PHjEVTKdtZbWsSXQqjGwc+eFaPPJVaLjIhDBBLJly4ajR49GcA/C33QpoOEfA7VABERABCKCAJ1+B46yMhuNcTe3QF6gbxegRhV3piP3UW2JQHQSyJkzJw4fPhydnQtRr8KmgK5caUUptqR8+fLxXeW+jRs34uqrr0aBAkqTEQ9GGyIgAiIQZgL7DgLPvwvMWuhuSPXKQD9L+SyYz71PWyIQCwRy586NAwcOxEJX06yPYXFC4qB16tQJ//zzT3zH+vXrh969e2P+/Plo1aoVNm3aFH9MGyIgAiIgAuEjMG8Z0PDZhMpnqybAZ+9I+QzfqOjK4SSQL18+Ey83nG2I9GuHZQb0vffeAwfPlg0bNuCPP/7A+PHjjQfmmDFj8OWXX8L2yrTL6V0EREAERCC0BD6dCPT+GDh7IeZ2zuzAu88DN/8vtO3Q1UTASQSYKWz3bo88s05qXIS0JeQKKD0s8+bNazJu2IzWrVtnvC7t8B9cgp86dap92Lzv3LnTpIizdx4/ftyEQHBKJoLTp614JJYwLIPdD7utTnln0Fx67Tk1hRhz69ptdAozz3bwf41x35zq+WjHpOP/oFO+F578uM02sn1ODeDMdvH/0KljTIah+o4cOQa8/mFGTJ+Vnpc1Ur7MObz/0mkUL0JG9l73u50fm/dDpzK079VOvQ/a3+Ng8rPrdI+UtlJLoEiRIkYB5b3Wqb/5qe1jWp8fUgV0y5YtmDBhAgYPHoyRI0fG92379u2gPYUtuXLlumhqe+7cuejY0UqzcUEqVaqE/fv3g8FgnSTMpexkiQSbFY6rk+XUqVNObh6OHLngmuzQVkZC7Dyn/w+mdfvWbcmAboPyYusut/J5543H0OHhg8hk3XKT+4oeO3YMfDlZnN6+YI5xMJVZJ49pKNtWtGhR80DNybFLLrkklJeOmmuFTAHlE9hbb72FF198EYyf5SneKa1Y1rtMvXr18Pvvv8ef1q1bN3AKnMFgnSD8gh86dMg4Tzn1aYjKJxV9pz7584ZLdp4PI04YW7sNnHli2A0+IDlR+L3Zt28f8uTJ45jvhTcneo1myZLFcQ+Odjv5HSZHTxMh+5hT3vk95hinlUyaHoceg+Nw8pQrq1HmTOfxWpvzuO+WzNYlCyV5Wc6AclmSHsLe9/AkTwzhQa6e8R7I/0MnCu8xfBUqlDTrQNrOkEGS4BIoWbKkqZCO01JAU8Y2ZAoo7TyXL18eP4vJZRDeBPbs2YNq1aolWF7nj6j3gPJm4XnDyJAhA6i48uUEsZVOvjulTd5cyJttc6oCyvbabfRuu1M+O3l87WV3J30vvMfN5ufk7wjb7NT2sW1p9R05aU3s9/wIGPcjr+KSUkWBAa/EoXwZlzJq70/s3V6Ct8c5sXLh3M+2pRXDYPSL7aME83/QrjMY7VMdLgJM2kBZv349atas6dqpvwERCJkCWq5cOcycOTO+cVyG51PZo48+amYO+/fvj82bNxvFk3aiNWrUiC+rDREQAREQgbQjsHk70P4tYPk69zVuvR54x3I2yqHJMzcUbYnABQJcgeVq3apVq8QkhQRCpoAm1T4uabZu3RpPPPGEWfoqVaoUHnrooaRO0TEREAEREIEgEJg+C+jyPnD4QlKX9NYE3IuPA481CkLlqkIEopgA45hzZVeSMgJhU0CfeeaZBC2+6667cPvttxsP2Rw5ciQ4pg8iIAIiIALBJWCZNKPfZ8CI8e56C1nR8T7oClxdyb1PWyIgAr4JVK5cGbNnz/Z9UHuTJeAyNkm2WGgK0KNdymdoWOsqIiACsUtg9z7gUUvR9FQ+a14JTPxQymfs/leo54ESuPLKK8EMjooyECg5V3lHKaAp64LOEgEREAER8JfAv4uB+9oBc5a4z3j6AeATywY0f9o517svpi0RiBIC11xzjYmasWDBgijpUWi7IQU0tLx1NREQAREICwErQhJGjHPNfO65EGo3t2XtNPQN4LmWsEKghaVZuqgIRCwBJs1hRJ5ZsyxDaknABHTLCRiZThABERCByCJwyMpN0LYH0MfK/2ElbjFS+TJgwkCgzrWR1Re1VgScQoCxbhlG8s8//3RKkyKqHVJAI2q41FgREAERCIzAsrVAo/bArx6+Eg/eCXzVByhWOLC6VFoERCAhgTp16pgkOXYM3IRH9SkpAlJAk6KjYyIgAiIQwQQYVP5BK5bnlh2uTmS1khm99xLQ/VmYlJoR3DU1XQQcQYBZGpn9a9GiRY5oTyQ1QgpoJI2W2ioCIiACfhA4cdIV2/O1AcCp064TyhS3shz1B+65yY8KVEQERMAvArVr1zapj3/66Se/yquQm4AUUDcLbYmACIhAxBPYsBVo2hGYNN3dlTtqA+Mt5bOcK321+4C2REAEUkUge/bsePjhhzFt2rRU1ROLJ0sBjcVRV59FQASiksA0yxeisWXvuWqDq3sZrVQjrz5tBZzvAmTPGpVdVqdEIOwE6IjEVON79+4Ne1siqQFSQCNptNRWERABEfBB4IyV1eidYUCHt4Gjx10FihQARr0HPHKPjxO0SwREIGgEGjZsaEWXOIdJkyYFrc5YqEgKaCyMsvooAiIQtQR27gGadwY+8/jtu+FqV1ajKytEbbfVMRFwDIESJUrguuuuw9ixYx3TpkhoiBTQSBgltVEEREAEfBD4x0rA0tDyaJ+/zHUwLg5o9wgwrAeQN5ePE7RLBEQgTQg8+OCDmD59Onbu3Jkm9UdjpVJAo3FU1ScREIGoJsCsRoNHA4+/Auw/5OpqHkvhHPGmFXD+IWU1iurBV+ccSYAKaJz1BPjVV185sn1ObJQUUCeOitokAiIgAokQOHQkDq1fBwZ8AVARpVxlLbVP+hCoZS29S0RABEJPoHDhwqhfvz5GjrTSjUn8IiAF1C9MKiQCIiAC4SeweJWVy/2VXPjjP3dbmltORl9YzkZ0OpKIgAiEj0CrVq2wePFizJ7tkXYsfM1x/JWlgDp+iNRAERABEbBSZ34HPPQisGNveoODYZU+6Aq8YoVZYrgliQiIQHgJ3HXXXShatCg++uij8DYkQq4uBTRCBkrNFAERiE0Cx04AL1oznD0GA6fPuBhcVsqV1aj+jbHJRL0WAScSyJAhA1q3bo0xY8Zgzx4rPIUkSQJSQJPEo4MiIAIiED4CazcB93cAvpvpbkP9Wicxth9wqZVaUyICIuAsAk899ZSJCTpsmBWYV5IkASmgSeLRQREQAREID4Gpv1nK53PA2s2u63OZvbsVcqlbm6PImiU8bdJVRUAEkiZQpEgRPPDAA/jwww9x6tSppAvH+FEpoDH+D6Dui4AIOIvAqdPAm0OAF3oBXH6nFCsMjH4fePBO1+fE/jIG4e+//44jR44kVkT7RUAE0pjACy+8gO3bt2PUqFFpfKXIrl4KaGSPn1ovAiIQRQS27QIefgn4coq7U3VrABMGAJUvc+/ztbVgwQJw9oVpAXPmzIl169b5KqZ9IiACaUzgyiuvNCGZevXqZZbj0/hyEVu9FNCIHTo1XAREIJoIMLRSo3YAQy1R0ll3544tgSGvA7lzuvYl9bdatWrm8P79+817y5bWyRIREIGwEHj55ZexatUqjBs3LizXj4SLSgGNhFFSG0VABKKWwLlzrqDyT3YDDhx2dTN/HuCTt4CnHoCVXcW/rnP201MWLVrk+VHbIiACISRw4403ok6dOnjzzTc1C5oIdymgiYDRbhEQARFIawL7DgJPvOZKq2lf65pKwMQPgZpX2nv8e3/uOctjyZJ0nDq1ZPjw4eZdf0RABMJDoHv37li6dCnGjh0bngY4/KoKX+zwAVLzREAEopPA/OXAc28DO/e6+/d4I+D5x4AMrljz7gN+bHXu3BmVKlXCv//+a2Zebr75Zj/OUhEREIG0IlC3bl3Uq1cPr7/+Opo0aQLGCZW4CYiGm4W2REAERCAkBD6bBPT+GDhz1nW5HNmAd58Hbrk+dZdnJha+JCIgAs4g8Pbbb6NmzZomR/yTTz7pjEY5pBVagnfIQKgZIiAC0U/gyDHXrOc7VoxqW/mscKnl5T4w9cpn9NNTD0Ug8ghcd911uO+++8Dl+GPHrBuAJJ6AFNB4FNoQAREQgbQjsGoD0MTKavTjn+5rNL4V+LovUPIS9z5tiYAIRBeBd955B7t27cL771vBfCXxBKSAxqPQhgiIgAikDYFvpwNNOwIbtrrqz5wJeMvyGXrL2sdtiQiIQPQSKF++PJiik3FBt23bFr0dDbBnUkADBKbiIiACziTw2WefmUDscVbcok8//TTgRk6fPh0VKlRAoUKF0K2bFRMpCMKsRt2sIPKdrYmPEyddFXK2k7OejW8LwgVUhQiIQEQQeOONN5ApUyZ07do1ItobikZKAQ0FZV1DBEQgTQnMnj0bjz76KJiKkvLYY49hzpw5fl9z/fr1uOWWW+LPZ+y+b7/91u/zfRXcssNKnWk5Fo390X30lv8B31gKKe0+JSIgArFDIH/+/MYO9IsvvsA///wTOx1PoqdSQJOAo0MiIAKRQWDZsmXInTt3fGO5zSwk/sqaNWvAHwhbOFOxfLkVJymF8utsK6tRe2DZWlcF6a077UutgA9fA3JmT2GlOk0ERCCiCTzzzDMmVFrbtm1x9uzZiO5LMBovBTQYFFWHCIhAWAnUrl0bBw9aUd0vCLdvvdXy8PFTqlevjr173QE5T506ZXKq+3l6fDH+prw/EnjmDeDQEdfuQvmAz3sBrRrHF9OGCIhADBJgHNBBgwZh/vz5GDx4cAwSSNhlKaAJeeiTCIhABBIoW7asyThSpkwZE/h58eLFxpbT367kzZsXW7duRfHixXHttdfCtgf193yW22OlYH/sZSsDkUfq5+uqukIsMbuRRAREQAT4sNyyZUu8+uqrMe+QpED0+j6IgAhEBYGKFSti3bp1Ke5L0aJFQcX19OnTKFiwYED1/LcE6PgOsNtSQm1hHvf2jwDpU5DVyK5D7yIgAtFHoE+fPvjuu+/w7LPPYsKECdHXQT97pBlQP0GpmAiIgAj4IjBiPNCyi1v5zJUD+Ki7pZC2lPLpi5f2iUCsEyhQoAD69u2LiRMn4ptvvolZHFJAY3bo1XEREIHUEDh8FGj7JtDnE+DsOVdNWeLWIseB5vhp0svYuHFjaqrXuSIgAlFMoEWLFrj99ttBh6R9+/ZFcU8T75oU0MTZ6IgIiIAI+CSw3PJub9QOmO4RTeXojk+w6MdKmPHzKHCJrXTp0gF54vu8kHaKgAhELYFhw4aZ9Jxcio9FkQIai6OuPouACKSYwPhpwANWfM/NVpxPSrq4k9iz/HFsmd8K58+dNPtoR0q54YYbsH37drOtPyIgAiLgSaBkyZJmKX706NExuRQvBdTzv0HbIiACIpAIAWYyerkf8Gp/gBmOKKWKnsPOubWxd4MVe8mHHDt2DJMmTfJxRLtEQAREAHjiiSdw5513mlSdO3ZceKqNETAR6wXPIK779+9HxowZHTFUdlDZAwcOgKkAnSiclaGtiVPbd+bMGROc16n2MOfPn4fdRieO77lzLkPEQ4cOIV06Zz5bkh9jbDq1ffyOcJy9/we37EiHlwfkwJpN7ltmvetO4uHbluPuCasT/Xc4evQoFixYcFF9iZ7gxwH7e+xH0ZAXITsK+33ixImQX9+fC9r36uPHj/tTPORl+B2heP8PpqYhTu1ravoUTed+/PHHqFKlisng9v333zv2NzrYzN1302DXnMb1pbdimzB2HzOWOEH4BafymSdPHivsijPjrvCGRmZOVUD37Nlj2LGNThT+cB05ciRBxh0ntZOKCRnmypXLMd8Lbz5UjrNmzeqYB0fv9vE7TI758lnR4y/Iz38DXaxc7kcv6CsZrK93pyeAFvdmtoLfl0kyownvT6VKlUpQn11vSt/5PfZsX0rrSYvzqIByFid79uzIli1bWlwi1XVyVpr3QP4fOlF4jzl8+HBQx9ipfXUi/3C0qUiRIhg+fDjuu+8+DBgwAB06dAhHM0J+TWdOk4Qcgy4oAiIgAgkJnLGyGvUaAbTr6VY+ixQARvWm8ukqS2W/atWqic7ocra3QYMGCSvWJxEQARHwItCwYUOzDN+5c2eTKcnrcFR+lAIalcOqTomACKSGwE4rK2eLzsBIjxjRtaoBEwcCV1Vw18yZtDFjxoDmD77MCj744ANceeWV7hO0JQIiIAKJEOjXrx/KlSuHBx54wMyCJ1IsanZLAY2aoVRHREAEgkFg7rKMuM+KijJvmas2mnS3fchKsWnF/Myb++IrFCtWzOShb9SokTFx4fJz5cqV8eWXX8bMUtrFVLRHBEQgUAI0lRg7dqxJC9y6detAT4+48hFrAxpxpNVgERABRxOg/8ynkzJj2LgsOOfypUGeXEDvl4Abr0m66VyKHzfOIwl80sV1VAREQAR8EmBK4SFDhph88QzjxkD10SpSQKN1ZNUvERABvwkcPGw5FvUBfpvjdkypWh7o/zJwSWBp4f2+pgqKgAiIgC8CzJL0559/4vnnn8c111yDmjVr+ioW8fu0BB/xQ6gOiIAIpIbA4lWurEa/zXHX8sjdwJe9A1c+GU7lvffeMyHi3LVpSwREQAQCIzBw4EDj4NikSRPs3LkzsJMjpLQU0AgZKDVTBEQg+ARGTwUeehHYustVd9bM59H9mUN4tQ2QMcD1Ic5SPPPMM3jttddMCJ1169YFv8GqUQREICYIZM6c2WRHYiSNxo0bm/Bw0dZxKaDRNqLqjwiIQLIEjlkx0l96D3hjEHDaFfcb5UpaXu89D+NmK8B8oLJmzRrMnj3bBNm3A+337ds30GpUXgREQATiCTBVJ52SeG+JRltQKaDxQ60NERCBWCCwbjPQ9Dlgykx3b++uC4z9AChdzJVNyn3Ev60sWbKAMxa2ZMiQwWdYJvu43kVABETAHwJ169ZF//79TaB6LstHk0gBjabRVF9EQASSJPD9b0CTDrBSarqKcZn9dcvJtHcnIFuWJE9N8mDx4sXRo0cPU4aZvAoXLgzNgCaJTAdFQAT8JEDTnqeffhodO3bEtGnT/DzL+cUCtHJyfofUQhEQARHwJsBl9l7DrSxGU9xHihUCPrC83Ktc7t6Xmq1OnTrhjjvuMCl5a9SoAc6CSkRABEQgGAQ4+7l69Wo0bdoUf//9NypVqhSMasNah+6QYcWvi4uACKQ1ge27gefeBhaudF+pzrWWQmo5H+XJ6d4XjK0qVaoEoxrVIQIiIAIJCPCBdvz48bj++utNet9Zs2aBOeQjWbQEH8mjp7aLgAgkSeCPucB97dzKZzrrjtehBfBR9+Arn0k2RAdFQAREIJUE8uTJg6lTp+L48eO46667cPTo0VTWGN7TpYCGl7+uLgIikAYErNTsGDgKaN0NOHDIdYF8VhrNT3oCbR4EmF5TIgIiIAKRRqBMmTKYMmUKli9fbpbjz549G2ldiG+vFNB4FNoQARGIdAJz587FzN8X4snXgEFfAUyvSbm6IjDxQ6DmVa7P0fqXAat/++03HD5spXaSiIAIRCUB2piPHj3aOCQ99dRTEdtH2YBG7NCp4SIgAp4Ebr75ZixYYS2tX/4pMrozauLR+4AXHwcypPcsHX3b8+fPx9VXXw164e/fvx9r167FpZdeGn0dVY9EQARwzz33YPDgwaACeskll+DNN9+MOCpSQCNuyNRgERABbwKTJk3CvLWVUahKH8Sly2gOZ8542gqvlBG31fIuHZ2fqXxSqHxSmjdvjr/++sts648IiED0EWjdujW2bduGN954w4R+e/bZZyOqk1qCj6jhUmNFQAS8CRw9Dnz6w5UoXLF/vPJ56shitL7zx5hRPsnE2yN20aJF3qj0WQSCTuD06dN48sknwdSzXbp0we233w4qQtu3bw/6tVThxQS6d+9uYoR26NABY8aMubiAg/dIAXXw4KhpIiACSRNYvdEKLN8eWLG5THzBw9s+w4a/rsNTre6I3xcLGwxSTUmf3mVrMHLkyFjotvoYZgJ0ghkxYgTuvPNO0AaZS8PTp0/HbbfdFuaWxc7lBw0aZPLFt2jRAj/++GPEdFxL8BEzVGqoCIiAJ4HJvwLdrMx0Jy6kbs+U8TyuK/sTytfbbdlF7Yy5QPAMhM/g1HPmzEHt2rVRr149T1zaFoE0JcAA6XY2sPLly+PWW281s6C0T5SkLYF0Vny5UaNGmdBMjRs3xk8//YRatZxveyQFNG3/L1S7CIhAkAmcOg28PRQY87274hJWPOYBr8ThirK3Wzv5ik1p0KCBCVIdm71Xr8NJgJ7ZtpQsWdJsRnqcSrs/kfCeKVMmTJw4Ebfccou5B8ycORNXXeXssB9ago+E/yy1UQREwBDYshN4yMpg5Kl81qsJTLBmQq8oK0giIALhIpDm/BY4AABAAElEQVQ9e/b4S3NGjnLejoMWf0QbaUmAY/D999+jVKlSxhZ35UqP9G9peeEU1i0FNIXgdJoIiEBoCcz8F2hkZTVastp13fTW3YvhlQZbweZXLv/PLPnlz58fBQoUMDdfhiGSiIAIiEAsEWAYNi7B586d28yGbtiwwbHdlwLq2KFRw0RABEiAiT76fQY83R04dMTFpGBey/P9HeCJJsDSpUtx7bXX4pdffsG+ffuwd+9e/PzzzyhXrhwYmF4iAiIgArFEoHDhwuZ+yJloLsk7NSKBFNBY+q9UX0XAwQS6detmgqjHWXkyf/3V8jCyZO8B4PFXgKFfuxteo4orq9G11vuRI0dQuXJl98ELW/bSH20imTfZH6ECS+eJQoUK4eWXX/bnFJURAREQAUcSoB0u72m0w6USumfPHse1Uwqo44ZEDRKB2CMwfvx4k8njwAFL47SEWY2+/3Un7rPiKs/2CGf55P3AyLeBAtYMKGXWrFnIly+f64OPvwwRQ6/w5ITLVPTa3bVrlyn6zjvvgMHtJSIgAkkTyJIli7H1vOmmm+ILcvWBD4GXXXZZ/D5thJ4A+XM1iOGxGBbLvr+GviW+rygF1DcX7RUBEQghgeXLlycIm1T0ilfw4vsFsWufqxE5Lf+Gwa8DLzzGOJfuhu3evdtaorfW6BORM2fOmJtvIofjd69evRq0H7WFHqUrVlh5PSUiIAIiEMEEuEI0bdo0k5qXsVq5auQUkQLqlJFQO0Qghglw9pHKYroMuVH06gnIWbonzp133Z4qWt7tEy0v93rXXQyIuc7twOsXHwXOnTuHsmWTd4+vXr26sR216zh16hTuu89KIi8RAREQgQgncM011xjveGZHY6KAEydOOKJHUkAdMQxqhAjENoGaNWti1Nj/UK7OAuQs7Fb8mta3Qi71BYpbcT59CfOfFy1aFLQb9Rbu4+xo1apVvQ9d9Jmeo8ypTLspxjOkDSrtQSUiIAIiEA0EGJj+22+/xT///GOyJjGFarhFgejDPQK6vgg4hADtg+gtyfAdzF7iS6lLq6Z+8xPw3pfXIC6T6wpZMgPd2wINb0n6ihkzZsSMGTNQsGBBZM6cGSdPutIiZcuWzcyMUqnMkMG/2xz7vHDhQvDGzPokIiACIhBNBGhbP27cODRq1AjNmjXD119/neQKUlr3XTOgaU1Y9YuAwwkwY8b//vc/M/vHFI5M58ilbeYXTms5eQp4pZ/1+gDgNqV0MWCstS855dNV2nJIsuJ+Hjp0CK+//rrx9mQKytdeew2bN29Gjhw57GJ6FwEREIGYJ3DXXXeZtJ10snz88cfDmizAv6mBmB8yARCB6CTwzTff4OGHH46fOTx8+LDpKGdDn332WUydOtXYDqVF7zdtB9q/BaxY56799lrAWx2BHNnc+/zZypkzJ7p27Wpe/pRXGREQARGIVQJNmzY14ZlatWoF3js//PDDsKDQDGhYsOuiIhB+Ar///juaNGkSr3z6ahFDeFCxC7b88rcrq5GtfGawPNu7tgb6WzE/A1U+g9021ScCIiAC0U7gsccewwcffGBWusIV9zhgBZRLXRIREAHnEejevbuJn9m8eXO/llW6dOmSbCfomf7RRx8l8BBP7CTaE91www2oVq1aosHfz1gRk/qMTIdne1pB5I+5aipsRT/64j2gRK5fUadOHWMC4MSgyYn1W/tFQAREIBIJtG/fHj169ADjHr/3nnUTDrEEvAR/+eWXg3Zijz76qMm3nFQIlBD3RZcTgZglwGX0r776yvSfMTW5rDJixIhEefBBcsmSJYke9zxAZ6TffvvNGK577vfcZu7hFi1agOGLmP6NHuTr169PYODOmJ7te2bDghXWdOcFub6apZB2AvbsXIWrLQN5Cq9HJyCaA8iG8wIovYmACIhAGhCgvTxNrjp37mwy0T355JNpcBXfVQY8A0oPqly5chkPquLFi+Oll14yuZh9V6+9IiACoSDw3XffJbgMbTuTEnq7M9i6P8JUlvQmT0o++eQTo3yyDGNvUnmkAmrLrIUwWY0WrHA/87ZpBox4E8iX23I6Gjs23ludGVQYFF553G16ehcBERCBtCPQp08f45D09NNPI7nfjmC2ImAF9MYbbzQzKzt27EC/fv3A2RYGOb322muNLcHBgweD2T7VJQIi4AeB66+/3sw8sihnEJNLuca4l1xe90cY3ihPnjxJFmU8Ts/VEF6/SJEilimAK48787kzrzsld87zGN4D6NAcVptd+xgs3jOj0d69e1G6dGnXQf0VAREQARFIMwL8zRg2bBjuvfde45TKOMihkIAVULtRWbNmxYMPPohu3brhiSeewIIFC9CpUycUK1YML774oomlZ5fVuwiIQNoS4MoEZx4Z85Kp15JTQLnEHUisSyq4SQlXQurWrWuKlChRwizvnz2fA23eAPp9xllR19kVy57FuH5ncGP1hLUxJt1TTz1ldhYqVAi//PILSpUqlbCQPomACIiACKQJAU4gjB492oTka9iwIebPn58m1/GsNEUK6Jo1a0CHBya65w/Thg0bMGbMGOzfv9/YijG+FI9LREAEQkOAtpK0v1y5ciXmzJljgskndWU+8b7wwgvJ2ljypkTHIsYFTUpYH5VGtmHVqlU4n6kSGrUHZv7rPuuhu4DhbxzFJYnEeB8yZIhxXuLSPQMmS0RABKKHAHOQr169GnIwdO6YcrWL2ZJ4v7/jjjsSmFGlRasDVkAZ5JmKJ222aKzKYM+0P2vcuLGxKeNyPHMo++vgkBadUp0iEKsEuDJBZdAfob3P7bffnmhRZhnisviECRMSLeN9gOdM+jULmr0AbN3pOpotC/B+Z6DbM0BGtwmo96nmc5YsWcAsRhIREIHoIDB58mRUr17dpMxlwoty5coZBWf48OFaKXXgENPH54cffgDvxfXr1/crAkpKuxGwAkpbr1mzZmHZsmVmyZ3p67yFrv10SpCIgAg4m8D48ePBGHBMv8lZVCqQVGLpRU/llKsafjsrnQA69bFSaFoxjU9fMC8tWwIYZ2U5alDH2RzUOhEQgeATuP/++9GyZUvjUEjHRNp200+EqxzUE+hseOKEdeOQOIoA9boff/zRzFbfc889aTZGASugdEKqWLFikrBoA8Z/LF/CfzaGdPnvv/8ucoLg8iHDuWiK3hc57ROBtCHQpk0bo3zS+9x2BOKPRevWrZN1PrJbtG4L0NTKYDTZw3b9rrqW8tkfKFvSLqV3ERCBWCHA2JJ8wE3MHp26wNGjR425jb8Okd7sqMxSUfJ8eZfR55QRqFChAmhOyWgkDLHH34dgS8AKKJ0NmHv5zjvvNF5T9Ib3V3bv3m08rLg8T6cJzwwr9Kjv3bu3MXxleqhNmzb5W63KiYAIpJAAZyT4wLh161bzY0BHJoZdovDJl0sxycmPfwD3dwBWb3SVTBd31iy3M74nl99DJbQxGzBggMnuYSvSobq2riMCIuAmwPuJP4HNeb+hPpDS0D/M1MYH5UGDBpnX4MGD3Y3QVqoJcMJx5MiR5kEiLbIlJWORdXH7OUs5b948Y/dJG45nnnkGNWrUAL2mmNYvKWeF6dOnG1vRhx56yMy08Adu48aNRrP+448/TCcZxJoOTV9++WUCBfXilmiPCIhAagnQ8YeG5ydPnvRZ1auvvmqM0X0d5DJ774+Bz791Hz19fCO2zb8fX8UVwkN3JYxN6i4V/C3OoNBsgHZL3O7YsaNRpPlZIgIiEFoCVCgTu6d4t4RJMag4PvDAA96Hkv1MT21Gz3jlFSvOmyRNCDBCCR3PGfHoiiuuMLOhwbpQwAooHRzoaMTX66+/bjxu6U3LKPpcVqdzUmLCsE228B+HsxQMBUOb0qpVq8bHMaSd6dSpU+2i5p0Kaq9eveL30VGBS/W0WXOC8EmOwhklf51AQt1u8nayeQMVB744U+5E4RIEx5me3k4Ue4mES17+/g/yoTCpHwo+cPoaj1370qHbh7mwdK37+3dk9w/YvvARnDu9DzNnZjcPqpxd9RT+D3Lpzd/2eZ6b1DbvO1SkbXsyKqNffPGFeTBO6jzvY2wfOfrqs3fZcH1mG53aPvt/kCYcXF51otj3as6YO1Hs9gVzjI8du5D3NkQdnj17dvxKij+XZDzxlAjDP9Jbm6unV111FW655Zag31tS0q5oO4fZklasWGFmm+mETmeyYEjACii/FDNmzAADlfJFzZjKI5VQzoD6I1RceS5tz6hIMisLnSBsoRcWFTlPYeBsKqa2rFu3zjhHOEUBpeJExYQOG8H+cbX7nNp3/jiTl1Pbxxsv2+av00tqeQR6PtvHcXZy+6icMBaoZ1D4xPpJxXPfPis/ZhJy+vRpY8PF2Jy2zF6UAa99mBUHD7sseOLizqNM7u/w88+NcM7iQ6Hywe+xNyt+R9g+rnQEU3gtzzqpAPGe4n395K5JJhznQM9Lrt5gHuf32KntowJKEw6OMV9OFH6HKU5tH79zwf4f9Od+EMyx4vcvELEfHAM5h2WpgFKohL711lsmOc73339v9ulPcAl8/PHHJoxWo0aNzGQjY76nVgK+QzAXPKfM2QgmsWdYJs8fJ38a9MYbbxhNmkorva345eAPpy28QdAT11MYXJsvW9q2bevzB84+Hup33nT548qZl1B/2f3tKxnzR9mpCihvvGTn+TDib99CUY78OGvi1PaRH2/k9GZPTkHhA9xdd90Fvicl/C4y1u+nn35q3eQbYMgY4MMvYTIc8by8uYC+XeJQ6dI6yD36DLJnz27awNRuDLfiLbx38Lsd7AfH5s2bY+DAgWZFhm2gWVDTpk29L5/sZ84ek6NTx5gdsL/HyXYmDAVsBZRj7NRwWpwN5D3Q+zcmDLh8XpL3mGD/D3J1IJRCPYEPhFSk/ZHChQv7U+yiMjQH5Coqr8WwkMy+xkkxX/eei07WjoAI0Jxp4sSJJqQW762ciEzudya5CwQ8DUFbi9q1a2PKlCl49913jaExHRX8WW5ZunRp/Mwmtee6desaTZr/QJ4zMdz2Fd4puc7ouAiIQPIE6Dh45ZVXmqD1SS2/syb+gNBs4577WuLep3Zi4Ci38lntCmCSFXLpf1fBPAzyR5PeqJyVeO6555JvSJBL/Pvvv/jnn39As4JQpZILchdUnQhEBYG7777b7wgaVGz8XT31hMN7F5eF7ZUPKtnMnka/EknaEKDexsgGnHBjGK3USsAKKNNsUvPlEjmnvPk03qVLF+TLl8/YgSbVIGZoGTFihCnCmRr+WPBJhXnk6QnHoPaccaFyyxkMiQhEEgF+F1K6lBSqfu7atcs83HGWxd/ZiSy5r0PpWvOweqt7lqJlQ8v5yDLJLlzA3XIuaTJrkudKhftoaLZq1qyJ6667LjQX01VEQAR8EqD3NB1W/FkN5D0zJcoMZ9/oBG1H6qB+sW3bNtSpo6DDPgclSDtr1aqFRx99FEOHDsWoUdaMRCokYAXUvhYVRT6BcDmDy2pcUuPyc1LCpxz+8D3++OPGc43/pPSEp/0WQykwpzyX0mg/Qk95iQhEAoG//vrLmKLkyZPHhCjjMtDzzz8fkBF+qPrZqVOngJa/85R8FiVr/o6MWV3BPM+fPYz3O51G19YXZzXi8jV/BDgrYTujhKpfuo4IiIBzCHBWkhNJfChPSgnlrCWT2qRkxZNmFDS7oSkg/UMaNGgA2ik61bbXOaOT+pbwgYFJBphNj/f7lErANqAMLsvUm/Ryo+0np9oZg4u2oMmFPKFt2ptvvmmUVpa1p87ZeNqjMfMKlVqWk4hAJBCYNm2aiYnrOZtIc5QPP/zQGMRTKXOKPSFto5jnl0vlyUlc+hwoUnk4chV1R644eXgxDq5qgc0rngLqPh1fBfvOB0guzfBB1Gbx999/o3z58vHltCECIhA7BOg4zKVa2gv+/vvvRhnlvZFKJ+1vixcvbmJMcqY0pUK9gyupXJHlKqxT/RtS2j8nn8fVbCr+jG5EfTAldsYBK6D0MLvppptMsGfmd03JgCdmnM4fr2A7Jzh5ANW2yCawaNEikyvXVy+o5PHJnw9WM2fOTHIWwD6fwZupJDLEWUoewngT5mwsnYaKFi1qVxv/ztBmydl8snCmHBVRtNo3yJyjQvy5B7d8hp1L2+D8ueMmRi+ffG3h9TjzaSue9n5m0uCPA5fF/RW2n32njapEBEQgsglwoom54NeuXWtM9xjxhooiTexSqj/4IpJY5kVfZbUvOAS4cj169GhwSZ5mmEwmFKgErIAyHieFthbeU69sUDBc8wPthMqLQDgI9O3bN0lPTy4/0fGOMW95s01KOFvILzJnDZh/nZnAvGNoJnU+FVfGZ6MZAGddFy5caMKjeZ7DJTHOSCQlOS95yJr5HIZ0GbKbYufOnsCuZe1xcMvw+NPoZEQTHC51MXUu4/96K592YSqqdqgUe19i77x3cIaE6fU6dOhgHnITK6v9IiACkUOgbNmy4EsSXQTov8OoRnRO52TLzTffHFAHA7YB5SwKf+T4Y8Gc8J4vZh+RiECsEKDSmJjiZTOggT0VtOSEyieFyidXFVq2bJncKQmO20vdVD4pvrKKJJk2Ny4TClUcjKJXfRmvfJ46tg6bZl2fQPlk3TSdYTsp9HpPisH69esTRLgwJ/n4Q1suPtRS+aR89NFHfiuuPqrTLhEQAREQgRAQYBIiBqanb0+g8V8DVkCZ9oq2mpwJ5Y+L54u2oBIRiBUC/mRE4iyoPzaXnktIdOBhVIhAxHt2wXt1gnUl1t4MWUpZjkZ/Im+pNvGXPLJzMjb+dQ1OHpofv8/eoGmB7e3P96Qcjlg2seva9fGdM5+eMeU4w0qHRYkIiIAIiIBzCXBC4lMrTjSTFL300ksBNTQgBZTe7rRT49Ijw62ULl06wYvxPCUiECsE/Ak3RHvGSpUqJYuES84U22OUaSQDEaZKo9iOff3797/odF+eptkL3mlCLGXNc60pf/68leZxZRdsndfQymrkmk31roh2pLapDe07k3Kyog2YP/cFzvhSUaUNOM+hOQ/vMRIREAEREAFnE6D5F6MRDBs2DH/++affjQ1IAeWPApVO2qdJRCDWCXA1gOYoSQmXp+mkk5xQgWSWiW7duuG3334zqwzJneN5nOHL6OzEAPCsx1dcPbbDHaIkHQpc1hPFrvkO6TPlM1WdObkDm/+9GfvWWQE+cd6z+gTbJUuWjFd0GzdunKT3KaNm2Ep1gkq8PlA55vIN7Yl69eoFxiuViIAIiIAIRAYBmmDSeZR2/1zB8kf8ckJihhEukVEefvhhE/+JRqdc9vP0WmdYJn9me/xpmMqIgNMJMARZs2bNMGTIEJ9NpbJHByTO6PkjDRs2BF8pFSqYTIHnuZzvWReNxPmEevBIBlxy5VfIXsBtMH5s72/YtuBBnD21w/OUi7YZaoNKpy1Me0knI86CMg4wnZy4lM6+M9VuILasnC3u2rWrXbXeRUAEREAEIoQAJxpou097UIYh9Ccbnl8KKI1L6c3rKcxA4C0MTDp27Fjv3fosAlFLYPDgwSb/8AcffGBmBWkPyRfDIH322WdmxSC5zjOZw8iRI/HNN9+YtJdcZXjkkUfMg54/Yc5oYzpmzBiTlYKrE7w2FeMWLVoksKuko9OlFZtgX8ZuyJilmGkW27pvfW/sWfmy9flsck018ftscwG7MFdGaAvKFJjLly83+eBvvfVWeb3agPQuAiIgAjFAgFno+LvDlSyuyiU2GWKjiLN+gBJfa7tQijZffhQzS22eM6L2RdLivW3btibulKfjQlpcx986OfNDD2TOAvuz5OhvvcEst2/fPhPmxx+lJpjX9bcu5hwnO4YicqLQoYiOMb5sHqlEzps3zywjX3rppWYm0h/OtKtm3M+dO3cm8CDkTCOXpVetWpVglcGbC+0maSvJcrYHOcsw1i7bxPZylpIycgLQ+5Pzltd6nPl89vQBbF/UEkd3TTafk/vD/rz99tsm5ltyZRM7zv4yCHWo7hOJtSOx/fwOU6H3x241sTrSej+/x4yl6ETh7wSjLfA7kli853C3m98L/i/z/9CJwu8szVF82WyntL20CafzMGPzhlPoqEKlxLZVD2dbdO20IcBoJrQJpYka/YWSEr9sQPljyGXE5F5O/VFJCoCOiUAwCPDHlorgHXfcYbL/+KN88qGFP9QM0uwdvoIPfZzNZKraxB7+aF/KHymGefJUPtkf/sjyJs9Zz/0HT6P9W0CvEYhXPundTi93f5VP/lhzSYUBhyUiIAIi4A8B3pfee+89kzGHD8I0EeJ7qVKl0K5dO/mT+AMxwspwBY6/FVwd3LJlS5Kt90sBTbIGHRQBETDhyDjzScWTyxBJxca0cY0aNcrcjJNSMJnijNmEfAljcHIGNKnzt+7OgQatT+Cnv9w1NLndmg196zhyZNnv12w9Ha2efPLJZJ9m3VfQlgiIQKwToEkOZ1zpHU1beD4Uc4WB5jp8uKa9YJUqVdCzZ89YRxV1/X/xxRdNak46lCYlUkCToqNjIuAHAc5WUvlkTFzK3LlzjTd7cqdOmjQp3rkvsbJcsv755599Hv7hhx+SPD9XsUeRt/LP2Hc4pzk/cybgneeBnlbEp7p1rgcD6Tdq1MiEPKIdp6fYoZD4A8F0a77COnmW17YIiIAI2AR++eUXNGjQwJhj2A7M9jH7nZ7SvL8xAsijjz5q79Z7FBCgGd2zzz6Ljz/+2MQHTaxLfjkhJXay9ouACMAsf9OD2w6cTltRO2VtUnxoD5WcsC7ah/oShiryNfsZly6LldVoIPKUeCL+tFJWavgBrwDly8TvMrMTdBpkO5hSk8HrN2zYYOyYuURGcwLa8khEQAREwF8CtIWnE2IgwvvQFVdcAWbVkUQHAYYCfP/9902UGIYX9CVSQH1R0T4RCIAAHVY4A8rsRfbS+2OPPZZsDeXKlcOcOXOSLMeZSJbzJVQOGe7IM+ZaxqyXomi18ciSu1r8KYWyz8c3A6ohR7b4XQk22H6GV5OIgAiIQGoJcOYrUKE9/LvvvotWrVqhQIECgZ5uJgF+//13MDSeLVzq5+oRbeGpEDvFYdluX7S/Fy5cGA8++CCGDh2Kl19+2SMGtbvnWoJ3s9CWCKSIAO0+GSu3YsWKqF69uom16c+SEpW+5Dz+aeN57733+mxX06ZNwZlXW3IUuhelas2NVz7PnztjZTV6AW93OJqo8mmfq3cREAERSC2BRYsWmVBsKamHpkwMRxeo0LbUVnTsc7nsX7VqVYwfPx5MhkFzAF+rRXZ5vacNAQalp1c8zcV8iRRQX1S0TwQCJMBIEYsXLzYzmnTY8Ud4U0wuS5Kd8MFXfbzB8sZrJfBEgfK9UPTqiUif0ZWZ6fSJrdgy5yY0u+OkUlr6gqd9IiACQSfw/fffm2QUKamYs6Djxo0L6FSuOjH7Dp2bPIXhf+rXr29iMTM1JM2jpk2b5llE2yEgwFTN5cuXNzGqfV1OCqgvKtonAgEQ4JM1PdUZboRLDXyK9zeV5JQpU3zGDOWy0QMPPGA8SJNqSo+3hqDq7fOQ/9JOxgOfZY/umY7Nf1+N6pXTYeDAgUmdrmMiIAIiEDQCq1evBmcyUypbt24N6FSGr2PCD3pde8rChQtx0003xe/i9qxZs+I/ayN0BDhJ8t133/l8MJECGrpx0JWikADtjG677TYwzeWrr75qlnuYKahIkSLGezy5LjNdJW+63stDtCX9+uuvzXJ+YnX8uxi47bEjOJmuqinCOvau6WnNfN6G0yd3gTZRrN8fYTpNZjerU6eOUXyZlUkiAiIgAoEQ8I5HHMi5LMtZ0ECEaR99rSLRmdIzCw8TNzBBgiT0BJhemmYSv/7660UXlxPSRUi0QwT8I0APdQaC51M4t22xg8rTDpRLP4ktyTOrUL9+/ezTfL7ToL9YsWLGhskuwNxlI8YDfT89ZymuLhvQs6f2YfvCR6zZz4S2NszJS2elNm3a2Kdf9M6lKdqZMgOQ7URFr1TamFIJloiACIiAPwRKly7tT7FEy3gqjYkW8uMAk+J4Omfy3uZpL+9HFSoSJAJXXXWVmZChQxjNzjxFM6CeNLQdswT27t1rYmIyU0dicTe94XCpnU/snsqnZxk6EHFJnrHuvIVG8gxRkZzwxumZfejQEaBtD+B9y1b//HnX1/f4gTnY8NfVFymfrJvnMy8v330Jl6poK8VlM1v5tMtNmDABffr0sT/qXQREQASSJMAVFCauSInQmbNu3bopOfWic5iNx3PGk9tlynjEoLvoDO1ISwL8v/AVmlAKaFpSV90RQYBPygz98e2334I2TFxSnzlzZrJt/+STT5K1d6Jy6sv4nTaj/qTrZCOYzozL9EvXAPe1A36d7W7a/o2DsWnWDThzYqN7p9cW28DYfL6EgYLTp0/v65CZQeAMqkQEREAE/CFAW8vE7ifJnU/FtVmzZskV8+s4l31pG8qlX3phT506NYFNqF+VqFDQCDA7IJ10OSnjKVJAPWloOyYJMHRIzpw5E8wAUhlNTjZv3pxcEbMEz7Rz3rJ9+/aLvozeZezPjPX51ZTTaGaZc269EJM+U8azOLz2Kexa1taaCk34pbbPs9+pgPJ6vmTp0qWJzuCyvD/B8n3Vq30iIAKxR4DL3B07djTZ1QLpPZXWSpUqGRv0QM5LrCwdX5jdjStatBNl3nmGyZOEhwAjtnAVbuXKlQkaIAU0AQ59iEUCJUuWNHacdt+zZctmsnLYnxN7p6d6ckLHIF9L9Hzap2KZnMSly4bspQfi48mlcerCKvqlJax0mm1W4tS+Ccmdbo6znYkti3GpKikhC4kIiIAI+Euga9euuPbaawMK/M57JB/6Uzp72rhxY+NpbbeRNqAM6cQwTWvXrgUdQyXhI2Bn1ONYeEryv6CepbUtAlFIgMvvf/31l+lZ7ty50bJlS7Ru3TrRntJW8oknnkg0RabniVzeZyBkLj94So0aNXwqpp5lMma7HKWun4UsBRnr0yV31gbGfQDUr1cuwYytfdzXO9twzTXX+DqEJk2aJKqc8seAS1kSERABEfCXAB94mdqXqTWTe4BlpjdmkaOJET3Vgy3+PugH+7qqLyEBTnTQ5IzmEJ4iBdSThrYjkgAVLCqQtGccPnw4fvnll4DDeTCUB2crGdB48ODBSXK48cYb8eWXXyZr/2lXwjq5BMEgzbYwTRmdfxKzA81RpAlK1/oPmXNWMadktCZLX33a8nzvAmTPCjO78NRTTyFrVutDEsIfA2ZcoomBL6H3O5VQb2HaOiraQ4YM8T6kzyIgAiKQJAHedxjajY6WjBRCRdC+V3F2ksom9zHKx3///YfkVmKSvJgOOp4AV/toErF///4EbU1+DTBBcX0QAWcRYLig559/3iicjMlJJZI3Ot4AmUXoueee86vB9FSnLSi9wZm5gaGPfCmH9ChnQGNvj3F/LtK8eXP8/fffpn7aw9ARie1NIHEZUbD8e8hXxt3uuLPb8fn7hVDtioTOQm+++aZZtlq2bFmCKuwP5EDFM7lg9FTaq1SpggEDBhhbUZ5zzz33gNlE/DETsK+ndxEQARHwJMBUjFwtmj9/vlFI6VBZsGBB45Feu3btRB+MPevQdnQQ4O+Rd5xXKaDRMbYx2Qt6aNO2x1sZtP/JaQxPJxsqWIkJz2Ww9k8//dQoW1QIOaPK9G60SfK0naTT0YgRIy66XmJ1e+/nTCiV5UmTJpnlKW/HoAyZi6FotbHImvf6+FOP7P4Ru5e2xOC+91zUDy6Rs3+cCWW8Tird7DvTgrIfXD5n37k/OWnfvj34koiACIhAMAnwIZY2oXxJYpcAf2u9J3WkgMbu/0NE9/yHH34wno3JdWLUqFFgINy2bS1vcR9yww03mCUg7ziZjFmWN29erFu3Lj5+HFNt2sqtj6qS3cUvIGdAGVtz586dCQIlZ8t/Cy658itkyFzQ1HP+/Dkrq9Eb1utN6/N5cKaXweSvvvrqi64zdOhQcDZ0xowZWL9+PUqVKmW8SbWsdREq7RABERABEQgDAYbE8rYJTn5qJAwN1SVFIDkCnTt3Tq6IOc5l+R49evgMeTRo0CCzDO6tfPJEziDyaY352G0v9smTJydQGv1qgFchKrBcEmeGJJfEIX/Z11D82mnxyueZk7utdJr1LeWzh1XEtUTP7EpJLaUXKlTIKMwMqE/FOSXK59y5c9GzZ09lP/IaM30UAREQARFIOQH+DvM3jw6/nqIZUE8a2o4IAvSY9BVbM7HGU4GcM2cOatWqlaBIUkvzLEgllIHpGTqCdku+MholqNCPD7QxtWNrpsuYz5r1HIUcBe+IP/P4/n+wbUFTK7D8lvh93GBbfvzxxwT7PD+MGTPGBHHmchdnWKlcM7e7v0I+9MyngwAVcgau79Wrl7+nq5wIiIAIiIAI+CRg/16XKGHFEPQQzYB6wNBmZBDgP3MgzjHMvrBhw4aLOucdk+yiAhd20MmHqdzoGR4MoTKZJXcNy8t9fgLlc/+G/tg0u85Fyqd9TTvHvP3Z871FixbmI+1XKQz9FIjQQYpizwZzWT815gaBXFtlRUAEREAEopfA8uXLTeeYGMBTpIB60tB2RBCgIkglzl/hUjodc7zFHyWW57Icr2kvxXvXE+jn3CXaoGTNP5Axa0lz6rkzh7FtflPsWm55vp/3nbM9uWvQacpT6G0aiDBLiKeB+MGDBwNiHMi1VFYEREAERCB2CHBFjWGYypQpk6DTUkAT4NCHSCDApyh7ps6f9tILvHLlyhcV9VbaLipg7aDjULVq1cwSvD276KucP/vi0mc3jkYFrxiIuHSu2dSTh5dgw1/VcXjHuGSrKFKkyP/bOw8wp4rujb+w9N6kw9IEBAGlF1EQ6UURQRRBQMDPTwVEEBARC34iKE1FRCwIin8ULBSliIBIEamKAlKk997r/u87681ms0k2u5vNZpP3PM9ubpmZO/O7N8nJmTnneCxDZyuK7bXPsCcJEcY+pVLPQPyU2bNnx1kwnpD2VFYEREAEREAESGD58uWoXbt2LCMHj0sBJQVJqiLAOJV33XWXz2nb6JBTvnz5OGMcNGiQIzhynJPWAVoEmzRpYmKC8tdbiRIl3BXz6ViGrLdYWY3WIEfhhxzlT++fit0rauHqhW2OY542uDbTW1Yixi49cuQIJk2ahN27dxvPf09tuTtO5ZZT7gwRRc//Vq1auSumYyIgAiIgAiLgMwF6vzNRzN133x2njhTQOEh0IKUI0AJHi6MvQkcbpnHzReiQ406YiWjEiBHuTiFbtmzGY4/xNW3p2LFjoqyC2Qs9ZJTPjNluMU1FpL2O0zv64tCmLoi6ccFu3utr1qxZ481nTOsnw0p5s5R6uwh5NmjQIM40ibc6OicCIiACIiACngjQeZbOty1btoxTRApoHCQ6EGgC48aNM9mBaGGklxynyz/66COv3aDFjyncKAzI7iq0XlIhmz9/vlGqXM/b+wy+zoxJzvHJ2B7TbdJxyXldJNPGOZez2/D4miYD8ld4B4Vv+xxp02U1xTKnO44ZYyPQ6s7zXq2vrm2OGTPGsHE9rn0REAEREAERCFYC06dPNzOQ7pbBSQEN1rsWJv1q1KgRnn/+eWzbtg001XMamNl9HnvsMfPnDQOn1bdu3Wqmm6ko2n+sQ2V2wYIFZgrdWxvMpjRq1ChzbbscnY0WLlyI4sWLx8r3TqsoFUFPedXt+nxNl6m4cTTKHRkTAP/Csbn4ZkIEKpaBmSqntTI+4ZiGDh2Krl27xldU50VABERABEQgaAgcO3bM+BPYUVZcOyYF1JWI9gNG4IUXXsDixYtjKX/OF6djDZ1jvMkff/wBBlDn9L39x/LMCDRr1ixvVY119Omnn3YbpJ4OR4z7aYc3shtq3bq1x2l7u0zWfM2sEEvrkDlXTXMoKuo6zu5+CRt+qIvIornsYk7B6B2H4mzQGmvHDY1z0uUAg9Bzsbev5V2qa1cEREAEREAE/Ebgww8/NMvqunXr5rZNKaBusehgchNgbM733nvP62VYhtZJT+tCaTFt3769xzbYvh0A112h+GJl0tOe61cOHDgQqzoDvH/++efIkydPrCl6+vTlvfkVFKk+DxEZ8po6168cRoZjvbB6fg+rfG5HO1SQGV80PqEizHWo8Xn9M2B+oUKF0KtXLxQtWhQbN26Mr2mdFwEREAEREIFkIcDvb2bv43c0v5vcSTp3B1PDMVq74vtSDuQ47BA97JO/4kX6u/9U5PhQOK9r9Pc1fG3PF+WLbZ04cQIHDx40YZBc2/71118d6Sddz3GfzwgtrA8//LC70z4paQzhtGrVKuMVzvvKPzJs166dycvOdS289xEZbjIhlrLmu8dxrQsnfkbR9G9iwU+fmTiirGcLrba+Cu8Xlxq4BvF1rm97+Z86dcoc7tChA37//XfnIkGxTX58j/DeBKPwPcK+Od+rYOun/T4Otn6xP/Z95XsiWBmyb3xPBWv/7O8Pf/bPbjMYnxn1KTQJfPLJJ2DWwgEDBngcYKpWQOlZ5ck65nHEyXTCVkDZJyotwShkxf4FgwJK66UvQpYsy367yvnz5x1feK7nuM97wuxB7uryvC8/YPiFyjy2bIP8+EFut8ec6wxwny5bTRS+fQbSZyrCZo2c2DkKR7c9jxpt25g+2nXs8+xXQp5db+Ngm6VKlcL27dvt5s2aWtdrOk6m4Ab58Ys1WL8Q2S/e82BkZ982+31s7wfTq7MCGqwMbQU0mLg598X5u8T5eFK2g/X9lpQxqW7wEuB7f/jw4bj33nu9hgRMtQooFRM6hfgrPWJSbyWdZ6iosE/uvLKT2r4/6lPhogNNMCigDALvy4cisxDdfPPNbodfo0YNr23wi7pOnToenYZKly6NTZs2uW3bPkhW1atXN22wv+fOnXO0x3td+JbBSJP3OSuwfHpT5frV01Z4pa44d+Qb4+XeoEEDR3m7Tb5WqlTJ5F13PuZpm19IFStWdNuOXWfYsGHgQm8+e+zn+PHjvZa36wX6lQpK5syZfR57oPtHdvb7JNDX9vV6wdw/3l/+MGRIrwRFjPB18H4oxx+0fF/zOQxG4WcMf6T54uzoa/+D5XvS1/6qXOomwKl3Wj/nzp3rdSDBaarz2mWdDAUCjGvJOJwMsO5JqEw98sgjnk4jf/78ePDBB93mhecXDM9TefQkjz/+ONgPb0Lll4qqq5yzDLh9/5cGaW8a4lA+L53ZYAWWr2aUT5bnj5JHH33UtarZr1q1qk9fMBwHlWgGwvcm5LR06VI888wzJpg8naskIiACIiACIhBIAkyIQusnHY9oaPEmUkC90dG5ZCVAJyFac9wtWaBiSgsFA857k4kTJ+Kee+4xFg1aXfhLn85BtDzu2LHDW1UTosmeMvRUkE49rhbtrbuAB/oA83+JqXVq74c4vukeRF3Zbdalckqcb0RPiiPb9BQgP6bV6DV1DBXli1BR7devH5o3b+5LcZURAREQAREQAb8SeO6558wyptdeey3edlPtFHy8I1OBoCfANZRcK0KHnmXLljkUPU6DtmjRwgSjd1X+XAdFC+H3338P5j5nui9OV9O6yBib7hRbuz5DFtGBKL6F/j179jRLKxiEnjJ3WXqMtGLkX/p3SWpGK6X7i/8FqpVthEWLRoBOQHQWolIc3xQkM0Mw9SVTbHKcHLctXFvKKX5aNcuUKWMf1qsIiIAIiIAIBCUBfl9NmTIFTC5ToECBePsoBTReRCqQnARosRw4cKCJXUmvbSqUNWvWBEMk+bpGix71zGZEb3UqcVQs+QbwNP3ONVa33HKLz44mDARfslQ5rPq7Eb6cn8WBo7gVWWL8EKBA7pPW1PdLRhHm+jdmc6LnX/fu3R1lPW1wkTbHzQD39Ng/ffq08fhv06YN+vbta/LQe6qr4yIgAiIgAiIQDAS45IwGG37vPvlkTAIWb32TAuqNjs4lOwGGUmLaS2dZsmSJyUfOUEW0ZnoTxvmkgw4VV3s6fcWKFaCDEjMhNW7cOE51hoegw5ivcv5ybvQfXQRRGWJWrNxTBxjxLJAh3RXLEz6PWcvK5QSULVu2mCxO7NtLL71kjnn7R4WZAXslIiACIiACIpAaCTCxDNNX83s7vplLe3wx36j2Eb2KQIAIcLq6Vq1aca5mK5K0DnoL5cLpczs2pl3HuTHGwmQcUVehAsqwRr5I1vytEWllNYrKUMEUj0gbheceA94ZCmSzjKF0/uFUv618OrdJqyaXFkhEQAREQAREIFQJ/Pzzzxg7dqxJGx2f45EzAymgzjS0HVACfGi5DtST0ErJtZ2ehDnjvXmx0yrKaW1XYd75+CUC+cq+jiJVv0VE+uj0mdmzXMSEoefRvV10bU73z58/32M8Tyq5M2fOjP9SKiECIiACIiACqZCAnbK6WrVqGDx4cIJGoCn4BOFSYX8SOHbsmFvLoX0NxvFkGU/CnOdUMj0Jrad0NnIWHnNnLXUuE5GhIArfNh1Z8jZwHL58aimebLUft5Vv6TjG9Z7ehNfZt2+ftyI6JwIiIAIiIAKplgDXezLiC40xjNudEJEFNCG0VNavBEqUKOE1kQCntd3F4LQ7wXN21hD7mPMrHZxKlizpfMhcz1vs0cy570QJa8rdVj6pRB7f8T+c3dYekUWzxWqLgaLpre5JeH2u75SIgAiIgAiIQKgR+OyzzzBt2jQz/W4vh0vIGKWAJoSWyvqVQN26dY3HtzsrJo8xDJG3h5rKZeXKlT0ueOYaU1cHJ7bLeJnuJHfJAShWazHSZbLc2y25fvUk9q9tjWPbhljhmi6hXr16saqxrR49episL7FO/LvDNareAum7q6NjIiACIiACIhDsBBhn+4knnjBhFOn9nhiRApoYaqrjFwK0HtJJh1ZGZ0si13UymPzOnTs9KpfsAJ1/5syZY0IvOde30wBy+t1dKCdOGTgHiE+bLicKV/0G+cuPtKb0I8zYLp3+Df/8UhXnj841fWjUqBFy5YpeC+o8eAbbdXWkogcgrZ/0xveURtS5DW2LgAiIgAiIQGohQONKx44djQ/HBx98kOhuJ2zCPtGXUUURcE+A6TJpqWS2H4ZN4hqSVq1aebUsOrdERyD+CqNnu73+hGtHGcje0/Q8MwUxVpkJfp/5VhS+/StkyBqTbvPUnok48ldfRN24bC7Fa4wcOdL5so5tWkGXWGGjpk+fjq+++sqsOaVVlmk+GR5KIgIiIAIiIAKhRGDQoEHYsGGDSZTizZE4vjFLAY2PkM4nOwFbUaQl1P7ztk7T7hC95BnygY5KruGavvvuO8yaNQu7d+9G8eLF7SrmlUojLaeRlZ5FrtKjkTYikzl+49p5HNr8OM4e+MxRnus8V69ebSyZVEQ9yUMPPQT+SURABERABEQgVAnwu5MhBv/3v/+By+iSIlJAk0JPdZNMgNbPyMhIE8qImRQonLp+9tlncfLkSbfT3izDKQCm+qInujvF0FZquW5z48aNZkqf9ShMo/nKe5mRp+yE6APW/2sXtmHv2vtx5dxmM33OZQDlypUDpxeYNUkiAiIgAiIgAuFMYP/+/ejatatJ8EIraFJFCmhSCap+ognQaunOfM/jXN/JXOnOOeKdL8RfYbRkulM+nctRiaUSyXSflH/2A71fA7b9Y3bNvwbVL6JGibXYUbeNFaC+IUpY3vkNGzaMNwtTTAvAhQsXTBqyNWvW4LnnnjNLCJzPa1sEREAEREAEUisBLm3r1KmTWeo2depUryEQfR2jFFBfSamc3wmsXLkSOXPmNPnPXRvnw84c7wwa784C+fXXX7ut59oOLaQMBk8FdMEvwODRwPloQyvSWf5GA3sCndtktqpx+jzxU+i0mNL5iAoxPQLphNSlSxfX7mhfBERABERABFIdAU650yD0ww8/mNlHfwxAXvD+oKg2EkVg165dXi2YtIIy1IM74dpOX+XQoaMYYTnq0fJpK58F8wHTRlH59LUVz+UYhJcOUM7WWMZGk4iACIiACIhAaiewatUqvPzyy2ZpXJMmTfw2HCmgfkOphhJKIF++fMZS6Kkep9hZxp3kzZvX3eE4xyIyFkaWm2fik69jTtW7Hfj6bVhZjWKOJWWLjkquIZqKFSuWlCZVVwREQAREQARSnMC5c+dMPGs6/DLsoD9FCqg/aaqtBBG48847vZY/ceKEx3WYTZs2BRU/b5Ilz90oeYeVSz5jVVPM0mfxVCfgg1eB3Dm91UzYOcYa5dpPCqfi27dvj8mTJyesEZUWAREQAREQgSAj0K9fP9D5iFmPuLTMnyIF1J801VaCCHD9J9dnugotn5Tt27d7fOA7dOhglD3XutH7aZCn9BAUrbkQERnym0O5ckQrnlRArZl9vwsdlxhCir8WZ8yY4ZcF2n7vpBoUAREQAREQAR8JML87nXhff/11t74YPjbjsVgyfBV7vJZOiEAcAg0aNDABbZlNiNZDpt/kMYZOKlWqVJzy9gFmSmKsT1dJmz4PilSbg5vKDreUwOjHu0q56Cn3O6INoa5VtC8CIiACIiACIuBE4OzZs8ahljOVffr0cTrjv015wfuPpVpKBAGGSRoyZAj++usvh7Vz7dq15hcXTf50RPIkNWrUME5Kbdq0wZ49exCVoSLyV/oC6TNHOqrQyei5HkD6eJ70v//+GwsXLjQB7StUqACm3rQzKzka04YIiIAIiIAIhAGBwYMH4+jRo1i8eHGyzejF87UcBpQ1xBQjwExGtGRS0bMDx9ud4TT23LlzcfjwYbf53O1ytJIy9eWxK42Rs9QbSJM2eo1K1PXzeHNQRrRuGP8jzqD3H330kQluf/XqVZOXnlPpDOGUJUsW+1J6FQEREAEREIGQJ0Cfhvfeew/Dhw9HmTJlkm28ns1LyXZJNSwC0QQeffRREzvTVfnkWcYBZbaj999/3yuu9g8+iiWbWyJXmTEO5fPy2c3YvaI6xv+vuVmX6a2BUaNG4d133zX56BlMngoolU+uQ61du7bpg7f6OicCIiACIiACoUKAvgxPPvmkyQTYv3//ZB2WFNBkxavGPRGgdZELnJ1jZ7qWZUYkb2Efft1wDGsPDkSOIpZn0b9yev80S/msicvntoBT+fzzJFRw2b5rHnmW55tw3759mDdvnqfqOi4CIiACIiACIUWAWY5oAR0/fjzSp0+frGOTApqseNW4JwL0cPdljSUV1IMHD8ZpZt5SoOewXMiQrYI5d+PGZRz64wkc2tQZUTcumGNUMOnM5EkYzJ7ZizwJ89SvW7fO02kdFwEREAEREIGQIcBlcS+88AJatWqFe+65J9nHFf8CuWTvgi4QjgR8UT7JhVPxzmWvXAVGWiE2p83m2ejH9+qFf7B/fXtcPvMbDzqEyqW3uGX8dUdLpydh/YwZM3o6reMiIAIiIAIiEDIEJk6caGJ+fv/99wEZkxTQgGDWRVwJ3HzzzV6VP7t87ty5YWc9OngU6PM/YNNW+yxw5dQC7F7TETeunYw5+O/WmTNncNddd8U5bh8oXry4yWBET3x3wrWp9IaXiIAIiIAIiEAoE6D184033sCDDz6IihUrBmSomoIPCGZdxJUALZNPPPGEVwsl6wwYMMCEYvrZWsrZ9qkY5ZPRmfp2AYY8ts+t8sm6DCNBJdOTMMQTf/G5E1o+e/fubRyR3J3XMREQAREQAREIFQKffvqpiTrDsIiBEllAA0Va14lD4NVXX8Xy5cuxdKm1oNNFmN6yb9+++M9//ou3pwETptMxKLpQHiuN5uiBQO3buN/dCuWUG507d0amTJlMAU7bP/PMMxg6dGh0BS//mzRpYhyVuN6FCim93+kJ/8gjj2Ds2LFeauqUCIiACIiACIQGAX7fNW/ePGDWT1KTAhoaz06qHAWVvSVLlhhFb8KECdi7d6+Zli9dujQYm7Ntu+7oYemQK9bHDK+q5XM0ZjBQIG/MsbZt24J54//8809Tv1y5cgmK31m1alUTcJfB8OkRz+vnypUr5gLaEgEREAEREIEQJcDvYX7/jR49OqAjlAIaUNy6mDsCjL957NgxR8D5f/75B8fPFcV91pT74eMxNbrdDzzbzfrV5MZxnVP6t91mTKIxFRKwRYejW2+9NQE1VFQEREAEREAEUj8BJmKJjIwEZwQDKVJAA0lb14pD4JNPPjGpOJ1P5IrsjcnzGlqB5aOPZrOSEb3eD2hc17mUtkVABERABERABJJC4OLFi/j6669Nvndvqa+Tcg1PdaWAeiKj48lOYP/+/ejWzTJp/itpIrKjUKUPkb1Qe/sQbo68jneGRiCysOOQNkRABERABERABPxAYMGCBSb7H73fAy3ygg80cV3PQeDnn392rLXMkO1WlKi7Jpbyeeno53iq7XIpnw5i2hABERABERAB/xGYPXs2SpQogUqVKvmvUR9bkgLqIygV8z8BOvww01GOwp0RWXe1ldWonLnIjeuXcPD3x3BuV2+kwWX/X1gtioAIiIAIiIAIYNGiRWjWrFmKkNAUfIpg10VJ4NZKVZGrzDhkKRgzDX/l/A4cWP8ALp/dYGKEyjFIz4oIiIAIiIAI+J/Anj17wGQvTZs29X/jPrQoBdQHSCrifwL7DgP/+6SSpXzGmP3PHv7GyuXe1Qosf9pcsF27dihcWIs//U9fLYqACIiACIQ7gVWrVmHDhg2oVq1aiqCQApoi2EProleuXMGyZcuwe/dukzudcTXLly9vAru7G+lPq4GBbwFnzkWfjYq6hpM7XsDp3WOQPiINsmTPbcJBfP755+6qxznGfO4bN27E77//bqb0y5QpYzIYOeeQj1NJB0RABERABEQgjAmsW7cON910E4oVK5YiFKSApgj20LnotGnT0L9/fzBvOnPJMowDY2oy1/ucOXOQP39+x2Ct5Z4YNxWYNMNxCDflBv5z/w58OXULfrtwk8lEVL9+fbzwwgsxhbxs/fbbb8aTft++faYPVEaZRjNr1qz44osvULeuYjd5wadTIiACIiACYUpg8+bNKeJ8ZOMOuALKNIdr1641QccrV65sFA67M1u3bjVWNFrQ8uXLZx/Wa5ASmDRpEh5//HG3vaNiWKBAAWzZsgXMTHTspBVE/g1g9aaY4rUqA7cVnoqnevTGmTNnwBSalBkzZmD69OmYO3cuWrRoEVPBZYvPS7169UALrLOcP3/eZEbiuZkzZ+L++60I9hIREAEREAEREAEHgW3btqFRo0aO/UBvBNQL/tChQ+jYsSNWrFgBuv536tTJpD7koMeMGYNRo0Zh/fr1eOyxx8DFsZLgJbB69WqPyid7TUskhQrkinWX0dbKauSsfPbqALSqNhP9+nTBqVOnHMon69AzntKyZUssXrzYbLv+Y+pNTvO7Kp+u5Xr06AG+ySQiIAIiIAIiIALRBPgdzWVzJUuWTDEkAVVAv/zyS6NU9OvXz0yxcq3ewoULwdSLjAlJi9rAgQPx0EMP4bPPPksxKLpw/ATefPPNWNZrTzUuZ+pk5XNPj6OWBZSSIxvw3jCgb5cb1tS9ld4oHhkwYIDbEnyWsmWzGotHzp49C+aZl4iACIiACIiACEQTYPprhkJMqfWf7EVAp+A5XZsmTRrH/T99+jSYBmrnzp3gdLydBopT8Jx+dZaDBw+CC2ZtYT2uObStZfbxlHq1LXH2OsiU6oe365IVuTnfA2/lvZ1bvny5w8rprlzadDlRsPInyFbgPtyINobillI3MKr/FRQpEIXNm7eDymF8QuslLec5c+aMVZTPx7lz/3oxxToTe4drU5npgeNOqnCJANvzR1tJ7Yu7+vZ7wY6v6q5MSh8jP/aPr8EoZMj7HKz3mMzs93Ew8rNnPvh56I/PmeQYYzD3jePlMjWKP59Bu03TsP6JgEWA36uUggULmteU+BdQBTRDhgyOMXJqlY4jzZs3x7fffhtLwciRIweOalKemQAALaZJREFUHz/uKMsNTs3TcmpLxYoVzdRt+vTp7UNB8cq1jMEsVPr9Id6Ux4zZq6Dw7TORIWtpx6Va33UeTz10Bhms22XNuGPv3r0+fUHRk51l7S82u8EjR47Ym/G+coqff/4S+8eGv9rzdzu+KOb+vmZC2qMCGuziz+clOcYa7P2j8uRPBSo5GF64cCE5mvVbm/68x6nhPec3cGrIJwK0gFLoBZ9SElAF1B7kd999B3pPjx492kyj0mvatt6wDK0jmTNntoub13vuuQcrV650HHv++eeNh7WzUus4mQIbtHxSuePNtC25KdANr5c8efKkSX3pD8sEncTo7OMqOYp2R4EK7yJtRCZz6sb1C6hZcg7eGPCAtR9zT2+77TafrGB8Lmgdd/2hQS97rkP1RYoWLWoconwp660M+8Ix8wdSMAqtHFwby8DCwfK+cOXEHy6ZMmWKcz9dy6XUPn9AkmPevHlTqgvxXpfvY97jYBT+UOSPQ75HXD/Dg6W/VDz5GRis/eNnDH9E0onTX8KoIBIRcCbAzxFKSn6WBFwBnTp1KubPn4+3337b8Qaj0rZp0yYHG36JFipUyLHPDX6h5smTx3GMSisVvWBR9myljq/B0icHrH837L7ZfXU9n5D9++67DxMnTnQ4kaVJm9lSPN9BzmLdHc1cObcVZ7Z3w3+HvRmHCQPMM8sRHdI8CTk2adLEhFVyLdO2bVuzTMN+E7met/ezZMmC9u3bx7m+fT4hr/xytRkmpF6gytrPHV/t7UBd29fr2PyCtX/2OIK5fzZDu6/B9GrPVARzH3lvg7l/7BvFn8+g3WYwPSvqS8oSsGdDXZe3BbJXAXVCmjdvHn788Ue89957DuWTg61Rowb++OMPM9VK6yc95GvWrBlIDmF/LU738B4w+oAdDskbFMb+zJ49uymSPksZFK+zMpbyefbglzjwW12UKHwFderUcdsUnYO8WerYDz4r7qR169YoXrx4vB/StHb07NnTXRM6JgIiIAIiIAJhSYBWdv7ISUnreEAV0I8++gg7duwwnvB33nkn+Ddu3DgzXdOrVy8wZE7nzp2Nc8rDDz8clg9FoAfNaWwGfo+MjETDhg3BqXF6xU2ZMiXOukvnvnFam5ELshVoi8i6vyFTjirmdNSNqzj8Z18c+/MRlIzMj19//dXjWk/GB73jjjucm3Vs843BcFye1qdwSv6nn34yyrLr9Dwb4dpRBqQ/fPiwWXbgaFgbIiACIiACIhDmBLjUgzOEKSkBnYL/6quvPI61VatWaNq0qZnS9SW8jseGdMJnAnQEa9asmcPr0q7IaW0qfyNGjDDpLd2ltLxmher8ZHZ+FKk6y66Gqxf34cCGDrh0aqVROunBTkcEd7+wuJ6S3neeHAFo/eQ6Yf5godXc3XQU167wTTRo0CATtot1WI5t00LK/jtnYnJ0VBsiIAIiIAIiEMYEgkEBDagFNL57TUuWlM/4KPnnPKMKMAOCp/AcVOKo/A0fPjzOBY+cADo+cwnf/BSzJvf8sYXY/cvtRvlkBXu9ZJs2bdxO6T/77LMmjJan67MNem4yoxJjjnoS/oIbP3486NHHfPAsf/ToUXCtcZEiRTxV03EREAEREAERCFsCVEDdGYcCCSSoFNBADjzcrzVs2DCPU+M2GyqHXCKxa9cu+xBWbYTJavTH9mgv96ioGzj29yvYt6YZrl+NDutgF6YSu2HDhjiORlx7wil+X0KDsCyjJcQXN5KL7LkmlFkd3E3J233SqwiIgAiIgAiEOwEpoOH+BKTQ+KkYLl261OsaT7trVEKZrYqZNd//P6D7EOD4vyE1r185jn2/tcDx7VZqI0Tncbfr2a/0tPv+++/tXfO6atUqMIqBr0Ll888///S1uMqJgAiIgAiIgAh4IUDjju1I7KVYsp4K6BrQZB2JGveZAD3efQ3LwV9JO/85hideBpb8GnOJK2fWYO/aB3Dt0p6Yg2627Kl851N0DPI29e5c1t5mHYkIiIAIiIAIiEDSCdA4lNIxrTUFn/T7mOpa4ENHxdAXyZ6vNn7c8lQs5bNTaytN3M528SqfdvuujkB0HnLn2GSXd/eaK1cud4d1TAREQAREQAREIIEEgiGhhRTQBN60UCjONZIVKlSIdyg5iz2OQtWW4Oyl6Mw/Waxln28NBIY+AdS/o7ZPVlQqm3fffXesazHua0IsoFSWmQ1JIgIiIAIiIAIikHQCdNZlRsOUFCmgKUnfT9fev38/Pv/8c4wZM8Y492zdujXe9Z1MZerJqpgmbRYUrDwVBW+diDRpM5peli4GfDUOaHlXdKcZiN6XDApUdps3bx5rpIztydSqvqwDZf1OnTq5zYYUq1HtiIAIiIAIiIAI+ETg0KFDJhSiT4WTqZAU0GQCG4hmuZbzwQcfNNbMJ5980sTDfPrpp1GrVi0T5H/fvn0eu3HvvffiP//5T5zz6bOWswLL/4qcRR5xnGvVAPjSUj5LWUqoLcxU9cYbb9i7cV65xpRZjphdiQHhXYWhk+JbAM2YnrSUMp6nRAREQAREQAREIOkEuP7z7NmzYEKZlBQ5IaUk/SRcm57h5cuXB9dxXLlyxdGSvf3LL7+YjEZbtmwBMw65k9dff90oeGPHjjVrQrMX7ICClSYjbbroFJvpIqLw/ONp8HArd7UBZq+iNfO///2vaYdhlah40rLJazIWp6dMRnzwt2/fbqYAGIuMzk7OwnWqjOPJcSg2rDMZbYuACIiACIhA4gns3LnTVGbYwpQUKaApST+R12bGH6YxPX78uMf4mAwET2F6TVoh8+SJCRpvX5YWUsb5vH4jLfLfMhq5S/S2T1lZjXajYqGPLeXzJccxdxtt27YFs1hRUWS80MyZM5t0nmXLlnWbvci5jbx58+LMmTP44IMPTCaj3bt3m9NUXh999FF06dIFmTJZC08lIiACIiACIiACfiFAwxTFk3HKLxfxoRFNwfsAKdiKLFmyBHyA4gvOzn5TwZs4cWKcIdBaWa1aNStpekEUr7UslvJ57sg8/PNLVSyaOwazZs2KU9f1ANdplipVClyLyoxEtMy6S53pWo/7nIbv168fFi1aZCypffr0wbJly4x1VcqnO2I6JgIiIAIiIAKJJ7Bp0yYzO1mgQIHEN+KHmrKA+gFioJv49ttvwTUcvgintv/v//4PdDpyll9//RVpstZHZKWJSJch2hMuOqvRMJzY8ZpVNApnrsIor/fff79z1TjbVHJLly5tpt+5ZpNW1b///jtOOU8HqAzTQss1o/R4f/HFF02WJO5LREAEREAEREAE/EdgzZo1qFq1qv8aTGRLsoAmElxKVqP1k9PwvsqBAwdiFWXVD2dlRs5yMxzK57XLR6x0mk0s5XO4VTZ6+p6VfMlANHLkSNO+HVqJ4R2Yk91XYVpOWju5fpUKKL3r586d62t1lRMBERABERABEfCBAGdOmY2wbt26PpRO3iKygCYv32RpPaFT05wit+XkGeC5UcCqrdUti2X00Qsnf8HB9Q/i2uX9djHHq3Ndx0GXDSqMzlPutM4mxHGI0/DOmZmCIUODyxC1KwIiIAIiIAKpngCVT86M0j8kpUUW0JS+A4m4PkMgJWR6umLFiuYqm7dH4P6ngeXrYi56YtcY7F3dwK3yyVJ0FPIk9MCnJz2diGzve5al0vr4448bK6YvltqOHTvi5ptvNpehE9MDDzyARo0aebqsjouACIiACIiACCSCwJw5c0wM8Dp16iSitn+ryALqX56Jao1KGj3AOXXNsEWRkZGxLIqujbZv3x5vvvlmLKXPtYy9T0tkz549MfU74I0PcuDa9Wiz541rZ3BwU3ecOzzTLur2lYuV6eU+e/bsWFbK1atXgx7wXP/pGkKJU/F0JNqwYQNuv/12LFy40Cilbi9gHaT1c+PGjViwYIEpFwy/zDz1VcdFQAREQAREILUSmDlzJlq2bJngdNjJMV5ZQJODqo9tci3GqFGjjMJJj3Q+FHylAvrZZ595zGbEEEcMURSfFZT51jNkzIUVOx7Aa5YjvK18XjqzyfJyrx6v8slhUJn88ccf0bVrV8eouC60fv36OHjwYBzl01HI2qByunz5ctNPX5ymqlSpAtta69yOtkVABERABERABJJGgNPvjL/90EMPJa0hP9WWAuonkAlthnE6ael79dVXwXicnM5mCCO+cr9bt25mWtp27HFtn57mjRs3jmWVdC7DdZW3VGmDmm334IefY27ztZMzsGdlbVy94LuX+qVLl0DPeyrFtNRSSfTUL+c+cJtORZySv++++9wq1OQwevRolClTxoRyYmBc5qmfPHmya1PaFwEREAEREAERSCSBSZMmoVChQmjatGkiW/BvtRjNxL/tqrV4CNSoUQMMhcR0WO6ECt4///yDAQMGuDttjnEtx4cffmgUVeZ1z5cvH3Lnzo3ChQvjga5TkLbYV9i1P3rKPYPlh1Sv7A84srkHom5c9NimpxO0YA4ZMsSESPLFMcm5HY5l3bp1Zire+Ti3GzRogKFDh2LHjh24cOGC+fvrr7/MsgEGo5eIgAiIgAiIgAgkjQCX+E2fPt18t3J2NBhECmgK3AUGkt+2bVu8azhpPWSIop9//tljL2kp3bp1K1asWIHXXnsN33w7Dz0H78OK7W1x6XK08lm0IPDFaGDN4uc8KrweL+B0gkoo14/4av10qmqm48eMGeN8yCi0XCtKxdOdMH7pO++84+6UjomACIiACIiACPhIgLOmFKbODhaRApoCd+LTTz/1WRGk0sfy3uTw4cNm2nroy++hy6B0+GJetOLJOnfXBmaNB4rkO21SZXprJ75zXNPpi1e7p3aYrtPO3kSv+ffff99TUXOcAerpbJWUa3q9gE6KgAiIgAiIQIgTYNrut99+G4899hhSOvuRM2opoM40ArTN6WhfhWsk165d67V47dq1kfWmFshxy4/IlLO6KZvWurP9uwHvDgVyZIPxsI+IiPDaTnwnqQg6h1uKr7zreXq7nzhxwhymBdgXOXXqlMl570tZlREBERABERABEYhNgLOjnLl0zYgYu1Tg94JjIUDgx52iV0zoFLY3pc+apUeuki8hY6ZHHQ5J1y4fwvSxBVGjUsww2UZSFVC2RoU4scLr22Ph8gJfhAHufS3rS3sqIwIiIAIiIALhQoDGHi5lGzhwoPEPCaZxywKaAnejRIkSCboq86y7k+OngO4vAJcyd3UonxeOL0Xr2z+MpXyyLuOL2tPf7try9VhCHZCc26XymT9/fnOIged9UcSptBYsaC1ilYiACIiACIiACCSIwJNPPmmm3QcNGpSgeoEoLAU0EJRdrsEYXPRa90WY5tJdzK51m4G2TwGrN9qtRKFi0ZV4q/8xjHlziH3Q8UoFlG0lRXLkyOFQdBPTDsMr2bFLs2TJgubNm3sNUE/l85FHHknMpVRHBERABERABMKawCeffIJFixZh/PjxyJo1a9CxkAKaAreEqSZp1XPOf+6uG5x+zpgxo8k45Hz+41lA54HAkejllMhuPVfvvpgGMyfVQfv27ZyLxtq+9957TXuxDiZgh6EbqlatGm+/3TVJ5ZULoJ3lvffeM1ZQ5zzy9nnmu+cbhk5IEhEQAREQAREQAd8J7N27F88884ylE7SPo0P43krylpQCmrx83bZO5eqnn34y6yndKV+sRGWPFkvGx6QSSjlnRSt6eriVUtOK0X79hjmECtbsPL3cG1ne7vEJTfGJ/RXEev379zcKYWIsqYxR2rlz51hdZMxSeroz1ScV1Dx58pg4ptymkk7vfn+sW411Ue2IgAiIgAiIQAgToMMwsyVS15gwYULQjlQKaArdGlpAmcrSk0LI9Zr0fmcud8rWXcD9TwMLV8R0uH0zYPpbVozPaaPADEJsiyEWGOfLneMO118yFAOnvxMiVIYzZ86Mfv36oU6dOiYYvT2V7ms7jGXq7rpsh1mWmB6MMU+ZbWnXrl2YOnWqefP42r7KiYAIiIAIiIAIwGRYXLp0KTgFT+NPsIoU0BS6M1QwmbHIUyYkWkY7dOhgHIdmLQQ6PAPsORjd2UyWQXREP+sh6w08/FA7E9CdWZMY0P3IkSMmviZTcboL8M71pEz/6avwFxSdpvbv3++wxNKszxietIR6W0bAuhzj5s2b4/W+4xpVhpPiHy2hEhEQAREQAREQgYQRWLBgAV555RXj9R4sKTc9jUAKqCcyyXycVj9P1k9emib0HTutnPAD9uN5K4HQ5SvRHSpRBPi/0cB991ivVqagWbNmxfEmZ11aQD15vdGS+eWXX8ar6NHy2aJFC6xfv97hPGRj6dq1q0mtec899xgrLZVGjoeKL39xcf+pp57Cxo0bTZB8u55eRUAEREAEREAE/E9g586dxmn5rrvuwvDh1nq9IBfFAU2hGzR37lycO3fO49XTZyltBZb/Cmu2RjrKNK0HvGZZQrP9O4NOBdSTMOQRlVN6v7kTrrFcvnw57PRc7spwevyLL77w6KnOfPb8tXXy5EmsWrXKrNnkVD2tnpyqpwIrEQEREAEREAERSF4CnE1t06aNMQJRN0gN/hPSEJL3mfDY+tGjRz2ey5b/XhSsPAUR6aPDJqWLsLIadQe6to1d5dChQ7EPuOwxjac38dYH1qMCykxEnB73JnQmYkgliQiIgAiIgAiIQGAJcMaTS/Z2795tDEvxfWcHtneer6YpeM9skvVMuXLlEMcDPk063FRuJIpU+8ahfGZKZ+WCfyOu8snOMa6mtzWYRYpY8/VepGLFio51ne6KsX958+Z1d0rHREAEREAEREAEgoAAHY8XLlyI6dOno0qVKkHQI9+6IAXUN05+L8W1k84m8oiMhVCs5o/IU2qA41rnjy3CSz03omoFx6FYG7169fI4Pc6Cffr0iVXedYcOSe480+1yjNsZR0m2T+pVBERABERABEQgRQnQ4WjSpElmuR1DGqYmkQKaAnfrwIED6N69uyM1ZuY8DVCi3npkyXOn6Q3zrR/fPhz7f2uKtm3uwvHjx932smbNmuYXD086K4oM3cS8r0888YTbevZBhm5avHix2XVer0lHooYNG2LkyJF2Ub2KgAiIgAiIgAgEEQFGoxk2bBief/55E34xiLrmU1e0BtQnTP4rxODqMVPjaSyL5yDkKzvcmkq3Fnpacv3KCRzc+AjOH/vecdFbb70V69atQ6FChRzH7I37778fv/32GyZPnowtW7agaNGiZi1I69at7SJeX2+77Tb89ddfpj693bmes127dm7Tf3ptSCdFQAREQAREQAQCQoCORpx679GjB1577bWAXNPfF5EC6m+iXtqjZZPZCTj1HpUmOwpV/hTZCsQoihdP/YoD69vj2qU9sVo5duyYecjmzJnjds1ntWrVwL/ESvny5ZXyMrHwVE8EREAEREAEAkiAUXSYWZAGKFpBU6toCj6Ad45WzDVr1iBd1iqIrLculvJ5cvcE7FlVP47yye4xaP3KlStNTM0AdleXEgEREAEREAERCCICP/74o0lVTT8SZg50Xn4XRN30qStSQH3C5J9C3333HdLkeBDF66xAhiwlTaM3rp3HgQ0P48ifTwJR/0abd3M5xgydPXu2mzM6JAIiIAIiIAIiEOoEmNKasT4ZZ3vmzJlxEsSktvFrCj5Ad+ziJWD2qnq4qUITxxUvn/sLB9a1w5XzfzmOedq4evWqie/l6byOi4AIiIAIiIAIhCYBzoK2bNkSt99+uzFGMelLapdUq4Ay8Coz8KRPnz4o7gH7Q2HgdtfYnHsOpsXz47LhQkSM8nnmwHQc+qMXoq57zobkOrA9e/bgxIkTrod93qcSy/qu/fO5gWQuyKUG5JiUMSZnF7mG1+5jcl4nsW0zBSvlzJkzQTstQ37M0hWs00Z8j/A+B+szyPtrv4+5HWxCdpTz58/j0iXrV3cQiv1ZffHixSDsXfSSK3bMn89gsI41KG9AEHbq119/RbNmzUzs73nz5nlN4x2E3ffYpVSrgNKRhx7bzNYTDMI3OJXPXLlyxYrv+cNyYIiVy/38v591UTeu4Mhf/XBqz7sJ7nbBggXjzd/urVF+oJFZsCqgdLay76u3caTUOX5xcSlEzpzRGapSqh+erkvFhAxz5MgRNO8L175SOeYv92D54ejaP76HyTFPnjyup4Jmn+/jYO0fFVBmaMuaNavXGMMpCfPChQvmMzBYLUj8jGFaRX/e42Ada0o+B6nl2oxy07RpU5QtWxbz5883n++ppe/x9TPVKqDxDSylz1+9Boz6EPj025ieZIw4gb9XNge93RMqtBhVqlQpodVUXgREQAREQAREIBUSoONykyZNUKpUKSxYsCBojR+JRSsFNLHkvNQ7dAx45nVgvdPSzjusKEnNq65HrxXbkZiJH1pWufhYIgIiIAIiIAIiENoENmzYgMaNGyMyMtKk2eTsZaiJvOD9fEdXbgDaPhWjfKZJA/R+BPjgFaBd20ZOQegTdmE+hAy7IBEBERABERABEQhdAps2bTLf90wss2jRIr8uxwgmalJA/XQ3uPb+09nZ0PPFtDh5JrrR3DmAD4cD/30Y1pqj6GMffPBBotZwMNerRAREQAREQAREIHQJbN68GY0aNTKZDxnzM2/evCE7WCmgfri1VDifGp4BH3+T3fKgjdY0b78F+OYdoO7tsS9Qq1atBMXz5GL+ZcuWoXr16rEb0p4IiIAIiIAIiEDIEGA6bSqf+fPnB5XPfPnyhczY3A1ECqg7Kgk4tmkrcP/TwIoN0bncWbXLvZY19A2ggIdn584778Qvv/yCYsWKeb0Sz9P8Xr9+fa/ldFIEREAEREAERCD1Eti+fbtRPhllhconldBQFzkhJeEOf2YlJhrxgRWXz/J4p2TOdAOv9Y1CiztjlNHoM3H/161bFzVr1sTevXvjnrSOMFQSy9SuXdvteR0UAREQAREQARFI/QQY45uWz0yZMmHx4sVgyMVwECmgibjLjOn54nhg7tKYymWK38CLjx9Dtcq+r9dgiAVPwnh6jP8lEQEREAEREAERCE0CjJtL5ZPf+bR8FilSJDQH6mZUUkDdQPF2aMce4OnXgJ1Ohst7GwEDH7uMyxejsyF5q+98LmPGjM67cbb5a0giAiIgAiIgAiIQegSYzZFxPpl4gL4eJUqUCL1BehmR1oB6geN6as4S4IE+Mcpnekt9f9la//nGs9b0u3dd0rUps8+8rp6UUGaK4XmJCIiACIiACIhAaBFgRq4WLVqYZXjMcMRMR+EmUkB9uONXrlqKppU5s/9I4OLl6ApFCgBfjAYebO5DAx6KDB48GJcvX46TGpNZj5gO8KWXXvJQU4dFQAREQAREQARSI4Fr167hgQcewMaNGzFnzhxUqVIlNQ4jyX2WAhoPwgNHgIf7A9PnxhRsWBP4+m2gYpmYY4nZYnwv/gqqWLGiiQ3KkAvMeESv9+PHj5uc2YlpV3VEQAREQAREQASCk0CPHj1MdqMZM2agXr16wdnJAPRKa0C9QF62BhgwCjh9LrqQZZhE3y5Az/YxgeW9VPfpVObMmc2voMOHDxtTfPHixU34BVpBJSIgAiIgAiIgAqFD4MUXX8SUKVMwefJktGrVKnQGloiRSAF1A+3GDeDtacB7X8SczGelYX1rIFCrcswxf21R2SxUqJD581ebakcEREAEREAERCB4CFDxfPXVVzF06FA89thjwdOxFOqJFFAX8CdOA89aQeSZ092W6rcCowcB+fPYR/QqAiIgAiIgAiIgAr4R+Pnnn9GrVy906tQJr7zyim+VQryUFFCnG7zuT2uK/XXgyPGYg4+1A57pCqSLiDmmLREQAREQAREQARHwhcDu3bvRrl07VKtWDR9++KEvVcKijBTQf2/zj6uAPlZ8z2v/hvLMliU6vFKjOmHxHGiQIiACIiACIiACfiZw8eJFtG3b1oRc/Prrrz2GXvTzZVNFc1JA/71N1StaudutJEb7La/38qWA8UOA4oVSxT1UJ0VABERABERABIKQwBNPPIE///wTnIIvUMCK3yhxEJAC+i+KnNmBcZbS+dV8YHAvIGMGByNtiIAIiIAIiIAIiECCCNDTnY5HkyZNQo0aNRJUNxwKSwF1usu33gzwTyICIiACIiACIiACiSWwefNm9O7dG507d0bPnj0T20xI1wv7YJOzZ89G48aNjWk8R44cKFmyJPr06YNdu3aF9I3X4ERABERABERABPxP4NKlS+jYsSOKFSuGCRMm+P8CIdJi2FpAmQqrdevWWLlyJU6ftmIv/Stnz57FxIkTMX78eLNm44477rBP6VUEREAEREAEREAEvBJ4/vnnsXXrVqNfZMuWzWvZcD4ZlhbQqKgok/5y/vz5sZRP+0G4cuWK2WzUqBEWL15sH9arCIiACIiACIiACHgksGzZMowdOxbDhg0zYZc8FtQJhKUCOm3aNDAuFxVRb0JFtFu3brAVUm9ldU4EREAEREAERCB8CTDkEjMcMd7noEFW9hqJVwJhqYCOGzcOly9f9grGPskp+R9//NHe1asIiIAIiIAIiIAIxCHADEc0bn300UeIiFD2mjiAXA6EnQJ6w0r0vmXLFhcMnndPnTqFFStWeC6gMyIgAiIgAiIgAmFNgLE+33rrLfTv3x+VKlUKaxa+Dj7snJDOnDmToF8mnKY/evSorzxVTgREQAREQAREIMwIFC1aFHQ+GjhwYJiNPPHDDTsFlKGWEiLp06dHZGRkQqqorAiIgAiIgAiIQBgRoG7x0ksvhdGIkz7UsJuCT5s2LerWreszuezZs+Puu+/2ubwKioAIiIAIiIAIiIAIeCcQdgoocdBMnitXLu9krLNUVsuXL4/q1avHW1YFREAEREAEREAEREAEfCMQlgpo/fr10a9fP6+EqHzSYemHH35I0JpRr43qpAiIgAiIgAiIgAiIQHjGAeV9Hzp0KKZMmYLcuXMjS5YsjkchTZo0yJMnD6pUqYL9+/eDU/ASERABERABERABERAB/xEIOyckZ3RdunRBixYtMHPmTJN2kyGXSpcujVatWqFhw4ZIly6s8Tij0rYIiIAIiIAIiIAI+I1A2GtY+fLlw+OPP27+/EZVDYmACIiACIiACIiACHgkEJZrQD3S0AkREAEREAEREAEREIFkJyAFNNkR6wIiIAIiIAIiIAIiIALOBKSAOtPQtgiIgAiIgAiIgAiIQLITkAKa7Ih1AREQAREQAREQAREQAWcCUkCdaWhbBERABERABERABEQg2QlIAU12xLqACIiACIiACIiACIiAMwEpoM40tC0CIiACIiACIiACIpDsBKSAJjtiXUAEREAEREAEREAERMCZQKoNRJ83b1706tXLeSwpun3t2jVcunQJWbNmBdN5BqNcv349qPPaX7hwAWnTpkWmTJmCEZ/pUzAzZN8uXryIzJkzB+19Zh95j4P1PcL38I0bN2Kl5w22hzGYn8GoqCicP38eGTNmRPr06YMNnekP7y+fv2B9Bq9cuQL+ZcuWzW/8Tp8+bTL8+a1BNSQCfiCQxvrAiPJDO2HfxPfff4++fftixYoVoHIsSTiBhx9+GPnz58fYsWMTXlk1sGXLFtx777347LPPUL16dRFJBIEXXngBmzZtwnfffZeI2qpy+fJlVK5cGcOHD0f79u0FJBEEJk+ejPHjx5vnMBHVVUUEUg0BTcGnmluljoqACIiACIiACIhAaBCQAhoa91GjEAEREAEREAEREIFUQyDiJUtSTW+DuKNcyZAjRw7UqVMnaNc+BTE+0zWubStfvjzKli0b7F0N2v5x3V3t2rXNsxi0nQzijvEZLFGihJlGDuJuBnXXyJDPIJfTSBJOgGtUya5WrVoJr6waIpCKCGgNaCq6WeqqCIiACIiACIiACIQCAU3Bh8Jd1BhEQAREQAREQAREIBURSLVhmIKF8cKFC9GwYUOkSxeD8tixY1i7dq2ZyitXrlywdDUo+3Hu3Dls3LgR9erVc/Rv/fr1oDetLZySz5Mnj72rVycC27dvx969e82UJ8Mv2aJn0Cbh/fXkyZPmvVqxYkUUKlTIUXjPnj04cOCAY5+RLW6++WbHvjZiCGzevBmHDh1CtWrVkCtXLscJvod/++03E+6oRo0aWprkIBN7g6G/Vq9ebUL43XbbbY7vEj2DsTlpL/QIaA1oEu7pzJkz8cYbb6Bz586ODw0qTwzHxA/i999/38S0vOWWW5JwldCtyg/eF198Edu2bUPjxo3NQBlP9dFHHwXj1jGsEP9KliyJAgUKhC6IRI6sX79+WLdunYlbydBVkZGRKFKkCPQM+gb022+/xVtvvYWcOXNi+vTpoDLPtYsUHl++fDn++ecf8wzyuWR4IUlsAv379zd8GH92woQJZg0818Jzv1u3biYm6C+//IKffvoJTZs2DdrYm7FHFbi9o0ePonv37kb5XLlyJRYtWoQmTZqYDugZDNx90JVSiADjgEoSRuDq1atRgwcPjrIC4UfdcccdUZYi5WigS5cuURs2bDD7llUgqlWrVlGWJcBxXhvRBHbs2BH14IMPGoYDBgxwYPn777+jLAXUsa8N9wR+//33qEceecRxcvHixVHPPPOM2dcz6MDiccNSKKPatWsXtXPnTlPGCp4e1bJly6gTJ06Y/Y4dO0bt3r3bY32diDLsevfu7UAxYsSIqI8//tjs83XMmDGOc/ystBQsx742oglYP3yirLi9ZofPZIsWLaKsHz1mX8+gnpJQJ6A1oIlQ/G0vz3fffTdWbVpJ9u3b57CU0GqXJUsW7N+/P1Y57QDMejRkyBA89NBDsXBYCiiKFi2KH374AbRQsZwkLoEKFSpg0qRJjhO0GNOirGfQgcTrRkREBKZMmWKs6yxIdlwOwvc2nzlLEQWtU9OmTTPvaa+NhelJzkyMGzfOjP7UqVNmKQ2t8BRak6tWrWq2+Y/bf/75p2NfG9EELCUTTMBB4cwFn7+bbrpJz2A0Hv0PcQJSQBNxg5lmrk2bNo5pd7uJI0eOxEnFyek9fplJYhO49dZbUalSpdgHrT1Ox2/duhVnz541rx06dADXM0piE2A6S3vNJ5+7qVOnwrJ8Qs9gbE7e9pg2l8KwN1SkmjVrhnz58sGyzps1yGvWrDEKAZfUzJ0711tTYX1uwYIFsKzJyJ07t2MtN9eEcireFm4fP37c3tWrC4Fhw4bBmsEw72EaLfQMugDSbkgSiPGcCcnhBXZQtKrwF6yz0CIVzLnNnfsaDNs9e/YE//ghTKEjA62h1nRzMHQv6Pqwa9cuDBw40Ky34/rFw4cP6xlMwF3i8/XKK6/AmuoyFnlW5Zrtr7/+2ihU3C9Tpgw++ugjWFP03JW4EOCaRWspkkkfyRSc5On6WcjPQfsHk0t17VoEXn75ZVjLFPDss88aZ7j69evrGdSTEfIEZAH14y2mp6y1liyWBzetn4ULF/bjVUK7KS5XoFJgS/HixXHw4EF7V69OBP766y9jNXnyySdhrTU2Z/QMOgGKZ5NT7XSiyZ49O1599VVkyJDB1OB0Mr3jbeEzSMWellJJDAEy4WwFhT8Y27Zta7zeuU9LsvPMjz4HSSWuMIKAbRmmA2GDBg0MQz2DcVnpSOgRkALqx3vKUEzMXvHdd9+ZVpctW2asKJyakvhGgMwmTpxoClNBsJxrTJgr32qHTykuS3juuefAqbu77rrLMXA9gw4U8W6QHcOkDRo0yFjs7ApcT8vpUHpy0zI6Z84cw5jLHiQxBPj+JDuun6XQ053WYgoteN9//705x2d1xYoVuP322805/YshwGUekydPNgfIkZ7wZKhnMIaRtkKXgDIhJfHe8oOWoTO4LpRiec4axYBTUPzCYpghpZb0DHnJkiWYN28eRo4caQqdOXPGhLZiDEY6gTA809NPP21Yem4l/M4w5A1DB6VJk8YxeMZK/eabb/QMOoh43qD1mFOeFGeG77zzjnEipIMS39ecOub6RVpIlVoyLk86aXENKD/v6JTUo0cPM+NDbpxW/uOPP8x7l8427du3j9tAmB+h4xtD+XHmh8u3+H3C8FXkqWcwzB+OMBi+FNBkusmcQnEOypxMlwnZZmld4YewrdiH7ECTcWB6BpMGl1PuVBCcnWmS1mJo1iYnWu/sddvOo6QzIdd+OifqcD6v7WgC/Lyjr4CrlV3PoJ6QUCYgBTSU767GJgIiIAIiIAIiIAJBSECLmoLwpqhLIiACIiACIiACIhDKBKSAhvLd1dhEQAREQAREQAREIAgJSAENwpuiLomACIiACIiACIhAKBOQAhrKd1djEwEREAEREAEREIEgJCAFNAhvirokAqmVwJUrVxxxIVPrGNRvERABERCB5CcgBTT5GesKIhAWBJg9qHLlytizZ09YjFeDFAEREAERSDwBKaCJZ6eaIiACTgQYd9ROzeh0WJsiIAIiIAIiEIdAxEuWxDmqAyIgAiLggQDTB7711lt49913wTSLJUqUMNmEevfuDea2Zo5w5lcfM2aMeY2MjHS0NGvWLJPasl69eo5j2hABERABEQg/ArKAht8914hFINEEVq9ejUaNGmHnzp1o27YtqFC2a9fOZLqpUqWKabdSpUooUqQIDh06hNGjR8e6ln7vxsKhHREQAREIWwLKhBS2t14DF4GEE2CuaiqazJlOoQW0Q4cOYG56pk0tVaqUmYYvW7assXRSOT148CCYp37Dhg2oXr069u7di0KFCiX84qohAiIgAiIQMgRkAQ2ZW6mBiEDyEoiKijJKZOPGjR0XypcvHxYvXozy5cs7jtkbzZo1M4rnjBkzzKFPP/0UTZs2lfJpA9KrCIiACIQxASmgYXzzNXQRSAiB8+fP49y5c8iaNatP1dKlS4fOnTtj2rRpuH79Oj7//HN07drVp7oqJAIiIAIiENoEpICG9v3V6ETAbwSyZcuG/Pnzx/J0v3HjBh544AEsXbrUOCLxYrSU2kKFc8WKFaAVlDFC27RpY5/SqwiIgAiIQBgTkAIaxjdfQxeBhBLo2bMn3nzzTSxYsMAolG+//TaWL1+OGjVqmOl2trd27VqcPn3aNF2hQgXUrFkT/fr1Q8eOHc060YReU+VFQAREQARCj4AU0NC7pxqRCCQbgSFDhqBJkyZo0aIFcubMaSybH3/8MbJkyYIcOXKA6z47deqEV155xdGHbt26GY94Tb87kGhDBERABMKegLzgw/4REAARSDiBy5cvg4HnCxQoEKfy2bNnjUIaERFhzn3wwQegpXTTpk1xyuqACIiACIhAeBJIF57D1qhFQASSQoAhl9wpn2yTQegpu3btMmk5R4wYgYEDB5pj+icCIiACIiACJKApeD0HIiACyULgyy+/xN13341atWqhR48eyXINNSoCIiACIpA6CWgKPnXeN/VaBFIFgUuXLiFTpkypoq/qpAiIgAiIQOAISAENHGtdSQREQAREQAREQAREwCKgKXg9BiIgAiIgAiIgAiIgAgElIAU0oLh1MREQAREQAREQAREQASmgegZEQAREQAREQAREQAQCSkAKaEBx62IiIAIiIAIiIAIiIAJSQPUMiIAIiIAIiIAIiIAIBJSAFNCA4tbFREAEREAEREAEREAEpIDqGRABERABERABERABEQgogf8H/wcX3zOeohYAAAAASUVORK5CYII=" /><!-- --></p>
</div>
<div id="相关系数图" class="section level3">
<h3>1.7 相关系数图</h3>
<p>相关系数图可以查看同一组数据中多个连续变量的相关性。</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggcorrplot)</span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Correlation matrix</span></span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(mtcars)</span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>corr <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">cor</span>(mtcars), <span class="dv">1</span>)</span>
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a><span class="fu">ggcorrplot</span>(corr, <span class="at">hc.order =</span> <span class="cn">TRUE</span>, </span>
<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a>           <span class="at">type =</span> <span class="st">&quot;lower&quot;</span>, </span>
<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a>           <span class="at">lab =</span> <span class="cn">TRUE</span>, </span>
<span id="cb11-11"><a href="#cb11-11" aria-hidden="true" tabindex="-1"></a>           <span class="at">lab_size =</span> <span class="dv">3</span>, </span>
<span id="cb11-12"><a href="#cb11-12" aria-hidden="true" tabindex="-1"></a>           <span class="at">method=</span><span class="st">&quot;circle&quot;</span>, </span>
<span id="cb11-13"><a href="#cb11-13" aria-hidden="true" tabindex="-1"></a>           <span class="at">colors =</span> <span class="fu">c</span>(<span class="st">&quot;tomato2&quot;</span>, <span class="st">&quot;white&quot;</span>, <span class="st">&quot;springgreen3&quot;</span>), </span>
<span id="cb11-14"><a href="#cb11-14" aria-hidden="true" tabindex="-1"></a>           <span class="at">title=</span><span class="st">&quot;Correlogram of mtcars&quot;</span>, </span>
<span id="cb11-15"><a href="#cb11-15" aria-hidden="true" tabindex="-1"></a>           <span class="at">ggtheme=</span>theme_bw)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
</div>
<div id="偏差" class="section level2">
<h2>2 偏差</h2>
<div id="发散条形图" class="section level3">
<h3>2.1 发散条形图</h3>
<p>发散条形图是一种可以同时处理负值和正值的条形图。这可以通过<code>geom_bar()</code>来实现。但是<code>geom_bar()</code>的用法可能会让人很困惑。这是因为，它既可以用来制作柱状图，也可以用来制作直方图。默认情况下，<code>geom_bar()</code>中<code>stat</code>参数的默认值为<code>count</code>。这意味着，当您只提供一个连续的
x 变量（而不提供 y
变量）时，它会尝试从数据中生成一个直方图。如果要制作条形图，需要做两件事：</p>
<ul>
<li>设置<code>stat=identity</code>;</li>
<li>在<code>aes()</code>中同时输入 x 和 y，其中 x 是字符或因子型变量，y
是数值型。</li>
</ul>
<p>为了确保得到的是发散条形图，数据需要满足：分类变量的两个类别在连续变量的某个阈值处改变其值。在下面的例子中，<code>mtcars</code>数据集的
mpg 通过计算 z 分数被规范化。那些 mpg 在 0 以上的车辆被标记为绿色，低于
0 的车辆被标记为红色。</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;mtcars&quot;</span>)  <span class="co"># load data</span></span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>mtcars<span class="sc">$</span><span class="st">`</span><span class="at">car name</span><span class="st">`</span> <span class="ot">&lt;-</span> <span class="fu">rownames</span>(mtcars)  <span class="co"># create new column for car names</span></span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>mtcars<span class="sc">$</span>mpg_z <span class="ot">&lt;-</span> <span class="fu">round</span>((mtcars<span class="sc">$</span>mpg <span class="sc">-</span> <span class="fu">mean</span>(mtcars<span class="sc">$</span>mpg))<span class="sc">/</span><span class="fu">sd</span>(mtcars<span class="sc">$</span>mpg), <span class="dv">2</span>)  <span class="co"># compute normalized mpg</span></span>
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>mtcars<span class="sc">$</span>mpg_type <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(mtcars<span class="sc">$</span>mpg_z <span class="sc">&lt;</span> <span class="dv">0</span>, <span class="st">&quot;below&quot;</span>, <span class="st">&quot;above&quot;</span>)  <span class="co"># above / below avg flag</span></span>
<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a>mtcars <span class="ot">&lt;-</span> mtcars[<span class="fu">order</span>(mtcars<span class="sc">$</span>mpg_z), ]  <span class="co"># sort</span></span>
<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a>mtcars<span class="sc">$</span><span class="st">`</span><span class="at">car name</span><span class="st">`</span> <span class="ot">&lt;-</span> <span class="fu">factor</span>(mtcars<span class="sc">$</span><span class="st">`</span><span class="at">car name</span><span class="st">`</span>, <span class="at">levels =</span> mtcars<span class="sc">$</span><span class="st">`</span><span class="at">car name</span><span class="st">`</span>)  <span class="co"># convert to factor to retain sorted order in plot.</span></span>
<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a><span class="co"># Diverging Barcharts</span></span>
<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(mtcars, <span class="fu">aes</span>(<span class="at">x=</span><span class="st">`</span><span class="at">car name</span><span class="st">`</span>, <span class="at">y=</span>mpg_z, <span class="at">label=</span>mpg_z)) <span class="sc">+</span> </span>
<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="at">stat=</span><span class="st">&#39;identity&#39;</span>, <span class="fu">aes</span>(<span class="at">fill=</span>mpg_type), <span class="at">width=</span>.<span class="dv">5</span>)  <span class="sc">+</span></span>
<span id="cb12-11"><a href="#cb12-11" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">name=</span><span class="st">&quot;Mileage&quot;</span>, </span>
<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a>                    <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;Above Average&quot;</span>, <span class="st">&quot;Below Average&quot;</span>), </span>
<span id="cb12-13"><a href="#cb12-13" aria-hidden="true" tabindex="-1"></a>                    <span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;above&quot;</span><span class="ot">=</span><span class="st">&quot;#00ba38&quot;</span>, <span class="st">&quot;below&quot;</span><span class="ot">=</span><span class="st">&quot;#f8766d&quot;</span>)) <span class="sc">+</span> </span>
<span id="cb12-14"><a href="#cb12-14" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">subtitle=</span><span class="st">&quot;Normalised mileage from &#39;mtcars&#39;&quot;</span>, </span>
<span id="cb12-15"><a href="#cb12-15" aria-hidden="true" tabindex="-1"></a>       <span class="at">title=</span> <span class="st">&quot;Diverging Bars&quot;</span>) <span class="sc">+</span> </span>
<span id="cb12-16"><a href="#cb12-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_flip</span>()</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="带标记的发散型棒棒糖图" class="section level3">
<h3>2.2 带标记的发散型棒棒糖图</h3>
<p>这个图形是发散条形图的一个变体。</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(mtcars, <span class="fu">aes</span>(<span class="at">x=</span><span class="st">`</span><span class="at">car name</span><span class="st">`</span>, <span class="at">y=</span>mpg_z, <span class="at">label=</span>mpg_z)) <span class="sc">+</span> </span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">stat=</span><span class="st">&#39;identity&#39;</span>, <span class="at">fill=</span><span class="st">&quot;black&quot;</span>, <span class="at">size=</span><span class="dv">6</span>)  <span class="sc">+</span></span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="fu">aes</span>(<span class="at">y =</span> <span class="dv">0</span>, </span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a>                   <span class="at">x =</span> <span class="st">`</span><span class="at">car name</span><span class="st">`</span>, </span>
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a>                   <span class="at">yend =</span> mpg_z, </span>
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a>                   <span class="at">xend =</span> <span class="st">`</span><span class="at">car name</span><span class="st">`</span>), </span>
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a>               <span class="at">color =</span> <span class="st">&quot;black&quot;</span>) <span class="sc">+</span></span>
<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_text</span>(<span class="at">color=</span><span class="st">&quot;white&quot;</span>, <span class="at">size=</span><span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Diverging Lollipop Chart&quot;</span>, </span>
<span id="cb13-10"><a href="#cb13-10" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;Normalized mileage from &#39;mtcars&#39;: Lollipop&quot;</span>) <span class="sc">+</span> </span>
<span id="cb13-11"><a href="#cb13-11" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylim</span>(<span class="sc">-</span><span class="fl">2.5</span>, <span class="fl">2.5</span>) <span class="sc">+</span></span>
<span id="cb13-12"><a href="#cb13-12" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_flip</span>()</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="发散性点图" class="section level3">
<h3>2.3 发散性点图</h3>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(mtcars, <span class="fu">aes</span>(<span class="at">x=</span><span class="st">`</span><span class="at">car name</span><span class="st">`</span>, <span class="at">y=</span>mpg_z, <span class="at">label=</span>mpg_z)) <span class="sc">+</span> </span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">stat=</span><span class="st">&#39;identity&#39;</span>, <span class="fu">aes</span>(<span class="at">col=</span>mpg_type), <span class="at">size=</span><span class="dv">6</span>)  <span class="sc">+</span></span>
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">name=</span><span class="st">&quot;Mileage&quot;</span>, </span>
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a>                     <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;Above Average&quot;</span>, <span class="st">&quot;Below Average&quot;</span>), </span>
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a>                     <span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;above&quot;</span><span class="ot">=</span><span class="st">&quot;#00ba38&quot;</span>, <span class="st">&quot;below&quot;</span><span class="ot">=</span><span class="st">&quot;#f8766d&quot;</span>)) <span class="sc">+</span> </span>
<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_text</span>(<span class="at">color=</span><span class="st">&quot;white&quot;</span>, <span class="at">size=</span><span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Diverging Dot Plot&quot;</span>, </span>
<span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;Normalized mileage from &#39;mtcars&#39;: Dotplot&quot;</span>) <span class="sc">+</span> </span>
<span id="cb14-9"><a href="#cb14-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylim</span>(<span class="sc">-</span><span class="fl">2.5</span>, <span class="fl">2.5</span>) <span class="sc">+</span></span>
<span id="cb14-10"><a href="#cb14-10" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_flip</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="面积图" class="section level3">
<h3>2.4 面积图</h3>
<p>面积图通常用于可视化特定指标（如股票回报率）与基线的对比情况。</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(quantmod)</span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;economics&quot;</span>, <span class="at">package =</span> <span class="st">&quot;ggplot2&quot;</span>)</span>
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a><span class="co"># 计算利润</span></span>
<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a>economics<span class="sc">$</span>returns_perc <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="fu">diff</span>(economics<span class="sc">$</span>psavert)<span class="sc">/</span>economics<span class="sc">$</span>psavert[<span class="sc">-</span><span class="fu">length</span>(economics<span class="sc">$</span>psavert)])</span>
<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a><span class="co"># 为轴刻度创建断点和标签</span></span>
<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a>brks <span class="ot">&lt;-</span> economics<span class="sc">$</span>date[<span class="fu">seq</span>(<span class="dv">1</span>, <span class="fu">length</span>(economics<span class="sc">$</span>date), <span class="dv">12</span>)]</span>
<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a>lbls <span class="ot">&lt;-</span> lubridate<span class="sc">::</span><span class="fu">year</span>(economics<span class="sc">$</span>date[<span class="fu">seq</span>(<span class="dv">1</span>, <span class="fu">length</span>(economics<span class="sc">$</span>date), <span class="dv">12</span>)])</span>
<span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
<span id="cb15-12"><a href="#cb15-12" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(economics[<span class="dv">1</span><span class="sc">:</span><span class="dv">100</span>, ], <span class="fu">aes</span>(date, returns_perc)) <span class="sc">+</span> </span>
<span id="cb15-13"><a href="#cb15-13" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_area</span>() <span class="sc">+</span> </span>
<span id="cb15-14"><a href="#cb15-14" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>(<span class="at">breaks=</span>brks, <span class="at">labels=</span>lbls) <span class="sc">+</span> </span>
<span id="cb15-15"><a href="#cb15-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle=</span><span class="dv">90</span>)) <span class="sc">+</span> </span>
<span id="cb15-16"><a href="#cb15-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Area Chart&quot;</span>, </span>
<span id="cb15-17"><a href="#cb15-17" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle =</span> <span class="st">&quot;Perc Returns for Personal Savings&quot;</span>, </span>
<span id="cb15-18"><a href="#cb15-18" aria-hidden="true" tabindex="-1"></a>       <span class="at">y=</span><span class="st">&quot;% Returns for Personal savings&quot;</span>, </span>
<span id="cb15-19"><a href="#cb15-19" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;Source: economics&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
</div>
<div id="排序" class="section level2">
<h2>3 排序</h2>
<div id="有序条形图" class="section level3">
<h3>3.1 有序条形图</h3>
<p>有序条形图是按 Y 轴变量排序的条形图，X 轴变量必须转换为因子型。</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="co"># 准备数据：按厂商分组计算平均城市里程。</span></span>
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>cty_mpg <span class="ot">&lt;-</span> <span class="fu">aggregate</span>(mpg<span class="sc">$</span>cty, <span class="at">by=</span><span class="fu">list</span>(mpg<span class="sc">$</span>manufacturer), <span class="at">FUN=</span>mean)  <span class="co"># aggregate</span></span>
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a><span class="fu">colnames</span>(cty_mpg) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;make&quot;</span>, <span class="st">&quot;mileage&quot;</span>)  <span class="co"># change column names</span></span>
<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a>cty_mpg <span class="ot">&lt;-</span> cty_mpg[<span class="fu">order</span>(cty_mpg<span class="sc">$</span>mileage), ]  <span class="co"># sort</span></span>
<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a>cty_mpg<span class="sc">$</span>make <span class="ot">&lt;-</span> <span class="fu">factor</span>(cty_mpg<span class="sc">$</span>make, <span class="at">levels =</span> cty_mpg<span class="sc">$</span>make)  <span class="co"># to retain the order in plot.</span></span>
<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(cty_mpg, <span class="dv">4</span>)</span></code></pre></div>
<pre><code>##          make  mileage
## 9     lincoln 11.33333
## 8  land rover 11.50000
## 3       dodge 13.13514
## 10    mercury 13.25000</code></pre>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(cty_mpg, <span class="fu">aes</span>(<span class="at">x=</span>make, <span class="at">y=</span>mileage)) <span class="sc">+</span> </span>
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="at">stat=</span><span class="st">&quot;identity&quot;</span>, <span class="at">width=</span>.<span class="dv">5</span>, <span class="at">fill=</span><span class="st">&quot;tomato3&quot;</span>) <span class="sc">+</span> </span>
<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Ordered Bar Chart&quot;</span>, </span>
<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;Make Vs Avg. Mileage&quot;</span>, </span>
<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;source: mpg&quot;</span>) <span class="sc">+</span> </span>
<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle=</span><span class="dv">65</span>, <span class="at">vjust=</span><span class="fl">0.6</span>))</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="棒棒糖图" class="section level3">
<h3>3.2 棒棒糖图</h3>
<p>棒棒糖图传达的信息与柱状图相同。通过将粗条转变为细线，减少了杂乱，使图形看起来更美观。</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(cty_mpg, <span class="fu">aes</span>(<span class="at">x=</span>make, <span class="at">y=</span>mileage)) <span class="sc">+</span> </span>
<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size=</span><span class="dv">3</span>) <span class="sc">+</span> </span>
<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="fu">aes</span>(<span class="at">x=</span>make, </span>
<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a>                   <span class="at">xend=</span>make, </span>
<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a>                   <span class="at">y=</span><span class="dv">0</span>, </span>
<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a>                   <span class="at">yend=</span>mileage)) <span class="sc">+</span> </span>
<span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Lollipop Chart&quot;</span>, </span>
<span id="cb19-8"><a href="#cb19-8" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;Make Vs Avg. Mileage&quot;</span>, </span>
<span id="cb19-9"><a href="#cb19-9" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;source: mpg&quot;</span>) <span class="sc">+</span> </span>
<span id="cb19-10"><a href="#cb19-10" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle=</span><span class="dv">65</span>, <span class="at">vjust=</span><span class="fl">0.6</span>))</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="点图" class="section level3">
<h3>3.3 点图</h3>
<p>点图非常类似于棒棒糖图，但没有线条，并且各标签都在水平位置上。它更强调项目的顺序与实际值有关。</p>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(cty_mpg, <span class="fu">aes</span>(<span class="at">x=</span>make, <span class="at">y=</span>mileage)) <span class="sc">+</span> </span>
<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">col=</span><span class="st">&quot;tomato2&quot;</span>, <span class="at">size=</span><span class="dv">3</span>) <span class="sc">+</span>   <span class="co"># Draw points</span></span>
<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_segment</span>(<span class="fu">aes</span>(<span class="at">x=</span>make, </span>
<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a>                   <span class="at">xend=</span>make, </span>
<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a>                   <span class="at">y=</span><span class="fu">min</span>(mileage), </span>
<span id="cb20-6"><a href="#cb20-6" aria-hidden="true" tabindex="-1"></a>                   <span class="at">yend=</span><span class="fu">max</span>(mileage)), </span>
<span id="cb20-7"><a href="#cb20-7" aria-hidden="true" tabindex="-1"></a>               <span class="at">linetype=</span><span class="st">&quot;dashed&quot;</span>, </span>
<span id="cb20-8"><a href="#cb20-8" aria-hidden="true" tabindex="-1"></a>               <span class="at">size=</span><span class="fl">0.1</span>) <span class="sc">+</span>   <span class="co"># Draw dashed lines</span></span>
<span id="cb20-9"><a href="#cb20-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Dot Plot&quot;</span>, </span>
<span id="cb20-10"><a href="#cb20-10" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;Make Vs Avg. Mileage&quot;</span>, </span>
<span id="cb20-11"><a href="#cb20-11" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;source: mpg&quot;</span>) <span class="sc">+</span>  </span>
<span id="cb20-12"><a href="#cb20-12" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_flip</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="坡度图" class="section level3">
<h3>3.4 坡度图</h3>
<p>坡度图是比较两点之间差异的绝佳方法。目前，还没有内置函数来构建这个图形。下面的代码可以作为一个示例框架，告诉您如何绘制这个图形。</p>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(scales)</span>
<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a><span class="co"># 数据准备</span></span>
<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">read.csv</span>(<span class="st">&quot;https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv&quot;</span>)</span>
<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a><span class="fu">colnames</span>(df) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;continent&quot;</span>, <span class="st">&quot;1952&quot;</span>, <span class="st">&quot;1957&quot;</span>)</span>
<span id="cb21-6"><a href="#cb21-6" aria-hidden="true" tabindex="-1"></a>left_label <span class="ot">&lt;-</span> <span class="fu">paste</span>(df<span class="sc">$</span>continent, <span class="fu">round</span>(df<span class="sc">$</span><span class="st">&#39;1952&#39;</span>),<span class="at">sep=</span><span class="st">&quot;, &quot;</span>)</span>
<span id="cb21-7"><a href="#cb21-7" aria-hidden="true" tabindex="-1"></a>right_label <span class="ot">&lt;-</span> <span class="fu">paste</span>(df<span class="sc">$</span>continent, <span class="fu">round</span>(df<span class="sc">$</span><span class="st">&#39;1957&#39;</span>),<span class="at">sep=</span><span class="st">&quot;, &quot;</span>)</span>
<span id="cb21-8"><a href="#cb21-8" aria-hidden="true" tabindex="-1"></a>df<span class="sc">$</span>class <span class="ot">&lt;-</span> <span class="fu">ifelse</span>((df<span class="sc">$</span><span class="st">&#39;1957&#39;</span> <span class="sc">-</span> df<span class="sc">$</span><span class="st">&#39;1952&#39;</span>) <span class="sc">&lt;</span> <span class="dv">0</span>, <span class="st">&quot;red&quot;</span>, <span class="st">&quot;green&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>p <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(df) <span class="sc">+</span> <span class="fu">geom_segment</span>(<span class="fu">aes</span>(<span class="at">x=</span><span class="dv">1</span>, <span class="at">xend=</span><span class="dv">2</span>, <span class="at">y=</span><span class="st">`</span><span class="at">1952</span><span class="st">`</span>, <span class="at">yend=</span><span class="st">`</span><span class="at">1957</span><span class="st">`</span>, <span class="at">col=</span>class), <span class="at">size=</span>.<span class="dv">75</span>, <span class="at">show.legend=</span>F) <span class="sc">+</span> </span>
<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a>                  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">1</span>, <span class="at">linetype=</span><span class="st">&quot;dashed&quot;</span>, <span class="at">size=</span>.<span class="dv">1</span>) <span class="sc">+</span> </span>
<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a>                  <span class="fu">geom_vline</span>(<span class="at">xintercept=</span><span class="dv">2</span>, <span class="at">linetype=</span><span class="st">&quot;dashed&quot;</span>, <span class="at">size=</span>.<span class="dv">1</span>) <span class="sc">+</span></span>
<span id="cb22-5"><a href="#cb22-5" aria-hidden="true" tabindex="-1"></a>                  <span class="fu">scale_color_manual</span>(<span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;Up&quot;</span>, <span class="st">&quot;Down&quot;</span>), </span>
<span id="cb22-6"><a href="#cb22-6" aria-hidden="true" tabindex="-1"></a>                                     <span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;green&quot;</span><span class="ot">=</span><span class="st">&quot;#00ba38&quot;</span>, <span class="st">&quot;red&quot;</span><span class="ot">=</span><span class="st">&quot;#f8766d&quot;</span>)) <span class="sc">+</span>  <span class="co"># color of lines</span></span>
<span id="cb22-7"><a href="#cb22-7" aria-hidden="true" tabindex="-1"></a>                  <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;&quot;</span>, <span class="at">y=</span><span class="st">&quot;Mean GdpPerCap&quot;</span>) <span class="sc">+</span>  <span class="co"># Axis labels</span></span>
<span id="cb22-8"><a href="#cb22-8" aria-hidden="true" tabindex="-1"></a>                  <span class="fu">xlim</span>(.<span class="dv">5</span>, <span class="fl">2.5</span>) <span class="sc">+</span> <span class="fu">ylim</span>(<span class="dv">0</span>,(<span class="fl">1.1</span><span class="sc">*</span>(<span class="fu">max</span>(df<span class="sc">$</span><span class="st">`</span><span class="at">1952</span><span class="st">`</span>, df<span class="sc">$</span><span class="st">`</span><span class="at">1957</span><span class="st">`</span>))))  <span class="co"># X and Y axis limits</span></span>
<span id="cb22-9"><a href="#cb22-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb22-10"><a href="#cb22-10" aria-hidden="true" tabindex="-1"></a><span class="co"># Add texts</span></span>
<span id="cb22-11"><a href="#cb22-11" aria-hidden="true" tabindex="-1"></a>p <span class="ot">&lt;-</span> p <span class="sc">+</span> <span class="fu">geom_text</span>(<span class="at">label=</span>left_label, <span class="at">y=</span>df<span class="sc">$</span><span class="st">`</span><span class="at">1952</span><span class="st">`</span>, <span class="at">x=</span><span class="fu">rep</span>(<span class="dv">1</span>, <span class="fu">NROW</span>(df)), <span class="at">hjust=</span><span class="fl">1.1</span>, <span class="at">size=</span><span class="fl">3.5</span>)</span>
<span id="cb22-12"><a href="#cb22-12" aria-hidden="true" tabindex="-1"></a>p <span class="ot">&lt;-</span> p <span class="sc">+</span> <span class="fu">geom_text</span>(<span class="at">label=</span>right_label, <span class="at">y=</span>df<span class="sc">$</span><span class="st">`</span><span class="at">1957</span><span class="st">`</span>, <span class="at">x=</span><span class="fu">rep</span>(<span class="dv">2</span>, <span class="fu">NROW</span>(df)), <span class="at">hjust=</span><span class="sc">-</span><span class="fl">0.1</span>, <span class="at">size=</span><span class="fl">3.5</span>)</span>
<span id="cb22-13"><a href="#cb22-13" aria-hidden="true" tabindex="-1"></a>p <span class="ot">&lt;-</span> p <span class="sc">+</span> <span class="fu">geom_text</span>(<span class="at">label=</span><span class="st">&quot;Time 1&quot;</span>, <span class="at">x=</span><span class="dv">1</span>, <span class="at">y=</span><span class="fl">1.1</span><span class="sc">*</span>(<span class="fu">max</span>(df<span class="sc">$</span><span class="st">`</span><span class="at">1952</span><span class="st">`</span>, df<span class="sc">$</span><span class="st">`</span><span class="at">1957</span><span class="st">`</span>)), <span class="at">hjust=</span><span class="fl">1.2</span>, <span class="at">size=</span><span class="dv">5</span>)  <span class="co"># title</span></span>
<span id="cb22-14"><a href="#cb22-14" aria-hidden="true" tabindex="-1"></a>p <span class="ot">&lt;-</span> p <span class="sc">+</span> <span class="fu">geom_text</span>(<span class="at">label=</span><span class="st">&quot;Time 2&quot;</span>, <span class="at">x=</span><span class="dv">2</span>, <span class="at">y=</span><span class="fl">1.1</span><span class="sc">*</span>(<span class="fu">max</span>(df<span class="sc">$</span><span class="st">`</span><span class="at">1952</span><span class="st">`</span>, df<span class="sc">$</span><span class="st">`</span><span class="at">1957</span><span class="st">`</span>)), <span class="at">hjust=</span><span class="sc">-</span><span class="fl">0.1</span>, <span class="at">size=</span><span class="dv">5</span>)  <span class="co"># title</span></span>
<span id="cb22-15"><a href="#cb22-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb22-16"><a href="#cb22-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb22-17"><a href="#cb22-17" aria-hidden="true" tabindex="-1"></a><span class="co">#设置主题</span></span>
<span id="cb22-18"><a href="#cb22-18" aria-hidden="true" tabindex="-1"></a>p <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">panel.background =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb22-19"><a href="#cb22-19" aria-hidden="true" tabindex="-1"></a>           <span class="at">panel.grid =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb22-20"><a href="#cb22-20" aria-hidden="true" tabindex="-1"></a>           <span class="at">axis.ticks =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb22-21"><a href="#cb22-21" aria-hidden="true" tabindex="-1"></a>           <span class="at">axis.text.x =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb22-22"><a href="#cb22-22" aria-hidden="true" tabindex="-1"></a>           <span class="at">panel.border =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb22-23"><a href="#cb22-23" aria-hidden="true" tabindex="-1"></a>           <span class="at">plot.margin =</span> <span class="fu">unit</span>(<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">1</span>,<span class="dv">2</span>), <span class="st">&quot;cm&quot;</span>))</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="哑铃图" class="section level3">
<h3>3.5 哑铃图</h3>
<p>哑铃图可以：1)比较两个时间点之间的相对位置（比如增长和下降）；2)
比较两类之间的距离。为了得到哑铃的正确顺序，Y
变量应该是一个因子，因子变量的水平应该与它在图中出现的顺序相同。</p>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>health <span class="ot">&lt;-</span> <span class="fu">read.csv</span>(<span class="st">&quot;https://raw.githubusercontent.com/selva86/datasets/master/health.csv&quot;</span>)</span>
<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>health<span class="sc">$</span>Area <span class="ot">&lt;-</span> <span class="fu">factor</span>(health<span class="sc">$</span>Area, <span class="at">levels=</span><span class="fu">as.character</span>(health<span class="sc">$</span>Area))  <span class="co"># for right ordering of the dumbells</span></span>
<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a><span class="co"># health$Area &lt;- factor(health$Area)</span></span>
<span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a>gg <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(health, <span class="fu">aes</span>(<span class="at">x=</span>pct_2013, <span class="at">xend=</span>pct_2014, <span class="at">y=</span>Area, <span class="at">group=</span>Area)) <span class="sc">+</span> </span>
<span id="cb23-6"><a href="#cb23-6" aria-hidden="true" tabindex="-1"></a>        <span class="fu">geom_dumbbell</span>(<span class="at">color=</span><span class="st">&quot;#a3c4dc&quot;</span>, </span>
<span id="cb23-7"><a href="#cb23-7" aria-hidden="true" tabindex="-1"></a>                      <span class="at">size=</span><span class="fl">0.75</span>, </span>
<span id="cb23-8"><a href="#cb23-8" aria-hidden="true" tabindex="-1"></a>                      <span class="at">point.colour.l=</span><span class="st">&quot;#0e668b&quot;</span>) <span class="sc">+</span> </span>
<span id="cb23-9"><a href="#cb23-9" aria-hidden="true" tabindex="-1"></a>        <span class="fu">scale_x_continuous</span>(<span class="at">label=</span>percent) <span class="sc">+</span> </span>
<span id="cb23-10"><a href="#cb23-10" aria-hidden="true" tabindex="-1"></a>        <span class="fu">labs</span>(<span class="at">x=</span><span class="cn">NULL</span>, </span>
<span id="cb23-11"><a href="#cb23-11" aria-hidden="true" tabindex="-1"></a>             <span class="at">y=</span><span class="cn">NULL</span>, </span>
<span id="cb23-12"><a href="#cb23-12" aria-hidden="true" tabindex="-1"></a>             <span class="at">title=</span><span class="st">&quot;Dumbbell Chart&quot;</span>, </span>
<span id="cb23-13"><a href="#cb23-13" aria-hidden="true" tabindex="-1"></a>             <span class="at">subtitle=</span><span class="st">&quot;Pct Change: 2013 vs 2014&quot;</span>, </span>
<span id="cb23-14"><a href="#cb23-14" aria-hidden="true" tabindex="-1"></a>             <span class="at">caption=</span><span class="st">&quot;Source: https://github.com/hrbrmstr/ggalt&quot;</span>) <span class="sc">+</span></span>
<span id="cb23-15"><a href="#cb23-15" aria-hidden="true" tabindex="-1"></a>        <span class="fu">theme</span>(<span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">hjust=</span><span class="fl">0.5</span>, <span class="at">face=</span><span class="st">&quot;bold&quot;</span>),</span>
<span id="cb23-16"><a href="#cb23-16" aria-hidden="true" tabindex="-1"></a>              <span class="at">plot.background=</span><span class="fu">element_rect</span>(<span class="at">fill=</span><span class="st">&quot;#f7f7f7&quot;</span>),</span>
<span id="cb23-17"><a href="#cb23-17" aria-hidden="true" tabindex="-1"></a>              <span class="at">panel.background=</span><span class="fu">element_rect</span>(<span class="at">fill=</span><span class="st">&quot;#f7f7f7&quot;</span>),</span>
<span id="cb23-18"><a href="#cb23-18" aria-hidden="true" tabindex="-1"></a>              <span class="at">panel.grid.minor=</span><span class="fu">element_blank</span>(),</span>
<span id="cb23-19"><a href="#cb23-19" aria-hidden="true" tabindex="-1"></a>              <span class="at">panel.grid.major.y=</span><span class="fu">element_blank</span>(),</span>
<span id="cb23-20"><a href="#cb23-20" aria-hidden="true" tabindex="-1"></a>              <span class="at">panel.grid.major.x=</span><span class="fu">element_line</span>(),</span>
<span id="cb23-21"><a href="#cb23-21" aria-hidden="true" tabindex="-1"></a>              <span class="at">axis.ticks=</span><span class="fu">element_blank</span>(),</span>
<span id="cb23-22"><a href="#cb23-22" aria-hidden="true" tabindex="-1"></a>              <span class="at">legend.position=</span><span class="st">&quot;top&quot;</span>,</span>
<span id="cb23-23"><a href="#cb23-23" aria-hidden="true" tabindex="-1"></a>              <span class="at">panel.border=</span><span class="fu">element_blank</span>())</span>
<span id="cb23-24"><a href="#cb23-24" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-25"><a href="#cb23-25" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(gg)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
</div>
<div id="分布" class="section level2">
<h2>4 分布</h2>
<div id="直方图" class="section level3">
<h3>4.1 直方图</h3>
<div id="连续变量的直方图" class="section level4">
<h4>4.4.1 连续变量的直方图</h4>
<p>连续变量的直方图可以使用<code>geom_bar()</code>或<code>geom_histogram()</code>来完成。当使用<code>geom_histogram()</code>时，可以使用<code>bins</code>参数来控制分箱的数量。也可以使用<code>binwidth</code>设置每个分箱覆盖的范围。<code>binwidth</code>的值与建立直方图的连续变量在同一个尺度上。</p>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="co"># 连续（数值）变量的直方图</span></span>
<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(displ)) <span class="sc">+</span> <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">&quot;Spectral&quot;</span>)</span>
<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a>g <span class="sc">+</span> <span class="fu">geom_histogram</span>(<span class="fu">aes</span>(<span class="at">fill=</span>class), </span>
<span id="cb24-5"><a href="#cb24-5" aria-hidden="true" tabindex="-1"></a>                   <span class="at">binwidth =</span> .<span class="dv">1</span>, </span>
<span id="cb24-6"><a href="#cb24-6" aria-hidden="true" tabindex="-1"></a>                   <span class="at">col=</span><span class="st">&quot;black&quot;</span>, </span>
<span id="cb24-7"><a href="#cb24-7" aria-hidden="true" tabindex="-1"></a>                   <span class="at">size=</span>.<span class="dv">1</span>) <span class="sc">+</span>  <span class="co"># change binwidth</span></span>
<span id="cb24-8"><a href="#cb24-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Histogram with Auto Binning&quot;</span>, </span>
<span id="cb24-9"><a href="#cb24-9" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;Engine Displacement across Vehicle Classes&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>g <span class="sc">+</span> <span class="fu">geom_histogram</span>(<span class="fu">aes</span>(<span class="at">fill=</span>class), </span>
<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>                   <span class="at">bins=</span><span class="dv">5</span>, </span>
<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a>                   <span class="at">col=</span><span class="st">&quot;black&quot;</span>, </span>
<span id="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a>                   <span class="at">size=</span>.<span class="dv">1</span>) <span class="sc">+</span>   <span class="co"># change number of bins</span></span>
<span id="cb25-5"><a href="#cb25-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Histogram with Fixed Bins&quot;</span>, </span>
<span id="cb25-6"><a href="#cb25-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;Engine Displacement across Vehicle Classes&quot;</span>) </span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="分类变量的直方图" class="section level4">
<h4>4.1.2分类变量的直方图</h4>
<p>分类变量的直方图实际上是根据每个类别的频率绘制的条形图。</p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_classic</span>())</span>
<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Histogram on a Categorical variable</span></span>
<span id="cb26-5"><a href="#cb26-5" aria-hidden="true" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(manufacturer))</span>
<span id="cb26-6"><a href="#cb26-6" aria-hidden="true" tabindex="-1"></a>g <span class="sc">+</span> <span class="fu">geom_bar</span>(<span class="fu">aes</span>(<span class="at">fill=</span>class), <span class="at">width =</span> <span class="fl">0.5</span>) <span class="sc">+</span> </span>
<span id="cb26-7"><a href="#cb26-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle=</span><span class="dv">65</span>, <span class="at">vjust=</span><span class="fl">0.6</span>)) <span class="sc">+</span> </span>
<span id="cb26-8"><a href="#cb26-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Histogram on Categorical Variable&quot;</span>, </span>
<span id="cb26-9"><a href="#cb26-9" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;Manufacturer across Vehicle Classes&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
</div>
<div id="密度图" class="section level3">
<h3>4.2 密度图</h3>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(cty))</span>
<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>g <span class="sc">+</span> <span class="fu">geom_density</span>(<span class="fu">aes</span>(<span class="at">fill=</span><span class="fu">factor</span>(cyl)), <span class="at">alpha=</span><span class="fl">0.8</span>) <span class="sc">+</span> </span>
<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a>    <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Density plot&quot;</span>, </span>
<span id="cb27-4"><a href="#cb27-4" aria-hidden="true" tabindex="-1"></a>         <span class="at">subtitle=</span><span class="st">&quot;City Mileage Grouped by Number of cylinders&quot;</span>,</span>
<span id="cb27-5"><a href="#cb27-5" aria-hidden="true" tabindex="-1"></a>         <span class="at">caption=</span><span class="st">&quot;Source: mpg&quot;</span>,</span>
<span id="cb27-6"><a href="#cb27-6" aria-hidden="true" tabindex="-1"></a>         <span class="at">x=</span><span class="st">&quot;City Mileage&quot;</span>,</span>
<span id="cb27-7"><a href="#cb27-7" aria-hidden="true" tabindex="-1"></a>         <span class="at">fill=</span><span class="st">&quot;# Cylinders&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="箱线图" class="section level3">
<h3>4.3 箱线图</h3>
<p>箱形图是研究数据分布的一个有用工具。它还可以显示多个组内的分布，以及中值、范围和异常值。箱子内的黑线表示中位数。箱顶是
75% 分位数，箱底是 25% 分位数。线的端点（又称晶须）距离为1.5*IQR，其中
IQR （四分位差）是 25% 到 75%
分位数之间距离。须外的点通常被认为是极值点。设置<code>varwidth=T</code>将调整盒子的宽度，使其与观察的数量成比例。</p>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(class, cty))</span>
<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>g <span class="sc">+</span> <span class="fu">geom_boxplot</span>(<span class="at">varwidth=</span>T, <span class="at">fill=</span><span class="st">&quot;plum&quot;</span>) <span class="sc">+</span> </span>
<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a>    <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Box plot&quot;</span>, </span>
<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a>         <span class="at">subtitle=</span><span class="st">&quot;City Mileage grouped by Class of vehicle&quot;</span>,</span>
<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a>         <span class="at">caption=</span><span class="st">&quot;Source: mpg&quot;</span>,</span>
<span id="cb28-6"><a href="#cb28-6" aria-hidden="true" tabindex="-1"></a>         <span class="at">x=</span><span class="st">&quot;Class of Vehicle&quot;</span>,</span>
<span id="cb28-7"><a href="#cb28-7" aria-hidden="true" tabindex="-1"></a>         <span class="at">y=</span><span class="st">&quot;City Mileage&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(class, cty))</span>
<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>g <span class="sc">+</span> <span class="fu">geom_boxplot</span>(<span class="fu">aes</span>(<span class="at">fill=</span><span class="fu">factor</span>(cyl))) <span class="sc">+</span> </span>
<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle=</span><span class="dv">65</span>, <span class="at">vjust=</span><span class="fl">0.6</span>)) <span class="sc">+</span> </span>
<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Box plot&quot;</span>, </span>
<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;City Mileage grouped by Class of vehicle&quot;</span>,</span>
<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;Source: mpg&quot;</span>,</span>
<span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">x=</span><span class="st">&quot;Class of Vehicle&quot;</span>,</span>
<span id="cb29-8"><a href="#cb29-8" aria-hidden="true" tabindex="-1"></a>       <span class="at">y=</span><span class="st">&quot;City Mileage&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="点图箱线图" class="section level3">
<h3>4.4 点图+箱线图</h3>
<p>在箱线图的基础上，添加点图可以提供更清晰的信息。这些点交错排列，每个点代表一次观测。</p>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(manufacturer, cty))</span>
<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>g <span class="sc">+</span> <span class="fu">geom_boxplot</span>() <span class="sc">+</span> </span>
<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_dotplot</span>(<span class="at">binaxis=</span><span class="st">&#39;y&#39;</span>, </span>
<span id="cb30-4"><a href="#cb30-4" aria-hidden="true" tabindex="-1"></a>               <span class="at">stackdir=</span><span class="st">&#39;center&#39;</span>, </span>
<span id="cb30-5"><a href="#cb30-5" aria-hidden="true" tabindex="-1"></a>               <span class="at">dotsize =</span> .<span class="dv">5</span>, </span>
<span id="cb30-6"><a href="#cb30-6" aria-hidden="true" tabindex="-1"></a>               <span class="at">fill=</span><span class="st">&quot;red&quot;</span>) <span class="sc">+</span></span>
<span id="cb30-7"><a href="#cb30-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle=</span><span class="dv">65</span>, <span class="at">vjust=</span><span class="fl">0.6</span>)) <span class="sc">+</span> </span>
<span id="cb30-8"><a href="#cb30-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Box plot + Dot plot&quot;</span>, </span>
<span id="cb30-9"><a href="#cb30-9" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;City Mileage vs Class: Each dot represents 1 row in source data&quot;</span>,</span>
<span id="cb30-10"><a href="#cb30-10" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;Source: mpg&quot;</span>,</span>
<span id="cb30-11"><a href="#cb30-11" aria-hidden="true" tabindex="-1"></a>       <span class="at">x=</span><span class="st">&quot;Class of Vehicle&quot;</span>,</span>
<span id="cb30-12"><a href="#cb30-12" aria-hidden="true" tabindex="-1"></a>       <span class="at">y=</span><span class="st">&quot;City Mileage&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="塔夫特箱线图" class="section level3">
<h3>4.5 塔夫特箱线图</h3>
<p>这是一个简化版的箱线图。</p>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggthemes)</span>
<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_tufte</span>())  <span class="co"># from ggthemes</span></span>
<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-5"><a href="#cb31-5" aria-hidden="true" tabindex="-1"></a><span class="co"># plot</span></span>
<span id="cb31-6"><a href="#cb31-6" aria-hidden="true" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(manufacturer, cty))</span>
<span id="cb31-7"><a href="#cb31-7" aria-hidden="true" tabindex="-1"></a>g <span class="sc">+</span> <span class="fu">geom_tufteboxplot</span>() <span class="sc">+</span> </span>
<span id="cb31-8"><a href="#cb31-8" aria-hidden="true" tabindex="-1"></a>      <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle=</span><span class="dv">65</span>, <span class="at">vjust=</span><span class="fl">0.6</span>)) <span class="sc">+</span> </span>
<span id="cb31-9"><a href="#cb31-9" aria-hidden="true" tabindex="-1"></a>      <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Tufte Styled Boxplot&quot;</span>, </span>
<span id="cb31-10"><a href="#cb31-10" aria-hidden="true" tabindex="-1"></a>           <span class="at">subtitle=</span><span class="st">&quot;City Mileage grouped by Class of vehicle&quot;</span>,</span>
<span id="cb31-11"><a href="#cb31-11" aria-hidden="true" tabindex="-1"></a>           <span class="at">caption=</span><span class="st">&quot;Source: mpg&quot;</span>,</span>
<span id="cb31-12"><a href="#cb31-12" aria-hidden="true" tabindex="-1"></a>           <span class="at">x=</span><span class="st">&quot;Class of Vehicle&quot;</span>,</span>
<span id="cb31-13"><a href="#cb31-13" aria-hidden="true" tabindex="-1"></a>           <span class="at">y=</span><span class="st">&quot;City Mileage&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="小提琴图" class="section level3">
<h3>4.6 小提琴图</h3>
<p>小提琴图类似于箱线图，但显示了组内的密度。</p>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(class, cty))</span>
<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>g <span class="sc">+</span> <span class="fu">geom_violin</span>() <span class="sc">+</span> </span>
<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Violin plot&quot;</span>, </span>
<span id="cb32-4"><a href="#cb32-4" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;City Mileage vs Class of vehicle&quot;</span>,</span>
<span id="cb32-5"><a href="#cb32-5" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;Source: mpg&quot;</span>,</span>
<span id="cb32-6"><a href="#cb32-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">x=</span><span class="st">&quot;Class of Vehicle&quot;</span>,</span>
<span id="cb32-7"><a href="#cb32-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">y=</span><span class="st">&quot;City Mileage&quot;</span>)</span></code></pre></div>
<p><img 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dRT5YMPPkicsx5gKPzcc8+Vr7/+2hotqJ9ai3Guf//+pi64JsRlugDRCR5DhgxJEp9Ij3Yecsghph/s8mdq89ChQ+WYY46RI444Qq655hpZsWJFophs6ohMn3zyiZkagPpcfPHFopZYiD9Y1zGF4JxzzpFPP/00cQ0cuOGFdJn6d/Xq1XLllVfKYYcdJpdeemninkXeRx99VJ5//nkcmvDFF1+YNLDwP/zww7J48WI9VeYz03XLZGAECZAACZAACZSAQOAEKIZuv/32WyOs7HhBgP797383pzDn8LnnnjPHGP6tUaOGbL/99tKhQwepWbOmXHvttWaYVcuBwPnmm2+kdu3aGlXm829/+5sRWNYTsJTpNTXeem2N008Inssuu0zatm0rXbt2NfNUTzjhBD1tBAgsqK1btzYiacOGDUZ0QYggYDoAxAhEa7du3UxZV199tUyaNMmcX79+veyxxx5GtMLiW7FiRendu3eChUn05z9VqlSRWbNmmWtq/NatWw2bypUrG7HTs2dPqVq1qhG85cqVM9f88ccfNXnSJ6YgVKhQwQjNpBPxLw0aNJAnnnhCdt5559RTGdsMYTVw4EDzAoA++N///icHH3ywKQeCLJs6QujiRQIiEEPtEyZMkF133dVwaNasmbRs2VLQ9i5dukjDhg2T6pqJFxJn6l+kwbxYTFXAC8Jbb71l+gfxCHi5GD9+vDnGiwxeQtBHmNYAgYw62wU317XLxzgSIAESIAESKDqBWMBC3PoWi0OK4TNTiFuRYvXr108k23PPPWO33Xab+b5x48ZYfMg89tRTTyXOxwVhLG59Sny3HsTnj8bioiW2ZMmSWFxgxeJC1ZyeNm2aKSduGYvFLaSxuJXPxFuvPXjw4Fhc+CbSx4d1Y/HpA4ni48P6pk0fffSRibvxxhtjcaGWOB8XKuYacYukiYtPBYj93//9X+L81KlTTf74Qh8Td8cdd8TiYju2atWqRBrEbbvttrG4uEzE6UF8fmasTp06sbi4N1FjxoyJxQVnbOXKlTEcxwVsLC7yzDnkj89njX3//feaPenziiuuiMUFXFKc3RfUDf0YF3/mdKY2x6dcxOLWykT9Z86caeqBOmdbR9QvPk0gqVqIi79EmLi49TbWtGnTpPPWL068cD849S/uO7Q7/sKQKDJuwTZxcUFs4uIvF7G49dUc77fffrHzzz8/kXbp0qWx+PSS2A8//BDL9r5KFMIDEiABEiABEigxgcDNAdU5hTrMnqtih0UPVksMdWJ4GUOtGIK3zim1K7tRo0ZmfimGvTt16mTmCcIiBYuZmwArZbzPBdZMWHI1wCKLcz169DCrpBctWmSsXbA0Il1cuJiFO3FRKHHxZSyimhcW3biA0q9miLxx48ZmqoFG/vzzz8aauWDBAoGVzxowrH3eeeeZlddoC6zGiMOc1r333tss9oK19tBDD5XDDz/cTAmANdMuoH/iIl1gRc1m/iQ8DKRrM66DfsG127RpY9qOKQ9YcAbrbjZ1BD9ME4gL8qTqo7x33303KS7dFydesE479S/qirDPPvskioe1GmHhwoVJ1neUA4u8deEbLLKw/iKMGzfOfOIfN/dVIjEPSIAESIAESKDEBAI3BI8h9LglT+JWP1t0EE8PPPCA7bnUSAjPuNVRIM6Q76ijjpK4xTQ1WZnvGALWeaAYfs8kWq0FxC1/RjRhKBcCTf8gpnRoGvVv1aqVEZAQcxie32abbUwxOu8xVUTWq1cvcRmILMxH1bLxifSYN4nj1AAxjiF/iHEM97/66qtGlCMdhDHmfGLeLM7FLa9maoCuZk8tq3PnziYdRF5qgKDCvEcVUNbzTm1GOiwqgxgDawxPY+gaYg48s6kj0iPgPrIG3FOY9+kmOPFy07+4hnWaB6Y1IICPNWAqxbp168zUEWu83bHb69rlZRwJkAAJkAAJFJ1A/EcvcCG+KCfWokWLGIYjrQHf4z/ssbgVz0Rbh8ERYR2C13xx0ReLix8zPB1fRa/RZT51CB4nli9fHou7foq98MILJl98LqZJ72YIPm5lSxp6Rkbkjy98iWFYOS7yYnFrakyH2/V8XLDG7rrrrhiG4zG8Hvd/ilMmxK3BsbiIiekQfHyleSwuOGNxQaVJYvE5qbH4HNcYpgrYhbhFNoZrYPjZmhdDyuCoAcPEceEXi3sa0Kikz7Vr18biVuLY6aefnhSPLygnfoPH4qLdTA/AMYbgM7UZeePunBLD9fge94FppgagrGzqCNbgGxfjKCYRMNStbco0BI9M6Xhl6l8dgo+L8MS14y6ZDBed1mAdgo+/eMQeeeSRRFr0Ke5vTNewDsFnum6iAB6QAAmQAAmQgA8IlDWHFV0CZ39BrASGb0ksxIHrJVgJYZGE+yUEDOfaBQwbx+fOmaFePQ8raFzYCaxQWJHsJsBKigUwWPSBlexYdOM2wJLXrl07ic95NFZc9Yd51VVXGasYhpRRT0wxwDA2rI4XXXSRWX2NtDiP62IBFVaaY9EPHPHH76VEFbB6G0Pt4ABrKIa24TUArNJNFYiLc7MoCouZsOreaimNi0l55ZVXjIUQq+tRJlxS2QVYI9EXsKbCqoupBpjaAD+oGErG4h/tJ82fqc1Ih2kIWM0PN1hoK/hgEZbWw20d0Vdw4YX6YbgcUxuU44knnqhVyviZjlem/s1YcEoCeB2AP1ksTPrtt9/MKnhYgPfaa6+klF5fN6lwfiEBEiABEiABrwn4QATnVAUs3MFCEiwWiTMxf/vuu2/S4iRY3KyLkOKuioz1y7rABNbDuACKxcWRYz2sFlAkfOaZZ8w14+58EvncWECROD6vM4a6wmoJayaO426WEuXEpwPE4ivgY3EhGov7o4zFh61j8aHnWHxFu0kDKx4seM2bN4/BQobzu+yyS+z2229PlIFFTjgHqyaswrDuoa1OAYuLwBJsrQEW4rhoNnUFK1jgdMGMNZ31OC5CY3FBH4sLUlMmLMZxMRWLTyEwyeJDxiZeFyFlajMsh2AAJmAGLg8++GDiktnUEVbauIsrs5gMfHA/4N7Q4MYCirTpeDn1b7YWUFiHsQALC9+wMCzuNSEWF86mqlYLKCKcrmsy8B8SIAESIAES8AmBcqiH16K22OXBh2N8FXfSvLp0dYAVCZYz9dkJ6ynmR8LCBldNxQxwA4S6wOJpF2DFxGIiWAitIb7qW3bfffdEPlhK0YZbb73VOHq3psX8ViycSmf5tKbNdAyrIxYmYQ6k24D2oX/iIs/sWJUpX7o2az60FW1CeTp3Us/hM5s6wqdnfNqGKctahlfHmfo3m+ugrpjnibmqmYKX1810LZ4nARIgARIggVwIhEKA5tJwDCVjhTmG8yFa3n///VyKKUkerJSHgIbDcnzCwXvcGmZ2Y4IQZSABEiABEiABEiABPxMI5BxQL4AuW7bMuMKBo2/MAQxSgNjEqnesAsdqeThShwshis8g9SLrSgIkQAIkQALRJRBZCyi6HIt6shlO9tttguFouA6Kz6/0W9VYHxIgARIgARIgARJISyDSAjQtFZ4gARIgARIgARIgARIoGIHIDsEXjCgLJgESIAESIAESIAEScCRAAeqIhydJgARIgARIgARIgAS8JkAB6jVRlkcCJEACJEACJEACJOBIgALUEQ9PkgAJkAAJkAAJkAAJeE0g2cO516UXoLy5c+eabSkLUHTRi9Q9AOwcqhe9MiG6ILkWpjPJtbBcUTqfBd4y5j3rLU8tjVyVhLefYeOKrbnjO0SmhRS4VfDYez2+dWXaBgXpBPZ5x97k8e0gg1Rt39cV7rXwH7latWq+r2uQKohdxPCHhwqDdwTgSi2+RavZVKJ8eQ5KeUUWz4D169cbV3upu8l5dY2olgOucP/nxQ57UWVo1248BxDC8NsFN5EzZsyQ+DbXdk01cYGzgMb3AZeBAwembVCQTmAbSPwHxjaiDN4RWLlypfGP2rBhQ+8KZUmybt06Wbt2rTRp0oQ0PCSAbVZXrFhhtsylUPIOLIQ9tlrG9sFh+EH3jkz+JS1evNi8MNWqVSv/wlhCgsDy5cvNKEj9+vUTcUE9wDbcF154oWP1+brtiIcnSYAESIAESIAESIAEvCZAAeo1UZZHAiRAAiRAAiRAAiTgSIAC1BEPT5IACZAACZAACZAACXhNgALUa6IsjwRIgARIgARIgARIwJEABagjHp4kARIgARIgARIgARLwmgAFqNdEWR4JkAAJkAAJkAAJkIAjAQpQRzw8SQIkQAIkQAIkQAIk4DUBClCvibI8EiABEiABEiABEiABRwIUoI54eJIESIAESIAESIAESMBrAhSgXhNleSRAAiRAAiRAAiRAAo4EirYVJ7ZE++qrr6Rdu3bSqlWrRKXmzZsnCxcuTHxv0KCBtG3bNvGdByRAAiRAAiRAAiRAAuEiUBQBOmrUKHn55Zdl//33l+eee05OPvlk6d27tyH55JNPCvaVxX69CB07dqQANST4DwmQAAmQAAmQAAmEk0DBBWgsFpP33ntPbrnlFmnZsqV06tRJHn744YQAnT59ugwaNEiaN28eTsJsFQmQAAmQAAmQAAmQQBKBggvQcuXKyUMPPWQu+vvvv8uECROMEEXEhg0bZMWKFbJ06VIZO3as9OzZU5o2bZpUQT2vkVu2bBGUE4YAcb5169bQtMcvfQKu+AvLfeIXrvi/h0Cu3vbI5s2bTYH4xH3L4A0BPFsRwvSb4Q2Z/EvB1LltttlGqlatmn9hLCFBQP//h+EZ6+Z5VnABqmTXrFkj/fr1k40bN8qQIUNM9MyZM2XTpk3yxRdfSLVq1eSSSy6R/v37J6yjSPTSSy/JAw88oMXILrvsIsuWLUt8D/oBHo6//vpr0Jvhy/qH6T7xE2ByLUxvrFy5sjAFR7zUtWvXCv4YvCGAUcuzzjpL2rdvL48//rg3hbKUJAJheMZCgKrRIqlxli9FE6C1a9eWN954w1g6zznnHBk5cqTstNNO8vrrr0u9evVMldq0aSNPPfVUkgA95phjZM8990xUGdbU+vXrJ74H+WD16tVSsWJFqVGjRpCb4bu6r1u3zliWcc8xeEcAL4/4C8v/P+/I5FfSb7/9Jrhn69SpIxUqVMivMOZOEIAFdNWqVVKzZk2pXLlyIp4H+RGoVKmSKQCGEz4L8mOZmhuGOowa16pVK/VU4L5DgGZ6nhVcgOLhitXv++yzjwHbo0cPefTRR2Xq1KlmKB7AVYBiHigWJOHBUb78Hx6imjRpIvjTAMFWpUoV/RroT9xo6KCwtMcvnYGpHRjKIFdve0SHhcjVW65aGkQSnm8M3hBQ60uYfjO8IZNfKSpAUQqfBfmxTM0N3QNdEAau0DZoi1MouB9Q3Kww03/++eemHtOmTZPly5dLixYtBBbAgQMHGqsKBANWy0Ogqvh0qjjPkQAJkAAJkAAJlIZAJnFRmlrxqkEiUPDXbdykmNsJETp48GCj7LEivlGjRubvuOOOk7PPPltgrsWQ6W233RYkfqwrCZAACZAACZAACZBAlgQKLkBRn912280IUMxzwnwcazjttNPk1FNPNXOgOGfPSobHJEACJEACJEACJBBOAgUfgrdiSxWfeg5D7hSfSoOfJEACJEACJOBPAhx692e/BLFWRRWgQQTEOpMACZAACZAACSQToBBN5sFv2ROgAM2eGXOQAAmQAAmQQCQJUHhGstsL0mgK0IJgZaEkQAIkQAIkEF4CFKLh7dtitYwCtFikeR0SIAESIAESCDgBFZ76GfDmsPolJEABWkL4vDQJkAAJkAAJBIkAhWeQesvfdaUA9Xf/sHYkQAIkQAIk4BsCFKC+6YrAV4QCNPBdyAaQAAmQAAmQQHEJUIgWl3cYr0YBGsZeZZtIgARIgARIoAAEVHjqZwEuwSIjQoACNCIdzWaSAAmQAAmQgFcEKEC9IhndcihAo9v3bDkJkAAJkAAJZEVAhad+ZpWZiUnAQoAC1AKDhyRAAiRAAiRAAukJqPDUz/QpeYYEnAlQgDrz4VkSIAESIAESIIE/CVB48lbwigAFqFckWQ4JkAAJkAAJhJyAClD9DHlz2bwCEqAALSBcFk0CJEACJEACYSJA4Rmm3ixtWyhAS8ufVycBEiABEiCBwBBQAaqfgak4K+o7AhSgvusSVogESIAESIAE/E2AAtTf/ROE2lGABqGXWEcSIAESIAESIAESCBEBCtAQdSabQgIkQAIkQAKFJKCWT/0s5LVYdrgJUICGu38j17qtW7fKyJEj5dVXX5WNGzdGrv1sMAmQAAkUkgCFZyHpRqvsitFqLlsbdgKzZ8+WO++80zSzffv20qNHj7A3me0jARIggaIToBAtOvLQXZAW0NB1abQbFIvFog2ArScBEiCBAhJQ4amfBbwUiw45AQrQkHdw1JrHh2LUepztJQESIAESCCIBCtAg9hrrnJZAhQoVEuesx4lIHpAACZAACZAACZScAAVoybuAFfCSgFV0li/P29tLtiyLBEiABDjKxHvAKwL8hfaKJMvxBQGrAK1YkWvsfNEprAQJkEDoCFCIhq5Li96gwP1Cb9myRVauXFl0UIW4INqyadOm0LSnEIyyLXPdunWJLHDDFJZ7JdGoEh5s3rzZXJ1Mve0EPAcQVq9eLbTae8dWFySuX79efv31V+8KjnhJa9asMQTg8o7PAm9vhjA9Y9EWfbaloxQ4AQoLV506ddK1J1Dxy5Ytk8qVK0vt2rUDVW8/VxYPRQ1169YNzb2ibSrlJ37IIfDD8v+vlCyt18ZL6KpVq6RWrVpCq72VTH7H+oJfvXp1qVq1an6FMXeCQM2aNRPHfBYkUHhyAEEPy3IYuEKAWkck7QAFToCiEWGxEuBGw19Y2mN3gxU7DoJeQ5UqVchWYXjwqUNuvF89gGkpQnniU48tp3mYIwG1gPIZmyPANNn0OUCuaQB5EB2G54CbNnAOqAc3C4vwDwGrAK1UqZJ/KsaakAAJkAAJkAAJJAhQgCZQ8CAMBKyi03ochraxDSRAAiRQagJqWdbPUteH1w8uAQrQ4PYda25DAGZ/nXditYbaJGUUCZAACZBAlgRUeOpnltmZnAQSBChAEyh4EBYCavmkAA1Lj7IdJEACfiNAAeq3HglefShAg9dnrHEGAipA9TNDcp4mARIgARJwSYDC0yUoJstIgAI0IyImCBoBtXzqZ9Dqz/qSAAmQgF8JqADVT7/Wk/XyPwEKUP/3EWuYJQH1pUgLaJbgmJwESIAEMhBQ4amfGZLzNAmkJUABmhYNTwSVAIQnFiO58UMW1Day3iRAAiRQCgK62QcFaCnoh+uaFKDh6k+2Jk4AApTWT94KJEACJOA9ARWeKkS9vwJLjAoBCtCo9HSE2okheB2Gj1Cz2VQSIAESKDgBCtCCI47MBShAI9PV0WkoBWh0+potJQESKC4BtXyqEC3u1Xm1MBGgAA1Tb7IthgAc0aszeiIhARIgARLwjsCWLVtMYSpEvSuZJUWNAAVo1Ho8Au2F+OQQfAQ6mk0kARIoOgG1fFKAFh196C5IARq6LmWDaAHlPUACJEAChSFAC2hhuEaxVArQKPZ6yNsM6yeH4EPeyWweCZBASQio5VM/S1IJXjQUBChAQ9GNbISVAH2AWmnwmARIgAS8I6DCUy2h3pXMkqJGgAI0aj0egfZSgEagk9lEEiCBkhBQ4alCtCSV4EVDQYACNBTdyEZYCZQrV467IFmB8JgESIAEPCJAAeoRSBYjFKC8CUJHABZQiFAGEiABEiABbwmoANVPb0tnaVEiQAEapd6OSFshPilAI9LZbCYJkEBRCajw1M+iXpwXCxUBCtBQdScbAwIUoLwPSIAESKAwBFR46mdhrsJSo0CAAjQKvRyxNlKARqzD2VwSIIGiEVDhuXnz5qJdkxcKJwEK0HD2K1tFAiRAAiRAAp4TUAGqn55fgAVGhgAFaGS6mg0lARIgARIggfwIqOWTAjQ/jswtXAXPm4AESIAESIAESMAdARWg9APqjhdTpSdAC2h6NjxDAiRAAiRAAiRgIaACFFHWY0sSHpKAKwJFE6BLliyRd955R2bPnl2mYtOmTZP33ntPli1bVuYcI0ggWwKxWEzwx0ACJEACJOAtAavotB57exWWFgUCRRGgo0aNkiuuuEIWLFgg119/vbz11lsJtvfff7/cc8898vXXX8uAAQNk3rx5iXM8IIFcCGBoiAI0F3LMQwIkQALOBKxzPylAnVnxrDOBis6n8z8LIQDr5i233CItW7aUTp06ycMPPyy9e/eWOXPmyLhx4+SVV14xWycOHz5cXnjhBbnmmmvyvzBLiDQBzk+KdPez8SRAAgUiYBWd1uMCXY7FhphAwQUofDI+9NBDBuHvv/8uEyZMMEIUEbNmzZKOHTsm9u3u3LlzknUUaV566SUZOnQoDk2oUaOGLF26VL8G+hP/efE2+dtvvwW6HX6rPLhCgIblPvELXxX15OptjyjXFStWcAcvD9HqKMiaNWtk3bp1HpYc7aJWr16dALB48WLB7zqDNwTUuhyGZ6zqGycyBRegenE8BPr16ycbN26UIUOGmOhFixZJnTp1NInUrl1bli9fnviOgyZNmkiXLl0ScZgvWrly5cT3IB/gZqtQoUJo2uOXvsAPD/7Ccp/4hSt+aCCWyNXbHsFzYNOmTVKpUqXEy7i3V4hmaXgG4PemYsWK5i+aFLxvtQp7lAwDE58H3jH+9ddfQ8O0fPnyGV+oiyZAIS7feOMNGTt2rJxzzjkycuRII75U8aMLoZirVauW1Jvdu3cX/Gno379/kmjV+CB+wvKJ/7xWER7EdvitznhAQiiRq7c9AysSRCi5essV4hN/tWrVolDyEC1+WyBAq1evXuZ3xcPLRK4oGE004PeazwOlkf8nNBBEfRiYoi0QoU7B+axTTpfnILImTpxoUgNsjx49pG7dujJ16lRp1KiRYNhJA45h8WQggXwIQHzqsGY+5TAvCZAACZBAMgEICw3WY43jJwm4JVBwAYphpccff1w+//xzUycMoWOYvUWLFrLXXnvJd999J/PnzzfWzzfffDNpuN1tI5iOBKwEYPmgALUS4TEJkAAJeEPAOueTAtQbplEtpeBD8LB6XnLJJUaEDh48WKpUqWJWxMP6iXD22WfLmWeeKfXr1zei9OSTT45qX7DdHhGAAOWD0SOYLIYESIAELASsz1arGLUk4SEJuCJQcAGKWuy2225GgGIOWc2aNZMqduSRR8phhx1m5kClnktKyC8k4JIABCj+GEiABEiABLwlYBWdVjHq7VVYWhQIFEWAKsh0AhPD9PhjIAEvCOChyAejFyRZBgmQAAkkE8CzFZ4F8GkVo8mp+I0EMhMo+BzQzFVgChLwlgAejLSAesuUpZEACZAACEB0qrcaClDeE/kQoADNhx7z+pIA38x92S2sFAmQQAgIWAUonrUMJJArAQrQXMkxn28J4AHJN3Pfdg8rRgIkEGACeLbCtyoCn7MB7kgfVJ0C1AedwCp4SwAPRTij5zC8t1xZGgmQAAnA6kkByvvACwIUoF5QZBm+IqBv5dgEgYEESIAESMA7Ani+cg6odzyjXBIFaJR7P6RtV+GpnyFtJptFAiRAAkUnAAFao0YNc1192S96JXjBUBCgAA1FN7IRVgL6UKQAtVLhMQmQAAnkT4AW0PwZsoQ/CFCA8k4IHQEVnvoZugayQSRAAiRQIgJ4rnIOaIngh+yyFKAh69CoN8cqOjdt2hR1HGw/CZAACXhKABbQqlWrSvny5bkK3lOy0SuMAjR6fR7qFlsFqPU41I1m40iABEigSAQgQHX3Qp3uVKRL8zIhI0ABGrIOjXpzrKLTehx1Lmw/CZAACXhBgALUC4osAwQoQHkfhIqA9Y2cAjRUXcvGkAAJ+IAAnrGVK1c2VlDr89YHVWMVAkaAAjRgHcbqOhOwik7rsXMuniUBEiABEnBDwGoB5TPWDTGmSUeAAjQdGcYHkoD1gci380B2IStNAiTgYwJ4xmIOKKygfMb6uKMCUDUK0AB0EqvonoD1gWgVo+5LYEoSIAESIIF0BPCM5RB8OjqMz4YABWg2tJjW9wSsotMqRn1fcVaQBEiABHxOYOvWrbJly5bEKnjr89bnVWf1fEiAAtSHncIq5U7AKjo3b96ce0HMSQIkQAIkkERAn6+wgOKPAjQJD79kSYACNEtgTO5vAlbRaT32d61ZOxIgARLwPwEVnJwD6v++CkINKUCD0Euso2sCVtFpPXZdABOSAAmQAAnYErBaQCFCVZDaJmYkCWQgQAGaARBPB4uAVXTqwzJYLWBtSYAESMCfBFRwYvi9SpUqFKD+7KbA1IoCNDBdxYq6IWAVoNZjN3mZhgRIgARIID0BqwDlHND0nHjGHQEKUHecmCogBLBCU4P1WOP4SQIkQAIkkBuBVAHKUabcODLXHwQoQHknhIqA1eppPQ5VI9kYEiABEigBAasAxRzQTZs2laAWvGRYCFCAhqUn2Q5DwCo6rcfEQwIkQAIkkB8BqwDlEHx+LJlbpGLQIMRiMfn111+DVm3b+sKpL0RSWNpj28giR27cuNFcUd/Oyda7DlBBT6beMUVJOowJa5Iy9vYK0SwNz1cE8C1Xrlw0IXjc6rVr15oSIT7xh3uWzwPvIOOexb0aBqZ4lkGvOYVAClB9sDg1LEjnwtaeUrLXH/Nq1aqZHx6y9a439GFCpt4xRUnKE58USt6xVa64b/XYu9KjWZIOuWMFvK6CJ1tv74Ww3K+4L/Q3Ix2hwAnQ8uXLS/Xq1dO1J1Dx69evl4oVK4amPX6Aj/sDoWrVquYzLPeKaUyJ/8EDBW/mZOptR+BHfcOGDYKXJjwPGLwhgEWIsNjBUge2DN4RUAsohuT5PPCOK0bw8BIaBqawgOrvcTpCnAOajgzjA0lAh9vwds7hzEB2IStNAiTgUwI6B1QtoGoR9Wl1WS2fE6AA9XkHsXrZEYDoxPxP/OlwfHYlMDUJkAAJkIAdARWgsIDqELxdOsaRgBsCFKBuKDFNYAhAdFKABqa7WFESIIEAEVCLp3UIPkDVZ1V9RoAC1GcdwurkRwBv6CpA9W09vxKZmwRIgARIAAT0maoWUGscCZFAtgQoQLMlxvS+JoAHJN/Ofd1FrBwJkEBACcACiqF3BDxnEdQqar7wHxLIggAFaBawmNT/BCBA8YDEw1Hf1v1fa9aQBEiABPxPwCpAVYhSgPq/3/xaQwpQv/YM65UTATwMdXiIAjQnhMxEAiRAArYEKEBtsTAyRwIUoDmCYzZ/ErBaQPlm7s8+Yq1IgASCSYACNJj95tdaU4D6tWdYr5wIWC2gFKA5IWQmEiABErAloM9XnNQheI402aJipAsCFKAuIDFJcAioBRQPRz4Yg9NvrCkJkID/CdAC6v8+ClINKUCD1Fusa0YCeEDCDRPmgdICmhEXE5AACZCAawJ4puo2x2oBxfa8DCSQCwEK0FyoMY9vCcARPR6MXAXv2y5ixUiABAJKgBbQgHacT6tNAerTjmG1ciOAYXeIT27FmRs/5iIBEiCBdAQgQPF8RVBLKEea0tFifCYCFKCZCPF8oAhwK85AdRcrSwIkECAC1iF4FaIUoAHqQJ9VlQLUZx3C6uRHYPPmzVKxYkVjAcUxAwmQAAmQgDcEMN9T537SAuoN0yiXQgEa5d4PYdshOitUqGBE6JYtW0LYQjaJBEiABEpDANZOFaD6SQtoafoiDFelAA1DL7INCQIQnbCAli9fXmgBTWDhAQmQAAnkTUDd3KEgPGPxrOUq+LyxRrYACtDIdn04G75161bzYIQVFMcMJEACJEAC3hCwDsGjRFhB6W/ZG7ZRLIUCNIq9HuI2Q3SWK1fO/MVisRC3lE0jARIggeISsA7B48oQoByCL24fhOlqFKBh6k22RSA6VYDSAsobggRIgAS8IwCxqYuPUCoEKIfgveMbtZIoQKPW4xForwrQCDSVTSQBEiCBohFQP8t6QVpAlQQ/cyFAAZoLNebxNQFYQTn87usuYuVIgAQCRgAjSrrTnFadc0CVBD9zIUABmgs15vEtAVg/NViPNY6fJEACJEAC2RPQxUYQnRrgjJ5zQJUGP7MlQAGaLTGm9zUBiE61gFKA+rqrWDkSIIEAEVChaRWgHIIPUAf6sKoUoD7sFFYpdwIUoLmzY04SIAESSEeAAjQdGcbnSoACNFdyzOdLAlYB6ssKslIkQAIkEEACKkB1D3g0gXNAA9iRPqpyxWLVZeXKlfLll1/KzjvvLE2aNElcdt68ebJw4cLE9wYNGkjbtm0T33lAAtkS4BB8tsSYngRIgAScCegcUKsAxfHq1audM/IsCaQhUBQBOnLkSHnllVeke/fu5rN9+/ZyySWXmCo9+eSTsnjxYqlbt6753rFjRwrQNJ3F6MwErBZQzgHNzIspSIAESMANARWgqXNANd5NGUxDAlYCBReg2Jv7ueeek3vuuUdatWol/fr1k5NOOklOO+00qVevnkyfPl0GDRokzZs3t9aLxySQEwGr6LQe51QYM5EACZAACRgCdkPwsIBSgPIGyZVAVgJ048aNAmvmTz/9JGeeeaYZOu/cubPZeztdBbAn99ChQ6VGjRomCXZNWLdunUCYbtiwQVasWCFLly6VsWPHSs+ePaVp06ZJRU2aNEkmTpyYiMN/grVr1ya+B/kAftXwnzcs7fFDX6TufkS23vWK/tCQqXdMUdLmzZtNgevXr3d8lnp71fCXps8C/OYo4/C3unAtXLVqlSk8dQgefPlM8IY7dBFCGHji/5z+H0xHx7UA/eGHH+Twww+XX375xRR61FFHyQ033GDmf7z22mvSuHHjdNdIiE9U5sEHH5RevXpJw4YNZcqUKcaH2BdffCHVqlUzw/L9+/eX3r17J8qaPHmyPPvss4nvsJTiQR2GgLmKYBKW9vihT6w3PPiSrXe9Ap4IZOodU5SkXPFCTqu9d2yVKwQSuebPVUVRqgCFUYjPhPz5ogS9Z8PA01MBOmDAANl3333liSeekN13393QfuaZZ+T444+XYcOGycCBAx17ADfprbfeagBfd911Ju1OO+0kr7/+uhmKR0SbNm3kqaeeShKgZ5xxhuBPAwSqk9jVdEH4hOUX/5nr1KkThOoGoo7ly5dP7AWPCoflXvEDfIxc4EeITL3tDTwbMRLUqFEjqVjRtU3A20qEsDRYk5YsWWLWF8DAwZAfAR3FtM4Bxe8XOPOZkB9bzb18+XLz+1W/fn2NCuwnBGim55krN0x4M//888+NgKxdu3YCyLbbbisXX3yxjB49OhFnd4D8l19+udSqVUtuu+02I7qQDiZ9rI7XAOsmFiRZrVh6jp8k4JYArB34433klhjTkQAJkIAzAZ2CU6lSpURCCFCNT0TygARcEnAlQKFiYVmyMwtPnTo147ylm266Sdq1aydXX321YE6oBrhvgOUUc0theh41apT06NEjY3man58kkEoAolMFqA5npKbhdxIgARIggewIqNBMHYLX+OxKY2oSEHE13oMb7tBDDzVi8e677zbcYNV88cUX5bHHHjPCMh1MzB3FIiL8jRgxIpHskUceEbhcOu644+Tss882k8RhXYWFlIEEciUAAYqXHPzRAporReYjARIggWQCKjStAhTWUI1PTs1vJJCZgCsBimIGDx4sxx57rHTp0sVYmA444AD5/fffpW/fvnLRRRelvRLmeY4bNy7tebhjOvXUU83KeOvwftoMPEECDgQgOmGtxx8FqAMoniIBEiCBLAjg9x7BKkBxjDmg+tzNojgmJQF3FlBw2m677YwVE2Lyxx9/NDfhbrvtJvjLN0AsUHzmS5H5QQAPQ1g/cU8h8MFoMPAfEiABEsiLACydmN5knUanYhTi1Lo4Ka8LMXNkCLi2gH7//ffmxx3O47EaXgPmgNasWdOsguMNqFT4WSoCKkB19R2+qxgtVZ14XRIgARIIOgGITBWc2hZdkARxyt9/pcJPtwRcC9CuXbs67vkKNxdYbHTVVVe5vTbTkYDnBFSAqujEd31Ien4xFkgCJEACESEAAZr6LNXvOjwfERRspkcEXK2Cx7VuueUW6dChg/EDOmfOHBk/frzAn2eDBg3M8eOPP26224RfTwYSKBUBFaBqAeUOKKXqCV6XBEggTARg5VTBqe1SiygFqBLhZzYEXFlAMY8O1k3seHTggQea8lu0aCHdunUTDMG/9957RqBiX/cxY8aYxUrZVIJpScArAur8VucpQZAykAAJkAAJ5EfAbgheBShXwufHNqq5XVlAsZUZfHXCUXxqwOKkRYsWmWicx84TDCRQKgKpFlAK0FL1BK9LAiQQJgJOQ/AUoGHq6eK1xZUArV69umAOKHYzmjZtWqJ2X331lWA7zv3228/MD33ppZfkoIMOSpznAQkUm4BaQHUInkNDxe4BXo8ESCCMBCAy1eKp7dPvfM4qEX5mQ8DVEDwKHDJkiPTp00fat28vGH7HsPyCBQvMPu3w43n++ecbcXr00Udnc32mJQFPCeBBCPGpApRzQD3Fy8JIgAQiSsDJAkoBGtGbIs9muxagbdq0kS+++EI++eQT+frrr6Vq1arG8rn77rubKlx66aVmEVKNGjXyrBKzk0BuBHS4HRPlKUBzY8hcJEACJGBHwM4CqouSOARvR4xxmQi4FqAoCE5oYQGFGEXADz6G4ZctW2a26jSR/IcESkRA38LxUNQHo8aVqEq8LAmQAAmEgoCdBZRD8KHo2pI1wrUAff755+Xcc8+V9evXl6nsWWedRQFahgojik1A38LxUNQHo8YVuy68HgmQAAmEiQAtoGHqTX+0xdUiJFT1iiuukBNOOMG4WapVq5bZlvOhhx6SJk2ayJ133umP1rAWkSYAbw0I2JEDU0QQNm3aZD75DwmQAAmQQO4EIEB1ZElL0Rd9jjQpEX5mQ8CVBXT16tXyyy+/yD//+U9p2rSpNGrUSOrWrSsXXnih+YG/44475N57783mukxLAp4TULEJ8akCFO7DGEiABEiABPIjAJEJjzjWoIKUI01WKjx2S8CVBRQ3HW40/VHHPNAJEyaYa+y9997y2Wefub0e05FAwQjo9BDcr/qg3LBhQ8Gux4JJgARIICoE7Ibg1QJKARqVu8DbdroSoBCfnTp1MrshrV27VnbbbTd5+eWXBW9Eo0ePllatWnlbK5ZGAjkQWLdunclVs2ZNwR+Cxpkv/IcESIAESCAnAhSgOWFjJgcCrobgkf/f//63HHXUUdKzZ0/BoqM99tjD/MhjJfwbb7zhcAmeIoHiEFizZo25UO3atc29Ca8NGlecGvAqJEACJBBOAhSg4ezXUrbKtQDda6+9ZN68eYKbENYl+ALFHvCHHHKIcUxfykbw2iQAAitXrjQg6tWrZ1yGYZ6yxpEQCZAACZBA7gTsBGj58uWNz2Wdf5976cwZRQKuBSjgWN3bYN/3M888M4rM2GafEoA/2mrVqiXmfzZs2FCWLl3q09qyWiRAAiQQHAIQmTrn01preB3hKngrER67JeBqDqgWhhXFw4cPl1tvvVUWLlwokyZNMlty6nl+kkApCcBTw7bbbpuoAo4XL16c+M4DEiABEiCB3AhAgEJspgaIUlpAU6nwuxsCri2gP/zwgxx++OHGHRP2gcd80BtuuEHgoum1116Txo0bu7ke05BAwQgsWLBAtttuu0T5OMZLEkN+BCZPnix33323bN682ex+hkWJ8IRx44035lcwc5MACQSGAESmesKxVhqiVH0wW+N5TAKZCLi2gA4YMED23XdfWbJkiTRr1syU+8wzzwjmgAwbNizTdXieBApOYO7cuUnzkVu0aCHz58+nlT5P8uPHj5dvvvnGWJch6uHaasSIERKLxfIsmdlJgASCQiCdBRSilBbQoPSiv+rpSoDiB+fzzz83Q+9YYawBQ5wXX3yxccWkcfwkgVIQwAR5iM0ddtghcfnWrVubuUmIZ8idAKYx4KXzwQcfNH/wgoE5XytWrMi9UOYkARIIDAGMeuL/vN0QPOIoQAPTlb6qqCsBWrFiRWPpVEff1hZMnTrVnLPG8ZgEik1g+vTpxtK54447Ji7drl07c/zjjz8m4niQPYFFixaVmVuLUjDnloEESCD8BHSI3W4IHnF6Pvwk2EIvCbgSoJhkfOihh8rAgQPliy++MNeHVfTFF1+Uxx57THr16uVlnVgWCWRN4LvvvjN5OnTokMi7/fbbmy1j9VziBA+yIgChmTq3FgVgISIDCZBA+AmowEwnQLnlcfjvgUK00JUAxYUHDx5s5n516dJFZs+eLQcccID069dPDjzwQLnooosKUTeWSQKuCXz11VeCIfdatWol5enYsaPxWZsUyS9ZEcDiLoh5DZh6U6FCBfn55581ip8kQAIhJqACFG7uUgMtoKlE+N0tAder4GEBmThxoowbN04wpAmrKLbkxB8DCZSaAFa7d+3atUw19txzT3nkkUfMBgp2PuzKZGBEEgHM88TUm6ZNmybiIT6bNGli5twmInlAAiQQWgJq4bSzgEKU0t9yaLu+oA1zFKBY+YqhdmvAfNBddtnFROHcJ598InD4bZ17Z03v9TG2/gzL4ge0BW+W+GTInQAscbDS7bPPPmUKgeeG++67Tz766COBGGXIjoBOX4BHAWvA9xkzZoTm/6K1bcU+xgIPBLi0w/axDN4QUC8N69atExVQ3pQcvVLUn7KdBRRxYByW3+VS9i4WeuEZEAaW6rbPiaejAD3llFMEi4wyhRNPPNG4ZcmUzovzsL7Ur1/fi6JKXgbeGmGVq1OnTsnrEuQKvP322+Y/rZ0FFC9L4IutYzGPmSE7AthdCqFVq1ZJGfEdoj4s/xeTGlfkL1hBjB8c3Kd4wWfwhgBe7OE2EFtH2wknb64SjVLg+xfBjmP16tXNCBOfBfnfC8uXLze/ZWFgCQEKveYUHJ92n376qSsfinpzOl2I50igUAQghDp16iTYAz41wE/t/vvvb8TSVVddlXqa3zMQgHeBRo0alXlJatOmjTz//PNmhAQ/QAwkQALhJaAjoTVq1CjTSIhSPV/mJCNIwIGAowD9/vvvzdvjzjvvLB9//LHZCcWuLCxK0GF5u/OMI4FCEcDQD16UnBbCHXTQQTJq1CiZNWuWWahUqLqEsdxp06bZTq/RKTcQqBD/DCRAAuEloALT7mUTcXo+vATYskIQcBSg2P0Iq4jhbqlPnz5mjpJdJYo5BG93fcZFl8CHH35oHCQ7Da/36NHDTHV499135bzzzosurBxajpfQ448/vkxObMWJuUqYokMBWgYPI0ggVARUYFKAhqpbS94YRwGKeXM6KR5zadIFDHMykEApCGD+JxzOt2zZMu3lMWzUvXt3QVoK0LSYypzA4q6VK1fajm6AKeaB6iKlMpkZQQIkEBoC8IQBLWA3BxTPAsy3xW509DQSmi4vSkMcBSh+fLAqK1PATRmGSbOZ2snz/iKwdu1aGTt2rFx44YUZK3bEEUfIpZdeKjNnzkzarjNjxggn+Pbbb03rMQpiFxCvaezOM44ESCAcBCBA7ayfaJ3OC8V0KOqAcPR3sVrhaLqEk3n4/8v0d+655xarvrwOCSQIvP/+++YFCeIyU8DGCXhReuuttzIl5fk/CUBcYgGS1Qm9FQ58AEPQ40WAgQRIILwEIEBVaKa2UuORhoEEsiHgaAHFjw/C3nvvLX//+9/T+lGEH1AGEig2AYhJWOGaNWuW8dJ4e4cIRR6nBUsZC4pQgi+//FJ23333tC3GOfhahL9geBpgIAESCCcBiEu4s7ILGk8BakeHcU4EHC2gWOCBFcZw8D1o0CA5/fTTBQs54O6mW7duiT/MwWMggWISgN9E3Ju9e/d2fVmknTNnjvzwww+u80Q1ITZIACcnAYqV8LB+YBtUBhIggfASwPC6WjpTW6nxSMNAAtkQcBSgKAji84EHHjA7zQwZMsQ49sWqYqx8veuuu8wPejYXZFoS8ILAmDFjzMT3ww8/3HVxWIiEh+Xo0aNd54lqQiwugiPhzp07p0WAxYcYhsdiRQYSIIHwEoC4VEtnais1nhbQVDL8nolARgGqBeDHpmfPnvL444/LokWLjEUULljgkBqLOxhIoJgE3nvvPSN+4IPWbcAKTdzDmDvK4EwAw+rYYKJDhw6OCSFAJ0+e7GrDCseCeJIESMC3BJwEqFpAORfct93n24q5FqDaArhawDD8sGHDjHNvrHpzcoGj+fhJAl4RwJv2xIkT5eCDD866yEMOOURmz55t/rLOHKEMU6ZMEfj6zORWBSMh6A8wZSABEggnAYjLWrVq2TZO4zkEb4uHkQ4EXAlQiE4s3sAcUFicTj31VGMdGTFihCxcuJCLOhwA85T3BDD3E+7BYM3MNmCxDPanxc5eDOkJYHTDze5maiGFw3oGEiCBcBKAuFShmdpCjJRUqVKF3jBSwfB7RgJ5yTrXAABAAElEQVSOAvSDDz6Q/v37G9F58sknmxWvL7zwgixevFiefPJJgTWpYkXHhfQZK8AEJJAtgU8++US22WYbadu2bbZZzTwmWO1QBoM9gY0bN8r8+fNtt+BMzQFPGViU+NNPP6We4ncSIIGQEHAagkcTIU45BB+Szi5iMxzV48UXXyw//vijsTT16tXLvOXA7x/+rAFCAOcZSKAYBCZNmiRdunTJ+VLIixcpuBDSnb5yLiyEGefOnWta1bp1a1etQzoOwbtCxUQkEEgCEJe62MiuARCgHIK3I8M4JwKOAhTzOxs0aCCYD4a/dOHoo4+mAE0Hh/GeEoB1bvr06XLSSSflXC5cC2Ex3axZs7grkg1FbMGJgA0o3ASkmzFjhpukTEMCJBAwAnDJBo8Y6Ybg0RyIU1pAA9axPqiuowDFNocMJOAnAhCfW7dulZ122innamleWPd32GGHnMsJa8Zly5aZpmGag5uAdJiXy0ACJBA+Aiosa9eunbZxHIJPi4YnHAg4zgF1yMdTJFASArBaIsD9V64BC+nwxq5l5VpOWPOtXr1aqlatmnEFvLa/Tp06gjwMJEAC4SOwZs0a0ygnCygFaPj6vRgtogAtBmVewzMCGB6GeIToySdgf3N4cGAoSwBDbtWqVSt7Ik0M0m7atCnNWUaTAAkEmYDO7cw0B1QtpUFuK+teXAIUoMXlzavlSWD58uWCldf5Bgwb61BzvmWFLf+WLVsEG0+4DbqQC1MjGEiABMJFQIVlJguoCtVwtZ6tKSQB978yhawFyyYBlwQwHOT0IHRZjCmDD0x7WnCtBhHqNmjabESr27KZjgRIoLQE3AhQLkIqbR8F9equBajOAwlqQ1nvcBDApghwepxvwA4/HDa2p4j5n/A24DYgLfIwkAAJhI+AGwEKowBf6MPX94VukWsBuuOOOxrXN6NHj87KOlLoBrD86BGA/06GwhGANQPiHLtNuQn44XGaH+amDKYhARLwJwH8/8aoiNOLPwQopuBgW14GEnBLwLUAffnllwVuGP7+978b/4BXXHGFYLs+BhIoJgFYLmEFzTegjEz7nOd7jaDmV3cravnI1A6MjmieTGl5ngRIIFgEIEAzTXvSF1BaQYPVt6WurWsBij20sf3mL7/8Ivfff7/88MMPsscee8hee+0ljz76KN2wlLonI3J9POi8eMihDH1oRgSd62bWrVvXpF25cqWrPHDBhO04GUiABMJHAM/KGjVqODZMn6VePJsdL8SToSLgWoBqq+FypW/fvnLjjTfKmWeeKd98841ceeWVArc2l19+uethOy2PnySQDQGII7fCyKlclKFCyyldFM+piyu3vj1XrVqVt1usKHJmm0kgCATcvKyrAOUQfBB61D91zEqAYru9m2++WbD3e9euXWXOnDkyfPhwIwg+/vhj+e9//2vO+6d5rEnYCDRs2FAgeNzOT0zX/qVLlwrKYihLQIfT3Q7BI12mIbqyV2EMCZBAEAhAVGaygOp5WkCD0KP+qaPjVpzWah544IHy4Ycfmi0QzzrrLDn11FOlSZMmiSQYjj/22GPlu+++S8RZD2Bx+vLLL2XnnXdOyoc006ZNk7lz50rnzp0pCqzQeFyGQOPGjQWLkJYsWWKs7mUSuIiA2yAIUOv96yJbZJJUr17dtHXDhg2u2ox0msdVBiYiARIIDIFsBCgtoIHpVl9U1LUAhTi88847Ze+9905b8Ysuusj2h2jkyJHyyiuvSPfu3c1n+/bt5ZJLLjHlYD4pFjPBqoq5pA8//LA0b9487TV4ItoEmjZtagBgRyRM+8glYB4zROh2222XS/bQ56lUqZJp4+bNm121FdZozeMqAxORAAkEhgBEJV78nYJaQClAnSjxXCoB10Pw3377rbFephbwzDPPyMknn2yimzVrJg0aNEhKgh/65557Tm699VaB5fS+++6TMWPGmGF7DOGPGzdOBg8eLFdddZVZYf/CCy8k5ecXErASUAE6b948a3RWx/Pnzzfpcb8ylCWgOxrpDkdlUyTHwAG95kk+w28kQAJBJwBRmWmEQ8+7HTUJOhPW3xsCjhbQzz77TN5//31zpfHjx8tdd92V5HAa4hLzPmG9TBcqVKggQ4cOTcwhwT7TmCeCvLNmzZKOHTsmtv2DlfWtt95KKgrz/VasWJGIQz63lplEJp8eYCgZP9xhaU8xMMPhef369c2UjVyvp+IVFlCyL0tR5366dS6PdPjhIcuyLN3E4JmGoJ9u8jBNZgL6UsRnbGZWTincTLHB7zzc2uG3nc8BJ5rpz0HrYCoiXvyhDXQxaPoc/j6D+yCTz25HAdqmTRu59NJLE06pIQ5xo2nAsFvLli3l2muv1SjbTzXP40Hw4IMPSq9evcxcz0WLFiVBxuIH7PVtDcOGDZMHHnggEbXLLruY+XuJiIAf4EcHopzBPQHM3VQR6T7XXynxnxyWerzZc8joLy56hBdDBLeuleBNANMaMK+WIXcC1hft3EthzlQC3MUvlUh23/GMhPebTAFpli1bxudAJlBpzsPHOjQRQosWLYzhLk3SQERDgGZ6qXYUoPiRnjBhgmns0UcfLS+++GLOvhOxswqG4aGIr7vuOlMmxKy1gqhw6o1+1FFHSadOnRLAH3vsMWMBS0QE+ABubrDDhAr0ADelqFVv3bq1wCNDrgECFP/BYUllKEtARbnbObJIh8WH5FmWpZsYbIoAyxEsHtYXfDd5mSY9ARg8YFWCiyBuOpGeU6Yz+O3WIXantJqGzwEnSunP4V494YQTzEYrmJoYdI7Qc5meZ44CFEPw+M+LleuXXXaZ4Ltd2HbbbQWWyXQBJnzM8cSiEeygpJVq1KiRTJ48OZENFoDUlcmY86fz/pBwyJAhjluCJQoLwAFM7WDhtMVZAJpR9CrC6v6///0v5+vCetqhQwdyT0Nw9uzZ5gcn08IDzb7DDjvIqFGjzL2MFyqG3AhAJJFfbuzscqlxI9M2knZ5GfcXAYzQuZmOA+MRFiTy9+wvdtkc4UUU0xkh+LFOJugcoW0yrSNw/LUYMGCAmaMJy2efPn3S7nZ04oknyogRI9Kyvummm6Rdu3ZywQUXJKXBLkoYkseiEAjPN998U7p06ZKUhl9IIJUAFg/BSoc3xlycyS9YsEAOO+yw1GL5/U8CWHAIgZ7p4aHA8PKJH54ff/zR8UVU0/OTBEggGATw/xpC3o0AhWDidLLc+lWHq8EQo8QQoVEIjgL066+/TvwIwe9iuoBVsOkCtuycOHGi+bOK1EceecSI27PPPtvsqARzM4ZFdUV9uvIYTwLqfgmumLIVoJj2APGqZZBmMgH84MBf72mnnZZ8wuHbbrvtZtwwffrppxSgDpx4igSCRkCFkBsBijQUoLn1MKyfCBgFwdQRiH58Ommr3K7kr1yOAtTq2y/XOTQ77bSTcbWUrtlHHnmksUbhRtftvNKlZTwJgIBO08CEbUwPySboJG8tI5u8UUj7xRdfGIEOn71uA4be9txzT7NRBVytMZAACYSDgFUYZWoRrHeaPlNank8mgBd/BBWgOEZc0Ifh0Q6nkN50+WcuKHG4X0pddXzEEUfI1VdfbdyvOF3AzTkIXYpPN6SYBgS22WYbA8LJKp+OlObBvGWGsgRGjx5t+MKqmU3AlIavvvrKrIbPJh/TkgAJ+JeAWkDdGKCQhgI0t75UAQotpIY/jcutxGDkchSgmCu37777ym233SaLFy9OahFctNx9991ywAEH0OyeRIZfCk0A/0GxYjjVZZeb68JNCAL3gS9La+PGjQIBilEJt/M/tRS4VsNiD+x6xkACJBAOAiqCKEAL25/KGc9QFaBREPOOAvSGG24Q/CjBxQoWDFkDdizC3M4pU6YIXCMxkEAxCWDOcC5+E1euXGn+g9PiXra3ID4xPxauQLINeCE9+OCDzWJEzF1iIAESCD4BFUYqipxahDSa3ikdz5UloNzAUFlrXNnU4YlxFKDvvvuu2T6zVatWti3GinVYS5COgQSKSQAWUCwoyjYgT9B3mMi2zW7TP//887L33nsL3CrlEuBIGaMmH3/8cS7ZmYcESMBnBFQEqShyqh6sd5reKR3PlSWg3ChALWxgDdl1110tMWUPe/ToYdzhlD3DGBIoHAFYMOG8O9uAPLR+lqWGxUfff/+9nHrqqWVPuoyBeIW7NWy9y0ACJBB8AnAPhABxmSkgjfpezZSW55MJqADFVAcV+xqXnDJc3xwtoF27djUrW52aDOf03bp1c0rCcyTgOQHsuoHpIdkGN/saZ1tmGNI/88wzAv+qBx10UF7NOf300wXumOATlIEESCDYBFRQunEHBAGqgjXYrS5+7XW+JwSozrfVuOLXpnhXdBSgBx54oGBYLt22hxhqw6KDnj17Fq/GvBIJxAnk6nMOqzrd+LSLEmRsTfrBBx8Y359ufmic2GBKDhZ4Pf30007JeI4ESCAABFSAurGA4tnB+d+5daqKTQpQC7+LL77Y7MPeuXNnsxIe2+1hqA4LkAYOHGgWHRx33HHcVcbCjIfFIYBhCv1Pm80VkUffMLPJF+a0zz77rJmWcPzxx+fdTLDt16+fvPXWW6IeB/IulAWQAAmUhIAKSjcvpkijgrUklQ3wRfW3DL9r+vukcQFuVsaqO1pAAeLll1+WM844QzBEd9RRR5mtMv/xj3/IO++8I/fdd5+xdCiwjFdjAhLwiECuwz0YIsIetQx/EMCc2Ndff92sfMe0Bi9C3759TTHDhw/3ojiWQQIkUCIC2BYSwY1bNjcitUTN8P1l1d8qHM+r83mN833l86igowBFufihf+CBB2TmzJnGFyjmfGIlMbbYvPDCC/O4NLOSQO4EICL17TybUpCHAvQvYhjVwGJDWC29CnCRhY0qsPVuLn3kVT1YDgmQQH4EVIC6LSXb9G7LDXs63cIU08MoQNP0NnaggeslriJOA4jRRSOQ63wjCCK+qf/VTa+++qrZbAILkLwMJ510knlhnTBhgpfFsiwSIIESEHBjAS1BtUJzSbV2QoDqGgUVpaFppE1DMlpAbfIwigRKTgAPxFysa8jDh+kf3Td//nyZPHmy9OnTx/P+3GOPPWT77bcXWFgZSIAEgk2Als3C9p8KUFg/VYBqXGGvXNrSKUBLy59Xz5EArJi5PBSRhxbQP6CPGTPGTEfI1/WSXRdC5B966KHy0Ucf5fSiYFcm40iABIpLQJ+Vbp61SMOX+9z6By4FwRqLkHQInhbQ3FgyFwkUnAAedG4eiqkV4UPyLyKffPKJ7LbbblK7du2/Ij086t69u9mkAg7uGUiABIJLwM2zlqNLufcvrJ0qPCFCIUZpAc2dJ3OSQEEJQIDmOgSvb/UFrWAACv/2228FQ+WFChC3YP3NN98U6hIslwRIoIAEdMGmm2ct0mj6AlYplEVDbOrQOxoIMUoLaCi7mo0KAwF4Z8jF5xzy8CEpsmTJEuPNon379gW7HeDWqUWLFvLTTz8V7BosmARIoHAE9Fnp5lkLAcqX+9z6Aj4/YfnUANeWkfcDqjD4SQJ+I4AHYy7bviGPm109/NZer+uzcOFCU6TXq99T69m0aVPRa6We43cSIAF/E8hGgPLZmntfYt93qz91HEd+L/jccTInCRSWQK7/QVPfNAtbS/+WDl++CHXr1i1oJevUqWMsrQW9CAsnARIoCAEVoG5e9jm6lHsXQGxaLaAwklCA5s6TOUmgoARynSNjnexd0Ar6vHCd01XoITP8gOm1fI6E1SMBEkghoKLIjRiiBTQFXhZfId6tz+KoPDcrZsGISUnANwQwYRvWzGznHWFid7Vq1XzTjlJVRCe8w/1HIcOGDRvI+0/AK1askHfffbeMIMcPNzhhgw/rjxCyQQAcffTRSQsUCtlfLJsErARUgLqxgKZa8azl8NiZAH7H1NqMlHgOuJl361yq/89SgPq/j1hDGwK6G5f+cNsksY3CtpNe7Xlue4GARDZq1MjUFIuR2rZtW7Bao3w4pGcQufHGG+X999/PGsXcuXPliiuuyDofM5BAvgRUgLqxgMIgYJ3HmO+1o5Q/1c1VVPypUoBG6S4PUVtVgK5ZsyarrWHXrl1bML+XQcKLxUd4y541a5Z069atYFWfPXu2dO3atWDlB6Xgjz/+2IjP22+/XY4//njX1b7vvvvkqaeekmOOOaagLwquK8SEkSKgAtTNimyIVL7ce3N7pApSb0r1XyncCcl/fcIauSCgi2cgQN0GiE8MdWBhTNQD5tDusMMOMmXKlIKhmDdvnqB/dt5554JdIwgFw+p+8803G5+rEJ+wbrj9O//886Vx48Zy/fXXlxm6D0LbWcdgE1CLphsBSgto7n2N4XfrkHvqkHzuJfs7JwWov/uHtUtDoF69euYM5tW5DStXrjRJNa/bfGFNt+eee8pnn31WsOZNnDjRlN25c+eCXSMIBf/rX/+SpUuXym233Zb1VoWYq4t8cOY/dOjQIDSXdQwRAd2dx82uPBSguXd86qKj1EVJuZfs75wUoP7uH9YuDYH69eubM8uXL0+Tomy0ptW8ZVNEK2a//faTX375RaZNm1aQhmPYuUOHDtKgQYOClB+EQrHd6YsvvigXX3yxsTjnUmdMYTjppJMEw/EzZ87MpQjmIYGcCGRjAaWHkZwQm0ypbpcgQKPgr5oCNPd7hjlLSABD8JifBMuS26Bpt9lmG7dZQp0Ocz/hEQArs70OGHYeN26cHHzwwV4XHZjy4Gv16quvFliABwwYkFe9UQ7uWyxGcrMgJK+LMTMJ/EkAU0XwnHVjAYWHEbWYEmB2BCA2rZ4GouJRIHCLkPBmsGrVqux616ep0Rb8xw5Le4qNGZa1xYsXu74s0uKBiuEOMv8DG6ygb775plx00UWuObpJiNXeuLf333//yLK+7rrrBPOO77777jLuldwwtKapUaOGKeeUU04RDOmfd9551tM8tiGgCzngKcONgLIpglFxAm59Luve5Xy2Zn/bQAtYXywxnSHoWgeCGm1wCoEToBAPtWrVcmpTYM7hJsPbZVjaU2zwTZo0MUPIbq+7aNEigfshzgH9ixgWxZx77rkyadIkwZxQr8Jrr70mHTt2jOwCJIj6MWPGyB133CFebXe6xx57yFlnnSVDhgwxluWoz63NdK/ixw+iCPNo1e9tpjw8X5YARklUXJY9+1cMRH7t2rX5e/YXEtdH0ABWAYpjvHQGWRtAgEKvOYXACVA0JlOjnBrsp3OwxsEVTljaU2y28C8JH4luAwTodtttR94WYN27dzdDuy+//LJnAhSr37G4CYtnonhv4z5D2zH9IBuXS5ZuSXt44YUXyvjx483Q/htvvGF+pNIm5glDgM/Y/G4EiHc3AhSbWsANUxT/z+dH+I8NJ1IFKCzPQWapIxBObDgH1IkOz/maAMTkwoULXddxwYIFRoC6zhCBhHjAnXDCCfL2228bl0leNHnEiBHmh+iII47worjAlXHttdeaYUuIUK8DRkzuuecegYN/DO0zkEChCcACmmnHNLgNwogeLc259Qb+X+scULCEeOMipNxYMhcJFIUAhjaxsj3Tw1Er8/PPP0vTpk31Kz//JIAV1nj7/u9//5s3E5Tz6quvypFHHpnVBgF5X9gnBbzyyiuCle+33HKLFMrbAvy3XnLJJTJ8+PCCutHyCVJWo8QE3AzBq4UUaRmyJ2BdhKRCFKI07IEW0LD3cIjbp2ISls1MAQsRIFa9mo+X6XpBOo+5tFgs9NJLL+VdbSw+gm/Wvn375l1W0ApAuwcNGiS9evWSQw45pKDVP/3002XXXXc123vC8sRAAoUi4MYCqkYACtDcegHTRHTIGhZQBEzRC3ugAA17D4e4fSomMecwU5g/f75JonkypY/a+b/97W8yY8YM+frrr/NqOobfsfNRFHc/gp9OLHzB6vdCB0yduPXWW80c6KeffrrQlwtc+c8884yZovDvf/9b7r33XnnggQdk3bp1gWuHHyoMUYkXeKeg57kVpxOlzOcgQlWIRkGABnIRUuZuZIooEMAcUAxdqLh0arOK1ObNmzsli+y5nj17Gg8BGD7ffffdc+KAKQ6ffvqp3HTTTTnlD3ImOPPH8Ptll11mFnUVoy1w8n/iiSfKE088YT4LNeRfjLZ4eQ1srnDnnXeaDRDULyXmirdt21Z69+7t5aUiURYEaCbXSrSAenMr6MJklKaWUG9K9mcptID6s19YKxcEYAWCCFVx6ZQFIhVzajDczFCWAFj26dNHRo8e7WrFa9kSREaOHCnYOQXzP6MWHnzwQdl2223ltNNOK2rT4b8VVtfBgwcX9bp+vtisWbNM9WAZ/vDDD80fXNrMnj3bz9X2bd3ATi2c6Sqp52kBTUfIOR5z5/EMRtDPTD40nUsMxlkK0GD0E2uZhgAsmm4EKNLAbRPm2jDYE4AAxQ5G+NHOJcD35QEHHGB8AeaSP6h5YP384IMPjHN43bqwWG2BX9uTTz5Zhg0bJitXrizWZX19HQhQ/D9v1apVop6tW7fmNqYJGtkdQFTiueAUKECd6GQ+h3nc+uzAqB4soVGY281f48z3BlP4mEA2ApTD784dueOOO0qbNm2MFdQ5ZdmzEGH44Y+i9XPo0KFmuPe4444rC6YIMf379zdWUC8WkRWhugW/xPTp081iQ/1BxwVxX2OOM0P2BCBAVWCmy60CFdZShuwJ6IYJmhPTHtSzgMaF8ZMCNIy9GqE2YVER5h5mGq6ABZQLkDLfGFjBPXbs2Kwffu+9957ZVx6O7aMUsNXmqFGjBIu4rIKnmAywR/yhhx5qvBjoAoZiXt9v1/rpp58EL1PWgO94QbI6+7ae53F6Am4EqApUDsGn5+h0BgvkatasmUiC4ygsmqMATXQ5D4JIAFZN+E3DwoN0AeIUixBoAU1H6K94uA/Cmzd8WWYTMATdrVu3yDmihvDGFoSlsn5qH2EzAdzj2FI1ygECHNb4du3aJWFo3769eU7MnDkzKZ5fMhOAVRPPBKeXfFpAM3N0SrF69WqpU6dOIgm2NM208CuROMAHFKAB7jxWXRJWTaeV8PhhxsOTFtDMdwx+qLGY5uOPP86c+M8U2JXnhx9+MPM/XWcKSUL4PYU/zlLfW3vvvbfUq1fP7D8fErQ5NQMjHRBDO+20U1J+3NcIuE8ZsiOgw+pq5bTLjXNB3zrSrl3FioMPYasXCxwjLuyBAjTsPRzy9qkzeicBqo7qSy0SgtIVcEqP/cbdBrWWIl+UAl5qPv/8c+PEv9TtxspZWKC1L0pdn1Jdf+rUqebSqX5o8YPeuHFj0fOlql8Qr6sCVK2cdm3AcLGmszvPOGcCeInHgkINmFaDuLAHCtCw93DI24eHHiw/KjLtmqvnVKzapWHcXwS6du1qeCq3v87YH0H0YJUxLKdRCnPmzDHWtlz9pnrNCvXAApwoLF5Ix27KlClmQZidu7VddtlFcJ4hOwIqLJ3mJEKcarrsSmdqEMAonfWexfGiRYtCD4cCNPRdHP4GQlg6iSWcg0jlBHl390KXLl1Mwi+++MJVBlgBMQQctTB37lzTZKu7n1IywEsA5kA6/V8oZf2Kce1vv/3WTImwu1bHjh3l+++/50IkOzgOcbo4JpMFVNM5FMVTNgQw/xOLGa0jdPhNW7p0qaiDf5tsoYiiAA1FN0a7EfjPipXw6QLO0fqZjk7ZeAwFtWzZUtwIULy54019r732KltQyGPww4GAlxs/hLp165pqaL38UKdi1gGLETHEvttuu9leFvHwrch5oLZ40kaqsHSygOKcpktbEE/YEtANEvDM1aDH+pKr8WH7pAANW49GsD1wMJ9JgCINg3sCe+yxh3z11VcZM2gapI9a0E0N/LJlnq5S1npFrT8gLDH9IN2UCCwWw1zZr7/+Ompo8mqvCksK0Lwwps2MaTMIO+ywQyIN/NYi6LnEiZAdcC/4lA6Fn7hbb721KCvQ8DaOHwvsfFDogIfymWeeWejLlKR8bMeJCdvW3SSsFcGQZLofJWs6Hv9FALywLzxcgahl7a+zfx1BgGJxB/6iFho2bGiavHjxYl/s/oR6IGi9zJcI/fPll18agYmhdruAKThYHY90xd4y1a4+QYlTAYph4nQB4rRBgwbpTjPegcCPP/5otpSuVatWIhX+D2PhHM4dddRRifiwHRRe+QSMGHzIjRgxQtq2bZvkFiFgzUiqLhZLfPrpp6EVoGrdxFBwixYtktqOFwrMpYFIZXBPQIcxMaeuR48eaTPifFTFvTo7/+6778zzIi2kIp3A8DMWguj/hyJd1jeXgQ/UDh06OM713nPPPQVbxjK4JwDhDstxJguoVUC5L50p8f8W921qgCeHsHttoABN6XWYwfHmgQf5Y489FviVfXC83K9fv1DP0VNxiWH4VAEKB/VYmBHVH+WU29v1V/w/gOXjm2++SStA4YAdb+hR3H4TIDFXFkNlH330kRx77LGu2RYq4YcffmgWg0VxCB7/xyFAM/UDBOgzzzxjdkXCoi0GdwTwLHCygOKcWkrdlchUIAADCUTmBRdcUAYILPm4VzHFJ6z/pzkHNKXbsQfrgw8+aH5YTznlFFm2bFlKiuB8xVynk08+2QwP3nbbbcGpeJY1VQFq57ZC54ZaXVxkWXwkk+OBhzlzsHCmC3hwYuFHp06d0iUJfXzv3r0Fu0CV2mk0VndjDmRUXwYwV27lypUZvTHoYjl4bmBwTwDWTScLKAQoLaDueWpK/L/F1LHOnTtrVOITI0vwPICtZcMaKEBtehZuaJ577jmzuvfUU091/I9nk90XURAH/fv3N6u/hw0bJnBsG9aAN29sXYYV2alBRSktoKlkMn+HsITfRFiX7ALEKeYvpzr9tksb1riTTjpJypUrZywVpWzj4MGDzf9xbKUaxfDZZ58ZKxEsnE4B85mxTSfSM7gn4GQBxUsoFn9RgLrnqSnxIlSpUiWxm7cMAQpDgBtvJFpe0D4pQNP0GG6IoUOHCnbYef7559Ok8m/0PffcYxzbog1RmBwOC6edAIUFFA9PPhyzv1fxf2DNmjWCOcR2YfLkyYJ5kNiCL6oBiwX69u0rzz77rOgioGKzwEvC22+/LWeffbZUrly52Jf3xfUmTpxoXoTcDAPvu+++gvTpXqx80SCfVQIv+HgW2AWNRxqG7AjgPsR8e7tnKO5lvNwjTVgDBahDz+IBBQtPEJ3B4o0Ub1Z4O41CgIXTToDCAqpD9FHg4GUb9a0cQtMuIF7T2J2PStz//d//mf9rd911V9GbjPlht9xyi8AZPoRwFAMYwKIJYekm7LPPPmbKRNhd3Lhh4TYNxGW6OaA6NO9G/Lu9XhTSYegd85ax81y6gHMQoOpiLV26oMZTgNr0HCw+cMV0wgknGCtiEF12XHnllWZHlF69esmTTz5p3OnYNDU0URCZOt/T2ijEUYBaibg/xiIbuFey274Q8+3g3ooC9A9H9JdffrmMHj1asBComAGWV/TPjTfeaERwMa/tl2thuhHEkVsBinmgWNUNzyAM7ghAXKqlMzWHxtMCmkrG+TvEJwxF++23X9qE+++/v5kCGFbftRSgf3Y9hs8w1I5FO4cddpj897//lQEDBhhfiFgVH7SASc1vvPGGdOvWTe69915zk2Ol3VtvvRXIOa2Z+ENk6op3a1pYRSlArUSyO8ZCJDsLqIpSCtA/eGIuKCxrN9xwg1kMkx3l3FLPmjVL7rvvPjnxxBMdrSi5lR6cXBCSGO2xW8hh1wqIKdzXFKB2dOzj6tSpQwFqjybn2I8//tjsorbLLrukLQPzQHG/jh07Nm2aIJ+ItACFC5lHH33UWDq7d+8u//znP82bMT7HjRsnAwcOdPQp5/eOx/aTEJ+wypx//vnG9cill15qfijhlP7FF180C6383g439YPIVJ+fmh5TKDgErzRy+8QPNVZXp07lgACFxwjr7h25XSEcubAQ6Y477pANGzbI9ddfX/BG4V6H1RVzUK+++uqCX8/PF4CQhPisWrWq62rCWooFIGEd2nQNwmVCzKFPNwRPC6hLiCnJ8LsM3eHkYglTAGEhhau3MIaiClDMFZkwYUISx3nz5pk5DpjngL9Cz8vBzi233367HHDAAdKnTx954oknzEMcbopQN6x+h0UBfkDDEjCMet5555khQgwTXnjhheZhgmkGPXv2NL7z4PM0yPvO6ip36zzQ5cuXGxcXtIDmfidDgGKu0owZM5IKgfN1OE92engmZYjAF9yDN998s4wZM0aGDx9e0Bbff//95sUAiw2jPPcO9yZ2NnI7/K6dgvRwcYP7mCEzAbWA2i3cogDNzC81Bfxz4/f2wAMPTD1V5ju0CjbIsZtiViZxwCKK5ogecx3wcMYPFoaFNWB+Ioa/dbs/DOlhFyIvAxxmwxXRCy+8IBC89erVMx1/8MEHm6GrbN6cvaxXKcqCxQp/55xzjkCg4c0KP5j//ve/5YEHHhDs6Q33TUFz56IiE/9JdRcfFaMqTkvBO+jXVBdL+KFu3759ojmYd4f5xQzJBI4++mgZP368sYZi+Awuf7wOGLr7z3/+IxdddJH5/+p1+UEqD67A8HzH9IdsAvoGHgNg9IiyH1u3zDC/E+ITRqRUjyIQoGBpt5LbbflRS/e///3PTBtxmv+pTLATHXQT/A3/4x//0OhQfBZFgGKu0rXXXit4i8KfNcDiOWjQIGnevLk12rNjCBIIKgjPgw46yAyPQQAXY/91zxpRoILgnun44483f3iwvPvuu0aoY64oLKMPP/xwYNy6oC14CKroBDJ9Y1RxWiCMoS4W/18h4OEwWQOcruOl0W77OE0T5U+8aGPe7CWXXGLmkGMrQ68CuF911VVGcGFUI+oBAhJ8YanPJuBZgWF75MfLOIMzAV1gtHr16jICFHGpv+vOpfEsjD54aXIzegGDGQxDFKA53jeYF3XdddcZixuGgDUgHj9m2Ksbk2whejBv0RpefvnlJD+ceHAgvdsAEQVTN/6DYNUuJu3jj8GegLqe+ihuGR05cqTpE/uU/ovddttt5amnnpJRo0aZymGlNhYnIGRzz5gM/CdBABZzLMrDUCeCuiXD1A5yTWBKOoAIhV9OrE7/17/+lXQu1y+YhwtRi3DNNdeY52muZYUlH6ZNQUjmYlDAhiOYgoV54rnkDwtDN+3QoXcdbrfmgQDFlDU+C6xU0h9j5BFbHMN9mtuA0dq7775bMHSvLwNu85YqHZ5XmeZYF8UCqqu8IGqsATAxfAJP/1jQgIcrrJXY3k4DXMFofsQhj4oKTeP0ecQRR2SE4JS/kOfQQVi8AJcgfgoYNsQDGUPZ2bAudRuwsAoiSW96cIV4wksLQ+4EsBsYpsjA3yL+cG9gXhLuE9y/DGUJYLrCZZddZobisTuPFz468eIMdyx4qYb4j3pw2kfbDRsI0IceesjMb87Wguqm/DClgRUOAWIzNUCUwsATpN+K1DYU87t6X3Az/1PrBQF65513Gn+30DRBCG5+G4oiQNPB2mmnneT11183czKRpk2bNsaCZRWgsIriTwMEqs4X1TinT6wyw58fA94YIY44fOFN7+BHHn+wfEKEYoUwQ/4E9P8QpmlgJSx2nWLITAD+gzF1AV41MLc9nykLGH7TeZ9Bm5+dmVRuKfCyiUVImbbfTFc6+gSiCd5Q4G+RIT2BZs2amZN2AnTVqlUCV4XZ/C6nv1L4z3zyySdm3nE222NjZBgv/MgLV5FBCDCwZTKulS9lQ3DjQixowDxQzHGClYWBBEiABIJOAEPxLVu2THieyKU92A4Y8z6xYAG7LjH8QQACFAIyV+slFs0gLzyjMDgTUCOJnQBVC6hzCTwLAvC8AAso1qNkG2AFxQJHvHSFJZRUgOJmhq9NzCnDHBPM3dMVX2EBzHaQAAlElwCmFmGYF3PdMW8z24AfG0xNwkIbuFxyM6yV7TWCmh7TESAg85lig/mjYd1lxst+BWN4i4HRKDXgd5zWz1Qq9t9hwcTUkVwEKIbsoZV0CN/+CsGKLakAxfy84447zkzWh1kZE3ODuO1lsLqctSUBEigmgdatWwv8DL///vtJCyrd1AGiExsBwEVaEHdkc9PGXNPABVO+LpQwzx2jcPCSwuBMAFZQOwsoRKlaSJ1L4Fk4n8dwOqYbZhvgEg/D9h+lrKXJthw/pS/qHNDU+ZwAAcGJRQ6YXxaU1V1+6kDWhQRIwP8EjjzySOPy56677jJzFq0+VdPVHv4+sdc7di+DpY7hLwJwt4bVxPkKUN1KFm6zCuUK8K9aB/sIVs5UAYp5fvjtpgB117fw9nPooYe6S5ySCqMfmI8fpm05S2oBVb5wskrxqTT4SQIkEEYCcEUH68cVV1yRcR6XDtnDVyDcOTEkE9AdjKweUpJTuPsG123wtKLlucsVzVQQoKlD8OqWiUPwme8JLHbDwuN8FkVjsRzcSc6ePTvzBQOQwhcCNACcWEUSIAESyIsA5oOqL79HH33UsSwM2cNFHSymnPdZFhW8C2BHHl2dXTaF+xh4Y7FutOA+Z7RS2g3BqyClAM18L2D+J1zYwf1XrgFbyOJ5gLLCEChAw9CLbAMJkEAgCGDI94wzzhBsQTxjxgzbOmPoHRt2YOU7XV7ZIjJ7Y7uZxmCfOzkWAhR7bTM4E4AAVcGpKXVInkPwSiT952effWamjOSzMxo4w50bygpDoAANQy+yDSRAAoEhgK1usZjg9ttvL1NnzKm74447BHuVn3jiiWXOM+IPAj/99JPsuOOOnuBAORBW3MnHGSec0avg1JQqSGkBVSL2n/DyA7dhufqstZa61157yaRJk6xRgT2mAA1s17HiJEACQSQAdzaXX365GUZLHUp79dVXZc6cOXLttddy6D1N52Jqws8//yzwLuBFgDcWhFmzZnlRXGjLgPXN6rcbDVVBSgHq3O24t7CJB/Z0zzdgQSIW4ME/cNADBWjQe5D1JwESCBwBbKfXtm1befzxxxN1x+5dgwcPFvj709XZiZM8SBCAyyRYlFq1apWIy+egRYsWJvvcuXPzKSb0eSEy4cMSvig1QJBit5uaNWtqFD9tCEyZMsXE5rppgrVIfTaEYeFcUd0wWSHymARIgASiSgALCQYMGCBXX321TJ8+3YhR+PfDCtdBgwZFFYurdoMRghcLkFAO5uQ1aNAgFBYltKdQQa2c3bp1S1wCGyVofCKSB2UIwJcvpt144csX88LBHGUefvjhZa4VpAgK0CD1FutKAiQQGgKwgmKf+Ndff12uvPJK84nhYC/miYUGkk1D4AMUoXHjxjZnc4vabrvtZNGiRblljkgurMDGfQorKHx/YhtUbGeqUxgigiGnZmLBoVdzllEBOLKfOXNmTnXxUyYKUD/1ButCAiQQGQL48caWfO+9955cdNFFxsH0OeecE5n259pQLBbCgph8tuBMvTasU0uWLEmN5ncLgRo1ahirPaIWL15sLMdwhcWQmQDmdWMjHq8Cpp+EYQtZzgH16o5gOSRAAiSQJYEePXqYod+RI0cav5/4zuBMAE76vRjKtF4F5aUusLGe5zEJ5EoA85V/+eUX2X777XMtokw+lKUjAWVOBiiCAjRAncWqkgAJhIuADrc//fTTxqIEn5QMzgSwmtjrnfNQnu7q43x1niWB7AjgxQbu1bDrllcBFvsNGzbI+vXrvSqyJOVQgJYEOy9KAiRAAmJ+lLAABlvrtWvXzqwoJhdnAliFjV2lvAwoz7q628uyWVa0CahlHdNGvApalvph9arcYpdDAVps4rweCZAACVgItGzZ0nzTT8spHtoQgDUJWxp6GbCgBuUykIDXBNRKiTm0XgUtS8v2qtxil0MBWmzivB4JkAAJWAhgBTaCflpO8ZAESCDgBOA1AAEvOV4FXYCnZXtVbrHLoQAtNnFejwRIgAQsBBo2bGi+6aflFA9tCOCH3OsfXpTnpUCwqTajSIAEUghQgKYA4VcSIAESKCYBbHGIoJ/FvHYQrwXH8ViA4WXAUCbKZSABrwnoi42XL03YAABBy/a6zsUqjwK0WKR5HRIgARKwIaDCR+d12SRhlIUAhLruQW6JzusQ5Xm9sj6vCjFzaAjo/2sv52vqC5g+O4IKiwI0qD3HepMACYSCQNWqVU07vF7ZHQo4No2A14Bly5bZnMk9CuVxCkTu/JgzPQEd2fByxbqWFfRtUClA0983PEMCJEACBSegw2j6WfALBvwC2IITW0Hiz6sAR+Febu3pVb1YTvAJwGVSuXLlZPny5Z41Bi9MWIhUs2ZNz8osRUEUoKWgzmuSAAmQwJ8EKlSoYI7Kl+fj2M1N0bRpU5NswYIFbpK7SjN//nzRcl1lYCIScEkA/78bNWokixYtcpkjczKU5aVj+8xXLEwKPvEKw5WlkgAJkIArArCOIOinq0wRTqT+UmfNmuUJBexrDif0Wq4nhbIQErAQwNaZP//8syUmv0OUFYYXJgrQ/O4D5iYBEiCBvAhgr2gG9wRg+cHQ44wZM9xnckg5ffp0c7ZNmzYOqXiKBHIn0Lx5c5k7d27uBaTknDdvnjRr1iwlNnhfKUCD12esMQmQQIgIbNmyxbRGP0PUtII1BduWTps2zZPyUQ7m39IC6glOFmJDAPeWVwIUzwlMGWnVqpXNlYIVRQEarP5ibUmABEJGQLeA1M+QNa8gzWnfvr388MMPnpSNcmD95CIwT3CyEBsCEItwnYTpHvkGzH2GT1EK0HxJMj8JkAAJRJyAOqhW59IRx+Gq+R06dDBz6tQdjatMaRJNnTpVUB4DCRSKQOvWrU3RM2fOzPsSOvdZy8y7wBIWULGE187p0pgv9euvv+aU12+Ztm7dKrB6hKU9fuELrvgjV297RC105Oot17Vr15oC16xZw3vWJdq2bdualN9//7107drVZa6yyeAcfPbs2dK3b1+yL4snbQx+h/nblRZPmRNw8YVFhhCP+dyvKBhlVKxY0fit9fOzGPdHpvntgRSgYZorhQ4KU3vK/M8rQQTEJwK5egufXL3lqaXpDinwa8l7Vqk4f2JOHfwgTpkyJa8fdAy/4xmMIX2yd2aeehbPAzJLpWL/HYIRInTOnDn2CbKIxQuTLkDyM3/ULXQCFL7ydGurLPrMl0kxJwTzjsLSHr9AxlAmbn5y9bZH8DD5//bOA1yq4vzDg4ICinRUQKQpUqQoYEFRNIKJvcQaDURFY4kYWzT6SKLGRBJ7IlETS+xdUaIGCyoWjAiCFStSlCIiCljw/Of9/s7m3HX3brmFPbu/73nu3VOmvjNnzne+aV999ZW41i5WFxRQ2gPV2fzhojTSfV4TmTVrlmOdxv79+7t11123JkFVlF8+lvgAUH3Nv9gZs8ns9ZoKE5D4ACt19lhAc61trElINa0N8i8CIiACNSAQdkj59NNPaxBK5Xnt3bt3jRVQuvCZgCTls/LqT33nmHU7a2PzBMIIFtD6zkNtxycFtLaJKjwREAERKIDAwoULzXVtzJAtINrEO2XiEC/jmmzJSRd8z549E89CGSh9Au3bt6/xbkj0QrFt7MYbb1z6Gc4jhVJA84AkJyIgAiJQVwTCDim1YR2pqzSWYrisBYoUux4oqw8woYOufIkI1DWBdu3a2VJMYchNMfEtXbrUJn+Vwzac5F8KaDG1QH5EQAREoBYIhLUBmzdvXisTFGohSYkJIuxcVOyOSEzmYJza5ptvnpg8K6HJJdC6dWtLfE2G2gS/rVq1Si6IWMqlgMZg6FAEREAE6pNA2AZy+PDhbvHixQ4LhyQ/AkzCoCuyWAU0rMnYrVu3/CKUKxGoAQE+MpFly5YVHUpY97ZFixZFh1FKHqWAllJpKC0iIAIVRYBZ3KwPuN9++1m+azqru6Lg+cwys7jYpW3w16RJE1sep9K4Kb/1T2D99de3SGsyZjl034ew6j8XtRujFNDa5anQREAERCBvAtOnT3dY4FgGCGVoxowZefuVQ+c23XTTovfYZm/uTp06CaMI1AsBnm9k5cqVRcfHkB0khFV0QCXiUQpoiRSEkiECIlB5BKZNm+YGDBhga1H27dvXvfzyy5UHoQY5RoGcP3++7XxWaDCsp1guy9kUmne5r38CjRs3tkhrsnsR6zAjISw7SfA/KaAJLjwlXQREILkEWHYJJWjQoEGWCX5feeUVmxiT3FzVb8pZ2obZ7IsWLSo4YhRX1maUiEB9EAhrzQYlspg4g182ASgHkQJaDqWoPIiACCSOwIsvvmhp3mabbex38ODBtkwLu/NI8iOAAoqgTBYi7JTGB0C5rKdYSN7lds0QCAooO/UVK1JAiyUnfyIgAiIgAikCU6dOtS5g9ohGGAfK1rxcl+RHICiQCxYsyM/D966wmKKEBv8FeZZjESiCAFZLJhzWpAsev+XS/Q5CWUCLqEjyIgIiIAI1JcB4z4EDB6aCwUKicaApHHkdtGnTxpR2docpRILCKgW0EGpyW1MCTZs2tV6OYsNhEhJhlItIAS2XklQ+REAEEkNg+fLltgsPE5DiwrlmwseJVH+MRYldYYJCWb3r/90N7oP1+X93dCQCdUeA5ZM+//zzoiPAb7NmzYr2X2oepYCWWokoPSIgAmVPgD3Ikd69e1fJK+csRq994atgqfYEK2ahY0CxmDLcoW3bttWGrZsiUJsE2MEo7GZUTLi0DeWyCxL5lwJaTC2QHxEQARGoAQG2gUTSd+EJ5+xRLsmPABORClVA586dawvQY0GViEB9EcBaX+hwkXja8Fsu+8CTLymg8dLVsQiIgAjUAwG6gFu2bPmDBaU7dOhgsdfkJVUPyS+pKFhKCYWyEJk3b56WYCoEmNzWCgHqKkuvFStz5swpq3rbsFgQ8icCIiACIlAcAcZyrb322m7ixIlVAoiiyM5rsl90lQAr4ITF5OEJs7Dfdq5s8yKPTwDL5V73RaA2CHTv3t3dfPPNNhGp0MlE7AO/ePHiH/Sa1Ea61lQYUkDXFHnFKwIiULEEmIzAy+SUU07JyKBc9nrOmLlavsh2nAhba7KKQC757rvvzAp1wAEH5HKq+yJQqwR69epl4b3++usFfwCF9YFDGLWasDUUmBTQNQRe0YqACFQugTFjxriRI0caABaXxrrRunVr17BhQ1srkO55SX4EunTpYg4ZN5uPAkp3PbsnBX/5xSJXIlBzAj179nSsB5q+BFs+IeNnvfXWc5tttlk+zhPhRmNAE1FMSqQIiEA5EVhrrbVsNiszWvlr0aJF6lzKZ2ElDS/+3n333bw8BndhwldenuRIBGqBAMrnVltt5Z577rmCQ3v++efNasrQnXIRKaDlUpLKhwiIgAhUKIHNN9/cvf3223nlHncs+t+pU6e83MuRCNQmgaFDh5oF9Isvvsg7WJZumj59usNvOYkU0HIqTeVFBERABCqQQCEK6FtvveWYDIIVWiIC9U1g1113tSEgTz75ZN5RP/HEE44JivgtJ9ETWE6lqbyIgAiIQAUS6NGjh60Fms8uM2+++abbYostKpCSslwKBDp37uyor+krYFSXtocfftj169fPldvWsVJAqyt13RMBERABESh5AkzuQFAuqxMmfLEJgBTQ6ijpXl0T2Guvvdwzzzxjkw9zxbVo0SL3wgsvuD333DOX08TdlwKauCJTgkVABERABOIE6IJnckbY4jR+L37M+E+WYQoKa/yejkWgvgiggH777bd5WUEnTJhgK2Psscce9ZW8eotHCmi9oVZEIiACIiACdUGA2cVdu3bNqYAGBVUW0LooBYWZL4GNNtrIbbvttu7+++/P6QU3O+64oy3TltNxwhzUqwLKrK8pU6b8ABGDwh977DFbmPkHN3VBBERABERABHIQQKnM1QXPfbY7bdasWY7QdFsE6pbAvvvu62bMmGFDQrLFRH1FP8JtOUq9KaCrVq1yY8eOdQ888EAVjpdeeqkbN26ce+WVV9xRRx3l2CJNIgIiIAIiIAKFEGBixzvvvONWr16d1Rtd8LiTiMCaJrDbbru5xo0bO7rYs8mDDz7o2BVtl112yeYk0dfrRQFlhwp2/Vi+fHkVWB988IENxL3mmmvcmWee6Q499FB3yy23VHGjExEQAREQARHIRYBxoOxwxJac2WT27NlltZNMtnzqeukTYFcjFMuHHnooY2JZdomZ8sOHD7d1azM6SvjFetmKc8WKFe63v/2tW7JkSZVBt2HrtLAeGzsEsNxAXLCMTp06NXWJWYyFLOCa8liCBwyGp8Esl/yUCmIGd8NWXGu3RHj2EHGtXa7UV4R2MrSFtRtDZYTGuDqEnY4YD5ouLObNlqcdO3ZUHU6HU+A5ytHXX38tjgVyS3eOAoqSSVd7+rjkV1991S1YsMANGzYskZzDezg9z/HzelFA+/TpY3E+9dRT8bgNbvPmzVPXNthgA1NSUxf8Afufjh8/PnWJ7dPK5QXIQ4yiFF5AqUzqoEYE4IqUSz2pEYxa9CyutQgzFlTg+uWXX9ps19gtHRZAgHGdTEaiZy2TYPBA2rVrp7YhE6ACrlFnUUAxoEiKJ9C3b1+zbj7++OM/UEAnTZpke7/36tUrkfW1ZBTQbMXDshnx8TokuEmTJlWcH3300Y6/IKNGjXLhSzdcS+ov63vRYMaV8KTmpZTSvXTpUqtXbdq0KaVkJT4tKPQMoymX569UCgTLMta5tm3buoYN68UmUCpZr/V0bLLJJlm74D/66COLj542jB2S4gl88sknrmnTpprMVTzClM/tttvOTZ482Z1wwgmpaxw8/fTTNvsdi30SBX0uV3tWL2NAs8GjwaXhDcJxua30H/KmXxEQAREQgbolwMt67ty5GSPhOoqnlM+MeHRxDREYMmSImzlzZhUrJ7oQ3fLcK2dZowrooEGD3KxZsxxfpmjLzAYbPHhwOfNW3kRABERABOqIAEssMW4uk8yfP18GjkxgdG2NEkDnYSje9OnTU+mYNm2aHaMjlbOsUQWUL9HRo0dbF/sRRxxh3XuHHXZYOfNW3kRABERABOqIwIYbbujoHs4kXNfwkUxkdG1NEmD1BoYeMukoCMcMzevSpUu4VJa/9TrgaOedd3b8xYX9TUeMGOEYB8V6VxIREAEREAERKIYAE4xWrlxp3Znp7xPG3Pfr16+YYOVHBOqMACtfMAM+7NJFRBwz+ajcZY1aQAPcRo0aSfkMMPQrAiIgAiJQFIHWrVubP5b8S5fFixeX5XaG6fnUefIIdO/e3TZRCClnKTGulbuUhAJa7pCVPxEQAREQgbon0KpVK4skPrmVCywbxBqg4X7dp0QxiED+BOhqZy4MY0FZ3orxyp07d84/gIS6rNcu+IQyUrJFQAREQAQSQKBFixaWSpTNuCxbtsxe7i1btoxf1rEIlAQBJs+xpirDRBiOyAdTUpdfKgSoFNBCaMmtCIiACIhAyRIICmYmBZREBwW1ZDOghFUkgTA5jolyq1atMgbhWjkDkQJazqWrvImACIhABRFg4hGLX7MZRVyCQioFNE5Fx6VCgDXRESygQQEN10oljXWRDimgdUFVYYqACIiACKwRAixfQ5d7XKSAxmnouNQIhLHJjF2mCx6phI8lKaClVhOVHhEQAREQgaIJ8OJmH+34TPiwDWclvNSLBiePa4wA64Cuu+667q677rKxyqyRzlbl5S5SQMu9hJU/ERABEaggArvuuqt76qmnbGeZ1atXO9ZZ5G/o0KHahrOC6kHSssp66K+99pole/jw4UlLflHplQJaFDZ5EgEREAERKEUCp556quMP5XPhwoXWlYmFSSICpUxg3LhxZrVv0KBBxSwXpnVAS7lGKm0iIAIiIAIiIAIiUIYEpICWYaEqSyIgAiIgAiIgAiJQygSkgJZy6ShtIiACIiACIiACIlCGBKSAlmGhKksiIAIiIAIiIAIiUMoEpICWcukobSIgAiIgAiIgAiJQhgSkgJZhC7RkiwAAH4VJREFUoSpLIiACIiACIiACIlDKBKSAlnLpKG0iIAIiIAIiIAIiUIYEpICWYaEqSyIgAiIgAiIgAiJQygSkgJZy6ShtIiACIiACIiACIlCGBKSAlmGhKksiIAIiIAIiIAIiUMoEpICWcukobSIgAiIgAiIgAiJQhgSkgJZhoSpLIiACIiACIiACIlDKBKSAlnLpKG0iIAIiIAIiIAIiUIYEpICWYaEqSyIgAiIgAiIgAiJQygQalnLiMqWtadOmbuTIkZluJe7aF1984dZee23XpEmTxKW9lBO8cuVKF0WRo65Iao/A119/7b766ivXrFmz2gtUIblvv/3WUWfXW289t9ZasgnUVpWgDaCNpX1t2DBxr7rawlAn4cC1UaNGbt11162T8Cs10BUrVrgGDRqUjU7QtWvXaouygX9Io2pd6GadEdhjjz3cwIED3e9+97s6i6MSAz711FPd3Llz3R133FGJ2a+zPF977bXuqquucjNmzKizOCox4GeeecYdffTR7rHHHnObbrppJSKokzwvWrTI7bDDDu6KK65wI0aMqJM4KjXQ7bbbzh1yyCHu5JNPrlQEdZLvUaNGucaNG7urr766TsIvtUD1uV1qJaL0iIAIiIAIiIAIiECZE5ACWuYFrOyJgAiIgAiIgAiIQKkRWHusl1JLVKWkh/Gfffv2dZ07d66ULNdLPhlH1717d9erV696ia9SIoFr+/btbdhIpeS5PvIJ1xYtWrjBgwdb91t9xFkJcTCWjjGKcG3VqlUlZLne8kid3XrrrV2HDh3qLc5KiAiuPXv2dJtttlklZNdpDGhFFLMyKQIiIAIiIAIiIAKlQ0Bd8KVTFkqJCIiACIiACIiACFQEASmgRRbzO++845588klbPqXIIHJ6Y8kbSWkQKPeyWLp0qZs8eXJG2C+99JKbN29exntcrM5vVk8VcuO1115zs2fPrja34lctnow382H2n//8x3355ZcZ/VfqRVZaYKmfpEq5t8NJLZdi0y0FtAhyv/71r93f//5399Zbb9mapFOnTi0ilOq9vPzyy+7Pf/5z9Y50t14IVEJZ8EJ/+umnM/K877773BtvvJHxHher85vVU4XceP311/NSQLOxrxBMBWcznzrHUja4k/yPwN/+9jf32Wef/e9Cgo5YL/fII49MUIqV1FwEpIDmIpR2f9asWY715caNG+eOO+44+7v99ttTrpYvX+6wjqZ/ZbLcKlakTz75JOU2HHz66adV/Hz33Xfugw8+sK/3+Bc8Dcf7779vC1cHv8uWLbPz+fPnh0uJ/IULixunCy+QODMaIdytXr3affjhhykWcF+yZEnKe3DH4ulz5sxxMI0LC3+/++67bvHixfHLdkzZffzxx3acrSx+4KmEL1BHEPL0+eef2zF8WCs1SKdOndyYMWPCqf1+9NFHbtWqVVWu4S+dW9wv4VNm8b/AHuvFe++9VxZWqVC/ctXDPffc0+2yyy7GMJQD9TlepwM/wgpuAnSe/2D1yVRn4+kIbUbwWw6/8KDtpK7G29TALJ7HbG0IbmAY6j5hhjrJvaCkBpbZ2gzclppkeh65RnsYhPzE3yNc59lOr2tcj7d9nAdJb4e5HvxX167kYkqdp2xpo3Ebl/S08P5bsGBBqrzibpN0nElHIK/hOScvlBflRp2Nc+FapvdkkvIfT6u2h4jTyOOYmdXXXHNNyiUPYXhJ0+XDYt3dunVzb775pjv++OPdbrvtZo0Bi6OHh5H7f/jDH0yJOvfcc+1l1Lx5c7Mycc4suAcffNAecBZT/8UvfuHGjx/vJk6c6Dp27GgK8F/+8hdHI8wXYYjvj3/8o82qTyUuAQc8aFiUefiYAdivXz9b3BjFEEbwpeFhRwXyB1fyzi4czHJduHChO/DAA92jjz7qvvnmG3vZH3PMMebuT3/6kzFn5jZK02WXXWYrDsD2X//6l4WJhWro0KHu9NNPN1qU7YQJE4wpD/p55533g7JIANYqSWTnMFZbQEF/++233c9//nPjxc47NHz//Oc/zZqPxf366693fBCdeOKJtuMRftZZZx0LD9YoqfDkuEuXLraJAj0BwS9lg3+EsuOj6/7777d42XCBcsQ94bMRQ1Il33r4j3/8w22wwQb2nFIOW265pT3vKEvDhg2zuh740aty2GGHWXlsuOGGpnjhh3rMh2+mOks6LrnkElOo2KGKev773/++bFYqIP+d/SohKIx8cB5xxBHugAMOqFJfs7UhoW7BmnpLu8DHwM9+9jN33XXXORijAO299942nAqW2dqMEFYp/WZ7HmfOnGnvC+oTwtAahovRniI8h7S1GDPgefjhh9v19LaP9pJ3W6Z2mBVcKJtc7Up1TImf9127du2sbeIdQBp4XjKl5aabbrJn4sILL7R3QRJ3t8qmI/B+p11lcX+EPNI+0LvK+z1cR79gofqjjjrK3CX+HzshSYoj4Bu2aP/994+ef/55C8DvChE98cQTduwfvOiee+6xY/8Cji6++GI79kpodNppp0Wvvvpq5C2l0fnnn2/X+XfLLbdE/oG083//+9+RV37s2L/QLR7/gNq5V5Ciyy+/3I5942nx+AbazpP279JLL418A2fJ9gpk5JXRyFuYI786WOSVmdT1E044IXrxxRcj37hGO++8c+S/uu3eSSedFPmXix1THnvttZcd487vghL5Rs7OfTdy5D8IIjidc845kVeM7Lq3HEc77rhj5F9Ekd/hJ/IWq8hbmuzeDTfcEHnFNoqXhd1I2L999903uvvuuy3V9957b7TTTjtF3qJh58cee2z0yiuvRF7BifwLxa5RJ//617/asVf+I//SjnzDGT3wwAOp+um/xKMrr7wy8i//Kn7Nk//nv9wj/2JLPQ88G34sqd32Sm106KGHWlkE90n7zbcewujGG2+07FEOXiG1Y+od5UC9i7OnzntF09xMnz498i+aautsqOfesmx+br755lQZ2YWE/4OZ/0CyXNAO+pdy5BWvKsyytSH77bdf5IfPRF6pj/wQhxQJnvHQftAe007Q9gSW6W1GymOJHWR7HqdNmxaNHj06lVo/7jM666yz7HyfffZJ8YRjaEuztX3Z2mECy6ddqY4p70feZUGo67w/s6XFK8ORNxYE54n8zaYj8NzfdtttqTxRXpQbZel3R7LrtBUwD++ulOMEH6gLvshPCN9ImRWHrbO23XZbC+XHP/6xfS1i0WPyAdZPhK9PLEHhy43ujKeeesq+bPj6pAvfv/Sdb1CqmOHNs//HVn187dHtTxheEbOvWl/vzEn//v3NGhjcJ+mXsYVsl4eQRyxobdq0MX58AYbr/mXtJk2aZOcbb7yxWS844atxm222sev4g23osthkk03MesLN7bff3qyiWFLOOOMM5x9ss4hecMEF9lWN9RRr6KBBg1JrMWIpHD58uIWd9H9YlhF4bb755rbuJOcwY0hJXODAVnsIlvk+ffrYMWyo13DBUooFs2nTpnYv/o9uIt+A2vaHlCFl4j+43COPPGL1l7F5dEMRVpIl33oYzyP1EMEvz2/oPQluYIo1H+H3Jz/5iT3b2eos7jbaaCOzRnNMnceqXU5CDwXSsmVLWx8Rq1pcsrUhuPEf8dauhPoc95fpOFObgZW0FCXf5zE97V7ptEtt27a1dxAW+GxtH89otnaYQPJpV7Ix9cqUrc+KVe/MM8+0rnisoNnSYolO+L9sOkK2bPFux8KPvsFcBHpAacPLRdQFX0RJ0uDxgj3llFMcilEQ9humoZsyZYq9POh6pNsAYTHk8LByTncZShDdZZjX6VbiPn7ThRcVFY8HNpM0adIk0+VEXEPBCQojCWY8EV0wKEYohUFQasKLID2/oYuYLvlswvg53BEm3RdDhgwxxZUuT9gj6WlBSUpXELKFX+rX48xYnLs6YXgDvIOEri6UJoaEeMucfQB566ljUkNcqKsXXXSRLVBN914QwvDWaVMGuEZdTvoi1nGm5CmfeshzHyRTfe3Ro4d1j/ISfvbZZ90vf/lLq4PZ6ixh8bxUivAc0wUZl/TnNrQhuGHoEx/2vnfJPpyCv9CWVKeshzaD7uZSlOqex5A/0k0+4kL3exAUPt4tKDnxdji0fdW1w4QRfwZytSu4jzP1lmsbE85HFx9anCPp5RnSUg71vDodIVOZ0UbABuMLQ0k4Lif5X00sp1zVYV4Ym4g1gi/ruPJJlGeffbZZdfjK4YuOAdM81Iw74oXCy6V3797u1ltvtfGejOsaOHCgKaBbbLGF8135qUaAlxmNA0I8fKXS4OCfAeTeXJ9Yq6dl6vt/5I0ZwDx8KD2+S93GfMKMJUPgx0sCizF5L0TgxIQwhAd4wIABZu1jbKLv0rePBd/9bPeJH0ssljruI4yhevzxx02xCGVhN8r8H5zIN9Zi6jsKJ+KHIth4Q+osL/ZNN93U6ngcB2O3GAMaxtRyD8sVZYdiwC9j7xhXFm9w42FU+jEvZMZ1Yv1AYcVCna3OVgIr3y1r2WQCG3WItjIu2doQ3LAjGnWRDyfGxyIoMliUkBdeeMF+w79MbUa4V2q/2Z5HFDg4BeU6PY9Y1nm2WR6M9pVx2dnavrpqh2FJW4vxZffdd7cx/Vi2q2uHw4dw3DBRamWSKz3ZdATKLNTJMFY/hAUfelGxgAbrdbiX9F9ZQAsswTvvvNMUJD+WI+WTbd6wdvIw8RVH9yQvDZQpHhoeYgaCM1mGLfeYvPGjH/3IXu5USJQhHipe/GEtRhpOJnb85je/sQHXBx98sPPj5qyLjS9yrpeD8PJgYDbs+JrGGkkXA8MXUEzJN42SH6dpVjMU+XyFr/exY8daV+f6669vHFGGiBOLEtc6+wkOxMdMTLqm/ZheSwvKPl2blCFfnvGyyDf+pLrDskm9xDqMRTNsC0c9RpH3Y0XtY4E6imU/KPkonn4MoqNrj3LDL8JEAybSURZ8fPHy437r1q2TiqhO003dx7J89NFHWzx0YWars3WakBIJHEWFoUpYz5hAE7cik8RsbUhIPs83dZaPHj4qscwz9IYPIZ55Xv5BMrUZ4V6p/WZ7HrFEbrXVVvb8wooPGZSaIEzm4tlmhjWGEgR3mdo+eoAytcMhrHx+szElDX6subvrrrvMmEKaaYezpSVsXe3HsVo7k8TtVbPpCBit6FFlghzvwTDsCb7UU/6YrBW3OOfDvtTdaCvOOighHmyUm3hXB9HQzYFCmt5VgXWDr/L0Ljle1CimwT1fqzTC6Q1wHWSh3oOkm4XxhOndXVyny41u4UIE6zIfA8xCJox0ZlgHiCuwjYcNc/7i4xvTyyLuvlyP4cZM+fR6zEsJ5bKYxpDxoHwESAonUF2dLTy0ZPjwE4ncVVddZe0jbWp6GxnPRbY2JO4mHPN806tBmEFytRnBXan9Znse4cEzGiyH8XRnu5ep7cMf7uuiHaZdZbWRTN3r2dJCftOHYcTzloTjbDpCpbWPsoDWQW3N9DARDS/zTBL/Ao/f58UfV5BoSNIVqbj7JB9ny1e264XkNVMYceUyPSyU3XSFN70s0v2U43kmbuSzJo2/lM/ia0p1dbb4UJPhM1tdjKc+HzfBfaZnPNzjt5Cw4v7WxHG257G6PGS7l41LNveF5DdTGLSr2d6X2dKSLb+FpGVNu82W50prH2UBXdM1UfHXCQGsyoxxYryiRAREIJkEnnvuORuaVIy1vdAcq80olFhu92Kam1Elu5ACWsmlr7yLgAiIgAiIgAiIwBogoFnwawC6ohQBERABERABERCBSiYgBbSSS195FwEREAEREAEREIE1QEAK6BqArihFQARqhwBLdLEzGHt4s0MTSz6FZaFCDOyhHnYXCtdK5ZdNLfzWvLZMEGtQBmEZN5Yc4n4mYR9w7s+ZMyfT7SrXWMaIXdSyCQwJK6xDmM0d10uZZXXp1j0REIHSIyAFtPTKRCkSARHIgwDLtLBMD2vEss0nE1XYoIGFylm4OQhKE/dLUdgdinVwWeMvPjOWtRNvuOEGd8UVV2RMNjussYYia9XmEr+3tino2dyhgLIuZr4KaKmyzJY/XRcBEShNAlqGqTTLRakSARHIQYBFm6dOneo++OCDKvsjH3nkkbbRwMyZM7MufZYj6Hq5zZq+7M4zceJEx0LUcWG9y1GjRrnLL7/c/sI2n8HNjTfeaAuqp18P9wv5JQyUeYkIiIAI1CcBWUDrk7biEgERqBUCKJ3sSnbttddWUT4JnA0I2E0IN5mELR3HjBljWwCy+wvd02wDGwTljr3q2Xf5rLPOsq1Fwz22bkUx3HXXXd0xxxxjCnC4l+mXZYQYGkB62D0tdLPTdc4+78j48ePdlVde+QPvxPPZZ5/9wHo7bdo0x6LpYackPLKw9TnnnGN5YqcftlJNF7byIy1YXS+77LLUTlVscEFe2AoxCFZjwkGZv+eee1JbBIf74TefeINb/YqACIhAnIAU0DgNHYuACCSCAPtbs5MVil26sMUnWy6y73y60M3ct29fUypRsNhm9PzzzzflDbd0bbMlHt36bBfKPuRsm4uwJSt7MbMQNgobVsohQ4ZUUdzM4ff/JkyYYOGwFiJbzE6ZMsVtueWWDgWYReXZehDp2bOnbQn5vbfUD1twkj+2N40LCjLx4g9hh7Wtt97asTc42xSyYQX7yaNEBiENbOVL/Phjq1XGzSLsRnPddde5efPm2fl5553nfvWrXzm2o2UdXbajpbs/XfKJN92PzkVABEQgRcBvqScRAREQgUQROP3006POnTvnlWavxEXe4mluvZIW+X3pIz/uMeXXWxojr3Da+ejRoyPce6XMzn0XeXTJJZdEfvu/aNKkSZFX7iKviNo93HDv9ddfT4UVPyB9fphA/JKl2SuCds0rhZFviCNvJa3iJn7irbyRV3gj3CLeUhv58aHR9ddfb+f883ucR36XtchbS6tc8/tHWz68Um3xeAto6v5BBx0UjRgxws79lpR2n/zNnz/fjidPnpxy6/fqjvy+3RZWnGWueFMB6EAEREAEMhDQGNCUKq4DERCBpBBo3769YyY41rv0veqry8Puu+9uVkwsm8ww98qj84pXajLPIYccYuMxu3fvbl3we+65p1kAsSpus802rlu3bmY1HT58uLmjixqLa7qwpzNDALySVuUW4RUyIx+LJvuV0w1Ol/zDDz9swwW8ApkKl3GwTEa66KKLUtewZmKxnTt3rl3Datu/f//Ufayv6ZZVbjLEgO1/sQAHOfDAAx1/6ZIrXiy4EhEQARHIRkBd8NnI6LoIiEDJEkCBWrFihSl56Yn0H9rujDPOsO7z9HssSdSlSxebpMTyTXTH77TTTilnw4YNc9OnT3coos8++6yNqUTxZCwmiiBKF4oecR9//PGua9euVWbch4Bwj3To0CFcsl9vlXTMOs9XmCDEUIGgLNL9Tld6fF94lF3OUcTDH8of41eDcu4tpKlj4g7X09OB0spqAgwvyCX5xJsrDN0XARGoXAKygFZu2SvnIpBYAiigbdu2tfGbvju6Sj5uvfVWm1jEuMh0GTt2rC3ThNWTMaQIimZQCrFONmvWzF144YX2h0Vw8ODBZrUcMGCAe+mll0zxRPlkAg6W0KuuusqhuMalU6dODuWRJYuGDh2aukX4cUtk6kY1B37IgOvXr5+NNcUCysSmuGCtfeyxx2wppaBYMrueMacwKkSw8KI8Y11maSgEBowLvf3226sEVZvxVglYJyIgAhVBQBbQiihmZVIEyosA1kg/NtEsg0ySQTFkZrkfk2kLu9OFzAz3dKGr+tNPP3UsgYSl9IEHHnB333238+MgzSlrZmJxnD17tt3/+OOPbQY4ihkycuRIc4/CigKKFTDcMwff/0O59eNJLX0ss0R8zNhn8tRPf/rTuNOcx3369HGDBg2yWe9MrGJiUFyOPfZY62pnMXnSs2DBAnf44Ye7hx56yJTguNtcx9tvv73r1auXO+6442yyFAvis7g/imzc6ko4tRlvrnTpvgiIQBkSyDAuVJdEQAREIBEEmCDDZBqvkNrkmUaNGkV+hnrklcxU+uMTZ7xiaROOvDIVtWrVKvLLKUV+rc3URB+vKEa++z3y4zptYo+3htr9EJhfvijq0aOH3WNCkp/dnpogFNyE3+XLl9uEJ6+MRn5cZdSxY8fo6quvDrfNn3+lVDsJKTi+5pprLH/e2houVfn1Vt/IWywtHr+gfeSV78grz+aGSUjkJy4XX3xx5BVbuxSfhMQFPzY28kpuRLqbN29uPPxYUnMbZ8mF6uI1D/onAiIgAlkINOB6GerVypIIiEAFEWAtS9bY9Eqe80pozpwvWbLErIN0t2cSJjcxkYfwMo2HxDLaokULW5Ipk//4NayrWBIJq66FNGOtrI0F6mHEhCSszbmkNuPNFZfui4AIlAcBKaDlUY7KhQiIgAiIgAiIgAgkhoDGgCamqJRQERABERABERABESgPAlJAy6MclQsREAEREAEREAERSAwBKaCJKSolVAREQAREQAREQATKg4AU0PIoR+VCBERABERABERABBJDQApoYopKCRUBERABERABERCB8iAgBbQ8ylG5EAEREAEREAEREIHEEJACmpiiUkJFQAREQAREQAREoDwISAEtj3JULkRABERABERABEQgMQQaJialSqgIiIAIiIAIiIAIJITAo48+6l5++WXbNW3YsGGuZ8+eCUl5/SRTFtD64axYREAEREAEREAEKoTA6NGj3emnn+4+//xz99///tehgN55550Vkvv8sqmtOPPjJFciIAIiIAIiIAIikJPAZ5995jbccEM3b94816ZNG3N/0003ucsuu8xNmzYt5X/hwoXuq6++cptssknq2pIlS1zLli3dWmv9v31w0aJFrm3btu6bb75xK1eutN8oilLhzpkzxzVv3tz+QiDcf++999w666xTJezly5e7Bg0auPXXXz84td8vvvjCNWrUyNJCfN26dbPr77//vtt4441d48aN7Zy0tWrVyr377rtuo402+kE4+F21apX5IcwWLVpUiSf9RApoOhGdi4AIiIAIiIAIiECRBFavXm0K2pgxYxyWUBRIlEL+UCznz5/vRo0a5RYvXuxQ2rbcckv34IMPurXXXtu1a9fOuu1RSgmnYcOGpnS+8MIL7uSTTzb3KJZ07e++++5uxYoV5m/o0KGm4C5dutSuo7CiCPft29fdd999pngec8wxrmnTpu7yyy+vkrNTTjnFvfPOO27BggWWpm233dZ9++237pNPPnEooXfccYcbMmSI69ixo+vVq5el64033nBnn322O/HEEy2so446yk2aNMltsMEGll8U3ccff7xKPOkn6oJPJ6JzERABERABERABESiSAIokyhcKWYcOHVz//v3deeedZ9ZBgjzttNPcZpttZkoklkqshbmUNfzNmjXLPf/88+7tt9925557rimDM2bMcC+++KJ78803TbGlm3/AgAFmaZ09e7Ypr/hBLrnkEnfBBRfYcfo/rKMvvfSS/RHGiBEj3DPPPONOPfVUd/vtt6ec77DDDpbW6dOnWz6w8t5///2WLuJ79dVXTQFNeajmQApoNXB0SwREQAREQAREQAQKJYDl8cknnzSl8KyzznKTJ092O++8swWDNfPAAw+0YyycBxxwgLvttttyRtG1a1dTaLGiTp061e29997mh+7zRx55xLVv397dddddZs0cOXKkwypJN//dd99t7po1a+b4yyQollgtsdbiBgUUIcy5c+emvOy33352jKUWRZfxrSi4e+yxh3X5Ewb5yUc0Cz4fSnIjAiIgAiIgAiIgAnkQwJqJQjhu3Dgbq3nwwQebwsn4SSyeWEW//vrrVEiM7aTLG0GBC8dYJeMSH7vJ2FK62YN8+OGHrnXr1naK8rjjjjuGWzamNHWS5SAeNk7CuM8szu0yllu69BknOnPmzJTTZcuWpY6rO5AFtDo6uicCIiACIiACIiACBRDo0aOHGz9+vLvnnnvcd999Zz5ZkglLZadOndxBBx3kbrnlFlM0UeJwt91225k7lNTXXnvNjidOnJg1VqyMjO1knCgTmbCuYu1E2aVLfuutt3aM5UQJpmsdobuccZ41EcaDIgwHQOkdOHCgwyqKws04UiYqkbd8RBbQfCjJjQiIgAiIgAiIgAjkQYDJOhMmTHDHHnusTTZi0hBd1ihpdLkfdthhpjwy2xxr5z777OOOO+44C5mJPUcccYQpqltttVVqtnt6tPvvv78pet27d7fZ6CeddJKjix4rKApt586dzW/v3r3doYceat7Hjh2bcRJSetjVnU+ZMsXWM0VxZmwoM/b5Y1zrLrvs4lC++/TpY8podeFwT7PgcxHSfREQAREQAREQAREogsCXX35pXeWZliRiljpd2CiocaF7nm55llfKJYTBmE0mPsWF9UcJN5+u9Li/6o5RrBnLipJL2hgugLAsE9ZQFFCEyU50yV9//fV2nu2fLKDZyOi6CIiACIiACIiACNSAwHrrrZfVdyalFMcojulKabZAsoXBckh1JelxsiYoC+0feeSRFiVd8Pfee2/O6GUBzYlIDkRABERABERABESgsgk8/PDDNtY0k1LN+qMsO4X1lvGoTLTKJVJAcxHSfREQAREQAREQAREQgVoloFnwtYpTgYmACIiACIiACIiACOQiIAU0FyHdFwEREAEREAEREAERqFUCUkBrFacCEwEREAEREAEREAERyEVACmguQrovAiIgAiIgAiIgAiJQqwSkgNYqTgUmAiIgAiIgAiIgAiKQi8D/AS2KnXWPV80eAAAAAElFTkSuQmCC" /><!-- --></p>
</div>
<div id="金字塔图" class="section level3">
<h3>4.7 金字塔图</h3>
<p>金字塔图提供了一种独特的方式来可视化每个类别包含的样本比例。下面的金字塔是一个很好的例子，说明了在一个营销活动中每个阶段的用户留存率。</p>
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggthemes)</span>
<span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a><span class="fu">options</span>(<span class="at">scipen =</span> <span class="dv">999</span>)  <span class="co"># turns of scientific notations like 1e+40</span></span>
<span id="cb33-4"><a href="#cb33-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb33-5"><a href="#cb33-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Read data</span></span>
<span id="cb33-6"><a href="#cb33-6" aria-hidden="true" tabindex="-1"></a>email_campaign_funnel <span class="ot">&lt;-</span> <span class="fu">read.csv</span>(<span class="st">&quot;https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv&quot;</span>)</span>
<span id="cb33-7"><a href="#cb33-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb33-8"><a href="#cb33-8" aria-hidden="true" tabindex="-1"></a><span class="co"># X Axis Breaks and Labels </span></span>
<span id="cb33-9"><a href="#cb33-9" aria-hidden="true" tabindex="-1"></a>brks <span class="ot">&lt;-</span> <span class="fu">seq</span>(<span class="sc">-</span><span class="dv">15000000</span>, <span class="dv">15000000</span>, <span class="dv">5000000</span>)</span>
<span id="cb33-10"><a href="#cb33-10" aria-hidden="true" tabindex="-1"></a>lbls <span class="ot">=</span> <span class="fu">paste0</span>(<span class="fu">as.character</span>(<span class="fu">c</span>(<span class="fu">seq</span>(<span class="dv">15</span>, <span class="dv">0</span>, <span class="sc">-</span><span class="dv">5</span>), <span class="fu">seq</span>(<span class="dv">5</span>, <span class="dv">15</span>, <span class="dv">5</span>))), <span class="st">&quot;m&quot;</span>)</span>
<span id="cb33-11"><a href="#cb33-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb33-12"><a href="#cb33-12" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
<span id="cb33-13"><a href="#cb33-13" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(email_campaign_funnel, <span class="fu">aes</span>(<span class="at">x =</span> Stage, <span class="at">y =</span> Users, <span class="at">fill =</span> Gender)) <span class="sc">+</span>   <span class="co"># Fill column</span></span>
<span id="cb33-14"><a href="#cb33-14" aria-hidden="true" tabindex="-1"></a>                              <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">&quot;identity&quot;</span>, <span class="at">width =</span> .<span class="dv">6</span>) <span class="sc">+</span>   <span class="co"># draw the bars</span></span>
<span id="cb33-15"><a href="#cb33-15" aria-hidden="true" tabindex="-1"></a>                              <span class="fu">scale_y_continuous</span>(<span class="at">breaks =</span> brks,   <span class="co"># Breaks</span></span>
<span id="cb33-16"><a href="#cb33-16" aria-hidden="true" tabindex="-1"></a>                                                 <span class="at">labels =</span> lbls) <span class="sc">+</span> <span class="co"># Labels</span></span>
<span id="cb33-17"><a href="#cb33-17" aria-hidden="true" tabindex="-1"></a>                              <span class="fu">coord_flip</span>() <span class="sc">+</span>  <span class="co"># Flip axes</span></span>
<span id="cb33-18"><a href="#cb33-18" aria-hidden="true" tabindex="-1"></a>                              <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Email Campaign Funnel&quot;</span>) <span class="sc">+</span></span>
<span id="cb33-19"><a href="#cb33-19" aria-hidden="true" tabindex="-1"></a>                              <span class="fu">theme_tufte</span>() <span class="sc">+</span>  <span class="co"># Tufte theme from ggfortify</span></span>
<span id="cb33-20"><a href="#cb33-20" aria-hidden="true" tabindex="-1"></a>                              <span class="fu">theme</span>(<span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">hjust =</span> .<span class="dv">5</span>), </span>
<span id="cb33-21"><a href="#cb33-21" aria-hidden="true" tabindex="-1"></a>                                    <span class="at">axis.ticks =</span> <span class="fu">element_blank</span>()) <span class="sc">+</span>   <span class="co"># Centre plot title</span></span>
<span id="cb33-22"><a href="#cb33-22" aria-hidden="true" tabindex="-1"></a>                              <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">&quot;Dark2&quot;</span>)  <span class="co"># Color palette</span></span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
</div>
<div id="组成" class="section level2">
<h2>5 组成</h2>
<div id="华夫饼图" class="section level3">
<h3>5.1 华夫饼图</h3>
<p>华夫图可以显示总体的组成成分。</p>
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a>var <span class="ot">&lt;-</span> mpg<span class="sc">$</span>class  <span class="co"># the categorical data </span></span>
<span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb34-3"><a href="#cb34-3" aria-hidden="true" tabindex="-1"></a><span class="do">## Prep data (nothing to change here)</span></span>
<span id="cb34-4"><a href="#cb34-4" aria-hidden="true" tabindex="-1"></a>nrows <span class="ot">&lt;-</span> <span class="dv">10</span></span>
<span id="cb34-5"><a href="#cb34-5" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">expand.grid</span>(<span class="at">y =</span> <span class="dv">1</span><span class="sc">:</span>nrows, <span class="at">x =</span> <span class="dv">1</span><span class="sc">:</span>nrows)</span>
<span id="cb34-6"><a href="#cb34-6" aria-hidden="true" tabindex="-1"></a>categ_table <span class="ot">&lt;-</span> <span class="fu">round</span>(<span class="fu">table</span>(var) <span class="sc">*</span> ((nrows<span class="sc">*</span>nrows)<span class="sc">/</span>(<span class="fu">length</span>(var))))</span>
<span id="cb34-7"><a href="#cb34-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb34-8"><a href="#cb34-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb34-9"><a href="#cb34-9" aria-hidden="true" tabindex="-1"></a>df<span class="sc">$</span>category <span class="ot">&lt;-</span> <span class="fu">factor</span>(<span class="fu">rep</span>(<span class="fu">names</span>(categ_table), categ_table))  </span>
<span id="cb34-10"><a href="#cb34-10" aria-hidden="true" tabindex="-1"></a><span class="co"># </span><span class="al">NOTE</span><span class="co">: if sum(categ_table) is not 100 (i.e. nrows^2), it will need adjustment to make the sum to 100.</span></span>
<span id="cb34-11"><a href="#cb34-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb34-12"><a href="#cb34-12" aria-hidden="true" tabindex="-1"></a><span class="do">## Plot</span></span>
<span id="cb34-13"><a href="#cb34-13" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(df, <span class="fu">aes</span>(<span class="at">x =</span> x, <span class="at">y =</span> y, <span class="at">fill =</span> category)) <span class="sc">+</span> </span>
<span id="cb34-14"><a href="#cb34-14" aria-hidden="true" tabindex="-1"></a>        <span class="fu">geom_tile</span>(<span class="at">color =</span> <span class="st">&quot;black&quot;</span>, <span class="at">size =</span> <span class="fl">0.5</span>) <span class="sc">+</span></span>
<span id="cb34-15"><a href="#cb34-15" aria-hidden="true" tabindex="-1"></a>        <span class="fu">scale_x_continuous</span>(<span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">0</span>)) <span class="sc">+</span></span>
<span id="cb34-16"><a href="#cb34-16" aria-hidden="true" tabindex="-1"></a>        <span class="fu">scale_y_continuous</span>(<span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">0</span>), <span class="at">trans =</span> <span class="st">&#39;reverse&#39;</span>) <span class="sc">+</span></span>
<span id="cb34-17"><a href="#cb34-17" aria-hidden="true" tabindex="-1"></a>        <span class="fu">scale_fill_brewer</span>(<span class="at">palette =</span> <span class="st">&quot;Set3&quot;</span>) <span class="sc">+</span></span>
<span id="cb34-18"><a href="#cb34-18" aria-hidden="true" tabindex="-1"></a>        <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Waffle Chart&quot;</span>, <span class="at">subtitle=</span><span class="st">&quot;&#39;Class&#39; of vehicles&quot;</span>,</span>
<span id="cb34-19"><a href="#cb34-19" aria-hidden="true" tabindex="-1"></a>             <span class="at">caption=</span><span class="st">&quot;Source: mpg&quot;</span>) <span class="sc">+</span> </span>
<span id="cb34-20"><a href="#cb34-20" aria-hidden="true" tabindex="-1"></a>        <span class="fu">theme</span>(<span class="at">panel.border =</span> <span class="fu">element_rect</span>(<span class="at">size =</span> <span class="dv">2</span>),</span>
<span id="cb34-21"><a href="#cb34-21" aria-hidden="true" tabindex="-1"></a>              <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="fu">rel</span>(<span class="fl">1.2</span>)),</span>
<span id="cb34-22"><a href="#cb34-22" aria-hidden="true" tabindex="-1"></a>              <span class="at">axis.text =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb34-23"><a href="#cb34-23" aria-hidden="true" tabindex="-1"></a>              <span class="at">axis.title =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb34-24"><a href="#cb34-24" aria-hidden="true" tabindex="-1"></a>              <span class="at">axis.ticks =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb34-25"><a href="#cb34-25" aria-hidden="true" tabindex="-1"></a>              <span class="at">legend.title =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb34-26"><a href="#cb34-26" aria-hidden="true" tabindex="-1"></a>              <span class="at">legend.position =</span> <span class="st">&quot;right&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="饼图" class="section level3">
<h3>5.2 饼图</h3>
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_classic</span>())</span>
<span id="cb35-3"><a href="#cb35-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb35-4"><a href="#cb35-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Source: Frequency table</span></span>
<span id="cb35-5"><a href="#cb35-5" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(<span class="fu">table</span>(mpg<span class="sc">$</span>class))</span>
<span id="cb35-6"><a href="#cb35-6" aria-hidden="true" tabindex="-1"></a><span class="fu">colnames</span>(df) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;class&quot;</span>, <span class="st">&quot;freq&quot;</span>)</span>
<span id="cb35-7"><a href="#cb35-7" aria-hidden="true" tabindex="-1"></a>pie <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(df, <span class="fu">aes</span>(<span class="at">x =</span> <span class="st">&quot;&quot;</span>, <span class="at">y=</span>freq, <span class="at">fill =</span> <span class="fu">factor</span>(class))) <span class="sc">+</span> </span>
<span id="cb35-8"><a href="#cb35-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="at">width =</span> <span class="dv">1</span>, <span class="at">stat =</span> <span class="st">&quot;identity&quot;</span>) <span class="sc">+</span></span>
<span id="cb35-9"><a href="#cb35-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.line =</span> <span class="fu">element_blank</span>(), </span>
<span id="cb35-10"><a href="#cb35-10" aria-hidden="true" tabindex="-1"></a>        <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">hjust=</span><span class="fl">0.5</span>)) <span class="sc">+</span> </span>
<span id="cb35-11"><a href="#cb35-11" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">fill=</span><span class="st">&quot;class&quot;</span>, </span>
<span id="cb35-12"><a href="#cb35-12" aria-hidden="true" tabindex="-1"></a>       <span class="at">x=</span><span class="cn">NULL</span>, </span>
<span id="cb35-13"><a href="#cb35-13" aria-hidden="true" tabindex="-1"></a>       <span class="at">y=</span><span class="cn">NULL</span>, </span>
<span id="cb35-14"><a href="#cb35-14" aria-hidden="true" tabindex="-1"></a>       <span class="at">title=</span><span class="st">&quot;Pie Chart of class&quot;</span>, </span>
<span id="cb35-15"><a href="#cb35-15" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;Source: mpg&quot;</span>)</span>
<span id="cb35-16"><a href="#cb35-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb35-17"><a href="#cb35-17" aria-hidden="true" tabindex="-1"></a>pie <span class="sc">+</span> <span class="fu">coord_polar</span>(<span class="at">theta =</span> <span class="st">&quot;y&quot;</span>, <span class="at">start=</span><span class="dv">0</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Source: Categorical variable.</span></span>
<span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a><span class="co"># mpg$class</span></span>
<span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a>pie <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(<span class="at">x =</span> <span class="st">&quot;&quot;</span>, <span class="at">fill =</span> <span class="fu">factor</span>(class))) <span class="sc">+</span> </span>
<span id="cb36-4"><a href="#cb36-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>(<span class="at">width =</span> <span class="dv">1</span>) <span class="sc">+</span></span>
<span id="cb36-5"><a href="#cb36-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.line =</span> <span class="fu">element_blank</span>(), </span>
<span id="cb36-6"><a href="#cb36-6" aria-hidden="true" tabindex="-1"></a>        <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">hjust=</span><span class="fl">0.5</span>)) <span class="sc">+</span> </span>
<span id="cb36-7"><a href="#cb36-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">fill=</span><span class="st">&quot;class&quot;</span>, </span>
<span id="cb36-8"><a href="#cb36-8" aria-hidden="true" tabindex="-1"></a>       <span class="at">x=</span><span class="cn">NULL</span>, </span>
<span id="cb36-9"><a href="#cb36-9" aria-hidden="true" tabindex="-1"></a>       <span class="at">y=</span><span class="cn">NULL</span>, </span>
<span id="cb36-10"><a href="#cb36-10" aria-hidden="true" tabindex="-1"></a>       <span class="at">title=</span><span class="st">&quot;Pie Chart of class&quot;</span>, </span>
<span id="cb36-11"><a href="#cb36-11" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;Source: mpg&quot;</span>)</span>
<span id="cb36-12"><a href="#cb36-12" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb36-13"><a href="#cb36-13" aria-hidden="true" tabindex="-1"></a>pie <span class="sc">+</span> <span class="fu">coord_polar</span>(<span class="at">theta =</span> <span class="st">&quot;y&quot;</span>, <span class="at">start=</span><span class="dv">0</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="条形图" class="section level3">
<h3>5.3 条形图</h3>
<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a> <span class="co">#prep frequency table</span></span>
<span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a>freqtable <span class="ot">&lt;-</span> <span class="fu">table</span>(mpg<span class="sc">$</span>manufacturer)</span>
<span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">as.data.frame.table</span>(freqtable)</span></code></pre></div>
<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_classic</span>())</span>
<span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb38-4"><a href="#cb38-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
<span id="cb38-5"><a href="#cb38-5" aria-hidden="true" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(df, <span class="fu">aes</span>(Var1, Freq))</span>
<span id="cb38-6"><a href="#cb38-6" aria-hidden="true" tabindex="-1"></a>g <span class="sc">+</span> <span class="fu">geom_bar</span>(<span class="at">stat=</span><span class="st">&quot;identity&quot;</span>, <span class="at">width =</span> <span class="fl">0.5</span>, <span class="at">fill=</span><span class="st">&quot;tomato2&quot;</span>) <span class="sc">+</span> </span>
<span id="cb38-7"><a href="#cb38-7" aria-hidden="true" tabindex="-1"></a>      <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Bar Chart&quot;</span>, </span>
<span id="cb38-8"><a href="#cb38-8" aria-hidden="true" tabindex="-1"></a>           <span class="at">subtitle=</span><span class="st">&quot;Manufacturer of vehicles&quot;</span>, </span>
<span id="cb38-9"><a href="#cb38-9" aria-hidden="true" tabindex="-1"></a>           <span class="at">caption=</span><span class="st">&quot;Source: Frequency of Manufacturers from &#39;mpg&#39; dataset&quot;</span>) <span class="sc">+</span></span>
<span id="cb38-10"><a href="#cb38-10" aria-hidden="true" tabindex="-1"></a>      <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle=</span><span class="dv">65</span>, <span class="at">vjust=</span><span class="fl">0.6</span>))</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb39-1"><a href="#cb39-1" aria-hidden="true" tabindex="-1"></a><span class="co"># From on a categorical column variable</span></span>
<span id="cb39-2"><a href="#cb39-2" aria-hidden="true" tabindex="-1"></a>g <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(mpg, <span class="fu">aes</span>(manufacturer))</span>
<span id="cb39-3"><a href="#cb39-3" aria-hidden="true" tabindex="-1"></a>g <span class="sc">+</span> <span class="fu">geom_bar</span>(<span class="fu">aes</span>(<span class="at">fill=</span>class), <span class="at">width =</span> <span class="fl">0.5</span>) <span class="sc">+</span> </span>
<span id="cb39-4"><a href="#cb39-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle=</span><span class="dv">65</span>, <span class="at">vjust=</span><span class="fl">0.6</span>)) <span class="sc">+</span></span>
<span id="cb39-5"><a href="#cb39-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Categorywise Bar Chart&quot;</span>, </span>
<span id="cb39-6"><a href="#cb39-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;Manufacturer of vehicles&quot;</span>, </span>
<span id="cb39-7"><a href="#cb39-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;Source: Manufacturers from &#39;mpg&#39; dataset&quot;</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
</div>
<div id="变化趋势" class="section level2">
<h2>6 变化趋势</h2>
<div id="时间序列图基于时间序列对象ts" class="section level3">
<h3>6.1 时间序列图：基于时间序列对象（ts）</h3>
<p><code>ggfortify</code>包可以对时间序列直接绘图。</p>
<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a><span class="do">## From Timeseries object (ts)</span></span>
<span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb40-3"><a href="#cb40-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggfortify)</span>
<span id="cb40-4"><a href="#cb40-4" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_classic</span>())</span>
<span id="cb40-5"><a href="#cb40-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb40-6"><a href="#cb40-6" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot </span></span>
<span id="cb40-7"><a href="#cb40-7" aria-hidden="true" tabindex="-1"></a><span class="fu">autoplot</span>(AirPassengers) <span class="sc">+</span> </span>
<span id="cb40-8"><a href="#cb40-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;AirPassengers&quot;</span>) <span class="sc">+</span> </span>
<span id="cb40-9"><a href="#cb40-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">hjust=</span><span class="fl">0.5</span>))</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="时间序列图基于数据框" class="section level3">
<h3>6.2 时间序列图：基于数据框</h3>
<div class="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb41-2"><a href="#cb41-2" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_classic</span>())</span>
<span id="cb41-3"><a href="#cb41-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb41-4"><a href="#cb41-4" aria-hidden="true" tabindex="-1"></a><span class="co"># 使用默认的时间跨度</span></span>
<span id="cb41-5"><a href="#cb41-5" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(economics, <span class="fu">aes</span>(<span class="at">x=</span>date)) <span class="sc">+</span> </span>
<span id="cb41-6"><a href="#cb41-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y=</span>returns_perc)) <span class="sc">+</span> </span>
<span id="cb41-7"><a href="#cb41-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Time Series Chart&quot;</span>, </span>
<span id="cb41-8"><a href="#cb41-8" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;Returns Percentage from &#39;Economics&#39; Dataset&quot;</span>, </span>
<span id="cb41-9"><a href="#cb41-9" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;Source: Economics&quot;</span>, </span>
<span id="cb41-10"><a href="#cb41-10" aria-hidden="true" tabindex="-1"></a>       <span class="at">y=</span><span class="st">&quot;Returns %&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<p>如果想设置特定的时间间隔，则需要使用<code>scale_x_date()</code>函数。</p>
<div class="sourceCode" id="cb42"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb42-2"><a href="#cb42-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lubridate)</span>
<span id="cb42-3"><a href="#cb42-3" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_bw</span>())</span>
<span id="cb42-4"><a href="#cb42-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb42-5"><a href="#cb42-5" aria-hidden="true" tabindex="-1"></a>economics_m <span class="ot">&lt;-</span> economics[<span class="dv">1</span><span class="sc">:</span><span class="dv">24</span>, ]</span>
<span id="cb42-6"><a href="#cb42-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb42-7"><a href="#cb42-7" aria-hidden="true" tabindex="-1"></a><span class="co"># 设定时间跨度为一个月</span></span>
<span id="cb42-8"><a href="#cb42-8" aria-hidden="true" tabindex="-1"></a>lbls <span class="ot">&lt;-</span> <span class="fu">paste0</span>(month.abb[<span class="fu">month</span>(economics_m<span class="sc">$</span>date)], <span class="st">&quot; &quot;</span>, lubridate<span class="sc">::</span><span class="fu">year</span>(economics_m<span class="sc">$</span>date))</span>
<span id="cb42-9"><a href="#cb42-9" aria-hidden="true" tabindex="-1"></a>brks <span class="ot">&lt;-</span> economics_m<span class="sc">$</span>date</span>
<span id="cb42-10"><a href="#cb42-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb42-11"><a href="#cb42-11" aria-hidden="true" tabindex="-1"></a><span class="co"># plot</span></span>
<span id="cb42-12"><a href="#cb42-12" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(economics_m, <span class="fu">aes</span>(<span class="at">x=</span>date)) <span class="sc">+</span> </span>
<span id="cb42-13"><a href="#cb42-13" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y=</span>returns_perc)) <span class="sc">+</span> </span>
<span id="cb42-14"><a href="#cb42-14" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Monthly Time Series&quot;</span>, </span>
<span id="cb42-15"><a href="#cb42-15" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;Returns Percentage from Economics Dataset&quot;</span>, </span>
<span id="cb42-16"><a href="#cb42-16" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;Source: Economics&quot;</span>, </span>
<span id="cb42-17"><a href="#cb42-17" aria-hidden="true" tabindex="-1"></a>       <span class="at">y=</span><span class="st">&quot;Returns %&quot;</span>) <span class="sc">+</span>  <span class="co"># title and caption</span></span>
<span id="cb42-18"><a href="#cb42-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>(<span class="at">labels =</span> lbls, </span>
<span id="cb42-19"><a href="#cb42-19" aria-hidden="true" tabindex="-1"></a>               <span class="at">breaks =</span> brks) <span class="sc">+</span>  <span class="co"># change to monthly ticks and labels</span></span>
<span id="cb42-20"><a href="#cb42-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">90</span>, <span class="at">vjust=</span><span class="fl">0.5</span>),  <span class="co"># rotate x axis text</span></span>
<span id="cb42-21"><a href="#cb42-21" aria-hidden="true" tabindex="-1"></a>        <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>())  <span class="co"># turn off minor grid</span></span></code></pre></div>
<p><img 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+eee25vmodgkwKrUp5++mmv/bWZrQhWIkAKV4SwYJanDgSFNSNR6k3KLEgy5neEe4sgLMgn2tso7hfN1EnHCgEhIASEgBDIOwKF1YDScWhBiZRGCxYE/NTsSzSlEKUgdZP7y1/+4pOUY3I95JBDfOT0iBEjap5bbQekCvL5xz/+0Y0aNaraIU1t22mnnTzZoR1GgpoqIIWD9913Xz/F5X777ec1oWQVuP/++30SfKb8xG+1lgSBTJ5Ykyifti6//PJei8qggAh2ovDZjjC5AJpP+nWTTTbxU4iS1/Okk07yfqj0XzMSpd64SODzSnQ++UYhrfilMrAJgp48IW7mmjpWCAgBISAEhEC3I1BYDSgdCwGFPKy11lqull8jx0EumIOcOdGZKQeTL+STVEcELzWb7oi0Q2j9IElB5DuXaEtM60k9119//bbKSupkSOBdd93l/T0xTRMkhe8qxJ+0TvUE4omGlCAlgrIIuALDM888062zzjp+H/6WJhBB0ipxPfBYbbXVHMFO5513nltxxRXtsEjLKPWG+OMegD8wmnSCskgZhVaW+kqEgBAQAkJACAiBngj0CQhY4+iYnucU+hezGDGbEQSykf9gI6AIXoLgtDvVJflC8VUkIOnEE09sdNmO70dLyIxKELVWMMSsTb7TKH1AXk60ogwe2pUo9YbokjuUtpHzVCIEhIAQEAJCQAj0RkAEtDcmuduC9o1pKklBZCmRctcIVVgICAEhIASEgBAoDAKF9gHNey+j7ST90Pjx433Qi8hn3ntU9RcCQkAICAEhUAwEREBz3M/4RhKI86tf/coH2+S4Kaq6EBACQkAICAEhUCAEZIIvUGerqUJACAgBISAEhIAQyAIChY6Cz0IHqA5CQAgIASEgBISAECgaAiKgRetxtVcICAEhIASEgBAQAh1GQAS0wx2gywsBISAEhIAQEAJCoGgIiIAWrcfVXiEgBISAEBACQkAIdBgBEdAOd4AuLwSEgBAQAkJACAiBoiEgAlq0Hld7hYAQEAJCQAgIASHQYQQKkwf07bff9nOHh/FmCsyFFlrILbvssv4vvK/ROlNyMo1mVuTUU0917777brk6TAM5cOBAt9FGG7n555+/vD2LK0wl+vXXX7vZZputY9W76qqr3HXXXeeGDBnijj322NTrwfSuzGhVS37wgx+4kSNH1tqd2e2XXnqp69evn9t4441jreMtt9zi7r777nKZs88+u7/f119/ffeNb3yjvD3qStrPc9rXi4qDjhMCQkAIpIVAYfKAkrB9ueWWc+uuu255XvBPP/3Uf8Qgbn/+85/9bEJRgL/++uvdMccc4+65554oh6dyzLBhwzyJY350BFL38ssvO+ZNhwRsscUWqdSj2YuA/corr+yuueYa961vfavZ02M5fuzYsZ7cgdGaa67pE/vHUnAThTz55JNumWWWcSNGjKg6sNlrr73cZptt1kSJ2Tj0Rz/6kRs6dKg744wzYq3QgQce6Bh0ce8gH3zwgb/fIXaHH364O+iggyJfL+3nmWlzweTQQw+NXEcdKASEgBDoNgQKowG1jjvuuON6aJLQvO26667ukEMOcbvvvrtDK9pIIAsfffRRo8NS3/+Tn/ykh/YOEgqp4oOXVQL63nvv+TnsUwcrdEH6c8455/REfYYZOuuV8pe//MWtuOKKodrle/XKK69MrAELLLCAu/XWW3uUDzHlWV5ppZXc2muv3WNfrR9pP8/3339/LgcTtfDTdiEgBIRAKwh09mvbSo1jPgfCsfnmm7sPP/zQawut+EcffdT9/Oc/d+utt57be++93RtvvOF3jR492l1xxRXutdde8/vR4N15551u//33t1P98j//+Y9jrnYEU+EJJ5zg/vWvf7lNN93UL++77z5PFh966CFPgDGx/ulPf3JfffWVP4d/f//7333d2IdGZ8qUKeV9UVYww//4xz92uB+89dZb5VP+8Y9/uK222srXBc3vl19+6fd99tlnvk0PPvigP482QdCRCy+80O2www7upz/9qW+/ncO+WlixD0J10003ufPPP9+3ZZtttnGYTxFIvGmq0AbdfPPNfvuLL77oMd9ggw0cGrQ//OEPXqPrdwb/2H/YYYc5cEHbBf78DkutNoaPYR2CdN5553ncf/GLX7h7773Xa9ZuvPFGt++++zpI/bPPPutPQ+O94447uu9///vu17/+tZswYUK5ONr5v//9z5111lkeVwYz1BMtNFOlbrnllu7yyy8vH9/OCv1D3biXTjvtNN+/Vh59/dvf/tbft/QX2Jt88cUXvn+pF0QNczWDE7TkJtx/Z555pvvhD3/oyz/ppJMc55k0287TTz/dXXTRRXa6mzRpkn8WNtlkE18HtM8mjzzyiNt5553dOuus4+v5wAMP2K7IS56z1VZbrYd2kfv697//ve8D2ow2+ZVXXvFlVnue2XHttde6XXbZxdeFe/6GG24o14FBEySXd8PWW2/tzj33XFcqlcr70cJyP3P/0gdhkswzzj1x9dVXu+OPP758jlaEgBAQAkVDoPAEFFLHRxxTHv6gyG233eZ/Q0ohDmgs8BOFhA4ePNiheenbt69bYYUVHCTvmWeecRdffHGPewdiiVkZee6557yJn4/WPPPM4z755BP3/PPPu5NPPtl/oBZccEGvsTn44IPLGkwI1D777ONWX311/5GjTrgPNCsQqTnmmMMNGjTInwpx2m+//dziiy/umEueD7NpRyEafEy32247N+usszo+tBB0SB4f7fnmm8+biPfcc08HsUDqYcV+CBDE7oILLvAfcwgOJGDcuHFupplm8m4RHIf5GbPkSy+95LGmX/h4U8+jjz66TCgmT57sy4GsQsAgEJCZf//73xTjpV4b7Rhbgv0iiyzi+5H+xG8WzHbbbTdHH3IP4MMIIaEvpk6d6sk5gwrqDJlDaCeadI6jn+666y5fLwY3+AovueSSvh/DhMvqEF5yb0DMK//sGO5FCBrXhSRCoBlkIAyGvve97zlMymDD4AFszLcU7OlfiDtElWMgRxApE0gX5BTccQeA0G244YZlgtVsOyHlYIHQJspiMAM5gxiuuuqqvs+pz1prreX9gBn49enTx+/j2WpWeJbRappwv+GGAm5cn3t21KhRHp9qzzP3Ns8Abi0MOD7++GOPGcQf2X777d3tt9/uj8EvF8IPTghtXH755T1hxWWCexw/bNqMcB/wPOKn+p3vfMdv0z8hIASEQCERCEbuhZDHHnsMFUUpePGXgg+L/wsCTkozzjhjKfiQlwItXhmH7373u35beUOwwrZAk+U3BZrNUkBIy7vPPvtsX255Q7ASkM1SQPD8pkD7568dkI/yIQHB9NsC39TytoD0lALtjf/NeqBpKwUkwv8eP358KdBGlQK/1fLx4ZVFF13UnxtoAkv87bHHHqVvf/vb/hrUFwk0eaWAUJYCslw+NSDC/pg77rijFPjR+fWAgJT3B6TbbwuIXnlbQPZKwQfa160RVkHwSSn40JYC8uPPZznvvPOWTjnlFP87IFK+fOqGBJqmUkCCysezLdCKlQLyx2qJugWkoYwL2yg/+LCz2rCN/qCKf+ecc04pGFSUtwYkpRT4o5YCLW95W0BSSwHxKP9mhW3bbrut30Y7l1hiiVJA4v3vgBj6dtFnJrThd7/7nf3ssXziiSf88dyj1f4CsuaP5/6w+5AN77zzTikwNZeefvrpUkCESoErQcmOZT/bAgJcCkhUKSC3vuxwHQIi77fRz4HG0a8H2jlO9WLbgiAt/7vZdgYktxQMQPy59HlA+HvUj/uU5yfQipcCslYKiKg/lvse7J566in/u/LfAQccUApIcuVm/zsgm+U2BRrXUjCI7FFOQND9/sAq4I+vfJ55fgJNdrls+jQYOJYCn1O/rX///qVAS1zeHww6SoHFw/8OXHxKAcEsBYO38n628a6xZzkg9qVgUFXerxUhIASEQBERKJwPKOZQAnXQpGFeDAiUN3XjA4iglQnIqtf2oQkyCYiq14jZ72aXaBSHDx/e4zQid9GimaCBNbMjpmq0Nd/85je99oUoYjSPaFRqCW4BFhiFhhZNHNpdND8IGr3gJndocmijCRo+9qG5QcI+iJhFqTvaPxM0pvxFxQpNmvlWskT7g2axmqAZQxOGliogVS4gIN5kj3YUoZ7ggobMBGxM29yojQQZRRGwoM8RNIsvB2ZT/IfDwnXDJm6C3Kx/LBiM9pighQ67Qtj28BJNWfiesH34JtN3uDv85je/sc1eYwtWyMMPP+z7HK28CRpQXBhwI0D7hqDpNTGtP5o762u7XziGvgN77hm0uUir7aR87qNw/UyTzv0AZmhe0cjSx5i+g8GKv2Yz/9BSI3PPPbe3VOD6Am5o4cFhzJgxfj/a5mpy5JFHercEtMtoYHlWOJagRQStaECcvVYTbTKazqWWWsrv4/kFr7B5/fXXX/caZ55PNO4SISAEhIAQcK5wJnhIGf6P+OjhO8jHJdBilX0d8d/CdAkpgyzZH35/ZuqsduNADsIS9ptjO6Z3I2F2HCQxLOy3cgig4KMJEcWECZGBGGIWryWYDTFN84ePGVHCYTLBuRAkCKW1iyXE1j6glB3+6GMahSiHCZ9dPypWmBzDYsQuvM3WyVYQaHO9OdtcH8KkkYFD5UccbE2ittGOr7UMY2CYV6b3IWUTZm2T8Dm2rdlUXRAwCF7lH/hDEiFqlXjatSBe1erI/nA9w+fbPcl9RzvBMryf62KmDp/faju5l8JlW71Z8rxB3iBumLwheJjAMXU3K5BG3EV4viCNPDtrrLGGN8OzDb/eeoJrDPcgdSE9FoMtMDDBb5rnK9B4+2ds6aWX9m4L7GewwjXCzxf3K77OhrWVo6UQEAJCoMgI1FanFQAVNBUEoKDFIGCIjwRaKkgDuTPDGi982SxCvpKMQegqNXr4MrYjaNbQypKTkj+0R2iu2E7gQyuCNhVijFYM/08EYkGwU60USGilICZ8iO0jTF3wC8WvrhFWjeppWBrxPuKII7ymjkAlI6oQcCNAEGU0fWEJayFbaWO4rGrraAnR2uEbCpEx4bqVWm3bl8QSkkYfoMUzf2AGSwyoGETQduoYFn4z6IAkGcbh/eF1zockMvCxdhGgxKAA/+R2BUIZuBr0KAYrBISTNqBlhXjyx+AGTShBT1Gj2SmY+5RnGh9ehGBA/FzxmbWBC9sQsEPsHmQdTWdg3nd//OMfPaZs497DH5njqetll13m85qiAWcbPso8o9y7YMi7gjRtRjgD9xkfiGh+2JQpEQJCQAgUHYHCaUArO9xMfUcddZQPDGI/2lFMoZh1+fhgssPMRgQvMmDAAG+iI7iIaHDIGx9MiBwEj/P++9//+mNb/Ydmlo8ewUoQB0y3XMtMu62Uy4ccrQ0R4wQBoR3io8kHt5amDqJKsMQvf/lL/xEPfA5d4EPoiTqankZYNaonWCIE56DBY1BAABJEgHajaSJ6HHM/QmAWxI9AKogRRDickLyVNvqC6/yDCENoiOYmwIe6BX6jjkwGBKnFKZA/NPOVfxaMQ4AO5AiSQ5otXCwg6ATD0EcvvPCCN7mTF5P71qLyGSQ1Ep6FhRde2N8f3HeYjLk3IL1h4t2onFr7CdKirmgQ0ebSVsgbZn5kp5128n3NM8fzhDax3v1OPxhORKlDVglqQkNLYBDC/UR5EGuE6HeCAREzqYefZ7ZzPs+bEU4C8Lj/OB5rADlNcc9hYEYdeCbQPDORAgF34IYZn/pD4NG48j4w1wPKx70knH2A60qEgBAQAoVCIPjIF0ICQoeN3AdaVDaYQIVAO1EKfA99oAABGwR6BBrPUvDB8cEm4cCNgHiWgg+bLy9I2+OLCzRQPqApICulIBK5FHyAegQhEYQQFoKQgg9ReFOJYIXA9Oq3BR+2UmB+98cEZksfXGKBOz1O+v8/CEIKtFTVdvXYFhCZUhAlXAq0Pj5YgnUCfxALQgqIS49zCHAhcIK2BX51vl7BR9Yf0wirQEtUCkhqj/IIXAo0RuVtgYnUYxmkFioRFEWwTkBuSwExKAUuBD5gKfi4lwKC6s8JZiwqBRo6j0kQ4VwKiJf/bQXWa6MdE15WC0IK0hOFD/HYEBwFBgGZ80FLQXR5+ZjKdtr9ZjhxYJBSqhQQyPI54ZVGQUiBhtAfDt4EqFEPMCHQjaAak0D7VyJIhjpy3xAsxr2EsOQZCHxG7XCPN9u4pxGC4rh/A+2dLyPwhS1RN5Nm2xkOQqKMgMT7fqV+gUbSB8xZ2YHp2wdyUe9Aa1sKXF7KfW7H2JIgJOptf5RHcGGgPS2FMef4gPj6QCyeWa5JkBJBRdQFqXyeg8GnL4vnk6CuIB2Zv+eDQag/Phh4lAK3EH+PBqTS1zkcYEiQX0DaPX7BwM73uwU8UQD3DeeFA998wfonBISAECgQAoWZCSn4UDUtaDPRnJB2qZqYz5ztwzyH5gnfwLgELQxBDNQhbCpst3y0jWhU0cZEFfwvgw+999erPKcRVpXHV/4GNzSqAbHyu7gWGiMLDrPj0QxTBwuoYTsaKszSYVM821tpI+fVEzRhaLxq3RP1zo1zH/Xg/qt2rwXvL6+FQ/tnbiPNXhstNPdbQGabPbXh8dSPexqtYbV7Gu0jvqhxTs2KthgLRr1paSufZzSZYGiBZZUNw+0GDWgt0zptZJ9pPsPnUx+eP+55iRAQAkKgiAiIgBax13PcZsysuABg0iRQ54477vD+g2wnh6VECAgBISAEhIAQyD4CIqDZ7yPVMIQAWiN8+CCg+EXif4t/Jr6hEiEgBISAEBACQiAfCIiA5qOfVMsqCGCGxhwvEQJCQAgIASEgBPKFgAhovvpLtRUCQkAICAEhIASEQO4RKHwaptz3oBogBISAEBACQkAICIGcISACmrMOU3WFgBAQAkJACAgBIZB3BERA896Dqr8QEAJCQAgIASEgBHKGgAhozjpM1RUCQkAICAEhIASEQN4RKMRc8EzFx3zMaQhJtk2qJdm2fe0s7RoqvzqKwqc6LuGtSWFk5XKtPN6fqn/4Lqm+bhjlsX9pkepfvV9ta9L4JNkHSddd5dtdEm3Zr1+/upN/FCIKnplVgqk13WqrrRYNtTaOguwyOwoznATTGbZRUu1TmXGJGYOSSkHELC3MbBRMiVi7Em3sUf3rgwf+/PHwJiG8RJmLnZmGas3y0+p1837/q/6Nez6N5zfJ+z/p+vPuJEVcUs9v3uvPHcYsYny/Wp2prdZdmlS5dr1PP/3UMTthUjOYJV1/7kvecUnV33BiCU4vvPCCC6YeDm/usV4IDSgtZtacNJKV08FMY8gUfHF/3K3nJk6c6MlDMM+0bYp1yUPAH9MQJiHUn5dPMK98EsX7uue9/kxNOt988yWCDy8GpphlusvZZ5891mvY/T9w4MDYPy5W0STvH4gP08Cq/oZ27yVT0TK9aJLPb5L3f9L1Z3D3/vvvJ/b85r3+DIBRCnH/xE2E3nzzTcd3MSnlCdP1MsCoNf1u76eluS1J15/pqXnHJVX/cGuZNGbPPfcMb+q1noyKrtdltEEICAEhIASEgBAQAkJACExDQARUd4IQEAJCQAgIASEgBIRAqgiIgKYKty4mBISAEBACQkAICAEhIAKqe0AICAEhIASEgBAQAkIgVQREQFOFWxcTAkJACAgBISAEhIAQEAHVPSAEhIAQEAJCQAgIASGQKgIioKnCrYsJASEgBISAEBACQkAIdIyATpo0yd10003u2WefbdgLzzzzjHv55Zd7HEe+wbvvvtvdc889Pi9Xj536IQSEgBAQAkJACAgBIZBZBDpCQB955BG38847u+eee87tv//+7qqrrqoJ0EsvveT2228/9/TTT5ePYaahnXbayd1+++3uoosu8mXYFFnlg7QiBISAEBACQkAICAEhkEkEOkJATz75ZHfMMcf4LPlnn322O//88312/kqErr76aj97UeWMG5dddplbccUV3aGHHurOOOMMx9Rk999/f+Xp+i0EhIAQEAJCQAgIASGQQQRSn4qT6Zlee+01t+yyy3o4hgwZ4qfjev31192iiy7aAyKmCTzvvPPcqaee6vr06VPex/yi6623Xvn39773PffUU0+5lVZaqbzt9NNPdxMmTPC/mfaR+U+ZRitp4ToIU7ElNRc8UynigpBUe5hqDI1yUuWr/vXvQvBHksLfrAVMGch9FKfY/c9Uinm8/1X/xncDGCX5/uEbgSR1/6v+9fs4afzt/YPiiGkh4xYspPYOjbts6sv3K6l7k/rmvf6GOfeRvU9tW+UydQLKPM7M0xomlGg4mT+9koCGSabdtDSAeWTD86CzDqkNyyuvvOIgqghzXjOvuT1Y4ePiXufmRADe1uO+BlhQdlLtsXonVT715y+p8lX/+necPUtJ3EOGfdL3f1L3j+pf/95hb9LPr320kno/qP71+zgN/KlBEu8fyrX6sx63UOek3j1W1yTrn/S9b21gGeX5TZ2AzjjjjL1uECo622yzheted72yDM5HWxqW3//+9+WfEFZM/QMHDixvS2oFzQBkun///m6mmZKBFxIPXmESHmd7PvzwQ8dfUnhRf7TSla4VcbWhG+qPBjEp/HmJvv32227OOefs9dy02wd2/zPom3nmmdstrur5Sd4/aDgmT57sB62qf1X43TvvvONmmWWWRJ/fJO//pOuPZQELWFLPb97rDwnim9yvXz9v/ax+l7W29c033/TlouRKQtB8ol1Nqm+Trj/xNryb11577STg6VEmvAyuVk9S9wGdd955XaXpD8I2//zz16tnj310PueYNHu+naelEBACQkAICAEhIASKgMABBxzgzjrrrMw0NXUCilaQAKJrrrnGgzBmzBivLURjiO8DpvNGsvrqq7sbbrjBffrpp450TqRi+u53v9voNO0XAkJACAgBISAEhEDhEHjjjTfc888/7+BPWZFkbMQNWrfHHnuU0y8RqHDYYYf5M8aNG+eOOuqoMjmtVcy6667rc4Buu+22PtBhm2226eU/WutcbRcCQkAICAEhIASEQJEQuPXWW31zC09AF154YUcqJfwp8EcwGTFiRFXyeeSRR9ohfokW9eijj3b4CeH7mZSvZY+L6ocQEAJCQAgIASEgBHKIAAR08cUXd/PNN19map+6CT7c8jD5DG+Puk4QhchnVLR0nBAQAkJACAgBIVBEBG677Ta35pprZqrpHSWgmUJClRECQkAICAEhIASEQJchwKyT5EUXAe2yjlVzhIAQEAJCQAgIASGQVQQwvxNvkyX/T7CSBjSrd4zqJQSEgBAQAkJACAiBNhGAgDJjZLtuj21Wo9fpIqC9INEGISAEhIAQEAJCQAjkHwES/99xxx1unXXWyVxjREAz1yWqkBAQAkJACAgBISAE2kfgkUce8bO7iYC2j6VKEAJCQAgIASEgBISAEIiAAOZ3ps5dddVVIxyd7iHSgKaLt64mBISAEBACQkAICIFUEICArrLKKq5v376pXK+Zi4iANoOWjhUCQkAICAEhIASEQA4Q+OKLL9xdd92VSf9P4BMBzcFNpCoKASEgBISAEBACQqAZBO6991730UcfiYA2A5qOFQJCQAgIASEgBISAEGgdAczvzBg5cuTI1gtJ8ExpQBMEV0ULASEgBISAEBACQqATCEBAmf0oq1OWi4B24q7QNYWAEBACQkAICAEhkBACH374oXvggQcya36n2SKgCXW+ihUCQkAICAEhIASEQCcQGDNmjCMIKYv5Pw0PEVBDQkshIASEgBAQAkJACHQBApjfBw8e7JZeeunMtkYENLNdo4oJASEgBISAEBACQqB5BCCgo0aNcn369Gn+5JTOEAFNCWhdRggIASEgBISAEBACSSMwadIk9/jjj2fa/A4GIqBJ3wkqXwgIASEgBISAEBACKSFw2223uVKp5DWgKV2ypcuIgLYEm04SAkJACAgBISAEhED2EMD8vuiii7phw4Zlr3KhGomAhsDQajoI7Lnnnu7SSy9N52K6ihAQAkJACAiBAiEAAc1y9Lt1hQioIaFlaghcd9117sorr0zterqQEBACQkAICIEiIPDqq6+68ePHi4AWobPVxuYQeO+99/zctM8++2xzJ+poISAEhIAQEAJCoC4Ct9xyi9+/9tpr1z0uCzulAc1CLxSoDq+//rpvLaO0zz77rEAtV1OFgBAQAkJACCSLAOb3ZZZZxg0ZMiTZC8VQughoDCCqiOgIGAElQu/JJ5+MfqKOFAJCQAgIASEgBOoiQAR8Hvw/aYQIaN2u1M64EXjjjTfKRT711FPlda0IASEgBISAEBACrSMwbtw499Zbb+WGgM7UelN1phBoHgE0oHPMMYebffbZnQho8/jpDCEgBISAEBAC1RDA/D7TTDO5NdZYo9ruzG0TAc1cl3R3hSCg8803nxs6dKgIaHd3tVonBISAEBACKSIAAR05cqSba665Urxq65eSCb517HRmCwgYAV1iiSVEQFvAT6cIASEgBISAEKhE4KuvvnKjR4/Ojfmd+ouAVvaifieKAASU6Lwll1zSvfzyy+7zzz9P9HoqXAgIASEgBIRAtyPw0EMPualTp4qAdntHq32tI0AQEiZ4NKBffvmle/rpp1svTGcKASEgBISAEBACDvM7sRUrr7xybtCQBjQ3XZX/in799ddu4sSJnoCiAUUUiJT/flULhIAQEAJCoLMIQEBXW201N+uss3a2Ik1cXQS0CbB0aHsIoP3ET4UApMGDB7u5557bkTZCIgSEgBAQAkJACLSGwKeffuruueeeXJnfaakIaGv9rbNaQAD/TwQTPLLssstKA+qR0D8hIASEgBAQAq0hcPfddztIaF4S0FsrRUANCS0TR8AIKBpQ5Dvf+Y4IaOKo6wJCQAgIASHQzQhgfp9nnnnc9773vVw1UwQ0V92V78pCQPv06eMGDRrkGwIBffHFF30wUr5bptoLASEgBISAEOgMAhDQtdde280wQ74oXb5q25m+1VVjQgAfULSfM888sy8RAvrFF1+4Z555JqYrqBghIASEgBAQAsVBgNRLY8eOzZ35nR4SAS3OfdrxlqIB/cY3vlGuBwQUUSR8GRKtCAEhIASEgBCIjMAdd9zhg3vz5v9JA0VAI3ezDmwXgUoCOv/887s555xTBLRdYHW+EBACQkAIFBIBzO98Sy21YZ5AEAHNU2/lvK6VBJTmLL300iKgOe9XVV8ICAEhIAQ6gwAENI/aT9ASAe3MPVPIq7711lt+pBZu/FJLLaVcoGFAtC4EhIAQEAJCIAICfFNxYRMBjQCWDikuAh988IGfpzbsAwoa+IGOHz/e+7AUFx21XAgIASEgBIRAcwig/URGjRrV3IkZOVoa0Ix0RLdXA/M7Uo2AfvbZZ+65557rdgjUPiEgBISAEBACsSEAAf3Wt77lFlxwwdjKTLMgEdA00S7wteoRUGBRJHyBbw41XQgIASEgBJpG4Lbbbsut+Z3GioA23eU6oRUEahHQBRZYwPXt21cEtBVQdY4QEAJCQAgUEoEXXnjBvfLKKyKghex9NbopBCCgs802m5t77rl7nMfMSIqE7wGJfggBISAEhIAQqIsA5ndmPmIGpLyKNKB57bmc1ZtZkGr5qRCIJBN8zjpU1RUCQkAICIGOIQABHT58uBswYEDH6tDuhUVA20VQ50dCAA1oZQCSnQgBff75593XX39tm7QUAkJACAgBISAEqiBQKpXc7bffnmvzO80SAa3SudoUPwL1CCi5QD/55BOHT4tECAgBISAEhIAQqI3AY4895iZNmiQCWhsi7REC0xGoR0A1J/x0nLQmBISAEBACQqAeApjfZ5llFrf66qvXOyzz+6QBzXwX5b+CmAsmTpzYaxYka9lCCy3kA5TkB2qIaCkEhIAQEAJCoDoCENCVVlrJZ5CpfkQ+toqA5qOfcl3LN998033xxRc1fUCJ5MMMLwKa625W5YWAEBACQiBhBPiW3nnnnbk3vwOTCGjCN4uKd44IeKRWEBL7FAkPChIhIASEgBAQArURuP/++92HH34oAlobIu0RAtMRwP8TaURAmY4Tc71ECAgBISAEhIAQ6I0A5vd+/fq5FVZYoffOnG2RBjRnHZbH6hoBHTJkSM3qowH96KOP3EsvvVTzGO0QAkJACAgBIVBkBCCga6yxhpt55plzD4MIaO67MPsNgIBCPmedddaalVUkfE1otEMICAEhIASEgFfSYIJfZ511ugINEdCu6MZsNwICOv/889et5KKLLurTSowbN67ucdopBISAEBACQqCICBB89Pnnn4uAFrHz1ebWECAIqZ7/J6XOOOOMCkRqDV6dJQSEgBAQAgVAAPP7wIED3bLLLtsVrZUGtCu6MduNQAPaiIDSAkXCZ7sfVTshIASEgBDoHAIQ0FGjRrk+ffp0rhIxXlkENEYwVVR1BJohoETCS4SAEBACQkAICIHpCEyePNkxBWe3+H/SMhHQ6f2rtQQQYI73qVOnRtaAvv/+++6VV15JoCYqUggIASEgBIRAPhG4/fbb3ddffy0Cms/uU607gcCECRP8ZRsFIXGQIuE70UO6phAQAkJACGQdAczvCy+8sFtsscWyXtXI9ZMGNDJUOrAVBKLMgmTlDhs2zOc205SchoiWQkAICAEhIAScg4B2k/mdPhUB1Z2dKAL4fyJRgpBIrLvEEktoTvhEe0SFCwEhIASEQJ4QwJL4/PPPi4DmqdNU184jAAGdZZZZ3IABAyJVZqmllnLKBRoJKh0kBISAEBACBUAA7SdCBHw3iTSg3dSbGWwLBHTBBReMXDP8QJ999tnIx+tAISAEhIAQEALdjAAEFOXM0KFDu6qZIqBd1Z3ZawwENEoAktUcAvree++51157zTZpKQSEgBAQAkKgsAjcdtttXWd+pzNFQAt7S6fTcAhoFP9Pq40i4Q0JLYWAEBACQqDoCDz99NOOYN5uC0CiX0VAi353J9z+KNNwhqvwzW9+00/LqUj4MCpaFwJCQAgIgSIigPmdqarXXHPNrmu+CGjXdWm2GvTWW281pQElYOlb3/qWIuGz1Y2qjRAQAkJACHQAAQjoiBEj3Nxzz92Bqyd7SRHQZPEtdOlvv/22+/zzz5sioACGGV4a0ELfOmq8EBACQqDwCDDz0R133NGV5nc6d6ZO9fCkSZPc2LFj3SKLLOJzP9aqBxHRTM34ve99zw0cOLB82COPPOI+++yz8m+0ZlFT/ZRP0kqiCOD/iTTjA8rxEFAeOokQEAJCQAgIgaIiAEciKLcb/T/p045oQCGPO++8s3vuuefc/vvv76666qqq99ef//xn94c//MFx/K677upeffVVf9yXX37pfvvb3/rzOJc/IztVC9LGjiBgfdJMFDwVhYBOnjzZvfnmmx2pty4qBISAEBAC+UPguuuucyi3ukUwv88222xulVVW6ZYm9WhHRwjoySef7I455hi35557urPPPtudf/753lQbrtnLL7/s7rzzTr//gAMOcNtuu6375z//6Q9h3wILLOBOPPHE8t8yyywTPl3rGUCgHQJK9WWGT6YTp06d6pZddll3++23J3OBhEv90Y9+5E466aSEr6LihYAQyBMCvNd+/vOfu3PPPTdP1a5bVwjoqquu6klo3QNzujN1Aor2khyPfACRIUOGuL59+/bSYL744ov+mBlmmFZFTPBGSJiSCgJ64403uquvvtp9/PHHOYW/u6tNBDxuE7POOmtTDcWdgn63/m7qZB3cEAHcG9ASPPDAAw2PzdoB+EQ9+OCDjrx4EiEgBISAIYBiCnnmmWf8Mu//cDG8++67u9b8Tv+k7gM6ceJEN8ccc7g+ffqU7w+iu6ZMmeIWXXTR8jbMr+Gor7nmmsubZTkA0z2+ocstt5x76aWX3DnnnOMuuOCCHj6i2223XXlKx/nmm88NHz48VZPuO++8U25LEisfffSR4y9JadcEzkBh8ODBVXFn0FBv4IBvMESjnTq0c24jXBvVv9H5UfYnVf8bbrjBX578ckldIykzGG44BLY9/vjjidUdcJKqv/V70vdP0vVHkVDv+bV2trNM6t6kTqp/455JEn+ujsaSv7jk4Ycf9kXBDZKue5Llv//++44/yOcnn3zieU7c14u7vGp9yDPGXz1JnYCSz+qrr77qUScqiZ9DWCqP45jZZ5/dH4KanT80pwgjBbSh22+/vf/Nv6222soZCSyVSm7ChAluzjnnLO9PaoV6ctNQN9qQhHz44Ydupplm6oVZXNcCTz7y7eIF/miqK8uJUn/8QNGCV54bpY1x1b/WtaLUv9a5UbYnXf97773XVwMy1wq+9dqQ9P2PVh3hBQ3JCg9a69Ur6r6k6089GDjybqh850WtY73j8l5/2sa7h2cg7nvTcEsSf9XfUK695HvMO5T7f+aZZ659YJN7bNBF0HJS986nn37q+QtKtCTkgw8+8BZD0hFioULxhgk+Li6RdP3DmPAuMgt2eHt4PXUCOu+88/oXMC8YM82i/awMVBk0aJDXclhlOQZNJoJvIaZdI6ALLbRQrxHP5ptvbqc6clHia9qvX7/ytqRWaBcElBsUkpiEoHngBk2yPV988UXb5ZOGaaWVVupVDvXnxVOv/rhotNNncdS/Vt9FqX+tc6Nu5yNcD5+o5VQeR3DXuHHj/POD9SDua9j9z7MZ58fF2mGBiPxG0xG37ze42wAyifpT7yTvnzTqDz6Nnl/a2apATriP4r43rT5J1x+Cq/ob2r2XRkD5/ts3vPdRzW8xrZ5pECs5RfMl9j4DUkX9k7o3jYDCH4iBWXvttXtYgnvXqLktKP+SrH+4NlEIaOo+oJCyFVdc0V1zzTW+rmPGjHH9+/f3f7wYGL0gI0eOdE8++aTXXNKQa6+91q2wwgrlc84880y/zsscfzA6SpItBBgoNJuCyVqABpQRrWmxbbuW7SEwevRo/wLCQsAAgY99ngTSidaTdwbZMSRCQAgIARDAB3SeeebxYOCml2eBROOC1q3pl6xvUiegXHiPPfZwl19+ucNPEy3XQQcd5OuDZobIeATV82677eZ+9rOfuR122MExMuB4ZMstt/QfTlI58SElQIk/SXYQQNX/7rvvtkVAaY0CkeLtUwKQsCSMGjXKF/zCCy/Ee4GES+PDwnSt+H+bz1fCl1TxQiBTCDz66KM+kDdTlcpAZSCgmKsRBqp5FhQFaCu7nYAmYyNu0PMLL7ywu+yyy3yCVRuxcArTTZlmlN8bb7yxW3/99XuZMyCnxx57rDdl4RthpnzOkWQDAUvB1KoGdMkll/SBagxKunEO3E71EqmXIJ/mOwkBJUAvL8KHZZNNNvEmYN4hEiFQNAR23HFH/0685JJLitb0uu2FgGIJhaDnXQNK+iUUBVgCu1k6ogE1QMPk07ZVLuv5GuE/IvJZiVg2frdLQAk4GzZsmDSgMXYnLg1G6IcOHeqDAPKkAcW3jvuK+wINKC4EFpQUI0wqSghkFgFc1PB1JEBTMh0B3g1Y3BZccEG3+OKL514DCgE1K9X0VnbfWkcJaPfBqRYZAkZA23EEZ/QnE7wh2v4S7ScO6GgJSIMGkcsTASWtF/XHBG95hOUH2v59oRLyg0A4g0V+ap18TY2Qk3UFAppnDSgDa+Jfut38zl0hApr8s1HIK0BA0V6TraBVEQFtFbnq5+H/yQsa4olA5PJEQM2vi3ovtthiPhJVfqDV+1pbuxMBI6CQFDJ9SKYhgPkdQQPK+wFNMZNW5FHw/0REQPPYe6pzJhDANIr/Z3jCgWYrBgHlRYtpRdI+AmhAwy81iChaxbwIBBT/byY3IL8cZnhpQPPSe6pnHAhAQHFdwxJAGjXJNAQgoHxr8JtEA0o6sjy928L9iKIAEk16yW4XaUC7vYc71D40oK0GIFmVzQEbv0VJewgwAxkzH6211lrlgtAi4k9GKrM8CGY1pmk1IfOFNKCGhpbdjgCZRR577DEfhEdbRUCn9zgEFO0nVjcIKJJXMzwENKwomN7K7lsTAe2+Ps1Ei+IgoEsssYRvi/xA2+9Smzu9koCiSRk/fnz7F0ihBDSgdk9wue9+97ve1CYNeQrg6xIdR+Chhx7ymr3NNtvM18XMzh2vWAYqABZM34xARMk3bi47fmNO/uE6wGQbIqA56TBVM5sIxEFAmU6NlF0ioO33MaNqUi/ZS5oS0YAiefEDraYBpf7SgoKCpNsRwPzODHhM5IIbigjo9B6HuPGtQCCfWEryqAFl9iOkKBPrSAPqu1v/4kaA6U/biYC3+iy11FI+dZD91rI1BPD/rHyp4SJBGrM8EFDuJ2YHCWtAcdHggyw/0NbuCZ2VLwQgoMsvv7x/ZiFbMsFP77+wBpStENA8akDvuusuP71wO8G701HJ/poIaPb7KHc1JN8k/krt+oDScEXCt9/9+HmiDQib3ymVQB60oHlw1rePSZiA4u/FXPDSgLZ/j6iE7CMAAV155ZV9RQlQkQZ0Wp+RA3Ty5Mk9rDu8J/KoAYWAVioKsn9ntl5DEdDWsdOZNRDA/I7ERUCJqGcqVklrCFTz/7SS8pKKCQJKlKsFGFj98QOVBtTQ0LJbEYBsYgUwAioN6PSeNk1w2L0IDSh45SXAkta89tprbsqUKW7kyJHTG9flayKgXd7BnWhe3ASUNpCYV9IaAvh/QjRxzq+UvBBQtBkMaOaYY44eTSASnn1oQSRCoFsRQPuJ4P+JoAF95513vKXJbyjwP9MEhwmoWUryFD9AlhLE6l6ELhUBLUIvp9xGI6DkZGtXmBMeydOLpN02x31+Nf9PuwYElJE3LhNZFjSg1V7MaEBJOE16GokQ6FYEIKAMIG0QCdkig4XNANSt7Y7SLgKQsI6ELW6Wri1PZni+cbhF8U4uioiAFqWnU2wnBLR///6ub9++bV917rnn9rP3iIC2BiXkkjRLlf6fVhom7Tx8yCCg9lGxurNkSs4ZZ5xRfqBhULTedQiE/T9pnCUpN+1f1zW4iQaBAeSTgEqTQYMGObKomO+4bc/yEg0omUoIrCyKiIAWpadTbKfNghTXJRWI1DqS5v+55pprVi3ERttZDkRiykH8vKppQBnksF1+oFW7Vxu7AAFLQG/+nzQJwoXWz/wfu6CZLTcBAopGuFL4buRNA1ptkF3Zrm76LQLaTb2ZkbagAQ2bQ9qtlgho6wji/wlBq9UfmPQYcWc5FRNmxi+//LIqAQUZzYjU+v2hM7OPwIMPPujnfQ8TULR9PNOQr6JLLQIKmcuTBvSZZ57pFWTZ7X0rAtrtPdyB9sVNQMkFOmHCBAWatNCX9fw/KQ7zNWafLBNQ+4hU04DSBvxAcdFg/meJEOg2BDC/QziHDx/eo2lo/URAncegmgaU90VefGQJKOOvMstHjw7vwh8ioF3YqZ1uUtwEFA0ofoqaE765nmVKNz5Qtfw/rbSsR8JDQPkA20wnVm9bogGFfCpTgiGiZTchAAG1BPThdjFwLLoJ/pNPPnHkna5GQNGATp061fE9yrpYBLwIaNZ7SvXLNAL465EUOI5ZkKyhioQ3JJpb3nrrrf6EWv6fVlrWCSh+XNSRCNFqYpoh+YFWQ0fb8o7AfffdV87/GW6LNKCuTMCrDU7NYpIHP1AsOPj0mk9+uJ+7eb36G72bW6y2JYoAUddoK2v5HLZy8QEDBjhSOikSvjn08P9Eezx06NC6JzLqRluaVRM2GlD7mFRryDzzzOOGDRumSPhq4GhbrhFAwxlOQB9uDBpQBvtFzoGLhQeppgHlnYDkgYCiAYVEx5E5xjc6J/9EQHPSUXmpJhHwSJwElPIUiAQKzQkENMq0boy6yaWZVX8pCGij6FDNiNTcvaGj84EA5ndkpZVW6lVhI11ZfW57VTiBDRBQNIcLLLBAr9L79evn01WZD3mvAzK0AeUK37iiiQho0Xo84faav40IaMJANyie3J9oNRv5f1KMmX2yGIiED9fEiRPrakBpA36gJKOHSEuEQLcgAAEl52e196kR0CL7gUJAcfeabbbZqnY5A9e8aEC//e1vV21DN28UAe3m3u1A2yCgRFaTCDhOYXTIjBc4nUsaI0D0O5qBRv6flITpZ6aZZspkJLxpL+qZ4GkDGlDmfbbj2SYRAnlHAAIaTr8Ubg9aP/yizQwd3leUddpuRLxam/OQiumDDz7wgVLSgFbrQW0TAk0gYBHwtQJGmiiqx6E8nGi3LFqwx0796IUA5vell1460kAA8slLPIsaUCOUUUzwgPDwww/3wkIbhEAeEWCw/fjjj9ckoDPPPLOfmrPIBBSlRLUAJOtvBq4c89VXX9mmzC0ttkEa0Mx1jSqUNwQgoHFGwFv77eFUKiZDpP4yqv+nlUIgUhYJKOazeeed1/9ZXastCbQiUE2R8NXQ0bY8IlAtAX1lO4qeiimKBpTMLFme6U0EtPKu1m8h0CICcU/DadXApD948GBFwhvI/edrAABAAElEQVQgdZaQNgYCUfw/rRj8QLP4kkYD2sj8bm3QjEiGhJbdgADm92oJ6MNtw3JRVA0oU5SSvB0Maom9O7LsB4pVj8Ez2TyKJvIBLVqPJ9xeM8EncRnM8DZaTKL8binT/D/XWGONyE2CgBK0xJSXWRIIaCPzu9UXP9BHH33UfmopBHKNAAR0xIgRfqrcWg1BA8pzW0Qh+IqUf/UIKFMN46pgrjxZxKmoEfD0hQhoFu/IHNfpzTffrBqxGUeTRECjoYj5fbnllmtotg6XBgGFfGYpopaPC1pZ02KE61ttHQ3ou+++m6k2VKuntgmBKAjUSkAfPhfyNWXKFPfee++FNxdi3TS/9Qgo/u1Zj4RHA2ouZoXouFAjRUBDYGi1PQR4ERKJXC1lSHslTzsbAgpB+uyzz+IormvLGD16dKT8n2EAspiKacKECT7rQVQCigYUkR9ouGe1nkcEyO359ttv1wxAsjahAUWyNHC0uiW9JLgIqZYDNHztLBNQ3Agg0nzbiigioEXs9YTajPkdSSIIiXJ5SIlmfOaZZ/gpqYIAo2m00M34f1IMHzLSZ2UpEMnMZlFN8GhC+vfvr0j4KveFNuULAczvSLUE9OGWmPbPtIHhfd2+TpvxnZx99tnrNpUBrL1L6h7YgZ18y8juIg1oB8DXJbsLgaRmQTKUbJQoP1BDpPcS/09SYDXj/0kp+EmRziRLgUgEDtAW0872bm3vLZoRqTcm2pI/BCCgPI+NBvPsx8xcRA0oBNQIeL0eZgCLNjmLU5bat8y+bfXa0Y37pAHtxl7tUJtMA8qoNAkZMmSI13DZQ5vENfJeJv6fkLBWIiohelnTgPKBIRI4quAHKhN8VLR0XFYRgIDWSkAfrjNWC4gqZKxo0gwBBZssfjewWA0YMMBneCla/9HelgkoAQLvv/9+ETFTm2sgAAGde+653ZxzzlnjiPY3k1xduUBr49iK/6eVlkUCGtX/09oA+cYF4a233rJNWgqBXCGAH329BPSVjWGQJgJaicr03/YOyWIqJkhxUbWf9FBLBPSAAw7w89PC3Ndee2139913T+9trRUWgSRTMBmoPKxZHMla/Tq5fPLJJ/286c36f1qdIaB8yLIya0gzKZisDWhAEc2IZIhomTcESEBPRoooGlDaVsRk9ASiTpw4MZIJfuDAgW6uuebKpB9okSPguXebJqD333+/Gzt2rLv11lt9+od99tnHHXTQQZQlKTgCENBGPkvtQgQBHT9+vGN2C0lPBPD/xCS3+uqr99wR8RcEFFwtujTiaYkcRnQoUfCmvYh6Efy95phjDpnhowKm4zKHAOb32WabzadSi1I5NKBFywXKQBkrbL1pOMPY8d3ImgaUQQYuT9KAhnvq/68TmbXTTjv1cm4m0IQRF1P3MaoYOXKkmzx5sr8ZqhSjTQVCIC0NKA9uVqMaO9nd+H8uv/zy/rlspR4800gW/EAJhuId1CwBJWiJHKjSgLZyB+icLCAAAW2UgD5cT77HuMNNmjQpvLmr183lAPIdRbKYiol3HAP+okbA0281NaC8yCGXmPN23313P7UfJ2y22WYOUx/m92WWWcaz99/85jeuT58+7JYUGIGkpuEMQ2qjRZnhw6g4PwBsx/+T0viQ8dxnIRLeBhhRUzCF0VAkfBgNrecNgSgJ6MNtMhJmpCy8r1vXra3MdBRFGMhiOcuS2DfMvmlZqltadalJQKnAr371K59zcdiwYW7FFVd0++67rx9lMULDFH/KKaf49AY777xzWvXVdTKKAFpJ5uVNKgm9NRsTP5p3e3hte9GXBC1giWDA2KoQbc4LPQsaUMxlmNJbuZ/wAyUtTRFnh2m173VeNhCAJOHbGNX/k1obAS1SKiYI6NChQ13fvn0jdRwDWbTEWOmyIvh/9uvXr2Ei/azUN4l61CWgXJAkr7/97W8dYBHhjHbhkEMOcYMGDXKjRo2qO09tEhVWmdlEAO0nJtNWCEOzLVpqqaVEQCtAw/+TfICrrbZaxZ7mfmYlEt4CkFqxrPCOQpSOqbm+19GdRwDlDtIoAX24phCxWWaZxZlWMLyvW9dpqxHvKG00S4pZVqKck/QxKFEwv7fyjku6bmmV35CAotm688473dSpU93hhx/unnjiCR+hRzqco48+2n3wwQdp1VXXyTACNrJMg4BispAGtOfNgP8nLjOMqNuRrBHQVtrCAIUPsvxAW0FP53QSAQgoxKqZXMq4zXBOkQgogZJRA5Doz8UWW8x3a5YCkYoeAU+H1CWgl19+uVdz/+xnP/P+nsyugvnzxBNP9JHwmFxh8H/84x+99sv3sP4VEgEjoElHwQMuBAM/RQZHEuefvTFjxjQ9/3s17AhEwpSHNruTwoei2QAkqy/kk3tEGlBDRMu8IAABbcb8bu0qWiqmZjWguPMstNBCmQle5f2KNrbI/p/cu3UJKETzgQce8EBNmTLFLbvssg5SiqD2P/XUU90999zj/URJnispLgKY4BmJM1tR0sJD+/nnn2ciWCbptkYp/9FHH3XvvvtuW/6fdh00oOTY62RaFwa2tKdVAkpb8AOVBtR6Vcs8IMA3FAtjKwS0SBpQ3v1MrUmbmxHeJ1nRgEKgP/nkk0JHwNN3dQkoPp9oETCzAxgfpcop/hhVnHvuuW2b/pq5kXRs9hBAA4rZiDyUSYuNGmWGn4Y0/p/M5b7qqqu2DT0EFOlkIJL5aZnfViuNwg+Uj40Gxq2gp3M6gQDKHqw6rRBQNKCdHDSmiRdcBA1iswSU94m9W9Ksb7Vr2bfLvmXVjinCtroE9Nhjj3UnnHCCJ5183IiQXX/99YuAi9rYJAIQ0DT8P6nWAgss4Ac89hA3WdWuOxz/T7JURI0IrQcAGS9wiu8kATUtRTsEFA0oMzo99thj9ZqrfUIgMwhgfm8mAX244pCxjz76yGsGw9u7cR0CijRLQNGAQtKz4LqF/ydZRxg4FFnqElA+akwLRoJbCMbpp59e6IitIt8ojdqeJgGFIDFyFAF1nmQRJMiUuHEIWS8YSHSSgKKlQJuOv3mrgrsQLiHyA20VQZ2XNgIQUAIJsWY0K0ZkipCKyWZqi5oD1LBkQEvi906+26wufLsgxGlYDO2aWVzWJaBW4f79+4t4GhhaVkUAAppGAJJdXAR0GhL4OZKhYq211jJo2l52OhIeAtqO9hMACDrgBS8/0LZvh64qgFy5BxxwgLv66qsz165WA5BoiGkDTTuYucbFWCHaOHjwYP+MN1OsvVOyYIZXBPy0notEQJvpZB1bTATSmAUpjCwElBdJp6O1w3XqxDr+n5hyWvEbq1VfIuE7ORsS/Qp5bFc0I1K7CHbX+ZdccokP+vjb3/7mUwoyl3hWBK0clsZWn2PycuOCUwQNKATUCHcz/UfaJt6V5uLTzLlxHwsBLbr/J5iKgMZ9ZxWwPDRw+B+l5QMKxDy8RGtnbXq1tLsf/0+SVmM6j0vQgL744ot+es+4yoxaDn6bXDsOAoofKNMGY3aTFBcB/P5+8IMfuO22284/K2RvgcTccsstmQEF7SfSTAL6cOVxS4KU0a5uF9pIW5sVXHJ4t3VaA4q1kFmZijwHvPWdCKghoWXLCPBAIWkSUPI8IuPGjfPLIv7Dmf6uu+6Kzf/TMOQl/emnn7rXXnvNNqW2RINDmpU4CCgaUMoq8j2SWsdl8EJYR5gumncFrhiXXXaZu+aaa9zWW2/tgz/OPPPMzNQaAoofJ+kNWxUR0MbI8V7ptAbUYhekAW1CA8rDfOONN/oXOt1M8vnNNtvMYc6QFBuBThBQzCmYnOxhLmIPPPTQQz5FWpz+n+AIAUU64axv2gnz1/IVafGfTckpP9AWAczxaZZPc++993ZbbbWVn0qaJYK2cJdddnHXXnute+uttzLRynb8P60BENhuN8FjzaDPWtGAghPvFXvHGG5pLzG/E3yEq1PRJbIGlPnfN954Y/fmm2+6iy++2B144IEeu7322suPLIsOZJHbbwQ0zSAkPiKYMIpMQPH/JG1Lq2a7WvesTVvXCQKKdoIoYIvqrVXHKNsJnuRDpUj4KGh1xzFo7g899FC3/PLLOyZPue2229x5553nuBfCgjmedwg5rDstuC8ZYW6nLtzrEyZM6IjrTDv1buZcIuBRhjUzDWe4fDSgEydOdB9++GF4c6rrfLMY5DNjW9ElMgH961//6q6//nrf8RdeeKHbaKONfCThUUcd5S699NKi41jo9kNAiTpuJ21OKwBiwigyAcX/c5VVVvGO9a3gV+sc+pLBRCcIKNoJCPBMM81Uq3pNbdeMSE3BleuDR48e7ZZbbjk/VfR+++3nSV2t9GTzzjuv23LLLT0B7XQgIwno8X1uNQDJOo1BGwScgNBuFfNxhWy3ImZZQQvZKVEE/HTkIxFQRpLMhoSpj+mj+PD98Ic/9KXwsSB6T1JcBHjhkRw+bYGAQliyFM2aFgaYou6+++7Y/T+t/ozQOxEJT3/aR8Lq0s4SMzzJ6DtNMtppg86tj8B7773ndtttN/8sMAjGNeX444/31oF6Z/7iF79waNRuuOGGeoclvg/zO0GE5K5tR4yUGUlrp6ysnmttYwbGVsR8yztphkdpIv/Pab0XiYBivsDfjoTXV1xxhfcD3WCDDfyMAjh21xpltnKD6Jz8IYAGNM0AJEOIh5ipFrvd78naG16iNcF0F7f/p10DAtoJDSgmePtIWF3aWaIBBadOBx600wadWxsBvke8B3ALIy7hvvvu81rQ2mdM37P66qt7N56zzjpr+sYOrEFAW01AH66uEdBufh9CQEk51a9fv3DTI6+j+WaK8U69D8hDi8JOEfDTuiwSAcVXBh9QpuH86U9/6v7v//7PR+ttscUW3seGpaS4CHSSgIJ6Ec3w+H8yKFxhhRUSufEgoGmnuMIvC216nATUApHkB5rIbdKxQnnnbL755o5vD5pD0m3tu+++Tc8s88tf/tK7lnUi44OBB2lu1/xOWZAr3GdMS2jld9OSthnRbrVdkL9OaUAtI4c0oNN6LxIB5VCCjsaOHesYrZ188sn+7F//+tdeS9Ku6WBaVfQ/rwh0ioDi80QQThEJKG4wq666amKO7BBQtMtp+pPZRyFOEzxTepLaRpHweX279Kw37jbEI/ABxwWFeASys7RKSnbYYQf/DJ1zzjk9L5TSL9xc2klAX1nNYcOGdTUBxWWi1QAkw4oBbqc0oPh/otCLc5Bt7crjMjIBpXFLLrmkn6MZ0xwPDn5/fKCIjJcUEwGc54kqTDMC3pAmlQX3pI0qbXu3L8ltec899yTq+mIpQtI0w9tHIe6XM1pQaUDz/1Tw8cZsvscee7hNN93Up1bafvvt22oY7mXkBSVSnndZ2oJCB4krk0W3p2KKQwPKADdt647dVyhLINBoqiVN5AHF1wZNAuDRgeE/NKGSYiJATjZe3J3wAQVxNCFF04BisiMYMCn/T3C1VExpBiKhAZ1nnnn8PM/UIS7BD1QENC400y+HAdeRRx7phg8f7rC2oPFE8zlw4MBYKoMZnnLJC5q2QEDRWg4ZMiSWS6MJhqR1ozDxBsquVrXdhgkDXIKqO+F2oQh464Vpy8gaUEad+Nzcf//9XvvJh8n+mNpMUkwEeHEjnSSgzzzzTKEi4fH/ZARN4EJSMuecc/qPYpoaUAhonOZ3wwYNKJk8MN9J8oUAmn767+ijj/axB1g7iEWIU1ZccUXvR9qJmZEgoHH4fxoeaEAhVt2Y9YHnF2VHuwTU3jHm8mPYpbFEWSL/z+lIRyKgzFuKmfXEE0/0QQ/4h4X/2pk+bHpVtJZHBLJAQAleYb7nogj+n5gi48qVWQs3nvE0CSgm+LjN77QNDSgiP1APQy7+8UwT7Lraaqv5iQnQ+v/pT3/ygXdJNGD33Xd3N998c6raQ9pI8FScBBRyhsa4E9q9JPolXKZpdtsloFh38MM0l5/wNZJcR+vK91IR8NNRjkRAya3GyIr8ahIhEEaAB2qGGWZoaw7jcHnNrttosihmeBJNozVJI/VZtxBQ3l2Y9mWGb/bp6szx5G1dc8013fnnn+/zefLdGTFiRKKV+clPfuJzcaaZkimuBPRhYIycdWMqJiOg7QYhkT2EPKJpE1D7Rtk3K9xvRV2PPN3IMccc4/bZZx8/suKFHta+DB482C211FJFxbDQ7YaA0v/h+yFNQBjNMqUZD/eGG26Y5qU7ci3I52effZao/6c1DAKK73cawn2ERsjMY3FfEzOuNKBxo5pMeXxnIAljxozxlrZkrtKzVFxOmJ4T0svsfkwHm7TwLNPOOLPIGAGFrEHiu0kwwZNqqtUcoGEseM+kbYLH/xORBnR6T0QmoPiATp061TF7RKUwpdm//vWvys36XQAEyILQKf9P4IX48jKx0WW3Q47/Jx9L5rpOWoiEhxQSaJa0m41pI5IwwYMTBFRTBid9x7Rf/lVXXeUefPBB949//CM18mm15ttGOibqsNVWW9nmxJYQUPy44xy8o+nnr1s1oEaw2+0U3jNpz4DFN4q0cPSPZBoCkUzwHEr0GZG31f7++c9/Cs+CIoDmqpMEFNiLFAmP/+caa6zRdMLtVm5PNKBIGn6gaCPwy7L0T63Ut945+IEyWHr77bfrHaZ9HUSAABMmPFlllVXcuuuum3pNGNRBCNMyw8eVgL4SKCyUZq6u3Jfn37QpLgKK0gKNKlMapyWKgO+NdGQCysP5v//9zyf+Jvl3+C8Nc0XvqmtLFhDIAgHF/cPMG1nAJKk6MPgjC0Ua/p+0IW0Cil8Wc2InIWhAEfmBJoFuPGX+7W9/888xKZc6JWhBsTIknX4MjT/TMsYZgGSYQdJEQA2N6ks0oKR1SmNwbTVAAyr/T0Nj2jISAeXDR+LWbkzt0BMO/WoWgSwQUB5q3EO6MfIz3B/M/EKEa5L5P8PXY85kci2m8ZJOKgWTtYcPDv528gM1RLK1JLgO4kmqv7iSsrfSwm222ca7uCStBcX8jiTRVjSg3WaChyxiwYhTAwr+afmBcn8zKJD/J6hPl0g+oGglTjjhBLf//vv7NAKYycKaCj5SAnY6qEVZ++ijj3xC307MghTG2EaVjDCZnatbBc0MpNC0eWm0M61IeDRCSQaRMWsWwR7SgKZx1zR/DXJJ4+Z17LHHNn9yjGeQX5fpOf/+97/7usw666wxlj69KAgoAZQEcMYtkDTIGi4N3PfdIBMmTPDtaTcC3rBYcMEFffCq+Z7b9qSW5KpGgWffqqSuk7dyI2lAaRSjU8wS5GYjETB+aPZ3+OGH563dqm8MCPBSQDrtA8qACEf+bg9Ewv+TyFbSXqUlYJu0BhStLtqBpAKQDCv8QKUBNTSys3zvvfe8gmPHHXfMxAd6t9128/OzX3755YmBBAFNwvxOhdGAojHspokXeD8gcWlAIeb4gaZFQM1FTIo6343lf5G/ZJhaSaRa7Y+IRUnxEGCUjXSagOKDDFHqZgKKtpno4LT8P+1uTkMDCsFFW8MHIUlBc4xpEncNSXYQwLqGm9cRRxyRiUqhKScQKqmZkcgswYxOSRFQI2lG2jIBapuVsLbgJx6XMOBNywTPt6l///6xTbkaFwadLicyAWU2pFp/vDwkxUOAQQnSaQJKHTBtdDMBveuuu3zEZlr+n2CKQEAhbO+88860DQn8Ny1EGhrQUqkkM3wCfdhqkbxDML9jWcMsmhVhfnieuSTeKQQSMuBKioCambqb/EAhoAMGDPAuSHHdI2lrQGV+791zkQkoqmOIRrW/arlBe19KW7oNAT4e+AJnIa9ZtxNQ/D95AceZtDrK/ZhGJDxaCLJqxKndqNa2pZde2icYlx9oNXQ6sw2tJxNJHHTQQZ2pQI2rktsajVUSWlDM7wTELbPMMjWu3t5m8gQTl2Faw/ZKy8bZtMU0u3HVCALKFOMo1pIWBjIioL1RjkxAGQ0++uij5b8777zTnXLKKd5sxhzxkuIhAAHNStAPD/e7777rk6Z3Y090wv8THNMioLhQkAc0SYHokLJLfqBJohy9bAYepF468MAD/eAq+pnJH8mAaKeddnIXXnihdw+I84oQ0BVWWCHWBPSV9YOsdRMBxZ/VNLuVbW31t1lcCBBKUizdk/w/e6McmYCiPVhuueXKf6uttprba6+93M477+yOO+643iU32DJp0iR30003NfTB4CVF/lGODwvTEZKW5p577kk1mWy4DkVfh4B2OgLe+sCmgsW3qtsEv+uxY8em7v8Jjmhd+UsyEAkTvH0Mku47/EClAU0a5WjlH3zwwd4nju9IFoVgJAKk4p5BK6kE9GEMuy0VUxIaUBtcJ+0HSvA2Ce+lAQ3fodPWIxPQ3qdO28LUUk888USt3VW38wGAuPLhIbUTU59Vkz//+c/uD3/4g/9g7Lrrru7VV1/1h+FzyugUs+RFF13ky8C3S5IuAgQhZcH/k1ZjTiGyMQmfrXRR7X01rA2MotP2/7Sa8KJOMjE3H4C0CCiR8Gg85LduvduZJX6QV155pSODCuboLMqSSy7pn7k4c4Jyr0+ZMiUx/0/DsZs0oPjLouygTXHKoEGD/Nzy5oMeZ9nhshQBH0aj53qkPKCc8sADD/iPoJ3OB5E5osnb1uyH8eSTT3bHHHOM16Yy5+7PfvYzt9FGG3lfICufEQ8fXlJhkHaGUShTfuIrdNlll7kVV1zR7b333v5wfFB5oSWR1Nfqo2VvBHgpkIorC0K+PvLqdSMBZaCFTxdWiE5IkgSUjzHWjaQj4A03NKB80B5//HH/DrHtWqaLAGZ3+nyXXXZJ98JNXo1vy7bbbusee+wx/71q8vReh2N+R5L+VqEBJa8qmre8z1RIuj/4RtwElH7gHkxaA8o3ifyyWQqyo+1ZkMgEdL311quavgRT/NFHHx25LdxIzFhjwRRDhgzxI2DIDA+NyYsvvuiPsZyHaC6uu+46vxtzIPUxYR+dHH6oH3roIZ8yimPQdvDRYTaCpIUHnhEV7gFW97iv+fHHHztwx6ctCaENaJTr4UVSXV5wJFKud1y1+lF2Ev2BFu3JJ5/0L6tG9a9Wr6jbkqq/XZ9nBDFcb7vtNrf66qs73E7iEJvRjPybUfwuefFff/315frUqwP3DkLZ9HEjMesJ17D2NjqnHfy5R2gzA1ZciirF6g/WUepfeX6U3+3Uv1H5du8kWX/un3aeX1yv8GlGoUB9rc7WNvsd9X6w86Ium6n/D37wAz/4+8tf/uJOO+20SJewe6ha/VGqDBs2zM+2VG1/lAtQfzCqdz6uURwHuTJTc5SyOaZe/aOWUe847n+E69Rrg5VhGkqsrVGOj1ou5aO0YHARpVyO576n/lGP5xy+Sbx3or6/m6k/5Tcj3DfN1r+Z8sPH2rXC2yrXIxNQzN9241ghsHoSgDcjRJ1xXvjDx+wuaELCBBRyw3aTueaay8+dy280r/w2Yb1yGkZyy9nHjYcRjSlBKmnIxRdf7M4+++xEL7Xddtu5P/7xj4leox5eb7/9tn8Y6aN6x9WqIA9j1AeyVhmV27l/IP5mXm2lXpVl1vqdRP0rr0X9idDEZWWLLbZoCefKMsO/Gcjw10iGDh3qfeEYFBIZHEWiRpYS2IgwkGmmv9rBHwKARYdI51qC322S0k79o9Qrq/XnG4LvJ+SfnLb1+rzevigY1DumGfyx0jEz0gEHHOC/XfXKDe+rVn9iFtDCV9sXPrfROgM8/mqJPad8A+edd95ah9Xd3m4d6xYe7Iz6/jGrFt/5KHXi/W/fgEZ1IIj2iiuuiFRuuKwo9bDjIaAEIEU9p5n62zWaXUatS7Plho+HgDYaxEdmjz/+8Y+9r2a/fv3C13AXXHCBDxKCdEUR/PQqK0VFiToMS+VxHGPTf9bbZ2X89a9/LT+ggM1Nhs9H0sJLAdeAfffdt2lyHrVuv/vd79zo0aMTaw8vBhKf18PLZtlgZFfvuGptmjx5ssNkXnkvVTu2mW3LL7+8zynIA8y90my9ol4rqfrb9cP4o6njo42LSlztQTNCG0jXUvncWR3Cy+HDh/ufBGQ0MpVz/5M3lA9glMEpA03cC5rR0jBYxaxI/VuRESNGOPyyquHZbP1buX679a93TbQn9FNU/OuVVWtfO/XnOwGhILC0Gv5cM3z/16pDO9ubrf+ee+7pzjjjDHfzzTe7n//85w0vXav+DArQSP7qV7+q2faGhQcHRKk/Sh6Ee6EWzv6AKv9q1b/KoS1t4n2G2w3vf/um1yuI9qLoQFvZSMhXHLVcymIwwPcCjWYUEzl9yDNGYGYUoa3jx4/3bhxR+qHZ+kepQ/iYZusfPrfZdTgbXK2e1CWgfPx46BDSMKFVDH+wIJL/+c9//Cw09S4S3sdoDHLDCNTm2eUGq4ymprPw0zLhGFTwCB8sfpuwXnnzhDsbENC4RvkgWpmtLsGEvJhcP6nrbbDBBu6SSy7xpv4kIutwHWiEF5pshNQYzbaTshuV3wr+llePlzwvlmbrFfWaSdXfrm+uG9R/zJgxPlLYXFbsmHaWEFCE5yIKRpY+hMTWzBBTT2xwGbVs3GkI9ohSj/B1wajZc+x8Biq8t/g4VPrHNYuNldnssp3617tWGvXn/m+l/pB78n4ylfP3v//9ms0I3/81D2pjR7P15/6kvueee67bfffdG17ZPrqV9yeZLLjncJ+q3New0NABUeqPthD3NiyXzV6rVv1DVWhrFQyQqO8IfECxbkVtRzP3pn0/se6ELbC1Gtjsu59yIbd8m5Kof6161toONrQhal1qlRN1O9eqJ3Wj4NFK3HDDDf5lDevHB5MXt/3deOONbpHAdwuTSlSh4ZjDr7nmGn8KH1hG6/wxEjHN2siRI73vhDkgX3vttT53GifhD0e96FhGUmbWiFqHvB8HCeBGQovQKcFnl5vLBgWdqkf4unwowCXpvG7haya9jq/cWmutlfRl6pbPgA8NRBKpmBgsoEVPU/AZZwBspr00r13kaxFNTnDp8ccfnzsYmBmJ/LFMh9uqEICEZtIGyq2WE/U8CBV4511oAzwjCUGryneM91ASogj4+qjW1YCircSnDtl0000d5pM4zKZ77LFHOf0ShOGwww7z1yCH41FHHeXJKSM48rARIY+6G00bfo/Iuuuu6+tFdCLnb7PNNpFGL/7kLvgHNiQyhoBaJoC0mwUBRcubVCBUK+3BnMNL1x76VsrI0jm4juAgn4WZxhiMxk1A0dZR5k9/+tNUYUc7juBbWy0QKdXKFORizH9O5hPe1YZ/nprO9w9faGZGQjnSilgCetMwtlJGM+dA2rqFgG6++ebNND3ysXwz4BYW6BT5xIgHMsjF0ovfuaQ3AnUJaPhw01iGt7W6ToeTSgn/lPA0jvhmha+z8cYbe3MN2oow8UWLSuQ9/gzcQGmpk1ttbxLnjRo1yvs7opmuNCMmcb3KMiGgWckBGq4bJpVu0YDi5wtJ67QGFHyTIKBYO7BiNPIrDfdvHOs2oEWjtVOQT1iSPAIETDKggoTmUfjGoAw56aST/F84QDZKezA749KGJjUtYTBOCrc8C+8/vjVJaUDBJslUTChDKD+tQUfe+rquCb6yMZjIyceJlpIk5KQ6Mp+jymOj/A6Tz1rHQ67C5DN8HEEIRSSfYAABRauAb24nhJdCpd9uJ+pRec1uIqB8PHBxSNtEXYkpv5MgoKZ16ET70MJpRqRqPR3/NvzF//SnP3lNfp41QRBQBkxMz9msYOIlVmHllVdu9tSWj4e0gT11zquQ3QYlC0qrpIT3j72L4r4GGlDzM4277G4oLzIBhckTjIDGgFEsaXiIxsahmrRIknQRwI+NUXin/ECzrAElkjCNNBNJ9zj+n6SqyYIwVzv+1kS4xyV8lNEMRIlujeuaVg7PDymgLCDCtmsZPwKm9Tz00EPjLzzFEiFBG264oWtlZqS0EtCH4UADyv1N8GBexVwIktaAYo2B6MYtWOMsiDPusruhvMgElKkwGb0xorKIc1Iw4YNJRLYkXQT4cOMLa1kK0r268xrwrJrgwSKpEW1aOKMtIYdfVggoGlAkzik5IaB8JDvhQoIGFAtCnO1J697I03UgPxA20tIRlZ13wR+bvI4WGxG1PRBQBnEE9KUlRtqMxKV13TivY3W3tsRZtpWFBpTsHXG/C1DSMGCXBtSQ7r2MREDJC0biZkzvBMCY8EL59a9/7WdJsW1apocAqUEwI4ZTUqVxdVwx8N/NIgG10WbeCSgfLLQXWfD/5J4yAhpnIBJ91AnzO+1BA4rgBypJDgGsZFhqfvOb3yR3kRRLZmYkkpc3qwXleU7T/A4kCy20kI/wzrsGFFe7KO56rd4G5oMe9zfDgmHtm9Rq/br5vEgEFD9LNJ3k76wUItfZJ0kfATSg+OCmrQW1WaeySEBJc4KGPu6XSdq9i4aFD50Rv7SvX3k9Bpv4YsdJQNGAdoqA4r/M7EvyA63s6fh+k8GBzCmHHHJIy5MGxFebeErC8kQy+n//+9+RB/7MCoYvYNoElOhr7nPTIsaDQLqlUPek/Yb5XoAV76M4hT7nfjGCG2fZ3VJWJOZIqh3mXt9nn33KedDQivJyYcYhEqNL0kcA3zkIStp+oJgWkCwSUOpFPtC8E1By22bF/A6mCPdaXASU9wcDmU6+nNGCSgM6rW+T+H/ggQf64JEoyduTuH5SZeKOhr8g03NGEaLfURSkTUCpG6brPBNQfDOTDEACIxRovIfi/magAeUbnaVUhbQ3SxKJgFJh5jbno0H+SVT6fBx/8pOf+GjsvfbaK0ttKlRdMMOnrQE1AprFKHg6H5NHnlMxEUTFyzBrBBQftrgIKP5WuBh0SgPKfaJIeFBIRgigY6IS0uV12weYgfcmm2wS2QyP+R3rwdJLL50M2HVKxcc67yb4JP0/DToIaBIaUPl/GsLVl5EJKGTjvvvu83OQk4wXQor5igAkZiuSdAYBNNPgnybhIgUXJgsmKsiioNkiWM5cBbJYx3p1uvPOO/3urPh/Wl3RgMblqG8v+04SUO6TyZMn++kKrY1axoMA2k+mj7XJQ+IpNTulEIzEPQzRbiQQUBQ3mGPTljxrQNEa8w5Pg4DyHkpCAyr/z/p3fEMCirYLsknuM4JP1lhjDT9D0U5BOiZyFDKzxf7771//KtqbGAKQFF5saZrhuSeyan4HaOb6RtqZNs8X0KF/5HYFX7QXWRIIKMSeCSDaFT7eBBd0cipXm5FHfqDt9mbP86+88kqfdJ0pN7s1PoCBP88nyph6gpYfE3wnzO/UizqSPo2MD3kTvjOff/55KgQUDSiWJ4Jr4xAwpzxpQOujWZeAou1gFIsPD0l4+SjyAUKuuOIKb+r873//65ixSNIZBIgOXHHFFUVAQ/AzYibylswNeRQmeLAo7SzVHwKKxGGGR9vQSf9P2kFwA/eJ/EBBIx4hnc3BBx/sFRVEjHerQKyZKvqqq67yRKNWO7FMkZO4UwTUtId59AO1OlsbamEcx3azxJhlpt0yFQEfDcG6BPT3v/+9n/+WDw6mKuZNZsTHrBZbbrmlnxOXnGhpz+UcrWnFOYrR+JgxY9yXX36ZSqOzrgEFBAhcHjWgTDvLM2Va3FQ6NOJF4iSgvOjtpR/x8rEf1qdPHzd8+HBFwseI7Pnnn+9N0yeeeGKMpWazqJ133tn7MdPmWoL5HVlppZVqHZLodrOi5NEPlAAkJOkgJK5h77a4zPBEwPN+ISBWUhuBugT0xRdfdFtssYVX4+NEveeee7pTTjnFHX744e700093N910k0tjdFK7+toDAgQiYRZtNjlyq+hBQLMagGRtgljkUbM1duxYH2GbRQ0ofd63b9+u0YByr4BzHu8Tu8+ztMRF68gjj3Q//OEPO0a40sSD1GS09ZxzzvFEtNq1IaBo+jvlL08qN7S1pk2sVsesbqPO8I4BAwYkXkUmCOAvTg0oeVhJCyipjUBdAsp0m8sss0z5bPJlkfQcLWi3pdYoNzKHKzi440+XRjQ8Pk1MvZplH1C6EAKK6SuuEW1atwWBfuTd7UTEbJQ2oiloNxCJ9wozhHRaA0p78QNlQIW/lqQ9BE499VT/bjj22GPbKyhHZxOMNH78+JrvXghop8zvwMgsY5DQPGpAIaCmwU3jlmCgENf3QnPAR+uxugS0sgg+jAjmd0l2EKBfSEqfRiASH2ocw7NOQPFdRvJmhidggbrPNtts2bnBQjWBgLbrA2pahiwQUNM0Swsa6uQWVhnsnXDCCY7g1CJF/pIqjfRk1WZGYpDViQT0ld0HicurBjRNCysE1N5NlRg2+xsf0CI9B83iY8c3RUDtpG7L62btyvMSMzwfUT4ESQraIiTrBHTo0KF+RqS8BSKhAR0xYkSSXdhW2XES0E4HIQEEPlqzzz67/EDbuiucI+L9008/dUcccUSbJeXrdPz80IJec8017s033+xReQaTWIw6qQGlQpA4EdAeXVP1BwNitNn0WTuCOxzpoxQB3xjFaSrNOseRfsk+4pheEdIu8eCZoLHZfvvt7aeWHUCAQCQiUG+55ZZENdR5IaB0wciRI3OlAcU0/eqrr/qcgR24hSJdEgLKh5ZpeVv1b0LLwACm1fMjVTTiQaQw4/0lDWhEwKocxsf2tNNOc0xIgrm3aILWl+lGzzvvPHfooYeWm4/5HdeoTrvToAG9+uqry/XKwwokMK0coIYHA2Lea+TVxn+zVVEEfHTk6mpAGTnxsWB0xx9ElFHCtddeW97GdtLGSDqLAFN+ES2YtBneCGgn8zdGRRoCynzUEPM8iEXMZl0DCpZoCloV/KyyYH63+mtGJEOitSVaT1xGSD5fRCHAiGBdgpFInm7C84x/fqdzofIdJ78lc9LnRZjshIwgaUTAGyb2TmrXDxS3C0QmeEO29rIuASXHJ3nMGv2dfPLJta+gPakhsMEGG7hbb7010etBQHnhZtVHMdx4CCjTxz7xxBPhzZldx/wOtgwmsir4uyHt+IEyqLWXfRbaiR8ohDpPH+gs4EYd0PZccMEFnnz2798/K9VKvR6//OUvvfXihhtu8NdGg9fJBPRhACyQh6w2eRFzGYA8pyW8d7HstktAeSZwASvy8xC1z+oS0KiF6LhsIIAZnmjHdh+geq1hZJp1/0+rvwWY5CUQiQ8WkwpkWeh7Bh+tRsKTq5YPYRb8Pw1nNKAQhkcffdQ2aRkRAUzPfGwxvxdZVlttNe/zZzMj8Xygdey0/yd9YiTOSF0e+snqanVPo86817heu4FIioCP3lsioNGxyvyRRGRi7knSDI8GNC8ElBEomjbzYc5yB2K6w5WlUwmro2KDhgBNQasaUMgnJDRLGlBSzZFJQn6gUe+CacehsWcmIPJCE8hVdEELigYU30WeZZ6VLDzPvK+5v/OUigkCio942vlTGRi3q8BRBHz0N4EIaHSsMn8khAufoyTzgeaJgNJheQlEYvYj5mvOwger0Y3eTiS8aReyREBnnXVWr73SnPCNer7nfnw+6cdddtml546C/tphhx0cGWL+9re/OSaUgMykkUS9EdwE2hFUY1rFRsdnYT91NdeBNOtDn9k7qpXrkgkCoq8I+GjoiYBGwyk3R2GGv+OOOxKbljOPBBSTCC+GLAvaJDQmEOasS7sElI90msEFUfDUjEhRUJp+DFaW0aNHu+OOO85BcCTOzTPPPG7rrbd2f//73zPj/2n9gmk5TwSUaTg78Y5gQEUmEnJdtyKQV6xZCkCKhp4IaDSccnMU+UAJprCI6jgrTlQiM2FlfRrOcJshdF988UXmzav4f5KTko9Y1oVAJAYiTL3YrGDegsBmjbTgB0qwZdYHKs3incTxfGCZchN/5R/96EdJXCK3ZWKGJ10hriZZ8P80INEm5s0ED2lOW9CAkjWlVR93i4CXBjRaz4mARsMpN0fxUWD+3CTM8JAOJC8+oNR1ueWW82Qn64FIaECzHoAEnggEkqCdVlIxoSHIkvl9WoumzQmPb2peMiZYvTuxvOKKK9y4ceP8zEeduH6Wr8kzbNNXZ4mAQubQKuZBeLeghewEAbV3U6t+oPh/4go3ZMiQPEDd8TqKgHa8C+KtAHP/jho1KpFAJJzrkTwR0L59+/oPQpYJKFP28eLKg/8n/W9poloJRIKAomXImjBQwQVCkfCNe+aUU05x66yzjltrrbUaH1zAI8gIMGzYMLfUUktlpvWQOSxjkydPzkydalWEiS6wtnWCgC644II+y0erfqCKgK/Vq9W3i4BWxyXXW/EDJQoTYhOnkIIJyRMBpb4EZmU5Eh5yzKg/LxpQAhoI3GmWgHI/MtuTaRnom6wIM9bgWqBApPo9AoFBOyTTe22ctt12W3fXXXd1PAF9uIYW0JMHP1CrYycIKINQBsjtaEDl/xm+8+qvi4DWxyeXe8PTcsbZAEzwaFgHDhwYZ7GJl4UfKGQpq4nGMb+jqe30lH1RO4JUX3zQmiWg9lLPIgGl7fiBSgNa/y6AWCF5GSzVb01x9hoBzYMfqLkKdCIIiTuiVQKKCw++o/L/jP5ciYBGxyo3R6LJQUsVdz5QCCgBSIwS8yQQUDSMWdWCEoBEHcnVlxfhHmuWgJpZK4smeHAnEp50WHxIJNURILiRQDlzw6h+lLZmDQEmCyD7hGkXs1a/cH2oIwPyQYMGhTents4A2d5VzVyU9yEBr9KARkdNBDQ6Vrk6cv3113e33HJLrHXO0yxI4Ybji8UsF1n1A4UY502jRCBSs5GiaEDJi5hVDToaUHzPmm1X+F7r9vV77rnHu7TkbRDa7f3SqH1YLdAo5kEDCgHthPndMGSAPGnSJD+TlW2LslQEfBSUeh4jAtoTj675hRmeVCDNaqnqAYAGNG/+n7QHzSLarSwSUPpo4sSJuQlAsvsDAkpQGoQtqqBVyKr5nTbY1K2KhK/eo2iG8S1HWy/JHwKY4fOiAe00AaV3ScvWjBBIyuxNBDJJoiEgAhoNp9wdlcS0nHkloHReVgOR8P+0+vmVnPyDgJIPEgIdVbJOQJn2b4EFFvBm+KhtKtJxTFVK7leeJUn+EIDUiYA27jdzETKf9cZnTDsCDSjmd1kHoiLmnAhodKxydSQf0xEjRsSaD5T0GHnUgNJxaG0g0ERhZ0nw/2TEnDdcIaBIVA07PriYtu3lnqU+CNcFM7w0oGFEpq9jfseaQMoqSf4QQANqAT5Zrn2ncoAaJuYm1KwfqOaANwSjL0VAo2OVuyOZFen222/3Mzu0W3l8YpglJk+zIIXbbGZDCF+WJE8J6MO44U9GRoSoBBRz/ccff5xpEzztg1wRiARhlvREAAIKQSdARJI/BNCAfvTRR97lJ6u1ZxYpvjOdioA3XHAVakYDyvsCwqoIeEMw2jI/YbfR2qOjQgjgB3rsscc6SM6qq64a2tP8KtpDJG+aOmspGru5557b+4FuuummtrmjS/wnSfuz1VZbdbQerVycqTT5oEUloKZNyLIPKDhAsPhIY4pnRjF8uiBc9hf+3ew6ZXBOXgUCusUWW+S1+oWvN88rQiDS4MGD/XrW/pmLgNW1U/XDUjN27NjIl6feDLAVAR8ZMn+gCGhzeOXqaKaCs2k5i05A8ctBC5qlQCSSnn/++ee5i4C3h4BUTFEjxiGgROKa6d7KyNqSQdsRRxzhg6vQxPBR4Q9SyhIXDlsP78M3MqrWFDM21zjkkEOy1vya9cEsyiB0lVVWqXmMdmQbAcsFClnKataNLBHQSy+91D/TUXw6FQHf2r0vAtoabrk4CxPpWmut5fOB8sFrR2wWpLya4Gk7BPTss89uB4ZYz0UzDRlZfvnlYy03rcIgk9dee22ky2HOwqzGDEpZFnIl7rbbbj5VFM9PVIF8QljD5LTW+llnneWuvPLKXBFQtJ9IluY3j9o3Om4aAmg9Z5999kynYoKAUsdOa2ix1DConDBhgs+p3egewv+TdxtTsEqiIyACGh2rXB6JRmfffff1swDNNddcLbcB7Uf//v1z7f9F9O7xxx/vxo8fn4lE2vij4nPICzePAgFFM0by5UZkDQ1o1s3v7fQBWhL6MUpfvvPOO+7www/3ZDUvJnkIKMFy/FF/Sf4Q4B5dJOOR8BBQ6thpsWBJ3ltM6tJI0IByDq5JkugIKAgpOla5PBICSv6+W2+9ta36Q0Dz6v9pDbdApKyY4fMagGR4QkC/+uqrSBoVXuT2Urfzi7pEiwhuplXMAw7UVdrPPPRU/TpmnYASpd/pACQQZKYvXIaiBiIpAr7+fVdrrwhoLWS6ZDtaJwIq2p2WEwKaZ/M73QmBHjJkSCb8QEk+z2h/pZVWyu2dhg8o0igQCdM0mtJu1oA204lEys4555xuzJgxzZzWsWMxRT7++OPy/+xYD8R3YfxAszwbUlY0oMycBxFm4BxFIKCKgI+CVM9jREB74tGVv5iW8+abb26rbXmdhrOy0TjfZ0EDivYTyWowQCVu1X6jTcGHtVEgEgSVpPUioNNQRLOCNjEvBBRXEdwsFIBU7SnI1zaeWQaDUQPm0m4ddaOOWRDeV1E0oHwbp06dqgj4FjpNBLQF0PJ2CvlA8XtsZ+TbDSZ4+g0/UKLPIUSdFD7q+NSaFrGTdWn12pBPtASNNKCmRZAJfjrSkLkHHnigqalMp5+d7hrmdzRCw4cPT/fCulrsCKABxSLBpCJZEzJMkFkiKwSU95W9u+phpQj4eujU3ycCWh+frti7zjrr+OnBbrrpppbaQ6ogEtHn3QeUxuMH+uGHH7px48a1hEVcJ5n/Z5QUH3FdM4ly8ANtREDRIpADE1cQyTQEIKAQAUho1gUCysCtUaBZ1tuh+rkyucPUnTWxOmWFgKIBRSNLvuZ6gvmd4KM8KxPqtS/JfSKgSaKbkbIHDhzoU/20aobHxIDJphsIqKU86qQZHu3rQw89lGv/T7u1oxBQC0DKO9m2NsexJOE9EfN5MMOjrZf5PY5e73wZlgu0HWtYUq2waUKzEIREG9GA8q5uNMBGA0rQUtZTzCXVb+2UKwLaDno5OredaTkxvyN5D0KiDfPOO69/WXSSgPLCev/997uCgDLqR3NBpoVaYgS01v4ibkebmAc/UPoO64cIaHfcpbz/SP1l2sYstYo64epBoGgWxHzWG5nhFQHfem+JgLaOXa7OJB3Tu+++21IAjiWh7wYNKJ3W6RmRLADJ0kLl6kaqqCwaUMhnvQ8aJnh7mVecXuifa6yxhrv33nt9SqasAmGpovKcrSGr2HaqXlmNhOcdgvYzK5YSXIawUjQKREKhoAj41u5mEdDWcMvdWWgw8MNrJR0TGlACTjo9O0VcoEP8nnjiCT8NZlxlNlMOJk0I2YABA5o5LZPHQkCRWmYqtGdTpkwRAa3SexDQDz74wD388MNV9mZjEwQUU+SgQYOyUSHVom0EIKD1BoxtX6DFAqhTVvw/aQJEGAtPPQ3o5MmT/cQMmgO+tU4XAW0Nt9ydxRSDNi1ns5WHgM4333w+MW+z52bxeAIqCKwiGr4TYgFInbh23NfkY0ZaoVoE1F7e0oD2Rp4UXJjis+wHCgGV+b133+V5CyRPBDRaD/LeqqcBVQR8NBxrHSUCWguZLtyOGR7tG1qXZqRbUjBZm0knA2nqhB8oEfi8tLrFpMnAhqnqGhFQpWD6f+2dB7wTxdrGB1BBBUQEBRUbFhRBUPQTEbGDvSH2hr37qdiwYO/Yywd6r72LDQQVES/YARWxK4riBUXE3lDz7X/unWNOSHKS3Z1kkzzv73dOki2zs8/Mzj7ztnG97+9PLBJo45NKQL/99luDf5sI6N9tVg3fmDSyxnm5U9FlYpmkHKCubg2lYuL5QFPaqVMnd4o+i0BABLQIsCr9UAKR8NcbN25cUbcCAa2GACR3082bNzedO3cuCwEl7Q4DfyUnoHc4us98kfBoD9q1a2datmzpDtdnGgKY4V944YVEJgbHP5XsFyKgaQ1WBV/RgGIBYlxPisyZM8f89NNPiViGMx0TNKCY2YmfyCYoE5iAE9glKR4BEdDiMavYM3CUJpCoWD/QatOA0oDlCkTC/I5je9euXSu2H2VWHD+pfBpQmd8zEfv7NwSUF9y0adP+3piQb5jfmTjIvy0hDRJTNdCAIklKxeRcAiDHSRJnuXGuRJl1UwR8JiLF/RYBLQ6vij8aM3yx+UCrZRnO9MaDgDKoYBIvpeACQS5SgrqqRdCA8jL7888/F7glMHaD+AI7tcFqF3EHmTBhQuLQcP6f1E9SPQg4kudIXxLuzNXF1S0JdaIObuzKRUAVAR+tpTSyRMOv4s7GDM/a3S7pb0M3gOnhl19+qYok9On3SiASpvBS+4FCQKvF/9PhCQHFpIcPV7pASFkCVhrQdFTqf19iiSXsEpdJ8wOl7Xg2ZH6v317V8KtVq1aGfpc0DSiJ3HHXSZKwXDIZILIFIhFLMXPmTFkIIjSYCGgE8Crx1C233NI6TRdqhnd+QtWSA9S12dprr20IoCklAWWWz3rH1UhAwTXTDM/9QkxFQF2vy/6JGT5pGtCpU6faYEUR0OxtVulbMcPzfCZFqEuScoCm45IrEAnzO6IcoOloFfddBLQ4vCr+aGZzLANY6wQU8gkOpSSgLgF9NQUg8UCsssoqdlKTSUCd2cqZsSr+4fF0AxBQ3Fwy8fN0uYKKxfyO6R1XFUn1IYCpO0kEFItcUpbgzGxtJtDZNKCOgMpHOhOxwn+LgBaOVdUciRmeSPhC0nA4DWg1RcG7hix1IBLmd3BkhY1qEpbP69ChwwIEikGbPJcQVEluBDbeeGO7M0lmeAholy5dlL0gd7NV9B40oEkzwSfN/9M1MBNoJodkhEgX/D9xGcBMLwmHgAhoONwq+iwCkVidZtKkSQ3eB5oZImFJXVRtAgFl5s1qPaUQNKDVZn53uOEHim9xuqABhXxWU8BV+v3F9R2rBGa8pBFQmd/jauHklQPZQ7lAWr4kCONwUgkoGlDiIDJ93BUBH73niIBGx7DiSujVq5dNBVSIGb4aUzC5BiMQCSE3p29xKy9VMwHNNCErAr7wXtW7d+/EEFD8lDHPioAW3n6VdiQaUMgnCenLLSgAyEaSVALqXIicS5HDSxHwDonwnyKg4bGr2DOJNuzTp09BfqDVTEAZWFq0aFESAvrGG2+Y3377raoS0Kc/AGhAp0+fXs+tAxO8ApDSUcr9HT9QTKLO5SX3kf73TJw40V5EBNQ/1uW6giN7SfADdXVwdSoXJrmu27FjR+sPne4H+uuvv9rnVf6fuVArbLsIaGE4Vd1RmOExCTeUB7OaCShBFj169ChJIBJYN2nSxOYArbrOFNwQBBSCTVoShH5F3xEBtXA0+A8Cijz//PMNHuv7APw/l156afnu+ga6jOU7spcEP1CXEjCpQUgobMArnYCiDSWGQhHw0TqxCGg0/Cr2bAKR5s+fb5577rm89wCJqMYAJHfT+IFOnjzZ/fT2SQASqx9V65JtrIaEkPcTcYO1M1/ZjfqXEwEC0zCLJsEPlCU4cdORVC8CWH6WWmop47SP5bxT6kBWkvbt25ezGnmvzUQ63QSvCPi8cBW8UwS0YKiq60DyYPLA51sVCR8h1uitthyg6S0JAcXnzc3C0/fF+R0NaLWlX0rHBzNVo0aN6iLhXUCSNKDpKOX/jha03AQULfaUKVPk/5m/qapiL1q9JGhAIaCsp57kFbeYSLtJNY2P/yfR70lLnF9pHVMEtNJaLMb6YobPF4g0a9Ysa2aodgIKpD7zgeJkj39ktQYggR/r26MpT9eAstrKMsssw25JAQhAQN97772SZWXIViWsAZBQ+X9mQ6e6tiUlGT0EFDKcZIGAEgWP7yeiCPh4WksENB4cK7IUzPCYFXJFQrqAiGomoPgdkQbHZyQ82k+kmjWg3B9+oOkEVNpPUClcIKDkGiynFhT/T8yh6667buEV15EViQCkD/JXbqkEmNR9ogAAQABJREFUAspYhs8nigREEfDx9BoR0HhwrMhSGlqWsxYIKA1HOiafGlD8P1l/udoJWSYBlf9nccMC+OEWU24Cut566xkWF5BUNwJoQMnzTIq4ckqSc4A6XNxYRqo5XNNwMVIEvEMn/KcIaHjsKv5MzKMExuQyw0NAidyudjMqfqD4vWWudBFXA6MBheTiI1nNAoFCQwCO+EtVO+H20Zbl9gMlAEnmdx8tm7wy0YCi1ctMsF7KmrIgyg8//JDYZTgdFgQJLrbYYtbHHb9ZAngVAe/QCf8pAhoeu6o4Ez/QZ5991g5EmTcEAYV8QkKrWSCg33//vfW/i/s+IWNoV6vZ/9NhRiQ8PlLkPP3pp59EQB0wRXxCQKdOnWr7YxGnxXIok4fZs2eLgMaCZvILQQOKlDMQybkAJN0HFOUB4xsaUBeMJA1o9D4uAhodw4ouAQI6d+5cqwHMvBHMM9Xs/+nul1ygiA8zPM7q3333XdX7f4IfGlBk7Nix9tOZrewP/SsIAQjon3/+aV544YWCjo/zIPw/kVqYLMWJW6WW5fJuOhJYjvtw1046AQUbLDqOgJJOj8h9STQERECj4VfxZ2+88cbW3yubGR4NaC0QUJJuMwD6CESqlQAkHgRSMSFo1BGXG9T+0L+CEOjcubNp3bp1WfxAIaBoxao5729BjVAjB2FSZuwrtwZ04YUXrog+x4TaEdBOnTpVvUtVKR4DEdBSoJzgaxBsgNYlWz7QWiGgNA9meB8aUAKQIGIkfa52ad68uXXZwITcoUMH6zNV7fcc9/1h6ivXuvAQUPl/xt2iyS6v3KmY0IAmPQeoa0EI6Lx58wxjuvw/HSrRPkVAo+FXFWdjhuflg99eutQaAYU44Vwep1R7AvpMrJwWVNrPTGQK/82EcNKkSeaXX34p/KSIR7J06rRp00zPnj0jlqTTKwkBLD/ODF6OehMB71wBynH9Yq7pgipZblj+n8Ugl/tYEdDc2NTMHvKBkopj/PjxdfdMUA6EtFbMcWhACaCBhMYl4Pf222/XlE+d8wOV/2f4XgQB5Xl07hvhSyr8TK6F76k0oIVjVg1HogEttwkeElwJkj6mSQMaT4uVjYCyOsxTTz1Vb33VbLdEonT8Ezk+XV5//XU7QDNw8kc6B0k4BLp06WJNp+lmeKJhkVrwAeU+SbyN+TNOMzxl8VKv9gT04OfEaUCdtsBt12fhCHTr1s3gzlDKfKBYQAisIC2bpHYQgPyxFLFb4afUd472tVIIKLmc27RpYyGSBjSenlIWAgp5POigg2w6g1NOOcU88sgjWe/mqquuMpdffrnh+IMPPrguXxmJYAcNGmTP41z+MBdLwiEA8UILmh6IVGsEtGXLltasEicBxVcIH9t11lknXMNU4FmOgMoEH77xFlpoIauJLDUBZaJU7SnXwrdKdZ6JBpRUceUww+NPiaWtUggoPQALD0FTbpyrzl5RurtaqHSX+vtKV199tbngggvsi3nAgAHmkEMOMdttt51dAs4dxQMxYcIE89BDD5nGjRub++67z9x9993m9NNPtw8LiWEvvfRSd7g+IyKAH+hdd91l8G9hpsc68EitaEC5V9IxxRkJj2YezSoDVq0IJlxMyCTel4RHAAwvuugi65Psu/9AQOirxx57bPgK68yKRMCRP963RHaXUvD/RFwdSnntsNdae+21rW+2JmphEax/XskJKNpLSI4z9ZDonHQQaDCZjTkhKTLHQD4RXuSjRo2y31kGCwI6ZswY89tvv1ntHWWky3XXXVe3xjlaKFZ8+Pbbb9MP8fIdkyvCzM7VPe4LcS/cd5z34wjDY489ZpgUQEAxyfnAzUf90zEmkIiXarH4MLhAwsl/mtmf0ssvtP6sKrPbbrsVXQ8XCFVs/dPrmO872CD4qNKP4hQ0yUwWGaB91b9Q/MPcl3t+WZ2lnM9v9+7dzc8//2yee+65osh8mPqzrjW5ahlvC2kzrhH3+JPeVrwjkELqkn5eod9V/7+RWmKJJeyPdF913/i78Yf3OILCI862JnjPjaH2AjH+Gzx4sB0346xvZvV81h/fch/v9Mx74Df9yI1H2fazreQE9KuvvrLEBrOvEx4CfDjTCSgEyD0cHMeLjYTpCCsR4BuKaRMH6uHDh5vbbrutzj+DYyC0kFiEDk6+M/dg2Y2e/tG4CMC773FfigeYsuO8H9IE4ddCDsf+/fvbFVHatWsX6zUcDtSfvzjr78rm0+FebPn0J86dPHly3mjgQurPJAvfKiZOxdYjbP3TMcj3nfojcfchVyafvvu/r/7jsC93/emLiyyyiJk4caLtQ2BaiISpP9pPBNJbSF8tpP8XUtdcx7iXViF1yVVGvu2q/9/oMFFECYQG1OFdCvypAUuAcv24382u/n/fZXzfUGY1bdq0Dqv4Sv67JJ/19933/76L/xDQ9N/ZvnsnoBDEkSNH2mtDIvv167cAK6bj07DpQsdMbwiOWXTRRe0hhx56qOHPaamYjaMN3XfffeuKuOSSS+q+4884bNiwegS1bmfMX6gLZHrJJZc0+HL5EEg8eIFnnLLtttuaO+64w+IKeSI/m3O6jvM61J+HOH2CEWf5pJThr9i69+nTx5rLmZnvsMMOOatUSP3HjRtnz8e3tth6UHc0cMWel7PCGTsgKbRvixYt6p6pjENC/3T9n0mfL9NxIfiHvQE0BEx0k1B/ViRiMlRMPwhT/7feesvmNXQZDBrCbs6cOZYc+3x+ffZ/3/XHsoAFrJh2awjz9P1x1x9/RsYDV1/f9YcE8U7mj3cMBDguQXFFAB/WOx+C5hPtqsMq7mv4rj+WDsYIX/VPxwPOBo/LJ96DkHgJMVDxxwsPTVum6Q/Clpnup23btvUi2zmmffv29l7QbvKic0InpuEk0RDAD5TB7c0337SDQy35f4IcpBgzZByBSAQgoUGmb0qEQBgE8ANlSU6n1QxTRiHnKAF9IShV7zFYHj8NNKClFnxAK8n/s9T41ML1vBNQfDX3339/+7frrrtarSDRlo8//rjFl0hPtIX84fvgHJPJy0hi5M8//9yqu5944ok6XyjOufnmm+35+Emhbdpss81qob283iMrsKBZxQwPoa81Agq4+MLGEYiEWVNranvtrlVfOAQUjQUTQl+CtheXJuX/9IVw8suFBJaDgGKCFwFNfv/wWUPvBDRb5Y866igb3b733ntb0ziR7QiO0C4SE/PyYYcdZiPk99tvP2uS5Hhk9913tyZWUjkRMIOfHX+SaAjg4sDa8OQDRRNaiwSUiQ++w2jcwwommilTpoiAhgVQ51kEWJUINx6f6ZjQfiIioBaGmvyHBpTxHstkKUUEtJRoJ/NafpwUG7hXlt66//77beQbvlZOSIPjNKNs23777U3fvn2tuR2/DieQ0wsvvNBGieJjgOlUEg8C+CyeeuqptrBMt4h4rpDsUiCgCGZ4+l4YQWNFYudaSkAfBiedkx8Bxjwm1hDQ448/Pv/BIfdCQFu3bm20cEBIAKvgNKeFRAvauXPnktwRPrJo9ytlGc6SgFKDFymLBtThnE4+3bbMT3xI08ln+n6CkEQ+0xGJ/h0/UCe1qAElEwAO7FH8QDG/MzFiQiURAlEQwAxPJLwvkf+nL2Qrp1yXfaaUS3LiWoc48ls5aKmmcSJQVgIa542orHgQIP2Li5CrRQIKcUTrFIWAEoCEJiHXxCmellIptYAABJSo//feey/22yVKddKkSTK/x45sZRXYoUMHm/O2lH6gIqCV1Ud81VYE1BeyFVou+VkJ6OLTZR2o0FsJXW3M8FECkRSAFBp6nZiBAD7ZPIs+/EBZ4pggTvl/ZoBeYz+xMqJsKLUGlMk+QcqS2kVABLR22z7nnbM06sCBA73lMc154YTsIBKeHHUkky9WiCr+6KOPFIBULHA6PisCZAdhhS4fBBTzO0FOzu85awW0sSYQKHUqJsZWSC8kVFK7CJQlCKl24a6MO0cj4pZKrYwax1tL90LGDF/sDB3zO6IApHjbpJZLY4EElsiNWyCguNy4BT3iLl/lVQ4C+GKS9rBUAgFVjuRSoZ3c60gDmty2Uc3KhMAqq6xiI4PD+IFCQMnS0KlTpzLVXpetNgTwA8VnLm4fvZdeeknm92rrLCHvBw1oqU3wioAP2VhVdJoIaBU1pm4lPgTQgoYhoPh/YsJv3FiPVnytUdslsUAEEqcZHkLLn/w/a7tvubtHAzpv3jy7hKjb5vOTvicC6hPhyihbb8nKaCfVssQIQECJEGbd4kKFYwle0gpIhSKm4wpBgCVdV1tttVgJKOZ3RAS0kBao/mNKmYppyJAhNgcovs2S2kZABLS22193nwMBtJjffvut+fDDD3McseDm999/354j/88FsdGWaAhgho9TAwoBZaEJ+eFFa5dqORsNKBK3m4ct9L///vrrL8MqiOedd55dWGGnnXZK363vNYiACGgNNrpuuWEEXBL5YszwmN8REdCG8dURxSEAAWUyRHaGOAQC2qtXrziKUhlVgADBlmRE8OUH+vvvv5u99trL3Hzzzeaaa66pW22vCqDTLURAQAQ0Ang6tXoRIAcqg3IxBJQApI4dO5q2bdtWLzC6s7IgAAFF4tCC/vLLL4blYmV+L0tTJvKipEMiIb0PDeiPP/5otttuO/PII4+Ye+65xxx77LGJxECVKj0CIqClx1xXrBAEig1EQgMq7WeFNG6FVRMTKQQhDgLKpGr+/PkioBXWB3xXlz4WtwZ0zpw5dmETMi6MHDnS7Lnnnr5vQ+VXEAIioBXUWKpqaRGAgLJaDEsWNiSsKEMePQUgNYSU9odFIC4/UMzvzZo1M927dw9bFZ1XhQjEnYx+xowZhpW8ILXjxo0zW2+9dRWipluKgoAIaBT0dG5VI0AgEubKQhI0EzEPUZUGtKq7RFlvDgJKXyRdThSBgDK5YglGiRBwCKABjcsETz/Fx5jxc+LEiTY1nbuOPoWAQ0AE1CGhTyGQgcB6661n1+EuxA8U/8+mTZuabt26ZZSin0IgHgQgoKT6mjBhQqQClYA+EnxVezIa0O+//9588803ke6RCQ59lQU5+K5FOSLBWdUni4BWdfPq5qIg0KpVK5t/sRACiv8nJs1FFlkkyiV1rhDIiQAvcgLcoviBfvDBB+brr7+W/2dOlGt3h0vF9Nlnn4UGYdSoUWbLLbc0a6yxhtV8FruUcegL68SKREAEtCKbTZUuFQKFBiKhAZX/Z6lapXavE9UPFI0U0rNnz9oFUXeeFQGXjD6sGf6OO+4wO++8s+nTp4959tln7XLGWS+kjULgvwiIgKorCIE8CEBA8Wf69ddfcx41c+ZM88UXX8j/MydC2hEXAhBQAuNIbRNGIKCsqqRUYWHQq+5zSD2HBYfgoWJl6NCh5sADDzQDBgwwjz/+uFlsscWKLULH1yACIqA12Oi65cIRIBCJ4KIpU6bkPMkloJcGNCdE2hETAhBQ+iN+nGEEAqr8n2GQq/5zGjdubFfGKpaAnnbaaeakk04yxx13nLnrrrsU3Fb9XSW2OxQBjQ1KFVSNCBBUxAoh+fxAMb8vs8wyxvlQVSMOuqdkINC1a1ezxBJLhPID/e6778w777wjApqMpkxkLTDDF0pA//zzTzNw4EBz6aWXmgsvvNBcffXVNmgzkTemSiUSgYUSWStVSggkBIFFF13UrL322nkJqBLQJ6SxaqAaaKnIrRgmEAmtKVH08v+sgY4S8haZRJM2qSHBJWmPPfYwBB0NGzbMHHrooQ2dov1CYAEEpAFdABJtEAL1EcgXiIQ5dPLkyQpAqg+ZfnlEADP8q6++an777beiroL5ndQ4nTt3Luo8HVw7CBSiAUWTTlL5p556yjz44IMin7XTPWK/UxHQ2CFVgdWGAAT0ww8/NAy8mTJ16lSbbFkJ6DOR0W9fCEBA0UBBQosRCCh+ymhRJUIgGwJoQFnVbe7cudl2m9mzZ9scn2+++aYZM2aM2WWXXbIep41CoBAENBIVgpKOqWkECETCdMlqR5mC/ycvdEiqRAiUAgEWSCDKuBgz/F9//WUJqwKQStFClXsNl4opWy7Qjz76yPoPf/nll+b55583m266aeXeqGqeCAREQBPRDKpEkhHAZIkvaDaNE/6fa621lmnRokWSb0F1qyIEWEITTWYxBBRN/Q8//KAApCrqBz5uxQVSfv755/WKJ/UXvseNGjUyL7zwglZ8q4eOfoRFQAQ0LHI6r2YQIAqeVY6yRcJDQJV+qWa6QmJuFDM8JnUikQsRjkVTL1eRQtCq3WPI5tGsWTOTrgEdP3681Xa2a9fOks+OHTvWLkC681gREAGNFU4VVq0IZAtEmjdvnvUNFQGt1lZP7n1BQElGny8/bXrtiYAnmwNBSBIhkAsBNJwrrLCCcRrQESNGmH79+lmNJ2Z3SKhECMSFgAhoXEiqnKpGAALKikc44TvB/xPfUGmVHCL6LBUCTHpYtaZQMzwaUPl/lqp1Kvs6joAOHz7crmwEASXinfyzEiEQJwIioHGiqbKqFgEXZJRuhoeA4vuJD6hECJQSAXySe/ToURABZdI0ffp0EdBSNlAFXws/UCYshx12mDnggAPMww8/bM3yFXxLqnpCERABTWjDqFrJQoD1s1u1alUvEAn/T4ip0tokq61qpTaY4UkajhY+n7hlO6UBzYeS9jkEiIT//fffDUts3nrrraZJkyZulz6FQKwIiIDGCqcKq1YE8I1C4+Q0oLz00YDK/7NaWzz59wUB/eabb8y0adPyVhZt1tJLL20UPJIXJu38LwL777+/ueeee8zFF18sTISAVwREQL3Cq8KrCQG0nS4X6Mcff2wIQpL/ZzW1cGXdS69evaz2vSE/UPl/Vla7lru2Sy65pHJ8lrsRauT6IqA10tC6zegIQEBZIeTTTz+t04RKAxodV5UQDgEi2rt165bXDxRTKkvFyvweDmOdJQSEgD8ERED9YauSqwwBF4j0xhtvWE0ozvqYNiVCoFwIYIafMGFCzstDPlkzXgQ0J0TaIQSEQJkQEAEtE/C6bOUhsPzyy9s8eKwKgile2s/Ka8NqqzEEdNasWYZlErMJ5nfSNbF8p0QICAEhkCQERECT1BqqS+IRYF14oooJ/BABTXxzVX0Fe/fube8xlx8oBHTddddVGp2q7wm6QSFQeQiIgFZem6nGZUQAM/ybb75pl0BUAFIZG0KXtgi0adPG5qHNR0B79uwptISAEBACiUNABDRxTaIKJRkB5we68MIL2/Xhk1xX1a02EMAMn42AfvLJJ3blLvl/1kY/0F0KgUpDQAS00lpM9S0rAo6AdunSxTRt2rSsddHFhQAIQEAhmywVmy6Y3xER0HRU9F0ICIGkICACmpSWUD0qAoHWrVubNdZYwzjfu4qotCpZ1Qi4vpipBYWArrjiimbZZZet6vvXzQkBIVCZCCxUmdVWrYVA+RAYOXKkIVmzRAgkAQGyM7B8IgS0f//+dVWCgEr7WQeHvggBIZAwBKQBTViDqDrJR4AE4AstpLlb8luqdmqY6Qf6448/mrfeeksEtHa6gO5UCFQcAiKgFddkqrAQEAJCoD4CENB3333XzJkzx+547bXXbKYGaUDr46RfQkAIJAcBEdDktIVqIgSEgBAIhQAEFJk4caL9JFft4osvbrp27Wp/658QEAJCIGkIiIAmrUVUHyEgBIRAkQisuuqqpn379vUIKIsmyFWkSCB1uBAQAiVDQAS0ZFDrQkJACAgBfwigBWVd+FQqZV5++WX5f/qDWiULASEQAwIioDGAqCKEgBAQAuVGAAI6depUM3nyZPPdd9+JgJa7QXR9ISAE8iIgApoXHu0UAkJACFQGAhBQtJ833XSTrfCGG25YGRVXLYWAEKhJBERAa7LZddNCQAhUGwKdO3e2+WlHjx5tOnXqZFg0QSIEhIAQSCoCIqBJbRnVSwgIASFQBAKNGjUyG2+8sT2jZ8+eRZypQ4WAEBACpUdABLT0mOuKQkAICAEvCDgCKvO7F3hVqBAQAjEiIAIaI5gqSggIASFQTgT69u1rTe+bb755OauhawsBISAEGkRA6wk2CJEOEAJCQAhUBgJrrrmmmTZtmmnTpk1lVFi1FAJCoGYRkAa0ZpteNy4EhIAQEAJCQAgIgfIgIAJaHtx1VSEgBISAEBACQkAI1CwCIqA12/S6cSEgBISAEBACQkAIlAcBEdDy4K6rCgEhIASEgBAQAkKgZhEQAa3ZpteNCwEhIASEgBAQAkKgPAiIgJYHd11VCAgBISAEhIAQEAI1i4AIaM02vW5cCAgBISAEhIAQEALlQUAEtDy466pCQAgIASEgBISAEKhZBERAa7bpdeNCQAgIASEgBISAECgPAiKg5cFdVxUCQkAICAEhIASEQM0iIAJas02vGxcCQkAICAEhIASEQHkQEAEtD+66qhAQAkJACAgBISAEahaBhWrlzl9//XVzxRVXeL/dP//80/zyyy9mscUWM40b++H3P//8s2nSpIlp2rSpl/v5/fffzfz5883iiy/upXzVPz+s4M9f8+bN8x8Ycm8qlTI//fSTadasmVlooXiHANf/F110UdtHQ1Yx72k++4/qnxd6u9Mn/lzAd//3XX/Gzt9++83b81vp9XfjD++vhRdeuOEOV8QRP/74o30vxl2uq8Kvv/5q/vrrL/t+d9vi/PRdf/olYxz8xLeAU0PSKOgMqYYOqvT9AP7pp5+W5Dbee+89c+2115pzzjnHLLPMMl6uefbZZ5t11lnH7Lbbbl7Kf+qpp8yYMWPMVVdd5aV86t+1a1fTv39/L+U//fTTZvTo0d7qT9uuvfbaZvfdd/dW/yeffNJcffXVXsrnBXbyySebgw46yKy//vqxXuPDDz+0uA8ePNgst9xysZbtChsyZIhZa621zIABA9ym2D4/+ugjM3ToUOOz/ueee67p1KmT2WOPPWKrtyvo448/NldeeaU544wzzPLLL+82x/p53nnnmdVXX93sueeesZbrCnv22WfNo48+aq677jq3KdbP888/36y22mre6v/cc8+Zhx9+2Fx//fWx1tsVRv07duxo9t57b7cp1k/q/9BDD5kbbrgh1nJdYRD0448/3uy7775mo402cptj+TzqqKPsc9WnT59Yysss5J577jGffPKJHR8y98Xxm/ozrm266aZxFLdAGb7rn3lBlCj5eFC86o/MqyfkN9pCHthSyJdffmnJLqD7uubs2bNNly5dvJUPXp9//rm38ql/586dvZVP/T/77DNv5dPGa665prfy0Ur6rP93331n+yiz4Lj76DfffOO9/3/11VeWAMVdd8aHefPm2fovvfTSsWPjxp85c+aYVVdd1Uv5pag/+K+yyipe6g9G//rXv8yMGTO8lU/9V1ppJW/lT5w40fYhH/0TfOg/K6ywgrf6v/DCC17rjxbx00AhhJUkbowod5FFFom9XHBH0CB+/fXX3sr3XX+sC/SfuHH/DzrF//djIy6+HjpDCAgBISAEhIAQEAJCoEYQEAGNuaHxm0S7wSzMlzB7b9Omja/izZJLLul1hrTyyiubtm3beqt/69atbRv4ugD4+6w/+NOHfAkaYspv2bJl7JdAq0HZvvyTqfCKK65o0FD6kFLV31f/QavtG3/6vy/8aVPf/d9n/6H+rVq1siZ+vvuQSq8/sRH00SWWWCJ2eCiX/uNLsGyiffYl1J/+40t4bn3Wv9h614QPaLGg6HghIASEgBAQAkJACAgBfwhIA+oPW5UsBISAEBACQkAICAEhkAUBEdAsoGiTEBACQkAICAEhIASEgD8ERED9YauShYAQEAJCQAgIASEgBLIg0CTIqTcky3ZtShACL7/8siE/GM7DvhyI+/XrZ0iCS35RglQqTXzWvxT4Vxre1VbfSZMmmfvvv98uvkCS/tNOO83cdtttdlEJUm7FnbC/0vD797//be6++26bHowcmhdeeKG5/PLLzfTp0w1BhVEDSli8Y9SoUWbcuHE2QOWZZ56xuRbJSUnZBN4kWXzX3zf+Sca22utG2i5y9w4bNsz+kYf7lVdeMcsuu6zXYL8k4KogpCS0QgN1eOmll8yNN95ojyIB+oEHHpg3uWsDxWXdvdVWW5mePXva/J8kCe7WrVvW45K60Wf9S4E/LzAGHl40e+21l5kwYYK54447bLQ9Cad79+4dGXpIFuVuscUWdpWWSy+91Oa022GHHew1o0Su+yw78o03UAAvgFtuucVssskmZvz48XYCRpL1NdZYwwwfPtwmhaZ/RRHa9fHHH7cvlB133NFcfPHF9iWz4YYbmgMOOMB06NAhSvFezyX3IX1w6623Nm+//bZdJY08oCTS50XJKnMXXXRRpDqQXJ2VU4gAfueddwzroxx33HGGnJEsiEE7lGL1lrA34bP+pcD/jz/+MP/4xz/Ma6+9Zt8BjRo1sgtJ8AwcfvjhsWTM4Dl78MEH7SSGXMTt2rWziyXQ/8kLHVZ8lRu2PsWcR85VxnmS8qP4AR8WOKH/33rrreawww4zjBFRhLzVLIrAZHHWrFl2QsciISTrZzEY2rpcIg1ouZAv4rozZ840X3zxhR2IeQGgfWDFpRYtWtiUE3EsO3bnnXeam2++2RKeSy65xK4kRLoM0j1FHfhLoUH0Wf9S4A/mJOgnfde9995rPvjgA3PqqafaFaMYPLbZZptIy9YxSNO+vFBYDQMiykAHsUDbxCAUNjmxz7LdY+JTA/TPf/7TvmQhWPT5b7/91v526XQeeOABs+WWW7qqFP0JgTjkkEOsZo8X/MiRI+1zRvtCsJ544gk7KSi64BKd8OKLL9oJy5FHHmlXrmEyDClkbIA40F/BJ+w4xMo4tME111xjX7ashLTzzjvbCTFaICwzLHBAipqw4rP/+K6/b/zBlAkEKwYyHjAO/PDDD2bQoEHWAoCme7vttgvdvpQP0WKMZmK38cYbm7lz59qV2BiPWHGJ9EZhVu7yVS51dgKBY7IOIaSurNLFmIelhBXZohA4+j0EnBXpmIRCDEeMGGGxYbVAVqTabLPNXFWK/mTVO6ynKB14prDkYMFAycGCD0wge/XqVXS5cZ0gH9AYkMT8y8udgcinoKGiM7HMG6ZyXgQQkziXtEQLxCyMFyYPN1qObbfd1s6Mw94bsznMaGDEg8wD7Ut81N/V1Rf+9BuWsGSJT7TP5KKEHKLt7tGjh9U8QRijCC91CM/AgQPtEq5oH/r27WtWCnI6ouFgf1jxWTZ1gsAde+yxdqBHQ8l31rGn//MCu+mmm8JW3Z7HoM/EDi0Q2jdWAXMCcYlq/mWQ5wUAzmjKmFwcffTR1p2GdmZlHl4UUQQNNKRw6tSpVtPBtViqFzcC8IsiEAP6J+VMmzbNrtUOLgiaeyZN9NmwAnHlxUiZkH/wZ0UYJ1HbwHf/8V1/3/gz/tDvTzzxRDvhhaig4UZjRv9E+//qq6+65gj1OXbsWHPMMcdYSw7LbzIOMRGD/Jx11llW4RGmYF/lurrwXJ5wwgmme/fu5vTTTzdYi5hsMQa9//771g3FHRvmk7YFeycs84liCWESENX1h3EBDequu+5q3ye8XyCejGlnnnlm3XPtrl/qz4VKfcFqvB4zRwZJ1OWlMF9D5iCG/DG4smxjnEKnx+TLH2YxNHNRtaBoMlhDmlnkPvvsYzbYYAM7I4NkRS0789591D/9GnHjn/4CA4tsL+D/+Z//Sa9C0d8dyWL2GzfJ8lk2N5pO4DDdMZjiMwVW+ETjusCLImw/QrvDYMwEiX5Jv99vv/2sdgONJdeKIrxkHnnkkQUIHBq9OAgc2hjnQnDFFVcs4ELACyeKCwFkhATWO+20k9WCoS373//9X6uhxBLDBDWKFghs0cgwuUbbyRjKJPujjz6ysENKO3XqFLoJfPcf3/X3jT/jD38oBtq3b29xZgLgxhyIUNS4AEe0WEIaiYto+SrXVjL4l07g2IbGk7568MEH2zHj0EMPtc91WPclCC1l8Yyybvpbb71lrr32WvPGG2/Y9yVKiSgCPlgonDB+shwnzysTbvfd7S/1pwhoTIgPHjzYvijPPfdc6y/DS5LZXRwrFqGl2n777bPWlI4PqYgqRxxxRNbZFiZJyGMc4jSIENAxY8ZYDRYDEavCYGqIIj7rXwr8fb6AwdUnyfJZNnX3TeB46TIxYo1nnlcGZiZGmAkhQ2GJLXVHfBMIp4EmWGqppZYyuLyg3UbQhKIpjkJAKQftDyZ4XpJM8NCg85KEqMfhv0r98EljQo0GCDMtWn+ug3kyivjuP9TNZ/0p3zf+AwYMsEQIiwsKDdoYAkrfefPNN23bU4+w4oto+SrX3advAsfEjskpkyRM+kOGDLHWhHnz5pm77rorlrEHhQmabEz6aLJpa6wWvHOwzPhctdHhmOtTQUi5kCliO4MP5gQIFi8vglbw65o8ebLdBimNOogWUZ3EHQoe+M1gfs8Up8GNg0Rnll1pv5mNuhcwA1D6CziqhslhkU6yGPQgWZiUopIsyvdZNkE7zz//vNXUnHLKKeayyy6rp4HDtyysgPtjjz1mmGhgasMFhQkSbQE2aCjiwB9NniNwmLQhcJjHohI4tJ/4q2I6xV8PdwJeXghklCAhyGMti8/+Uy240j/RxPEcoKmkz2PZY4IWR//n3eiIFlYG3DYY53hvRhl/fJXr2pWMGB9//HE9AkfQkCNwaEHDSqnGHqwJuOfg/sCkDiGuBOtVOUUENAb00wloenHp5uuo67tCZt99910bLcdLhVkTplQGipNOOslqPtKvXex3zC9E42Fawyftvvvus4MRzuEHHXRQpEg8Iu94ABh0fInP+lNn3/j7wsWV63Og81m2qz+fvggckyP8nQm6QNsDGcWdhpfu7bffbjNC8MJJqvB84UIwY8YM60KAfxdmecyFzoWAVElRxHf/9/38cu+++g9l+66/b/y5B5/ia4zwVW4mFr4IXKWPPZk4Ff07CBCRREQgMB+ngllYxFJynx5EgKYCk34qGIRS33//fSrQdKSefvrpVOCnkwpMh6nAoTv3yQXuCZyqUwEBtfcRmHtSQbRfKjCPpwLznr128HIrsKTshwW+NKkgCtLuDDSiqUCLlQrcClJcK9CcZT+piK0+618K/AM/21Sg1UsFAQGpYGaaCvwOU4HPXSogQinwiirBhCV18sknp4IXpe07QRBMKiBbqWBWnAomMHVtE+Y6Pst29fHZf4LgrFSgkbSXol9OmTLFXdY+Y0E6oLrfYb/4rL+r05w5c+xX+lBAQFMBkU4FZj23O/RnKfq/z+eXG/eNv8/6lwL/Sh1/NPY0/Fj77vsN1yD3EeSbkiQcgcAcmAqS1Npajh49OhUEGtSr8f77758KZvf1thXzI0gFkwrSb9hTgqAIS3yCwKq6IoJUESke9LDiewD1XX/f+IOrzxcY5fskWT7Lpu6++891112XCszYXCoVRI3bP/sj+Bfk7kxdcMEF7meoT9/1D1WpIk7y3f99P7++8fddf9/40xUqdfzR2JP/Qfbd9/NfveG9SsNUtM54wRMwv+B7hS8KPjNDhw61/lgEAGAujyo4QgezGBshStoZ0j8QeY9wbSJH8S0LK853lXJJb4O7ALkvEe4JnzJM8WEFx2dSVa277rrW1EmAAW4LmApJx8E1iM4LK77r7xt/fA3xxSSgw6VEIagKX6zNN9/cpv4ItHJh4bHncQ/4UCKYZvEHcsJ3nOHDis+yqZPv/kMePnzTMLuT8YEVf0hDFryUrQkeH9Ao4rv+mCHxW8XczrNE1Cv3hF8s/qHBayBK9W0QmMaf3BBq/MmNjdvja4zwVa6rt+9nt9LHHodT2E9FwYdFLu08EsMTZITPGEnDeSiuvvpqm0cNYkp+vihLaJLsmeAIfDHx+eQlQ44/yCh+oaQviZomA/83nK0pk/qz2hKEEUJNupgoqzFQHsl1cdqmfL5DoKlzHASapvBZf9/4p7/AwMdNAOgzbgJAVoUowkCHrzBEi0TTRE4T5EQgAD6E9Nuw4rNs6uS7/xB1zTJ4BOsQbAD+BEXgoM9kKWqUqO/6P/nkk5akE0XO0pX4s9LWzocVQh3Fh9V3/6eNfT6/vvH3XX/f+Ffy+KOxh96XW0rR93NfveE9CkJqGKO8R6C9QovHIB+YYqzWAYJF+iIkMOnVRajmLaiAnZBBnNEDXy+bn5P0RSyfGUV7lX5Z6s+qGxAScr+1bt3a5t8jfUNUIccipAcCHfiu2pVN0gk0GtKo4rP+1M0n/miYmMiACSlQaIf0CQCZFOKIRHUkC41rnCQLfHyWXYr+wz34Ep/1Z+LoVloKTJJ2DGLCihCYhEWGVYaiis/+T918Pr8+8Xe4+qw/1/CJf6WPPxp7XC9c8LMUfX/Bqxa2RQS0MJzyHkVCeJKsQx7QHPLdaa/I34b2CqIYRdAU+lzPFU2b77WAfQ6gvuvvG3/6hu8XWJT+l4RzffYfosYxY5MDMc51qtNx81V/xgU02bgKELWPoBlCSAeHiwV5iqOI7/7v+/nl3n3hT9m+6+8bf+5B4w8oZBeffaeSx57saBW+VQS0cKxyHul79sgqL7xcSCa7+uqr27W7MQuSo5CXJt/JjRhFgkAL6//JetgswYYpjyTcaFzR6EJOoyy3F6VuhZzrs/6lwN/3CwwMfQ50PssupP2jHIPJmhRkmKlxC+GZ4lnDd5IE9fiGRnFBoW4+CQTWCkzuiHOvYAKc7l7hcv/Zg4r8V4r+7/P5LfJ2Qx3us/6lwL+Sxx+NPfm7rM+xJ/+VG94rAtowRgUd4XP2SHJgTAwQQoT8ovhTou0gaIhEuGhB8OUJIwS+oDEhebVzHcBkh+8RJBfyyYsNH7Ow4vMh8F1/3/iDqc8XGOX7JFk+y6buiM/+g3sDVgrcQxCWwyOQhyUnWakLNxqOCSulIBDUzZcZ0nf/9/38go3P/uO7/r7xB59KHX809tB6uaVUY0/uGuTfoyCk/PgUtJfZIxoUolBZxxtfPQIY8L9aKYhkbtmyZUHl5DoIR2Kf67n6XguYh+CEE06wWiXcFZ555hmrtXUaXAK1omhwfdffN/68wFhUwE0A8Lml76CJRhOHhotozCgTgLFjx5pjjjmmjmQReONIFkutQrLCavl8ls0z4bv/0L5uUQeuB+kEHwTs8cmNIlhIwNYFksW9njR1y6UFInsFE8ko4rv/+35+ffcf3/X3jX8ljz8ae/I/2aUYe/LXIP/eaCNr/rJrZu8ll1xiNZGkbck0X5OKKar5mrWkfa/n6nMt4FI8BD7r7xt/3y8wHkSfJMtn2dTdd/+p9PWk0QIFizxkdSEIFjSI7ELgu//Txj6fX9/9x3f9feNfyeOPxh56X27xPXnJfeUC9zScKlRH5EMgyMGXCszhqfTE7UEKplSg5bOnsVLR+PHj8xVR8D5Wa3n44YdTQZR63TlBvs6671G/BOvy2tVTWCEnMPPb4liVx30PW36Q2iYV5LWsO53kuKzmFLgt2JV/SKQfZBOo2x/2i6/6u/r4xH/kyJGpbbbZJnXWWWelAneIVODza3FnRaog/VZkfAITpF15KkjZZVeh4lrcT2C2Te2yyy6pYF1yd5tFf/osm8qUov8EWiC7etBTTz2VCjRmFgP6aRwrCVEYCbP79++fCgIUbTuw6hjPVWARqFtkwl40xL8hQ4bYFa3cqaxuxQpjSDAhTp199tluV6RPn/2fivl6fkvRf3zW3zWaT/wrdfzR2ON6R+5Pn2NP7qsWtkc+oAUS9XyH4T+JFrR9+/b2MNZTx9SM2Q1fyvXWW8/mE8xXRrXvI1UMORYxL2NORuNB0Ae+rFtssYX1Y612DBq6P9aqxt8Ltw38EXHlIPqSfhVHCiZcRcgDGpAq07t3bxukErz0re8wKZmiiM+yqVc19B9f60kTKMVCFIw5SLB6k108ggwcRMCztn0wsbH7avVfNfQf321XqeOPxp6Ge4avsafhK+c/QgQ0Pz4F7R01apQJNFWmR48eNo0LPmPDhw83N910kzUfkocrajLrgiqS8IOS+hAkHDZV778I+Oo/rFYGEc8lRJC7vJq5jinn9q+++spmySDyHSJKENW1115rV0gjJdw555xj1llnnXJWMRHX9tV/EnFzqoRXBHz1nUofe6KCLgIaFcH/nu9z9kjidlY8yieBSTXf7rz7SKY/bty4vMfwAo6SyiVv4RF3+q6/b/wj3n5Bp/sc6HyWXdDNRTyIRSSYJDKBzBYwSBAPGvski08tkO/+7/v59d1uvuvvG3/f+FC+rzHCV7mlwIRrVMPYEwUrBSFFQS/tXJbv23HHHdO2GEN0cRxCiifSLBGItNpqq2UtMgoBpcDAB8iuMd+rV6+s5aNdCUtASzGA+qy/b/x9v8BoUNwfGiJZYbV8Psum7r77zz777MNlzLRp0yKlW7KFZPnnu/5cEqtL5rO75JJLZqlN8Zt8939q5PP5LQX+PuvvG/9KHn809uR/nkvR9/PXIP9eaUDz45OYvaSGwbcUf6+llloq9nqhQTn66KPtGvMko49T3nvvPZsoOx+BjpJnkbr6rD/l+8SfF8CJJ56YdwKABi5qOh1m25Csiy++mFuKVXyWXYr+EwQRWlzIqUvqojilFPWPs77ZyvLZ/7mez+e3FPj7rD/4+MS/0scfjT30kOxSir6f/cqFbRUBLQynnEeVYvboLk4wAaubxE0QXfkMcg899JAZNGiQ2xTbp88B1FXSZ/25hk/8fb/AqL9PkuWzbOpeiv7DdXyJz/qXygzps/+Du8/n1yf+rs/4rD/X8Il/JY8/GntcD8z+WYq+n/3KDW8VAW0Yo7xHlGr2mLcSFbLT5wBaIRDkrabvF1jei1fAzkrvP77qX+t+ZIV2XV/4F3r9pB+n8Sd3C1V630lq/UVAc/e5gveUYvZYcGV0oBAQAjWHgE8zZM2BqRsWAkKgJAg0LslVqvwiBACQZy5I4F7ld6rbEwJCIIkI7LnnnmbxxRe3a54nsX6qkxAQAkIgEwFpQDMR0W8hIARqEgF8KUn8v+WWW2ZNx1SToOimhYAQ8I5ArY490oB671qVewFcCypNHnjgATNlyhQTLATmpeo//vijefrpp23Zc+bMMffdd5/BD7gSJVgKMtZq+8I8vZI+r8G6ySScJi0TKwexxnolPgPpeFXy90rEXuNP4T0uzvHH57jg7sjnNWp17JEG1PWuCviE/AwePHiBmuICQGqmYK1ps/rqqy+wv5gNkClWUWFJyB9++MF07NjRpmdad911iykm57G8VKhvugRrbpvWrVunbwr9/ZlnnjEPPvigYWGAfv36GfKjuiVSQxeaduIhhxxil7Fk+VXw+b//+z9D/S+66KK0o5L9dcSIEeaee+4xX3/9tenWrZvp06eP2WGHHRZol2LvgiUgP/vsM3PMMccUe2pBx9Omhx9+uLn33ntN48b+5s48A5DPsWPH2vypLFu60047mQ4dOhRUz2o9SONPwy2r8adhjHyMPxp7GsY9iUfUZwJJrKHqVIdAs2bNrGaPBNOQBtb0Rhu33HLL2aT3kNMbb7zRtG3btu6cYr+wpCgv4CuuuMIsvfTSVpt4/vnn20T4XCeKsNzhhRdeaMt25cyfP9+uCc+gxP1Fla222srwBxF66qmnbH5N7oMcqqyxHkVmz55tlzqEfCItWrQwJ598shk4cKD5+eefTdT11CkTrertt99uffnSZ9z77bdfLMspvvbaawYtzfHHH2/WXnttM336dHPnnXea77//3rj7oh5hBB9EEh9nm2SEKS/znEaNGtmyIc60qS8Bd1K78Mm98JzxbLFS0nHHHRfpspMmTbJrtdNf0oXnLemi8afhFtL4kx8jX+OPxp78uLM3iWOPCGjD7ZaYI+bOnWs1e9dcc43hZYxssskmZsiQIZZooZF7/fXXI+UJdetIQ66QLbbYwpKUyZMnW6JrNxb5D0K7//77m1mzZtmXOuQ5Xbp06RIL+UwvE5IOSSGpOGtlU4eowjKNrLzByiSOLH/33Xf2vhZeeOGoxdvzr7rqKpvrFc1tuqY4Kvl3laN9d999d6vFZdt6661n/R3ResdBQD/88EOrTWXVrCZNmtjLQtyOOOIIV4XQn+DBimN77LGHnXCRE9fJLbfc4r6G/pwxY4Yl42g/6ZPbbrutYS112paJElrQAw88MLR/KET27LPPtib+StSmavwpvGtp/MmOla/xBwKqsSc75mxN6tgjApq7zQragyYD7Vo+Oeyww+yLPt8xhexbZJFFbEdCK+MID6QTbZ8Tt939LvYTrdjo0aPtikgQCO4P8nnCCScUW1Td8U2bNrVr3mJCveOOO6xJ3+2EVDii4rZF+XzllVdswmYyEmy88cZWO9m1a9coRdadi4YTUzUEbv3117fasalTpxoikKPi7i7y+eefm+uuu84SLbctzk9M7mhYIbhOY4sWPQ6MVl11VYO2PFNYASsOoR+hufUlYL/yyitbstymTZt6l6F90YI2b9683vZifqDdxqXFLf1ZzLn5jr366qvNu+++m/MQJhmMQVFF40/DCGr8yY+Rr/FHY09+3H2NPfmv2vBe+YA2jFHeIyCDzOryCabfuNZlZhnFt99+2/Ts2dOaTRnw9tprL8MDiB/ibbfdZk3D+eqTbx/aVVZDQgOKlub999+3mjjWgkc22GCDSNos/MhefPFFq6XloXj22WfNLrvsYiAXcQjrnVPXzTbbzGoS4ygzswxI5zvvvGMgVpBcpy3OPC7M75tuusn6w+622271NKBhyko/Z+jQobbObCPhNAEA+AtjzkZDfNBBB1mynn5Osd8pk/5HII8zYf/+++9WW3nKKacUW1ze4yk/zolL3ovFtJM6H3zwweacc86xRDemYi3eTERzCVpjiHUcovEnP4oaf7Lj43v80diTHXe31dfY48oP+ykCGha5LOcNHz7cvtwzd0EQMenFJQQIQUIJPCJAgiCbL774whKXdLNkmOtBTlDXIzzUmcEemKEdGQ1TfimDeDC7x0VsM+8V/0D+MvHJPK7Y3/jwEmSDSWnZZZetO/2oo46yPoh1G4r8Qrs2RFKitCvVIQADDXffvn3N+PHjrZaVgDAI15prrllkjbMfjo/p5ZdfbljjGI1c9+7d7dKxcUzwfL/EmHzhQ4qpEBeF9InLP/7xj+w3XMRWUrkQjJEpjD2MQXGJxp/CkNT48zdOvscfjT1/Y53tm++xJ9s1C9kmAloISgUeg6maiGiEGQemcTSUvDDjiPKGGBLEk03iisLGv/HRRx81zz33nCXNEE78KCEVUYUgnksuucRgMkwXgniuv/76OpNw+r5iv+MyQJANA9LMmTMN6S2InMZXNg6hTcHn+eeft/6BkEW0WnFpmNInAOn1RRsdlynblRu3FpEJGH1lu+22sz6/rM6DBh1SNGjQIHfZ0J8QxF133dX6YW600Ubml19+sZHqr776qs1GELrg/57o+yWGtQTinE1wfYkquBCkl0/Q38iRIw2Tlw033DBq8XZiqvEnP4waf/Ljk743zvFHY086sgt+9z32LHjFwrbIB7QwnAo6Cl+rTMEEibYSTWVUwQ8N07ITXsBoPdCExiXDhg2rS2EEgaPe+L7hIxg1nVEpgngYiDDtE+xB4A6Rf5h/8L2LGsjDQ3zSSSfZYB2ugWCCh1STjikOIbgJAu0mMpAuItT33ntvm/kgjmsQZDNmzBgbUEXbki2AICHnExr2GkyyCMrCNI4GF9M+vpT0oziEcmjHnXfeua44yP+4ceOsdjddo1h3QBFf0K7i3wuBdn7QEEMIdBwaXDRipL/KJnFMIJmkZAY3UX9cauIgoBp/srVc/W0af+rjke2Xj/FHY082pP/e5nvs+ftKxX0TAS0Or6KPxuyJJiIOweRImo902XHHHQ0peiBH6VHT6ccU890RNsyEvPDR7PFCJn0G14oipQjiIQk9GmeXpmfTTTc1H3zwgU0nFZWAomEixdX2229ft+wquUZ5wdPOUQkQ2BKAhNsAWFEefwzYcRAIysd/lWucccYZljivssoqBvMMWl1IbhQhuwG+pPg8o6HE9I6LAn7DcQhma1YqgihyDQR/UzJCRAkOsgUF/3y/xEpB4Ny9uE8mLxp//oOGxh/XK3J/Vur4o7End5uypxxjT/4a/WevCGghKBV4zJVXXmnJDofjH8jgj5YyjgjUXFWgfMgPn3EQIHzpSDWULpDSdK1T+r5iv4MFZMoF8WCajaPerh4E1kCeHQFlOwSUIK2oAjaQcmaTTtBUQkyj+t668kgFRCASgW2k1Dr00EPt5ILfpDOKKrQlCxa4qHfqjV8upD0qAUXbSU5R8IGAovGGTLO0ZRyCNYFJFhMup+kDe66LJhSJ4ivr+yXmewKJ3y0uIU5oB/A5/fTT3abYPzX+1IdU4099PDJ/+Rp/NPZkIl3/t++xp/7VCv8lAlo4Vg0eSZ7A9EAPGh3iE1cgDGWT+NwJPjT4VbJKUVwkDg0W5mu0TeQ+JGUMvocQiqiCeZb0SPipUSZBL1HNvpl1Ik0SL9x11lnHkhQGPDRkvXr1yjy06N9EE5MbEqIGEUKzxL3su+++sWifqRAkl4kL/YaAHgStK8Q3DgKKli/dT5DywQiyGIeAkRP8NeMU3BNOPfXUvEU6Ypr3oBw7fb/Esl02TgKHuT093yquEASycV9xiMafhlHU+JMfI5/jj8ae/Nhn7o1z7Mksu9DfCkIqFKkEHIeZPT3lE8QK4oAZNU6BeEJK0CRC3PC7iypoISHPaCYhV2izIKNrrLGGOffcc2PLo0k9Wd1nwoQJVhOHD2sc/nvp9w/xpHy03GjN0ge+9OPCfCcnJwFZaLKOPvpoixVtQaL4qFHq1IdBh6UyIdBox8CGtkF7H3YZV9oP034uIWVY+sQp13FhtvM84D7A2u1Jl3wEjoT3SReNP4W1kMaf3DjFPf5o7MmNdfqepI49IqDprRTiOw2bTdMDOUSLiPmRVYB8ic8XMEQLohJVu0r0PsnaSdi/+eab10EBCWUJQvxTokRJY2rE9JtPWNEpaqQ6Jk5S0BAJj+kaIk2+TiLt4xSIP2UT6DRq1CibaogEznEKvpMvvfSSvQ5J9WnnsILLwCOPPGLrSX/PxBn/TJ6FuIRIYwKnHnvsMesTSmBeFDNzqV5ivggcWmF8nzMFK8Naa61ln62oz3Bm2e63xh9jJ7oaf1yPKOwzrvFHY09hePsaewq7eu6jREBzY1PQHqKUMY9mCtoxknxfdtllhiTc2SLkM88p9HfcL2CuS3AH0eK4EaA1JC/hXXfdZckhJucoyzROnDjRrlmfTcsDecTMTyBPWKEMok8zhbYZO3asNWmzxCU5I8MK2BAMhMYTrS3X5OVLOib8JzH5J1mIvsaNYqX/Bu/EmQKF+yaQCXLOwgKseEXwF6Q/PZdpVHx4aaHtREuMWwv3w8pIUd04Sv0Si4pD5vlffvml7Y+Z2xknyMPKZClODbTGn/pIa/ypj0e2Xz7HH4092RCvjG3h1R6VcX/ea0mUby7T6Iorrmj9MyF3cRDQbC9gEpdHfQED0plnnmlJFMEpXIcBAx9ETPz4hUJKw5r60ehB2rIJRALtG/6hYfNcUgZm5XQBc1ZtwTwOQcx1/fRzcn0nNyfaT1JUpWvyyAqAZpJ1yIkeDSul0KKTxQAMIKAsWoDGOVdKoDD3gZYT/1j+0JxDzElPxcx7wIABlpCGKZdzcBFgAkDAF1kgbr31VjsZYJIXR98/8sgjbWATBJr0VHETaLTYmblvuS/6LT6r+G1GmcCQezWXkKUhPTAp13GFbNf4kx0ljT/ZcUnf6nP80diTjnT9777HnvpXK/6XCGjxmBV0BrMyNIj4CkaNgvf9AuaFO2vWLKtFJHAB0yaR6s60zHdymYYloEiqI/AAAA/uSURBVJTDyz2bQFYwwYcln5llQkowB0MWcY2APFN+FMGni9Vk0smnKw9iHoV8Ug45MyFVmZKuRe/cuXMsk5jMa/j4zQsBUgRemPkh8GhEwwouD5BmlmylnCiBRrnq4PMlhtY820po+Fqzhju5WJ944gkbLJerfmG28/JBA5rNRaiY8jT+FI6Wxp/CsfJxpMae+qiWa+ypX4vcv0RAc2MTaQ8DEVHwmH6jLhPo+wWMBg5Np1tbmxWdMJ86wVczCokjkAkT+YgRI2w6J7d8JS4K559/fr1ruWuG+UTridYNUovGqVOnTmGKWeAcfFhzBRqBm1u6dIETC9xQSi16gVUq+jA0nWg5WEEL0klwE/6+mMgh2FEEv1La8sknn7QLAbDwAj6NcWQFyFavuF9ilMdfNsFfFhcAJqxx+2kS8EEar6h5WDX+ZGu5Bbdp/FkQk1Js0diTG+VyjT25a1R/jwhofTxi+8XLhCUm4xDfL2AStJP6h0hmOuy0adNsEnHqDpljOdE99tgj9K1gJmWllwsuuMD885//tJpVtD+fBgnFWaP6wAMPDF22OxGtJ6sRka8U3CH/cQsDXTYh4MyHxKlFp34QZdqZCQc+oHx3ghtEWHP27bffbn14V1ttNTuZOPbYY2MnU2jRsSSQ7xOiyxKT9CUyKWy99daRc436fIk5jDM/SYeFeZzsA3GTT65Fvtc4RONPwyhq/GkYIx/jj8aehnHPdoTvsSfbNbNtUxBSNlQSvA3i4F7ALMNJDtA4XsCYyCGJaDvRmkAMKR+NIlHGaLKiCnWn45P+hxcuZkk0iFEFQoXvIUQwF/Ekt2nY9eBZihE/0nyCq0Xcwn1hQsXMD7mLImQgIJ1TLqGNswWJ5To+fTtlkzQfX7hsAu6YmeMWXEcISIKMupypYa6R+RKjvj4IYWbdsDTgWsByruRHrATR+LNgK2n8WRCTzC2+xh+NPZlIF/Y7KWOPCGhh7ZXIo+J6AbubQysJAXUmUzSUmPEwf/rS8rlrR/l0vpL5yoDo5iJI+c5jH1GuaCPzCWbhJAttS1aAXIIbQFg3i7lz52aNwnbXIgo7qhuKK8vHZ7leYj7upZRlavz5D9oafxrudb7GH409DWOf5CNEQJPcOqqbEBAC3hGo9JeYd4B0ASEgBLwgUOtjjwiol26lQoWAEBACQkAICAEhIARyIdA41w5tFwJCQAgIASEgBISAEBACPhAQAfWBqsoUAkJACAgBISAEhIAQyImACGhOaLRDCAgBIeAXAZbRZC36hoLc0muRnj4rfbu+CwEhIAQqCQER0EpqLdVVCAiBqkJg9uzZZsiQIQUTUJacvfbaa6sKA92MEBACtYmACGhttrvuWggIgQpEgEUhJEJACAiBakBAKyFVQyvqHoSAEKgIBMizO2zYMPPMM8+YZZdd1my//fb16k3O2WuuucYuNoGpfY011rDLj6644ormyiuvtKuHPfbYY3bZ3NNPP92e+8Ybb5gbbrjBzJgxw6y11lrmlFNOsWXXK1g/hIAQEAIJQ0Aa0IQ1iKojBIRA9SJw9NFH2yVpN9poI7vowz777FPvZvv27Wvuu+8+u6TpNttsY8aNG2c233xzu4gAC0KwSARL50I0Efb37NnTmvB33313u2xu165dDeuSS4SAEBACSUZAeUCT3DqqmxAQAlWDwNSpU023bt3MtGnT6gjk0KFDrYZz1qxZdiWqI4880gYlrbnmmva+R48ebZeZxVd0mWWWMeuvv77ZaaedzJlnnmn3sxTvSiutZEaMGFGHE9sguNdff33dNn0RAkJACCQNAZngk9Yiqo8QEAJViQCmckik015yk/369bMElO8s5/rAAw8YjrvtttvM+++/b/71r3+xyy6Ja7+k/cNc/+abb5r27dub0047rW5PkyZNzKRJk+p+64sQEAJCIIkIyASfxFZRnYSAEKg6BFg7/a+//jKsHe5koYX+1gH8+uuvlpBusskm1gy/2GKLmUwTvTuPT3xEKa958+amcePGdX9bbbWV2W233dIP1XchIASEQOIQ+Hv0S1zVVCEhIASEQPUggGn8q6++shrO7t272xt79tln627w0UcfNfyePn266dChg93ONgSimSlt27Y1LVu2tAFHF110Ud3up59+2prz6zboixAQAkIggQhIA5rARlGVhIAQqD4EevToYTp37mzOO+88M3PmTINPKBHxTtq1a2f+/PNPQ3J6hKj2wYMH2+9oRxHM9O+++67BZxTBZ/TOO+80jz/+uD0Xkz0+ol9//bXdr39CQAgIgaQiIAKa1JZRvYSAEKgqBJo1a2ZGjhxpI9RXXXVVs8EGG9gAI3eTm266qRk4cKCNgMevs3fv3ubss882rVq1Mq+//ro9bOeddzYPPfSQPZcN55xzjtlzzz1N//79TYsWLcwBBxxgBg0aZIiIlwgBISAEkoyAouCT3DqqmxAQAlWJwJw5cyxhhJRmCrlC0WCSJzSbsP+PP/4w+Ig6mT9/vtWcLr/88m6TPoWAEBACiUZABDTRzaPKCQEhIASEgBAQAkKg+hCQCb762lR3JASEgBAQAkJACAiBRCMgApro5lHlhIAQEAJCQAgIASFQfQiIgFZfm+qOhIAQEAJCQAgIASGQaAREQBPdPKqcEBACQkAICAEhIASqDwER0OprU92REBACQkAICAEhIAQSjYAIaKKbR5UTAkJACAgBISAEhED1ISACWn1tqjsSAkJACAgBISAEhECiERABTXTzqHJCQAgIASEgBISAEKg+BBaqvlvSHQkBISAEhIAQEAK1iMB7771nxo4da3788UfTvXt307dv30TC8Ntvv5nbb789a90OO+ywrNvLufGee+4x22+/vWnZsmVs1ZAGNDYoVZAQEAJCQAgIASFQLgRGjhxptthiC/PGG2+Yb7/91px44olmn332KVd18l73p59+Mocffrh57bXXzOuvv17vL++JZdp53333me+//z7Wq2spzljhVGFCQAgIASEgBIRAORDo16+f2XXXXY3TIH799ddmpZVWsoR01VVXravShx9+aJZZZpk6bR7a0iZNmphFF13UHgPRatq0qf2bO3euPe7zzz83q6yyit3/559/mo8//th07NjRnucK5npfffWVWX311c1CC/3HwDx//nxL3JZaail3mP385ptvDNsgoosttli9fek/fvjhB8OxK664Yvpm8/vvv5vp06fbOiy88MJ2H3WlTOqaSqXMCiusUPA5M2bMMC1atDCtW7c21Jnf6ZhRdqtWreru97PPPjNLLLGE/XMXmTdvnr32yiuvbMty23N9SgOaCxltFwJCQAgIASEgBCoGAUjaww8/bN5++21b5zZt2pjvvvuujkg9/vjjZu2117YEtUOHDuamm26yx5166ql139lw4IEHmhEjRth9HI/pef311zcTJ060f+3atTNHH3206dy5sxk9erQ97vTTT7dlo9VcbbXVzPvvv2+3jx8/3nTp0sV+L/bf4MGDbd0PPvhg06NHD3svlDFo0CB77YEDB9r906ZNs0Wvs846Zo899jC777676dmzpznhhBPqLpnvnD333NPsv//+luRedtllZt111zUHHHCA6dq1q4FsI926dTOffPKJJdOUvcMOO5jNNtus7hqY6Ndbbz1z9tlnmzXXXNPwu0EJWLJECAgBISAEhIAQEAIVjUCgyUwdeuihqUAzl1p22WVTAYlKvfPOO/ae2Mf25557zv4ONHyppZdeOhVoElNHHXVU6sorr6y791122SUVECj7OyCbqRtuuCH1119/1f1+8skn7fdXX301td9++6UCrWcqILSpX3/91W6/5ZZbUgH5s98DX8/UnDlz7Pf0f4FGMRUQtFSnTp1Sa621Vt3fscceaw+bMGGCrV+gIbW/zz///NRdd92VeuSRR1IB0U657VdddVXqkEMOsccst9xyqXPOOcd+D8hiKtDCpgIC2eA51113nT3nxhtvtOe4+gZEM/X888/bfcsvv3wq0BynqF9AfO02sAt8bFNffPFFKnB9SD344IN2+6RJk1LXX3+9/Z7vn4KQGqToOkAICAEhIASEgBBIOgKLL764GTZsmAnIj3nxxRdNQIisJhDNZUAOTbNmzUyfPn3sbWCexlROwFJDsskmm5hGjRpZ8zImcfxMEbSid9xxh73mIossYn062Y5Z/ZVXXjFDhw41bEcTm0vuv/9+Wy+3v3nz5vYr52+11VZ15vkzzzzTbkdbu91229VtHzBggNW8BuTR7kdbi+B6EJA/8/PPP5uXXnop7zm9e/e25+BigPbT1Tcg8SYgl3af+xeQboO2F8H0P2bMGPsdrTF/w4cPNzvttFNBvrcioBY6/RMCQkAICAEhIAQqFQEIJmbhRx991EBEN910U/sHARs1apQJNJXWnBxoMuv8GNn3xx9/WHLJpxN8QtPFkUJ8JPEVhdg5CTSshjLxlzziiCPc5oI/OS+bDygkEF9MJwRVQWwDTaSZOnWq22yJNRH11AtZcskl6/Y1btzY1rWhc9z9caLzg60rJONLZr3wFcXvdN999zXbbruteeKJJ8ydd95pXRreeuutjLPr/5QPaH089EsICAEhIASEgBCoMATQbhKYc9JJJ1mtH9WfOXOm1f6RjgltXmDutgSJffhN8offIgTK+Y3Onj3bTJkyhUMWEIJwNtpoo7oyxo0bZ/DP7N+/v5k8ebIh+GbDDTc0BDldccUVlthCZkkNVazsuOOOJjDDG4J/ELSOaEt32203q3UMzOR2OxrYDTbYwEA2c0mYc/KVFbgBWDIP8d00IPoEXgVuC+bll1+2vqOBC4L1F00n0NnKkwY0GyraJgSEgBAQAkJACFQUAg899JAlQIFvp41chwCdccYZZuutt7b3EfhRWtPwWWedZYjYvvfeey0xDXxF7TGBL6ZBG0iQTS7BFE6gzwUXXGBN59dcc401WZPyCZO+i4CHhCGYvyn/3//+d9Yi0apmSuBDaXOYHnPMMVazCrHFpB74e9pruuCj9u3b2yh0FzCVWY77Dfku9hx3buYnWQbuvvtuWy+wCnxCbXaAk08+2VDfc88915rtqauLzs8sw/1WGiaHhD6FgBAQAkJACAiBikeAyG00hESrZxP2tW3bdoFdubYvcGCwwaU8St+HGR8f0XQzePr+MN/R6qJpzCSqbMeFAK1soRLmnFxl4xLgXBLSjyFlFHXKp5F1x4uAOiT0KQSEgBAQAkJACAgBIVASBHI7DZTk8rqIEBACQkAICAEhIASEQK0hIAJaay2u+xUCQkAICAEhIASEQJkREAEtcwPo8kJACAgBISAEhIAQqDUEREBrrcV1v0JACAgBISAEhIAQKDMCIqBlbgBdXggIASEgBISAEBACtYaACGittbjuVwgIASEgBISAEBACZUZABLTMDaDLCwEhIASEgBAQAkKg1hD4fzQdxtMVbvcoAAAAAElFTkSuQmCC" /><!-- --></p>
<p>设置时间跨度为 1 年：</p>
<div class="sourceCode" id="cb43"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb43-2"><a href="#cb43-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lubridate)</span>
<span id="cb43-3"><a href="#cb43-3" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_bw</span>())</span>
<span id="cb43-4"><a href="#cb43-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-5"><a href="#cb43-5" aria-hidden="true" tabindex="-1"></a>economics_y <span class="ot">&lt;-</span> economics[<span class="dv">1</span><span class="sc">:</span><span class="dv">90</span>, ]</span>
<span id="cb43-6"><a href="#cb43-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-7"><a href="#cb43-7" aria-hidden="true" tabindex="-1"></a><span class="co"># labels and breaks for X axis text</span></span>
<span id="cb43-8"><a href="#cb43-8" aria-hidden="true" tabindex="-1"></a>brks <span class="ot">&lt;-</span> economics_y<span class="sc">$</span>date[<span class="fu">seq</span>(<span class="dv">1</span>, <span class="fu">length</span>(economics_y<span class="sc">$</span>date), <span class="dv">12</span>)]</span>
<span id="cb43-9"><a href="#cb43-9" aria-hidden="true" tabindex="-1"></a>lbls <span class="ot">&lt;-</span> lubridate<span class="sc">::</span><span class="fu">year</span>(brks)</span>
<span id="cb43-10"><a href="#cb43-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-11"><a href="#cb43-11" aria-hidden="true" tabindex="-1"></a><span class="co"># plot</span></span>
<span id="cb43-12"><a href="#cb43-12" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(economics_y, <span class="fu">aes</span>(<span class="at">x=</span>date)) <span class="sc">+</span> </span>
<span id="cb43-13"><a href="#cb43-13" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y=</span>returns_perc)) <span class="sc">+</span> </span>
<span id="cb43-14"><a href="#cb43-14" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Yearly Time Series&quot;</span>, </span>
<span id="cb43-15"><a href="#cb43-15" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;Returns Percentage from Economics Dataset&quot;</span>, </span>
<span id="cb43-16"><a href="#cb43-16" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;Source: Economics&quot;</span>, </span>
<span id="cb43-17"><a href="#cb43-17" aria-hidden="true" tabindex="-1"></a>       <span class="at">y=</span><span class="st">&quot;Returns %&quot;</span>) <span class="sc">+</span>  <span class="co"># title and caption</span></span>
<span id="cb43-18"><a href="#cb43-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>(<span class="at">labels =</span> lbls, </span>
<span id="cb43-19"><a href="#cb43-19" aria-hidden="true" tabindex="-1"></a>               <span class="at">breaks =</span> brks) <span class="sc">+</span>  <span class="co"># change to monthly ticks and labels</span></span>
<span id="cb43-20"><a href="#cb43-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">90</span>, <span class="at">vjust=</span><span class="fl">0.5</span>),  <span class="co"># rotate x axis text</span></span>
<span id="cb43-21"><a href="#cb43-21" aria-hidden="true" tabindex="-1"></a>        <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>())  <span class="co"># turn off minor grid</span></span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="多个时间序列" class="section level3">
<h3>6.3 多个时间序列</h3>
<p>在本例中，基于长数据格式进行可视化。这意味着，所有列的列名和各自的值被存放在两个变量中（分别是<code>variable</code>和<code>value</code>）。</p>
<div class="sourceCode" id="cb44"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb44-1"><a href="#cb44-1" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(economics_long, <span class="at">package =</span> <span class="st">&quot;ggplot2&quot;</span>)</span>
<span id="cb44-2"><a href="#cb44-2" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(economics_long)</span></code></pre></div>
<pre><code>## # A tibble: 6 × 4
##   date       variable value  value01
##   &lt;date&gt;     &lt;chr&gt;    &lt;dbl&gt;    &lt;dbl&gt;
## 1 1967-07-01 pce       507. 0       
## 2 1967-08-01 pce       510. 0.000265
## 3 1967-09-01 pce       516. 0.000762
## 4 1967-10-01 pce       512. 0.000471
## 5 1967-11-01 pce       517. 0.000916
## 6 1967-12-01 pce       525. 0.00157</code></pre>
<p>在下面的代码中，在<code>geom_line()</code>函数中设置绘图对象为<code>value</code>，颜色匹配对象为<code>variable</code>。这样，只要调用一次geom_line，就会绘制多条彩色线，每条线代表<code>variable</code>列中的每个唯一<code>value</code>。<code>scale_x_date()</code>将更改
X 轴断点和标签，<code>scale_color_manual</code>将更改行颜色。</p>
<div class="sourceCode" id="cb46"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb46-1"><a href="#cb46-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb46-2"><a href="#cb46-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lubridate)</span>
<span id="cb46-3"><a href="#cb46-3" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_bw</span>())</span>
<span id="cb46-4"><a href="#cb46-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb46-5"><a href="#cb46-5" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> economics_long[economics_long<span class="sc">$</span>variable <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">&quot;psavert&quot;</span>, <span class="st">&quot;uempmed&quot;</span>), ]</span>
<span id="cb46-6"><a href="#cb46-6" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> df[lubridate<span class="sc">::</span><span class="fu">year</span>(df<span class="sc">$</span>date) <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">1967</span><span class="sc">:</span><span class="dv">1981</span>), ]</span>
<span id="cb46-7"><a href="#cb46-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb46-8"><a href="#cb46-8" aria-hidden="true" tabindex="-1"></a><span class="co"># labels and breaks for X axis text</span></span>
<span id="cb46-9"><a href="#cb46-9" aria-hidden="true" tabindex="-1"></a>brks <span class="ot">&lt;-</span> df<span class="sc">$</span>date[<span class="fu">seq</span>(<span class="dv">1</span>, <span class="fu">length</span>(df<span class="sc">$</span>date), <span class="dv">12</span>)]</span>
<span id="cb46-10"><a href="#cb46-10" aria-hidden="true" tabindex="-1"></a>lbls <span class="ot">&lt;-</span> lubridate<span class="sc">::</span><span class="fu">year</span>(brks)</span>
<span id="cb46-11"><a href="#cb46-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb46-12"><a href="#cb46-12" aria-hidden="true" tabindex="-1"></a><span class="co"># plot</span></span>
<span id="cb46-13"><a href="#cb46-13" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(df, <span class="fu">aes</span>(<span class="at">x=</span>date)) <span class="sc">+</span> </span>
<span id="cb46-14"><a href="#cb46-14" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y=</span>value, <span class="at">col=</span>variable)) <span class="sc">+</span> </span>
<span id="cb46-15"><a href="#cb46-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Time Series of Returns Percentage&quot;</span>, </span>
<span id="cb46-16"><a href="#cb46-16" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;Drawn from Long Data format&quot;</span>, </span>
<span id="cb46-17"><a href="#cb46-17" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;Source: Economics&quot;</span>, </span>
<span id="cb46-18"><a href="#cb46-18" aria-hidden="true" tabindex="-1"></a>       <span class="at">y=</span><span class="st">&quot;Returns %&quot;</span>, </span>
<span id="cb46-19"><a href="#cb46-19" aria-hidden="true" tabindex="-1"></a>       <span class="at">color=</span><span class="cn">NULL</span>) <span class="sc">+</span>  <span class="co"># title and caption</span></span>
<span id="cb46-20"><a href="#cb46-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>(<span class="at">labels =</span> lbls, <span class="at">breaks =</span> brks) <span class="sc">+</span>  <span class="co"># change to monthly ticks and labels</span></span>
<span id="cb46-21"><a href="#cb46-21" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;psavert&quot;</span>, <span class="st">&quot;uempmed&quot;</span>), </span>
<span id="cb46-22"><a href="#cb46-22" aria-hidden="true" tabindex="-1"></a>                     <span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;psavert&quot;</span><span class="ot">=</span><span class="st">&quot;#00ba38&quot;</span>, <span class="st">&quot;uempmed&quot;</span><span class="ot">=</span><span class="st">&quot;#f8766d&quot;</span>)) <span class="sc">+</span>  <span class="co"># line color</span></span>
<span id="cb46-23"><a href="#cb46-23" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">90</span>, <span class="at">vjust=</span><span class="fl">0.5</span>, <span class="at">size =</span> <span class="dv">8</span>),  <span class="co"># rotate x axis text</span></span>
<span id="cb46-24"><a href="#cb46-24" aria-hidden="true" tabindex="-1"></a>        <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>())  <span class="co"># turn off minor grid</span></span></code></pre></div>
<p><img 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" /><!-- --></p>
<p>如果从一个宽格式创建一个时间序列，则必须通过对每条线调用一次<code>geom_line()</code>制。因此，默认情况下不会绘制图例，需要手动添加。</p>
<div class="sourceCode" id="cb47"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb47-1"><a href="#cb47-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb47-2"><a href="#cb47-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lubridate)</span>
<span id="cb47-3"><a href="#cb47-3" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_bw</span>())</span>
<span id="cb47-4"><a href="#cb47-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb47-5"><a href="#cb47-5" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> economics[, <span class="fu">c</span>(<span class="st">&quot;date&quot;</span>, <span class="st">&quot;psavert&quot;</span>, <span class="st">&quot;uempmed&quot;</span>)]</span>
<span id="cb47-6"><a href="#cb47-6" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> df[lubridate<span class="sc">::</span><span class="fu">year</span>(df<span class="sc">$</span>date) <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">1967</span><span class="sc">:</span><span class="dv">1981</span>), ]</span>
<span id="cb47-7"><a href="#cb47-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb47-8"><a href="#cb47-8" aria-hidden="true" tabindex="-1"></a><span class="co"># labels and breaks for X axis text</span></span>
<span id="cb47-9"><a href="#cb47-9" aria-hidden="true" tabindex="-1"></a>brks <span class="ot">&lt;-</span> df<span class="sc">$</span>date[<span class="fu">seq</span>(<span class="dv">1</span>, <span class="fu">length</span>(df<span class="sc">$</span>date), <span class="dv">12</span>)]</span>
<span id="cb47-10"><a href="#cb47-10" aria-hidden="true" tabindex="-1"></a>lbls <span class="ot">&lt;-</span> lubridate<span class="sc">::</span><span class="fu">year</span>(brks)</span>
<span id="cb47-11"><a href="#cb47-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb47-12"><a href="#cb47-12" aria-hidden="true" tabindex="-1"></a><span class="co"># plot</span></span>
<span id="cb47-13"><a href="#cb47-13" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(df, <span class="fu">aes</span>(<span class="at">x=</span>date)) <span class="sc">+</span> </span>
<span id="cb47-14"><a href="#cb47-14" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y=</span>psavert, <span class="at">col=</span><span class="st">&quot;psavert&quot;</span>)) <span class="sc">+</span> </span>
<span id="cb47-15"><a href="#cb47-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y=</span>uempmed, <span class="at">col=</span><span class="st">&quot;uempmed&quot;</span>)) <span class="sc">+</span> </span>
<span id="cb47-16"><a href="#cb47-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Time Series of Returns Percentage&quot;</span>, </span>
<span id="cb47-17"><a href="#cb47-17" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;Drawn From Wide Data format&quot;</span>, </span>
<span id="cb47-18"><a href="#cb47-18" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;Source: Economics&quot;</span>, <span class="at">y=</span><span class="st">&quot;Returns %&quot;</span>) <span class="sc">+</span>  <span class="co"># title and caption</span></span>
<span id="cb47-19"><a href="#cb47-19" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>(<span class="at">labels =</span> lbls, <span class="at">breaks =</span> brks) <span class="sc">+</span>  <span class="co"># change to monthly ticks and labels</span></span>
<span id="cb47-20"><a href="#cb47-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">name=</span><span class="st">&quot;&quot;</span>, </span>
<span id="cb47-21"><a href="#cb47-21" aria-hidden="true" tabindex="-1"></a>                     <span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;psavert&quot;</span><span class="ot">=</span><span class="st">&quot;#00ba38&quot;</span>, <span class="st">&quot;uempmed&quot;</span><span class="ot">=</span><span class="st">&quot;#f8766d&quot;</span>)) <span class="sc">+</span>  <span class="co"># line color</span></span>
<span id="cb47-22"><a href="#cb47-22" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>())  <span class="co"># turn off minor grid</span></span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="堆叠面积图" class="section level3">
<h3>6.4 堆叠面积图</h3>
<p>堆叠面积图与折线图类似，只是图下方的区域全部着色。应用场景有：</p>
<ul>
<li>想要描述数量或体积（而不是价格之类的变量）随时间的变化；</li>
<li>有很多数据点。对于很少的数据点，可以考虑绘制柱状图。</li>
<li>希望展示各个类别的贡献。</li>
</ul>
<div class="sourceCode" id="cb48"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb48-1"><a href="#cb48-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb48-2"><a href="#cb48-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lubridate)</span>
<span id="cb48-3"><a href="#cb48-3" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_bw</span>())</span>
<span id="cb48-4"><a href="#cb48-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb48-5"><a href="#cb48-5" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> economics[, <span class="fu">c</span>(<span class="st">&quot;date&quot;</span>, <span class="st">&quot;psavert&quot;</span>, <span class="st">&quot;uempmed&quot;</span>)]</span>
<span id="cb48-6"><a href="#cb48-6" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> df[lubridate<span class="sc">::</span><span class="fu">year</span>(df<span class="sc">$</span>date) <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">1967</span><span class="sc">:</span><span class="dv">1981</span>), ]</span>
<span id="cb48-7"><a href="#cb48-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb48-8"><a href="#cb48-8" aria-hidden="true" tabindex="-1"></a><span class="co"># labels and breaks for X axis text</span></span>
<span id="cb48-9"><a href="#cb48-9" aria-hidden="true" tabindex="-1"></a>brks <span class="ot">&lt;-</span> df<span class="sc">$</span>date[<span class="fu">seq</span>(<span class="dv">1</span>, <span class="fu">length</span>(df<span class="sc">$</span>date), <span class="dv">12</span>)]</span>
<span id="cb48-10"><a href="#cb48-10" aria-hidden="true" tabindex="-1"></a>lbls <span class="ot">&lt;-</span> lubridate<span class="sc">::</span><span class="fu">year</span>(brks)</span>
<span id="cb48-11"><a href="#cb48-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb48-12"><a href="#cb48-12" aria-hidden="true" tabindex="-1"></a><span class="co"># plot</span></span>
<span id="cb48-13"><a href="#cb48-13" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(df, <span class="fu">aes</span>(<span class="at">x=</span>date)) <span class="sc">+</span> </span>
<span id="cb48-14"><a href="#cb48-14" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_area</span>(<span class="fu">aes</span>(<span class="at">y=</span>psavert<span class="sc">+</span>uempmed, <span class="at">fill=</span><span class="st">&quot;psavert&quot;</span>)) <span class="sc">+</span> </span>
<span id="cb48-15"><a href="#cb48-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_area</span>(<span class="fu">aes</span>(<span class="at">y=</span>uempmed, <span class="at">fill=</span><span class="st">&quot;uempmed&quot;</span>)) <span class="sc">+</span> </span>
<span id="cb48-16"><a href="#cb48-16" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Area Chart of Returns Percentage&quot;</span>, </span>
<span id="cb48-17"><a href="#cb48-17" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;From Wide Data format&quot;</span>, </span>
<span id="cb48-18"><a href="#cb48-18" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;Source: Economics&quot;</span>, </span>
<span id="cb48-19"><a href="#cb48-19" aria-hidden="true" tabindex="-1"></a>       <span class="at">y=</span><span class="st">&quot;Returns %&quot;</span>) <span class="sc">+</span>  <span class="co"># title and caption</span></span>
<span id="cb48-20"><a href="#cb48-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_date</span>(<span class="at">labels =</span> lbls, <span class="at">breaks =</span> brks) <span class="sc">+</span>  <span class="co"># change to monthly ticks and labels</span></span>
<span id="cb48-21"><a href="#cb48-21" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">name=</span><span class="st">&quot;&quot;</span>, </span>
<span id="cb48-22"><a href="#cb48-22" aria-hidden="true" tabindex="-1"></a>                    <span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;psavert&quot;</span><span class="ot">=</span><span class="st">&quot;#00ba38&quot;</span>, <span class="st">&quot;uempmed&quot;</span><span class="ot">=</span><span class="st">&quot;#f8766d&quot;</span>)) <span class="sc">+</span>  <span class="co"># line color</span></span>
<span id="cb48-23"><a href="#cb48-23" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>())  <span class="co"># turn off minor grid</span></span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="日历热力图" class="section level3">
<h3>6.5 日历热力图</h3>
<p>当您想要在实际的日历上看到像股票价格这类指标的变化，特别是高点和低点时，日历热力图是一个很好的工具。它强调随着时间的推移视觉上的变化，而不是实际数值的变化。这可以通过使用<code>geom_tile()</code>来实现。</p>
<div class="sourceCode" id="cb49"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb49-1"><a href="#cb49-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb49-2"><a href="#cb49-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(plyr)</span>
<span id="cb49-3"><a href="#cb49-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(scales)</span>
<span id="cb49-4"><a href="#cb49-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(zoo)</span>
<span id="cb49-5"><a href="#cb49-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb49-6"><a href="#cb49-6" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">read.csv</span>(<span class="st">&quot;https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv&quot;</span>)</span>
<span id="cb49-7"><a href="#cb49-7" aria-hidden="true" tabindex="-1"></a>df<span class="sc">$</span>date <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(df<span class="sc">$</span>date)  <span class="co"># format date</span></span>
<span id="cb49-8"><a href="#cb49-8" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> df[df<span class="sc">$</span>year <span class="sc">&gt;=</span> <span class="dv">2012</span>, ]  <span class="co"># filter reqd years</span></span>
<span id="cb49-9"><a href="#cb49-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb49-10"><a href="#cb49-10" aria-hidden="true" tabindex="-1"></a><span class="co"># Create Month Week</span></span>
<span id="cb49-11"><a href="#cb49-11" aria-hidden="true" tabindex="-1"></a>df<span class="sc">$</span>yearmonth <span class="ot">&lt;-</span> <span class="fu">as.yearmon</span>(df<span class="sc">$</span>date)</span>
<span id="cb49-12"><a href="#cb49-12" aria-hidden="true" tabindex="-1"></a>df<span class="sc">$</span>yearmonthf <span class="ot">&lt;-</span> <span class="fu">factor</span>(df<span class="sc">$</span>yearmonth)</span>
<span id="cb49-13"><a href="#cb49-13" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">ddply</span>(df,.(yearmonthf), transform, <span class="at">monthweek=</span><span class="dv">1</span><span class="sc">+</span>week<span class="sc">-</span><span class="fu">min</span>(week))  <span class="co"># compute week number of month</span></span>
<span id="cb49-14"><a href="#cb49-14" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> df[, <span class="fu">c</span>(<span class="st">&quot;year&quot;</span>, <span class="st">&quot;yearmonthf&quot;</span>, <span class="st">&quot;monthf&quot;</span>, <span class="st">&quot;week&quot;</span>, <span class="st">&quot;monthweek&quot;</span>, <span class="st">&quot;weekdayf&quot;</span>, <span class="st">&quot;VIX.Close&quot;</span>)]</span>
<span id="cb49-15"><a href="#cb49-15" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(df)</span></code></pre></div>
<pre><code>##   year yearmonthf monthf week monthweek weekdayf VIX.Close
## 1 2012   Jan 2012    Jan    1         1      Tue     22.97
## 2 2012   Jan 2012    Jan    1         1      Wed     22.22
## 3 2012   Jan 2012    Jan    1         1      Thu     21.48
## 4 2012   Jan 2012    Jan    1         1      Fri     20.63
## 5 2012   Jan 2012    Jan    2         2      Mon     21.07
## 6 2012   Jan 2012    Jan    2         2      Tue     20.69</code></pre>
<div class="sourceCode" id="cb51"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb51-1"><a href="#cb51-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(df, <span class="fu">aes</span>(monthweek, weekdayf, <span class="at">fill =</span> VIX.Close)) <span class="sc">+</span> </span>
<span id="cb51-2"><a href="#cb51-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_tile</span>(<span class="at">colour =</span> <span class="st">&quot;white&quot;</span>) <span class="sc">+</span> </span>
<span id="cb51-3"><a href="#cb51-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_grid</span>(year<span class="sc">~</span>monthf) <span class="sc">+</span> </span>
<span id="cb51-4"><a href="#cb51-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_gradient</span>(<span class="at">low=</span><span class="st">&quot;red&quot;</span>, <span class="at">high=</span><span class="st">&quot;green&quot;</span>) <span class="sc">+</span></span>
<span id="cb51-5"><a href="#cb51-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x=</span><span class="st">&quot;Week of Month&quot;</span>,</span>
<span id="cb51-6"><a href="#cb51-6" aria-hidden="true" tabindex="-1"></a>       <span class="at">y=</span><span class="st">&quot;&quot;</span>,</span>
<span id="cb51-7"><a href="#cb51-7" aria-hidden="true" tabindex="-1"></a>       <span class="at">title =</span> <span class="st">&quot;Time-Series Calendar Heatmap&quot;</span>, </span>
<span id="cb51-8"><a href="#cb51-8" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;Yahoo Closing Price&quot;</span>, </span>
<span id="cb51-9"><a href="#cb51-9" aria-hidden="true" tabindex="-1"></a>       <span class="at">fill=</span><span class="st">&quot;Close&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="坡度图-1" class="section level3">
<h3>6.6 坡度图</h3>
<p>坡度图可以可视化数值和类别排名之间的变化。这更适用于时间点很少的时间序列。</p>
<div class="sourceCode" id="cb52"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb52-1"><a href="#cb52-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span>
<span id="cb52-2"><a href="#cb52-2" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_classic</span>())</span>
<span id="cb52-3"><a href="#cb52-3" aria-hidden="true" tabindex="-1"></a>source_df <span class="ot">&lt;-</span> <span class="fu">read.csv</span>(<span class="st">&quot;https://raw.githubusercontent.com/jkeirstead/r-slopegraph/master/cancer_survival_rates.csv&quot;</span>)</span>
<span id="cb52-4"><a href="#cb52-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-5"><a href="#cb52-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Define functions. Source: https://github.com/jkeirstead/r-slopegraph</span></span>
<span id="cb52-6"><a href="#cb52-6" aria-hidden="true" tabindex="-1"></a>tufte_sort <span class="ot">&lt;-</span> <span class="cf">function</span>(df, <span class="at">x=</span><span class="st">&quot;year&quot;</span>, <span class="at">y=</span><span class="st">&quot;value&quot;</span>, <span class="at">group=</span><span class="st">&quot;group&quot;</span>, <span class="at">method=</span><span class="st">&quot;tufte&quot;</span>, <span class="at">min.space=</span><span class="fl">0.05</span>) {</span>
<span id="cb52-7"><a href="#cb52-7" aria-hidden="true" tabindex="-1"></a>    <span class="do">## First rename the columns for consistency</span></span>
<span id="cb52-8"><a href="#cb52-8" aria-hidden="true" tabindex="-1"></a>    ids <span class="ot">&lt;-</span> <span class="fu">match</span>(<span class="fu">c</span>(x, y, group), <span class="fu">names</span>(df))</span>
<span id="cb52-9"><a href="#cb52-9" aria-hidden="true" tabindex="-1"></a>    df <span class="ot">&lt;-</span> df[,ids]</span>
<span id="cb52-10"><a href="#cb52-10" aria-hidden="true" tabindex="-1"></a>    <span class="fu">names</span>(df) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;x&quot;</span>, <span class="st">&quot;y&quot;</span>, <span class="st">&quot;group&quot;</span>)</span>
<span id="cb52-11"><a href="#cb52-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-12"><a href="#cb52-12" aria-hidden="true" tabindex="-1"></a>    <span class="do">## Expand grid to ensure every combination has a defined value</span></span>
<span id="cb52-13"><a href="#cb52-13" aria-hidden="true" tabindex="-1"></a>    tmp <span class="ot">&lt;-</span> <span class="fu">expand.grid</span>(<span class="at">x=</span><span class="fu">unique</span>(df<span class="sc">$</span>x), <span class="at">group=</span><span class="fu">unique</span>(df<span class="sc">$</span>group))</span>
<span id="cb52-14"><a href="#cb52-14" aria-hidden="true" tabindex="-1"></a>    tmp <span class="ot">&lt;-</span> <span class="fu">merge</span>(df, tmp, <span class="at">all.y=</span><span class="cn">TRUE</span>)</span>
<span id="cb52-15"><a href="#cb52-15" aria-hidden="true" tabindex="-1"></a>    df <span class="ot">&lt;-</span> <span class="fu">mutate</span>(tmp, <span class="at">y=</span><span class="fu">ifelse</span>(<span class="fu">is.na</span>(y), <span class="dv">0</span>, y))</span>
<span id="cb52-16"><a href="#cb52-16" aria-hidden="true" tabindex="-1"></a>  </span>
<span id="cb52-17"><a href="#cb52-17" aria-hidden="true" tabindex="-1"></a>    <span class="do">## Cast into a matrix shape and arrange by first column</span></span>
<span id="cb52-18"><a href="#cb52-18" aria-hidden="true" tabindex="-1"></a>    <span class="fu">require</span>(reshape2)</span>
<span id="cb52-19"><a href="#cb52-19" aria-hidden="true" tabindex="-1"></a>    tmp <span class="ot">&lt;-</span> <span class="fu">dcast</span>(df, group <span class="sc">~</span> x, <span class="at">value.var=</span><span class="st">&quot;y&quot;</span>)</span>
<span id="cb52-20"><a href="#cb52-20" aria-hidden="true" tabindex="-1"></a>    ord <span class="ot">&lt;-</span> <span class="fu">order</span>(tmp[,<span class="dv">2</span>])</span>
<span id="cb52-21"><a href="#cb52-21" aria-hidden="true" tabindex="-1"></a>    tmp <span class="ot">&lt;-</span> tmp[ord,]</span>
<span id="cb52-22"><a href="#cb52-22" aria-hidden="true" tabindex="-1"></a>    </span>
<span id="cb52-23"><a href="#cb52-23" aria-hidden="true" tabindex="-1"></a>    min.space <span class="ot">&lt;-</span> min.space<span class="sc">*</span><span class="fu">diff</span>(<span class="fu">range</span>(tmp[,<span class="sc">-</span><span class="dv">1</span>]))</span>
<span id="cb52-24"><a href="#cb52-24" aria-hidden="true" tabindex="-1"></a>    yshift <span class="ot">&lt;-</span> <span class="fu">numeric</span>(<span class="fu">nrow</span>(tmp))</span>
<span id="cb52-25"><a href="#cb52-25" aria-hidden="true" tabindex="-1"></a>    <span class="do">## Start at &quot;bottom&quot; row</span></span>
<span id="cb52-26"><a href="#cb52-26" aria-hidden="true" tabindex="-1"></a>    <span class="do">## Repeat for rest of the rows until you hit the top</span></span>
<span id="cb52-27"><a href="#cb52-27" aria-hidden="true" tabindex="-1"></a>    <span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">2</span><span class="sc">:</span><span class="fu">nrow</span>(tmp)) {</span>
<span id="cb52-28"><a href="#cb52-28" aria-hidden="true" tabindex="-1"></a>        <span class="do">## Shift subsequent row up by equal space so gap between</span></span>
<span id="cb52-29"><a href="#cb52-29" aria-hidden="true" tabindex="-1"></a>        <span class="do">## two entries is &gt;= minimum</span></span>
<span id="cb52-30"><a href="#cb52-30" aria-hidden="true" tabindex="-1"></a>        mat <span class="ot">&lt;-</span> <span class="fu">as.matrix</span>(tmp[(i<span class="dv">-1</span>)<span class="sc">:</span>i, <span class="sc">-</span><span class="dv">1</span>])</span>
<span id="cb52-31"><a href="#cb52-31" aria-hidden="true" tabindex="-1"></a>        d.min <span class="ot">&lt;-</span> <span class="fu">min</span>(<span class="fu">diff</span>(mat))</span>
<span id="cb52-32"><a href="#cb52-32" aria-hidden="true" tabindex="-1"></a>        yshift[i] <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(d.min <span class="sc">&lt;</span> min.space, min.space <span class="sc">-</span> d.min, <span class="dv">0</span>)</span>
<span id="cb52-33"><a href="#cb52-33" aria-hidden="true" tabindex="-1"></a>    }</span>
<span id="cb52-34"><a href="#cb52-34" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-35"><a href="#cb52-35" aria-hidden="true" tabindex="-1"></a>    </span>
<span id="cb52-36"><a href="#cb52-36" aria-hidden="true" tabindex="-1"></a>    tmp <span class="ot">&lt;-</span> <span class="fu">cbind</span>(tmp, <span class="at">yshift=</span><span class="fu">cumsum</span>(yshift))</span>
<span id="cb52-37"><a href="#cb52-37" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-38"><a href="#cb52-38" aria-hidden="true" tabindex="-1"></a>    scale <span class="ot">&lt;-</span> <span class="dv">1</span></span>
<span id="cb52-39"><a href="#cb52-39" aria-hidden="true" tabindex="-1"></a>    tmp <span class="ot">&lt;-</span> <span class="fu">melt</span>(tmp, <span class="at">id=</span><span class="fu">c</span>(<span class="st">&quot;group&quot;</span>, <span class="st">&quot;yshift&quot;</span>), <span class="at">variable.name=</span><span class="st">&quot;x&quot;</span>, <span class="at">value.name=</span><span class="st">&quot;y&quot;</span>)</span>
<span id="cb52-40"><a href="#cb52-40" aria-hidden="true" tabindex="-1"></a>    <span class="do">## Store these gaps in a separate variable so that they can be scaled ypos = a*yshift + y</span></span>
<span id="cb52-41"><a href="#cb52-41" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-42"><a href="#cb52-42" aria-hidden="true" tabindex="-1"></a>    tmp <span class="ot">&lt;-</span> <span class="fu">transform</span>(tmp, <span class="at">ypos=</span>y <span class="sc">+</span> scale<span class="sc">*</span>yshift)</span>
<span id="cb52-43"><a href="#cb52-43" aria-hidden="true" tabindex="-1"></a>    <span class="fu">return</span>(tmp)</span>
<span id="cb52-44"><a href="#cb52-44" aria-hidden="true" tabindex="-1"></a>   </span>
<span id="cb52-45"><a href="#cb52-45" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb52-46"><a href="#cb52-46" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-47"><a href="#cb52-47" aria-hidden="true" tabindex="-1"></a>plot_slopegraph <span class="ot">&lt;-</span> <span class="cf">function</span>(df) {</span>
<span id="cb52-48"><a href="#cb52-48" aria-hidden="true" tabindex="-1"></a>    ylabs <span class="ot">&lt;-</span> <span class="fu">subset</span>(df, x<span class="sc">==</span><span class="fu">head</span>(x,<span class="dv">1</span>))<span class="sc">$</span>group</span>
<span id="cb52-49"><a href="#cb52-49" aria-hidden="true" tabindex="-1"></a>    yvals <span class="ot">&lt;-</span> <span class="fu">subset</span>(df, x<span class="sc">==</span><span class="fu">head</span>(x,<span class="dv">1</span>))<span class="sc">$</span>ypos</span>
<span id="cb52-50"><a href="#cb52-50" aria-hidden="true" tabindex="-1"></a>    fontSize <span class="ot">&lt;-</span> <span class="dv">3</span></span>
<span id="cb52-51"><a href="#cb52-51" aria-hidden="true" tabindex="-1"></a>    gg <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(df,<span class="fu">aes</span>(<span class="at">x=</span>x,<span class="at">y=</span>ypos)) <span class="sc">+</span></span>
<span id="cb52-52"><a href="#cb52-52" aria-hidden="true" tabindex="-1"></a>        <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">group=</span>group),<span class="at">colour=</span><span class="st">&quot;grey80&quot;</span>) <span class="sc">+</span></span>
<span id="cb52-53"><a href="#cb52-53" aria-hidden="true" tabindex="-1"></a>        <span class="fu">geom_point</span>(<span class="at">colour=</span><span class="st">&quot;white&quot;</span>,<span class="at">size=</span><span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb52-54"><a href="#cb52-54" aria-hidden="true" tabindex="-1"></a>        <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label=</span>y), <span class="at">size=</span>fontSize, <span class="at">family=</span><span class="st">&quot;American Typewriter&quot;</span>) <span class="sc">+</span></span>
<span id="cb52-55"><a href="#cb52-55" aria-hidden="true" tabindex="-1"></a>        <span class="fu">scale_y_continuous</span>(<span class="at">name=</span><span class="st">&quot;&quot;</span>, <span class="at">breaks=</span>yvals, <span class="at">labels=</span>ylabs)</span>
<span id="cb52-56"><a href="#cb52-56" aria-hidden="true" tabindex="-1"></a>    <span class="fu">return</span>(gg)</span>
<span id="cb52-57"><a href="#cb52-57" aria-hidden="true" tabindex="-1"></a>}    </span>
<span id="cb52-58"><a href="#cb52-58" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-59"><a href="#cb52-59" aria-hidden="true" tabindex="-1"></a><span class="do">## Prepare data    </span></span>
<span id="cb52-60"><a href="#cb52-60" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">tufte_sort</span>(source_df, </span>
<span id="cb52-61"><a href="#cb52-61" aria-hidden="true" tabindex="-1"></a>                 <span class="at">x=</span><span class="st">&quot;year&quot;</span>, </span>
<span id="cb52-62"><a href="#cb52-62" aria-hidden="true" tabindex="-1"></a>                 <span class="at">y=</span><span class="st">&quot;value&quot;</span>, </span>
<span id="cb52-63"><a href="#cb52-63" aria-hidden="true" tabindex="-1"></a>                 <span class="at">group=</span><span class="st">&quot;group&quot;</span>, </span>
<span id="cb52-64"><a href="#cb52-64" aria-hidden="true" tabindex="-1"></a>                 <span class="at">method=</span><span class="st">&quot;tufte&quot;</span>, </span>
<span id="cb52-65"><a href="#cb52-65" aria-hidden="true" tabindex="-1"></a>                 <span class="at">min.space=</span><span class="fl">0.05</span>)</span>
<span id="cb52-66"><a href="#cb52-66" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-67"><a href="#cb52-67" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">transform</span>(df, </span>
<span id="cb52-68"><a href="#cb52-68" aria-hidden="true" tabindex="-1"></a>                <span class="at">x=</span><span class="fu">factor</span>(x, <span class="at">levels=</span><span class="fu">c</span>(<span class="dv">5</span>,<span class="dv">10</span>,<span class="dv">15</span>,<span class="dv">20</span>), </span>
<span id="cb52-69"><a href="#cb52-69" aria-hidden="true" tabindex="-1"></a>                            <span class="at">labels=</span><span class="fu">c</span>(<span class="st">&quot;5 years&quot;</span>,<span class="st">&quot;10 years&quot;</span>,<span class="st">&quot;15 years&quot;</span>,<span class="st">&quot;20 years&quot;</span>)), </span>
<span id="cb52-70"><a href="#cb52-70" aria-hidden="true" tabindex="-1"></a>                <span class="at">y=</span><span class="fu">round</span>(y))</span>
<span id="cb52-71"><a href="#cb52-71" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-72"><a href="#cb52-72" aria-hidden="true" tabindex="-1"></a><span class="do">## Plot</span></span>
<span id="cb52-73"><a href="#cb52-73" aria-hidden="true" tabindex="-1"></a><span class="fu">plot_slopegraph</span>(df) <span class="sc">+</span> <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Estimates of % survival rates&quot;</span>) <span class="sc">+</span> </span>
<span id="cb52-74"><a href="#cb52-74" aria-hidden="true" tabindex="-1"></a>                      <span class="fu">theme</span>(<span class="at">axis.title=</span><span class="fu">element_blank</span>(),</span>
<span id="cb52-75"><a href="#cb52-75" aria-hidden="true" tabindex="-1"></a>                            <span class="at">axis.ticks =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb52-76"><a href="#cb52-76" aria-hidden="true" tabindex="-1"></a>                            <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">hjust=</span><span class="fl">0.5</span>,</span>
<span id="cb52-77"><a href="#cb52-77" aria-hidden="true" tabindex="-1"></a>                                                      <span class="at">family =</span> <span class="st">&quot;American Typewriter&quot;</span>,</span>
<span id="cb52-78"><a href="#cb52-78" aria-hidden="true" tabindex="-1"></a>                                                      <span class="at">face=</span><span class="st">&quot;bold&quot;</span>),</span>
<span id="cb52-79"><a href="#cb52-79" aria-hidden="true" tabindex="-1"></a>                            <span class="at">axis.text =</span> <span class="fu">element_text</span>(<span class="at">family =</span> <span class="st">&quot;American Typewriter&quot;</span>,</span>
<span id="cb52-80"><a href="#cb52-80" aria-hidden="true" tabindex="-1"></a>                                                     <span class="at">face=</span><span class="st">&quot;bold&quot;</span>))</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="季节图" class="section level3">
<h3>6.7 季节图</h3>
<p>如果您正在处理<code>ts</code>或<code>xts</code>类型的时间序列对象，您可以通过使用<code>forecast::ggseasonplot</code>绘制的季节图来查看季节波动。下面是一个使用<code>AirPassengers</code>和<code>nottem</code>数据集绘制的例子。</p>
<div class="sourceCode" id="cb53"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb53-1"><a href="#cb53-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb53-2"><a href="#cb53-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(forecast)</span>
<span id="cb53-3"><a href="#cb53-3" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_classic</span>())</span>
<span id="cb53-4"><a href="#cb53-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb53-5"><a href="#cb53-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Subset data</span></span>
<span id="cb53-6"><a href="#cb53-6" aria-hidden="true" tabindex="-1"></a>nottem_small <span class="ot">&lt;-</span> <span class="fu">window</span>(nottem, <span class="at">start=</span><span class="fu">c</span>(<span class="dv">1920</span>, <span class="dv">1</span>), <span class="at">end=</span><span class="fu">c</span>(<span class="dv">1925</span>, <span class="dv">12</span>))  <span class="co"># subset a smaller timewindow</span></span>
<span id="cb53-7"><a href="#cb53-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb53-8"><a href="#cb53-8" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot</span></span>
<span id="cb53-9"><a href="#cb53-9" aria-hidden="true" tabindex="-1"></a><span class="fu">ggseasonplot</span>(AirPassengers) <span class="sc">+</span> <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Seasonal plot: International Airline Passengers&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb54"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb54-1"><a href="#cb54-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggseasonplot</span>(nottem_small) <span class="sc">+</span> <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Seasonal plot: Air temperatures at Nottingham Castle&quot;</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
</div>
<div id="群体" class="section level2">
<h2>7 群体</h2>
<div id="谱系图" class="section level3">
<h3>7.1 谱系图</h3>
<div class="sourceCode" id="cb55"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb55-1"><a href="#cb55-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb55-2"><a href="#cb55-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggdendro)</span>
<span id="cb55-3"><a href="#cb55-3" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_bw</span>())</span>
<span id="cb55-4"><a href="#cb55-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb55-5"><a href="#cb55-5" aria-hidden="true" tabindex="-1"></a>hc <span class="ot">&lt;-</span> <span class="fu">hclust</span>(<span class="fu">dist</span>(USArrests), <span class="st">&quot;ave&quot;</span>)  <span class="co"># hierarchical clustering</span></span>
<span id="cb55-6"><a href="#cb55-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb55-7"><a href="#cb55-7" aria-hidden="true" tabindex="-1"></a><span class="co"># plot</span></span>
<span id="cb55-8"><a href="#cb55-8" aria-hidden="true" tabindex="-1"></a><span class="fu">ggdendrogram</span>(hc, <span class="at">rotate =</span> <span class="cn">TRUE</span>, <span class="at">size =</span> <span class="dv">2</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="聚类图" class="section level3">
<h3>7.2 聚类图</h3>
<p>可以使用<code>geom_surround()</code>来显示不同的簇或组。如果数据集有多个特征，还可以计算主成分，并使用
PC1 和 PC2 作为 X 和 Y
轴绘制散点图。<code>geom_encircle()</code>可用于框选所需的组。</p>
<div class="sourceCode" id="cb56"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb56-1"><a href="#cb56-1" aria-hidden="true" tabindex="-1"></a><span class="co"># devtools::install_github(&quot;hrbrmstr/ggalt&quot;)</span></span>
<span id="cb56-2"><a href="#cb56-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb56-3"><a href="#cb56-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggalt)</span>
<span id="cb56-4"><a href="#cb56-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggfortify)</span>
<span id="cb56-5"><a href="#cb56-5" aria-hidden="true" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_classic</span>())</span>
<span id="cb56-6"><a href="#cb56-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb56-7"><a href="#cb56-7" aria-hidden="true" tabindex="-1"></a><span class="co"># Compute data with principal components ------------------</span></span>
<span id="cb56-8"><a href="#cb56-8" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> iris[<span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">4</span>)]</span>
<span id="cb56-9"><a href="#cb56-9" aria-hidden="true" tabindex="-1"></a>pca_mod <span class="ot">&lt;-</span> <span class="fu">prcomp</span>(df)  <span class="co"># compute principal components</span></span>
<span id="cb56-10"><a href="#cb56-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb56-11"><a href="#cb56-11" aria-hidden="true" tabindex="-1"></a><span class="co"># Data frame of principal components ----------------------</span></span>
<span id="cb56-12"><a href="#cb56-12" aria-hidden="true" tabindex="-1"></a>df_pc <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(pca_mod<span class="sc">$</span>x, <span class="at">Species=</span>iris<span class="sc">$</span>Species)  <span class="co"># dataframe of principal components</span></span>
<span id="cb56-13"><a href="#cb56-13" aria-hidden="true" tabindex="-1"></a>df_pc_vir <span class="ot">&lt;-</span> df_pc[df_pc<span class="sc">$</span>Species <span class="sc">==</span> <span class="st">&quot;virginica&quot;</span>, ]  <span class="co"># df for &#39;virginica&#39;</span></span>
<span id="cb56-14"><a href="#cb56-14" aria-hidden="true" tabindex="-1"></a>df_pc_set <span class="ot">&lt;-</span> df_pc[df_pc<span class="sc">$</span>Species <span class="sc">==</span> <span class="st">&quot;setosa&quot;</span>, ]  <span class="co"># df for &#39;setosa&#39;</span></span>
<span id="cb56-15"><a href="#cb56-15" aria-hidden="true" tabindex="-1"></a>df_pc_ver <span class="ot">&lt;-</span> df_pc[df_pc<span class="sc">$</span>Species <span class="sc">==</span> <span class="st">&quot;versicolor&quot;</span>, ]  <span class="co"># df for &#39;versicolor&#39;</span></span>
<span id="cb56-16"><a href="#cb56-16" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb56-17"><a href="#cb56-17" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot ----------------------------------------------------</span></span>
<span id="cb56-18"><a href="#cb56-18" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(df_pc, <span class="fu">aes</span>(PC1, PC2, <span class="at">col=</span>Species)) <span class="sc">+</span> </span>
<span id="cb56-19"><a href="#cb56-19" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">shape=</span>Species), <span class="at">size=</span><span class="dv">2</span>) <span class="sc">+</span>   <span class="co"># draw points</span></span>
<span id="cb56-20"><a href="#cb56-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title=</span><span class="st">&quot;Iris Clustering&quot;</span>, </span>
<span id="cb56-21"><a href="#cb56-21" aria-hidden="true" tabindex="-1"></a>       <span class="at">subtitle=</span><span class="st">&quot;With principal components PC1 and PC2 as X and Y axis&quot;</span>,</span>
<span id="cb56-22"><a href="#cb56-22" aria-hidden="true" tabindex="-1"></a>       <span class="at">caption=</span><span class="st">&quot;Source: Iris&quot;</span>) <span class="sc">+</span> </span>
<span id="cb56-23"><a href="#cb56-23" aria-hidden="true" tabindex="-1"></a>  <span class="fu">coord_cartesian</span>(<span class="at">xlim =</span> <span class="fl">1.2</span> <span class="sc">*</span> <span class="fu">c</span>(<span class="fu">min</span>(df_pc<span class="sc">$</span>PC1), <span class="fu">max</span>(df_pc<span class="sc">$</span>PC1)), </span>
<span id="cb56-24"><a href="#cb56-24" aria-hidden="true" tabindex="-1"></a>                  <span class="at">ylim =</span> <span class="fl">1.2</span> <span class="sc">*</span> <span class="fu">c</span>(<span class="fu">min</span>(df_pc<span class="sc">$</span>PC2), <span class="fu">max</span>(df_pc<span class="sc">$</span>PC2))) <span class="sc">+</span>   <span class="co"># change axis limits</span></span>
<span id="cb56-25"><a href="#cb56-25" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_encircle</span>(<span class="at">data =</span> df_pc_vir, <span class="fu">aes</span>(<span class="at">x=</span>PC1, <span class="at">y=</span>PC2)) <span class="sc">+</span>   <span class="co"># draw circles</span></span>
<span id="cb56-26"><a href="#cb56-26" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_encircle</span>(<span class="at">data =</span> df_pc_set, <span class="fu">aes</span>(<span class="at">x=</span>PC1, <span class="at">y=</span>PC2)) <span class="sc">+</span> </span>
<span id="cb56-27"><a href="#cb56-27" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_encircle</span>(<span class="at">data =</span> df_pc_ver, <span class="fu">aes</span>(<span class="at">x=</span>PC1, <span class="at">y=</span>PC2))</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
</div>
</section>



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================================================
FILE: 2022年/2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/50pictures_ggplot.rmd
================================================
---
title: "ggplot绘图手册"
author: "赵子茜"
date: "`r Sys.Date()`"
output: 
  prettydoc::html_pretty:
    toc: true
    toc_depth: 4
    theme: cayman
    highlight: github
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,warning = F,message = F)
```

## 加载数据集

```{r}
library(ggplot2)
library(plotrix)
options(scipen=999)  # 关掉像1e+48这样的科学符号
data("midwest", package = "ggplot2") #加载数据集
```

## 全局主题设置

```{r}
# 颜色设置（灰色系列）
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

# 颜色设置（黑色系列）
cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")


ggplot <- function(...) ggplot2::ggplot(...) + 
  scale_color_manual(values = cbp1) +
  scale_fill_manual(values = cbp1) + # 注意: 使用连续色阶时需要重写
  theme_bw()

```




## 1. 相关关系
### 1.1 两个变量
展示两个变量之间的相关关系，最常使用的是**散点图**。在 ggplot 中，使用`geom_point()`绘制。此外，默认情况下，`geom_smooth`会绘制一条平滑线（基于 losses ），可以通过设置`method='lm'`来调整以绘制最佳拟合的线。


```{r}
# Scatterplot
gg <- ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) + 
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.1)) + 
  ylim(c(0, 500000)) + 
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot", 
       caption = "Source: midwest")

plot(gg)

```


### 1.2 环绕式散点图
使用 ggalt 包中的`geom_encircle()`函数可实现在散点图中圈选特定区域：
```{r message=FALSE, warning=FALSE}
# install.packages("ggalt")
library(ggalt)
options(scipen = 999)
library(ggplot2)
library(ggalt)

## 设定筛选条件
midwest_select <- midwest[midwest$poptotal > 350000 & 
                            midwest$poptotal <= 500000 & 
                            midwest$area > 0.01 & 
                            midwest$area < 0.1, ]

# Plot
ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) +   # draw points
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.1)) + 
  ylim(c(0, 500000)) +   # draw smoothing line
  geom_encircle(aes(x=area, y=poptotal), 
                data=midwest_select, 
                color="red", 
                size=2, 
                expand=0.08) +   # encircle
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot + Encircle", 
       caption="Source: midwest")
```




### 1.3 抖动图

抖动图可以解决数据点重叠的问题。通过`geom_jitter`函数中的`width`参数设置抖动范围，重叠点在其原始位置周围随机抖动。
```{r}
g <- ggplot(mpg, aes(cty, hwy))
g + geom_jitter(width = .5, size=1) +
  labs(subtitle="mpg: city vs highway mileage", 
       y="hwy", 
       x="cty", 
       title="Jittered Points")

```

### 1.4 计数图

克服数据点重叠问题的第二个选择是使用计数图 `geom_count()`。重叠点越多，圆就越大。

```{r}
g + geom_count(col="tomato3", show.legend=F) +
  labs(subtitle="mpg: city vs highway mileage", 
       y="hwy", 
       x="cty", 
       title="Counts Plot")
```

### 1.5 气泡图
气泡图适合 4 维数据，其中两个是数值型（分别是 X 和 Y），另一个是分类变量（用`color`表示）和另一个数值变量（用`size`表示）。
```{r}
data(mpg, package="ggplot2")


mpg_select <- mpg[mpg$manufacturer %in% c("audi", "ford", "honda", "hyundai"), ]

# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
g <- ggplot(mpg_select, aes(displ, cty)) + 
  labs(subtitle="mpg: Displacement vs City Mileage",
       title="Bubble chart")

g + geom_jitter(aes(col=manufacturer, size=hwy)) + 
  geom_smooth(aes(col=manufacturer), method="lm", se=F)
```


<!-- ### 1.6 动态气泡图 -->

<!-- ```{r} -->
<!-- # install.packages("gganimate") -->
<!-- # install.packages("gapminder") -->
<!-- library(gganimate) -->
<!-- library(gapminder) -->

<!-- g <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, frame = year)) + -->
<!--   geom_point() + -->
<!--   geom_smooth(aes(group = year), -->
<!--               method = "lm", -->
<!--               show.legend = FALSE) + -->
<!--   facet_wrap(~continent, scales = "free") + -->
<!--   scale_x_log10()  # convert to log scale -->

<!-- gganimate(g, interval=0.2) -->
<!-- ``` -->


### 1.6 边际直方图/箱线图

如果您想在同一个图中显示变量的关系和分布，可以使用边际直方图。更改`ggMarginal()`函数中的`type`参数可以将边际直方图换成箱线图或者密度图。

```{r}
library(ggExtra)
data(mpg, package="ggplot2")


# Scatterplot
theme_set(theme_bw())  # pre-set the bw theme.
mpg_select <- mpg[mpg$hwy >= 35 & mpg$cty > 27, ]
g <- ggplot(mpg, aes(cty, hwy)) + 
  geom_count() + 
  geom_smooth(method="lm", se=F)

ggMarginal(g, type = "histogram", fill="transparent")

```
```{r}
ggMarginal(g, type = "boxplot", fill="transparent")

```

```{r}
ggMarginal(g, type = "density", fill="transparent")
```

### 1.7 相关系数图

相关系数图可以查看同一组数据中多个连续变量的相关性。
```{r}
library(ggcorrplot)

# Correlation matrix
data(mtcars)
corr <- round(cor(mtcars), 1)

# Plot
ggcorrplot(corr, hc.order = TRUE, 
           type = "lower", 
           lab = TRUE, 
           lab_size = 3, 
           method="circle", 
           colors = c("tomato2", "white", "springgreen3"), 
           title="Correlogram of mtcars", 
           ggtheme=theme_bw)
```

## 2 偏差
### 2.1 发散条形图
发散条形图是一种可以同时处理负值和正值的条形图。这可以通过 `geom_bar()` 来实现。但是 `geom_bar()` 的用法可能会让人很困惑。这是因为，它既可以用来制作柱状图，也可以用来制作直方图。默认情况下，`geom_bar()` 中 `stat` 参数的默认值为 `count`。这意味着，当您只提供一个连续的 x 变量（而不提供 y 变量）时，它会尝试从数据中生成一个直方图。如果要制作条形图，需要做两件事：

- 设置 `stat=identity`;
- 在 `aes()` 中同时输入 x 和 y，其中 x 是字符或因子型变量，y 是数值型。

为了确保得到的是发散条形图，数据需要满足：分类变量的两个类别在连续变量的某个阈值处改变其值。在下面的例子中，`mtcars` 数据集的 mpg 通过计算 z 分数被规范化。那些 mpg 在 0 以上的车辆被标记为绿色，低于 0 的车辆被标记为红色。

```{r}
data("mtcars")  # load data
mtcars$`car name` <- rownames(mtcars)  # create new column for car names
mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg
mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, "below", "above")  # above / below avg flag
mtcars <- mtcars[order(mtcars$mpg_z), ]  # sort
mtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`) 

# Diverging Barcharts
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +
  scale_fill_manual(name="Mileage", 
                    labels = c("Above Average", "Below Average"), 
                    values = c("above"="#00ba38", "below"="#f8766d")) + 
  labs(subtitle="Normalised mileage from 'mtcars'", 
       title= "Diverging Bars") + 
  coord_flip()
```


### 2.2 带标记的发散型棒棒糖图
这个图形是发散条形图的一个变体。
```{r}
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_point(stat='identity', fill="black", size=6)  +
  geom_segment(aes(y = 0, 
                   x = `car name`, 
                   yend = mpg_z, 
                   xend = `car name`), 
               color = "black") +
  geom_text(color="white", size=2) +
  labs(title="Diverging Lollipop Chart", 
       subtitle="Normalized mileage from 'mtcars': Lollipop") + 
  ylim(-2.5, 2.5) +
  coord_flip()
```


### 2.3 发散性点图

```{r}
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + 
  geom_point(stat='identity', aes(col=mpg_type), size=6)  +
  scale_color_manual(name="Mileage", 
                     labels = c("Above Average", "Below Average"), 
                     values = c("above"="#00ba38", "below"="#f8766d")) + 
  geom_text(color="white", size=2) +
  labs(title="Diverging Dot Plot", 
       subtitle="Normalized mileage from 'mtcars': Dotplot") + 
  ylim(-2.5, 2.5) +
  coord_flip()
```


### 2.4 面积图
面积图通常用于可视化特定指标（如股票回报率）与基线的对比情况。
```{r}
library(quantmod)
data("economics", package = "ggplot2")

# 计算利润
economics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])

# 为轴刻度创建断点和标签
brks <- economics$date[seq(1, length(economics$date), 12)]
lbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])

# Plot
ggplot(economics[1:100, ], aes(date, returns_perc)) + 
  geom_area() + 
  scale_x_date(breaks=brks, labels=lbls) + 
  theme(axis.text.x = element_text(angle=90)) + 
  labs(title="Area Chart", 
       subtitle = "Perc Returns for Personal Savings", 
       y="% Returns for Personal savings", 
       caption="Source: economics")

```

## 3 排序
### 3.1 有序条形图
有序条形图是按 Y 轴变量排序的条形图，X 轴变量必须转换为因子型。
```{r}
# 准备数据：按厂商分组计算平均城市里程。
cty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate
colnames(cty_mpg) <- c("make", "mileage")  # change column names
cty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort
cty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.
head(cty_mpg, 4)

```

```{r}
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_bar(stat="identity", width=.5, fill="tomato3") + 
  labs(title="Ordered Bar Chart", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

### 3.2 棒棒糖图
棒棒糖图传达的信息与柱状图相同。通过将粗条转变为细线，减少了杂乱，使图形看起来更美观。
```{r}
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_point(size=3) + 
  geom_segment(aes(x=make, 
                   xend=make, 
                   y=0, 
                   yend=mileage)) + 
  labs(title="Lollipop Chart", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

### 3.3 点图
点图非常类似于棒棒糖图，但没有线条，并且各标签都在水平位置上。它更强调项目的顺序与实际值有关。
```{r}
ggplot(cty_mpg, aes(x=make, y=mileage)) + 
  geom_point(col="tomato2", size=3) +   # Draw points
  geom_segment(aes(x=make, 
                   xend=make, 
                   y=min(mileage), 
                   yend=max(mileage)), 
               linetype="dashed", 
               size=0.1) +   # Draw dashed lines
  labs(title="Dot Plot", 
       subtitle="Make Vs Avg. Mileage", 
       caption="source: mpg") +  
  coord_flip()
```

### 3.4 坡度图
坡度图是比较两点之间差异的绝佳方法。目前，还没有内置函数来构建这个图形。下面的代码可以作为一个示例框架，告诉您如何绘制这个图形。
```{r}
library(scales)

# 数据准备
df <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv")
colnames(df) <- c("continent", "1952", "1957")
left_label <- paste(df$continent, round(df$'1952'),sep=", ")
right_label <- paste(df$continent, round(df$'1957'),sep=", ")
df$class <- ifelse((df$'1957' - df$'1952') < 0, "red", "green")
head(df)
```

```{r}
library(ggplot2)
p <- ggplot(df) + geom_segment(aes(x=1, xend=2, y=`1952`, yend=`1957`, col=class), size=.75, show.legend=F) + 
                  geom_vline(xintercept=1, linetype="dashed", size=.1) + 
                  geom_vline(xintercept=2, linetype="dashed", size=.1) +
                  scale_color_manual(labels = c("Up", "Down"), 
                                     values = c("green"="#00ba38", "red"="#f8766d")) +  # color of lines
                  labs(x="", y="Mean GdpPerCap") +  # Axis labels
                  xlim(.5, 2.5) + ylim(0,(1.1*(max(df$`1952`, df$`1957`))))  # X and Y axis limits

# Add texts
p <- p + geom_text(label=left_label, y=df$`1952`, x=rep(1, NROW(df)), hjust=1.1, size=3.5)
p <- p + geom_text(label=right_label, y=df$`1957`, x=rep(2, NROW(df)), hjust=-0.1, size=3.5)
p <- p + geom_text(label="Time 1", x=1, y=1.1*(max(df$`1952`, df$`1957`)), hjust=1.2, size=5)  # title
p <- p + geom_text(label="Time 2", x=2, y=1.1*(max(df$`1952`, df$`1957`)), hjust=-0.1, size=5)  # title


#设置主题
p + theme(panel.background = element_blank(),
           panel.grid = element_blank(),
           axis.ticks = element_blank(),
           axis.text.x = element_blank(),
           panel.border = element_blank(),
           plot.margin = unit(c(1,2,1,2), "cm"))


```

### 3.5 哑铃图
哑铃图可以：1)比较两个时间点之间的相对位置（比如增长和下降）；2) 比较两类之间的距离。为了得到哑铃的正确顺序，Y 变量应该是一个因子，因子变量的水平应该与它在图中出现的顺序相同。
```{r}
health <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/health.csv")
health$Area <- factor(health$Area, levels=as.character(health$Area))  # for right ordering of the dumbells

# health$Area <- factor(health$Area)
gg <- ggplot(health, aes(x=pct_2013, xend=pct_2014, y=Area, group=Area)) + 
        geom_dumbbell(color="#a3c4dc", 
                      size=0.75, 
                      point.colour.l="#0e668b") + 
        scale_x_continuous(label=percent) + 
        labs(x=NULL, 
             y=NULL, 
             title="Dumbbell Chart", 
             subtitle="Pct Change: 2013 vs 2014", 
             caption="Source: https://github.com/hrbrmstr/ggalt") +
        theme(plot.title = element_text(hjust=0.5, face="bold"),
              plot.background=element_rect(fill="#f7f7f7"),
              panel.background=element_rect(fill="#f7f7f7"),
              panel.grid.minor=element_blank(),
              panel.grid.major.y=element_blank(),
              panel.grid.major.x=element_line(),
              axis.ticks=element_blank(),
              legend.position="top",
              panel.border=element_blank())

plot(gg)

```

## 4 分布
### 4.1 直方图
#### 4.4.1 连续变量的直方图
连续变量的直方图可以使用`geom_bar()`或`geom_histogram()`来完成。当使用`geom_histogram()`时，可以使用`bins`参数来控制分箱的数量。也可以使用`binwidth`设置每个分箱覆盖的范围。`binwidth`的值与建立直方图的连续变量在同一个尺度上。
```{r}
# 连续（数值）变量的直方图
g <- ggplot(mpg, aes(displ)) + scale_fill_brewer(palette = "Spectral")

g + geom_histogram(aes(fill=class), 
                   binwidth = .1, 
                   col="black", 
                   size=.1) +  # change binwidth
  labs(title="Histogram with Auto Binning", 
       subtitle="Engine Displacement across Vehicle Classes")

 
```


```{r}

g + geom_histogram(aes(fill=class), 
                   bins=5, 
                   col="black", 
                   size=.1) +   # change number of bins
  labs(title="Histogram with Fixed Bins", 
       subtitle="Engine Displacement across Vehicle Classes") 

 
```

#### 4.1.2分类变量的直方图
分类变量的直方图实际上是根据每个类别的频率绘制的条形图。
```{r}
library(ggplot2)
theme_set(theme_classic())

# Histogram on a Categorical variable
g <- ggplot(mpg, aes(manufacturer))
g + geom_bar(aes(fill=class), width = 0.5) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Histogram on Categorical Variable", 
       subtitle="Manufacturer across Vehicle Classes")

 
```

### 4.2 密度图

```{r}
g <- ggplot(mpg, aes(cty))
g + geom_density(aes(fill=factor(cyl)), alpha=0.8) + 
    labs(title="Density plot", 
         subtitle="City Mileage Grouped by Number of cylinders",
         caption="Source: mpg",
         x="City Mileage",
         fill="# Cylinders")
 
```

### 4.3 箱线图
箱形图是研究数据分布的一个有用工具。它还可以显示多个组内的分布，以及中值、范围和异常值。箱子内的黑线表示中位数。箱顶是 75% 分位数，箱底是 25% 分位数。线的端点（又称晶须）距离为1.5*IQR，其中 IQR （四分位差）是 25% 到 75% 分位数之间距离。须外的点通常被认为是极值点。设置`varwidth=T`将调整盒子的宽度，使其与观察的数量成比例。
```{r}
g <- ggplot(mpg, aes(class, cty))
g + geom_boxplot(varwidth=T, fill="plum") + 
    labs(title="Box plot", 
         subtitle="City Mileage grouped by Class of vehicle",
         caption="Source: mpg",
         x="Class of Vehicle",
         y="City Mileage")
 
```

```{r}
g <- ggplot(mpg, aes(class, cty))
g + geom_boxplot(aes(fill=factor(cyl))) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot", 
       subtitle="City Mileage grouped by Class of vehicle",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
 
```

### 4.4 点图+箱线图
在箱线图的基础上，添加点图可以提供更清晰的信息。这些点交错排列，每个点代表一次观测。
```{r}
g <- ggplot(mpg, aes(manufacturer, cty))
g + geom_boxplot() + 
  geom_dotplot(binaxis='y', 
               stackdir='center', 
               dotsize = .5, 
               fill="red") +
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Box plot + Dot plot", 
       subtitle="City Mileage vs Class: Each dot represents 1 row in source data",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
 
```

### 4.5 塔夫特箱线图
这是一个简化版的箱线图。
```{r}

library(ggthemes)
library(ggplot2)
theme_set(theme_tufte())  # from ggthemes

# plot
g <- ggplot(mpg, aes(manufacturer, cty))
g + geom_tufteboxplot() + 
      theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
      labs(title="Tufte Styled Boxplot", 
           subtitle="City Mileage grouped by Class of vehicle",
           caption="Source: mpg",
           x="Class of Vehicle",
           y="City Mileage")


```

### 4.6 小提琴图
小提琴图类似于箱线图，但显示了组内的密度。
```{r}
g <- ggplot(mpg, aes(class, cty))
g + geom_violin() + 
  labs(title="Violin plot", 
       subtitle="City Mileage vs Class of vehicle",
       caption="Source: mpg",
       x="Class of Vehicle",
       y="City Mileage")
 
```

### 4.7 金字塔图
金字塔图提供了一种独特的方式来可视化每个类别包含的样本比例。下面的金字塔是一个很好的例子，说明了在一个营销活动中每个阶段的用户留存率。
```{r}library(ggplot2)
library(ggthemes)
options(scipen = 999)  # turns of scientific notations like 1e+40

# Read data
email_campaign_funnel <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv")

# X Axis Breaks and Labels 
brks <- seq(-15000000, 15000000, 5000000)
lbls = paste0(as.character(c(seq(15, 0, -5), seq(5, 15, 5))), "m")

# Plot
ggplot(email_campaign_funnel, aes(x = Stage, y = Users, fill = Gender)) +   # Fill column
                              geom_bar(stat = "identity", width = .6) +   # draw the bars
                              scale_y_continuous(breaks = brks,   # Breaks
                                                 labels = lbls) + # Labels
                              coord_flip() +  # Flip axes
                              labs(title="Email Campaign Funnel") +
                              theme_tufte() +  # Tufte theme from ggfortify
                              theme(plot.title = element_text(hjust = .5), 
                                    axis.ticks = element_blank()) +   # Centre plot title
                              scale_fill_brewer(palette = "Dark2")  # Color palette
```

## 5 组成
### 5.1 华夫饼图
华夫图可以显示总体的组成成分。
```{r}
var <- mpg$class  # the categorical data 

## Prep data (nothing to change here)
nrows <- 10
df <- expand.grid(y = 1:nrows, x = 1:nrows)
categ_table <- round(table(var) * ((nrows*nrows)/(length(var))))


df$category <- factor(rep(names(categ_table), categ_table))  
# NOTE: if sum(categ_table) is not 100 (i.e. nrows^2), it will need adjustment to make the sum to 100.

## Plot
ggplot(df, aes(x = x, y = y, fill = category)) + 
        geom_tile(color = "black", size = 0.5) +
        scale_x_continuous(expand = c(0, 0)) +
        scale_y_continuous(expand = c(0, 0), trans = 'reverse') +
        scale_fill_brewer(palette = "Set3") +
        labs(title="Waffle Chart", subtitle="'Class' of vehicles",
             caption="Source: mpg") + 
        theme(panel.border = element_rect(size = 2),
              plot.title = element_text(size = rel(1.2)),
              axis.text = element_blank(),
              axis.title = element_blank(),
              axis.ticks = element_blank(),
              legend.title = element_blank(),
              legend.position = "right")

```

### 5.2 饼图

```{r}
library(ggplot2)
theme_set(theme_classic())

# Source: Frequency table
df <- as.data.frame(table(mpg$class))
colnames(df) <- c("class", "freq")
pie <- ggplot(df, aes(x = "", y=freq, fill = factor(class))) + 
  geom_bar(width = 1, stat = "identity") +
  theme(axis.line = element_blank(), 
        plot.title = element_text(hjust=0.5)) + 
  labs(fill="class", 
       x=NULL, 
       y=NULL, 
       title="Pie Chart of class", 
       caption="Source: mpg")

pie + coord_polar(theta = "y", start=0)

# Source: Categorical variable.
# mpg$class
pie <- ggplot(mpg, aes(x = "", fill = factor(class))) + 
  geom_bar(width = 1) +
  theme(axis.line = element_blank(), 
        plot.title = element_text(hjust=0.5)) + 
  labs(fill="class", 
       x=NULL, 
       y=NULL, 
       title="Pie Chart of class", 
       caption="Source: mpg")
  
pie + coord_polar(theta = "y", start=0)
```



### 5.3 条形图
```{r}
 #prep frequency table
freqtable <- table(mpg$manufacturer)
df <- as.data.frame.table(freqtable)

```

```{r}
library(ggplot2)
theme_set(theme_classic())

# Plot
g <- ggplot(df, aes(Var1, Freq))
g + geom_bar(stat="identity", width = 0.5, fill="tomato2") + 
      labs(title="Bar Chart", 
           subtitle="Manufacturer of vehicles", 
           caption="Source: Frequency of Manufacturers from 'mpg' dataset") +
      theme(axis.text.x = element_text(angle=65, vjust=0.6))
```

```{r}
# From on a categorical column variable
g <- ggplot(mpg, aes(manufacturer))
g + geom_bar(aes(fill=class), width = 0.5) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) +
  labs(title="Categorywise Bar Chart", 
       subtitle="Manufacturer of vehicles", 
       caption="Source: Manufacturers from 'mpg' dataset")
```



## 6 变化趋势
### 6.1 时间序列图：基于时间序列对象（ts）
`ggfortify`包可以对时间序列直接绘图。
```{r}
## From Timeseries object (ts)
library(ggplot2)
library(ggfortify)
theme_set(theme_classic())

# Plot 
autoplot(AirPassengers) + 
  labs(title="AirPassengers") + 
  theme(plot.title = element_text(hjust=0.5))
```

### 6.2 时间序列图：基于数据框
```{r}
library(ggplot2)
theme_set(theme_classic())

# 使用默认的时间跨度
ggplot(economics, aes(x=date)) + 
  geom_line(aes(y=returns_perc)) + 
  labs(title="Time Series Chart", 
       subtitle="Returns Percentage from 'Economics' Dataset", 
       caption="Source: Economics", 
       y="Returns %")

```

如果想设置特定的时间间隔，则需要使用`scale_x_date()`函数。
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

economics_m <- economics[1:24, ]

# 设定时间跨度为一个月
lbls <- paste0(month.abb[month(economics_m$date)], " ", lubridate::year(economics_m$date))
brks <- economics_m$date

# plot
ggplot(economics_m, aes(x=date)) + 
  geom_line(aes(y=returns_perc)) + 
  labs(title="Monthly Time Series", 
       subtitle="Returns Percentage from Economics Dataset", 
       caption="Source: Economics", 
       y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, 
               breaks = brks) +  # change to monthly ticks and labels
  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text
        panel.grid.minor = element_blank())  # turn off minor grid
```

设置时间跨度为 1 年：
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

economics_y <- economics[1:90, ]

# labels and breaks for X axis text
brks <- economics_y$date[seq(1, length(economics_y$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(economics_y, aes(x=date)) + 
  geom_line(aes(y=returns_perc)) + 
  labs(title="Yearly Time Series", 
       subtitle="Returns Percentage from Economics Dataset", 
       caption="Source: Economics", 
       y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, 
               breaks = brks) +  # change to monthly ticks and labels
  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text
        panel.grid.minor = element_blank())  # turn off minor grid
```

### 6.3 多个时间序列
在本例中，基于长数据格式进行可视化。这意味着，所有列的列名和各自的值被存放在两个变量中（分别是`variable`和`value`）。

```{r}
data(economics_long, package = "ggplot2")
head(economics_long)
```

在下面的代码中，在`geom_line()`函数中设置绘图对象为`value`，颜色匹配对象为`variable`。这样，只要调用一次geom_line，就会绘制多条彩色线，每条线代表`variable`列中的每个唯一`value`。`scale_x_date()`将更改 X 轴断点和标签，`scale_color_manual`将更改行颜色。
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

df <- economics_long[economics_long$variable %in% c("psavert", "uempmed"), ]
df <- df[lubridate::year(df$date) %in% c(1967:1981), ]

# labels and breaks for X axis text
brks <- df$date[seq(1, length(df$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(df, aes(x=date)) + 
  geom_line(aes(y=value, col=variable)) + 
  labs(title="Time Series of Returns Percentage", 
       subtitle="Drawn from Long Data format", 
       caption="Source: Economics", 
       y="Returns %", 
       color=NULL) +  # title and caption
  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels
  scale_color_manual(labels = c("psavert", "uempmed"), 
                     values = c("psavert"="#00ba38", "uempmed"="#f8766d")) +  # line color
  theme(axis.text.x = element_text(angle = 90, vjust=0.5, size = 8),  # rotate x axis text
        panel.grid.minor = element_blank())  # turn off minor grid
```

如果从一个宽格式创建一个时间序列，则必须通过对每条线调用一次`geom_line()`制。因此，默认情况下不会绘制图例，需要手动添加。
```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

df <- economics[, c("date", "psavert", "uempmed")]
df <- df[lubridate::year(df$date) %in% c(1967:1981), ]

# labels and breaks for X axis text
brks <- df$date[seq(1, length(df$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(df, aes(x=date)) + 
  geom_line(aes(y=psavert, col="psavert")) + 
  geom_line(aes(y=uempmed, col="uempmed")) + 
  labs(title="Time Series of Returns Percentage", 
       subtitle="Drawn From Wide Data format", 
       caption="Source: Economics", y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels
  scale_color_manual(name="", 
                     values = c("psavert"="#00ba38", "uempmed"="#f8766d")) +  # line color
  theme(panel.grid.minor = element_blank())  # turn off minor grid
```



### 6.4 堆叠面积图
堆叠面积图与折线图类似，只是图下方的区域全部着色。应用场景有：

- 想要描述数量或体积（而不是价格之类的变量）随时间的变化；
- 有很多数据点。对于很少的数据点，可以考虑绘制柱状图。
- 希望展示各个类别的贡献。

```{r}
library(ggplot2)
library(lubridate)
theme_set(theme_bw())

df <- economics[, c("date", "psavert", "uempmed")]
df <- df[lubridate::year(df$date) %in% c(1967:1981), ]

# labels and breaks for X axis text
brks <- df$date[seq(1, length(df$date), 12)]
lbls <- lubridate::year(brks)

# plot
ggplot(df, aes(x=date)) + 
  geom_area(aes(y=psavert+uempmed, fill="psavert")) + 
  geom_area(aes(y=uempmed, fill="uempmed")) + 
  labs(title="Area Chart of Returns Percentage", 
       subtitle="From Wide Data format", 
       caption="Source: Economics", 
       y="Returns %") +  # title and caption
  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels
  scale_fill_manual(name="", 
                    values = c("psavert"="#00ba38", "uempmed"="#f8766d")) +  # line color
  theme(panel.grid.minor = element_blank())  # turn off minor grid
```


### 6.5 日历热力图

当您想要在实际的日历上看到像股票价格这类指标的变化，特别是高点和低点时，日历热力图是一个很好的工具。它强调随着时间的推移视觉上的变化，而不是实际数值的变化。这可以通过使用`geom_tile()`来实现。
```{r}
library(ggplot2)
library(plyr)
library(scales)
library(zoo)

df <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv")
df$date <- as.Date(df$date)  # format date
df <- df[df$year >= 2012, ]  # filter reqd years

# Create Month Week
df$yearmonth <- as.yearmon(df$date)
df$yearmonthf <- factor(df$yearmonth)
df <- ddply(df,.(yearmonthf), transform, monthweek=1+week-min(week))  # compute week number of month
df <- df[, c("year", "yearmonthf", "monthf", "week", "monthweek", "weekdayf", "VIX.Close")]
head(df)
```

```{r}
ggplot(df, aes(monthweek, weekdayf, fill = VIX.Close)) + 
  geom_tile(colour = "white") + 
  facet_grid(year~monthf) + 
  scale_fill_gradient(low="red", high="green") +
  labs(x="Week of Month",
       y="",
       title = "Time-Series Calendar Heatmap", 
       subtitle="Yahoo Closing Price", 
       fill="Close")
```

### 6.6 坡度图
坡度图可以可视化数值和类别排名之间的变化。这更适用于时间点很少的时间序列。
```{r}
library(dplyr)
theme_set(theme_classic())
source_df <- read.csv("https://raw.githubusercontent.com/jkeirstead/r-slopegraph/master/cancer_survival_rates.csv")

# Define functions. Source: https://github.com/jkeirstead/r-slopegraph
tufte_sort <- function(df, x="year", y="value", group="group", method="tufte", min.space=0.05) {
    ## First rename the columns for consistency
    ids <- match(c(x, y, group), names(df))
    df <- df[,ids]
    names(df) <- c("x", "y", "group")

    ## Expand grid to ensure every combination has a defined value
    tmp <- expand.grid(x=unique(df$x), group=unique(df$group))
    tmp <- merge(df, tmp, all.y=TRUE)
    df <- mutate(tmp, y=ifelse(is.na(y), 0, y))
  
    ## Cast into a matrix shape and arrange by first column
    require(reshape2)
    tmp <- dcast(df, group ~ x, value.var="y")
    ord <- order(tmp[,2])
    tmp <- tmp[ord,]
    
    min.space <- min.space*diff(range(tmp[,-1]))
    yshift <- numeric(nrow(tmp))
    ## Start at "bottom" row
    ## Repeat for rest of the rows until you hit the top
    for (i in 2:nrow(tmp)) {
        ## Shift subsequent row up by equal space so gap between
        ## two entries is >= minimum
        mat <- as.matrix(tmp[(i-1):i, -1])
        d.min <- min(diff(mat))
        yshift[i] <- ifelse(d.min < min.space, min.space - d.min, 0)
    }

    
    tmp <- cbind(tmp, yshift=cumsum(yshift))

    scale <- 1
    tmp <- melt(tmp, id=c("group", "yshift"), variable.name="x", value.name="y")
    ## Store these gaps in a separate variable so that they can be scaled ypos = a*yshift + y

    tmp <- transform(tmp, ypos=y + scale*yshift)
    return(tmp)
   
}

plot_slopegraph <- function(df) {
    ylabs <- subset(df, x==head(x,1))$group
    yvals <- subset(df, x==head(x,1))$ypos
    fontSize <- 3
    gg <- ggplot(df,aes(x=x,y=ypos)) +
        geom_line(aes(group=group),colour="grey80") +
        geom_point(colour="white",size=8) +
        geom_text(aes(label=y), size=fontSize, family="American Typewriter") +
        scale_y_continuous(name="", breaks=yvals, labels=ylabs)
    return(gg)
}    

## Prepare data    
df <- tufte_sort(source_df, 
                 x="year", 
                 y="value", 
                 group="group", 
                 method="tufte", 
                 min.space=0.05)

df <- transform(df, 
                x=factor(x, levels=c(5,10,15,20), 
                            labels=c("5 years","10 years","15 years","20 years")), 
                y=round(y))

## Plot
plot_slopegraph(df) + labs(title="Estimates of % survival rates") + 
                      theme(axis.title=element_blank(),
                            axis.ticks = element_blank(),
                            plot.title = element_text(hjust=0.5,
                                                      family = "American Typewriter",
                                                      face="bold"),
                            axis.text = element_text(family = "American Typewriter",
                                                     face="bold"))
```

### 6.7 季节图
如果您正在处理`ts`或`xts`类型的时间序列对象，您可以通过使用`forecast::ggseasonplot`绘制的季节图来查看季节波动。下面是一个使用`AirPassengers`和`nottem`数据集绘制的例子。
```{r}
library(ggplot2)
library(forecast)
theme_set(theme_classic())

# Subset data
nottem_small <- window(nottem, start=c(1920, 1), end=c(1925, 12))  # subset a smaller timewindow

# Plot
ggseasonplot(AirPassengers) + labs(title="Seasonal plot: International Airline Passengers")

```

```{r}
ggseasonplot(nottem_small) + labs(title="Seasonal plot: Air temperatures at Nottingham Castle")
```

## 7 群体
### 7.1 谱系图
```{r}

library(ggplot2)
library(ggdendro)
theme_set(theme_bw())

hc <- hclust(dist(USArrests), "ave")  # hierarchical clustering

# plot
ggdendrogram(hc, rotate = TRUE, size = 2)
```

### 7.2 聚类图
可以使用`geom_surround()`来显示不同的簇或组。如果数据集有多个特征，还可以计算主成分，并使用 PC1 和 PC2 作为 X 和 Y 轴绘制散点图。`geom_encircle()`可用于框选所需的组。
```{r}
# devtools::install_github("hrbrmstr/ggalt")
library(ggplot2)
library(ggalt)
library(ggfortify)
theme_set(theme_classic())

# Compute data with principal components ------------------
df <- iris[c(1, 2, 3, 4)]
pca_mod <- prcomp(df)  # compute principal components

# Data frame of principal components ----------------------
df_pc <- data.frame(pca_mod$x, Species=iris$Species)  # dataframe of principal components
df_pc_vir <- df_pc[df_pc$Species == "virginica", ]  # df for 'virginica'
df_pc_set <- df_pc[df_pc$Species == "setosa", ]  # df for 'setosa'
df_pc_ver <- df_pc[df_pc$Species == "versicolor", ]  # df for 'versicolor'
 
# Plot ----------------------------------------------------
ggplot(df_pc, aes(PC1, PC2, col=Species)) + 
  geom_point(aes(shape=Species), size=2) +   # draw points
  labs(title="Iris Clustering", 
       subtitle="With principal components PC1 and PC2 as X and Y axis",
       caption="Source: Iris") + 
  coord_cartesian(xlim = 1.2 * c(min(df_pc$PC1), max(df_pc$PC1)), 
                  ylim = 1.2 * c(min(df_pc$PC2), max(df_pc$PC2))) +   # change axis limits
  geom_encircle(data = df_pc_vir, aes(x=PC1, y=PC2)) +   # draw circles
  geom_encircle(data = df_pc_set, aes(x=PC1, y=PC2)) + 
  geom_encircle(data = df_pc_ver, aes(x=PC1, y=PC2))
```



================================================
FILE: 2022年/2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/midwest.csv
================================================
"","PID","county","state","area","poptotal","popdensity","popwhite","popblack","popamerindian","popasian","popother","percwhite","percblack","percamerindan","percasian","percother","popadults","perchsd","percollege","percprof","poppovertyknown","percpovertyknown","percbelowpoverty","percchildbelowpovert","percadultpoverty","percelderlypoverty","inmetro","category"
"1",561,"ADAMS","IL",0.052,66090,1270.96154,63917,1702,98,249,124,96.7120593,2.57527614,0.14828264,0.37675897,0.18762294,43298,75.1073953,19.6313918,4.35585939,63628,96.2747768,13.1514428,18.0117169,11.0097759,12.4438118,0,"AAR"
"2",562,"ALEXANDER","IL",0.014,10626,759,7054,3496,19,48,9,66.3843403,32.9004329,0.1788067,0.45172219,0.08469791,6724,59.7263534,11.2433076,2.87031529,10529,99.0871447,32.2442777,45.8265139,27.3856467,25.2289759,0,"LHR"
"3",563,"BOND","IL",0.022,14991,681.409091,14477,429,35,16,34,96.5712761,2.86171703,0.23347342,0.10673071,0.22680275,9669,69.3349881,17.0338194,4.48857172,14235,94.9569742,12.0688444,14.036061,10.85209,12.69741,0,"AAR"
"4",564,"BOONE","IL",0.017,30806,1812.11765,29344,127,46,150,1139,95.2541713,0.41225735,0.14932156,0.48691813,3.69733169,19272,75.4721876,17.2789539,4.19779992,30337,98.4775693,7.20901869,11.1795358,5.53601292,6.21704745,1,"ALU"
"5",565,"BROWN","IL",0.018,5836,324.222222,5264,547,14,5,6,90.1987663,9.37285812,0.23989034,0.08567512,0.10281014,3979,68.861523,14.475999,3.36768032,4815,82.5051405,13.5202492,13.0228887,11.1432109,19.2,0,"AAR"
"6",566,"BUREAU","IL",0.05,35688,713.76,35157,50,65,195,221,98.5121049,0.14010312,0.18213405,0.54640215,0.61925577,23444,76.6294148,18.9046238,3.27589149,35107,98.3720018,10.3996354,14.1588185,8.17928731,11.0085864,0,"AAR"
"7",567,"CALHOUN","IL",0.017,5322,313.058824,5298,1,8,15,0,99.5490417,0.01878993,0.15031943,0.28184893,0,3583,62.8244488,11.9173877,3.20960089,5241,98.4780158,15.1497806,13.7877614,12.9323308,21.0852713,0,"LAR"
"8",568,"CARROLL","IL",0.027,16805,622.407407,16519,111,30,61,84,98.2981256,0.6605177,0.1785183,0.36298721,0.49985123,11323,75.9516029,16.1971209,3.05572728,16455,97.9172865,11.7107262,17.2254616,10.0270366,9.52505219,0,"AAR"
"9",569,"CASS","IL",0.024,13437,559.875,13384,16,8,23,6,99.6055667,0.1190742,0.0595371,0.17116916,0.04465282,8825,72.2719547,14.1076487,3.20679887,13081,97.3505991,13.875086,17.9947841,11.9143432,13.6601796,0,"AAR"
"10",570,"CHAMPAIGN","IL",0.058,173025,2983.18966,146506,16559,331,8033,1596,84.6733131,9.57029331,0.19130183,4.64268169,0.92241006,95971,87.4993488,41.2958081,17.7574476,154934,89.5442855,15.5724373,14.1322336,17.5627276,8.1050169,1,"HAU"
"11",571,"CHRISTIAN","IL",0.042,34418,819.47619,34176,82,51,89,20,99.2968795,0.23824743,0.14817828,0.25858562,0.05810913,22945,73.074744,13.567226,3.08999782,33788,98.1695624,11.7082988,16.3206125,9.56969969,11.4906408,0,"AAR"
"12",572,"CLARK","IL",0.03,15921,530.7,15842,10,26,36,7,99.5038,0.06281012,0.16330632,0.22611645,0.04396709,10734,71.3340786,15.1108627,2.77622508,15615,98.0780102,12.0076849,15.3215475,10.1317752,12.5954198,0,"AAR"
"13",573,"CLAY","IL",0.028,14460,516.428571,14403,4,17,29,7,99.6058091,0.02766252,0.1175657,0.20055325,0.04840941,9647,65.5644242,13.6830103,2.78843164,14248,98.5338866,16.7742841,20.5825776,14.4641142,17.6700783,0,"LAR"
"14",574,"CLINTON","IL",0.029,33944,1170.48276,32688,1021,48,104,83,96.2997879,3.00789536,0.14140938,0.30638699,0.24452039,21563,67.1659788,15.3874693,2.87529565,32190,94.8326656,10.2236719,13.2994025,9.25383422,8.32317604,1,"LAU"
"15",575,"COLES","IL",0.03,51644,1721.46667,50177,925,92,341,109,97.159399,1.79110836,0.17814267,0.66028968,0.21106034,29136,76.105162,25.1750412,8.14456343,45693,88.4768802,16.7487361,16.3419409,18.7929138,10.9936084,0,"AAR"
"16",576,"COOK","IL",0.058,5105067,88018.3966,3204947,1317147,10289,188565,384119,62.7797245,25.8007779,0.20154486,3.69368316,7.52426951,3291995,73.4058223,28.0181167,8.32996405,5023523,98.402685,14.1983027,22.2934971,11.6655424,10.8252688,1,"AAU"
"17",577,"CRAWFORD","IL",0.026,19464,748.615385,19300,63,34,48,19,99.1574188,0.32367448,0.17468146,0.24660912,0.09761611,13317,76.0306375,16.9858076,3.33408425,19123,98.2480477,10.5370496,13.809825,8.87024273,10.8033875,0,"AAR"
"18",578,"CUMBERLAND","IL",0.02,10670,533.5,10627,5,6,26,6,99.5970009,0.04686036,0.05623243,0.24367385,0.05623243,6727,72.2461721,14.5978891,2.69064962,10590,99.2502343,12.0491029,13.6031851,9.8222638,15.2846257,0,"AAR"
"19",579,"DE KALB","IL",0.038,77932,2050.84211,72968,2069,123,1751,1021,93.6303444,2.65487861,0.1578299,2.24683057,1.31011651,41817,83.8797618,32.835928,11.1509673,69127,88.7016887,13.5446352,8.67823765,17.0474188,6.45390792,1,"HAU"
"20",580,"DE WITT","IL",0.023,16516,718.086957,16387,25,37,43,24,99.2189392,0.15136837,0.22402519,0.2603536,0.14531364,10941,74.6458276,16.1959601,3.30865552,16238,98.3167837,10.3153098,13.568426,7.95599305,12.2553448,0,"AAR"
"21",581,"DOUGLAS","IL",0.025,19464,778.56,19280,16,19,41,108,99.054665,0.08220304,0.09761611,0.21064529,0.55487053,12550,74.0318725,16.8685259,3.84063745,19140,98.3353884,9.62904911,13.2935431,7.92479531,8.71436256,0,"AAR"
"22",582,"DU PAGE","IL",0.02,781666,39083.3,714905,15462,962,39634,10703,91.4591398,1.97808271,0.12307047,5.07045209,1.3692549,502321,88.5768264,42.7688669,11.9632665,771641,98.7174829,2.71473392,2.94525227,2.39906403,3.83824912,1,"HLU"
"23",583,"EDGAR","IL",0.036,19595,544.305556,19469,68,24,24,10,99.3569788,0.3470273,0.12248022,0.12248022,0.05103343,13082,73.5208684,16.8399327,3.67680783,19258,98.2801735,16.0037387,22.8276281,14.4809567,11.7620137,0,"AAR"
"24",584,"EDWARDS","IL",0.014,7440,531.428571,7401,6,8,19,6,99.4758065,0.08064516,0.10752688,0.25537634,0.08064516,5019,69.6752341,16.9356446,2.45068739,7350,98.7903226,12.2040816,15.1598677,10.9502501,11.8595279,0,"AAR"
"25",585,"EFFINGHAM","IL",0.028,31704,1132.28571,31523,12,45,95,29,99.4290941,0.03785011,0.14193793,0.29964673,0.09147111,19477,74.9550752,20.2751964,3.7839503,31229,98.5017663,8.99484454,11.599832,6.97446574,10.5174981,0,"AAR"
"26",586,"FAYETTE","IL",0.044,20893,474.840909,20148,599,40,35,71,96.4342124,2.86698894,0.19145168,0.16752022,0.33982674,13894,68.7922844,13.6029941,2.98690082,19452,93.1029531,13.618137,16.6276347,12.1341463,13.4358289,0,"AAR"
"27",587,"FORD","IL",0.03,14275,475.833333,14157,43,14,40,21,99.17338,0.30122592,0.09807356,0.28021016,0.14711033,9592,77.1684737,17.8273561,3.89908257,13860,97.0928196,9.26406926,13.5712279,7.32528768,8.78740157,0,"AAR"
"28",588,"FRANKLIN","IL",0.025,40319,1612.76,40068,36,106,84,25,99.3774647,0.08928793,0.26290335,0.2083385,0.06200551,27214,66.6642169,14.7056662,2.43257147,39703,98.4721843,20.764174,31.074742,19.3135372,13.6794859,0,"LHR"
"29",589,"FULTON","IL",0.052,38080,732.307692,37117,668,83,105,107,97.4711134,1.75420168,0.21796218,0.27573529,0.28098739,25592,73.847296,15.5321976,2.99703032,36393,95.5698529,15.494738,22.8122252,13.9965731,11.0196033,0,"AAR"
"30",590,"GALLATIN","IL",0.019,6909,363.631579,6842,42,10,11,4,99.0302504,0.60790274,0.14473875,0.15921262,0.0578955,4680,58.4188034,11.3675214,2.69230769,6737,97.5104936,21.4338726,29.7246558,20.005596,16.2300319,0,"LHR"
"31",591,"GREENE","IL",0.033,15317,464.151515,15231,14,50,17,5,99.4385323,0.09140171,0.32643468,0.11098779,0.03264347,10031,69.0459575,13.0495464,2.71159406,15096,98.5571587,15.5339163,20.0300827,13.444049,14.9431818,0,"AAR"
"32",592,"GRUNDY","IL",0.026,32337,1243.73077,31864,21,45,113,294,98.5372793,0.06494109,0.13915948,0.34944491,0.90917525,20541,78.9640232,18.3778784,3.93846453,31979,98.8929091,6.6168423,6.93047172,5.32733914,10.4620084,1,"ALU"
"33",593,"HAMILTON","IL",0.025,8499,339.96,8462,3,11,21,2,99.5646547,0.03529827,0.12942699,0.24708789,0.02353218,5859,59.9931729,14.0467657,2.61136713,8383,98.6351335,19.8496958,21.9232686,17.0501332,23.1419671,0,"LHR"
"34",594,"HANCOCK","IL",0.047,21373,454.744681,21272,26,25,37,13,99.5274412,0.12164881,0.11697001,0.17311561,0.0608244,14322,77.5380533,20.6605223,3.86119257,20943,97.9881158,11.7843671,14.6202065,9.82946027,12.9152053,0,"AAR"
"35",595,"HARDIN","IL",0.009,5189,576.555556,5062,85,16,13,13,97.5525149,1.63808056,0.30834458,0.25052997,0.25052997,3492,59.7365407,14.3184422,2.60595647,4954,95.4711891,26.7460638,35.1417004,25.1742835,21.1961302,0,"LHR"
"36",596,"HENDERSON","IL",0.023,8096,352,8037,8,29,10,12,99.2712451,0.09881423,0.35820158,0.12351779,0.14822134,5467,73.349186,13.7735504,2.17669654,8030,99.1847826,12.9389788,15.6080748,10.2823529,16.2950257,0,"AAR"
"37",597,"HENRY","IL",0.051,51159,1003.11765,49969,657,63,128,342,97.6739186,1.28423151,0.12314549,0.25020036,0.66850408,33423,77.1953445,18.7236334,4.22164378,50472,98.6571278,10.4790775,15.1772634,8.63526345,9.01154039,1,"AAU"
"38",598,"IROQUOIS","IL",0.067,30787,459.507463,30154,164,43,69,357,97.9439374,0.53269237,0.13966934,0.22412057,1.15958034,20578,73.4376519,15.1666829,3.22674701,30189,98.0576217,9.19871476,11.6762716,7.9592631,9.10027978,0,"AAR"
"39",599,"JACKSON","IL",0.036,61067,1696.30556,51991,6342,109,2178,447,85.1376357,10.3853145,0.17849248,3.56657442,0.7319829,32172,78.767251,36.6436653,14.0898918,54230,88.8041004,28.37175,26.3922105,32.4584833,13.8153008,0,"AHR"
"40",600,"JASPER","IL",0.029,10609,365.827586,10574,1,11,17,6,99.6700914,0.00942596,0.10368555,0.1602413,0.05655575,6835,69.7000732,15.0256035,2.76517922,10511,99.076256,13.1005613,17.4379611,10.0076104,14.5622525,0,"AAR"
"41",601,"JEFFERSON","IL",0.033,37020,1121.81818,34856,1924,56,129,55,94.1545111,5.19719071,0.15126958,0.34846029,0.14856834,24023,69.9038422,18.3698955,3.67980685,36495,98.5818476,16.0569941,21.1134991,13.6358867,15.631524,0,"AAR"
"42",602,"JERSEY","IL",0.023,20539,893,20346,96,43,32,22,99.0603243,0.46740348,0.20935781,0.15580116,0.1071133,12847,71.9156223,14.7583093,3.13691913,19611,95.4817664,9.74453113,11.8255728,8.8343212,9.29971989,1,"AAU"
"43",603,"JO DAVIESS","IL",0.035,21821,623.457143,21732,14,20,29,26,99.592136,0.06415838,0.09165483,0.1328995,0.11915128,14409,74.092581,16.4133528,3.4284128,21610,99.0330416,8.28782971,9.29164459,6.28398253,11.8393235,0,"AAR"
"44",604,"JOHNSON","IL",0.02,11347,567.35,10230,1046,26,14,31,90.1559884,9.21829558,0.22913545,0.12338063,0.27319996,7922,66.1701591,14.3524363,3.43347639,9180,80.9024412,15.5555556,18.5350603,12.2665006,19.8775318,0,"LAR"
"45",605,"KANE","IL",0.029,317471,10947.2759,269675,19006,620,4474,23696,84.9447666,5.98668855,0.19529343,1.40926258,7.46398884,191807,77.7192699,27.5938834,7.01329983,310740,97.8798063,6.84655982,9.59040311,5.55167946,6.31380264,1,"ALU"
"46",606,"KANKAKEE","IL",0.039,96255,2468.07692,80194,14399,150,644,868,83.3141136,14.9592229,0.15583606,0.66905615,0.90177134,59821,73.0863744,17.6409622,3.99023754,92594,96.1965612,13.2967579,19.5983276,10.888062,10.2479647,1,"AAU"
"47",607,"KENDALL","IL",0.018,39413,2189.61111,38019,210,73,224,887,96.4630959,0.53281912,0.18521808,0.5683404,2.25052648,24175,83.6773526,24.8893485,5.25336091,39199,99.4570319,3.38529044,4.42017237,2.35504872,5.87851078,1,"HLU"
"48",608,"KNOX","IL",0.042,56393,1342.69048,52413,2860,85,320,715,92.9423865,5.07155143,0.15072793,0.56744631,1.26788786,37723,76.5660207,19.3436365,4.21228428,53106,94.1712624,13.8684894,19.6733764,12.8831741,9.73871734,0,"AAR"
"49",609,"LAKE","IL",0.028,516418,18443.5,450666,34771,1198,12588,17195,87.2676785,6.73311155,0.23198262,2.43756027,3.32966705,318475,84.7269016,37.8340529,11.4640082,495312,95.9130007,5.15977808,6.76712484,4.19193902,6.16443033,1,"HLU"
"50",610,"LA SALLE","IL",0.068,106913,1572.25,103805,1153,206,523,1226,97.0929634,1.07844696,0.19268003,0.48918279,1.14672678,70357,73.127052,16.8995267,3.43817957,104473,97.7177705,11.1014329,15.2417866,9.21209673,10.6971468,0,"AAR"
"51",611,"LAWRENCE","IL",0.022,15972,726,15759,151,31,21,10,98.6664162,0.94540446,0.19408966,0.13148009,0.06260957,10950,69.2328767,14.5388128,1.94520548,15347,96.0869021,19.8475272,31.1128734,16.7597057,14.8832156,0,"AHR"
"52",612,"LEE","IL",0.043,34392,799.813953,32530,1222,84,181,375,94.5859502,3.5531519,0.24424285,0.52628518,1.09036985,22661,76.2808349,18.476678,4.04218702,32162,93.5159339,8.8240781,10.5378832,7.71542507,9.5268748,0,"AAR"
"53",613,"LIVINGSTON","IL",0.062,39301,633.887097,36551,2115,62,131,442,93.0027226,5.38154245,0.1577568,0.33332485,1.12465332,26084,74.1450698,14.5031437,2.94816746,35431,90.1529223,9.32516723,13.7011589,8.02005013,6.77040677,0,"AAR"
"54",614,"LOGAN","IL",0.036,30798,855.5,29223,1291,37,143,104,94.8860316,4.19183064,0.12013767,0.46431586,0.33768427,20324,75.9004133,17.9492226,3.79846487,28104,91.2526787,10.7813834,12.8258458,9.96479107,10.3613632,0,"AAR"
"55",615,"MCDONOUGH","IL",0.034,35244,1036.58824,32992,1254,65,802,131,93.6102599,3.55805243,0.18442856,2.27556464,0.37169447,18784,80.302385,27.9280239,10.3598807,28821,81.7756214,19.0520801,16.0093168,22.4033816,12.8843841,0,"HHR"
"56",616,"MCHENRY","IL",0.036,183241,5090.02778,178895,310,299,1293,2444,97.6282601,0.16917611,0.16317309,0.70562811,1.33376264,114721,84.5076316,28.0541488,6.41730808,181636,99.1241043,3.49159858,4.06964667,2.87024568,5.0644731,1,"HLU"
"57",617,"MCLEAN","IL",0.068,129180,1899.70588,121057,5563,203,1624,733,93.7118749,4.30639418,0.15714507,1.25716055,0.5674253,72957,84.671793,33.8185507,9.00393382,117663,91.0845332,11.8754409,10.0105088,13.715404,7.2666025,1,"HAU"
"58",618,"MACON","IL",0.035,117206,3348.74286,102197,14135,157,506,211,87.1943416,12.0599628,0.13395219,0.43171851,0.18002491,76297,76.2113845,19.8618556,4.98446859,114042,97.3004795,12.7417969,19.2574786,10.9095835,8.98486907,1,"AAU"
"59",619,"MACOUPIN","IL",0.05,47679,953.58,47077,379,94,88,41,98.7373896,0.79489922,0.19715179,0.18456763,0.08599174,31217,72.8192972,14.0083929,3.2546369,46387,97.2902116,13.2127536,18.5988943,10.8171077,12.460907,0,"AAR"
"60",620,"MADISON","IL",0.045,249238,5538.62222,230217,16136,683,1420,782,92.3683387,6.47413316,0.27403526,0.56973656,0.31375633,161517,75.8143106,19.6994744,4.94932422,245370,98.4480697,11.3387945,16.6709192,9.90305061,8.29181097,1,"AAU"
"61",621,"MARION","IL",0.035,41561,1187.45714,39647,1519,106,232,57,95.394721,3.65486875,0.2550468,0.55821563,0.13714781,27077,70.1259371,16.4493851,3.31277468,40800,98.1689565,16.3676471,22.3493101,13.9285714,14.7224835,0,"AAR"
"62",622,"MARSHALL","IL",0.023,12846,558.521739,12752,17,30,28,19,99.2682547,0.13233691,0.23353573,0.21796668,0.14790596,8600,77.7790698,17.5348837,2.56976744,12563,97.7969796,9.38470111,11.4717933,7.50307503,11.3305613,0,"AAR"
"63",623,"MASON","IL",0.033,16269,493,16180,8,27,38,16,99.4529473,0.04917327,0.1659598,0.23357305,0.09834655,10729,72.5789915,13.5054525,2.8986858,16025,98.5002151,15.5070203,24.1131352,12.8400955,11.6038007,0,"AAR"
"64",624,"MASSAC","IL",0.014,14752,1053.71429,13804,870,37,31,10,93.5737527,5.89750542,0.25081345,0.210141,0.06778742,10068,65.3059197,14.034565,2.93007549,14348,97.2613883,16.7410092,22.9808251,14.9682372,14.3283582,0,"LAR"
"65",625,"MENARD","IL",0.018,11164,620.222222,11101,9,29,14,11,99.4356861,0.08061627,0.25976353,0.12540308,0.09853099,7390,77.3071719,18.511502,3.51826793,10964,98.2085274,9.55855527,12.4503311,7.89163722,10.1449275,1,"AAU"
"66",626,"MERCER","IL",0.033,17290,523.939394,17155,30,33,35,37,99.2192019,0.1735107,0.19086177,0.20242915,0.21399653,11357,77.3531743,16.4744211,3.11702034,17006,98.357432,10.1317182,11.4663727,9.01086957,11.4338123,0,"AAR"
"67",627,"MONROE","IL",0.023,22422,974.869565,22262,13,53,57,37,99.2864151,0.05797877,0.23637499,0.25421461,0.1650165,14613,75.8639568,19.9206186,3.31896257,22071,98.4345732,4.81174392,4.77462437,4.21197857,6.83692471,1,"ALU"
"68",628,"MONTGOMERY","IL",0.041,30728,749.463415,29956,559,49,66,98,97.4876334,1.81918771,0.15946368,0.21478782,0.31892736,20386,72.1917002,11.723732,2.5262435,29003,94.3862275,14.0295831,17.7958446,12.2912264,13.4700076,0,"AAR"
"69",629,"MORGAN","IL",0.033,36397,1102.93939,34561,1510,48,130,148,94.9556282,4.14869357,0.131879,0.35717229,0.40662692,23605,75.9034103,20.7244228,5.65981784,33746,92.7164327,11.1954009,12.3290901,10.1418555,12.6183885,0,"AAR"
"70",630,"MOULTRIE","IL",0.021,13930,663.333333,13884,8,22,13,3,99.6697775,0.05743001,0.15793252,0.09332376,0.02153625,9282,70.2865762,15.7185951,2.99504417,13356,95.879397,11.3432165,15.2669633,9.10269336,11.856585,0,"AAR"
"71",631,"OGLE","IL",0.045,45957,1021.26667,44895,66,87,136,773,97.6891442,0.14361251,0.1893074,0.2959288,1.68200709,29575,77.602705,18.7793745,3.47252747,45229,98.4159105,7.24535143,8.54618474,6.26131745,8.28708375,1,"ALU"
"72",632,"PEORIA","IL",0.038,182827,4811.23684,154298,24892,312,2225,1100,84.3956308,13.6150569,0.17065313,1.21699749,0.60166168,115963,77.9326164,25.838414,6.59003303,176647,96.6197553,14.5278437,21.8711768,12.4275854,10.3180428,1,"AAU"
"73",633,"PERRY","IL",0.026,21412,823.538462,20901,399,26,63,23,97.6134878,1.86344106,0.12142724,0.29422754,0.1074164,13921,67.839954,13.5622441,2.20530134,21090,98.4961704,15.8368895,20.8596713,13.3475898,15.8660508,0,"LAR"
"74",634,"PIATT","IL",0.025,15548,621.92,15508,8,16,11,5,99.7427322,0.05145356,0.10290713,0.07074865,0.03215848,10458,82.950851,22.011857,5.19219736,15315,98.501415,6.13124388,7.06766917,4.61225953,9.16779432,0,"HLR"
"75",635,"PIKE","IL",0.049,17577,358.714286,17499,8,24,32,14,99.5562383,0.04551402,0.13654207,0.1820561,0.07964954,11820,69.856176,12.1742809,2.30964467,17365,98.7938784,17.9326231,23.3325587,15.0834773,18.2773589,0,"AHR"
"76",636,"POPE","IL",0.022,4373,198.772727,4072,266,15,6,14,93.1168534,6.0827807,0.34301395,0.13720558,0.32014635,2821,65.1542006,14.2502659,2.48138958,4160,95.1292019,25.1682692,36.4729459,24.4148692,15.3611394,0,"LHR"
"77",637,"PULASKI","IL",0.011,7523,683.909091,5032,2466,8,7,10,66.8882095,32.7794763,0.10634056,0.09304799,0.13292569,4816,59.7799003,14.3687708,2.20099668,7441,98.9100093,30.184115,40.0374181,23.867741,31.1619718,0,"LHR"
"78",638,"PUTNAM","IL",0.01,5730,573,5616,9,7,7,91,98.0104712,0.15706806,0.12216405,0.12216405,1.58813264,3783,75.8128469,16.5212794,3.43642612,5722,99.8603839,7.54980776,10.4780616,6.22734761,7.15517241,0,"AAR"
"79",639,"RANDOLPH","IL",0.036,34583,960.638889,31532,2852,54,83,62,91.1777463,8.24682648,0.15614608,0.24000231,0.17927884,22847,64.2228739,12.7412789,2.95881297,30685,88.7285661,11.0021183,14.2925089,8.99560578,11.678487,0,"LAR"
"80",640,"RICHLAND","IL",0.022,16545,752.045455,16442,17,24,43,19,99.3774554,0.10275008,0.14505893,0.25989725,0.11483832,10917,73.4908858,21.5077402,3.62737016,16228,98.0840133,13.9758442,18.6600613,12.0681606,12.94943,0,"AAR"
"81",641,"ROCK ISLAND","IL",0.028,148723,5311.53571,133428,10488,354,1017,3436,89.7157803,7.05203634,0.2380264,0.6838216,2.31033532,96715,77.3747609,21.3007289,4.49775112,144662,97.2694203,13.1845267,19.375,11.7520934,9.05748897,1,"AAU"
"82",642,"ST CLAIR","IL",0.04,262852,6571.3,187866,71275,585,2007,1119,71.4721592,27.1160197,0.2225587,0.76354755,0.42571485,162550,72.5868963,21.184251,5.38911104,257438,97.9402858,17.43177,26.7291879,14.2654008,11.8686334,1,"AAU"
"83",643,"SALINE","IL",0.022,26551,1206.86364,25452,931,66,40,62,95.8607962,3.50645927,0.24857821,0.15065346,0.23351286,18020,63.2241953,15.4883463,3.63485017,25545,96.2110655,20.183989,26.6991079,18.6944068,16.8465816,0,"LHR"
"84",644,"SANGAMON","IL",0.051,178386,3497.76471,162013,14364,290,1377,342,90.8215891,8.05220141,0.16256881,0.77192156,0.19171908,117686,81.7709838,28.9881549,8.169196,175796,98.5480923,9.88816583,13.7008576,8.21267648,9.76442912,1,"HAU"
"85",645,"SCHUYLER","IL",0.026,7498,288.384615,7479,2,9,6,2,99.7465991,0.02667378,0.12003201,0.08002134,0.02667378,5090,69.410609,14.6365422,2.75049116,7365,98.2261937,16.496945,21.9134153,13.6461126,16.7800454,0,"AAR"
"86",646,"SCOTT","IL",0.015,5644,376.266667,5634,1,6,3,0,99.8228207,0.01771793,0.10630758,0.05315379,0,3732,73.6066452,13.0760986,2.25080386,5578,98.8306166,11.5274292,14.2564802,9.37067773,13.3606557,0,"AAR"
"87",647,"SHELBY","IL",0.044,22261,505.931818,22190,14,27,27,3,99.6810566,0.06289026,0.12128835,0.12128835,0.01347648,14745,72.7229569,15.9511699,3.78433367,22010,98.8724675,10.0045434,12.0917304,8.6534177,10.629843,0,"AAR"
"88",648,"STARK","IL",0.017,6534,384.352941,6496,8,8,21,1,99.4184267,0.12243649,0.12243649,0.32139578,0.01530456,4396,77.0245678,16.8789809,2.9799818,6404,98.0104071,12.4609619,19.0391459,10.4477612,9.38748336,0,"AAR"
"89",649,"STEPHENSON","IL",0.033,48052,1456.12121,44524,3081,58,304,85,92.6579539,6.41180388,0.12070257,0.63264796,0.1768917,31555,76.691491,19.8795753,3.8821106,47225,98.2789478,9.90788777,13.5032583,8.35417318,9.50578339,0,"AAR"
"90",650,"TAZEWELL","IL",0.039,123692,3171.58974,122639,186,221,432,214,99.1486919,0.15037351,0.1786696,0.3492546,0.17301038,80310,78.5941975,20.0448263,3.92105591,121970,98.6078324,9.13257358,12.8933916,7.91641669,7.28836792,1,"AAU"
"91",651,"UNION","IL",0.024,17619,734.125,17313,122,34,53,97,98.2632385,0.6924343,0.19297349,0.30081162,0.55054203,12092,64.2242805,17.8795898,4.00264638,16823,95.48215,18.1596624,24.9001996,14.6790039,19.010096,0,"LHR"
"92",652,"VERMILION","IL",0.052,88257,1697.25,78956,7841,165,507,788,89.4614591,8.88428113,0.18695401,0.57445868,0.89284703,58087,72.751218,17.0571729,4.06114965,85119,96.4444747,15.2386659,22.5401872,13.0433821,11.4093571,0,"AAR"
"93",653,"WABASH","IL",0.012,13111,1092.58333,12955,40,11,80,25,98.8101594,0.30508733,0.08389902,0.61017466,0.19067958,8520,74.870892,24.7300469,3.92018779,12849,98.001678,12.9426415,17.0275883,11.5272938,11.3899614,0,"AAR"
"94",654,"WARREN","IL",0.033,19181,581.242424,18630,356,20,70,105,97.1273656,1.85600334,0.10426985,0.36494448,0.54741671,12220,76.309329,20.4582651,4.57446809,18323,95.5268234,14.2334771,18.9009662,12.8320964,11.7299162,0,"AAR"
"95",655,"WASHINGTON","IL",0.033,14965,453.484848,14856,46,31,26,6,99.2716338,0.3073839,0.20715002,0.17373872,0.04009355,9906,65.7278417,15.9600242,2.44296386,14620,97.6946208,9.28180575,9.81610982,7.66360123,12.3771499,0,"LAR"
"96",656,"WAYNE","IL",0.042,17241,410.5,17141,9,31,44,16,99.4199872,0.05220115,0.17980396,0.25520561,0.09280204,11613,63.0586412,15.6807027,2.75553259,17054,98.9153761,14.3954497,19.6855936,11.6169183,14.7752126,0,"LAR"
"97",657,"WHITE","IL",0.029,16522,569.724138,16397,41,37,35,12,99.243433,0.24815398,0.22394383,0.21183876,0.07263043,11451,66.2212907,15.9287398,3.24862457,16152,97.7605617,19.0564636,27.0340525,16.4872384,16.7161961,0,"LHR"
"98",658,"WHITESIDE","IL",0.041,60186,1467.95122,57135,417,85,187,2362,94.9307148,0.69285216,0.14122886,0.31070349,3.92450071,39020,73.2880574,16.5530497,3.54177345,58927,97.9081514,10.9610196,14.7232262,9.50370254,9.8152526,0,"AAR"
"99",659,"WILL","IL",0.05,357313,7146.26,303420,38361,692,4774,10066,84.9171455,10.7359654,0.19366774,1.33608349,2.81713792,215823,80.43026,24.7568609,5.88723167,348384,97.5010705,6.03472031,7.46308545,4.79919768,8.5633985,1,"HLU"
"100",660,"WILLIAMSON","IL",0.025,57733,2309.32,56135,1147,112,252,87,97.2320856,1.98673203,0.1939965,0.43649213,0.15069371,38733,71.7811685,21.0595616,4.9802494,56318,97.5490621,15.8013424,22.3945816,13.6188171,13.8197853,0,"AAR"
"101",661,"Winnebago","IL",0.03,252913,8430.43333,222439,23256,651,2986,3581,87.9507973,9.19525687,0.25740077,1.18064315,1.41590191,163047,76.2964053,22.652364,5.1819414,248507,98.257899,10.11722,14.5814097,8.22470776,9.62654586,1,"AAU"
"102",662,"WOODFORD","IL",0.032,32653,1020.40625,32388,64,54,102,45,99.188436,0.19600037,0.16537531,0.31237559,0.13781276,20469,80.0136792,22.8052176,4.97337437,31693,97.0599945,7.18770706,10.0139231,5.62835021,7.15055695,1,"HLU"
"103",663,"ADAMS","IN",0.021,31095,1480.71429,30530,36,42,60,427,98.1829876,0.11577424,0.13506995,0.19295707,1.37321113,18119,74.3970418,16.1156797,4.86229924,30490,98.0543496,11.6366022,17.1945239,9.10188775,8.71402697,1,"AAU"
"104",664,"ALLEN","IN",0.041,300836,7337.46341,264086,30314,892,2644,2900,87.7840418,10.0765866,0.29650707,0.87888418,0.96398037,187856,81.1552466,27.3597862,6.85950941,296184,98.4536425,7.90353294,10.533689,6.7890146,7.30149415,1,"HAU"
"105",665,"BARTHOLOMEW","IN",0.022,63657,2893.5,61774,1005,97,610,171,97.0419593,1.57877374,0.15237916,0.95826068,0.26862717,41218,76.9032947,22.8468145,6.84409724,62784,98.6285876,8.54517074,10.736855,6.99242014,10.8119434,0,"AAR"
"106",666,"BENTON","IN",0.024,9441,393.375,9389,6,16,1,29,99.4492109,0.06355259,0.16947357,0.0105921,0.30717085,6053,77.0857426,13.4643978,4.01453825,9300,98.5065141,8.04301075,8.34921845,6.84232894,10.5022831,0,"AAR"
"107",667,"BLACKFORD","IN",0.01,14067,1406.7,13978,7,44,16,22,99.3673136,0.04976185,0.3127888,0.11374138,0.1563944,9259,72.9884437,12.9819635,4.42812399,13903,98.8341508,9.85398835,12.3237451,8.33224671,10.9375,0,"AAR"
"108",668,"BOONE","IN",0.024,38147,1589.45833,37814,83,90,94,66,99.1270611,0.21757936,0.23592943,0.24641518,0.17301492,24915,82.4683925,27.8306241,8.81396749,37402,98.0470286,6.29645474,8.02175391,5.23959855,7.08942537,1,"HLU"
"109",669,"BROWN","IN",0.019,14080,741.052632,13968,13,47,18,34,99.2045455,0.09232955,0.33380682,0.12784091,0.24147727,9510,76.4353312,19.8002103,6.16193481,13948,99.0625,6.86836822,8.09384164,6.16327041,7.44387554,0,"ALR"
"110",670,"CARROLL","IN",0.022,18809,854.954545,18720,19,22,3,45,99.5268223,0.10101547,0.11696528,0.01594981,0.23924717,12241,76.1702475,15.4562536,3.79870926,18498,98.3465362,7.49270191,9.38326888,5.63980573,10.1830664,0,"AAR"
"111",671,"CASS","IN",0.024,38413,1600.54167,37765,330,138,114,66,98.3130711,0.85908416,0.35925338,0.29677453,0.17181683,25123,75.8906182,13.2149823,4.02420093,37306,97.1181631,10.3468611,13.3524412,9.47268075,8.55725506,0,"AAR"
"112",672,"CLARK","IN",0.022,87777,3989.86364,82289,4703,192,356,237,93.7477927,5.35789558,0.21873612,0.40557321,0.27000239,56970,72.7575917,16.8123574,4.05476567,86270,98.2831493,10.0649125,13.752694,8.18791412,10.9526235,1,"AAU"
"113",673,"CLAY","IN",0.021,24705,1176.42857,24522,113,41,15,14,99.2592593,0.45739729,0.16595831,0.06071645,0.05666869,16197,75.884423,14.1137248,4.29709205,24343,98.5347096,11.8432404,15.1636813,9.70500549,12.9662175,1,"AAU"
"114",674,"CLINTON","IN",0.023,30974,1346.69565,30657,36,51,61,169,98.976561,0.11622651,0.16465423,0.19693937,0.54561891,19875,76.1610063,16.045283,4.5081761,30143,97.3171047,9.4051687,11.7393903,7.80756609,10.5207037,1,"AAU"
"115",675,"CRAWFORD","IN",0.018,9914,550.777778,9868,9,26,11,0,99.5360097,0.09078071,0.2622554,0.11095421,0,6297,59.5680483,8.54375099,2.93790694,9780,98.648376,18.4662577,22.923588,14.3980528,24.0462428,0,"LHR"
"116",676,"DAVIESS","IN",0.026,27533,1058.96154,27372,99,26,20,16,99.4152472,0.35956852,0.09443214,0.0726401,0.05811208,17267,66.2187989,13.2391267,3.35900851,26896,97.6864127,15.5413444,20.7750953,12.3878965,15.9292035,0,"LAR"
"117",677,"DEARBORN","IN",0.019,38835,2043.94737,38440,252,53,75,15,98.9828763,0.64889919,0.13647483,0.19312476,0.03862495,24335,73.4785289,15.5742757,4.24491473,38254,98.5039269,8.48015894,10.7832521,7.1425195,8.94133289,1,"AAU"
"118",678,"DECATUR","IN",0.022,23645,1074.77273,23444,39,19,129,14,99.149926,0.16493973,0.08035525,0.54556989,0.05920914,14625,72.3350427,13.9213675,4.56068376,23245,98.3083104,9.06431491,11.116045,7.66800916,10.0627287,0,"AAR"
"119",679,"DE KALB","IN",0.022,35324,1605.63636,35009,37,91,88,99,99.108255,0.10474465,0.25761522,0.24912241,0.28026271,21801,77.5469015,15.581854,4.45392413,34833,98.6100102,6.45652112,8.76933688,4.93601463,7.47427333,1,"ALU"
"120",680,"DELAWARE","IN",0.023,119659,5202.56522,111232,7167,274,641,345,92.9574875,5.98952022,0.22898403,0.53568892,0.28831931,70609,74.4664278,21.1970145,8.38844906,111645,93.302635,16.7477272,17.661107,17.8898595,11.7679641,1,"AAU"
"121",681,"DUBOIS","IN",0.026,36616,1408.30769,36466,33,30,55,32,99.590343,0.09012454,0.0819314,0.15020756,0.08739349,22921,72.2307055,17.1676628,4.47188168,35883,97.9981429,6.13661065,5.50713154,4.41161892,13.4687333,0,"ALR"
"122",682,"ELKHART","IN",0.027,156198,5785.11111,146505,7106,453,997,1137,93.7944148,4.54935403,0.29001652,0.63829242,0.72792225,96003,72.8060581,18.3723425,5.36649896,152953,97.9225086,7.04203252,9.86358867,5.80470664,6.30558874,1,"ALU"
"123",683,"FAYETTE","IN",0.012,26015,2167.91667,25462,435,37,69,12,97.8743033,1.67211224,0.14222564,0.2652316,0.04612723,16744,63.9094601,11.4249881,3.78643096,25571,98.2932923,10.7856556,14.6890163,8.84701144,10.8691186,0,"LAR"
"124",684,"FLOYD","IN",0.009,64404,7156,61415,2642,92,175,80,95.3589839,4.10222968,0.14284827,0.27172225,0.12421589,41499,73.2234512,20.5498928,6.40015422,63308,98.2982423,11.0080874,16.0648533,8.19145697,12.6123514,1,"AAU"
"125",685,"FOUNTAIN","IN",0.025,17808,712.32,17726,4,25,27,26,99.5395328,0.02246181,0.14038634,0.15161725,0.1460018,11700,72.965812,11.1452991,3.29059829,17573,98.6803684,9.83895749,13.1136951,7.04975016,12.6510878,0,"AAR"
"126",686,"FRANKLIN","IN",0.023,19580,851.304348,19496,10,34,27,13,99.5709908,0.05107252,0.17364658,0.13789581,0.06639428,12029,65.2672708,12.9935988,3.54975476,19375,98.9530133,10.6270968,12.6787252,8.36868492,14.2276423,0,"LAR"
"127",687,"FULTON","IN",0.021,18840,897.142857,18555,151,47,36,51,98.4872611,0.8014862,0.24946921,0.1910828,0.27070064,12405,75.3164047,13.929867,4.15961306,18611,98.7845011,10.3379722,12.8706879,9.1876013,10.0211528,0,"AAR"
"128",688,"GIBSON","IN",0.029,31913,1100.44828,31146,596,38,104,29,97.5965907,1.86757748,0.11907373,0.325886,0.09087206,20949,72.8053845,15.50432,3.50852069,31364,98.2796979,9.63843897,11.1341717,7.94889993,12.2426177,0,"AAR"
"129",689,"GRANT","IN",0.024,74169,3090.375,67817,5047,298,373,634,91.4357751,6.80472974,0.40178511,0.50290553,0.85480457,47541,71.7570097,15.5381671,5.48579121,70618,95.2122855,13.0816506,18.2321973,11.6630942,10.2415235,0,"AAR"
"130",690,"GREENE","IN",0.033,30410,921.515152,30248,10,47,65,40,99.4672805,0.03288392,0.15455442,0.21374548,0.13153568,20124,71.5861658,14.9522958,4.35301133,29957,98.5103584,13.1788897,18.2160478,11.4086437,11.5055672,0,"AAR"
"131",691,"HAMILTON","IN",0.024,108936,4539,106764,676,163,1190,143,98.0061688,0.62054784,0.14962914,1.09238452,0.13126974,69127,88.6961679,42.1311499,12.0893428,107945,99.0902915,3.59164389,4.25498008,2.92675381,5.45073375,1,"HLU"
"132",692,"HANCOCK","IN",0.017,45527,2678.05882,45173,44,59,176,75,99.2224394,0.09664595,0.12959343,0.38658379,0.16473741,29024,80.1095645,20.4899394,6.60832415,44988,98.8160872,4.46341247,5.62774014,3.50094256,6.07751938,1,"HLU"
"133",693,"HARRISON","IN",0.028,29890,1067.5,29641,124,58,36,31,99.1669455,0.41485447,0.19404483,0.12044162,0.10371362,18829,71.1243295,13.1074407,3.83982155,29559,98.8926062,9.83118509,12.58419,7.53536948,12.9673655,1,"AAU"
"134",694,"HENDRICKS","IN",0.024,75717,3154.875,74519,685,157,275,81,98.4177926,0.90468455,0.20735106,0.36319453,0.1069773,48047,84.0656024,24.2200345,7.15965617,73207,96.6850245,3.66904804,4.22803082,2.84384454,6.13503237,1,"HLU"
"135",695,"HENRY","IN",0.024,48139,2005.79167,47446,474,78,78,63,98.5604188,0.98464862,0.16203079,0.16203079,0.13087102,31989,71.374535,13.2576823,4.41714339,47341,98.3423004,12.3508164,17.2683924,10.6034222,11.0881168,0,"AAR"
"136",696,"HOWARD","IN",0.016,80827,5051.6875,75420,4398,226,457,326,93.3104037,5.44125107,0.27960954,0.56540512,0.40333057,52042,78.453941,20.2298144,5.75881019,79738,98.6526779,11.5152123,16.9615277,9.59316926,9.26146753,1,"AAU"
"137",697,"HUNTINGTON","IN",0.023,35427,1540.30435,35012,52,154,133,76,98.8285771,0.1467807,0.4346967,0.37541988,0.21452564,22188,78.5559762,16.9731386,4.84946818,34397,97.092613,6.56452598,7.3208402,5.38424668,9.07244414,1,"ALU"
"138",698,"JACKSON","IN",0.03,37730,1257.66667,37289,138,66,189,48,98.8311688,0.36575669,0.17492711,0.50092764,0.12721972,24151,69.3263219,13.1423129,3.82592853,37228,98.6694938,10.4921027,13.5361596,8.64372075,11.5976685,0,"AAR"
"139",699,"JASPER","IN",0.034,24960,734.117647,24659,111,60,40,90,98.7940705,0.44471154,0.24038462,0.16025641,0.36057692,14984,75.5205553,14.8558462,5.23224773,23850,95.5528846,7.9706499,10.070028,6.37300843,9.37019969,0,"AAR"
"140",700,"JAY","IN",0.022,21512,977.818182,21313,30,32,67,70,99.0749349,0.13945705,0.14875418,0.31145407,0.32539978,13843,68.8795781,12.771798,4.43545474,21179,98.4520268,9.74078096,11.8595994,8.39788732,10.5313547,0,"AAR"
"141",701,"JEFFERSON","IN",0.021,29797,1418.90476,29181,363,58,122,73,97.9326778,1.21824345,0.19465047,0.40943719,0.24499111,18876,70.285018,17.5460903,5.50434414,27849,93.4624291,11.63417,15.6190216,9.61010784,12.1604938,0,"AAR"
"142",702,"JENNINGS","IN",0.021,23661,1126.71429,23347,209,31,51,23,98.6729217,0.88331009,0.13101729,0.21554457,0.09720637,14942,64.0811136,10.1458975,3.38642752,22854,96.5893242,12.8030104,16.947001,10.3086514,14.7733961,0,"LAR"
"143",703,"JOHNSON","IN",0.018,88109,4894.94444,86455,845,139,534,136,98.1227797,0.95903937,0.15775914,0.60606748,0.15435427,55137,80.4069862,22.308069,6.24988665,85547,97.092238,6.90263832,8.75542692,5.76260508,8.17939609,1,"HLU"
"144",704,"KNOX","IN",0.031,39884,1286.58065,39107,486,69,178,44,98.0518504,1.21853375,0.1730017,0.44629425,0.11031993,24740,74.5028294,19.9757478,5.29506871,37005,92.7815665,15.6978787,19.8819543,14.454248,13.9807342,0,"AAR"
"145",705,"KOSCIUSKO","IN",0.032,65294,2040.4375,64058,309,118,322,487,98.1070236,0.4732441,0.18072105,0.49315404,0.7458572,40321,77.475757,19.2629151,5.91751197,64110,98.1866634,6.64170956,8.2147651,5.32189084,8.34152093,0,"ALR"
"146",706,"LAGRANGE","IN",0.022,29477,1339.86364,29156,44,59,92,126,98.9110154,0.14926892,0.20015605,0.31210775,0.42745191,16100,56.6521739,11.1118012,3.46583851,28954,98.2257353,11.5079091,15.9615575,7.96751242,13.4746404,0,"LAR"
"147",707,"LAKE","IN",0.03,475594,15853.1333,334203,116688,865,2772,21066,70.270651,24.5352128,0.18187782,0.58285008,4.42940828,298552,73.478322,17.6686138,4.93917308,469774,98.7762672,13.8051489,21.5110371,11.0934323,9.95578032,1,"AAU"
"148",708,"LA PORTE","IN",0.036,107066,2974.05556,96286,9580,259,431,510,89.9314442,8.94775185,0.24190686,0.40255543,0.4763417,70102,73.9080197,17.1792531,4.87860546,100574,93.9364504,10.0542884,14.6622858,8.53452003,7.91819325,0,"AAR"
"149",709,"LAWRENCE","IN",0.027,42836,1586.51852,42536,109,88,77,26,99.2996545,0.25445887,0.20543468,0.17975535,0.06069661,28005,69.7089805,13.1476522,3.84931262,42152,98.4032123,9.72195863,12.5923191,7.86502438,11.2748803,0,"AAR"
"150",710,"MADISON","IN",0.026,130669,5025.73077,119734,9870,299,415,351,91.631527,7.55343655,0.22882244,0.31759637,0.26861765,84886,73.4502745,16.401998,5.07268572,125156,95.7809427,12.7249193,19.8256124,10.5838631,9.4521245,1,"AAU"
"151",711,"MARION","IN",0.023,797159,34659.087,615039,169654,1698,7579,3189,77.1538677,21.2823289,0.21300644,0.95075136,0.40004566,511309,76.8046328,26.7446886,7.68654571,780649,97.928895,12.058044,18.1554118,9.76327509,10.7259641,1,"AAU"
"152",712,"MARSHALL","IN",0.026,42182,1622.38462,41508,76,72,151,375,98.4021621,0.18017164,0.17068892,0.35797259,0.88900479,26511,73.9692958,17.7775263,5.35249519,41497,98.3760846,7.54512374,9.9233426,5.89902569,8.81351121,0,"AAR"
"153",713,"MARTIN","IN",0.021,10369,493.761905,10321,12,14,14,8,99.5370817,0.11572958,0.13501784,0.13501784,0.07715305,6625,64.4075472,13.5396226,3.38113208,10211,98.4762272,13.8478112,17.9383712,11.3361763,15.455035,0,"LAR"
"154",714,"MIAMI","IN",0.024,36897,1537.375,34784,1115,571,224,203,94.2732471,3.0219259,1.54755129,0.60709543,0.55018023,22509,76.4272069,14.4075703,3.88733396,35971,97.4903109,10.9393678,15.2701644,9.26941094,9.05255757,0,"AAR"
"155",715,"MONROE","IN",0.024,108978,4540.75,102752,2835,216,2713,462,94.2869203,2.60144249,0.19820514,2.48949329,0.42393878,57368,82.1032631,37.7422954,17.2012272,93693,85.9742333,19.4390189,14.3463367,22.9989983,9.5751634,1,"HHU"
"156",716,"MONTGOMERY","IN",0.03,34436,1147.86667,33971,201,68,138,58,98.649669,0.58369149,0.19746777,0.40074341,0.16842839,22174,80.0487057,16.9252277,5.80860467,33157,96.2858636,9.39771391,11.8232951,7.65231519,11.2698928,0,"HAR"
"157",717,"MORGAN","IN",0.024,55920,2330,55635,9,137,91,48,99.4903433,0.01609442,0.24499285,0.16273247,0.08583691,35089,73.5786144,14.6798142,4.43443814,55262,98.823319,6.67728276,8.36697487,5.18163722,9.53302374,1,"ALU"
"158",718,"NEWTON","IN",0.024,13551,564.625,13436,9,39,24,43,99.1513541,0.06641576,0.28780164,0.1771087,0.31731975,8567,72.4407611,12.1746236,3.18664643,13310,98.2215335,8.8580015,12.6107348,7.11798301,8.02442215,0,"AAR"
"159",719,"NOBLE","IN",0.025,37877,1515.08,37456,58,83,98,182,98.8885075,0.15312723,0.21913034,0.25873221,0.48050268,23151,72.0832793,12.6387629,3.5592415,37198,98.2073554,8.04881983,11.6240876,6.00627082,8.47923103,0,"AAR"
"160",720,"OHIO","IN",0.005,5315,1063,5255,41,8,9,2,98.8711195,0.77140169,0.1505174,0.16933208,0.03762935,3457,67.7176743,10.7029216,1.79346254,5271,99.1721543,9.88427243,8.62688713,8.58398107,15.9609121,1,"LAU"
"161",721,"ORANGE","IN",0.023,18409,800.391304,18213,127,40,21,8,98.9353034,0.68987995,0.21728502,0.11407464,0.043457,11902,64.9302638,9.78827088,2.83985885,18103,98.3377696,15.3455228,19.423275,12.7880658,16.7800454,0,"LAR"
"162",722,"OWEN","IN",0.023,17281,751.347826,17167,44,46,18,6,99.340316,0.25461489,0.2661883,0.10416064,0.03472021,11151,66.2720832,11.1649179,3.25531342,17046,98.640125,13.5515663,17.9775281,11.1626436,14.4039735,0,"LAR"
"163",723,"PARKE","IN",0.027,15410,570.740741,15222,118,42,16,12,98.780013,0.76573653,0.27255029,0.10382868,0.07787151,10217,76.7348537,14.6129001,4.31633552,14849,96.3595068,12.1961075,13.0620985,10.3266992,15.9411012,0,"AAR"
"164",724,"PERRY","IN",0.022,19107,868.5,18819,210,32,32,14,98.492699,1.09907364,0.16747789,0.16747789,0.07327158,12271,65.3573466,11.0422948,2.90929835,18163,95.0594023,11.6225293,14.1656417,9.28652322,14.5016797,0,"LAR"
"165",725,"PIKE","IN",0.02,12509,625.45,12469,3,16,19,2,99.6802302,0.02398273,0.12790791,0.15189064,0.01598849,8387,65.482294,13.842852,3.89889114,12357,98.7848749,13.2637372,19.8807157,10.5921723,12.6115166,0,"LAR"
"166",726,"PORTER","IN",0.026,128932,4958.92308,126329,454,243,944,962,97.9811063,0.3521236,0.18847144,0.7321689,0.74612974,79625,82.4075353,24.455887,7.83924647,125118,97.0418515,6.11662591,7.48438387,5.48997025,5.97111701,1,"HLU"
"167",727,"POSEY","IN",0.025,25968,1038.72,25588,283,38,34,25,98.5366605,1.08980283,0.14633395,0.13093038,0.09627234,16513,76.2974626,17.2106825,4.59032278,25679,98.8870918,7.62490751,8.9456869,5.92997509,11.3455809,1,"AAU"
"168",728,"PULASKI","IN",0.026,12643,486.269231,12509,65,21,20,28,98.940125,0.51411848,0.16609982,0.1581903,0.22146642,7974,71.8961625,14.1710559,3.51141209,12467,98.6079253,10.8285875,13.6955919,8.91951838,11.4992151,0,"AAR"
"169",729,"PUTNAM","IN",0.029,30315,1045.34483,29196,826,77,154,62,96.308758,2.72472373,0.25399967,0.50799934,0.20451921,18437,76.0535879,15.734664,5.5811683,26072,86.0036286,8.27324333,10.7356154,6.72192811,9.37950938,0,"AAR"
"170",730,"RANDOLPH","IN",0.026,27148,1044.15385,26947,56,49,37,59,99.259614,0.20627671,0.18049212,0.13628997,0.21732724,17694,71.8548661,12.6200972,4.53826156,26789,98.677619,11.3516742,15.7303371,9.68322036,10.1874188,0,"AAR"
"171",731,"RIPLEY","IN",0.025,24616,984.64,24501,16,44,38,17,99.5328242,0.06499838,0.17874553,0.15437114,0.06906077,15331,68.8148196,13.5281456,3.99191181,24262,98.561911,10.547358,12.9624319,8.24702723,13.6374324,0,"AAR"
"172",732,"RUSH","IN",0.023,18129,788.217391,17901,142,15,60,11,98.7423465,0.78327542,0.08274036,0.33096144,0.06067626,11404,73.5882147,12.8463697,3.89337075,17589,97.021347,11.1774404,13.4143785,9.50972105,12.7012522,0,"AAR"
"173",733,"ST JOSEPH","IN",0.028,247052,8823.28571,216984,24190,846,2507,2525,87.8292829,9.79146091,0.34243803,1.01476612,1.02205204,154443,76.0630135,24.611669,8.10007576,234575,94.9496462,9.65490781,13.8132517,8.19188249,8.14209511,1,"AAU"
"174",734,"SCOTT","IN",0.011,20991,1908.27273,20850,16,25,50,50,99.3282836,0.07622314,0.11909866,0.23819732,0.23819732,13060,59.992343,10.5895865,3.27718224,20636,98.308799,18.9813917,26.8822813,15.8872136,16.0839161,1,"LHU"
"175",735,"SHELBY","IN",0.024,40307,1679.45833,39743,330,67,142,25,98.6007393,0.81871635,0.16622423,0.35229613,0.06202397,25585,74.1254641,14.7000195,3.70529607,39745,98.6057012,7.23109825,9.41501612,5.43236263,9.82587065,1,"ALU"
"176",736,"SPENCER","IN",0.024,19490,812.083333,19295,111,37,33,14,98.9994869,0.56952283,0.18984094,0.1693176,0.07183171,12509,71.9242146,14.1338236,4.44479974,19102,98.0092355,9.85760653,10.7415902,8.0898243,14.2547258,0,"AAR"
"177",737,"STARKE","IN",0.019,22747,1197.21053,22446,73,86,48,94,98.6767486,0.32092144,0.37807183,0.21101684,0.41324131,14260,59.9158485,10.0140252,3.00841515,22293,98.0041324,13.425739,18.5185185,11.8391095,10.2341299,0,"LAR"
"178",738,"STEUBEN","IN",0.021,27446,1306.95238,27146,51,64,132,53,98.9069445,0.18581943,0.23318516,0.4809444,0.19310646,17256,78.9986092,18.6080204,5.44738062,26541,96.702616,5.63279454,5.03225806,5.17698213,7.88116361,0,"ALR"
"179",739,"SULLIVAN","IN",0.027,18993,703.444444,18905,15,39,15,19,99.5366714,0.07897647,0.20533881,0.07897647,0.10003686,12602,74.0676083,15.4816696,4.12632915,18678,98.3414942,12.5120463,15.4133002,9.67541222,15.6844106,0,"AAR"
"180",740,"SWITZERLAND","IN",0.013,7738,595.230769,7695,15,16,10,2,99.4443009,0.19384854,0.20677178,0.12923236,0.02584647,5015,65.8025922,9.47158524,3.17048853,7589,98.0744378,15.2193965,20.3518783,11.365332,18.4300341,0,"LAR"
"181",741,"TIPPECANOE","IN",0.03,130598,4353.26667,122013,2660,320,4821,784,93.4263924,2.03678464,0.24502672,3.69148073,0.60031547,69148,85.1781686,36.2454446,15.2571296,114062,87.3382441,14.3816521,10.5675147,17.1884131,7.86494538,1,"HAU"
"182",742,"TIPTON","IN",0.016,16119,1007.4375,15990,10,20,51,48,99.1997022,0.06203859,0.12407718,0.3163968,0.29778522,10511,77.0335839,15.4219389,4.87108743,15933,98.8460823,6.43946526,8.11649558,5.04670768,8.19285233,1,"ALU"
"183",743,"UNION","IN",0.01,6976,697.6,6915,20,15,21,5,99.1255734,0.28669725,0.21502294,0.30103211,0.07167431,4397,71.2531271,12.6449852,3.91175802,6836,97.9931193,9.47922762,11.5527292,7.66578249,11.9592875,0,"AAR"
"184",744,"VANDERBURGH","IN",0.013,165058,12696.7692,151216,12410,284,917,231,91.6138569,7.51856923,0.17206073,0.55556229,0.13995081,109217,75.2044096,21.7585174,6.47426683,160427,97.1943196,12.4767028,17.096145,10.8453217,11.4168894,1,"AAU"
"185",745,"VERMILLION","IN",0.016,16773,1048.3125,16690,15,32,28,8,99.5051571,0.08942944,0.19078281,0.16693495,0.0476957,11163,72.0594822,12.2726866,3.52951715,16494,98.3366124,11.6709106,14.7683398,9.87012987,12.5608011,1,"AAU"
"186",746,"VIGO","IN",0.024,106107,4421.125,98411,5916,297,1161,322,92.7469441,5.57550397,0.27990613,1.09417852,0.30346725,66140,75.9842758,22.7230118,8.7526459,97228,91.6320318,14.6943267,18.5088374,13.7729456,12.5077272,1,"AAU"
"187",747,"WABASH","IN",0.023,35069,1524.73913,34462,138,259,123,87,98.2691266,0.39350994,0.73854401,0.35073712,0.24808235,22008,74.3865867,16.0805162,4.98455107,33017,94.1486783,9.1922343,10.4414545,8.93758669,8.08468078,0,"AAR"
"188",748,"WARREN","IN",0.021,8176,389.333333,8140,1,17,15,3,99.5596869,0.01223092,0.20792564,0.1834638,0.03669276,5403,71.6083657,13.399963,3.03535073,8075,98.7646771,9.16408669,11.1265647,7.32876712,11.6384915,0,"AAR"
"189",749,"WARRICK","IN",0.023,44920,1953.04348,44274,371,83,157,35,98.5618878,0.82591273,0.18477293,0.34951024,0.0779163,28368,80.1254935,23.8155668,5.85166385,44151,98.2880677,6.58195737,8.66166853,5.20934196,8.198136,1,"HLU"
"190",750,"WASHINGTON","IN",0.03,23717,790.566667,23625,23,28,18,23,99.6120926,0.09697685,0.11805878,0.07589493,0.09697685,14989,66.1685236,10.8145974,2.88211355,23390,98.6212421,14.296708,18.0585852,11.5780578,17.1385238,0,"LAR"
"191",751,"WAYNE","IN",0.024,71951,2997.95833,67532,3795,153,296,175,93.8583202,5.27442287,0.21264472,0.41139109,0.24322108,46603,71.2443405,15.5891252,5.13486256,69370,96.4128365,14.8926049,20.8136394,13.2868788,11.6376769,0,"AAR"
"192",752,"WELLS","IN",0.021,25948,1235.61905,25758,10,40,41,99,99.2677663,0.03853862,0.15415446,0.15800832,0.3815323,16396,79.0009758,18.5655038,5.58672847,25464,98.134731,5.57257304,6.61065969,4.43388757,7.49419954,1,"ALU"
"193",753,"WHITE","IN",0.03,23265,775.5,23127,2,50,41,45,99.4068343,0.0085966,0.21491511,0.17623039,0.1934236,15292,77.8773215,16.5838347,5.16609992,22974,98.7491941,7.70436145,8.13656669,6.16872291,11.0403397,0,"AAR"
"194",754,"WHITLEY","IN",0.02,27651,1382.55,27473,29,73,37,39,99.356262,0.10487867,0.26400492,0.13381071,0.14104372,17369,78.8761587,15.809776,4.22016236,27251,98.5533977,5.22549631,6.12323492,3.90956194,7.95652174,1,"ALU"
"195",1197,"ALCONA","MI",0.041,10145,247.439024,10026,27,56,26,10,98.8270084,0.26614096,0.55199606,0.25628388,0.09857072,7368,68.553203,14.1150923,3.24375679,10040,98.9650074,17.250996,27.2045028,16.6173986,11.5069357,0,"AAR"
"196",1198,"ALGER","MI",0.051,8972,175.921569,8422,213,304,24,9,93.8698172,2.37405261,3.38831922,0.26749889,0.10031208,6009,73.0237976,16.2755866,3.82759195,8452,94.2041908,14.4817795,19.2080109,12.3091161,13.9679255,0,"AAR"
"197",1199,"ALLEGAN","MI",0.049,90509,1847.12245,86760,1448,543,411,1347,95.8578705,1.5998409,0.59994034,0.45409849,1.48824979,55740,74.4384643,18.0660208,4.03659849,88882,98.2023887,9.49798609,11.8957846,7.93385737,10.4949449,1,"AAU"
"198",1200,"ALPENA","MI",0.034,30605,900.147059,30372,35,93,85,20,99.2386865,0.1143604,0.30387192,0.2777324,0.0653488,20165,73.597818,18.9337962,4.23010166,30329,99.0981866,13.4755515,17.0423241,12.1669518,12.2930256,0,"AAR"
"199",1201,"ANTRIM","MI",0.031,18185,586.612903,17895,23,211,24,32,98.4052791,0.12647787,1.16029695,0.1319769,0.17596921,12185,76.3807961,19.039803,5.01436192,17942,98.6637338,13.2148033,18.7581416,12.0375423,9.68128445,0,"AAR"
"200",1202,"ARENAC","MI",0.021,14931,711,14695,10,139,38,49,98.4193959,0.06697475,0.93094903,0.25450405,0.32817628,9782,65.3956246,11.8278471,2.58638315,14664,98.2117742,20.6492089,28.9513678,18.2323366,16.0544649,0,"LHR"
"201",1203,"BARAGA","MI",0.054,7954,147.296296,6971,49,918,10,6,87.6414383,0.61604224,11.5413628,0.12572291,0.07543374,5166,70.4800619,14.5760743,3.36817654,7700,96.8066382,16.8181818,22.3873442,14.2559291,15.8131683,0,"AAR"
"202",1204,"BARRY","MI",0.034,50057,1472.26471,49429,104,188,144,192,98.7454302,0.20776315,0.37557185,0.28767205,0.38356274,31892,78.2641415,17.2519754,3.86303775,49091,98.0702,9.07498319,11.6531558,8.00437796,8.27717737,0,"AAR"
"203",1205,"BAY","MI",0.026,111723,4297.03846,107747,1242,726,428,1580,96.4411983,1.11167799,0.64982143,0.38309032,1.41421193,71684,73.9872217,18.2258245,3.66190503,110453,98.86326,12.5302165,17.5860997,10.9708597,9.98930263,1,"AAU"
"204",1206,"BENZIE","MI",0.02,12200,610,11863,30,237,35,35,97.2377049,0.24590164,1.94262295,0.28688525,0.28688525,8333,76.6470659,21.4208568,4.68018721,12077,98.9918033,12.8591538,18.6411745,11.3047574,10.3274559,0,"AAR"
"205",1207,"BERRIEN","MI",0.033,161378,4890.24242,133259,24872,685,1487,1075,82.5756919,15.4122619,0.42446926,0.92143911,0.66613789,102485,74.7270332,23.742011,6.30140996,158312,98.1001128,14.7057709,22.975087,11.852965,10.9998965,1,"AAU"
"206",1208,"BRANCH","MI",0.029,41502,1431.10345,40278,705,221,156,142,97.0507445,1.69871332,0.53250446,0.3758855,0.34215219,26446,73.7880965,16.066702,3.51281857,39759,95.8002024,14.0848613,20.8565121,11.6031599,10.717385,0,"AAR"
"207",1209,"CALHOUN","MI",0.042,135982,3237.66667,118737,14383,696,1068,1098,87.3181745,10.5771352,0.51183245,0.78539807,0.80745981,86623,76.8387149,21.4885192,4.76778685,131766,96.8995897,14.2920025,21.3346103,12.370767,9.57053103,1,"AAU"
"208",1210,"CASS","MI",0.03,49477,1649.23333,44827,3725,469,191,265,90.6016937,7.52875073,0.94791519,0.38603796,0.5356024,31841,72.2778807,15.8443516,3.42639992,48946,98.9267741,11.8824827,17.7992278,9.57841732,10.2969621,0,"AAR"
"209",1211,"CHARLEVOIX","MI",0.022,21468,975.818182,20993,17,378,41,39,97.7874045,0.07918763,1.7607602,0.19098193,0.18166574,13963,79.6963403,22.6527251,5.06338179,21250,98.9845351,10.3952941,13.8431922,8.11281337,12.0059289,0,"AAR"
"210",1212,"CHEBOYGAN","MI",0.048,21398,445.791667,20837,15,478,57,11,97.3782597,0.07010001,2.23385363,0.26638004,0.05140667,14207,73.5412121,14.6477089,3.01259942,21097,98.5933265,15.5614542,21.159054,13.3028806,14.1585593,0,"AAR"
"211",1213,"CHIPPEWA","MI",0.078,34604,443.641026,28353,2184,3820,152,95,81.9356144,6.31140909,11.0391862,0.43925558,0.27453474,21848,73.5948371,16.9397657,3.80812889,29486,85.2098023,17.0759004,20.9559748,16.1035046,14.3391521,0,"AAR"
"212",1214,"CLARE","MI",0.034,24952,733.882353,24665,40,160,53,34,98.8497916,0.16030779,0.64123116,0.21240782,0.13626162,16401,66.9105542,12.0419487,2.76812389,24522,98.2766912,23.7134002,35.978586,22.1719457,13.5634167,0,"LHR"
"213",1215,"CLINTON","MI",0.034,57883,1702.44118,56639,218,276,199,551,97.850837,0.37662181,0.47682394,0.34379697,0.95192025,35745,83.6844314,22.8647363,4.94614631,57463,99.2743984,6.01778536,7.36650485,4.98168716,7.71621622,1,"HLU"
"214",1216,"CRAWFORD","MI",0.034,12260,360.588235,11802,264,145,42,7,96.2642741,2.15334421,1.18270799,0.34257749,0.05709625,8057,73.2034256,18.7787018,4.98945017,11569,94.3637847,14.5907166,20.8293153,12.8239203,10.9926169,0,"AAR"
"215",1217,"DELTA","MI",0.069,37780,547.536232,36819,16,809,99,37,97.4563261,0.04235045,2.14134463,0.26204341,0.09793542,24476,76.8875633,18.912404,3.52590293,37242,98.5759661,14.6125342,18.591133,13.127551,13.1073145,0,"AAR"
"216",1218,"DICKINSON","MI",0.046,26831,583.282609,26532,23,135,106,35,98.8856174,0.08572174,0.50314934,0.39506541,0.13044613,17972,78.5221456,20.4206544,3.80035611,26455,98.5986359,9.8998299,12.2511263,7.1444387,13.4107884,0,"AAR"
"217",1219,"EATON","MI",0.034,92879,2731.73529,87549,3310,438,559,1023,94.2613508,3.56377653,0.4715813,0.60185833,1.10143305,58205,85.5304527,26.9787819,6.14723821,91580,98.6014061,6.81371478,8.91257154,5.70213714,7.35389086,1,"HLU"
"218",1220,"EMMET","MI",0.027,25040,927.407407,24122,133,683,69,33,96.3338658,0.53115016,2.72763578,0.27555911,0.13178914,16448,81.535749,26.8725681,6.49319066,24581,98.1669329,8.51470648,10.2641509,7.11646468,10.1717902,0,"HAR"
"219",1221,"GENESEE","MI",0.037,430459,11634.027,336651,84257,3132,2902,3517,78.2074483,19.5737573,0.72759543,0.67416409,0.81703484,265430,76.7660023,20.1454244,4.80201936,425331,98.8087135,16.4631781,25.2357066,13.84395,9.70689451,1,"AAU"
"220",1222,"GLADWIN","MI",0.031,21896,706.322581,21694,19,114,40,29,99.0774571,0.08677384,0.52064304,0.18268177,0.13244428,14388,64.8040033,11.4331387,2.20322491,21641,98.8354037,22.3233677,33.1810193,20.1047987,14.9024246,0,"LHR"
"221",1223,"GOGEBIC","MI",0.068,18052,265.470588,17486,243,283,26,14,96.8646133,1.34611123,1.56769333,0.14402836,0.07755373,12497,76.2743058,21.1730815,3.35280467,17385,96.3051185,14.8806442,19.5447798,13.5987339,13.257945,0,"AAR"
"222",1224,"GRAND TRAVERSE","MI",0.03,64273,2142.43333,63019,259,555,318,122,98.0489475,0.40296859,0.86350412,0.49476452,0.18981532,41094,84.861537,30.9753249,7.0253565,62872,97.8202356,8.54911566,11.2048541,7.49803127,7.69153931,0,"HAR"
"223",1225,"GRATIOT","MI",0.033,38982,1181.27273,37827,328,144,98,585,97.037094,0.84141399,0.36940126,0.25139808,1.50069263,23966,77.0675123,16.4441292,4.20595844,36654,94.0280129,14.0803187,19.1593729,12.4962511,10.7022876,0,"AAR"
"224",1226,"HILLSDALE","MI",0.034,43431,1277.38235,42919,113,143,112,144,98.8211186,0.26018282,0.3292579,0.25788032,0.33156041,26657,75.211014,16.4684698,4.14900401,42138,97.0228639,12.8031705,16.4067013,11.2972735,11.509408,0,"AAR"
"225",1227,"HOUGHTON","MI",0.06,35446,590.766667,34469,158,153,610,56,97.2436946,0.44574846,0.4316425,1.72092761,0.1579868,20646,73.9416836,24.7650877,7.32829604,32448,91.542064,21.0367357,21.1989796,23.6971485,14.5784943,0,"AHR"
"226",1228,"HURON","MI",0.05,34951,699.02,34627,22,89,60,153,99.0729879,0.06294527,0.25464221,0.17166891,0.43775572,22798,67.9928064,14.3082727,3.08799017,34484,98.6638437,15.0098596,17.9445876,13.4359815,14.9548881,0,"LAR"
"227",1229,"INGHAM","MI",0.034,281912,8291.52941,237183,27837,1941,7562,7389,84.1337013,9.87435796,0.68851273,2.68239734,2.62103068,158966,83.8676195,36.7600619,12.8599826,261491,92.7562502,16.6181628,18.9472436,17.0660584,9.3982718,1,"HAU"
"228",1230,"IONIA","MI",0.034,57024,1677.17647,53141,3003,221,120,539,93.1905864,5.2662037,0.38755612,0.21043771,0.94521605,33466,77.2156816,15.116835,3.40046614,51695,90.654812,11.3299159,14.9650878,9.34628975,11.1096292,0,"AAR"
"229",1231,"IOSCO","MI",0.033,30209,915.424242,28966,632,228,269,114,95.8853322,2.09209176,0.75474196,0.89046311,0.37737098,19194,76.3155153,16.2342399,3.63134313,29106,96.3487702,14.1585927,18.5825964,12.2557611,13.2756867,0,"AAR"
"230",1232,"IRON","MI",0.07,13175,188.214286,13028,4,102,32,9,98.8842505,0.03036053,0.77419355,0.24288425,0.0683112,9594,72.9726913,15.4575777,3.28330206,12886,97.8064516,17.1193543,23.3241189,14.4561646,16.5122357,0,"AAR"
"231",1233,"ISABELLA","MI",0.034,54624,1606.58824,52212,635,1020,456,301,95.5843585,1.16249268,1.86731107,0.83479789,0.55103984,26492,79.7448286,27.2497358,10.0030198,48498,88.7851494,24.8546332,21.2696866,28.4791467,13.966294,0,"AHR"
"232",1234,"JACKSON","MI",0.043,149756,3482.69767,135557,11983,655,653,908,90.5185769,8.00168274,0.43737814,0.43604263,0.60631961,97049,77.6916815,21.2057826,4.08144339,140520,93.8326344,12.0132365,17.1500264,10.1758057,9.9349205,1,"AAU"
"233",1235,"KALAMAZOO","MI",0.033,223411,6770.0303,197427,19879,1017,3168,1920,88.3694178,8.89795041,0.45521483,1.41801433,0.85940263,134684,83.4241632,34.5571857,10.8520685,212670,95.192269,13.4748672,16.1371424,13.5013362,8.63585267,1,"HAU"
"234",1236,"KALKASKA","MI",0.033,13497,409,13321,10,114,23,29,98.6960065,0.07409054,0.84463214,0.17040824,0.21486256,8485,69.6169711,11.4555097,2.38067177,13321,98.6960065,14.2256587,17.2608361,12.5250501,14.2446634,0,"AAR"
"235",1237,"KENT","MI",0.05,500631,10012.62,444112,40314,2756,5380,8069,88.7104474,8.05263757,0.55050526,1.0746438,1.61176595,305356,80.2882537,28.6383762,6.46131073,489425,97.7616248,9.15135108,12.4240562,7.78036993,8.09425626,1,"HAU"
"236",1238,"KEWEENAW","MI",0.02,1701,85.05,1688,1,4,6,2,99.2357437,0.05878895,0.23515579,0.35273369,0.1175779,1287,64.3356643,14.4522145,3.57420357,1696,99.7060553,20.5778302,21.8844985,21.2765957,19.0243902,0,"LHR"
"237",1239,"LAKE","MI",0.035,8583,245.228571,7337,1146,81,9,10,85.4829314,13.3519748,0.94372597,0.10485844,0.11650938,5931,61.288147,11.3134379,2.00640701,8387,97.7164162,26.4099201,37.6986038,25.0707123,18.8764973,0,"LHR"
"238",1240,"LAPEER","MI",0.038,74768,1967.57895,73049,483,319,282,635,97.7008881,0.64599829,0.42665311,0.3771667,0.84929382,45437,77.6019544,16.1542355,3.2572573,73344,98.0954419,8.19289921,10.8173406,6.89849757,7.908968,1,"AAU"
"239",1241,"LEELANAU","MI",0.021,16527,787,15958,16,451,45,57,96.5571489,0.09681128,2.72886791,0.27228172,0.34489018,11127,85.099308,32.5963872,8.25918936,16422,99.364676,9.00621118,12.8336041,7.04916163,9.20502092,0,"HAR"
"240",1242,"LENAWEE","MI",0.043,91476,2127.34884,86323,1431,303,486,2933,94.3668285,1.56434475,0.33123442,0.53128689,3.20630548,56323,76.3258349,18.7880617,4.58604833,87535,95.6917661,10.4015537,13.9918186,8.62787596,10.0333983,1,"AAU"
"241",1243,"LIVINGSTON","MI",0.034,115645,3401.32353,113566,673,705,480,221,98.2022569,0.58195339,0.60962428,0.41506334,0.19110208,72343,85.5687489,27.5866359,6.47747536,114160,98.7158978,4.13104415,5.29198774,3.22646163,6.05403732,1,"HLU"
"242",1244,"LUCE","MI",0.055,5763,104.781818,5418,2,331,6,6,94.0135346,0.03470415,5.74353635,0.10411244,0.10411244,3811,69.5880346,14.4056678,3.33245867,5566,96.5816415,17.6787639,22.9434447,16.8765743,12.8350934,0,"AHR"
"243",1245,"MACKINAC","MI",0.059,10674,180.915254,8955,5,1691,11,12,83.8954469,0.0468428,15.8422335,0.10305415,0.11242271,7156,71.3806596,15.2878703,2.78088317,10570,99.0256699,16.4049196,22.2423146,14.9934567,12.9071661,0,"AAR"
"244",1246,"MACOMB","MI",0.028,717400,25621.4286,693686,10400,2639,9112,1563,96.6944522,1.4496794,0.36785615,1.27014218,0.21787009,472323,76.9287119,20.6866911,4.43001929,710217,98.9987455,5.19306071,7.35610433,4.10136736,5.95219197,1,"ALU"
"245",1247,"MANISTEE","MI",0.032,21265,664.53125,20851,54,189,54,117,98.053139,0.2539384,0.88878439,0.2539384,0.55019986,14619,73.295027,16.2528217,3.39284493,21000,98.7538208,17.6238095,25.9354583,15.3846154,14.1412138,0,"AAR"
"246",1248,"MARQUETTE","MI",0.11,70887,644.427273,68027,1170,943,538,209,95.9654097,1.6505142,1.33028623,0.75895439,0.29483544,42386,81.7722833,26.763554,6.7994149,66398,93.6673861,12.6088135,14.2621599,11.84465,12.523891,0,"HAR"
"247",1249,"MASON","MI",0.029,25537,880.586207,24957,155,188,75,162,97.7287857,0.60696245,0.73618671,0.29369151,0.63437365,16796,76.1252679,18.5401286,4.06644439,25147,98.4728042,14.0613194,20.0209958,12.4061605,10.7592659,0,"AAR"
"248",1250,"MECOSTA","MI",0.034,37308,1097.29412,35739,978,258,187,146,95.7944677,2.62142168,0.69154069,0.50123298,0.39133698,19005,77.7426993,25.0460405,7.84530387,32627,87.4530932,25.0681951,25.5436141,28.2299641,13.1685725,0,"AHR"
"249",1251,"MENOMINEE","MI",0.064,24920,389.375,24464,7,382,60,7,98.1701445,0.02808989,1.5329053,0.24077047,0.02808989,16514,74.282427,15.084171,2.65229502,24562,98.5634029,12.7514046,14.0686721,11.3086771,14.551258,0,"AAR"
"250",1252,"MIDLAND","MI",0.031,75651,2440.35484,73466,719,334,804,328,97.1117368,0.95041705,0.4415011,1.06277511,0.43356995,47213,83.2440218,35.6088365,11.1854786,74135,97.9960609,11.0851824,14.5056449,10.1340285,8.30024213,1,"HAU"
"251",1253,"MISSAUKEE","MI",0.033,12147,368.090909,12015,3,74,25,30,98.9133119,0.02469746,0.60920392,0.20581213,0.24697456,7628,69.3890928,13.280021,2.39905611,11973,98.5675475,17.2889,23.7570621,15.07431,13.2467532,0,"AAR"
"252",1254,"MONROE","MI",0.033,133600,4048.48485,129421,2339,481,574,785,96.872006,1.7507485,0.36002994,0.42964072,0.58757485,82291,74.0882964,17.4296096,4.00529827,132283,99.0142216,8.6398101,11.9992591,6.77596351,9.38262438,1,"AAU"
"253",1255,"MONTCALM","MI",0.042,53059,1263.30952,51216,960,384,157,342,96.5265082,1.80930662,0.72372265,0.29589702,0.64456548,32959,73.3851148,14.160017,2.93698231,50918,95.9648693,15.3187478,19.1582873,13.6934028,13.7735631,0,"AAR"
"254",1256,"MONTMORENCY","MI",0.033,8936,270.787879,8861,1,48,10,16,99.1606983,0.01119069,0.53715309,0.11190689,0.17905103,6279,67.5903806,13.4575569,2.7552158,8803,98.5116383,17.5167557,26.8328446,16.2082515,12.4022346,0,"LAR"
"255",1257,"MUSKEGON","MI",0.028,158983,5677.96429,133931,21617,1338,555,1542,84.2423404,13.5970513,0.84159942,0.34909393,0.96991502,99720,74.2468913,19.0433213,3.44965905,154086,96.9197965,15.2551173,23.0023063,12.7038182,10.3740797,1,"AAU"
"256",1258,"NEWAYGO","MI",0.051,38202,749.058824,36758,468,248,103,625,96.2200932,1.22506675,0.64918067,0.26961939,1.63604,23989,71.0742424,15.6946934,3.84759682,37725,98.7513743,15.8807157,21.3676748,13.1306466,14.869186,0,"AAR"
"257",1259,"OAKLAND","MI",0.055,1083592,19701.6727,970674,77488,3948,25103,6379,89.5792881,7.15103102,0.36434378,2.31664686,0.58869021,717210,84.6344864,36.9635114,11.220563,1070844,98.8235424,6.04364408,8.45200666,4.94988864,6.47730339,1,"HLU"
"258",1260,"OCEANA","MI",0.03,22454,748.466667,21211,58,242,50,893,94.464238,0.25830587,1.07775897,0.22267747,3.97701968,14069,73.3172223,16.9024096,3.91641197,22156,98.6728423,17.8868027,24.6226559,15.3972083,14.0566274,0,"AHR"
"259",1261,"OGEMAW","MI",0.032,18681,583.78125,18489,18,140,19,15,98.9722178,0.09635458,0.74942455,0.10170762,0.08029549,12379,63.0341708,11.6568382,2.73850876,18364,98.3030887,21.7545197,30.5464927,19.9889197,15.6257046,0,"LHR"
"260",1262,"ONTONAGON","MI",0.078,8854,113.512821,8723,4,109,15,3,98.5204427,0.04517732,1.231082,0.16941495,0.03388299,6198,74.5724427,15.9567602,2.66214908,8712,98.3962051,13.1887052,16.516374,11.8080357,12.8,0,"AAR"
"261",1263,"OSCEOLA","MI",0.034,20146,592.529412,19899,57,117,43,30,98.7739502,0.28293458,0.58076045,0.21344187,0.14891294,12491,72.0598831,13.6658394,2.64190217,19853,98.545617,18.5463154,24.083319,16.2693393,16.2198036,0,"AHR"
"262",1264,"OSCODA","MI",0.033,7842,237.636364,7781,2,41,5,13,99.2221372,0.0255037,0.52282581,0.06375925,0.16577404,5433,66.5746365,12.9210381,2.68728143,7718,98.4187707,17.8025395,24.3506494,17.0002669,13.5186057,0,"LHR"
"263",1265,"OTSEGO","MI",0.031,17957,579.258065,17737,18,103,82,17,98.774851,0.10023946,0.57359247,0.45664643,0.0946706,11358,79.4506075,20.0211305,4.43740095,17682,98.4685638,9.46725484,11.4552313,7.837492,11.080502,0,"AAR"
"264",1266,"OTTAWA","MI",0.033,187768,5689.93939,179675,997,638,2451,4007,95.6898939,0.53097439,0.33978101,1.30533424,2.13401645,110737,79.7709889,26.9656935,6.15963951,181937,96.894572,5.9866877,6.29788172,5.89117375,5.68246591,1,"ALU"
"265",1267,"PRESQUE ISLE","MI",0.042,13743,327.214286,13648,11,43,30,11,99.308739,0.08004075,0.31288656,0.21829295,0.08004075,9285,65.6758212,13.8933764,3.27409801,13627,99.1559339,14.6987598,16.6811468,12.5114434,16.7679558,0,"LAR"
"266",1268,"ROSCOMMON","MI",0.034,19776,581.647059,19597,37,101,23,18,99.0948625,0.18709547,0.51072006,0.11630259,0.09101942,14435,69.3869068,13.363353,2.81260824,19602,99.1201456,18.0542802,26.0370913,17.9957969,13.0744236,0,"AHR"
"267",1269,"SAGINAW","MI",0.048,211946,4415.54167,165430,36849,915,1272,7480,78.0529003,17.3860323,0.43171374,0.60015287,3.52920083,131154,74.8128155,19.7081294,4.48709151,208790,98.5109415,17.2489104,26.2936776,14.5334371,10.8787742,1,"AAU"
"268",1270,"ST CLAIR","MI",0.04,145607,3640.175,140294,2987,745,475,1106,96.351137,2.05141236,0.51165123,0.32622058,0.75957887,91241,74.8008023,17.641192,4.07711446,144101,98.9657091,10.9326098,15.0850124,9.30644722,9.42984334,1,"AAU"
"269",1271,"ST JOSEPH","MI",0.029,58913,2031.48276,56661,1600,226,258,168,96.1774142,2.71586916,0.38361652,0.4379339,0.28516626,36757,73.7601001,17.0117257,3.81423946,58033,98.506272,11.4917375,16.6746699,9.77610991,8.22842179,0,"AAR"
"270",1272,"SANILAC","MI",0.056,39928,713,39232,39,195,71,391,98.2568624,0.09767582,0.48837908,0.17782008,0.97926267,25404,72.1461187,13.7970398,3.11368288,39486,98.8930074,14.2962063,18.7488829,11.9235869,14.1056347,0,"AAR"
"271",1273,"SCHOOLCRAFT","MI",0.075,8302,110.693333,7755,7,519,13,8,93.4112262,0.08431703,6.25150566,0.15658877,0.09636232,5643,71.5931242,14.5667198,2.76448698,8218,98.9881956,16.6098807,20.7288216,15.3212121,14.8989899,0,"AAR"
"272",1274,"SHIAWASSEE","MI",0.031,69770,2250.64516,68686,93,397,223,371,98.4463236,0.13329511,0.56901247,0.31962161,0.53174717,43097,78.7456203,17.2912268,3.4712393,69020,98.9250394,10.5882353,14.5300886,9.05735814,8.88030888,0,"AAR"
"273",1275,"TUSCOLA","MI",0.048,55498,1156.20833,54051,478,345,206,418,97.3926988,0.8612923,0.62164402,0.37118455,0.75318029,34607,72.9505591,14.1243101,2.5572861,54175,97.6161303,12.8583295,17.2158102,11.4048489,10.0748013,0,"AAR"
"274",1276,"VAN BUREN","MI",0.037,70060,1893.51351,63189,4690,646,217,1318,90.192692,6.69426206,0.9220668,0.30973451,1.88124465,43758,71.8405777,17.8047443,4.48146625,69093,98.6197545,15.1332262,21.0408367,12.4853114,13.4497364,1,"AAU"
"275",1277,"WASHTENAW","MI",0.041,282937,6900.90244,236390,31720,1076,11724,2027,83.5486345,11.2109763,0.38029667,4.14367863,0.7164139,167214,87.209803,48.0785102,20.7913213,261261,92.3389306,12.1629329,10.7978164,13.4554559,7.17477744,1,"HAU"
"276",1278,"WAYNE","MI",0.035,2111687,60333.9143,1212007,849109,8048,21704,20819,57.3952011,40.2099838,0.38111709,1.02780384,0.98589422,1324635,69.9515716,19.4104036,4.98575079,2084529,98.7139193,20.0653001,30.56427,17.0662258,13.2473501,1,"AHU"
"277",1279,"WEXFORD","MI",0.034,26360,775.294118,26040,34,178,87,21,98.7860395,0.12898331,0.67526555,0.33004552,0.07966616,16597,74.6158944,18.2322106,3.59703561,26038,98.7784522,14.6401413,20.1308237,12.4565469,12.323275,0,"AAR"
"278",2009,"ADAMS","OH",0.035,25371,724.885714,25212,47,67,30,15,99.3733002,0.18525088,0.26408104,0.11824524,0.05912262,15569,58.3852527,8.74173036,2.15813475,25028,98.6480627,28.5280486,35.0246846,25.7522586,26.1800599,0,"LHR"
"279",2010,"ALLEN","OH",0.024,109755,4573.125,96177,12313,202,572,491,87.6288096,11.2186233,0.18404628,0.52116077,0.44736003,68893,76.1499717,17.3660604,3.47930849,104543,95.2512414,12.6665583,17.8579862,10.5679214,10.7444235,1,"AAU"
"280",2011,"ASHLAND","OH",0.025,47507,1900.28,46686,460,49,271,41,98.2718336,0.96827836,0.10314269,0.57044225,0.08630307,29403,76.0024487,17.2669455,4.15943951,45486,95.7458901,11.3441498,17.2289909,9.15943446,8.67428431,0,"AAR"
"281",2012,"ASHTABULA","OH",0.041,99821,2434.65854,95465,3138,196,350,672,95.6361888,3.14362709,0.19635147,0.35062762,0.67320504,63850,72.3790133,13.2435395,2.88332028,97541,97.7159115,16.117325,23.8186247,13.6699531,11.9077683,1,"AAU"
"282",2013,"ATHENS","OH",0.03,59549,1984.96667,56163,1678,167,1374,167,94.3139263,2.81784749,0.28044132,2.30734353,0.28044132,30179,74.6114848,29.1494085,12.0447994,51002,85.6471141,28.6733854,29.2581157,31.7442813,14.3249554,0,"AHR"
"283",2014,"AUGLAIZE","OH",0.024,44585,1857.70833,44225,66,50,177,67,99.1925535,0.14803185,0.11214534,0.3969945,0.15027476,27676,76.4561353,16.1764706,3.24468854,43911,98.4882808,6.26949967,8.14294535,5.28379951,6.08875129,1,"ALU"
"284",2015,"BELMONT","OH",0.031,71074,2292.70968,69520,1308,81,129,36,97.8135464,1.84033543,0.11396573,0.18150097,0.05065143,48645,72.3424812,14.1576729,3.22746428,69952,98.4213636,17.4190874,25.849973,15.7629051,12.5753241,1,"AAU"
"285",2016,"BROWN","OH",0.028,34966,1248.78571,34487,406,28,30,15,98.6300978,1.16112795,0.08007779,0.08579763,0.04289882,21769,64.9088153,11.4336901,3.1742386,34439,98.4928216,14.1554633,18.6900797,11.1457233,16.3148479,1,"LAU"
"286",2017,"BUTLER","OH",0.028,291479,10409.9643,274892,13134,379,2659,415,94.3093671,4.50598499,0.13002652,0.91224411,0.14237732,176989,75.9736481,24.2252343,6.75917712,279692,95.9561409,10.6499292,13.1730731,10.0032732,8.52227905,1,"AAU"
"287",2018,"CARROLL","OH",0.024,26521,1105.04167,26254,135,65,29,38,98.9932506,0.50903058,0.2450888,0.10934731,0.14328268,17124,71.5253445,11.323289,3.07171222,26075,98.3183138,11.746884,14.6183522,10.9407318,9.75813178,1,"AAU"
"288",2019,"CHAMPAIGN","OH",0.026,36019,1385.34615,34698,992,68,113,148,96.3324912,2.754102,0.18878925,0.31372331,0.41089425,23023,75.3898276,13.9121748,2.8971029,35404,98.2925678,8.82668625,12.3796568,6.82405567,9.93365922,0,"AAR"
"289",2020,"CLARK","OH",0.024,147548,6147.83333,133242,13031,294,653,328,90.3041722,8.83170223,0.19925719,0.44256784,0.22230054,93950,73.360298,17.784992,4.60883449,143046,96.9487895,13.4166632,20.4814416,11.095735,10.4346131,1,"AAU"
"290",2021,"CLERMONT","OH",0.026,150187,5776.42308,148084,1291,218,453,141,98.5997457,0.85959504,0.14515238,0.30162398,0.09388296,91613,72.7833386,19.6893454,4.5528473,148417,98.8214692,8.69374802,11.9078721,7.0066905,9.0945467,1,"AAU"
"291",2022,"CLINTON","OH",0.024,35415,1475.625,34471,716,59,138,31,97.3344628,2.0217422,0.16659608,0.3896654,0.08753353,21969,74.3320133,15.7631208,3.77349902,34521,97.4756459,12.2505142,14.7371682,10.6142298,13.4121622,0,"AAR"
"292",2023,"COLUMBIANA","OH",0.031,108276,3492.77419,106369,1409,174,219,105,98.2387602,1.30130407,0.16070043,0.20226089,0.0969744,70249,71.8003103,13.0905778,2.8797563,106943,98.7688869,15.8916432,23.2200959,13.6442561,12.0377504,1,"AAU"
"293",2024,"COSHOCTON","OH",0.034,35427,1041.97059,34819,415,68,112,13,98.2837948,1.17142293,0.191944,0.31614305,0.03669518,22878,71.3305359,11.6793426,2.42154034,34833,98.3233127,13.188643,17.751112,11.1867496,12.1505376,0,"AAR"
"294",2025,"CRAWFORD","OH",0.023,47870,2081.30435,47361,253,67,116,73,98.9367036,0.52851473,0.1399624,0.24232296,0.15249634,30958,73.8193682,14.258027,2.9717682,47189,98.5773971,11.5916845,15.418153,10.1218333,10.4140383,1,"AAU"
"295",2026,"CUYAHOGA","OH",0.026,1412140,54313.0769,1025756,350185,2533,18085,15581,72.638407,24.7981787,0.17937315,1.28068039,1.10336086,943924,74.0130561,25.0853882,7.35673635,1388547,98.3292733,13.7661167,21.5809764,11.6755057,10.213391,1,"AAU"
"296",2027,"DARKE","OH",0.036,53619,1489.41667,53067,184,96,114,158,98.9705142,0.34316194,0.17904101,0.2126112,0.29467166,34048,73.4580592,13.475094,3.21898496,52557,98.0193588,8.98643378,12.1345723,7.14033102,9.59320612,0,"AAR"
"297",2028,"DEFIANCE","OH",0.023,39350,1710.86957,36962,493,80,121,1694,93.931385,1.25285896,0.20330368,0.30749682,4.30495553,24362,76.7670963,18.1881619,3.44388802,38386,97.5501906,8.7584015,11.4322065,7.11092484,9.76641842,0,"AAR"
"298",2029,"DELAWARE","OH",0.027,66929,2478.85185,64888,1424,104,385,128,96.9504998,2.12762778,0.15538855,0.57523644,0.19124744,41799,84.3728319,31.5940573,9.34232876,63986,95.602803,5.67311599,6.5723747,4.52441077,9.06414817,1,"HLU"
"299",2030,"ERIE","OH",0.014,76779,5484.21429,69613,6312,150,265,439,90.6667188,8.22099793,0.19536592,0.34514646,0.57177093,50112,76.2392241,18.7380268,4.63761175,75406,98.2117506,8.98602233,12.6682741,7.13811263,9.33003597,0,"AAR"
"300",2031,"FAIRFIELD","OH",0.029,103461,3567.62069,101610,1153,193,378,127,98.2109201,1.11442959,0.18654372,0.36535506,0.12275157,65269,78.751015,21.0773874,4.84763058,100916,97.5401359,8.77759721,11.6821249,6.9252938,10.5373526,1,"AAU"
"301",2032,"FAYETTE","OH",0.024,27466,1144.41667,26593,662,50,102,59,96.8215248,2.41025268,0.18204325,0.37136824,0.21481104,17854,65.2850902,11.8740898,2.14517755,26886,97.8882983,16.2203377,21.9505379,13.5346455,15.9820151,0,"LAR"
"302",2033,"FRANKLIN","OH",0.034,961437,28277.5588,783714,152840,2056,19437,3390,81.5148574,15.8970375,0.21384657,2.02166133,0.35259721,597303,80.9987561,32.204593,9.14092178,935142,97.2650314,12.9900058,17.6378685,11.8183828,9.74934856,1,"HAU"
"303",2034,"FULTON","OH",0.025,38498,1539.92,37097,93,62,137,1109,96.3608499,0.24157099,0.16104733,0.35586264,2.88066913,23846,78.2563113,16.4765579,3.3296989,37995,98.6934386,6.22976707,7.58154831,4.93982309,8.1255161,1,"ALU"
"304",2035,"GALLIA","OH",0.026,30954,1190.53846,29831,871,79,136,37,96.3720359,2.81385281,0.25521742,0.43936163,0.11953221,19586,64.1784948,14.9341366,4.32451751,29824,96.3494217,22.4885998,28.4607819,19.7789588,21.7653936,0,"LHR"
"305",2036,"GEAUGA","OH",0.024,81129,3380.375,79629,1056,83,312,49,98.1510927,1.30163074,0.1023062,0.38457272,0.06039764,51290,82.0413336,31.6279977,9.12263599,80419,99.1248505,5.55217051,8.49137931,4.10188194,5.40397817,1,"HLU"
"306",2037,"GREENE","OH",0.025,136731,5469.24,124081,9611,398,2133,508,90.7482575,7.02913019,0.29108249,1.55999737,0.37153242,83757,82.3740105,31.956732,11.061762,130134,95.175198,9.49098621,12.0735213,8.7175598,7.88356241,1,"HAU"
"307",2038,"GUERNSEY","OH",0.032,39024,1219.5,38166,616,70,141,31,97.801353,1.57851579,0.17937679,0.36131611,0.07943829,25188,71.3593775,13.5143719,2.57265364,38112,97.6629766,17.4721872,24.3214731,15.0734936,14.3717608,0,"AAR"
"308",2039,"HAMILTON","OH",0.025,866228,34649.12,672972,181145,1204,9198,1709,77.6899384,20.9119308,0.13899343,1.06184515,0.19729217,551233,75.6496799,29.7883109,8.6172272,846909,97.7697558,13.2924553,19.570569,11.169832,10.6960613,1,"AAU"
"309",2040,"HANCOCK","OH",0.031,65536,2114.06452,63572,591,91,401,881,97.0031738,0.90179443,0.13885498,0.61187744,1.34429932,41492,82.919599,24.4504965,5.54564735,64198,97.958374,7.27748528,8.45006003,6.51807196,7.93665845,0,"HLR"
"310",2041,"HARDIN","OH",0.028,31111,1111.10714,30661,236,66,115,33,98.5535663,0.75857414,0.21214361,0.36964418,0.10607181,18589,73.9523374,16.0309861,4.32513852,29111,93.5714056,16.3821236,21.816574,14.9368203,12.5945537,0,"AAR"
"311",2042,"HARRISON","OH",0.025,16085,643.4,15645,393,22,15,10,97.2645322,2.44327013,0.13677339,0.09325459,0.06216972,10726,69.7557337,10.5537945,1.96718255,15808,98.2778987,19.6988866,29.3435341,18.2995358,11.9607843,0,"AHR"
"312",2043,"HENRY","OH",0.025,29108,1164.32,27951,147,53,95,862,96.0251477,0.5050158,0.18208053,0.32637076,2.96138519,18245,75.4343656,15.2589751,2.65826254,28491,97.8803078,6.96360254,8.67297428,5.1076843,9.92209922,0,"ALR"
"313",2044,"HIGHLAND","OH",0.034,35728,1050.82353,34876,692,73,71,16,97.6153157,1.93685625,0.20432154,0.19872369,0.0447828,22784,66.4808638,12.3112711,2.49297753,35314,98.841245,16.4835476,19.4384226,14.3413749,18.1541141,0,"LAR"
"314",2045,"HOCKING","OH",0.024,25533,1063.875,25199,234,55,25,20,98.6918889,0.91646105,0.21540751,0.09791251,0.07833,16368,67.8457967,12.85435,2.71260997,24857,97.3524459,15.7098604,20.7745956,13.4820216,14.9915234,0,"LAR"
"315",2046,"HOLMES","OH",0.025,32849,1313.96,32706,52,24,43,24,99.5646747,0.1583001,0.07306158,0.13090201,0.07306158,17780,46.912261,9.33070866,1.72665917,31830,96.8979269,17.2447377,24.5171128,12.3522422,15.3579391,0,"LAR"
"316",2047,"HURON","OH",0.03,56240,1874.66667,54982,597,85,153,423,97.7631579,1.06152205,0.15113798,0.27204836,0.75213371,34521,74.131688,13.4497842,2.95472321,55535,98.7464438,9.50391645,13.0375596,8.02076192,8.16840178,0,"AAR"
"317",2048,"JACKSON","OH",0.024,30230,1259.58333,29895,218,53,39,25,98.8918293,0.72113794,0.17532253,0.12901092,0.08269931,19136,60.8800167,10.9845318,2.95255017,29874,98.8223619,24.1882573,30.1736614,21.0996006,24.1633681,0,"LHR"
"318",2049,"JEFFERSON","OH",0.022,80298,3649.90909,75270,4488,167,266,107,93.7383247,5.5891803,0.20797529,0.33126603,0.13325363,54294,71.879029,13.8431503,2.81799094,78510,97.7732945,17.1494077,26.2505981,15.7419355,10.8577511,1,"AAU"
"319",2050,"KNOX","OH",0.03,47473,1582.43333,46747,381,93,195,57,98.4707097,0.80256146,0.19590083,0.4107598,0.12006825,29992,75.2000533,16.771139,4.2011203,44269,93.2509005,12.4511509,18.1717025,10.5853414,9.88372093,0,"AAR"
"320",2051,"LAKE","OH",0.013,215499,16576.8462,209879,3528,250,1447,395,97.3920993,1.63713057,0.11600982,0.67146483,0.18329551,142348,81.0520696,23.6828055,5.20274257,213036,98.8570713,4.89729435,7.14098582,3.93348514,4.90623387,1,"HLU"
"321",2052,"LAWRENCE","OH",0.026,61834,2378.23077,60115,1559,57,75,28,97.2199761,2.52126662,0.09218229,0.12129249,0.04528253,39219,65.8787832,12.2083684,3.14643413,61007,98.6625481,23.5399216,33.2300024,21.1064359,16.3875266,1,"LHU"
"322",2053,"LICKING","OH",0.04,128300,3207.5,125181,2217,247,475,180,97.568979,1.72798129,0.19251754,0.37022603,0.14029618,81642,76.4251243,18.1793685,4.53565567,124678,97.1769291,10.4998476,14.7950733,8.84336678,9.29430267,1,"AAU"
"323",2054,"LOGAN","OH",0.027,42310,1567.03704,41156,804,58,240,52,97.2725124,1.90025999,0.13708343,0.56724179,0.12290239,26780,74.4884242,13.3233757,3.33457804,41566,98.2415505,10.4676899,13.5485,9.22106393,9.52197379,0,"AAR"
"324",2055,"LORAIN","OH",0.029,271126,9349.17241,241549,21230,738,1479,6130,89.0910499,7.83030768,0.27219817,0.54550283,2.26094141,169492,75.2820192,18.8616572,4.283978,265062,97.7634015,11.4912737,17.386122,9.42837411,8.42662074,1,"AAU"
"325",2056,"LUCAS","OH",0.021,462361,22017.1905,380155,68456,1164,4981,7605,82.2203862,14.805747,0.25175134,1.07729674,1.64481866,289965,76.2340282,23.5504285,6.00589726,454351,98.2675874,15.268812,21.4916528,13.4944573,11.4228016,1,"AAU"
"326",2057,"MADISON","OH",0.028,37068,1323.85714,33947,2764,96,157,104,91.5803388,7.45656631,0.25898349,0.42354592,0.28056545,23896,69.4928021,13.3788082,2.67408771,32904,88.7665911,8.4275468,11.2765492,7.12069334,8.11343048,1,"AAU"
"327",2058,"MAHONING","OH",0.024,264806,11033.5833,221109,39681,444,985,2587,83.4984857,14.9849324,0.16766992,0.37197042,0.97694161,176658,74.6006408,18.1757973,4.52342945,260264,98.2847821,15.9196047,25.0271849,13.8501553,10.7028532,1,"AAU"
"328",2059,"MARION","OH",0.024,64274,2678.08333,60948,2707,148,285,186,94.8252793,4.21165635,0.23026418,0.44341413,0.28938607,41239,73.7578506,15.0270375,3.04323577,61526,95.7245543,12.7133244,18.2830355,10.7493876,10.0189036,0,"AAR"
"329",2060,"MEDINA","OH",0.025,122354,4894.16,120504,850,172,684,144,98.4879939,0.69470553,0.14057571,0.55903362,0.11769129,76962,82.3978067,23.7026065,4.88552792,121055,98.9383265,5.52063112,7.85156137,4.21102969,6.32405892,1,"HLU"
"330",2061,"MEIGS","OH",0.025,22987,919.48,22734,177,44,20,12,98.8993779,0.77000044,0.19141254,0.0870057,0.05220342,14772,64.0265367,11.677498,2.38288654,22665,98.5992082,26.0092654,34.9729464,23.6672906,19.9579341,0,"LHR"
"331",2062,"MERCER","OH",0.028,39443,1408.67857,39131,14,85,100,113,99.2089851,0.03549426,0.21550085,0.25353041,0.28648936,23780,75.4793944,14.1000841,3.32632464,38961,98.7779834,6.70414004,7.62704884,5.68931069,8.04411765,0,"ALR"
"332",2063,"MIAMI","OH",0.024,93182,3882.58333,90519,1779,158,606,120,97.1421519,1.90916701,0.16956064,0.65034019,0.12878024,59893,76.5665437,19.2777119,4.43457499,92127,98.8678071,8.35151476,12.3240006,6.30440618,8.91394114,1,"AAU"
"333",2064,"MONROE","OH",0.026,15497,596.038462,15437,19,26,12,3,99.6128283,0.12260438,0.16777441,0.07743434,0.01935859,10196,69.4488035,10.513927,2.59905845,15276,98.5739175,21.4912281,28.809219,18.6889987,19.7591146,0,"AHR"
"334",2065,"MONTGOMERY","OH",0.027,573809,21252.1852,463551,101817,1065,5886,1490,80.7848953,17.7440577,0.18560183,1.02577687,0.25966829,371530,77.7571125,26.5504804,7.0042796,561952,97.933633,12.6286587,19.4520723,10.6043371,9.44086822,1,"AAU"
"335",2066,"MORGAN","OH",0.025,14194,567.76,13524,570,64,12,24,95.2796956,4.01578132,0.45089474,0.08454276,0.16908553,8980,71.5812918,11.3808463,2.31625835,13924,98.0977878,21.2079862,31.325,17.9487179,14.8148148,0,"AHR"
"336",2067,"MORROW","OH",0.024,27749,1156.20833,27579,64,49,38,19,99.3873653,0.23063894,0.17658294,0.13694187,0.06847094,17158,71.3253293,12.1051405,2.54691689,27440,98.8864464,11.0750729,17.3749057,8.39833593,8.89809849,0,"AAR"
"337",2068,"MUSKINGUM","OH",0.041,82068,2001.65854,78125,3468,214,152,109,95.1954477,4.225764,0.26075937,0.18521226,0.13281669,51692,71.140602,15.3234543,3.57308674,80009,97.4911049,14.7208439,21.3569812,12.6231667,11.137507,0,"AAR"
"338",2069,"NOBLE","OH",0.024,11336,472.333333,11301,7,15,9,4,99.6912491,0.06175018,0.13232181,0.07939308,0.03528582,7235,69.8548721,8.84588804,1.57567381,11176,98.5885674,16.3743737,22.7357901,12.8071997,16.4845173,0,"AAR"
"339",2070,"OTTAWA","OH",0.016,40029,2501.8125,39029,265,51,94,590,97.5018112,0.66202004,0.12740763,0.23482975,1.4739314,26931,75.8568193,19.338309,4.21447403,39392,98.4086537,6.61301787,9.54335557,5.25392428,6.72342557,0,"ALR"
"340",2071,"PAULDING","OH",0.025,20488,819.52,19920,236,54,20,258,97.2276455,1.15189379,0.26356892,0.09761812,1.25927372,12456,72.2302505,11.4884393,2.82594733,20298,99.0726279,9.78914179,11.9703215,8.16975645,11.2109012,0,"AAR"
"341",2072,"PERRY","OH",0.023,31557,1372.04348,31408,57,46,21,25,99.5278385,0.18062553,0.14576798,0.06654625,0.07922173,19411,68.5539127,10.1076709,2.18432847,31255,99.0430016,19.0657495,25.3889985,16.3782957,16.7218221,0,"AHR"
"342",2073,"PICKAWAY","OH",0.03,48255,1608.5,44867,3036,127,95,130,92.9789659,6.291576,0.26318516,0.19687079,0.26940213,31380,69.7100064,13.0943276,2.93499044,42392,87.8499637,12.0777505,16.3463189,9.94214435,12.4391393,1,"AAU"
"343",2074,"PIKE","OH",0.026,24249,932.653846,23807,327,72,41,2,98.1772444,1.34850922,0.29691946,0.16907914,0.00824776,15099,60.7722366,12.4445328,3.23862507,23830,98.2720937,26.5757449,36.915078,22.2239587,22.7062546,0,"LHR"
"344",2075,"PORTAGE","OH",0.03,142585,4752.83333,136998,3906,292,1191,198,96.0816355,2.73941859,0.20479013,0.83529123,0.13886454,82726,79.3100114,21.8818751,6.58680463,133447,93.5911912,11.9088477,13.3814451,12.1537341,7.97863462,1,"AAU"
"345",2076,"PREBLE","OH",0.025,40113,1604.52,39819,147,53,65,29,99.2670705,0.36646474,0.13212674,0.16204223,0.07229576,25469,72.4606384,11.233264,2.39899486,39614,98.7560143,10.1883173,15.565561,8.12505639,8.24067022,0,"AAR"
"346",2077,"PUTNAM","OH",0.029,33819,1166.17241,33197,26,44,25,527,98.1607972,0.07687986,0.13010438,0.07392294,1.55829563,20028,77.5064909,15.7080088,2.8060715,33390,98.7314823,5.75621444,7.14017991,4.23869967,7.95176182,0,"ALR"
"347",2078,"RICHLAND","OH",0.029,126137,4349.55172,115078,9981,223,578,277,91.2325487,7.91282494,0.1767919,0.45823192,0.2196025,81363,73.5321952,16.7975615,3.42170274,122328,96.9802675,11.2517167,16.4937951,8.9708687,10.6330901,1,"AAU"
"348",2079,"ROSS","OH",0.041,69330,1690.97561,64362,4467,155,266,80,92.8342709,6.44309823,0.22356844,0.38367229,0.11539016,45531,67.5803299,14.1156575,2.99795744,63449,91.5173806,17.7496887,24.8676325,15.0094603,15.4179338,0,"LHR"
"349",2080,"SANDUSKY","OH",0.024,61963,2581.79167,58282,1553,94,142,1892,94.059358,2.50633443,0.15170344,0.22916902,3.05343511,38993,76.6163157,16.2824097,3.40061037,60811,98.140826,8.99672757,12.030513,7.50127931,8.76451954,0,"AAR"
"350",2081,"SCIOTO","OH",0.035,80327,2295.05714,77253,2458,409,126,81,96.1731423,3.05999228,0.50916877,0.15685884,0.10083783,51585,63.7569061,13.7772608,3.17534167,76736,95.5295231,25.7923269,37.6523745,23.5231677,15.1068091,0,"LHR"
"351",2082,"SENECA","OH",0.033,59733,1810.09091,57474,1172,90,234,763,96.2181709,1.96206452,0.15067048,0.39174326,1.27735088,36666,75.2604593,15.6221022,3.03005509,57655,96.5211859,10.7518862,14.3172859,9.29836624,9.27675988,0,"AAR"
"352",2083,"SHELBY","OH",0.025,44915,1796.6,43789,615,49,393,69,97.4930424,1.36925303,0.10909496,0.87498608,0.15362351,27365,72.943541,17.3762105,3.66892015,44127,98.245575,7.74582455,10.54634,5.95581568,8.73122118,0,"AAR"
"353",2084,"STARK","OH",0.034,367585,10811.3235,339421,25052,950,1529,633,92.3380987,6.81529442,0.25844363,0.41595821,0.17220507,241153,76.0090067,18.9896041,4.62030329,359231,97.7273284,11.0605711,16.399197,9.51066888,8.45844662,1,"AAU"
"354",2085,"SUMMIT","OH",0.024,514990,21457.9167,446902,61185,1065,4989,849,86.7787724,11.8808132,0.20680013,0.96875667,0.16485757,337442,78.3393887,24.7331393,6.25529721,506100,98.2737529,12.1499704,18.0813265,10.8654209,8.22432118,1,"AAU"
"355",2086,"TRUMBULL","OH",0.037,227813,6157.10811,210915,15221,341,973,363,92.5825129,6.68135708,0.14968417,0.42710469,0.15934121,149822,75.2492958,15.5998451,3.78849568,225230,98.8661753,11.4047862,16.9653149,9.38910671,9.85596375,1,"AAU"
"356",2087,"TUSCARAWAS","OH",0.034,84090,2473.23529,83107,623,138,187,35,98.8310144,0.74087287,0.16410988,0.22238078,0.04162207,55192,71.9180316,13.0834179,3.02036527,82852,98.5277679,11.1222421,14.39584,9.78198126,10.4047334,1,"AAU"
"357",2088,"UNION","OH",0.026,31969,1229.57692,30563,1168,57,132,49,95.6019894,3.65353937,0.17829773,0.4129,0.15327348,20527,76.1631023,16.9825108,3.84858966,30117,94.2068879,7.43101903,9.46814728,5.77602449,9.76408912,0,"AAR"
"358",2089,"VAN WERT","OH",0.024,30464,1269.33333,29900,193,31,78,262,98.1486345,0.63353466,0.10175945,0.25603992,0.86003151,19497,79.2327025,15.1100169,3.1902344,30007,98.4998687,7.09167861,7.60100707,6.06022585,9.22109675,0,"ALR"
"359",2090,"VINTON","OH",0.024,11098,462.416667,11071,4,16,3,4,99.7567129,0.03604253,0.14417012,0.0270319,0.03604253,6963,58.6959644,7.91325578,1.56541721,10937,98.5492882,23.6079364,31.7708333,22.2259753,14.9176955,0,"LHR"
"360",2091,"WARREN","OH",0.023,113909,4952.56522,110526,2415,231,627,110,97.0300854,2.12011342,0.20279346,0.55043939,0.09656831,73151,75.4671843,24.4193518,5.90832661,109393,96.0354318,6.35232602,8.71549016,5.07858441,7.22380639,1,"ALU"
"361",2092,"WASHINGTON","OH",0.038,62254,1638.26316,61129,774,111,185,55,98.1928872,1.2432936,0.1783018,0.29716966,0.08834774,40411,77.5061246,19.0096756,4.35524981,60627,97.3865133,13.6737757,17.9894516,12.0116635,12.6052655,1,"AAU"
"362",2093,"WAYNE","OH",0.033,101461,3074.57576,99131,1557,130,535,108,97.7035511,1.53457979,0.12812805,0.5272962,0.10644484,62178,73.5967706,18.4454309,4.79590852,98285,96.8697332,11.6558987,17.2130572,9.51515592,8.91176271,0,"AAR"
"363",2094,"WILLIAMS","OH",0.025,36956,1478.24,36366,23,46,127,394,98.4035069,0.06223617,0.12447235,0.34365191,1.0661327,23340,76.092545,14.4987147,2.70779777,36499,98.7633943,7.55363161,10.6865843,5.92573754,7.44215134,0,"AAR"
"364",2095,"WOOD","OH",0.037,113269,3061.32432,109303,1168,197,1028,1573,96.4986007,1.03117358,0.17392226,0.90757401,1.38872948,64052,83.7616312,29.0951102,8.33541498,104553,92.3050437,10.5726282,8.76044005,12.2235902,6.93142742,1,"HAU"
"365",2096,"WYANDOT","OH",0.024,22254,927.25,22087,20,20,65,62,99.2495731,0.08987148,0.08987148,0.29208232,0.2786016,14361,76.4709978,13.7107444,2.54856904,21743,97.7037836,8.49468795,9.99170124,7.31604292,9.76552435,0,"AAR"
"366",2981,"ADAMS","WI",0.041,15682,382.487805,15001,375,125,56,125,95.6574417,2.39127662,0.79709221,0.35709731,0.79709221,11378,66.9801371,12.4274917,2.39057831,14534,92.6795052,14.4351177,22.3008305,13.3100559,10.1867572,0,"LAR"
"367",2982,"ASHLAND","WI",0.054,16307,301.981481,14749,17,1478,46,17,90.4458208,0.10424971,9.06359232,0.28208745,0.10424971,10262,75.3069577,18.3200156,4.36562074,15573,95.4988655,16.2203814,20.3220685,15.1600512,13.2975552,0,"AAR"
"368",2983,"BARRON","WI",0.053,40750,768.867925,40346,40,209,95,60,99.008589,0.09815951,0.51288344,0.23312883,0.14723926,26198,72.9750363,19.425147,3.24452248,39931,97.990184,11.5524279,14.4092998,9.94820158,11.7280176,0,"AAR"
"369",2984,"BAYFIELD","WI",0.089,14008,157.393258,12707,29,1240,24,8,90.71245,0.20702456,8.85208452,0.17133067,0.05711022,9418,78.5410915,25.8228923,5.83988108,13833,98.7507139,16.5835321,23.4879306,14.4289143,13.2549788,0,"AAR"
"370",2985,"BROWN","WI",0.032,194594,6081.0625,186621,1012,3869,2522,570,95.9027514,0.52005714,1.98824219,1.29603174,0.29291756,120575,82.6439975,26.2931785,4.62450757,189369,97.3149224,9.16570294,12.0873618,8.08561665,7.96336775,1,"HAU"
"371",2986,"BUFFALO","WI",0.04,13584,339.6,13521,5,22,29,7,99.5362191,0.03680801,0.16195524,0.21348645,0.05153121,8918,72.6059655,17.9412424,3.13971743,13337,98.1816843,11.9067257,15.2720243,9.63907813,13.1345403,0,"AAR"
"372",2987,"BURNETT","WI",0.053,13084,246.867925,12497,22,532,24,9,95.5136044,0.1681443,4.06603485,0.18343014,0.0687863,9045,72.2830293,13.6760641,2.99613046,12816,97.9516967,15.4884519,21.0293653,13.9379475,13.1688466,0,"AAR"
"373",2988,"CALUMET","WI",0.023,34291,1490.91304,33910,29,146,173,33,98.8889213,0.0845703,0.42576769,0.50450556,0.09623516,20940,79.6561605,19.7803247,3.01337154,33952,99.0114024,4.87158341,6.22784325,3.72723875,6.39097744,1,"ALU"
"374",2989,"CHIPPEWA","WI",0.063,52360,831.111111,51854,31,150,276,49,99.0336134,0.0592055,0.28647823,0.52711994,0.09358289,33195,74.9269468,18.1533363,2.78957674,50983,97.3701299,10.476041,13.9566861,8.59508956,10.579373,1,"AAU"
"375",2990,"CLARK","WI",0.072,31647,439.541667,31437,29,91,38,52,99.33643,0.09163586,0.287547,0.12007457,0.16431257,19702,67.5362907,13.84631,2.50228403,30947,97.7881,13.7719327,18.0444728,11.4184117,13.0776485,0,"LAR"
"376",2991,"COLUMBIA","WI",0.046,45088,980.173913,44469,243,136,136,104,98.6271292,0.53894606,0.30163236,0.30163236,0.23066004,29637,78.2535344,20.852313,3.54624287,43744,97.0191625,7.49359912,9.98292058,5.91446379,8.3589919,0,"AAR"
"377",2992,"CRAWFORD","WI",0.035,15940,455.428571,15791,50,26,56,17,99.0652447,0.31367629,0.16311167,0.35131744,0.10664994,10169,72.3964992,16.599469,2.92064116,15562,97.6286073,14.6382213,19.4600675,12.3815621,13.4865437,0,"AAR"
"378",2993,"DANE","WI",0.073,367085,5028.56164,344617,10511,1201,8666,2090,93.8793467,2.86336952,0.32717218,2.36076113,0.56935042,225973,88.8986737,43.6264509,13.4715209,351558,95.7701895,10.4930054,8.76778118,12.1680107,4.97435064,1,"HAU"
"379",2994,"DODGE","WI",0.054,76559,1417.75926,74700,1142,215,197,305,97.5718074,1.49166003,0.28082916,0.25731789,0.39838556,49694,72.2783435,15.9254638,2.82327846,72840,95.1423085,6.64470071,8.39387394,5.17740489,8.25578798,0,"ALR"
"380",2995,"DOOR","WI",0.028,25690,917.5,25387,29,178,47,49,98.8205527,0.11288439,0.69287661,0.18295056,0.19073569,17369,79.5670447,22.4883413,4.42167079,25318,98.5519657,9.82305079,13.938754,8.2382666,8.65316276,0,"AAR"
"381",2996,"DOUGLAS","WI",0.078,41758,535.358974,40454,170,805,266,63,96.8772451,0.40710762,1.92777432,0.63700369,0.15086929,27060,77.2172949,20.9940872,4.97782705,40426,96.8101921,14.9210904,22.9435598,13.1049683,9.24704418,1,"AAU"
"382",2997,"DUNN","WI",0.052,35909,690.557692,34929,172,95,633,80,97.2708792,0.47898855,0.26455763,1.76278927,0.22278537,19755,77.6613516,26.2870159,7.50696026,32837,91.4450416,16.6336754,15.1954674,18.6613878,11.8962601,0,"AAR"
"383",2998,"EAU CLAIRE","WI",0.038,85183,2241.65789,82202,238,467,2124,152,96.5004754,0.27939847,0.54823145,2.49345527,0.17843936,49336,82.7894438,29.8544673,7.55634831,80458,94.4531186,15.9437222,18.7430181,16.7685011,8.57514739,1,"HAU"
"384",2999,"FLORENCE","WI",0.03,4590,153,4562,4,14,4,6,99.3899782,0.08714597,0.30501089,0.08714597,0.13071895,3057,75.171737,15.6035329,2.78050376,4513,98.3224401,13.1398183,18.8417618,10.3640257,12.605042,0,"AAR"
"385",3000,"FOND DU LAC","WI",0.044,90083,2047.34091,88760,257,297,448,321,98.5313544,0.28529245,0.32969595,0.49731914,0.35633804,56764,77.5068706,20.8318653,3.66253259,87203,96.8029484,7.64423242,10.1088839,6.25657867,8.04270463,0,"AAR"
"386",3001,"FOREST","WI",0.06,8776,146.266667,7842,127,780,14,13,89.3573382,1.44712853,8.88787603,0.15952598,0.14813127,5608,64.1226819,13.5699001,2.2467903,8581,97.778031,21.7806782,31.751365,19.6937066,14.349332,0,"LHR"
"387",3002,"GRANT","WI",0.069,49264,713.971014,48838,76,76,234,40,99.1352712,0.15427087,0.15427087,0.47499188,0.08119519,29160,77.8806584,21.6803841,5.24348422,46314,94.0118545,13.7150754,15.1552225,12.9747224,13.6286394,0,"AAR"
"388",3003,"GREEN","WI",0.034,30339,892.323529,30173,23,51,66,26,99.4528495,0.07581001,0.16810046,0.21754178,0.08569828,19708,76.7759286,19.8751776,3.34381977,29821,98.2926267,7.83675933,10.2412605,6.84171136,7.20466184,0,"AAR"
"389",3004,"GREEN LAKE","WI",0.022,18651,847.772727,18386,21,42,103,99,98.5791647,0.1125945,0.225189,0.55224921,0.53080264,12453,74.5523167,17.3050671,3.33253031,18351,98.3915072,9.9722086,13.5313531,8.35054909,9.42870413,0,"AAR"
"390",3005,"IOWA","WI",0.046,20150,438.043478,20093,7,21,19,10,99.7171216,0.03473945,0.10421836,0.0942928,0.04962779,12747,80.5993567,20.483251,3.94602652,19890,98.7096774,9.99497235,12.4825175,8.10377358,11.6246499,0,"HAR"
"391",3006,"IRON","WI",0.047,6153,130.914894,6121,1,25,2,4,99.4799285,0.01625223,0.40630587,0.03250447,0.06500894,4447,74.7020463,18.7317293,2.67596132,6021,97.854705,13.0709185,15.6467854,12.1790423,12.6623377,0,"AAR"
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"393",3008,"JEFFERSON","WI",0.035,67783,1936.65714,66702,189,176,287,429,98.4052048,0.27883098,0.25965213,0.42341,0.63290206,42214,76.9649879,22.1656323,4.73302696,64692,95.4398596,7.1956347,7.99721432,6.72824313,7.46902655,0,"ALR"
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"395",3010,"KENOSHA","WI",0.016,128181,8011.3125,119187,5295,472,669,2558,92.9833595,4.13087743,0.3682293,0.52191823,1.99561557,80794,75.087259,20.3282422,4.06094512,125494,97.9037455,10.2140341,15.6521483,8.56810674,6.94973343,1,"AAU"
"396",3011,"KEWAUNEE","WI",0.02,18878,943.9,18766,24,52,23,13,99.4067168,0.12713211,0.27545291,0.12183494,0.06886323,11945,73.5454165,14.3909586,2.15989954,18662,98.855811,8.21991212,9.8546347,6.05500821,11.6542392,0,"AAR"
"397",3012,"LA CROSSE","WI",0.028,97904,3496.57143,94319,438,340,2667,140,96.3382497,0.44737702,0.34727897,2.72409707,0.14299722,58586,82.5760421,30.4885126,7.34475813,93254,95.2504494,13.4085401,14.452894,14.0575375,9.52813067,1,"HAU"
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"400",3015,"LINCOLN","WI",0.053,26993,509.301887,26712,84,96,78,23,98.9589894,0.31119179,0.35564776,0.28896381,0.08520728,17567,71.0593727,17.4417943,3.44395742,26273,97.3326418,10.4175389,13.7597459,8.59200576,10.8202124,0,"AAR"
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"404",3019,"MARQUETTE","WI",0.027,12321,456.333333,12174,31,49,18,49,98.806915,0.25160295,0.39769499,0.14609204,0.39769499,8499,69.714084,14.0016473,2.57677374,12182,98.8718448,11.6072894,15.6430268,9.678511,11.4586466,0,"AAR"
"405",3020,"MENOMINEE","WI",0.021,3890,185.238095,416,0,3469,0,5,10.6940874,0,89.1773779,0,0.1285347,1922,62.6951093,7.33610822,0.52029136,3820,98.2005141,48.6910995,64.3084767,43.3124644,18.2186235,0,"LHR"
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"407",3022,"MONROE","WI",0.054,36633,678.388889,35983,141,301,143,65,98.2256435,0.38489886,0.82166353,0.39035842,0.17743565,23025,75.7263844,17.3724213,2.84473398,35485,96.8662135,13.0026772,18.0921979,10.5513257,11.6072842,0,"AAR"
"408",3023,"OCONTO","WI",0.06,30226,503.766667,29926,18,212,36,34,99.007477,0.05955138,0.70138292,0.11910276,0.11248594,19760,69.4078947,13.4311741,2.37854251,29750,98.4251969,12.0941176,14.1166317,9.63442252,15.5886158,0,"AAR"
"409",3024,"ONEIDA","WI",0.071,31679,446.183099,31320,58,223,56,22,98.8667572,0.18308659,0.70393636,0.17677326,0.06944664,22153,77.5967138,21.9338239,4.97449555,30890,97.5093911,9.61799935,13.9667073,8.10961622,8.559414,0,"AAR"
"410",3025,"OUTAGAMIE","WI",0.037,140510,3797.56757,136043,206,1965,1904,392,96.8208668,0.14660878,1.39847698,1.3550637,0.2789837,86689,81.5328358,24.8578251,4.36272191,137496,97.8549569,6.20236225,8.20300676,5.02296542,6.85592403,1,"HLU"
"411",3026,"OZAUKEE","WI",0.015,72831,4855.4,71676,492,127,438,98,98.4141368,0.67553652,0.1743763,0.60139226,0.13455809,47058,86.8991457,37.4197798,9.36503889,71600,98.3097857,2.1801676,1.91895478,1.9385043,3.54706685,1,"HLU"
"412",3027,"PEPIN","WI",0.014,7107,507.642857,7070,2,18,9,8,99.4793865,0.02814127,0.25327142,0.12663571,0.11256508,4578,71.0353866,15.7492355,2.81782438,6944,97.7064866,12.6152074,14.8821218,10.1239669,15.1315789,0,"AAR"
"413",3028,"PIERCE","WI",0.034,32765,963.676471,32366,82,87,172,58,98.7822371,0.25026705,0.26552724,0.5249504,0.17701816,18623,81.0717929,24.464372,5.42340117,30557,93.2611018,10.4165985,11.0331339,10.3370278,9.47141832,1,"HAU"
"414",3029,"POLK","WI",0.055,34773,632.236364,34348,23,321,50,31,98.7777874,0.06614327,0.92313001,0.14378972,0.08914963,22515,77.9835665,18.5920497,3.39329336,34105,98.0789693,11.8223134,16.0155849,9.75664969,11.1643221,0,"AAR"
"415",3030,"PORTAGE","WI",0.048,61405,1279.27083,59972,161,255,786,231,97.6663138,0.26219363,0.41527563,1.28002606,0.37619086,35004,79.7337447,24.6343275,6.01931208,57805,94.1372852,12.8950783,13.5589979,13.1821824,10.5094407,0,"AAR"
"416",3031,"PRICE","WI",0.075,15600,208,15479,7,77,27,10,99.224359,0.04487179,0.49358974,0.17307692,0.06410256,10414,73.324371,15.527175,2.81352026,15271,97.8910256,10.8899221,12.5549585,8.14521452,14.7695725,0,"AAR"
"417",3032,"RACINE","WI",0.02,175034,8751.7,152098,16999,521,1004,4412,86.8962602,9.71182742,0.29765646,0.57360284,2.52065313,110593,76.3809644,22.9065131,4.93611711,172392,98.490579,10.1512831,15.556437,8.42926105,6.81166261,1,"AAU"
"418",3033,"RICHLAND","WI",0.034,17521,515.323529,17411,12,34,38,26,99.372182,0.06848924,0.19405285,0.2168826,0.14839336,11309,73.720046,17.4993368,3.14793527,17254,98.4761144,13.4403617,15.7928118,12.6371115,12.3541888,0,"AAR"
"419",3034,"ROCK","WI",0.042,139510,3321.66667,130803,6638,369,985,715,93.7588703,4.75808186,0.26449717,0.70604258,0.51250806,88072,78.1814879,19.9064402,4.37028795,135919,97.425991,9.87720628,15.2857608,8.14384062,6.8982497,1,"AAU"
"420",3035,"RUSK","WI",0.055,15079,274.163636,14821,31,82,114,31,98.2890112,0.20558392,0.54380264,0.7560183,0.20558392,9704,70.3112119,16.7559769,3.01937345,14748,97.8048942,16.5785191,20.7524272,15.806851,13.1289074,0,"AAR"
"421",3036,"ST CROIX","WI",0.044,50251,1142.06818,49895,44,121,148,43,99.2915564,0.08756045,0.24079123,0.2945215,0.08557044,30873,84.4232825,28.5880867,5.38010559,49388,98.2826212,6.40236495,7.89210789,4.97251286,9.30721916,1,"HLU"
"422",3037,"SAUK","WI",0.05,46975,939.5,46459,54,288,79,95,98.9015434,0.11495476,0.61309207,0.16817456,0.20223523,30347,74.6597687,19.833921,3.97073846,46193,98.3352847,9.69194467,13.1041039,8.14808827,9.14033299,0,"AAR"
"423",3038,"SAWYER","WI",0.079,14181,179.506329,11962,18,2167,15,19,84.3523024,0.1269304,15.2810098,0.10577533,0.13398209,9600,73.7395833,17.9479167,4.04166667,13897,97.9973204,20.5440023,29.0735695,19.1953682,14.1793182,0,"AHR"
"424",3039,"SHAWANO","WI",0.054,37157,688.092593,35251,42,1762,70,32,94.8704147,0.11303388,4.74204053,0.18838981,0.08612105,24271,69.453257,14.8077953,2.45560546,36389,97.9330947,11.2973701,14.932266,9.10511287,11.9495299,0,"AAR"
"425",3040,"SHEBOYGAN","WI",0.032,103877,3246.15625,100389,430,357,2061,640,96.6421826,0.41395112,0.34367569,1.98407732,0.61611329,66938,77.4298605,20.8371926,3.83638591,101088,97.3150938,6.48642767,8.67791176,5.14637904,7.23178521,1,"ALU"
"426",3041,"TAYLOR","WI",0.057,18901,331.596491,18807,2,39,44,9,99.5026718,0.01058145,0.20633829,0.23279192,0.04761653,11676,68.8078109,15.0650908,3.0061665,18603,98.4233638,12.664624,15.9650375,10.265319,13.8896828,0,"AAR"
"427",3042,"TREMPEALEAU","WI",0.043,25263,587.511628,25160,12,32,46,13,99.5922891,0.0475003,0.12666746,0.18208447,0.05145865,16648,71.6842864,17.5516579,2.87121576,24533,97.1103986,10.6754168,12.9199756,7.8841449,14.5652992,0,"AAR"
"428",3043,"VERNON","WI",0.048,25617,533.6875,25509,12,36,42,18,99.578405,0.04684389,0.14053168,0.16395362,0.07026584,16883,69.211633,18.9421311,3.62494817,25087,97.9310614,15.8249292,21.2011051,13.4496431,14.5714286,0,"AAR"
"429",3044,"VILAS","WI",0.06,17707,295.116667,16116,9,1534,38,10,91.0148529,0.05082736,8.66324053,0.21460439,0.05647484,12815,76.1451424,19.2118611,4.31525556,17446,98.5260067,14.7369024,23.0433667,14.2519778,9.17322835,0,"AAR"
"430",3045,"WALWORTH","WI",0.032,75000,2343.75,72747,454,201,494,1104,96.996,0.60533333,0.268,0.65866667,1.472,46742,79.0210089,23.1569039,6.08232425,71553,95.404,9.64180398,8.69961284,10.9266098,6.89418232,0,"AAR"
"431",3046,"WASHBURN","WI",0.05,13772,275.44,13585,25,122,33,7,98.6421725,0.18152774,0.88585536,0.23961661,0.05082777,9297,74.8521028,19.0168872,4.02280305,13532,98.2573337,15.8660952,21.4185982,13.6424832,14.329455,0,"AAR"
"432",3047,"WASHINGTON","WI",0.025,95328,3813.12,94465,125,208,337,193,99.0947046,0.13112622,0.21819402,0.35351628,0.20245888,59583,81.3403152,23.3909001,4.01456791,94143,98.7569235,3.23762786,4.06985404,2.58450033,4.28088917,1,"HLU"
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"434",3049,"WAUPACA","WI",0.045,46104,1024.53333,45695,22,125,92,170,99.1128752,0.0477182,0.27112615,0.19954885,0.36873156,30109,72.137899,16.5498688,3.13859643,44412,96.3300364,8.48869675,10.071411,6.95379864,10.338641,0,"AAR"
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"436",3051,"WINNEBAGO","WI",0.035,140320,4009.14286,136822,697,685,1728,388,97.5071266,0.49672178,0.4881699,1.23147092,0.27651083,88960,80.6193795,24.9955036,5.65984712,133950,95.4603763,8.80403135,10.5920306,8.66058681,6.66109376,1,"HAU"
"437",3052,"WOOD","WI",0.048,73605,1533.4375,72157,90,481,722,155,98.0327423,0.1222743,0.65348821,0.98091162,0.21058352,46796,78.2951534,21.6663817,4.5837251,72685,98.7500849,8.52583064,11.1629965,7.37565628,7.88291776,0,"AAR"


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FILE: 2022年/2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/readme.txt
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# 代码解释见推文：https://mp.weixin.qq.com/s/B0AhboAduKpAYrtPr5Yozw
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》
# 代码来源：http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html





![image-20230513221952622](/Users/liangliangzhuang/Library/Application Support/typora-user-images/image-20230513221952622.png)


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FILE: 2022年/2022.11.15 R绘图案例｜基于分面的折线图绘制/.Rhistory
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ggplot(data, aes(x=time, y=value, color = Method)) +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0,0),
breaks = c(1:7),
limits = c(1,7))
ggplot(data, aes(x=time, y=value, color = Method)) +
geom_line() +
facet_wrap(vars(Cri), nrow =1)
ggplot(data, aes(x=time, y=value, color = Method)) +
geom_line()
data["Cri"] = rep(rep(c("RMSE","MAE","MAPE"),each = 7),5)
dat = read.csv("data_wuhan.csv",header = F)
colnames(dat) = c("Method","Cri",1:7)
library(tidyverse)
data = dat[,3:9] %>% pivot_longer(1:7,
names_to = "time",
values_to = "value",
names_transform = list(time = as.integer)
)
data["Cri"] = rep(rep(c("RMSE","MAE","MAPE"),each = 7),5)
data["Method"] = rep(c("FNN","RNN","LSTM","Bi-LSTM","ENDC-LSTM"),each= 21)
# library
library(ggplot2)
library(viridis)
library(hrbrthemes)
head(data)
# Stacked
ggplot(data, aes(x=time, y=value, color = Method)) +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0,0),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0,0),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method)) +
geom_point() +
geom_line(shape=21) +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0,0),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method)) +
geom_point(shape=21) +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0,0),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method)) +
geom_point(shape=21,fill=Method) +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0,0),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,fill=Method)) +
geom_point(shape=21) +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0,0),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method)) +
geom_point(shape=21) +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0,0),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method)) +
geom_point(aes(shape=method)) +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0,0),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method)) +
geom_point(data, aes(shape=method)) +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0,0),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape=method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0,0),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method)) +
geom_point(data, aes(shape = Method)) +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0,0),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0,0),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0,0),
breaks = c(0:7),
limits = c(0,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0.5,0.5),
breaks = c(0:7),
limits = c(0,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(0:7),
limits = c(0,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T,direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7))
as.factor(data["Method"])
data["Method"] = as.factor(data["Method"], levels =  c("FNN", "RNN", "LSTM", "Bi-LSTM", "ENDC-LSTM"))
data["Method"] = as.factor(data["Method"], levels = c("FNN", "RNN", "LSTM", "Bi-LSTM", "ENDC-LSTM"))
as.factor(data["Method"], levels = c("FNN", "RNN", "LSTM", "Bi-LSTM", "ENDC-LSTM"))
data["Method"] = afactor(data["Method"], levels = c("FNN", "RNN", "LSTM", "Bi-LSTM", "ENDC-LSTM"))
data["Method"] = factor(data["Method"], levels = c("FNN", "RNN", "LSTM", "Bi-LSTM", "ENDC-LSTM"))
# library
library(ggplot2)
library(viridis)
library(hrbrthemes)
head(data)
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T,direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7))
# library
library(ggplot2)
# create a dataset
specie <- c(rep("sorgho" , 3) , rep("poacee" , 3) , rep("banana" , 3) , rep("triticum" , 3) )
condition <- rep(c("normal" , "stress" , "Nitrogen") , 4)
value <- abs(rnorm(12 , 0 , 15))
dat = read.csv("data_wuhan.csv",header = F)
colnames(dat) = c("Method","Cri",1:7)
library(tidyverse)
data = dat[,3:9] %>% pivot_longer(1:7,
names_to = "time",
values_to = "value",
names_transform = list(time = as.integer)
)
data["Cri"] = rep(rep(c("RMSE","MAE","MAPE"),each = 7),5)
data["Method"] = rep(c("FNN","RNN","LSTM","Bi-LSTM","ENDC-LSTM"),each= 21)
data["Method"] = factor(data["Method"], levels = c("FNN", "RNN", "LSTM", "Bi-LSTM", "ENDC-LSTM"))
# library
library(ggplot2)
library(viridis)
library(hrbrthemes)
head(data)
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T,direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7))
data["Method"]
dat = read.csv("data_wuhan.csv",header = F)
colnames(dat) = c("Method","Cri",1:7)
library(tidyverse)
data = dat[,3:9] %>% pivot_longer(1:7,
names_to = "time",
values_to = "value",
names_transform = list(time = as.integer)
)
data["Cri"] = rep(rep(c("RMSE","MAE","MAPE"),each = 7),5)
data["Method"] = rep(c("FNN","RNN","LSTM","Bi-LSTM","ENDC-LSTM"),each= 21)
data["Method"]
factor(data["Method"], levels = c("FNN", "RNN", "LSTM", "Bi-LSTM", "ENDC-LSTM"))
# library
library(ggplot2)
library(viridis)
library(hrbrthemes)
head(data)
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T,direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis2(discrete = T,direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T,option = "A", direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T,option = "B", direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T,option = "C", direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T,option = "D", direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T,option = "E", direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T,option = "F", direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7))
# Stacked
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
scale_color_viridis(discrete = T,option = "G", direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7))
# plot
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
scale_color_viridis(discrete = T,option = "G", direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7)) +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right")
# plot
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
scale_color_viridis(discrete = T,option = "G", direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7)) +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
xlab("zzx love me") +
ylab("add your text")
library(tidyverse)
# library
library(ggplot2)
library(viridis)
library(hrbrthemes)
# data reprocess
dat = read.csv("data_wuhan.csv",header = F)
colnames(dat) = c("Method","Cri",1:7)
library(tidyverse)
data = dat[,3:9] %>% pivot_longer(1:7,
names_to = "time",
values_to = "value",
names_transform = list(time = as.integer)
)
data["Cri"] = rep(rep(c("RMSE","MAE","MAPE"),each = 7),5)
data["Method"] = rep(c("FNN","RNN","LSTM","Bi-LSTM","ENDC-LSTM"),each= 21)
factor(data["Method"], levels = c("FNN", "RNN", "LSTM", "Bi-LSTM", "ENDC-LSTM"))
head(data)
# plot
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
scale_color_viridis(discrete = T,option = "G", direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7)) +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
xlab("zzx love me") +
ylab("add your text")
# library
library(ggplot2)
library(viridis)
library(hrbrthemes)
# data reprocess
dat = read.csv("data_wuhan.csv",header = F)
colnames(dat) = c("Method","Cri",1:7)
library(tidyverse)
data = dat[,3:9] %>% pivot_longer(1:7,
names_to = "time",
values_to = "value",
names_transform = list(time = as.integer)
)
data["Cri"] = rep(rep(c("RMSE","MAE","MAPE"),each = 7),5)
data["Method"] = rep(c("FNN","RNN","LSTM","Bi-LSTM","ENDC-LSTM"),each= 21)
factor(data["Method"], levels = c("FNN", "RNN", "LSTM", "Bi-LSTM", "ENDC-LSTM"))
head(data)
# plot
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
scale_color_viridis(discrete = T,option = "G", direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7)) +
facet_wrap(vars(Cri), nrow =1) +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
xlab("zzx love me") +
ylab("add your text")
# plot
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
scale_color_viridis(discrete = T,option = "G", direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7)) +
facet_wrap(vars(Cri), nrow =1,strip.position = "bottom") +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
xlab("zzx love me") +
ylab("add your text")
# plot
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
scale_color_viridis(discrete = T,option = "G", direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7)) +
facet_wrap(vars(Cri), nrow =1,strip.position = "bottom") +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
xlab("") +
ylab("add your text")
# plot
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +
geom_point() +
geom_line() +
scale_color_viridis(discrete = T,option = "G", direction = -1) +
scale_x_continuous(expand = c(0.1,0.1),
breaks = c(1:7),
limits = c(1,7)) +
facet_wrap(vars(Cri), nrow =1,strip.position = "top") +
theme_bw() +
theme(panel.grid = element_blank(), legend.position="right") +
xlab("") +
ylab("add your text")


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FILE: 2022年/2022.11.15 R绘图案例｜基于分面的折线图绘制/.Rproj.user/C6560784/sources/session-417D82C1/3B4320B1-contents
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# library
library(ggplot2)
library(viridis)
library(tidyverse)
# data reprocess
dat = read.csv("data_city.csv",header = F)
colnames(dat) = c("Method","Cri",1:7)
head(dat)
data = dat[,3:9] %>% pivot_longer(1:7,
                      names_to = "time",
                      values_to = "value",
                      names_transform = list(time = as.integer)
                      )
data["Cri"] = rep(rep(c("RMSE","MAE","MAPE"),each = 7),5)
data["Method"] = rep(c("FNN","RNN","LSTM","Bi-LSTM","ENDC-LSTM"),each= 21)
factor(data["Method"], levels = c("FNN", "RNN", "LSTM", "Bi-LSTM", "ENDC-LSTM"))

head(data)

# plot
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) + 
  geom_point() +
  geom_line() + 
  facet_wrap(vars(Cri), nrow =1,strip.position = "top") +
  scale_color_viridis(discrete = T,option = "G", direction = -1) +
  scale_x_continuous(expand = c(0.1,0.1),
                     breaks = c(1:7),
                     limits = c(1,7)) +
  theme_bw() +  
  theme(panel.grid = element_blank(), legend.position="right") +
  xlab("Time") +
  ylab("Value")





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/Users/liangliangzhuang/Documents/我的项目/zzx_plot/line_point.R="CE695975"


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FILE: 2022年/2022.11.15 R绘图案例｜基于分面的折线图绘制/data_city.csv
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SO-FNN,RMSE,12.26,24.37,31.06,34.24,35.32,36.62,37.06
,MAE,9.53,17.48,22.52,25.22,26.29,27.47,28.12
,MAPE,11.72,22.19,28.24,30.53,31.85,34.47,36.77
SO-RNN,RMSE,40.12,52.06,59.53,62.89,64.27,62.79,62.09
,MAE,37.12,43.41,47.92,50.8,52.26,50.01,48.4
,MAPE,42.99,44.71,47.5,50.14,51.93,49.51,47.56
SO-LSTM,RMSE,6.78,23.79,32.53,36.11,37.53,37.58,38.1
,MAE,4.71,16.86,22.99,25.55,26.55,26.74,27.46
,MAPE,6.11,21.72,29.02,30.62,31.54,31.86,33.54
SO-Bi-LSTM,RMSE,4.38,23.74,32.67,38.65,39.54,38.71,37.22
,MAE,3.09,16.51,22.71,26.72,27.5,27.36,26.98
,MAPE,3.91,20.41,27.13,30.82,31.89,32.36,32.33
SO-ENDC-LSTM,RMSE,13.11,27.49,35.23,40.78,42.1,42.72,42.76
,MAE,9.75,19.29,23.96,27.63,28.91,29.85,30.42
,MAPE,13.7,24.24,28.54,31.45,33.18,35.38,36.88


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FILE: 2022年/2022.11.15 R绘图案例｜基于分面的折线图绘制/line_point.R
================================================

# 代码解释见推文：https://mp.weixin.qq.com/s/apu5-4ykHe0QTUbdJKy-MQ
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》


# library
library(ggplot2)
library(viridis)
library(tidyverse)
# data reprocess
dat = read.csv("data_city.csv",header = F)
colnames(dat) = c("Method","Cri",1:7)

data = dat[,3:9] %>% pivot_longer(1:7,
                      names_to = "time",
                      values_to = "value",
                      names_transform = list(time = as.integer)
                      )
data["Cri"] = rep(rep(c("RMSE","MAE","MAPE"),each = 7),5)
data["Method"] = rep(c("FNN","RNN","LSTM","Bi-LSTM","ENDC-LSTM"),each= 21)
factor(data["Method"], levels = c("FNN", "RNN", "LSTM", "Bi-LSTM", "ENDC-LSTM"))

head(data)

# plot
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) + 
  geom_point() +
  geom_line() + 
  scale_color_viridis(discrete = T,option = "G", direction = -1) +
  scale_x_continuous(expand = c(0.1,0.1),
                     breaks = c(1:7),
                     limits = c(1,7)) +
  facet_wrap(vars(Cri), nrow =1,strip.position = "top") +
  theme_bw() +  
  theme(panel.grid = element_blank(), legend.position="right") +
  xlab("Time") +
  ylab("Value")





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FILE: 2022年/2022.11.19 分面中添加不同的直线/facet-line.r
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# 代码解释见推文：https://mp.weixin.qq.com/s/JdfKl9vEqkE0rcEfz6lBBg
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》

dat3 = read_excel("test.xlsx",sheet=3,na="NA")
head(dat3)
dat3 %>%
  pivot_longer(
    cols = `官方结果`:`XGBoost`,
    names_to = "模型",
    names_transform = list(模型 = as.character),
    values_to = "value") -> dat_3_new

dat_3_new$ 模型 = fct_relevel(dat_3_new$ 模型, 
                           "官方结果","朴素贝叶斯","支持向量机","决策树","随机森林","XGBoost")

head(dat_3_new)

dummy2 <- data.frame(叶类名称 = dat3$ 叶类名称, Z = dat3$ 官方结果)
dat_3_new %>% ggplot(aes(`模型`,value,fill = `模型`)) + 
  geom_col() +
  facet_wrap(vars(`叶类名称`)) +
  geom_hline(data = dummy2,aes(yintercept = Z)) +
  # scale_fill_manual(values = cols[1:6]) +
  theme_bw() + ylab("准确度") + xlab("") + #主题设置
  scale_fill_viridis(discrete = T,option = "D") +
  theme(panel.grid = element_blank(),
        legend.position = 'bottom',
        legend.direction = "horizontal",
       axis.text.x = element_blank(),
       axis.ticks.x = element_blank())

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NumSpacesForTab: 2
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LaTeX: XeLaTeX

BuildType: Website


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FILE: 2022年/2022.11.29 R绘图案例｜基于分面的面积图绘制/.Rhistory
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# install.packages("readxl")
library(readxl)
library(ggplot2)
library(tidyverse)
library(cowplot)
library(viridis)
library(showtext)
showtext_auto()
### 绘制不同方法的区域图===========
dat = read_excel("test.xlsx",sheet=1,na="NA")
head(dat)
colnames(dat) = c("Id",paste("X",1:6,sep=''))
cols <- c("#85BA8F", "#A3C8DC","#349839","#EA5D2D","#EABB77","#F09594")
dat %>% select(c(Id,X4,X1)) %>%
pivot_longer(
cols = c(X4,X1),
names_to = "method",
names_transform = list(method = as.character),
values_to = "Acc") -> dat1
cols <- c("#85BA8F", "#A3C8DC","#349839","#EA5D2D","#EABB77","#F09594")
p1 = ggplot(dat1) + geom_area(aes(Id,Acc),fill = cols[1]) +
facet_wrap(vars(method),nrow = 2,strip.position = "top") +
theme_bw() + ylab("精确率") + xlab("叶类") + #主题设置
theme(panel.grid = element_blank()) +
p1
#==
dat %>% select(c(Id,X5,X2)) %>%
pivot_longer(
cols = c(X5,X2),
names_to = "method",
names_transform = list(method = as.character),
values_to = "Acc") -> dat2
p2 = ggplot(dat2) + geom_area(aes(Id,Acc),fill = cols[2]) + facet_wrap(vars(method),nrow = 2,strip.position = "top") +
theme_bw() + ylab("召回率") + xlab("叶类") + #主题设置
theme(panel.grid = element_blank())
p2
#==
dat %>% select(c(Id,X3,X6)) %>%
pivot_longer(
cols = c(X3,X6),
names_to = "method",
# names_transform = list(method = as.factor),
values_to = "Acc") -> dat3
# levels(dat3$method) = c("","")
cols <- c("#85BA8F", "#A3C8DC","#349839","#EA5D2D","#EABB77","#F09594")
p3 = ggplot(dat3) + geom_area(aes(Id,Acc),fill = cols[4]) + facet_wrap(vars(method),nrow = 2,strip.position = "top") +
theme_bw() + ylab("F1得分") + xlab("叶类") + #主题设置
theme(panel.grid = element_blank())
p3
plot_grid(p1,p2,p3,ncol = 3)
dat_all = read_excel("test.xlsx",sheet=2,na="NA")
head(dat_all)
dat_all %>%
pivot_longer(
cols = `准确率`:`F1得分`,
names_to = "method",
names_transform = list(method = as.character),
values_to = "value") -> dat_all_new
dat_all_new$method = fct_relevel(dat_all_new$method, "准确率", "精确率", "召回率","F1得分")
dat_all_new %>% ggplot(aes(method ,
value,fill=模型)) +
geom_col(position = "dodge")+
facet_wrap(vars(文本向量化方法)) +
theme_bw() + ylab("数值") + xlab("评价指标") + #主题设置
# scale_fill_viridis(discrete = T) +
# geom_hline(aes(yintercept = 0.75),color = "gray") +
scale_fill_manual(values =cols[c(1,2,4)]) +
theme(panel.grid = element_blank())
dat_3_new$模型 = fct_relevel(dat_3_new$模型,
"官方结果","朴素贝叶斯","支持向量机","决策树","随机森林","XGBoost")
dat3 = read_excel("test.xlsx",sheet=3,na="NA")
head(dat3)
dat3 %>%
pivot_longer(
cols = `官方结果`:`XGBoost`,
names_to = "模型",
names_transform = list(模型 = as.character),
values_to = "value") -> dat_3_new
dummy2 <- data.frame(X = dat3$叶类名称, Z = dat3$官方结果)
dat_3_new %>% ggplot(aes(`模型`,value,fill = `模型`)) +
geom_col() +
facet_wrap(vars(`叶类名称`)) +
geom_hline(data = dummy2,aes(yintercept = Z)) +
scale_fill_manual(values = cols[1:6]) +
theme_bw() + ylab("准确度") + xlab("") + #主题设置
# scale_fill_viridis(discrete = T) +
theme(panel.grid = element_blank(),
legend.position = 'bottom',
legend.direction = "horizontal",
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
head(dat3)
dat3 %>%
pivot_longer(
cols = `官方结果`:`XGBoost`,
names_to = "模型",
names_transform = list(模型 = as.character),
values_to = "value") -> dat_3_new
dummy2 <- data.frame(X = dat3$叶类名称, Z = dat3$官方结果)
dat_3_new %>% ggplot(aes(`模型`,value,fill = `模型`)) +
geom_col() +
facet_wrap(vars(`叶类名称`)) +
geom_hline(data = dummy2,aes(yintercept = Z)) +
scale_fill_manual(values = cols[1:6]) +
theme_bw() + ylab("准确度") + xlab("") + #主题设置
# scale_fill_viridis(discrete = T) +
theme(panel.grid = element_blank(),
legend.position = 'bottom',
legend.direction = "horizontal",
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
dummy2 <- data.frame(模型 = dat3$叶类名称, Z = dat3$官方结果)
dat_3_new %>% ggplot(aes(`模型`,value,fill = `模型`)) +
geom_col() +
facet_wrap(vars(`叶类名称`)) +
geom_hline(data = dummy2,aes(yintercept = Z)) +
scale_fill_manual(values = cols[1:6]) +
theme_bw() + ylab("准确度") + xlab("") + #主题设置
# scale_fill_viridis(discrete = T) +
theme(panel.grid = element_blank(),
legend.position = 'bottom',
legend.direction = "horizontal",
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
# dummy2 <- data.frame(X = dat3$叶类名称, Z = dat3$官方结果)
dat_3_new %>% ggplot(aes(`模型`,value,fill = `模型`)) +
geom_col() +
facet_wrap(vars(`叶类名称`)) +
# geom_hline(data = dummy2,aes(yintercept = Z)) +
scale_fill_manual(values = cols[1:6]) +
theme_bw() + ylab("准确度") + xlab("") + #主题设置
# scale_fill_viridis(discrete = T) +
theme(panel.grid = element_blank(),
legend.position = 'bottom',
legend.direction = "horizontal",
axis.text.x = element_blank(),
axis.ticks.x = element_blank())


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FILE: 2022年/2022.11.29 R绘图案例｜基于分面的面积图绘制/.Rproj.user/C6560784/sources/session-23478791/0BC0007C-contents
================================================
# library
library(ggplot2)
library(viridis)
library(tidyverse)
# data reprocess
dat = read.csv("data_city.csv",header = F)
colnames(dat) = c("Method","Cri",1:7)

data = dat[,3:9] %>% pivot_longer(1:7,
                      names_to = "time",
                      values_to = "value",
                      names_transform = list(time = as.integer)
                      )
data["Cri"] = rep(rep(c("RMSE","MAE","MAPE"),each = 7),5)
data["Method"] = rep(c("FNN","RNN","LSTM","Bi-LSTM","ENDC-LSTM"),each= 21)
factor(data["Method"], levels = c("FNN", "RNN", "LSTM", "Bi-LSTM", "ENDC-LSTM"))

head(data)

# plot
ggplot(data, aes(x=time, y=value, color = Method,shape = Method)) + 
  geom_point() +
  geom_line() + 
  scale_color_viridis(discrete = T,option = "G", direction = -1) +
  scale_x_continuous(expand = c(0.1,0.1),
                     breaks = c(1:7),
                     limits = c(1,7)) +
  facet_wrap(vars(Cri), nrow =1,strip.position = "top") +
  theme_bw() +  
  theme(panel.grid = element_blank(), legend.position="right") +
  xlab("Time") +
  ylab("Value")





================================================
FILE: 2022年/2022.11.29 R绘图案例｜基于分面的面积图绘制/.Rproj.user/C6560784/sources/session-23478791/AF358D71-contents
================================================
---
title: "r-sparkline"
author: "袁凡"
date: '2022-11-07'
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
```

这篇笔记一共有两章，第一章记录 sparkline 包中可改动的参数细节，第二章记录将 sparkline 与 formattable 包、DT 包、reactable包结合使用的案例，其中一部分数据操作分 tidy 版本和 data.table 版本。

据笔者所知，R 中现在有两个包可以画 sparkline（迷你图）：

+ 一个是[基于dataui包](https://timelyportfolio.github.io/dataui/articles/dataui_replicate_examples.html)的 reactablefmtr 包，其[提供的案例](https://kcuilla.github.io/reactablefmtr/articles/sparklines.html)有相当多的细节，若要应用直接看官网案例即可，不需要单独鼓捣一篇笔记出来；

+ 二个是 Rstudio 他们家出的 sparkline 包，此包的[文档](https://cran.r-project.org/web/packages/sparkline/sparkline.pdf)仅有7页，有[简洁的网站案例](https://cran.r-project.org/web/packages/sparkline/vignettes/intro_sparkline.html)，最近一次更新是2016年11月12日，不过由于这个包就是[jQuery Sparklines](https://omnipotent.net/jquery.sparkline/#s-about)的 R 版本，有些细节需要对照两种文档来看，所以笔者决定还是鼓捣一篇笔记出来。

# 一、sparkline 包的参数细节

这一章记录了sparkline包可以画的图形基本类型，如'line'（面积图/折线图）、'bar'（柱形图/堆叠柱形图）、'tristate'、'discrete'、'bullet'、'box'（箱线图）、'pie'（饼图），还记录了一些可以改动的图形细节，如图形区域的高度和宽度、颜色、标注点和区域等。

## 1.1. 迷你图的基本类型

首先，编造一些用于展示迷你图的基础数据。

```{r}
library(htmlwidgets)
library(sparkline)
library(dplyr) # tidy版操作数据
library(tibble) #使用tibble类型的数据
library(purrr) #使用 map_chr 函数
library(data.table) # data.table版操作数据
library(formattable)
library(DT)
library(reactable)

set.seed(1234)
x = rnorm(10)
y = c(rnorm(10), rep(0, 3))
z = c(rep(1, 3))
u = data.frame(serie1 = c(1:4), serie2 = c(4:1)) #堆叠柱形图的数据
u = as.matrix(u) # 转换成矩阵
```

为了方便显示代码，下表中“代码”一列中均增加了`eval = FALSE`这一参数。

![](https://yuanfan.rbind.io/images/2022/2022-06-01-1.png)

运用 sparkline 包中的`spk_composite()`函数可以将两个不同的迷你图一起展示。

```{r}
# 第一种写法
s1 <- sparkline(abs(x), type = 'bar')
s2 <- sparkline(y, type = "line", fillColor = FALSE)
spk_composite(s1, s2)

# 第二种写法
spk_composite(sparkline(abs(x), type = 'bar'),
              sparkline(y, type = "line", fillColor = FALSE))
```

![](https://yuanfan.rbind.io/images/2022/2022-06-01-2.png)

## 1.2. 迷你图的图形细节

原 jQuery Sparklines 的文档中详细介绍了迷你图的[通用参数](https://omnipotent.net/jquery.sparkline/#common)和[基于不同图形类型的个性参数](https://omnipotent.net/jquery.sparkline/#line)。这一小节仅记录部分细节。

### 1.2.1. 图形区域的高度和宽度

`sparkline()`函数中默认宽度和高度是`width = 60, height = 20`。

![](https://yuanfan.rbind.io/images/2022/2022-06-01-3.png)

宽度`width`对柱形图和 'tristate' 不起作用，需要通过修改`barWidth`（单个柱子的宽度）和`barSpacing`（柱子之间的空隙）来达到改变整个迷你图宽度的效果。

![](https://yuanfan.rbind.io/images/2022/2022-06-01-4.png)

### 1.2.2. 颜色

+ 一般折线图的颜色设置
    - 'lineColor'：指定线的颜色
    - 'fillColor'：指定折线下面积的填充颜色，设定为`fillColor = FALSE`时表示不展示折线下的面积。

![](https://yuanfan.rbind.io/images/2022/2022-06-01-5.png)

+ 柱形图的颜色设置
    - 'barColor'：指定普通柱子的颜色，通常就是指数值为正数的柱子
    - 'negBarColor'：指定数值为负数的柱子的颜色
    - 'zeroColor'：指定数值为零的柱子的颜色

![](https://yuanfan.rbind.io/images/2022/2022-06-01-6.png)

+ 堆叠柱形图的颜色设置
    - 'stackedBarColor'：指定堆叠的柱形图中柱子的颜色，如果是只堆叠了两个不同的数据系列，那么只需要指定两个不同的颜色。

![](https://yuanfan.rbind.io/images/2022/2022-06-01-7.png)

+ 饼图颜色设置
    - 'sliceColors'：指定饼图中各个扇形的颜色。

![](https://yuanfan.rbind.io/images/2022/2022-06-01-8.png)

### 1.2.3. 坐标轴

+ Y 轴最大、最小值
    - 'chartRangeMin'：指定直角坐标系下 Y 轴最大值
    - 'chartRangeMax'：指定直角坐标系下 Y 轴最小值

本文案例中'x'最大值为1.23，最小值为-2.42，因此若指定Y轴最小值为-5时，横轴之下的柱子都会变矮，同理若指定 Y 轴最大值为2时，横轴之上的柱子也会变矮。

![](https://yuanfan.rbind.io/images/2022/2022-06-01-9.png)

+ 旋转角度
    - 'offset'：指定饼图的旋转角度，取值范围是[-90,90]

![](https://yuanfan.rbind.io/images/2022/2022-06-01-10.png)

### 1.2.4. 标注点和区域

+ 标注点
    - 'spotColor'：折线图默认标记最大值和最小值点，设置`spotColor=FALSE`时表示不标注
    - 'spotRadius'：标记点的半径,默认值为1.5
    - 'minSpotColor'：指定标记的最小值的点的颜色
    - 'maxSpotColor'：指定标记的最大值的点的颜色

![](https://yuanfan.rbind.io/images/2022/2022-06-01-11.png)

+ 标注区域
    - `normalRangeMin` 和`normalRangeMax`这两个参数需要同时设置，只设置一个会不起作用。

![](https://yuanfan.rbind.io/images/2022/2022-06-01-12.png)

# 二、在表格中使用 sparkline

这一节记录了在 formattable 包和 DT 包中使用 sparkline 的案例，需使用 sparkline 包中的`spk_add_deps()`和`spk_chr()`函数。

## 2.1. 直接展示

一般来讲一个表格中的一个单元格里仅展示一个数值，如果一个单元格里要显示一个迷你图，那么迷你图中需要写入一串数据。若直接在表格中增加 展示迷你图，那么就需要在原来用于展示的数据框中写入列表，作为迷你图的那一列中的每一行数据都需要单独写入。这一小节的案例中使用的数据如下：

```{r}
set.seed(1234)
x = rnorm(10)

table1 <-
  tibble(
    column1 = c('坂田银时', '神乐', '志村新八', '定春'), # 第一列
    column2 = c(100, 10000, 10, 100),                 # 第二列
    column3 = c(1:4), # 第三列
    sparkline = list( # 第四列
      v1 = x,      # 第四列第一行
      v2 = abs(x), # 第四列第二行
      v3 = x,      # 第四列第三行
      v4 = abs(x)  # 第四列第四行
    )
  )
```

### 2.1.1. 与 formattable 包结合

```{r}
table1.formattable <- as.htmlwidget(formattable(
  data.frame(
    column1 = c('坂田银时', '神乐', '志村新八', '定春'), # 第一列
    column2 = c(100, 10000, 10, 100),                    # 第二列
    column3 = c(1:4), # 第三列
    sparkline = c(    # 第四列
      spk_chr(x, type = 'line'),       # 第四列第一行
      spk_chr(abs(x), type = 'line'),  # 第四列第二行
      spk_chr(x, type = 'bar'),        # 第四列第三行
      spk_chr(abs(x), type = 'bar')    # 第四列第四行
    ),
    stringsAsFactors = FALSE)))

spk_add_deps(table1.formattable)
```

![](https://yuanfan.rbind.io/images/2022/2022-06-01-13.png)

### 2.1.2. 与 DT 包结合

```{r}
table1.dt <- table1

table1.dt$sparkline <- table1.dt$sparkline %>%
  map(~ sparkline(.x, type = "box")) %>%
  map(htmltools::as.tags) %>%
  map_chr(as.character)

DT::datatable(table1.dt, escape = FALSE) %>% spk_add_deps()
```

![](https://yuanfan.rbind.io/images/2022/2022-06-01-14.png)

### 2.1.3. 与 reactable 结合

在写入需要展示迷你图的那列数据时，有两种写法。

+ 写法一：

写法是`columns = list( 列名 = colDef(cell = function(values){sparkline(values)}) )`。

```{r}
reactable(table1,
          columns = list(sparkline = colDef(
            cell = function(values) {
              sparkline(values,
                        type = "line")
            }
          )))
```

+ 写法二

写法是`columns = list( 列名 = colDef( cell = function(value, index){sparkline( 表名$列名[[index] )}))`。

```{r}
reactable(table1,
          columns = list(sparkline = colDef(
            cell = function(value, index) {
              sparkline(table1$sparkline[[index]], type = "line")
            }
          )))
```

![](https://yuanfan.rbind.io/images/2022/2022-06-01-15.png)

+ 写法3

reactable 包的案例中，还有一种[特别的写法](https://glin.github.io/reactable/articles/examples.html#footers)。

```{r}
reactable(
  iris[1:20, ],
  defaultPageSize = 5,
  bordered = TRUE,
  defaultColDef = colDef(footer = function(values) {
    if (!is.numeric(values)) return()
    sparkline(values, type = "box", width = 100, height = 30)
  })
)
```

![](https://yuanfan.rbind.io/images/2022/2022-06-01-16.png)

## 2.2. 分组聚合后展示

若是用于表格展示的数据需要先进行一些分组聚合的操作，而后又需要增加展示迷你图，那么……这一小节的案例中使用的数据如下：

```{r}
table2 <- data.frame(
  column1 = c(rep(c('坂田银时', '神乐', '志村新八', '定春'), 10)),
  column2 = sample(1:4, 40, replace = T),
  column3 = sample(10:20, 40, replace = T))
```

### 2.2.1. 与 formattable 结合

+ tidy 版本

```{r}
table2 %>%
  group_by(column1) %>%
  summarise(
    column2_mean = mean(column2),
    sparkline1 = spk_chr(column2, type = 'line'),
    sparkline2 = spk_chr(column3, type = 'bar')
  ) %>%
  formattable() %>%
  formattable::as.htmlwidget() %>%
  spk_add_deps()
```

![](https://yuanfan.rbind.io/images/2022/2022-06-01-17.png)

+ data.table 版本

```{r}
table2.dt <- as.data.table(table2)

table2.dt[, by = .(column1), .(
  column2_mean = mean(column2),
  sparkline1 = spk_chr(column2, type = 'line'),
  sparkline2 = spk_chr(column3, type = 'bar')
)] |>
  formattable() |>
  formattable::as.htmlwidget() |>
  spk_add_deps()
```

### 2.2.2. 与 DT 结合

+ tidy 版本

```{r}
table2.tidy <- table2 %>%
  group_by(column1) %>%
  summarise(column2_mean = mean(column2))

table2.tidy$sparkline1 <- table2$column2 %>%
  split(table2$column1) %>%
  map( ~ sparkline(.x, type = "line")) %>%
  map(htmltools::as.tags) %>%
  map_chr(as.character)

table2.tidy$sparkline2 <- table2$column3 %>%
  split(table2$column1) %>%
  map( ~ sparkline(.x, type = "bar")) %>%
  map(htmltools::as.tags) %>%
  map_chr(as.character)

DT::datatable(table2.tidy, escape = FALSE) %>% spk_add_deps()
```

![](https://yuanfan.rbind.io/images/2022/2022-06-01-18.png)

+ data.table 版本

下面的写法来源于统计之都论坛上的[一个帖子](<https://d.cosx.org/d/423198-tidy-datatable>)。不熟 data.table 包的时候，可以使用`dtplyr::lazy_dt()`把tidy版本转换成data.table版本，然后用`dtplyr:::dt_call()`提取出具体的代码。

```{r}
table2.dt.DT <- table2.dt[, .(
  column2_mean = mean(column2),
  sparkline1 = as.character(htmltools::as.tags(sparkline(column2, type = "line"))),
  sparkline2 = as.character(htmltools::as.tags(sparkline(column2, type = "bar")))
), keyby = .(column1)]

DT::datatable(table2.dt.DT, escape = FALSE) |> spk_add_deps()
```

### 2.2.3. 与reactable 结合

+ tidy 版本

```{r}
table2.tidy.react <- table2 %>%
  group_by(column1) %>%
  summarise(
    column2_mean = mean(column2),
    sparkline1 = list(column2),
    sparkline2 = list(column3)
  )

reactable(table2.tidy.react,
          columns = list(
            sparkline1 = colDef(
              cell = function(values) {
                sparkline(values,
                          type = "line")
              }
            ),
            sparkline2 = colDef(
              cell = function(value, index) {
                sparkline(table2.tidy.react$sparkline2[[index]], type = "bar")
              }
            )
          ))
```

![](https://yuanfan.rbind.io/images/2022/2022-06-01-19.png)

+ data.table 版本

```{r}
table2.dt.react <-
  table2.dt[, .(
    column2_mean = mean(column2),
    sparkline1 = list(column2),
    sparkline2 = list(column3)), keyby = .(column1)]

reactable(table2.dt.react,
          columns = list(
            sparkline1 = colDef(
              cell = function(values) {
                sparkline(values,
                          type = "line")
              }
            ),
            sparkline2 = colDef(
              cell = function(value, index) {
                sparkline(table2.dt.react$sparkline2[[index]], type = "bar")
              }
            )
          ))
```

## 2.3. 把 tidy 代码转换成 data.table 代码

举个栗子：

```{r}
library(dtplyr)
a <- lazy_dt(table2) %>%
  group_by(column1) %>%
  summarise(
    column2_mean = mean(column2),
    sparkline1 = list(column2),
    sparkline2 = list(column3)
  )

dtplyr:::dt_call(a)
```

得到：

```
`_DT2`[, .(column2_mean = mean(column2), sparkline1 = list(column2), 
    sparkline2 = list(column3)), keyby = .(column1)]
```



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# install.packages("readxl")
library(readxl)
library(ggplot2)
library(tidyverse)
library(cowplot)
library(viridis)
library(showtext)
showtext_auto()


### 绘制不同方法的区域图===========
dat = read_excel("test.xlsx",sheet=1,na="NA")
colnames(dat) = c("Id",paste("X",1:6,sep=''))
head(dat)


dat %>% select(c(Id,X1,X4)) %>% rename("随机森林"=X1, "XGBoost"=X4) %>% 
  pivot_longer(
             cols = c("随机森林","XGBoost"),
             names_to = "method",
             names_transform = list(method = as.character),
             values_to = "Acc") -> dat1
head(dat1)
cols <- c("#85BA8F", "#A3C8DC","#349839","#EA5D2D","#EABB77","#F09594")
p1 = ggplot(dat1) + 
  geom_area(aes(Id,Acc),fill = cols[1]) + 
  facet_wrap(vars(method),nrow = 2,strip.position = "top") +
  theme_bw() + 
  ylab("精确率") + 
  xlab("叶类") + #主题设置
  theme(panel.grid = element_blank()) 
p1

#==
dat %>% select(c(Id,X2,X5)) %>% rename("随机森林"=X2, "XGBoost"=X5) %>% 
  pivot_longer(
    cols = c("随机森林","XGBoost"),
    names_to = "method",
    names_transform = list(method = as.character),
    values_to = "Acc") -> dat2


p2 = ggplot(dat2) + geom_area(aes(Id,Acc),fill = cols[2]) + facet_wrap(vars(method),nrow = 2,strip.position = "top") +
  theme_bw() + ylab("召回率") + xlab("叶类") + #主题设置
  theme(panel.grid = element_blank())
p2

#==
dat %>% select(c(Id,X3,X6)) %>% rename("随机森林"=X3, "XGBoost"=X6) %>% 
  pivot_longer(
    cols = c("随机森林","XGBoost"),
    names_to = "method",
    # names_transform = list(method = as.factor),
    values_to = "Acc") -> dat3

p3 = ggplot(dat3) + 
  geom_area(aes(Id,Acc),fill = cols[4]) + 
  facet_wrap(vars(method),nrow = 2,strip.position = "top") +
  theme_bw() + 
  ylab("F1得分") + 
  xlab("叶类") + #主题设置
  theme(panel.grid = element_blank())
p3

plot_grid(p1,p2,p3,ncol = 3)


#==========================

dat_all = read_excel("test.xlsx",sheet=2,na="NA")
head(dat_all)

dat_all %>%
  pivot_longer(
    cols = `准确率`:`F1得分`,
    names_to = "method",
    names_transform = list(method = as.character),
    values_to = "value") -> dat_all_new

dat_all_new$method = fct_relevel(dat_all_new$method, "准确率", "精确率", "召回率","F1得分")
dat_all_new %>% ggplot(aes(method ,
                           value,fill=模型)) +
  geom_col(position = "dodge")+
  facet_wrap(vars(文本向量化方法)) +
  theme_bw() + ylab("数值") + xlab("评价指标") + #主题设置
  # scale_fill_viridis(discrete = T) +
  # geom_hline(aes(yintercept = 0.75),color = "gray") +
  scale_fill_manual(values =cols[c(1,2,4)]) +
  theme(panel.grid = element_blank())


# =========

dat_3_new$模型 = fct_relevel(dat_3_new$模型, 
     "官方结果","朴素贝叶斯","支持向量机","决策树","随机森林","XGBoost")


dat3 = read_excel("test.xlsx",sheet=3,na="NA")
head(dat3)
dat3 %>%
  pivot_longer(
    cols = `官方结果`:`XGBoost`,
    names_to = "模型",
    names_transform = list(模型 = as.character),
    values_to = "value") -> dat_3_new
dat_3_new$模型 = factor(dat_3_new$模型,levels = c("官方结果","朴素贝叶斯"))
dummy2 <- data.frame(叶类名称 = dat3$叶类名称, Z = dat3$官方结果)
dat_3_new %>% ggplot(aes(`模型`,value,fill = `模型`)) + 
  geom_col() +
  facet_wrap(vars(`叶类名称`)) +
  geom_hline(data = dummy2,aes(yintercept = Z)) +
  scale_fill_manual(values = cols[1:6]) +
  theme_bw() + ylab("准确度") + xlab("") + #主题设置
  # scale_fill_viridis(discrete = T) +
  theme(panel.grid = element_blank(),
        legend.position = 'bottom',
        legend.direction = "horizontal",
       axis.text.x = element_blank(),
       axis.ticks.x = element_blank())
  




================================================
FILE: 2022年/2022.11.29 R绘图案例｜基于分面的面积图绘制/.Rproj.user/C6560784/sources/session-23478791/lock_file
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FILE: 2022年/2022.11.29 R绘图案例｜基于分面的面积图绘制/.Rproj.user/C6560784/sources/session-51C83C65/8752579D-contents
================================================
# install.packages("readxl")
library(readxl)
library(ggplot2)
library(tidyverse)
library(cowplot)
library(viridis)
library(showtext)
showtext_auto()


### 绘制不同方法的区域图===========
dat = read_excel("test.xlsx",sheet=1,na="NA")
colnames(dat) = c("Id",paste("X",1:6,sep=''))
head(dat)


dat %>% select(c(Id,X1,X4)) %>% rename("随机森林"=X1, "XGBoost"=X4) %>% 
  pivot_longer(
             cols = c("随机森林","XGBoost"),
             names_to = "method",
             names_transform = list(method = as.character),
             values_to = "Acc") -> dat1
head(dat1)
cols <- c("#85BA8F", "#A3C8DC","#349839","#EA5D2D","#EABB77","#F09594")
p1 = ggplot(dat1) + 
  geom_area(aes(Id,Acc),fill = cols[1]) + 
  facet_wrap(vars(method),nrow = 2,strip.position = "top") +
  theme_bw() + 
  ylab("精确率") + 
  xlab("叶类") + #主题设置
  theme(panel.grid = element_blank()) 
p1

#==
dat %>% select(c(Id,X2,X5)) %>% rename("随机森林"=X2, "XGBoost"=X5) %>% 
  pivot_longer(
    cols = c("随机森林","XGBoost"),
    names_to = "method",
    names_transform = list(method = as.character),
    values_to = "Acc") -> dat2


p2 = ggplot(dat2) + geom_area(aes(Id,Acc),fill = cols[2]) + facet_wrap(vars(method),nrow = 2,strip.position = "top") +
  theme_bw() + ylab("召回率") + xlab("叶类") + #主题设置
  theme(panel.grid = element_blank())
p2

#==
dat %>% select(c(Id,X3,X6)) %>% rename("随机森林"=X3, "XGBoost"=X6) %>% 
  pivot_longer(
    cols = c("随机森林","XGBoost"),
    names_to = "method",
    # names_transform = list(method = as.factor),
    values_to = "Acc") -> dat3

p3 = ggplot(dat3) + 
  geom_area(aes(Id,Acc),fill = cols[4]) + 
  facet_wrap(vars(method),nrow = 2,strip.position = "top") +
  theme_bw() + 
  ylab("F1得分") + 
  xlab("叶类") + #主题设置
  theme(panel.grid = element_blank())
p3

plot_grid(p1,p2,p3,ncol = 3)


#==========================

dat_all = read_excel("test.xlsx",sheet=2,na="NA")
head(dat_all)

dat_all %>%
  pivot_longer(
    cols = `准确率`:`F1得分`,
    names_to = "method",
    names_transform = list(method = as.character),
    values_to = "value") -> dat_all_new

dat_all_new$method = fct_relevel(dat_all_new$method, "准确率", "精确率", "召回率","F1得分")
dat_all_new %>% ggplot(aes(method ,
                           value,fill=模型)) +
  geom_col(position = "dodge")+
  facet_wrap(vars(文本向量化方法)) +
  theme_bw() + ylab("数值") + xlab("评价指标") + #主题设置
  # scale_fill_viridis(discrete = T) +
  # geom_hline(aes(yintercept = 0.75),color = "gray") +
  scale_fill_manual(values =cols[c(1,2,4)]) +
  theme(panel.grid = element_blank())


# =========



dat3 = read_excel("test.xlsx",sheet=3,na="NA")
head(dat3)
dat3 %>%
  pivot_longer(
    cols = `官方结果`:`XGBoost`,
    names_to = "模型",
    names_transform = list(模型 = as.character),
    values_to = "value") -> dat_3_new

dat_3_new$模型 = fct_relevel(dat_3_new$模型, 
                           "官方结果","朴素贝叶斯","支持向量机","决策树","随机森林","XGBoost")

head(dat_3_new)

dummy2 <- data.frame(叶类名称 = dat3$叶类名称, Z = dat3$官方结果)
dat_3_new %>% ggplot(aes(`模型`,value,fill = `模型`)) + 
  geom_col() +
  facet_wrap(vars(`叶类名称`)) +
  geom_hline(data = dummy2,aes(yintercept = Z)) +
  # scale_fill_manual(values = cols[1:6]) +
  theme_bw() + ylab("准确度") + xlab("") + #主题设置
  scale_fill_viridis(discrete = T,option = "D") +
  theme(panel.grid = element_blank(),
        legend.position = 'bottom',
        legend.direction = "horizontal",
       axis.text.x = element_blank(),
       axis.ticks.x = element_blank())
  




================================================
FILE: 2022年/2022.11.29 R绘图案例｜基于分面的面积图绘制/.Rproj.user/C6560784/sources/session-51C83C65/lock_file
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FILE: 2022年/2022.11.29 R绘图案例｜基于分面的面积图绘制/.Rproj.user/shared/notebooks/patch-chunk-names
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FILE: 2022年/2022.11.29 R绘图案例｜基于分面的面积图绘制/.Rproj.user/shared/notebooks/paths
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/Users/liangliangzhuang/我的日记/wechat/推文整理/2022/袁凡/R-SPARKLINE.Rmd="1ED8D323"


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FILE: 2022年/2022.11.29 R绘图案例｜基于分面的面积图绘制/R-plot.Rproj
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Version: 1.0

RestoreWorkspace: Default
SaveWorkspace: Default
AlwaysSaveHistory: Default

EnableCodeIndexing: Yes
UseSpacesForTab: Yes
NumSpacesForTab: 2
Encoding: UTF-8

RnwWeave: Sweave
LaTeX: XeLaTeX

BuildType: Website


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FILE: 2022年/2022.11.29 R绘图案例｜基于分面的面积图绘制/rev_plot.R
================================================
# 代码解释见推文：https://mp.weixin.qq.com/s/alyuUumkwqC08RJilXmfeA
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》



# install.packages("readxl")
library(readxl)
library(ggplot2)
library(tidyverse)
library(cowplot)
library(viridis)
library(showtext)
showtext_auto()


### 绘制不同方法的区域图===========
dat = read_excel("test.xlsx",sheet=1,na="NA")
colnames(dat) = c("Id",paste("X",1:6,sep=''))
head(dat)


dat %>% select(c(Id,X1,X4)) %>% rename("随机森林"=X1, "XGBoost"=X4) %>% 
  pivot_longer(
             cols = c("随机森林","XGBoost"),
             names_to = "method",
             names_transform = list(method = as.character),
             values_to = "Acc") -> dat1
head(dat1)
cols <- c("#85BA8F", "#A3C8DC","#349839","#EA5D2D","#EABB77","#F09594")
p1 = ggplot(dat1) + 
  geom_area(aes(Id,Acc),fill = cols[1]) + 
  facet_wrap(vars(method),nrow = 2,strip.position = "top") +
  theme_bw() + 
  ylab("精确率") + 
  xlab("叶类") + #主题设置
  theme(panel.grid = element_blank()) 
p1

#==
dat %>% select(c(Id,X2,X5)) %>% rename("随机森林"=X2, "XGBoost"=X5) %>% 
  pivot_longer(
    cols = c("随机森林","XGBoost"),
    names_to = "method",
    names_transform = list(method = as.character),
    values_to = "Acc") -> dat2


p2 = ggplot(dat2) + geom_area(aes(Id,Acc),fill = cols[2]) + facet_wrap(vars(method),nrow = 2,strip.position = "top") +
  theme_bw() + ylab("召回率") + xlab("叶类") + #主题设置
  theme(panel.grid = element_blank())
p2

#==
dat %>% select(c(Id,X3,X6)) %>% rename("随机森林"=X3, "XGBoost"=X6) %>% 
  pivot_longer(
    cols = c("随机森林","XGBoost"),
    names_to = "method",
    # names_transform = list(method = as.factor),
    values_to = "Acc") -> dat3

p3 = ggplot(dat3) + 
  geom_area(aes(Id,Acc),fill = cols[4]) + 
  facet_wrap(vars(method),nrow = 2,strip.position = "top") +
  theme_bw() + 
  ylab("F1得分") + 
  xlab("叶类") + #主题设置
  theme(panel.grid = element_blank())
p3

plot_grid(p1,p2,p3,ncol = 3)


#==========================

dat_all = read_excel("test.xlsx",sheet=2,na="NA")
head(dat_all)

dat_all %>%
  pivot_longer(
    cols = `准确率`:`F1得分`,
    names_to = "method",
    names_transform = list(method = as.character),
    values_to = "value") -> dat_all_new

dat_all_new$method = fct_relevel(dat_all_new$method, "准确率", "精确率", "召回率","F1得分")
dat_all_new %>% ggplot(aes(method ,
                           value,fill=模型)) +
  geom_col(position = "dodge")+
  facet_wrap(vars(文本向量化方法)) +
  theme_bw() + ylab("数值") + xlab("评价指标") + #主题设置
  # scale_fill_viridis(discrete = T) +
  # geom_hline(aes(yintercept = 0.75),color = "gray") +
  scale_fill_manual(values =cols[c(1,2,4)]) +
  theme(panel.grid = element_blank())


# =========

dat_3_new$模型 = fct_relevel(dat_3_new$模型, 
     "官方结果","朴素贝叶斯","支持向量机","决策树","随机森林","XGBoost")


dat3 = read_excel("test.xlsx",sheet=3,na="NA")
head(dat3)
dat3 %>%
  pivot_longer(
    cols = `官方结果`:`XGBoost`,
    names_to = "模型",
    names_transform = list(模型 = as.character),
    values_to = "value") -> dat_3_new
dat_3_new$模型 = factor(dat_3_new$模型,levels = c("官方结果","朴素贝叶斯"))
dummy2 <- data.frame(叶类名称 = dat3$叶类名称, Z = dat3$官方结果)
dat_3_new %>% ggplot(aes(`模型`,value,fill = `模型`)) + 
  geom_col() +
  facet_wrap(vars(`叶类名称`)) +
  geom_hline(data = dummy2,aes(yintercept = Z)) +
  scale_fill_manual(values = cols[1:6]) +
  theme_bw() + ylab("准确度") + xlab("") + #主题设置
  # scale_fill_viridis(discrete = T) +
  theme(panel.grid = element_blank(),
        legend.position = 'bottom',
        legend.direction = "horizontal",
       axis.text.x = element_blank(),
       axis.ticks.x = element_blank())
  




================================================
FILE: 2022年/2022.12.31 2023年日历大派送/.Rhistory
================================================
day.size = 3, # 定义日期的字体大小
special.days = "weekend",  # 定义周末为特殊日期
special.col = "lightblue",  # 特殊日期背景颜色
low.col = "white", # 非特殊日期背景颜色
lty = 0,  # 去掉边框线
col = "white",
# 定义背景、放置方向、生成文件
# font.family = "kaishu", # 设置字体
orientation = "p",  # 垂直放置
papersize = "A4", # 设置纸张大小
bg.img = "5.jpg", # 设置背景图片
pdf = TRUE, # 生成pdf文件
doc_name = "calendar2023-2"  # 给pdf文件命名
)
# showtext_auto(FALSE)
# ===============
calendR(year = 2023,
start = "M",
title.col = "white",
# Weeks start on Monday
mbg.col = "#cd853f",               # Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "#a9a9a9", # Color of the special.days
lty = 0,                   # Line type (no line)
weeknames = c("Mo", "Tu",  # Week names
"We", "Th",
"Fr", "Sa",
"Su"),
title.size = 40,   # Title size
orientation = "p",
bg.img = 5,
pdf = TRUE,
doc_name = "My_calendar_brown"
)
invisible(sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            title.col = "white",
# Weeks start on Monday
mbg.col = "#cd853f",
# Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "gray60", # Color of the special.days
lty = 0,                   # Line type (no line)
bg.img = img,
doc_name = file.path("C:\\Users\\ZLL\\Documents\\calendar", paste0("Calendar(gray)_2021_", i))))
)
invisible(sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            title.col = "white",
# Weeks start on Monday
mbg.col = "#cd853f",
# Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "gray60", # Color of the special.days
lty = 0,                   # Line type (no line)
bg.img = img,
doc_name = file.path("\\month", paste0("Calendar(gray)_2021_", i))))
)
invisible(sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            title.col = "white",
# Weeks start on Monday
mbg.col = "#cd853f",
# Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "gray60", # Color of the special.days
lty = 0,                   # Line type (no line)
bg.img = 5,
doc_name = file.path("\\month", paste0("Calendar(gray)_2021_", i))))
)
invisible(sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            title.col = "white",
# Weeks start on Monday
mbg.col = "#cd853f",
# Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "gray60", # Color of the special.days
lty = 0,                   # Line type (no line)
bg.img = 5,
doc_name = file.path("month", paste0("Calendar(gray)_2023_", i))))
)
invisible(sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            title.col = "white",
# Weeks start on Monday
mbg.col = "#cd853f",
# Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "gray60", # Color of the special.days
lty = 0,                   # Line type (no line)
bg.img = "5.jpg",
doc_name = file.path("month", paste0("Calendar(gray)_2023_", i))))
)
sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,
title.col = "white",
# Weeks start on Monday
mbg.col = "#cd853f",
# Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "gray60", # Color of the special.days
lty = 0,                   # Line type (no line)
bg.img = "5.jpg",
doc_name = file.path("month", paste0("Calendar(gray)_2023_", i))))
sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,
title.col = "white",
# Weeks start on Monday
mbg.col = "#cd853f",
# Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "gray60", # Color of the special.days
lty = 0,                   # Line type (no line)
bg.img = "5.jpg",
doc_name = file.path("month", paste0("Calendar(gray)_2023_", i))))
library(calendR)
library(showtext)
showtext_auto()
calendR(year = 2023,
start = "M",
title.col = "white",
# Weeks start on Monday
mbg.col = "#cd853f",               # Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "#a9a9a9", # Color of the special.days
lty = 0,                   # Line type (no line)
weeknames = c("Mo", "Tu",  # Week names
"We", "Th",
"Fr", "Sa",
"Su"),
title.size = 40,   # Title size
orientation = "p",
bg.img = 5,
pdf = TRUE,
doc_name = "version1/My_calendar_brown"
)
calendR(year = 2023,
start = "M",
title.col = "white",
# Weeks start on Monday
mbg.col = "#cd853f",               # Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "#a9a9a9", # Color of the special.days
lty = 0,                   # Line type (no line)
weeknames = c("Mo", "Tu",  # Week names
"We", "Th",
"Fr", "Sa",
"Su"),
title.size = 40,   # Title size
orientation = "p",
bg.img = "4.jpg",
pdf = TRUE,
doc_name = "version1/My_calendar_brown"
)
library(calendR)
library(showtext)
showtext_auto()
calendR(year = 2023,
start = "M",
title.col = "white",
# Weeks start on Monday
mbg.col = "#cd853f",               # Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "#a9a9a9", # Color of the special.days
lty = 0,                   # Line type (no line)
weeknames = c("Mo", "Tu",  # Week names
"We", "Th",
"Fr", "Sa",
"Su"),
title.size = 40,   # Title size
orientation = "p",
bg.img = "4.jpg",
pdf = TRUE,
doc_name = "version1/My_calendar"
)
sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,
title.col = "white",
# Weeks start on Monday
mbg.col = "#cd853f",
# Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "gray60", # Color of the special.days
lty = 0,                   # Line type (no line)
bg.img = "5.jpg",
doc_name = file.path("month", paste0("Calendar(gray)_2023_", i))))
library(calendR)
library(showtext)
showtext_auto()
calendR(year = 2023,
start = "M",
title.col = "white",
# Weeks start on Monday
mbg.col = "#cd853f",               # Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "#a9a9a9", # Color of the special.days
lty = 0,                   # Line type (no line)
weeknames = c("Mo", "Tu",  # Week names
"We", "Th",
"Fr", "Sa",
"Su"),
title.size = 40,   # Title size
orientation = "p",
bg.img = "4.jpg",
pdf = TRUE,
doc_name = "version1/calendar2023"
)
sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,
title.col = "white",
# Weeks start on Monday
mbg.col = "#cd853f",
# Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "gray60", # Color of the special.days
lty = 0,                   # Line type (no line)
bg.img = "5.jpg",
doc_name = file.path("version1/month", paste0("Calendar(gray)_2023_", i))))
calendR(year = 2023,
start = "M",
title.col = "white",
# Weeks start on Monday
mbg.col = "#cd853f",               # Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "#a9a9a9", # Color of the special.days
lty = 0,                   # Line type (no line)
weeknames = c("Mo", "Tu",  # Week names
"We", "Th",
"Fr", "Sa",
"Su"),
title.size = 40,   # Title size
orientation = "p",
bg.img = "5.jpg",
pdf = TRUE,
doc_name = "version1/calendar2023"
)
library(calendR)
library(showtext)
# setwd("C:\\Users\\ZLL\\Desktop\\wechat\\庄闪闪原创\\R\\R可视乎\\2020.11.13日历\\wechat\\calendar\\读者（楚新元）")
# font_add("kaishu", "simkai.ttf")
showtext_auto()
# 判断文件是否存在，如果存在先删除
# if (file.exists("calendar2023.pdf") == T) {
#   file.remove("calendar2023.pdf")
# }
calendR(
# 定义标题
# title, # 如果缺失，则以年或年月替代
year = 2023,
title.size = 40,
title.col = "white",  # 年份字体颜色
# 定义副标题
# subtitle = "每天好心情",
subtitle.size = 10,
subtitle.col = "gray30",
# 定义月份
mbg.col = "#274652",   # 月份背景颜色
months.col = "white",  # 月份字体颜色
months.size = 10, # 定义月份字体大小
months.pos = 0.5, # 定义月份水平居中
# 定义周
weeknames = c(
"Mo", "Tu", "We", "Th", "Fr",
"Sa", "Su"
),  # 定义周名称
weeknames.col = "white",  # 周字体颜色
weeknames.size = 4.5, # 定义周字体大小
start = "M",  # 设置从周一开始
# 定义日期
days.col = "gray30", # 定义日期的颜色
day.size = 3, # 定义日期的字体大小
special.days = "weekend",  # 定义周末为特殊日期
special.col = "lightblue",  # 特殊日期背景颜色
low.col = "white", # 非特殊日期背景颜色
lty = 0,  # 去掉边框线
col = "white",
# 定义背景、放置方向、生成文件
# font.family = "kaishu", # 设置字体
orientation = "p",  # 垂直放置
papersize = "A4", # 设置纸张大小
bg.img = "4.jpg", # 设置背景图片
pdf = TRUE, # 生成pdf文件
doc_name = "calendar2023"  # 给pdf文件命名
)
library(calendR)
library(showtext)
# setwd("C:\\Users\\ZLL\\Desktop\\wechat\\庄闪闪原创\\R\\R可视乎\\2020.11.13日历\\wechat\\calendar\\读者（楚新元）")
# font_add("kaishu", "simkai.ttf")
showtext_auto()
# 判断文件是否存在，如果存在先删除
# if (file.exists("calendar2023.pdf") == T) {
#   file.remove("calendar2023.pdf")
# }
calendR(
# 定义标题
# title, # 如果缺失，则以年或年月替代
year = 2023,
title.size = 40,
title.col = "white",  # 年份字体颜色
# 定义副标题
# subtitle = "每天好心情",
subtitle.size = 10,
subtitle.col = "gray30",
# 定义月份
mbg.col = "#274652",   # 月份背景颜色
months.col = "white",  # 月份字体颜色
months.size = 10, # 定义月份字体大小
months.pos = 0.5, # 定义月份水平居中
# 定义周
weeknames = c(
"Mo", "Tu", "We", "Th", "Fr",
"Sa", "Su"
),  # 定义周名称
weeknames.col = "white",  # 周字体颜色
weeknames.size = 4.5, # 定义周字体大小
start = "M",  # 设置从周一开始
# 定义日期
days.col = "gray30", # 定义日期的颜色
day.size = 3, # 定义日期的字体大小
special.days = "weekend",  # 定义周末为特殊日期
special.col = "lightblue",  # 特殊日期背景颜色
low.col = "white", # 非特殊日期背景颜色
lty = 0,  # 去掉边框线
col = "white",
# 定义背景、放置方向、生成文件
# font.family = "kaishu", # 设置字体
orientation = "p",  # 垂直放置
papersize = "A4", # 设置纸张大小
bg.img = "4.jpg", # 设置背景图片
pdf = TRUE, # 生成pdf文件
doc_name = "version2/calendar2023"  # 给pdf文件命名
)
# showtext_auto(FALSE)
# ===============
sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,
title.col = "white",
# Weeks start on Monday
mbg.col = "lightblue",
# Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "gray60", # Color of the special.days
lty = 0,                   # Line type (no line)
bg.img = "4.jpg",
doc_name = file.path("version2/month", paste0("Calendar(gray)_2023_", i))))
sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,
title.col = "white",
# Weeks start on Monday
mbg.col = "lightblue",
# Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "gray60", # Color of the special.days
lty = 0,                   # Line type (no line)
bg.img = "4.jpg",
doc_name = file.path("version2/month", paste0("Calendar(blue)_2023_", i))))
calendR(
# 定义标题
# title, # 如果缺失，则以年或年月替代
year = 2023,
title.size = 40,
title.col = "white",  # 年份字体颜色
# 定义副标题
# subtitle = "每天好心情",
subtitle.size = 10,
subtitle.col = "gray30",
# 定义月份
mbg.col = "#274652",   # 月份背景颜色
months.col = "white",  # 月份字体颜色
months.size = 10, # 定义月份字体大小
months.pos = 0.5, # 定义月份水平居中
# 定义周
weeknames = c(
"Mo", "Tu", "We", "Th", "Fr",
"Sa", "Su"
),  # 定义周名称
weeknames.col = "white",  # 周字体颜色
weeknames.size = 4.5, # 定义周字体大小
start = "M",  # 设置从周一开始
# 定义日期
days.col = "gray30", # 定义日期的颜色
day.size = 3, # 定义日期的字体大小
special.days = "weekend",  # 定义周末为特殊日期
special.col = "lightblue",  # 特殊日期背景颜色
low.col = "white", # 非特殊日期背景颜色
lty = 0,  # 去掉边框线
col = "white",
# 定义背景、放置方向、生成文件
# font.family = "kaishu", # 设置字体
orientation = "l",  # 垂直放置 "l"
papersize = "A4", # 设置纸张大小
bg.img = "4.jpg", # 设置背景图片
pdf = TRUE, # 生成pdf文件
doc_name = "version2/calendar2023-2"  # 给pdf文件命名
)
calendR(year = 2023,
start = "M",
title.col = "white",
# Weeks start on Monday
mbg.col = "#cd853f",               # Background color of the month names
months.col = "white",      # Color of the text of the month names
weeknames.col = "white",
special.days = "weekend",  # Color the weekends
special.col = "#a9a9a9", # Color of the special.days
lty = 0,                   # Line type (no line)
weeknames = c("Mo", "Tu",  # Week names
"We", "Th",
"Fr", "Sa",
"Su"),
title.size = 40,   # Title size
orientation = "l",
bg.img = "5.jpg",
pdf = TRUE,
doc_name = "version1/calendar2023-2"
)
calendR(
# 定义标题
# title, # 如果缺失，则以年或年月替代
year = 2023,
title.size = 40,
title.col = "white",  # 年份字体颜色
# 定义副标题
# subtitle = "每天好心情",
subtitle.size = 10,
subtitle.col = "gray30",
# 定义月份
mbg.col = "#7FCF60",   # 月份背景颜色
months.col = "white",  # 月份字体颜色
months.size = 10, # 定义月份字体大小
months.pos = 0.5, # 定义月份水平居中
# 定义周
weeknames = c(
"Mo", "Tu", "We", "Th", "Fr",
"Sa", "Su"
),  # 定义周名称
weeknames.col = "white",  # 周字体颜色
weeknames.size = 4.5, # 定义周字体大小
start = "M",  # 设置从周一开始
# 定义日期
days.col = "gray30", # 定义日期的颜色
day.size = 3, # 定义日期的字体大小
special.days = "weekend",  # 定义周末为特殊日期
special.col = "lightblue",  # 特殊日期背景颜色
low.col = "white", # 非特殊日期背景颜色
lty = 0,  # 去掉边框线
col = "white",
# 定义背景、放置方向、生成文件
# font.family = "kaishu", # 设置字体
orientation = "p",  # 垂直放置 "l"
papersize = "A4", # 设置纸张大小
bg.img = "4.jpg", # 设置背景图片
pdf = TRUE, # 生成pdf文件
doc_name = "version2/calendar2023"  # 给pdf文件命名
)
calendR(
# 定义标题
# title, # 如果缺失，则以年或年月替代
year = 2023,
title.size = 40,
title.col = "white",  # 年份字体颜色
# 定义副标题
# subtitle = "每天好心情",
subtitle.size = 10,
subtitle.col = "gray30",
# 定义月份
mbg.col = "#7FCF60",   # 月份背景颜色
months.col = "white",  # 月份字体颜色
months.size = 10, # 定义月份字体大小
months.pos = 0.5, # 定义月份水平居中
# 定义周
weeknames = c(
"Mo", "Tu", "We", "Th", "Fr",
"Sa", "Su"
),  # 定义周名称
weeknames.col = "white",  # 周字体颜色
weeknames.size = 4.5, # 定义周字体大小
start = "M",  # 设置从周一开始
# 定义日期
days.col = "gray30", # 定义日期的颜色
day.size = 3, # 定义日期的字体大小
special.days = "weekend",  # 定义周末为特殊日期
special.col = "lightblue",  # 特殊日期背景颜色
low.col = "white", # 非特殊日期背景颜色
lty = 0,  # 去掉边框线
col = "white",
# 定义背景、放置方向、生成文件
# font.family = "kaishu", # 设置字体
orientation = "l",  # 垂直放置 "l"
papersize = "A4", # 设置纸张大小
bg.img = "4.jpg", # 设置背景图片
pdf = TRUE, # 生成pdf文件
doc_name = "version2/calendar2023-2"  # 给pdf文件命名
)


================================================
FILE: 2022年/2022.12.31 2023年日历大派送/.Rproj.user/C6560784/jobs/E803742C-output.json
================================================
[1,"Installing 'calendR' ...\n"]
[1,"[1/4] Installing suncalc...\n"]
[2,"trying URL 'https://cran.rstudio.com/bin/macosx/big-sur-arm64/contrib/4.2/suncalc_0.5.1.tgz'\n"]
[2,"Content type 'application/x-gzip' length 60786 bytes (59 KB)\n==========================="]
[2,"======="]
[1,"The downloaded binary packages are in\n"]
[1,"\t/var/folders/wt/t3n70vjs6g536860y9y_yr9r0000gn/T//Rtmp5QxrBE/downloaded_packages\n"]
[1,"[2/4] Installing ggimage...\n"]
[2,"================\ndownloaded 59 KB\n\n"]
[2,"trying URL 'https://cran.rstudio.com/bin/macosx/big-sur-arm64/contrib/4.2/ggimage_0.3.1.tgz'\n"]
[2,"Content type 'application/x-gzip' length 147327 bytes (143 KB)\n==========="]
[2,"======"]
[2,"=========="]
[2,"======="]
[2,"================\ndownloaded 143 KB\n\n"]
[1,"The downloaded binary packages are in\n"]
[1,"\t/var/folders/wt/t3n70vjs6g536860y9y_yr9r0000gn/T//Rtmp5QxrBE/downloaded_packages\n"]
[1,"[3/4] Installing gggibbous...\n"]
[2,"trying URL 'https://cran.rstudio.com/bin/macosx/big-sur-arm64/contrib/4.2/gggibbous_0.1.1.tgz'\n"]
[2,"Content type 'application/x-gzip' length 515549 bytes (503 KB)\n="]
[2,"="]
[2,"=="]
[2,"==="]
[2,"=="]
[2,"==="]
[2,"=="]
[2,"======="]
[2,"=="]
[2,"==========="]
[2,"====="]
[2,"="]
[2,"==========\ndownloaded 503 KB\n\n"]
[1,"The downloaded binary packages are in\n"]
[1,"\t/var/folders/wt/t3n70vjs6g536860y9y_yr9r0000gn/T//Rtmp5QxrBE/downloaded_packages\n"]
[1,"[4/4] Installing calendR...\n"]
[2,"trying URL 'https://cran.rstudio.com/bin/macosx/big-sur-arm64/contrib/4.2/calendR_1.1.tgz'\n"]
[2,"Content type 'application/x-gzip' length 38987 bytes (38 KB)\n================"]
[2,"==================================\ndownloaded 38 KB\n\n"]
[1,"The downloaded binary packages are in\n"]
[1,"\t/var/folders/wt/t3n70vjs6g536860y9y_yr9r0000gn/T//Rtmp5QxrBE/downloaded_packages\n"]
[2,"\n\n✔ Package 'calendR' successfully installed.\n"]


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FILE: 2022年/2022.12.31 2023年日历大派送/.Rproj.user/C6560784/sources/per/t/13B3FE41-contents
================================================
library(calendR)
library(showtext)
showtext_auto()

calendR(year = 2023,
        start = "M", 
        title.col = "white",
        # Weeks start on Monday
        mbg.col = "#cd853f",               # Background color of the month names
        months.col = "white",      # Color of the text of the month names
        weeknames.col = "white",
        
        special.days = "weekend",  # Color the weekends
        special.col = "#a9a9a9", # Color of the special.days
        lty = 0,                   # Line type (no line)
        weeknames = c("Mo", "Tu",  # Week names
                      "We", "Th",
                      "Fr", "Sa",
                      "Su"),
        title.size = 40,   # Title size
        orientation = "p",
        bg.img = "5.jpg",
        pdf = TRUE,
        doc_name = "version1/calendar2023"
)


sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            
                                  title.col = "white",
                                  # Weeks start on Monday
                                  mbg.col = "#cd853f",  
                                  # Background color of the month names
                                  months.col = "white",      # Color of the text of the month names
                                  weeknames.col = "white",
                                  special.days = "weekend",  # Color the weekends
                                  special.col = "gray60", # Color of the special.days
                                  lty = 0,                   # Line type (no line)
                                  bg.img = "5.jpg",
                                  doc_name = file.path("version1/month", paste0("Calendar(gray)_2023_", i))))




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}

================================================
FILE: 2022年/2022.12.31 2023年日历大派送/.Rproj.user/C6560784/sources/per/t/873D867D-contents
================================================
library(calendR)
library(showtext)
showtext_auto()

# 判断文件是否存在，如果存在先删除
# if (file.exists("calendar2023.pdf") == T) {
#   file.remove("calendar2023.pdf")
# } 



calendR(
  # 定义标题
  # title, # 如果缺失，则以年或年月替代
  year = 2023,
  title.size = 40,
  title.col = "white",  # 年份字体颜色
  
  # 定义副标题
  # subtitle = "每天好心情",
  subtitle.size = 10,
  subtitle.col = "gray30",
  
  # 定义月份
  mbg.col = "#7FCF60",   # 月份背景颜色
  months.col = "white",  # 月份字体颜色 
  months.size = 10, # 定义月份字体大小
  months.pos = 0.5, # 定义月份水平居中
  
  # 定义周
  weeknames = c(
    "Mo", "Tu", "We", "Th", "Fr", 
    "Sa", "Su"
  ),  # 定义周名称
  weeknames.col = "white",  # 周字体颜色
  weeknames.size = 4.5, # 定义周字体大小
  start = "M",  # 设置从周一开始
  
  # 定义日期
  days.col = "gray30", # 定义日期的颜色
  day.size = 3, # 定义日期的字体大小
  special.days = "weekend",  # 定义周末为特殊日期
  special.col = "lightblue",  # 特殊日期背景颜色
  low.col = "white", # 非特殊日期背景颜色
  lty = 0,  # 去掉边框线 
  col = "white",
  
  # 定义背景、放置方向、生成文件
  # font.family = "kaishu", # 设置字体
  orientation = "p",  # 垂直放置 "l"
  papersize = "A4", # 设置纸张大小
  bg.img = "4.jpg", # 设置背景图片
  pdf = TRUE, # 生成pdf文件
  doc_name = "version2/calendar2023"  # 给pdf文件命名
)

# showtext_auto(FALSE)


# ===============

sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            
                                  title.col = "white",
                                  # Weeks start on Monday
                                  mbg.col = "lightblue",  
                                  # Background color of the month names
                                  months.col = "white",      # Color of the text of the month names
                                  weeknames.col = "white",
                                  special.days = "weekend",  # Color the weekends
                                  special.col = "gray60", # Color of the special.days
                                  lty = 0,                   # Line type (no line)
                                  bg.img = "4.jpg",
                                  doc_name = file.path("version2/month", paste0("Calendar(blue)_2023_", i))))



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FILE: 2022年/2022.12.31 2023年日历大派送/version1.R
================================================
# 代码解释见推文：https://mp.weixin.qq.com/s/BdE2Zb97iDyp2NpsoELouQ
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》

library(calendR)
library(showtext)
showtext_auto()

calendR(year = 2023,
        start = "M", 
        title.col = "white",
        # Weeks start on Monday
        mbg.col = "#cd853f",               # Background color of the month names
        months.col = "white",      # Color of the text of the month names
        weeknames.col = "white",
        
        special.days = "weekend",  # Color the weekends
        special.col = "#a9a9a9", # Color of the special.days
        lty = 0,                   # Line type (no line)
        weeknames = c("Mo", "Tu",  # Week names
                      "We", "Th",
                      "Fr", "Sa",
                      "Su"),
        title.size = 40,   # Title size
        orientation = "p",
        bg.img = "5.jpg",
        pdf = TRUE,
        doc_name = "version1/calendar2023"
)


sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            
                                  title.col = "white",
                                  # Weeks start on Monday
                                  mbg.col = "#cd853f",  
                                  # Background color of the month names
                                  months.col = "white",      # Color of the text of the month names
                                  weeknames.col = "white",
                                  special.days = "weekend",  # Color the weekends
                                  special.col = "gray60", # Color of the special.days
                                  lty = 0,                   # Line type (no line)
                                  bg.img = "5.jpg",
                                  doc_name = file.path("version1/month", paste0("Calendar(gray)_2023_", i))))




================================================
FILE: 2022年/2022.12.31 2023年日历大派送/version2.R
================================================
# 代码解释见推文：https://mp.weixin.qq.com/s/BdE2Zb97iDyp2NpsoELouQ
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》

library(calendR)
library(showtext)
showtext_auto()

# 判断文件是否存在，如果存在先删除
# if (file.exists("calendar2023.pdf") == T) {
#   file.remove("calendar2023.pdf")
# } 



calendR(
  # 定义标题
  # title, # 如果缺失，则以年或年月替代
  year = 2023,
  title.size = 40,
  title.col = "white",  # 年份字体颜色
  
  # 定义副标题
  # subtitle = "每天好心情",
  subtitle.size = 10,
  subtitle.col = "gray30",
  
  # 定义月份
  mbg.col = "#7FCF60",   # 月份背景颜色
  months.col = "white",  # 月份字体颜色 
  months.size = 10, # 定义月份字体大小
  months.pos = 0.5, # 定义月份水平居中
  
  # 定义周
  weeknames = c(
    "Mo", "Tu", "We", "Th", "Fr", 
    "Sa", "Su"
  ),  # 定义周名称
  weeknames.col = "white",  # 周字体颜色
  weeknames.size = 4.5, # 定义周字体大小
  start = "M",  # 设置从周一开始
  
  # 定义日期
  days.col = "gray30", # 定义日期的颜色
  day.size = 3, # 定义日期的字体大小
  special.days = "weekend",  # 定义周末为特殊日期
  special.col = "lightblue",  # 特殊日期背景颜色
  low.col = "white", # 非特殊日期背景颜色
  lty = 0,  # 去掉边框线 
  col = "white",
  
  # 定义背景、放置方向、生成文件
  # font.family = "kaishu", # 设置字体
  orientation = "p",  # 垂直放置 "l"
  papersize = "A4", # 设置纸张大小
  bg.img = "4.jpg", # 设置背景图片
  pdf = TRUE, # 生成pdf文件
  doc_name = "version2/calendar2023"  # 给pdf文件命名
)

# showtext_auto(FALSE)


# ===============

sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            
                                  title.col = "white",
                                  # Weeks start on Monday
                                  mbg.col = "lightblue",  
                                  # Background color of the month names
                                  months.col = "white",      # Color of the text of the month names
                                  weeknames.col = "white",
                                  special.days = "weekend",  # Color the weekends
                                  special.col = "gray60", # Color of the special.days
                                  lty = 0,                   # Line type (no line)
                                  bg.img = "4.jpg",
                                  doc_name = file.path("version2/month", paste0("Calendar(blue)_2023_", i))))



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================================================
FILE: 2023年/2022.08.31 分面+双轴/.Rhistory
================================================
# 加载数据=====
df = read_csv("df.csv")
head(df)
library(tidyverse)
# 加载数据=====
df = read_csv("df.csv")
head(df)
# 双轴绘制 ======
p1 = df %>%
filter(type == "Adult") %>%
group_by(station) %>%
mutate(Percent = 100 * number/sum(number),
station=factor(station)) %>%
ggplot(aes(station, fill = species)) +
geom_bar(aes(y = Percent), position = "stack", stat = "identity") +
geom_line(aes(y = depth /4, group = 1)) +
scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = "Depth"))
p1
# 双轴绘制 ======
p1 = df %>%
filter(type == "Adult") %>%
group_by(station) %>%
mutate(Percent = 100 * number/sum(number),
station=factor(station)) %>%
ggplot(aes(station, fill = species)) +
geom_bar(aes(y = Percent), position = "stack", stat = "identity") +
geom_line(aes(y = depth /4, group = 1)) +
scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = "Depth"))
p1
# 双轴 + 分面
library(viridis)
p1 + theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
panel.grid = element_blank()) +
# scale_fill_viridis(discrete = T) +
#scale_fill_manual(name = "Species", values=PZ) +
facet_grid(~factor(zone,levels = c("A1","A2","A3")), scale = "free_x", space = "free_x")
# 链接：https://community.rstudio.com/t/add-secondary-axis-on-ggplot/138671/7
library(tidyverse)
# 加载数据=====
df = read_csv("df.csv")
head(df)
# 双轴绘制 ======
p1 = df %>%
filter(type == "Adult") %>%
group_by(station) %>%
mutate(Percent = 100 * number/sum(number),
station=factor(station)) %>%
ggplot(aes(station, fill = species)) +
geom_bar(aes(y = Percent), position = "stack", stat = "identity") +
geom_line(aes(y = depth /4, group = 1)) +
scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = "Depth"))
p1
# 双轴 + 分面
library(viridis)
p1 + theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
panel.grid = element_blank()) +
# scale_fill_viridis(discrete = T) +
#scale_fill_manual(name = "Species", values=PZ) +
facet_grid(~factor(zone,levels = c("A1","A2","A3")), scale = "free_x", space = "free_x")
head(df)
# 双轴绘制 ======
p1 = df %>%
filter(type == "Adult") %>%
group_by(station) %>%
mutate(Percent = 100 * number/sum(number),
station=factor(station)) %>%
ggplot(aes(station, fill = species)) +
geom_bar(aes(y = Percent), position = "stack", stat = "identity") # +
p1
# 双轴绘制 ======
p1 = df %>%
filter(type == "Adult") %>%
group_by(station) %>%
mutate(Percent = 100 * number/sum(number),
station=factor(station)) %>%
ggplot(aes(station, fill = species)) +
geom_bar(aes(y = Percent), position = "stack", stat = "identity") +
geom_line(aes(y = depth /4, group = 1))# +
p1
# 双轴绘制 ======
p1 = df %>%
filter(type == "Adult") %>%
group_by(station) %>%
mutate(Percent = 100 * number/sum(number),
station=factor(station)) %>%
ggplot(aes(station, fill = species)) +
geom_bar(aes(y = Percent), position = "stack", stat = "identity") +
geom_line(aes(y = depth /4, group = 1))
p1
p1 + scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = "Depth"))
p1 + scale_y_continuous(sec.axis = sec_axis(~ . * 40, name = "Depth"))
# 双轴绘制 ======
p1 = df %>%
filter(type == "Adult") %>%
group_by(station) %>%
mutate(Percent = 100 * number/sum(number),
station=factor(station)) %>%
ggplot(aes(station, fill = species)) +
geom_bar(aes(y = Percent), position = "stack", stat = "identity") +
geom_line(aes(y = depth, group = 1))
p1
# 双轴绘制 ======
p1 = df %>%
filter(type == "Adult") %>%
group_by(station) %>%
mutate(Percent = 100 * number/sum(number),
station=factor(station)) %>%
ggplot(aes(station, fill = species)) +
geom_bar(aes(y = Percent), position = "stack", stat = "identity") +
geom_line(aes(y = depth /4, group = 1))
p1
p1 + scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = "Depth"))
# 双轴绘制 ======
p1 = df %>%
filter(type == "Adult") %>%
group_by(station) %>%
mutate(Percent = 100 * number/sum(number),
station=factor(station)) %>%
ggplot(aes(station, fill = species)) +
geom_bar(aes(y = Percent), position = "stack", stat = "identity") +
geom_line(aes(y = depth /4, group = 1))
p1
p1 + scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = "Depth"))
p1 + theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
panel.grid = element_blank()) +
facet_grid(~factor(zone,levels = c("A1","A2","A3")), scale = "free_x", space = "free_x")
library(viridis)
p1 + theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
panel.grid = element_blank()) +
scale_fill_viridis(discrete = T) +
#scale_fill_manual(name = "Species", values=PZ) +
facet_grid(~factor(zone,levels = c("A1","A2","A3")), scale = "free_x", space = "free_x")
p1 + theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
panel.grid = element_blank()) +
# scale_fill_viridis(discrete = T) +
#scale_fill_manual(name = "Species", values=PZ) +
facet_grid(~factor(zone,levels = c("A1","A2","A3")), scale = "free_x", space = "free_x")
p1 + theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
panel.grid = element_blank()) +
scale_fill_viridis(discrete = T) +
#scale_fill_manual(name = "Species", values=PZ) +
facet_grid(~factor(zone,levels = c("A1","A2","A3")), scale = "free_x", space = "free_x")
p1 + theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
panel.grid = element_blank()) +
scale_fill_viridis(discrete = T) +
facet_grid(~factor(zone,levels = c("A1","A2","A3")), scale = "free_x")
p1 + theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
panel.grid = element_blank()) +
scale_fill_viridis(discrete = T) +
facet_grid(~factor(zone,levels = c("A1","A2","A3")), scale = "free_x", space = "free_x")
# 双轴绘制 ======
p1 = df %>%
filter(type == "Adult") %>%
group_by(station) %>%
mutate(Percent = 100 * number/sum(number),
station=factor(station)) %>%
ggplot(aes(station, fill = species)) +
geom_bar(aes(y = Percent), position = "stack", stat = "identity") +
geom_line(aes(y = depth /4, group = 1)) + geom_point(aes(y = depth /4, group = 1))
p1
p1 + scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = "Depth"))
# 双轴 + 分面
library(viridis)
p1 + theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
panel.grid = element_blank()) +
scale_fill_viridis(discrete = T) +
facet_grid(~factor(zone,levels = c("A1","A2","A3")), scale = "free_x", space = "free_x")
p1
p1 + scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = "Depth"))
# 双轴 + 分面
library(viridis)
p1 + theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
panel.grid = element_blank()) +
scale_fill_viridis(discrete = T) +
facet_grid(~factor(zone,levels = c("A1","A2","A3")), scale = "free_x", space = "free_x")


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FILE: 2023年/2022.08.31 分面+双轴/.Rproj.user/C6560784/sources/per/t/EA22C3C6-contents
================================================
# ======== 使用分面展示不同组别的双 Y 轴图形 =======
# 出自：庄闪闪
# 参考资料：https://community.rstudio.com/t/add-secondary-axis-on-ggplot/138671/7
library(tidyverse)

# 加载数据=====
df = read_csv("df.csv")
head(df)
# 双轴绘制 ======
p1 = df %>% 
  filter(type == "Adult") %>%
  group_by(station) %>% 
  mutate(Percent = 100 * number/sum(number),
         station=factor(station)) %>%
  ggplot(aes(station, fill = species)) + 
  geom_bar(aes(y = Percent), position = "stack", stat = "identity") +
  geom_line(aes(y = depth /4, group = 1)) +
  scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = "Depth")) 
p1 
# 双轴 + 分面
library(viridis)
p1 + theme_bw() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
        panel.grid = element_blank()) +
  # scale_fill_viridis(discrete = T) +
  #scale_fill_manual(name = "Species", values=PZ) + 
  facet_grid(~factor(zone,levels = c("A1","A2","A3")), scale = "free_x", space = "free_x") 





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FILE: 2023年/2022.08.31 分面+双轴/.Rproj.user/C6560784/sources/prop/D4B371E0
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FILE: 2023年/2022.08.31 分面+双轴/.Rproj.user/C6560784/sources/prop/INDEX
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~%2F%E6%88%91%E7%9A%84%E6%97%A5%E8%AE%B0%2Fwechat%2F%2B%2B%2B%E6%9C%AA%E5%AE%8C%E6%88%90%E6%8E%A8%E6%96%87%2F2022.08.31%20%E5%88%86%E9%9D%A2%2B%E5%8F%8C%E8%BD%B4%2F%E6%9C%AA%E5%91%BD%E5%90%8D.R="D4B371E0"
~%2F%E6%88%91%E7%9A%84%E6%97%A5%E8%AE%B0%2Fwechat%2F%2B%2B%2B%E6%9C%AA%E5%AE%8C%E6%88%90%E6%8E%A8%E6%96%87%2F2022.08.31%20%E5%88%86%E9%9D%A2%2B%E5%8F%8C%E8%BD%B4%2FAdd%20secondary%20axis.R="F02804FC"


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/Users/liangliangzhuang/我的日记/wechat/+++未完成推文/2022.08.31 分面+双轴/未命名.R="5A9690EE"


================================================
FILE: 2023年/2022.08.31 分面+双轴/Add secondary axis.R
================================================
# ======== 使用分面展示不同组别的双 Y 轴图形 =======
# 出自：庄闪闪
# 参考资料：https://community.rstudio.com/t/add-secondary-axis-on-ggplot/138671/7
library(tidyverse)

# 加载数据=====
df = read_csv("df.csv")
head(df)
# 双轴绘制 ======
p1 = df %>% 
  filter(type == "Adult") %>%
  group_by(station) %>% 
  mutate(Percent = 100 * number/sum(number),
         station=factor(station)) %>%
  ggplot(aes(station, fill = species)) + 
  geom_bar(aes(y = Percent), position = "stack", stat = "identity") +
  geom_line(aes(y = depth /4, group = 1)) +
  scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = "Depth")) 
p1 
# 双轴 + 分面
library(viridis)
p1 + theme_bw() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
        panel.grid = element_blank()) +
  # scale_fill_viridis(discrete = T) +
  #scale_fill_manual(name = "Species", values=PZ) + 
  facet_grid(~factor(zone,levels = c("A1","A2","A3")), scale = "free_x", space = "free_x") 





================================================
FILE: 2023年/2022.08.31 分面+双轴/df.csv
================================================
zone,type,station,species,number,depth
A1,Adult,1,Atlanticus,2,200
A1,Adult,1,Olrikii,1,450
A1,Larvae,2,Medius,5,345
A1,Adult,3,Calo,8,402
A1,Larvae,3,Pryi,15,298
A1,Zoo,4,Galk,2,336
A1,Adult,4,Spu,9,315
A2,Adult,5,Dryu,17,269
A2,Adult,5,Spu,3,256
A2,Larvae,5,Olrikii,13,311
A2,Zoo,6,Calo,5,289
A2,Zoo,6,Unidentified,3,405
A2,Adult,6,Glacialis,2,380
A2,Larvae,7,Medius,4,310
A2,Larvae,7,Glou,7,269
A3,Zoo,8,Capilatta,17,265
A3,Zoo,8,Calo,17,295
A3,Adult,9,Dryu,17,378
A3,Zoo,9,Glou,17,368
A3,Zoo,9,Azu,17,490
A3,Adult,10,Olrikii,1,410


================================================
FILE: 2023年/2023.03.07 高亮柱状图/高亮柱状图.R
================================================
# 代码细节解释见推文：https://mp.weixin.qq.com/s/Rml11BcLE5_fLKckfob9Wg
# 作者：庄亮亮/庄闪闪


# 导入 ggplot2 和 reshape2 包
library(ggplot2)
library(reshape2)

# 创建数据框
data <- data.frame(
  Method = c("A", "B", "C", "D"),
  RMSE = c(1.5, 2.3, 1.9, 2.1),
  RB = c(0.8, 1.2, 1.5, 0.7)
)
head(data)

## ggplot2版本 ===========

# 使用ggplot2创建柱状图
h1 <- c("red", "grey", "grey", "grey")
p1 = ggplot(data, aes(x = Method, y = RMSE, fill = Method)) +
  geom_col() + 
  scale_fill_manual(values = h1)  

h2 <- c("grey", "grey", "grey","red")
p2 = ggplot(data, aes(x = Method, y = RB, fill = Method)) +
  geom_col() + 
  scale_fill_manual(values = h2)  

library(cowplot)
plot_grid(p1,p2)

# ==============================================================================
## ggcharts 包

library(ggcharts)
library(dplyr)
data("biomedicalrevenue")
revenue2018 <- biomedicalrevenue %>%
  filter(year == 2018)
head(revenue2018)

ggcharts_set_theme("theme_ggcharts")
bar_chart(
  revenue2018,
  company,
  revenue,
  top_n = 10,
  highlight = "Roche"
)


ggcharts_set_theme("theme_ng")
bar_chart(
  revenue2018,
  company,
  revenue,
  top_n = 10,
  highlight = "Roche"
)

ggcharts_set_theme("theme_ggcharts")
biomedicalrevenue %>%
  filter(year %in% c(2012, 2014, 2016, 2018)) %>%
  bar_chart(
    company,
    revenue,
    facet = year,
    top_n = 12,
    highlight = "Bayer"
  )
head(biomedicalrevenue)



## ==========
data_long <- tidyr::gather(data, Metric, Value, -Method)
head(data_long)
data_long %>%
  bar_chart(
    Method,
    Value,
    facet = as.factor(Metric),
    top_n = 12,
    highlight = "A",
    horizontal = T
  )









================================================
FILE: 2023年/2023.03.08 基于 ggridges 绘制剩余使用寿命密度图/rul_ggbridges.R
================================================

# 代码解释见推文：https://mp.weixin.qq.com/s/ECquLeoHaJ2byWgrRpqftw
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》


## 基于ggridges的剩余使用寿命密度图
library(tidyverse)
library(ggridges)
library(viridis)

mean1 = seq(21,1,length.out =5) # 刻画不同时间对应密度函数的均值
std1 = seq(5,3,length.out =5) # 刻画不同时间对应密度函数的标准差
len = 10000 #分布中离散数据的个数
y <- c(1,6,11,16,21) # 未来时间
z = matrix(NA,len,5) # 存储抽样得到的后验
for(i in 1:length(mean1)){
  z[,i] = rnorm(len,mean1[i],std1[i])
}
# 合并数据
dat = as.data.frame(cbind(y,z))
colnames(dat) = c("Cur_time",paste("z",1:5,sep = ""))
head(dat)

dat %>% pivot_longer(`z1`:`z5`,
                     names_to = "Class",
                     values_to = "Value"
                     ) -> new_dat
head(new_dat)


##============================== 绘图 ==============================
### 初级版本
ggplot(new_dat, aes(x = Value, y = Class, fill = Class)) +
  geom_density_ridges()

### 进阶版本
ggplot(new_dat, aes(x = Value, y = Class, fill = Class)) +
  geom_density_ridges() +
  scale_fill_viridis(discrete = T) +
  scale_y_discrete(labels = paste("Day",mean1 = seq(1,21,length.out =5) ,seq='')) +
  theme_bw() + 
  theme(legend.position = "none",
        panel.grid = element_blank()) 


# 添加均值1
ggplot(new_dat, aes(x = Value, y = Class, fill = Class)) +
  geom_density_ridges() +
  geom_density_ridges(quantile_lines=TRUE,
                      quantile_fun=function(x,...)mean(x))+ 
  scale_fill_viridis(discrete = T) +
  scale_y_discrete(labels = paste("Day",mean1 = seq(1,21,length.out =5) ,seq='')) +
  theme_bw() + 
  theme(legend.position = "none",
        panel.grid = element_blank()) 

# 添加均值2
ggplot(new_dat, aes(x = Value, y = Class, fill = Class)) +
  geom_density_ridges() +
  stat_summary(fun = "mean", geom = "point", shape = 21, size = 2,fill = "red",color = 'red') +
  scale_fill_viridis(discrete = T) +
  scale_y_discrete(labels = paste("Day",mean1 = seq(1,21,length.out =5) ,seq='')) +
  theme_bw() + 
  theme(legend.position = "none",
                     panel.grid = element_blank()) 

# 添加分位数
ggplot(new_dat, aes(x = Value, y = Class, fill = factor(after_stat(quantile)))) +
  stat_density_ridges(
    geom = "density_ridges_gradient", 
    calc_ecdf = TRUE,
    quantiles = c(0.025, 0.975)) +
  scale_fill_manual(
    name = "Probability", values = c("#FF0000A0", "#A0A0A0A0", "#0000FFA0"),
    labels = c("(0, 0.025]", "(0.025, 0.975]", "(0.975, 1]")
  ) +
  theme_bw() + 
  theme(panel.grid = element_blank())

















================================================
FILE: 2023年/2023.03.09基于 ggdensity 包的等高线绘制/等高线绘制.R
================================================
# 代码解释见推文：https://mp.weixin.qq.com/s/IRSvobzOfqmYtfBXDpbdJw
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》


# 基于ggdensity包的等高线绘制

library(ggdensity)
library(ggblanket)
library(ggsci)

head(iris)
## ggplot 直接版本
ggplot(iris,aes(x = Sepal.Width, y = Sepal.Length, fill = Species)) +
  ggdensity::geom_hdr() + 
  scale_fill_aaas() +
  facet_wrap(vars(Species)) 

## 添加其他geom
ggplot(iris,aes(x = Sepal.Width, y = Sepal.Length, fill = Species)) +
  ggdensity::geom_hdr() + 
  geom_point(shape = 21) +
  scale_fill_aaas() +
  facet_wrap(vars(Species)) 

ggplot(iris,aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  ggdensity::geom_hdr_lines() +
  geom_point() +
  scale_color_aaas() +
  facet_wrap(vars(Species)) 

ggplot(iris,aes(x = Sepal.Width, y = Sepal.Length, fill = Species)) +
  ggdensity::geom_hdr() + 
  geom_point(shape = 21) +
  scale_fill_aaas() +
  facet_wrap(vars(Species)) +
  geom_hdr_rug()

## ggblanket 版本
iris |>
  gg_blank(
    x = Sepal.Width,
    y = Sepal.Length,
    col = Species,
    facet = Species,
    col_legend_place = "r") +
  ggdensity::geom_hdr(colour = NA) +
  labs(alpha = "Probs") +
  theme(legend.title = element_text(margin = margin(t = 5)))

# mutate(Species = stringr::str_to_sentence(Species)) |> 

### 细节处理
## 修改x轴标签大小写问题：
# 使用stringr::str_to_sentene()修改Species列中的文本。并在col_labels中设置为：stringr::str_to_sentence

iris |>
  mutate(Species = stringr::str_to_sentence(Species)) |> 
  gg_blank(
    x = Sepal.Width,
    y = Sepal.Length,
    col = Species,
    facet = Species,
    col_legend_place = "r",
    col_labels = stringr::str_to_sentence) +
  ggdensity::geom_hdr(colour = NA) +
  labs(alpha = "Probs") +
  theme(legend.title = element_text(margin = margin(t = 5)))



================================================
FILE: 2023年/2023.03.16使用 ggTimeSeries 包构建日历图/timeseries.R
================================================

# 代码解释见推文：https://mp.weixin.qq.com/s/rAlPbNh2bYuNkqyT9dxosQ
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》

# 构建数据
set.seed(20230309)
library(tidyverse)
dtData <- tibble(
  DateCol = seq(
    as.Date("1/01/2020", "%d/%m/%Y"),
    as.Date("31/12/2022", "%d/%m/%Y"), "days"
  ),
  ValueCol = sample(
    c('a', 'b', 'c', 'd', 'e'),
    1096,
    prob = c(0.7, 0.1, 0.1, 0.05, 0.05),
    replace = TRUE
  )
)

head(dtData)

library(ggTimeSeries)
library(ggplot2)

p = ggplot_calendar_heatmap(
  dtData, dayBorderSize = 1,
  monthBorderSize = 1,dayBorderColour = "white",
  'DateCol',monthBorderColour = "white",
  'ValueCol'
)

# adding some formatting
col <- c("#EBEDF0", "#CBE491", "#89C876", "#459944", "#2C602C")
p + 
  scale_fill_manual(values = col) + 
  facet_wrap(~Year, ncol = 1,strip.position = "right") +
  theme( panel.background = element_blank(),
         legend.text = element_blank(),
         panel.border = element_rect(colour="grey60",fill=NA),
         strip.background = element_blank(),
         strip.text = element_text(size=13,face="plain",color="black"),
         axis.line=element_line(colour="black",size=0.25),
         axis.title=element_text(size=10,face="plain",color="black"),
         axis.text = element_text(size=10,face="plain",color="black")) +
  xlab('') + 
  ylab('') +  
  labs(fill = "Freq") 



================================================
FILE: 2023年/2023.03.16使用 ggTimeSeries 包构建日历图/日历图.html
================================================
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<h1 class="title">ggtimeseries</h1>
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    <div class="quarto-title-meta-heading">Author</div>
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             <p>庄闪闪 </p>
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</header>

<section id="构建数据" class="level1">
<h1>构建数据</h1>
<div class="cell">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">20230309</span>)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>dtData <span class="ot">&lt;-</span> <span class="fu">tibble</span>(</span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>  <span class="at">DateCol =</span> <span class="fu">seq</span>(</span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>    <span class="fu">as.Date</span>(<span class="st">"1/01/2020"</span>, <span class="st">"%d/%m/%Y"</span>),</span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>    <span class="fu">as.Date</span>(<span class="st">"31/12/2022"</span>, <span class="st">"%d/%m/%Y"</span>), <span class="st">"days"</span></span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>  <span class="at">ValueCol =</span> <span class="fu">sample</span>(</span>
<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>    <span class="fu">c</span>(<span class="st">'a'</span>, <span class="st">'b'</span>, <span class="st">'c'</span>, <span class="st">'d'</span>, <span class="st">'e'</span>),</span>
<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>    <span class="dv">1096</span>,</span>
<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>    <span class="at">prob =</span> <span class="fu">c</span>(<span class="fl">0.7</span>, <span class="fl">0.1</span>, <span class="fl">0.1</span>, <span class="fl">0.05</span>, <span class="fl">0.05</span>),</span>
<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>    <span class="at">replace =</span> <span class="cn">TRUE</span></span>
<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(dtData)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code># A tibble: 6 × 2
  DateCol    ValueCol
  &lt;date&gt;     &lt;chr&gt;   
1 2020-01-01 a       
2 2020-01-02 b       
3 2020-01-03 a       
4 2020-01-04 a       
5 2020-01-05 d       
6 2020-01-06 a       </code></pre>
</div>
</div>
<section id="可视化" class="level2">
<h2 class="anchored" data-anchor-id="可视化">可视化</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggTimeSeries)</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>p <span class="ot">=</span> <span class="fu">ggplot_calendar_heatmap</span>(</span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>  dtData, <span class="at">dayBorderSize =</span> <span class="dv">1</span>,</span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a>  <span class="at">monthBorderSize =</span> <span class="dv">1</span>,<span class="at">dayBorderColour =</span> <span class="st">"white"</span>,</span>
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>  <span class="st">'DateCol'</span>,<span class="at">monthBorderColour =</span> <span class="st">"white"</span>,</span>
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a>  <span class="st">'ValueCol'</span></span>
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a>col <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"#EBEDF0"</span>, <span class="st">"#CBE491"</span>, <span class="st">"#89C876"</span>, <span class="st">"#459944"</span>, <span class="st">"#2C602C"</span>)</span>
<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a>p <span class="sc">+</span> </span>
<span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values =</span> col) <span class="sc">+</span> </span>
<span id="cb3-14"><a href="#cb3-14" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>Year, <span class="at">ncol =</span> <span class="dv">1</span>,<span class="at">strip.position =</span> <span class="st">"right"</span>) <span class="sc">+</span></span>
<span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>( <span class="at">panel.background =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb3-16"><a href="#cb3-16" aria-hidden="true" tabindex="-1"></a>         <span class="at">legend.text =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb3-17"><a href="#cb3-17" aria-hidden="true" tabindex="-1"></a>         <span class="at">panel.border =</span> <span class="fu">element_rect</span>(<span class="at">colour=</span><span class="st">"grey60"</span>,<span class="at">fill=</span><span class="cn">NA</span>),</span>
<span id="cb3-18"><a href="#cb3-18" aria-hidden="true" tabindex="-1"></a>         <span class="at">strip.background =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb3-19"><a href="#cb3-19" aria-hidden="true" tabindex="-1"></a>         <span class="at">strip.text =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">13</span>,<span class="at">face=</span><span class="st">"plain"</span>,<span class="at">color=</span><span class="st">"black"</span>),</span>
<span id="cb3-20"><a href="#cb3-20" aria-hidden="true" tabindex="-1"></a>         <span class="at">axis.line=</span><span class="fu">element_line</span>(<span class="at">colour=</span><span class="st">"black"</span>,<span class="at">size=</span><span class="fl">0.25</span>),</span>
<span id="cb3-21"><a href="#cb3-21" aria-hidden="true" tabindex="-1"></a>         <span class="at">axis.title=</span><span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">10</span>,<span class="at">face=</span><span class="st">"plain"</span>,<span class="at">color=</span><span class="st">"black"</span>),</span>
<span id="cb3-22"><a href="#cb3-22" aria-hidden="true" tabindex="-1"></a>         <span class="at">axis.text =</span> <span class="fu">element_text</span>(<span class="at">size=</span><span class="dv">10</span>,<span class="at">face=</span><span class="st">"plain"</span>,<span class="at">color=</span><span class="st">"black"</span>)) <span class="sc">+</span></span>
<span id="cb3-23"><a href="#cb3-23" aria-hidden="true" tabindex="-1"></a>  <span class="fu">xlab</span>(<span class="st">''</span>) <span class="sc">+</span> </span>
<span id="cb3-24"><a href="#cb3-24" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">''</span>) <span class="sc">+</span>  </span>
<span id="cb3-25"><a href="#cb3-25" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">fill =</span> <span class="st">"Freq"</span>) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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================================================
FILE: 2023年/2023.03.16使用 ggTimeSeries 包构建日历图/日历图.qmd
================================================
---
title: "ggtimeseries"
format: html
author: 庄闪闪
editor: visual
---

# 构建数据

```{r warning=FALSE,message=FALSE}
set.seed(20230309)
library(tidyverse)
dtData <- tibble(
  DateCol = seq(
    as.Date("1/01/2020", "%d/%m/%Y"),
    as.Date("31/12/2022", "%d/%m/%Y"), "days"
  ),
  ValueCol = sample(
    c('a', 'b', 'c', 'd', 'e'),
    1096,
    prob = c(0.7, 0.1, 0.1, 0.05, 0.05),
    replace = TRUE
  )
)

head(dtData)

```

## 可视化


```{r warning=FALSE,message=FALSE}
library(ggTimeSeries)
library(ggplot2)

p = ggplot_calendar_heatmap(
  dtData, dayBorderSize = 1,
  monthBorderSize = 1,dayBorderColour = "white",
  'DateCol',monthBorderColour = "white",
  'ValueCol'
)

col <- c("#EBEDF0", "#CBE491", "#89C876", "#459944", "#2C602C")
p + 
  scale_fill_manual(values = col) + 
  facet_wrap(~Year, ncol = 1,strip.position = "right") +
  theme( panel.background = element_blank(),
         legend.text = element_blank(),
         panel.border = element_rect(colour="grey60",fill=NA),
         strip.background = element_blank(),
         strip.text = element_text(size=13,face="plain",color="black"),
         axis.line=element_line(colour="black",size=0.25),
         axis.title=element_text(size=10,face="plain",color="black"),
         axis.text = element_text(size=10,face="plain",color="black")) +
  xlab('') + 
  ylab('') +  
  labs(fill = "Freq") 

```







================================================
FILE: 2023年/2023.03.16使用 ggTimeSeries 包构建日历图/日历图_files/libs/bootstrap/bootstrap-icons.css
================================================
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.bi-send-x::before { content: "\f6bf"; }
.bi-send::before { content: "\f6c0"; }
.bi-steam::before { content: "\f6c1"; }
.bi-terminal-dash-1::before { content: "\f6c2"; }
.bi-terminal-dash::before { content: "\f6c3"; }
.bi-terminal-plus::before { content: "\f6c4"; }
.bi-terminal-split::before { content: "\f6c5"; }
.bi-ticket-detailed-fill::before { content: "\f6c6"; }
.bi-ticket-detailed::before { content: "\f6c7"; }
.bi-ticket-fill::before { content: "\f6c8"; }
.bi-ticket-perforated-fill::before { content: "\f6c9"; }
.bi-ticket-perforated::before { content: "\f6ca"; }
.bi-ticket::before { content: "\f6cb"; }
.bi-tiktok::before { content: "\f6cc"; }
.bi-window-dash::before { content: "\f6cd"; }
.bi-window-desktop::before { content: "\f6ce"; }
.bi-window-fullscreen::before { content: "\f6cf"; }
.bi-window-plus::before { content: "\f6d0"; }
.bi-window-split::before { content: "\f6d1"; }
.bi-window-stack::before { content: "\f6d2"; }
.bi-window-x::before { content: "\f6d3"; }
.bi-xbox::before { content: "\f6d4"; }
.bi-ethernet::before { content: "\f6d5"; }
.bi-hdmi-fill::before { content: "\f6d6"; }
.bi-hdmi::before { content: "\f6d7"; }
.bi-usb-c-fill::before { content: "\f6d8"; }
.bi-usb-c::before { content: "\f6d9"; }
.bi-usb-fill::before { content: "\f6da"; }
.bi-usb-plug-fill::before { content: "\f6db"; }
.bi-usb-plug::before { content: "\f6dc"; }
.bi-usb-symbol::before { content: "\f6dd"; }
.bi-usb::before { content: "\f6de"; }
.bi-boombox-fill::before { content: "\f6df"; }
.bi-displayport-1::before { content: "\f6e0"; }
.bi-displayport::before { content: "\f6e1"; }
.bi-gpu-card::before { content: "\f6e2"; }
.bi-memory::before { content: "\f6e3"; }
.bi-modem-fill::before { content: "\f6e4"; }
.bi-modem::before { content: "\f6e5"; }
.bi-motherboard-fill::before { content: "\f6e6"; }
.bi-motherboard::before { content: "\f6e7"; }
.bi-optical-audio-fill::before { content: "\f6e8"; }
.bi-optical-audio::before { content: "\f6e9"; }
.bi-pci-card::before { content: "\f6ea"; }
.bi-router-fill::before { content: "\f6eb"; }
.bi-router::before { content: "\f6ec"; }
.bi-ssd-fill::before { content: "\f6ed"; }
.bi-ssd::before { content: "\f6ee"; }
.bi-thunderbolt-fill::before { content: "\f6ef"; }
.bi-thunderbolt::before { content: "\f6f0"; }
.bi-usb-drive-fill::before { content: "\f6f1"; }
.bi-usb-drive::before { content: "\f6f2"; }
.bi-usb-micro-fill::before { content: "\f6f3"; }
.bi-usb-micro::before { content: "\f6f4"; }
.bi-usb-mini-fill::before { content: "\f6f5"; }
.bi-usb-mini::before { content: "\f6f6"; }
.bi-cloud-haze2::before { content: "\f6f7"; }
.bi-device-hdd-fill::before { content: "\f6f8"; }
.bi-device-hdd::before { content: "\f6f9"; }
.bi-device-ssd-fill::before { content: "\f6fa"; }
.bi-device-ssd::before { content: "\f6fb"; }
.bi-displayport-fill::before { content: "\f6fc"; }
.bi-mortarboard-fill::before { content: "\f6fd"; }
.bi-mortarboard::before { content: "\f6fe"; }
.bi-terminal-x::before { content: "\f6ff"; }
.bi-arrow-through-heart-fill::before { content: "\f700"; }
.bi-arrow-through-heart::before { content: "\f701"; }
.bi-badge-sd-fill::before { content: "\f702"; }
.bi-badge-sd::before { content: "\f703"; }
.bi-bag-heart-fill::before { content: "\f704"; }
.bi-bag-heart::before { content: "\f705"; }
.bi-balloon-fill::before { content: "\f706"; }
.bi-balloon-heart-fill::before { content: "\f707"; }
.bi-balloon-heart::before { content: "\f708"; }
.bi-balloon::before { content: "\f709"; }
.bi-box2-fill::before { content: "\f70a"; }
.bi-box2-heart-fill::before { content: "\f70b"; }
.bi-box2-heart::before { content: "\f70c"; }
.bi-box2::before { content: "\f70d"; }
.bi-braces-asterisk::before { content: "\f70e"; }
.bi-calendar-heart-fill::before { content: "\f70f"; }
.bi-calendar-heart::before { content: "\f710"; }
.bi-calendar2-heart-fill::before { content: "\f711"; }
.bi-calendar2-heart::before { content: "\f712"; }
.bi-chat-heart-fill::before { content: "\f713"; }
.bi-chat-heart::before { content: "\f714"; }
.bi-chat-left-heart-fill::before { content: "\f715"; }
.bi-chat-left-heart::before { content: "\f716"; }
.bi-chat-right-heart-fill::before { content: "\f717"; }
.bi-chat-right-heart::before { content: "\f718"; }
.bi-chat-square-heart-fill::before { content: "\f719"; }
.bi-chat-square-heart::before { content: "\f71a"; }
.bi-clipboard-check-fill::before { content: "\f71b"; }
.bi-clipboard-data-fill::before { content: "\f71c"; }
.bi-clipboard-fill::before { content: "\f71d"; }
.bi-clipboard-heart-fill::before { content: "\f71e"; }
.bi-clipboard-heart::before { content: "\f71f"; }
.bi-clipboard-minus-fill::before { content: "\f720"; }
.bi-clipboard-plus-fill::before { content: "\f721"; }
.bi-clipboard-pulse::before { content: "\f722"; }
.bi-clipboard-x-fill::before { content: "\f723"; }
.bi-clipboard2-check-fill::before { content: "\f724"; }
.bi-clipboard2-check::before { content: "\f725"; }
.bi-clipboard2-data-fill::before { content: "\f726"; }
.bi-clipboard2-data::before { content: "\f727"; }
.bi-clipboard2-fill::before { content: "\f728"; }
.bi-clipboard2-heart-fill::before { content: "\f729"; }
.bi-clipboard2-heart::before { content: "\f72a"; }
.bi-clipboard2-minus-fill::before { content: "\f72b"; }
.bi-clipboard2-minus::before { content: "\f72c"; }
.bi-clipboard2-plus-fill::before { content: "\f72d"; }
.bi-clipboard2-plus::before { content: "\f72e"; }
.bi-clipboard2-pulse-fill::before { content: "\f72f"; }
.bi-clipboard2-pulse::before { content: "\f730"; }
.bi-clipboard2-x-fill::before { content: "\f731"; }
.bi-clipboard2-x::before { content: "\f732"; }
.bi-clipboard2::before { content: "\f733"; }
.bi-emoji-kiss-fill::before { content: "\f734"; }
.bi-emoji-kiss::before { content: "\f735"; }
.bi-envelope-heart-fill::before { content: "\f736"; }
.bi-envelope-heart::before { content: "\f737"; }
.bi-envelope-open-heart-fill::before { content: "\f738"; }
.bi-envelope-open-heart::before { content: "\f739"; }
.bi-envelope-paper-fill::before { content: "\f73a"; }
.bi-envelope-paper-heart-fill::before { content: "\f73b"; }
.bi-envelope-paper-heart::before { content: "\f73c"; }
.bi-envelope-paper::before { content: "\f73d"; }
.bi-filetype-aac::before { content: "\f73e"; }
.bi-filetype-ai::before { content: "\f73f"; }
.bi-filetype-bmp::before { content: "\f740"; }
.bi-filetype-cs::before { content: "\f741"; }
.bi-filetype-css::before { content: "\f742"; }
.bi-filetype-csv::before { content: "\f743"; }
.bi-filetype-doc::before { content: "\f744"; }
.bi-filetype-docx::before { content: "\f745"; }
.bi-filetype-exe::before { content: "\f746"; }
.bi-filetype-gif::before { content: "\f747"; }
.bi-filetype-heic::before { content: "\f748"; }
.bi-filetype-html::before { content: "\f749"; }
.bi-filetype-java::before { content: "\f74a"; }
.bi-filetype-jpg::before { content: "\f74b"; }
.bi-filetype-js::before { content: "\f74c"; }
.bi-filetype-jsx::before { content: "\f74d"; }
.bi-filetype-key::before { content: "\f74e"; }
.bi-filetype-m4p::before { content: "\f74f"; }
.bi-filetype-md::before { content: "\f750"; }
.bi-filetype-mdx::before { content: "\f751"; }
.bi-filetype-mov::before { content: "\f752"; }
.bi-filetype-mp3::before { content: "\f753"; }
.bi-filetype-mp4::before { content: "\f754"; }
.bi-filetype-otf::before { content: "\f755"; }
.bi-filetype-pdf::before { content: "\f756"; }
.bi-filetype-php::before { content: "\f757"; }
.bi-filetype-png::before { content: "\f758"; }
.bi-filetype-ppt-1::before { content: "\f759"; }
.bi-filetype-ppt::before { content: "\f75a"; }
.bi-filetype-psd::before { content: "\f75b"; }
.bi-filetype-py::before { content: "\f75c"; }
.bi-filetype-raw::before { content: "\f75d"; }
.bi-filetype-rb::before { content: "\f75e"; }
.bi-filetype-sass::before { content: "\f75f"; }
.bi-filetype-scss::before { content: "\f760"; }
.bi-filetype-sh::before { content: "\f761"; }
.bi-filetype-svg::before { content: "\f762"; }
.bi-filetype-tiff::before { content: "\f763"; }
.bi-filetype-tsx::before { content: "\f764"; }
.bi-filetype-ttf::before { content: "\f765"; }
.bi-filetype-txt::before { content: "\f766"; }
.bi-filetype-wav::before { content: "\f767"; }
.bi-filetype-woff::before { content: "\f768"; }
.bi-filetype-xls-1::before { content: "\f769"; }
.bi-filetype-xls::before { content: "\f76a"; }
.bi-filetype-xml::before { content: "\f76b"; }
.bi-filetype-yml::before { content: "\f76c"; }
.bi-heart-arrow::before { content: "\f76d"; }
.bi-heart-pulse-fill::before { content: "\f76e"; }
.bi-heart-pulse::before { content: "\f76f"; }
.bi-heartbreak-fill::before { content: "\f770"; }
.bi-heartbreak::before { content: "\f771"; }
.bi-hearts::before { content: "\f772"; }
.bi-hospital-fill::before { content: "\f773"; }
.bi-hospital::before { content: "\f774"; }
.bi-house-heart-fill::before { content: "\f775"; }
.bi-house-heart::before { content: "\f776"; }
.bi-incognito::before { content: "\f777"; }
.bi-magnet-fill::before { content: "\f778"; }
.bi-magnet::before { content: "\f779"; }
.bi-person-heart::before { content: "\f77a"; }
.bi-person-hearts::before { content: "\f77b"; }
.bi-phone-flip::before { content: "\f77c"; }
.bi-plugin::before { content: "\f77d"; }
.bi-postage-fill::before { content: "\f77e"; }
.bi-postage-heart-fill::before { content: "\f77f"; }
.bi-postage-heart::before { content: "\f780"; }
.bi-postage::before { content: "\f781"; }
.bi-postcard-fill::before { content: "\f782"; }
.bi-postcard-heart-fill::before { content: "\f783"; }
.bi-postcard-heart::before { content: "\f784"; }
.bi-postcard::before { content: "\f785"; }
.bi-search-heart-fill::before { content: "\f786"; }
.bi-search-heart::before { content: "\f787"; }
.bi-sliders2-vertical::before { content: "\f788"; }
.bi-sliders2::before { content: "\f789"; }
.bi-trash3-fill::before { content: "\f78a"; }
.bi-trash3::before { content: "\f78b"; }
.bi-valentine::before { content: "\f78c"; }
.bi-valentine2::before { content: "\f78d"; }
.bi-wrench-adjustable-circle-fill::before { content: "\f78e"; }
.bi-wrench-adjustable-circle::before { content: "\f78f"; }
.bi-wrench-adjustable::before { content: "\f790"; }
.bi-filetype-json::before { content: "\f791"; }
.bi-filetype-pptx::before { content: "\f792"; }
.bi-filetype-xlsx::before { content: "\f793"; }


================================================
FILE: 2023年/2023.03.16使用 ggTimeSeries 包构建日历图/日历图_files/libs/quarto-html/quarto-syntax-highlighting.css
================================================
/* quarto syntax highlight colors */
:root {
  --quarto-hl-ot-color: #003B4F;
  --quarto-hl-at-color: #657422;
  --quarto-hl-ss-color: #20794D;
  --quarto-hl-an-color: #5E5E5E;
  --quarto-hl-fu-color: #4758AB;
  --quarto-hl-st-color: #20794D;
  --quarto-hl-cf-color: #003B4F;
  --quarto-hl-op-color: #5E5E5E;
  --quarto-hl-er-color: #AD0000;
  --quarto-hl-bn-color: #AD0000;
  --quarto-hl-al-color: #AD0000;
  --quarto-hl-va-color: #111111;
  --quarto-hl-bu-color: inherit;
  --quarto-hl-ex-color: inherit;
  --quarto-hl-pp-color: #AD0000;
  --quarto-hl-in-color: #5E5E5E;
  --quarto-hl-vs-color: #20794D;
  --quarto-hl-wa-color: #5E5E5E;
  --quarto-hl-do-color: #5E5E5E;
  --quarto-hl-im-color: #00769E;
  --quarto-hl-ch-color: #20794D;
  --quarto-hl-dt-color: #AD0000;
  --quarto-hl-fl-color: #AD0000;
  --quarto-hl-co-color: #5E5E5E;
  --quarto-hl-cv-color: #5E5E5E;
  --quarto-hl-cn-color: #8f5902;
  --quarto-hl-sc-color: #5E5E5E;
  --quarto-hl-dv-color: #AD0000;
  --quarto-hl-kw-color: #003B4F;
}

/* other quarto variables */
:root {
  --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
}

pre > code.sourceCode > span {
  color: #003B4F;
}

code span {
  color: #003B4F;
}

code.sourceCode > span {
  color: #003B4F;
}

div.sourceCode,
div.sourceCode pre.sourceCode {
  color: #003B4F;
}

code span.ot {
  color: #003B4F;
}

code span.at {
  color: #657422;
}

code span.ss {
  color: #20794D;
}

code span.an {
  color: #5E5E5E;
}

code span.fu {
  color: #4758AB;
}

code span.st {
  color: #20794D;
}

code span.cf {
  color: #003B4F;
}

code span.op {
  color: #5E5E5E;
}

code span.er {
  color: #AD0000;
}

code span.bn {
  color: #AD0000;
}

code span.al {
  color: #AD0000;
}

code span.va {
  color: #111111;
}

code span.pp {
  color: #AD0000;
}

code span.in {
  color: #5E5E5E;
}

code span.vs {
  color: #20794D;
}

code span.wa {
  color: #5E5E5E;
  font-style: italic;
}

code span.do {
  color: #5E5E5E;
  font-style: italic;
}

code span.im {
  color: #00769E;
}

code span.ch {
  color: #20794D;
}

code span.dt {
  color: #AD0000;
}

code span.fl {
  color: #AD0000;
}

code span.co {
  color: #5E5E5E;
}

code span.cv {
  color: #5E5E5E;
  font-style: italic;
}

code span.cn {
  color: #8f5902;
}

code span.sc {
  color: #5E5E5E;
}

code span.dv {
  color: #AD0000;
}

code span.kw {
  color: #003B4F;
}

.prevent-inlining {
  content: "</";
}

/*# sourceMappingURL=debc5d5d77c3f9108843748ff7464032.css.map */


================================================
FILE: 2023年/2023.03.16使用 ggTimeSeries 包构建日历图/日历图_files/libs/quarto-html/quarto.js
================================================
const sectionChanged = new CustomEvent("quarto-sectionChanged", {
  detail: {},
  bubbles: true,
  cancelable: false,
  composed: false,
});

window.document.addEventListener("DOMContentLoaded", function (_event) {
  const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]');
  const sidebarEl = window.document.getElementById("quarto-sidebar");
  const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left");
  const marginSidebarEl = window.document.getElementById(
    "quarto-margin-sidebar"
  );
  // function to determine whether the element has a previous sibling that is active
  const prevSiblingIsActiveLink = (el) => {
    const sibling = el.previousElementSibling;
    if (sibling && sibling.tagName === "A") {
      return sibling.classList.contains("active");
    } else {
      return false;
    }
  };

  // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior)
  function fireSlideEnter(e) {
    const event = window.document.createEvent("Event");
    event.initEvent("slideenter", true, true);
    window.document.dispatchEvent(event);
  }
  const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]');
  tabs.forEach((tab) => {
    tab.addEventListener("shown.bs.tab", fireSlideEnter);
  });

  // fire slideEnter for tabby tab activations (for htmlwidget resize behavior)
  document.addEventListener("tabby", fireSlideEnter, false);

  // Track scrolling and mark TOC links as active
  // get table of contents and sidebar (bail if we don't have at least one)
  const tocLinks = tocEl
    ? [...tocEl.querySelectorAll("a[data-scroll-target]")]
    : [];
  const makeActive = (link) => tocLinks[link].classList.add("active");
  const removeActive = (link) => tocLinks[link].classList.remove("active");
  const removeAllActive = () =>
    [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link));

  // activate the anchor for a section associated with this TOC entry
  tocLinks.forEach((link) => {
    link.addEventListener("click", () => {
      if (link.href.indexOf("#") !== -1) {
        const anchor = link.href.split("#")[1];
        const heading = window.document.querySelector(
          `[data-anchor-id=${anchor}]`
        );
        if (heading) {
          // Add the class
          heading.classList.add("reveal-anchorjs-link");

          // function to show the anchor
          const handleMouseout = () => {
            heading.classList.remove("reveal-anchorjs-link");
            heading.removeEventListener("mouseout", handleMouseout);
          };

          // add a function to clear the anchor when the user mouses out of it
          heading.addEventListener("mouseout", handleMouseout);
        }
      }
    });
  });

  const sections = tocLinks.map((link) => {
    const target = link.getAttribute("data-scroll-target");
    if (target.startsWith("#")) {
      return window.document.getElementById(decodeURI(`${target.slice(1)}`));
    } else {
      return window.document.querySelector(decodeURI(`${target}`));
    }
  });

  const sectionMargin = 200;
  let currentActive = 0;
  // track whether we've initialized state the first time
  let init = false;

  const updateActiveLink = () => {
    // The index from bottom to top (e.g. reversed list)
    let sectionIndex = -1;
    if (
      window.innerHeight + window.pageYOffset >=
      window.document.body.offsetHeight
    ) {
      sectionIndex = 0;
    } else {
      sectionIndex = [...sections].reverse().findIndex((section) => {
        if (section) {
          return window.pageYOffset >= section.offsetTop - sectionMargin;
        } else {
          return false;
        }
      });
    }
    if (sectionIndex > -1) {
      const current = sections.length - sectionIndex - 1;
      if (current !== currentActive) {
        removeAllActive();
        currentActive = current;
        makeActive(current);
        if (init) {
          window.dispatchEvent(sectionChanged);
        }
        init = true;
      }
    }
  };

  const inHiddenRegion = (top, bottom, hiddenRegions) => {
    for (const region of hiddenRegions) {
      if (top <= region.bottom && bottom >= region.top) {
        return true;
      }
    }
    return false;
  };

  const categorySelector = "header.quarto-title-block .quarto-category";
  const activateCategories = (href) => {
    // Find any categories
    // Surround them with a link pointing back to:
    // #category=Authoring
    try {
      const categoryEls = window.document.querySelectorAll(categorySelector);
      for (const categoryEl of categoryEls) {
        const categoryText = categoryEl.textContent;
        if (categoryText) {
          const link = `${href}#category=${encodeURIComponent(categoryText)}`;
          const linkEl = window.document.createElement("a");
          linkEl.setAttribute("href", link);
          for (const child of categoryEl.childNodes) {
            linkEl.append(child);
          }
          categoryEl.appendChild(linkEl);
        }
      }
    } catch {
      // Ignore errors
    }
  };
  function hasTitleCategories() {
    return window.document.querySelector(categorySelector) !== null;
  }

  function offsetRelativeUrl(url) {
    const offset = getMeta("quarto:offset");
    return offset ? offset + url : url;
  }

  function offsetAbsoluteUrl(url) {
    const offset = getMeta("quarto:offset");
    const baseUrl = new URL(offset, window.location);

    const projRelativeUrl = url.replace(baseUrl, "");
    if (projRelativeUrl.startsWith("/")) {
      return projRelativeUrl;
    } else {
      return "/" + projRelativeUrl;
    }
  }

  // read a meta tag value
  function getMeta(metaName) {
    const metas = window.document.getElementsByTagName("meta");
    for (let i = 0; i < metas.length; i++) {
      if (metas[i].getAttribute("name") === metaName) {
        return metas[i].getAttribute("content");
      }
    }
    return "";
  }

  async function findAndActivateCategories() {
    const currentPagePath = offsetAbsoluteUrl(window.location.href);
    const response = await fetch(offsetRelativeUrl("listings.json"));
    if (response.status == 200) {
      return response.json().then(function (listingPaths) {
        const listingHrefs = [];
        for (const listingPath of listingPaths) {
          const pathWithoutLeadingSlash = listingPath.listing.substring(1);
          for (const item of listingPath.items) {
            if (
              item === currentPagePath ||
              item === currentPagePath + "index.html"
            ) {
              // Resolve this path against the offset to be sure
              // we already are using the correct path to the listing
              // (this adjusts the listing urls to be rooted against
              // whatever root the page is actually running against)
              const relative = offsetRelativeUrl(pathWithoutLeadingSlash);
              const baseUrl = window.location;
              const resolvedPath = new URL(relative, baseUrl);
              listingHrefs.push(resolvedPath.pathname);
              break;
            }
          }
        }

        // Look up the tree for a nearby linting and use that if we find one
        const nearestListing = findNearestParentListing(
          offsetAbsoluteUrl(window.location.pathname),
          listingHrefs
        );
        if (nearestListing) {
          activateCategories(nearestListing);
        } else {
          // See if the referrer is a listing page for this item
          const referredRelativePath = offsetAbsoluteUrl(document.referrer);
          const referrerListing = listingHrefs.find((listingHref) => {
            const isListingReferrer =
              listingHref === referredRelativePath ||
              listingHref === referredRelativePath + "index.html";
            return isListingReferrer;
          });

          if (referrerListing) {
            // Try to use the referrer if possible
            activateCategories(referrerListing);
          } else if (listingHrefs.length > 0) {
            // Otherwise, just fall back to the first listing
            activateCategories(listingHrefs[0]);
          }
        }
      });
    }
  }
  if (hasTitleCategories()) {
    findAndActivateCategories();
  }

  const findNearestParentListing = (href, listingHrefs) => {
    if (!href || !listingHrefs) {
      return undefined;
    }
    // Look up the tree for a nearby linting and use that if we find one
    const relativeParts = href.substring(1).split("/");
    while (relativeParts.length > 0) {
      const path = relativeParts.join("/");
      for (const listingHref of listingHrefs) {
        if (listingHref.startsWith(path)) {
          return listingHref;
        }
      }
      relativeParts.pop();
    }

    return undefined;
  };

  const manageSidebarVisiblity = (el, placeholderDescriptor) => {
    let isVisible = true;

    return (hiddenRegions) => {
      if (el === null) {
        return;
      }

      // Find the last element of the TOC
      const lastChildEl = el.lastElementChild;

      if (lastChildEl) {
        // Find the top and bottom o the element that is being managed
        const elTop = el.offsetTop;
        const elBottom =
          elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;

        // Converts the sidebar to a menu
        const convertToMenu = () => {
          for (const child of el.children) {
            child.style.opacity = 0;
            child.style.overflow = "hidden";
          }

          const toggleContainer = window.document.createElement("div");
          toggleContainer.style.width = "100%";
          toggleContainer.classList.add("zindex-over-content");
          toggleContainer.classList.add("quarto-sidebar-toggle");
          toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
          toggleContainer.id = placeholderDescriptor.id;
          toggleContainer.style.position = "fixed";

          const toggleIcon = window.document.createElement("i");
          toggleIcon.classList.add("quarto-sidebar-toggle-icon");
          toggleIcon.classList.add("bi");
          toggleIcon.classList.add("bi-caret-down-fill");

          const toggleTitle = window.document.createElement("div");
          const titleEl = window.document.body.querySelector(
            placeholderDescriptor.titleSelector
          );
          if (titleEl) {
            toggleTitle.append(titleEl.innerText, toggleIcon);
          }
          toggleTitle.classList.add("zindex-over-content");
          toggleTitle.classList.add("quarto-sidebar-toggle-title");
          toggleContainer.append(toggleTitle);

          const toggleContents = window.document.createElement("div");
          toggleContents.classList = el.classList;
          toggleContents.classList.add("zindex-over-content");
          toggleContents.classList.add("quarto-sidebar-toggle-contents");
          for (const child of el.children) {
            if (child.id === "toc-title") {
              continue;
            }

            const clone = child.cloneNode(true);
            clone.style.opacity = 1;
            clone.style.display = null;
            toggleContents.append(clone);
          }
          toggleContents.style.height = "0px";
          toggleContainer.append(toggleContents);
          el.parentElement.prepend(toggleContainer);

          // Process clicks
          let tocShowing = false;
          // Allow the caller to control whether this is dismissed
          // when it is clicked (e.g. sidebar navigation supports
          // opening and closing the nav tree, so don't dismiss on click)
          const clickEl = placeholderDescriptor.dismissOnClick
            ? toggleContainer
            : toggleTitle;

          const closeToggle = () => {
            if (tocShowing) {
              toggleContainer.classList.remove("expanded");
              toggleContents.style.height = "0px";
              tocShowing = false;
            }
          };

          const positionToggle = () => {
            // position the element (top left of parent, same width as parent)
            const elRect = el.getBoundingClientRect();
            toggleContainer.style.left = `${elRect.left}px`;
            toggleContainer.style.top = `${elRect.top}px`;
            toggleContainer.style.width = `${elRect.width}px`;
          };

          // Get rid of any expanded toggle if the user scrolls
          window.document.addEventListener(
            "scroll",
            throttle(() => {
              closeToggle();
            }, 50)
          );

          // Handle positioning of the toggle
          window.addEventListener(
            "resize",
            throttle(() => {
              positionToggle();
            }, 50)
          );
          positionToggle();

          // Process the click
          clickEl.onclick = () => {
            if (!tocShowing) {
              toggleContainer.classList.add("expanded");
              toggleContents.style.height = null;
              tocShowing = true;
            } else {
              closeToggle();
            }
          };
        };

        // Converts a sidebar from a menu back to a sidebar
        const convertToSidebar = () => {
          for (const child of el.children) {
            child.style.opacity = 1;
            child.style.overflow = null;
          }

          const placeholderEl = window.document.getElementById(
            placeholderDescriptor.id
          );
          if (placeholderEl) {
            placeholderEl.remove();
          }

          el.classList.remove("rollup");
        };

        if (isReaderMode()) {
          convertToMenu();
          isVisible = false;
        } else {
          if (!isVisible) {
            // If the element is current not visible reveal if there are
            // no conflicts with overlay regions
            if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
              convertToSidebar();
              isVisible = true;
            }
          } else {
            // If the element is visible, hide it if it conflicts with overlay regions
            // and insert a placeholder toggle (or if we're in reader mode)
            if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
              convertToMenu();
              isVisible = false;
            }
          }
        }
      }
    };
  };

  // Find any conflicting margin elements and add margins to the
  // top to prevent overlap
  const marginChildren = window.document.querySelectorAll(
    ".column-margin.column-container > * "
  );

  nexttick(() => {
    let lastBottom = 0;
    for (const marginChild of marginChildren) {
      const top = marginChild.getBoundingClientRect().top + window.scrollY;
      if (top < lastBottom) {
        const margin = lastBottom - top;
        marginChild.style.marginTop = `${margin}px`;
      }
      const styles = window.getComputedStyle(marginChild);
      const marginTop = parseFloat(styles["marginTop"]);

      lastBottom = top + marginChild.getBoundingClientRect().height + marginTop;
    }
  });

  // Manage the visibility of the toc and the sidebar
  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
    id: "quarto-toc-toggle",
    titleSelector: "#toc-title",
    dismissOnClick: true,
  });
  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
    id: "quarto-sidebarnav-toggle",
    titleSelector: ".title",
    dismissOnClick: false,
  });
  let tocLeftScrollVisibility;
  if (leftTocEl) {
    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
      id: "quarto-lefttoc-toggle",
      titleSelector: "#toc-title",
      dismissOnClick: true,
    });
  }

  // Find the first element that uses formatting in special columns
  const conflictingEls = window.document.body.querySelectorAll(
    '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
  );

  // Filter all the possibly conflicting elements into ones
  // the do conflict on the left or ride side
  const arrConflictingEls = Array.from(conflictingEls);
  const leftSideConflictEls = arrConflictingEls.filter((el) => {
    if (el.tagName === "ASIDE") {
      return false;
    }
    return Array.from(el.classList).find((className) => {
      return (
        className !== "column-body" &&
        className.startsWith("column-") &&
        !className.endsWith("right") &&
        !className.endsWith("container") &&
        className !== "column-margin"
      );
    });
  });
  const rightSideConflictEls = arrConflictingEls.filter((el) => {
    if (el.tagName === "ASIDE") {
      return true;
    }

    const hasMarginCaption = Array.from(el.classList).find((className) => {
      return className == "margin-caption";
    });
    if (hasMarginCaption) {
      return true;
    }

    return Array.from(el.classList).find((className) => {
      return (
        className !== "column-body" &&
        !className.endsWith("container") &&
        className.startsWith("column-") &&
        !className.endsWith("left")
      );
    });
  });

  const kOverlapPaddingSize = 10;
  function toRegions(els) {
    return els.map((el) => {
      const top =
        el.getBoundingClientRect().top +
        document.documentElement.scrollTop -
        kOverlapPaddingSize;
      return {
        top,
        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
      };
    });
  }

  const hideOverlappedSidebars = () => {
    marginScrollVisibility(toRegions(rightSideConflictEls));
    sidebarScrollVisiblity(toRegions(leftSideConflictEls));
    if (tocLeftScrollVisibility) {
      tocLeftScrollVisibility(toRegions(leftSideConflictEls));
    }
  };

  window.quartoToggleReader = () => {
    // Applies a slow class (or removes it)
    // to update the transition speed
    const slowTransition = (slow) => {
      const manageTransition = (id, slow) => {
        const el = document.getElementById(id);
        if (el) {
          if (slow) {
            el.classList.add("slow");
          } else {
            el.classList.remove("slow");
          }
        }
      };

      manageTransition("TOC", slow);
      manageTransition("quarto-sidebar", slow);
    };

    const readerMode = !isReaderMode();
    setReaderModeValue(readerMode);

    // If we're entering reader mode, slow the transition
    if (readerMode) {
      slowTransition(readerMode);
    }
    highlightReaderToggle(readerMode);
    hideOverlappedSidebars();

    // If we're exiting reader mode, restore the non-slow transition
    if (!readerMode) {
      slowTransition(!readerMode);
    }
  };

  const highlightReaderToggle = (readerMode) => {
    const els = document.querySelectorAll(".quarto-reader-toggle");
    if (els) {
      els.forEach((el) => {
        if (readerMode) {
          el.classList.add("reader");
        } else {
          el.classList.remove("reader");
        }
      });
    }
  };

  const setReaderModeValue = (val) => {
    if (window.location.protocol !== "file:") {
      window.localStorage.setItem("quarto-reader-mode", val);
    } else {
      localReaderMode = val;
    }
  };

  const isReaderMode = () => {
    if (window.location.protocol !== "file:") {
      return window.localStorage.getItem("quarto-reader-mode") === "true";
    } else {
      return localReaderMode;
    }
  };
  let localReaderMode = null;

  // Walk the TOC and collapse/expand nodes
  // Nodes are expanded if:
  // - they are top level
  // - they have children that are 'active' links
  // - they are directly below an link that is 'active'
  const walk = (el, depth) => {
    // Tick depth when we enter a UL
    if (el.tagName === "UL") {
      depth = depth + 1;
    }

    // It this is active link
    let isActiveNode = false;
    if (el.tagName === "A" && el.classList.contains("active")) {
      isActiveNode = true;
    }

    // See if there is an active child to this element
    let hasActiveChild = false;
    for (child of el.children) {
      hasActiveChild = walk(child, depth) || hasActiveChild;
    }

    // Process the collapse state if this is an UL
    if (el.tagName === "UL") {
      if (depth === 1 || hasActiveChild || prevSiblingIsActiveLink(el)) {
        el.classList.remove("collapse");
      } else {
        el.classList.add("collapse");
      }

      // untick depth when we leave a UL
      depth = depth - 1;
    }
    return hasActiveChild || isActiveNode;
  };

  // walk the TOC and expand / collapse any items that should be shown

  if (tocEl) {
    walk(tocEl, 0);
    updateActiveLink();
  }

  // Throttle the scroll event and walk peridiocally
  window.document.addEventListener(
    "scroll",
    throttle(() => {
      if (tocEl) {
        updateActiveLink();
        walk(tocEl, 0);
      }
      if (!isReaderMode()) {
        hideOverlappedSidebars();
      }
    }, 5)
  );
  window.addEventListener(
    "resize",
    throttle(() => {
      if (!isReaderMode()) {
        hideOverlappedSidebars();
      }
    }, 10)
  );
  hideOverlappedSidebars();
  highlightReaderToggle(isReaderMode());
});

// grouped tabsets
window.addEventListener("pageshow", (_event) => {
  function getTabSettings() {
    const data = localStorage.getItem("quarto-persistent-tabsets-data");
    if (!data) {
      localStorage.setItem("quarto-persistent-tabsets-data", "{}");
      return {};
    }
    if (data) {
      return JSON.parse(data);
    }
  }

  function setTabSettings(data) {
    localStorage.setItem(
      "quarto-persistent-tabsets-data",
      JSON.stringify(data)
    );
  }

  function setTabState(groupName, groupValue) {
    const data = getTabSettings();
    data[groupName] = groupValue;
    setTabSettings(data);
  }

  function toggleTab(tab, active) {
    const tabPanelId = tab.getAttribute("aria-controls");
    const tabPanel = document.getElementById(tabPanelId);
    if (active) {
      tab.classList.add("active");
      tabPanel.classList.add("active");
    } else {
      tab.classList.remove("active");
      tabPanel.classList.remove("active");
    }
  }

  function toggleAll(selectedGroup, selectorsToSync) {
    for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
      const active = selectedGroup === thisGroup;
      for (const tab of tabs) {
        toggleTab(tab, active);
      }
    }
  }

  function findSelectorsToSyncByLanguage() {
    const result = {};
    const tabs = Array.from(
      document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
    );
    for (const item of tabs) {
      const div = item.parentElement.parentElement.parentElement;
      const group = div.getAttribute("data-group");
      if (!result[group]) {
        result[group] = {};
      }
      const selectorsToSync = result[group];
      const value = item.innerHTML;
      if (!selectorsToSync[value]) {
        selectorsToSync[value] = [];
      }
      selectorsToSync[value].push(item);
    }
    return result;
  }

  function setupSelectorSync() {
    const selectorsToSync = findSelectorsToSyncByLanguage();
    Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
      Object.entries(tabSetsByValue).forEach(([value, items]) => {
        items.forEach((item) => {
          item.addEventListener("click", (_event) => {
            setTabState(group, value);
            toggleAll(value, selectorsToSync[group]);
          });
        });
      });
    });
    return selectorsToSync;
  }

  const selectorsToSync = setupSelectorSync();
  for (const [group, selectedName] of Object.entries(getTabSettings())) {
    const selectors = selectorsToSync[group];
    // it's possible that stale state gives us empty selections, so we explicitly check here.
    if (selectors) {
      toggleAll(selectedName, selectors);
    }
  }
});

function throttle(func, wait) {
  let waiting = false;
  return function () {
    if (!waiting) {
      func.apply(this, arguments);
      waiting = true;
      setTimeout(function () {
        waiting = false;
      }, wait);
    }
  };
}

function nexttick(func) {
  return setTimeout(func, 0);
}


================================================
FILE: 2023年/2023.03.16使用 ggTimeSeries 包构建日历图/日历图_files/libs/quarto-html/tippy.css
================================================
.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 8px;border-left-color:initial;right:-7px;transform-origin:center left}.tippy-box[data-placement^=right]>.tippy-arrow{left:0}.tippy-box[data-placement^=right]>.tippy-arrow:before{left:-7px;border-width:8px 8px 8px 0;border-right-color:initial;transform-origin:center right}.tippy-box[data-inertia][data-state=visible]{transition-timing-function:cubic-bezier(.54,1.5,.38,1.11)}.tippy-arrow{width:16px;height:16px;color:#333}.tippy-arrow:before{content:"";position:absolute;border-color:transparent;border-style:solid}.tippy-content{position:relative;padding:5px 9px;z-index:1}

================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/.Rhistory
================================================
)+
xlab("developmental stage (days)") +
ylab(expression(paste("tracheal length (",~mu*m,")")))
panel_C
# Panel E ----
library(ggplot2)
base_size = 12
my_theme <-  function() {
theme(
aspect.ratio = 1,
axis.line =element_line(colour = "black"),
# shift axis text closer to axis bc ticks are facing inwards
axis.text.x = element_text(size = base_size*0.8, color = "black",
lineheight = 0.9,
margin=unit(c(0.3,0.3,0.3,0.3), "cm")),
axis.text.y = element_text(size = base_size*0.8, color = "black",
lineheight = 0.9,
margin=unit(c(0.3,0.3,0.3,0.3), "cm")),
axis.ticks = element_line(color = "black", size  =  0.2),
axis.title.x = element_text(size = base_size,
color = "black",
margin = margin(t = -5)),
# t (top), r (right), b (bottom), l (left)
axis.title.y = element_text(size = base_size,
color = "black", angle = 90,
margin = margin(r = -5)),
axis.ticks.length = unit(-0.3, "lines"),
legend.background = element_rect(color = NA,
fill = NA),
legend.key = element_rect(color = "black",
fill = "white"),
legend.key.size = unit(0.5, "lines"),
legend.key.height =NULL,
legend.key.width = NULL,
legend.text = element_text(size = 0.6*base_size,
color = "black"),
legend.title = element_text(size = 0.6*base_size,
face = "bold",
hjust = 0,
color = "black"),
legend.text.align = NULL,
legend.title.align = NULL,
legend.direction = "vertical",
legend.box = NULL,
panel.background = element_rect(fill = "white",
color  =  NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(size = base_size,
color = "black"),
)
}
data_Ea = read.csv("./data_Ea.csv")
data_Eb = read.csv("./data_Eb.csv")
data_Ec = read.csv("./data_Ec.csv")
data_E = data.frame("gene"=c(rep("gene a",nrow(data_Ea)),
rep("gene b",nrow(data_Eb)),
rep("gene c",nrow(data_Ec))),
"t"=c(data_Ea$t,data_Eb$t,data_Ec$t),
"C"=c(data_Ea$C,data_Eb$C,data_Ec$C),
"pos_err"=c(data_Ea$err,data_Eb$pos_err,data_Ec$pos_err_C),
"neg_err"=c(data_Ea$err,data_Eb$neg_err,data_Ec$neg_err_C),
"pos_err_t"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$pos_err_t),
"neg_err_t"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$neg_err_t)
)
head(data_E)
f1 = function(t) 0.2*exp(-t/48)
f2 = function(t) 0.3*exp(-t/60)
f3 = function(t) 0.4*exp(-t/72)
t = seq(0,96,1)
ribbon = data.frame(
"f2" = 0.3*exp(-t/60),
"t" = t
)
head(ribbon)
manual_pch =c(15,16,17) # available pch: type ?pch
panel_E <- ggplot(data=data_E) +
geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +
geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),
fill="black",alpha=0.1) +
stat_function(fun=f1, geom="line",linetype="dashed") +
stat_function(fun=f2, geom="line") +
stat_function(fun=f3, geom="line",linetype="dotted") +
geom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err),
width=2) +
geom_errorbarh(aes(y=C,xmin=t-neg_err_t,xmax=t+pos_err_t))+
scale_shape_manual(values=manual_pch) +
my_theme() + theme(legend.title=element_blank())+
theme(legend.position = c(0.9,0.95)) +
scale_x_continuous(expand = c(0, 0),
breaks = c(seq(0,96,12)),
limits = c(0,96)
) +
scale_y_continuous(expand = c(0, 0),
breaks = c(seq(0,0.4,0.05)),
limits = c(0,0.4)
) +
theme(
panel.grid.major = element_line("gray95", size = 0.1),
# putting label closer to axis bc exponent makes it bigger
axis.title.y = element_text(margin = margin(r = -9))
) +
xlab("time (h)") +
ylab(expression(paste("concentration (mmol",~cm^-3,")"))) +
coord_cartesian(clip = "off") # to allow for plotting outside axes
panel_E
data_Ea = read.csv("./data_Ea.csv")
data_Eb = read.csv("./data_Eb.csv")
data_Ec = read.csv("./data_Ec.csv")
data_E = data.frame("gene"=c(rep("gene a",nrow(data_Ea)),
rep("gene b",nrow(data_Eb)),
rep("gene c",nrow(data_Ec))),
"t"=c(data_Ea$t,data_Eb$t,data_Ec$t),
"C"=c(data_Ea$C,data_Eb$C,data_Ec$C),
"pos_err"=c(data_Ea$err,data_Eb$pos_err,data_Ec$pos_err_C),
"neg_err"=c(data_Ea$err,data_Eb$neg_err,data_Ec$neg_err_C),
"pos_err_t"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$pos_err_t),
"neg_err_t"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$neg_err_t)
)
head(data_E)
data_Ea = read.csv("./data_Ea.csv")
data_Eb = read.csv("./data_Eb.csv")
data_Ec = read.csv("./data_Ec.csv")
data_E = data.frame("gene"=c(rep("gene a",nrow(data_Ea)),
rep("gene b",nrow(data_Eb)),
rep("gene c",nrow(data_Ec))),
"t"=c(data_Ea$t,data_Eb$t,data_Ec$t),
"C"=c(data_Ea$C,data_Eb$C,data_Ec$C),
"pos_err"=c(data_Ea$err,data_Eb$pos_err,data_Ec$pos_err_C),
"neg_err"=c(data_Ea$err,data_Eb$neg_err,data_Ec$neg_err_C),
"pos_err_t"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$pos_err_t),
"neg_err_t"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$neg_err_t)
)
head(data_E)
t = seq(0,96,1)
t
1/60
1/48
ribbon = data.frame(
"f2" = 0.3*exp(-t/60),
"t" = t
)
ribbon
ggplot(data=data_E) +
geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +
geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),
fill="black",alpha=0.1) +
stat_function(fun=f1, geom="line",linetype="dashed") +
stat_function(fun=f2, geom="line") +
stat_function(fun=f3, geom="line",linetype="dotted")
ggplot(data=data_E) +
geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +
geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),
fill="black",alpha=0.1)
ggplot(data=data_E) +
geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +
geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),
fill="black",alpha=0.1)
ggplot(data=data_E) +
geom_point(aes(x=t,y=C,pch=factor(gene)),size=2)
ggplot(data=data_E) +
geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +
geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),
fill="black",alpha=0.1) +
stat_function(fun=f1, geom="line",linetype="dashed") +
stat_function(fun=f2, geom="line") +
stat_function(fun=f3, geom="line",linetype="dotted")
ggplot(data=data_E) +
geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +
geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),
fill="black",alpha=0.1) +
stat_function(fun=f1, geom="line",linetype="dashed") +
stat_function(fun=f2, geom="line") +
stat_function(fun=f3, geom="line",linetype="dotted") +
geom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err),
width=2)
ggplot(data=data_E) +
geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +
geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),
fill="black",alpha=0.1) +
stat_function(fun=f1, geom="line",linetype="dashed") +
stat_function(fun=f2, geom="line") +
stat_function(fun=f3, geom="line",linetype="dotted") +
geom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err),
width=2) +
geom_errorbarh(aes(y=C,xmin=t-neg_err_t,xmax=t+pos_err_t))
ggplot(data=data_E) +
geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +
geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),
fill="black",alpha=0.1) +
stat_function(fun=f1, geom="line",linetype="dashed") +
stat_function(fun=f2, geom="line") +
stat_function(fun=f3, geom="line",linetype="dotted") +
geom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err),
width=2) +
geom_errorbarh(aes(y=C,xmin=t-neg_err_t,xmax=t+pos_err_t))+
scale_shape_manual(values=manual_pch) +
my_theme() + theme(legend.title=element_blank())+
theme(legend.position = c(0.9,0.95)) +
scale_x_continuous(expand = c(0, 0),
breaks = c(seq(0,96,12)),
limits = c(0,96)
) +
scale_y_continuous(expand = c(0, 0),
breaks = c(seq(0,0.4,0.05)),
limits = c(0,0.4)
) +
theme(
panel.grid.major = element_line("gray95", size = 0.1),
# putting label closer to axis bc exponent makes it bigger
axis.title.y = element_text(margin = margin(r = -9))
) +
xlab("time (h)") +
ylab(expression(paste("concentration (mmol",~cm^-3,")")))
panel_E <- ggplot(data=data_E) +
geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +
geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),
fill="black",alpha=0.1) +
stat_function(fun=f1, geom="line",linetype="dashed") +
stat_function(fun=f2, geom="line") +
stat_function(fun=f3, geom="line",linetype="dotted") +
geom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err),
width=2) +
geom_errorbarh(aes(y=C,xmin=t-neg_err_t,xmax=t+pos_err_t))+
scale_shape_manual(values=manual_pch) +
my_theme() + theme(legend.title=element_blank())+
theme(legend.position = c(0.9,0.95)) +
scale_x_continuous(expand = c(0, 0),
breaks = c(seq(0,96,12)),
limits = c(0,96)
) +
scale_y_continuous(expand = c(0, 0),
breaks = c(seq(0,0.4,0.05)),
limits = c(0,0.4)
) +
theme(
panel.grid.major = element_line("gray95", size = 0.1),
# putting label closer to axis bc exponent makes it bigger
axis.title.y = element_text(margin = margin(r = -9))
) +
xlab("time (h)") +
ylab(expression(paste("concentration (mmol",~cm^-3,")"))) +
coord_cartesian(clip = "off") # to allow for plotting outside axes
panel_E
# Panel F ----
library(ggplot2)
base_size = 12
my_theme <-  function() {
theme(
aspect.ratio = 1,
axis.line =element_line(colour = "black"),
# shift axis text closer to axis bc ticks are facing inwards
axis.text.x = element_text(size = base_size*0.8, color = "black",
lineheight = 0.9,
margin=unit(c(0.3,0.3,0.3,0.3), "cm")),
axis.text.y = element_text(size = base_size*0.8, color = "black",
lineheight = 0.9,
margin=unit(c(0.3,0.3,0.3,0.3), "cm")),
axis.ticks = element_line(color = "black", size  =  0.2),
axis.title.x = element_text(size = base_size,
color = "black",
margin = margin(t = -5)),
# t (top), r (right), b (bottom), l (left)
axis.title.y = element_text(size = base_size,
color = "black", angle = 90,
margin = margin(r = -5)),
axis.ticks.length = unit(-0.3, "lines"),
legend.background = element_rect(color = NA,
fill = NA),
legend.key = element_rect(color = "black",
fill = "white"),
legend.key.size = unit(0.5, "lines"),
legend.key.height =NULL,
legend.key.width = NULL,
legend.text = element_text(size = 0.6*base_size,
color = "black"),
legend.title = element_text(size = 0.6*base_size,
face = "bold",
hjust = 0,
color = "black"),
legend.text.align = NULL,
legend.title.align = NULL,
legend.direction = "vertical",
legend.box = NULL,
panel.background = element_rect(fill = "white",
color  =  NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(size = base_size,
color = "black"),
)
}
data_F = read.csv("./data_F.csv")
panel_F <- ggplot(data=data_F,
aes(x=K,y=n,
size=amplitude,
fill=duration))+
geom_point(pch=21) +
my_theme() +
scale_x_continuous(expand = c(0, 0),
trans = 'log10',
labels=c(1,10,100),
breaks=c(1,10,100),
limits = c(1,100)) +
scale_y_continuous(expand = c(0, 0),
breaks=c(seq(0,4,by=0.5)),
limits = c(0,4)) +
annotation_logticks(sides='b') +
scale_size(range = c(1, 3)) +
scale_fill_viridis(option="D") + # a color palette from the viridis package
theme(legend.position = c(0.9,0.35)) +
coord_cartesian(clip = "off") +
xlab(expression(paste("dissociation constant",~~italic("K")," (M)"))) +
ylab("Hill coefficient n")
library(viridis)
base_size = 12
my_theme <-  function() {
theme(
aspect.ratio = 1,
axis.line =element_line(colour = "black"),
# shift axis text closer to axis bc ticks are facing inwards
axis.text.x = element_text(size = base_size*0.8, color = "black",
lineheight = 0.9,
margin=unit(c(0.3,0.3,0.3,0.3), "cm")),
axis.text.y = element_text(size = base_size*0.8, color = "black",
lineheight = 0.9,
margin=unit(c(0.3,0.3,0.3,0.3), "cm")),
axis.ticks = element_line(color = "black", size  =  0.2),
axis.title.x = element_text(size = base_size,
color = "black",
margin = margin(t = -5)),
# t (top), r (right), b (bottom), l (left)
axis.title.y = element_text(size = base_size,
color = "black", angle = 90,
margin = margin(r = -5)),
axis.ticks.length = unit(-0.3, "lines"),
legend.background = element_rect(color = NA,
fill = NA),
legend.key = element_rect(color = "black",
fill = "white"),
legend.key.size = unit(0.5, "lines"),
legend.key.height =NULL,
legend.key.width = NULL,
legend.text = element_text(size = 0.6*base_size,
color = "black"),
legend.title = element_text(size = 0.6*base_size,
face = "bold",
hjust = 0,
color = "black"),
legend.text.align = NULL,
legend.title.align = NULL,
legend.direction = "vertical",
legend.box = NULL,
panel.background = element_rect(fill = "white",
color  =  NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(size = base_size,
color = "black"),
)
}
data_F = read.csv("./data_F.csv")
panel_F <- ggplot(data=data_F,
aes(x=K,y=n,
size=amplitude,
fill=duration))+
geom_point(pch=21) +
my_theme() +
scale_x_continuous(expand = c(0, 0),
trans = 'log10',
labels=c(1,10,100),
breaks=c(1,10,100),
limits = c(1,100)) +
scale_y_continuous(expand = c(0, 0),
breaks=c(seq(0,4,by=0.5)),
limits = c(0,4)) +
annotation_logticks(sides='b') +
scale_size(range = c(1, 3)) +
scale_fill_viridis(option="D") + # a color palette from the viridis package
theme(legend.position = c(0.9,0.35)) +
coord_cartesian(clip = "off") +
xlab(expression(paste("dissociation constant",~~italic("K")," (M)"))) +
ylab("Hill coefficient n")
panel_F
head(data_F)
data_F = read.csv("./data_F.csv")
head(data_F)
ggplot(data=data_F,
aes(x=K,y=n,
size=amplitude,
fill=duration))+
geom_point(pch=21)
ggplot(data=data_F,
aes(x=K,y=n,
size=amplitude,
fill=duration))+
geom_point(pch=21) +
my_theme() +
scale_x_continuous(expand = c(0, 0),
trans = 'log10',
labels=c(1,10,100),
breaks=c(1,10,100),
limits = c(1,100)) +
scale_y_continuous(expand = c(0, 0),
breaks=c(seq(0,4,by=0.5)),
limits = c(0,4))
ggplot(data=data_F,
aes(x=K,y=n,
size=amplitude,
fill=duration))+
geom_point(pch=21) +
my_theme() +
scale_x_continuous(expand = c(0, 0),
trans = 'log10',
labels=c(1,10,100),
breaks=c(1,10,100),
limits = c(1,100)) +
scale_y_continuous(expand = c(0, 0),
breaks=c(seq(0,4,by=0.5)),
limits = c(0,4)) +
annotation_logticks(sides='b')
ggplot(data=data_F,
aes(x=K,y=n,
size=amplitude,
fill=duration))+
geom_point(pch=21) +
my_theme() +
scale_x_continuous(expand = c(0, 0),
trans = 'log10',
labels=c(1,10,100),
breaks=c(1,10,100),
limits = c(1,100)) +
scale_y_continuous(expand = c(0, 0),
breaks=c(seq(0,4,by=0.5)),
limits = c(0,4)) +
annotation_logticks(sides='b') +
scale_size(range = c(1, 3)) +
scale_fill_viridis(option="D") + # a color palette from the viridis package
theme(legend.position = c(0.9,0.35))
ggplot(data=data_F,
aes(x=K,y=n,
size=amplitude,
fill=duration))+
geom_point(pch=21) +
my_theme() +
scale_x_continuous(expand = c(0, 0),
trans = 'log10',
labels=c(1,10,100),
breaks=c(1,10,100),
limits = c(1,100)) +
scale_y_continuous(expand = c(0, 0),
breaks=c(seq(0,4,by=0.5)),
limits = c(0,4)) +   xlab(expression(paste("dissociation constant",~~italic("K")," (M)"))) +
ylab("Hill coefficient n")
ggplot(data=data_F,
aes(x=K,y=n,
size=amplitude,
fill=duration))+
geom_point(pch=21) +
my_theme() +
scale_x_continuous(expand = c(0, 0),
trans = 'log10',
labels=c(1,10,100),
breaks=c(1,10,100),
limits = c(1,100)) +
scale_y_continuous(expand = c(0, 0),
breaks=c(seq(0,4,by=0.5)),
limits = c(0,4)) +
xlab(expression(paste("dissociation constant",~~italic("K")," (M)"))) +
ylab("Hill coefficient n")
ggplot(data=data_F,
aes(x=K,y=n,
size=amplitude,
fill=duration))+
geom_point(pch=21) +
my_theme() +
scale_x_continuous(expand = c(0, 0),
trans = 'log10',
labels=c(1,10,100),
breaks=c(1,10,100),
limits = c(1,100)) +
scale_y_continuous(expand = c(0, 0),
breaks=c(seq(0,4,by=0.5)),
limits = c(0,4)) +
xlab(expression(paste("dissociation constant",~~italic("K")," (M)"))) +
ylab("Hill coefficient n") +
annotation_logticks(sides='b') +
scale_size(range = c(1, 3)) +
scale_fill_viridis(option="D") + # a color palette from the viridis package
theme(legend.position = c(0.9,0.35))
panel_F <- ggplot(data=data_F,
aes(x=K,y=n,
size=amplitude,
fill=duration))+
geom_point(pch=21) +
my_theme() +
scale_x_continuous(expand = c(0, 0),
trans = 'log10',
labels=c(1,10,100),
breaks=c(1,10,100),
limits = c(1,100)) +
scale_y_continuous(expand = c(0, 0),
breaks=c(seq(0,4,by=0.5)),
limits = c(0,4)) +
xlab(expression(paste("dissociation constant",~~italic("K")," (M)"))) +
ylab("Hill coefficient n") +
annotation_logticks(sides='b') +
scale_size(range = c(1, 3)) +
scale_fill_viridis(option="D") + # a color palette from the viridis package
theme(legend.position = c(0.9,0.35)) +
coord_cartesian(clip = "off")
panel_F


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================================================
# Panel F ----
library(ggplot2) 
library(vi)
base_size = 12 
my_theme <-  function() {
  theme(
    aspect.ratio = 1,
    axis.line =element_line(colour = "black"),  
    
    # shift axis text closer to axis bc ticks are facing inwards
    axis.text.x = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")), 
    axis.text.y = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")),  
    
    axis.ticks = element_line(color = "black", size  =  0.2),  
    axis.title.x = element_text(size = base_size, 
                                color = "black", 
                                margin = margin(t = -5)), 
    # t (top), r (right), b (bottom), l (left)
    axis.title.y = element_text(size = base_size, 
                                color = "black", angle = 90,
                                margin = margin(r = -5)),  
    axis.ticks.length = unit(-0.3, "lines"),  
    legend.background = element_rect(color = NA, 
                                     fill = NA), 
    legend.key = element_rect(color = "black",  
                              fill = "white"),  
    legend.key.size = unit(0.5, "lines"),  
    legend.key.height =NULL,  
    legend.key.width = NULL,      
    legend.text = element_text(size = 0.6*base_size, 
                               color = "black"),  
    legend.title = element_text(size = 0.6*base_size, 
                                face = "bold", 
                                hjust = 0, 
                                color = "black"),  
    legend.text.align = NULL,  
    legend.title.align = NULL,  
    legend.direction = "vertical",  
    legend.box = NULL, 
    panel.background = element_rect(fill = "white", 
                                    color  =  NA),  
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    plot.title = element_text(size = base_size, 
                              color = "black"), 
  ) 
  
}

data_F = read.csv("./data_F.csv")
head(data_F)
panel_F <- ggplot(data=data_F,
                  aes(x=K,y=n,
                      size=amplitude,
                      fill=duration))+
  geom_point(pch=21) +
  my_theme() + 
  scale_x_continuous(expand = c(0, 0),
                     trans = 'log10',
                     labels=c(1,10,100),
                     breaks=c(1,10,100),
                     limits = c(1,100)) +
  scale_y_continuous(expand = c(0, 0),
                     breaks=c(seq(0,4,by=0.5)),
                     limits = c(0,4)) +   
  xlab(expression(paste("dissociation constant",~~italic("K")," (M)"))) +
  ylab("Hill coefficient n") +
  annotation_logticks(sides='b') +
  scale_size(range = c(1, 3)) +
  scale_fill_viridis(option="D") + # a color palette from the viridis package
  theme(legend.position = c(0.9,0.35)) +
  coord_cartesian(clip = "off")

panel_F


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# Set working directory 
# setwd(getwd()) 

# Libraries/packages 

library(ggplot2) # Grammar of graphics
library(cowplot) # Arranging multiple plots into a grid
library(png)     # Load JPEG, PNG and TIFF format 
library(scales)  # Generic plot scaling methods
library(viridis) # Default color maps from 'matplotlib'
library(grid)    # A rewrite of the graphics layout capabilities
library(magick)  # graphics and image processing
library(rsvg)    # Render svg image into a high quality bitmap
library(ggforce) # Collection of additional ggplot stats + geoms

# global font size
base_size = 12 

# Manual theme for most panels
# documentation: https://ggplot2.tidyverse.org/reference/theme.html
my_theme <-  function() {
  theme(
    aspect.ratio = 1,
    axis.line =element_line(colour = "black"),  
    
    # shift axis text closer to axis bc ticks are facing inwards
    axis.text.x = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")), 
    axis.text.y = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")),  
    
    axis.ticks = element_line(color = "black", size  =  0.2),  
    axis.title.x = element_text(size = base_size, 
                                color = "black", 
                                margin = margin(t = -5)), 
    # t (top), r (right), b (bottom), l (left)
    axis.title.y = element_text(size = base_size, 
                                color = "black", angle = 90,
                                margin = margin(r = -5)),  
    axis.ticks.length = unit(-0.3, "lines"),  
    legend.background = element_rect(color = NA, 
                                     fill = NA), 
    legend.key = element_rect(color = "black",  
                              fill = "white"),  
    legend.key.size = unit(0.5, "lines"),  
    legend.key.height =NULL,  
    legend.key.width = NULL,      
    legend.text = element_text(size = 0.6*base_size, 
                               color = "black"),  
    legend.title = element_text(size = 0.6*base_size, 
                                face = "bold", 
                                hjust = 0, 
                                color = "black"),  
    legend.text.align = NULL,  
    legend.title.align = NULL,  
    legend.direction = "vertical",  
    legend.box = NULL, 
    panel.background = element_rect(fill = "white", 
                                    color  =  NA),  
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    plot.title = element_text(size = base_size, 
                              color = "black"), 
  ) 
  
  
}

# Panel A ----

img1 <- magick::image_flip(magick::image_read("./image1.jpg"))
img2 <-  magick::image_flip(magick::image_read("./image2.png"))

panel_A <- ggplot() +
  annotation_custom(rasterGrob(image =  img1, 
                               x=0.27,
                               y=0.49,
                               width = unit(0.45,"npc"),
                               height = unit(0.87,"npc")), 
                    -Inf, Inf, -Inf, Inf) +
  annotation_custom(rasterGrob(image = img2, 
                               x=0.73,
                               y=0.49,
                               width = unit(0.45,"npc"),
                               height = unit(0.87,"npc")), 
                    -Inf, Inf, -Inf, Inf) +
  
  geom_ellipse(aes(x0 = 0.25, 
                            y0 = 0.3,
                            a = 0.1, 
                            b = 0.04, 
                            angle = 0),
                        color="yellow",
                        size=1)+
  scale_x_continuous(limits = c(0,1))+
  scale_y_continuous(limits=c(0,1)) +
  geom_segment(aes(x=0.15,
                   xend=0.2,
                   y=0.75,
                   yend=0.7),
               arrow = arrow(length=unit(0.30,"cm"),
                             ends="last", 
                             type = "closed"),
               size = 1,
               color="white") +
  geom_segment(aes(x=0.3,
                   xend=0.9,
                   y=0.7,
                   yend=0.7),
               arrow = arrow(length=unit(0.30,"cm"),
                             ends="both", 
                             type = "closed"),
               size = 1,
               color="red") +

  annotate("text", x = 0.25, y = 0.5, label = "PNG",color="white") +
  annotate("text", x = 0.75, y = 0.5, label = "JPEG",color="white") +
  annotate("text", x = 0.25, y = 1, label = "image 1",color="black") +
  annotate("text", x = 0.75, y = 1, label = "image 2",color="black") +
  annotate("text", x = 0.39, y = 0.07, label = "20~mu*m",color="white",parse=T) +
  annotate("text", x = 0.89, y = 0.07, label = "20~mu*m",color="white",parse=T) +
  geom_segment(aes(x=0.33,xend=0.45,y=0.03,yend=0.03), size = 2,color="white") +
  geom_segment(aes(x=0.83,xend=0.95,y=0.03,yend=0.03),size = 2,color="white")  + 
  theme_void() +# blank plot w/o axes etc.
  theme(plot.margin = unit(c(-0,0,1,0), "cm"),
        aspect.ratio = 1)

panel_A
# Panel B ----

data_B = read.csv("./data_B.csv")

# format data to ggplot's liking
data_B = data.frame("n"=c(data_B$n,data_B$n),
                    "sample"=c(rep("apical side",nrow(data_B)),
                               rep("basal side",nrow(data_B))),
                    "fraction"=c(data_B$fraction1,data_B$fraction2),
                    "err"=c(data_B$err1,data_B$err2)
)

# define lognormal distribution to be called via ggplot2::stat_function
sigma = 0.14
n = seq(3,10,1)
logn_dist <- function(n) exp(-(log(n)-log(6))^2/(2*sigma^2))/(sqrt(2*pi)*sigma*n)



panel_B <- 
  ggplot(data=data_B,(aes(x=n, # aes: aesthetics
                          y=fraction,
                          fill=sample)))+
  geom_bar(stat = "identity",
           position=position_dodge()) + # dodge overlapping objects side-to-side
  stat_function(fun=logn_dist, 
                geom="line",
                linetype="solid",
                aes(x=n,
                    y=logn_dist(n))) +
  geom_errorbar(aes(ymin=fraction-err, 
                    ymax=fraction+err), 
                width=.2, 
                position=position_dodge(.9)) +
  scale_fill_manual(values=c('#f2a340','#998fc2'))+
  scale_x_continuous(expand = c(0, 0), # prevent gap between origin and first tick
                     breaks=c(seq(from =  min(data_B$n), 
                                  to = max(data_B$n),
                                  by = 1)),
                     limits = c(min(data_B$n),max(data_B$n))) +
  scale_y_continuous(expand = c(0, 0),
                     breaks=c(seq(0,0.6,0.1)),
                     limits = c(0,0.6)
  )+
  my_theme() +
  # some extra theme tweaking 
  theme(legend.position = c(0.18,0.95),
        legend.title = element_blank(),
        axis.ticks.x=element_blank(),
        axis.text.x = element_text(vjust=-0.5),
        axis.title.x = element_text(vjust=-0.5),
        legend.key.size = unit(0.4, "lines")
  ) +
  xlab(expression(paste("number of neighbors ",italic("n")))) +
  ylab("fraction of cells (%)") 

panel_B


inset_curve <- function(n) 180*(n-2)/n 
# The equation is added as an image bc the annotate() function output looks too ugly
inset_equation <- image_read_svg("./panel_b_inset_equation.svg",width = 583,height = 240)

# now comes the inset plot

inset <- ggplot() + 
  stat_function(fun=inset_curve, 
                geom="line",
                linetype="solid",
                aes(x=n,
                    y=inset_curve(n))) +
  # annotate(geom="text",label="180*degree*(n-2)/n",x = 5.5,y=160,
  #          parse=TRUE,size=2) +
  annotation_custom(rasterGrob(image = inset_equation, 
                               x=0.5,
                               y=0.8,
                               width = unit(0.3125,"npc"),
                               height = unit(0.126,"npc")), 
                    -Inf, Inf, -Inf, Inf) +
  scale_x_continuous(expand = c(0, 0),
                     breaks=c(seq(3,10,1)),
                     limits = c(3,10)) +
  scale_y_continuous(expand = c(0, 0),
                     breaks=c(seq(60,180,20)),
                     limits = c(60,180)
                    ) +
  my_theme() +
  xlab(expression(italic(n)))+ 
  # expression doc: https://stat.ethz.ch/R-manual/R-devel/library/grDevices/html/plotmath.html
  ylab(expression(interior~angle~italic(theta[n])(degree))) +
  # some extra theme tweaking 
  theme( 
    panel.background = element_blank(),
    plot.background = element_blank(),
    axis.ticks.length = unit(-0.1, "lines"), # make ticks a bit shorter
    axis.title.x = element_text(size = 0.5*base_size, 
                                color = "black", 
                                margin = margin(t = -4)), 
    # top (t), right (r), bottom (b), left (l) 
    axis.title.y = element_text(size = 0.5*base_size, 
                                color = "black", angle = 90,
                                margin = margin(r = -4)),  
    axis.text.x = element_text(size = base_size*0.5, 
                               color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.1,0.1,0.1,0.1), "cm")),
    axis.text.y = element_text(size = base_size*0.5, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.1,0.1,0.1,0.1), "cm"))
  ) 

inset
# create  grob (grid graphical object) from the inset plot
panel_B_inset <- ggplotGrob(x = inset) 


# now for some regular polygon drawing
polygons <- 
  ggplot() +
    ggforce::geom_regon(aes(x0=c(seq(3,10,1)),
                            y0=rep(-0.3,8),
                            sides = c(seq(3,10,1)), 
                            angle=0, r=0.3),
                        fill=NA,
                        color="black") +
  theme_void() + coord_fixed()
polygons


# finally, we can combine everything to plot panel B

panel_B <- 
  panel_B + 
  annotation_custom(grob = panel_B_inset, 
                    xmin=6.6, xmax=10.1, ymin=0.1, ymax=0.6) +
  annotation_custom(grob = ggplotGrob(polygons), 
                    xmin = 2.36, xmax = 10.67, ymin = -0.172, ymax = 0.115) 


# Panel C ----

data_Cwt_E8.5  = read.csv( "./data_Cwt_E8.5.csv")
data_Cwt_E9.5  = read.csv( "./data_Cwt_E9.5.csv")
data_Cwt_E10.5 = read.csv("./data_Cwt_E10.5.csv")
data_Cwt_E11.5 = read.csv("./data_Cwt_E11.5.csv")
data_Cmu_E8.5  = read.csv( "./data_Cmu_E8.5.csv")
data_Cmu_E9.5  = read.csv( "./data_Cmu_E9.5.csv")
data_Cmu_E10.5 = read.csv("./data_Cmu_E10.5.csv")
data_Cmu_E11.5 = read.csv("./data_Cmu_E11.5.csv")

# format data to ggplot's liking
data_C = data.frame(
  "type" = c(
    rep(
      "wildtype",
      nrow(data_Cwt_E8.5) +
        nrow(data_Cwt_E9.5) +
        nrow(data_Cwt_E10.5) +
        nrow(data_Cwt_E11.5)
    ),
    rep(
      "mutant",
      nrow(data_Cmu_E8.5) +
        nrow(data_Cmu_E9.5) +
        nrow(data_Cmu_E10.5) +
        nrow(data_Cmu_E11.5)
    )
  ),
  "dev_stage" = c(
    rep("E8.5", nrow(data_Cwt_E8.5)),
    rep("E9.5", nrow(data_Cwt_E9.5)),
    rep("E10.5", nrow(data_Cwt_E10.5)),
    rep("E11.5", nrow(data_Cwt_E11.5)),
    rep("E8.5", nrow(data_Cmu_E8.5)),
    rep("E9.5", nrow(data_Cmu_E9.5)),
    rep("E10.5", nrow(data_Cmu_E10.5)),
    rep("E11.5", nrow(data_Cmu_E11.5))
  ),
  "trachea_length" = c(
    data_Cwt_E8.5[, 1] ,
    data_Cwt_E9.5[, 1] ,
    data_Cwt_E10.5[, 1] ,
    data_Cwt_E11.5[, 1] ,
    data_Cmu_E8.5[, 1] ,
    data_Cmu_E9.5[, 1] ,
    data_Cmu_E10.5[, 1] ,
    data_Cmu_E11.5[, 1]
  )
)

panel_C <- 
  ggplot(data=data_C,
         aes(x=dev_stage,
             y=trachea_length,
             fill=type))+
  geom_boxplot() + 
  geom_point(position=position_jitterdodge(), # jitter for h-dist, dodge for grouped dists
             pch=21,
             alpha=0.4) +  # transparency
  scale_x_discrete(limits=c("E8.5","E9.5","E10.5","E11.5")) +
  scale_fill_manual(values=c('#f2a340','#998fc2'))+ # custom colors in hex code
  my_theme() +
  theme(legend.position = c(0.18,0.95),
        legend.title = element_blank(),
        legend.key = element_blank()
  ) +
  scale_y_continuous(expand = c(0, 0),
                     breaks=c(seq(20,120,by=20)),
                     limits = c(20,120)
  )+
  xlab("developmental stage (days)") + 
  ylab(expression(paste("tracheal length (",~mu*m,")")))

panel_C
# Panel D ----

data_D1 = read.csv("./data_D1.csv")
data_D2 = read.csv("./data_D2.csv")
# format data to ggplot's liking
data_D = data.frame("width"=c(data_D1$width,data_D2$width),
                    "unit"=c(rep("shear_stress",nrow(data_D1)),
                             rep("velocity",nrow(data_D2))),
                    "value"=c(data_D1$shear_stress,data_D2$velocity)
)

curve_D1 = data.frame(width=data_D1$width,
                      shear_stress=33.28/(pi*18*data_D1$width^2))


panel_D1 <- ggplot(data=data_D1, 
                   aes(x=width,
                       y=shear_stress)) + 
  geom_point(fill="red",
             size=3,
             pch=22) +
  geom_line(data=curve_D1) +
  theme_void()+
  scale_x_log10(expand=c(0,0), # prevent gap between origin and first tick
                breaks=c(0.5,1,2,5,10,20,50),
                labels=c(0.5,1,2,5,10,20,50), 
                limits=c(0.5,50)) +
  scale_y_log10( expand = c(0, 0),
                 # using trans_format from the scales package, but one can also use expressions
                 labels = trans_format('log10', math_format(10^.x)), 
                 breaks=c(0.001,0.01,0.1,1),
                 limits = c(0.001,1)
  ) +
  annotation_logticks(sides = "l") +
  theme(
    line = element_blank(),
    # exclude everything outside axes bc it messes with positioning of grob in panel_D
    text = element_blank(),
    title = element_blank(),
    axis.line.y = element_line(colour = "black"),
    aspect.ratio = 1
  ) +
  ylab("shear stress (Pa)")



panel_D <- 
  ggplot(data=data_D2, 
         aes(x=width,
             y=velocity))+ 
  # add plot of first dataset as grob as a trick to introduce two y-axes with different scalings
  annotation_custom(ggplotGrob(panel_D1)) + 
  geom_point(fill="blue",
             size=3,
             pch=21) + 
  my_theme() + 
  geom_line(color="blue") +
  geom_vline(xintercept = 1.1,
             linetype="dashed") +
  geom_hline(yintercept = 0.9,
             linetype="dashed") +
  scale_x_log10(expand=c(0,0),
                breaks=c(0.5,1,2,5,10,20,50),
                labels=c(0.5,1,2,5,10,20,50), 
                limits=c(0.5,50)) + 
  annotation_logticks(sides = "b") + 
  scale_y_continuous(expand = c(0,0),
                     breaks = seq(0,1,0.1),
                     limits = c(0,1), 
                     # putting the y axis of the second plot to the right
                     position = "right", 
                     # now the secondary axis becomes the left axis
                     # we need the axis text+title for panel_D1
                     # They were excluded in panel_D1 bc they were messing with the positioning
                     sec.axis = sec_axis(~., 
                                         name = "shear stress (Pa)",
                                         # rescale breaks bc sec_axis inherits scale from primary y axis
                                         breaks=rescale(c(-3,-2,-1,0), 
                                                        to = c(0,1)),
                                         labels = c(expression("10"^"-3",
                                                               "10"^"-2",
                                                               "10"^"-1",
                                                               "10"^"0")))
                     
  )  +
  # some extra theme tweaking 
  theme(
    aspect.ratio = 1,
    plot.margin = unit(c(0.1,0,0,0.5), "cm"), # to match other panels
    axis.title.y = element_text(margin = margin(r=1)), 
    axis.text.y = element_text(margin = margin(r=6)),
    axis.text.y.right = element_text(margin = margin(l=7)),
    axis.title.y.right = element_text(angle = 90)
  ) +
  xlab(expression(lumen~width~(mu*m))) +
  ylab("relative flow velocity") + 
  annotate(geom = "text",x =6 ,y =0.85 ,label = "tau == 0.5~Pa",parse=T) +
  annotate(geom = "text",x =1.4 ,y =0.4 ,label = "b == 1.1*mu*m",parse=T,angle=90) +
  annotate(geom = "text",x =5.6 ,y =0.6 ,label = "tau == frac(4*mu*Q,pi*a*b^2)",parse=T) 
  
panel_D 

# Panel E ----

data_Ea = read.csv("./data_Ea.csv")
data_Eb = read.csv("./data_Eb.csv")
data_Ec = read.csv("./data_Ec.csv")
data_E = data.frame("gene"=c(rep("gene a",nrow(data_Ea)),
                             rep("gene b",nrow(data_Eb)),
                             rep("gene c",nrow(data_Ec))),
                    "t"=c(data_Ea$t,data_Eb$t,data_Ec$t),
                    "C"=c(data_Ea$C,data_Eb$C,data_Ec$C),
                    "pos_err"=c(data_Ea$err,data_Eb$pos_err,data_Ec$pos_err_C),
                    "neg_err"=c(data_Ea$err,data_Eb$neg_err,data_Ec$neg_err_C),
                    "pos_err_t"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$pos_err_t),
                    "neg_err_t"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$neg_err_t)
)

f1 = function(t) 0.2*exp(-t/48)
f2 = function(t) 0.3*exp(-t/60)
f3 = function(t) 0.4*exp(-t/72)
t = seq(0,96,1)

ribbon = data.frame(
  "f2" = 0.3*exp(-t/60),
  "t" = t
)

manual_pch =c(15,16,17) # available pch: type ?pch 

panel_E <- ggplot(data=data_E) +
  geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +
  geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),
              fill="black",alpha=0.1) +
  stat_function(fun=f1, geom="line",linetype="dashed") +
  stat_function(fun=f2, geom="line") +
  stat_function(fun=f3, geom="line",linetype="dotted") +
  geom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err), 
                width=2) +
  geom_errorbarh(aes(y=C,xmin=t-neg_err_t,xmax=t+pos_err_t))+
  scale_shape_manual(values=manual_pch) +
  my_theme() + theme(legend.title=element_blank())+
  theme(legend.position = c(0.9,0.95)) +
  scale_x_continuous(expand = c(0, 0), 
                     breaks = c(seq(0,96,12)),
                     limits = c(0,96)
  ) +
  scale_y_continuous(expand = c(0, 0),
                     breaks = c(seq(0,0.4,0.05)),
                     limits = c(0,0.4)
  ) +
  theme(
    panel.grid.major = element_line("gray95", size = 0.1),
    # putting label closer to axis bc exponent makes it bigger 
    axis.title.y = element_text(margin = margin(r = -9)) 
  ) +
  xlab("time (h)") +
  ylab(expression(paste("concentration (mmol",~cm^-3,")"))) +  
  coord_cartesian(clip = "off") # to allow for plotting outside axes

panel_E
# Panel F ----

data_F = read.csv("./data_F.csv")

panel_F <- ggplot(data=data_F,
                  aes(x=K,y=n,
                      size=amplitude,
                      fill=duration))+
  geom_point(pch=21) +
  my_theme() +

  scale_x_continuous(expand = c(0, 0),
                     trans = 'log10',
                     labels=c(1,10,100),
                     breaks=c(1,10,100),
                     limits = c(1,100)) +
  scale_y_continuous(expand = c(0, 0),
                     breaks=c(seq(0,4,by=0.5)),
                     limits = c(0,4)) +
  annotation_logticks(sides='b') +
  scale_size(range = c(1, 3)) +
  scale_fill_viridis(option="D") + # a color palette from the viridis package
  theme( legend.position = c(0.9,0.35)) +
  coord_cartesian(clip = "off") +
  xlab(expression(paste("dissociation constant",~~italic("K")," (M)"))) +
  ylab("Hill coefficient n")

panel_F

# Plot the whole figure using plot_grid from the cowplot package


row1 <- plot_grid(panel_A,panel_B,panel_C,
                  ncol=3,
                  labels = c('A','B','C'))#,rel_widths = c(0.9,0.9))

row2 <- plot_grid(panel_D,panel_E,panel_F,
                  ncol=3,
                  labels = c('D','E','F'))

plot_grid(row1,row2,nrow=2)

# RStudio instructions for saving the final plot:
# Whenever you resize the 'Plots' window, click 'refresh current plot'
# For best results save as SVG with a resolution of 1148 x 686 
# open SVG in inkscape, (do some optional post-processing) and 'save as' PDF



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FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/.Rproj.user/C6560784/sources/per/t/A32C5A91
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FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/.Rproj.user/C6560784/sources/per/t/A32C5A91-contents
================================================
library(ggplot2) 
base_size = 12 
my_theme <-  function() {
  theme(
    aspect.ratio = 1,
    axis.line =element_line(colour = "black"),  
    
    # shift axis text closer to axis bc ticks are facing inwards
    axis.text.x = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")), 
    axis.text.y = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")),  
    
    axis.ticks = element_line(color = "black", size  =  0.2),  
    axis.title.x = element_text(size = base_size, 
                                color = "black", 
                                margin = margin(t = -5)), 
    # t (top), r (right), b (bottom), l (left)
    axis.title.y = element_text(size = base_size, 
                                color = "black", angle = 90,
                                margin = margin(r = -5)),  
    axis.ticks.length = unit(-0.3, "lines"),  
    legend.background = element_rect(color = NA, 
                                     fill = NA), 
    legend.key = element_rect(color = "black",  
                              fill = "white"),  
    legend.key.size = unit(0.5, "lines"),  
    legend.key.height =NULL,  
    legend.key.width = NULL,      
    legend.text = element_text(size = 0.6*base_size, 
                               color = "black"),  
    legend.title = element_text(size = 0.6*base_size, 
                                face = "bold", 
                                hjust = 0, 
                                color = "black"),  
    legend.text.align = NULL,  
    legend.title.align = NULL,  
    legend.direction = "vertical",  
    legend.box = NULL, 
    panel.background = element_rect(fill = "white", 
                                    color  =  NA),  
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    plot.title = element_text(size = base_size, 
                              color = "black"), 
  ) 
  
  
}

data_D1 = read.csv("./data_D1.csv")
data_D2 = read.csv("./data_D2.csv")
# format data to ggplot's liking
data_D = data.frame("width"=c(data_D1$width,data_D2$width),
                    "unit"=c(rep("shear_stress",nrow(data_D1)),
                             rep("velocity",nrow(data_D2))),
                    "value"=c(data_D1$shear_stress,data_D2$velocity)
)
head(data_D)
curve_D1 = data.frame(width=data_D1$width,
                      shear_stress=33.28/(pi*18*data_D1$width^2))
curve_D1

panel_D1 <- ggplot(data=data_D1, 
                   aes(x=width,
                       y=shear_stress)) + 
  geom_point(fill="red",
             size=3,
             pch=22) +
  geom_line(data=curve_D1) +
  scale_x_log10(expand=c(0,0), # prevent gap between origin and first tick
                breaks=c(0.5,1,2,5,10,20,50),
                labels=c(0.5,1,2,5,10,20,50), 
                limits=c(0.5,50)) +
  scale_y_log10( expand = c(0, 0),
                 # using trans_format from the scales package, but one can also use expressions
                 labels = trans_format('log10', math_format(10^.x)), 
                 breaks=c(0.001,0.01,0.1,1),
                 limits = c(0.001,1)
  ) +
  annotation_logticks(sides = "l") +
  theme_void()+
  theme(
    line = element_blank(),
    # exclude everything outside axes bc it messes with positioning of grob in panel_D
    text = element_blank(),
    title = element_blank(),
    axis.line.y = element_line(colour = "black")
  ) +
  ylab("shear stress (Pa)")

panel_D1

panel_D <- 
  ggplot(data=data_D2, 
         aes(x=width,
             y=velocity))+ 
  # add plot of first dataset as grob as a trick to introduce two y-axes with different scalings
  geom_point(fill="blue",
             size=3,
             pch=21) +
  annotation_custom(ggplotGrob(panel_D1)) + 
  scale_y_continuous(expand = c(0,0),
                     breaks = seq(0,1,0.1),
                     limits = c(0,1), 
                     # putting the y axis of the second plot to the right
                     position = "right", 
                     # now the secondary axis becomes the left axis
                     # we need the axis text+title for panel_D1
                     # They were excluded in panel_D1 bc they were messing with the positioning
                     sec.axis = sec_axis(~., 
                                         name = "shear stress (Pa)",
                                         # rescale breaks bc sec_axis inherits scale from primary y axis
                                         breaks=rescale(c(-3,-2,-1,0), 
                                                        to = c(0,1)),
                                         labels = c(expression("10"^"-3",
                                                               "10"^"-2",
                                                               "10"^"-1",
                                                               "10"^"0")))
                     
  ) +
  scale_x_log10(expand=c(0,0),
                breaks=c(0.5,1,2,5,10,20,50),
                labels=c(0.5,1,2,5,10,20,50), 
                limits=c(0.5,50)) + 
  annotation_logticks(sides = "b") + 
  my_theme() + 
  geom_line(color="blue") +
  geom_vline(xintercept = 1.1,
             linetype="dashed") +
  geom_hline(yintercept = 0.9,
             linetype="dashed") +
  theme(
    plot.margin = unit(c(0.1,0,0,0.5), "cm"), # to match other panels
    axis.title.y = element_text(margin = margin(r=1)), 
    axis.text.y = element_text(margin = margin(r=6)),
    axis.text.y.right = element_text(margin = margin(l=7)),
    axis.title.y.right = element_text(angle = 90)
  ) +
  xlab(expression(lumen~width~(mu*m))) +
  ylab("relative flow velocity") + 
  annotate(geom = "text",x =6 ,y =0.85 ,label = "tau == 0.5~Pa",parse=T) +
  annotate(geom = "text",x =1.4 ,y =0.4 ,label = "b == 1.1*mu*m",parse=T,angle=90) +
  annotate(geom = "text",x =5.6 ,y =0.6 ,label = "tau == frac(4*mu*Q,pi*a*b^2)",parse=T) 

panel_D 



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# Panel E ----
library(ggplot2) 
base_size = 12 
my_theme <-  function() {
  theme(
    aspect.ratio = 1,
    axis.line =element_line(colour = "black"),  
    
    # shift axis text closer to axis bc ticks are facing inwards
    axis.text.x = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")), 
    axis.text.y = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")),  
    
    axis.ticks = element_line(color = "black", size  =  0.2),  
    axis.title.x = element_text(size = base_size, 
                                color = "black", 
                                margin = margin(t = -5)), 
    # t (top), r (right), b (bottom), l (left)
    axis.title.y = element_text(size = base_size, 
                                color = "black", angle = 90,
                                margin = margin(r = -5)),  
    axis.ticks.length = unit(-0.3, "lines"),  
    legend.background = element_rect(color = NA, 
                                     fill = NA), 
    legend.key = element_rect(color = "black",  
                              fill = "white"),  
    legend.key.size = unit(0.5, "lines"),  
    legend.key.height =NULL,  
    legend.key.width = NULL,      
    legend.text = element_text(size = 0.6*base_size, 
                               color = "black"),  
    legend.title = element_text(size = 0.6*base_size, 
                                face = "bold", 
                                hjust = 0, 
                                color = "black"),  
    legend.text.align = NULL,  
    legend.title.align = NULL,  
    legend.direction = "vertical",  
    legend.box = NULL, 
    panel.background = element_rect(fill = "white", 
                                    color  =  NA),  
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    plot.title = element_text(size = base_size, 
                              color = "black"), 
  ) 
  
}

data_Ea = read.csv("./data_Ea.csv")
data_Eb = read.csv("./data_Eb.csv")
data_Ec = read.csv("./data_Ec.csv")
data_E = data.frame("gene"=c(rep("gene a",nrow(data_Ea)),
                             rep("gene b",nrow(data_Eb)),
                             rep("gene c",nrow(data_Ec))),
                    "t"=c(data_Ea$t,data_Eb$t,data_Ec$t),
                    "C"=c(data_Ea$C,data_Eb$C,data_Ec$C),
                    "pos_err"=c(data_Ea$err,data_Eb$pos_err,data_Ec$pos_err_C),
                    "neg_err"=c(data_Ea$err,data_Eb$neg_err,data_Ec$neg_err_C),
                    "pos_err_t"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$pos_err_t),
                    "neg_err_t"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$neg_err_t)
)

head(data_E)

f1 = function(t) 0.2*exp(-t/48)
f2 = function(t) 0.3*exp(-t/60)
f3 = function(t) 0.4*exp(-t/72)
t = seq(0,96,1)

ribbon = data.frame(
  "f2" = 0.3*exp(-t/60),
  "t" = t
)
head(ribbon)

manual_pch =c(15,16,17) # available pch: type ?pch 

panel_E <- ggplot(data=data_E) +
  geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +
  geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),
              fill="black",alpha=0.1) +
  stat_function(fun=f1, geom="line",linetype="dashed") +
  stat_function(fun=f2, geom="line") +
  stat_function(fun=f3, geom="line",linetype="dotted") +
  geom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err), 
                width=2) +
  geom_errorbarh(aes(y=C,xmin=t-neg_err_t,xmax=t+pos_err_t))+
  scale_shape_manual(values=manual_pch) +
  my_theme() + theme(legend.title=element_blank())+
  theme(legend.position = c(0.9,0.95)) +
  scale_x_continuous(expand = c(0, 0), 
                     breaks = c(seq(0,96,12)),
                     limits = c(0,96)
  ) +
  scale_y_continuous(expand = c(0, 0),
                     breaks = c(seq(0,0.4,0.05)),
                     limits = c(0,0.4)
  ) +
  theme(
    panel.grid.major = element_line("gray95", size = 0.1),
    # putting label closer to axis bc exponent makes it bigger 
    axis.title.y = element_text(margin = margin(r = -9)) 
  ) +
  xlab("time (h)") +
  ylab(expression(paste("concentration (mmol",~cm^-3,")"))) +  
  coord_cartesian(clip = "off") # to allow for plotting outside axes

panel_E


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FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/LICENSE
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                    GNU GENERAL PUBLIC LICENSE
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================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/Panel_C.R
================================================
# Panel C ----
library(ggplot2) 
base_size = 12 
my_theme <-  function() {
  theme(
    aspect.ratio = 1,
    axis.line =element_line(colour = "black"),  
    
    # shift axis text closer to axis bc ticks are facing inwards
    axis.text.x = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")), 
    axis.text.y = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")),  
    
    axis.ticks = element_line(color = "black", size  =  0.2),  
    axis.title.x = element_text(size = base_size, 
                                color = "black", 
                                margin = margin(t = -5)), 
    # t (top), r (right), b (bottom), l (left)
    axis.title.y = element_text(size = base_size, 
                                color = "black", angle = 90,
                                margin = margin(r = -5)),  
    axis.ticks.length = unit(-0.3, "lines"),  
    legend.background = element_rect(color = NA, 
                                     fill = NA), 
    legend.key = element_rect(color = "black",  
                              fill = "white"),  
    legend.key.size = unit(0.5, "lines"),  
    legend.key.height =NULL,  
    legend.key.width = NULL,      
    legend.text = element_text(size = 0.6*base_size, 
                               color = "black"),  
    legend.title = element_text(size = 0.6*base_size, 
                                face = "bold", 
                                hjust = 0, 
                                color = "black"),  
    legend.text.align = NULL,  
    legend.title.align = NULL,  
    legend.direction = "vertical",  
    legend.box = NULL, 
    panel.background = element_rect(fill = "white", 
                                    color  =  NA),  
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    plot.title = element_text(size = base_size, 
                              color = "black"), 
  ) 
  
  
}

data_Cwt_E8.5  = read.csv( "./data_Cwt_E8.5.csv")
data_Cwt_E9.5  = read.csv( "./data_Cwt_E9.5.csv")
data_Cwt_E10.5 = read.csv("./data_Cwt_E10.5.csv")
data_Cwt_E11.5 = read.csv("./data_Cwt_E11.5.csv")
data_Cmu_E8.5  = read.csv( "./data_Cmu_E8.5.csv")
data_Cmu_E9.5  = read.csv( "./data_Cmu_E9.5.csv")
data_Cmu_E10.5 = read.csv("./data_Cmu_E10.5.csv")
data_Cmu_E11.5 = read.csv("./data_Cmu_E11.5.csv")

head(data_Cwt_E8.5)
# format data to ggplot's liking
data_C = data.frame(
  "type" = c(
    rep(
      "wildtype",
      nrow(data_Cwt_E8.5) +
        nrow(data_Cwt_E9.5) +
        nrow(data_Cwt_E10.5) +
        nrow(data_Cwt_E11.5)
    ),
    rep(
      "mutant",
      nrow(data_Cmu_E8.5) +
        nrow(data_Cmu_E9.5) +
        nrow(data_Cmu_E10.5) +
        nrow(data_Cmu_E11.5)
    )
  ),
  "dev_stage" = c(
    rep("E8.5", nrow(data_Cwt_E8.5)),
    rep("E9.5", nrow(data_Cwt_E9.5)),
    rep("E10.5", nrow(data_Cwt_E10.5)),
    rep("E11.5", nrow(data_Cwt_E11.5)),
    rep("E8.5", nrow(data_Cmu_E8.5)),
    rep("E9.5", nrow(data_Cmu_E9.5)),
    rep("E10.5", nrow(data_Cmu_E10.5)),
    rep("E11.5", nrow(data_Cmu_E11.5))
  ),
  "trachea_length" = c(
    data_Cwt_E8.5[, 1] ,
    data_Cwt_E9.5[, 1] ,
    data_Cwt_E10.5[, 1] ,
    data_Cwt_E11.5[, 1] ,
    data_Cmu_E8.5[, 1] ,
    data_Cmu_E9.5[, 1] ,
    data_Cmu_E10.5[, 1] ,
    data_Cmu_E11.5[, 1]
  )
)

head(data_C)

panel_C <- 
  ggplot(data=data_C,
         aes(x=dev_stage,
             y=trachea_length,
             fill=type))+
  geom_boxplot() + 
  geom_point(position=position_jitterdodge(), # jitter for h-dist, dodge for grouped dists
             pch=21, #圆形
             alpha=0.4) +  # transparency
  scale_x_discrete(limits=c("E8.5","E9.5","E10.5","E11.5")) +
  scale_fill_manual(values=c('#f2a340','#998fc2')) + # custom colors in hex code
  my_theme() +
  theme(legend.position = c(0.18,0.95),
        legend.title = element_blank(),
        legend.key = element_blank()
  ) +
  scale_y_continuous(expand = c(0, 0),
                     breaks=c(seq(20,120,by=20)),
                     limits = c(20,120)
  )+
  xlab("developmental stage (days)") + 
  ylab(expression(paste("tracheal length (",~mu*m,")")))

panel_C


================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/Panel_D.R
================================================
library(ggplot2) 
base_size = 12 
my_theme <-  function() {
  theme(
    aspect.ratio = 1,
    axis.line =element_line(colour = "black"),  
    
    # shift axis text closer to axis bc ticks are facing inwards
    axis.text.x = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")), 
    axis.text.y = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")),  
    
    axis.ticks = element_line(color = "black", size  =  0.2),  
    axis.title.x = element_text(size = base_size, 
                                color = "black", 
                                margin = margin(t = -5)), 
    # t (top), r (right), b (bottom), l (left)
    axis.title.y = element_text(size = base_size, 
                                color = "black", angle = 90,
                                margin = margin(r = -5)),  
    axis.ticks.length = unit(-0.3, "lines"),  
    legend.background = element_rect(color = NA, 
                                     fill = NA), 
    legend.key = element_rect(color = "black",  
                              fill = "white"),  
    legend.key.size = unit(0.5, "lines"),  
    legend.key.height =NULL,  
    legend.key.width = NULL,      
    legend.text = element_text(size = 0.6*base_size, 
                               color = "black"),  
    legend.title = element_text(size = 0.6*base_size, 
                                face = "bold", 
                                hjust = 0, 
                                color = "black"),  
    legend.text.align = NULL,  
    legend.title.align = NULL,  
    legend.direction = "vertical",  
    legend.box = NULL, 
    panel.background = element_rect(fill = "white", 
                                    color  =  NA),  
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    plot.title = element_text(size = base_size, 
                              color = "black"), 
  ) 
  
  
}

data_D1 = read.csv("./data_D1.csv")
data_D2 = read.csv("./data_D2.csv")
# format data to ggplot's liking
data_D = data.frame("width"=c(data_D1$width,data_D2$width),
                    "unit"=c(rep("shear_stress",nrow(data_D1)),
                             rep("velocity",nrow(data_D2))),
                    "value"=c(data_D1$shear_stress,data_D2$velocity)
)
head(data_D)
curve_D1 = data.frame(width=data_D1$width,
                      shear_stress=33.28/(pi*18*data_D1$width^2))
curve_D1

panel_D1 <- ggplot(data=data_D1, 
                   aes(x=width,
                       y=shear_stress)) + 
  geom_point(fill="red",
             size=3,
             pch=22) +
  geom_line(data=curve_D1) +
  scale_x_log10(expand=c(0,0), # prevent gap between origin and first tick
                breaks=c(0.5,1,2,5,10,20,50),
                labels=c(0.5,1,2,5,10,20,50), 
                limits=c(0.5,50)) +
  scale_y_log10( expand = c(0, 0),
                 # using trans_format from the scales package, but one can also use expressions
                 labels = trans_format('log10', math_format(10^.x)), 
                 breaks=c(0.001,0.01,0.1,1),
                 limits = c(0.001,1)
  ) +
  annotation_logticks(sides = "l") +
  theme_void()+
  theme(
    line = element_blank(),
    # exclude everything outside axes bc it messes with positioning of grob in panel_D
    text = element_blank(),
    title = element_blank(),
    axis.line.y = element_line(colour = "black")
  ) +
  ylab("shear stress (Pa)")

panel_D1

panel_D <- 
  ggplot(data=data_D2, 
         aes(x=width,
             y=velocity))+ 
  # add plot of first dataset as grob as a trick to introduce two y-axes with different scalings
  geom_point(fill="blue",
             size=3,
             pch=21) +
  annotation_custom(ggplotGrob(panel_D1)) + 
  scale_y_continuous(expand = c(0,0),
                     breaks = seq(0,1,0.1),
                     limits = c(0,1), 
                     # putting the y axis of the second plot to the right
                     position = "right", 
                     # now the secondary axis becomes the left axis
                     # we need the axis text+title for panel_D1
                     # They were excluded in panel_D1 bc they were messing with the positioning
                     sec.axis = sec_axis(~., 
                                         name = "shear stress (Pa)",
                                         # rescale breaks bc sec_axis inherits scale from primary y axis
                                         breaks=rescale(c(-3,-2,-1,0), 
                                                        to = c(0,1)),
                                         labels = c(expression("10"^"-3",
                                                               "10"^"-2",
                                                               "10"^"-1",
                                                               "10"^"0")))
                     
  ) +
  scale_x_log10(expand=c(0,0),
                breaks=c(0.5,1,2,5,10,20,50),
                labels=c(0.5,1,2,5,10,20,50), 
                limits=c(0.5,50)) + 
  annotation_logticks(sides = "b") + 
  my_theme() + 
  geom_line(color="blue") +
  geom_vline(xintercept = 1.1,
             linetype="dashed") +
  geom_hline(yintercept = 0.9,
             linetype="dashed") +
  theme(
    plot.margin = unit(c(0.1,0,0,0.5), "cm"), # to match other panels
    axis.title.y = element_text(margin = margin(r=1)), 
    axis.text.y = element_text(margin = margin(r=6)),
    axis.text.y.right = element_text(margin = margin(l=7)),
    axis.title.y.right = element_text(angle = 90)
  ) +
  xlab(expression(lumen~width~(mu*m))) +
  ylab("relative flow velocity") + 
  annotate(geom = "text",x =6 ,y =0.85 ,label = "tau == 0.5~Pa",parse=T) +
  annotate(geom = "text",x =1.4 ,y =0.4 ,label = "b == 1.1*mu*m",parse=T,angle=90) +
  annotate(geom = "text",x =5.6 ,y =0.6 ,label = "tau == frac(4*mu*Q,pi*a*b^2)",parse=T) 

panel_D 



================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/Panel_E.R
================================================
# Panel E ----
library(ggplot2) 
base_size = 12 
my_theme <-  function() {
  theme(
    aspect.ratio = 1,
    axis.line =element_line(colour = "black"),  
    
    # shift axis text closer to axis bc ticks are facing inwards
    axis.text.x = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")), 
    axis.text.y = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")),  
    
    axis.ticks = element_line(color = "black", size  =  0.2),  
    axis.title.x = element_text(size = base_size, 
                                color = "black", 
                                margin = margin(t = -5)), 
    # t (top), r (right), b (bottom), l (left)
    axis.title.y = element_text(size = base_size, 
                                color = "black", angle = 90,
                                margin = margin(r = -5)),  
    axis.ticks.length = unit(-0.3, "lines"),  
    legend.background = element_rect(color = NA, 
                                     fill = NA), 
    legend.key = element_rect(color = "black",  
                              fill = "white"),  
    legend.key.size = unit(0.5, "lines"),  
    legend.key.height =NULL,  
    legend.key.width = NULL,      
    legend.text = element_text(size = 0.6*base_size, 
                               color = "black"),  
    legend.title = element_text(size = 0.6*base_size, 
                                face = "bold", 
                                hjust = 0, 
                                color = "black"),  
    legend.text.align = NULL,  
    legend.title.align = NULL,  
    legend.direction = "vertical",  
    legend.box = NULL, 
    panel.background = element_rect(fill = "white", 
                                    color  =  NA),  
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    plot.title = element_text(size = base_size, 
                              color = "black"), 
  ) 
  
}

data_Ea = read.csv("./data_Ea.csv")
data_Eb = read.csv("./data_Eb.csv")
data_Ec = read.csv("./data_Ec.csv")
data_E = data.frame("gene"=c(rep("gene a",nrow(data_Ea)),
                             rep("gene b",nrow(data_Eb)),
                             rep("gene c",nrow(data_Ec))),
                    "t"=c(data_Ea$t,data_Eb$t,data_Ec$t),
                    "C"=c(data_Ea$C,data_Eb$C,data_Ec$C),
                    "pos_err"=c(data_Ea$err,data_Eb$pos_err,data_Ec$pos_err_C),
                    "neg_err"=c(data_Ea$err,data_Eb$neg_err,data_Ec$neg_err_C),
                    "pos_err_t"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$pos_err_t),
                    "neg_err_t"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$neg_err_t)
)

head(data_E)

f1 = function(t) 0.2*exp(-t/48)
f2 = function(t) 0.3*exp(-t/60)
f3 = function(t) 0.4*exp(-t/72)
t = seq(0,96,1)

ribbon = data.frame(
  "f2" = 0.3*exp(-t/60),
  "t" = t
)
head(ribbon)

manual_pch =c(15,16,17) # available pch: type ?pch 

panel_E <- ggplot(data=data_E) +
  geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +
  geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),
              fill="black",alpha=0.1) +
  stat_function(fun=f1, geom="line",linetype="dashed") +
  stat_function(fun=f2, geom="line") +
  stat_function(fun=f3, geom="line",linetype="dotted") +
  geom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err), 
                width=2) +
  geom_errorbarh(aes(y=C,xmin=t-neg_err_t,xmax=t+pos_err_t))+
  scale_shape_manual(values=manual_pch) +
  my_theme() + theme(legend.title=element_blank())+
  theme(legend.position = c(0.9,0.95)) +
  scale_x_continuous(expand = c(0, 0), 
                     breaks = c(seq(0,96,12)),
                     limits = c(0,96)
  ) +
  scale_y_continuous(expand = c(0, 0),
                     breaks = c(seq(0,0.4,0.05)),
                     limits = c(0,0.4)
  ) +
  theme(
    panel.grid.major = element_line("gray95", size = 0.1),
    # putting label closer to axis bc exponent makes it bigger 
    axis.title.y = element_text(margin = margin(r = -9)) 
  ) +
  xlab("time (h)") +
  ylab(expression(paste("concentration (mmol",~cm^-3,")"))) +  
  coord_cartesian(clip = "off") # to allow for plotting outside axes

panel_E


================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/Panel_F.R
================================================
# Panel F ----
library(ggplot2) 
library(vi)
base_size = 12 
my_theme <-  function() {
  theme(
    aspect.ratio = 1,
    axis.line =element_line(colour = "black"),  
    
    # shift axis text closer to axis bc ticks are facing inwards
    axis.text.x = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")), 
    axis.text.y = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")),  
    
    axis.ticks = element_line(color = "black", size  =  0.2),  
    axis.title.x = element_text(size = base_size, 
                                color = "black", 
                                margin = margin(t = -5)), 
    # t (top), r (right), b (bottom), l (left)
    axis.title.y = element_text(size = base_size, 
                                color = "black", angle = 90,
                                margin = margin(r = -5)),  
    axis.ticks.length = unit(-0.3, "lines"),  
    legend.background = element_rect(color = NA, 
                                     fill = NA), 
    legend.key = element_rect(color = "black",  
                              fill = "white"),  
    legend.key.size = unit(0.5, "lines"),  
    legend.key.height =NULL,  
    legend.key.width = NULL,      
    legend.text = element_text(size = 0.6*base_size, 
                               color = "black"),  
    legend.title = element_text(size = 0.6*base_size, 
                                face = "bold", 
                                hjust = 0, 
                                color = "black"),  
    legend.text.align = NULL,  
    legend.title.align = NULL,  
    legend.direction = "vertical",  
    legend.box = NULL, 
    panel.background = element_rect(fill = "white", 
                                    color  =  NA),  
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    plot.title = element_text(size = base_size, 
                              color = "black"), 
  ) 
  
}

data_F = read.csv("./data_F.csv")
head(data_F)
panel_F <- ggplot(data=data_F,
                  aes(x=K,y=n,
                      size=amplitude,
                      fill=duration))+
  geom_point(pch=21) +
  my_theme() + 
  scale_x_continuous(expand = c(0, 0),
                     trans = 'log10',
                     labels=c(1,10,100),
                     breaks=c(1,10,100),
                     limits = c(1,100)) +
  scale_y_continuous(expand = c(0, 0),
                     breaks=c(seq(0,4,by=0.5)),
                     limits = c(0,4)) +   
  xlab(expression(paste("dissociation constant",~~italic("K")," (M)"))) +
  ylab("Hill coefficient n") +
  annotation_logticks(sides='b') +
  scale_size(range = c(1, 3)) +
  scale_fill_viridis(option="D") + # a color palette from the viridis package
  theme(legend.position = c(0.9,0.35)) +
  coord_cartesian(clip = "off")

panel_F


================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/README.md
================================================
# 代码解释见推文：https://mp.weixin.qq.com/s/j9ribyJ809pErDGQXL_Usw
# 作者：庄亮亮/庄闪闪 
# 公众号：《庄闪闪的R语言手册》

代码来源：https://github.com/marco-meer/scifig_plot_examples_R



================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_B.csv
================================================
n,fraction1,fraction2,err1,err2
3,0.01,0.005,0.002,0.001
4,0.05,0.04,0.01,0.008
5,0.24,0.225,0.04,0.04
6,0.45,0.53,0.06,0.05
7,0.19,0.14,0.034,0.03
8,0.045,0.04,0.009,0.008
9,0.01,0.02,0.002,0.004
10,0.005,0,0.001,0


================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_Cmu_E10.5.csv
================================================
65.8205529524502
41.6531035937204
83.152551557937
49.8115199468345
49.7305565969044
44.9995256006525
45.1256605549988
54.4260875488319
59.3898067636063
55.2654589200939
59.1173471779051
46.235427610441
57.6267963516239
48.5291927139915
68.6366348486041
74.2734952793967
60.6487719565292
84.786057725313
48.5480400989702
65.3386633487001
52.3899352375807
48.6507554603649
57.5822109522762
67.8970383500416
45.0397364373011
36.5613573799657
60.0366499016365
52.6932735705296
53.9281089566023
67.5736606957021
53.9476022283962
54.2741479383546
70.4077851758678
54.6481556145846
60.4091424302168
49.1581406653753
59.655677151569
57.092707914815
44.6581069131501
54.8138933538391
55.310677780828
68.9534191538903
50.6759544087888
65.1494597486622
52.0866432423026
48.7163381473606
56.9623907788505
53.0820740059476
63.8609173377662
69.3508728072075
43.918594861742
40.1339485419691
47.9636860176302
52.3626294120593
47.6899666215299
66.70934623327
47.6036564420278
46.4462375984477
37.1354633751926
28.4352412345882
49.317284429323
39.7967553834712
51.1200504829997
89.9445381630164
58.9838425665504
48.9544765236798
39.4502495746855
53.1342817405542
77.4269573461966
49.4436483287547
82.239850092544
70.58437635767
30.262491641059
64.3093089866025
60.2999565031281
72.3560256197538
40.5564946289917
55.3711507422376
56.0267992773933
56.5769380613516
66.9992559088336
59.5371870721189
63.3197974950725
53.4387071182716
53.8879690976582
64.2194530009369
38.13817438162
46.6385040560028
53.5027322154622
53.0976496113571
46.0243049939109
55.431571319898
60.9328007658431
60.8185985209417
55.6353020926962
48.7799678522732
52.5451326562458
54.5798650090393
61.774285453737
57.0345994953582
37.5665098680396
51.4414802520879
50.3611512216014
61.3682749102604
55.4435480146538
64.8662834741187
62.841494873935
46.2169685002261
57.5914131965011
58.0086668582521


================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_Cmu_E11.5.csv
================================================
59.1890088518618
56.2107835047841
55.2994926733609
70.7916698405769
64.7330529679417
48.2574994441115
63.2107626882808
55.8564954899177
54.5760807254418
51.3230864223238
60.2021409743821
63.4194391191852
48.3437334986212
52.7239783061468
56.8960726372144
58.0139878340251
67.0538371096838
56.6622318629482
60.5850110851269
59.2510534999053
50.4949741224361
57.0558476030154
61.0584025897667
52.7791703590774
64.4631659684564
52.2445438695031
56.7234439395831
44.5300127340189
50.9568980312489
58.5473725355373
60.5454816944244
57.3988404666032
66.7227183053284
60.4688087395229
56.2592131452591
55.0777274410811
57.383926844346
67.5864019282867
65.4738619046073
66.9529866798847
58.3114215280906
56.5273850456214
56.2353365698152
61.684464515395
55.3505158559685
46.3484877537309
55.6909136265621
72.1333458665144
54.9871118794021
63.009606324092
57.097978297995
55.4169629881021
51.5280071936532
48.5373743865034
55.7150222248605
49.2113588380889
53.795783611417
62.179585698278
57.5116264374945
43.8912875539574
68.1005600287051
63.0091092848727
61.5165781695328
60.0060966410832
53.4578934689505
57.631665252223
59.3242253821183
58.9981706659672
64.668851282844
55.4409232072789


================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_Cmu_E8.5.csv
================================================
40.0376253078157
34.4494870737266
33.4971571900859
37.9076623810452
38.3799653610893
39.4948119596739
39.8803763629151
48.0129233665844
32.9249865794136
40.5200505165726
40.9368041901928
49.4189987182495
45.3459455596609
45.155245507597
38.5315921920013
48.7421625990379
50.1490461451389
40.1161666571257
49.7231699091656
37.7391126683174
46.137918759145
42.1751325231794
43.4554162345088
35.477688506143
40.7509010376925
34.4153490978095
42.0358914711044
47.1545142231053
40.0933644472997
46.0360314164461
41.2959367267308
40.9928945811862
52.726017022066
37.9632282071833
44.5186590565088
48.3752989961283
49.6666360005074
38.2912150907391
32.5576871579266
45.5331939582947
38.7116576687106
45.6548028199712
55.9027372937827
45.7735695134433
39.124797055581
50.0512933266502
39.7125511748254
45.5582603117211
45.2862627845952
47.810825400476


================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_Cmu_E9.5.csv
================================================
57.3251816935626
47.8349114415409
37.4338126448888
40.3207636941618
43.3047032564298
54.2942279159674
41.9509080856433
40.9257512485931
52.9415103414648
49.3862574078503
43.568729625025
32.0442993806243
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FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_Cwt_E10.5.csv
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FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_Cwt_E11.5.csv
================================================
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================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_Cwt_E8.5.csv
================================================
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FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_Cwt_E9.5.csv
================================================
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================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_D1.csv
================================================
width,shear_stress
20,0.00174
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1,0.65696
4,0.02312


================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_D2.csv
================================================
width,velocity
0.6000000,0.01726544
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================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_Ea.csv
================================================
t,C,err
12,0.27,0.03
24,0.19,0.02
36,0.17,0.01
48,0.13,0.04
60,0.09,0.03
72,0.10,0.01
84,0.09,0.02
96,0.07,0.02


================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_Eb.csv
================================================
t,C,pos_err,neg_err
12,0.16,0.03,0.01
24,0.11,0.02,0.02
36,0.08,0.01,0.03
48,0.06,0.02,0.02
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72,0.06,0.01,0.01
84,0.03,0.02,0.02
96,0.01,0.02,0.02


================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_Ec.csv
================================================
t,C,neg_err_C,pos_err_C,neg_err_t,pos_err_t
12,0.37,0.03,0.01,2,2
24,0.31,0.02,0.02,3,1
36,0.22,0.01,0.03,2,4
48,0.23,0.02,0.02,4,2
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72,0.14,0.01,0.01,2,5
84,0.13,0.02,0.02,5,4
96,0.11,0.02,0.02,1,3


================================================
FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_F.csv
================================================
K,n,amplitude,duration
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FILE: 2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/figure_example.R
================================================

# Set working directory 
# setwd(getwd()) 

# Libraries/packages 

library(ggplot2) # Grammar of graphics
library(cowplot) # Arranging multiple plots into a grid
library(png)     # Load JPEG, PNG and TIFF format 
library(scales)  # Generic plot scaling methods
library(viridis) # Default color maps from 'matplotlib'
library(grid)    # A rewrite of the graphics layout capabilities
library(magick)  # graphics and image processing
library(rsvg)    # Render svg image into a high quality bitmap
library(ggforce) # Collection of additional ggplot stats + geoms

# global font size
base_size = 12 

# Manual theme for most panels
# documentation: https://ggplot2.tidyverse.org/reference/theme.html
my_theme <-  function() {
  theme(
    aspect.ratio = 1,
    axis.line =element_line(colour = "black"),  
    
    # shift axis text closer to axis bc ticks are facing inwards
    axis.text.x = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")), 
    axis.text.y = element_text(size = base_size*0.8, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.3,0.3,0.3,0.3), "cm")),  
    
    axis.ticks = element_line(color = "black", size  =  0.2),  
    axis.title.x = element_text(size = base_size, 
                                color = "black", 
                                margin = margin(t = -5)), 
    # t (top), r (right), b (bottom), l (left)
    axis.title.y = element_text(size = base_size, 
                                color = "black", angle = 90,
                                margin = margin(r = -5)),  
    axis.ticks.length = unit(-0.3, "lines"),  
    legend.background = element_rect(color = NA, 
                                     fill = NA), 
    legend.key = element_rect(color = "black",  
                              fill = "white"),  
    legend.key.size = unit(0.5, "lines"),  
    legend.key.height =NULL,  
    legend.key.width = NULL,      
    legend.text = element_text(size = 0.6*base_size, 
                               color = "black"),  
    legend.title = element_text(size = 0.6*base_size, 
                                face = "bold", 
                                hjust = 0, 
                                color = "black"),  
    legend.text.align = NULL,  
    legend.title.align = NULL,  
    legend.direction = "vertical",  
    legend.box = NULL, 
    panel.background = element_rect(fill = "white", 
                                    color  =  NA),  
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    plot.title = element_text(size = base_size, 
                              color = "black"), 
  ) 
  
  
}

# Panel A ----

img1 <- magick::image_flip(magick::image_read("./image1.jpg"))
img2 <-  magick::image_flip(magick::image_read("./image2.png"))

panel_A <- ggplot() +
  annotation_custom(rasterGrob(image =  img1, 
                               x=0.27,
                               y=0.49,
                               width = unit(0.45,"npc"),
                               height = unit(0.87,"npc")), 
                    -Inf, Inf, -Inf, Inf) +
  annotation_custom(rasterGrob(image = img2, 
                               x=0.73,
                               y=0.49,
                               width = unit(0.45,"npc"),
                               height = unit(0.87,"npc")), 
                    -Inf, Inf, -Inf, Inf) +
  
  geom_ellipse(aes(x0 = 0.25, 
                            y0 = 0.3,
                            a = 0.1, 
                            b = 0.04, 
                            angle = 0),
                        color="yellow",
                        size=1)+
  scale_x_continuous(limits = c(0,1))+
  scale_y_continuous(limits=c(0,1)) +
  geom_segment(aes(x=0.15,
                   xend=0.2,
                   y=0.75,
                   yend=0.7),
               arrow = arrow(length=unit(0.30,"cm"),
                             ends="last", 
                             type = "closed"),
               size = 1,
               color="white") +
  geom_segment(aes(x=0.3,
                   xend=0.9,
                   y=0.7,
                   yend=0.7),
               arrow = arrow(length=unit(0.30,"cm"),
                             ends="both", 
                             type = "closed"),
               size = 1,
               color="red") +

  annotate("text", x = 0.25, y = 0.5, label = "PNG",color="white") +
  annotate("text", x = 0.75, y = 0.5, label = "JPEG",color="white") +
  annotate("text", x = 0.25, y = 1, label = "image 1",color="black") +
  annotate("text", x = 0.75, y = 1, label = "image 2",color="black") +
  annotate("text", x = 0.39, y = 0.07, label = "20~mu*m",color="white",parse=T) +
  annotate("text", x = 0.89, y = 0.07, label = "20~mu*m",color="white",parse=T) +
  geom_segment(aes(x=0.33,xend=0.45,y=0.03,yend=0.03), size = 2,color="white") +
  geom_segment(aes(x=0.83,xend=0.95,y=0.03,yend=0.03),size = 2,color="white")  + 
  theme_void() +# blank plot w/o axes etc.
  theme(plot.margin = unit(c(-0,0,1,0), "cm"),
        aspect.ratio = 1)

panel_A
# Panel B ----

data_B = read.csv("./data_B.csv")

# format data to ggplot's liking
data_B = data.frame("n"=c(data_B$n,data_B$n),
                    "sample"=c(rep("apical side",nrow(data_B)),
                               rep("basal side",nrow(data_B))),
                    "fraction"=c(data_B$fraction1,data_B$fraction2),
                    "err"=c(data_B$err1,data_B$err2)
)

# define lognormal distribution to be called via ggplot2::stat_function
sigma = 0.14
n = seq(3,10,1)
logn_dist <- function(n) exp(-(log(n)-log(6))^2/(2*sigma^2))/(sqrt(2*pi)*sigma*n)



panel_B <- 
  ggplot(data=data_B,(aes(x=n, # aes: aesthetics
                          y=fraction,
                          fill=sample)))+
  geom_bar(stat = "identity",
           position=position_dodge()) + # dodge overlapping objects side-to-side
  stat_function(fun=logn_dist, 
                geom="line",
                linetype="solid",
                aes(x=n,
                    y=logn_dist(n))) +
  geom_errorbar(aes(ymin=fraction-err, 
                    ymax=fraction+err), 
                width=.2, 
                position=position_dodge(.9)) +
  scale_fill_manual(values=c('#f2a340','#998fc2'))+
  scale_x_continuous(expand = c(0, 0), # prevent gap between origin and first tick
                     breaks=c(seq(from =  min(data_B$n), 
                                  to = max(data_B$n),
                                  by = 1)),
                     limits = c(min(data_B$n),max(data_B$n))) +
  scale_y_continuous(expand = c(0, 0),
                     breaks=c(seq(0,0.6,0.1)),
                     limits = c(0,0.6)
  )+
  my_theme() +
  # some extra theme tweaking 
  theme(legend.position = c(0.18,0.95),
        legend.title = element_blank(),
        axis.ticks.x=element_blank(),
        axis.text.x = element_text(vjust=-0.5),
        axis.title.x = element_text(vjust=-0.5),
        legend.key.size = unit(0.4, "lines")
  ) +
  xlab(expression(paste("number of neighbors ",italic("n")))) +
  ylab("fraction of cells (%)") 

panel_B


inset_curve <- function(n) 180*(n-2)/n 
# The equation is added as an image bc the annotate() function output looks too ugly
inset_equation <- image_read_svg("./panel_b_inset_equation.svg",width = 583,height = 240)

# now comes the inset plot

inset <- ggplot() + 
  stat_function(fun=inset_curve, 
                geom="line",
                linetype="solid",
                aes(x=n,
                    y=inset_curve(n))) +
  # annotate(geom="text",label="180*degree*(n-2)/n",x = 5.5,y=160,
  #          parse=TRUE,size=2) +
  annotation_custom(rasterGrob(image = inset_equation, 
                               x=0.5,
                               y=0.8,
                               width = unit(0.3125,"npc"),
                               height = unit(0.126,"npc")), 
                    -Inf, Inf, -Inf, Inf) +
  scale_x_continuous(expand = c(0, 0),
                     breaks=c(seq(3,10,1)),
                     limits = c(3,10)) +
  scale_y_continuous(expand = c(0, 0),
                     breaks=c(seq(60,180,20)),
                     limits = c(60,180)
                    ) +
  my_theme() +
  xlab(expression(italic(n)))+ 
  # expression doc: https://stat.ethz.ch/R-manual/R-devel/library/grDevices/html/plotmath.html
  ylab(expression(interior~angle~italic(theta[n])(degree))) +
  # some extra theme tweaking 
  theme( 
    panel.background = element_blank(),
    plot.background = element_blank(),
    axis.ticks.length = unit(-0.1, "lines"), # make ticks a bit shorter
    axis.title.x = element_text(size = 0.5*base_size, 
                                color = "black", 
                                margin = margin(t = -4)), 
    # top (t), right (r), bottom (b), left (l) 
    axis.title.y = element_text(size = 0.5*base_size, 
                                color = "black", angle = 90,
                                margin = margin(r = -4)),  
    axis.text.x = element_text(size = base_size*0.5, 
                               color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.1,0.1,0.1,0.1), "cm")),
    axis.text.y = element_text(size = base_size*0.5, color = "black", 
                               lineheight = 0.9,
                               margin=unit(c(0.1,0.1,0.1,0.1), "cm"))
  ) 

inset
# create  grob (grid graphical object) from the inset plot
panel_B_inset <- ggplotGrob(x = inset) 


# now for some regular polygon drawing
polygons <- 
  ggplot() +
    ggforce::geom_regon(aes(x0=c(seq(3,10,1)),
                            y0=rep(-0.3,8),
                            sides = c(seq(3,10,1)), 
                            angle=0, r=0.3),
                        fill=NA,
                        color="black") +
  theme_void() + coord_fixed()
polygons


# finally, we can combine everything to plot panel B

panel_B <- 
  panel_B + 
  annotation_custom(grob = panel_B_inset, 
                    xmin=6.6, xmax=10.1, ymin=0.1, ymax=0.6) +
  annotation_custom(grob = ggplotGrob(polygons), 
                    xmin = 2.36, xmax = 10.67, ymin = -0.172, ymax = 0.115) 


# Panel C ----

data_Cwt_E8.5  = read.csv( "./data_Cwt_E8.5.csv")
data_Cwt_E9.5  = read.csv( "./data_Cwt_E9.5.csv")
data_Cwt_E10.5 = read.csv("./data_Cwt_E10.5.csv")
data_Cwt_E11.5 = read.csv("./data_Cwt_E11.5.csv")
data_Cmu_E8.5  = read.csv( "./data_Cmu_E8.5.csv")
data_Cmu_E9.5  = read.csv( "./data_Cmu_E9.5.csv")
data_Cmu_E10.5 = read.csv("./data_Cmu_E10.5.csv")
data_Cmu_E11.5 = read.csv("./data_Cmu_E11.5.csv")

# format data to ggplot's liking
data_C = data.frame(
  "type" = c(
    rep(
      "wildtype",
      nrow(data_Cwt_E8.5) +
        nrow(data_Cwt_E9.5) +
        nrow(data_Cwt_E10.5) +
        nrow(data_Cwt_E11.5)
    ),
    rep(
      "mutant",
      nrow(data_Cmu_E8.5) +
        nrow(data_Cmu_E9.5) +
        nrow(data_Cmu_E10.5) +
        nrow(data_Cmu_E11.5)
    )
  ),
  "dev_stage" = c(
    rep("E8.5", nrow(data_Cwt_E8.5)),
    rep("E9.5", nrow(data_Cwt_E9.5)),
    rep("E10.5", nrow(data_Cwt_E10.5)),
    rep("E11.5", nrow(data_Cwt_E11.5)),
    rep("E8.5", nrow(data_Cmu_E8.5)),
    rep("E9.5", nrow(data_Cmu_E9.5)),
    rep("E10.5", nrow(data_Cmu_E10.5)),
    rep("E11.5", nrow(data_Cmu_E11.5))
  ),
  "trachea_length" = c(
    data_Cwt_E8.5[, 1] ,
    data_Cwt_E9.5[, 1] ,
    data_Cwt_E10.5[, 1] ,
    data_Cwt_E11.5[, 1] ,
    data_Cmu_E8.5[, 1] ,
    data_Cmu_E9.5[, 1] ,
    data_Cmu_E10.5[, 1] ,
    data_Cmu_E11.5[, 1]
  )
)

panel_C <- 
  ggplot(data=data_C,
         aes(x=dev_stage,
             y=trachea_length,
             fill=type))+
  geom_boxplot() + 
  geom_point(position=position_jitterdodge(), # jitter for h-dist, dodge for grouped dists
             pch=21,
             alpha=0.4) +  # transparency
  scale_x_discrete(limits=c("E8.5","E9.5","E10.5","E11.5")) +
  scale_fill_manual(values=c('#f2a340','#998fc2'))+ # custom colors in hex code
  my_theme() +
  theme(legend.position = c(0.18,0.95),
        legend.title = element_blank(),
        legend.key = element_blank()
  ) +
  scale_y_continuous(expand = c(0, 0),
                     breaks=c(seq(20,120,by=20)),
                     limits = c(20,120)
  )+
  xlab("developmental stage (days)") + 
  ylab(expression(paste("tracheal length (",~mu*m,")")))

panel_C
# Panel D ----

data_D1 = read.csv("./data_D1.csv")
data_D2 = read.csv("./data_D2.csv")
# format data to ggplot's liking
data_D = data.frame("width"=c(data_D1$width,data_D2$width),
                    "unit"=c(rep("shear_stress",nrow(data_D1)),
                             rep("velocity",nrow(data_D2))),
                    "value"=c(data_D1$shear_stress,data_D2$velocity)
)

curve_D1 = data.frame(width=data_D1$width,
                      shear_stress=33.28/(pi*18*data_D1$width^2))


panel_D1 <- ggplot(data=data_D1, 
                   aes(x=width,
                       y=shear_stress)) + 
  geom_point(fill="red",
             size=3,
             pch=22) +
  geom_line(data=curve_D1) +
  theme_void()+
  scale_x_log10(expand=c(0,0), # prevent gap between origin and first tick
                breaks=c(0.5,1,2,5,10,20,50),
                labels=c(0.5,1,2,5,10,20,50), 
                limits=c(0.5,50)) +
  scale_y_log10( expand = c(0, 0),
                 # using trans_format from the scales package, but one can also use expressions
                 labels = trans_format('log10', math_format(10^.x)), 
                 breaks=c(0.001,0.01,0.1,1),
                 limits = c(0.001,1)
  ) +
  annotation_logticks(sides = "l") +
  theme(
    line = element_blank(),
    # exclude everything outside axes bc it messes with positioning of grob in panel_D
    text = element_blank(),
    title = element_blank(),
    axis.line.y = element_line(colour = "black"),
    aspect.ratio = 1
  ) +
  ylab("shear stress (Pa)")



panel_D <- 
  ggplot(data=data_D2, 
         aes(x=width,
             y=velocity))+ 
  # add plot of first dataset as grob as a trick to introduce two y-axes with different scalings
  annotation_custom(ggplotGrob(panel_D1)) + 
  geom_point(fill="blue",
             size=3,
             pch=21) + 
  my_theme() + 
  geom_line(color="blue") +
  geom_vline(xintercept = 1.1,
             linetype="dashed") +
  geom_hline(yintercept = 0.9,
             linetype="dashed") +
  scale_x_log10(expand=c(0,0),
                breaks=c(0.5,1,2,5,10,20,50),
                labels=c(0.5,1,2,5,10,20,50), 
                limits=c(0.5,50)) + 
  annotation_logticks(sides = "b") + 
  scale_y_continuous(expand = c(0,0),
                     breaks = seq(0,1,0.1),
                     limits = c(0,1), 
                     # putting the y axis of the second plot to the right
                     position = "right", 
                     # now the secondary axis becomes the left axis
                     # we need the axis text+title for panel_D1
                     # They were excluded in panel_D1 bc they were messing with the positioning
                     sec.axis = sec_axis(~., 
                                         name = "shear stress (Pa)",
                                         # rescale breaks bc sec_axis inherits scale from primary y axis
                                         breaks=rescale(c(-3,-2,-1,0), 
                                                        to = c(0,1)),
                                         labels = c(expression("10"^"-3",
                                                               "10"^"-2",
                                                               "10"^"-1",
                                                               "10"^"0")))
                     
  )  +
  # some extra theme tweaking 
  theme(
    aspect.ratio = 1,
    plot.margin = unit(c(0.1,0,0,0.5), "cm"), # to match other panels
    axis.title.y = element_text(margin = margin(r=1)), 
    axis.text.y = element_text(margin = margin(r=6)),
    axis.text.y.right = element_text(margin = margin(l=7)),
    axis.title.y.right = element_text(angle = 90)
  ) +
  xlab(expression(lumen~width~(mu*m))) +
  ylab("relative flow velocity") + 
  annotate(geom = "text",x =6 ,y =0.85 ,label = "tau == 0.5~Pa",parse=T) +
  annotate(geom = "text",x =1.4 ,y =0.4 ,label = "b == 1.1*mu*m",parse=T,angle=90) +
  annotate(geom = "text",x =5.6 ,y =0.6 ,label = "tau == frac(4*mu*Q,pi*a*b^2)",parse=T) 
  
panel_D 

# Panel E ----

data_Ea = read.csv("./data_Ea.csv")
data_Eb = read.csv("./data_Eb.csv")
data_Ec = read.csv("./data_Ec.csv")
data_E = data.frame("gene"=c(rep("gene a",nrow(data_Ea)),
                             rep("gene b",nrow(data_Eb)),
                             rep("gene c",nrow(data_Ec))),
                    "t"=c(data_Ea$t,data_Eb$t,data_Ec$t),
                    "C"=c(data_Ea$C,data_Eb$C,data_Ec$C),
                    "pos_err"=c(data_Ea$err,data_Eb$pos_err,data_Ec$pos_err_C),
                    "neg_err"=c(data_Ea$err,data_Eb$neg_err,data_Ec$neg_err_C),
                    "pos_err_t"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$pos_err_t),
                    "neg_err_t"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$neg_err_t)
)

f1 = function(t) 0.2*exp(-t/48)
f2 = function(t) 0.3*exp(-t/60)
f3 = function(t) 0.4*exp(-t/72)
t = seq(0,96,1)

ribbon = data.frame(
  "f2" = 0.3*exp(-t/60),
  "t" = t
)

manual_pch =c(15,16,17) # available pch: type ?pch 

panel_E <- ggplot(data=data_E) +
  geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +
  geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),
              fill="black",alpha=0.1) +
  stat_function(fun=f1, geom="line",linetype="dashed") +
  stat_function(fun=f2, geom="line") +
  stat_function(fun=f3, geom="line",linetype="dotted") +
  geom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err), 
                width=2) +
  geom_errorbarh(aes(y=C,xmin=t-neg_err_t,xmax=t+pos_err_t))+
  scale_shape_manual(values=manual_pch) +
  my_theme() + theme(legend.title=element_blank())+
  theme(legend.position = c(0.9,0.95)) +
  scale_x_continuous(expand = c(0, 0), 
                     breaks = c(seq(0,96,12)),
                     limits = c(0,96)
  ) +
  scale_y_continuous(expand = c(0, 0),
                     breaks = c(seq(0,0.4,0.05)),
                     limits = c(0,0.4)
  ) +
  theme(
    panel.grid.major = element_line("gray95", size = 0.1),
    # putting label closer to axis bc exponent makes it bigger 
    axis.title.y = element_text(margin = margin(r = -9)) 
  ) +
  xlab("time (h)") +
  ylab(expression(paste("concentration (mmol",~cm^-3,")"))) +  
  coord_cartesian(clip = "off") # to allow for plotting outside axes

panel_E
# Panel F ----

data_F = read.csv("./data_F.csv")

panel_F <- ggplot(data=data_F,
                  aes(x=K,y=n,
                      size=amplitude,
                      fill=duration))+
  geom_point(pch=21) +
  my_theme() +

  scale_x_continuous(expand = c(0, 0),
                     trans = 'log10',
                     labels=c(1,10,100),
                     breaks=c(1,10,100),
                     limits = c(1,100)) +
  scale_y_continuous(expand = c(0, 0),
                     breaks=c(seq(0,4,by=0.5)),
                     limits = c(0,4)) +
  annotation_logticks(sides='b') +
  scale_size(range = c(1, 3)) +
  scale_fill_viridis(option="D") + # a color palette from the viridis package
  theme( legend.position = c(0.9,0.35)) +
  coord_cartesian(clip = "off") +
  xlab(expression(paste("dissociation constant",~~italic("K")," (M)"))) +
  ylab("Hill coefficient n")

panel_F

# Plot the whole figure using plot_grid from the cowplot package


row1 <- plot_grid(panel_A,panel_B,panel_C,
                  ncol=3,
                  labels = c('A','B','C'))#,rel_widths = c(0.9,0.9))

row2 <- plot_grid(panel_D,panel_E,panel_F,
                  ncol=3,
                  labels = c('D','E','F'))

plot_grid(row1,row2,nrow=2)

# RStudio instructions for saving the final plot:
# Whenever you resize the 'Plots' window, click 'refresh current plot'
# For best results save as SVG with a resolution of 1148 x 686 
# open SVG in inkscape, (do some optional post-processing) and 'save as' PDF



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suppressPackageStartupMessages(library(binom))
data(ghcnd_stations, package = "StatCompLab")
data(ghcnd_values, package = "StatCompLab")
suppressPackageStartupMessages(library(styler))
data1 <- ghcnd_stations
data2 <- ghcnd_values
rm(ghcnd_stations,ghcnd_values)
data2 <- data2 %>% mutate(season=ifelse(Month %in% 4:9,"Summer","Winter"))
data2 <- data2 %>% mutate(Summer=ifelse(Month %in% 4:9,T,F))
data2 <- data2 %>% left_join(data1,by='ID')
year_2018 <- data2 %>% filter(Year==2018)


tmin <- year_2018 %>% filter(Element=='TMIN') %>% group_by(season) %>% 
  summarise(mean=mean(Value)) %>% ggplot(aes(x=season,y=mean))+
  geom_col()+ggtitle('The min temperature Summer vs Winter')+
  ylab("The average min temperature")

tmax <- year_2018 %>% filter(Element=='TMAX') %>% group_by(season) %>% 
  summarise(mean=mean(Value)) %>% ggplot(aes(x=season,y=mean))+
  geom_col()+ggtitle('The max temperature Summer vs Winter')+
  ylab("The average max temperature")

prcp_plot <- year_2018 %>% filter(Element=='PRCP') %>% group_by(season) %>% 
  summarise(mean=mean(Value)) %>% ggplot(aes(x=season,y=mean))+
  geom_col()+ggtitle('The precipitation Summer vs Winter')+
  ylab("The average precipitation")
g <- year_2018 %>%
  filter(Element %in% c("TMIN", "TMAX")) %>%
  ggplot(aes(DecYear, Value, colour = Element)) +
  geom_line() +
  facet_wrap(~ ID,nrow=8)+
  scale_y_continuous(breaks = seq(-20,30,10))
t_average <- year_2018 %>%  filter(Element=='TMAX'|Element=='TMIN') %>% select(-DecYear,-season) %>% 
  pivot_wider(names_from = Element,values_from = Value) %>% mutate(taver=(TMAX+TMIN)/2,
    season=ifelse(Month%in% 4:9,'Summer','Winter'))
t_average_sup <- t_average %>% ggplot(aes(x=taver))+geom_histogram(bins = 30)+
  facet_wrap(~season,nrow=2)+ggtitle('The average temperature of Summer vs Winter')+
  xlab("The average temperature value")


prcp <- data2 %>% filter(Element=='PRCP')
options(dplyr.summarise.info=F)
observed_diff <- prcp %>% group_by(Name,Summer) %>% 
  summarise(mean=mean(Value),.groups = "drop")
observed_diff <- observed_diff %>% pivot_wider(names_from = Summer,
  values_from = mean)
names(observed_diff) <- c("StationName","Winter","Summer")
observed_diff <- observed_diff  %>%  mutate(diff=abs(Winter-Summer))

permutation_diff <- data.frame(StationName=observed_diff$StationName,
  p_value=0,sd=0,ci95_lower=0,ci95_upper=0)
mean_diff <- function(x, y) {
  mean(x) - mean(y)
}
p_value_CI <- function(perm_stats,observed_diff,StationName,n_perms){
  ci <- binom.confint(sum(perm_stats >= observed_diff[observed_diff$StationName==StationName,]$diff), 
    n_perms, method = "wilson")
  ci
}
mc_permutation <- function(){
  
  for(j in 1:nrow(permutation_diff)){
    StationName <- permutation_diff$StationName[j]
    prcp_winter <- prcp %>% filter(Summer==F,Name==StationName) %>% pull(Value)
    prcp_summer <- prcp %>% filter(Summer==T,Name==StationName) %>% pull(Value)
    n_perms <- 1000
    perm_stats <- numeric(n_perms)
    for (i in 1:n_perms) {
      perm <- sample(c(prcp_winter, prcp_summer))
      perm_stats[i] <- mean_diff(perm[1:length(prcp_winter)], 
        perm[(length(prcp_winter)+1):(length(prcp_winter)+length(prcp_summer))])
    }
    p_value <- sum(perm_stats>=observed_diff[observed_diff$StationName==StationName,]$diff)/n_perms
    ci <- p_value_CI(perm_stats,observed_diff,StationName,n_perms)
    permutation_diff[j,]$p_value <<- p_value
    permutation_diff[j,]$sd <<- sd(perm_stats)
    permutation_diff[j,]$ci95_lower <<- ci$lower
    permutation_diff[j,]$ci95_upper <<- ci$upper
  }
  
  knitr::kable(permutation_diff)
  
  
}

prcp_month <- data2 %>% group_by(Month) %>% summarise(mean=sqrt(mean(Value)))
data2 <- data2 %>% mutate(Value_sqrt_avg=0)
for(i in 1:nrow(prcp_month)){
  data2[data2$Month==prcp_month$Month[i],]$Value_sqrt_avg <- prcp_month$mean[i]
}
cs <- function(k,x){
  if(k==1){
    return(cos(2*pi*x)+sin(2*pi*x))
  }else{
    temp <- cos(2*pi*x)+sin(2*pi*x)
    for(i in 2:k){
      temp <- temp+cos(2*pi*x*i)+sin(2*pi*x*i)
    }
    return(temp)
  }
}
data2 <- data2 %>% mutate(cov_m1=cs(1,DecYear),
  cov_m2=cs(2,DecYear),
  cov_m3=cs(3,DecYear),
  cov_m4=cs(4,DecYear)
)
spatial_model <- function(k,data){
  if(k==0){
    m <- lm(Value_sqrt_avg~Longitude+Latitude+Elevation+DecYear,data=data)
  }else{
    temp <- cs(k,data$DecYear)
    data$cov <- temp
    m <- lm(Value_sqrt_avg~Longitude+Latitude+Elevation+DecYear+cov,data=data)
  }
  return(m)
}

station_model <- function(){
  names <- data1$Name
  df <- data.frame(name=names,se=0,ds=0)
  for (i in 1:length(names)) {
    name <- names[i]
    temp <- cs(1,data2$DecYear)
    data2$cov <- temp
    train <- data2 %>% filter(Name!=name)
    test <- data2 %>% filter(Name==name)
    m <- spatial_model(1,train)
    pred <- predict(m,newdata=test)
    score <- cbind(pred, test) %>% mutate(
      se = proper_score("se", Value_sqrt_avg, mean = mean(pred)),
      ds = proper_score("ds", Value_sqrt_avg, mean = mean(pred), sd = sd(pred))
    )
    df$name[i] <- name
    df$se[i] <- mean(score$se)
    df$ds[i] <- mean(score$ds)
  }
  knitr::kable(df)
}
month_model <- function(){
  months <- 1:12
  df2 <- data.frame(months=months,se=0,ds=0)
  for (i in 1:length(months)) {
    month <- months[i]
    temp <- cs(1,data2$DecYear)
    data2$cov <- temp
    train <- data2 %>% filter(Month!=month)
    test <- data2 %>% filter(Month==month)
    m <- spatial_model(1,train)
    pred <- predict(m,newdata=test)
    score2 <- cbind(pred, test) %>% mutate(
      se = proper_score("se", Value_sqrt_avg, mean = mean(pred)),
      ds = proper_score("ds", Value_sqrt_avg, mean = mean(pred), sd = sd(pred))
    )
    df2$months[i] <- month
    df2$se[i] <- mean(score2$se)
    df2$ds[i] <- mean(score2$ds)
  }
  knitr::kable(df2)
}


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---
title: "基于`r params$new_title`学生信息的探索性分析"
author: "庄闪闪"
date: "2023/04/12"
documentclass: ctexart
output:
  rticles::ctex:
    fig_caption: yes
    number_sections: yes
    toc: yes
params:
  new_title: A中学
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo=T, message=F, warning=F, fig.height=4)
```

# 背景

假设我们得知不同学校学生信息，包括：姓名，身高，体重。如：

![](images/9631681396915_.pic.jpg)

\*领导/硕导/博导\* 要求分别给出不同学校的学生信息分析报告。此时，可以使用 R Markdown 开展这项工作。

## 加载数据

首先，我们加载 `r params$new_title` 学生的信息。

```{r tab2}
library(tidyverse)
library(knitr)
df = read.csv(paste0("data/",params$new_title,".csv",sep=''))
knitr::kable(df, align = "c", caption = '学生信息')
```

## 探索性分析

根据该数据，我们对不同性别的身高和体重进行探索性分析。

```{r mytable}
df %>% group_by(`性别`) %>% summarise("平均身高" = mean(`身高`),
                                    "身高标准差" = sd(`身高`),
                                    "平均体重" = mean(`体重`),
                                    "体重标准差" = sd(`体重`)) -> df_summary
knitr::kable(df_summary, align = "c",
             caption = "不同性别的身高和体重情况")
```

```{r fig1, fig.align='center',fig.cap="不同性别学生的身高和体重分布情况"}
library(showtext)
library(ggsci)
showtext.auto()
df %>% pivot_longer(`身高`:`体重`,
                    names_to = "Class",
                    values_to = "数值") %>% 
  ggplot() + 
  geom_boxplot(aes(性别,数值,fill=性别)) +
  scale_fill_aaas() +
  facet_wrap(vars(Class),scales = "free")
```

# 总结

这个项目主要测试 RMarkdown 的参数化报告，主要参考：

1.  [R Markdown 指南 第 7 章：使用 R Markdown 开展项目工作](https://cosname.github.io/rmarkdown-guide/rmarkdown-project.html#render-rmd)

2.  [Iterate multiple RMarkdown reports](https://community.rstudio.com/t/iterate-multiple-rmarkdown-reports/43208/7)

> 由于时间有限，这个报告后续没有添加了。

# 未来展望


- 对于不同的数据，虽然可以自动化绘制不同的报表，但是由于数据不同，结果也会不同。不同结果如何达到自动化分析呢？这个问题还需要再商讨。个人觉得如果能把类似 ChatGPT 结合起来，或许真能实现自动给出完整的分析报告了。类似的 ChatGPT 的 R 包有：[chatgpt](https://github.com/jcrodriguez1989/chatgpt "chatgpt")，[gptstudio](https://github.com/MichelNivard/gptstudio "gptstudio")。教程有：[1](https://rpubs.com/nirmal/setting_chat_gpt_R "1")，[2](https://www.karada-good.net/en/ranalytics/r-e767/ "2")，供参考。

- 制作该可重复性报告的前提是：不同数据需要一致的数据结构。这点是否可以进行拓展？是否有专门的包能够自动对数据处理，并给出统一的数据格式？

- 制作这个教程花费较长时间，欢迎大家一键三连呀～ 教程对应的项目代码已经开源在 [GitHub](https://github.com/liangliangzhuang/R_example "GitHub") 中，欢迎 Fork 和 star。或者公众号后台回复[`批量制作数据分析报告`]免费获得。




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FILE: 2023年/2023.04.12 Rmarkdown重复性报告/libs/header-attrs-2.20/header-attrs.js
================================================
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});


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FILE: 2023年/2023.04.12 Rmarkdown重复性报告/libs/remark-css-0.0.1/default.css
================================================
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FILE: 2023年/2023.04.12 Rmarkdown重复性报告/test.R
================================================
library(rmarkdown)
school = c("A中学","B中学")
for(v in school){
  render("中文.Rmd",
         output_file=paste0("result/exploratory_", v, ".pdf"),
         params=list(new_title=v))
}


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FILE: 2023年/2023.04.12 Rmarkdown重复性报告/zh-CN.css
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FILE: 2023年/2023.04.12 Rmarkdown重复性报告/中文.Rmd
================================================
---
title: "基于`r params$new_title`学生信息的探索性分析"
author: "庄闪闪"
date: "2023/04/12"
documentclass: ctexart
output:
  rticles::ctex:
    fig_caption: yes
    number_sections: yes
    toc: yes
params:
  new_title: A中学
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo=T, message=F, warning=F, fig.height=4)
```

# 背景

假设我们得知不同学校学生信息，包括：姓名，身高，体重。如：

![](images/9631681396915_.pic.jpg)

\*领导/硕导/博导\* 要求分别给出不同学校的学生信息分析报告。此时，可以使用 R Markdown 开展这项工作。

## 加载数据

首先，我们加载 `r params$new_title` 学生的信息。

```{r tab2}
library(tidyverse)
library(knitr)
df = read.csv(paste0("data/",params$new_title,".csv",sep=''))
knitr::kable(df, align = "c", caption = '学生信息')
```

## 探索性分析

根据该数据，我们对不同性别的身高和体重进行探索性分析。

```{r mytable}
df %>% group_by(`性别`) %>% summarise("平均身高" = mean(`身高`),
                                    "身高标准差" = sd(`身高`),
                                    "平均体重" = mean(`体重`),
                                    "体重标准差" = sd(`体重`)) -> df_summary
knitr::kable(df_summary, align = "c",
             caption = "不同性别的身高和体重情况")
```

```{r fig1, fig.align='center',fig.cap="不同性别学生的身高和体重分布情况"}
library(showtext)
library(ggsci)
showtext.auto()
df %>% pivot_longer(`身高`:`体重`,
                    names_to = "Class",
                    values_to = "数值") %>% 
  ggplot() + 
  geom_boxplot(aes(性别,数值,fill=性别)) +
  scale_fill_aaas() +
  facet_wrap(vars(Class),scales = "free")
```

# 总结

这个项目主要测试 RMarkdown 的参数化报告，主要参考：

1.  [R Markdown 指南 第 7 章：使用 R Markdown 开展项目工作](https://cosname.github.io/rmarkdown-guide/rmarkdown-project.html#render-rmd)

2.  [Iterate multiple RMarkdown reports](https://community.rstudio.com/t/iterate-multiple-rmarkdown-reports/43208/7)

> 由于时间有限，这个报告后续没有添加了。

# 未来展望


- 对于不同的数据，虽然可以自动化绘制不同的报表，但是由于数据不同，结果也会不同。不同结果如何达到自动化分析呢？这个问题还需要再商讨。个人觉得如果能把类似 ChatGPT 结合起来，或许真能实现自动给出完整的分析报告了。类似的 ChatGPT 的 R 包有：[chatgpt](https://github.com/jcrodriguez1989/chatgpt "chatgpt")，[gptstudio](https://github.com/MichelNivard/gptstudio "gptstudio")。教程有：[1](https://rpubs.com/nirmal/setting_chat_gpt_R "1")，[2](https://www.karada-good.net/en/ranalytics/r-e767/ "2")，供参考。

- 制作该可重复性报告的前提是：不同数据需要一致的数据结构。这点是否可以进行拓展？是否有专门的包能够自动对数据处理，并给出统一的数据格式？

- 制作这个教程花费较长时间，欢迎大家一键三连呀～ 教程对应的项目代码已经开源在 [GitHub](https://github.com/liangliangzhuang/R_example "GitHub") 中，欢迎 Fork 和 star。或者公众号后台回复[`批量制作数据分析报告`]免费获得。




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FILE: 2023年/2023.05.04 如何一步步提高图形 B 格？以 ggplot 绘图为例/.Rhistory
================================================


================================================
FILE: 2023年/2023.05.04 如何一步步提高图形 B 格？以 ggplot 绘图为例/line.R
================================================

# 代码解释见推文：https://mp.weixin.qq.com/s/k5VgsK7e4d9RDrTkctJP6g
# 作者：庄亮亮/庄闪闪
## 主要参考书籍《THE HITCHHIKER’S GUIDE TO GGPLOT2》

library(ggplot2) 
library(ggthemes) 
library(dplyr) 
library(readr)

chilean.exports = "year,product,export,percentage
                  2006,copper,4335009500,81 
                  2006,others,1016726518,19 
                  2007,copper,9005361914,86 
                  2007,others,1523085299,14 
                  2008,copper,6907056354,80 
                  2008,others,1762684216,20 
                  2009,copper,10529811075,81 
                  2009,others,2464094241,19 
                  2010,copper,14828284450,85 
                  2010,others,2543015596,15 
                  2011,copper,15291679086,82 
                  2011,others,3447972354,18 
                  2012,copper,14630686732,80 
                  2012,others,3583968218,20 
                  2013,copper,15244038840,79 
                  2013,others,4051281128,21 
                  2014,copper,14703374241,78 
                  2014,others,4251484600,22 
                  2015,copper,13155922363,78 
                  2015,others,3667286912,22
"

charts.data = read_csv(chilean.exports)

p1 = ggplot(aes(y = export, x = year, colour = product), data = charts.data) + 
  geom_line() 
p1

p1 = ggplot(aes(y = export, x = year, colour = product), data = charts.data) + 
  geom_line(linewidth = 1.5) 
p1


p1 = p1 + scale_x_continuous(breaks = seq(2006,2015,1))
p1


p1 = p1 +
  labs(title = "Composition of Exports to China ($)", subtitle = "Source: The Observatory of Economic Complexity") +
  labs(x = "Year", y = "USD million")
p1

colour = c("#5F9EA0", "#E1B378") 
p1 = p1 + scale_colour_manual(values = colour) 
p1

library(viridis)
p1 = p1 + scale_color_viridis(discrete=T) 


p1 + theme_bw() + 
  theme(panel.grid = element_blank(),
        legend.position = "bottom",
        legend.direction = "horizontal")

library(ggthemes)
p1 + geom_point(size = 3) +
  theme_economist() + 
  scale_colour_economist() +
  theme(panel.grid = element_blank(),
        legend.position = "bottom",
        legend.direction = "horizontal")

library(showtext)


font_add("ZSS","SanJiLuRongTi-2.ttf")
font_add("ZSS2","No.019-Sounso-Quality-2.ttf")


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FILE: 2023年/2023.05.08个性化树状图/ggparty.R
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# 代码来源：https://github.com/martin-borkovec/ggparty
# 代码解析/推文：https://mp.weixin.qq.com/s/8pP7IWLH-RaIxEtnyuCjhA


library(ggparty)
library(ARE)

data("TeachingRatings", package = "AER")
str(TeachingRatings)
tr <- subset(TeachingRatings, credits == "more")

tr_tree <- lmtree(eval ~ beauty | minority + age + gender + division + native +
                    tenure, data = tr, weights = students, caseweights = FALSE)

ggparty(tr_tree,
        terminal_space = 0.5,
        add_vars = list(p.value = "$node$info$p.value")) +
  geom_edge(size = 1.5) +
  geom_edge_label(colour = "grey", size = 6) +
  geom_node_plot(gglist = list(geom_point(aes(x = beauty,
                                              y = eval,
                                              col = tenure,
                                              shape = minority),
                                          alpha = 0.8),
                               theme_bw(base_size = 15)),
                 scales = "fixed",
                 id = "terminal",
                 shared_axis_labels = T,
                 shared_legend = T,
                 legend_separator = T,
                 predict = "beauty",
                 predict_gpar = list(col = "blue",
                                     size = 1.2)
  ) +
  geom_node_label(aes(col = splitvar),
                  line_list = list(aes(label = paste("Node", id)),
                                   aes(label = splitvar),
                                   aes(label = paste("p =", formatC(p.value, format = "e", digits = 2)))),
                  line_gpar = list(list(size = 12, col = "black", fontface = "bold"),
                                   list(size = 20),
                                   list(size = 12)),
                  ids = "inner") +
  geom_node_label(aes(label = paste0("Node ", id, ", N = ", nodesize)),
                  fontface = "bold",
                  ids = "terminal",
                  size = 5, 
                  nudge_y = 0.01) +
  theme(legend.position = "none")



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FILE: 2023年/2023.05.13 高级棒棒图/棒棒图.R
================================================

# 代码解释见推文：
# 作者：庄亮亮/庄闪闪



# 加载包
library(ggplot2)
library(dplyr)
library(hrbrthemes)

# 创建数据
dim1 = c(30,11) # 第一项x轴，第二项y轴
combinations <- expand.grid(1:dim1[1],1:dim1[2])
dat = cbind(1:dim(combinations)[1],combinations)
colnames(dat) = c("id","value1","value2")

# 拟合点数
dis1 = 10; dis2 = 4
dat$class = 0
for(i in 1:dim1[2]){
  dat$class[((dim1[1]+1)*(i-1) + 1):((dim1[1]+1)*(i-1) + dis1)] = 1
  dat$class[((dim1[1]+1)*(i-1) + dis1 + 1):((dim1[1]+1)*(i-1) + dis1 + dis2 - 1)] = 2
}

# 绘图
ggplot(data = dat,aes(value1,value2)) + 
  geom_segment(data = dat[dat$value1!=dim1[1], ],aes(x=value1, xend=value1+1, y=value2, yend=value2),color="grey",size=1) +
  geom_point(aes(color=as.factor(class)), size=3, shape = 19) + 
  scale_color_manual(values = c("grey","#2F6DA3","#CC9034"),name = "Class",labels = c("Nothing","Window","Forecasting horizon")) +
  scale_y_reverse(breaks = seq(1,12,3)) +
  scale_x_continuous(breaks = c(1,10,20,30)) +
  xlab("Time") + ylab("Window number") +
  theme_bw() +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        legend.position = "bottom",
        axis.line = element_line(colour = "black"))


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[1,"Installing 'ggraph' ...\n"]
[1,"[1/3] Installing tidygraph...\n"]
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[1,"\t/var/folders/wt/t3n70vjs6g536860y9y_yr9r0000gn/T//RtmpCasmua/downloaded_packages\n"]
[1,"[2/3] Installing graphlayouts...\n"]
[2,"Installing package into ‘/Users/liangliangzhuang/Library/R/arm64/4.2/library’\n(as ‘lib’ is unspecified)\n"]
[2,"trying URL 'https://cran.rstudio.com/bin/macosx/big-sur-arm64/contrib/4.2/graphlayouts_1.0.0.tgz'\n"]
[2,"Content type 'application/x-gzip' length 4936619 bytes (4.7 MB)\n"]
[2,"="]
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[2,"====\ndownloaded 4.7 MB\n\n"]
[1,"The downloaded binary packages are in\n"]
[1,"\t/var/folders/wt/t3n70vjs6g536860y9y_yr9r0000gn/T//RtmpCasmua/downloaded_packages\n"]
[1,"[3/3] Installing ggraph...\n"]
[2,"Installing package into ‘/Users/liangliangzhuang/Library/R/arm64/4.2/library’\n(as ‘lib’ is unspecified)\n"]
[2,"trying URL 'https://cran.rstudio.com/bin/macosx/big-sur-arm64/contrib/4.2/ggraph_2.1.0.tgz'\n"]
[2,"Content type 'application/x-gzip' length 4688013 bytes (4.5 MB)\n"]
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[2,"===\ndownloaded 4.5 MB\n\n"]
[1,"The downloaded binary packages are in\n"]
[1,"\t/var/folders/wt/t3n70vjs6g536860y9y_yr9r0000gn/T//RtmpCasmua/downloaded_packages\n"]
[2,"\n\n✔ Package 'ggraph' successfully installed.\n"]


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FILE: 2023年/2023.06.29 旭日图/.Rproj.user/C6560784/sources/prop/INDEX
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~%2F%E6%88%91%E7%9A%84%E6%97%A5%E8%AE%B0%2Fwechat%2F%2B%2B%2B%E6%9C%AA%E5%AE%8C%E6%88%90%E6%8E%A8%E6%96%87%2F2023.06.29%20%E6%97%AD%E6%97%A5%E5%9B%BE%2F%E5%9B%BE9-3-1%E5%92%8C-2%E6%97%AD%E6%97%A5%E5%9B%BE-%E5%86%B0%E6%9F%B1%E5%9B%BE.R="1E4518E3"
~%2F%E6%88%91%E7%9A%84%E6%97%A5%E8%AE%B0%2Fwechat%2F%2B%2B%2B%E6%9C%AA%E5%AE%8C%E6%88%90%E6%8E%A8%E6%96%87%2F2023.06.29%20%E6%97%AD%E6%97%A5%E5%9B%BE%2F%E6%97%AD%E6%97%A5%E5%9B%BE.R="5E635AFA"


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FILE: 2023年/2023.06.29 旭日图/.Rproj.user/C6560784/sources/session-ef7e8bd6/3A928347-contents
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#EasyCharts团队出品，
#如需使用与深入学习，请联系微信：EasyCharts

library(ggraph)
library(igraph)
library(RColorBrewer) 
library(dplyr)

df<-read.csv('旭日图.csv',header=TRUE,stringsAsFactors=FALSE)

#--------------------------------分割角度均等平分----------------------------------------
edges<- data.frame(rbind(
  cbind(rep('origin',4),unique(as.character(df$Season))),
  as.matrix(df[!duplicated(df[c('Season','Month')]),1:2]),
  as.matrix(df[!duplicated(df[c('Month','Week')]),2:3])))
colnames(edges)<-c('from','to')

vertices0<-data.frame(name=unique(c(as.character(edges$from), as.character(edges$to))))
df_leaf<-df[,c('Week','Value','label')]
df_leaf$angle<-90-(1:nrow(df_leaf))/nrow(df_leaf)*360
df_leaf$angle<-ifelse(df_leaf$angle< -90, df_leaf$angle+180, df_leaf$angle)

vertices<-left_join(vertices0,df_leaf,by=c('name'='Week'))

graph <- graph_from_data_frame(edges, vertices = vertices)

#图9-3-1 旭日图(a) 分割角度均等平分
ggraph(graph, layout ='partition', circular = TRUE) +
  geom_node_arc_bar(aes(filter =(depth<=2 & depth>0 )),fill='#FEE5D9',color='black',size=0.1)+#,size=size
  geom_node_arc_bar(aes(filter =(depth==3 ),fill = Value),size=0.1)+
  geom_node_text( aes(filter =(depth<=2 & depth>0 ),label=name,size=-depth),angle=0,colour="black") +
  geom_node_text(aes(label=label,angle=angle),size=2,colour="black") +
  scale_size(range=c(3,5))+
  coord_fixed()+
  scale_fill_distiller(palette='Reds')+
  guides(size="none")+
  theme_void()

#图9-3-2 冰柱图(a) 分割角度均等平分
ggraph(graph, layout ='partition') + 
  geom_node_tile(aes(filter =(depth<=2)),fill='#FEE5D9',color='black',size=0.1)+#,size=size
  geom_node_tile(aes(filter =(depth==3 ),fill = Value),size=0.1)+
  
  geom_node_text(aes(filter =(depth<=2 & depth>0),label=name,size=-depth),angle=90,colour="black") +
  geom_node_text(aes(label=label),size=2,angle=90,colour="black")+
  scale_size(range=c(3,4))+
  scale_y_reverse()+
  scale_fill_distiller(palette="Reds",na.value = "#FFF2EC")+
  guides(size="none")+
  theme_void()

#-------------------------------分割角度与某个数值成比例----------------------------------
fake_circle<-c()
for (i in 1:nrow(df)){
  fake_circle<-append(fake_circle,rep(df$Week[i],round(10*df$Value[i])))
}

edges<- data.frame(rbind(
  cbind(rep('origin',4),unique(as.character(df$Season))),
  as.matrix(df[!duplicated(df[c('Season','Month')]),1:2]),
  as.matrix(df[!duplicated(df[c('Month','Week')]),2:3]),
  cbind(fake_circle,as.character(1:length(fake_circle)))
))

colnames(edges)<-c('from','to') 

vertices0<-data.frame(name=unique(c(as.character(edges$from), as.character(edges$to))))
df_leaf<-df[,c('Week','Value','label')]
df_leaf$angle<-90-(cumsum(df_leaf$Value)-df_leaf$Value/2)/sum(df_leaf$Value)*360
df_leaf$angle<-ifelse(df_leaf$angle< -90, df_leaf$angle+180, df_leaf$angle)

vertices<-left_join(vertices0,df_leaf,by=c('name'='Week'))


df_color<- data.frame(rbind(
  as.matrix(df[!duplicated(df[c('Season','Season')]),c(1,1)]),
  as.matrix(df[!duplicated(df[c('Season','Month')]),c(1,2)]),
  as.matrix(df[!duplicated(df[c('Season','Week')]),c(1,3)])
))

colnames(df_color)<-c('Season','name') 

vertices<-left_join(vertices,df_color,by='name')

graph <- graph_from_data_frame(edges, vertices = vertices)

#图9-3-1 旭日图(b) 分割角度与某个数值成比例
ggraph(graph, layout ='partition', circular = TRUE) + 
  geom_node_arc_bar(aes(filter =(depth<=3 & depth>0 ),fill = Season),size=0.1)+
  geom_node_text(aes(filter =(depth<=2 & depth>0),label=name,size=-depth),angle=0,colour="black") +
  geom_node_text(aes(label=label,angle=angle),size=2,colour="black")+
  scale_size(range=c(3,4))+
  coord_fixed()+
  scale_fill_brewer(palette="Reds",direction=-1)+
  guides(size="none",fill="none")+
  theme_void()

#图9-3-2 冰柱图(b) 分割角度与某个数值成比例aph(graph, layout ='partition')+ 
  geom_node_tile(aes(filter =(depth<=3 ),fill = Season),size = 0.1)+
  geom_node_text(aes(filter =(depth<=2 & depth>0),label=name,size=-depth),angle=90,colour="black") +
  geom_node_text(aes(label=label),size=2,angle=90,colour="black")+
  scale_size(range=c(3,4))+
  scale_y_reverse()+
  scale_fill_brewer(palette="Reds",na.value = "#FFF2EC")+
  guides(size="none",fill="none")+
  theme_void()



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FILE: 2023年/2023.06.29 旭日图/.Rproj.user/C6560784/sources/session-ef7e8bd6/F749EB52-contents
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library(plotly)

# 数据导入
d <- data.frame(
  ids = c(
    "North America", "Europe", "Australia", "North America - Football", "Soccer",
    "North America - Rugby", "Europe - Football", "Rugby",
    "Europe - American Football","Australia - Football", "Association",
    "Australian Rules", "Autstralia - American Football", "Australia - Rugby",
    "Rugby League", "Rugby Union"
  ),
  labels = c(
    "North<br>America", "Europe", "Australia", "Football", "Soccer", "Rugby",
    "Football", "Rugby", "American<br>Football", "Football", "Association",
    "Australian<br>Rules", "American<br>Football", "Rugby", "Rugby<br>League",
    "Rugby<br>Union"
  ),
  parents = c(
    "", "", "", "North America", "North America", "North America", "Europe",
    "Europe", "Europe","Australia", "Australia - Football", "Australia - Football",
    "Australia - Football", "Australia - Football", "Australia - Rugby",
    "Australia - Rugby"
  ),
  stringsAsFactors = FALSE
)

# 另一种方法，导入数据集
write.csv(d,"d.csv",row.names = FALSE)
d = read.csv("d.csv")

# 绘图
fig <- plot_ly(d, ids = ~ids, labels = ~labels, parents = ~parents, 
               type = 'sunburst') #maxdepth=3
fig

# 保存 PDF 图形
# 首先需要在操作系统上安装 [orca](https://github.com/plotly/orca#installation "orca")
orca(fig, "FINAL_plot.pdf")





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FILE: 2023年/2023.06.29 旭日图/.Rproj.user/C6560784/sources/session-ef7e8bd6/lock_file
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FILE: 2023年/2023.06.29 旭日图/.Rproj.user/shared/notebooks/patch-chunk-names
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FILE: 2023年/2023.06.29 旭日图/.Rproj.user/shared/notebooks/paths
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/Users/liangliangzhuang/我的日记/wechat/+++未完成推文/2023.06.29 旭日图/图9-3-1和-2旭日图-冰柱图.R="7031FA84"
/Users/liangliangzhuang/我的日记/wechat/+++未完成推文/2023.06.29 旭日图/旭日图.R="C53897E4"


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FILE: 2023年/2023.06.29 旭日图/d.csv
================================================
"ids","labels","parents"
"North America","North<br>America",""
"Europe","Europe",""
"Australia","Australia",""
"North America - Football","Football","North America"
"Soccer","Soccer","North America"
"North America - Rugby","Rugby","North America"
"Europe - Football","Football","Europe"
"Rugby","Rugby","Europe"
"Europe - American Football","American<br>Football","Europe"
"Australia - Football","Football","Australia"
"Association","Association","Australia - Football"
"Australian Rules","Australian<br>Rules","Australia - Football"
"Autstralia - American Football","American<br>Football","Australia - Football"
"Australia - Rugby","Rugby","Australia - Football"
"Rugby League","Rugby<br>League","Australia - Rugby"
"Rugby Union","Rugby<br>Union","Australia - Rugby"


================================================
FILE: 2023年/2023.06.29 旭日图/旭日图.R
================================================
library(plotly)

# 数据导入
d <- data.frame(
  ids = c(
    "North America", "Europe", "Australia", "North America - Football", "Soccer",
    "North America - Rugby", "Europe - Football", "Rugby",
    "Europe - American Football","Australia - Football", "Association",
    "Australian Rules", "Autstralia - American Football", "Australia - Rugby",
    "Rugby League", "Rugby Union"
  ),
  labels = c(
    "North<br>America", "Europe", "Australia", "Football", "Soccer", "Rugby",
    "Football", "Rugby", "American<br>Football", "Football", "Association",
    "Australian<br>Rules", "American<br>Football", "Rugby", "Rugby<br>League",
    "Rugby<br>Union"
  ),
  parents = c(
    "", "", "", "North America", "North America", "North America", "Europe",
    "Europe", "Europe","Australia", "Australia - Football", "Australia - Football",
    "Australia - Football", "Australia - Football", "Australia - Rugby",
    "Australia - Rugby"
  ),
  stringsAsFactors = FALSE
)

# 另一种方法，导入数据集
write.csv(d,"d.csv",row.names = FALSE)
d = read.csv("d.csv")

# 绘图
fig <- plot_ly(gg, ids = ~ids, labels = ~labels, parents = ~parents, 
               type = 'sunburst') #maxdepth=3
fig

# 保存 PDF 图形
# 首先需要在操作系统上安装 [orca](https://github.com/plotly/orca#installation "orca")
orca(fig, "FINAL_plot.pdf")





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FILE: 2023年/2023.06.29 旭日图/未命名.Rproj
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Version: 1.0

RestoreWorkspace: Default
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FILE: 2023年/2023.06.29 旭日图/未命名.html
================================================
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<style>body{background-color:white;}</style>
<script>(function() {
  // If window.HTMLWidgets is already defined, then use it; otherwise create a
  // new object. This allows preceding code to set options that affect the
  // initialization process (though none currently exist).
  window.HTMLWidgets = window.HTMLWidgets || {};

  // See if we're running in a viewer pane. If not, we're in a web browser.
  var viewerMode = window.HTMLWidgets.viewerMode =
      /\bviewer_pane=1\b/.test(window.location);

  // See if we're running in Shiny mode. If not, it's a static document.
  // Note that static widgets can appear in both Shiny and static modes, but
  // obviously, Shiny widgets can only appear in Shiny apps/documents.
  var shinyMode = window.HTMLWidgets.shinyMode =
      typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;

  // We can't count on jQuery being available, so we implement our own
  // version if necessary.
  function querySelectorAll(scope, selector) {
    if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
      return scope.find(selector);
    }
    if (scope.querySelectorAll) {
      return scope.querySelectorAll(selector);
    }
  }

  function asArray(value) {
    if (value === null)
      return [];
    if ($.isArray(value))
      return value;
    return [value];
  }

  // Implement jQuery's extend
  function extend(target /*, ... */) {
    if (arguments.length == 1) {
      return target;
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    for (var i = 1; i < arguments.length; i++) {
      var source = arguments[i];
      for (var prop in source) {
        if (source.hasOwnProperty(prop)) {
          target[prop] = source[prop];
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    return target;
  }

  // IE8 doesn't support Array.forEach.
  function forEach(values, callback, thisArg) {
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    } else {
      for (var i = 0; i < values.length; i++) {
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  }

  // Replaces the specified method with the return value of funcSource.
  //
  // Note that funcSource should not BE the new method, it should be a function
  // that RETURNS the new method. funcSource receives a single argument that is
  // the overridden method, it can be called from the new method. The overridden
  // method can be called like a regular function, it has the target permanently
  // bound to it so "this" will work correctly.
  function overrideMethod(target, methodName, funcSource) {
    var superFunc = target[methodName] || function() {};
    var superFuncBound = function() {
      return superFunc.apply(target, arguments);
    };
    target[methodName] = funcSource(superFuncBound);
  }

  // Add a method to delegator that, when invoked, calls
  // delegatee.methodName. If there is no such method on
  // the delegatee, but there was one on delegator before
  // delegateMethod was called, then the original version
  // is invoked instead.
  // For example:
  //
  // var a = {
  //   method1: function() { console.log('a1'); }
  //   method2: function() { console.log('a2'); }
  // };
  // var b = {
  //   method1: function() { console.log('b1'); }
  // };
  // delegateMethod(a, b, "method1");
  // delegateMethod(a, b, "method2");
  // a.method1();
  // a.method2();
  //
  // The output would be "b1", "a2".
  function delegateMethod(delegator, delegatee, methodName) {
    var inherited = delegator[methodName];
    delegator[methodName] = function() {
      var target = delegatee;
      var method = delegatee[methodName];

      // The method doesn't exist on the delegatee. Instead,
      // call the method on the delegator, if it exists.
      if (!method) {
        target = delegator;
        method = inherited;
      }

      if (method) {
        return method.apply(target, arguments);
      }
    };
  }

  // Implement a vague facsimilie of jQuery's data method
  function elementData(el, name, value) {
    if (arguments.length == 2) {
      return el["htmlwidget_data_" + name];
    } else if (arguments.length == 3) {
      el["htmlwidget_data_" + name] = value;
      return el;
    } else {
      throw new Error("Wrong number of arguments for elementData: " +
        arguments.length);
    }
  }

  // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
  function escapeRegExp(str) {
    return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
  }

  function hasClass(el, className) {
    var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
    return re.test(el.className);
  }

  // elements - array (or array-like object) of HTML elements
  // className - class name to test for
  // include - if true, only return elements with given className;
  //   if false, only return elements *without* given className
  function filterByClass(elements, className, include) {
    var results = [];
    for (var i = 0; i < elements.length; i++) {
      if (hasClass(elements[i], className) == include)
        results.push(elements[i]);
    }
    return results;
  }

  function on(obj, eventName, func) {
    if (obj.addEventListener) {
      obj.addEventListener(eventName, func, false);
    } else if (obj.attachEvent) {
      obj.attachEvent(eventName, func);
    }
  }

  function off(obj, eventName, func) {
    if (obj.removeEventListener)
      obj.removeEventListener(eventName, func, false);
    else if (obj.detachEvent) {
      obj.detachEvent(eventName, func);
    }
  }

  // Translate array of values to top/right/bottom/left, as usual with
  // the "padding" CSS property
  // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
  function unpackPadding(value) {
    if (typeof(value) === "number")
      value = [value];
    if (value.length === 1) {
      return {top: value[0], right: value[0], bottom: value[0], left: value[0]};
    }
    if (value.length === 2) {
      return {top: value[0], right: value[1], bottom: value[0], left: value[1]};
    }
    if (value.length === 3) {
      return {top: value[0], right: value[1], bottom: value[2], left: value[1]};
    }
    if (value.length === 4) {
      return {top: value[0], right: value[1], bottom: value[2], left: value[3]};
    }
  }

  // Convert an unpacked padding object to a CSS value
  function paddingToCss(paddingObj) {
    return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
  }

  // Makes a number suitable for CSS
  function px(x) {
    if (typeof(x) === "number")
      return x + "px";
    else
      return x;
  }

  // Retrieves runtime widget sizing information for an element.
  // The return value is either null, or an object with fill, padding,
  // defaultWidth, defaultHeight fields.
  function sizingPolicy(el) {
    var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
    if (!sizingEl)
      return null;
    var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
    if (viewerMode) {
      return sp.viewer;
    } else {
      return sp.browser;
    }
  }

  // @param tasks Array of strings (or falsy value, in which case no-op).
  //   Each element must be a valid JavaScript expression that yields a
  //   function. Or, can be an array of objects with "code" and "data"
  //   properties; in this case, the "code" property should be a string
  //   of JS that's an expr that yields a function, and "data" should be
  //   an object that will be added as an additional argument when that
  //   function is called.
  // @param target The object that will be "this" for each function
  //   execution.
  // @param args Array of arguments to be passed to the functions. (The
  //   same arguments will be passed to all functions.)
  function evalAndRun(tasks, target, args) {
    if (tasks) {
      forEach(tasks, function(task) {
        var theseArgs = args;
        if (typeof(task) === "object") {
          theseArgs = theseArgs.concat([task.data]);
          task = task.code;
        }
        var taskFunc = tryEval(task);
        if (typeof(taskFunc) !== "function") {
          throw new Error("Task must be a function! Source:\n" + task);
        }
        taskFunc.apply(target, theseArgs);
      });
    }
  }

  // Attempt eval() both with and without enclosing in parentheses.
  // Note that enclosing coerces a function declaration into
  // an expression that eval() can parse
  // (otherwise, a SyntaxError is thrown)
  function tryEval(code) {
    var result = null;
    try {
      result = eval("(" + code + ")");
    } catch(error) {
      if (!(error instanceof SyntaxError)) {
        throw error;
      }
      try {
        result = eval(code);
      } catch(e) {
        if (e instanceof SyntaxError) {
          throw error;
        } else {
          throw e;
        }
      }
    }
    return result;
  }

  function initSizing(el) {
    var sizing = sizingPolicy(el);
    if (!sizing)
      return;

    var cel = document.getElementById("htmlwidget_container");
    if (!cel)
      return;

    if (typeof(sizing.padding) !== "undefined") {
      document.body.style.margin = "0";
      document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
    }

    if (sizing.fill) {
      document.body.style.overflow = "hidden";
      document.body.style.width = "100%";
      document.body.style.height = "100%";
      document.documentElement.style.width = "100%";
      document.documentElement.style.height = "100%";
      if (cel) {
        cel.style.position = "absolute";
        var pad = unpackPadding(sizing.padding);
        cel.style.top = pad.top + "px";
        cel.style.right = pad.right + "px";
        cel.style.bottom = pad.bottom + "px";
        cel.style.left = pad.left + "px";
        el.style.width = "100%";
        el.style.height = "100%";
      }

      return {
        getWidth: function() { return cel.offsetWidth; },
        getHeight: function() { return cel.offsetHeight; }
      };

    } else {
      el.style.width = px(sizing.width);
      el.style.height = px(sizing.height);

      return {
        getWidth: function() { return el.offsetWidth; },
        getHeight: function() { return el.offsetHeight; }
      };
    }
  }

  // Default implementations for methods
  var defaults = {
    find: function(scope) {
      return querySelectorAll(scope, "." + this.name);
    },
    renderError: function(el, err) {
      var $el = $(el);

      this.clearError(el);

      // Add all these error classes, as Shiny does
      var errClass = "shiny-output-error";
      if (err.type !== null) {
        // use the classes of the error condition as CSS class names
        errClass = errClass + " " + $.map(asArray(err.type), function(type) {
          return errClass + "-" + type;
        }).join(" ");
      }
      errClass = errClass + " htmlwidgets-error";

      // Is el inline or block? If inline or inline-block, just display:none it
      // and add an inline error.
      var display = $el.css("display");
      $el.data("restore-display-mode", display);

      if (display === "inline" || display === "inline-block") {
        $el.hide();
        if (err.message !== "") {
          var errorSpan = $("<span>").addClass(errClass);
          errorSpan.text(err.message);
          $el.after(errorSpan);
        }
      } else if (display === "block") {
        // If block, add an error just after the el, set visibility:none on the
        // el, and position the error to be on top of the el.
        // Mark it with a unique ID and CSS class so we can remove it later.
        $el.css("visibility", "hidden");
        if (err.message !== "") {
          var errorDiv = $("<div>").addClass(errClass).css("position", "absolute")
            .css("top", el.offsetTop)
            .css("left", el.offsetLeft)
            // setting width can push out the page size, forcing otherwise
            // unnecessary scrollbars to appear and making it impossible for
            // the element to shrink; so use max-width instead
            .css("maxWidth", el.offsetWidth)
            .css("height", el.offsetHeight);
          errorDiv.text(err.message);
          $el.after(errorDiv);

          // Really dumb way to keep the size/position of the error in sync with
          // the parent element as the window is resized or whatever.
          var intId = setInterval(function() {
            if (!errorDiv[0].parentElement) {
              clearInterval(intId);
              return;
            }
            errorDiv
              .css("top", el.offsetTop)
              .css("left", el.offsetLeft)
              .css("maxWidth", el.offsetWidth)
              .css("height", el.offsetHeight);
          }, 500);
        }
      }
    },
    clearError: function(el) {
      var $el = $(el);
      var display = $el.data("restore-display-mode");
      $el.data("restore-display-mode", null);

      if (display === "inline" || display === "inline-block") {
        if (display)
          $el.css("display", display);
        $(el.nextSibling).filter(".htmlwidgets-error").remove();
      } else if (display === "block"){
        $el.css("visibility", "inherit");
        $(el.nextSibling).filter(".htmlwidgets-error").remove();
      }
    },
    sizing: {}
  };

  // Called by widget bindings to register a new type of widget. The definition
  // object can contain the following properties:
  // - name (required) - A string indicating the binding name, which will be
  //   used by default as the CSS classname to look for.
  // - initialize (optional) - A function(el) that will be called once per
  //   widget element; if a value is returned, it will be passed as the third
  //   value to renderValue.
  // - renderValue (required) - A function(el, data, initValue) that will be
  //   called with data. Static contexts will cause this to be called once per
  //   element; Shiny apps will cause this to be called multiple times per
  //   element, as the data changes.
  window.HTMLWidgets.widget = function(definition) {
    if (!definition.name) {
      throw new Error("Widget must have a name");
    }
    if (!definition.type) {
      throw new Error("Widget must have a type");
    }
    // Currently we only support output widgets
    if (definition.type !== "output") {
      throw new Error("Unrecognized widget type '" + definition.type + "'");
    }
    // TODO: Verify that .name is a valid CSS classname

    // Support new-style instance-bound definitions. Old-style class-bound
    // definitions have one widget "object" per widget per type/class of
    // widget; the renderValue and resize methods on such widget objects
    // take el and instance arguments, because the widget object can't
    // store them. New-style instance-bound definitions have one widget
    // object per widget instance; the definition that's passed in doesn't
    // provide renderValue or resize methods at all, just the single method
    //   factory(el, width, height)
    // which returns an object that has renderValue(x) and resize(w, h).
    // This enables a far more natural programming style for the widget
    // author, who can store per-instance state using either OO-style
    // instance fields or functional-style closure variables (I guess this
    // is in contrast to what can only be called C-style pseudo-OO which is
    // what we required before).
    if (definition.factory) {
      definition = createLegacyDefinitionAdapter(definition);
    }

    if (!definition.renderValue) {
      throw new Error("Widget must have a renderValue function");
    }

    // For static rendering (non-Shiny), use a simple widget registration
    // scheme. We also use this scheme for Shiny apps/documents that also
    // contain static widgets.
    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
    // Merge defaults into the definition; don't mutate the original definition.
    var staticBinding = extend({}, defaults, definition);
    overrideMethod(staticBinding, "find", function(superfunc) {
      return function(scope) {
        var results = superfunc(scope);
        // Filter out Shiny outputs, we only want the static kind
        return filterByClass(results, "html-widget-output", false);
      };
    });
    window.HTMLWidgets.widgets.push(staticBinding);

    if (shinyMode) {
      // Shiny is running. Register the definition with an output binding.
      // The definition itself will not be the output binding, instead
      // we will make an output binding object that delegates to the
      // definition. This is because we foolishly used the same method
      // name (renderValue) for htmlwidgets definition and Shiny bindings
      // but they actually have quite different semantics (the Shiny
      // bindings receive data that includes lots of metadata that it
      // strips off before calling htmlwidgets renderValue). We can't
      // just ignore the difference because in some widgets it's helpful
      // to call this.renderValue() from inside of resize(), and if
      // we're not delegating, then that call will go to the Shiny
      // version instead of the htmlwidgets version.

      // Merge defaults with definition, without mutating either.
      var bindingDef = extend({}, defaults, definition);

      // This object will be our actual Shiny binding.
      var shinyBinding = new Shiny.OutputBinding();

      // With a few exceptions, we'll want to simply use the bindingDef's
      // version of methods if they are available, otherwise fall back to
      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
      // methods in the future, and we want them to be overrideable by
      // HTMLWidget binding definitions, then we'll need to add them to this
      // list.
      delegateMethod(shinyBinding, bindingDef, "getId");
      delegateMethod(shinyBinding, bindingDef, "onValueChange");
      delegateMethod(shinyBinding, bindingDef, "onValueError");
      delegateMethod(shinyBinding, bindingDef, "renderError");
      delegateMethod(shinyBinding, bindingDef, "clearError");
      delegateMethod(shinyBinding, bindingDef, "showProgress");

      // The find, renderValue, and resize are handled differently, because we
      // want to actually decorate the behavior of the bindingDef methods.

      shinyBinding.find = function(scope) {
        var results = bindingDef.find(scope);

        // Only return elements that are Shiny outputs, not static ones
        var dynamicResults = results.filter(".html-widget-output");

        // It's possible that whatever caused Shiny to think there might be
        // new dynamic outputs, also caused there to be new static outputs.
        // Since there might be lots of different htmlwidgets bindings, we
        // schedule execution for later--no need to staticRender multiple
        // times.
        if (results.length !== dynamicResults.length)
          scheduleStaticRender();

        return dynamicResults;
      };

      // Wrap renderValue to handle initialization, which unfortunately isn't
      // supported natively by Shiny at the time of this writing.

      shinyBinding.renderValue = function(el, data) {
        Shiny.renderDependencies(data.deps);
        // Resolve strings marked as javascript literals to objects
        if (!(data.evals instanceof Array)) data.evals = [data.evals];
        for (var i = 0; data.evals && i < data.evals.length; i++) {
          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
        }
        if (!bindingDef.renderOnNullValue) {
          if (data.x === null) {
            el.style.visibility = "hidden";
            return;
          } else {
            el.style.visibility = "inherit";
          }
        }
        if (!elementData(el, "initialized")) {
          initSizing(el);

          elementData(el, "initialized", true);
          if (bindingDef.initialize) {
            var result = bindingDef.initialize(el, el.offsetWidth,
              el.offsetHeight);
            elementData(el, "init_result", result);
          }
        }
        bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
        evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
      };

      // Only override resize if bindingDef implements it
      if (bindingDef.resize) {
        shinyBinding.resize = function(el, width, height) {
          // Shiny can call resize before initialize/renderValue have been
          // called, which doesn't make sense for widgets.
          if (elementData(el, "initialized")) {
            bindingDef.resize(el, width, height, elementData(el, "init_result"));
          }
        };
      }

      Shiny.outputBindings.register(shinyBinding, bindingDef.name);
    }
  };

  var scheduleStaticRenderTimerId = null;
  function scheduleStaticRender() {
    if (!scheduleStaticRenderTimerId) {
      scheduleStaticRenderTimerId = setTimeout(function() {
        scheduleStaticRenderTimerId = null;
        window.HTMLWidgets.staticRender();
      }, 1);
    }
  }

  // Render static widgets after the document finishes loading
  // Statically render all elements that are of this widget's class
  window.HTMLWidgets.staticRender = function() {
    var bindings = window.HTMLWidgets.widgets || [];
    forEach(bindings, function(binding) {
      var matches = binding.find(document.documentElement);
      forEach(matches, function(el) {
        var sizeObj = initSizing(el, binding);

        if (hasClass(el, "html-widget-static-bound"))
          return;
        el.className = el.className + " html-widget-static-bound";

        var initResult;
        if (binding.initialize) {
          initResult = binding.initialize(el,
            sizeObj ? sizeObj.getWidth() : el.offsetWidth,
            sizeObj ? sizeObj.getHeight() : el.offsetHeight
          );
          elementData(el, "init_result", initResult);
        }

        if (binding.resize) {
          var lastSize = {
            w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
            h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
          };
          var resizeHandler = function(e) {
            var size = {
              w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
              h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
            };
            if (size.w === 0 && size.h === 0)
              return;
            if (size.w === lastSize.w && size.h === lastSize.h)
              return;
            lastSize = size;
            binding.resize(el, size.w, size.h, initResult);
          };

          on(window, "resize", resizeHandler);

          // This is needed for cases where we're running in a Shiny
          // app, but the widget itself is not a Shiny output, but
          // rather a simple static widget. One example of this is
          // an rmarkdown document that has runtime:shiny and widget
          // that isn't in a render function. Shiny only knows to
          // call resize handlers for Shiny outputs, not for static
          // widgets, so we do it ourselves.
          if (window.jQuery) {
            window.jQuery(document).on(
              "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
              resizeHandler
            );
            window.jQuery(document).on(
              "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
              resizeHandler
            );
          }

          // This is needed for the specific case of ioslides, which
          // flips slides between display:none and display:block.
          // Ideally we would not have to have ioslide-specific code
          // here, but rather have ioslides raise a generic event,
          // but the rmarkdown package just went to CRAN so the
          // window to getting that fixed may be long.
          if (window.addEventListener) {
            // It's OK to limit this to window.addEventListener
            // browsers because ioslides itself only supports
            // such browsers.
            on(document, "slideenter", resizeHandler);
            on(document, "slideleave", resizeHandler);
          }
        }

        var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
        if (scriptData) {
          var data = JSON.parse(scriptData.textContent || scriptData.text);
          // Resolve strings marked as javascript literals to objects
          if (!(data.evals instanceof Array)) data.evals = [data.evals];
          for (var k = 0; data.evals && k < data.evals.length; k++) {
            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
          }
          binding.renderValue(el, data.x, initResult);
          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
        }
      });
    });

    invokePostRenderHandlers();
  }


  function has_jQuery3() {
    if (!window.jQuery) {
      return false;
    }
    var $version = window.jQuery.fn.jquery;
    var $major_version = parseInt($version.split(".")[0]);
    return $major_version >= 3;
  }

  /*
  / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
  / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
  / really means $(setTimeout(fn)).
  / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
  /
  / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
  / one tick later than it did before, which means staticRender() is
  / called renderValue() earlier than (advanced) widget authors might be expecting.
  / https://github.com/rstudio/shiny/issues/2630
  /
  / For a concrete example, leaflet has some methods (e.g., updateBounds)
  / which reference Shiny methods registered in initShiny (e.g., setInputValue).
  / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
  / delay execution of those methods (until Shiny methods are ready)
  / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
  /
  / Ideally widget authors wouldn't need to use this setTimeout() hack that
  / leaflet uses to call Shiny methods on a staticRender(). In the long run,
  / the logic initShiny should be broken up so that method registration happens
  / right away, but binding happens later.
  */
  function maybeStaticRenderLater() {
    if (shinyMode && has_jQuery3()) {
      window.jQuery(window.HTMLWidgets.staticRender);
    } else {
      window.HTMLWidgets.staticRender();
    }
  }

  if (document.addEventListener) {
    document.addEventListener("DOMContentLoaded", function() {
      document.removeEventListener("DOMContentLoaded", arguments.callee, false);
      maybeStaticRenderLater();
    }, false);
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    document.attachEvent("onreadystatechange", function() {
      if (document.readyState === "complete") {
        document.detachEvent("onreadystatechange", arguments.callee);
        maybeStaticRenderLater();
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    });
  }


  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
    // If no key, default to the first item
    if (typeof(key) === "undefined")
      key = 1;

    var link = document.getElementById(depname + "-" + key + "-attachment");
    if (!link) {
      throw new Error("Attachment " + depname + "/" + key + " not found in document");
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    return link.getAttribute("href");
  };

  window.HTMLWidgets.dataframeToD3 = function(df) {
    var names = [];
    var length;
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            item[names[col]] = df[names[col]][row];
        }
        results.push(item);
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    return results;
  };

  window.HTMLWidgets.transposeArray2D = function(array) {
      if (array.length === 0) return array;
      var newArray = array[0].map(function(col, i) {
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        currentResult += value[pos];
        escapeMode = false;
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    }
    if (currentResult !== "") {
      results.push(currentResult);
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    return results;
  }
  // Function authored by Yihui/JJ Allaire
  window.HTMLWidgets.evaluateStringMember = function(o, member) {
    var parts = splitWithEscape(member, '.', '\\');
    for (var i = 0, l = parts.length; i < l; i++) {
      var part = parts[i];
      // part may be a character or 'numeric' member name
      if (o !== null && typeof o === "object" && part in o) {
        if (i == (l - 1)) { // if we are at the end of the line then evalulate
          if (typeof o[part] === "string")
            o[part] = tryEval(o[part]);
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  };

  // Retrieve the HTMLWidget instance (i.e. the return value of an
  // HTMLWidget binding's initialize() or factory() function)
  // associated with an element, or null if none.
  window.HTMLWidgets.getInstance = function(el) {
    return elementData(el, "init_result");
  };

  // Finds the first element in the scope that matches the selector,
  // and returns the HTMLWidget instance (i.e. the return value of
  // an HTMLWidget binding's initialize() or factory() function)
  // associated with that element, if any. If no element matches the
  // selector, or the first matching element has no HTMLWidget
  // instance associated with it, then null is returned.
  //
  // The scope argument is optional, and defaults to window.document.
  window.HTMLWidgets.find = function(scope, selector) {
    if (arguments.length == 1) {
      selector = scope;
      scope = document;
    }

    var el = scope.querySelector(selector);
    if (el === null) {
      return null;
    } else {
      return window.HTMLWidgets.getInstance(el);
    }
  };

  // Finds all elements in the scope that match the selector, and
  // returns the HTMLWidget instances (i.e. the return values of
  // an HTMLWidget binding's initialize() or factory() function)
  // associated with the elements, in an array. If elements that
  // match the selector don't have an associated HTMLWidget
  // instance, the returned array will contain nulls.
  //
  // The scope argument is optional, and defaults to window.document.
  window.HTMLWidgets.findAll = function(scope, selector) {
    if (arguments.length == 1) {
      selector = scope;
      scope = document;
    }

    var nodes = scope.querySelectorAll(selector);
    var results = [];
    for (var i = 0; i < nodes.length; i++) {
      results.push(window.HTMLWidgets.getInstance(nodes[i]));
    }
    return results;
  };

  var postRenderHandlers = [];
  function invokePostRenderHandlers() {
    while (postRenderHandlers.length) {
      var handler = postRenderHandlers.shift();
      if (handler) {
        handler();
      }
    }
  }

  // Register the given callback function to be invoked after the
  // next time static widgets are rendered.
  window.HTMLWidgets.addPostRenderHandler = function(callback) {
    postRenderHandlers.push(callback);
  };

  // Takes a new-style instance-bound definition, and returns an
  // old-style class-bound definition. This saves us from having
  // to rewrite all the logic in this file to accomodate both
  // types of definitions.
  function createLegacyDefinitionAdapter(defn) {
    var result = {
      name: defn.name,
      type: defn.type,
      initialize: function(el, width, height) {
        return defn.factory(el, width, height);
      },
      renderValue: function(el, x, instance) {
        return instance.renderValue(x);
      },
      resize: function(el, width, height, instance) {
        return instance.resize(width, height);
      }
    };

    if (defn.find)
      result.find = defn.find;
    if (defn.renderError)
      result.renderError = defn.renderError;
    if (defn.clearError)
      result.clearError = defn.clearError;

    return result;
  }
})();

</script>
<script>
HTMLWidgets.widget({
  name: "plotly",
  type: "output",

  initialize: function(el, width, height) {
    return {};
  },

  resize: function(el, width, height, instance) {
    if (instance.autosize) {
      var width = instance.width || width;
      var height = instance.height || height;
      Plotly.relayout(el.id, {width: width, height: height});
    }
  },  
  
  renderValue: function(el, x, instance) {
    
    // Plotly.relayout() mutates the plot input object, so make sure to 
    // keep a reference to the user-supplied width/height *before*
    // we call Plotly.plot();
    var lay = x.layout || {};
    instance.width = lay.width;
    instance.height = lay.height;
    instance.autosize = lay.autosize || true;
    
    /* 
    / 'inform the world' about highlighting options this is so other
    / crosstalk libraries have a chance to respond to special settings 
    / such as persistent selection. 
    / AFAIK, leaflet is the only library with such intergration
    / https://github.com/rstudio/leaflet/pull/346/files#diff-ad0c2d51ce5fdf8c90c7395b102f4265R154
    */
    var ctConfig = crosstalk.var('plotlyCrosstalkOpts').set(x.highlight);
      
    if (typeof(window) !== "undefined") {
      // make sure plots don't get created outside the network (for on-prem)
      window.PLOTLYENV = window.PLOTLYENV || {};
      window.PLOTLYENV.BASE_URL = x.base_url;
      
      // Enable persistent selection when shift key is down
      // https://stackoverflow.com/questions/1828613/check-if-a-key-is-down
      var persistOnShift = function(e) {
        if (!e) window.event;
        if (e.shiftKey) { 
          x.highlight.persistent = true; 
          x.highlight.persistentShift = true;
        } else {
          x.highlight.persistent = false; 
          x.highlight.persistentShift = false;
        }
      };
      
      // Only relevant if we haven't forced persistent mode at command line
      if (!x.highlight.persistent) {
        window.onmousemove = persistOnShift;
      }
    }

    var graphDiv = document.getElementById(el.id);
    
    // TODO: move the control panel injection strategy inside here...
    HTMLWidgets.addPostRenderHandler(function() {
      
      // lower the z-index of the modebar to prevent it from highjacking hover
      // (TODO: do this via CSS?)
      // https://github.com/ropensci/plotly/issues/956
      // https://www.w3schools.com/jsref/prop_style_zindex.asp
      var modebars = document.querySelectorAll(".js-plotly-plot .plotly .modebar");
      for (var i = 0; i < modebars.length; i++) {
        modebars[i].style.zIndex = 1;
      }
    });
      
      // inject a "control panel" holding selectize/dynamic color widget(s)
    if ((x.selectize || x.highlight.dynamic) && !instance.plotly) {
      var flex = document.createElement("div");
      flex.class = "plotly-crosstalk-control-panel";
      flex.style = "display: flex; flex-wrap: wrap";
      
      // inject the colourpicker HTML container into the flexbox
      if (x.highlight.dynamic) {
        var pickerDiv = document.createElement("div");
        
        var pickerInput = document.createElement("input");
        pickerInput.id = el.id + "-colourpicker";
        pickerInput.placeholder = "asdasd";
        
        var pickerLabel = document.createElement("label");
        pickerLabel.for = pickerInput.id;
        pickerLabel.innerHTML = "Brush color&nbsp;&nbsp;";
        
        pickerDiv.appendChild(pickerLabel);
        pickerDiv.appendChild(pickerInput);
        flex.appendChild(pickerDiv);
      }
      
      // inject selectize HTML containers (one for every crosstalk group)
      if (x.selectize) {
        var ids = Object.keys(x.selectize);
        
        for (var i = 0; i < ids.length; i++) {
          var container = document.createElement("div");
          container.id = ids[i];
          container.style = "width: 80%; height: 10%";
          container.class = "form-group crosstalk-input-plotly-highlight";
          
          var label = document.createElement("label");
          label.for = ids[i];
          label.innerHTML = x.selectize[ids[i]].group;
          label.class = "control-label";
          
          var selectDiv = document.createElement("div");
          var select = document.createElement("select");
          select.multiple = true;
          
          selectDiv.appendChild(select);
          container.appendChild(label);
          container.appendChild(selectDiv);
          flex.appendChild(container);
        }
      }
      
      // finally, insert the flexbox inside the htmlwidget container,
      // but before the plotly graph div
      graphDiv.parentElement.insertBefore(flex, graphDiv);
      
      if (x.highlight.dynamic) {
        var picker = $("#" + pickerInput.id);
        var colors = x.highlight.color || [];
        // TODO: let users specify options?
        var opts = {
          value: colors[0],
          showColour: "both",
          palette: "limited",
          allowedCols: colors.join(" "),
          width: "20%",
          height: "10%"
        };
        picker.colourpicker({changeDelay: 0});
        picker.colourpicker("settings", opts);
        picker.colourpicker("value", opts.value);
        // inform crosstalk about a change in the current selection colour
        var grps = x.highlight.ctGroups || [];
        for (var i = 0; i < grps.length; i++) {
          crosstalk.group(grps[i]).var('plotlySelectionColour')
            .set(picker.colourpicker('value'));
        }
        picker.on("change", function() {
          for (var i = 0; i < grps.length; i++) {
            crosstalk.group(grps[i]).var('plotlySelectionColour')
              .set(picker.colourpicker('value'));
          }
        });
      }
    }
    
    // if no plot exists yet, create one with a particular configuration
    if (!instance.plotly) {
      
      var plot = Plotly.newPlot(graphDiv, x);
      instance.plotly = true;
      
    } else if (x.layout.transition) {
      
      var plot = Plotly.react(graphDiv, x);
    
    } else {
      
      // this is essentially equivalent to Plotly.newPlot(), but avoids creating 
      // a new webgl context
      // https://github.com/plotly/plotly.js/blob/2b24f9def901831e61282076cf3f835598d56f0e/src/plot_api/plot_api.js#L531-L532

      // TODO: restore crosstalk selections?
      Plotly.purge(graphDiv);
      // TODO: why is this necessary to get crosstalk working?
      graphDiv.data = undefined;
      graphDiv.layout = undefined;
      var plot = Plotly.newPlot(graphDiv, x);
    }
    
    // Trigger plotly.js calls defined via `plotlyProxy()`
    plot.then(function() {
      if (HTMLWidgets.shinyMode) {
        Shiny.addCustomMessageHandler("plotly-calls", function(msg) {
          var gd = document.getElementById(msg.id);
          if (!gd) {
            throw new Error("Couldn't find plotly graph with id: " + msg.id);
          }
          // This isn't an official plotly.js method, but it's the only current way to 
          // change just the configuration of a plot 
          // https://community.plot.ly/t/update-config-function/9057
          if (msg.method == "reconfig") {
            Plotly.react(gd, gd.data, gd.layout, msg.args);
            return;
          }
          if (!Plotly[msg.method]) {
            throw new Error("Unknown method " + msg.method);
          }
          var args = [gd].concat(msg.args);
          Plotly[msg.method].apply(null, args);
        });
      }
      
      // plotly's mapbox API doesn't currently support setting bounding boxes
      // https://www.mapbox.com/mapbox-gl-js/example/fitbounds/
      // so we do this manually...
      // TODO: make sure this triggers on a redraw and relayout as well as on initial draw
      var mapboxIDs = graphDiv._fullLayout._subplots.mapbox || [];
      for (var i = 0; i < mapboxIDs.length; i++) {
        var id = mapboxIDs[i];
        var mapOpts = x.layout[id] || {};
        var args = mapOpts._fitBounds || {};
        if (!args) {
          continue;
        }
        var mapObj = graphDiv._fullLayout[id]._subplot.map;
        mapObj.fitBounds(args.bounds, args.options);
      }
      
    });
    
    // Attach attributes (e.g., "key", "z") to plotly event data
    function eventDataWithKey(eventData) {
      if (eventData === undefined || !eventData.hasOwnProperty("points")) {
        return null;
      }
      return eventData.points.map(function(pt) {
        var obj = {
          curveNumber: pt.curveNumber, 
          pointNumber: pt.pointNumber, 
          x: pt.x,
          y: pt.y
        };
        
        // If 'z' is reported with the event data, then use it!
        if (pt.hasOwnProperty("z")) {
          obj.z = pt.z;
        }
        
        if (pt.hasOwnProperty("customdata")) {
          obj.customdata = pt.customdata;
        }
        
        /* 
          TL;DR: (I think) we have to select the graph div (again) to attach keys...
          
          Why? Remember that crosstalk will dynamically add/delete traces 
          (see traceManager.prototype.updateSelection() below)
          For this reason, we can't simply grab keys from x.data (like we did previously)
          Moreover, we can't use _fullData, since that doesn't include 
          unofficial attributes. It's true that click/hover events fire with 
          pt.data, but drag events don't...
        */
        var gd = document.getElementById(el.id);
        var trace = gd.data[pt.curveNumber];
        
        if (!trace._isSimpleKey) {
          var attrsToAttach = ["key"];
        } else {
          // simple keys fire the whole key
          obj.key = trace.key;
          var attrsToAttach = [];
        }
        
        for (var i = 0; i < attrsToAttach.length; i++) {
          var attr = trace[attrsToAttach[i]];
          if (Array.isArray(attr)) {
            if (typeof pt.pointNumber === "number") {
              obj[attrsToAttach[i]] = attr[pt.pointNumber];
            } else if (Array.isArray(pt.pointNumber)) {
              obj[attrsToAttach[i]] = attr[pt.pointNumber[0]][pt.pointNumber[1]];
            } else if (Array.isArray(pt.pointNumbers)) {
              obj[attrsToAttach[i]] = pt.pointNumbers.map(function(idx) { return attr[idx]; });
            }
          }
        }
        return obj;
      });
    }
    
    
    var legendEventData = function(d) {
      // if legendgroup is not relevant just return the trace
      var trace = d.data[d.curveNumber];
      if (!trace.legendgroup) return trace;
      
      // if legendgroup was specified, return all traces that match the group
      var legendgrps = d.data.map(function(trace){ return trace.legendgroup; });
      var traces = [];
      for (i = 0; i < legendgrps.length; i++) {
        if (legendgrps[i] == trace.legendgroup) {
          traces.push(d.data[i]);
        }
      }
      
      return traces;
    };

    
    // send user input event data to shiny
    if (HTMLWidgets.shinyMode && Shiny.setInputValue) {
      
      // Some events clear other input values
      // TODO: always register these?
      var eventClearMap = {
        plotly_deselect: ["plotly_selected", "plotly_selecting", "plotly_brushed", "plotly_brushing", "plotly_click"],
        plotly_unhover: ["plotly_hover"],
        plotly_doubleclick: ["plotly_click"]
      };
    
      Object.keys(eventClearMap).map(function(evt) {
        graphDiv.on(evt, function() {
          var inputsToClear = eventClearMap[evt];
          inputsToClear.map(function(input) {
            Shiny.setInputValue(input + "-" + x.source, null, {priority: "event"});
          });
        });
      });
      
      var eventDataFunctionMap = {
        plotly_click: eventDataWithKey,
        plotly_sunburstclick: eventDataWithKey,
        plotly_hover: eventDataWithKey,
        plotly_unhover: eventDataWithKey,
        // If 'plotly_selected' has already been fired, and you click
        // on the plot afterwards, this event fires `undefined`?!?
        // That might be considered a plotly.js bug, but it doesn't make 
        // sense for this input change to occur if `d` is falsy because,
        // even in the empty selection case, `d` is truthy (an object),
        // and the 'plotly_deselect' event will reset this input
        plotly_selected: function(d) { if (d) { return eventDataWithKey(d); } },
        plotly_selecting: function(d) { if (d) { return eventDataWithKey(d); } },
        plotly_brushed: function(d) {
          if (d) { return d.range ? d.range : d.lassoPoints; }
        },
        plotly_brushing: function(d) {
          if (d) { return d.range ? d.range : d.lassoPoints; }
        },
        plotly_legendclick: legendEventData,
        plotly_legenddoubleclick: legendEventData,
        plotly_clickannotation: function(d) { return d.fullAnnotation }
      };
      
      var registerShinyValue = function(event) {
        var eventDataPreProcessor = eventDataFunctionMap[event] || function(d) { return d ? d : el.id };
        // some events are unique to the R package
        var plotlyJSevent = (event == "plotly_brushed") ? "plotly_selected" : (event == "plotly_brushing") ? "plotly_selecting" : event;
        // register the event
        graphDiv.on(plotlyJSevent, function(d) {
          Shiny.setInputValue(
            event + "-" + x.source,
            JSON.stringify(eventDataPreProcessor(d)),
            {priority: "event"}
          );
        });
      }
    
      var shinyEvents = x.shinyEvents || [];
      shinyEvents.map(registerShinyValue);
    }
    
    // Given an array of {curveNumber: x, pointNumber: y} objects,
    // return a hash of {
    //   set1: {value: [key1, key2, ...], _isSimpleKey: false}, 
    //   set2: {value: [key3, key4, ...], _isSimpleKey: false}
    // }
    function pointsToKeys(points) {
      var keysBySet = {};
      for (var i = 0; i < points.length; i++) {
        
        var trace = graphDiv.data[points[i].curveNumber];
        if (!trace.key || !trace.set) {
          continue;
        }
        
        // set defaults for this keySet
        // note that we don't track the nested property (yet) since we always 
        // emit the union -- http://cpsievert.github.io/talks/20161212b/#21
        keysBySet[trace.set] = keysBySet[trace.set] || {
          value: [],
          _isSimpleKey: trace._isSimpleKey
        };
        
        // Use pointNumber by default, but aggregated traces should emit pointNumbers
        var ptNum = points[i].pointNumber;
        var hasPtNum = typeof ptNum === "number";
        var ptNum = hasPtNum ? ptNum : points[i].pointNumbers;
        
        // selecting a point of a "simple" trace means: select the 
        // entire key attached to this trace, which is useful for,
        // say clicking on a fitted line to select corresponding observations 
        var key = trace._isSimpleKey ? trace.key : Array.isArray(ptNum) ? ptNum.map(function(idx) { return trace.key[idx]; }) : trace.key[ptNum];
        // http://stackoverflow.com/questions/10865025/merge-flatten-an-array-of-arrays-in-javascript
        var keyFlat = trace._isNestedKey ? [].concat.apply([], key) : key;
        
        // TODO: better to only add new values?
        keysBySet[trace.set].value = keysBySet[trace.set].value.concat(keyFlat);
      }
      
      return keysBySet;
    }
    
    
    x.highlight.color = x.highlight.color || [];
    // make sure highlight color is an array
    if (!Array.isArray(x.highlight.color)) {
      x.highlight.color = [x.highlight.color];
    }

    var traceManager = new TraceManager(graphDiv, x.highlight);

    // Gather all *unique* sets.
    var allSets = [];
    for (var curveIdx = 0; curveIdx < x.data.length; curveIdx++) {
      var newSet = x.data[curveIdx].set;
      if (newSet) {
        if (allSets.indexOf(newSet) === -1) {
          allSets.push(newSet);
        }
      }
    }

    // register event listeners for all sets
    for (var i = 0; i < allSets.length; i++) {
      
      var set = allSets[i];
      var selection = new crosstalk.SelectionHandle(set);
      var filter = new crosstalk.FilterHandle(set);
      
      var filterChange = function(e) {
        removeBrush(el);
        traceManager.updateFilter(set, e.value);
      };
      filter.on("change", filterChange);
      
      
      var selectionChange = function(e) {
        
        // Workaround for 'plotly_selected' now firing previously selected
        // points (in addition to new ones) when holding shift key. In our case,
        // we just want the new keys 
        if (x.highlight.on === "plotly_selected" && x.highlight.persistentShift) {
          // https://stackoverflow.com/questions/1187518/how-to-get-the-difference-between-two-arrays-in-javascript
          Array.prototype.diff = function(a) {
              return this.filter(function(i) {return a.indexOf(i) < 0;});
          };
          e.value = e.value.diff(e.oldValue);
        }
        
        // array of "event objects" tracking the selection history
        // this is used to avoid adding redundant selections
        var selectionHistory = crosstalk.var("plotlySelectionHistory").get() || [];
        
        // Construct an event object "defining" the current event. 
        var event = {
          receiverID: traceManager.gd.id,
          plotlySelectionColour: crosstalk.group(set).var("plotlySelectionColour").get()
        };
        event[set] = e.value;
        // TODO: is there a smarter way to check object equality?
        if (selectionHistory.length > 0) {
          var ev = JSON.stringify(event);
          for (var i = 0; i < selectionHistory.length; i++) {
            var sel = JSON.stringify(selectionHistory[i]);
            if (sel == ev) {
              return;
            }
          }
        }
        
        // accumulate history for persistent selection
        if (!x.highlight.persistent) {
          selectionHistory = [event];
        } else {
          selectionHistory.push(event);
        }
        crosstalk.var("plotlySelectionHistory").set(selectionHistory);
        
        // do the actual updating of traces, frames, and the selectize widget
        traceManager.updateSelection(set, e.value);
        // https://github.com/selectize/selectize.js/blob/master/docs/api.md#methods_items
        if (x.selectize) {
          if (!x.highlight.persistent || e.value === null) {
            selectize.clear(true);
          }
          selectize.addItems(e.value, true);
          selectize.close();
        }
      }
      selection.on("change", selectionChange);
      
      // Set a crosstalk variable selection value, triggering an update
      var turnOn = function(e) {
        if (e) {
          var selectedKeys = pointsToKeys(e.points);
          // Keys are group names, values are array of selected keys from group.
          for (var set in selectedKeys) {
            if (selectedKeys.hasOwnProperty(set)) {
              selection.set(selectedKeys[set].value, {sender: el});
            }
          }
        }
      };
      if (x.highlight.debounce > 0) {
        turnOn = debounce(turnOn, x.highlight.debounce);
      }
      graphDiv.on(x.highlight.on, turnOn);
      
      graphDiv.on(x.highlight.off, function turnOff(e) {
        // remove any visual clues
        removeBrush(el);
        // remove any selection history
        crosstalk.var("plotlySelectionHistory").set(null);
        // trigger the actual removal of selection traces
        selection.set(null, {sender: el});
      });
          
      // register a callback for selectize so that there is bi-directional
      // communication between the widget and direct manipulation events
      if (x.selectize) {
        var selectizeID = Object.keys(x.selectize)[i];
        var items = x.selectize[selectizeID].items;
        var first = [{value: "", label: "(All)"}];
        var opts = {
          options: first.concat(items),
          searchField: "label",
          valueField: "value",
          labelField: "label",
          maxItems: 50
        };
        var select = $("#" + selectizeID).find("select")[0];
        var selectize = $(select).selectize(opts)[0].selectize;
        // NOTE: this callback is triggered when *directly* altering 
        // dropdown items
        selectize.on("change", function() {
          var currentItems = traceManager.groupSelections[set] || [];
          if (!x.highlight.persistent) {
            removeBrush(el);
            for (var i = 0; i < currentItems.length; i++) {
              selectize.removeItem(currentItems[i], true);
            }
          }
          var newItems = selectize.items.filter(function(idx) { 
            return currentItems.indexOf(idx) < 0;
          });
          if (newItems.length > 0) {
            traceManager.updateSelection(set, newItems);
          } else {
            // Item has been removed...
            // TODO: this logic won't work for dynamically changing palette 
            traceManager.updateSelection(set, null);
            traceManager.updateSelection(set, selectize.items);
          }
        });
      }
    } // end of selectionChange
    
  } // end of renderValue
}); // end of widget definition

/**
 * @param graphDiv The Plotly graph div
 * @param highlight An object with options for updating selection(s)
 */
function TraceManager(graphDiv, highlight) {
  // The Plotly graph div
  this.gd = graphDiv;

  // Preserve the original data.
  // TODO: try using Lib.extendFlat() as done in  
  // https://github.com/plotly/plotly.js/pull/1136 
  this.origData = JSON.parse(JSON.stringify(graphDiv.data));
  
  // avoid doing this over and over
  this.origOpacity = [];
  for (var i = 0; i < this.origData.length; i++) {
    this.origOpacity[i] = this.origData[i].opacity === 0 ? 0 : (this.origData[i].opacity || 1);
  }

  // key: group name, value: null or array of keys representing the
  // most recently received selection for that group.
  this.groupSelections = {};
  
  // selection parameters (e.g., transient versus persistent selection)
  this.highlight = highlight;
}

TraceManager.prototype.close = function() {
  // TODO: Unhook all event handlers
};

TraceManager.prototype.updateFilter = function(group, keys) {

  if (typeof(keys) === "undefined" || keys === null) {
    
    this.gd.data = JSON.parse(JSON.stringify(this.origData));
    
  } else {
  
    var traces = [];
    for (var i = 0; i < this.origData.length; i++) {
      var trace = this.origData[i];
      if (!trace.key || trace.set !== group) {
        continue;
      }
      var matchFunc = getMatchFunc(trace);
      var matches = matchFunc(trace.key, keys);
      
      if (matches.length > 0) {
        if (!trace._isSimpleKey) {
          // subsetArrayAttrs doesn't mutate trace (it makes a modified clone)
          trace = subsetArrayAttrs(trace, matches);
        }
        traces.push(trace);
      }
    }
  }
  
  this.gd.data = traces;
  Plotly.redraw(this.gd);
  
  // NOTE: we purposely do _not_ restore selection(s), since on filter,
  // axis likely will update, changing the pixel -> data mapping, leading 
  // to a likely mismatch in the brush outline and highlighted marks
  
};

TraceManager.prototype.updateSelection = function(group, keys) {
  
  if (keys !== null && !Array.isArray(keys)) {
    throw new Error("Invalid keys argument; null or array expected");
  }
  
  // if selection has been cleared, or if this is transient
  // selection, delete the "selection traces"
  var nNewTraces = this.gd.data.length - this.origData.length;
  if (keys === null || !this.highlight.persistent && nNewTraces > 0) {
    var tracesToRemove = [];
    for (var i = 0; i < this.gd.data.length; i++) {
      if (this.gd.data[i]._isCrosstalkTrace) tracesToRemove.push(i);
    }
    Plotly.deleteTraces(this.gd, tracesToRemove);
    this.groupSelections[group] = keys;
  } else {
    // add to the groupSelection, rather than overwriting it
    // TODO: can this be removed?
    this.groupSelections[group] = this.groupSelections[group] || [];
    for (var i = 0; i < keys.length; i++) {
      var k = keys[i];
      if (this.groupSelections[group].indexOf(k) < 0) {
        this.groupSelections[group].push(k);
      }
    }
  }
  
  if (keys === null) {
    
    Plotly.restyle(this.gd, {"opacity": this.origOpacity});
    
  } else if (keys.length >= 1) {
    
    // placeholder for new "selection traces"
    var traces = [];
    // this variable is set in R/highlight.R
    var selectionColour = crosstalk.group(group).var("plotlySelectionColour").get() || 
      this.highlight.color[0];

    for (var i = 0; i < this.origData.length; i++) {
      // TODO: try using Lib.extendFlat() as done in  
      // https://github.com/plotly/plotly.js/pull/1136 
      var trace = JSON.parse(JSON.stringify(this.gd.data[i]));
      if (!trace.key || trace.set !== group) {
        continue;
      }
      // Get sorted array of matching indices in trace.key
      var matchFunc = getMatchFunc(trace);
      var matches = matchFunc(trace.key, keys);
      
      if (matches.length > 0) {
        // If this is a "simple" key, that means select the entire trace
        if (!trace._isSimpleKey) {
          trace = subsetArrayAttrs(trace, matches);
        }
        // reach into the full trace object so we can properly reflect the 
        // selection attributes in every view
        var d = this.gd._fullData[i];
        
        /* 
        / Recursively inherit selection attributes from various sources, 
        / in order of preference:
        /  (1) official plotly.js selected attribute
        /  (2) highlight(selected = attrs_selected(...))
        */
        // TODO: it would be neat to have a dropdown to dynamically specify these!
        $.extend(true, trace, this.highlight.selected);
        
        // if it is defined, override color with the "dynamic brush color""
        if (d.marker) {
          trace.marker = trace.marker || {};
          trace.marker.color =  selectionColour || trace.marker.color || d.marker.color;
        }
        if (d.line) {
          trace.line = trace.line || {};
          trace.line.color =  selectionColour || trace.line.color || d.line.color;
        }
        if (d.textfont) {
          trace.textfont = trace.textfont || {};
          trace.textfont.color =  selectionColour || trace.textfont.color || d.textfont.color;
        }
        if (d.fillcolor) {
          // TODO: should selectionColour inherit alpha from the existing fillcolor?
          trace.fillcolor = selectionColour || trace.fillcolor || d.fillcolor;
        }
        // attach a sensible name/legendgroup
        trace.name = trace.name || keys.join("<br />");
        trace.legendgroup = trace.legendgroup || keys.join("<br />");
        
        // keep track of mapping between this new trace and the trace it targets
        // (necessary for updating frames to reflect the selection traces)
        trace._originalIndex = i;
        trace._newIndex = this.gd._fullData.length + traces.length;
        trace._isCrosstalkTrace = true;
        traces.push(trace);
      }
    }
    
    if (traces.length > 0) {
      
      Plotly.addTraces(this.gd, traces).then(function(gd) {
        // incrementally add selection traces to frames
        // (this is heavily inspired by Plotly.Plots.modifyFrames() 
        // in src/plots/plots.js)
        var _hash = gd._transitionData._frameHash;
        var _frames = gd._transitionData._frames || [];
        
        for (var i = 0; i < _frames.length; i++) {
          
          // add to _frames[i].traces *if* this frame references selected trace(s)
          var newIndices = [];
          for (var j = 0; j < traces.length; j++) {
            var tr = traces[j];
            if (_frames[i].traces.indexOf(tr._originalIndex) > -1) {
              newIndices.push(tr._newIndex);
              _frames[i].traces.push(tr._newIndex);
            }
          }
          
          // nothing to do...
          if (newIndices.length === 0) {
            continue;
          }
          
          var ctr = 0;
          var nFrameTraces = _frames[i].data.length;
          
          for (var j = 0; j < nFrameTraces; j++) {
            var frameTrace = _frames[i].data[j];
            if (!frameTrace.key || frameTrace.set !== group) {
              continue;
            }
            
            var matchFunc = getMatchFunc(frameTrace);
            var matches = matchFunc(frameTrace.key, keys);
            
            if (matches.length > 0) {
              if (!trace._isSimpleKey) {
                frameTrace = subsetArrayAttrs(frameTrace, matches);
              }
              var d = gd._fullData[newIndices[ctr]];
              if (d.marker) {
                frameTrace.marker = d.marker;
              }
              if (d.line) {
                frameTrace.line = d.line;
              }
              if (d.textfont) {
                frameTrace.textfont = d.textfont;
              }
              ctr = ctr + 1;
              _frames[i].data.push(frameTrace);
            }
          }
          
          // update gd._transitionData._frameHash
          _hash[_frames[i].name] = _frames[i];
        }
      
      });
      
      // dim traces that have a set matching the set of selection sets
      var tracesToDim = [],
          opacities = [],
          sets = Object.keys(this.groupSelections),
          n = this.origData.length;
          
      for (var i = 0; i < n; i++) {
        var opacity = this.origOpacity[i] || 1;
        // have we already dimmed this trace? Or is this even worth doing?
        if (opacity !== this.gd._fullData[i].opacity || this.highlight.opacityDim === 1) {
          continue;
        }
        // is this set an element of the set of selection sets?
        var matches = findMatches(sets, [this.gd.data[i].set]);
        if (matches.length) {
          tracesToDim.push(i);
          opacities.push(opacity * this.highlight.opacityDim);
        }
      }
      
      if (tracesToDim.length > 0) {
        Plotly.restyle(this.gd, {"opacity": opacities}, tracesToDim);
        // turn off the selected/unselected API
        Plotly.restyle(this.gd, {"selectedpoints": null});
      }
      
    }
    
  }
};

/* 
Note: in all of these match functions, we assume needleSet (i.e. the selected keys)
is a 1D (or flat) array. The real difference is the meaning of haystack.
findMatches() does the usual thing you'd expect for 
linked brushing on a scatterplot matrix. findSimpleMatches() returns a match iff 
haystack is a subset of the needleSet. findNestedMatches() returns 
*/

function getMatchFunc(trace) {
  return (trace._isNestedKey) ? findNestedMatches : 
    (trace._isSimpleKey) ? findSimpleMatches : findMatches;
}

// find matches for "flat" keys
function findMatches(haystack, needleSet) {
  var matches = [];
  haystack.forEach(function(obj, i) {
    if (obj === null || needleSet.indexOf(obj) >= 0) {
      matches.push(i);
    }
  });
  return matches;
}

// find matches for "simple" keys
function findSimpleMatches(haystack, needleSet) {
  var match = haystack.every(function(val) {
    return val === null || needleSet.indexOf(val) >= 0;
  });
  // yes, this doesn't make much sense other than conforming 
  // to the output type of the other match functions
  return (match) ? [0] : []
}

// find matches for a "nested" haystack (2D arrays)
function findNestedMatches(haystack, needleSet) {
  var matches = [];
  for (var i = 0; i < haystack.length; i++) {
    var hay = haystack[i];
    var match = hay.every(function(val) { 
      return val === null || needleSet.indexOf(val) >= 0; 
    });
    if (match) {
      matches.push(i);
    }
  }
  return matches;
}

function isPlainObject(obj) {
  return (
    Object.prototype.toString.call(obj) === '[object Object]' &&
    Object.getPrototypeOf(obj) === Object.prototype
  );
}

function subsetArrayAttrs(obj, indices) {
  var newObj = {};
  Object.keys(obj).forEach(function(k) {
    var val = obj[k];

    if (k.charAt(0) === "_") {
      newObj[k] = val;
    } else if (k === "transforms" && Array.isArray(val)) {
      newObj[k] = val.map(function(transform) {
        return subsetArrayAttrs(transform, indices);
      });
    } else if (k === "colorscale" && Array.isArray(val)) {
      newObj[k] = val;
    } else if (isPlainObject(val)) {
      newObj[k] = subsetArrayAttrs(val, indices);
    } else if (Array.isArray(val)) {
      newObj[k] = subsetArray(val, indices);
    } else {
      newObj[k] = val;
    }
  });
  return newObj;
}

function subsetArray(arr, indices) {
  var result = [];
  for (var i = 0; i < indices.length; i++) {
    result.push(arr[indices[i]]);
  }
  return result;
}

// Convenience function for removing plotly's brush 
function removeBrush(el) {
  var outlines = el.querySelectorAll(".select-outline");
  for (var i = 0; i < outlines.length; i++) {
    outlines[i].remove();
  }
}


// https://davidwalsh.name/javascript-debounce-function

// Returns a function, that, as long as it continues to be invoked, will not
// be triggered. The function will be called after it stops being called for
// N milliseconds. If `immediate` is passed, trigger the function on the
// leading edge, instead of the trailing.
function debounce(func, wait, immediate) {
	var timeout;
	return function() {
		var context = this, args = arguments;
		var later = function() {
			timeout = null;
			if (!immediate) func.apply(context, args);
		};
		var callNow = immediate && !timeout;
		clearTimeout(timeout);
		timeout = setTimeout(later, wait);
		if (callNow) func.apply(context, args);
	};
};
</script>
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u=l>>>26,f=67108863&l,h=Math.min(c,e.length-1),p=Math.max(0,c-t.length+1);p<=h;p++){var d=c-p|0;u+=(o=(i=0|t.words[d])*(a=0|e.words[p])+f)/67108864|0,f=67108863&o}r.words[c]=0|f,l=0|u}return 0!==l?r.words[c]=0|l:r.length--,r.strip()}a.prototype.toString=function(t,e){var r;if(e=0|e||1,16===(t=t||10)||"hex"===t){r="";for(var i=0,a=0,o=0;o<this.length;o++){var s=this.words[o],l=(16777215&(s<<i|a)).toString(16);r=0!==(a=s>>>24-i&16777215)||o!==this.length-1?u[6-l.length]+l+r:l+r,(i+=2)>=26&&(i-=26,o--)}for(0!==a&&(r=a.toString(16)+r);r.length%e!=0;)r="0"+r;return 0!==this.negative&&(r="-"+r),r}if(t===(0|t)&&t>=2&&t<=36){var c=f[t],p=h[t];r="";var d=this.clone();for(d.negative=0;!d.isZero();){var m=d.modn(p).toString(t);r=(d=d.idivn(p)).isZero()?m+r:u[c-m.length]+m+r}for(this.isZero()&&(r="0"+r);r.length%e!=0;)r="0"+r;return 0!==this.negative&&(r="-"+r),r}n(!1,"Base should be between 2 and 36")},a.prototype.toNumber=function(){var t=this.words[0];return 2===this.length?t+=67108864*this.words[1]:3===this.length&&1===this.words[2]?t+=4503599627370496+67108864*this.words[1]:this.length>2&&n(!1,"Number can only safely store up to 53 bits"),0!==this.negative?-t:t},a.prototype.toJSON=function(){return this.toString(16)},a.prototype.toBuffer=function(t,e){return n(void 0!==o),this.toArrayLike(o,t,e)},a.prototype.toArray=function(t,e){return this.toArrayLike(Array,t,e)},a.prototype.toArrayLike=function(t,e,r){var i=this.byteLength(),a=r||Math.max(1,i);n(i<=a,"byte array longer than desired length"),n(a>0,"Requested array length <= 0"),this.strip();var o,s,l="le"===e,c=new t(a),u=this.clone();if(l){for(s=0;!u.isZero();s++)o=u.andln(255),u.iushrn(8),c[s]=o;for(;s<a;s++)c[s]=0}else{for(s=0;s<a-i;s++)c[s]=0;for(s=0;!u.isZero();s++)o=u.andln(255),u.iushrn(8),c[a-s-1]=o}return c},Math.clz32?a.prototype._countBits=function(t){return 32-Math.clz32(t)}:a.prototype._countBits=function(t){var e=t,r=0;return 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e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n("number"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n("number"==typeof t&&t>=0);var r=t/26|0,i=t%26;return 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0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,m=p>>>13,g=0|o[2],v=8191&g,y=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,mt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(m,U)|0,a=Math.imul(m,V);var vt=(c+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,q)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,$)|0,a=a+Math.imul(m,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,q)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,q)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var At=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,mt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,mt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,mt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,mt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,mt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=gt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function m(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?p(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):m(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],m=n[u+p],g=r[u+p+o],v=n[u+p+o],y=f*g-h*v;v=f*v+h*g,g=y,r[u+p]=d+g,n[u+p]=m+v,r[u+p+o]=d-g,n[u+p+o]=m-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),m(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n("number"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n("number"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n("number"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,"imaskn works only with positive numbers"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n("number"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n("number"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if("mod"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),"div"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),"mod"!==e&&(i=s.div.neg()),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),"mod"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?"div"===e?{div:this.divn(t.words[0]),mod:null}:"mod"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,"div",!1).div},a.prototype.mod=function(t){return this.divmod(t,"mod",!1).mod},a.prototype.umod=function(t){return this.divmod(t,"mod",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n("number"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,"Number is too big");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,"Already a number in reduction context"),n(0===this.negative,"red works only with positives"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,"fromRed works only with numbers in reduction context"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,"Already a number in reduction context"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,"redAdd works only with red numbers"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,"redIAdd works only with red numbers"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,"redSub works only with red numbers"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,"redISub works only with red numbers"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,"redShl works only with red numbers"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,"redMul works only with red numbers"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,"redMul works only with red numbers"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,"redSqr works only with red numbers"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,"redISqr works only with red numbers"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,"redSqrt works only with red numbers"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,"redInvm works only with red numbers"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,"redNeg works only with red numbers"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,"redPow(normalNum)"),this.red._verify1(this),this.red.pow(this,t)};var v={k256:null,p224:null,p192:null,p25519:null};function y(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){y.call(this,"k256","ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f")}function b(){y.call(this,"p224","ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001")}function _(){y.call(this,"p192","ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff")}function w(){y.call(this,"25519","7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed")}function T(t){if("string"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),"modulus must be greater than 1"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}y.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},y.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},y.prototype.split=function(t,e){t.iushrn(this.n,0,e)},y.prototype.imulK=function(t){return t.imul(this.k)},i(x,y),x.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0|t.words[n];t.words[n-10]=(4194303&a)<<4|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,y),i(_,y),i(w,y),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if("k256"===t)e=new x;else if("p224"===t)e=new b;else if("p192"===t)e=new _;else{if("p25519"!==t)throw new Error("Unknown prime "+t);e=new w}return v[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,"red works only with positives"),n(t.red,"red works only with red numbers")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),"red works only with positives"),n(t.red&&t.red===e.red,"red works only with red numbers")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var m=p,g=0;0!==m.cmp(s);g++)m=m.redSqr();n(g<d);var v=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(v),f=v.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var f=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4===++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var 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i=e.geoCentroid({type:"MultiPoint",coordinates:[t,r,n]}),a=[-i[0],-i[1]],o=e.geoRotation(a),s=at(ot(o(t)),ot(o(r)),ot(o(n)));s.invert=H(s);var l=e.geoProjection(s).rotate(a),c=l.center;return delete l.rotate,l.center=function(t){return arguments.length?c(o(t)):o.invert(c())},l.clipAngle(90)}function lt(t,e){var r=L(1-m(e));return[2/T*t*r,T*(1-r)]}function ct(t){var e=g(t);function r(t,r){return[t,(t?t/m(t):1)*(m(r)*o(t)-e*o(r))]}return r.invert=e?function(t,r){t&&(r*=m(t)/t);var n=o(t);return[t,2*a(L(n*n+e*e-r*r)-n,e-r)]}:function(t,e){return[t,S(t?e*g(t)/t:e)]},r}lt.invert=function(t,e){var r=(r=e/T-1)*r;return[r>0?t*L(y/r)/2:0,S(1-r)]};var ut=L(3);function ft(t,e){return[ut*t*(2*o(2*e/3)-1)/T,ut*T*m(e/3)]}function ht(t){var e=o(t);function r(t,r){return[t*e,m(r)/e]}return r.invert=function(t,r){return[t/e,S(r*e)]},r}function pt(t){var e=o(t);function r(t,r){return[t*e,(1+e)*g(r/2)]}return r.invert=function(t,r){return[t/e,2*i(r/(1+e))]},r}function dt(t,e){var r=L(8/(3*y));return[r*t*(1-n(e)/y),r*e]}function mt(t,e){var r=L(4-3*m(n(e)));return[2/L(6*y)*t*r,d(e)*L(2*y/3)*(2-r)]}function gt(t,e){var r=L(y*(4+y));return[2/r*t*(1+L(1-4*e*e/(y*y))),4/r*e]}function vt(t,e){var r=(2+x)*m(e);e/=2;for(var i=0,a=1/0;i<10&&n(a)>v;i++){var s=o(e);e-=a=(e+m(e)*(s+2)-r)/(2*s*(1+s))}return[2/L(y*(4+y))*t*(1+o(e)),2*L(y/(4+y))*m(e)]}function yt(t,e){return[t*(1+o(e))/L(2+y),2*e/L(2+y)]}function xt(t,e){for(var r=(1+x)*m(e),i=0,a=1/0;i<10&&n(a)>v;i++)e-=a=(e+m(e)-r)/(1+o(e));return r=L(2+y),[t*(1+o(e))/r,2*e/r]}ft.invert=function(t,e){var r=3*S(e/(ut*T));return[T*t/(ut*(2*o(2*r/3)-1)),r]},dt.invert=function(t,e){var r=L(8/(3*y)),i=e/r;return[t/(r*(1-n(i)/y)),i]},mt.invert=function(t,e){var r=2-n(e)/L(2*y/3);return[t*L(6*y)/(2*r),d(e)*S((4-r*r)/3)]},gt.invert=function(t,e){var r=L(y*(4+y))/2;return[t*r/(1+L(1-e*e*(4+y)/(4*y))),e*r/2]},vt.invert=function(t,e){var r=e*L((4+y)/y)/2,n=S(r),i=o(n);return[t/(2/L(y*(4+y))*(1+i)),S((n+r*(i+2))/(2+x))]},yt.invert=function(t,e){var r=L(2+y),n=e*r/2;return[r*t/(1+o(n)),n]},xt.invert=function(t,e){var r=1+x,n=L(r/2);return[2*t*n/(1+o(e*=n)),S((e+m(e))/r)]};var bt=3+2*w;function _t(t,e){var r=m(t/=2),n=o(t),a=L(o(e)),s=o(e/=2),l=m(e)/(s+w*n*a),u=L(2/(1+l*l)),f=L((w*s+(n+r)*a)/(w*s+(n-r)*a));return[bt*(u*(f-1/f)-2*c(f)),bt*(u*l*(f+1/f)-2*i(l))]}_t.invert=function(t,e){if(!(r=D.invert(t/1.2,1.065*e)))return null;var r,a=r[0],s=r[1],l=20;t/=bt,e/=bt;do{var h=a/2,p=s/2,d=m(h),g=o(h),y=m(p),b=o(p),T=o(s),k=L(T),A=y/(b+w*g*k),M=A*A,S=L(2/(1+M)),E=(w*b+(g+d)*k)/(w*b+(g-d)*k),C=L(E),P=C-1/C,I=C+1/C,O=S*P-2*c(C)-t,z=S*A*I-2*i(A)-e,R=y&&_*k*d*M/y,F=(w*g*b+k)/(2*(b+w*g*k)*(b+w*g*k)*k),B=-.5*A*S*S*S,N=B*R,j=B*F,U=(U=2*b+w*k*(g-d))*U*C,V=(w*g*b*k+T)/U,H=-w*d*y/(k*U),q=P*N-2*V/C+S*(V+V/E),G=P*j-2*H/C+S*(H+H/E),Y=A*I*N-2*R/(1+M)+S*I*R+S*A*(V-V/E),W=A*I*j-2*F/(1+M)+S*I*F+S*A*(H-H/E),X=G*Y-W*q;if(!X)break;var 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b=g*g,_=t+b*(e+b*(r+b*i)),w=g*(a-1+b*(s-c+b*l)),T=_*_+w*w,k=2*w,A=T/k,M=A*A,E=S(_/A)/y,C=d*E,P=_*_,I=(2*e+b*(4*r+6*b*i))*g,O=a+b*(3*s+5*b*l),z=(2*(_*I+w*(O-1))*k-T*(2*(O-1)))/(k*k),D=o(C),R=m(C),F=A*D,B=A*R,N=d/y*(1/L(1-P/M))*(I*A-_*z)/M,j=B-u,U=g*(1+b*c)+A-F-f,V=z*R+F*N,H=F*E,q=1+z-(z*D-B*N),G=B*E,Y=V*G-q*H;if(!Y)break;d-=h=(U*V-j*q)/Y,g-=p=(j*G-U*H)/Y}while((n(h)>v||n(p)>v)&&--x>0);return[d,g]},u}Tt.invert=function(t,e){var r=e/(1+wt);return[t&&t/(wt*L(1-r*r)),2*i(r)]},kt.invert=function(t,e){var r=i(e/T),n=o(r),a=2*r;return[t*T/2/(o(a)*n*n),a]};var It=Pt(2.8284,-1.6988,.75432,-.18071,1.76003,-.38914,.042555);var Ot=Pt(2.583819,-.835827,.170354,-.038094,1.543313,-.411435,.082742);var zt=Pt(5/6*y,-.62636,-.0344,0,1.3493,-.05524,0,.045);function Dt(t,e){var r=t*t,n=e*e;return[t*(1-.162388*n)*(.87-952426e-9*r*r),e*(1+n/12)]}Dt.invert=function(t,e){var r,i=t,a=e,o=50;do{var s=a*a;a-=r=(a*(1+s/12)-e)/(1+s/4)}while(n(r)>v&&--o>0);o=50,t/=1-.162388*s;do{var 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e=t.height,r=t.data,n=3;n<r.length;n+=4)if(0!==r[n])return Math.floor(.25*(n-3)/e)}function o(t){for(var e=t.height,r=t.data,n=r.length-1;n>0;n-=4)if(0!==r[n])return Math.floor(.25*(n-3)/e)}e.exports=n,n.canvas=document.createElement("canvas"),n.cache={}},{}],247:[function(t,e,r){"use strict";e.exports=function(t){return new s(t||m,null)};function n(t,e,r,n,i,a){this._color=t,this.key=e,this.value=r,this.left=n,this.right=i,this._count=a}function i(t){return new n(t._color,t.key,t.value,t.left,t.right,t._count)}function a(t,e){return new n(t,e.key,e.value,e.left,e.right,e._count)}function o(t){t._count=1+(t.left?t.left._count:0)+(t.right?t.right._count:0)}function s(t,e){this._compare=t,this.root=e}var l=s.prototype;function c(t,e){var r;if(e.left&&(r=c(t,e.left)))return r;return(r=t(e.key,e.value))||(e.right?c(t,e.right):void 0)}function u(t,e,r,n){if(e(t,n.key)<=0){var i;if(n.left)if(i=u(t,e,r,n.left))return i;if(i=r(n.key,n.value))return i}if(n.right)return u(t,e,r,n.right)}function 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p=l[f-2];if(p.left===h)if(h.left===i){if(!(d=p.right)||0!==d._color){if(p._color=0,p.left=h.right,h._color=1,h.right=p,l[f-2]=h,l[f-1]=i,o(p),o(h),f>=3)(m=l[f-3]).left===p?m.left=h:m.right=h;break}h._color=1,p.right=a(1,d),p._color=0,f-=1}else{if(!(d=p.right)||0!==d._color){if(h.right=i.left,p._color=0,p.left=i.right,i._color=1,i.left=h,i.right=p,l[f-2]=i,l[f-1]=h,o(p),o(h),o(i),f>=3)(m=l[f-3]).left===p?m.left=i:m.right=i;break}h._color=1,p.right=a(1,d),p._color=0,f-=1}else if(h.right===i){if(!(d=p.left)||0!==d._color){if(p._color=0,p.right=h.left,h._color=1,h.left=p,l[f-2]=h,l[f-1]=i,o(p),o(h),f>=3)(m=l[f-3]).right===p?m.right=h:m.left=h;break}h._color=1,p.left=a(1,d),p._color=0,f-=1}else{var d;if(!(d=p.left)||0!==d._color){var m;if(h.left=i.right,p._color=0,p.right=i.left,i._color=1,i.right=h,i.left=p,l[f-2]=i,l[f-1]=h,o(p),o(h),o(i),f>=3)(m=l[f-3]).right===p?m.right=i:m.left=i;break}h._color=1,p.left=a(1,d),p._color=0,f-=1}}return l[0]._color=1,new 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f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,"key",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,"value",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,"index",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,"hasNext",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error("Can't update empty node!");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,"hasPrev",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],248:[function(t,e,r){e.exports=function(t,e){if("string"!=typeof t)throw new TypeError("must specify type string");if(e=e||{},"undefined"==typeof document&&!e.canvas)return null;var r=e.canvas||document.createElement("canvas");"number"==typeof e.width&&(r.width=e.width);"number"==typeof e.height&&(r.height=e.height);var n,i=e;try{var a=[t];0===t.indexOf("webgl")&&a.push("experimental-"+t);for(var o=0;o<a.length;o++)if(n=r.getContext(a[o],i))return n}catch(t){n=null}return n||null}},{}],249:[function(t,e,r){"use strict";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t("./lib/text.js"),i=t("./lib/lines.js"),a=t("./lib/background.js"),o=t("./lib/cube.js"),s=t("./lib/ticks.js"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=["auto","auto","auto"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont="sans-serif",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=["auto","auto","auto"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if("bounds"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if("ticks"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a("tickSpacing")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&("ticks"in t||"tickSpacing"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o("tickEnable"),l("tickFont")&&(u=!0),a("tickSize"),a("tickAngle"),a("tickPad"),c("tickColor");var m=l("labels");l("labelFont")&&(m=!0),o("labelEnable"),a("labelSize"),a("labelPad"),c("labelColor"),o("lineEnable"),o("lineMirror"),a("lineWidth"),c("lineColor"),o("lineTickEnable"),o("lineTickMirror"),a("lineTickLength"),a("lineTickWidth"),c("lineTickColor"),o("gridEnable"),a("gridWidth"),c("gridColor"),o("zeroEnable"),c("zeroLineColor"),a("zeroLineWidth"),o("backgroundEnable"),c("backgroundColor"),this._text?this._text&&(m||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var m=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E=m;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=h[A]:E[A]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var L=[0,0,0];h[A]>0?L[A]=a[1][A]:L[A]=a[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,I=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,I=(A+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var O=c(v,M[A].primalMinor),z=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(H[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]="auto"):this.tickAlign[A]=-1,F=1,"auto"===(S=[this.tickAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(""+S[0]),B=[0,0,0],j(A,U,V);for(C=0;C<3;++C)H[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,B,S)}if(this.labelEnable[A]){F=0,B=[0,0,0],this.labels[A].length>4&&(N(A),F=1),"auto"===(S=[this.labelAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(""+S[0]);for(C=0;C<3;++C)H[C]+=k*U[C]*this.labelPad[C]/r[5*C];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{"./lib/background.js":250,"./lib/cube.js":251,"./lib/lines.js":252,"./lib/text.js":254,"./lib/ticks.js":255}],250:[function(t,e,r){"use strict";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var m=-1;m<=1;m+=2)f[u]=m,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=c;c=u,u=g}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders":253,"gl-buffer":257,"gl-vao":343}],251:[function(t,e,r){"use strict";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);var 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e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var m=[1,1,1],g=[0,0,0],v={cubeEdges:m,axis:g}},{"bit-twiddle":101,"gl-mat4/multiply":289,"robust-orientation":524,"split-polygon":541}],252:[function(t,e,r){"use strict";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var m=+r[f][d].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){m=+r[f][v].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}g=o.length/3|0;c[f]=h,u[f]=g-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var m=f(l);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{"./shaders":253,"gl-buffer":257,"gl-vao":343}],253:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\nuniform float lineWidth;\nuniform vec2 screenShape;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nvoid main() {\n  vec3 major = position.x * majorAxis;\n  vec3 minor = position.y * minorAxis;\n\n  vec3 vPosition = major + minor + offset;\n  vec3 pPosition = project(vPosition);\n  vec3 offset = project(vPosition + screenAxis * position.z);\n\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\n\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.line=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"}])};var s=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, axis, alignDir, alignOpt;\nuniform float scale, angle, pixelScale;\nuniform vec2 resolution;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nfloat computeViewAngle(vec3 a, vec3 b) {\n  vec3 A = project(a);\n  vec3 B = project(b);\n\n  return atan(\n    (B.y - A.y) * resolution.y,\n    (B.x - A.x) * resolution.x\n  );\n}\n\nconst float PI = 3.141592;\nconst float TWO_PI = 2.0 * PI;\nconst float HALF_PI = 0.5 * PI;\nconst float ONE_AND_HALF_PI = 1.5 * PI;\n\nint option = int(floor(alignOpt.x + 0.001));\nfloat hv_ratio =       alignOpt.y;\nbool enableAlign =    (alignOpt.z != 0.0);\n\nfloat mod_angle(float a) {\n  return mod(a, PI);\n}\n\nfloat positive_angle(float a) {\n  return mod_angle((a < 0.0) ?\n    a + TWO_PI :\n    a\n  );\n}\n\nfloat look_upwards(float a) {\n  float b = positive_angle(a);\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\n    b - PI :\n    b;\n}\n\nfloat look_horizontal_or_vertical(float a, float ratio) {\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\n  // if ratio is set to 0.5 then it is 50%, 50%.\n  // when using a higher ratio e.g. 0.75 the result would\n  // likely be more horizontal than vertical.\n\n  float b = positive_angle(a);\n\n  return\n    (b < (      ratio) * HALF_PI) ? 0.0 :\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\n    (b < (4.0 - ratio) * HALF_PI) ? 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HALF_PI :\n                      computeViewAngle(dataPosition, dataPosition + axis);\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\n\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\n\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\n\n    beta += applyAlignOption(clipAngle, flip * PI);\n  }\n\n  //Compute plane offset\n  vec2 planeCoord = position.xy * pixelScale;\n\n  mat2 planeXform = scale * mat2(\n     cos(beta), sin(beta),\n    -sin(beta), cos(beta)\n  );\n\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\n\n  //Compute clip position\n  vec3 clipPosition = project(dataPosition);\n\n  //Apply text offset in clip coordinates\n  clipPosition += vec3(viewOffset, 0.0);\n\n  //Done\n  gl_Position = vec4(clipPosition, 1.0);\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.text=function(t){return i(t,s,l,null,[{name:"position",type:"vec3"}])};var c=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec3 normal;\n\nuniform mat4 model, view, projection;\nuniform vec3 enable;\nuniform vec3 bounds[2];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\n\n  vec3 realNormal = signAxis * normal;\n\n  if(dot(realNormal, enable) > 0.0) {\n    vec3 minRange = min(bounds[0], bounds[1]);\n    vec3 maxRange = max(bounds[0], bounds[1]);\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\n  } else {\n    gl_Position = vec4(0,0,0,0);\n  }\n\n  colorChannel = abs(realNormal);\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 colors[3];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n  gl_FragColor = colorChannel.x * colors[0] +\n                 colorChannel.y * colors[1] +\n                 colorChannel.z * colors[2];\n}"]);r.bg=function(t){return i(t,c,u,null,[{name:"position",type:"vec3"},{name:"normal",type:"vec3"}])}},{"gl-shader":323,glslify:424}],254:[function(t,e,r){(function(r){(function(){"use strict";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t("gl-buffer"),i=t("gl-vao"),a=t("vectorize-text"),o=t("./shaders").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:"'+t+'" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:"center",textBaseline:"middle",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=u.positions,p=u.cells,d=0,m=p.length;d<m;++d)for(var g=p[d],v=2;v>=0;--v){var y=h[g[v]];o.push(f*y[0],-f*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(o.length/3|0)-f[d],c[d]=o.length/3|0;for(var m=0;m<n[d].length;++m)n[d][m].text&&s(n[d][m].x,n[d][m].text,n[d][m].font||i,n[d][m].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t("_process"))},{"./shaders":253,_process:504,"gl-buffer":257,"gl-vao":343,"vectorize-text":596}],255:[function(t,e,r){"use 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h=e.model||c,p=e.view||c,v=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,v,x,y)).axis;o(u,p,h),o(u,v,u);for(var _=m,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=y?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var I=d(g,u,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(I[C])))}}}}return _};var n=t("extract-frustum-planes"),i=t("split-polygon"),a=t("./lib/cube.js"),o=t("gl-mat4/multiply"),s=t("gl-mat4/transpose"),l=t("gl-vec4/transformMat4"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var m=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},{"./lib/cube.js":251,"extract-frustum-planes":241,"gl-mat4/multiply":289,"gl-mat4/transpose":300,"gl-vec4/transformMat4":414,"split-polygon":541}],257:[function(t,e,r){"use strict";var n=t("typedarray-pool"),i=t("ndarray-ops"),a=t("ndarray"),o=["uint8","uint8_clamped","uint16","uint32","int8","int16","int32","float32"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error("gl-buffer: If resizing buffer, must not specify offset");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if("number"!=typeof e&&(e=-1),this.bind(),"object"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r="float32"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension("OES_element_index_uint")&&"uint16"!==r?"uint32":"uint16";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,"uint16"):u(t,"float32"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if("object"==typeof t&&"number"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if("number"!=typeof t&&void 0!==t)throw new Error("gl-buffer: Invalid data type");if(e>=0)throw new Error("gl-buffer: Cannot specify offset when resizing buffer");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error("gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error("gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:462,"ndarray-ops":457,"typedarray-pool":590}],258:[function(t,e,r){"use strict";var n=t("gl-vec3");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,m=[],g=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),v=!1):v=!0}v||(p=x,d=b),m.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A;y=0;for(var M=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=m[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t("./lib/shaders");e.exports.createMesh=t("./create_mesh"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:"cone"})}},{"./create_mesh":259,"./lib/shaders":260,"gl-vec3":362}],259:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("gl-mat4/multiply"),l=t("gl-mat4/invert"),c=t("ndarray"),u=t("colormap"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function m(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var v=p[g];d=Math.min(d,v),m=Math.max(m,v)}else for(g=0;g<n.length;++g){v=n[g][2];d=Math.min(d,v),m=Math.max(m,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(g=0;g<n.length;++g)for(var y=n[g],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(m-d),0]:[(y[2]-d)/(m-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return"cone"===this.traceType?i.index=Math.floor(r[1]/48):"streamtube"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=m(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,g,v,x,r.traceType||"cone");return b.update(e),b}},{colormap:132,"gl-buffer":257,"gl-mat4/invert":287,"gl-mat4/multiply":289,"gl-shader":323,"gl-texture2d":338,"gl-vao":343,ndarray:462}],260:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, coneScale, coneOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * conePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\n  vec4 t_position  = view * conePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = conePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float vectorScale, coneScale, coneOffset;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n  gl_Position = projection * view * conePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},{glslify:424}],261:[function(t,e,r){e.exports={0:"NONE",1:"ONE",2:"LINE_LOOP",3:"LINE_STRIP",4:"TRIANGLES",5:"TRIANGLE_STRIP",6:"TRIANGLE_FAN",256:"DEPTH_BUFFER_BIT",512:"NEVER",513:"LESS",514:"EQUAL",515:"LEQUAL",516:"GREATER",517:"NOTEQUAL",518:"GEQUAL",519:"ALWAYS",768:"SRC_COLOR",769:"ONE_MINUS_SRC_COLOR",770:"SRC_ALPHA",771:"ONE_MINUS_SRC_ALPHA",772:"DST_ALPHA",773:"ONE_MINUS_DST_ALPHA",774:"DST_COLOR",775:"ONE_MINUS_DST_COLOR",776:"SRC_ALPHA_SATURATE",1024:"STENCIL_BUFFER_BIT",1028:"FRONT",1029:"BACK",1032:"FRONT_AND_BACK",1280:"INVALID_ENUM",1281:"INVALID_VALUE",1282:"INVALID_OPERATION",1285:"OUT_OF_MEMORY",1286:"INVALID_FRAMEBUFFER_OPERATION",2304:"CW",2305:"CCW",2849:"LINE_WIDTH",2884:"CULL_FACE",2885:"CULL_FACE_MODE",2886:"FRONT_FACE",2928:"DEPTH_RANGE",2929:"DEPTH_TEST",2930:"DEPTH_WRITEMASK",2931:"DEPTH_CLEAR_VALUE",2932:"DEPTH_FUNC",2960:"STENCIL_TEST",2961:"STENCIL_CLEAR_VALUE",2962:"STENCIL_FUNC",2963:"STENCIL_VALUE_MASK",2964:"STENCIL_FAIL",2965:"STENCIL_PASS_DEPTH_FAIL",2966:"STENCIL_PASS_DEPTH_PASS",2967:"STENCIL_REF",2968:"STENCIL_WRITEMASK",2978:"VIEWPORT",3024:"DITHER",3042:"BLEND",3088:"SCISSOR_BOX",3089:"SCISSOR_TEST",3106:"COLOR_CLEAR_VALUE",3107:"COLOR_WRITEMASK",3317:"UNPACK_ALIGNMENT",3333:"PACK_ALIGNMENT",3379:"MAX_TEXTURE_SIZE",3386:"MAX_VIEWPORT_DIMS",3408:"SUBPIXEL_BITS",3410:"RED_BITS",3411:"GREEN_BITS",3412:"BLUE_BITS",3413:"ALPHA_BITS",3414:"DEPTH_BITS",3415:"STENCIL_BITS",3553:"TEXTURE_2D",4352:"DONT_CARE",4353:"FASTEST",4354:"NICEST",5120:"BYTE",5121:"UNSIGNED_BYTE",5122:"SHORT",5123:"UNSIGNED_SHORT",5124:"INT",5125:"UNSIGNED_INT",5126:"FLOAT",5386:"INVERT",5890:"TEXTURE",6401:"STENCIL_INDEX",6402:"DEPTH_COMPONENT",6406:"ALPHA",6407:"RGB",6408:"RGBA",6409:"LUMINANCE",6410:"LUMINANCE_ALPHA",7680:"KEEP",7681:"REPLACE",7682:"INCR",7683:"DECR",7936:"VENDOR",7937:"RENDERER",7938:"VERSION",9728:"NEAREST",9729:"LINEAR",9984:"NEAREST_MIPMAP_NEAREST",9985:"LINEAR_MIPMAP_NEAREST",9986:"NEAREST_MIPMAP_LINEAR",9987:"LINEAR_MIPMAP_LINEAR",10240:"TEXTURE_MAG_FILTER",10241:"TEXTURE_MIN_FILTER",10242:"TEXTURE_WRAP_S",10243:"TEXTURE_WRAP_T",10497:"REPEAT",10752:"POLYGON_OFFSET_UNITS",16384:"COLOR_BUFFER_BIT",32769:"CONSTANT_COLOR",32770:"ONE_MINUS_CONSTANT_COLOR",32771:"CONSTANT_ALPHA",32772:"ONE_MINUS_CONSTANT_ALPHA",32773:"BLEND_COLOR",32774:"FUNC_ADD",32777:"BLEND_EQUATION_RGB",32778:"FUNC_SUBTRACT",32779:"FUNC_REVERSE_SUBTRACT",32819:"UNSIGNED_SHORT_4_4_4_4",32820:"UNSIGNED_SHORT_5_5_5_1",32823:"POLYGON_OFFSET_FILL",32824:"POLYGON_OFFSET_FACTOR",32854:"RGBA4",32855:"RGB5_A1",32873:"TEXTURE_BINDING_2D",32926:"SAMPLE_ALPHA_TO_COVERAGE",32928:"SAMPLE_COVERAGE",32936:"SAMPLE_BUFFERS",32937:"SAMPLES",32938:"SAMPLE_COVERAGE_VALUE",32939:"SAMPLE_COVERAGE_INVERT",32968:"BLEND_DST_RGB",32969:"BLEND_SRC_RGB",32970:"BLEND_DST_ALPHA",32971:"BLEND_SRC_ALPHA",33071:"CLAMP_TO_EDGE",33170:"GENERATE_MIPMAP_HINT",33189:"DEPTH_COMPONENT16",33306:"DEPTH_STENCIL_ATTACHMENT",33635:"UNSIGNED_SHORT_5_6_5",33648:"MIRRORED_REPEAT",33901:"ALIASED_POINT_SIZE_RANGE",33902:"ALIASED_LINE_WIDTH_RANGE",33984:"TEXTURE0",33985:"TEXTURE1",33986:"TEXTURE2",33987:"TEXTURE3",33988:"TEXTURE4",33989:"TEXTURE5",33990:"TEXTURE6",33991:"TEXTURE7",33992:"TEXTURE8",33993:"TEXTURE9",33994:"TEXTURE10",33995:"TEXTURE11",33996:"TEXTURE12",33997:"TEXTURE13",33998:"TEXTURE14",33999:"TEXTURE15",34e3:"TEXTURE16",34001:"TEXTURE17",34002:"TEXTURE18",34003:"TEXTURE19",34004:"TEXTURE20",34005:"TEXTURE21",34006:"TEXTURE22",34007:"TEXTURE23",34008:"TEXTURE24",34009:"TEXTURE25",34010:"TEXTURE26",34011:"TEXTURE27",34012:"TEXTURE28",34013:"TEXTURE29",34014:"TEXTURE30",34015:"TEXTURE31",34016:"ACTIVE_TEXTURE",34024:"MAX_RENDERBUFFER_SIZE",34041:"DEPTH_STENCIL",34055:"INCR_WRAP",34056:"DECR_WRAP",34067:"TEXTURE_CUBE_MAP",34068:"TEXTURE_BINDING_CUBE_MAP",34069:"TEXTURE_CUBE_MAP_POSITIVE_X",34070:"TEXTURE_CUBE_MAP_NEGATIVE_X",34071:"TEXTURE_CUBE_MAP_POSITIVE_Y",34072:"TEXTURE_CUBE_MAP_NEGATIVE_Y",34073:"TEXTURE_CUBE_MAP_POSITIVE_Z",34074:"TEXTURE_CUBE_MAP_NEGATIVE_Z",34076:"MAX_CUBE_MAP_TEXTURE_SIZE",34338:"VERTEX_ATTRIB_ARRAY_ENABLED",34339:"VERTEX_ATTRIB_ARRAY_SIZE",34340:"VERTEX_ATTRIB_ARRAY_STRIDE",34341:"VERTEX_ATTRIB_ARRAY_TYPE",34342:"CURRENT_VERTEX_ATTRIB",34373:"VERTEX_ATTRIB_ARRAY_POINTER",34466:"NUM_COMPRESSED_TEXTURE_FORMATS",34467:"COMPRESSED_TEXTURE_FORMATS",34660:"BUFFER_SIZE",34661:"BUFFER_USAGE",34816:"STENCIL_BACK_FUNC",34817:"STENCIL_BACK_FAIL",34818:"STENCIL_BACK_PASS_DEPTH_FAIL",34819:"STENCIL_BACK_PASS_DEPTH_PASS",34877:"BLEND_EQUATION_ALPHA",34921:"MAX_VERTEX_ATTRIBS",34922:"VERTEX_ATTRIB_ARRAY_NORMALIZED",34930:"MAX_TEXTURE_IMAGE_UNITS",34962:"ARRAY_BUFFER",34963:"ELEMENT_ARRAY_BUFFER",34964:"ARRAY_BUFFER_BINDING",34965:"ELEMENT_ARRAY_BUFFER_BINDING",34975:"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING",35040:"STREAM_DRAW",35044:"STATIC_DRAW",35048:"DYNAMIC_DRAW",35632:"FRAGMENT_SHADER",35633:"VERTEX_SHADER",35660:"MAX_VERTEX_TEXTURE_IMAGE_UNITS",35661:"MAX_COMBINED_TEXTURE_IMAGE_UNITS",35663:"SHADER_TYPE",35664:"FLOAT_VEC2",35665:"FLOAT_VEC3",35666:"FLOAT_VEC4",35667:"INT_VEC2",35668:"INT_VEC3",35669:"INT_VEC4",35670:"BOOL",35671:"BOOL_VEC2",35672:"BOOL_VEC3",35673:"BOOL_VEC4",35674:"FLOAT_MAT2",35675:"FLOAT_MAT3",35676:"FLOAT_MAT4",35678:"SAMPLER_2D",35680:"SAMPLER_CUBE",35712:"DELETE_STATUS",35713:"COMPILE_STATUS",35714:"LINK_STATUS",35715:"VALIDATE_STATUS",35716:"INFO_LOG_LENGTH",35717:"ATTACHED_SHADERS",35718:"ACTIVE_UNIFORMS",35719:"ACTIVE_UNIFORM_MAX_LENGTH",35720:"SHADER_SOURCE_LENGTH",35721:"ACTIVE_ATTRIBUTES",35722:"ACTIVE_ATTRIBUTE_MAX_LENGTH",35724:"SHADING_LANGUAGE_VERSION",35725:"CURRENT_PROGRAM",36003:"STENCIL_BACK_REF",36004:"STENCIL_BACK_VALUE_MASK",36005:"STENCIL_BACK_WRITEMASK",36006:"FRAMEBUFFER_BINDING",36007:"RENDERBUFFER_BINDING",36048:"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE",36049:"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME",36050:"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL",36051:"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE",36053:"FRAMEBUFFER_COMPLETE",36054:"FRAMEBUFFER_INCOMPLETE_ATTACHMENT",36055:"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT",36057:"FRAMEBUFFER_INCOMPLETE_DIMENSIONS",36061:"FRAMEBUFFER_UNSUPPORTED",36064:"COLOR_ATTACHMENT0",36096:"DEPTH_ATTACHMENT",36128:"STENCIL_ATTACHMENT",36160:"FRAMEBUFFER",36161:"RENDERBUFFER",36162:"RENDERBUFFER_WIDTH",36163:"RENDERBUFFER_HEIGHT",36164:"RENDERBUFFER_INTERNAL_FORMAT",36168:"STENCIL_INDEX8",36176:"RENDERBUFFER_RED_SIZE",36177:"RENDERBUFFER_GREEN_SIZE",36178:"RENDERBUFFER_BLUE_SIZE",36179:"RENDERBUFFER_ALPHA_SIZE",36180:"RENDERBUFFER_DEPTH_SIZE",36181:"RENDERBUFFER_STENCIL_SIZE",36194:"RGB565",36336:"LOW_FLOAT",36337:"MEDIUM_FLOAT",36338:"HIGH_FLOAT",36339:"LOW_INT",36340:"MEDIUM_INT",36341:"HIGH_INT",36346:"SHADER_COMPILER",36347:"MAX_VERTEX_UNIFORM_VECTORS",36348:"MAX_VARYING_VECTORS",36349:"MAX_FRAGMENT_UNIFORM_VECTORS",37440:"UNPACK_FLIP_Y_WEBGL",37441:"UNPACK_PREMULTIPLY_ALPHA_WEBGL",37442:"CONTEXT_LOST_WEBGL",37443:"UNPACK_COLORSPACE_CONVERSION_WEBGL",37444:"BROWSER_DEFAULT_WEBGL"}},{}],262:[function(t,e,r){var n=t("./1.0/numbers");e.exports=function(t){return n[t]}},{"./1.0/numbers":261}],263:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=(t._ortho||!1?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*u,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=u[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){"lineWidth"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var u=r[l],h=0;h<3;++h)if(isNaN(u[h])||!isFinite(u[h]))continue t;var p=n[l],d=e[s];if(Array.isArray(d[0])&&(d=e[l]),3===d.length?d=[d[0],d[1],d[2],1]:4===d.length&&(d=[d[0],d[1],d[2],d[3]],!this.hasAlpha&&d[3]<1&&(this.hasAlpha=!0)),!isNaN(p[0][s])&&!isNaN(p[1][s])){var m;if(p[0][s]<0)(m=u.slice())[s]+=p[0][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s);if(p[1][s]>0)(m=u.slice())[s]+=p[1][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{"./shaders/index":264,"gl-buffer":257,"gl-vao":343}],264:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, offset;\nattribute vec4 color;\nuniform mat4 model, view, projection;\nuniform float capSize;\nvarying vec4 fragColor;\nvarying vec3 fragPosition;\n\nvoid main() {\n  vec4 worldPosition  = model * vec4(position, 1.0);\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\n  gl_Position         = projection * view * worldPosition;\n  fragColor           = color;\n  fragPosition        = position;\n}"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float opacity;\nvarying vec3 fragPosition;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  gl_FragColor = opacity * fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},{"gl-shader":323,glslify:424}],265:[function(t,e,r){"use strict";var n=t("gl-texture2d");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension("WEBGL_draw_buffers");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,c);Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]);if("number"!=typeof e)throw new Error("gl-fbo: Missing shape parameter");var u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>u||r<0||r>u)throw new Error("gl-fbo: Parameters are too large for FBO");var f=1;if("color"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error("gl-fbo: Must specify a nonnegative number of colors");if(f>1){if(!c)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(f>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error("gl-fbo: Context does not support "+f+" draw buffers")}}var h=t.UNSIGNED_BYTE,p=t.getExtension("OES_texture_float");if(n.float&&f>0){if(!p)throw new Error("gl-fbo: Context does not support floating point textures");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var m=!0;"depth"in n&&(m=!!n.depth);var g=!1;"stencil"in n&&(g=!!n.stencil);return new d(t,e,r,h,f,m,g,c)};var i,a,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error("gl-fbo: Framebuffer unsupported");case a:throw new Error("gl-fbo: Framebuffer incomplete attachment");case o:throw new Error("gl-fbo: Framebuffer incomplete dimensions");case s:throw new Error("gl-fbo: Framebuffer incomplete missing attachment");default:throw new Error("gl-fbo: Framebuffer failed for unspecified reason")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var m=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return m._shape[0]},set:function(t){return m.width=t}},1:{get:function(){return m._shape[1]},set:function(t){return m.height=t}}}),this._shapeVector=g,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,m=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension("WEBGL_depth_texture");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):m&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var m=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(m,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error("gl-fbo: Shape vector must be length 2");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),m.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},m.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{"gl-texture2d":338}],266:[function(t,e,r){var n=t("sprintf-js").sprintf,i=t("gl-constants/lookup"),a=t("glsl-shader-name"),o=t("add-line-numbers");e.exports=function(t,e,r){"use strict";var s=a(e)||"of unknown name (see npm glsl-shader-name)",l="unknown type";void 0!==r&&(l=r===i.FRAGMENT_SHADER?"fragment":"vertex");for(var c=n("Error compiling %s shader %s:\n",l,s),u=n("%s%s",c,t),f=t.split("\n"),h={},p=0;p<f.length;p++){var d=f[p];if(""!==d&&"\0"!==d){var m=parseInt(d.split(":")[2]);if(isNaN(m))throw new Error(n("Could not parse error: %s",d));h[m]=d}}var g=o(e).split("\n");for(p=0;p<g.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=g[p];if(c+=v+"\n",h[p+1]){var y=h[p+1];y=y.substr(y.split(":",3).join(":").length+1).trim(),c+=n("^^^ %s\n\n",y)}}return{long:c.trim(),short:u.trim()}}},{"add-line-numbers":68,"gl-constants/lookup":262,"glsl-shader-name":416,"sprintf-js":542}],267:[function(t,e,r){"use strict";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t("binary-search-bounds"),i=t("iota-array"),a=t("typedarray-pool"),o=t("gl-shader"),s=t("gl-buffer"),l=t("./lib/shaders");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(O+U):C*b+O],q=n.le(d,H);if(q<0)F=m[0],B=m[1],N=m[2],j=m[3];else if(q===g-1)F=m[4*g-4],B=m[4*g-3],N=m[4*g-2],j=m[4*g-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Y=1-G,W=4*q,X=4*(q+1);F=Y*m[W]+G*m[X],B=Y*m[W+1]+G*m[X+1],N=Y*m[W+2]+G*m[X+2],j=Y*m[W+3]+G*m[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{"./lib/shaders":268,"binary-search-bounds":100,"gl-buffer":257,"gl-shader":323,"iota-array":431,"typedarray-pool":590}],268:[function(t,e,r){"use strict";var n=t("glslify");e.exports={fragment:n(["precision lowp float;\n#define GLSLIFY 1\nvarying vec4 fragColor;\nvoid main() {\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\n}\n"]),vertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 color;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  fragColor = color;\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"]),pickFragment:n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nuniform vec2 shape;\nuniform vec4 pickOffset;\n\nvoid main() {\n  vec2 d = step(.5, vWeight);\n  vec4 id = fragId + pickOffset;\n  id.x += d.x + d.y*shape.x;\n\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  gl_FragColor = id/255.;\n}\n"]),pickVertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nvoid main() {\n  vWeight = weight;\n\n  fragId = pickId;\n\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"])}},{glslify:424}],269:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, nextPosition;\nattribute float arcLength, lineWidth;\nattribute vec4 color;\n\nuniform vec2 screenShape;\nuniform float pixelRatio;\nuniform mat4 model, view, projection;\n\nvarying vec4 fragColor;\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  vec4 startPoint = project(position);\n  vec4 endPoint   = project(nextPosition);\n\n  vec2 A = startPoint.xy / startPoint.w;\n  vec2 B =   endPoint.xy /   endPoint.w;\n\n  float clipAngle = atan(\n    (B.y - A.y) * screenShape.y,\n    (B.x - A.x) * screenShape.x\n  );\n\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\n    sin(clipAngle),\n    -cos(clipAngle)\n  ) / screenShape;\n\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3      clipBounds[2];\nuniform sampler2D dashTexture;\nuniform float     dashScale;\nuniform float     opacity;\n\nvarying vec3    worldPosition;\nvarying float   pixelArcLength;\nvarying vec4    fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n  if(dashWeight < 0.5) {\n    discard;\n  }\n  gl_FragColor = fragColor * opacity;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\nvec4 packFloat(float v) {\n  float av = abs(v);\n\n  //Handle special cases\n  if(av < FLOAT_MIN) {\n    return vec4(0.0, 0.0, 0.0, 0.0);\n  } else if(v > FLOAT_MAX) {\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n  } else if(v < -FLOAT_MAX) {\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n  }\n\n  vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  float e = floor(log2(av));\n  float m = av * pow(2.0, -e) - 1.0;\n\n  //Unpack mantissa\n  c[1] = floor(128.0 * m);\n  m -= c[1] / 128.0;\n  c[2] = floor(32768.0 * m);\n  m -= c[2] / 32768.0;\n  c[3] = floor(8388608.0 * m);\n\n  //Unpack exponent\n  float ebias = e + 127.0;\n  c[0] = floor(ebias / 2.0);\n  ebias -= c[0] * 2.0;\n  c[1] += floor(ebias) * 128.0;\n\n  //Unpack sign bit\n  c[0] += 128.0 * step(0.0, -v);\n\n  //Scale back to range\n  return c / 255.0;\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\n\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\n}"]),l=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{"gl-shader":323,glslify:424}],270:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new v(e,r,o,s,l,d);return m.update(t),m};var n=t("gl-buffer"),i=t("gl-vao"),a=t("gl-texture2d"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t("binary-search-bounds"),c=t("ndarray"),u=t("./lib/shaders"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,"dashes"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,h,this.points[h])}},{"./lib/shaders":269,"binary-search-bounds":100,"gl-buffer":257,"gl-texture2d":338,"gl-vao":343,ndarray:462}],271:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15];return t[0]=s*(h*v-p*g)-f*(l*v-c*g)+m*(l*p-c*h),t[1]=-(n*(h*v-p*g)-f*(i*v-a*g)+m*(i*p-a*h)),t[2]=n*(l*v-c*g)-s*(i*v-a*g)+m*(i*c-a*l),t[3]=-(n*(l*p-c*h)-s*(i*p-a*h)+f*(i*c-a*l)),t[4]=-(o*(h*v-p*g)-u*(l*v-c*g)+d*(l*p-c*h)),t[5]=r*(h*v-p*g)-u*(i*v-a*g)+d*(i*p-a*h),t[6]=-(r*(l*v-c*g)-o*(i*v-a*g)+d*(i*c-a*l)),t[7]=r*(l*p-c*h)-o*(i*p-a*h)+u*(i*c-a*l),t[8]=o*(f*v-p*m)-u*(s*v-c*m)+d*(s*p-c*f),t[9]=-(r*(f*v-p*m)-u*(n*v-a*m)+d*(n*p-a*f)),t[10]=r*(s*v-c*m)-o*(n*v-a*m)+d*(n*c-a*s),t[11]=-(r*(s*p-c*f)-o*(n*p-a*f)+u*(n*c-a*s)),t[12]=-(o*(f*g-h*m)-u*(s*g-l*m)+d*(s*h-l*f)),t[13]=r*(f*g-h*m)-u*(n*g-i*m)+d*(n*h-i*f),t[14]=-(r*(s*g-l*m)-o*(n*g-i*m)+d*(n*l-i*s)),t[15]=r*(s*h-l*f)-o*(n*h-i*f)+u*(n*l-i*s),t}},{}],272:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],273:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],274:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],275:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],f=t[10],h=t[11],p=t[12],d=t[13],m=t[14],g=t[15];return(e*o-r*a)*(f*g-h*m)-(e*s-n*a)*(u*g-h*d)+(e*l-i*a)*(u*m-f*d)+(r*s-n*o)*(c*g-h*p)-(r*l-i*o)*(c*m-f*p)+(n*l-i*s)*(c*d-u*p)}},{}],276:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=r*o,u=n*o,f=n*s,h=i*o,p=i*s,d=i*l,m=a*o,g=a*s,v=a*l;return t[0]=1-f-d,t[1]=u+v,t[2]=h-g,t[3]=0,t[4]=u-v,t[5]=1-c-d,t[6]=p+m,t[7]=0,t[8]=h+g,t[9]=p-m,t[10]=1-c-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],277:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o=r[0],s=r[1],l=r[2],c=Math.sqrt(o*o+s*s+l*l);if(Math.abs(c)<1e-6)return null;return o*=c=1/c,s*=c,l*=c,n=Math.sin(e),i=Math.cos(e),a=1-i,t[0]=o*o*a+i,t[1]=s*o*a+l*n,t[2]=l*o*a-s*n,t[3]=0,t[4]=o*s*a-l*n,t[5]=s*s*a+i,t[6]=l*s*a+o*n,t[7]=0,t[8]=o*l*a+s*n,t[9]=s*l*a-o*n,t[10]=l*l*a+i,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],278:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=n*s,f=n*l,h=n*c,p=i*l,d=i*c,m=a*c,g=o*s,v=o*l,y=o*c;return t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],279:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],280:[function(t,e,r){e.exports=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t}},{}],281:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],282:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=0,t[2]=-r,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=r,t[9]=0,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],283:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=0,t[4]=-r,t[5]=n,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],284:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(r-e),l=1/(i-n),c=1/(a-o);return t[0]=2*a*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=2*a*l,t[6]=0,t[7]=0,t[8]=(r+e)*s,t[9]=(i+n)*l,t[10]=(o+a)*c,t[11]=-1,t[12]=0,t[13]=0,t[14]=o*a*2*c,t[15]=0,t}},{}],285:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],286:[function(t,e,r){e.exports={create:t("./create"),clone:t("./clone"),copy:t("./copy"),identity:t("./identity"),transpose:t("./transpose"),invert:t("./invert"),adjoint:t("./adjoint"),determinant:t("./determinant"),multiply:t("./multiply"),translate:t("./translate"),scale:t("./scale"),rotate:t("./rotate"),rotateX:t("./rotateX"),rotateY:t("./rotateY"),rotateZ:t("./rotateZ"),fromRotation:t("./fromRotation"),fromRotationTranslation:t("./fromRotationTranslation"),fromScaling:t("./fromScaling"),fromTranslation:t("./fromTranslation"),fromXRotation:t("./fromXRotation"),fromYRotation:t("./fromYRotation"),fromZRotation:t("./fromZRotation"),fromQuat:t("./fromQuat"),frustum:t("./frustum"),perspective:t("./perspective"),perspectiveFromFieldOfView:t("./perspectiveFromFieldOfView"),ortho:t("./ortho"),lookAt:t("./lookAt"),str:t("./str")}},{"./adjoint":271,"./clone":272,"./copy":273,"./create":274,"./determinant":275,"./fromQuat":276,"./fromRotation":277,"./fromRotationTranslation":278,"./fromScaling":279,"./fromTranslation":280,"./fromXRotation":281,"./fromYRotation":282,"./fromZRotation":283,"./frustum":284,"./identity":285,"./invert":287,"./lookAt":288,"./multiply":289,"./ortho":290,"./perspective":291,"./perspectiveFromFieldOfView":292,"./rotate":293,"./rotateX":294,"./rotateY":295,"./rotateZ":296,"./scale":297,"./str":298,"./translate":299,"./transpose":300}],287:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*m-f*d,A=u*g-h*d,M=u*v-p*d,S=f*g-h*m,E=f*v-p*m,L=h*v-p*g,C=y*L-x*E+b*S+_*M-w*A+T*k;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*T-g*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-m*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*x-d*_-g*y)*C,t[15]=(u*_-f*x+h*y)*C,t}},{}],288:[function(t,e,r){var n=t("./identity");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,m=e[0],g=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(m-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{"./identity":285}],289:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],290:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],291:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],292:[function(t,e,r){e.exports=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),c=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*c*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t}},{}],293:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],294:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],295:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],296:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],297:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],298:[function(t,e,r){e.exports=function(t){return"mat4("+t[0]+", "+t[1]+", "+t[2]+", "+t[3]+", "+t[4]+", "+t[5]+", "+t[6]+", "+t[7]+", "+t[8]+", "+t[9]+", "+t[10]+", "+t[11]+", "+t[12]+", "+t[13]+", "+t[14]+", "+t[15]+")"}},{}],299:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],300:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],301:[function(t,e,r){"use strict";var n=t("barycentric"),i=t("polytope-closest-point/lib/closest_point_2d.js");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,m=0;m<2;++m)d+=Math.pow(u[f][m]-e[m],2);d<p&&(p=d,h=f)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;v+=g[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,g),g]}},{barycentric:81,"polytope-closest-point/lib/closest_point_2d.js":488}],302:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, normal;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model\n           , view\n           , projection\n           , inverseModel;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  gl_Position      = project(position);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  f_color          = color;\n  f_data           = position;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (f_color.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\n  ) discard;\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\n\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * f_color.a;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model, view, projection;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_color = color;\n  f_data  = position;\n  f_uv    = uv;\n}"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\n\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\nattribute float pointSize;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\n  } else {\n    gl_Position = projection * view * model * vec4(position, 1.0);\n  }\n  gl_PointSize = pointSize;\n  f_color = color;\n  f_uv = uv;\n}"]),c=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\n  if(dot(pointR, pointR) > 0.25) {\n    discard;\n  }\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_id        = id;\n  f_position  = position;\n}"]),f=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]),h=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3  position;\nattribute float pointSize;\nattribute vec4  id;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\n  } else {\n    gl_Position  = projection * view * model * vec4(position, 1.0);\n    gl_PointSize = pointSize;\n  }\n  f_id         = id;\n  f_position   = position;\n}"]),p=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n}"]),d=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec3 contourColor;\n\nvoid main() {\n  gl_FragColor = vec4(contourColor, 1.0);\n}\n"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:"position",type:"vec3"}]}},{glslify:424}],303:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("normals"),l=t("gl-mat4/multiply"),c=t("gl-mat4/invert"),u=t("ndarray"),f=t("colormap"),h=t("simplicial-complex-contour"),p=t("typedarray-pool"),d=t("./lib/shaders"),m=t("./lib/closest-point"),g=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=v,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],m=n[f][1],g=i[f],v=1-g,y=this.positions[d],x=this.positions[m],b=0;b<3;++b)o[s++]=g*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,"opacity"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),"opacityscale"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:"rgba"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],m=[],g=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],m.push(Z[0],Z[1]),g.push(R)}H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=m(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension("OES_standard_derivatives")||t.getExtension("MOZ_OES_standard_derivatives")||t.getExtension("WEBKIT_OES_standard_derivatives");if(!r)throw new Error("derivatives not supported");var n=M(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),m=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,m,g,v,x,b,k,_,w,A,I,R,O,z,D,F,B,N);return j.update(e),j}},{"./lib/closest-point":301,"./lib/shaders":302,colormap:132,"gl-buffer":257,"gl-mat4/invert":287,"gl-mat4/multiply":289,"gl-shader":323,"gl-texture2d":338,"gl-vao":343,ndarray:462,normals:465,"simplicial-complex-contour":532,"typedarray-pool":590}],304:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":307,"gl-buffer":257,"gl-shader":323}],305:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("binary-search-bounds"),o=t("./shaders");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,m=0;m<2;++m){var g=a[m],v=a[m+2]-g,y=.5*(o[m+2]+o[m]),x=o[m+2]-o[m];u[m]=2*v/x,c[m]=2*(g-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(m=0;m<2;++m){f[0]=f[1]=0,f[m]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[m]/(s[m+2]-s[m])*d,r.uniforms.color=h[m];var _=6*n[m].length;p[m]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,m=s.viewBox,g=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(m[0]-E[1])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[1]*g/y,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[1]-E[0])/x-1,o[0]=L[0]*g/y,o[1]=E[0]*g/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(m[2]+E[3])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[3]*g/y,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[3]+E[2])/x-1,o[0]=L[2]*g/y,o[1]=E[2]*g/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var m=0;m<6;++m)n[i++]=d,n[i++]=h[m],n[i++]=p[m]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{"./shaders":307,"binary-search-bounds":100,"gl-buffer":257,"gl-shader":323}],306:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":307,"gl-buffer":257,"gl-shader":323}],307:[function(t,e,r){"use strict";var n=t("glslify"),i=n(["precision lowp float;\n#define GLSLIFY 1\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\n}\n"]);e.exports={lineVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 start, end;\nuniform float width;\n\nvec2 perp(vec2 v) {\n  return vec2(v.y, -v.x);\n}\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  vec2 delta = normalize(perp(start - end));\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\n}\n"]),lineFrag:i,textVert:n(["#define GLSLIFY 1\nattribute vec3 textCoordinate;\n\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\nuniform float angle;\n\nvoid main() {\n  float dataOffset  = textCoordinate.z;\n  vec2 glyphOffset  = textCoordinate.xy;\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\n    glyphMatrix * glyphOffset * textScale + screenOffset;\n  gl_Position = vec4(screenCoordinate, 0, 1);\n}\n"]),textFrag:i,gridVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale;\nuniform float lineWidth;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\n  gl_Position = vec4(pos, 0, 1);\n}\n"]),gridFrag:i,boxVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 lo, hi;\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\n}\n"]),tickVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\n}\n"])}},{glslify:424}],308:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("text-cache"),o=t("binary-search-bounds"),s=t("./shaders");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,m=l.prototype;m.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,m=e.tickPad,g=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*m[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*m[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],m=a[o+2]-f,g=i[o],v=i[o+2]-g;p[o]=2*l/u*m/v,h[o]=2*(s-c)/u*m/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,m=p.text,g=p.font||"sans-serif";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=m.split("\n"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:"center"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},m.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":307,"binary-search-bounds":100,"gl-buffer":257,"gl-shader":323,"text-cache":570}],309:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t("gl-select-static"),i=t("./lib/grid"),a=t("./lib/text"),o=t("./lib/line"),s=t("./lib/box");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],h[d]):o.drawLine(e[0],m,e[2],m,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),g[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),g[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),g[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!("titleEnable"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||["x","y"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||["sans-serif","sans-serif"],title:t.title||"",titleSize:t.titleSize||18,titleFont:t.titleFont||"sans-serif"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{"./lib/box":304,"./lib/grid":305,"./lib/line":306,"./lib/text":308,"gl-select-static":322}],310:[function(t,e,r){"use strict";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];"distanceLimits"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);"zoomMin"in e&&(r[0]=e.zoomMin);"zoomMax"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||"orbit",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:"rotate",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&"orthographic"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),"turntable"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener("contextmenu",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s="rotate"===o,l="pan"===o,u="zoom"===o,h=!!a.control,p=!!a.alt,m=!!a.shift,g=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!m||g&&!h&&!p&&m)&&c.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!m||v||g&&h&&!p&&!m)&&c.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&g&&!h&&!p&&!m||y||g&&!h&&p&&!m)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener("touchstart",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener("touchmove",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener("touchend",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t("right-now"),i=t("3d-view"),a=t("mouse-change"),o=t("mouse-wheel"),s=t("mouse-event-offset"),l=t("has-passive-events")},{"3d-view":55,"has-passive-events":426,"mouse-change":449,"mouse-event-offset":450,"mouse-wheel":452,"right-now":518}],311:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\nattribute vec2 position;\nvarying vec2 uv;\nvoid main() {\n  uv = position;\n  gl_Position = vec4(position, 0, 1);\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D accumBuffer;\nvarying vec2 uv;\n\nvoid main() {\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\n  gl_FragColor = min(vec4(1,1,1,1), accum);\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec2"}])}},{"gl-shader":323,glslify:424}],312:[function(t,e,r){"use strict";var n=t("./camera.js"),i=t("gl-axes3d"),a=t("gl-axes3d/properties"),o=t("gl-spikes3d"),s=t("gl-select-static"),l=t("gl-fbo"),c=t("a-big-triangle"),u=t("mouse-change"),f=t("gl-mat4/perspective"),h=t("gl-mat4/ortho"),p=t("./lib/shader"),d=t("is-mobile")({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return"boolean"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement("canvas"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext("webgl",e))||(r=t.getContext("experimental-webgl",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error("webgl not supported");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&"orthographic"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||"turntable",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:A,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||"absolute",o.left="0px",o.top="0px",o.width=r+"px",o.height=n+"px",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function H(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener("resize",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=A,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener("resize",U),e.removeEventListener("webglcontextlost",H),N.mouseListener.enabled=!1,!N.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener("webglcontextlost",H);var q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[q[0].slice(),q[1].slice()];function Y(){if(!H()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,A.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=q[0],i=q[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var m=.05*(i[p]-n[p]);n[p]=n[p]-m,i[p]=i[p]+m}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:y}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var m=0;for(d=0;d<3;++d)m=Math.max(m,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/m,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!H()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;A.enable&&(M=M||A.isTransparent(),A.draw(R)),S.axes=A,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=A,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(M=!0)}if(M){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{"./camera.js":310,"./lib/shader":311,"a-big-triangle":66,"gl-axes3d":249,"gl-axes3d/properties":256,"gl-fbo":265,"gl-mat4/ortho":290,"gl-mat4/perspective":291,"gl-select-static":322,"gl-spikes3d":332,"is-mobile":435,"mouse-change":449}],313:[function(t,e,r){var n=t("glslify");r.pointVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform float pointCloud;\n\nhighp float rand(vec2 co) {\n  highp float a = 12.9898;\n  highp float b = 78.233;\n  highp float c = 43758.5453;\n  highp float d = dot(co.xy, vec2(a, b));\n  highp float e = mod(d, 3.14);\n  return fract(sin(e) * c);\n}\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n    // if we don't jitter the point size a bit, overall point cloud\n    // saturation 'jumps' on zooming, which is disturbing and confusing\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    // get the same square surface as circle would be\n    gl_PointSize *= 0.886;\n  }\n}"]),r.pointFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color, borderColor;\nuniform float centerFraction;\nuniform float pointCloud;\n\nvoid main() {\n  float radius;\n  vec4 baseColor;\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    if(centerFraction == 1.0) {\n      gl_FragColor = color;\n    } else {\n      gl_FragColor = mix(borderColor, color, centerFraction);\n    }\n  } else {\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\n    if(radius > 1.0) {\n      discard;\n    }\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\n  }\n}\n"]),r.pickVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform vec4 pickOffset;\n\nvarying vec4 fragId;\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n  gl_PointSize = pointSize;\n\n  vec4 id = pickId + pickOffset;\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  fragId = id;\n}\n"]),r.pickFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\n\nvoid main() {\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\n  if(radius > 1.0) {\n    discard;\n  }\n  gl_FragColor = fragId / 255.0;\n}\n"])},{glslify:424}],314:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("typedarray-pool"),o=t("./lib/shader");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r("sizeMin",.5),this.sizeMax=r("sizeMax",20),this.color=r("color",[1,0,0,1]).slice(),this.areaRatio=r("areaRatio",1),this.borderColor=r("borderColor",[0,0,0,1]).slice(),this.blend=r("blend",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{"./lib/shader":313,"gl-buffer":257,"gl-shader":323,"typedarray-pool":590}],315:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],m=r[2],g=r[3];(a=c*p+u*d+f*m+h*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*m,t[3]=s*h+l*g,t}},{}],316:[function(t,e,r){"use strict";e.exports=function(t){return t||0===t?t.toString():""}},{}],317:[function(t,e,r){"use strict";var n=t("vectorize-text");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:"center",textBaseline:"middle",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{"vectorize-text":596}],318:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform vec4 highlightId;\nuniform float highlightScale;\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = 1.0;\n    if(distance(highlightId, id) < 0.0001) {\n      scale = highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1);\n    vec4 viewPosition = view * worldPosition;\n    viewPosition = viewPosition / viewPosition.w;\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float highlightScale, pixelRatio;\nuniform vec4 highlightId;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = pixelRatio;\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\n      scale *= highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1.0);\n    vec4 viewPosition = view * worldPosition;\n    vec4 clipPosition = projection * viewPosition;\n    clipPosition /= clipPosition.w;\n\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform float highlightScale;\nuniform vec4 highlightId;\nuniform vec3 axes[2];\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float scale, pixelRatio;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float lscale = pixelRatio * scale;\n    if(distance(highlightId, id) < 0.0001) {\n      lscale *= highlightScale;\n    }\n\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = dataPosition;\n  }\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float opacity;\n\nvarying vec4 interpColor;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\n    interpColor.a * opacity == 0.\n  ) discard;\n  gl_FragColor = interpColor * opacity;\n}\n"]),c=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float pickGroup;\n\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\n\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\n}"]),u=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,m)},r.createPickProject=function(t){return v(t,g)}},{"gl-shader":323,glslify:424}],319:[function(t,e,r){"use strict";var n=t("is-string-blank"),i=t("gl-buffer"),a=t("gl-vao"),o=t("typedarray-pool"),s=t("gl-mat4/multiply"),l=t("./lib/shaders"),c=t("./lib/glyphs"),u=t("./lib/get-simple-string"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function m(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),m=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new g(e,r,n,o,f,h,p,d,m,s,c,u);return v.update(t),v};var v=g.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,m=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=m,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*g]=0,i[g]<0?v[12+g]=d[0][g]:v[12+g]=d[1][g],s(v,c,v),l.model=v;var C=(g+1)%3,P=(g+2)%3,I=M(x),O=M(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,m[0],g,-1e8),l.fragClipBounds[1]=E(k,m[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a="\u25bc",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if("perspective"in(t=t||{})&&(this.useOrtho=!t.perspective),"orthographic"in t&&(this.useOrtho=!!t.orthographic),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"project"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if("projectScale"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,"projectOpacity"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=m(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,"opacity"in t&&(this.opacity=m(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||"normal",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var 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i=0|Math.min(Math.max(t-r,0),n[0]),o=0|Math.min(Math.max(t+r,0),n[0]),l=0|Math.min(Math.max(e-r,0),n[1]),c=0|Math.min(Math.max(e+r,0),n[1]);if(o<=i||c<=l)return null;var u=[o-i,c-l],f=a(this.buffer,[u[0],u[1],4],[4,4*n[0],1],4*(i+n[0]*l)),h=function(t,e,r){for(var n=1e8,i=-1,a=-1,o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++){var u=t.get(l,c,0),f=t.get(l,c,1),h=t.get(l,c,2),p=t.get(l,c,3);if(u<255||f<255||h<255||p<255){var d=e-l,m=r-c,g=d*d+m*m;g<n&&(n=g,i=l,a=c)}}return[i,a,n]}(f.hi(u[0],u[1],1),r,r),p=h[0],d=h[1];return p<0||Math.pow(this.radius,2)<h[2]?null:new s(p+i|0,d+l|0,f.get(p,d,0),[f.get(p,d,1),f.get(p,d,2),f.get(p,d,3)],Math.sqrt(h[2]))},c.dispose=function(){this.gl&&(this.fbo.dispose(),i.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{"bit-twiddle":101,"gl-fbo":265,ndarray:462,"typedarray-pool":590}],323:[function(t,e,r){"use strict";var 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w=c[y];if(w.type.indexOf("mat")>=0){for(var T=0|w.type.charAt(w.type.length-1),k=new Array(T),A=0;A<T;++A)k[A]=_.length,b.push(w.name+"["+A+"]"),"number"==typeof w.location?_.push(w.location+A):Array.isArray(w.location)&&w.location.length===T&&"number"==typeof w.location[A]?_.push(0|w.location[A]):_.push(-1);x.push({name:w.name,type:w.type,locations:k})}else x.push({name:w.name,type:w.type,locations:[_.length]}),b.push(w.name),"number"==typeof w.location?_.push(0|w.location):_.push(-1)}var M=0;for(y=0;y<_.length;++y)if(_[y]<0){for(;_.indexOf(M)>=0;)M+=1;_[y]=M}var S=new Array(r.length);function E(){h.program=o.program(p,h._vref,h._fref,b,_);for(var t=0;t<r.length;++t)S[t]=p.getUniformLocation(h.program,r[t].name)}E(),h._relink=E,h.types={uniforms:a(r),attributes:a(c)},h.attributes=i(p,h,x,_),Object.defineProperty(h,"uniforms",n(p,h,r,S))},e.exports=function(t,e,r,n,i){var a=new c(t);return 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radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, tubeScale;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * tubePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\n  vec4 t_position  = view * tubePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = tubePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float tubeScale;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  gl_Position = projection * view * tubePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},{glslify:424}],334:[function(t,e,r){"use strict";var n=t("gl-vec3"),i=t("gl-vec4"),a=["xyz","xzy","yxz","yzx","zxy","zyx"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],m=0,g=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(m,g,g,g,m,m);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=y;y=v,v=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var m=u[s],g=f.length;f=f.concat(m.positions),p=p.concat(m.vectors),d=d.concat(m.vertexIntensity);for(c=0;c<m.cells.length;c++){var v=m.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||"+x+y+z",p={};-1!==h.indexOf("-x")&&(p.reversedX=!0),-1!==h.indexOf("-y")&&(p.reversedY=!0),-1!==h.indexOf("-z")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,"").replace(/\+/g,""));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),m=s(a[1],c),g=s(a[2],u),v=d+1,y=m+1,x=g+1;if(d=l(d,0,f-1),v=l(v,0,f-1),m=l(m,0,h-1),y=l(y,0,h-1),g=l(g,0,p-1),x=l(x,0,p-1),d<0||m<0||g<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][v],E=a[1][m],L=a[1][y],C=a[2][g],P=(o-M)/(S-M),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(m=h-1-m,y=h-1-y),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=m*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=g,A=x,b=d*p,_=v*p,w=m*p*f,T=y*p*f;break;case 3:w=m,T=y,k=g*h,A=x*h,b=d*h*p,_=v*h*p;break;case 2:w=m,T=y,b=d*h,_=v*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=v,k=g*f,A=x*f,w=m*f*p,T=y*f*p;break;default:b=d,_=v,w=m*f,T=y*f,k=g*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(H,D,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,q,I),n.lerp(W,H,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},m=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),m=c(r),g=c(n),v=Math.min(d,m,g);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=m(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-A>-1e-4*A){P.push(N),z=N,I.push(O);R=m(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*M/S),U};var u=t("./lib/shaders"),f=t("gl-cone3d").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:"streamtube"})}},{"./lib/shaders":333,"gl-cone3d":258,"gl-vec3":362,"gl-vec4":398}],335:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute vec3 f;\nattribute vec3 normal;\n\nuniform vec3 objectOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 lightPosition, eyePosition;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 localCoordinate = vec3(uv.zw, f.x);\n  worldCoordinate = objectOffset + localCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n  vec4 clipPosition = projection * view * worldPosition;\n  gl_Position = clipPosition;\n  kill = f.y;\n  value = f.z;\n  planeCoordinate = uv.xy;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * worldPosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  lightDirection = lightPosition - cameraCoordinate.xyz;\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\n}\n"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat beckmannSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness) {\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 lowerBound, upperBound;\nuniform float contourTint;\nuniform vec4 contourColor;\nuniform sampler2D colormap;\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform float vertexColor;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  if (\n    kill > 0.0 ||\n    vColor.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\n  ) discard;\n\n  vec3 N = normalize(surfaceNormal);\n  vec3 V = normalize(eyeDirection);\n  vec3 L = normalize(lightDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  //decide how to interpolate color \u2014 in vertex or in fragment\n  vec4 surfaceColor =\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\n    step(.5, vertexColor) * vColor;\n\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\n}\n"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute float f;\n\nuniform vec3 objectOffset;\nuniform mat3 permutation;\nuniform mat4 model, view, projection;\nuniform float height, zOffset;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\n  worldCoordinate = objectOffset + dataCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n\n  vec4 clipPosition = projection * view * worldPosition;\n  clipPosition.z += zOffset;\n\n  gl_Position = clipPosition;\n  value = f + objectOffset.z;\n  kill = -1.0;\n  planeCoordinate = uv.zw;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Don't do lighting for contours\n  surfaceNormal   = vec3(1,0,0);\n  eyeDirection    = vec3(0,1,0);\n  lightDirection  = vec3(0,0,1);\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec2 shape;\nuniform vec3 clipBounds[2];\nuniform float pickId;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 surfaceNormal;\n\nvec2 splitFloat(float v) {\n  float vh = 255.0 * v;\n  float upper = floor(vh);\n  float lower = fract(vh);\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\n}\n\nvoid main() {\n  if ((kill > 0.0) ||\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\n\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\n}\n"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{"gl-shader":323,glslify:424}],336:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||"jet",g.update(v),g};var n=t("bit-twiddle"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("typedarray-pool"),l=t("colormap"),c=t("ndarray-ops"),u=t("ndarray-pack"),f=t("ndarray"),h=t("surface-nets"),p=t("gl-mat4/multiply"),d=t("gl-mat4/invert"),m=t("binary-search-bounds"),g=t("ndarray-gradient"),v=t("./lib/shaders"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function M(t,e,r,n,i,a,o,l,c,u,h,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:"rgba"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var m=0;m<this.contourLevels[o].length;++m)m===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==m&&m-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][m]&&(f.uniforms.height=this.contourLevels[o][m],h.draw(r.LINES,this._contourCounts[o][m],this._contourOffsets[o][m]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,g=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*g;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=m.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,"contourWidth"in t&&(this.contourWidth=R(t.contourWidth,Number)),"showContour"in t&&(this.showContour=R(t.showContour,Boolean)),"showSurface"in t&&(this.showSurface=!!t.showSurface),"contourTint"in t&&(this.contourTint=R(t.contourTint,Boolean)),"contourColor"in t&&(this.contourColor=B(t.contourColor)),"contourProject"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),"surfaceProject"in t&&(this.surfaceProject=t.surfaceProject),"dynamicColor"in t&&(this.dynamicColor=B(t.dynamicColor)),"dynamicTint"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),"dynamicWidth"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),"opacity"in t&&(this.opacity=t.opacity),"opacityscale"in t&&(this.opacityscale=t.opacityscale),"colorBounds"in t&&(this.colorBounds=t.colorBounds),"vertexColor"in t&&(this.vertexColor=t.vertexColor?1:0),"colormap"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in t||"coords"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error("gl-surface: invalid coordinates for x/y");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error("gl-surface: coords have incorrect shape");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error("gl-surface: invalid ticks");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error("gl-surface: invalid tick length");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),y[o],"mirror");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),A=x.get(1,o,v,1),M=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-A*M,L=M*w-S*_,C=_*A-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+v+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Y=o+T[q][0],W=v+T[q][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if("levels"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var mt=($+dt+1)%3;for(V=0;V<2;++V){var gt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var vt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[mt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=gt*vt;nt[dt]+=yt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),m=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<m.length;++e)for(var v=m[e],y=0;y<2;++y){var x=g[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,M)+P*f.get(_,S)+I*f.get(w,M)+O*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+I*p.get(w,M)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{"./lib/shaders":335,"binary-search-bounds":100,"bit-twiddle":101,colormap:132,"gl-buffer":257,"gl-mat4/invert":287,"gl-mat4/multiply":289,"gl-texture2d":338,"gl-vao":343,ndarray:462,"ndarray-gradient":455,"ndarray-ops":457,"ndarray-pack":458,"surface-nets":565,"typedarray-pool":590}],337:[function(t,e,r){"use strict";var n=t("css-font"),i=t("pick-by-alias"),a=t("regl"),o=t("gl-util/context"),s=t("es6-weak-map"),l=t("color-normalize"),c=t("font-atlas"),u=t("typedarray-pool"),f=t("parse-rect"),h=t("is-plain-obj"),p=t("parse-unit"),d=t("to-px"),m=t("detect-kerning"),g=t("object-assign"),v=t("font-measure"),y=t("flatten-vertex-data"),x=t("bit-twiddle").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement("div"));w.style.font="italic small-caps bold condensed 16px/2 cursive",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return"function"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:"uint8",usage:"stream"}),this.sizeBuffer=this.regl.buffer({type:"float",usage:"stream"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop("count"),offset:t.prop("offset"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this("sizeBuffer")},width:{offset:0,stride:8,buffer:t.this("sizeBuffer")},char:t.this("charBuffer"),position:t.this("position")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop("color"),opacity:t.prop("opacity"),viewport:t.this("viewportArray"),scale:t.this("scale"),align:t.prop("align"),baseline:t.prop("baseline"),translate:t.this("translate"),positionOffset:t.prop("positionOffset")},primitive:"points",viewport:t.this("viewport"),vert:"\n\t\t\tprecision highp float;\n\t\t\tattribute float width, charOffset, char;\n\t\t\tattribute vec2 position;\n\t\t\tuniform float fontSize, charStep, em, align, baseline;\n\t\t\tuniform vec4 viewport;\n\t\t\tuniform vec4 color;\n\t\t\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\n\t\t\tvarying vec2 charCoord, charId;\n\t\t\tvarying float charWidth;\n\t\t\tvarying vec4 fontColor;\n\t\t\tvoid main () {\n\t\t\t\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\n\t\t\t\t\t+ vec2(positionOffset.x, -positionOffset.y)))\n\t\t\t\t\t/ (viewport.zw * scale.xy);\n\n\t\t\t\tvec2 position = (position + translate) * scale;\n\t\t\t\tposition += offset * scale;\n\n\t\t\t\tcharCoord = position * viewport.zw + viewport.xy;\n\n\t\t\t\tgl_Position = vec4(position * 2. - 1., 0, 1);\n\n\t\t\t\tgl_PointSize = charStep;\n\n\t\t\t\tcharId.x = mod(char, atlasDim.x);\n\t\t\t\tcharId.y = floor(char / atlasDim.x);\n\n\t\t\t\tcharWidth = width * em;\n\n\t\t\t\tfontColor = color / 255.;\n\t\t\t}",frag:"\n\t\t\tprecision highp float;\n\t\t\tuniform float fontSize, charStep, opacity;\n\t\t\tuniform vec2 atlasSize;\n\t\t\tuniform vec4 viewport;\n\t\t\tuniform sampler2D atlas;\n\t\t\tvarying vec4 fontColor;\n\t\t\tvarying vec2 charCoord, charId;\n\t\t\tvarying float charWidth;\n\n\t\t\tfloat lightness(vec4 color) {\n\t\t\t\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\n\t\t\t}\n\n\t\t\tvoid main () {\n\t\t\t\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\n\t\t\t\tfloat halfCharStep = floor(charStep * .5 + .5);\n\n\t\t\t\t// invert y and shift by 1px (FF expecially needs that)\n\t\t\t\tuv.y = charStep - uv.y;\n\n\t\t\t\t// ignore points outside of character bounding box\n\t\t\t\tfloat halfCharWidth = ceil(charWidth * .5);\n\t\t\t\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\n\t\t\t\t\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\n\n\t\t\t\tuv += charId * charStep;\n\t\t\t\tuv = uv / atlasSize;\n\n\t\t\t\tvec4 color = fontColor;\n\t\t\t\tvec4 mask = texture2D(atlas, uv);\n\n\t\t\t\tfloat maskY = lightness(mask);\n\t\t\t\t// float colorY = lightness(color);\n\t\t\t\tcolor.a *= maskY;\n\t\t\t\tcolor.a *= opacity;\n\n\t\t\t\t// color.a += .1;\n\n\t\t\t\t// antialiasing, see yiq color space y-channel formula\n\t\t\t\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\n\n\t\t\t\tgl_FragColor = color;\n\t\t\t}"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if("string"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:"position positions coord coords coordinates",font:"font fontFace fontface typeface cssFont css-font family fontFamily",fontSize:"fontSize fontsize size font-size",text:"text texts chars characters value values symbols",align:"align alignment textAlign textbaseline",baseline:"baseline textBaseline textbaseline",direction:"dir direction textDirection",color:"color colour fill fill-color fillColor textColor textcolor",kerning:"kerning kern",range:"range dataBox",viewport:"vp viewport viewBox viewbox viewPort",opacity:"opacity alpha transparency visible visibility opaque",offset:"offset positionOffset padding shift indent indentation"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&("number"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+"px sans-serif");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if("string"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+"px "+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(", "),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:"top",fontSize:T.baseFontSize,fontStyle:u.join(" ")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),"string"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join("")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);g(t.kerning,m(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),A=0,M=0;A<this.counts.length;A++){var S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:"float",data:k}):this.position=this.regl.buffer({type:"float",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),P=u.mallocFloat(2*this.count);this.textWidth=[];for(var I=0,O=0;I<this.counts.length;I++){for(var z=this.counts[I],D=this.font[I]||this.font[0],R=this.fontAtlas[I]||this.fontAtlas[0],F=0;F<z;F++){var B=this.text.charAt(O),N=this.text.charAt(O-1);if(C[O]=R.ids[B],P[2*O]=D.width[B],F){var j=P[2*O-2],U=P[2*O],V=P[2*O-1]+.5*j+.5*U;if(this.kerning){var H=D.kerning[N+B];H&&(V+=.001*H)}P[2*O+1]=V}else P[2*O+1]=.5*P[2*O];O++}this.textWidth.push(P.length?.5*P[2*O-2]+P[2*O-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:"uint8",usage:"stream"}),this.sizeBuffer({data:P,type:"float",usage:"stream"}),u.freeUint8(C),u.freeFloat(P),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if("number"==typeof n)return n;switch(n){case"right":case"end":return-t;case"center":case"centre":case"middle":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+="number"==typeof t?t-n.baseline:-n[t],i*=-1}))),null!=t.color)if(t.color||(t.color="transparent"),"string"!=typeof t.color&&isNaN(t.color)){var q;if("number"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;q=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)q.set(l(Y(W,W+4),"uint8"),W)}else{var X=t.color.length;q=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)q.set(l(t.color[Z]||0,"uint8"),4*Z)}this.color=q}else this.color=l(t.color,"uint8");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var K=0;K<this.batch.length;K++)this.batch[K]={count:this.counts.length>1?this.counts[K]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[K]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*K,4*K+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[K]:this.opacity,baseline:null!=this.baselineOffset[K]?this.baselineOffset[K]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[K]?this.alignOffset[K]:this.alignOffset[0]:0,atlas:this.fontAtlas[K]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*K,2*K+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text="",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.maxAtlasSize=1024,T.atlasCanvas=document.createElement("canvas"),T.atlasContext=T.atlasCanvas.getContext("2d",{alpha:!1}),T.baseFontSize=64,T.fonts={},e.exports=T},{"bit-twiddle":101,"color-normalize":126,"css-font":146,"detect-kerning":173,"es6-weak-map":233,"flatten-vertex-data":244,"font-atlas":245,"font-measure":246,"gl-util/context":339,"is-plain-obj":437,"object-assign":466,"parse-rect":471,"parse-unit":473,"pick-by-alias":475,regl:516,"to-px":574,"typedarray-pool":590}],338:[function(t,e,r){"use strict";var n=t("ndarray"),i=t("ndarray-ops"),a=t("typedarray-pool");e.exports=function(t){if(arguments.length<=1)throw new Error("gl-texture2d: Missing arguments for texture2d constructor");o||c(t);if("number"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if("object"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error("gl-texture2d: Invalid arguments for texture2d constructor")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||"undefined"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function 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y=u.size,x=c.indexOf(o)<0;x&&c.push(o);if(d===l&&v)0===u.offset&&u.data.length===y?x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,u.data):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,u.data):x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,u.data.subarray(u.offset,u.offset+y)):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,u.data.subarray(u.offset,u.offset+y));else{var b;b=l===t.FLOAT?a.mallocFloat32(y):a.mallocUint8(y);var _=n(b,p,[p[2],p[2]*p[0],1]);d===t.FLOAT&&l===t.UNSIGNED_BYTE?f(_,u):i.assign(_,u),x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,b.subarray(0,y)):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,b.subarray(0,y)),l===t.FLOAT?a.freeFloat32(b):a.freeUint8(b)}}(s,e,r,o,this.format,this.type,this._mipLevels,t)}}},{ndarray:462,"ndarray-ops":457,"typedarray-pool":590}],339:[function(t,e,r){(function(r){(function(){"use strict";var n=t("pick-by-alias");function 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Use headless-gl.");t.container=document.body||document.documentElement,t.canvas=o(),t.container.appendChild(t.canvas),i(t)}return t.gl||["webgl","experimental-webgl","webgl-experimental"].some((function(e){try{t.gl=t.canvas.getContext(e,t.attrs)}catch(t){}return t.gl})),t.gl}}).call(this)}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{"pick-by-alias":475}],340:[function(t,e,r){"use strict";e.exports=function(t,e,r){e?e.bind():t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null);var n=0|t.getParameter(t.MAX_VERTEX_ATTRIBS);if(r){if(r.length>n)throw new Error("gl-vao: Too many vertex attributes");for(var i=0;i<r.length;++i){var a=r[i];if(a.buffer){var o=a.buffer,s=a.size||4,l=a.type||t.FLOAT,c=!!a.normalized,u=a.stride||0,f=a.offset||0;o.bind(),t.enableVertexAttribArray(i),t.vertexAttribPointer(i,s,l,c,u,f)}else{if("number"==typeof a)t.vertexAttrib1f(i,a);else if(1===a.length)t.vertexAttrib1f(i,a[0]);else 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0===s?jr(e.default,r.default):n(e,r,s,i,a)}}}(this._parameters,this._specification))};function Qr(t){var e=t.key,r=t.value,n=t.valueSpec||{},i=t.objectElementValidators||{},a=t.style,o=t.styleSpec,s=[],l=Fr(r);if("object"!==l)return[new Ct(e,r,"object expected, "+l+" found")];for(var c in r){var u=c.split(".")[0],f=n[u]||n["*"],h=void 0;if(i[u])h=i[u];else if(n[u])h=kn;else if(i["*"])h=i["*"];else{if(!n["*"]){s.push(new Ct(e,r[c],'unknown property "'+c+'"'));continue}h=kn}s=s.concat(h({key:(e?e+".":e)+c,value:r[c],valueSpec:f,style:a,styleSpec:o,object:r,objectKey:c},r))}for(var p in n)i[p]||n[p].required&&void 0===n[p].default&&void 0===r[p]&&s.push(new Ct(e,r,'missing required property "'+p+'"'));return s}function $r(t){var e=t.value,r=t.valueSpec,n=t.style,i=t.styleSpec,a=t.key,o=t.arrayElementValidator||kn;if("array"!==Fr(e))return[new Ct(a,e,"array expected, "+Fr(e)+" found")];if(r.length&&e.length!==r.length)return[new Ct(a,e,"array length "+r.length+" expected, length 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0===t.value.property,l=!s,c="array"===Fr(t.value.stops)&&"array"===Fr(t.value.stops[0])&&"object"===Fr(t.value.stops[0][0]),u=Qr({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if("identity"===a)return[new Ct(t.key,t.value,'identity function may not have a "stops" property')];var e=[],r=t.value;e=e.concat($r({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),"array"===Fr(r)&&0===r.length&&e.push(new Ct(t.key,r,"array must have at least one stop"));return e},default:function(t){return kn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return"identity"===a&&s&&u.push(new Ct(t.key,t.value,'missing required property "property"')),"identity"===a||t.value.stops||u.push(new Ct(t.key,t.value,'missing required property "stops"')),"exponential"===a&&t.valueSpec.expression&&!Rr(t.valueSpec)&&u.push(new Ct(t.key,t.value,"exponential functions not supported")),t.styleSpec.$version>=8&&(l&&!zr(t.valueSpec)?u.push(new Ct(t.key,t.value,"property functions not supported")):s&&!Dr(t.valueSpec)&&u.push(new Ct(t.key,t.value,"zoom functions not supported"))),"categorical"!==a&&!c||void 0!==t.value.property||u.push(new Ct(t.key,t.value,'"property" property is required')),u;function f(t){var e=[],a=t.value,s=t.key;if("array"!==Fr(a))return[new Ct(s,a,"array expected, "+Fr(a)+" found")];if(2!==a.length)return[new Ct(s,a,"array length 2 expected, length "+a.length+" found")];if(c){if("object"!==Fr(a[0]))return[new Ct(s,a,"object expected, "+Fr(a[0])+" found")];if(void 0===a[0].zoom)return[new Ct(s,a,"object stop key must have zoom")];if(void 0===a[0].value)return[new Ct(s,a,"object stop key must have value")];if(n&&n>Ot(a[0].zoom))return[new Ct(s,a[0].zoom,"stop zoom values must appear in ascending order")];Ot(a[0].zoom)!==n&&(n=Ot(a[0].zoom),r=void 0,o={}),e=e.concat(Qr({key:s+"[0]",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:tn,value:h}}))}else e=e.concat(h({key:s+"[0]",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Yr(zt(a[1]))?e.concat([new Ct(s+"[1]",a[1],"expressions are not allowed in function stops.")]):e.concat(kn({key:s+"[1]",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Fr(t.value),l=Ot(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new Ct(t.key,c,s+" stop domain type must match previous stop domain type "+e)]}else e=s;if("number"!==s&&"string"!==s&&"boolean"!==s)return[new Ct(t.key,c,"stop domain value must be a number, string, or boolean")];if("number"!==s&&"categorical"!==a){var u="number expected, "+s+" found";return zr(i)&&void 0===a&&(u+='\nIf you intended to use a categorical function, specify `"type": "categorical"`.'),[new Ct(t.key,c,u)]}return"categorical"!==a||"number"!==s||isFinite(l)&&Math.floor(l)===l?"categorical"!==a&&"number"===s&&void 0!==r&&l<r?[new Ct(t.key,c,"stop domain values must appear in ascending order")]:(r=l,"categorical"===a&&l in o?[new Ct(t.key,c,"stop domain values must be unique")]:(o[l]=!0,[])):[new Ct(t.key,c,"integer expected, found "+l)]}}function rn(t){var e=("property"===t.expressionContext?Jr:Wr)(zt(t.value),t.valueSpec);if("error"===e.result)return e.value.map((function(e){return new Ct(""+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if("property"===t.expressionContext&&"text-font"===t.propertyKey&&!r.outputDefined())return[new Ct(t.key,t.value,'Invalid data expression for "'+t.propertyKey+'". Output values must be contained as literals within the expression.')];if("property"===t.expressionContext&&"layout"===t.propertyType&&!Be(r))return[new Ct(t.key,t.value,'"feature-state" data expressions are not supported with layout properties.')];if("filter"===t.expressionContext&&!Be(r))return[new Ct(t.key,t.value,'"feature-state" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf("cluster")){if(!Ne(r,["zoom","feature-state"]))return[new Ct(t.key,t.value,'"zoom" and "feature-state" expressions are not supported with cluster properties.')];if("cluster-initial"===t.expressionContext&&!Fe(r))return[new Ct(t.key,t.value,"Feature data expressions are not supported with initial expression part of cluster properties.")]}return[]}function nn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ot(r))&&i.push(new Ct(e,r,"expected one of ["+n.values.join(", ")+"], "+JSON.stringify(r)+" found")):-1===Object.keys(n.values).indexOf(Ot(r))&&i.push(new Ct(e,r,"expected one of ["+Object.keys(n.values).join(", ")+"], "+JSON.stringify(r)+" found")),i}function an(t){if(!0===t||!1===t)return!0;if(!Array.isArray(t)||0===t.length)return!1;switch(t[0]){case"has":return t.length>=2&&"$id"!==t[1]&&"$type"!==t[1];case"in":return t.length>=3&&("string"!=typeof t[1]||Array.isArray(t[2]));case"!in":case"!has":case"none":return!1;case"==":case"!=":case">":case">=":case"<":case"<=":return 3!==t.length||Array.isArray(t[1])||Array.isArray(t[2]);case"any":case"all":for(var e=0,r=t.slice(1);e<r.length;e+=1){var n=r[e];if(!an(n)&&"boolean"!=typeof n)return!1}return!0;default:return!0}}Kr.deserialize=function(t){return new Kr(t._parameters,t._specification)},Kr.serialize=function(t){return{_parameters:t._parameters,_specification:t._specification}};var on={type:"boolean",default:!1,transition:!1,"property-type":"data-driven",expression:{interpolated:!1,parameters:["zoom","feature"]}};function sn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};an(t)||(t=cn(t));var e=Wr(t,on);if("error"===e.result)throw new Error(e.value.map((function(t){return t.key+": "+t.message})).join(", "));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if("within"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function ln(t,e){return t<e?-1:t>e?1:0}function cn(t){if(!t)return!0;var e,r=t[0];return t.length<=1?"any"!==r:"=="===r?un(t[1],t[2],"=="):"!="===r?pn(un(t[1],t[2],"==")):"<"===r||">"===r||"<="===r||">="===r?un(t[1],t[2],r):"any"===r?(e=t.slice(1),["any"].concat(e.map(cn))):"all"===r?["all"].concat(t.slice(1).map(cn)):"none"===r?["all"].concat(t.slice(1).map(cn).map(pn)):"in"===r?fn(t[1],t.slice(2)):"!in"===r?pn(fn(t[1],t.slice(2))):"has"===r?hn(t[1]):"!has"===r?pn(hn(t[1])):"within"!==r||t}function un(t,e,r){switch(t){case"$type":return["filter-type-"+r,e];case"$id":return["filter-id-"+r,e];default:return["filter-"+r,t,e]}}function fn(t,e){if(0===e.length)return!1;switch(t){case"$type":return["filter-type-in",["literal",e]];case"$id":return["filter-id-in",["literal",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?["filter-in-large",t,["literal",e.sort(ln)]]:["filter-in-small",t,["literal",e]]}}function hn(t){switch(t){case"$type":return!0;case"$id":return["filter-has-id"];default:return["filter-has",t]}}function pn(t){return["!",t]}function dn(t){return an(zt(t.value))?rn(It({},t,{expressionContext:"filter",valueSpec:{value:"boolean"}})):function t(e){var r=e.value,n=e.key;if("array"!==Fr(r))return[new Ct(n,r,"array expected, "+Fr(r)+" found")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new Ct(n,r,"filter array must have at least 1 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Ct(n,r,'filter array for "'+r[0]+'" operator must have 2 elements')):"string"!==i&&o.push(new Ct(n+"[1]",r[1],"string expected, "+i+" found"));break;case"within":i=Fr(r[1]),2!==r.length?o.push(new Ct(n,r,'filter array for "'+r[0]+'" operator must have 2 elements')):"object"!==i&&o.push(new Ct(n+"[1]",r[1],"object expected, "+i+" found"))}return o}(t)}function mn(t,e){var r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+"_"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if("paint"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property "'+o+'"')];if("string"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'"'+o+'" does not support interpolation syntax\nUse an identity property function instead: `{ "type": "identity", "property": '+JSON.stringify(c[1])+" }`.")];var f=[];return"symbol"===t.layerType&&("text-field"===o&&n&&!n.glyphs&&f.push(new Ct(r,a,'use of "text-field" requires a style "glyphs" property')),"text-font"===o&&Br(zt(a))&&"identity"===Ot(a.type)&&f.push(new Ct(r,a,'"text-font" does not support identity functions'))),f.concat(kn({key:t.key,value:a,valueSpec:u,style:n,styleSpec:i,expressionContext:"property",propertyType:e,propertyKey:o}))}function gn(t){return mn(t,"paint")}function vn(t){return mn(t,"layout")}function yn(t){var e=[],r=t.value,n=t.key,i=t.style,a=t.styleSpec;r.type||r.ref||e.push(new Ct(n,r,'either "type" or "ref" is required'));var o,s=Ot(r.type),l=Ot(r.ref);if(r.id)for(var c=Ot(r.id),u=0;u<t.arrayIndex;u++){var f=i.layers[u];Ot(f.id)===c&&e.push(new Ct(n,r.id,'duplicate layer id "'+r.id+'", previously used at line '+f.id.__line__))}if("ref"in r)["type","source","source-layer","filter","layout"].forEach((function(t){t in r&&e.push(new Ct(n,r[t],'"'+t+'" is prohibited for ref 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e.push(new Ct(n,r,'missing required property "source"'));return e=e.concat(Qr({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(){return[]},type:function(){return kn({key:n+".type",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:"type"})},filter:dn,layout:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return vn(It({layerType:s},t))}}})},paint:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return gn(It({layerType:s},t))}}})}}}))}function xn(t){var e=t.value,r=t.key,n=Fr(e);return"string"!==n?[new Ct(r,e,"string expected, "+n+" found")]:[]}var bn={promoteId:function(t){var e=t.key,r=t.value;if("string"===Fr(r))return xn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,xn({key:e+"."+i,value:r[i]}));return n}};function _n(t){var e=t.value,r=t.key,n=t.styleSpec,i=t.style;if(!e.type)return[new Ct(r,e,'"type" is required')];var a,o=Ot(e.type);switch(o){case"vector":case"raster":case"raster-dem":return a=Qr({key:r,value:e,valueSpec:n["source_"+o.replace("-","_")],style:t.style,styleSpec:n,objectElementValidators:bn});case"geojson":if(a=Qr({key:r,value:e,valueSpec:n.source_geojson,style:i,styleSpec:n,objectElementValidators:bn}),e.cluster)for(var s in e.clusterProperties){var l=e.clusterProperties[s],c=l[0],u=l[1],f="string"==typeof c?[c,["accumulated"],["get",s]]:c;a.push.apply(a,rn({key:r+"."+s+".map",value:u,expressionContext:"cluster-map"})),a.push.apply(a,rn({key:r+"."+s+".reduce",value:f,expressionContext:"cluster-reduce"}))}return a;case"video":return Qr({key:r,value:e,valueSpec:n.source_video,style:i,styleSpec:n});case"image":return Qr({key:r,value:e,valueSpec:n.source_image,style:i,styleSpec:n});case"canvas":return[new Ct(r,null,"Please use runtime APIs to add canvas sources, rather 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vi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};vi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var yi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};yi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},yi.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var xi=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};xi.prototype.getValue=function(t){return x(this._values[t].value)},xi.prototype.setValue=function(t,e){this._values[t]=new di(this._values[t].property,null===e?void 0:x(e))},xi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},xi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};bi.prototype.isConstant=function(){return"constant"===this.value.kind},bi.prototype.constantOr=function(t){return"constant"===this.value.kind?this.value.value:t},bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var _i=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};_i.prototype.get=function(t){return this._values[t]};var wi=function(t){this.specification=t};wi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},wi.prototype.interpolate=function(t,e,r){var n=Ge[this.specification.type];return n?n(t,e,r):t};var Ti=function(t,e){this.specification=t,this.overrides=e};Ti.prototype.possiblyEvaluate=function(t,e,r,n){return"constant"===t.expression.kind||"camera"===t.expression.kind?new bi(this,{kind:"constant",value:t.expression.evaluate(e,null,{},r,n)},e):new bi(this,t.expression,e)},Ti.prototype.interpolate=function(t,e,r){if("constant"!==t.value.kind||"constant"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new bi(this,{kind:"constant",value:void 0},t.parameters);var n=Ge[this.specification.type];return n?new bi(this,{kind:"constant",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ti.prototype.evaluate=function(t,e,r,n,i,a){return"constant"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new bi(this,{kind:"constant",value:void 0},e);if("constant"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a="resolvedImage"===t.property.specification.type&&"string"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new bi(this,{kind:"constant",value:o},e)}if("camera"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new bi(this,{kind:"constant",value:s},e)}return new bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if("source"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return"composite"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ti),Ai=function(t){this.specification=t};Ai.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if("constant"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new pi(Math.floor(e.zoom),e)),t.expression.evaluate(new pi(Math.floor(e.zoom+1),e)),e)}},Ai.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Ai.prototype.interpolate=function(t){return t};var Mi=function(t){this.specification=t};Mi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Mi.prototype.interpolate=function(){return!1};var Si=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new di(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new mi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Nn("DataDrivenProperty",Ti),Nn("DataConstantProperty",wi),Nn("CrossFadedDataDrivenProperty",ki),Nn("CrossFadedProperty",Ai),Nn("ColorRampProperty",Mi);var Ei=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},"custom"!==e.type&&(e=e,this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,"background"!==e.type&&(this.source=e.source,this.sourceLayer=e["source-layer"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new xi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new gi(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new _i(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return"visibility"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n="layers."+this.id+".layout."+t;if(this._validate(In,n,t,e,r))return}"visibility"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return g(t,"-transition")?this._transitionablePaint.getTransition(t.slice(0,-"-transition".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n="layers."+this.id+".paint."+t;if(this._validate(Pn,n,t,e,r))return!1}if(g(t,"-transition"))return this._transitionablePaint.setTransition(t.slice(0,-"-transition".length),e||void 0),!1;var i=this._transitionablePaint._values[t],a="cross-faded-data-driven"===i.property.specification["property-type"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||"none"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,"source-layer":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||"layout"===e&&!Object.keys(t).length||"paint"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&(("source"===e.value.kind||"composite"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error("_refreshViews() must be implemented by each concrete StructArray layout")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn("StructArrayLayout2i4",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn("StructArrayLayout4i8",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn("StructArrayLayout2i4i12",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn("StructArrayLayout2i4ub8",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn("StructArrayLayout8ui2ub18",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn("StructArrayLayout4i4ui4i24",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn("StructArrayLayout3f12",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn("StructArrayLayout1ul4",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn("StructArrayLayout6i1ul2ui20",Vi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Hi.prototype.bytesPerElement=12,Nn("StructArrayLayout2i2i2i12",Hi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);qi.prototype.bytesPerElement=16,Nn("StructArrayLayout2f1f2i16",qi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn("StructArrayLayout2ub2f12",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn("StructArrayLayout3ui6",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=m,this.uint32[x+10]=g,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn("StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=c,this.uint16[L+9]=u,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=m,this.uint16[L+15]=g,this.uint16[L+16]=v,this.uint16[L+17]=y,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Pi);Xi.prototype.bytesPerElement=68,Nn("StructArrayLayout8i15ui1ul4f68",Xi);var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Pi);Zi.prototype.bytesPerElement=4,Nn("StructArrayLayout1f4",Zi);var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Pi);Ji.prototype.bytesPerElement=6,Nn("StructArrayLayout3i6",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Pi);Ki.prototype.bytesPerElement=8,Nn("StructArrayLayout1ul2ui8",Ki);var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Pi);Qi.prototype.bytesPerElement=4,Nn("StructArrayLayout2ui4",Qi);var $i=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Pi);$i.prototype.bytesPerElement=2,Nn("StructArrayLayout1ui2",$i);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn("StructArrayLayout2f8",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Pi);ea.prototype.bytesPerElement=16,Nn("StructArrayLayout4f16",ea);var ra=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new i(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Ci);ra.prototype.size=20;var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ra(this,t)},e}(Vi);Nn("CollisionBoxArray",na);var ia=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ci);ia.prototype.size=48;var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ia(this,t)},e}(Wi);Nn("PlacedSymbolArray",aa);var oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return this._structArray.uint16[this._pos2+22]},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+12]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+12]=t},r.textBoxScale.get=function(){return this._structArray.float32[this._pos4+13]},r.textOffset0.get=function(){return this._structArray.float32[this._pos4+14]},r.textOffset1.get=function(){return this._structArray.float32[this._pos4+15]},r.collisionCircleDiameter.get=function(){return this._structArray.float32[this._pos4+16]},Object.defineProperties(e.prototype,r),e}(Ci);oa.prototype.size=68;var sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new oa(this,t)},e}(Xi);Nn("SymbolInstanceArray",sa);var la=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getoffsetX=function(t){return this.float32[1*t+0]},e}(Zi);Nn("GlyphOffsetArray",la);var ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getx=function(t){return this.int16[3*t+0]},e.prototype.gety=function(t){return this.int16[3*t+1]},e.prototype.gettileUnitDistanceFromAnchor=function(t){return this.int16[3*t+2]},e}(Ji);Nn("SymbolLineVertexArray",ca);var ua=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0}};return r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+0]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+3]},Object.defineProperties(e.prototype,r),e}(Ci);ua.prototype.size=8;var fa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ua(this,t)},e}(Ki);Nn("FeatureIndexArray",fa);var ha=Ii([{name:"a_pos",components:2,type:"Int16"}],4).members,pa=function(t){void 0===t&&(t=[]),this.segments=t};function da(t,e){return 256*(t=l(Math.floor(t),0,255))+(e=l(Math.floor(e),0,255))}pa.prototype.prepareSegment=function(t,e,r,n){var i=this.segments[this.segments.length-1];return t>pa.MAX_VERTEX_ARRAY_LENGTH&&_("Max vertices per segment is "+pa.MAX_VERTEX_ARRAY_LENGTH+": bucket requested "+t),(!i||i.vertexLength+t>pa.MAX_VERTEX_ARRAY_LENGTH||i.sortKey!==n)&&(i={vertexOffset:e.length,primitiveOffset:r.length,vertexLength:0,primitiveLength:0},void 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t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof bi&&zr(o.property.specification)){var s=Va(a,t.type),l=o.value,c=o.property.specification.type,u=o.property.useIntegerZoom,f=o.property.specification["property-type"],h="cross-faded"===f||"cross-faded-data-driven"===f;if("constant"===l.kind)this.binders[a]=h?new Ra(l.value,s):new Da(l.value,s,c),i.push("/u_"+a);else if("source"===l.kind||h){var p=Ha(a,c,"source");this.binders[a]=h?new Na(l,c,u,e,p,t.id):new Fa(l,s,c,p),i.push("/a_"+a)}else{var d=Ha(a,c,"composite");this.binders[a]=new Ba(l,s,c,u,e,d),i.push("/z_"+a)}}}this.cacheKey=i.sort().join("")};ja.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof Fa||e instanceof Ba?e.maxValue:0},ja.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof Fa||o instanceof Ba||o instanceof Na)&&o.populatePaintArray(t,e,r,n,i)}},ja.prototype.setConstantPatternPositions=function(t,e){for(var r in 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c=t.readVarint();n=7&c,a=c>>3}if(a--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new i(o,s));else{if(7!==n)throw new Error("unknown command "+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error("unknown command "+r)}return[o,l,s,c]},ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=ws.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];u(l=f);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=ks(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}r&&i.push(r);return i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c="Multi"+c;var h={type:"Feature",geometry:{type:c,coordinates:l},properties:this.properties};return"id"in this&&(h.id=this.id),h};var As=Ms;function Ms(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Ss,this,e),this.length=this._features.length}function Ss(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){var e=null,r=t.readVarint()+t.pos;for(;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new As(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ms.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error("feature index out of bounds");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:As},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds("fill-extrusion",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(ms("fill-extrusion",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var m=0,g=0;g<d.length;g++){var v=d[g];if(g>=1){var y=d[g-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);m+b>32768&&(m=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,m),m+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,m);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),"Polygon"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=Do(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn("FillExtrusionBucket",Os,{omit:["layers","features"]});var Rs={paint:new Si({"fill-extrusion-opacity":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-opacity"]),"fill-extrusion-color":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-color"]),"fill-extrusion-translate":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-translate"]),"fill-extrusion-translate-anchor":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-translate-anchor"]),"fill-extrusion-pattern":new ki(Lt["paint_fill-extrusion"]["fill-extrusion-pattern"]),"fill-extrusion-height":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-height"]),"fill-extrusion-base":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-base"]),"fill-extrusion-vertical-gradient":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-vertical-gradient"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get("fill-extrusion-translate"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get("fill-extrusion-translate"),this.paint.get("fill-extrusion-translate-anchor"),o.angle,s),u=this.paint.get("fill-extrusion-height").evaluate(e,r),f=this.paint.get("fill-extrusion-base").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,m=0,g=t;m<g.length;m+=1){for(var v=g[m],y=[],x=[],b=0,_=v;b<_.length;b+=1){var w=_[b],T=w.x,k=w.y,A=n[0]*T+n[4]*k+n[12],M=n[1]*T+n[5]*k+n[13],S=n[2]*T+n[6]*k+n[14],E=n[3]*T+n[7]*k+n[15],L=S+c,C=E+u,P=A+f,I=M+h,O=S+p,z=E+d,D=new i((A+s)/C,(M+l)/C);D.z=L/C,y.push(D);var R=new i(P/z,I/z);R.z=O/z,x.push(R)}a.push(y),o.push(x)}return[a,o]}(n,f,u,l);return function(t,e,r){var n=1/0;Ka(r,e)&&(n=Ns(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],c=a[s+1],u=o[s],f=o[s+1],h=[l,c,f,u,l];Za(r,h)&&(n=Math.min(n,Ns(r,h)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ei);function Bs(t,e){return t.x*e.x+t.y*e.y}function Ns(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Bs(s,s),f=Bs(s,l),h=Bs(l,l),p=Bs(c,s),d=Bs(c,l),m=u*h-f*f,g=(h*p-f*d)/m,v=(u*d-f*p)/m,y=1-g-v,x=i.z*y+r.z*g+a.z*v;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var js=Ii([{name:"a_pos_normal",components:2,type:"Int16"},{name:"a_data",components:4,type:"Uint8"}],4).members,Us=Ls.VectorTileFeature.types,Vs=Math.cos(Math.PI/180*37.5),Hs=Math.pow(2,14)/.5,qs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Fi,this.indexArray=new Yi,this.programConfigurations=new Ua(js,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};qs.prototype.populate=function(t,e,r){this.hasPattern=ds("line",this.layers,e);for(var n=this.layers[0].layout.get("line-sort-key"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r):void 0,m={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(m)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,v=i;g<v.length;g+=1){var y=v[g],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=ms("line",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},qs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},qs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},qs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},qs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},qs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},qs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},qs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get("line-join").evaluate(t,{}),s=a.get("line-cap"),l=a.get("line-miter-limit"),c=a.get("line-round-limit"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},qs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty("mapbox_clip_start")&&e.properties.hasOwnProperty("mapbox_clip_end")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s="Polygon"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){"bevel"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,m=void 0,g=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],g=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 0:t[v+1])||!t[v].equals(d)){g&&(m=g),u&&(p=u),u=t[v],g=d?d.sub(u)._unit()._perp():m;var y=(m=m||g).add(g);0===y.x&&0===y.y||y._unit();var x=m.x*g.x+m.y*g.y,b=y.x*g.x+y.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<Vs&&p&&d,k=m.x*g.y-m.y*g.x>0;if(T&&v>c){var A=u.dist(p);if(A>2*f){var M=u.sub(u.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,m,0,0,h),p=M}}var S=p&&d,E=S?r:s?"butt":n;if(S&&"round"===E&&(_<a?E="miter":_<=2&&(E="fakeround")),"miter"===E&&_>i&&(E="bevel"),"bevel"===E&&(_>2&&(E="flipbevel"),_<i&&(E="miter")),p&&this.updateDistance(p,u),"miter"===E)y._mult(_),this.addCurrentVertex(u,y,0,0,h);else if("flipbevel"===E){if(_>100)y=g.mult(-1);else{var L=_*m.add(g).mag()/m.sub(g).mag();y._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(u,y,0,0,h),this.addCurrentVertex(u,y.mult(-1),0,0,h)}else if("bevel"===E||"fakeround"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,I=k?0:C;if(p&&this.addCurrentVertex(u,m,P,I,h),"fakeround"===E)for(var O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(m)._mult(D)._add(m)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,g,-P,-I,h)}else if("butt"===E)this.addCurrentVertex(u,y,0,0,h);else if("square"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else"round"===E&&(p&&(this.addCurrentVertex(u,m,0,0,h),this.addCurrentVertex(u,m,1,1,h,!0)),d&&(this.addCurrentVertex(u,g,-1,-1,h,!0),this.addCurrentVertex(u,g,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,g,0,0,h),u=j}}}}},qs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>Hs/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},qs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6);var u=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,u),o.primitiveLength++),i?this.e2=u:this.e1=u},qs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Hs-1):this.distance},qs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Nn("LineBucket",qs,{omit:["layers","patternFeatures"]});var Gs=new Si({"line-cap":new wi(Lt.layout_line["line-cap"]),"line-join":new Ti(Lt.layout_line["line-join"]),"line-miter-limit":new wi(Lt.layout_line["line-miter-limit"]),"line-round-limit":new wi(Lt.layout_line["line-round-limit"]),"line-sort-key":new Ti(Lt.layout_line["line-sort-key"])}),Ys={paint:new Si({"line-opacity":new Ti(Lt.paint_line["line-opacity"]),"line-color":new Ti(Lt.paint_line["line-color"]),"line-translate":new wi(Lt.paint_line["line-translate"]),"line-translate-anchor":new wi(Lt.paint_line["line-translate-anchor"]),"line-width":new Ti(Lt.paint_line["line-width"]),"line-gap-width":new Ti(Lt.paint_line["line-gap-width"]),"line-offset":new Ti(Lt.paint_line["line-offset"]),"line-blur":new Ti(Lt.paint_line["line-blur"]),"line-dasharray":new Ai(Lt.paint_line["line-dasharray"]),"line-pattern":new ki(Lt.paint_line["line-pattern"]),"line-gradient":new Mi(Lt.paint_line["line-gradient"])}),layout:Gs},Ws=new(function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=u({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ti))(Ys.paint.properties["line-width"].specification);Ws.useIntegerZoom=!0;var Xs=function(t){function e(e){t.call(this,e,Ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){"line-gradient"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var 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Eo({width:o||1,height:s||1});for(var c in t){var u=t[c],f=r[c].paddedRect;Eo.copy(u.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},u.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,m=d.x+1,g=d.y+1,v=p.data.width,y=p.data.height;Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g},p.data),Eo.copy(p.data,l,{x:0,y:y-1},{x:m,y:g-1},{width:v,height:1}),Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g+y},{width:v,height:1}),Eo.copy(p.data,l,{x:v-1,y:0},{x:m-1,y:g},{width:1,height:y}),Eo.copy(p.data,l,{x:0,y:0},{x:m+v,y:g},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Ll.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new Sl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Ll.prototype.patchUpdatedImages=function(t,e){for(var r in 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e=0,r=t;e<r.length;e+=1){if(0!==r[e].positionedGlyphs.length)return!1}return!0}(L)&&C}Il.fromFeature=function(t,e){for(var r=new Il,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},Il.prototype.length=function(){return this.text.length},Il.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},Il.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},Il.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},Il.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e="",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;(!n||!Jn(n)||rl[t[r+1]])&&(!i||!Jn(i)||rl[t[r-1]])&&rl[t[r]]?e+=rl[t[r]]:e+=t[r]}return e}(this.text)},Il.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&zl[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&zl[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},Il.prototype.substring=function(t,e){var r=new Il;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},Il.prototype.toString=function(){return this.text},Il.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},Il.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(Pl.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},Il.prototype.addImageSection=function(t){var e=t.image?t.image.name:"";if(0!==e.length){var r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(Pl.forImage(e)),this.sectionIndex.push(this.sections.length-1)):_("Reached maximum number of 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l=t.getSection(s);o+=Rl(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf("\u200b")>=0,f=0,h=0;h<t.length();h++){var p=t.getSection(h),d=t.getCharCode(h);if(zl[d]||(f+=Rl(d,p,n,i,e,o)),h<t.length()-1){var m=!!(!((s=d)<11904)&&(Yn["Bopomofo Extended"](s)||Yn.Bopomofo(s)||Yn["CJK Compatibility Forms"](s)||Yn["CJK Compatibility Ideographs"](s)||Yn["CJK Compatibility"](s)||Yn["CJK Radicals Supplement"](s)||Yn["CJK Strokes"](s)||Yn["CJK Symbols and Punctuation"](s)||Yn["CJK Unified Ideographs Extension A"](s)||Yn["CJK Unified Ideographs"](s)||Yn["Enclosed CJK Letters and Months"](s)||Yn["Halfwidth and Fullwidth Forms"](s)||Yn.Hiragana(s)||Yn["Ideographic Description Characters"](s)||Yn["Kangxi Radicals"](s)||Yn["Katakana Phonetic Extensions"](s)||Yn.Katakana(s)||Yn["Vertical Forms"](s)||Yn["Yi Radicals"](s)||Yn["Yi Syllables"](s)));(Dl[d]||m||p.imageName)&&l.push(Nl(h+1,f,c,l,Bl(d,t.getCharCode(h+1),m&&u),!1))}}return function t(e){return e?t(e.priorBreak).concat(e.index):[]}(Nl(t.length(),f,c,l,0,!0))}function Ul(t){var e=.5,r=.5;switch(t){case"right":case"top-right":case"bottom-right":e=1;break;case"left":case"top-left":case"bottom-left":e=0}switch(t){case"bottom":case"bottom-right":case"bottom-left":r=1;break;case"top":case"top-right":case"top-left":r=0}return{horizontalAlign:e,verticalAlign:r}}function Vl(t,e,r,n,i){if(n||i)for(var a=t[r],o=a.metrics.advance*a.scale,s=(t[r].x+o)*n,l=e;l<=r;l++)t[l].x-=s,t[l].y+=i}function Hl(t,e,r,n,i,a){var o,s=t.image;if(s.content){var l=s.content,c=s.pixelRatio||1;o=[l[0]/c,l[1]/c,s.displaySize[0]-l[2]/c,s.displaySize[1]-l[3]/c]}var u,f,h,p,d=e.left*a,m=e.right*a;"width"===r||"both"===r?(p=i[0]+d-n[3],f=i[0]+m+n[1]):f=(p=i[0]+(d+m-s.displaySize[0])/2)+s.displaySize[0];var 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F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];C._matMult(N),P._matMult(N),O._matMult(N),I._matMult(N)}var j=n.stretch+n.fixed,U=l.stretch+l.fixed,V=a.stretch+a.fixed,H=c.stretch+c.fixed;return{tl:C,tr:P,bl:O,br:I,tex:{x:o.paddedRect.x+1+j,y:o.paddedRect.y+1+V,w:U-j,h:H-V},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:z,pixelOffsetBR:D,minFontScaleX:k/s/u,minFontScaleY:M/s/f,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var L=ic(h,v,m),C=ic(p,y,g),P=0;P<L.length-1;P++)for(var I=L[P],O=L[P+1],z=0;z<C.length-1;z++){var D=C[z],R=C[z+1];a.push(E(I,D,O,R))}else a.push(E({fixed:0,stretch:-1},{fixed:0,stretch:-1},{fixed:0,stretch:l+1},{fixed:0,stretch:c+1}));return a}function nc(t,e,r){for(var n=0,i=0,a=t;i<a.length;i+=1){var o=a[i];n+=Math.max(e,Math.min(r,o[1]))-Math.max(e,Math.min(r,o[0]))}return n}function ic(t,e,r){for(var n=[{fixed:-1,stretch:0}],i=0,a=t;i<a.length;i+=1){var 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0===e&&(e=cc),this.data=t,this.length=this.data.length,this.compare=e,this.length>0)for(var r=(this.length>>1)-1;r>=0;r--)this._down(r)};function cc(t,e){return t<e?-1:t>e?1:0}function uc(t,e,r){void 0===e&&(e=1),void 0===r&&(r=!1);for(var n=1/0,a=1/0,o=-1/0,s=-1/0,l=t[0],c=0;c<l.length;c++){var u=l[c];(!c||u.x<n)&&(n=u.x),(!c||u.y<a)&&(a=u.y),(!c||u.x>o)&&(o=u.x),(!c||u.y>s)&&(s=u.y)}var f=o-n,h=s-a,p=Math.min(f,h),d=p/2,m=new lc([],fc);if(0===p)return new i(n,a);for(var g=n;g<o;g+=p)for(var v=a;v<s;v+=p)m.push(new hc(g+d,v+d,d,t));for(var y=function(t){for(var e=0,r=0,n=0,i=t[0],a=0,o=i.length,s=o-1;a<o;s=a++){var l=i[a],c=i[s],u=l.x*c.y-c.x*l.y;r+=(l.x+c.x)*u,n+=(l.y+c.y)*u,e+=3*u}return new hc(r/e,n/e,0,t)}(t),x=m.length;m.length;){var b=m.pop();(b.d>y.d||!y.d)&&(y=b,r&&console.log("found best %d after %d probes",Math.round(1e4*b.d)/1e4,x)),b.max-y.d<=e||(d=b.h/2,m.push(new hc(b.p.x-d,b.p.y-d,d,t)),m.push(new hc(b.p.x+d,b.p.y-d,d,t)),m.push(new hc(b.p.x-d,b.p.y+d,d,t)),m.push(new 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mc(t){switch(t){case"right":case"top-right":case"bottom-right":return"right";case"left":case"top-left":case"bottom-left":return"left"}return"center"}function gc(t,e,r,n,a,o,s,l,c,u,f,h,p,d,m){var g=function(t,e,r,n,a,o,s,l){for(var c=n.layout.get("text-rotate").evaluate(o,{})*Math.PI/180,u=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];if(g.rect){var v=g.rect||{},y=4,x=!0,b=1,_=0,w=(a||l)&&g.vertical,T=g.metrics.advance*g.scale/2;if(l&&e.verticalizable){var k=24*(g.scale-1),A=(24-g.metrics.width*g.scale)/2;_=p.lineOffset/2-(g.imageName?-A:k)}if(g.imageName){var M=s[g.imageName];x=M.sdf,y=1/(b=M.pixelRatio)}var S=a?[g.x+T,g.y]:[0,0],E=a?[0,0]:[g.x+T+r[0],g.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(g.metrics.left-y)*g.scale-T+E[0],P=(-g.metrics.top-y)*g.scale+E[1],I=C+v.w*g.scale/b,O=P+v.h*g.scale/b,z=new i(C,P),D=new i(I,P),R=new i(C,O),F=new i(I,O);if(w){var B=new i(-T,T- -17),N=-Math.PI/2,j=12-T,U=g.imageName?j:0,V=new i(22-j,-U),H=new(Function.prototype.bind.apply(i,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(H),D._rotateAround(N,B)._add(V)._add(H),R._rotateAround(N,B)._add(V)._add(H),F._rotateAround(N,B)._add(V)._add(H)}if(c){var q=Math.sin(c),G=Math.cos(c),Y=[G,-q,q,G];z._matMult(Y),D._matMult(Y),R._matMult(Y),F._matMult(Y)}var W=new i(0,0),X=new i(0,0);u.push({tl:z,tr:D,bl:R,br:F,tex:v,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:W,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;"source"===v.kind?(y=[128*a.layout.get("text-size").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for "text-size" is >= 255. 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Reduce your "text-size".'),t.addSymbols(t.text,g,y,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,m);for(var x=0,b=f;x<b.length;x+=1){h[b[x]]=t.text.placedSymbolArray.length-1}return 4*g.length}function vc(t){for(var e in t)return t[e];return null}function yc(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var xc=Ls.VectorTileFeature.types,bc=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function _c(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=s?Math.min(32640,Math.round(s[0])):0,d=s?Math.min(32640,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*f,256*h)}function wc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Tc(t){for(var e=0,r=t.sections;e<r.length;e+=1){if($n(r[e].text))return!0}return!1}var kc=function(t){this.layoutVertexArray=new Ni,this.indexArray=new Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn("SymbolBuffers",kc);var Ac=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Ac.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn("CollisionBuffers",Ac);var Mc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e["text-size"]),this.iconSizeData=Gl(this.zoom,e["icon-size"]);var r=this.layers[0].layout,n=r.get("symbol-sort-key"),i=r.get("symbol-z-order");this.sortFeaturesByKey="viewport-y"!==i&&void 0!==n.constantOr(1);var a="viewport-y"===i||"auto"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get("text-allow-overlap")||r.get("icon-allow-overlap")||r.get("text-ignore-placement")||r.get("icon-ignore-placement")),"point"===r.get("symbol-placement")&&(this.writingModes=r.get("text-writing-mode").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Mc.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Mc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Mc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get("text-font"),o=i.get("text-field"),s=i.get("icon-image"),l=("constant"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&("constant"!==a.value.kind||a.value.value.length>0),c="constant"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get("symbol-sort-key");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),m=0,g=t;m<g.length;m+=1){var v=g[m],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var A=n.getValueAndResolveTokens("text-field",T,r,p),M=ne.factory(A);Tc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||"unavailable"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens("icon-image",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(","),I="map"===i.get("text-rotation-alignment")&&"point"!==i.get("symbol-placement");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}"line"===i.get("symbol-placement")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+":"+n.x+":"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var m=s(p,d,f),g=o(p,d,n[m].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[m].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Mc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},Mc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Mc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Mc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Mc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Mc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Mc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Mc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),m=this.glyphOffsetArray.length,g=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,m,this.glyphOffsetArray.length-m,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Mc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},Mc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},Mc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Mc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Ac(Hi,$s.members,Qi),this.iconCollisionBox=new Ac(Hi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Mc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var m=t.get(d);c.iconBox={x1:m.x1,y1:m.y1,x2:m.x2,y2:m.y2,anchorPointX:m.anchorPointX,anchorPointY:m.anchorPointY},c.iconFeatureIndex=m.featureIndex;break}for(var g=s;g<l;g++){var v=t.get(g);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Mc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},Mc.prototype.hasTextData=function(){return this.text.segments.get().length>0},Mc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Mc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Mc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Mc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Mc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},Mc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},Mc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},Mc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn("SymbolBucket",Mc,{omit:["layers","collisionBoxArray","features","compareText"]}),Mc.MAX_GLYPHS=65535,Mc.addDynamicAttributes=wc;var Sc=new Si({"symbol-placement":new wi(Lt.layout_symbol["symbol-placement"]),"symbol-spacing":new wi(Lt.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new wi(Lt.layout_symbol["symbol-avoid-edges"]),"symbol-sort-key":new Ti(Lt.layout_symbol["symbol-sort-key"]),"symbol-z-order":new wi(Lt.layout_symbol["symbol-z-order"]),"icon-allow-overlap":new wi(Lt.layout_symbol["icon-allow-overlap"]),"icon-ignore-placement":new wi(Lt.layout_symbol["icon-ignore-placement"]),"icon-optional":new wi(Lt.layout_symbol["icon-optional"]),"icon-rotation-alignment":new wi(Lt.layout_symbol["icon-rotation-alignment"]),"icon-size":new Ti(Lt.layout_symbol["icon-size"]),"icon-text-fit":new wi(Lt.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new wi(Lt.layout_symbol["icon-text-fit-padding"]),"icon-image":new Ti(Lt.layout_symbol["icon-image"]),"icon-rotate":new Ti(Lt.layout_symbol["icon-rotate"]),"icon-padding":new wi(Lt.layout_symbol["icon-padding"]),"icon-keep-upright":new wi(Lt.layout_symbol["icon-keep-upright"]),"icon-offset":new Ti(Lt.layout_symbol["icon-offset"]),"icon-anchor":new Ti(Lt.layout_symbol["icon-anchor"]),"icon-pitch-alignment":new wi(Lt.layout_symbol["icon-pitch-alignment"]),"text-pitch-alignment":new wi(Lt.layout_symbol["text-pitch-alignment"]),"text-rotation-alignment":new wi(Lt.layout_symbol["text-rotation-alignment"]),"text-field":new Ti(Lt.layout_symbol["text-field"]),"text-font":new Ti(Lt.layout_symbol["text-font"]),"text-size":new Ti(Lt.layout_symbol["text-size"]),"text-max-width":new Ti(Lt.layout_symbol["text-max-width"]),"text-line-height":new wi(Lt.layout_symbol["text-line-height"]),"text-letter-spacing":new Ti(Lt.layout_symbol["text-letter-spacing"]),"text-justify":new Ti(Lt.layout_symbol["text-justify"]),"text-radial-offset":new Ti(Lt.layout_symbol["text-radial-offset"]),"text-variable-anchor":new wi(Lt.layout_symbol["text-variable-anchor"]),"text-anchor":new Ti(Lt.layout_symbol["text-anchor"]),"text-max-angle":new wi(Lt.layout_symbol["text-max-angle"]),"text-writing-mode":new wi(Lt.layout_symbol["text-writing-mode"]),"text-rotate":new Ti(Lt.layout_symbol["text-rotate"]),"text-padding":new wi(Lt.layout_symbol["text-padding"]),"text-keep-upright":new wi(Lt.layout_symbol["text-keep-upright"]),"text-transform":new Ti(Lt.layout_symbol["text-transform"]),"text-offset":new Ti(Lt.layout_symbol["text-offset"]),"text-allow-overlap":new wi(Lt.layout_symbol["text-allow-overlap"]),"text-ignore-placement":new wi(Lt.layout_symbol["text-ignore-placement"]),"text-optional":new wi(Lt.layout_symbol["text-optional"])}),Ec={paint:new Si({"icon-opacity":new Ti(Lt.paint_symbol["icon-opacity"]),"icon-color":new Ti(Lt.paint_symbol["icon-color"]),"icon-halo-color":new Ti(Lt.paint_symbol["icon-halo-color"]),"icon-halo-width":new Ti(Lt.paint_symbol["icon-halo-width"]),"icon-halo-blur":new Ti(Lt.paint_symbol["icon-halo-blur"]),"icon-translate":new wi(Lt.paint_symbol["icon-translate"]),"icon-translate-anchor":new wi(Lt.paint_symbol["icon-translate-anchor"]),"text-opacity":new Ti(Lt.paint_symbol["text-opacity"]),"text-color":new Ti(Lt.paint_symbol["text-color"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),"text-halo-color":new Ti(Lt.paint_symbol["text-halo-color"]),"text-halo-width":new Ti(Lt.paint_symbol["text-halo-width"]),"text-halo-blur":new Ti(Lt.paint_symbol["text-halo-blur"]),"text-translate":new wi(Lt.paint_symbol["text-translate"]),"text-translate-anchor":new wi(Lt.paint_symbol["text-translate-anchor"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn("FormatSectionOverride",Lc,{omit:["defaultValue"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),"auto"===this.layout.get("icon-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["icon-rotation-alignment"]="map":this.layout._values["icon-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["text-rotation-alignment"]="map":this.layout._values["text-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-pitch-alignment")&&(this.layout._values["text-pitch-alignment"]=this.layout.get("text-rotation-alignment")),"auto"===this.layout.get("icon-pitch-alignment")&&(this.layout._values["icon-pitch-alignment"]=this.layout.get("icon-rotation-alignment")),"point"===this.layout.get("symbol-placement")){var n=this.layout.get("text-writing-mode");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values["text-writing-mode"]=i}else this.layout._values["text-writing-mode"]=["horizontal"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):""}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Mc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s="constant"===i.value.kind||"source"===i.value.kind?new Xr("source",o):new Zr("composite",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get("text-field"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if("constant"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if("source"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({"background-color":new wi(Lt.paint_background["background-color"]),"background-pattern":new Ai(Lt.paint_background["background-pattern"]),"background-opacity":new wi(Lt.paint_background["background-opacity"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({"raster-opacity":new wi(Lt.paint_raster["raster-opacity"]),"raster-hue-rotate":new wi(Lt.paint_raster["raster-hue-rotate"]),"raster-brightness-min":new wi(Lt.paint_raster["raster-brightness-min"]),"raster-brightness-max":new wi(Lt.paint_raster["raster-brightness-max"]),"raster-saturation":new wi(Lt.paint_raster["raster-saturation"]),"raster-contrast":new wi(Lt.paint_raster["raster-contrast"]),"raster-resampling":new wi(Lt.paint_raster["raster-resampling"]),"raster-fade-duration":new wi(Lt.paint_raster["raster-fade-duration"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return"3d"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,"fill-extrusion":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Hc=function(t){var e=this;this._callback=t,this._triggered=!1,"undefined"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Hc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Hc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var qc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m(["receive","process"],this),this.invoker=new Hc(this.process),this.target.addEventListener("message",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}qc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Hn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:"<cancel>",targetMapId:n,sourceMapId:a.mapId})}}},qc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if("<cancel>"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},qc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},qc.prototype.processTask=function(t,e){var r=this;if("<response>"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(qn(e.error)):n(null,qn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:"<response>",sourceMapId:r.mapId,error:e?Hn(e):null,data:Hn(n,a)},a)}:function(t){i=!0},s=null,l=qn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(".");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error("Could not find function "+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},qc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener("message",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return"LngLatBounds("+this._sw.toString()+", "+this._ne.toString()+")"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error("Invalid LngLat object: ("+t+", "+e+")");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error("Invalid LngLat latitude value: must be between -90 and 90")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return"LngLat("+this.lng+", "+this.lat+")"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&"object"==typeof t&&null!==t)return new Wc(Number("lng"in t?t.lng:t.lon),Number(t.lat));throw new Error("`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+","+a[1]+","+o[0]+","+o[1]),l=function(t,e,r){for(var n,i="",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace("{prefix}",(this.x%16).toString(16)+(this.y%16).toString(16)).replace("{z}",String(this.z)).replace("{x}",String(this.x)).replace("{y}",String("tms"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace("{quadkey}",l).replace("{bbox-epsg-3857}",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+"/"+this.x+"/"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+"/"+this.canonical.x+"/"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn("CanonicalTileID",eu),Nn("OverscaledTileID",nu,{omit:["posMatrix"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError("DEM tiles must be square");if(r&&"mapbox"!==r&&"terrarium"!==r)return _('"'+r+'" is not a valid encoding type. Valid types include "mapbox" and "terrarium".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||"mapbox";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return("terrarium"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return"terrarium"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError("out of range source coordinates for DEM data");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error("dem dimension mismatch");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn("DEMData",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type="Feature",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)"_geometry"!==e&&"_vectorTileFeature"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),m=0,g=d;m<g.length;m+=1){var v=g[m];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),m=0;m<f.length;m++){var g=f[m];if(!(a&&a.indexOf(g)<0)){var v=s[g];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||"_geojsonTileLayer",d));var x=l[g];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[g];void 0===w&&(w=t[g]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n="string"==typeof this.promoteId?this.promoteId:this.promoteId[e];"boolean"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn("FeatureIndex",uu,{omit:["rawTileData","sourceLayerCoder"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state="loading"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return"errored"===this.state||"loaded"===this.state||"reloading"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state="loaded",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Mc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof Mc&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||"deferred"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state="unloaded"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:"",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),m=new su(p,l,c,u,d);m.tile=f,t.push(m)}}}},du.prototype.hasData=function(){return"loaded"===this.state||"reloading"===this.state||"expired"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=A(t.cacheControl);r["max-age"]&&(this.expirationTime=Date.now()+1e3*r["max-age"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state="expired"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||"_geojsonTileLayer",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var mu=self.performance,gu=function(t){this._marks={start:[t.url,"start"].join("#"),end:[t.url,"end"].join("#"),measure:t.url.toString()},mu.mark(this._marks.start)};gu.prototype.finish=function(){mu.mark(this._marks.end);var t=mu.getEntriesByName(this._marks.measure);return 0===t.length&&(mu.measure(this._marks.measure,this._marks.start,this._marks.end),t=mu.getEntriesByName(this._marks.measure),mu.clearMarks(this._marks.start),mu.clearMarks(this._marks.end),mu.clearMeasures(this._marks.measure)),t},t.Actor=qc,t.AlphaImage=So,t.CanonicalTileID=eu,t.CollisionBoxArray=na,t.Color=te,t.DEMData=au,t.DataConstantProperty=wi,t.DictionaryCoder=ou,t.EXTENT=8192,t.ErrorEvent=St,t.EvaluationParameters=pi,t.Event=Mt,t.Evented=Et,t.FeatureIndex=uu,t.FillBucket=gs,t.FillExtrusionBucket=Os,t.ImageAtlas=Ll,t.ImagePosition=Sl,t.LineBucket=qs,t.LngLat=Wc,t.LngLatBounds=Yc,t.MercatorCoordinate=tu,t.ONE_EM=24,t.OverscaledTileID=nu,t.Point=i,t.Point$1=i,t.Properties=Si,t.Protobuf=al,t.RGBAImage=Eo,t.RequestManager=H,t.RequestPerformance=gu,t.ResourceType=dt,t.SegmentVector=pa,t.SourceFeatureState=cu,t.StructArrayLayout1ui2=$i,t.StructArrayLayout2f1f2i16=qi,t.StructArrayLayout2i4=zi,t.StructArrayLayout3ui6=Yi,t.StructArrayLayout4i8=Di,t.SymbolBucket=Mc,t.Texture=Vc,t.Tile=du,t.Transitionable=gi,t.Uniform1f=Sa,t.Uniform1i=Ma,t.Uniform2f=Ea,t.Uniform3f=La,t.Uniform4f=Ca,t.UniformColor=Pa,t.UniformMatrix4f=Oa,t.UnwrappedTileID=ru,t.ValidationError=Ct,t.WritingMode=Cl,t.ZoomHistory=Gn,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=wc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=o,t.bindAll=m,t.browser=R,t.cacheEntryPossiblyAdded=function(t){++ht>ot&&(t.getActor().send("enforceCacheSizeLimit",at),ht=0)},t.clamp=l,t.clearTileCache=function(t){var e=self.caches.delete("mapbox-tiles");t&&e.catch(t).then((function(){return t()}))},t.clipLine=ec,t.clone=function(t){var e=new fo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=x,t.clone$2=function(t){var e=new fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=tl,t.config=F,t.create=function(){var t=new fo(16);return 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vt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function yt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,xt(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),f>o.maxY&&(o.maxY=f)}return o}function xt(t,e,r,n){var i=e.geometry,a=e.type,o=[];if("Point"===a||"MultiPoint"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if("LineString"===a)bt(o,i,t,r,!1,!1);else if("MultiLineString"===a||"Polygon"===a)for(s=0;s<i.length;s++)bt(o,i[s],t,r,"Polygon"===a,0===s);else if("MultiPolygon"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)bt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags||null;if("LineString"===a&&n.lineMetrics){for(var f in u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:"Polygon"===a||"MultiPolygon"===a?3:"LineString"===a||"MultiLineString"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time("preprocess data"),e.maxZoom<0||e.maxZoom>24)throw new Error("maxZoom should be in the 0-24 range");if(e.promoteId&&e.generateId)throw new Error("promoteId and generateId cannot be used together.");var n=function(t,e){var r=[];if("FeatureCollection"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else"Feature"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd("preprocess data"),console.log("index: maxZoom: %d, maxPoints: %d",e.indexMaxZoom,e.indexMaxPoints),console.time("generate tiles"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log("features: %d, points: %d",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd("generate tiles"),console.log("tiles generated:",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time("total time");var o="prepare "+t.length+" points";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log("z%d: %d clusters in %dms",c,s.length,+Date.now()-u)}return r&&console.timeEnd("total time"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?q(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n="No cluster with the specified id.",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?q(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,m=o&&h>1?this._map(c,!0):null,g=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=g,o&&(m||(m=this._map(c,!0)),o(m,this._map(b)))}}1===h?r.push(c):(c.parentId=g,r.push(V(p/h,d/h,g,h,m)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time("creation"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log("tile z%d-%d-%d (features: %d, points: %d, simplified: %d)",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd("creation"));var p="z"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time("clipping");var m,g,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;m=g=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(m=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),g=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd("clipping"),s.push(m||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return gt(this.tiles[s],i);a>1&&console.log("drilling down to z%d-%d-%d",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log("found parent tile z%d-%d-%d",c,u,f),a>1&&console.time("drilling down"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd("drilling down"),this.tiles[s]?gt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&"Idle"!==this._state?this._state="NeedsLoadData":(this._state="Coalescing",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if("object"!=typeof o)return r(new Error("Input data given to '"+n.source+"' is not a valid GeoJSON object."));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],m=t.createExpression(d),g=t.createExpression("string"==typeof p?[p,["accumulated"],["get",f]]:p);i[f]=m.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){"Coalescing"===this._state?this._state="Idle":"NeedsLoadData"===this._state&&(this._state="Coalescing",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if("string"!=typeof e.data)return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l);var At=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:kt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name "'+t+'" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error("RTL text plugin already registered.");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return At.prototype.setReferrer=function(t,e){this.referrer=e},At.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in 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JSON support":"insufficient Object support":"insufficient Function support":"insufficent Array support"}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=e,window.mapboxgl.notSupportedReason=r);var n={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}})),r={create:function(e,r,n){var i=t.window.document.createElement(e);return void 0!==r&&(i.className=r),n&&n.appendChild(i),i},createNS:function(e,r){return t.window.document.createElementNS(e,r)}},n=t.window.document.documentElement.style;function i(t){if(!n)return t[0];for(var e=0;e<t.length;e++)if(t[e]in n)return t[e];return t[0]}var a,o=i(["userSelect","MozUserSelect","WebkitUserSelect","msUserSelect"]);r.disableDrag=function(){n&&o&&(a=n[o],n[o]="none")},r.enableDrag=function(){n&&o&&(n[o]=a)};var s=i(["transform","WebkitTransform"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var c=Object.defineProperty({},"passive",{get:function(){l=!0}});t.window.addEventListener("test",c,c),t.window.removeEventListener("test",c,c)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)};var u=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener("click",u,!0)};function f(t){var e=t.userImage;if(e&&e.render&&e.render())return t.data.replace(new Uint8Array(e.data.buffer)),!0;return!1}r.suppressClick=function(){t.window.addEventListener("click",u,!0),t.window.setTimeout((function(){t.window.removeEventListener("click",u,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var 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r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event("styleimagemissing",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image "'+o+'" could not be loaded. Please make sure you have added the image with map.addImage() or a "sprite" property in your style. You can provide missing images by listening for the "styleimagemissing" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,m=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||"sans-serif",this.fontWeight=a||"normal",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement("canvas"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext("2d"),this.ctx.font=this.fontWeight+" "+this.fontSize+"px "+this.fontFamily,this.ctx.textBaseline="middle",this.ctx.fillStyle="black",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf("Gecko/")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-m,n[1]=+m;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+m}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?m:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?m:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error("glyphs > 65535 not supported"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1){(0,o[i])(t,e)}delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar["CJK Unified Ideographs"](e)||t.isChar["Hangul Syllables"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o="400";/bold/i.test(r)?o="900":/medium/i.test(r)?o="500":/light/i.test(r)&&(o="200"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace("{fontstack}",e).replace("{range}",o+"-"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,"-transition")?this._transitionable.setTransition(n.slice(0,-"-transition".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(",")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(h*h+m*m)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce("LineAtlas out of space"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name="Worker "+a,this.actors.push(s)}};function A(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),["tiles","minzoom","maxzoom","attribution","mapbox_logo","bounds","scheme","tileSize","encoding"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var M=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};M.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},M.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type="vector",this.minzoom=0,this.maxzoom=22,this.scheme="xyz",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,["url","scheme","tileSize","promoteId"])),this._options=t.extend({type:"vector"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error("vector tile sources must have a tileSize of 512");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&"expired"!==e.state?"loading"===e.state?e.reloadCallback=r:e.request=e.actor.send("reloadTile",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send("loadTile",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send("abortTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type="raster",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme="xyz",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:"raster"},n),t.extend(this,t.pick(n,["url","scheme","tileSize"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(i)e.state="errored",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state="loaded",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type="raster-dem",this.maxzoom=22,this._options=t.extend({type:"raster-dem"},n),this.encoding=n.encoding||"mapbox"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:"raster-dem",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state="errored",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state="loaded",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(n)e.state="errored",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&"expired"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send("loadDEMTile",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state="unloaded",t.actor&&t.actor.send("removeDEMTile",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type="geojson",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:"source",sourceDataType:"metadata"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event("data",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:"source",sourceDataType:"content"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event("data",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send("geojson.getClusterExpansionZoom",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send("geojson.getClusterChildren",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send("geojson.getClusterLeaves",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;"string"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+".loadData",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+".coalesce",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?"reloadTile":"loadTile";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,"reloadTile"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send("removeSource",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type="image",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state="errored",e(null))},r.prototype.serialize=function(){return{type:"image",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type="video",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener("playing",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError("sources."+this.id,null,"Playback for this video can be set only between the "+r.start(0)+" and "+r.end(0)+"-second mark."))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"video",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return"number"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"coordinates" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "coordinates"'))),n.animate&&"boolean"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'optional "animate" property must be a boolean value'))),n.canvas?"string"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"canvas" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "canvas"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error("Canvas dimensions cannot be less than or equal to zero."))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];"loaded"!==a.state&&(a.state="loaded",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"canvas",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(I),D={vector:S,raster:E,"raster-dem":L,geojson:C,video:O,image:I,canvas:z};function R(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function F(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&"fill-extrusion"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&"fill-extrusion"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(B);for(var c=[],u=0,f=l;u<f.length;u+=1){var h=f[u];c.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,R(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var c in o)for(var u=o[c],f=l[c]=l[c]||{},h=e[c]=e[c]||[],p=0,d=u;p<d.length;p+=1){var m=d[p];f[m.featureIndex]||(f[m.featureIndex]=!0,h.push(m))}}return e}(c);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer["source-layer"],r.id);r.source=r.layer.source,r.layer["source-layer"]&&(r.sourceLayer=r.layer["source-layer"]),r.state=n}));return p}function B(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var N=function(t,e){this.max=t,this.onRemove=e,this.reset()};N.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},N.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},N.prototype.has=function(t){return t.wrapped().key in this.data},N.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},N.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},N.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},N.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},N.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},N.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},N.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var j=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};j.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},j.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},j.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var U={Int8:"BYTE",Uint8:"UNSIGNED_BYTE",Int16:"SHORT",Uint16:"UNSIGNED_SHORT",Int32:"INT",Uint32:"UNSIGNED_INT",Float32:"FLOAT"},V=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};V.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},V.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},V.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var 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At=function(t,e,r){this.func=t,this.mask=e,this.range=r};At.ReadOnly=!1,At.ReadWrite=!0,At.disabled=new At(519,At.ReadOnly,[0,1]);var Mt=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Mt.disabled=new Mt({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension("OES_vertex_array_object"),this.clearColor=new q(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new mt(this),this.bindElementBuffer=new gt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension("OES_texture_half_float"),this.extTextureHalfFloat&&(t.getExtension("OES_texture_half_float_linear"),this.extRenderToTextureHalfFloat=t.getExtension("EXT_color_buffer_half_float")),this.extTimerQuery=t.getExtension("EXT_disjoint_timer_query")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on("data",(function(t){"source"===t.dataType&&"metadata"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&"source"===t.dataType&&"content"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on("error",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error("Expected Source id to be "+e+" instead of "+a.id);return t.bindAll(["load","abort","unload","serialize","prepare"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if("loaded"!==e.state&&"errored"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)"errored"!==this._tiles[t].state&&this._reloadTile(t,"reloading")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&("loading"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state="errored",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),"expired"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),"raster-dem"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event("data",{dataType:"source",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n="number"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],m=this._tiles[p];if(m&&!(m.fadeEndTime&&m.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),c[g.tileID.key]=g.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],m=this._tiles[d.key];if(!m.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],v=this.getTile(g);if(v&&v.hasData()){n[g.key]=g;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=m.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(m=this.getTile(_))&&x&&(m=this._addTile(_)),m&&(n[_.key]=_,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event("dataloading",{tile:r,coord:r.tileID,dataType:"source"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,"expired"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&"reloading"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,m=0,g=c;m<g.length;m+=1){var v=g[m];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,m=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/m,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-g<t.EXTENT&&v[0].y-g<t.EXTENT&&v[1].x+g>=0&&v[1].y+g>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:m})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||"_geojsonTileLayer",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,"reloading")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return"raster"===t||"image"===t||"video"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt="mapboxgl_preloaded_worker_pool",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)"ref"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)"ref"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:"setStyle",addLayer:"addLayer",removeLayer:"removeLayer",setPaintProperty:"setPaintProperty",setLayoutProperty:"setLayoutProperty",setFilter:"setFilter",addSource:"addSource",removeSource:"removeSource",setGeoJSONSourceData:"setGeoJSONSourceData",setLayerZoomRange:"setLayerZoomRange",setLayerProperty:"setLayerProperty",setCenter:"setCenter",setZoom:"setZoom",setBearing:"setBearing",setPitch:"setPitch",setSprite:"setSprite",setGlyphs:"setGlyphs",setTransition:"setTransition",setLight:"setLight"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function Ht(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function qt(t,e,r,n){Ht(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Ht(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||("geojson"===e[a].type&&"geojson"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):qt(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),m=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],m[m.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.lastIndexOf(o,m.length-a),1)):a++,c=m[m.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),m.splice(m.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s["source-layer"],l["source-layer"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=m[m.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn("Unable to compute style diff:",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!g)ue(y.numGlyphs,h);else{g=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),A=te(k,a).point,M={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),m=e.getoffsetX(f-1),g=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!g)return null;var v=le(t*m,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:g,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=r/24,v=e.lineOffsetX*g,y=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(g,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var A=ae(e.writingMode,T,k,d);if(A)return A}m=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)m.push(le(g*l.getoffsetX(M),v,y,n,f,h,e.segment,b,_,c,o,p));m.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(g*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};m=[O]}for(var z=0,D=m;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=c)return null;if(y=v,w.push(v),void 0===(v=h[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[g]=k.point;else{var A=g-d;v=se(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var M=(_-x)/b,S=v.sub(y),E=S.mult(M)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=m+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,v=0,y=m;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var m=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[m+0],f[m+1],f[m+2],f[m+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var v=this.circles,y=0,x=g;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],m=new t.Point(r.anchorX,r.anchorY),g=te(m,o),v=ee(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(m,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,m,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var m=d[p],g=m.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var v=[new t.Point(m.x1,m.y1),new t.Point(m.x2,m.y1),new t.Point(m.x2,m.y2),new t.Point(m.x1,m.y2)];t.polygonIntersectsPolygon(r,v)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h="map"===l.get("text-pitch-alignment"),p="map"===l.get("text-rotation-alignment"),d=pe(n,1,this.transform.zoom),m=Qt(f,h,p,this.transform,d),g=null;if(h){var v=$t(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:m,labelToScreenMatrix:g,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=[f.textOffset0,f.textOffset1],v=be(t,r,n,g,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(m=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get("text-optional"),v=s.get("icon-optional"),y=s.get("text-allow-overlap"),x=s.get("icon-allow-overlap"),b="map"===s.get("text-rotation-alignment"),_="map"===s.get("text-pitch-alignment"),w="none"!==s.get("icon-text-fit"),T="viewport-y"===s.get("symbol-z-order"),k=y&&(x||!o.hasIconData()||v),A=x&&(y||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get("text-variable-anchor")){var N=s.get("text-variable-anchor");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,b,_,f,l,m,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get("text-padding"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,m.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,m.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,z,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,O,m.ID)),M&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get("icon-ignore-placement"),o.bucketInstanceId,D,m.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get("text-ignore-placement"),o.bucketInstanceId,O,m.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new me(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:"number"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get("text-allow-overlap"),l=a.get("icon-allow-overlap"),c=a.get("text-variable-anchor"),u="map"===a.get("text-rotation-alignment"),f="map"===a.get("text-pitch-alignment"),h="none"!==a.get("icon-text-fit"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get("icon-optional")),l&&(s||!e.hasTextData()||a.get("text-optional")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},m=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,m=a.crossTileID,g=r[m],v=i.opacities[m];g?v=o:v||(v=p,i.opacities[m]=v),r[m]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var A=w?Ie:T;d(e.text,l,A);var M=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,"left",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},g=0;g<e.symbolInstances.length;g++)m(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Ae=Math.pow(2,24),Me=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Ae+r*Me+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles="viewport-y"!==t.layout.get("symbol-z-order")&&void 0!==t.layout.get("symbol-sort-key").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if("symbol"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return"source.canvas"!==t.identifier})))},Ue=t.pick(Ut,["addLayer","removeLayer","setPaintProperty","setLayoutProperty","setFilter","addSource","removeSource","setLayerZoomRange","setLight","setTransition","setGeoJSONSourceData"]),Ve=t.pick(Ut,["setCenter","setZoom","setBearing","setPitch"]),He=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a="version"===n?r:"array"===i.type?[]:{})&&(e[n]=a)}}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast("setReferrer",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast("syncRTLPluginState",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on("data",(function(t){if("source"===t.dataType&&"metadata"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"}));var i="boolean"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event("dataloading",{dataType:"style"})),this._load(He,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast("setLayers",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event("data",{dataType:"style"})),this.fire(new t.Event("style.load"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?"@2x":"",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,".json"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,".png"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast("setImages",r._availableImages),r.fire(new t.Event("data",{dataType:"style"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();("geojson"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer "'+n+'" does not exist on source "'+i.id+'" as specified by style layer "'+e.id+'"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];"custom"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error("Style is not done loading")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];"reload"===o?this._reloadSource(a):"clear"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event("data",{dataType:"style"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies(["icons","patterns"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast("updateLayers",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error("Unimplemented: "+i.map((function(t){return t.command})).join(", ")+".");return n.forEach((function(t){"setTransition"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error("An image with this name already exists.")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error("No image with this name exists.")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error("There is already a source with this ID");if(!r.type)throw new Error("The type property must be defined, but the only the following properties were given: "+Object.keys(r).join(", ")+".");if(!(["vector","raster","geojson","video","image"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,"sources."+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error("There is no source with this ID");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source "'+e+'" cannot be removed while layer "'+r+'" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event("data",{sourceDataType:"metadata",dataType:"source",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id "'+i+'" already exists on this map')));else{var a;if("custom"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if("object"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,"layers."+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&"custom"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]="clear":(this._updatedSources[a.source]="reload",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be moved.")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be removed.")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot have zoom extent.")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,"layers."+i.id+".filter",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be filtered.")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style.")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;"geojson"===o&&i?this.fire(new t.ErrorEvent(new Error("GeoJSON sources cannot have a sourceLayer parameter."))):"vector"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o="vector"===a?e.sourceLayer:void 0;"vector"!==a||o?r&&"string"!=typeof e.id&&"number"!=typeof e.id?this.fire(new t.ErrorEvent(new Error("A feature id is requred to remove its specific state property."))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if("vector"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+r+"' does not exist in the map's style.")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&"raster"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]="reload",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return"fill-extrusion"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var v=i[g].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][m];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,"queryRenderedFeatures.filter",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error("parameters.layers must be an Array."))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error("The layer '"+s+"' does not exist in the map's style and cannot be queried for features."))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=m[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,m=c;d<m.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer["source-layer"],i.id);i.source=i.layer.source,i.layer["source-layer"]&&(i.sourceLayer=i.layer["source-layer"]),i.state=o}))};for(var v in s)g(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,"querySourceFeatures.filter",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called "'+t+'" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast("loadWorkerSource",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off("pluginStateChange",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if("symbol"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var m=0,g=this._order;m<g.length;m+=1){var v=g[m],y=this._layers[v];"symbol"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:"a_pos",type:"Int16",components:2}]),Ye=yr("#ifdef GL_ES\nprecision mediump float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif","#ifdef GL_ES\nprecision highp float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}"),We=yr("uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Xe=yr("uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}"),Ze=yr("varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main(void) {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}"),Je=yr("void main() {gl_FragColor=vec4(1.0);}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Ke=yr("uniform highp float u_intensity;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#define GAUSS_COEF 0.3989422804014327\nvoid main() {\n#pragma mapbox: initialize highp float weight\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#pragma mapbox: define mediump float radius\nconst highp float ZERO=1.0/255.0/16.0;\n#define GAUSS_COEF 0.3989422804014327\nvoid main(void) {\n#pragma mapbox: initialize highp float weight\n#pragma mapbox: initialize mediump float radius\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}"),Qe=yr("uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(0.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}"),$e=yr("varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}","attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}"),tr=yr("varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}","attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}"),er=yr("uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}","attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}"),rr=yr("#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_FragColor=color*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);}"),nr=yr("varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),ir=yr("uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),ar=yr("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}"),or=yr("varying vec4 v_color;void main() {gl_FragColor=v_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\n#pragma mapbox: define highp float base\n#pragma mapbox: define highp float height\n#pragma mapbox: define highp vec4 color\nvoid main() {\n#pragma mapbox: initialize highp float base\n#pragma mapbox: initialize highp float height\n#pragma mapbox: initialize highp vec4 color\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}"),sr=yr("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n? a_pos\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}"),lr=yr("#ifdef GL_ES\nprecision highp float;\n#endif\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}"),cr=yr("uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n#define PI 3.141592653589793\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}"),ur=yr("uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),fr=yr("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define MAX_LINE_DISTANCE 32767.0\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),hr=yr("uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}"),pr=yr("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}"),dr=yr("uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}"),mr=yr("uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}"),gr=yr("#define SDF_PX 8.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}"),vr=yr("#define SDF_PX 8.0\n#define SDF 1.0\n#define ICON 0.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}");function yr(t,e){var r=/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nvarying "+r+" "+i+" "+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"\n#ifdef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o="float"===i?"vec2":"vec4",s=a.match(/color/)?"color":o;return n[a]?"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float u_"+a+"_t;\nattribute "+r+" "+o+" a_"+a+";\nvarying "+r+" "+i+" "+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"vec4"===s?"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = a_"+a+";\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = unpack_mix_"+s+"(a_"+a+", u_"+a+"_t);\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float u_"+a+"_t;\nattribute "+r+" "+o+" a_"+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"vec4"===s?"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = a_"+a+";\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = unpack_mix_"+s+"(a_"+a+", u_"+a+"_t);\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:mr,symbolSDF:gr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,m=r;d<m.length;d+=1){var g=m[d];g.bind(),g.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push("#define OVERDRAW_INSPECTOR;");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join("\n"),l=o.concat(Ye.vertexSource,e.vertexSource).join("\n"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var m=a.getActiveAttrib(this.program,d);m&&(this.attributes[m.name]=a.getAttribLocation(this.program,m.name))}for(var g=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<g;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[v.LINES]=2,g[v.TRIANGLES]=3,g[v.LINE_STRIP]=1,g)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,m),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get("position"),s=[o.x,o.y,o.z],l=t.create$1();"viewport"===a.properties.get("anchor")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get("color");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get("intensity"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Ar=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(Ar(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if("map"===i.paint.get("circle-pitch-alignment")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else 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o=e.transform,s=e.lineAtlas,l=jr(r,o),c="round"===n.layout.get("line-cap"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get("line-translate"),r.paint.get("line-translate-anchor"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get("raster-opacity"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get("raster-brightness-min"),u_brightness_high:i.paint.get("raster-brightness-max"),u_saturation_factor:(o=i.paint.get("raster-saturation"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get("raster-contrast"),a>0?1/(1-a):1+a),u_spin_weights:Hr(i.paint.get("raster-hue-rotate"))};var a,o};function Hr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var qr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+("constant"===t||"source"===t),u_is_size_feature_constant:+("constant"===t||"camera"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram("collisionBox"),f=[],h=0,p=0,d=0;d<i.length;d++){var m=i[d],g=r.getTile(m),v=g.getBucket(n);if(v){var y=m.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(m.posMatrix,g,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram("collisionCircle"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,I,O,z,0),k.emplace(A++,P,I,O,z,1),k.emplace(A++,P,I,O,z,2),k.emplace(A++,P,I,O,z,3)}(!qr||qr.length<2*h)&&(qr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(qr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,m={};p.clear();for(var g=0;g<h.length;g++){var v=h.get(g),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=$r(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?te(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(m[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=m[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?"symbolTextAndIcon":t?"symbolSDF":"symbolIcon"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,m=e.transform,g="map"===l,v="map"===c,y=g&&"point"!==n.layout.get("symbol-placement"),x=g&&!v&&!y,b=void 0!==n.layout.get("symbol-sort-key").constantOr(1),_=e.depthModeForSublayer(0,At.ReadOnly),w=n.layout.get("text-variable-anchor"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==m.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,m.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var 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rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get("fill-pattern"),d=p&&p.constantOr(1),m=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty("fill-outline-color")?"fillOutlinePattern":"fillOutline",s=h.LINES):(l=d?"fillPattern":"fill",s=h.TRIANGLES);for(var g=0,v=n;g<v.length;g+=1){var y=v[g],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(m));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get("fill-translate"),r.paint.get("fill-translate-anchor"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c="fillOutlinePattern"===l&&d?Er(S,t,m,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,m,x):Ar(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get("fill-extrusion-pattern"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get("fill-extrusion-opacity"),p=0,d=n;p<d.length;p+=1){var m=d[p],g=e.getTile(m),v=g.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?"fillExtrusionPattern":"fillExtrusion",y);u&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(m.posMatrix,g,r.paint.get("fill-extrusion-translate"),r.paint.get("fill-extrusion-translate-anchor")),A=r.paint.get("fill-extrusion-vertical-gradient"),M=u?kr(k,t,A,h,m,f,g):Tr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,M,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram("hillshade");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get("hillshade-shadow-color"),i=r.paint.get("hillshade-highlight-color"),a=r.paint.get("hillshade-accent-color"),o=r.paint.get("hillshade-illumination-direction")*(Math.PI/180);"viewport"===r.paint.get("hillshade-illumination-anchor")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get("hillshade-exaggeration"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var m=r.fbo;if(!m){var g=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);g.bind(c.LINEAR,c.CLAMP_TO_EDGE),(m=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(m.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram("hillshadePrepare").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get("raster-fade-duration");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function mn(t){var e=t.transform.padding;gn(t,t.transform.height-(e.top||0),3,un),gn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function gn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram("debug"),l=At.disabled,c=Mt.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),"$debug",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,m=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=" => "+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext("2d");i.clearRect(0,0,r.width,r.height),i.shadowColor="white",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle="white",i.textBaseline="top",i.font="bold 36px Open Sans, sans-serif",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+" 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_=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(m,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),a),0!==n.paint.get("icon-opacity").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),n.layout.get("icon-rotation-alignment"),n.layout.get("icon-pitch-alignment"),n.layout.get("icon-keep-upright"),o,s),0!==n.paint.get("text-opacity").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),n.layout.get("text-keep-upright"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),!0),Kr(e,r,n,i,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),!1))}},circle:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("circle-opacity"),o=n.paint.get("circle-stroke-width"),s=n.paint.get("circle-stroke-opacity"),l=void 0!==n.layout.get("circle-sort-key").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,At.ReadOnly),h=Mt.disabled,p=e.colorModeForRenderPass(),d=[],m=0;m<i.length;m++){var g=i[m],v=r.getTile(g),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram("circle",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,g,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=M.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get("heatmap-opacity"))if("offscreen"===e.renderPass){var a=e.context,o=a.gl,s=Mt.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram("heatmap",p),m=e.transform.zoom;d.draw(a,o.TRIANGLES,At.disabled,s,l,Et.disabled,zr(u.posMatrix,f,m,n.paint.get("heatmap-intensity")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else"translucent"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram("heatmapTexture").draw(n,i.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get("heatmap-opacity")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("line-opacity"),o=n.paint.get("line-width");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,At.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get("line-dasharray"),u=n.paint.get("line-pattern"),f=u.constantOr(1),h=n.paint.get("line-gradient"),p=n.getCrossfadeParameters(),d=f?"linePattern":c?"lineSDF":h?"lineGradient":"line",m=e.context,g=m.gl,v=!0;if(h){m.activeTexture.set(g.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(m,n.gradient,g.RGBA)),y.bind(g.LINEAR,g.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=v||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(m.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(m.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(m)),M.draw(m,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get("fill-color"),o=n.paint.get("fill-opacity");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get("fill-pattern"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?"opaque":"translucent";if(e.renderPass===c){var u=e.depthModeForSublayer(1,"opaque"===e.renderPass?At.ReadWrite:At.ReadOnly);an(e,r,n,i,u,s,!1)}if("translucent"===e.renderPass&&n.paint.get("fill-antialias")){var f=e.depthModeForSublayer(n.getPaintProperty("fill-outline-color")?2:0,At.ReadOnly);an(e,r,n,i,f,s,!0)}}},"fill-extrusion":function(t,e,r,n){var i=r.paint.get("fill-extrusion-opacity");if(0!==i&&"translucent"===t.renderPass){var a=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get("fill-extrusion-pattern").constantOr(1))on(t,e,r,n,a,Mt.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,Mt.disabled,o)}}},hillshade:function(t,e,r,n){if("offscreen"===t.renderPass||"translucent"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,At.ReadOnly),s=t.colorModeForRenderPass(),l="translucent"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&"offscreen"===t.renderPass?ln(t,p,r,a,o,Mt.disabled,s):"translucent"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if("translucent"===t.renderPass&&0!==r.paint.get("raster-opacity")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram("raster"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,m=f;d<m.length;d+=1){var g=m[d],v=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get("raster-opacity")?At.ReadWrite:At.ReadOnly,a.LESS),y=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);y.registerFadeDuration(r.paint.get("raster-fade-duration"));var b=e.findLoadedParent(g,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k="nearest"===r.paint.get("raster-resampling")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,Mt.disabled,l,Et.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[g.overscaledZ],l,Et.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get("background-color"),i=r.paint.get("background-opacity");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get("background-pattern");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?"opaque":"translucent";if(t.renderPass===u){var f=Mt.disabled,h=t.depthModeForSublayer(0,"opaque"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?"backgroundPattern":"background"),m=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),v=0,y=m;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},g):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if("offscreen"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if("translucent"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Mt.disabled);var o="3d"===i.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram("clippingMask").draw(e,r.TRIANGLES,At.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),"$clipping",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var i=this.useProgram("clippingMask");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),"$clipping",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Mt({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Mt({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Mt({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Mt.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return"opaque"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return At.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new At(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var m=0;m<i.length;m++){var g=i[m];if(this.style._layers[g].is3D()){this.opaquePassCutoff=m;break}}this.renderPass="offscreen";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];("custom"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass="opaque",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass="translucent",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=("symbol"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&mn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||("background"===r.type||"custom"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?"map"===i?this.transform.angle:0:"viewport"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=""+t+(e?e.cacheKey:"")+(this._showOverdrawInspector?"/overdraw":"");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement("canvas"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error("Invalid value for edge-insets, top, bottom, left and right must all be numbers");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var An=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Mn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};An.prototype.clone=function(){var t=new An(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Mn.minZoom.get=function(){return this._minZoom},Mn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Mn.maxZoom.get=function(){return this._maxZoom},Mn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Mn.minPitch.get=function(){return this._minPitch},Mn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Mn.maxPitch.get=function(){return this._maxPitch},Mn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Mn.renderWorldCopies.get=function(){return this._renderWorldCopies},Mn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Mn.worldSize.get=function(){return this.tileSize*this.scale},Mn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Mn.size.get=function(){return new t.Point(this.width,this.height)},Mn.bearing.get=function(){return-this.angle/Math.PI*180},Mn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Mn.pitch.get=function(){return this._pitch/Math.PI*180},Mn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Mn.fov.get=function(){return this._fov/Math.PI*180},Mn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Mn.zoom.get=function(){return this._zoom},Mn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Mn.center.get=function(){return this._center},Mn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Mn.padding.get=function(){return this._edgeInsets.toJSON()},Mn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Mn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},An.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},An.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},An.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},An.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},An.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,v=m.y,y=m.fullyVisible;if(!y){var x=m.aabb.intersects(s);if(0===x)continue;y=2===x}var b=m.aabb.distanceX(o),_=m.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-m.zoom)-2;if(m.zoom===h||w>T&&m.zoom>=l)f.push({tileID:new t.OverscaledTileID(m.zoom===h?p:m.zoom,m.wrap,m.zoom,g,v),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-v])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(v<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:A,y:M,wrap:m.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},An.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Mn.unmodified.get=function(){return this._unmodified},An.prototype.zoomScale=function(t){return Math.pow(2,t)},An.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},An.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},An.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Mn.point.get=function(){return this.project(this.center)},An.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},An.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},An.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},An.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},An.prototype.coordinateLocation=function(t){return t.toLngLat()},An.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},An.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},An.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},An.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},An.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},An.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},An.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},An.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var m=p.y,g=c.y/2;m-g<a&&(i=a+g),m+g>o&&(i=o-g)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},An.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*h+m*p,v=l-Math.round(l)+d*p+m*h,y=new Float64Array(f);if(t.translate(y,y,[g>.5?g-1:g,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error("failed to invert matrix");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},An.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},An.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},An.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(An.prototype,Mn);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll(["_getCurrentHash","_onHashChange","_updateHash"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u="";if(u+=e?"/"+o+"/"+s+"/"+n:n+"/"+s+"/"+o,(l||c)&&(u+="/"+Math.round(10*l)/10),c&&(u+="/"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split("&").map((function(t){var e=t.split("=")[0];return e===f?(h=!0,e+"="+u):t})).filter((function(t){return t}));return h||p.push(f+"="+u),"#"+p.join("&")}return"#"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace("#","");return this._hashName?(n.split("&").map((function(t){return t.split("=")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||"").split("/")):n.split("/")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,"",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o="touchend"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn("contextmenu",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens("contextmenu")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create("div","mapboxgl-boxzoom",this._container),this._container.classList.add("mapboxgl-crosshair"),this._fireEvent("boxzoomstart",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,"translate("+a+"px,"+s+"px)"),this._box.style.width=o-a+"px",this._box.style.height=l-s+"px"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event("boxzoomend",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent("boxzoomcancel",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent("boxzoomcancel",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove("mapboxgl-crosshair"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Hn=function(t){this.reset(),this.numTouches=t.numTouches};Hn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Hn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},Hn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Hn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var qn=function(t){this.singleTap=new Hn(t),this.numTaps=t.numTaps,this.reset()};qn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},qn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},qn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},qn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new qn({numTouches:1,numTaps:2}),this._zoomOut=new qn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&"center"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:"keyboardHandler",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll(["_onWheel","_onTimeout","_onScrollFrame","_onScrollFinished"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&"center"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type="wheel":0!==r&&Math.abs(r)<4?this._type="trackpad":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type="wheel",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n="wheel"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a="number"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),"wheel"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s="number"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if("wheel"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new qn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add("mapboxgl-touch-drag-pan")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove("mapboxgl-touch-drag-pan")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add("mapboxgl-touch-zoom-rotate")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove("mapboxgl-touch-zoom-rotate")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var mi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll(["handleEvent","handleWindowEvent"],this);var i=this._el;this._listeners=[[i,"touchstart",{passive:!1}],[i,"touchmove",{passive:!1}],[i,"touchend",void 0],[i,"touchcancel",void 0],[i,"mousedown",void 0],[i,"mousemove",void 0],[i,"mouseup",void 0],[t.window.document,"mousemove",{capture:!0}],[t.window.document,"mouseup",void 0],[i,"mouseover",void 0],[i,"mouseout",void 0],[i,"dblclick",void 0],[i,"click",void 0],[i,"keydown",{capture:!1}],[i,"keyup",void 0],[i,"wheel",{passive:!1}],[i,"contextmenu",void 0],[t.window,"blur",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add("mapEvent",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add("boxZoom",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add("tapZoom",i),this._add("clickZoom",a);var o=new fi;this._add("tapDragZoom",o);var s=e.touchPitch=new ii;this._add("touchPitch",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add("mouseRotate",l,["mousePitch"]),this._add("mousePitch",c,["mouseRotate"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add("mousePan",u),this._add("touchPan",f,["touchZoom","touchRotate"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add("touchRotate",h,["touchPan","touchZoom"]),this._add("touchZoom",p,["touchPan","touchRotate"]);var d=e.scrollZoom=new li(e,this);this._add("scrollZoom",d,["mousePan"]);var m=e.keyboard=new oi;this._add("keyboard",m),this._add("blockableMapEvent",new jn(e));for(var g=0,v=["boxZoom","doubleClickZoom","tapDragZoom","touchPitch","dragRotate","dragPan","touchZoomRotate","scrollZoom","keyboard"];g<v.length;g+=1){var y=v[g];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(mi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+"Window")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if("blur"!==t.type){this._updatingCamera=!0;for(var n="renderFrame"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var m=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(m=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,m,h,n),m&&m.needsRenderFrame&&this._triggerRenderFrame()),(m||p.isActive())&&(o[h]=p)}}var g={};for(var v in this._previousActiveHandlers)o[v]||(g[v]=n);this._previousActiveHandlers=o,(Object.keys(g).length||vi(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=mi(this._eventsInProgress),a=mi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+"start"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent("movestart",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent("move",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var m=this._eventsInProgress[d],g=m.handlerName,v=m.originalEvent;this._handlersById[g].isActive()||(delete this._eventsInProgress[d],h=r[g]||v,p[d+"end"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=mi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event("moveend",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new gi("renderFrame",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll(["_renderFrameCallback"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if("number"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),m=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/m.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/m.y;if(!(v<0||g<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(g,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce("Map cannot fit within canvas with the given bounds, padding, and/or offset.")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return"zoom"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),"bearing"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),"pitch"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event("movestart",r)).fire(new t.Event("move",r)),i&&this.fire(new t.Event("zoomstart",r)).fire(new t.Event("zoom",r)).fire(new t.Event("zoomend",r)),a&&this.fire(new t.Event("rotatestart",r)).fire(new t.Event("rotate",r)).fire(new t.Event("rotateend",r)),o&&this.fire(new t.Event("pitchstart",r)).fire(new t.Event("pitch",r)).fire(new t.Event("pitchend",r)),this.fire(new t.Event("moveend",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c="zoom"in e?+e.zoom:a,u="bearing"in e?this._normalizeBearing(e.bearing,o):o,f="pitch"in e?+e.pitch:s,h="padding"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),m=i.pointLocation(d),g=t.LngLat.convert(e.center||m);this._normalizeCenter(g);var v,y,x=i.project(m),b=i.project(g).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var m=i.zoomScale(i.zoom-a),g=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event("movestart",e)),this._zooming&&!n.zooming&&this.fire(new t.Event("zoomstart",e)),this._rotating&&!n.rotating&&this.fire(new t.Event("rotatestart",e)),this._pitching&&!n.pitching&&this.fire(new t.Event("pitchstart",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event("move",e)),this._zooming&&this.fire(new t.Event("zoom",e)),this._rotating&&this.fire(new t.Event("rotate",e)),this._pitching&&this.fire(new t.Event("pitch",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event("zoomend",e)),i&&this.fire(new t.Event("rotateend",e)),a&&this.fire(new t.Event("pitchend",e)),this.fire(new t.Event("moveend",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,["center","zoom","bearing","pitch","around"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u="zoom"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f="bearing"in e?this._normalizeBearing(e.bearing,s):s,h="pitch"in e?+e.pitch:l,p="padding"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),v=a.pointLocation(g),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if("minZoom"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if("duration"in e)e.duration=+e.duration;else{var R="screenSpeed"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll(["_updateEditLink","_updateData","_updateCompact"],this)};bi.prototype.getDefaultPosition=function(){return"bottom-right"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-attrib"),this._innerContainer=r.create("div","mapboxgl-ctrl-attrib-inner",this._container),e&&this._container.classList.add("mapboxgl-compact"),this._updateAttributions(),this._updateEditLink(),this._map.on("styledata",this._updateData),this._map.on("sourcedata",this._updateData),this._map.on("moveend",this._updateEditLink),void 0===e&&(this._map.on("resize",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off("styledata",this._updateData),this._map.off("sourcedata",this._updateData),this._map.off("moveend",this._updateEditLink),this._map.off("resize",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(".mapbox-improve-map"));var r=[{key:"owner",value:this.styleOwner},{key:"id",value:this.styleId},{key:"access_token",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+"="+e.value+(n<r.length-1?"&":"")),t}),"?");e.href=t.config.FEEDBACK_URL+"/"+n+(this._map._hash?this._map._hash.getHashString(!0):""),e.rel="noopener nofollow"}},bi.prototype._updateData=function(t){!t||"metadata"!==t.sourceDataType&&"style"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return"string"!=typeof t?"":t}))):"string"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(" | ");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove("mapboxgl-attrib-empty")):this._container.classList.add("mapboxgl-attrib-empty"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add("mapboxgl-compact"):this._container.classList.remove("mapboxgl-compact")};var _i=function(){t.bindAll(["_updateLogo"],this),t.bindAll(["_updateCompact"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create("div","mapboxgl-ctrl");var e=r.create("a","mapboxgl-ctrl-logo");return e.target="_blank",e.rel="noopener nofollow",e.href="https://www.mapbox.com/",e.setAttribute("aria-label",this._map._getUIString("LogoControl.Title")),e.setAttribute("rel","noopener nofollow"),this._container.appendChild(e),this._container.style.display="none",this._map.on("sourcedata",this._updateLogo),this._updateLogo(),this._map.on("resize",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off("sourcedata",this._updateLogo),this._map.off("resize",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return"bottom-left"},_i.prototype._updateLogo=function(t){t&&"metadata"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?"block":"none")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add("mapboxgl-compact"):e.classList.remove("mapboxgl-compact")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={"FullscreenControl.Enter":"Enter fullscreen","FullscreenControl.Exit":"Exit fullscreen","GeolocateControl.FindMyLocation":"Find my location","GeolocateControl.LocationNotAvailable":"Location not available","LogoControl.Title":"Mapbox logo","NavigationControl.ResetBearing":"Reset bearing to north","NavigationControl.ZoomIn":"Zoom in","NavigationControl.ZoomOut":"Zoom out","ScaleControl.Feet":"ft","ScaleControl.Meters":"m","ScaleControl.Kilometers":"km","ScaleControl.Miles":"mi","ScaleControl.NauticalMiles":"nm"},ki=t.window.HTMLImageElement,Ai=t.window.HTMLElement,Mi=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:"sans-serif",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error("maxZoom must be greater than or equal to minZoom");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error("maxPitch must be greater than or equal to minPitch");if(null!=e.minPitch&&e.minPitch<0)throw new Error("minPitch must be greater than or equal to 0");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error("maxPitch must be less than or equal to 60");var i=new An(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),"string"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error("Container '"+e.container+"' not found.")}else{if(!(e.container instanceof Ai))throw new Error("Invalid type: 'container' must be a String or HTMLElement.");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll(["_onWindowOnline","_onWindowResize","_contextLost","_contextRestored"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error("Failed to initialize WebGL.");this.on("move",(function(){return r._update(!1)})),this.on("moveend",(function(){return r._update(!1)})),this.on("zoom",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener("online",this._onWindowOnline,!1),t.window.addEventListener("resize",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a="string"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on("style.load",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on("data",(function(e){r._update("style"===e.dataType),r.fire(new t.Event(e.dataType+"data",e))})),this.on("dataloading",(function(e){r.fire(new t.Event(e.dataType+"dataloading",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r="top-right"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf("bottom")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event("movestart",e)).fire(new t.Event("move",e))),this.fire(new t.Event("resize",e)),a&&this.fire(new t.Event("moveend",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error("minZoom must be between -2 and the current maxZoom, inclusive")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error("maxZoom must be greater than the current minZoom")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error("minPitch must be greater than or equal to 0");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error("minPitch must be between 0 and the current maxPitch, inclusive")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error("maxPitch must be less than or equal to 60");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error("maxPitch must be greater than the current minPitch")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if("mouseenter"===t||"mouseover"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if("mouseleave"===t||"mouseout"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||"number"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error("Missing UI string '"+t+"'");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),"string"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if("string"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else"object"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce("Unable to perform style diff: "+(n.message||n.error||n)+".  Rebuilding the style from scratch."),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce("There is no style added to the map.")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error("There is no source with ID '"+e+"'")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if("loaded"!==i.state&&"errored"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof ki||Mi&&r instanceof Mi){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));var p=r.width,d=r.height,m=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(m)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error("The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.")));var i=r instanceof ki||Mi&&r instanceof Mi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error("The width and height of the updated image must be that same as the previous version of the image")));var l=!(r instanceof ki||Mi&&r instanceof Mi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error("Missing required image id"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){"rgb(250, 128, 114)"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue("background-color")&&t.warnOnce("This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add("mapboxgl-map"),(this._missingCSSCanary=r.create("div","mapboxgl-canary",t)).style.visibility="hidden",this._detectMissingCSS();var e=this._canvasContainer=r.create("div","mapboxgl-canvas-container",t);this._interactive&&e.classList.add("mapboxgl-interactive"),this._canvas=r.create("canvas","mapboxgl-canvas",e),this._canvas.addEventListener("webglcontextlost",this._contextLost,!1),this._canvas.addEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.setAttribute("tabindex","0"),this._canvas.setAttribute("aria-label","Map");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create("div","mapboxgl-control-container",t),a=this._controlPositions={};["top-left","top-right","bottom-left","bottom-right"].forEach((function(t){a[t]=r.create("div","mapboxgl-ctrl-"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+"px",this._canvas.style.height=r+"px"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext("webgl",r)||this._canvas.getContext("experimental-webgl",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error("Failed to initialize WebGL")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event("webglcontextlost",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event("webglcontextrestored",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens("gpu-timing-frame")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens("gpu-timing-layer")}),this.fire(new t.Event("render")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event("load"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens("gpu-timing-frame")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event("gpu-timing-frame",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens("gpu-timing-layer")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event("gpu-timing-layer",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event("idle"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener("resize",this._onWindowResize,!1),t.window.removeEventListener("online",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension("WEBGL_lose_context");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove("mapboxgl-map"),this._removed=!0,this.fire(new t.Event("remove"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll(["_setButtonTitle","_updateZoomButtons"],this),this._zoomInButton=this._createButton("mapboxgl-ctrl-zoom-in",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomInButton).setAttribute("aria-hidden",!0),this._zoomOutButton=this._createButton("mapboxgl-ctrl-zoom-out",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomOutButton).setAttribute("aria-hidden",!0)),this.options.showCompass&&(t.bindAll(["_rotateCompassArrow"],this),this._compass=this._createButton("mapboxgl-ctrl-compass",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create("span","mapboxgl-ctrl-icon",this._compass),this._compassIcon.setAttribute("aria-hidden",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?"scale("+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+") rotateX("+this._map.transform.pitch+"deg) rotateZ("+this._map.transform.angle*(180/Math.PI)+"deg)":"rotate("+this._map.transform.angle*(180/Math.PI)+"deg)";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,"ZoomIn"),this._setButtonTitle(this._zoomOutButton,"ZoomOut"),this._map.on("zoom",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,"ResetBearing"),this.options.visualizePitch&&this._map.on("pitch",this._rotateCompassArrow),this._map.on("rotate",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off("zoom",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off("pitch",this._rotateCompassArrow),this._map.off("rotate",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create("button",t,this._container);return n.type="button",n.addEventListener("click",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString("NavigationControl."+e);t.title=r,t.setAttribute("aria-label",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll(["mousedown","mousemove","mouseup","touchstart","touchmove","touchend","reset"],this),r.addEventListener(n,"mousedown",this.mousedown),r.addEventListener(n,"touchstart",this.touchstart,{passive:!1}),r.addEventListener(n,"touchmove",this.touchmove),r.addEventListener(n,"touchend",this.touchend),r.addEventListener(n,"touchcancel",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,"mousedown",this.mousedown),r.removeEventListener(t,"touchstart",this.touchstart,{passive:!1}),r.removeEventListener(t,"touchmove",this.touchmove),r.removeEventListener(t,"touchend",this.touchend),r.removeEventListener(t,"touchcancel",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,"mousemove",this.mousemove),r.removeEventListener(t.window,"mouseup",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,"mousemove",this.mousemove),r.addEventListener(t.window,"mouseup",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:"mousedown",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:"translate(-50%,-50%)",top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove("mapboxgl-"+r+"-anchor-"+i);n.add("mapboxgl-"+r+"-anchor-"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll(["_update","_onMove","_onUp","_addDragHandler","_onMapClick","_onKeyPress"],this),this._anchor=n&&n.anchor||"center",this._color=n&&n.color||"#3FB1CE",this._draggable=n&&n.draggable||!1,this._state="inactive",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||"auto",this._pitchAlignment=n&&n.pitchAlignment&&"auto"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create("div"),this._element.setAttribute("aria-label","Map marker");var o=r.createNS("http://www.w3.org/2000/svg","svg");o.setAttributeNS(null,"display","block"),o.setAttributeNS(null,"height","41px"),o.setAttributeNS(null,"width","27px"),o.setAttributeNS(null,"viewBox","0 0 27 41");var s=r.createNS("http://www.w3.org/2000/svg","g");s.setAttributeNS(null,"stroke","none"),s.setAttributeNS(null,"stroke-width","1"),s.setAttributeNS(null,"fill","none"),s.setAttributeNS(null,"fill-rule","evenodd");var l=r.createNS("http://www.w3.org/2000/svg","g");l.setAttributeNS(null,"fill-rule","nonzero");var c=r.createNS("http://www.w3.org/2000/svg","g");c.setAttributeNS(null,"transform","translate(3.0, 29.0)"),c.setAttributeNS(null,"fill","#000000");for(var u=0,f=[{rx:"10.5",ry:"5.25002273"},{rx:"10.5",ry:"5.25002273"},{rx:"9.5",ry:"4.77275007"},{rx:"8.5",ry:"4.29549936"},{rx:"7.5",ry:"3.81822308"},{rx:"6.5",ry:"3.34094679"},{rx:"5.5",ry:"2.86367051"},{rx:"4.5",ry:"2.38636864"}];u<f.length;u+=1){var h=f[u],p=r.createNS("http://www.w3.org/2000/svg","ellipse");p.setAttributeNS(null,"opacity","0.04"),p.setAttributeNS(null,"cx","10.5"),p.setAttributeNS(null,"cy","5.80029008"),p.setAttributeNS(null,"rx",h.rx),p.setAttributeNS(null,"ry",h.ry),c.appendChild(p)}var d=r.createNS("http://www.w3.org/2000/svg","g");d.setAttributeNS(null,"fill",this._color);var m=r.createNS("http://www.w3.org/2000/svg","path");m.setAttributeNS(null,"d","M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z"),d.appendChild(m);var g=r.createNS("http://www.w3.org/2000/svg","g");g.setAttributeNS(null,"opacity","0.25"),g.setAttributeNS(null,"fill","#000000");var v=r.createNS("http://www.w3.org/2000/svg","path");v.setAttributeNS(null,"d","M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z"),g.appendChild(v);var y=r.createNS("http://www.w3.org/2000/svg","g");y.setAttributeNS(null,"transform","translate(6.0, 7.0)"),y.setAttributeNS(null,"fill","#FFFFFF");var x=r.createNS("http://www.w3.org/2000/svg","g");x.setAttributeNS(null,"transform","translate(8.0, 8.0)");var b=r.createNS("http://www.w3.org/2000/svg","circle");b.setAttributeNS(null,"fill","#000000"),b.setAttributeNS(null,"opacity","0.25"),b.setAttributeNS(null,"cx","5.5"),b.setAttributeNS(null,"cy","5.5"),b.setAttributeNS(null,"r","5.4999962");var _=r.createNS("http://www.w3.org/2000/svg","circle");_.setAttributeNS(null,"fill","#FFFFFF"),_.setAttributeNS(null,"cx","5.5"),_.setAttributeNS(null,"cy","5.5"),_.setAttributeNS(null,"r","5.4999962"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(g),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add("mapboxgl-marker"),this._element.addEventListener("dragstart",(function(t){t.preventDefault()})),this._element.addEventListener("mousedown",(function(t){t.preventDefault()})),this._element.addEventListener("focus",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,"marker"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on("move",this._update),t.on("moveend",this._update),this.setDraggable(this._draggable),this._update(),this._map.on("click",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off("click",this._onMapClick),this._map.off("move",this._update),this._map.off("moveend",this._update),this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler),this._map.off("mouseup",this._onUp),this._map.off("touchend",this._onUp),this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener("keypress",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute("tabindex")),t){if(!("offset"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],"top-left":[0,0],"top-right":[0,0],bottom:[0,-38.1],"bottom-left":[e,-1*(24.6+e)],"bottom-right":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute("tabindex"),this._originalTabIndex||this._element.setAttribute("tabindex","0"),this._element.addEventListener("keypress",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;"Space"!==e&&"Enter"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e="";"viewport"===this._rotationAlignment||"auto"===this._rotationAlignment?e="rotateZ("+this._rotation+"deg)":"map"===this._rotationAlignment&&(e="rotateZ("+(this._rotation-this._map.getBearing())+"deg)");var n="";"viewport"===this._pitchAlignment||"auto"===this._pitchAlignment?n="rotateX(0deg)":"map"===this._pitchAlignment&&(n="rotateX("+this._map.getPitch()+"deg)"),t&&"moveend"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+" translate("+this._pos.x+"px, "+this._pos.y+"px) "+n+" "+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents="none","pending"===this._state&&(this._state="active",this.fire(new t.Event("dragstart"))),this.fire(new t.Event("drag"))},n.prototype._onUp=function(){this._element.style.pointerEvents="auto",this._positionDelta=null,this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),"active"===this._state&&this.fire(new t.Event("dragend")),this._state="inactive"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state="pending",this._map.on("mousemove",this._onMove),this._map.on("touchmove",this._onMove),this._map.once("mouseup",this._onUp),this._map.once("touchend",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on("mousedown",this._addDragHandler),this._map.on("touchstart",this._addDragHandler)):(this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||"auto",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&"auto"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll(["_onSuccess","_onError","_onZoom","_finish","_setupUI","_updateCamera","_updateMarker"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:"geolocation"}).then((function(t){Ri="denied"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off("zoom",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case"WAITING_ACTIVE":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"ACTIVE_LOCK":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting");break;case"BACKGROUND":this._watchState="BACKGROUND_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event("outofmaxbounds",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"BACKGROUND":case"BACKGROUND_ERROR":this._watchState="BACKGROUND",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background")}this.options.showUserLocation&&"OFF"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&"ACTIVE_LOCK"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("geolocate",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+"px",this._circleElement.style.height=i+"px"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.disabled=!0;var r=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.title=r,this._geolocateButton.setAttribute("aria-label",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}"OFF"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("error",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this._geolocateButton=r.create("button","mapboxgl-ctrl-geolocate",this._container),r.create("span","mapboxgl-ctrl-icon",this._geolocateButton).setAttribute("aria-hidden",!0),this._geolocateButton.type="button",!1===e){t.warnOnce("Geolocation support is not available so the GeolocateControl will be disabled.");var i=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute("aria-label",i)}else{var a=this._map._getUIString("GeolocateControl.FindMyLocation");this._geolocateButton.title=a,this._geolocateButton.setAttribute("aria-label",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute("aria-pressed","false"),this._watchState="OFF"),this.options.showUserLocation&&(this._dotElement=r.create("div","mapboxgl-user-location-dot"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create("div","mapboxgl-user-location-accuracy-circle"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:"map"}),this.options.trackUserLocation&&(this._watchState="OFF"),this._map.on("zoom",this._onZoom)),this._geolocateButton.addEventListener("click",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on("movestart",(function(e){var r=e.originalEvent&&"resize"===e.originalEvent.type;e.geolocateSource||"ACTIVE_LOCK"!==n._watchState||r||(n._watchState="BACKGROUND",n._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background"),n._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),n.fire(new t.Event("trackuserlocationend")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce("Geolocate control triggered before added to a map"),!1;if(this.options.trackUserLocation){switch(this._watchState){case"OFF":this._watchState="WAITING_ACTIVE",this.fire(new t.Event("trackuserlocationstart"));break;case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":case"BACKGROUND_ERROR":Ni--,ji=!1,this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this.fire(new t.Event("trackuserlocationend"));break;case"BACKGROUND":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event("trackuserlocationstart"))}switch(this._watchState){case"WAITING_ACTIVE":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_LOCK":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"BACKGROUND":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background");break;case"BACKGROUND_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error")}if("OFF"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","true"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","false"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:"metric"},Hi=function(e){this.options=t.extend({},Vi,e),t.bindAll(["_onMove","setUnit"],this)};function qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&"imperial"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString("ScaleControl.Miles"));else Gi(e,n,l,t._getUIString("ScaleControl.Feet"))}else if(r&&"nautical"===r.unit){Gi(e,n,s/1852,t._getUIString("ScaleControl.NauticalMiles"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString("ScaleControl.Kilometers")):Gi(e,n,s,t._getUIString("ScaleControl.Meters"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(""+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+"px",t.innerHTML=s+"&nbsp;"+n}Hi.prototype.getDefaultPosition=function(){return"bottom-left"},Hi.prototype._onMove=function(){qi(this._map,this._container,this.options)},Hi.prototype.onAdd=function(t){return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-scale",t.getContainer()),this._map.on("move",this._onMove),this._onMove(),this._container},Hi.prototype.onRemove=function(){r.remove(this._container),this._map.off("move",this._onMove),this._map=void 0},Hi.prototype.setUnit=function(t){this.options.unit=t,qi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce("Full screen control 'container' must be a DOM element.")),t.bindAll(["_onClickFullscreen","_changeIcon"],this),"onfullscreenchange"in t.window.document?this._fullscreenchange="fullscreenchange":"onmozfullscreenchange"in t.window.document?this._fullscreenchange="mozfullscreenchange":"onwebkitfullscreenchange"in t.window.document?this._fullscreenchange="webkitfullscreenchange":"onmsfullscreenchange"in t.window.document&&(this._fullscreenchange="MSFullscreenChange")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display="none",t.warnOnce("This device does not support fullscreen mode.")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create("button","mapboxgl-ctrl-fullscreen",this._controlContainer);r.create("span","mapboxgl-ctrl-icon",e).setAttribute("aria-hidden",!0),e.type="button",this._updateTitle(),this._fullscreenButton.addEventListener("click",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute("aria-label",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?"FullscreenControl.Exit":"FullscreenControl.Enter")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle("mapboxgl-ctrl-shrink"),this._fullscreenButton.classList.toggle("mapboxgl-ctrl-fullscreen"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:"",maxWidth:"240px"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll(["_update","_onClose","remove","_onMouseMove","_onMouseUp","_onDrag"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on("click",this._onClose),this.options.closeOnMove&&this._map.on("move",this._onClose),this._map.on("remove",this.remove),this._update(),this._trackPointer?(this._map.on("mousemove",this._onMouseMove),this._map.on("mouseup",this._onMouseUp),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")):this._map.on("move",this._update),this.fire(new t.Event("open")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off("move",this._update),this._map.off("move",this._onClose),this._map.off("click",this._onClose),this._map.off("remove",this.remove),this._map.off("mousemove",this._onMouseMove),this._map.off("mouseup",this._onMouseUp),this._map.off("drag",this._onDrag),delete this._map),this.fire(new t.Event("close")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on("move",this._update),this._map.off("mousemove",this._onMouseMove),this._container&&this._container.classList.remove("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.remove("mapboxgl-track-pointer")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off("move",this._update),this._map.on("mousemove",this._onMouseMove),this._map.on("drag",this._onDrag),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement("body");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create("div","mapboxgl-popup-content",this._container),this.options.closeButton&&(this._closeButton=r.create("button","mapboxgl-popup-close-button",this._content),this._closeButton.type="button",this._closeButton.setAttribute("aria-label","Close popup"),this._closeButton.innerHTML="&#215;",this._closeButton.addEventListener("click",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create("div","mapboxgl-popup",this._map.getContainer()),this._tip=r.create("div","mapboxgl-popup-tip",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(" ").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add("mapboxgl-popup-track-pointer")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if("number"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),"top-left":new t.Point(n,n),"top-right":new t.Point(-n,n),bottom:new t.Point(0,-r),"bottom-left":new t.Point(n,-n),"bottom-right":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,"top-left":i,"top-right":i,bottom:i,"bottom-left":i,"bottom-right":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),"top-left":t.Point.convert(r["top-left"]||[0,0]),"top-right":t.Point.convert(r["top-right"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),"bottom-left":t.Point.convert(r["bottom-left"]||[0,0]),"bottom-right":t.Point.convert(r["bottom-right"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?["top"]:a.y>this._map.transform.height-u?["bottom"]:[],a.x<c/2?l.push("left"):a.x>this._map.transform.width-c/2&&l.push("right"),o=0===l.length?"bottom":l.join("-")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+" translate("+f.x+"px,"+f.y+"px)"),Di(this._container,o,"popup")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);var Zi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ei,NavigationControl:Pi,GeolocateControl:Ui,AttributionControl:bi,ScaleControl:Hi,FullscreenControl:Yi,Popup:Xi,Marker:Fi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(zt)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(zt),Rt=null):console.warn("Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:""};return Zi})),r}))},{}],442:[function(t,e,r){var n=t("./normalize"),i=t("gl-mat4/create"),a=t("gl-mat4/clone"),o=t("gl-mat4/determinant"),s=t("gl-mat4/invert"),l=t("gl-mat4/transpose"),c={length:t("gl-vec3/length"),normalize:t("gl-vec3/normalize"),dot:t("gl-vec3/dot"),cross:t("gl-vec3/cross")},u=i(),f=i(),h=[0,0,0,0],p=[[0,0,0],[0,0,0],[0,0,0]],d=[0,0,0];function m(t,e,r,n,i){t[0]=e[0]*n+r[0]*i,t[1]=e[1]*n+r[1]*i,t[2]=e[2]*n+r[2]*i}e.exports=function(t,e,r,i,g,v){if(e||(e=[0,0,0]),r||(r=[0,0,0]),i||(i=[0,0,0]),g||(g=[0,0,0,1]),v||(v=[0,0,0,1]),!n(u,t))return!1;if(a(f,u),f[3]=0,f[7]=0,f[11]=0,f[15]=1,Math.abs(o(f)<1e-8))return!1;var y,x,b,_,w,T,k,A=u[3],M=u[7],S=u[11],E=u[12],L=u[13],C=u[14],P=u[15];if(0!==A||0!==M||0!==S){if(h[0]=A,h[1]=M,h[2]=S,h[3]=P,!s(f,f))return!1;l(f,f),y=g,b=f,_=(x=h)[0],w=x[1],T=x[2],k=x[3],y[0]=b[0]*_+b[4]*w+b[8]*T+b[12]*k,y[1]=b[1]*_+b[5]*w+b[9]*T+b[13]*k,y[2]=b[2]*_+b[6]*w+b[10]*T+b[14]*k,y[3]=b[3]*_+b[7]*w+b[11]*T+b[15]*k}else g[0]=g[1]=g[2]=0,g[3]=1;if(e[0]=E,e[1]=L,e[2]=C,function(t,e){t[0][0]=e[0],t[0][1]=e[1],t[0][2]=e[2],t[1][0]=e[4],t[1][1]=e[5],t[1][2]=e[6],t[2][0]=e[8],t[2][1]=e[9],t[2][2]=e[10]}(p,u),r[0]=c.length(p[0]),c.normalize(p[0],p[0]),i[0]=c.dot(p[0],p[1]),m(p[1],p[1],p[0],1,-i[0]),r[1]=c.length(p[1]),c.normalize(p[1],p[1]),i[0]/=r[1],i[1]=c.dot(p[0],p[2]),m(p[2],p[2],p[0],1,-i[1]),i[2]=c.dot(p[1],p[2]),m(p[2],p[2],p[1],1,-i[2]),r[2]=c.length(p[2]),c.normalize(p[2],p[2]),i[1]/=r[2],i[2]/=r[2],c.cross(d,p[1],p[2]),c.dot(p[0],d)<0)for(var I=0;I<3;I++)r[I]*=-1,p[I][0]*=-1,p[I][1]*=-1,p[I][2]*=-1;return v[0]=.5*Math.sqrt(Math.max(1+p[0][0]-p[1][1]-p[2][2],0)),v[1]=.5*Math.sqrt(Math.max(1-p[0][0]+p[1][1]-p[2][2],0)),v[2]=.5*Math.sqrt(Math.max(1-p[0][0]-p[1][1]+p[2][2],0)),v[3]=.5*Math.sqrt(Math.max(1+p[0][0]+p[1][1]+p[2][2],0)),p[2][1]>p[1][2]&&(v[0]=-v[0]),p[0][2]>p[2][0]&&(v[1]=-v[1]),p[1][0]>p[0][1]&&(v[2]=-v[2]),!0}},{"./normalize":443,"gl-mat4/clone":272,"gl-mat4/create":274,"gl-mat4/determinant":275,"gl-mat4/invert":287,"gl-mat4/transpose":300,"gl-vec3/cross":350,"gl-vec3/dot":355,"gl-vec3/length":365,"gl-vec3/normalize":372}],443:[function(t,e,r){e.exports=function(t,e){var r=e[15];if(0===r)return!1;for(var n=1/r,i=0;i<16;i++)t[i]=e[i]*n;return!0}},{}],444:[function(t,e,r){var n=t("gl-vec3/lerp"),i=t("mat4-recompose"),a=t("mat4-decompose"),o=t("gl-mat4/determinant"),s=t("quat-slerp"),l=f(),c=f(),u=f();function f(){return{translate:h(),scale:h(1),skew:h(),perspective:[0,0,0,1],quaternion:[0,0,0,1]}}function h(t){return[t||0,t||0,t||0]}e.exports=function(t,e,r,f){if(0===o(e)||0===o(r))return!1;var h=a(e,l.translate,l.scale,l.skew,l.perspective,l.quaternion),p=a(r,c.translate,c.scale,c.skew,c.perspective,c.quaternion);return!(!h||!p)&&(n(u.translate,l.translate,c.translate,f),n(u.skew,l.skew,c.skew,f),n(u.scale,l.scale,c.scale,f),n(u.perspective,l.perspective,c.perspective,f),s(u.quaternion,l.quaternion,c.quaternion,f),i(t,u.translate,u.scale,u.skew,u.perspective,u.quaternion),!0)}},{"gl-mat4/determinant":275,"gl-vec3/lerp":366,"mat4-decompose":442,"mat4-recompose":445,"quat-slerp":505}],445:[function(t,e,r){var n={identity:t("gl-mat4/identity"),translate:t("gl-mat4/translate"),multiply:t("gl-mat4/multiply"),create:t("gl-mat4/create"),scale:t("gl-mat4/scale"),fromRotationTranslation:t("gl-mat4/fromRotationTranslation")},i=(n.create(),n.create());e.exports=function(t,e,r,a,o,s){return n.identity(t),n.fromRotationTranslation(t,s,e),t[3]=o[0],t[7]=o[1],t[11]=o[2],t[15]=o[3],n.identity(i),0!==a[2]&&(i[9]=a[2],n.multiply(t,t,i)),0!==a[1]&&(i[9]=0,i[8]=a[1],n.multiply(t,t,i)),0!==a[0]&&(i[8]=0,i[4]=a[0],n.multiply(t,t,i)),n.scale(t,t,r),t}},{"gl-mat4/create":274,"gl-mat4/fromRotationTranslation":278,"gl-mat4/identity":285,"gl-mat4/multiply":289,"gl-mat4/scale":297,"gl-mat4/translate":299}],446:[function(t,e,r){"use strict";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],447:[function(t,e,r){"use strict";var n=t("binary-search-bounds"),i=t("mat4-interpolate"),a=t("gl-mat4/invert"),o=t("gl-mat4/rotateX"),s=t("gl-mat4/rotateY"),l=t("gl-mat4/rotateZ"),c=t("gl-mat4/lookAt"),u=t("gl-mat4/translate"),f=(t("gl-mat4/scale"),t("gl-vec3/normalize")),h=[0,0,0];function p(t){this._components=t.slice(),this._time=[0],this.prevMatrix=t.slice(),this.nextMatrix=t.slice(),this.computedMatrix=t.slice(),this.computedInverse=t.slice(),this.computedEye=[0,0,0],this.computedUp=[0,0,0],this.computedCenter=[0,0,0],this.computedRadius=[0],this._limits=[-1/0,1/0]}e.exports=function(t){return new p((t=t||{}).matrix||[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1])};var d=p.prototype;d.recalcMatrix=function(t){var e=this._time,r=n.le(e,t),o=this.computedMatrix;if(!(r<0)){var s=this._components;if(r===e.length-1)for(var l=16*r,c=0;c<16;++c)o[c]=s[l++];else{var u=e[r+1]-e[r],h=(l=16*r,this.prevMatrix),p=!0;for(c=0;c<16;++c)h[c]=s[l++];var d=this.nextMatrix;for(c=0;c<16;++c)d[c]=s[l++],p=p&&h[c]===d[c];if(u<1e-6||p)for(c=0;c<16;++c)o[c]=h[c];else i(o,h,d,(t-e[r])/u)}var m=this.computedUp;m[0]=o[1],m[1]=o[5],m[2]=o[9],f(m,m);var g=this.computedInverse;a(g,o);var v=this.computedEye,y=g[15];v[0]=g[12]/y,v[1]=g[13]/y,v[2]=g[14]/y;var x=this.computedCenter,b=Math.exp(this.computedRadius[0]);for(c=0;c<3;++c)x[c]=v[c]-o[2+4*c]*b}},d.idle=function(t){if(!(t<this.lastT())){for(var e=this._components,r=e.length-16,n=0;n<16;++n)e.push(e[r++]);this._time.push(t)}},d.flush=function(t){var e=n.gt(this._time,t)-2;e<0||(this._time.splice(0,e),this._components.splice(0,16*e))},d.lastT=function(){return this._time[this._time.length-1]},d.lookAt=function(t,e,r,n){this.recalcMatrix(t),e=e||this.computedEye,r=r||h,n=n||this.computedUp,this.setMatrix(t,c(this.computedMatrix,e,r,n));for(var i=0,a=0;a<3;++a)i+=Math.pow(r[a]-e[a],2);i=Math.log(Math.sqrt(i)),this.computedRadius[0]=i},d.rotate=function(t,e,r,n){this.recalcMatrix(t);var i=this.computedInverse;e&&s(i,i,e),r&&o(i,i,r),n&&l(i,i,n),this.setMatrix(t,a(this.computedMatrix,i))};var m=[0,0,0];d.pan=function(t,e,r,n){m[0]=-(e||0),m[1]=-(r||0),m[2]=-(n||0),this.recalcMatrix(t);var 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/*
object-assign
(c) Sindre Sorhus
@license MIT
*/
"use strict";var n=Object.getOwnPropertySymbols,i=Object.prototype.hasOwnProperty,a=Object.prototype.propertyIsEnumerable;function o(t){if(null==t)throw new TypeError("Object.assign cannot be called with null or undefined");return Object(t)}e.exports=function(){try{if(!Object.assign)return!1;var t=new String("abc");if(t[5]="de","5"===Object.getOwnPropertyNames(t)[0])return!1;for(var e={},r=0;r<10;r++)e["_"+String.fromCharCode(r)]=r;if("0123456789"!==Object.getOwnPropertyNames(e).map((function(t){return e[t]})).join(""))return!1;var n={};return"abcdefghijklmnopqrst".split("").forEach((function(t){n[t]=t})),"abcdefghijklmnopqrst"===Object.keys(Object.assign({},n)).join("")}catch(t){return!1}}()?Object.assign:function(t,e){for(var r,s,l=o(t),c=1;c<arguments.length;c++){for(var u in r=Object(arguments[c]))i.call(r,u)&&(l[u]=r[u]);if(n){s=n(r);for(var f=0;f<s.length;f++)a.call(r,s[f])&&(l[s[f]]=r[s[f]])}}return l}},{}],467:[function(t,e,r){"use 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n("chain_add_tail",{index:t,pt:e})},chainConnect:function(t,e){return n("chain_con",{index1:t,index2:e})},chainReverse:function(t){return n("chain_rev",{index:t})},chainJoin:function(t,e){return n("chain_join",{index1:t,index2:e})},done:function(){return n("done")}}}},{}],482:[function(t,e,r){e.exports=function(t){"number"!=typeof t&&(t=1e-10);var e={epsilon:function(e){return"number"==typeof e&&(t=e),t},pointAboveOrOnLine:function(e,r,n){var i=r[0],a=r[1],o=n[0],s=n[1],l=e[0];return(o-i)*(e[1]-a)-(s-a)*(l-i)>=-t},pointBetween:function(e,r,n){var i=e[1]-r[1],a=n[0]-r[0],o=e[0]-r[0],s=n[1]-r[1],l=o*a+i*s;return!(l<t)&&!(l-(a*a+s*s)>-t)},pointsSameX:function(e,r){return Math.abs(e[0]-r[0])<t},pointsSameY:function(e,r){return Math.abs(e[1]-r[1])<t},pointsSame:function(t,r){return e.pointsSameX(t,r)&&e.pointsSameY(t,r)},pointsCompare:function(t,r){return e.pointsSameX(t,r)?e.pointsSameY(t,r)?0:t[1]<r[1]?-1:1:t[0]<r[0]?-1:1},pointsCollinear:function(e,r,n){var i=e[0]-r[0],a=e[1]-r[1],o=r[0]-n[0],s=r[1]-n[1];return Math.abs(i*s-o*a)<t},linesIntersect:function(e,r,n,i){var a=r[0]-e[0],o=r[1]-e[1],s=i[0]-n[0],l=i[1]-n[1],c=a*l-o*s;if(Math.abs(c)<t)return!1;var u=e[0]-n[0],f=e[1]-n[1],h=(s*f-l*u)/c,p=(a*f-o*u)/c,d={alongA:0,alongB:0,pt:[e[0]+h*a,e[1]+h*o]};return d.alongA=h<=-t?-2:h<t?-1:h-1<=-t?0:h-1<t?1:2,d.alongB=p<=-t?-2:p<t?-1:p-1<=-t?0:p-1<t?1:2,d},pointInsideRegion:function(e,r){for(var n=e[0],i=e[1],a=r[r.length-1][0],o=r[r.length-1][1],s=!1,l=0;l<r.length;l++){var c=r[l][0],u=r[l][1];u-i>t!=o-i>t&&(a-c)*(i-u)/(o-u)+c-n>t&&(s=!s),a=c,o=u}return s}};return e}},{}],483:[function(t,e,r){var n={toPolygon:function(t,e){function r(e){if(e.length<=0)return t.segments({inverted:!1,regions:[]});function r(e){var r=e.slice(0,e.length-1);return t.segments({inverted:!1,regions:[r]})}for(var n=r(e[0]),i=1;i<e.length;i++)n=t.selectDifference(t.combine(n,r(e[i])));return n}if("Polygon"===e.type)return t.polygon(r(e.coordinates));if("MultiPolygon"===e.type){for(var n=t.segments({inverted:!1,regions:[]}),i=0;i<e.coordinates.length;i++)n=t.selectUnion(t.combine(n,r(e.coordinates[i])));return t.polygon(n)}throw new Error("PolyBool: Cannot convert GeoJSON object to PolyBool polygon")},fromPolygon:function(t,e,r){function n(t,r){return e.pointInsideRegion([.5*(t[0][0]+t[1][0]),.5*(t[0][1]+t[1][1])],r)}function i(t){return{region:t,children:[]}}r=t.polygon(t.segments(r));var a=i(null);function o(t,e){for(var r=0;r<t.children.length;r++){if(n(e,(s=t.children[r]).region))return void o(s,e)}var a=i(e);for(r=0;r<t.children.length;r++){var s;n((s=t.children[r]).region,e)&&(a.children.push(s),t.children.splice(r,1),r--)}t.children.push(a)}for(var s=0;s<r.regions.length;s++){var l=r.regions[s];l.length<3||o(a,l)}function c(t,e){for(var r=0,n=t[t.length-1][0],i=t[t.length-1][1],a=[],o=0;o<t.length;o++){var s=t[o][0],l=t[o][1];a.push([s,l]),r+=l*n-s*i,n=s,i=l}return r<0!==e&&a.reverse(),a.push([a[0][0],a[0][1]]),a}var u=[];function f(t){var e=[c(t.region,!1)];u.push(e);for(var r=0;r<t.children.length;r++)e.push(h(t.children[r]))}function h(t){for(var e=0;e<t.children.length;e++)f(t.children[e]);return c(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return u.length<=0?{type:"Polygon",coordinates:[]}:1==u.length?{type:"Polygon",coordinates:u[0]}:{type:"MultiPolygon",coordinates:u}}};e.exports=n},{}],484:[function(t,e,r){var n=t("./linked-list");e.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return function(t,r,n,i,a,o){var s=e.pointsCompare(r,a);return 0!==s?s:e.pointsSame(n,o)?0:t!==i?t?1:-1:e.pointAboveOrOnLine(n,i?a:o,i?o:a)?1:-1}(t.isStart,t.pt,r,n.isStart,n.pt,n.other.pt)<0}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var i=n.node({isStart:!1,pt:e.end,seg:e,primary:r,other:t,status:null});t.other=i,o(i,t.pt)}(r,t,e),r}function l(t,e){var n=i(e,t.seg.end,t.seg);return function(t,e){r&&r.segmentChop(t.seg,e),t.other.remove(),t.seg.end=e,t.other.pt=e,o(t.other,t.pt)}(t,e),s(n,t.primary)}function c(i,o){var s=n.create();function c(t){return s.findTransition((function(r){var n,i,a,o,s,l;return(n=t,i=r.ev,a=n.seg.start,o=n.seg.end,s=i.seg.start,l=i.seg.end,e.pointsCollinear(a,s,l)?e.pointsCollinear(o,s,l)||e.pointAboveOrOnLine(o,s,l)?1:-1:e.pointAboveOrOnLine(a,s,l)?1:-1)>0}))}function u(t,n){var i=t.seg,a=n.seg,o=i.start,s=i.end,c=a.start,u=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,c,u);if(!1===f){if(!e.pointsCollinear(o,s,c))return!1;if(e.pointsSame(o,u)||e.pointsSame(s,c))return!1;var h=e.pointsSame(o,c),p=e.pointsSame(s,u);if(h&&p)return n;var d=!h&&e.pointBetween(o,c,u),m=!p&&e.pointBetween(s,c,u);if(h)return m?l(n,s):l(t,u),n;d&&(p||(m?l(n,s):l(t,u)),l(n,o))}else 0===f.alongA&&(-1===f.alongB?l(t,c):0===f.alongB?l(t,f.pt):1===f.alongB&&l(t,u)),0===f.alongB&&(-1===f.alongA?l(n,o):0===f.alongA?l(n,f.pt):1===f.alongA&&l(n,s));return!1}for(var f=[];!a.isEmpty();){var h=a.getHead();if(r&&r.vert(h.pt[0]),h.isStart){r&&r.segmentNew(h.seg,h.primary);var p=c(h),d=p.before?p.before.ev:null,m=p.after?p.after.ev:null;function g(){if(d){var t=u(h,d);if(t)return t}return!!m&&u(h,m)}r&&r.tempStatus(h.seg,!!d&&d.seg,!!m&&m.seg);var v,y=g();if(y){var x;if(t)(x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(y.seg.myFill.above=!y.seg.myFill.above);else 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n,i,a,o=t[t.length-1],l=0;l<t.length;l++){n=o,o=t[l];var c=e.pointsCompare(n,o);0!==c&&s((i=c<0?n:o,a=c<0?o:n,{id:r?r.segmentId():-1,start:i,end:a,myFill:{above:null,below:null},otherFill:null}),!0)}},calculate:function(t){return c(t,!1)}}:{calculate:function(t,e,r,n){return t.forEach((function(t){s(i(t.start,t.end,t),!0)})),r.forEach((function(t){s(i(t.start,t.end,t),!1)})),c(e,n)}}}},{"./linked-list":485}],485:[function(t,e,r){e.exports={create:function(){var t={root:{root:!0,next:null},exists:function(e){return null!==e&&e!==t.root},isEmpty:function(){return null===t.root.next},getHead:function(){return t.root.next},insertBefore:function(e,r){for(var n=t.root,i=t.root.next;null!==i;){if(r(i))return e.prev=i.prev,e.next=i,i.prev.next=e,void(i.prev=e);n=i,i=i.next}n.next=e,e.prev=n,e.next=null},findTransition:function(e){for(var r=t.root,n=t.root.next;null!==n&&!e(n);)r=n,n=n.next;return{before:r===t.root?null:r,after:n,insert:function(t){return 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p=s.index,d=s.matches_pt1?o:a,m=s.matches_head,g=n[p],v=m?g[0]:g[g.length-1],y=m?g[1]:g[g.length-2],x=m?g[g.length-1]:g[0],b=m?g[g.length-2]:g[1];return e.pointsCollinear(y,v,d)&&(m?(r&&r.chainRemoveHead(s.index,d),g.shift()):(r&&r.chainRemoveTail(s.index,d),g.pop()),v=y),e.pointsSame(x,d)?(n.splice(p,1),e.pointsCollinear(b,x,v)&&(m?(r&&r.chainRemoveTail(s.index,v),g.pop()):(r&&r.chainRemoveHead(s.index,v),g.shift())),r&&r.chainClose(s.index),void i.push(g)):void(m?(r&&r.chainAddHead(s.index,d),g.unshift(d)):(r&&r.chainAddTail(s.index,d),g.push(d)))}var _=s.index,w=l.index;r&&r.chainConnect(_,w);var T=n[_].length<n[w].length;s.matches_head?l.matches_head?T?(A(_),M(_,w)):(A(w),M(w,_)):M(w,_):l.matches_head?M(_,w):T?(A(_),M(w,_)):(A(w),M(_,w))}function k(t,e,r){return c.index=t,c.matches_head=e,c.matches_pt1=r,c===s?(c=l,!1):(c=null,!0)}function A(t){r&&r.chainReverse(t),n[t].reverse()}function M(t,i){var a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],c=o[0],u=o[1];e.pointsCollinear(l,s,c)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,c,u)&&(r&&r.chainRemoveHead(i,c),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},{}],487:[function(t,e,r){function n(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var i={union:function(t,e){return n(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],e)},intersect:function(t,e){return n(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],e)},difference:function(t,e){return n(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],e)},differenceRev:function(t,e){return n(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],e)},xor:function(t,e){return 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n=e,i=0;i<r.length;)if(t[n++]!==r[i++])return!1;return!0},r.str2arr=function(t,e){var r=[],n=0;if(e&&"hex"===e)for(;n<t.length;)r.push(parseInt(t.slice(n,n+2),16)),n+=2;else for(;n<t.length;n++)r.push(255&t.charCodeAt(n));return r},r.readUInt16LE=function(t,e){return t[e]|t[e+1]<<8},r.readUInt16BE=function(t,e){return t[e+1]|t[e]<<8},r.readUInt32LE=function(t,e){return t[e]|t[e+1]<<8|t[e+2]<<16|16777216*t[e+3]},r.readUInt32BE=function(t,e){return t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:543,"stream-parser":558}],490:[function(t,e,r){"use strict";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if("II*\0"!==i&&"MM\0*"!==i)throw n("invalid TIFF 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0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n("unexpected EOF","EBADDATA");for(var d=[],m=h,g=0;g<c;g++,m+=u){var v=this.exif_format_read(l,m);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&"\0"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],491:[function(t,e,r){"use strict";var n=t("./common").readUInt16BE,i=t("./common").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case"ispe":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case"irot":e.transforms.push({type:"irot",value:3&n.data[0]});break;case"imir":e.transforms.push({type:"imir",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),m=s(t,l+r,i);e.item_loc[u]={length:m,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if("infe"===s.boxtype){for(var l=n(s.data,4),c="",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;"ipco"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;"iprp"===n.boxtype&&u(n.data,e),"iloc"===n.boxtype&&l(n.data,e),"iinf"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if("imir"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),"irot"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var n=8;n<t.length;n+=4)r[String.fromCharCode.apply(null,t.slice(n,n+4))]=!0;if(r.mif1||r.msf1||r.miaf)return"avif"===e||"avis"===e||"avio"===e?{type:"avif",mime:"image/avif"}:"heic"===e||"heix"===e?{type:"heic",mime:"image/heic"}:"hevc"===e||"hevx"===e?{type:"heic",mime:"image/heic-sequence"}:r.avif||r.avis?{type:"avif",mime:"image/avif"}:r.heic||r.heix||r.hevc||r.hevx||r.heis?r.msf1?{type:"heif",mime:"image/heif-sequence"}:{type:"heif",mime:"image/heif"}:{type:"avif",mime:"image/avif"}}},{"./common":489}],492:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=t("../miaf_utils"),s=t("../exif_utils"),l=n("ftyp");e.exports=function(t){if(i(t,4,l)){var e=o.unbox(t,0);if(e){var r=o.getMimeType(e.data);if(r){for(var n,c=e.end;;){var u=o.unbox(t,c);if(!u)break;if(c=u.end,"mdat"===u.boxtype)return;if("meta"===u.boxtype){n=u.data;break}}if(n){var f=o.readSizeFromMeta(n);if(f){var h={width:f.width,height:f.height,type:r.type,mime:r.mime,wUnits:"px",hUnits:"px"};if(f.variants.length>1&&(h.variants=f.variants),f.orientation&&(h.orientation=f.orientation),f.exif_location&&f.exif_location.offset+f.exif_location.length<=t.length){var p=a(t,f.exif_location.offset),d=t.slice(f.exif_location.offset+p+4,f.exif_location.offset+f.exif_location.length),m=s.get_orientation(d);m>0&&(h.orientation=m)}return h}}}}}}},{"../common":489,"../exif_utils":490,"../miaf_utils":491}],493:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=n("BM");e.exports=function(t){if(!(t.length<26)&&i(t,0,o))return{width:a(t,18),height:a(t,22),type:"bmp",mime:"image/bmp",wUnits:"px",hUnits:"px"}}},{"../common":489}],494:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=n("GIF87a"),s=n("GIF89a");e.exports=function(t){if(!(t.length<10)&&(i(t,0,o)||i(t,0,s)))return{width:a(t,6),height:a(t,8),type:"gif",mime:"image/gif",wUnits:"px",hUnits:"px"}}},{"../common":489}],495:[function(t,e,r){"use strict";var n=t("../common").readUInt16LE;e.exports=function(t){var e=n(t,0),r=n(t,2),i=n(t,4);if(0===e&&1===r&&i){for(var a=[],o={width:0,height:0},s=0;s<i;s++){var l=t[6+16*s]||256,c=t[6+16*s+1]||256,u={width:l,height:c};a.push(u),(l>o.width||c>o.height)&&(o=u)}return{width:o.width,height:o.height,variants:a,type:"ico",mime:"image/x-icon",wUnits:"px",hUnits:"px"}}}},{"../common":489}],496:[function(t,e,r){"use strict";var n=t("../common").readUInt16BE,i=t("../common").str2arr,a=t("../common").sliceEq,o=t("../exif_utils"),s=i("Exif\0\0");e.exports=function(t){if(!(t.length<2)&&255===t[0]&&216===t[1])for(var e=2;;){if(t.length-e<2)return;if(255!==t[e++])return;for(var r,i,l=t[e++];255===l;)l=t[e++];if(208<=l&&l<=217||1===l)r=0;else{if(!(192<=l&&l<=254))return;if(t.length-e<2)return;r=n(t,e)-2,e+=2}if(217===l||218===l)return;if(225===l&&r>=10&&a(t,e,s)&&(i=o.get_orientation(t.slice(e+6,e+r))),r>=5&&192<=l&&l<=207&&196!==l&&200!==l&&204!==l){if(t.length-e<r)return;var c={width:n(t,e+3),height:n(t,e+1),type:"jpg",mime:"image/jpeg",wUnits:"px",hUnits:"px"};return i>0&&(c.orientation=i),c}e+=r}}},{"../common":489,"../exif_utils":490}],497:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=n("\x89PNG\r\n\x1a\n"),s=n("IHDR");e.exports=function(t){if(!(t.length<24)&&i(t,0,o)&&i(t,12,s))return{width:a(t,16),height:a(t,20),type:"png",mime:"image/png",wUnits:"px",hUnits:"px"}}},{"../common":489}],498:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=n("8BPS\0\x01");e.exports=function(t){if(!(t.length<22)&&i(t,0,o))return{width:a(t,18),height:a(t,14),type:"psd",mime:"image/vnd.adobe.photoshop",wUnits:"px",hUnits:"px"}}},{"../common":489}],499:[function(t,e,r){"use strict";function n(t){return"number"==typeof t&&isFinite(t)&&t>0}var i=/<[-_.:a-zA-Z0-9][^>]*>/,a=/^<([-_.:a-zA-Z0-9]+:)?svg\s/,o=/[^-]\bwidth="([^%]+?)"|[^-]\bwidth='([^%]+?)'/,s=/\bheight="([^%]+?)"|\bheight='([^%]+?)'/,l=/\bview[bB]ox="(.+?)"|\bview[bB]ox='(.+?)'/,c=/in$|mm$|cm$|pt$|pc$|px$|em$|ex$/;function u(t){return c.test(t)?t.match(c)[0]:"px"}e.exports=function(t){if(function(t){var e,r=0,n=t.length;for(239===t[0]&&187===t[1]&&191===t[2]&&(r=3);r<n&&(32===(e=t[r])||9===e||13===e||10===e);)r++;return r!==n&&60===t[r]}(t)){for(var e="",r=0;r<t.length;r++)e+=String.fromCharCode(t[r]);var c=(e.match(i)||[""])[0];if(a.test(c)){var f=function(t){var e=t.match(o),r=t.match(s),n=t.match(l);return{width:e&&(e[1]||e[2]),height:r&&(r[1]||r[2]),viewbox:n&&(n[1]||n[2])}}(c),h=parseFloat(f.width),p=parseFloat(f.height);if(f.width&&f.height){if(!n(h)||!n(p))return;return{width:h,height:p,type:"svg",mime:"image/svg+xml",wUnits:u(f.width),hUnits:u(f.height)}}var d=(f.viewbox||"").split(" 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e=77===t[0],r=h(t,4,e)-8;if(!(r<0)){var n=r+8;if(!(t.length-n<2)){var a=12*f(t,n+0,e);if(!(a<=0||(n+=2,t.length-n<a))){var o,s,l,d;for(o=0;o<a;o+=12)256===(d=f(t,n+o,e))?s=p(t,n+o,e):257===d&&(l=p(t,n+o,e));return s&&l?{width:s,height:l,type:"tiff",mime:"image/tiff",wUnits:"px",hUnits:"px"}:void 0}}}}}},{"../common":489}],501:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=t("../common").readUInt32LE,s=t("../exif_utils"),l=n("RIFF"),c=n("WEBP");function u(t,e){if(157===t[e+3]&&1===t[e+4]&&42===t[e+5])return{width:16383&a(t,e+6),height:16383&a(t,e+8),type:"webp",mime:"image/webp",wUnits:"px",hUnits:"px"}}function f(t,e){if(47===t[e]){var r=o(t,e+1);return{width:1+(16383&r),height:1+(r>>14&16383),type:"webp",mime:"image/webp",wUnits:"px",hUnits:"px"}}}function h(t,e){return{width:1+(t[e+6]<<16|t[e+5]<<8|t[e+4]),height:1+(t[e+9]<<e|t[e+8]<<8|t[e+7]),type:"webp",mime:"image/webp",wUnits:"px",hUnits:"px"}}e.exports=function(t){if(!(t.length<16)&&(i(t,0,l)||i(t,8,c))){var e=12,r=null,n=0,a=o(t,4)+8;if(!(a>t.length)){for(;e+8<a;)if(0!==t[e]){var p=String.fromCharCode.apply(null,t.slice(e,e+4)),d=o(t,e+4);"VP8 "===p&&d>=10?r=r||u(t,e+8):"VP8L"===p&&d>=9?r=r||f(t,e+8):"VP8X"===p&&d>=10?r=r||h(t,e+8):"EXIF"===p&&(n=s.get_orientation(t.slice(e+8,e+8+d)),e=1/0),e+=8+d}else e++;if(r)return n>0&&(r.orientation=n),r}}}},{"../common":489,"../exif_utils":490}],502:[function(t,e,r){"use 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fragColor;\n\t\t\tgl_FragColor.a *= opacity;\n\t\t}\n\t\t",uniforms:{range:t.prop("range"),lineWidth:t.prop("lineWidth"),capSize:t.prop("capSize"),opacity:t.prop("opacity"),scale:t.prop("scale"),translate:t.prop("translate"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{color:{buffer:d,offset:function(t,e){return 4*e.offset},divisor:1},position:{buffer:c,offset:function(t,e){return 8*e.offset},divisor:1},positionFract:{buffer:p,offset:function(t,e){return 8*e.offset},divisor:1},error:{buffer:m,offset:function(t,e){return 16*e.offset},divisor:1},direction:{buffer:g,stride:24,offset:0},lineOffset:{buffer:g,stride:24,offset:8},capOffset:{buffer:g,stride:24,offset:16}},primitive:"triangles",blend:{enable:!0,color:[0,0,0,0],equation:{rgb:"add",alpha:"add"},func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst 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n=t("color-normalize"),i=t("array-bounds"),a=t("object-assign"),o=t("glslify"),s=t("pick-by-alias"),l=t("flatten-vertex-data"),c=t("earcut"),u=t("array-normalize"),f=t("to-float32"),h=f.float32,p=f.fract32,d=t("es6-weak-map"),m=t("parse-rect"),g=t("array-find-index");function v(t,e){if(!(this instanceof v))return new v(t,e);if("function"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension("ANGLE_instanced_arrays"))throw Error("regl-error2d: `ANGLE_instanced_arrays` extension should be enabled");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=v.shaders.has(t)?v.shaders.get(t):v.shaders.set(t,v.createShaders(t)).get(t),this.update(e)}e.exports=v,v.dashMult=2,v.maxPatternLength=256,v.precisionThreshold=3e6,v.maxPoints=1e4,v.maxLines=2048,v.shaders=new d,v.createShaders=function(t){var e,r=t.buffer({usage:"static",type:"float",data:[0,1,0,0,1,1,1,0]}),n={primitive:"triangle 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enableStartMiter, enableEndMiter;\n\nconst float REVERSE_THRESHOLD = -.875;\nconst float MIN_DIFF = 1e-6;\n\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\n// TODO: precalculate dot products, normalize things beforehead etc.\n// TODO: refactor to rectangular algorithm\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nbool isNaN( float val ){\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\n}\n\nvoid main() {\n\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\n\n  vec2 adjustedScale;\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\n\n  vec2 scaleRatio = adjustedScale * viewport.zw;\n\tvec2 normalWidth = thickness / scaleRatio;\n\n\tfloat lineStart = 1. - lineEnd;\n\tfloat lineBot = 1. - lineTop;\n\n\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\n\n\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\n\n\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\n\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\n\n\tvec2 prevDiff = aCoord - prevCoord;\n\tvec2 currDiff = bCoord - aCoord;\n\tvec2 nextDiff = nextCoord - bCoord;\n\n\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\n\tvec2 currTangent = normalize(currDiff * scaleRatio);\n\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\n\n\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\n\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\n\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\n\n\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\n\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\n\n\t// collapsed/unidirectional segment cases\n\t// FIXME: there should be more elegant solution\n\tvec2 prevTanDiff = abs(prevTangent - currTangent);\n\tvec2 nextTanDiff = abs(nextTangent - currTangent);\n\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\n\t\tstartJoinDirection = currNormal;\n\t}\n\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\n\t\tendJoinDirection = currNormal;\n\t}\n\tif (aCoord == bCoord) {\n\t\tendJoinDirection = startJoinDirection;\n\t\tcurrNormal = prevNormal;\n\t\tcurrTangent = prevTangent;\n\t}\n\n\ttangent = currTangent;\n\n\t//calculate join shifts relative to normals\n\tfloat startJoinShift = dot(currNormal, startJoinDirection);\n\tfloat endJoinShift = dot(currNormal, endJoinDirection);\n\n\tfloat startMiterRatio = abs(1. / startJoinShift);\n\tfloat endMiterRatio = abs(1. / endJoinShift);\n\n\tvec2 startJoin = startJoinDirection * startMiterRatio;\n\tvec2 endJoin = endJoinDirection * endMiterRatio;\n\n\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\n\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\n\tstartBotJoin = -startTopJoin;\n\n\tendTopJoin = sign(endJoinShift) * endJoin * .5;\n\tendBotJoin = -endTopJoin;\n\n\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\n\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\n\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\n\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\n\n\t//miter anti-clipping\n\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\n\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\n\n\t//prevent close to reverse direction switch\n\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\n\tif (prevReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\n\t\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\n\t\t//handle miter clipping\n\t\tbTopCoord -= normalWidth * endTopJoin;\n\t\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\n\t}\n\n\tif (nextReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\n\t\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\n\t\t//handle miter clipping\n\t\taBotCoord -= normalWidth * startBotJoin;\n\t\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\n\t}\n\n\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\n\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\n\n\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\n\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\n\n\t//position is normalized 0..1 coord on the screen\n\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\n\n\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\n\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\n\n\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\n\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\n\n\t//bevel miter cutoffs\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n\n\t//round miter cutoffs\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nvoid main() {\n\tfloat alpha = 1., distToStart, distToEnd;\n\tfloat cutoff = thickness * .5;\n\n\t//bevel miter\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToStart + 1., 0.), 1.);\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToEnd + 1., 0.), 1.);\n\t\t}\n\t}\n\n\t// round miter\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - startCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - endCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\t}\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop("colorBuffer"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:"triangle",elements:function(t,e){return e.triangles},offset:0,vert:o(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec2 position, positionFract;\n\nuniform vec4 color;\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float pixelRatio, id;\nuniform vec4 viewport;\nuniform float opacity;\n\nvarying vec4 fragColor;\n\nconst float MAX_LINES = 256.;\n\nvoid main() {\n\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\n\n\tvec2 position = position * scale + translate\n       + positionFract * scale + translateFract\n       + position * scaleFract\n       + positionFract * scaleFract;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n\tfragColor.a *= opacity;\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n\tgl_FragColor = fragColor;\n}\n"]),uniforms:{scale:t.prop("scale"),color:t.prop("fill"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),translate:t.prop("translate"),opacity:t.prop("opacity"),pixelRatio:t.context("pixelRatio"),id:t.prop("id"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop("positionBuffer"),stride:8,offset:8},positionFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},v.defaults={dashes:null,join:"miter",miterLimit:1,thickness:10,cap:"square",color:"black",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},v.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},v.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);"number"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||"rect"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?"number"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if("number"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:"positions points data coords",thickness:"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth",join:"lineJoin linejoin join type mode",miterLimit:"miterlimit miterLimit",dashes:"dash dashes dasharray dash-array dashArray",color:"color colour stroke colors colours stroke-color strokeColor",fill:"fill fill-color fillColor",opacity:"alpha opacity",overlay:"overlay crease overlap intersect",close:"closed close closed-path closePath",range:"range dataBox",viewport:"viewport viewBox",hole:"holes hole hollow",splitNull:"splitNull"}),d||(e.passes[f]=d={id:f,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:r.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:"linear",min:"linear"}),colorBuffer:r.buffer({usage:"dynamic",type:"uint8",data:new Uint8Array}),positionBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array}),positionFractBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array})},t=a({},v.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<v.maxLines&&(d.depth=2*(v.maxLines-1-f%v.maxLines)/v.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,"uint8"):null),null!=t.viewport&&(d.viewport=m(t.viewport)),d.viewport||(d.viewport=m([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var y,x;if(t.positions.x&&t.positions.y){var b=t.positions.x,_=t.positions.y;x=d.count=Math.max(b.length,_.length),y=new Float64Array(2*x);for(var w=0;w<x;w++)y[2*w]=b[w],y[2*w+1]=_[w]}else y=l(t.positions,"float64"),x=d.count=Math.floor(y.length/2);var T=d.bounds=i(y,2);if(d.fill){for(var k=[],A={},M=0,S=0,E=0,L=d.count;S<L;S++){var C=y[2*S],P=y[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=y[2*M],P=y[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var I=Object.keys(A).map(Number).sort((function(t,e){return t-e})),O=[],z=0,D=null!=d.hole?d.hole[0]:null;if(null!=D){var R=g(I,(function(t){return t>=D}));(I=I.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*z,2*I[t]).concat(D?k.slice(2*D):[]),r=(d.hole||[]).map((function(e){return e-D+(I[t]-z)})),n=c(e,r);n=n.map((function(e){return e+z+(e+z<I[t]?0:D-I[t])})),O.push.apply(O,n),z=I[t]+1},B=0;B<I.length;B++)F(B);for(var N=0,j=O.length;N<j;N++)null!=A[O[N]]&&(O[N]=A[O[N]]);d.triangles=O}else{for(var U=c(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(y);u(q,2,T);var G=new Float64Array(2*x+6);d.close?y[0]===y[2*x-2]&&y[1]===y[2*x-1]?(G[0]=q[2*x-4],G[1]=q[2*x-3]):(G[0]=q[2*x-2],G[1]=q[2*x-1]):(G[0]=q[0],G[1]=q[1]),G.set(q,2),d.close?y[0]===y[2*x-2]&&y[1]===y[2*x-1]?(G[2*x+2]=q[2],G[2*x+3]=q[3],d.count-=1):(G[2*x+2]=q[0],G[2*x+3]=q[1],G[2*x+4]=q[2],G[2*x+5]=q[3]):(G[2*x+2]=q[2*x-2],G[2*x+3]=q[2*x-1],G[2*x+4]=q[2*x-2],G[2*x+5]=q[2*x-1]);var Y=h(G);d.positionBuffer(Y);var W=p(G,Y);d.positionFractBuffer(W)}if(t.range?d.range=t.range:d.range||(d.range=d.bounds),(t.range||t.positions)&&d.count){var X=d.bounds,Z=X[2]-X[0],J=X[3]-X[1],K=d.range[2]-d.range[0],Q=d.range[3]-d.range[1];d.scale=[Z/K,J/Q],d.translate=[-d.range[0]/K+X[0]/K||0,-d.range[1]/Q+X[1]/Q||0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var $,tt=0;if(!t.dashes||t.dashes.length<2)tt=1,$=new Uint8Array([255,255,255,255,255,255,255,255]);else{tt=0;for(var et=0;et<t.dashes.length;++et)tt+=t.dashes[et];$=new Uint8Array(tt*v.dashMult);for(var rt=0,nt=255,it=0;it<2;it++)for(var at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*v.dashMult*.5;ot<st;++ot)$[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:$,width:$.length,height:1,mag:"linear",min:"linear"},0,0)}if(t.color){var lt=d.count,ct=t.color;ct||(ct="transparent");var ut=new Uint8Array(4*lt+4);if(Array.isArray(ct)&&"number"!=typeof ct[0]){for(var ft=0;ft<lt;ft++){var ht=n(ct[ft],"uint8");ut.set(ht,4*ft)}ut.set(n(ct[0],"uint8"),4*lt)}else for(var pt=n(ct,"uint8"),dt=0;dt<lt+1;dt++)ut.set(pt,4*dt);d.colorBuffer({usage:"dynamic",type:"uint8",data:ut})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var y=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&y.push(this.passes[x]);return this.passes=y,this}},v.prototype.destroy=function(){return 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function(t){if(Array.isArray(t))return o(t)}(t)||function(t){if("undefined"!=typeof Symbol&&null!=t[Symbol.iterator]||null!=t["@@iterator"])return Array.from(t)}(t)||a(t)||function(){throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}()}function a(t,e){if(t){if("string"==typeof t)return o(t,e);var r=Object.prototype.toString.call(t).slice(8,-1);return"Object"===r&&t.constructor&&(r=t.constructor.name),"Map"===r||"Set"===r?Array.from(t):"Arguments"===r||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(r)?o(t,e):void 0}}function o(t,e){(null==e||e>t.length)&&(e=t.length);for(var r=0,n=new Array(e);r<e;r++)n[r]=t[r];return n}var s=t("color-normalize"),l=t("array-bounds"),c=t("color-id"),u=t("@plotly/point-cluster"),f=t("object-assign"),h=t("glslify"),p=t("pick-by-alias"),d=t("update-diff"),m=t("flatten-vertex-data"),g=t("is-iexplorer"),v=t("to-float32"),y=t("parse-rect"),x=b;function 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e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return e.borderSize.length?{buffer:e.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(255*e.borderSize/r.maxSize)]}},colorId:function(t,e){return e.color.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(4*e.color,4*e.color+4):[e.color]}},borderColorId:function(t,e){return e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},scissor:{enable:!0,box:t.prop("viewport")},viewport:t.prop("viewport"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop("elements"),count:t.prop("count"),offset:t.prop("offset"),primitive:"points"},s=f({},o);s.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nuniform float opacity;\nuniform sampler2D markerTexture;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat smoothStep(float x, float y) {\n  return 1.0 / (1.0 + exp(50.0*(x - y)));\n}\n\nvoid main() {\n  float dist = texture2D(markerTexture, gl_PointCoord).r, delta = fragWidth;\n\n  // max-distance alpha\n  if (dist < 0.003) discard;\n\n  // null-border case\n  if (fragBorderColorLevel == fragColorLevel || fragBorderColor.a == 0.) {\n    float colorAmt = smoothstep(.5 - delta, .5 + delta, dist);\n    gl_FragColor = vec4(fragColor.rgb, colorAmt * fragColor.a * opacity);\n  }\n  else {\n    float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);\n    float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);\n\n    vec4 color = fragBorderColor;\n    color.a *= borderColorAmt;\n    color = mix(color, fragColor, colorAmt);\n    color.a *= opacity;\n\n    gl_FragColor = color;\n  }\n\n}\n"]),s.vert=h(["precision highp float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\nuniform bool constPointSize;\nuniform float pixelRatio;\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\nuniform sampler2D paletteTexture;\n\nconst float maxSize = 100.;\nconst float borderLevel = .5;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n\nbool isDirect = (paletteSize.x < 1.);\n\nvec4 getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = 2. * size * pointSizeScale;\n  fragPointSize = size * pixelRatio;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragColor = color;\n  fragBorderColor = borderColor;\n  fragWidth = 1. / gl_PointSize;\n\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\n}"]),this.drawMarker=t(s);var l=f({},o);l.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragBorderRadius, fragWidth;\n\nuniform float opacity;\n\nfloat smoothStep(float edge0, float edge1, float x) {\n\tfloat t;\n\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\n\treturn t * t * (3.0 - 2.0 * t);\n}\n\nvoid main() {\n\tfloat radius, alpha = 1.0, delta = fragWidth;\n\n\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\n\n\tif (radius > 1.0 + delta) {\n\t\tdiscard;\n\t}\n\n\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\n\n\tfloat borderRadius = fragBorderRadius;\n\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\n\tvec4 color = mix(fragColor, fragBorderColor, ratio);\n\tcolor.a *= alpha * opacity;\n\tgl_FragColor = color;\n}\n"]),l.vert=h(["precision highp float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\nuniform bool constPointSize;\nuniform float pixelRatio;\nuniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\nuniform sampler2D paletteTexture;\n\nconst float maxSize = 100.;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragBorderRadius, fragWidth;\n\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n\nbool isDirect = (paletteSize.x < 1.);\n\nvec4 getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = (size + borderSize) * pointSizeScale;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\n  fragColor = color;\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\n  fragWidth = 1. / gl_PointSize;\n}\n"]),g&&(l.frag=l.frag.replace("smoothstep","smoothStep"),s.frag=s.frag.replace("smoothstep","smoothStep")),this.drawCircle=t(l)}b.defaults={color:"black",borderColor:"transparent",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if("number"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),m=0;m<r.length;m++){var g=r[m];d[g]=p?p[g]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:"function"==typeof e?e={ondraw:e}:"number"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:"positions data points",snap:"snap cluster lod tree",size:"sizes size radius",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",color:"colors color fill fill-color fillColor",borderColor:"borderColors borderColor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range dataBox databox",viewport:"viewport viewPort viewBox viewbox",opacity:"opacity alpha transparency",bounds:"bound bounds boundaries limits",tooManyColors:"tooManyColors palette paletteMode optimizePalette enablePalette"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:"stream",type:"uint8"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"})},e=f({},b.defaults,e)),e.positions&&!("marker"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!("positions"in e)&&(e.positions=n.positions,delete n.positions);var g=0,x=0;if(d(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=b.defaults.size),g+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=b.defaults.borderSize),g+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=b.defaults.color),e=t.updateColor(e),x++,e},borderColor:function(e,r){return null==e&&(e=b.defaults.borderColor),e=t.updateColor(e),x++,e},bounds:function(t,e,r){return"range"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,s=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=m(t,"float64");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:"points",usage:"static",data:e.tree,type:"uint32"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=v.float32(t);return i({data:p,usage:"dynamic"}),a({data:v.fract32(t,p),usage:"dynamic"}),s({data:new Uint8Array(c),type:"uint8",usage:"stream"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&"number"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:"uint8",usage:"static"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=v.fract(e.scale),e.translateFract=v.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),g){var _=n,w=_.count,T=_.size,k=_.borderSize,A=_.sizeBuffer,M=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)M[2*S]=Math.round(255*(null==T[S]?T:T[S])/s),M[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/s);A({data:M,usage:"dynamic"})}if(x){var E,L=n,C=L.count,P=L.color,I=L.borderColor,O=L.colorBuffer;if(t.tooManyColors){if(P.length||I.length){E=new Uint8Array(8*C);for(var z=0;z<C;z++){var D=P[z];E[8*z]=h[4*D],E[8*z+1]=h[4*D+1],E[8*z+2]=h[4*D+2],E[8*z+3]=h[4*D+3];var R=I[z];E[8*z+4]=h[4*R],E[8*z+5]=h[4*R+1],E[8*z+6]=h[4*R+2],E[8*z+7]=h[4*R+3]}}}else if(P.length||I.length){E=new Uint8Array(4*C+2);for(var F=0;F<C;F++)null!=P[F]&&(E[4*F]=P[F]%c,E[4*F+1]=Math.floor(P[F]/c)),null!=I[F]&&(E[4*F+2]=I[F]%c,E[4*F+3]=Math.floor(I[F]/c))}O({data:E||new Uint8Array(0),type:"uint8",usage:"dynamic"})}return n}))}},b.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:"linear",min:"linear"})),a},b.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if("number"==typeof t[0]){var a=[];if(Array.isArray(t))for(var o=0;o<t.length;o+=4)a.push(t.slice(o,o+4));else for(var l=0;l<t.length;l+=4)a.push(t.subarray(l,l+4));t=a}for(var u=0;u<t.length;u++){var f=t[u];f=s(f,"uint8");var h=c(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[u]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},b.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},b.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var _=t("object-assign");e.exports=function(t,e){var r=new x(t,e),n=r.render.bind(r);return _(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},{"@plotly/point-cluster":59,"array-bounds":72,"color-id":124,"color-normalize":126,"flatten-vertex-data":244,glslify:424,"is-iexplorer":434,"object-assign":466,"parse-rect":471,"pick-by-alias":475,"to-float32":573,"update-diff":594}],515:[function(t,e,r){"use strict";var n=t("regl-scatter2d"),i=t("pick-by-alias"),a=t("array-bounds"),o=t("raf"),s=t("array-range"),l=t("parse-rect"),c=t("flatten-vertex-data");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if("number"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,c=0;c<l.length;c++)i.push(this.passes[l[c]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},u.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:"data items columns rows values dimensions samples x",snap:"snap cluster",size:"sizes size radius",color:"colors color fill fill-color fillColor",opacity:"opacity alpha transparency opaque",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",borderColor:"borderColors borderColor bordercolor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range ranges databox dataBox",viewport:"viewport viewBox viewbox",domain:"domain domains area areas",padding:"pad padding paddings pads margin margins",transpose:"transpose transposed",diagonal:"diagonal diag showDiagonal",upper:"upper 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+"."+Math.floor(t/20)+"."+t%20},forYear:function(t){if((t=t.split(".")).length<3)throw"Invalid Mayan year";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw"Invalid Mayan year";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{"../main":617,"object-assign":466}],611:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new 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9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,"d").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 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n.baseCalendar,i(a.prototype,{name:"UmmAlQura",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Umm al-Qura",epochs:["BH","AH"],monthNames:["Al-Muharram","Safar","Rabi' al-awwal","Rabi' Al-Thani","Jumada Al-Awwal","Jumada Al-Thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-Ahad","Yawm al-Ithnain","Yawm al-Thal\u0101th\u0101\u2019","Yawm al-Arba\u2018\u0101\u2019","Yawm al-Kham\u012bs","Yawm al-Jum\u2018a","Yawm al-Sabt"],dayNamesMin:["Ah","Ith","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\{0\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t("object-assign");function i(){this.regionalOptions=[],this.regionalOptions[""]={invalidCalendar:"Calendar {0} not found",invalidDate:"Invalid {0} date",invalidMonth:"Invalid {0} month",invalidYear:"Invalid {0} year",differentCalendars:"Cannot mix {0} and {1} dates"},this.local=this.regionalOptions[""],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name)}function o(t,e){return"000000".substring(0,e-(t=""+t).length)+t}function s(){this.shortYearCutoff="+10"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[""]}n(i.prototype,{instance:function(t,e){t=(t||"gregorian").toLowerCase(),e=e||"";var r=this._localCals[t+"-"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+"-"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[""].invalidCalendar).replace(/\{0\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():"string"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+"").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n="",i=0;r>0;){var a=r%10;n=(0===a?"":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,"y")},month:function(t){return 0===arguments.length?this._month:this.set(t,"m")},day:function(t){return 0===arguments.length?this._day:this.set(t,"d")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[""].differentCalendars).replace(/\{0\}/,this._calendar.local.name).replace(/\{1\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?"-":"")+o(Math.abs(this.year()),4)+"-"+o(this.month(),2)+"-"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return(e.year()<0?"-":"")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[""].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,"d"===r||"w"===r){var n=t.toJD()+e*("w"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+("y"===r?e:0),o=t.monthOfYear()+("m"===r?e:0);i=t.day();"y"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):"m"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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i.local.unexpectedText||i.regionalOptions[""].unexpectedText;if(-1===h?h=this.today().year():h<100&&g&&(h+=-1===n?1900:this.today().year()-this.today().year()%100-(h<=n?0:100)),"string"==typeof p&&(p=s.call(this,h,p)),m>-1){p=1,d=m;for(var E=this.daysInMonth(h,p);d>E;E=this.daysInMonth(h,p))p++,d-=E}return f>-1?this.fromJD(f):this.newDate(h,p,d)},determineDate:function(t,e,r,n,i){r&&"object"!=typeof r&&(i=n,n=r,r=null),"string"!=typeof n&&(i=n,n="");var a=this;return e=e?e.newDate():null,t=null==t?e:"string"==typeof t?function(t){try{return a.parseDate(n,t,i)}catch(t){}for(var e=((t=t.toLowerCase()).match(/^c/)&&r?r.newDate():null)||a.today(),o=/([+-]?[0-9]+)\s*(d|w|m|y)?/g,s=o.exec(t);s;)e.add(parseInt(s[1],10),s[2]||"d"),s=o.exec(t);return e}(t):"number"==typeof t?isNaN(t)||t===1/0||t===-1/0?e:a.today().add(t,"d"):a.newDate(t)}})},{"./main":617,"object-assign":466}],619:[function(t,e,r){"use strict";var n,i=function(){return function(t,e,r,n,i,a){var o=t[0],s=r[0],l=[0],c=s;n|=0;var 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":r._dfltTitle.annotation),a("textangle"),n.coerceFont(a,"font",r.font),a("width"),a("align"),a("height")&&a("valign"),u){var f,h,p=a("arrowside");-1!==p.indexOf("end")&&(f=a("arrowhead"),h=a("arrowsize")),-1!==p.indexOf("start")&&(a("startarrowhead",f),a("startarrowsize",h)),a("arrowcolor",l?e.bordercolor:i.defaultLine),a("arrowwidth",2*(l&&c||1)),a("standoff"),a("startstandoff")}var d=a("hovertext"),m=r.hoverlabel||{};if(d){var g=a("hoverlabel.bgcolor",m.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a("hoverlabel.bordercolor",m.bordercolor||i.contrast(g));n.coerceFont(a,"hoverlabel.font",{family:m.font.family,size:m.font.size,color:m.font.color||v})}a("captureevents",!!d)}},{"../../lib":776,"../color":639}],626:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../../lib/to_log_range");e.exports=function(t,e,r,a){e=e||{};var o="log"===r&&"linear"===e.type,s="linear"===r&&"log"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c="annotations["+h+"].",l[f+"ref"]===e._id&&p(f),l["a"+f+"ref"]===e._id&&p("a"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{"../../lib/to_log_range":804,"fast-isnumeric":242}],627:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../plots/cartesian/axes"),a=t("../../plots/array_container_defaults"),o=t("./common_defaults"),s=t("./attributes");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a("visible"),c=a("clicktoshow");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=["x","y"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var m=f[d],g=i.coerceRef(t,e,p,m,"","paper");if("paper"!==g)i.getFromId(p,g)._annIndices.push(e._index);if(i.coercePosition(e,p,a,g,m,.5),u){var v="a"+m,y=i.coerceRef(t,e,p,v,"pixel",["pixel","paper"]);"pixel"!==y&&y!==g&&(y=e[v]="pixel");var x="pixel"===y?h[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(m+"anchor"),a(m+"shift")}if(n.noneOrAll(t,e,["x","y"]),u&&n.noneOrAll(t,e,["ax","ay"]),c){var b=a("xclick"),_=a("yclick");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 0===_?e.y:i.cleanPosition(_,p,e.yref)}}}e.exports=function(t,e){a(t,e,{name:"annotations",handleItemDefaults:l})}},{"../../lib":776,"../../plots/array_container_defaults":822,"../../plots/cartesian/axes":827,"./attributes":622,"./common_defaults":625}],628:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../registry"),a=t("../../plots/plots"),o=t("../../lib"),s=o.strTranslate,l=t("../../plots/cartesian/axes"),c=t("../color"),u=t("../drawing"),f=t("../fx"),h=t("../../lib/svg_text_utils"),p=t("../../lib/setcursor"),d=t("../dragelement"),m=t("../../plot_api/plot_template").arrayEditor,g=t("./draw_arrow_head");function v(t,e){var r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+"ref"],s=-1!==r.indexOf("y"),c="domain"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b="annotation-"+a,_=a+".annotations"):(b="annotation",_="annotations");var A=m(t.layout,_,e),M=A.modifyBase,S=A.modifyItem,E=A.getUpdateObj;w._infolayer.selectAll("."+b+'[data-index="'+r+'"]').remove();var L="clip"+w._uid+"_ann"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append("g").classed(b,!0).attr("data-index",String(r)).style("opacity",e.opacity),O=I.append("g").classed("annotation-text-g",!0),z=k[e.showarrow?"annotationTail":"annotationPosition"],D=e.captureevents||k.annotationText||z,R=O.append("g").style("pointer-events",D?"all":null).call(p,"pointer").on("click",(function(){t._dragging=!1,t.emit("plotly_clickannotation",Y(n.event))}));e.hovertext&&R.on("mouseover",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on("mouseout",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append("rect").attr("class","bg").style("stroke-width",F+"px").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll("#"+L).data(U?[0]:[]);V.enter().append("clipPath").classed("annclip",!0).attr("id",L).append("rect"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append("text").classed("annotation-text",!0).text(q);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on("edit",(function(r){e.text=r,this.call(W),S("text",r),v&&v.autorange&&M(v._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0),i.call("_guiRelayout",t,E())})):G.call(W)}else n.selectAll("#"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,H).attr({"text-anchor":{left:"start",right:"end"}[e.align]||"middle"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll("a");1===r.size()&&r.text()===G.text()&&R.insert("a",":first-child").attr({"xlink:xlink:href":r.attr("xlink:href"),"xlink:xlink:show":r.attr("xlink:show")}).style({cursor:"pointer"}).node().appendChild(j.node());var n=R.select(".annotation-text-math-group"),f=!n.empty(),m=u.bBox((f?n:G).node()),b=m.width,_=m.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),H=Math.round(D+2*N);function q(t,e){return"auto"===e&&(e=t<1/3?"left":t>2/3?"right":"center"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=["x","y"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+"ref"]||et,nt=e["a"+et+"ref"],it={x:v,y:x}[et],at=(P+("x"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+"anchor"],ut=e[et+"shift"]*("x"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&"domain"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e["a"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt="domain"===ht;"x"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var mt=e["a"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);"domain"===gt?("y"===et&&(mt=1-mt),ft.tail=it._offset+it._length*mt):"paper"===gt?"y"===et?(mt=1-mt,ft.tail=T.t+T.h*mt):ft.tail=T.l+T.w*mt:ft.tail=it._offset+it.r2p(mt),K=tt}else ft.tail=J+mt,K=tt+mt;ft.text=ft.tail+tt;var vt=w["x"===et?"width":"height"];if("paper"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),"pixel"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*q($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e["_"+et+"padplus"]=lt/2+K,e["_"+et+"padminus"]=lt/2-K,e["_"+et+"size"]=lt,e["_"+et+"shift"]=tt}if(W)R.remove();else{var bt=0,_t=0;if("left"!==e.align&&(bt=(A-b)*("center"===e.align?.5:1)),"top"!==e.valign&&(_t=(D-_)*("middle"===e.valign?.5:1)),f)n.select("svg").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-m.top,Tt=N+bt-m.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select("rect").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,H-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),O.attr({transform:"rotate("+P+","+C.x.text+","+C.y.text+")"});var kt,At=function(r,n){I.selectAll(".annotation-arrow-g").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,m=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,m,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr("width"),z=+j.attr("height"),D=m-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,H=e.arrowcolor,q=e.arrowside,G=I.append("g").style({opacity:c.opacity(H)}).classed("annotation-arrow-g",!0),Y=G.append("path").attr("d","M"+h+","+p+"L"+l+","+f).style("stroke-width",V+"px").call(c.stroke,c.rgb(H));if(g(Y,q,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var J,K,Q=G.append("path").classed("annotation-arrow",!0).classed("anndrag",!0).classed("cursor-move",!0).attr({d:"M3,3H-3V-3H3ZM0,0L"+(h-W)+","+(p-X),transform:s(W,X)}).style("stroke-width",V+6+"px").call(c.stroke,"rgba(0,0,0,0)").call(c.fill,"rgba(0,0,0,0)");d.init({element:Q.node(),gd:t,prepFn:function(){var t=u.getTranslate(R);J=t.x,K=t.y,v&&v.autorange&&M(v._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0)},moveFn:function(t,r){var n=w(J,K),i=n[0]+t,a=n[1]+r;R.call(u.setTranslate,i,a),S("x",y(v,t,"x",T,e)),S("y",y(x,r,"y",T,e)),e.axref===e.xref&&S("ax",y(v,t,"ax",T,e)),e.ayref===e.yref&&S("ay",y(x,r,"ay",T,e)),G.attr("transform",s(t,r)),O.attr({transform:"rotate("+P+","+i+","+a+")"})},doneFn:function(){i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&At(0,0),z)d.init({element:R.node(),gd:t,prepFn:function(){kt=O.attr("transform")},moveFn:function(t,r){var n="pointer";if(e.showarrow)e.axref===e.xref?S("ax",y(v,t,"ax",T,e)):S("ax",e.ax+t),e.ayref===e.yref?S("ay",y(x,r,"ay",T.w,e)):S("ay",e.ay+r),At(t,r);else{if(a)return;var i,o;if(v)i=y(v,t,"x",T,e);else{var l=e._xsize/T.w,c=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(c+t/T.w,l,0,1,e.xanchor)}if(x)o=y(x,r,"y",T,e);else{var u=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-u/2;o=d.align(f-r/T.h,u,0,1,e.yanchor)}S("x",i),S("y",o),v&&x||(n=d.getCursor(v?.5:i,x?.5:o,e.xanchor,e.yanchor))}O.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit("plotly_clickannotation",Y(n))},doneFn:function(){p(R),i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(".annotation").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&v(t,r);return a.previousPromises(t)},drawOne:v,drawRaw:x}},{"../../lib":776,"../../lib/setcursor":797,"../../lib/svg_text_utils":802,"../../plot_api/plot_template":816,"../../plots/cartesian/axes":827,"../../plots/plots":890,"../../registry":904,"../color":639,"../dragelement":658,"../drawing":661,"../fx":679,"./draw_arrow_head":629,"@plotly/d3":58}],629:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../color"),a=t("./arrow_paths"),o=t("../../lib"),s=o.strScale,l=o.strRotate,c=o.strTranslate;e.exports=function(t,e,r){var o,u,f,h,p=t.node(),d=a[r.arrowhead||0],m=a[r.startarrowhead||0],g=(r.arrowwidth||1)*(r.arrowsize||1),v=(r.arrowwidth||1)*(r.startarrowsize||1),y=e.indexOf("start")>=0,x=e.indexOf("end")>=0,b=d.backoff*g+r.standoff,_=m.backoff*v+r.startstandoff;if("line"===p.nodeName){o={x:+t.attr("x1"),y:+t.attr("y1")},u={x:+t.attr("x2"),y:+t.attr("y2")};var w=o.x-u.x,T=o.y-u.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);u.x+=k,u.y+=A,t.attr({x2:u.x,y2:u.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if("path"===p.nodeName){var E=p.getTotalLength(),L="";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L="0px,"+_+"px,";var I=p.getPointAtLength(E),O=p.getPointAtLength(E-.1);h=Math.atan2(I.y-O.y,I.x-O.x),u=p.getPointAtLength(Math.max(0,E-b)),L+=E-(L?_+b:b)+"px,"+E+"px",t.style("stroke-dasharray",L)}function z(){t.style("stroke-dasharray","0px,100px")}function D(e,a,o,u){e.path&&(e.noRotate&&(o=0),n.select(p.parentNode).append("path").attr({class:t.attr("class"),d:e.path,transform:c(a.x,a.y)+l(180*o/Math.PI)+s(u)}).style({fill:i.rgb(r.arrowcolor),"stroke-width":0}))}y&&D(m,o,f,v),x&&D(d,u,h,g)}},{"../../lib":776,"../color":639,"./arrow_paths":621,"@plotly/d3":58}],630:[function(t,e,r){"use strict";var n=t("./draw"),i=t("./click");e.exports={moduleType:"component",name:"annotations",layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),includeBasePlot:t("../../plots/cartesian/include_components")("annotations"),calcAutorange:t("./calc_autorange"),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:t("./convert_coords")}},{"../../plots/cartesian/include_components":840,"./attributes":622,"./calc_autorange":623,"./click":624,"./convert_coords":626,"./defaults":627,"./draw":628}],631:[function(t,e,r){"use strict";var n=t("../annotations/attributes"),i=t("../../plot_api/edit_types").overrideAll,a=t("../../plot_api/plot_template").templatedArray;e.exports=i(a("annotation",{visible:n.visible,x:{valType:"any"},y:{valType:"any"},z:{valType:"any"},ax:{valType:"number"},ay:{valType:"number"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),"calc","from-root")},{"../../plot_api/edit_types":809,"../../plot_api/plot_template":816,"../annotations/attributes":622}],632:[function(t,e,r){"use 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b=y.width/2+S;g<b?(g=b,e.attr("d","M-"+(b-M)+",0L-"+(b-2*M)+","+x+M+"H"+(S+y.width/2)+"v"+x+(2*S+y.height)+"H-"+b+"V"+x+M+"Z")):g>i.width-b?(g=i.width-b,e.attr("d","M"+(b-M)+",0L"+b+","+x+M+"v"+x+(2*S+y.height)+"H-"+b+"V"+x+M+"H"+(b-2*M)+"Z")):e.attr("d","M0,0L"+M+","+x+M+"H"+(S+y.width/2)+"v"+x+(2*S+y.height)+"H-"+(S+y.width/2)+"V"+x+M+"H-"+M+"Z")}else{var _,C,P;"right"===L.side?(_="start",C=1,P="",g=E._offset+E._length):(_="end",C=-1,P="-",g=E._offset),v=L._offset+(A.y0+A.y1)/2,l.attr("text-anchor",_),e.attr("d","M0,0L"+P+M+","+M+"V"+(S+y.height/2)+"h"+P+(2*S+y.width)+"V-"+(S+y.height/2)+"H"+P+M+"V-"+M+"Z");var O,z=y.height/2,R=D-y.top-z,F="clip"+i._uid+"commonlabel"+L._id;if(g<y.width+2*S+M){O="M-"+(M+S)+"-"+z+"h-"+(y.width-S)+"V"+z+"h"+(y.width-S)+"Z";var B=y.width-g+S;u.positionText(l,B,R),"end"===_&&l.selectAll("tspan").each((function(){var t=n.select(this),e=h.tester.append("text").text(t.text()).call(h.font,m),i=V(r,e.node());Math.round(i.width)<Math.round(y.width)&&t.attr("x",B-i.width),e.remove()}))}else u.positionText(l,C*(S+M),R),O=null;var N=i._topclips.selectAll("#"+F).data(O?[0]:[]);N.enter().append("clipPath").attr("id",F).append("path"),N.exit().remove(),N.select("path").attr("d",O),h.setClipUrl(l,O?F:null,r)}t.attr("transform",s(g,v))})),v.isUnifiedHover(a)){if(d.selectAll("g.hovertext").remove(),0===t.length)return;var G=i.hoverlabel,Y=G.font,W={showlegend:!0,legend:{title:{text:I,font:Y},font:Y,bgcolor:G.bgcolor,bordercolor:G.bordercolor,borderwidth:1,tracegroupgap:7,traceorder:i.legend?i.legend.traceorder:void 0,orientation:"v"}},X={};x(W,X,r._fullData);var Z=X.legend;Z.entries=[];for(var J=0;J<t.length;J++){var K=O(t[J],!0,a,i,I),Q=K[0],$=K[1],tt=t[J];tt.name=$,tt.text=""!==$?$+" : "+Q:Q;var 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G,Y,W=(H+M+S+E)*i._invScaleX;if(c)t.pos=P,G=z+j/2+W<=F,Y=z-j/2-W>=0,"top"!==t.idealAlign&&G||!Y?G?(z+=j/2,t.anchor="start"):t.anchor="middle":(z-=j/2,t.anchor="end");else if(t.pos=z,G=P+N/2+W<=R,Y=P-N/2-W>=0,"left"!==t.idealAlign&&G||!Y)if(G)P+=N/2,t.anchor="start";else{t.anchor="middle";var X=W/2,Z=P+X-R,J=P-X;Z>0&&(P-=Z),J<0&&(P+=-J)}else P-=N/2,t.anchor="end";w.attr("text-anchor",t.anchor),E&&A.attr("text-anchor",t.anchor),e.attr("transform",s(P,z)+(c?l(_):""))})),yt}function O(t,e,r,n,i,a){var s="",l="";void 0!==t.nameOverride&&(t.name=t.nameOverride),t.name&&(t.trace._meta&&(t.name=o.templateString(t.name,t.trace._meta)),s=B(t.name,t.nameLength));var c=r.charAt(0),u="x"===c?"y":"x";void 0!==t.zLabel?(void 0!==t.xLabel&&(l+="x: "+t.xLabel+"<br>"),void 0!==t.yLabel&&(l+="y: "+t.yLabel+"<br>"),"choropleth"!==t.trace.type&&"choroplethmapbox"!==t.trace.type&&(l+=(l?"z: ":"")+t.zLabel)):e&&t[c+"Label"]===i?l=t[u+"Label"]||"":void 0===t.xLabel?void 0!==t.yLabel&&"scattercarpet"!==t.trace.type&&(l=t.yLabel):l=void 0===t.yLabel?t.xLabel:"("+t.xLabel+", "+t.yLabel+")",!t.text&&0!==t.text||Array.isArray(t.text)||(l+=(l?"<br>":"")+t.text),void 0!==t.extraText&&(l+=(l?"<br>":"")+t.extraText),a&&""===l&&!t.hovertemplate&&(""===s&&a.remove(),l=s);var f=t.hovertemplate||!1;if(f){var h=t.hovertemplateLabels||t;t[c+"Label"]!==i&&(h[c+"other"]=h[c+"Val"],h[c+"otherLabel"]=h[c+"Label"]),l=(l=o.hovertemplateString(f,h,n._d3locale,t.eventData[0]||{},t.trace._meta)).replace(P,(function(e,r){return s=B(r,t.nameLength),""}))}return[l,s]}function z(t,e,r,i){var a=function(t){return t*r},o=function(t){return t*i};t.each((function(t){var r=n.select(this);if(t.del)return r.remove();var 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c=Array.isArray(r)?function(t,e){var i=o.castOption(a,r,t);return l(i)?i:o.extractOption({},n,"",e)}:function(t,e){return o.extractOption(s,n,t,e)};function u(e,r,n){var i=c(r,n);l(i)&&(t[e]=i)}if(u("hoverinfo","hi","hoverinfo"),u("bgcolor","hbg","hoverlabel.bgcolor"),u("borderColor","hbc","hoverlabel.bordercolor"),u("fontFamily","htf","hoverlabel.font.family"),u("fontSize","hts","hoverlabel.font.size"),u("fontColor","htc","hoverlabel.font.color"),u("nameLength","hnl","hoverlabel.namelength"),u("textAlign","hta","hoverlabel.align"),t.posref="y"===e||"closest"===e&&"h"===n.orientation?t.xa._offset+(t.x0+t.x1)/2:t.ya._offset+(t.y0+t.y1)/2,t.x0=o.constrain(t.x0,0,t.xa._length),t.x1=o.constrain(t.x1,0,t.xa._length),t.y0=o.constrain(t.y0,0,t.ya._length),t.y1=o.constrain(t.y1,0,t.ya._length),void 0!==t.xLabelVal&&(t.xLabel="xLabel"in t?t.xLabel:m.hoverLabelText(t.xa,t.xLabelVal,n.xhoverformat),t.xVal=t.xa.c2d(t.xLabelVal)),void 0!==t.yLabelVal&&(t.yLabel="yLabel"in t?t.yLabel:m.hoverLabelText(t.ya,t.yLabelVal,n.yhoverformat),t.yVal=t.ya.c2d(t.yLabelVal)),void 0!==t.zLabelVal&&void 0===t.zLabel&&(t.zLabel=String(t.zLabelVal)),!(isNaN(t.xerr)||"log"===t.xa.type&&t.xerr<=0)){var f=m.tickText(t.xa,t.xa.c2l(t.xerr),"hover").text;void 0!==t.xerrneg?t.xLabel+=" +"+f+" / -"+m.tickText(t.xa,t.xa.c2l(t.xerrneg),"hover").text:t.xLabel+=" \xb1 "+f,"x"===e&&(t.distance+=1)}if(!(isNaN(t.yerr)||"log"===t.ya.type&&t.yerr<=0)){var h=m.tickText(t.ya,t.ya.c2l(t.yerr),"hover").text;void 0!==t.yerrneg?t.yLabel+=" +"+h+" / -"+m.tickText(t.ya,t.ya.c2l(t.yerrneg),"hover").text:t.yLabel+=" \xb1 "+h,"y"===e&&(t.distance+=1)}var p=t.hoverinfo||t.trace.hoverinfo;return p&&"all"!==p&&(-1===(p=Array.isArray(p)?p:p.split("+")).indexOf("x")&&(t.xLabel=void 0),-1===p.indexOf("y")&&(t.yLabel=void 0),-1===p.indexOf("z")&&(t.zLabel=void 0),-1===p.indexOf("text")&&(t.text=void 0),-1===p.indexOf("name")&&(t.name=void 0)),t}function R(t,e,r){var n,i,o=r.container,s=r.fullLayout,l=s._size,c=r.event,u=!!e.hLinePoint,f=!!e.vLinePoint;if(o.selectAll(".spikeline").remove(),f||u){var d=p.combine(s.plot_bgcolor,s.paper_bgcolor);if(u){var g,v,y=e.hLinePoint;n=y&&y.xa,"cursor"===(i=y&&y.ya).spikesnap?(g=c.pointerX,v=c.pointerY):(g=n._offset+y.x,v=i._offset+y.y);var x,b,_=a.readability(y.color,d)<1.5?p.contrast(d):y.color,w=i.spikemode,T=i.spikethickness,k=i.spikecolor||_,A=m.getPxPosition(t,i);if(-1!==w.indexOf("toaxis")||-1!==w.indexOf("across")){if(-1!==w.indexOf("toaxis")&&(x=A,b=g),-1!==w.indexOf("across")){var M=i._counterDomainMin,S=i._counterDomainMax;"free"===i.anchor&&(M=Math.min(M,i.position),S=Math.max(S,i.position)),x=l.l+M*l.w,b=l.l+S*l.w}o.insert("line",":first-child").attr({x1:x,x2:b,y1:v,y2:v,"stroke-width":T,stroke:k,"stroke-dasharray":h.dashStyle(i.spikedash,T)}).classed("spikeline",!0).classed("crisp",!0),o.insert("line",":first-child").attr({x1:x,x2:b,y1:v,y2:v,"stroke-width":T+2,stroke:d}).classed("spikeline",!0).classed("crisp",!0)}-1!==w.indexOf("marker")&&o.insert("circle",":first-child").attr({cx:A+("right"!==i.side?T:-T),cy:v,r:T,fill:k}).classed("spikeline",!0)}if(f){var E,L,C=e.vLinePoint;n=C&&C.xa,i=C&&C.ya,"cursor"===n.spikesnap?(E=c.pointerX,L=c.pointerY):(E=n._offset+C.x,L=i._offset+C.y);var P,I,O=a.readability(C.color,d)<1.5?p.contrast(d):C.color,z=n.spikemode,D=n.spikethickness,R=n.spikecolor||O,F=m.getPxPosition(t,n);if(-1!==z.indexOf("toaxis")||-1!==z.indexOf("across")){if(-1!==z.indexOf("toaxis")&&(P=F,I=L),-1!==z.indexOf("across")){var B=n._counterDomainMin,N=n._counterDomainMax;"free"===n.anchor&&(B=Math.min(B,n.position),N=Math.max(N,n.position)),P=l.t+(1-N)*l.h,I=l.t+(1-B)*l.h}o.insert("line",":first-child").attr({x1:E,x2:E,y1:P,y2:I,"stroke-width":D,stroke:R,"stroke-dasharray":h.dashStyle(n.spikedash,D)}).classed("spikeline",!0).classed("crisp",!0),o.insert("line",":first-child").attr({x1:E,x2:E,y1:P,y2:I,"stroke-width":D+2,stroke:d}).classed("spikeline",!0).classed("crisp",!0)}-1!==z.indexOf("marker")&&o.insert("circle",":first-child").attr({cx:E,cy:F-("top"!==n.side?D:-D),r:D,fill:R}).classed("spikeline",!0)}}}function F(t,e){return!e||(e.vLinePoint!==t._spikepoints.vLinePoint||e.hLinePoint!==t._spikepoints.hLinePoint)}function B(t,e){return u.plainText(t||"",{len:e,allowedTags:["br","sub","sup","b","i","em"]})}function N(t,e,r){var n=e[t+"a"],i=e[t+"Val"],a=e.cd[0];if("category"===n.type)i=n._categoriesMap[i];else if("date"===n.type){var o=e.trace[t+"periodalignment"];if(o){var 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y=l._imageLowerLayer.selectAll("image").data(f),x=l._imageUpperLayer.selectAll("image").data(c);y.enter().append("image"),x.enter().append("image"),y.exit().remove(),x.exit().remove(),y.each((function(t){g.bind(this)(t),v.bind(this)(t)})),x.each((function(t){g.bind(this)(t),v.bind(this)(t)}));var b=Object.keys(l._plots);for(r=0;r<b.length;r++){e=b[r];var _=l._plots[e];if(_.imagelayer){var w=_.imagelayer.selectAll("image").data(u[e]||[]);w.enter().append("image"),w.exit().remove(),w.each((function(t){g.bind(this)(t),v.bind(this)(t)}))}}}},{"../../constants/xmlns_namespaces":753,"../../plots/cartesian/axes":827,"../../plots/cartesian/axis_ids":831,"../drawing":661,"@plotly/d3":58}],688:[function(t,e,r){"use 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b=g[a].label;h[x][b]||(m(x,{label:b,color:g[a].color,i:g[a].i,trace:y,pts:g[a].pts}),h[x][b]=!0,d=Math.max(d,(b||"").length))}else m(x,v),d=Math.max(d,(y.name||"").length)}if(!u.length)return[];var _=!f||!s,w=[];for(r=0;r<u.length;r++){var T=c[u[r]];_?w.push(T[0]):w.push(T)}for(_&&(w=[w]),r=0;r<w.length;r++){var k=1/0;for(a=0;a<w[r].length;a++){var A=w[r][a].trace.legendrank;k>A&&(k=A)}w[r][0]._groupMinRank=k,w[r][0]._preGroupSort=r}var M=function(t,e){return t.trace.legendrank-e.trace.legendrank||t._preSort-e._preSort};for(w.forEach((function(t,e){t[0]._preGroupSort=e})),w.sort((function(t,e){return t[0]._groupMinRank-e[0]._groupMinRank||t[0]._preGroupSort-e[0]._preGroupSort})),r=0;r<w.length;r++){w[r].forEach((function(t,e){t._preSort=e})),w[r].sort(M);var S=w[r][0].trace,E=null;for(a=0;a<w[r].length;a++){var L=w[r][a].trace.legendgrouptitle;if(L&&L.text){E=L,o&&(L.font=e._groupTitleFont);break}}if(l&&w[r].reverse(),E){var 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Select")},attr:"dragmode",val:"select",icon:o.selectbox,click:f},u.lasso2d={name:"lasso2d",_cat:"lasso",title:function(t){return c(t,"Lasso Select")},attr:"dragmode",val:"lasso",icon:o.lasso,click:f},u.drawclosedpath={name:"drawclosedpath",title:function(t){return c(t,"Draw closed freeform")},attr:"dragmode",val:"drawclosedpath",icon:o.drawclosedpath,click:f},u.drawopenpath={name:"drawopenpath",title:function(t){return c(t,"Draw open freeform")},attr:"dragmode",val:"drawopenpath",icon:o.drawopenpath,click:f},u.drawline={name:"drawline",title:function(t){return c(t,"Draw line")},attr:"dragmode",val:"drawline",icon:o.drawline,click:f},u.drawrect={name:"drawrect",title:function(t){return c(t,"Draw rectangle")},attr:"dragmode",val:"drawrect",icon:o.drawrect,click:f},u.drawcircle={name:"drawcircle",title:function(t){return c(t,"Draw circle")},attr:"dragmode",val:"drawcircle",icon:o.drawcircle,click:f},u.eraseshape={name:"eraseshape",title:function(t){return c(t,"Erase active shape")},icon:o.eraseshape,click:s},u.zoomIn2d={name:"zoomIn2d",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:f},u.zoomOut2d={name:"zoomOut2d",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:f},u.autoScale2d={name:"autoScale2d",_cat:"autoscale",title:function(t){return c(t,"Autoscale")},attr:"zoom",val:"auto",icon:o.autoscale,click:f},u.resetScale2d={name:"resetScale2d",_cat:"resetscale",title:function(t){return c(t,"Reset axes")},attr:"zoom",val:"reset",icon:o.home,click:f},u.hoverClosestCartesian={name:"hoverClosestCartesian",_cat:"hoverclosest",title:function(t){return c(t,"Show closest data on hover")},attr:"hovermode",val:"closest",icon:o.tooltip_basic,gravity:"ne",click:f},u.hoverCompareCartesian={name:"hoverCompareCartesian",_cat:"hoverCompare",title:function(t){return c(t,"Compare data on hover")},attr:"hovermode",val:function(t){return t._fullLayout._isHoriz?"y":"x"},icon:o.tooltip_compare,gravity:"ne",click:f},u.zoom3d={name:"zoom3d",_cat:"zoom",title:function(t){return c(t,"Zoom")},attr:"scene.dragmode",val:"zoom",icon:o.zoombox,click:h},u.pan3d={name:"pan3d",_cat:"pan",title:function(t){return c(t,"Pan")},attr:"scene.dragmode",val:"pan",icon:o.pan,click:h},u.orbitRotation={name:"orbitRotation",title:function(t){return c(t,"Orbital rotation")},attr:"scene.dragmode",val:"orbit",icon:o["3d_rotate"],click:h},u.tableRotation={name:"tableRotation",title:function(t){return c(t,"Turntable rotation")},attr:"scene.dragmode",val:"turntable",icon:o["z-axis"],click:h},u.resetCameraDefault3d={name:"resetCameraDefault3d",_cat:"resetCameraDefault",title:function(t){return c(t,"Reset camera to default")},attr:"resetDefault",icon:o.home,click:p},u.resetCameraLastSave3d={name:"resetCameraLastSave3d",_cat:"resetCameraLastSave",title:function(t){return c(t,"Reset camera to last save")},attr:"resetLastSave",icon:o.movie,click:p},u.hoverClosest3d={name:"hoverClosest3d",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:function(t,e){var r=d(t,e);n.call("_guiRelayout",t,r)}},u.zoomInGeo={name:"zoomInGeo",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:m},u.zoomOutGeo={name:"zoomOutGeo",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:m},u.resetGeo={name:"resetGeo",_cat:"reset",title:function(t){return c(t,"Reset")},attr:"reset",val:null,icon:o.autoscale,click:m},u.hoverClosestGeo={name:"hoverClosestGeo",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:v},u.hoverClosestGl2d={name:"hoverClosestGl2d",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:v},u.hoverClosestPie={name:"hoverClosestPie",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:"closest",icon:o.tooltip_basic,gravity:"ne",click:v},u.resetViewSankey={name:"resetSankeyGroup",title:function(t){return c(t,"Reset view")},icon:o.home,click:function(t){for(var e={"node.groups":[],"node.x":[],"node.y":[]},r=0;r<t._fullData.length;r++){var i=t._fullData[r]._viewInitial;e["node.groups"].push(i.node.groups.slice()),e["node.x"].push(i.node.x.slice()),e["node.y"].push(i.node.y.slice())}n.call("restyle",t,e)}},u.toggleHover={name:"toggleHover",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:function(t,e){var r=d(t,e);r.hovermode=g(t),n.call("_guiRelayout",t,r)}},u.resetViews={name:"resetViews",title:function(t){return c(t,"Reset views")},icon:o.home,click:function(t,e){var r=e.currentTarget;r.setAttribute("data-attr","zoom"),r.setAttribute("data-val","reset"),f(t,e),r.setAttribute("data-attr","resetLastSave"),p(t,e),x(t,"geo"),x(t,"mapbox")}},u.toggleSpikelines={name:"toggleSpikelines",title:function(t){return c(t,"Toggle Spike Lines")},icon:o.spikeline,attr:"_cartesianSpikesEnabled",val:"on",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled="on"===r?"off":"on",n.call("_guiRelayout",t,function(t){for(var e="on"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+".showspikes"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:"resetViewMapbox",_cat:"resetView",title:function(t){return c(t,"Reset view")},attr:"reset",icon:o.home,click:function(t){x(t,"mapbox")}},u.zoomInMapbox={name:"zoomInMapbox",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:y},u.zoomOutMapbox={name:"zoomOutMapbox",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:y}},{"../../fonts/ploticon":755,"../../lib":776,"../../plots/cartesian/axis_ids":831,"../../plots/plots":890,"../../registry":904,"../shapes/draw":723}],700:[function(t,e,r){"use strict";var n=t("./buttons"),i=Object.keys(n),a=["drawline","drawopenpath","drawclosedpath","drawcircle","drawrect","eraseshape"],o=["v1hovermode","hoverclosest","hovercompare","togglehover","togglespikelines"].concat(a),s=[];i.forEach((function(t){!function(t){if(-1===o.indexOf(t._cat||t.name)){var 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n=t[r];n._module&&n._module.selectPoints&&(a.traceIs(n,"scatter-like")?(i.hasMarkers(n)||i.hasText(n))&&(e=!0):a.traceIs(n,"box-violin")&&"all"!==n.boxpoints&&"all"!==n.points||(e=!0))}return e})(r)&&O.push("select2d","lasso2d");var z=[],D=function(t){-1===z.indexOf(t)&&-1!==P.indexOf(t)&&z.push(t)};if(Array.isArray(p)){for(var R=[],F=0;F<p.length;F++){var B=p[F];"string"==typeof B?(B=B.toLowerCase(),-1!==c.indexOf(B)?(e._has("mapbox")||e._has("cartesian"))&&O.push(B):"togglespikelines"===B?D("toggleSpikelines"):"togglehover"===B?D("toggleHover"):"hovercompare"===B?D("hoverCompareCartesian"):"hoverclosest"===B?(D("hoverClosestCartesian"),D("hoverClosestGeo"),D("hoverClosest3d"),D("hoverClosestGl2d"),D("hoverClosestPie")):"v1hovermode"===B&&(D("toggleHover"),D("hoverClosestCartesian"),D("hoverCompareCartesian"),D("hoverClosestGeo"),D("hoverClosest3d"),D("hoverClosestGl2d"),D("hoverClosestPie"))):R.push(B)}p=R}return E(O),E(C.concat(I)),E(z),function(t,e){if(e.length)if(Array.isArray(e[0]))for(var r=0;r<e.length;r++)t.push(e[r]);else t.push(e);return t}(S,p)}(t),u?u.update(t,f):e._modeBar=s(t,f)}else u&&(u.destroy(),delete e._modeBar)}},{"../../plots/cartesian/axis_ids":831,"../../registry":904,"../../traces/scatter/subtypes":1216,"../fx/helpers":675,"./buttons":699,"./constants":700,"./modebar":704}],704:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("fast-isnumeric"),a=t("../../lib"),o=t("../../fonts/ploticon"),s=new DOMParser;function l(t){this.container=t.container,this.element=document.createElement("div"),this.update(t.graphInfo,t.buttons),this.container.appendChild(this.element)}var c=l.prototype;c.update=function(t,e){this.graphInfo=t;var r=this.graphInfo._context,n=this.graphInfo._fullLayout,i="modebar-"+n._uid;this.element.setAttribute("id",i),this._uid=i,this.element.className="modebar","hover"===r.displayModeBar&&(this.element.className+=" modebar--hover 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e=this;this.buttons=t,this.buttonElements=[],this.buttonsNames=[],this.buttons.forEach((function(t){var r=e.createGroup();t.forEach((function(t){var n=t.name;if(!n)throw new Error("must provide button 'name' in button config");if(-1!==e.buttonsNames.indexOf(n))throw new Error("button name '"+n+"' is taken");e.buttonsNames.push(n);var i=e.createButton(t);e.buttonElements.push(i),r.appendChild(i)})),e.element.appendChild(r)}))},c.createGroup=function(){var t=document.createElement("div");return t.className="modebar-group",t},c.createButton=function(t){var e=this,r=document.createElement("a");r.setAttribute("rel","tooltip"),r.className="modebar-btn";var i=t.title;void 0===i?i=t.name:"function"==typeof i&&(i=i(this.graphInfo)),(i||0===i)&&r.setAttribute("data-title",i),void 0!==t.attr&&r.setAttribute("data-attr",t.attr);var a=t.val;if(void 0!==a&&("function"==typeof a&&(a=a(this.graphInfo)),r.setAttribute("data-val",a)),"function"!=typeof t.click)throw new Error("must provide button 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e.setAttribute("height","1em"),e.setAttribute("width","1em"),e},c.updateActiveButton=function(t){var e=this.graphInfo._fullLayout,r=void 0!==t?t.getAttribute("data-attr"):null;this.buttonElements.forEach((function(t){var i=t.getAttribute("data-val")||!0,o=t.getAttribute("data-attr"),s="true"===t.getAttribute("data-toggle"),l=n.select(t);if(s)o===r&&l.classed("active",!l.classed("active"));else{var c=null===o?o:a.nestedProperty(e,o).get();l.classed("active",c===i)}}))},c.hasButtons=function(t){var e=this.buttons;if(!e)return!1;if(t.length!==e.length)return!1;for(var r=0;r<t.length;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;n++)if(t[r][n].name!==e[r][n].name)return!1}return!0},c.getLogo=function(){var t=this.createGroup(),e=document.createElement("a");return e.href="https://plotly.com/",e.target="_blank",e.setAttribute("data-title",a._(this.graphInfo,"Produced with Plotly")),e.className="modebar-btn plotlyjsicon 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strict";e.exports={name:"sliders",containerClassName:"slider-container",groupClassName:"slider-group",inputAreaClass:"slider-input-area",railRectClass:"slider-rail-rect",railTouchRectClass:"slider-rail-touch-rect",gripRectClass:"slider-grip-rect",tickRectClass:"slider-tick-rect",inputProxyClass:"slider-input-proxy",labelsClass:"slider-labels",labelGroupClass:"slider-label-group",labelClass:"slider-label",currentValueClass:"slider-current-value",railHeight:5,menuIndexAttrName:"slider-active-index",autoMarginIdRoot:"slider-",minWidth:30,minHeight:30,textPadX:40,arrowOffsetX:4,railRadius:2,railWidth:5,railBorder:4,railBorderWidth:1,railBorderColor:"#bec8d9",railBgColor:"#f8fafc",railInset:8,stepInset:10,gripRadius:10,gripWidth:20,gripHeight:20,gripBorder:20,gripBorderWidth:1,gripBorderColor:"#bec8d9",gripBgColor:"#f6f8fa",gripBgActiveColor:"#dbdde0",labelPadding:8,labelOffset:0,tickWidth:1,tickColor:"#333",tickOffset:25,tickLength:7,minorTickOffset:25,minorTickColor:"#333",minorTickLength:4,currentValuePadding:8,currentValueInset:0}},{}],734:[function(t,e,r){"use 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T={y:e.y,b:u.height*m[w],t:u.height*d[w]};"fraction"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*m[b]):(T.x=e.x,T.l=u.outerLength*d[b],T.r=u.outerLength*m[b]),i.autoMargin(t,g(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case"right":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i="left";break;case"center":n=.5*a.inputAreaLength,i="middle";break;default:n=f.currentValueInset,i="left"}var l=s.ensureSingle(t,"text",f.labelClass,(function(t){t.attr({"text-anchor":i,"data-notex":1})})),u=e.currentvalue.prefix?e.currentvalue.prefix:"";if("string"==typeof r)u+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),u+=h}e.currentvalue.suffix&&(u+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(u).call(c.convertToTspans,e._gd);var m=c.lineCount(l),g=(a.currentValueMaxLines+1-m)*e.currentvalue.font.size*p;return c.positionText(l,n,g),l}}function 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n=t("../../plots/font_attributes"),i=t("../color/attributes"),a=t("../../lib/extend").extendFlat,o=t("../../plot_api/edit_types").overrideAll,s=t("../../plots/pad_attributes"),l=t("../../plot_api/plot_template").templatedArray,c=l("button",{visible:{valType:"boolean"},method:{valType:"enumerated",values:["restyle","relayout","animate","update","skip"],dflt:"restyle"},args:{valType:"info_array",freeLength:!0,items:[{valType:"any"},{valType:"any"},{valType:"any"}]},args2:{valType:"info_array",freeLength:!0,items:[{valType:"any"},{valType:"any"},{valType:"any"}]},label:{valType:"string",dflt:""},execute:{valType:"boolean",dflt:!0}});e.exports=o(l("updatemenu",{_arrayAttrRegexps:[/^updatemenus\[(0|[1-9][0-9]+)\]\.buttons/],visible:{valType:"boolean"},type:{valType:"enumerated",values:["dropdown","buttons"],dflt:"dropdown"},direction:{valType:"enumerated",values:["left","right","up","down"],dflt:"down"},active:{valType:"integer",min:-1,dflt:0},showactive:{valType:"boolean",dflt:!0},buttons:c,x:{valType:"number",min:-2,max:3,dflt:-.05},xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"right"},y:{valType:"number",min:-2,max:3,dflt:1},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"top"},pad:a(s({editType:"arraydraw"}),{}),font:n({}),bgcolor:{valType:"color"},bordercolor:{valType:"color",dflt:i.borderLine},borderwidth:{valType:"number",min:0,dflt:1,editType:"arraydraw"}}),"arraydraw","from-root")},{"../../lib/extend":766,"../../plot_api/edit_types":809,"../../plot_api/plot_template":816,"../../plots/font_attributes":856,"../../plots/pad_attributes":889,"../color/attributes":638}],739:[function(t,e,r){"use 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k=m>w,A=s.barLength+2*s.barPad,M=s.barWidth+2*s.barPad,S=d,E=g+v;E+M>c&&(E=c-M);var L=this.container.selectAll("rect.scrollbar-horizontal").data(k?[0]:[]);L.exit().on(".drag",null).remove(),L.enter().append("rect").classed("scrollbar-horizontal",!0).call(i.fill,s.barColor),k?(this.hbar=L.attr({rx:s.barRadius,ry:s.barRadius,x:S,y:E,width:A,height:M}),this._hbarXMin=S+A/2,this._hbarTranslateMax=w-A):(delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax);var C=v>T,P=s.barWidth+2*s.barPad,I=s.barLength+2*s.barPad,O=d+m,z=g;O+P>l&&(O=l-P);var D=this.container.selectAll("rect.scrollbar-vertical").data(C?[0]:[]);D.exit().on(".drag",null).remove(),D.enter().append("rect").classed("scrollbar-vertical",!0).call(i.fill,s.barColor),C?(this.vbar=D.attr({rx:s.barRadius,ry:s.barRadius,x:O,y:z,width:P,height:I}),this._vbarYMin=z+I/2,this._vbarTranslateMax=T-I):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,B=C?f+P+.5:f+.5,N=h-.5,j=k?p+M+.5:p+.5,U=o._topdefs.selectAll("#"+R).data(k||C?[0]:[]);if(U.exit().remove(),U.enter().append("clipPath").attr("id",R).append("rect"),k||C?(this._clipRect=U.select("rect").attr({x:Math.floor(F),y:Math.floor(N),width:Math.ceil(B)-Math.floor(F),height:Math.ceil(j)-Math.floor(N)}),this.container.call(a.setClipUrl,R,this.gd),this.bg.attr({x:d,y:g,width:m,height:v})):(this.bg.attr({width:0,height:0}),this.container.on("wheel",null).on(".drag",null).call(a.setClipUrl,null),delete this._clipRect),k||C){var V=n.behavior.drag().on("dragstart",(function(){n.event.sourceEvent.preventDefault()})).on("drag",this._onBoxDrag.bind(this));this.container.on("wheel",null).on("wheel",this._onBoxWheel.bind(this)).on(".drag",null).call(V);var H=n.behavior.drag().on("dragstart",(function(){n.event.sourceEvent.preventDefault(),n.event.sourceEvent.stopPropagation()})).on("drag",this._onBarDrag.bind(this));k&&this.hbar.on(".drag",null).call(H),C&&this.vbar.on(".drag",null).call(H)}this.setTranslate(e,r)},s.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on("wheel",null).on(".drag",null).call(a.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(".drag",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(".drag",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},s.prototype._onBoxDrag=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t-=n.event.dx),this.vbar&&(e-=n.event.dy),this.setTranslate(t,e)},s.prototype._onBoxWheel=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t+=n.event.deltaY),this.vbar&&(e+=n.event.deltaY),this.setTranslate(t,e)},s.prototype._onBarDrag=function(){var t=this.translateX,e=this.translateY;if(this.hbar){var r=t+this._hbarXMin,i=r+this._hbarTranslateMax;t=(o.constrain(n.event.x,r,i)-r)/(i-r)*(this.position.w-this._box.w)}if(this.vbar){var a=e+this._vbarYMin,s=a+this._vbarTranslateMax;e=(o.constrain(n.event.y,a,s)-a)/(s-a)*(this.position.h-this._box.h)}this.setTranslate(t,e)},s.prototype.setTranslate=function(t,e){var r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=o.constrain(t||0,0,r),e=o.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(a.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var i=t/r;this.hbar.call(a.setTranslate,t+i*this._hbarTranslateMax,e)}if(this.vbar){var s=e/n;this.vbar.call(a.setTranslate,t,e+s*this._vbarTranslateMax)}}},{"../../lib":776,"../color":639,"../drawing":661,"@plotly/d3":58}],744:[function(t,e,r){"use strict";e.exports={FROM_BL:{left:0,center:.5,right:1,bottom:0,middle:.5,top:1},FROM_TL:{left:0,center:.5,right:1,bottom:1,middle:.5,top:0},FROM_BR:{left:1,center:.5,right:0,bottom:0,middle:.5,top:1},LINE_SPACING:1.3,CAP_SHIFT:.7,MID_SHIFT:.35,OPPOSITE_SIDE:{left:"right",right:"left",top:"bottom",bottom:"top"}}},{}],745:[function(t,e,r){"use strict";e.exports={axisRefDescription:function(t,e,r){return["If set to a",t,"axis id (e.g. *"+t+"* or","*"+t+"2*), the `"+t+"` position refers to a",t,"coordinate. 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e=t.searchInfo.cd[0].trace,r=t.pointNumber,n=t.pointNumbers,i=n.length>0?n[0]:r;return!!e.selectedpoints&&e.selectedpoints.indexOf(i)>-1}function U(t,e,r){var n,a,o,s;for(n=0;n<e.length;n++){var l=e[n].cd[0].trace._fullInput,c=t._fullLayout._tracePreGUI[l.uid]||{};void 0===c.selectedpoints&&(c.selectedpoints=l._input.selectedpoints||null)}if(r){var u=r.points||[];for(n=0;n<e.length;n++)(s=e[n].cd[0].trace)._input.selectedpoints=s._fullInput.selectedpoints=[],s._fullInput!==s&&(s.selectedpoints=[]);for(n=0;n<u.length;n++){var f=u[n],h=f.data,p=f.fullData;f.pointIndices?([].push.apply(h.selectedpoints,f.pointIndices),s._fullInput!==s&&[].push.apply(p.selectedpoints,f.pointIndices)):(h.selectedpoints.push(f.pointIndex),s._fullInput!==s&&p.selectedpoints.push(f.pointIndex))}}else for(n=0;n<e.length;n++)delete(s=e[n].cd[0].trace).selectedpoints,delete s._input.selectedpoints,s._fullInput!==s&&delete s._fullInput.selectedpoints;var d=!1;for(n=0;n<e.length;n++){s=(o=(a=e[n]).cd)[0].trace,i.traceIs(s,"regl")&&(d=!0);var m=a._module,g=m.styleOnSelect||m.style;g&&(g(t,o,o[0].node3),o[0].nodeRangePlot3&&g(t,o,o[0].nodeRangePlot3))}d&&(w(t),T(t))}function V(t,e){if(Array.isArray(t))for(var r=e.cd,n=e.cd[0].trace,i=0;i<t.length;i++)t[i]=l(t[i],n,r);return t}function H(t,e){for(var r=[],n=0;n<t.length;n++){r[n]=[];for(var i=0;i<t[n].length;i++){r[n][i]=[],r[n][i][0]=i?"L":"M";for(var a=0;a<t[n][i].length;a++)r[n][i].push(t[n][i][a])}e||r[n].push(["Z",r[n][0][1],r[n][0][2]])}return r}e.exports={prepSelect:function(t,e,r,i,l){var c=u(l),v=f(l),x=p(l),_=h(l),w=d(l),T="drawcircle"===l,E="drawline"===l||T,L=i.gd,z=L._fullLayout,D=z._zoomlayer,j=i.element.getBoundingClientRect(),q=i.plotinfo,G=I(q),Y=e-j.left,W=r-j.top;z._calcInverseTransform(L);var X=y.apply3DTransform(z._invTransform)(Y,W);Y=X[0],W=X[1];var Z,J,K,Q,$,tt,et,rt=z._invScaleX,nt=z._invScaleY,it=Y,at=W,ot="M"+Y+","+W,st=i.xaxes[0]._length,lt=i.yaxes[0]._length,ct=i.xaxes.concat(i.yaxes),ut=t.altKey&&!(h(l)&&x);F(t,L,i),c&&(Z=M([[Y,W]],k.BENDPX));var ft=D.selectAll("path.select-outline-"+q.id).data(_?[0]:[1,2]),ht=z.newshape;ft.enter().append("path").attr("class",(function(t){return"select-outline select-outline-"+t+" select-outline-"+q.id})).style(_?{opacity:ht.opacity/2,fill:x?void 0:ht.fillcolor,stroke:ht.line.color,"stroke-dasharray":a(ht.line.dash,ht.line.width),"stroke-width":ht.line.width+"px"}:{}).attr("fill-rule",ht.fillrule).classed("cursor-move",!!_).attr("transform",G).attr("d",ot+"Z");var pt,dt=D.append("path").attr("class","zoombox-corners").style({fill:o.background,stroke:o.defaultLine,"stroke-width":1}).attr("transform",G).attr("d","M0,0Z"),mt=z._uid+k.SELECTID,gt=[],vt=N(L,i.xaxes,i.yaxes,i.subplot);function yt(t,e){return t-e}pt=q.fillRangeItems?q.fillRangeItems:v?function(t,e){var 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n,i,o=a.nestedProperty(t,e).get();if(i=(i="date"===t.type?a.dfltRange(t.calendar):"y"===p?T.DFLTRANGEY:r.dfltRange||T.DFLTRANGEX).slice(),"tozero"!==t.rangemode&&"nonnegative"!==t.rangemode||(i[0]=0),o&&2===o.length)for("date"!==t.type||t.autorange||(o[0]=a.cleanDate(o[0],m,t.calendar),o[1]=a.cleanDate(o[1],m,t.calendar)),n=0;n<2;n++)if("date"===t.type){if(!a.isDateTime(o[n],t.calendar)){t[e]=i;break}if(t.r2l(o[0])===t.r2l(o[1])){var l=a.constrain(t.r2l(o[0]),a.MIN_MS+1e3,a.MAX_MS-1e3);o[0]=t.l2r(l-1e3),o[1]=t.l2r(l+1e3);break}}else{if(!s(o[n])){if(!s(o[1-n])){t[e]=i;break}o[n]=o[1-n]*(n?10:.1)}if(o[n]<-d?o[n]=-d:o[n]>d&&(o[n]=d),o[0]===o[1]){var c=Math.max(1,Math.abs(1e-6*o[0]));o[0]-=c,o[1]+=c}}else a.nestedProperty(t,e).set(i)},t.setScale=function(r){var n=e._size;if(t.overlaying){var i=w.getFromId({_fullLayout:e},t.overlaying);t.domain=i.domain}var a=r&&t._r?"_r":"range",o=t.calendar;t.cleanRange(a);var s,l,c=t.r2l(t[a][0],o),u=t.r2l(t[a][1],o),f="y"===p;if((f?(t._offset=n.t+(1-t.domain[1])*n.h,t._length=n.h*(t.domain[1]-t.domain[0]),t._m=t._length/(c-u),t._b=-t._m*u):(t._offset=n.l+t.domain[0]*n.w,t._length=n.w*(t.domain[1]-t.domain[0]),t._m=t._length/(u-c),t._b=-t._m*c),t._rangebreaks=[],t._lBreaks=0,t._m2=0,t._B=[],t.rangebreaks)&&(t._rangebreaks=t.locateBreaks(Math.min(c,u),Math.max(c,u)),t._rangebreaks.length)){for(s=0;s<t._rangebreaks.length;s++)l=t._rangebreaks[s],t._lBreaks+=Math.abs(l.max-l.min);var h=f;c>u&&(h=!h),h&&t._rangebreaks.reverse();var d=h?-1:1;for(t._m2=d*t._length/(Math.abs(u-c)-t._lBreaks),t._B.push(-t._m2*(f?u:c)),s=0;s<t._rangebreaks.length;s++)l=t._rangebreaks[s],t._B.push(t._B[t._B.length-1]-d*t._m2*(l.max-l.min));for(s=0;s<t._rangebreaks.length;s++)(l=t._rangebreaks[s]).pmin=B(l.min),l.pmax=B(l.max)}if(!isFinite(t._m)||!isFinite(t._b)||t._length<0)throw e._replotting=!1,new Error("Something went wrong with axis scaling")},t.maskBreaks=function(e){var 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n=t("@plotly/d3"),i=t("../../lib"),a=t("../../registry"),o=Math.PI/180,s=180/Math.PI,l={cursor:"pointer"},c={cursor:"auto"};function u(t,e){return n.behavior.zoom().translate(e.translate()).scale(e.scale())}function f(t,e,r){var n=t.id,o=t.graphDiv,s=o.layout,l=s[n],c=o._fullLayout,u=c[n],f={},h={};function p(t,e){f[n+"."+t]=i.nestedProperty(l,t).get(),a.call("_storeDirectGUIEdit",s,c._preGUI,f);var r=i.nestedProperty(u,t);r.get()!==e&&(r.set(e),i.nestedProperty(l,t).set(e),h[n+"."+t]=e)}r(p),p("projection.scale",e.scale()/t.fitScale),p("fitbounds",!1),o.emit("plotly_relayout",h)}function h(t,e){var r=u(0,e);function i(r){var n=e.invert(t.midPt);r("center.lon",n[0]),r("center.lat",n[1])}return r.on("zoomstart",(function(){n.select(this).style(l)})).on("zoom",(function(){e.scale(n.event.scale).translate(n.event.translate),t.render();var r=e.invert(t.midPt);t.graphDiv.emit("plotly_relayouting",{"geo.projection.scale":e.scale()/t.fitScale,"geo.center.lon":r[0],"geo.center.lat":r[1]})})).on("zoomend",(function(){n.select(this).style(c),f(t,e,i)})),r}function p(t,e){var r,i,a,o,s,h,p,d,m,g=u(0,e);function v(t){return e.invert(t)}function y(r){var n=e.rotate(),i=e.invert(t.midPt);r("projection.rotation.lon",-n[0]),r("center.lon",i[0]),r("center.lat",i[1])}return g.on("zoomstart",(function(){n.select(this).style(l),r=n.mouse(this),i=e.rotate(),a=e.translate(),o=i,s=v(r)})).on("zoom",(function(){if(h=n.mouse(this),function(t){var r=v(t);if(!r)return!0;var n=e(r);return Math.abs(n[0]-t[0])>2||Math.abs(n[1]-t[1])>2}(r))return g.scale(e.scale()),void g.translate(e.translate());e.scale(n.event.scale),e.translate([a[0],n.event.translate[1]]),s?v(h)&&(d=v(h),p=[o[0]+(d[0]-s[0]),i[1],i[2]],e.rotate(p),o=p):s=v(r=h),m=!0,t.render();var 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a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=["x","y"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=["sans-serif","sans-serif"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title="",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont="sans-serif",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=["xaxis","yaxis"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?"":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]="auto"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf("all"):!!a,f=c?"allticks"===a:-1!==String(a).indexOf("ticks"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return"xaxis"===t?"bottom"===r:"yaxis"===t?"left"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return"xaxis"===t?"top"===r:"yaxis"===t?"right"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return"xaxis"===t?"top"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:"yaxis"===t?"right"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return"outside"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return"inside"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{"../../lib/str2rgbarray":801,"../cartesian/axes":827}],867:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("./scene2d"),a=t("../layout_attributes"),o=t("../../constants/xmlns_namespaces"),s=t("../cartesian/constants"),l=t("../cartesian"),c=t("../../components/fx/layout_attributes"),u=t("../get_data").getSubplotData;r.name="gl2d",r.attr=["xaxis","yaxis"],r.idRoot=["x","y"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t("../cartesian/attributes"),r.supplyLayoutDefaults=function(t,e,r){e._has("cartesian")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,"plot","from-root"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},"plot","nested"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,"gl2d",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,"gl2d",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage("png");e._glimages.append("svg:image").attr({xmlns:o.svg,"xlink:href":a,x:0,y:0,width:"100%",height:"100%",preserveAspectRatio:"none"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{"../../components/fx/layout_attributes":680,"../../constants/xmlns_namespaces":753,"../../plot_api/edit_types":809,"../cartesian":841,"../cartesian/attributes":825,"../cartesian/constants":834,"../get_data":864,"../layout_attributes":881,"./scene2d":868}],868:[function(t,e,r){"use strict";var n,i,a=t("../../registry"),o=t("../../plots/cartesian/axes"),s=t("../../components/fx"),l=t("gl-plot2d"),c=t("gl-spikes2d"),u=t("gl-select-box"),f=t("webgl-context"),h=t("./convert"),p=t("./camera"),d=t("../../lib/show_no_webgl_msg"),m=t("../cartesian/constraints"),g=m.enforce,v=m.clean,y=t("../cartesian/autorange").doAutoRange,x=t("../../components/dragelement/helpers"),b=x.drawMode,_=x.selectMode,w=["xaxis","yaxis"],T=t("../cartesian/constants").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement("canvas"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error("Error creating static canvas/context for image server");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(".gl-canvas-focus"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width="100%",r.style.height="100%",r.style.position="absolute",r.style.top="0px",r.style.left="0px",r.style["pointer-events"]="none",this.updateSize(r);var a=this.svgContainer=document.createElementNS("http://www.w3.org/2000/svg","svg");a.style.position="absolute",a.style.top=a.style.left="0px",a.style.width=a.style.height="100%",a.style["z-index"]=20,a.style["pointer-events"]="none";var o=this.mouseContainer=document.createElement("div");o.style.position="absolute",o.style["pointer-events"]="auto",this.pickCanvas=this.container.querySelector(".gl-canvas-pick");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener("mouseout",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener("mouseover",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t="png"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement("canvas");f.width=r,f.height=i;var h,p=f.getContext("2d"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case"jpeg":h=f.toDataURL("image/jpeg");break;case"webp":h=f.toDataURL("image/webp");break;default:h=f.toDataURL("image/png")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+"";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r="xaxis"+e[1],n="yaxis"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+".range"]=e.range.slice(),s=i[r._name+".range"]=r.range.slice();i[e._name+".autorange"]=e.autorange,i[r._name+".autorange"]=r.autorange,a.call("_storeDirectGUIEdit",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit("plotly_relayout",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod("annotations","drawOne")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener("wheel",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+"px",this.mouseContainer.style.height=u.h*(h[1]-h[0])+"px",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+"px",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+"px",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style["pointer-events"]="none",this.mouseContainer.style["pointer-events"]="none"):(this.pickCanvas.style["pointer-events"]="auto",this.mouseContainer.style["pointer-events"]="auto"),this.mouseContainer.style.cursor="pan"===t?"move":"zoom"===t?"crosshair":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&"zoom"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,"plotly_click"),a&&"skip"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var m=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,"plotly_hover");var g=this.fullData[m.trace.index]||{},v=m.pointIndex,y=s.castHoverinfo(g,i,v);if(y&&"all"!==y){var x=y.split("+");-1===x.indexOf("x")&&(m.traceCoord[0]=void 0),-1===x.indexOf("y")&&(m.traceCoord[1]=void 0),-1===x.indexOf("z")&&(m.traceCoord[2]=void 0),-1===x.indexOf("text")&&(m.textLabel=void 0),-1===x.indexOf("name")&&(m.name=void 0)}s.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter("xaxis",m.traceCoord[0]),yLabel:this.hoverFormatter("yaxis",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:s.castHoverOption(g,v,"bgcolor")||m.color,borderColor:s.castHoverOption(g,v,"bordercolor"),fontFamily:s.castHoverOption(g,v,"font.family"),fontSize:s.castHoverOption(g,v,"font.size"),fontColor:s.castHoverOption(g,v,"font.color"),nameLength:s.castHoverOption(g,v,"namelength"),textAlign:s.castHoverOption(g,v,"align")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit("plotly_unhover"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),"hover").text}}},{"../../components/dragelement/helpers":657,"../../components/fx":679,"../../lib/show_no_webgl_msg":798,"../../plots/cartesian/axes":827,"../../registry":904,"../cartesian/autorange":826,"../cartesian/constants":834,"../cartesian/constraints":835,"./camera":865,"./convert":866,"gl-plot2d":309,"gl-select-box":321,"gl-spikes2d":330,"webgl-context":602}],869:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("../../components/fx/layout_attributes"),a=t("./scene"),o=t("../get_data").getSubplotData,s=t("../../lib"),l=t("../../constants/xmlns_namespaces");r.name="gl3d",r.attr="scene",r.idRoot="scene",r.idRegex=r.attrRegex=s.counterRegex("scene"),r.attributes=t("./layout/attributes"),r.layoutAttributes=t("./layout/layout_attributes"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},"plot","nested"),r.supplyLayoutDefaults=t("./layout/defaults"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,"gl3d",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(".annotation-"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:"none"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return"1"===e&&(e=""),"scene"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{"../../components/fx/layout_attributes":680,"../../constants/xmlns_namespaces":753,"../../lib":776,"../../plot_api/edit_types":809,"../get_data":864,"./layout/attributes":870,"./layout/defaults":874,"./layout/layout_attributes":875,"./scene":879}],870:[function(t,e,r){"use strict";e.exports={scene:{valType:"subplotid",dflt:"scene",editType:"calc+clearAxisTypes"}}},{}],871:[function(t,e,r){"use strict";var n=t("../../../components/color"),i=t("../../cartesian/layout_attributes"),a=t("../../../lib/extend").extendFlat,o=t("../../../plot_api/edit_types").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:"boolean",dflt:!0},spikesides:{valType:"boolean",dflt:!0},spikethickness:{valType:"number",min:0,dflt:2},spikecolor:{valType:"color",dflt:n.defaultLine},showbackground:{valType:"boolean",dflt:!1},backgroundcolor:{valType:"color",dflt:"rgba(204, 204, 204, 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204, 204)"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},"plot","from-root")},{"../../../components/color":639,"../../../lib/extend":766,"../../../plot_api/edit_types":809,"../../cartesian/layout_attributes":842}],872:[function(t,e,r){"use strict";var n=t("tinycolor2").mix,i=t("../../../lib"),a=t("../../../plot_api/plot_template"),o=t("./axis_attributes"),s=t("../../cartesian/type_defaults"),l=t("../../cartesian/axis_defaults"),c=["xaxis","yaxis","zaxis"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h("gridcolor",n(f.color,r.bgColor,13600/187).toRgbString()),h("title.text",d[0]),f.setScale=i.noop,h("showspikes")&&(h("spikesides"),h("spikethickness"),h("spikecolor",f.color)),h("showaxeslabels"),h("showbackground")&&h("backgroundcolor")}}},{"../../../lib":776,"../../../plot_api/plot_template":816,"../../cartesian/axis_defaults":829,"../../cartesian/type_defaults":853,"./axis_attributes":871,tinycolor2:572}],873:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"];function 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o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,"font"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),"showline"in o&&(this.lineEnable[r]=o.showline),"linecolor"in o&&(this.lineColor[r]=n(o.linecolor)),"linewidth"in o&&(this.lineWidth[r]=o.linewidth),"showgrid"in o&&(this.gridEnable[r]=o.showgrid),"gridcolor"in o&&(this.gridColor[r]=n(o.gridcolor)),"gridwidth"in o&&(this.gridWidth[r]=o.gridwidth),"log"===o.type?this.zeroEnable[r]=!1:"zeroline"in o&&(this.zeroEnable[r]=o.zeroline),"zerolinecolor"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),"zerolinewidth"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),"ticks"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,"ticklen"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),"tickcolor"in o&&(this.lineTickColor[r]=n(o.tickcolor)),"tickwidth"in o&&(this.lineTickWidth[r]=o.tickwidth),"tickangle"in o&&(this.tickAngle[r]="auto"===o.tickangle?-3600:Math.PI*-o.tickangle/180),"showticklabels"in o&&(this.tickEnable[r]=o.showticklabels),"tickfont"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),"mirror"in o?-1!==["ticks","all","allticks"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,"showbackground"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{"../../../lib":776,"../../../lib/str2rgbarray":801}],874:[function(t,e,r){"use strict";var n=t("../../../lib"),i=t("../../../components/color"),a=t("../../../registry"),o=t("../../subplot_defaults"),s=t("./axis_defaults"),l=t("./layout_attributes"),c=t("../../get_data").getSubplotData;function u(t,e,r,n){for(var o=r("bgcolor"),l=i.combine(o,n.paper_bgcolor),u=["up","center","eye"],f=0;f<u.length;f++)r("camera."+u[f]+".x"),r("camera."+u[f]+".y"),r("camera."+u[f]+".z");r("camera.projection.type");var h=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),p=r("aspectmode",h?"manual":"auto");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},"manual"===p&&(e.aspectmode="auto"),t.aspectmode=e.aspectmode);var d=c(n.fullData,"gl3d",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod("annotations3d","handleDefaults")(t,e,n);var m=n.getDfltFromLayout("dragmode");if(!1!==m&&!m)if(m="orbit",t.camera&&t.camera.up){var g=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(g&&v&&y?y/Math.sqrt(g*g+v*v+y*y)>.999&&(m="turntable"):m="turntable")}else m="turntable";r("dragmode",m),r("hovermode",n.getDfltFromLayout("hovermode"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:"gl3d",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{"../../../components/color":639,"../../../lib":776,"../../../registry":904,"../../get_data":864,"../../subplot_defaults":898,"./axis_defaults":872,"./layout_attributes":875}],875:[function(t,e,r){"use strict";var n=t("./axis_attributes"),i=t("../../domain").attributes,a=t("../../../lib/extend").extendFlat,o=t("../../../lib").counterRegex;function s(t,e,r){return{x:{valType:"number",dflt:t,editType:"camera"},y:{valType:"number",dflt:e,editType:"camera"},z:{valType:"number",dflt:r,editType:"camera"},editType:"camera"}}e.exports={_arrayAttrRegexps:[o("scene",".annotations",!0)],bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"plot"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:"enumerated",values:["perspective","orthographic"],dflt:"perspective",editType:"calc"},editType:"calc"},editType:"camera"},domain:i({name:"scene",editType:"plot"}),aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto",editType:"plot",impliedEdits:{"aspectratio.x":void 0,"aspectratio.y":void 0,"aspectratio.z":void 0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},uirevision:{valType:"any",editType:"none"},editType:"plot",_deprecated:{cameraposition:{valType:"info_array",editType:"camera"}}}},{"../../../lib":776,"../../../lib/extend":766,"../../domain":855,"./axis_attributes":871}],876:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=["xaxis","yaxis","zaxis"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{"../../../lib/str2rgbarray":801}],877:[function(t,e,r){"use strict";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if("auto"===u.tickmode){u.tickmode="linear";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],"date"===u.type&&(p[d].text=p[d].text.replace(/\<br\>/g," "));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t("../../cartesian/axes"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"],o=[0,0,0]},{"../../../lib":776,"../../cartesian/axes":827}],878:[function(t,e,r){"use strict";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],879:[function(t,e,r){"use strict";var n,i,a=t("gl-plot3d"),o=a.createCamera,s=a.createScene,l=t("webgl-context"),c=t("has-passive-events"),u=t("../../registry"),f=t("../../lib"),h=f.preserveDrawingBuffer(),p=t("../../plots/cartesian/axes"),d=t("../../components/fx"),m=t("../../lib/str2rgbarray"),g=t("../../lib/show_no_webgl_msg"),v=t("./project"),y=t("./layout/convert"),x=t("./layout/spikes"),b=t("./layout/tick_marks");function _(t,e){var r=document.createElement("div"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS("http://www.w3.org/2000/svg","svg");i.style.position="absolute",i.style.top=i.style.left="0px",i.style.width=i.style.height="100%",i.style["z-index"]=20,i.style["pointer-events"]="none",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position="absolute",r.style.top=r.style.left="0px",r.style.width=r.style.height="100%",n.appendChild(r),this.fullLayout=e,this.id=t.id||"scene",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod("annotations3d","convert"),this.drawAnnotations=u.getComponentMethod("annotations3d","draw"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement("canvas"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error("error creating static canvas/context for image server");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn(["webgl setup failed possibly due to","false preserveDrawingBuffer config.","The mobile/tablet device may not be detected by is-mobile module.","Enabling preserveDrawingBuffer in second attempt to create webgl scene..."].join(" "));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e="orthographic"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:"orbit"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+".camera"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+".aspectratio"]=t.glplot.getAspectratio(),"manual"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+".aspectmode"]="manual")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit("plotly_relayout",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener("mouseup",(function(){i(t)})),t.glplot.canvas.addEventListener("wheel",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener("mousemove",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit("plotly_relayouting",e)}})),t.staticMode||t.glplot.canvas.addEventListener("webglcontextlost",(function(r){e&&e.emit&&e.emit("plotly_webglcontextlost",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,"viewBox","0 0 "+s+" "+l),n.setAttributeNS(null,"width",s),n.setAttributeNS(null,"height",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c,u=Object.keys(e.traces),h=null,m=e.glplot.selection,g=0;g<u.length;++g)"skip"!==(t=e.traces[u[g]]).data.hoverinfo&&t.handlePick(m)&&(h=t),t.setContourLevels&&t.setContourLevels();function y(t,r,n){var i=e.fullSceneLayout[t+"axis"];return"log"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==h){var x=v(e.glplot.cameraParams,m.dataCoordinate);t=h.data;var _,w=r._fullData[t.index],T=m.index,k={xLabel:y("x",m.traceCoordinate[0],t.xhoverformat),yLabel:y("y",m.traceCoordinate[1],t.yhoverformat),zLabel:y("z",m.traceCoordinate[2],t.zhoverformat)},A=d.castHoverinfo(w,e.fullLayout,T),M=(A||"").split("+"),S=A&&"all"===A;w.hovertemplate||S||(-1===M.indexOf("x")&&(k.xLabel=void 0),-1===M.indexOf("y")&&(k.yLabel=void 0),-1===M.indexOf("z")&&(k.zLabel=void 0),-1===M.indexOf("text")&&(m.textLabel=void 0),-1===M.indexOf("name")&&(h.name=void 0));var E=[];"cone"===t.type||"streamtube"===t.type?(k.uLabel=y("x",m.traceCoordinate[3],t.uhoverformat),(S||-1!==M.indexOf("u"))&&E.push("u: "+k.uLabel),k.vLabel=y("y",m.traceCoordinate[4],t.vhoverformat),(S||-1!==M.indexOf("v"))&&E.push("v: "+k.vLabel),k.wLabel=y("z",m.traceCoordinate[5],t.whoverformat),(S||-1!==M.indexOf("w"))&&E.push("w: "+k.wLabel),k.normLabel=m.traceCoordinate[6].toPrecision(3),(S||-1!==M.indexOf("norm"))&&E.push("norm: "+k.normLabel),"streamtube"===t.type&&(k.divergenceLabel=m.traceCoordinate[7].toPrecision(3),(S||-1!==M.indexOf("divergence"))&&E.push("divergence: "+k.divergenceLabel)),m.textLabel&&E.push(m.textLabel),_=E.join("<br>")):"isosurface"===t.type||"volume"===t.type?(k.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(m.traceCoordinate[3]),t.valuehoverformat),E.push("value: "+k.valueLabel),m.textLabel&&E.push(m.textLabel),_=E.join("<br>")):_=m.textLabel;var L={x:m.traceCoordinate[0],y:m.traceCoordinate[1],z:m.traceCoordinate[2],data:w._input,fullData:w,curveNumber:w.index,pointNumber:T};d.appendArrayPointValue(L,w,T),t._module.eventData&&(L=w._module.eventData(L,m,w,{},T));var C={points:[L]};if(e.fullSceneLayout.hovermode){var P=[];d.loneHover({trace:w,x:(.5+.5*x[0]/x[3])*s,y:(.5-.5*x[1]/x[3])*l,xLabel:k.xLabel,yLabel:k.yLabel,zLabel:k.zLabel,text:_,name:h.name,color:d.castHoverOption(w,T,"bgcolor")||h.color,borderColor:d.castHoverOption(w,T,"bordercolor"),fontFamily:d.castHoverOption(w,T,"font.family"),fontSize:d.castHoverOption(w,T,"font.size"),fontColor:d.castHoverOption(w,T,"font.color"),nameLength:d.castHoverOption(w,T,"namelength"),textAlign:d.castHoverOption(w,T,"align"),hovertemplate:f.castOption(w,T,"hovertemplate"),hovertemplateLabels:f.extendFlat({},L,k),eventData:[L]},{container:n,gd:r,inOut_bbox:P}),L.bbox=P[0]}m.buttons&&m.distance<5?r.emit("plotly_click",C):r.emit("plotly_hover",C),c=C}else d.loneUnhover(n),r.emit("plotly_unhover",c);e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error("Catastrophic and unrecoverable WebGL error. Context lost.")};requestAnimationFrame(e)};var k=["xaxis","yaxis","zaxis"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+"calendar"],u=e["_"+o+"length"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(m(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&A(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,g=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){g[0][a]=1/0,g[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,M=w._trace.data._pad||0;"ErrorBars"===w.constructor.name&&s._lowerLogErrorBound?g[0][a]=Math.min(g[0][a],s._lowerLogErrorBound):g[0][a]=Math.min(g[0][a],T[0][a]/h[a]-M),g[1][a]=Math.max(g[1][a],T[1][a]/h[a]+M)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);g[0][a]=Math.min(g[0][a],E),g[1][a]=Math.max(g[1][a],E)}}if("rangemode"in s&&"tozero"===s.rangemode&&(g[0][a]=Math.min(g[0][a],0),g[1][a]=Math.max(g[1][a],0)),g[0][a]>g[1][a])g[0][a]=-1,g[1][a]=1;else{var L=g[1][a]-g[0][a];g[0][a]-=L/32,g[1][a]+=L/32}if("reversed"===s.autorange){var C=g[0][a];g[0][a]=g[1][a],g[1][a]=C}}else{var P=s.range;g[0][a]=s.r2l(P[0]),g[1][a]=s.r2l(P[1])}g[0][a]===g[1][a]&&(g[0][a]-=1,g[1][a]+=1),v[a]=g[1][a]-g[0][a],this.glplot.setBounds(a,{min:g[0][a]*h[a],max:g[1][a]*h[a]})}var I=c.aspectmode;if("cube"===I)d=[1,1,1];else if("manual"===I){var O=c.aspectratio;d=[O.x,O.y,O.z]}else{if("auto"!==I&&"data"!==I)throw new Error("scene.js aspectRatio was not one of the enumerated types");var z=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];z[a]=Math.pow(D.acc,1/D.count)/h[a]}d="data"===I||Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position="absolute",B.left=F.l+R.x[0]*F.w+"px",B.top=F.t+(1-R.y[1])*F.h+"px",B.width=F.w*(R.x[1]-R.x[0])+"px",B.height=F.h*(R.y[1]-R.y[0])+"px"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener("wheel",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?"orthographic":"perspective"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),"orthographic"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+".camera").get();function n(t,e,r,n){var i=["up","center","eye"],a=["x","y","z"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+".aspectratio").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+".camera")).get(),p[this.id+".camera"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+".aspectratio")).get(),p[this.id+".aspectratio"]=o),u.call("_storeDirectGUIEdit",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+".camera").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+".aspectratio").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if("orbit"===t)r.mode="orbit",r.keyBindingMode="rotate";else if("turntable"===t){r.up=[0,0,1],r.mode="turntable",r.keyBindingMode="rotate";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+".camera.up",h={x:0,y:0,z:1},p={};p[c]=h;var d=n.layout;u.call("_storeDirectGUIEdit",d,i._preGUI,p),a.up=h,f.nestedProperty(d,c).set(h)}}else r.keyBindingMode=t;this.fullSceneLayout.hovermode=e},w.toImage=function(t){t||(t="png"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a),function(t,e,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<e;++a)for(var o=0;o<4;++o){var s=4*(e*n+a)+o,l=4*(e*i+a)+o,c=t[s];t[s]=t[l],t[l]=c}}(a,r,i),function(t,e,r){for(var n=0;n<r;++n)for(var i=0;i<e;++i){var a=4*(e*n+i),o=t[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(a,r,i);var o=document.createElement("canvas");o.width=r,o.height=i;var s,l=o.getContext("2d"),c=l.createImageData(r,i);switch(c.data.set(a),l.putImageData(c,0,0),t){case"jpeg":s=o.toDataURL("image/jpeg");break;case"webp":s=o.toDataURL("image/webp");break;default:s=o.toDataURL("image/png")}return this.staticMode&&this.container.removeChild(n),s},w.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[k[t]];p.setConvert(e,this.fullLayout),e.setScale=f.noop}},w.make4thDimension=function(){var t=this.graphDiv._fullLayout;this._mockAxis={type:"linear",showexponent:"all",exponentformat:"B"},p.setConvert(this._mockAxis,t)},e.exports=_},{"../../components/fx":679,"../../lib":776,"../../lib/show_no_webgl_msg":798,"../../lib/str2rgbarray":801,"../../plots/cartesian/axes":827,"../../registry":904,"./layout/convert":873,"./layout/spikes":876,"./layout/tick_marks":877,"./project":878,"gl-plot3d":312,"has-passive-events":426,"webgl-context":602}],880:[function(t,e,r){"use strict";e.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;a<n;a++)i[a]=[t[a],e[a],r[a]];return i}},{}],881:[function(t,e,r){"use strict";var n=t("./font_attributes"),i=t("./animation_attributes"),a=t("../components/color/attributes"),o=t("../components/shapes/draw_newshape/attributes"),s=t("./pad_attributes"),l=t("../lib/extend").extendFlat,c=n({editType:"calc"});c.family.dflt='"Open Sans", verdana, arial, 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n=t("../../lib/sort_object_keys"),i='\xa9 <a target="_blank" href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors',a=['\xa9 <a target="_blank" href="https://carto.com/">Carto</a>',i].join(" "),o=['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://www.openstreetmap.org/copyright">ODbL</a>'].join(" "),s={"open-street-map":{id:"osm",version:8,sources:{"plotly-osm-tiles":{type:"raster",attribution:i,tiles:["https://a.tile.openstreetmap.org/{z}/{x}/{y}.png","https://b.tile.openstreetmap.org/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-osm-tiles",type:"raster",source:"plotly-osm-tiles",minzoom:0,maxzoom:22}]},"white-bg":{id:"white-bg",version:8,sources:{},layers:[{id:"white-bg",type:"background",paint:{"background-color":"#FFFFFF"},minzoom:0,maxzoom:22}]},"carto-positron":{id:"carto-positron",version:8,sources:{"plotly-carto-positron":{type:"raster",attribution:a,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-positron",type:"raster",source:"plotly-carto-positron",minzoom:0,maxzoom:22}]},"carto-darkmatter":{id:"carto-darkmatter",version:8,sources:{"plotly-carto-darkmatter":{type:"raster",attribution:a,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-darkmatter",type:"raster",source:"plotly-carto-darkmatter",minzoom:0,maxzoom:22}]},"stamen-terrain":{id:"stamen-terrain",version:8,sources:{"plotly-stamen-terrain":{type:"raster",attribution:o,tiles:["https://stamen-tiles.a.ssl.fastly.net/terrain/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-terrain",type:"raster",source:"plotly-stamen-terrain",minzoom:0,maxzoom:22}]},"stamen-toner":{id:"stamen-toner",version:8,sources:{"plotly-stamen-toner":{type:"raster",attribution:o,tiles:["https://stamen-tiles.a.ssl.fastly.net/toner/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-toner",type:"raster",source:"plotly-stamen-toner",minzoom:0,maxzoom:22}]},"stamen-watercolor":{id:"stamen-watercolor",version:8,sources:{"plotly-stamen-watercolor":{type:"raster",attribution:['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://creativecommons.org/licenses/by-sa/3.0">CC BY SA</a>'].join(" "),tiles:["https://stamen-tiles.a.ssl.fastly.net/watercolor/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-watercolor",type:"raster",source:"plotly-stamen-watercolor",minzoom:0,maxzoom:22}]}},l=n(s);e.exports={requiredVersion:"1.10.1",styleUrlPrefix:"mapbox://styles/mapbox/",styleUrlSuffix:"v9",styleValuesMapbox:["basic","streets","outdoors","light","dark","satellite","satellite-streets"],styleValueDflt:"basic",stylesNonMapbox:s,styleValuesNonMapbox:l,traceLayerPrefix:"plotly-trace-layer-",layoutLayerPrefix:"plotly-layout-layer-",wrongVersionErrorMsg:["Your custom plotly.js bundle is not using the correct mapbox-gl 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16.59,8.09 14.74,6.25 Z m -2.27,4.09 -0.91,1.87 -0.9,-1.87 -1.86,-0.91 1.86,-0.9 0.9,-1.87 0.91,1.87 1.86,0.9 z" style="opacity:0.35;enable-background:new" class="st1"/%3E%3Cpolygon points="11.56,12.21 10.66,10.34 8.8,9.43 10.66,8.53 11.56,6.66 12.47,8.53 14.33,9.43 12.47,10.34 " style="opacity:0.9;fill:%23ffffff;enable-background:new" class="st0"/%3E%3C/g%3E%3C/svg%3E\')'}}},{"../../lib/sort_object_keys":799}],883:[function(t,e,r){"use strict";var n=t("../../lib");e.exports=function(t,e){var r=t.split(" "),i=r[0],a=r[1],o=n.isArrayOrTypedArray(e)?n.mean(e):e,s=.5+o/100,l=1.5+o/100,c=["",""],u=[0,0];switch(i){case"top":c[0]="top",u[1]=-l;break;case"bottom":c[0]="bottom",u[1]=l}switch(a){case"left":c[1]="right",u[0]=-s;break;case"right":c[1]="left",u[0]=s}return{anchor:c[0]&&c[1]?c.join("-"):c[0]?c[0]:c[1]?c[1]:"center",offset:u}}},{"../../lib":776}],884:[function(t,e,r){"use strict";var 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s=e[r[i]],h=s.domain,d=s._subplot.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":d,x:n.l+n.w*h.x[0],y:n.t+n.h*(1-h.y[1]),width:n.w*(h.x[1]-h.x[0]),height:n.h*(h.y[1]-h.y[0]),preserveAspectRatio:"none"});var m=c.select(s._subplot.div);if(!(null===m.select(".mapboxgl-ctrl-logo").node().offsetParent)){var g=e._glimages.append("g");g.attr("transform",a(n.l+n.w*h.x[0]+10,n.t+n.h*(1-h.y[0])-31)),g.append("path").attr("d",p.mapboxLogo.path0).style({opacity:.9,fill:"#ffffff","enable-background":"new"}),g.append("path").attr("d",p.mapboxLogo.path1).style("opacity",.35).style("enable-background","new"),g.append("path").attr("d",p.mapboxLogo.path2).style("opacity",.35).style("enable-background","new"),g.append("polygon").attr("points",p.mapboxLogo.polygon).style({opacity:.9,fill:"#ffffff","enable-background":"new"})}var v=m.select(".mapboxgl-ctrl-attrib").text().replace("Improve this 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i=t.symbol,o=a(i.textposition,i.iconsize);n.extendFlat(e,{"icon-image":i.icon+"-15","icon-size":i.iconsize/10,"text-field":i.text,"text-size":i.textfont.size,"text-anchor":o.anchor,"text-offset":o.offset,"symbol-placement":i.placement}),n.extendFlat(r,{"icon-color":t.color,"text-color":i.textfont.color,"text-opacity":t.opacity});break;case"raster":n.extendFlat(r,{"raster-fade-duration":0,"raster-opacity":t.opacity})}return{layout:e,paint:r}}l.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=c(t)},l.needsNewImage=function(t){return this.subplot.map.getSource(this.idSource)&&"image"===this.sourceType&&"image"===t.sourcetype&&(this.source!==t.source||JSON.stringify(this.coordinates)!==JSON.stringify(t.coordinates))},l.needsNewSource=function(t){return this.sourceType!==t.sourcetype||JSON.stringify(this.source)!==JSON.stringify(t.source)||this.layerType!==t.type},l.needsNewLayer=function(t){return this.layerType!==t.type||this.below!==this.subplot.belowLookup["layout-"+this.index]},l.lookupBelow=function(){return this.subplot.belowLookup["layout-"+this.index]},l.updateImage=function(t){this.subplot.map.getSource(this.idSource).updateImage({url:t.source,coordinates:t.coordinates});var e=this.findFollowingMapboxLayerId(this.lookupBelow());null!==e&&this.subplot.map.moveLayer(this.idLayer,e)},l.updateSource=function(t){var e=this.subplot.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,c(t)){var r=function(t){var e,r=t.sourcetype,n=t.source,a={type:r};"geojson"===r?e="data":"vector"===r?e="string"==typeof 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n=t("../../components/color/attributes"),i=t("../domain").attributes,a=t("../cartesian/layout_attributes"),o=t("../../plot_api/edit_types").overrideAll,s=t("../../lib/extend").extendFlat,l={title:{text:a.title.text,font:a.title.font},color:a.color,tickmode:a.tickmode,nticks:s({},a.nticks,{dflt:6,min:1}),tick0:a.tick0,dtick:a.dtick,tickvals:a.tickvals,ticktext:a.ticktext,ticks:a.ticks,ticklen:a.ticklen,tickwidth:a.tickwidth,tickcolor:a.tickcolor,showticklabels:a.showticklabels,showtickprefix:a.showtickprefix,tickprefix:a.tickprefix,showticksuffix:a.showticksuffix,ticksuffix:a.ticksuffix,showexponent:a.showexponent,exponentformat:a.exponentformat,minexponent:a.minexponent,separatethousands:a.separatethousands,tickfont:a.tickfont,tickangle:a.tickangle,tickformat:a.tickformat,tickformatstops:a.tickformatstops,hoverformat:a.hoverformat,showline:s({},a.showline,{dflt:!0}),linecolor:a.linecolor,linewidth:a.linewidth,showgrid:s({},a.showgrid,{dflt:!0}),gridcolor:a.gridcolor,gridwidth:a.gridwidth,layer:a.layer,min:{valType:"number",dflt:0,min:0},_deprecated:{title:a._deprecated.title,titlefont:a._deprecated.titlefont}},c=e.exports=o({domain:i({name:"ternary"}),bgcolor:{valType:"color",dflt:n.background},sum:{valType:"number",dflt:1,min:0},aaxis:l,baxis:l,caxis:l},"plot","from-root");c.uirevision={valType:"any",editType:"none"},c.aaxis.uirevision=c.baxis.uirevision=c.caxis.uirevision={valType:"any",editType:"none"}},{"../../components/color/attributes":638,"../../lib/extend":766,"../../plot_api/edit_types":809,"../cartesian/layout_attributes":842,"../domain":855}],902:[function(t,e,r){"use 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r=e[this.id],n=e._size;this._hasClipOnAxisFalse=!1;for(var i=0;i<t.length;i++){if(!1===t[i][0].trace.cliponaxis){this._hasClipOnAxisFalse=!0;break}}this.updateLayers(r),this.adjustLayout(r,n),p.generalUpdatePerTraceModule(this.graphDiv,this,t,r),this.layers.plotbg.select("path").call(c.fill,r.bgcolor)},S.makeFramework=function(t){var e=this.graphDiv,r=t[this.id],n=this.clipId="clip"+this.layoutId+this.id,i=this.clipIdRelative="clip-relative"+this.layoutId+this.id;this.clipDef=o.ensureSingleById(t._clips,"clipPath",n,(function(t){t.append("path").attr("d","M0,0Z")})),this.clipDefRelative=o.ensureSingleById(t._clips,"clipPath",i,(function(t){t.append("path").attr("d","M0,0Z")})),this.plotContainer=o.ensureSingle(this.container,"g",this.id),this.updateLayers(r),u.setClipUrl(this.layers.backplot,n,e),u.setClipUrl(this.layers.grids,n,e)},S.updateLayers=function(t){var e=this.layers,r=["draglayer","plotbg","backplot","grids"];"below traces"===t.aaxis.layer&&r.push("aaxis","aline"),"below 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r,n,i,a,o,l,p=this,d=t.domain,m=(d.x[0]+d.x[1])/2,g=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],y=d.y[1]-d.y[0],x=v*e.w,b=y*e.h,_=t.sum,w=t.aaxis.min,T=t.baxis.min,k=t.caxis.min;x>E*b?i=(a=b)*E:a=(i=x)/E,o=v*i/x,l=y*a/b,r=e.l+e.w*m-i/2,n=e.t+e.h*(1-g)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:"linear",range:[w+2*k-_,_-w-2*T],domain:[m-o/2,m+o/2],_id:"x"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:"linear",range:[w,_-T-k],domain:[g-l/2,g+l/2],_id:"y"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var A=p.yaxis.domain[0],M=p.aaxis=h({},t.aaxis,{range:[w,_-T-k],side:"left",tickangle:(+t.aaxis.tickangle||0)-30,domain:[A,A+l*E],anchor:"free",position:0,_id:"y",_length:i});f(M,p.graphDiv._fullLayout),M.setScale();var 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n=t("../../components/colorscale/attributes"),i=t("../../plots/template_attributes").hovertemplateAttrs,a=t("../../plots/attributes"),o=t("../scattermapbox/attributes"),s=t("../../lib/extend").extendFlat;e.exports=s({lon:o.lon,lat:o.lat,z:{valType:"data_array",editType:"calc"},radius:{valType:"number",editType:"plot",arrayOk:!0,min:1,dflt:30},below:{valType:"string",editType:"plot"},text:o.text,hovertext:o.hovertext,hoverinfo:s({},a.hoverinfo,{flags:["lon","lat","z","text","name"]}),hovertemplate:i(),showlegend:s({},a.showlegend,{dflt:!1})},n("",{cLetter:"z",editTypeOverride:"calc"}))},{"../../components/colorscale/attributes":646,"../../lib/extend":766,"../../plots/attributes":823,"../../plots/template_attributes":899,"../scattermapbox/attributes":1256}],1029:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../../lib").isArrayOrTypedArray,a=t("../../constants/numerical").BADNUM,o=t("../../components/colorscale/calc"),s=t("../../lib")._;e.exports=function(t,e){for(var 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e=i.colormodel[t.colormodel],r=(e.colormodel||t.colormodel).length;t._sArray=[];for(var n=0;n<r;n++)e.min[n]!==t.zmin[n]||e.max[n]!==t.zmax[n]?t._sArray.push(c(t.zmin[n],(e.max[n]-e.min[n])/(t.zmax[n]-t.zmin[n]),e.min[n],e.max[n])):t._sArray.push(u(e.min[n],e.max[n]));return function(e){for(var n=e.slice(0,r),i=0;i<r;i++){var o=n[i];if(!a(o))return!1;n[i]=t._sArray[i](o)}return n}}(e),[{x0:m,y0:g,z:e.z,w:n,h:r}]}},{"../../lib":776,"../../plots/cartesian/axes":827,"./constants":1112,"./helpers":1115,"fast-isnumeric":242}],1112:[function(t,e,r){"use strict";e.exports={colormodel:{rgb:{min:[0,0,0],max:[255,255,255],fmt:function(t){return t.slice(0,3)},suffix:["","",""]},rgba:{min:[0,0,0,0],max:[255,255,255,1],fmt:function(t){return t.slice(0,4)},suffix:["","","",""]},rgba256:{colormodel:"rgba",zminDflt:[0,0,0,0],zmaxDflt:[255,255,255,255],min:[0,0,0,0],max:[255,255,255,1],fmt:function(t){return t.slice(0,4)},suffix:["","","",""]},hsl:{min:[0,0,0],max:[360,100,100],fmt:function(t){var e=t.slice(0,3);return e[1]=e[1]+"%",e[2]=e[2]+"%",e},suffix:["\xb0","%","%"]},hsla:{min:[0,0,0,0],max:[360,100,100,1],fmt:function(t){var e=t.slice(0,4);return e[1]=e[1]+"%",e[2]=e[2]+"%",e},suffix:["\xb0","%","%",""]}},pixelatedStyle:["image-rendering: optimizeSpeed","image-rendering: -moz-crisp-edges","image-rendering: -o-crisp-edges","image-rendering: -webkit-optimize-contrast","image-rendering: optimize-contrast","image-rendering: crisp-edges","image-rendering: pixelated",""].join("; ")}},{}],1113:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./attributes"),a=t("./constants"),o=t("../../snapshot/helpers").IMAGE_URL_PREFIX;e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r("source"),e.source&&!e.source.match(o)&&delete e.source,e._hasSource=!!e.source;var s,l=r("z");(e._hasZ=!(void 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n(r)}},{"../../snapshot/helpers":908,"buffer/":112,"probe-image-size/sync":503}],1116:[function(t,e,r){"use strict";var n=t("../../components/fx"),i=t("../../lib"),a=t("./constants");e.exports=function(t,e,r){var o=t.cd[0],s=o.trace,l=t.xa,c=t.ya;if(!(n.inbox(e-o.x0,e-(o.x0+o.w*s.dx),0)>0||n.inbox(r-o.y0,r-(o.y0+o.h*s.dy),0)>0)){var u,f=Math.floor((e-o.x0)/s.dx),h=Math.floor(Math.abs(r-o.y0)/s.dy);if(s._hasZ?u=o.z[h][f]:s._hasSource&&(u=s._canvas.el.getContext("2d").getImageData(f,h,1,1).data),u){var p,d=o.hi||s.hoverinfo;if(d){var m=d.split("+");-1!==m.indexOf("all")&&(m=["color"]),-1!==m.indexOf("color")&&(p=!0)}var g,v=a.colormodel[s.colormodel],y=v.colormodel||s.colormodel,x=y.length,b=s._scaler(u),_=v.suffix,w=[];(s.hovertemplate||p)&&(w.push("["+[b[0]+_[0],b[1]+_[1],b[2]+_[2]].join(", ")),4===x&&w.push(", "+b[3]+_[3]),w.push("]"),w=w.join(""),t.extraText=y.toUpperCase()+": "+w),Array.isArray(s.hovertext)&&Array.isArray(s.hovertext[h])?g=s.hovertext[h][f]:Array.isArray(s.text)&&Array.isArray(s.text[h])&&(g=s.text[h][f]);var T=c.c2p(o.y0+(h+.5)*s.dy),k=o.x0+(f+.5)*s.dx,A=o.y0+(h+.5)*s.dy,M="["+u.slice(0,s.colormodel.length).join(", ")+"]";return[i.extendFlat(t,{index:[h,f],x0:l.c2p(o.x0+f*s.dx),x1:l.c2p(o.x0+(f+1)*s.dx),y0:T,y1:T,color:b,xVal:k,xLabelVal:k,yVal:A,yLabelVal:A,zLabelVal:M,text:g,hovertemplateLabels:{zLabel:M,colorLabel:w,"color[0]Label":b[0]+_[0],"color[1]Label":b[1]+_[1],"color[2]Label":b[2]+_[2],"color[3]Label":b[3]+_[3]}})]}}}},{"../../components/fx":679,"../../lib":776,"./constants":1112}],1117:[function(t,e,r){"use 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d,m,g,v,y,x,b=l.z,_=l.x0,w=l.y0,T=l.w,k=l.h,A=c.dx,M=c.dy;for(x=0;void 0===d&&x<T;)d=u.c2p(_+x*A),x++;for(x=T;void 0===m&&x>0;)m=u.c2p(_+x*A),x--;for(x=0;void 0===v&&x<k;)v=f.c2p(w+x*M),x++;for(x=k;void 0===y&&x>0;)y=f.c2p(w+x*M),x--;if(m<d&&(g=m,m=d,d=g),y<v&&(g=v,v=y,y=g),!p){d=Math.max(-.5*u._length,d),m=Math.min(1.5*u._length,m),v=Math.max(-.5*f._length,v),y=Math.min(1.5*f._length,y)}var S=Math.round(m-d),E=Math.round(y-v);if(S<=0||E<=0){r.selectAll("image").data([]).exit().remove()}else{var L=r.selectAll("image").data([e]);L.enter().append("svg:image").attr({xmlns:o.svg,preserveAspectRatio:"none"}),L.exit().remove();var C=!1===c.zsmooth?s.pixelatedStyle:"";if(p){var P=i.simpleMap(u.range,u.r2l),I=i.simpleMap(f.range,f.r2l),O=P[1]<P[0],z=I[1]>I[0];if(O||z){var D=d+S/2,R=v+E/2;C+="transform:"+a(D+"px",R+"px")+"scale("+(O?-1:1)+","+(z?-1:1)+")"+a(-D+"px",-R+"px")+";"}}L.attr("style",C);var F=new Promise((function(t){if(c._hasZ)t();else if(c._hasSource)if(c._canvas&&c._canvas.el.width===T&&c._canvas.el.height===k&&c._canvas.source===c.source)t();else{var e=document.createElement("canvas");e.width=T,e.height=k;var r=e.getContext("2d");c._image=c._image||new Image;var n=c._image;n.onload=function(){r.drawImage(n,0,0),c._canvas={el:e,source:c.source},t()},n.setAttribute("src",c.source)}})).then((function(){var t;if(c._hasZ)t=B((function(t,e){return b[e][t]})).toDataURL("image/png");else if(c._hasSource)if(p)t=c.source;else{var e=c._canvas.el.getContext("2d").getImageData(0,0,T,k).data;t=B((function(t,r){var n=4*(r*T+t);return[e[n],e[n+1],e[n+2],e[n+3]]})).toDataURL("image/png")}L.attr({"xlink:href":t,height:E,width:S,x:d,y:v})}));t._promises.push(F)}function B(t){var e=document.createElement("canvas");e.width=S,e.height=E;var r,n=e.getContext("2d"),a=function(t){return i.constrain(Math.round(u.c2p(_+t*A)-d),0,S)},o=function(t){return i.constrain(Math.round(f.c2p(w+t*M)-v),0,E)},h=s.colormodel[c.colormodel],p=h.colormodel||c.colormodel,m=h.fmt;for(x=0;x<l.w;x++){var g=a(x),y=a(x+1);if(y!==g&&!isNaN(y)&&!isNaN(g))for(var b=0;b<l.h;b++){var T=o(b),k=o(b+1);k===T||isNaN(k)||isNaN(T)||!t(x,b)||(r=c._scaler(t(x,b)),n.fillStyle=r?p+"("+m(r).join(",")+")":"rgba(0,0,0,0)",n.fillRect(g,T,y-g,k-T))}}return e}}))}},{"../../constants/xmlns_namespaces":753,"../../lib":776,"./constants":1112,"@plotly/d3":58}],1119:[function(t,e,r){"use strict";var n=t("@plotly/d3");e.exports=function(t){n.select(t).selectAll(".im image").style("opacity",(function(t){return t[0].trace.opacity}))}},{"@plotly/d3":58}],1120:[function(t,e,r){"use strict";var n=t("../../lib/extend").extendFlat,i=t("../../lib/extend").extendDeep,a=t("../../plot_api/edit_types").overrideAll,o=t("../../plots/font_attributes"),s=t("../../components/color/attributes"),l=t("../../plots/domain").attributes,c=t("../../plots/cartesian/layout_attributes"),u=t("../../plot_api/plot_template").templatedArray,f=t("../../constants/delta.js"),h=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers,p=o({editType:"plot",colorEditType:"plot"}),d={color:{valType:"color",editType:"plot"},line:{color:{valType:"color",dflt:s.defaultLine,editType:"plot"},width:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"calc"},thickness:{valType:"number",min:0,max:1,dflt:1,editType:"plot"},editType:"calc"},m={valType:"info_array",items:[{valType:"number",editType:"plot"},{valType:"number",editType:"plot"}],editType:"plot"},g=u("step",i({},d,{range:m}));e.exports={mode:{valType:"flaglist",editType:"calc",flags:["number","delta","gauge"],dflt:"number"},value:{valType:"number",editType:"calc",anim:!0},align:{valType:"enumerated",values:["left","center","right"],editType:"plot"},domain:l({name:"indicator",trace:!0,editType:"calc"}),title:{text:{valType:"string",editType:"plot"},align:{valType:"enumerated",values:["left","center","right"],editType:"plot"},font:n({},p,{}),editType:"plot"},number:{valueformat:{valType:"string",dflt:"",editType:"plot",description:h("value")},font:n({},p,{}),prefix:{valType:"string",dflt:"",editType:"plot"},suffix:{valType:"string",dflt:"",editType:"plot"},editType:"plot"},delta:{reference:{valType:"number",editType:"calc"},position:{valType:"enumerated",values:["top","bottom","left","right"],dflt:"bottom",editType:"plot"},relative:{valType:"boolean",editType:"plot",dflt:!1},valueformat:{valType:"string",editType:"plot",description:h("value")},increasing:{symbol:{valType:"string",dflt:f.INCREASING.SYMBOL,editType:"plot"},color:{valType:"color",dflt:f.INCREASING.COLOR,editType:"plot"},editType:"plot"},decreasing:{symbol:{valType:"string",dflt:f.DECREASING.SYMBOL,editType:"plot"},color:{valType:"color",dflt:f.DECREASING.COLOR,editType:"plot"},editType:"plot"},font:n({},p,{}),editType:"calc"},gauge:{shape:{valType:"enumerated",editType:"plot",dflt:"angular",values:["angular","bullet"]},bar:i({},d,{color:{dflt:"green"}}),bgcolor:{valType:"color",editType:"plot"},bordercolor:{valType:"color",dflt:s.defaultLine,editType:"plot"},borderwidth:{valType:"number",min:0,dflt:1,editType:"plot"},axis:a({range:m,visible:n({},c.visible,{dflt:!0}),tickmode:c.tickmode,nticks:c.nticks,tick0:c.tick0,dtick:c.dtick,tickvals:c.tickvals,ticktext:c.ticktext,ticks:n({},c.ticks,{dflt:"outside"}),ticklen:c.ticklen,tickwidth:c.tickwidth,tickcolor:c.tickcolor,showticklabels:c.showticklabels,tickfont:o({}),tickangle:c.tickangle,tickformat:c.tickformat,tickformatstops:c.tickformatstops,tickprefix:c.tickprefix,showtickprefix:c.showtickprefix,ticksuffix:c.ticksuffix,showticksuffix:c.showticksuffix,separatethousands:c.separatethousands,exponentformat:c.exponentformat,minexponent:c.minexponent,showexponent:c.showexponent,editType:"plot"},"plot"),steps:g,threshold:{line:{color:n({},d.line.color,{}),width:n({},d.line.width,{dflt:1}),editType:"plot"},thickness:n({},d.thickness,{dflt:.85}),value:{valType:"number",editType:"calc",dflt:!1},editType:"plot"},editType:"plot"}}},{"../../components/color/attributes":638,"../../constants/delta.js":746,"../../lib/extend":766,"../../plot_api/edit_types":809,"../../plot_api/plot_template":816,"../../plots/cartesian/axis_format_attributes":830,"../../plots/cartesian/layout_attributes":842,"../../plots/domain":855,"../../plots/font_attributes":856}],1121:[function(t,e,r){"use 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n=t("../../lib"),i=t("./attributes"),a=t("../../plots/domain").defaults,o=t("../../plot_api/plot_template"),s=t("../../plots/array_container_defaults"),l=t("./constants.js"),c=t("../../plots/cartesian/tick_value_defaults"),u=t("../../plots/cartesian/tick_mark_defaults"),f=t("../../plots/cartesian/tick_label_defaults");function h(t,e){function r(r,a){return n.coerce(t,e,i.gauge.steps,r,a)}r("color"),r("line.color"),r("line.width"),r("range"),r("thickness")}e.exports={supplyDefaults:function(t,e,r,p){function d(r,a){return n.coerce(t,e,i,r,a)}a(e,p,d),d("mode"),e._hasNumber=-1!==e.mode.indexOf("number"),e._hasDelta=-1!==e.mode.indexOf("delta"),e._hasGauge=-1!==e.mode.indexOf("gauge");var m=d("value");e._range=[0,"number"==typeof m?1.5*m:1];var g,v,y,x,b,_,w=new Array(2);function T(t,e){return n.coerce(y,x,i.gauge,t,e)}function k(t,e){return n.coerce(b,_,i.gauge.axis,t,e)}if(e._hasNumber&&(d("number.valueformat"),d("number.font.color",p.font.color),d("number.font.family",p.font.family),d("number.font.size"),void 0===e.number.font.size&&(e.number.font.size=l.defaultNumberFontSize,w[0]=!0),d("number.prefix"),d("number.suffix"),g=e.number.font.size),e._hasDelta&&(d("delta.font.color",p.font.color),d("delta.font.family",p.font.family),d("delta.font.size"),void 0===e.delta.font.size&&(e.delta.font.size=(e._hasNumber?.5:1)*(g||l.defaultNumberFontSize),w[1]=!0),d("delta.reference",e.value),d("delta.relative"),d("delta.valueformat",e.delta.relative?"2%":""),d("delta.increasing.symbol"),d("delta.increasing.color"),d("delta.decreasing.symbol"),d("delta.decreasing.color"),d("delta.position"),v=e.delta.font.size),e._scaleNumbers=(!e._hasNumber||w[0])&&(!e._hasDelta||w[1])||!1,d("title.font.color",p.font.color),d("title.font.family",p.font.family),d("title.font.size",.25*(g||v||l.defaultNumberFontSize)),d("title.text"),e._hasGauge){(y=t.gauge)||(y={}),x=o.newContainer(e,"gauge"),T("shape"),(e._isBullet="bullet"===e.gauge.shape)||d("title.align","center"),(e._isAngular="angular"===e.gauge.shape)||d("align","center"),T("bgcolor",p.paper_bgcolor),T("borderwidth"),T("bordercolor"),T("bar.color"),T("bar.line.color"),T("bar.line.width"),T("bar.thickness",l.valueThickness*("bullet"===e.gauge.shape?.5:1)),s(y,x,{name:"steps",handleItemDefaults:h}),T("threshold.value"),T("threshold.thickness"),T("threshold.line.width"),T("threshold.line.color"),b={},y&&(b=y.axis||{}),_=o.newContainer(x,"axis"),k("visible"),e._range=k("range",e._range);var A={outerTicks:!0};c(b,_,k,"linear"),f(b,_,k,"linear",A),u(b,_,k,A)}else d("title.align","center"),d("align","center"),e._isAngular=e._isBullet=!1;e._length=null}}},{"../../lib":776,"../../plot_api/plot_template":816,"../../plots/array_container_defaults":822,"../../plots/cartesian/tick_label_defaults":849,"../../plots/cartesian/tick_mark_defaults":850,"../../plots/cartesian/tick_value_defaults":851,"../../plots/domain":855,"./attributes":1120,"./constants.js":1123}],1125:[function(t,e,r){"use strict";e.exports={moduleType:"trace",name:"indicator",basePlotModule:t("./base_plot"),categories:["svg","noOpacity","noHover"],animatable:!0,attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,calc:t("./calc").calc,plot:t("./plot"),meta:{}}},{"./attributes":1120,"./base_plot":1121,"./calc":1122,"./defaults":1124,"./plot":1126}],1126:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("d3-interpolate").interpolate,a=t("d3-interpolate").interpolateNumber,o=t("../../lib"),s=o.strScale,l=o.strTranslate,c=o.rad2deg,u=t("../../constants/alignment").MID_SHIFT,f=t("../../components/drawing"),h=t("./constants"),p=t("../../lib/svg_text_utils"),d=t("../../plots/cartesian/axes"),m=t("../../plots/cartesian/axis_defaults"),g=t("../../plots/cartesian/position_defaults"),v=t("../../plots/cartesian/layout_attributes"),y=t("../../components/color"),x={left:"start",center:"middle",right:"end"},b={left:0,center:.5,right:1},_=/[yzafpn\xb5mkMGTPEZY]/;function w(t){return t&&t.duration>0}function T(t){t.each((function(t){y.stroke(n.select(this),t.line.color)})).each((function(t){y.fill(n.select(this),t.color)})).style("stroke-width",(function(t){return t.line.width}))}function k(t,e,r){var n=t._fullLayout,i=o.extendFlat({type:"linear",ticks:"outside",range:r,showline:!0},e),a={type:"linear",_id:"x"+e._id},s={letter:"x",font:n.font,noHover:!0,noTickson:!0};function l(t,e){return o.coerce(i,a,v,t,e)}return m(i,a,l,s,n),g(i,a,l,s),a}function A(t,e,r){return[Math.min(e/t.width,r/t.height),t,e+"x"+r]}function M(t,e,r,i){var a=document.createElementNS("http://www.w3.org/2000/svg","text"),o=n.select(a);return o.text(t).attr("x",0).attr("y",0).attr("text-anchor",r).attr("data-unformatted",t).call(p.convertToTspans,i).call(f.font,e),f.bBox(o.node())}function S(t,e,r,n,i,a){var s="_cache"+e;t[s]&&t[s].key===i||(t[s]={key:i,value:r});var l=o.aggNums(a,null,[t[s].value,n],2);return t[s].value=l,l}e.exports=function(t,e,r,m){var g,v=t._fullLayout;w(r)&&m&&(g=m()),o.makeTraceGroups(v._indicatorlayer,e,"trace").each((function(e){var m,E,L,C,P,I=e[0].trace,O=n.select(this),z=I._hasGauge,D=I._isAngular,R=I._isBullet,F=I.domain,B={w:v._size.w*(F.x[1]-F.x[0]),h:v._size.h*(F.y[1]-F.y[0]),l:v._size.l+v._size.w*F.x[0],r:v._size.r+v._size.w*(1-F.x[1]),t:v._size.t+v._size.h*(1-F.y[1]),b:v._size.b+v._size.h*F.y[0]},N=B.l+B.w/2,j=B.t+B.h/2,U=Math.min(B.w/2,B.h),V=h.innerRadius*U,H=I.align||"center";if(E=j,z){if(D&&(m=N,E=j+U/2,L=function(t){return function(t,e){var r=Math.sqrt(t.width/2*(t.width/2)+t.height*t.height);return[e/r,t,e]}(t,.9*V)}),R){var q=h.bulletPadding,G=1-h.bulletNumberDomainSize+q;m=B.l+(G+(1-G)*b[H])*B.w,L=function(t){return A(t,(h.bulletNumberDomainSize-q)*B.w,B.h)}}}else m=B.l+b[H]*B.w,L=function(t){return A(t,B.w,B.h)};!function(t,e,r,i){var c,u,h,m=r[0].trace,g=i.numbersX,v=i.numbersY,T=m.align||"center",A=x[T],E=i.transitionOpts,L=i.onComplete,C=o.ensureSingle(e,"g","numbers"),P=[];m._hasNumber&&P.push("number");m._hasDelta&&(P.push("delta"),"left"===m.delta.position&&P.reverse());var I=C.selectAll("text").data(P);function O(e,r,n,i){if(!e.match("s")||n>=0==i>=0||r(n).slice(-1).match(_)||r(i).slice(-1).match(_))return r;var a=e.slice().replace("s","f").replace(/\d+/,(function(t){return parseInt(t)-1})),o=k(t,{tickformat:a});return function(t){return Math.abs(t)<1?d.tickText(o,t).text:r(t)}}I.enter().append("text"),I.attr("text-anchor",(function(){return A})).attr("class",(function(t){return t})).attr("x",null).attr("y",null).attr("dx",null).attr("dy",null),I.exit().remove();var z,D=m.mode+m.align;m._hasDelta&&(z=function(){var e=k(t,{tickformat:m.delta.valueformat},m._range);e.setScale(),d.prepTicks(e);var i=function(t){return d.tickText(e,t).text},o=function(t){return m.delta.relative?t.relativeDelta:t.delta},s=function(t,e){return 0===t||"number"!=typeof t||isNaN(t)?"-":(t>0?m.delta.increasing.symbol:m.delta.decreasing.symbol)+e(t)},l=function(t){return t.delta>=0?m.delta.increasing.color:m.delta.decreasing.color};void 0===m._deltaLastValue&&(m._deltaLastValue=o(r[0]));var c=C.select("text.delta");function h(){c.text(s(o(r[0]),i)).call(y.fill,l(r[0])).call(p.convertToTspans,t)}return c.call(f.font,m.delta.font).call(y.fill,l({delta:m._deltaLastValue})),w(E)?c.transition().duration(E.duration).ease(E.easing).tween("text",(function(){var t=n.select(this),e=o(r[0]),c=m._deltaLastValue,u=O(m.delta.valueformat,i,c,e),f=a(c,e);return m._deltaLastValue=e,function(e){t.text(s(f(e),u)),t.call(y.fill,l({delta:f(e)}))}})).each("end",(function(){h(),L&&L()})).each("interrupt",(function(){h(),L&&L()})):h(),u=M(s(o(r[0]),i),m.delta.font,A,t),c}(),D+=m.delta.position+m.delta.font.size+m.delta.font.family+m.delta.valueformat,D+=m.delta.increasing.symbol+m.delta.decreasing.symbol,h=u);m._hasNumber&&(!function(){var e=k(t,{tickformat:m.number.valueformat},m._range);e.setScale(),d.prepTicks(e);var i=function(t){return d.tickText(e,t).text},o=m.number.suffix,s=m.number.prefix,l=C.select("text.number");function u(){var e="number"==typeof r[0].y?s+i(r[0].y)+o:"-";l.text(e).call(f.font,m.number.font).call(p.convertToTspans,t)}w(E)?l.transition().duration(E.duration).ease(E.easing).each("end",(function(){u(),L&&L()})).each("interrupt",(function(){u(),L&&L()})).attrTween("text",(function(){var t=n.select(this),e=a(r[0].lastY,r[0].y);m._lastValue=r[0].y;var l=O(m.number.valueformat,i,r[0].lastY,r[0].y);return function(r){t.text(s+l(e(r))+o)}})):u(),c=M(s+i(r[0].y)+o,m.number.font,A,t)}(),D+=m.number.font.size+m.number.font.family+m.number.valueformat+m.number.suffix+m.number.prefix,h=c);if(m._hasDelta&&m._hasNumber){var R,F,B=[(c.left+c.right)/2,(c.top+c.bottom)/2],N=[(u.left+u.right)/2,(u.top+u.bottom)/2],j=.75*m.delta.font.size;"left"===m.delta.position&&(R=S(m,"deltaPos",0,-1*(c.width*b[m.align]+u.width*(1-b[m.align])+j),D,Math.min),F=B[1]-N[1],h={width:c.width+u.width+j,height:Math.max(c.height,u.height),left:u.left+R,right:c.right,top:Math.min(c.top,u.top+F),bottom:Math.max(c.bottom,u.bottom+F)}),"right"===m.delta.position&&(R=S(m,"deltaPos",0,c.width*(1-b[m.align])+u.width*b[m.align]+j,D,Math.max),F=B[1]-N[1],h={width:c.width+u.width+j,height:Math.max(c.height,u.height),left:c.left,right:u.right+R,top:Math.min(c.top,u.top+F),bottom:Math.max(c.bottom,u.bottom+F)}),"bottom"===m.delta.position&&(R=null,F=u.height,h={width:Math.max(c.width,u.width),height:c.height+u.height,left:Math.min(c.left,u.left),right:Math.max(c.right,u.right),top:c.bottom-c.height,bottom:c.bottom+u.height}),"top"===m.delta.position&&(R=null,F=c.top,h={width:Math.max(c.width,u.width),height:c.height+u.height,left:Math.min(c.left,u.left),right:Math.max(c.right,u.right),top:c.bottom-c.height-u.height,bottom:c.bottom}),z.attr({dx:R,dy:F})}(m._hasNumber||m._hasDelta)&&C.attr("transform",(function(){var t=i.numbersScaler(h);D+=t[2];var e,r=S(m,"numbersScale",1,t[0],D,Math.min);m._scaleNumbers||(r=1),e=m._isAngular?v-r*h.bottom:v-r*(h.top+h.bottom)/2,m._numbersTop=r*h.top+e;var n=h[T];"center"===T&&(n=(h.left+h.right)/2);var a=g-r*n;return a=S(m,"numbersTranslate",0,a,D,Math.max),l(a,e)+s(r)}))}(t,O,e,{numbersX:m,numbersY:E,numbersScaler:L,transitionOpts:r,onComplete:g}),z&&(C={range:I.gauge.axis.range,color:I.gauge.bgcolor,line:{color:I.gauge.bordercolor,width:0},thickness:1},P={range:I.gauge.axis.range,color:"rgba(0, 0, 0, 0)",line:{color:I.gauge.bordercolor,width:I.gauge.borderwidth},thickness:1});var Y=O.selectAll("g.angular").data(D?e:[]);Y.exit().remove();var W=O.selectAll("g.angularaxis").data(D?e:[]);W.exit().remove(),D&&function(t,e,r,a){var o,s,f,h,p=r[0].trace,m=a.size,g=a.radius,v=a.innerRadius,y=a.gaugeBg,x=a.gaugeOutline,b=[m.l+m.w/2,m.t+m.h/2+g/2],_=a.gauge,A=a.layer,M=a.transitionOpts,S=a.onComplete,E=Math.PI/2;function L(t){var e=p.gauge.axis.range[0],r=(t-e)/(p.gauge.axis.range[1]-e)*Math.PI-E;return r<-E?-E:r>E?E:r}function C(t){return n.svg.arc().innerRadius((v+g)/2-t/2*(g-v)).outerRadius((v+g)/2+t/2*(g-v)).startAngle(-E)}function P(t){t.attr("d",(function(t){return C(t.thickness).startAngle(L(t.range[0])).endAngle(L(t.range[1]))()}))}_.enter().append("g").classed("angular",!0),_.attr("transform",l(b[0],b[1])),A.enter().append("g").classed("angularaxis",!0).classed("crisp",!0),A.selectAll("g.xangularaxistick,path,text").remove(),(o=k(t,p.gauge.axis)).type="linear",o.range=p.gauge.axis.range,o._id="xangularaxis",o.ticklabeloverflow="allow",o.setScale();var I=function(t){return(o.range[0]-t.x)/(o.range[1]-o.range[0])*Math.PI+Math.PI},O={},z=d.makeLabelFns(o,0).labelStandoff;O.xFn=function(t){var e=I(t);return Math.cos(e)*z},O.yFn=function(t){var e=I(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(z+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*u)},O.anchorFn=function(t){var e=I(t),r=Math.cos(e);return Math.abs(r)<.1?"middle":r>0?"start":"end"},O.heightFn=function(t,e,r){var n=I(t);return-.5*(1+Math.sin(n))*r};var D=function(t){return l(b[0]+g*Math.cos(t),b[1]-g*Math.sin(t))};f=function(t){return D(I(t))};if(s=d.calcTicks(o),h=d.getTickSigns(o)[2],o.visible){h="inside"===o.ticks?-1:1;var R=(o.linewidth||1)/2;d.drawTicks(t,o,{vals:s,layer:A,path:"M"+h*R+",0h"+h*o.ticklen,transFn:function(t){var e=I(t);return D(e)+"rotate("+-c(e)+")"}}),d.drawLabels(t,o,{vals:s,layer:A,transFn:f,labelFns:O})}var F=[y].concat(p.gauge.steps),B=_.selectAll("g.bg-arc").data(F);B.enter().append("g").classed("bg-arc",!0).append("path"),B.select("path").call(P).call(T),B.exit().remove();var N=C(p.gauge.bar.thickness),j=_.selectAll("g.value-arc").data([p.gauge.bar]);j.enter().append("g").classed("value-arc",!0).append("path");var U=j.select("path");w(M)?(U.transition().duration(M.duration).ease(M.easing).each("end",(function(){S&&S()})).each("interrupt",(function(){S&&S()})).attrTween("d",(V=N,H=L(r[0].lastY),q=L(r[0].y),function(){var t=i(H,q);return function(e){return V.endAngle(t(e))()}})),p._lastValue=r[0].y):U.attr("d","number"==typeof r[0].y?N.endAngle(L(r[0].y)):"M0,0Z");var V,H,q;U.call(T),j.exit().remove(),F=[];var G=p.gauge.threshold.value;(G||0===G)&&F.push({range:[G,G],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var Y=_.selectAll("g.threshold-arc").data(F);Y.enter().append("g").classed("threshold-arc",!0).append("path"),Y.select("path").call(P).call(T),Y.exit().remove();var W=_.selectAll("g.gauge-outline").data([x]);W.enter().append("g").classed("gauge-outline",!0).append("path"),W.select("path").call(P).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Y,layer:W,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var X=O.selectAll("g.bullet").data(R?e:[]);X.exit().remove();var Z=O.selectAll("g.bulletaxis").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,f=n.gauge,p=n.layer,m=n.gaugeBg,g=n.gaugeOutline,v=n.size,x=u.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append("g").classed("bullet",!0),f.attr("transform",l(v.l,v.t)),p.enter().append("g").classed("bulletaxis",!0).classed("crisp",!0),p.selectAll("g.xbulletaxistick,path,text").remove();var A=v.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-h.bulletNumberDomainSize:1);(i=k(t,u.gauge.axis))._id="xbulletaxis",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(d.drawTicks(t,i,{vals:"inside"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,c,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,c)}));function L(t){t.attr("width",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr("x",(function(t){return i.c2p(t.range[0])})).attr("y",(function(t){return.5*(1-t.thickness)*A})).attr("height",(function(t){return t.thickness*A}))}var C=[m].concat(u.gauge.steps),P=f.selectAll("g.bg-bullet").data(C);P.enter().append("g").classed("bg-bullet",!0).append("rect"),P.select("rect").call(L).call(T),P.exit().remove();var I=f.selectAll("g.value-bullet").data([u.gauge.bar]);I.enter().append("g").classed("value-bullet",!0).append("rect"),I.select("rect").attr("height",M).attr("y",(A-M)/2).call(T),w(b)?I.select("rect").transition().duration(b.duration).ease(b.easing).each("end",(function(){_&&_()})).each("interrupt",(function(){_&&_()})).attr("width",Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y)))):I.select("rect").attr("width","number"==typeof r[0].y?Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y))):0);I.exit().remove();var O=r.filter((function(){return u.gauge.threshold.value||0===u.gauge.threshold.value})),z=f.selectAll("g.threshold-bullet").data(O);z.enter().append("g").classed("threshold-bullet",!0).append("line"),z.select("line").attr("x1",i.c2p(u.gauge.threshold.value)).attr("x2",i.c2p(u.gauge.threshold.value)).attr("y1",(1-u.gauge.threshold.thickness)/2*A).attr("y2",(1-(1-u.gauge.threshold.thickness)/2)*A).call(y.stroke,u.gauge.threshold.line.color).style("stroke-width",u.gauge.threshold.line.width),z.exit().remove();var D=f.selectAll("g.gauge-outline").data([g]);D.enter().append("g").classed("gauge-outline",!0).append("rect"),D.select("rect").call(L).call(T),D.exit().remove()}(t,0,e,{gauge:X,layer:Z,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var J=O.selectAll("text.title").data(e);J.exit().remove(),J.enter().append("text").classed("title",!0),J.attr("text-anchor",(function(){return R?x.right:x[I.title.align]})).text(I.title.text).call(f.font,I.title.font).call(p.convertToTspans,t),J.attr("transform",(function(){var t,e=B.l+B.w*b[I.title.align],r=h.titlePadding,n=f.bBox(J.node());if(z){if(D)if(I.gauge.axis.visible)t=f.bBox(W.node()).top-r-n.bottom;else t=B.t+B.h/2-U/2-n.bottom-r;R&&(t=E-(n.top+n.bottom)/2,e=B.l-h.bulletPadding*B.w)}else t=I._numbersTop-r-n.bottom;return l(e,t)}))}))}},{"../../components/color":639,"../../components/drawing":661,"../../constants/alignment":744,"../../lib":776,"../../lib/svg_text_utils":802,"../../plots/cartesian/axes":827,"../../plots/cartesian/axis_defaults":829,"../../plots/cartesian/layout_attributes":842,"../../plots/cartesian/position_defaults":845,"./constants":1123,"@plotly/d3":58,"d3-interpolate":164}],1127:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;var 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i._trace=a,a.update(e),t.glplot.add(i),a}}},{"../../components/colorscale":651,"../../lib/gl_format_color":772,"../../lib/str2rgbarray":801,"../../plots/gl3d/zip3":880,"gl-mesh3d":303}],1130:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../registry"),a=t("./attributes"),o=t("../../components/colorscale/defaults");function s(t,e,r,n,a){var s=a("isomin"),l=a("isomax");null!=l&&null!=s&&s>l&&(e.isomin=null,e.isomax=null);var c=a("x"),u=a("y"),f=a("z"),h=a("value");c&&c.length&&u&&u.length&&f&&f.length&&h&&h.length?(i.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],n),a("valuehoverformat"),["x","y","z"].forEach((function(t){a(t+"hoverformat");var e="caps."+t;a(e+".show")&&a(e+".fill");var 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n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../surface/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat;e.exports=l({x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},i:{valType:"data_array",editType:"calc"},j:{valType:"data_array",editType:"calc"},k:{valType:"data_array",editType:"calc"},text:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertext:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertemplate:a({editType:"calc"}),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),delaunayaxis:{valType:"enumerated",values:["x","y","z"],dflt:"z",editType:"calc"},alphahull:{valType:"number",dflt:-1,editType:"calc"},intensity:{valType:"data_array",editType:"calc"},intensitymode:{valType:"enumerated",values:["vertex","cell"],dflt:"vertex",editType:"calc"},color:{valType:"color",editType:"calc"},vertexcolor:{valType:"data_array",editType:"calc"},facecolor:{valType:"data_array",editType:"calc"},transforms:void 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n=t("../../components/colorscale/calc");e.exports=function(t,e){e.intensity&&n(t,e,{vals:e.intensity,containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":647}],1134:[function(t,e,r){"use strict";var n=t("gl-mesh3d"),i=t("delaunay-triangulate"),a=t("alpha-shape"),o=t("convex-hull"),s=t("../../lib/gl_format_color").parseColorScale,l=t("../../lib/str2rgbarray"),c=t("../../components/colorscale").extractOpts,u=t("../../plots/gl3d/zip3");function f(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name="",this.color="#fff",this.data=null,this.showContour=!1}var h=f.prototype;function p(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=l(t[n]);return e}function d(t,e,r,n){for(var i=[],a=e.length,o=0;o<a;o++)i[o]=t.d2l(e[o],0,n)*r;return i}function m(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=Math.round(t[n]);return e}function g(t,e){for(var r=t.length,n=0;n<r;n++)if(t[n]<=-.5||t[n]>=e-.5)return!1;return!0}h.handlePick=function(t){if(t.object===this.mesh){var e=t.index=t.data.index;t.data._cellCenter?t.traceCoordinate=t.data.dataCoordinate:t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]];var r=this.data.hovertext||this.data.text;return Array.isArray(r)&&void 0!==r[e]?t.textLabel=r[e]:r&&(t.textLabel=r),!0}},h.update=function(t){var e=this.scene,r=e.fullSceneLayout;this.data=t;var n,f=t.x.length,h=u(d(r.xaxis,t.x,e.dataScale[0],t.xcalendar),d(r.yaxis,t.y,e.dataScale[1],t.ycalendar),d(r.zaxis,t.z,e.dataScale[2],t.zcalendar));if(t.i&&t.j&&t.k){if(t.i.length!==t.j.length||t.j.length!==t.k.length||!g(t.i,f)||!g(t.j,f)||!g(t.k,f))return;n=u(m(t.i),m(t.j),m(t.k))}else n=0===t.alphahull?o(h):t.alphahull>0?a(t.alphahull,h):function(t,e){for(var r=["x","y","z"].indexOf(t),n=[],a=e.length,o=0;o<a;o++)n[o]=[e[o][(r+1)%3],e[o][(r+2)%3]];return i(n)}(t.delaunayaxis,h);var v={positions:h,cells:n,lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:l(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading};if(t.intensity){var y=c(t);this.color="#fff";var x=t.intensitymode;v[x+"Intensity"]=t.intensity,v[x+"IntensityBounds"]=[y.min,y.max],v.colormap=s(t)}else t.vertexcolor?(this.color=t.vertexcolor[0],v.vertexColors=p(t.vertexcolor)):t.facecolor?(this.color=t.facecolor[0],v.cellColors=p(t.facecolor)):(this.color=t.color,v.meshColor=l(t.color));this.mesh.update(v)},h.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new f(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},{"../../components/colorscale":651,"../../lib/gl_format_color":772,"../../lib/str2rgbarray":801,"../../plots/gl3d/zip3":880,"alpha-shape":71,"convex-hull":137,"delaunay-triangulate":172,"gl-mesh3d":303}],1135:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("../../components/colorscale/defaults"),o=t("./attributes");e.exports=function(t,e,r,s){function l(r,n){return i.coerce(t,e,o,r,n)}function c(t){var e=t.map((function(t){var e=l(t);return e&&i.isArrayOrTypedArray(e)?e:null}));return e.every((function(t){return t&&t.length===e[0].length}))&&e}c(["x","y","z"])?(c(["i","j","k"]),(!e.i||e.j&&e.k)&&(!e.j||e.k&&e.i)&&(!e.k||e.i&&e.j)?(n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],s),["lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lighting.vertexnormalsepsilon","lighting.facenormalsepsilon","lightposition.x","lightposition.y","lightposition.z","flatshading","alphahull","delaunayaxis","opacity"].forEach((function(t){l(t)})),l("contour.show")&&(l("contour.color"),l("contour.width")),"intensity"in t?(l("intensity"),l("intensitymode"),a(t,e,s,l,{prefix:"",cLetter:"c"})):(e.showscale=!1,"facecolor"in t?l("facecolor"):"vertexcolor"in 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n=t("../../lib").extendFlat,i=t("../scatter/attributes"),a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,o=t("../../components/drawing/attributes").dash,s=t("../../components/fx/attributes"),l=t("../../constants/delta.js"),c=l.INCREASING.COLOR,u=l.DECREASING.COLOR,f=i.line;function h(t){return{line:{color:n({},f.color,{dflt:t}),width:f.width,dash:o,editType:"style"},editType:"style"}}e.exports={xperiod:i.xperiod,xperiod0:i.xperiod0,xperiodalignment:i.xperiodalignment,xhoverformat:a("x"),yhoverformat:a("y"),x:{valType:"data_array",editType:"calc+clearAxisTypes"},open:{valType:"data_array",editType:"calc"},high:{valType:"data_array",editType:"calc"},low:{valType:"data_array",editType:"calc"},close:{valType:"data_array",editType:"calc"},line:{width:n({},f.width,{}),dash:n({},o,{}),editType:"style"},increasing:h(c),decreasing:h(u),text:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertext:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},tickwidth:{valType:"number",min:0,max:.5,dflt:.3,editType:"calc"},hoverlabel:n({},s.hoverlabel,{split:{valType:"boolean",dflt:!1,editType:"style"}})}},{"../../components/drawing/attributes":660,"../../components/fx/attributes":670,"../../constants/delta.js":746,"../../lib":776,"../../plots/cartesian/axis_format_attributes":830,"../scatter/attributes":1191}],1138:[function(t,e,r){"use 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r=a.getFromId(t,e.xaxis),i=a.getFromId(t,e.yaxis),s=function(t,e,r){var i=r._minDiff;if(!i){var a,s=t._fullData,l=[];for(i=1/0,a=0;a<s.length;a++){var c=s[a];if("ohlc"===c.type&&!0===c.visible&&c.xaxis===e._id){l.push(c);var u=e.makeCalcdata(c,"x");c._origX=u;var f=o(r,e,"x",u).vals;c._xcalc=f;var h=n.distinctVals(f).minDiff;h&&isFinite(h)&&(i=Math.min(i,h))}}for(i===1/0&&(i=1),a=0;a<l.length;a++)l[a]._minDiff=i}return i*r.tickwidth}(t,r,e),u=e._minDiff;e._minDiff=null;var f=e._origX;e._origX=null;var h=e._xcalc;e._xcalc=null;var p=c(t,e,f,h,i,l);return e._extremes[r._id]=a.findExtremes(r,h,{vpad:u/2}),p.length?(n.extendFlat(p[0].t,{wHover:u/2,tickLen:s}),p):[{t:{empty:!0}}]},calcCommon:c}},{"../../constants/numerical":752,"../../lib":776,"../../plots/cartesian/align_period":824,"../../plots/cartesian/axes":827}],1139:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./ohlc_defaults"),a=t("../scatter/period_defaults"),o=t("./attributes");function 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n=t("@plotly/d3"),i=t("../../lib");e.exports=function(t,e,r,a){var o=e.yaxis,s=e.xaxis,l=!!s.rangebreaks;i.makeTraceGroups(a,r,"trace ohlc").each((function(t){var e=n.select(this),r=t[0],a=r.t;if(!0!==r.trace.visible||a.empty)e.remove();else{var c=a.tickLen,u=e.selectAll("path").data(i.identity);u.enter().append("path"),u.exit().remove(),u.attr("d",(function(t){if(t.empty)return"M0,0Z";var e=s.c2p(t.pos-c,!0),r=s.c2p(t.pos+c,!0),n=l?(e+r)/2:s.c2p(t.pos,!0);return"M"+e+","+o.c2p(t.o,!0)+"H"+n+"M"+n+","+o.c2p(t.h,!0)+"V"+o.c2p(t.l,!0)+"M"+r+","+o.c2p(t.c,!0)+"H"+n}))}}))}},{"../../lib":776,"@plotly/d3":58}],1144:[function(t,e,r){"use strict";e.exports=function(t,e){var r,n=t.cd,i=t.xaxis,a=t.yaxis,o=[],s=n[0].t.bPos||0;if(!1===e)for(r=0;r<n.length;r++)n[r].selected=0;else for(r=0;r<n.length;r++){var l=n[r];e.contains([i.c2p(l.pos+s),a.c2p(l.yc)],null,l.i,t)?(o.push({pointNumber:l.i,x:i.c2d(l.pos),y:a.c2d(l.yc)}),l.selected=1):l.selected=0}return o}},{}],1145:[function(t,e,r){"use 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n=t("../../lib/extend").extendFlat,i=t("../../plots/attributes"),a=t("../../plots/font_attributes"),o=t("../../components/colorscale/attributes"),s=t("../../plots/template_attributes").hovertemplateAttrs,l=t("../../plots/domain").attributes,c=n({editType:"calc"},o("line",{editTypeOverride:"calc"}),{shape:{valType:"enumerated",values:["linear","hspline"],dflt:"linear",editType:"plot"},hovertemplate:s({editType:"plot",arrayOk:!1},{keys:["count","probability"]})});e.exports={domain:l({name:"parcats",trace:!0,editType:"calc"}),hoverinfo:n({},i.hoverinfo,{flags:["count","probability"],editType:"plot",arrayOk:!1}),hoveron:{valType:"enumerated",values:["category","color","dimension"],dflt:"category",editType:"plot"},hovertemplate:s({editType:"plot",arrayOk:!1},{keys:["count","probability","category","categorycount","colorcount","bandcolorcount"]}),arrangement:{valType:"enumerated",values:["perpendicular","freeform","fixed"],dflt:"perpendicular",editType:"plot"},bundlecolors:{valType:"boolean",dflt:!0,editType:"plot"},sortpaths:{valType:"enumerated",values:["forward","backward"],dflt:"forward",editType:"plot"},labelfont:a({editType:"calc"}),tickfont:a({editType:"calc"}),dimensions:{_isLinkedToArray:"dimension",label:{valType:"string",editType:"calc"},categoryorder:{valType:"enumerated",values:["trace","category 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h(t,e,r,i){var a=t.map(F.bind(0,e,r)),o=i.selectAll("g.parcatslayer").data([null]);o.enter().append("g").attr("class","parcatslayer").style("pointer-events","all");var u=o.selectAll("g.trace.parcats").data(a,p),h=u.enter().append("g").attr("class","trace parcats");u.attr("transform",(function(t){return l(t.x,t.y)})),h.append("g").attr("class","paths");var y=u.select("g.paths").selectAll("path.path").data((function(t){return t.paths}),p);y.attr("fill",(function(t){return t.model.color}));var x=y.enter().append("path").attr("class","path").attr("stroke-opacity",0).attr("fill",(function(t){return t.model.color})).attr("fill-opacity",0);_(x),y.attr("d",(function(t){return t.svgD})),x.empty()||y.sort(m),y.exit().remove(),y.on("mouseover",g).on("mouseout",v).on("click",b),h.append("g").attr("class","dimensions");var w=u.select("g.dimensions").selectAll("g.dimension").data((function(t){return t.dimensions}),p);w.enter().append("g").attr("class","dimension"),w.attr("transform",(function(t){return l(t.x,0)})),w.exit().remove();var A=w.selectAll("g.category").data((function(t){return t.categories}),p),M=A.enter().append("g").attr("class","category");A.attr("transform",(function(t){return l(0,t.y)})),M.append("rect").attr("class","catrect").attr("pointer-events","none"),A.select("rect.catrect").attr("fill","none").attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})),T(M);var S=A.selectAll("rect.bandrect").data((function(t){return t.bands}),p);S.each((function(){s.raiseToTop(this)})),S.attr("fill",(function(t){return t.color}));var E=S.enter().append("rect").attr("class","bandrect").attr("stroke-opacity",0).attr("fill",(function(t){return t.color})).attr("fill-opacity",0);S.attr("fill",(function(t){return t.color})).attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})).attr("y",(function(t){return t.y})).attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"perpendicular"===t.parcatsViewModel.arrangement?"ns-resize":"move"})),k(E),S.exit().remove(),M.append("text").attr("class","catlabel").attr("pointer-events","none");var z=e._fullLayout.paper_bgcolor;A.select("text.catlabel").attr("text-anchor",(function(t){return d(t)?"start":"end"})).attr("alignment-baseline","middle").style("text-shadow",f.makeTextShadow(z)).style("fill","rgb(0, 0, 0)").attr("x",(function(t){return d(t)?t.width+5:-5})).attr("y",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),M.append("text").attr("class","dimlabel"),A.select("text.dimlabel").attr("text-anchor","middle").attr("alignment-baseline","baseline").attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"ew-resize"})).attr("x",(function(t){return t.width/2})).attr("y",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),A.selectAll("rect.bandrect").on("mouseover",L).on("mouseout",C),A.exit().remove(),w.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on("dragstart",P).on("drag",I).on("dragend",O)),u.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll("g.paths").selectAll("path.path"),t.dimensionSelection=n.select(this).selectAll("g.dimensions").selectAll("g.dimension")})),u.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function m(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){s.raiseToTop(this),w(n.select(this));var e=y(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit("plotly_hover",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none")){var i,a,l,c=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),m=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=c&&c<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],v=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+v.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,["black","white"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf("count")&&S.push(["Count:",M.countLabel].join(" ")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf("probability")&&S.push(["P:",M.probabilityLabel].join(" "));var E=S.join("<br>"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+m.left,y:_-d.top+m.top,text:E,color:t.model.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:10,fontColor:T,idealAlign:L<b?"right":"left",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function v(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(m),-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip"))){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_unhover",{points:e,event:n.event,constraints:r})}}function y(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_click",{points:e,event:n.event,constraints:r})}}function _(t){t.attr("fill",(function(t){return t.model.color})).attr("fill-opacity",.6).attr("stroke","lightgray").attr("stroke-width",.2).attr("stroke-opacity",1)}function w(t){t.attr("fill-opacity",.8).attr("stroke",(function(t){return u.mostReadable(t.model.color,["black","white"])})).attr("stroke-width",.3)}function T(t){t.select("rect.catrect").attr("stroke","black").attr("stroke-width",1).attr("stroke-opacity",1)}function k(t){t.attr("stroke","black").attr("stroke-width",.2).attr("stroke-opacity",1).attr("fill-opacity",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll("rect.bandrect"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,y(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,y(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select("rect.catrect"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a="left"):(i=c.left+c.width,a="right");var m=u.model.count,g=u.model.categoryLabel,v=m/u.parcatsViewModel.model.count,y={countLabel:m,categoryLabel:g,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf("count")&&x.push(["Count:",y.countLabel].join(" ")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf("probability")&&x.push(["P("+y.categoryLabel+"):",y.probabilityLabel].join(" "));var b=x.join("<br>");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:"lightgray",borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:12,fontColor:"black",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:y,eventData:[{data:p._input,fullData:p,count:m,category:g,probability:v}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron;if("color"===l?(!function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll("rect.bandrect").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr("stroke","black").attr("stroke-width",1.5)}))}(this),S(this,"plotly_hover",n.event)):(!function(t){n.select(t.parentNode).selectAll("rect.bandrect").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select("rect.catrect").attr("stroke","black").attr("stroke-width",2.5)}(this),M(this,"plotly_hover",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none"))"category"===l?e=E(r,a,this):"color"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),c=n.select(r).datum(),f=c.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,m=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a="left"):(i=l.left+l.width,a="right");var g=f.model.categoryLabel,v=c.parcatsViewModel.model.count,y=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===c.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf("count")&&A.push(["Count:",k.countLabel].join(" ")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf("probability")&&(A.push("P(color \u2229 "+g+"): "+k.probabilityLabel),A.push("P("+g+" | color): "+w.toFixed(3)),A.push("P(color | "+g+"): "+T.toFixed(3)));var M=A.join("<br>"),S=u.mostReadable(c.color,["black","white"]);return{trace:d,x:o*(i-e.left),y:s*(m-e.top),text:M,color:c.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,a,this):"dimension"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll("g.category").select("rect.catrect").each((function(){i.push(E(t,e,this))})),i}(r,a,this)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(_(e.pathSelection),T(e.dimensionSelection.selectAll("g.category")),k(e.dimensionSelection.selectAll("g.category").selectAll("rect.bandrect")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(m),-1===e.hoverinfoItems.indexOf("skip"))){"color"===t.parcatsViewModel.hoveron?S(this,"plotly_unhover",n.event):M(this,"plotly_unhover",n.event)}}function P(t){"fixed"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll("g.category").select("rect.catrect").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll("rect.bandrect").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if("fixed"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||"freeform"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function O(t){if("fixed"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll("text").attr("font-weight","normal");var e={},r=z(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e["dimensions["+i+"].displayindex"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return t.categoryLabel}));e["dimensions["+t.model.containerInd+"].categoryarray"]=[u],e["dimensions["+t.model.containerInd+"].ticktext"]=[f],e["dimensions["+t.model.containerInd+"].categoryorder"]="array"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")&&!t.dragHasMoved&&t.potentialClickBand&&("color"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,"plotly_click",n.event.sourceEvent):M(t.potentialClickBand,"plotly_click",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease("cubic-in-out").each((function(){R(t.parcatsViewModel,!0),D(t.parcatsViewModel,!0)})).each("end",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function z(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function D(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data((function(t){return t.paths}),p),(r=t.pathSelection,e?r.transition():r).attr("d",(function(t){return t.svgD}))}function R(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data((function(t){return t.dimensions}),p);var i=t.dimensionSelection.selectAll("g.category").data((function(t){return t.categories}),p);r(t.dimensionSelection).attr("transform",(function(t){return l(t.x,0)})),r(i).attr("transform",(function(t){return l(0,t.y)})),i.select(".dimlabel").text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})),i.select(".catlabel").attr("text-anchor",(function(t){return d(t)?"start":"end"})).attr("x",(function(t){return d(t)?t.width+5:-5})).each((function(t){var e,r;d(t)?(e=t.width+5,r="start"):(e=-5,r="end"),n.select(this).selectAll("tspan").attr("x",e).attr("text-anchor",r)}));var a=i.selectAll("rect.bandrect").data((function(t){return t.bands}),p),o=a.enter().append("rect").attr("class","bandrect").attr("cursor","move").attr("stroke-opacity",0).attr("fill",(function(t){return t.color})).attr("fill-opacity",0);a.attr("fill",(function(t){return t.color})).attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})).attr("y",(function(t){return t.y})),k(o),a.each((function(){s.raiseToTop(this)})),a.exit().remove()}function F(t,e,r){var n,i=r[0],a=e.margin||{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,c=e.height,u=Math.floor(l*(s.x[1]-s.x[0])),f=Math.floor(c*(s.y[1]-s.y[0])),h=s.x[0]*l+a.l,p=e.height-s.y[1]*e.height+a.t,d=o.line.shape;n="all"===o.hoverinfo?["count","probability"]:(o.hoverinfo||"").split("+");var m={trace:o,key:o.uid,model:i,x:h,y:p,width:u,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};return i.dimensions&&(j(m),N(m)),m}function B(t,e,r,n,a){var o,s,l=[],c=[];for(s=0;s<r.length-1;s++)o=i(r[s]+t[s],t[s+1]),l.push(o(a)),c.push(o(1-a));var u="M "+t[0]+","+e[0];for(u+="l"+r[0]+",0 ",s=1;s<r.length;s++)u+="C"+l[s-1]+","+e[s-1]+" "+c[s-1]+","+e[s]+" "+t[s]+","+e[s],u+="l"+r[s]+",0 ";for(u+="l0,"+n+" ",u+="l -"+r[r.length-1]+",0 ",s=r.length-2;s>=0;s--)u+="C"+c[s]+","+(e[s+1]+n)+" "+l[s]+","+(e[s]+n)+" "+(t[s]+r[s])+","+(e[s]+n),u+="l-"+r[s]+",0 ";return u+="Z"}function N(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),c=[];for(var u in r.paths)r.paths.hasOwnProperty(u)&&c.push(r.paths[u]);function f(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}c.sort((function(e,r){var n=f(e),i=f(r);return"backward"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),n<i?-1:n>i?1:0}));for(var h=new Array(c.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),m=0;m<c.length;m++){var g,v=c[m];g=p>0?d*(v.count/p):0;for(var y,x=new Array(n.length),b=0;b<v.categoryInds.length;b++){var _=v.categoryInds[b],w=i[b][_],T=a[b];x[T]=n[T][w],n[T][w]+=g;var k=t.dimensions[T].categories[w],A=k.bands.length,M=k.bands[A-1];if(void 0===M||v.rawColor!==M.rawColor){var S=void 0===M?0:M.y+M.height;k.bands.push({key:S,color:v.color,rawColor:v.rawColor,height:g,width:k.width,count:v.count,y:S,categoryViewModel:k,parcatsViewModel:t})}else{var E=k.bands[A-1];E.height+=g,E.count+=v.count}}y="hspline"===t.pathShape?B(s,x,l,g,.5):B(s,x,l,g,0),h[m]={key:v.valueInds[0],model:v,height:g,leftXs:s,topYs:x,dimWidths:l,svgD:y,parcatsViewModel:t}}t.paths=h}function j(t){var e=t.model.dimensions.map((function(t){return{displayInd:t.displayInd,dimensionInd:t.dimensionInd}}));e.sort((function(t,e){return t.displayInd-e.displayInd}));var r=[];for(var n in e){var i=e[n].dimensionInd,a=t.model.dimensions[i];r.push(U(t,a))}t.dimensions=r}function U(t,e){var r,n=t.model.dimensions.length,i=e.displayInd;r=40+(n>1?(t.width-80-16)/(n-1):0)*i;var a,o,s,l,c,u=[],f=t.model.maxCats,h=e.categories.length,p=e.count,d=t.height-8*(f-1),m=8*(f-h)/2,g=e.categories.map((function(t){return{displayInd:t.displayInd,categoryInd:t.categoryInd}}));for(g.sort((function(t,e){return t.displayInd-e.displayInd})),c=0;c<h;c++)l=g[c].categoryInd,o=e.categories[l],a=p>0?o.count/p*d:0,s={key:o.valueInds[0],model:o,width:16,height:a,y:null!==o.dragY?o.dragY:m,bands:[],parcatsViewModel:t},m=m+a+8,u.push(s);return{key:e.dimensionInd,x:null!==e.dragX?e.dragX:r,y:0,width:16,model:e,categories:u,parcatsViewModel:t,dragCategoryDisplayInd:null,dragDimensionDisplayInd:null,initialDragDimensionDisplayInds:null,initialDragCategoryDisplayInds:null,dragHasMoved:null,potentialClickBand:null}}e.exports=function(t,e,r,n){h(r,t,n,e)}},{"../../components/drawing":661,"../../components/fx":679,"../../lib":776,"../../lib/svg_text_utils":802,"../../plot_api/plot_api":813,"@plotly/d3":58,"d3-interpolate":164,tinycolor2:572}],1152:[function(t,e,r){"use strict";var n=t("./parcats");e.exports=function(t,e,r,i){var a=t._fullLayout,o=a._paper,s=a._size;n(t,o,e,{width:s.w,height:s.h,margin:{t:s.t,r:s.r,b:s.b,l:s.l}},r,i)}},{"./parcats":1151}],1153:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/layout_attributes"),a=t("../../plots/font_attributes"),o=t("../../plots/domain").attributes,s=t("../../lib/extend").extendFlat,l=t("../../plot_api/plot_template").templatedArray;e.exports={domain:o({name:"parcoords",trace:!0,editType:"plot"}),labelangle:{valType:"angle",dflt:0,editType:"plot"},labelside:{valType:"enumerated",values:["top","bottom"],dflt:"top",editType:"plot"},labelfont:a({editType:"plot"}),tickfont:a({editType:"plot"}),rangefont:a({editType:"plot"}),dimensions:l("dimension",{label:{valType:"string",editType:"plot"},tickvals:s({},i.tickvals,{editType:"plot"}),ticktext:s({},i.ticktext,{editType:"plot"}),tickformat:s({},i.tickformat,{editType:"plot"}),visible:{valType:"boolean",dflt:!0,editType:"plot"},range:{valType:"info_array",items:[{valType:"number",editType:"plot"},{valType:"number",editType:"plot"}],editType:"plot"},constraintrange:{valType:"info_array",freeLength:!0,dimensions:"1-2",items:[{valType:"any",editType:"plot"},{valType:"any",editType:"plot"}],editType:"plot"},multiselect:{valType:"boolean",dflt:!0,editType:"plot"},values:{valType:"data_array",editType:"calc"},editType:"calc"}),line:s({editType:"calc"},n("line",{colorscaleDflt:"Viridis",autoColorDflt:!1,editTypeOverride:"calc"}))}},{"../../components/colorscale/attributes":646,"../../lib/extend":766,"../../plot_api/plot_template":816,"../../plots/cartesian/layout_attributes":842,"../../plots/domain":855,"../../plots/font_attributes":856}],1154:[function(t,e,r){"use strict";var n=t("./constants"),i=t("@plotly/d3"),a=t("../../lib/gup").keyFun,o=t("../../lib/gup").repeat,s=t("../../lib").sorterAsc,l=t("../../lib").strTranslate,c=n.bar.snapRatio;function u(t,e){return t*(1-c)+e*c}var f=n.bar.snapClose;function h(t,e){return t*(1-f)+e*f}function p(t,e,r,n){if(function(t,e){for(var r=0;r<e.length;r++)if(t>=e[r][0]&&t<=e[r][1])return!0;return!1}(r,n))return r;var i=t?-1:1,a=0,o=e.length-1;if(i<0){var s=a;a=o,o=s}for(var l=e[a],c=l,f=a;i*f<i*o;f+=i){var p=f+i,d=e[p];if(i*r<i*h(l,d))return u(l,c);if(i*r<i*d||p===o)return u(d,l);c=l,l=d}}function d(t){t.attr("x",-n.bar.captureWidth/2).attr("width",n.bar.captureWidth)}function m(t){t.attr("visibility","visible").style("visibility","visible").attr("fill","yellow").attr("opacity",0)}function g(t){if(!t.brush.filterSpecified)return"0,"+t.height;for(var e,r,n,i=v(t.brush.filter.getConsolidated(),t.height),a=[0],o=i.length?i[0][0]:null,s=0;s<i.length;s++)r=(e=i[s])[1]-e[0],a.push(o),a.push(r),(n=s+1)<i.length&&(o=i[n][0]-e[1]);return a.push(t.height),a}function v(t,e){return t.map((function(t){return t.map((function(t){return Math.max(0,t*e)})).sort(s)}))}function y(){i.select(document.body).style("cursor",null)}function x(t){t.attr("stroke-dasharray",g)}function b(t,e){var r=i.select(t).selectAll(".highlight, .highlight-shadow");x(e?r.transition().duration(n.bar.snapDuration).each("end",e):r)}function _(t,e){var r,i=t.brush,a=NaN,o={};if(i.filterSpecified){var s=t.height,l=i.filter.getConsolidated(),c=v(l,s),u=NaN,f=NaN,h=NaN;for(r=0;r<=c.length;r++){var p=c[r];if(p&&p[0]<=e&&e<=p[1]){u=r;break}if(f=r?r-1:NaN,p&&p[0]>e){h=r;break}}if(a=u,isNaN(a)&&(a=isNaN(f)||isNaN(h)?isNaN(f)?h:f:e-c[f][1]<c[h][0]-e?f:h),!isNaN(a)){var d=c[a],m=function(t,e){var r=n.bar.handleHeight;if(!(e>t[1]+r||e<t[0]-r))return e>=.9*t[1]+.1*t[0]?"n":e<=.9*t[0]+.1*t[1]?"s":"ns"}(d,e);m&&(o.interval=l[a],o.intervalPix=d,o.region=m)}}if(t.ordinal&&!o.region){var g=t.unitTickvals,y=t.unitToPaddedPx.invert(e);for(r=0;r<g.length;r++){var x=[.25*g[Math.max(r-1,0)]+.75*g[r],.25*g[Math.min(r+1,g.length-1)]+.75*g[r]];if(y>=x[0]&&y<=x[1]){o.clickableOrdinalRange=x;break}}}return o}function w(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.brush.svgBrush;a.wasDragged=!0,a._dragging=!0,a.grabbingBar?a.newExtent=[r-a.grabPoint,r+a.barLength-a.grabPoint].map(e.unitToPaddedPx.invert):a.newExtent=[a.startExtent,e.unitToPaddedPx.invert(r)].sort(s),e.brush.filterSpecified=!0,a.extent=a.stayingIntervals.concat([a.newExtent]),a.brushCallback(e),b(t.parentNode)}function T(t,e){var r=_(e,e.height-i.mouse(t)[1]-2*n.verticalPadding),a="crosshair";r.clickableOrdinalRange?a="pointer":r.region&&(a=r.region+"-resize"),i.select(document.body).style("cursor",a)}function k(t){t.on("mousemove",(function(t){i.event.preventDefault(),t.parent.inBrushDrag||T(this,t)})).on("mouseleave",(function(t){t.parent.inBrushDrag||y()})).call(i.behavior.drag().on("dragstart",(function(t){!function(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.unitToPaddedPx.invert(r),o=e.brush,s=_(e,r),l=s.interval,c=o.svgBrush;if(c.wasDragged=!1,c.grabbingBar="ns"===s.region,c.grabbingBar){var u=l.map(e.unitToPaddedPx);c.grabPoint=r-u[0]-n.verticalPadding,c.barLength=u[1]-u[0]}c.clickableOrdinalRange=s.clickableOrdinalRange,c.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(c.stayingIntervals=c.stayingIntervals.filter((function(t){return t[0]!==l[0]&&t[1]!==l[1]}))),c.startExtent=s.region?l["s"===s.region?1:0]:a,e.parent.inBrushDrag=!0,c.brushStartCallback()}(this,t)})).on("drag",(function(t){w(this,t)})).on("dragend",(function(t){!function(t,e){var r=e.brush,n=r.filter,a=r.svgBrush;a._dragging||(T(t,e),w(t,e),e.brush.svgBrush.wasDragged=!1),a._dragging=!1,i.event.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,e.parent.inBrushDrag=!1,y(),!a.wasDragged)return a.wasDragged=void 0,a.clickableOrdinalRange?r.filterSpecified&&e.multiselect?a.extent.push(a.clickableOrdinalRange):(a.extent=[a.clickableOrdinalRange],r.filterSpecified=!0):o?(a.extent=a.stayingIntervals,0===a.extent.length&&M(r)):M(r),a.brushCallback(e),b(t.parentNode),void a.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);var s=function(){n.set(n.getConsolidated())};if(e.ordinal){var l=e.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),a.newExtent=[p(0,l,a.newExtent[0],a.stayingIntervals),p(1,l,a.newExtent[1],a.stayingIntervals)];var c=a.newExtent[1]>a.newExtent[0];a.extent=a.stayingIntervals.concat(c?[a.newExtent]:[]),a.extent.length||M(r),a.brushCallback(e),c?b(t.parentNode,s):(s(),b(t.parentNode))}else s();a.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}(this,t)})))}function A(t,e){return t[0]-e[0]}function M(t){t.filterSpecified=!1,t.svgBrush.extent=[[-1/0,1/0]]}function S(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return 1===n.length&&n[0][0]>n[0][1]&&(n=[]),n}e.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){1===(r=n.map((function(t){return t.slice().sort(s)})).sort(A)).length&&r[0][0]===-1/0&&r[0][1]===1/0&&(r=[[0,-1]]),t=S(r),e=r.reduce((function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]}),[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=i,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map((function(t){return t.slice()}))}(e).slice();e.filter.set(r),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t,e){var r=t.selectAll("."+n.cn.axisBrush).data(o,a);r.enter().append("g").classed(n.cn.axisBrush,!0),function(t,e){var r=t.selectAll(".background").data(o);r.enter().append("rect").classed("background",!0).call(d).call(m).style("pointer-events","auto").attr("transform",l(0,n.verticalPadding)),r.call(k).attr("height",(function(t){return t.height-n.verticalPadding}));var i=t.selectAll(".highlight-shadow").data(o);i.enter().append("line").classed("highlight-shadow",!0).attr("x",-n.bar.width/2).attr("stroke-width",n.bar.width+n.bar.strokeWidth).attr("stroke",e).attr("opacity",n.bar.strokeOpacity).attr("stroke-linecap","butt"),i.attr("y1",(function(t){return t.height})).call(x);var a=t.selectAll(".highlight").data(o);a.enter().append("line").classed("highlight",!0).attr("x",-n.bar.width/2).attr("stroke-width",n.bar.width-n.bar.strokeWidth).attr("stroke",n.bar.fillColor).attr("opacity",n.bar.fillOpacity).attr("stroke-linecap","butt"),a.attr("y1",(function(t){return t.height})).call(x)}(r,e)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(A)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},{"../../lib":776,"../../lib/gup":773,"./constants":1157,"@plotly/d3":58}],1155:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../plots/get_data").getModuleCalcData,a=t("./plot"),o=t("../../constants/xmlns_namespaces");r.name="parcoords",r.plot=function(t){var e=i(t.calcdata,"parcoords")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has("parcoords"),a=e._has&&e._has("parcoords");i&&!a&&(n._paperdiv.selectAll(".parcoords").remove(),n._glimages.selectAll("*").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(".svg-container");r.filter((function(t,e){return e===r.size()-1})).selectAll(".gl-canvas-context, .gl-canvas-focus").each((function(){var t=this.toDataURL("image/png");e.append("svg:image").attr({xmlns:o.svg,"xlink:href":t,preserveAspectRatio:"none",x:0,y:0,width:this.style.width,height:this.style.height})})),window.setTimeout((function(){n.selectAll("#filterBarPattern").attr("id","filterBarPattern")}),60)}},{"../../constants/xmlns_namespaces":753,"../../plots/get_data":864,"./plot":1164,"@plotly/d3":58}],1156:[function(t,e,r){"use strict";var n=t("../../lib").isArrayOrTypedArray,i=t("../../components/colorscale"),a=t("../../lib/gup").wrap;e.exports=function(t,e){var r,o;return i.hasColorscale(e,"line")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:"line",cLetter:"c"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},{"../../components/colorscale":651,"../../lib":776,"../../lib/gup":773}],1157:[function(t,e,r){"use strict";e.exports={maxDimensionCount:60,overdrag:45,verticalPadding:2,tickDistance:50,canvasPixelRatio:1,blockLineCount:5e3,layers:["contextLineLayer","focusLineLayer","pickLineLayer"],axisTitleOffset:28,axisExtentOffset:10,deselectedLineColor:"#777",bar:{width:4,captureWidth:10,fillColor:"magenta",fillOpacity:1,snapDuration:150,snapRatio:.25,snapClose:.01,strokeOpacity:1,strokeWidth:1,handleHeight:8,handleOpacity:1,handleOverlap:0},cn:{axisExtentText:"axis-extent-text",parcoordsLineLayers:"parcoords-line-layers",parcoordsLineLayer:"parcoords-lines",parcoords:"parcoords",parcoordsControlView:"parcoords-control-view",yAxis:"y-axis",axisOverlays:"axis-overlays",axis:"axis",axisHeading:"axis-heading",axisTitle:"axis-title",axisExtent:"axis-extent",axisExtentTop:"axis-extent-top",axisExtentTopText:"axis-extent-top-text",axisExtentBottom:"axis-extent-bottom",axisExtentBottomText:"axis-extent-bottom-text",axisBrush:"axis-brush"},id:{filterBarPattern:"filter-bar-pattern"}}},{}],1158:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/colorscale/helpers").hasColorscale,a=t("../../components/colorscale/defaults"),o=t("../../plots/domain").defaults,s=t("../../plots/array_container_defaults"),l=t("../../plots/cartesian/axes"),c=t("./attributes"),u=t("./axisbrush"),f=t("./constants").maxDimensionCount,h=t("./merge_length");function p(t,e,r,i){function a(r,i){return n.coerce(t,e,c.dimensions,r,i)}var o=a("values"),s=a("visible");if(o&&o.length||(s=e.visible=!1),s){a("label"),a("tickvals"),a("ticktext"),a("tickformat");var f=a("range");e._ax={_id:"y",type:"linear",showexponent:"all",exponentformat:"B",range:f},l.setConvert(e._ax,i.layout),a("multiselect");var h=a("constraintrange");h&&(e.constraintrange=u.cleanRanges(h,e))}}e.exports=function(t,e,r,l){function u(r,i){return n.coerce(t,e,c,r,i)}var d=t.dimensions;Array.isArray(d)&&d.length>f&&(n.log("parcoords traces support up to "+f+" dimensions at the moment"),d.splice(f));var m=s(t,e,{name:"dimensions",layout:l,handleItemDefaults:p}),g=function(t,e,r,o,s){var l=s("line.color",r);if(i(t,"line")&&n.isArrayOrTypedArray(l)){if(l.length)return s("line.colorscale"),a(t,e,o,s,{prefix:"line.",cLetter:"c"}),l.length;e.line.color=r}return 1/0}(t,e,r,l,u);o(e,l,u),Array.isArray(m)&&m.length||(e.visible=!1),h(e,m,"values",g);var v={family:l.font.family,size:Math.round(l.font.size/1.2),color:l.font.color};n.coerceFont(u,"labelfont",v),n.coerceFont(u,"tickfont",v),n.coerceFont(u,"rangefont",v),u("labelangle"),u("labelside")}},{"../../components/colorscale/defaults":649,"../../components/colorscale/helpers":650,"../../lib":776,"../../plots/array_container_defaults":822,"../../plots/cartesian/axes":827,"../../plots/domain":855,"./attributes":1153,"./axisbrush":1154,"./constants":1157,"./merge_length":1162}],1159:[function(t,e,r){"use strict";var n=t("../../lib").isTypedArray;r.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},r.isOrdinal=function(t){return!!t.tickvals},r.isVisible=function(t){return t.visible||!("visible"in t)}},{"../../lib":776}],1160:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),plot:t("./plot"),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcoords",basePlotModule:t("./base_plot"),categories:["gl","regl","noOpacity","noHover"],meta:{}}},{"./attributes":1153,"./base_plot":1155,"./calc":1156,"./defaults":1158,"./plot":1164}],1161:[function(t,e,r){"use strict";var n=t("glslify"),i=n(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\n               p17_20, p21_24, p25_28, p29_32,\n               p33_36, p37_40, p41_44, p45_48,\n               p49_52, p53_56, p57_60, colors;\n\nuniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\n\nuniform vec2 resolution, viewBoxPos, viewBoxSize;\nuniform float maskHeight;\nuniform float drwLayer; // 0: context, 1: focus, 2: pick\nuniform vec4 contextColor;\nuniform sampler2D maskTexture, palette;\n\nbool isPick    = (drwLayer > 1.5);\nbool isContext = (drwLayer < 0.5);\n\nconst vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\nconst vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\n\nfloat val(mat4 p, mat4 v) {\n    return dot(matrixCompMult(p, v) * UNITS, UNITS);\n}\n\nfloat axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\n    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\n    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\n    return y1 * (1.0 - ratio) + y2 * ratio;\n}\n\nint iMod(int a, int b) {\n    return a - b * (a / b);\n}\n\nbool fOutside(float p, float lo, float hi) {\n    return (lo < hi) && (lo > p || p > hi);\n}\n\nbool vOutside(vec4 p, vec4 lo, vec4 hi) {\n    return (\n        fOutside(p[0], lo[0], hi[0]) ||\n        fOutside(p[1], lo[1], hi[1]) ||\n        fOutside(p[2], lo[2], hi[2]) ||\n        fOutside(p[3], lo[3], hi[3])\n    );\n}\n\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\n    return (\n        vOutside(p[0], lo[0], hi[0]) ||\n        vOutside(p[1], lo[1], hi[1]) ||\n        vOutside(p[2], lo[2], hi[2]) ||\n        vOutside(p[3], lo[3], hi[3])\n    );\n}\n\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\n    return mOutside(A, loA, hiA) ||\n           mOutside(B, loB, hiB) ||\n           mOutside(C, loC, hiC) ||\n           mOutside(D, loD, hiD);\n}\n\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\n    mat4 pnts[4];\n    pnts[0] = A;\n    pnts[1] = B;\n    pnts[2] = C;\n    pnts[3] = D;\n\n    for(int i = 0; i < 4; ++i) {\n        for(int j = 0; j < 4; ++j) {\n            for(int k = 0; k < 4; ++k) {\n                if(0 == iMod(\n                    int(255.0 * texture2D(maskTexture,\n                        vec2(\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\n                        ))[3]\n                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\n                    2\n                )) return true;\n            }\n        }\n    }\n    return false;\n}\n\nvec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\n    float x = 0.5 * sign(v) + 0.5;\n    float y = axisY(x, A, B, C, D);\n    float z = 1.0 - abs(v);\n\n    z += isContext ? 0.0 : 2.0 * float(\n        outsideBoundingBox(A, B, C, D) ||\n        outsideRasterMask(A, B, C, D)\n    );\n\n    return vec4(\n        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\n        z,\n        1.0\n    );\n}\n\nvoid main() {\n    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\n    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\n    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\n    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\n\n    float v = colors[3];\n\n    gl_Position = position(isContext, v, A, B, C, D);\n\n    fragColor =\n        isContext ? vec4(contextColor) :\n        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\n}\n"]),a=n(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n    gl_FragColor = fragColor;\n}\n"]),o=t("./constants").maxDimensionCount,s=t("../../lib"),l=new Uint8Array(4),c=new Uint8Array(4),u={shape:[256,1],format:"rgba",type:"uint8",mag:"nearest",min:"nearest"};function f(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function h(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(!function(t){t.read({x:0,y:0,width:1,height:1,data:l})}(t),r.drawCompleted=!0),function s(l){var c=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete 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I(t){r=t.model,n=t.viewModel,l=n.dimensions.slice(),g=l[0]?l[0].values.length:0;var e=r.lines,i=w?e.color.map((function(t,r){return r/e.color.length})):e.color,a=function(t,e,r){for(var n,i=new Array(t*(o+4)),a=0,s=0;s<t;s++){for(var l=0;l<o;l++)i[a++]=l<e.length?e[l].paddedUnitValues[s]:.5;i[a++]=d(s,2),i[a++]=d(s,1),i[a++]=d(s,0),i[a++]=(n=r[s],Math.max(1e-6,Math.min(.999999,n)))}return i}(g,l,i);!function(t,e,r){for(var n=0;n<=o;n+=4)t[v(n)](m(n/4,e,r))}(E,g,a),_||w||(L=T.texture(s.extendFlat({data:p(r.unitToColor,255)},u)))}return{render:function(t,e,n){var i,a,o,s=t.length,c=1/0,u=-1/0;for(i=0;i<s;i++)t[i].dim0.canvasX<c&&(c=t[i].dim0.canvasX,a=i),t[i].dim1.canvasX>u&&(u=t[i].dim1.canvasX,o=i);0===s&&f(T,0,0,r.canvasWidth,r.canvasHeight);var p=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&n<l.length?l[n].brush.filter.getBounds():[-1/0,1/0];i[0][n]=a[0],i[1][n]=a[1]}var o=new Array(16384);for(e=0;e<16384;e++)o[e]=255;if(!t)for(e=0;e<l.length;e++){var 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Uint8Array(4*r*n);return T.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style["pointer-events"]="none",L.destroy(),b&&b.destroy(),E)E[e].destroy()},update:I}}},{"../../lib":776,"./constants":1157,glslify:424}],1162:[function(t,e,r){"use strict";e.exports=function(t,e,r,n){var i,a;for(n||(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},{}],1163:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../lib"),a=i.numberFormat,o=t("color-rgba"),s=t("../../plots/cartesian/axes"),l=i.strRotate,c=i.strTranslate,u=t("../../lib/svg_text_utils"),f=t("../../components/drawing"),h=t("../../components/colorscale"),p=t("../../lib/gup"),d=p.keyFun,m=p.repeat,g=p.unwrap,v=t("./helpers"),y=t("./constants"),x=t("./axisbrush"),b=t("./lines");function _(t,e,r){return i.aggNums(t,null,e,r)}function w(t,e){return k(_(Math.min,t,e),_(Math.max,t,e))}function 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n=t("fast-isnumeric"),i=t("../../lib"),a=t("./attributes"),o=t("../../plots/domain").defaults,s=t("../bar/defaults").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c("labels"),c("values")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c("label0"),c("dlabel")),f){e._length=f,c("marker.line.width")&&c("marker.line.color"),c("marker.colors"),c("scalegroup");var h,p=c("text"),d=c("texttemplate");if(d||(h=c("textinfo",Array.isArray(p)?"text+percent":"percent")),c("hovertext"),c("hovertemplate"),d||h&&"none"!==h){var m=c("textposition");s(t,e,n,c,m,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(m)||"auto"===m||"outside"===m)&&c("automargin"),("inside"===m||"auto"===m||Array.isArray(m))&&c("insidetextorientation")}o(e,n,c);var g=c("hole");if(c("title.text")){var v=c("title.position",g?"middle center":"top center");g||"middle center"!==v||(e.title.position="top center"),i.coerceFont(c,"title.font",n.font)}c("sort"),c("direction"),c("rotation"),c("pull")}else e.visible=!1}}},{"../../lib":776,"../../plots/domain":855,"../bar/defaults":918,"./attributes":1165,"fast-isnumeric":242}],1169:[function(t,e,r){"use strict";var n=t("../../components/fx/helpers").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),"funnelarea"===e.type&&(delete r.v,delete r.i),r}},{"../../components/fx/helpers":675}],1170:[function(t,e,r){"use strict";var n=t("../../lib");function i(t){return-1!==t.indexOf("e")?t.replace(/[.]?0+e/,"e"):-1!==t.indexOf(".")?t.replace(/[.]?0+$/,""):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+"%"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||""===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return("auto"===t?0:t)*Math.PI/180}},{"../../lib":776}],1171:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,supplyLayoutDefaults:t("./layout_defaults"),layoutAttributes:t("./layout_attributes"),calc:t("./calc").calc,crossTraceCalc:t("./calc").crossTraceCalc,plot:t("./plot").plot,style:t("./style"),styleOne:t("./style_one"),moduleType:"trace",name:"pie",basePlotModule:t("./base_plot"),categories:["pie-like","pie","showLegend"],meta:{}}},{"./attributes":1165,"./base_plot":1166,"./calc":1167,"./defaults":1168,"./layout_attributes":1172,"./layout_defaults":1173,"./plot":1174,"./style":1175,"./style_one":1176}],1172:[function(t,e,r){"use strict";e.exports={hiddenlabels:{valType:"data_array",editType:"calc"},piecolorway:{valType:"colorlist",editType:"calc"},extendpiecolors:{valType:"boolean",dflt:!0,editType:"calc"}}},{}],1173:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./layout_attributes");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r("hiddenlabels"),r("piecolorway",e.colorway),r("extendpiecolors")}},{"../../lib":776,"./layout_attributes":1172}],1174:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../plots/plots"),a=t("../../components/fx"),o=t("../../components/color"),s=t("../../components/drawing"),l=t("../../lib"),c=l.strScale,u=l.strTranslate,f=t("../../lib/svg_text_utils"),h=t("../bar/uniform_text"),p=h.recordMinTextSize,d=h.clearMinTextSize,m=t("../bar/constants").TEXTPAD,g=t("./helpers"),v=t("./event_data"),y=t("../../lib").isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u="funnelarea"===c.type;"_hasHoverLabel"in c||(c._hasHoverLabel=!1),"_hasHoverEvent"in c||(c._hasHoverEvent=!1),t.on("mouseover",(function(t){var r=e._fullLayout,f=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:c._module},r,0)),"all"===h&&(h="label+text+value+percent+name"),f.hovertemplate||"none"!==h&&"skip"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),m=s+t.pxmid[1]*(1-p),y=r.separators,x=[];if(h&&-1!==h.indexOf("label")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf("text")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,y),h&&-1!==h.indexOf("value")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,y),h&&-1!==h.indexOf("percent")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:m,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:m-p*i.r,_y1:u?s+t.BL[1]:m+p*i.r,text:x.join("<br>"),name:f.hovertemplate||-1!==h.indexOf("name")?f.name:void 0,idealAlign:t.pxmid[0]<0?"left":"right",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit("plotly_hover",{points:[v(t,f)],event:n.event})}})),t.on("mouseout",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit("plotly_unhover",{points:[v(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on("click",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append("text").attr("data-notex",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f="horizontal"===u,h="tangential"===u,p="radial"===u,d="auto"===u,m=[];if(!d){var g,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a="tan"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,m.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)v(Math.PI*g,"tan");for(g=4;g>=-4;g-=2)v(Math.PI*(g+1),"tan")}if(f||p){for(g=4;g>=-4;g-=2)v(Math.PI*(g+1.5),"rad");for(g=4;g>=-4;g-=2)v(Math.PI*(g+.5),"rad")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;m.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a));for(var x=0,b=0,_=0;_<m.length;_++){var w=m[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return m[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf("top")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf("bottom")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf("left")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf("center")?f*=2:-1!==a.title.position.indexOf("right")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&"middle center"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;"funnelarea"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf("bottom")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if("pie"===i.type)l=r.r*r.r;else if("funnelarea"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;"funnelarea"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&"none"!==o){var s,c=o.split("+"),u=function(t){return-1!==c.indexOf(t)},f=u("label"),h=u("text"),p=u("value"),d=u("percent"),m=n.separators;if(s=f?[e.label]:[],h){var v=g.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(g.formatPieValue(e.v,m)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,m)),e.text=s.join("<br>")}if(a){var x=l.castOption(i,e.i,"texttemplate");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,"customdata")}}(e),_=g.getFirstFilled(i.text,e.pts);(y(_)||""===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=""}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d("pie",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,"trace").each((function(e){var h=n.select(this),d=e[0],m=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c="px0",u="px1";if("counterclockwise"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c="px1",u="px0"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr("stroke-linejoin","round"),h.each((function(){var v=n.select(this).selectAll("g.slice").data(e);v.enter().append("g").classed("slice",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll("path,g").remove();else{i.pointNumber=i.i,i.curveNumber=m.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll("path.surface").data([i]);if(h.enter().append("path").classed("surface",!0).style({"pointer-events":"all"}),u.call(x,t,e),m.pull){var v=+g.castOption(m.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=m.hole;if(i.v===d.vTotal){var k="M"+(o+i.px0[0])+","+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+"Z";T?h.attr("d","M"+(o+T*i.px0[0])+","+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+"Z"+k):h.attr("d",k)}else{var A=C(i.px0,i.px1,!0,1);if(T){var M=1-T;h.attr("d","M"+(o+T*i.px1[0])+","+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+"l"+M*i.px0[0]+","+M*i.px0[1]+A+"Z")}else h.attr("d","M"+o+","+c+"l"+i.px0[0]+","+i.px0[1]+A+"Z")}D(t,i,d);var S=g.castOption(m.textposition,i.pts),E=u.selectAll("g.slicetext").data(i.text&&"none"!==S?[0]:[]);E.enter().append("g").classed("slicetext",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),"text","",(function(t){t.attr("data-notex",1)})),h=l.ensureUniformFontSize(t,"outside"===S?function(t,e,r){var n=g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,i=g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(m,i,r.font):b(m,i,r.font));u.text(i.text).attr({class:"slicetext",transform:"","text-anchor":"middle"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if("outside"===S)v=L(y,i);else if(v=w(y,i,d),"auto"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,m.outsidetextfont);u.call(s.font,x),v=L(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:z(d.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=c+k[1]*v.rCenter+(v.y||0),R(v,y),v.outside){var A=v.targetY;i.yLabelMin=A-y.height/2,i.yLabelMid=A,i.yLabelMax=A+y.height/2,i.labelExtraX=0,i.labelExtraY=0,_=!0}v.fontSize=h.size,p(m.type,v,r),e[a].transform=v,u.attr("transform",l.getTextTransform(v))}))}function C(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return"a"+n*d.r+","+n*d.r+" 0 "+i.largeArc+(r?" 1 ":" 0 ")+a+","+o}}));var T=n.select(this).selectAll("g.titletext").data(m.title.text?[0]:[]);if(T.enter().append("g").classed("titletext",!0),T.exit().remove(),T.each((function(){var e,r=l.ensureSingle(n.select(this),"text","",(function(t){t.attr("data-notex",1)})),i=m.title.text;m._meta&&(i=l.templateString(i,m._meta)),r.text(i).attr({class:"titletext",transform:"","text-anchor":"middle"}).call(s.font,m.title.font).call(f.convertToTspans,t),e="middle center"===m.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(d):C(d,a),r.attr("transform",u(e.x,e.y)+c(Math.min(1,e.scale))+u(e.tx,e.ty))})),_&&function(t,e){var r,n,i,a,o,s,l,c,u,f,h,p,d;function m(t,e){return t.pxmid[1]-e.pxmid[1]}function v(t,e){return e.pxmid[1]-t.pxmid[1]}function y(t,r){r||(r={});var i,c,u,h,p=r.labelExtraY+(n?r.yLabelMax:r.yLabelMin),d=n?t.yLabelMin:t.yLabelMax,m=n?t.yLabelMax:t.yLabelMin,v=t.cyFinal+o(t.px0[1],t.px1[1]),y=p-d;if(y*l>0&&(t.labelExtraY=y),Array.isArray(e.pull))for(c=0;c<f.length;c++)(u=f[c])===t||(g.castOption(e.pull,t.pts)||0)>=(g.castOption(e.pull,u.pts)||0)||((t.pxmid[1]-u.pxmid[1])*l>0?(y=u.cyFinal+o(u.px0[1],u.px1[1])-d-t.labelExtraY)*l>0&&(t.labelExtraY+=y):(m+t.labelExtraY-v)*l>0&&(i=3*s*Math.abs(c-f.indexOf(t)),(h=u.cxFinal+a(u.px0[0],u.px1[0])+i-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*s>0&&(t.labelExtraX+=h)))}for(n=0;n<2;n++)for(i=n?m:v,o=n?Math.max:Math.min,l=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,(c=t[n][r]).sort(i),u=t[1-n][r],f=u.concat(c),p=[],h=0;h<c.length;h++)void 0!==c[h].yLabelMid&&p.push(c[h]);for(d=!1,h=0;n&&h<u.length;h++)if(void 0!==u[h].yLabelMid){d=u[h];break}for(h=0;h<p.length;h++){var x=h&&p[h-1];d&&!h&&(x=d),y(p[h],x)}}}(y,m),function(t,e){t.each((function(t){var r=n.select(this);if(t.labelExtraX||t.labelExtraY){var i=r.select("g.slicetext text");t.transform.targetX+=t.labelExtraX,t.transform.targetY+=t.labelExtraY,i.attr("transform",l.getTextTransform(t.transform));var a=t.cxFinal+t.pxmid[0],s="M"+a+","+(t.cyFinal+t.pxmid[1]),c=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var u=t.labelExtraX*t.pxmid[1]/t.pxmid[0],f=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(u)>Math.abs(f)?s+="l"+f*t.pxmid[0]/t.pxmid[1]+","+f+"H"+(a+t.labelExtraX+c):s+="l"+t.labelExtraX+","+u+"v"+(f-u)+"h"+c}else s+="V"+(t.yLabelMid+t.labelExtraY)+"h"+c;l.ensureSingle(r,"path","textline").call(o.stroke,e.outsidetextfont.color).attr({"stroke-width":Math.min(2,e.outsidetextfont.size/8),d:s,fill:"none"})}else r.select("path.textline").remove()}))}(v,m),_&&m.automargin){var k=s.bBox(h.node()),A=m.domain,M=a.w*(A.x[1]-A.x[0]),S=a.h*(A.y[1]-A.y[0]),E=(.5*M-d.r)/a.w,P=(.5*S-d.r)/a.h;i.autoMargin(t,"pie."+m.uid+".automargin",{xl:A.x[0]-E,xr:A.x[1]+E,yb:A.y[0]-P,yt:A.y[1]+P,l:Math.max(d.cx-d.r-k.left,0),r:Math.max(k.right-(d.cx+d.r),0),b:Math.max(k.bottom-(d.cy+d.r),0),t:Math.max(d.cy-d.r-k.top,0),pad:5})}}))}));setTimeout((function(){h.selectAll("tspan").each((function(){var t=n.select(this);t.attr("dy")&&t.attr("dy",t.attr("dy"))}))}),0)},formatSliceLabel:D,transformInsideText:w,determineInsideTextFont:b,positionTitleOutside:C,prerenderTitles:_,layoutAreas:O,attachFxHandlers:x,computeTransform:R}},{"../../components/color":639,"../../components/drawing":661,"../../components/fx":679,"../../lib":776,"../../lib/svg_text_utils":802,"../../plots/plots":890,"../bar/constants":916,"../bar/uniform_text":930,"./event_data":1169,"./helpers":1170,"@plotly/d3":58}],1175:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("./style_one"),a=t("../bar/uniform_text").resizeText;e.exports=function(t){var e=t._fullLayout._pielayer.selectAll(".trace");a(t,e,"pie"),e.each((function(t){var e=t[0].trace,r=n.select(this);r.style({opacity:e.opacity}),r.selectAll("path.surface").each((function(t){n.select(this).call(i,t,e)}))}))}},{"../bar/uniform_text":930,"./style_one":1176,"@plotly/d3":58}],1176:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("./helpers").castOption;e.exports=function(t,e,r){var a=r.marker.line,o=i(a.color,e.pts)||n.defaultLine,s=i(a.width,e.pts)||0;t.style("stroke-width",s).call(n.fill,e.color).call(n.stroke,o)}},{"../../components/color":639,"./helpers":1170}],1177:[function(t,e,r){"use strict";var n=t("../scatter/attributes");e.exports={x:n.x,y:n.y,xy:{valType:"data_array",editType:"calc"},indices:{valType:"data_array",editType:"calc"},xbounds:{valType:"data_array",editType:"calc"},ybounds:{valType:"data_array",editType:"calc"},text:n.text,marker:{color:{valType:"color",arrayOk:!1,editType:"calc"},opacity:{valType:"number",min:0,max:1,dflt:1,arrayOk:!1,editType:"calc"},blend:{valType:"boolean",dflt:null,editType:"calc"},sizemin:{valType:"number",min:.1,max:2,dflt:.5,editType:"calc"},sizemax:{valType:"number",min:.1,dflt:20,editType:"calc"},border:{color:{valType:"color",arrayOk:!1,editType:"calc"},arearatio:{valType:"number",min:0,max:1,dflt:0,editType:"calc"},editType:"calc"},editType:"calc"},transforms:void 0}},{"../scatter/attributes":1191}],1178:[function(t,e,r){"use strict";var n=t("gl-pointcloud2d"),i=t("../../lib/str2rgbarray"),a=t("../../plots/cartesian/autorange").findExtremes,o=t("../scatter/get_trace_color");function s(t,e){this.scene=t,this.uid=e,this.type="pointcloud",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color="rgb(0, 0, 0)",this.name="",this.hoverinfo="all",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var l=s.prototype;l.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},l.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=o(t,{})},l.updateFast=function(t){var e,r,n,o,s,l,c=this.xData=this.pickXData=t.x,u=this.yData=this.pickYData=t.y,f=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,p=t.indices,d=this.bounds;if(f){if(n=f,e=f.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(l=0;l<e;l++)o=n[2*l],s=n[2*l+1],o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);if(p)r=p;else for(r=new Int32Array(e),l=0;l<e;l++)r[l]=l}else for(e=c.length,n=new Float32Array(2*e),r=new 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n,i=y(e),a=i.trace,u=a.domain,f="h"===a.orientation,p=a.node.pad,d=a.node.thickness,m=t.width*(u.x[1]-u.x[0]),g=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[m,g]:[g,m]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn("node.pad was reduced to ",n.nodePadding()," to fit within the figure."),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+":"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*m,a.node.y[_]*g];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if("snap"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(v=k.nodes));n.update(k)}return{circular:b,key:r,trace:a,guid:h.randstr(),horizontal:f,width:m,height:g,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:f?g:m,dragPerpendicular:f?m:g,arrangement:a.arrangement,sankey:n,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function A(t,e,r){var n=c(e.color),i=e.source.label+"|"+e.target.label+"__"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,"top"===e.circularLinkType?"M "+n.targetX+" "+(n.targetY+r)+" L"+n.rightInnerExtent+" "+(n.targetY+r)+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightSmallArcRadius+r)+" 0 0 1 "+(n.rightFullExtent-r)+" "+(n.targetY-n.rightSmallArcRadius)+"L"+(n.rightFullExtent-r)+" "+n.verticalRightInnerExtent+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightLargeArcRadius+r)+" 0 0 1 "+n.rightInnerExtent+" "+(n.verticalFullExtent-r)+"L"+n.leftInnerExtent+" "+(n.verticalFullExtent-r)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftLargeArcRadius+r)+" 0 0 1 "+(n.leftFullExtent+r)+" "+n.verticalLeftInnerExtent+"L"+(n.leftFullExtent+r)+" "+(n.sourceY-n.leftSmallArcRadius)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftSmallArcRadius+r)+" 0 0 1 "+n.leftInnerExtent+" "+(n.sourceY+r)+"L"+n.sourceX+" "+(n.sourceY+r)+"L"+n.sourceX+" "+(n.sourceY-r)+"L"+n.leftInnerExtent+" "+(n.sourceY-r)+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftSmallArcRadius-r)+" 0 0 0 "+(n.leftFullExtent-r)+" "+(n.sourceY-n.leftSmallArcRadius)+"L"+(n.leftFullExtent-r)+" "+n.verticalLeftInnerExtent+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftLargeArcRadius-r)+" 0 0 0 "+n.leftInnerExtent+" "+(n.verticalFullExtent+r)+"L"+n.rightInnerExtent+" "+(n.verticalFullExtent+r)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightLargeArcRadius-r)+" 0 0 0 "+(n.rightFullExtent+r)+" "+n.verticalRightInnerExtent+"L"+(n.rightFullExtent+r)+" "+(n.targetY-n.rightSmallArcRadius)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightSmallArcRadius-r)+" 0 0 0 "+n.rightInnerExtent+" "+(n.targetY-r)+"L"+n.targetX+" "+(n.targetY-r)+"Z":"M "+n.targetX+" "+(n.targetY-r)+" L"+n.rightInnerExtent+" "+(n.targetY-r)+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightSmallArcRadius+r)+" 0 0 0 "+(n.rightFullExtent-r)+" "+(n.targetY+n.rightSmallArcRadius)+"L"+(n.rightFullExtent-r)+" "+n.verticalRightInnerExtent+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightLargeArcRadius+r)+" 0 0 0 "+n.rightInnerExtent+" "+(n.verticalFullExtent+r)+"L"+n.leftInnerExtent+" "+(n.verticalFullExtent+r)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftLargeArcRadius+r)+" 0 0 0 "+(n.leftFullExtent+r)+" "+n.verticalLeftInnerExtent+"L"+(n.leftFullExtent+r)+" "+(n.sourceY+n.leftSmallArcRadius)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftSmallArcRadius+r)+" 0 0 0 "+n.leftInnerExtent+" "+(n.sourceY-r)+"L"+n.sourceX+" "+(n.sourceY-r)+"L"+n.sourceX+" "+(n.sourceY+r)+"L"+n.leftInnerExtent+" "+(n.sourceY+r)+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftSmallArcRadius-r)+" 0 0 1 "+(n.leftFullExtent-r)+" "+(n.sourceY+n.leftSmallArcRadius)+"L"+(n.leftFullExtent-r)+" "+n.verticalLeftInnerExtent+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftLargeArcRadius-r)+" 0 0 1 "+n.leftInnerExtent+" "+(n.verticalFullExtent-r)+"L"+n.rightInnerExtent+" "+(n.verticalFullExtent-r)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightLargeArcRadius-r)+" 0 0 1 "+(n.rightFullExtent+r)+" "+n.verticalRightInnerExtent+"L"+(n.rightFullExtent+r)+" "+(n.targetY+n.rightSmallArcRadius)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightSmallArcRadius-r)+" 0 0 1 "+n.rightInnerExtent+" "+(n.targetY+r)+"L"+n.targetX+" "+(n.targetY+r)+"Z";var e,r,n,a=t.link.source.x1,o=t.link.target.x0,s=i(a,o),l=s(.5),c=s(.5),u=t.link.y0-t.link.width/2,f=t.link.y0+t.link.width/2,h=t.link.y1-t.link.width/2,p=t.link.y1+t.link.width/2;return"M"+a+","+u+"C"+l+","+u+" "+c+","+h+" "+o+","+h+"L"+o+","+p+"C"+c+","+p+" "+l+","+f+" "+a+","+f+"Z"}}function S(t,e){var r=c(e.color),n=l.nodePadAcross,i=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var a=e.dx,o=Math.max(.5,e.dy),s="node_"+e.pointNumber;return e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:u.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join("_"),interactionState:t.interactionState,figure:t}}function E(t){t.attr("transform",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function C(t,e){t.call(L),e.attr("d",M())}function P(t){t.attr("width",(function(t){return t.node.x1-t.node.x0})).attr("height",(function(t){return t.visibleHeight}))}function I(t){return t.link.width>1||t.linkLineWidth>0}function O(t){return p(t.translateX,t.translateY)+(t.horizontal?"matrix(1 0 0 1 0 0)":"matrix(0 1 1 0 0 0)")}function z(t,e,r){t.on(".basic",null).on("mouseover.basic",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.hover(this,t,e),t.interactionState.hovered=[this,t])})).on("mousemove.basic",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.follow(this,t),t.interactionState.hovered=[this,t])})).on("mouseout.basic",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.unhover(this,t,e),t.interactionState.hovered=!1)})).on("click.basic",(function(t){t.interactionState.hovered&&(r.unhover(this,t,e),t.interactionState.hovered=!1),t.interactionState.dragInProgress||t.partOfGroup||r.select(this,t,e)}))}function D(t,e,r,i){var o=a.behavior.drag().origin((function(t){return{x:t.node.x0+t.visibleWidth/2,y:t.node.y0+t.visibleHeight/2}})).on("dragstart",(function(a){if("fixed"!==a.arrangement&&(h.ensureSingle(i._fullLayout._infolayer,"g","dragcover",(function(t){i._fullLayout._dragCover=t})),h.raiseToTop(this),a.interactionState.dragInProgress=a.node,F(a.node),a.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,a.interactionState.hovered),a.interactionState.hovered=!1),"snap"===a.arrangement)){var o=a.traceId+"|"+a.key;a.forceLayouts[o]?a.forceLayouts[o].alpha(1):function(t,e,r,i){!function(t){for(var e=0;e<t.length;e++)t[e].y=(t[e].y0+t[e].y1)/2,t[e].x=(t[e].x0+t[e].x1)/2}(r.graph.nodes);var a=r.graph.nodes.filter((function(t){return t.originalX===r.node.originalX})).filter((function(t){return!t.partOfGroup}));r.forceLayouts[e]=n.forceSimulation(a).alphaDecay(0).force("collide",n.forceCollide().radius((function(t){return t.dy/2+r.nodePad/2})).strength(1).iterations(l.forceIterations)).force("constrain",function(t,e,r,n){return function(){for(var t=0,i=0;i<r.length;i++){var a=r[i];a===n.interactionState.dragInProgress?(a.x=a.lastDraggedX,a.y=a.lastDraggedY):(a.vx=(a.originalX-a.x)/l.forceTicksPerFrame,a.y=Math.min(n.size-a.dy/2,Math.max(a.dy/2,a.y))),t=Math.max(t,Math.abs(a.vx),Math.abs(a.vy))}!n.interactionState.dragInProgress&&t<.1&&n.forceLayouts[e].alpha()>0&&n.forceLayouts[e].alpha(0)}}(0,e,a,r)).stop()}(0,o,a),function(t,e,r,n,i){window.requestAnimationFrame((function a(){var o;for(o=0;o<l.forceTicksPerFrame;o++)r.forceLayouts[n].tick();if(function(t){for(var e=0;e<t.length;e++)t[e].y0=t[e].y-t[e].dy/2,t[e].y1=t[e].y0+t[e].dy,t[e].x0=t[e].x-t[e].dx/2,t[e].x1=t[e].x0+t[e].dx}(r.graph.nodes),r.sankey.update(r.graph),C(t.filter(B(r)),e),r.forceLayouts[n].alpha()>0)window.requestAnimationFrame(a);else{var s=r.node.originalX;r.node.x0=s-r.visibleWidth/2,r.node.x1=s+r.visibleWidth/2,R(r,i)}}))}(t,e,a,o,i)}})).on("drag",(function(r){if("fixed"!==r.arrangement){var n=a.event.x,i=a.event.y;"snap"===r.arrangement?(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2,r.node.y0=i-r.visibleHeight/2,r.node.y1=i+r.visibleHeight/2):("freeform"===r.arrangement&&(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2),i=Math.max(0,Math.min(r.size-r.visibleHeight/2,i)),r.node.y0=i-r.visibleHeight/2,r.node.y1=i+r.visibleHeight/2),F(r.node),"snap"!==r.arrangement&&(r.sankey.update(r.graph),C(t.filter(B(r)),e))}})).on("dragend",(function(t){if("fixed"!==t.arrangement){t.interactionState.dragInProgress=!1;for(var e=0;e<t.node.childrenNodes.length;e++)t.node.childrenNodes[e].x=t.node.x,t.node.childrenNodes[e].y=t.node.y;"snap"!==t.arrangement&&R(t,i)}}));t.on(".drag",null).call(o)}function R(t,e){for(var r=[],n=[],i=0;i<t.graph.nodes.length;i++){var a=(t.graph.nodes[i].x0+t.graph.nodes[i].x1)/2,o=(t.graph.nodes[i].y0+t.graph.nodes[i].y1)/2;r.push(a/t.figure.width),n.push(o/t.figure.height)}b.call("_guiRestyle",e,{"node.x":[r],"node.y":[n]},t.trace.index).then((function(){e._fullLayout._dragCover&&e._fullLayout._dragCover.remove()}))}function F(t){t.lastDraggedX=t.x0+t.dx/2,t.lastDraggedY=t.y0+t.dy/2}function B(t){return function(e){return e.node.originalX===t.node.originalX}}e.exports=function(t,e,r,n,i){var o=!1;h.ensureSingle(t._fullLayout._infolayer,"g","first-render",(function(){o=!0}));var s=t._fullLayout._dragCover,m=r.filter((function(t){return y(t).trace.visible})).map(k.bind(null,n)),b=e.selectAll("."+l.cn.sankey).data(m,g);b.exit().remove(),b.enter().append("g").classed(l.cn.sankey,!0).style("box-sizing","content-box").style("position","absolute").style("left",0).style("shape-rendering","geometricPrecision").style("pointer-events","auto").attr("transform",O),b.each((function(e,r){t._fullData[r]._sankey=e;var n="bgsankey-"+e.trace.uid+"-"+r;h.ensureSingle(t._fullLayout._draggers,"rect",n),t._fullData[r]._bgRect=a.select("."+n),t._fullData[r]._bgRect.style("pointer-events","all").attr("width",e.width).attr("height",e.height).attr("x",e.translateX).attr("y",e.translateY).classed("bgsankey",!0).style({fill:"transparent","stroke-width":0})})),b.transition().ease(l.ease).duration(l.duration).attr("transform",O);var _=b.selectAll("."+l.cn.sankeyLinks).data(v,g);_.enter().append("g").classed(l.cn.sankeyLinks,!0).style("fill","none");var L=_.selectAll("."+l.cn.sankeyLink).data((function(t){return t.graph.links.filter((function(t){return t.value})).map(A.bind(null,t))}),g);L.enter().append("path").classed(l.cn.sankeyLink,!0).call(z,b,i.linkEvents),L.style("stroke",(function(t){return I(t)?u.tinyRGB(c(t.linkLineColor)):t.tinyColorHue})).style("stroke-opacity",(function(t){return I(t)?u.opacity(t.linkLineColor):t.tinyColorAlpha})).style("fill",(function(t){return t.tinyColorHue})).style("fill-opacity",(function(t){return t.tinyColorAlpha})).style("stroke-width",(function(t){return I(t)?t.linkLineWidth:1})).attr("d",M()),L.style("opacity",(function(){return t._context.staticPlot||o||s?1:0})).transition().ease(l.ease).duration(l.duration).style("opacity",1),L.exit().transition().ease(l.ease).duration(l.duration).style("opacity",0).remove();var C=b.selectAll("."+l.cn.sankeyNodeSet).data(v,g);C.enter().append("g").classed(l.cn.sankeyNodeSet,!0),C.style("cursor",(function(t){switch(t.arrangement){case"fixed":return"default";case"perpendicular":return"ns-resize";default:return"move"}}));var R=C.selectAll("."+l.cn.sankeyNode).data((function(t){var e=t.graph.nodes;return function(t){var e,r=[];for(e=0;e<t.length;e++)t[e].originalX=(t[e].x0+t[e].x1)/2,t[e].originalY=(t[e].y0+t[e].y1)/2,-1===r.indexOf(t[e].originalX)&&r.push(t[e].originalX);for(r.sort((function(t,e){return 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y={padded:!0},x={padded:!0};s&&(y.ppad=x.ppad=s);var b=c<2||i[0]!==i[c-1]||o[0]!==o[c-1];b&&("tozerox"===v||"tonextx"===v&&(p||"h"===d))?y.tozero=!0:(e.error_y||{}).visible||"tonexty"!==v&&"tozeroy"!==v&&(l.hasMarkers(e)||l.hasText(e))||(y.padded=!1,y.ppad=0),b&&("tozeroy"===v||"tonexty"===v&&(p||"v"===d))?x.tozero=!0:"tonextx"!==v&&"tozerox"!==v||(x.padded=!1),f&&(e._extremes[f]=a.findExtremes(r,i,y)),h&&(e._extremes[h]=a.findExtremes(n,o,x))}function p(t,e){if(l.hasMarkers(t)){var r,n=t.marker,o=1.6*(t.marker.sizeref||1);if(r="area"===t.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/o),3)}:function(t){return Math.max((t||0)/o,3)},i.isArrayOrTypedArray(n.size)){var s={type:"linear"};a.setConvert(s);for(var c=s.makeCalcdata(t.marker,"size"),u=new Array(e),f=0;f<e;f++)u[f]=r(c[f]);return u}return r(n.size)}}function d(t,e){var r=m(e),n=t._firstScatter;n[r]||(n[r]=e.uid)}function m(t){var e=t.stackgroup;return t.xaxis+t.yaxis+t.type+(e?"-"+e:"")}function g(t,e,r,n){var 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n=t("./subtypes");e.exports=function(t,e){var r,i,a,o,s=t.cd,l=t.xaxis,c=t.yaxis,u=[],f=s[0].trace;if(!n.hasMarkers(f)&&!n.hasText(f))return[];if(!1===e)for(r=0;r<s.length;r++)s[r].selected=0;else for(r=0;r<s.length;r++)i=s[r],a=l.c2p(i.x),o=c.c2p(i.y),null!==i.i&&e.contains([a,o],!1,r,t)?(u.push({pointNumber:i.i,x:l.c2d(i.x),y:c.c2d(i.y)}),i.selected=1):i.selected=0;return u}},{"./subtypes":1216}],1214:[function(t,e,r){"use strict";var n=["orientation","groupnorm","stackgaps"];e.exports=function(t,e,r,i){var a=r._scatterStackOpts,o=i("stackgroup");if(o){var s=e.xaxis+e.yaxis,l=a[s];l||(l=a[s]={});var c=l[o],u=!1;c?c.traces.push(e):(c=l[o]={traceIndices:[],traces:[e]},u=!0);for(var f={orientation:e.x&&!e.y?"h":"v"},h=0;h<n.length;h++){var p=n[h],d=p+"Found";if(!c[d]){var m=void 0!==t[p],g="orientation"===p;if((m||u)&&(c[p]=i(p,f[p]),g&&(c.fillDflt="h"===c[p]?"tonextx":"tonexty"),m&&(c[d]=!0,!u&&(delete c.traces[0][p],g))))for(var v=0;v<c.traces.length-1;v++){var 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n=t("../../lib"),i=t("../../registry");e.exports=function(t,e,r,a){var o,s=a("x"),l=a("y");if(i.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y"],r),s){var c=n.minRowLength(s);l?o=Math.min(c,n.minRowLength(l)):(o=c,a("y0"),a("dy"))}else{if(!l)return 0;o=n.minRowLength(l),a("x0"),a("dx")}return e._length=o,o}},{"../../lib":776,"../../registry":904}],1219:[function(t,e,r){"use strict";var n=t("../scatter/attributes"),i=t("../../components/colorscale/attributes"),a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,o=t("../../plots/template_attributes").hovertemplateAttrs,s=t("../../plots/template_attributes").texttemplateAttrs,l=t("../../plots/attributes"),c=t("../../constants/gl3d_dashes"),u=t("../../constants/gl3d_markers"),f=t("../../lib/extend").extendFlat,h=t("../../plot_api/edit_types").overrideAll,p=t("../../lib/sort_object_keys"),d=n.line,m=n.marker,g=m.line,v=f({width:d.width,dash:{valType:"enumerated",values:p(c),dflt:"solid"}},i("line"));var 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n=t("gl-line3d"),i=t("gl-scatter3d"),a=t("gl-error3d"),o=t("gl-mesh3d"),s=t("delaunay-triangulate"),l=t("../../lib"),c=t("../../lib/str2rgbarray"),u=t("../../lib/gl_format_color").formatColor,f=t("../scatter/make_bubble_size_func"),h=t("../../constants/gl3d_dashes"),p=t("../../constants/gl3d_markers"),d=t("../../plots/cartesian/axes"),m=t("../../components/fx/helpers").appendArrayPointValue,g=t("./calc_errors");function v(t,e){this.scene=t,this.uid=e,this.linePlot=null,this.scatterPlot=null,this.errorBars=null,this.textMarkers=null,this.delaunayMesh=null,this.color=null,this.mode="",this.dataPoints=[],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.textLabels=null,this.data=null}var y=v.prototype;function x(t){return null==t?0:t.indexOf("left")>-1?-1:t.indexOf("right")>-1?1:0}function b(t){return null==t?0:t.indexOf("top")>-1?-1:t.indexOf("bottom")>-1?1:0}function _(t,e){return e(4*t)}function w(t){return p[t]}function T(t,e,r,n,i){var 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t.object.highlight&&t.object.highlight(null),this.scatterPlot&&(t.object=this.scatterPlot,this.scatterPlot.highlight(t.data)),t.textLabel="",this.textLabels&&(Array.isArray(this.textLabels)?(this.textLabels[e]||0===this.textLabels[e])&&(t.textLabel=this.textLabels[e]):t.textLabel=this.textLabels),t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]],!0}},y.update=function(t){var e,r,l,c,u=this.scene.glplot.gl,f=h.solid;this.data=t;var p=k(this.scene,t);"mode"in p&&(this.mode=p.mode),"lineDashes"in p&&p.lineDashes in 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d=t.opacity;if(t.marker&&t.marker.opacity&&(d*=t.marker.opacity),r={gl:this.scene.glplot.gl,position:p.position,color:p.scatterColor,size:p.scatterSize,glyph:p.scatterMarker,opacity:d,orthographic:!0,lineWidth:p.scatterLineWidth,lineColor:p.scatterLineColor,project:p.project,projectScale:p.projectScale,projectOpacity:p.projectOpacity},-1!==this.mode.indexOf("markers")?this.scatterPlot?this.scatterPlot.update(r):(this.scatterPlot=i(r),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),c={gl:this.scene.glplot.gl,position:p.position,glyph:p.text,color:p.textColor,size:p.textSize,angle:p.textAngle,alignment:p.textOffset,font:p.textFont,orthographic:!0,lineWidth:0,project:!1,opacity:t.opacity},this.textLabels=t.hovertext||t.text,-1!==this.mode.indexOf("text")?this.textMarkers?this.textMarkers.update(c):(this.textMarkers=i(c),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),l={gl:this.scene.glplot.gl,position:p.position,color:p.errorColor,error:p.errorBounds,lineWidth:p.errorLineWidth,capSize:p.errorCapSize,opacity:t.opacity},this.errorBars?p.errorBounds?this.errorBars.update(l):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):p.errorBounds&&(this.errorBars=a(l),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),p.delaunayAxis>=0){var 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n=t("../../lib"),i=t("../scatter/subtypes"),a=t("../scatter/marker_defaults"),o=t("../scatter/line_defaults"),s=t("../scatter/text_defaults"),l=t("../scatter/fillcolor_defaults"),c=t("./attributes");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}var h,p=f("locations");if(p&&p.length){var d,m=f("geojson");("string"==typeof m&&""!==m||n.isPlainObject(m))&&(d="geojson-id"),"geojson-id"===f("locationmode",d)&&f("featureidkey"),h=p.length}else{var g=f("lon")||[],v=f("lat")||[];h=Math.min(g.length,v.length)}h?(e._length=h,f("text"),f("hovertext"),f("hovertemplate"),f("mode"),i.hasLines(e)&&(o(t,e,r,u,f),f("connectgaps")),i.hasMarkers(e)&&a(t,e,r,u,f,{gradient:!0}),i.hasText(e)&&(f("texttemplate"),s(t,e,u,f)),f("fill"),"none"!==e.fill&&l(t,e,r,f),n.coerceSelectionMarkerOpacity(e,f)):e.visible=!1}},{"../../lib":776,"../scatter/fillcolor_defaults":1199,"../scatter/line_defaults":1204,"../scatter/marker_defaults":1210,"../scatter/subtypes":1216,"../scatter/text_defaults":1217,"./attributes":1233}],1236:[function(t,e,r){"use strict";e.exports=function(t,e,r,n,i){t.lon=e.lon,t.lat=e.lat,t.location=e.loc?e.loc:null;var a=n[i];return a.fIn&&a.fIn.properties&&(t.properties=a.fIn.properties),t}},{}],1237:[function(t,e,r){"use strict";var n=t("../../plots/cartesian/axes");e.exports=function(t,e,r){var i={},a=r[e.geo]._subplot.mockAxis,o=t.lonlat;return 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n=t("@plotly/d3"),i=t("../../lib"),a=t("../../lib/topojson_utils").getTopojsonFeatures,o=t("../../lib/geojson_utils"),s=t("../../lib/geo_location_utils"),l=t("../../plots/cartesian/autorange").findExtremes,c=t("../../constants/numerical").BADNUM,u=t("../scatter/calc").calcMarkerSize,f=t("../scatter/subtypes"),h=t("./style");e.exports={calcGeoJSON:function(t,e){var r,n,i=t[0].trace,o=e[i.geo],f=o._subplot,h=i._length;if(Array.isArray(i.locations)){var p=i.locationmode,d="geojson-id"===p?s.extractTraceFeature(t):a(i,f.topojson);for(r=0;r<h;r++){n=t[r];var m="geojson-id"===p?n.fOut:s.locationToFeature(p,n.loc,d);n.lonlat=m?m.properties.ct:[c,c]}}var g,v,y={padded:!0};if("geojson"===o.fitbounds&&"geojson-id"===i.locationmode){var x=s.computeBbox(s.getTraceGeojson(i));g=[x[0],x[2]],v=[x[1],x[3]]}else{for(g=new Array(h),v=new 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T=i.getComponentMethod("errorbars","supplyDefaults");T(t,e,_||w||r,{axis:"y"}),T(t,e,_||w||r,{axis:"x",inherit:"y"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},{"../../lib":776,"../../registry":904,"../scatter/constants":1195,"../scatter/fillcolor_defaults":1199,"../scatter/line_defaults":1204,"../scatter/marker_defaults":1210,"../scatter/period_defaults":1211,"../scatter/subtypes":1216,"../scatter/text_defaults":1217,"../scatter/xy_defaults":1218,"./attributes":1243,"./helpers":1250}],1248:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/color"),a=t("../../constants/interactions").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},m=c.textUnselectedOptions[u]||{},g=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=m.color,x=p.color,b=Array.isArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(g)}}},{"../../components/color":639,"../../constants/interactions":751,"../../lib":776}],1249:[function(t,e,r){"use strict";var n=t("../scatter/format_labels");e.exports=function(t,e,r){var i=t.i;return"x"in t||(t.x=e._x[i]),"y"in t||(t.y=e._y[i]),n(t,e,r)}},{"../scatter/format_labels":1200}],1250:[function(t,e,r){"use strict";var n=t("./constants");r.isOpenSymbol=function(t){return"string"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return"string"==typeof t?n.DOT_RE.test(t):t>200}},{"./constants":1245}],1251:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("../scatter/get_trace_color");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var m=d&&d.line;m&&(h.mlc=Array.isArray(m.color)?m.color[f]:m.color,h.mlw=i.isArrayOrTypedArray(m.width)?m.width[f]:m.width);var g=d&&d.gradient;g&&"none"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:A?A[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod("errorbars","hoverInfo")(h,o,M),M}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d,m=t.cd,g=m[0].t,v=m[0].trace,y=t.xa,x=t.ya,b=g.x,_=g.y,w=y.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=y.p2c(w-k),M=y.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i="x"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if("x"===n){var C=!!v.xperiodalignment,P=!!v.yperiodalignment;for(u=0;u<i.length;u++){if(l=b[a=i[u]],f=Math.abs(y.c2p(l)-w),C){var I=y.c2p(v._xStarts[a]),O=y.c2p(v._xEnds[a]);f=w>=Math.min(I,O)&&w<=Math.max(I,O)?0:1/0}if(f<L){if(L=f,c=_[a],h=x.c2p(c)-T,P){var z=x.c2p(v._yStarts[a]),D=x.c2p(v._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[u]}}}else for(u=i.length-1;u>-1;u--)l=b[a=i[u]],c=_[a],f=y.c2p(l)-w,h=x.c2p(c)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,v)]},calcHover:o}},{"../../lib":776,"../../registry":904,"../scatter/get_trace_color":1201}],1252:[function(t,e,r){"use strict";var n=t("./hover");e.exports={moduleType:"trace",name:"scattergl",basePlotModule:t("../../plots/cartesian"),categories:["gl","regl","cartesian","symbols","errorBarsOK","showLegend","scatter-like"],attributes:t("./attributes"),supplyDefaults:t("./defaults"),crossTraceDefaults:t("../scatter/cross_trace_defaults"),colorbar:t("../scatter/marker_colorbar"),formatLabels:t("./format_labels"),calc:t("./calc"),plot:t("./plot"),hoverPoints:n.hoverPoints,selectPoints:t("./select"),meta:{}}},{"../../plots/cartesian":841,"../scatter/cross_trace_defaults":1197,"../scatter/marker_colorbar":1209,"./attributes":1243,"./calc":1244,"./defaults":1247,"./format_labels":1249,"./hover":1251,"./plot":1253,"./select":1255}],1253:[function(t,e,r){"use strict";var n=t("regl-scatter2d"),i=t("regl-line2d"),a=t("regl-error2d"),o=t("gl-text"),s=t("../../lib"),l=t("../../components/dragelement/helpers").selectMode,c=t("../../lib/prepare_regl"),u=t("../scatter/subtypes"),f=t("../scatter/link_traces"),h=t("./edit_style").styleTextSelection;function p(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,c=i.r*n,u=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-c-(1-e.domain[1])*f,o-u-(1-r.domain[1])*h]}e.exports=function(t,e,r){if(r.length){var d,m,g=t._fullLayout,v=e._scene,y=e.xaxis,x=e.yaxis;if(v)if(c(t,["ANGLE_instanced_arrays","OES_element_index_uint"])){var b=v.count,_=g._glcanvas.data()[0].regl;if(f(t,e,r),v.dirty){if(!0===v.error2d&&(v.error2d=a(_)),!0===v.line2d&&(v.line2d=i(_)),!0===v.scatter2d&&(v.scatter2d=n(_)),!0===v.fill2d&&(v.fill2d=i(_)),!0===v.glText)for(v.glText=new Array(b),d=0;d<b;d++)v.glText[d]=new o(_);if(v.glText){if(b>v.glText.length){var w=b-v.glText.length;for(d=0;d<w;d++)v.glText.push(new o(_))}else if(b<v.glText.length){var T=v.glText.length-b;v.glText.splice(b,T).forEach((function(t){t.destroy()}))}for(d=0;d<b;d++)v.glText[d].update(v.textOptions[d])}if(v.line2d&&(v.line2d.update(v.lineOptions),v.lineOptions=v.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),v.line2d.update(v.lineOptions)),v.error2d){var k=(v.errorXOptions||[]).concat(v.errorYOptions||[]);v.error2d.update(k)}v.scatter2d&&v.scatter2d.update(v.markerOptions),v.fillOrder=s.repeat(null,b),v.fill2d&&(v.fillOptions=v.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=v.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(v.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if("tozeroy"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if("tozerox"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else if("toself"===s.fill||"tonext"===s.fill){for(p=[],i=0,t.splitNull=!0,a=0;a<d.length;a+=2)(isNaN(d[a])||isNaN(d[a+1]))&&((p=p.concat(d.slice(i,a))).push(d[i],d[i+1]),p.push(null,null),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var m=s._nexttrace;if(m){var g=v.lineOptions[e+1];if(g){var y=g.positions;if("tonexty"===s.fill){for(p=d.slice(),e=Math.floor(y.length/2);e--;){var x=y[2*e],b=y[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=m.fillcolor}}}}if(s._prevtrace&&"tonext"===s._prevtrace.fill){var _=v.lineOptions[e-1].positions,w=p.length/2,T=[i=w];for(a=0;a<_.length;a+=2)(isNaN(_[a])||isNaN(_[a+1]))&&(T.push(a/2+w+1),i=a+2);p=p.concat(_),t.hole=T}return t.fillmode=s.fill,t.opacity=s.opacity,t.positions=p,t}})),v.fill2d.update(v.fillOptions))}var A=g.dragmode,M=l(A),S=g.clickmode.indexOf("select")>-1;for(d=0;d<b;d++){var E=r[d][0],L=E.trace,C=E.t,P=C.index,I=L._length,O=C.x,z=C.y;if(L.selectedpoints||M||S){if(M||(M=!0),L.selectedpoints){var D=v.selectBatch[P]=s.selIndices2selPoints(L),R={};for(m=0;m<D.length;m++)R[D[m]]=1;var F=[];for(m=0;m<I;m++)R[m]||F.push(m);v.unselectBatch[P]=F}var B=C.xpx=new Array(I),N=C.ypx=new Array(I);for(m=0;m<I;m++)B[m]=y.c2p(O[m]),N[m]=x.c2p(z[m])}else C.xpx=C.ypx=null}if(M){if(v.select2d||(v.select2d=n(g._glcanvas.data()[1].regl)),v.scatter2d){var j=new Array(b);for(d=0;d<b;d++)j[d]=v.selectBatch[d].length||v.unselectBatch[d].length?v.markerUnselectedOptions[d]:{};v.scatter2d.update(j)}v.select2d&&(v.select2d.update(v.markerOptions),v.select2d.update(v.markerSelectedOptions)),v.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};u.hasText(e)&&h(t)}))}else v.scatter2d&&v.scatter2d.update(v.markerOptions);var U={viewport:p(g,y,x,t._context.plotGlPixelRatio),range:[(y._rl||y.range)[0],(x._rl||x.range)[0],(y._rl||y.range)[1],(x._rl||x.range)[1]]},V=s.repeat(U,v.count);v.fill2d&&v.fill2d.update(V),v.line2d&&v.line2d.update(V),v.error2d&&v.error2d.update(V.concat(V)),v.scatter2d&&v.scatter2d.update(V),v.select2d&&v.select2d.update(V),v.glText&&v.glText.forEach((function(t){t.update(U)}))}else v.init()}}},{"../../components/dragelement/helpers":657,"../../lib":776,"../../lib/prepare_regl":789,"../scatter/link_traces":1207,"../scatter/subtypes":1216,"./edit_style":1248,"gl-text":337,"regl-error2d":512,"regl-line2d":513,"regl-scatter2d":514}],1254:[function(t,e,r){"use strict";var n=t("../../lib");e.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,c=r.selectBatch,u=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(u[f].length){var h=n.repeat([],r.count);h[f]=u[f],o.draw(h)}else c[f].length||o.draw(f);s[f]&&r.textOptions[f]&&s[f].render()}l&&l.draw(c),r.dirty=!1},r.destroy=function(){r.fill2d&&r.fill2d.destroy&&r.fill2d.destroy(),r.scatter2d&&r.scatter2d.destroy&&r.scatter2d.destroy(),r.error2d&&r.error2d.destroy&&r.error2d.destroy(),r.line2d&&r.line2d.destroy&&r.line2d.destroy(),r.select2d&&r.select2d.destroy&&r.select2d.destroy(),r.glText&&r.glText.forEach((function(t){t.destroy&&t.destroy()})),r.lineOptions=null,r.fillOptions=null,r.markerOptions=null,r.markerSelectedOptions=null,r.markerUnselectedOptions=null,r.errorXOptions=null,r.errorYOptions=null,r.textOptions=null,r.textSelectedOptions=null,r.textUnselectedOptions=null,r.selectBatch=null,r.unselectBatch=null,e._scene=null}),r.dirty||n.extendFlat(r,i),r}},{"../../lib":776}],1255:[function(t,e,r){"use strict";var n=t("../scatter/subtypes"),i=t("./edit_style").styleTextSelection;e.exports=function(t,e){var 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n=t("../../plots/template_attributes").hovertemplateAttrs,i=t("../../plots/template_attributes").texttemplateAttrs,a=t("../scattergeo/attributes"),o=t("../scatter/attributes"),s=t("../../plots/mapbox/layout_attributes"),l=t("../../plots/attributes"),c=t("../../components/colorscale/attributes"),u=t("../../lib/extend").extendFlat,f=t("../../plot_api/edit_types").overrideAll,h=a.line,p=a.marker;e.exports=f({lon:a.lon,lat:a.lat,mode:u({},o.mode,{dflt:"markers"}),text:u({},o.text,{}),texttemplate:i({editType:"plot"},{keys:["lat","lon","text"]}),hovertext:u({},o.hovertext,{}),line:{color:h.color,width:h.width},connectgaps:o.connectgaps,marker:u({symbol:{valType:"string",dflt:"circle",arrayOk:!0},angle:{valType:"number",dflt:"auto",arrayOk:!0},allowoverlap:{valType:"boolean",dflt:!1},opacity:p.opacity,size:p.size,sizeref:p.sizeref,sizemin:p.sizemin,sizemode:p.sizemode},c("marker")),fill:a.fill,fillcolor:o.fillcolor,textfont:s.layers.symbol.textfont,textposition:s.layers.symbol.textposition,below:{valType:"string"},selected:{marker:o.selected.marker},unselected:{marker:o.unselected.marker},hoverinfo:u({},l.hoverinfo,{flags:["lon","lat","text","name"]}),hovertemplate:n()},"calc","nested")},{"../../components/colorscale/attributes":646,"../../lib/extend":766,"../../plot_api/edit_types":809,"../../plots/attributes":823,"../../plots/mapbox/layout_attributes":886,"../../plots/template_attributes":899,"../scatter/attributes":1191,"../scattergeo/attributes":1233}],1257:[function(t,e,r){"use 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n=t("../../lib"),i=t("../scatter/subtypes"),a=t("../scatter/marker_defaults"),o=t("../scatter/line_defaults"),s=t("../scatter/line_shape_defaults"),l=t("../scatter/text_defaults"),c=t("../scatter/fillcolor_defaults"),u=t("../scatter/constants").PTS_LINESONLY,f=t("./attributes");function h(t,e,r,n){var i,a=n("r"),o=n("theta");if(a)o?i=Math.min(a.length,o.length):(i=a.length,n("theta0"),n("dtheta"));else{if(!o)return 0;i=e.theta.length,n("r0"),n("dr")}return e._length=i,i}e.exports={handleRThetaDefaults:h,supplyDefaults:function(t,e,r,p){function d(r,i){return n.coerce(t,e,f,r,i)}var m=h(t,e,p,d);if(m){d("thetaunit"),d("mode",m<u?"lines+markers":"lines"),d("text"),d("hovertext"),"fills"!==e.hoveron&&d("hovertemplate"),i.hasLines(e)&&(o(t,e,r,p,d),s(t,e,d),d("connectgaps")),i.hasMarkers(e)&&a(t,e,r,p,d,{gradient:!0}),i.hasText(e)&&(d("texttemplate"),l(t,e,p,d));var 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n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c={x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},u:{valType:"data_array",editType:"calc"},v:{valType:"data_array",editType:"calc"},w:{valType:"data_array",editType:"calc"},starts:{x:{valType:"data_array",editType:"calc"},y:{valType:"data_array",editType:"calc"},z:{valType:"data_array",editType:"calc"},editType:"calc"},maxdisplayed:{valType:"integer",min:0,dflt:1e3,editType:"calc"},sizeref:{valType:"number",editType:"calc",min:0,dflt:1},text:{valType:"string",dflt:"",editType:"calc"},hovertext:{valType:"string",dflt:"",editType:"calc"},hovertemplate:a({editType:"calc"},{keys:["tubex","tubey","tubez","tubeu","tubev","tubew","norm","divergence"]}),uhoverformat:i("u",1),vhoverformat:i("v",1),whoverformat:i("w",1),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),showlegend:l({},s.showlegend,{dflt:!1})};l(c,n("",{colorAttr:"u/v/w 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r=t.data.data;r.color=e._hasColorscale?B(N[r.i]):F(N[r.i],r.id)})),w[0].hierarchy=D,w}},r._runCrossTraceCalc=function(t,e){var r=e._fullLayout,n=e.calcdata,i=r[t+"colorway"],a=r["_"+t+"colormap"];r["extend"+t+"colors"]&&(i=l(i,"icicle"===t?p:"treemap"===t?h:f));var o,s=0;function c(t){var e=t.data.data,r=e.id;!1===e.color&&(a[r]?e.color=a[r]:t.parent?t.parent.parent?e.color=t.parent.data.data.color:(a[r]=e.color=i[s%i.length],s++):e.color=o)}for(var u=0;u<n.length;u++){var d=n[u][0];d.trace.type===t&&d.hierarchy&&(o=d.trace.root.color,d.hierarchy.each(c))}},r.crossTraceCalc=function(t){return r._runCrossTraceCalc("sunburst",t)}},{"../../components/colorscale":651,"../../constants/numerical":752,"../../lib":776,"../pie/calc":1167,"d3-hierarchy":163,"fast-isnumeric":242}],1306:[function(t,e,r){"use 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n.castOption(e,a,"outsidetextfont."+t)||n.castOption(e,a,"textfont."+t)||i.size},r.isOutsideText=function(t,e){return!t._hasColorscale&&r.isHierarchyRoot(e)},r.determineTextFont=function(t,e,a,o){return r.isOutsideText(t,e)?function(t,e,n){return{color:r.getOutsideTextFontKey("color",t,e,n),family:r.getOutsideTextFontKey("family",t,e,n),size:r.getOutsideTextFontKey("size",t,e,n)}}(t,e,a):function(t,e,a,o){var s=(o||{}).onPathbar,l=e.data.data,c=l.i,u=n.castOption(t,c,(s?"pathbar.textfont":"insidetextfont")+".color");return!u&&t._input.textfont&&(u=n.castOption(t._input,c,"textfont.color")),{color:u||i.contrast(l.color),family:r.getInsideTextFontKey("family",t,e,a,o),size:r.getInsideTextFontKey("size",t,e,a,o)}}(t,e,a,o)},r.hasTransition=function(t){return!!(t&&t.duration>0)},r.getMaxDepth=function(t){return t.maxdepth>=0?t.maxdepth:1/0},r.isHeader=function(t,e){return!(r.isLeaf(t)||t.depth===e._maxDepth-1)},r.getParent=function(t,e){return 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D=null,R={};v&&_.each((function(t){R[b.getPtId(t)]={rpx0:t.rpx0,rpx1:t.rpx1,x0:t.x0,x1:t.x1,transform:t.transform},!D&&b.isEntry(t)&&(D=t)}));var F=function(t){return i.partition().size([2*Math.PI,t.height+1])(t)}(M).descendants(),B=M.height+1,N=0,j=S;T.hasMultipleRoots&&b.isHierarchyRoot(M)&&(F=F.slice(1),B-=1,N=1,j+=1),F=F.filter((function(t){return t.y1<=j}));var U=p(k.rotation);U&&F.forEach((function(t){t.x0+=U,t.x1+=U}));var V=Math.min(B,S),H=function(t){return(t-N)/V*I},q=function(t,e){return[t*Math.cos(e),-t*Math.sin(e)]},G=function(t){return s.pathAnnulus(t.rpx0,t.rpx1,t.x0,t.x1,O,z)},Y=function(t){return O+w(t)[0]*(t.transform.rCenter||0)+(t.transform.x||0)},W=function(t){return z+w(t)[1]*(t.transform.rCenter||0)+(t.transform.y||0)};(_=_.data(F,b.getPtId)).enter().append("g").classed("slice",!0),v?_.exit().transition().each((function(){var t=n.select(this);t.select("path.surface").transition().attrTween("d",(function(t){var e=function(t){var e,r=b.getPtId(t),n=R[r],i=R[b.getPtId(M)];if(i){var o=(t.x1>i.x1?2*Math.PI:0)+U;e=t.rpx1<i.rpx1?{x0:t.x0,x1:t.x1,rpx0:0,rpx1:0}:{x0:o,x1:o,rpx0:t.rpx0,rpx1:t.rpx1}}else{var s,l=b.getPtId(t.parent);_.each((function(t){if(b.getPtId(t)===l)return s=t}));var c,u=s.children;u.forEach((function(t,e){if(b.getPtId(t)===r)return c=e}));var f=u.length,h=a(s.x0,s.x1);e={rpx0:I,rpx1:I,x0:h(c/f),x1:h((c+1)/f)}}return a(n,e)}(t);return function(t){return G(e(t))}})),t.select("g.slicetext").attr("opacity",0)})).remove():_.exit().remove(),_.order();var X=null;if(v&&D){var Z=b.getPtId(D);_.each((function(t){null===X&&b.getPtId(t)===Z&&(X=t.x1)}))}var J=_;function K(t){var e=t.parent,r=R[b.getPtId(e)],n={};if(r){var i=e.children,o=i.indexOf(t),s=i.length,l=a(r.x0,r.x1);n.x0=l(o/s),n.x1=l(o/s)}else n.x0=n.x1=0;return n}v&&(J=J.transition().each("end",(function(){var e=n.select(this);b.setSliceCursor(e,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:!1})}))),J.each((function(i){var c=n.select(this),f=s.ensureSingle(c,"path","surface",(function(t){t.style("pointer-events","all")}));i.rpx0=H(i.y0),i.rpx1=H(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=q(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),v?f.transition().attrTween("d",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(D)if(t.parent)if(X){var i=(t.x1>X?2*Math.PI:0)+U;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,K(t));else e={rpx0:0,rpx1:0};else e={x0:U,x1:U};return a(e,n)}(t);return function(t){return G(e(t))}})):f.attr("d",G),c.call(y,M,t,e,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),f.call(g,i,k);var p=s.ensureSingle(c,"g","slicetext"),_=s.ensureSingle(p,"text","",(function(t){t.attr("data-notex",1)})),w=s.ensureUniformFontSize(t,b.determineTextFont(k,i,h.font));_.text(r.formatSliceLabel(i,M,k,e,h)).classed("slicetext",!0).attr("text-anchor","middle").call(o.font,w).call(l.convertToTspans,t);var A=o.bBox(_.node());i.transform=m(A,i,T),i.transform.targetX=Y(i),i.transform.targetY=W(i);var S=function(t,e){var r=t.transform;return d(r,e),r.fontSize=w.size,u(k.type,r,h),s.getTextTransform(r)};v?_.transition().attrTween("transform",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n=t.transform;if(r)e=r;else 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a,o,s=t._fullLayout,l=s._sunburstlayer,c=!r,u=!s.uniformtext.mode&&b.hasTransition(r);(f("sunburst",s),(a=l.selectAll("g.trace.sunburst").data(e,(function(t){return t[0].trace.uid}))).enter().append("g").classed("trace",!0).classed("sunburst",!0).attr("stroke-linejoin","round"),a.order(),u)?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each("end",(function(){o&&o()})).each("interrupt",(function(){o&&o()})).each((function(){l.selectAll("g.trace").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&v(t,s._sunburstlayer.selectAll(".trace"),"sunburst"));c&&a.exit().remove()},r.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&"none"!==o))return"";var l=i.separators,c=n[0],u=t.data.data,f=c.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var m,g=o.split("+"),v=function(t){return-1!==g.indexOf(t)},y=[];if(v("label")&&u.label&&y.push(u.label),u.hasOwnProperty("v")&&v("value")&&y.push(b.formatValue(u.v,l)),!h){v("current path")&&y.push(b.getPath(t.data));var x=0;v("percent parent")&&x++,v("percent entry")&&x++,v("percent root")&&x++;var _=x>1;if(x){var w,T=function(t){m=b.formatPercent(w,l),_&&(m+=" of "+t),y.push(m)};v("percent parent")&&!h&&(w=d/b.getValue(p),T("parent")),v("percent entry")&&(w=d/b.getValue(e),T("entry")),v("percent root")&&(w=d/b.getValue(f),T("root"))}}return v("text")&&(m=s.castOption(r,u.i,"text"),s.isValidTextValue(m)&&y.push(m)),y.join("<br>")}var k=s.castOption(r,u.i,"texttemplate");if(!k)return"";var A={};u.label&&(A.label=u.label),u.hasOwnProperty("v")&&(A.value=u.v,A.valueLabel=b.formatValue(u.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),u.hasOwnProperty("color")&&(A.color=u.color);var M=s.castOption(r,u.i,"text");return(s.isValidTextValue(M)||""===M)&&(A.text=M),A.customdata=s.castOption(r,u.i,"customdata"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},{"../../components/drawing":661,"../../lib":776,"../../lib/svg_text_utils":802,"../bar/style":928,"../bar/uniform_text":930,"../pie/helpers":1170,"../pie/plot":1174,"./constants":1306,"./fx":1308,"./helpers":1309,"./style":1314,"@plotly/d3":58,"d3-hierarchy":163,"d3-interpolate":164}],1314:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../components/color"),a=t("../../lib"),o=t("../bar/uniform_text").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,"marker.line.color")||i.defaultLine,c=a.castOption(r,s,"marker.line.width")||0;t.style("stroke-width",c).call(i.fill,n.color).call(i.stroke,l).style("opacity",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(".trace");o(t,e,"sunburst"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style("opacity",r.opacity),e.selectAll("path.surface").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{"../../components/color":639,"../../lib":776,"../bar/uniform_text":930,"@plotly/d3":58}],1315:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("../../components/colorscale/attributes"),a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,o=t("../../plots/template_attributes").hovertemplateAttrs,s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;function u(t){return{show:{valType:"boolean",dflt:!1},start:{valType:"number",dflt:null,editType:"plot"},end:{valType:"number",dflt:null,editType:"plot"},size:{valType:"number",dflt:null,min:0,editType:"plot"},project:{x:{valType:"boolean",dflt:!1},y:{valType:"boolean",dflt:!1},z:{valType:"boolean",dflt:!1}},color:{valType:"color",dflt:n.defaultLine},usecolormap:{valType:"boolean",dflt:!1},width:{valType:"number",min:1,max:16,dflt:2},highlight:{valType:"boolean",dflt:!0},highlightcolor:{valType:"color",dflt:n.defaultLine},highlightwidth:{valType:"number",min:1,max:16,dflt:2}}}var f=e.exports=c(l({z:{valType:"data_array"},x:{valType:"data_array"},y:{valType:"data_array"},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:o(),xhoverformat:a("x"),yhoverformat:a("y"),zhoverformat:a("z"),connectgaps:{valType:"boolean",dflt:!1,editType:"calc"},surfacecolor:{valType:"data_array"}},i("",{colorAttr:"z or 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0},{"../../components/color":639,"../../components/colorscale/attributes":646,"../../lib/extend":766,"../../plot_api/edit_types":809,"../../plots/attributes":823,"../../plots/cartesian/axis_format_attributes":830,"../../plots/template_attributes":899}],1316:[function(t,e,r){"use strict";var n=t("../../components/colorscale/calc");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:"",cLetter:"c"}):n(t,e,{vals:e.z,containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":647}],1317:[function(t,e,r){"use strict";var n=t("gl-surface3d"),i=t("ndarray"),a=t("ndarray-linear-interpolate").d2,o=t("../heatmap/interp2d"),s=t("../heatmap/find_empties"),l=t("../../lib").isArrayOrTypedArray,c=t("../../lib/gl_format_color").parseColorScale,u=t("../../lib/str2rgbarray"),f=t("../../components/colorscale").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||"",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function m(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function g(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(m(t,n))}return e}function v(t){for(var e=g(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++)r(i,[l,c],n),t.set(l,c,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=v(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(u,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,u,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=c(t),m=l.dataScale,g=t.z[0].length,v=t._ylength,y=l.contourLevels;this.data=t;var x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<g;r++)x[e][r]=[];for(r=0;r<g;r++)for(n=0;n<v;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=s(x[2]),o(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<g;r++)for(t._interpolatedZ[r]=[],n=0;n<v;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=m[e];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)this.objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=this.objectOffset[e]);var b=[i(new Float32Array(g*v),[g,v]),i(new Float32Array(g*v),[g,v]),i(new Float32Array(g*v),[g,v])];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)b[e].set(r,n,x[e][r][n]);x=[];var w={colormap:d,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!t.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:t.opacityscale,opacity:t.opacity},T=f(t);if(w.intensityBounds=[T.min,T.max],t.surfacecolor){var k=i(new Float32Array(g*v),[g,v]);for(r=0;r<g;r++)for(n=0;n<v;n++)k.set(r,n,t.surfacecolor[n][r]);b.push(k)}else w.intensityBounds[0]*=m[2],w.intensityBounds[1]*=m[2];(_<b[0].shape[0]||_<b[0].shape[1])&&(this.refineData=!1),!0===this.refineData&&(this.dataScaleX=this.estimateScale(b[0].shape[0],0),this.dataScaleY=this.estimateScale(b[0].shape[1],1),1===this.dataScaleX&&1===this.dataScaleY||this.refineCoords(b)),t.surfacecolor&&(w.intensity=b.pop());var A=[!0,!0,!0],M=["x","y","z"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=y[e],p.highlightColor[e]=w.contourColor[e]=u(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=u(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,"lighting"in t&&(p.ambientLight=t.lighting.ambient,p.diffuseLight=t.lighting.diffuse,p.specularLight=t.lighting.specular,p.roughness=t.lighting.roughness,p.fresnel=t.lighting.fresnel),"lightposition"in t&&(p.lightPosition=[t.lightposition.x,t.lightposition.y,t.lightposition.z])},p.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new h(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},{"../../components/colorscale":651,"../../lib":776,"../../lib/gl_format_color":772,"../../lib/str2rgbarray":801,"../heatmap/find_empties":1064,"../heatmap/interp2d":1067,"gl-surface3d":336,ndarray:462,"ndarray-linear-interpolate":456}],1318:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("../../components/colorscale/defaults"),o=t("./attributes");function s(t,e,r,n){var i=n("opacityscale");"max"===i?e.opacityscale=[[0,.1],[1,1]]:"min"===i?e.opacityscale=[[0,1],[1,.1]]:"extremes"===i?e.opacityscale=function(t,e){for(var r=[],n=0;n<32;n++){var i=n/31,a=e+(1-e)*(1-Math.pow(Math.sin(t*i*Math.PI),2));r.push([i,Math.max(0,Math.min(1,a))])}return r}(1,.1):function(t){var e=0;if(!Array.isArray(t)||t.length<2)return!1;if(!t[0]||!t[t.length-1])return!1;if(0!=+t[0][0]||1!=+t[t.length-1][0])return!1;for(var r=0;r<t.length;r++){var n=t[r];if(2!==n.length||+n[0]<e)return!1;e=+n[0]}return!0}(i)||(e.opacityscale=void 0)}function l(t,e,r){e in t&&!(r in t)&&(t[r]=t[e])}e.exports={supplyDefaults:function(t,e,r,c){var u,f;function h(r,n){return i.coerce(t,e,o,r,n)}var p=h("x"),d=h("y"),m=h("z");if(!m||!m.length||p&&p.length<1||d&&d.length<1)e.visible=!1;else{e._xlength=Array.isArray(p)&&i.isArrayOrTypedArray(p[0])?m.length:m[0].length,e._ylength=m.length,n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],c),h("text"),h("hovertext"),h("hovertemplate"),h("xhoverformat"),h("yhoverformat"),h("zhoverformat"),["lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lightposition.x","lightposition.y","lightposition.z","hidesurface","connectgaps","opacity"].forEach((function(t){h(t)}));var g=h("surfacecolor"),v=["x","y","z"];for(u=0;u<3;++u){var y="contours."+v[u],x=h(y+".show"),b=h(y+".highlight");if(x||b)for(f=0;f<3;++f)h(y+".project."+v[f]);x&&(h(y+".color"),h(y+".width"),h(y+".usecolormap")),b&&(h(y+".highlightcolor"),h(y+".highlightwidth")),h(y+".start"),h(y+".end"),h(y+".size")}g||(l(t,"zmin","cmin"),l(t,"zmax","cmax"),l(t,"zauto","cauto")),a(t,e,c,h,{prefix:"",cLetter:"c"}),s(t,e,c,h),e._length=null}},opacityscaleDefaults:s}},{"../../components/colorscale/defaults":649,"../../lib":776,"../../registry":904,"./attributes":1315}],1319:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,colorbar:{min:"cmin",max:"cmax"},calc:t("./calc"),plot:t("./convert"),moduleType:"trace",name:"surface",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","2dMap","showLegend"],meta:{}}},{"../../plots/gl3d":869,"./attributes":1315,"./calc":1316,"./convert":1317,"./defaults":1318}],1320:[function(t,e,r){"use strict";var n=t("../../components/annotations/attributes"),i=t("../../lib/extend").extendFlat,a=t("../../plot_api/edit_types").overrideAll,o=t("../../plots/font_attributes"),s=t("../../plots/domain").attributes,l=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers;(e.exports=a({domain:s({name:"table",trace:!0}),columnwidth:{valType:"number",arrayOk:!0,dflt:null},columnorder:{valType:"data_array"},header:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell value")},prefix:{valType:"string",arrayOk:!0,dflt:null},suffix:{valType:"string",arrayOk:!0,dflt:null},height:{valType:"number",dflt:28},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:"number",arrayOk:!0,dflt:1},color:{valType:"color",arrayOk:!0,dflt:"grey"}},fill:{color:{valType:"color",arrayOk:!0,dflt:"white"}},font:i({},o({arrayOk:!0}))},cells:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell value")},prefix:{valType:"string",arrayOk:!0,dflt:null},suffix:{valType:"string",arrayOk:!0,dflt:null},height:{valType:"number",dflt:20},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:"number",arrayOk:!0,dflt:1},color:{valType:"color",arrayOk:!0,dflt:"grey"}},fill:{color:{valType:"color",arrayOk:!0,dflt:"white"}},font:i({},o({arrayOk:!0}))}},"calc","from-root")).transforms=void 0},{"../../components/annotations/attributes":622,"../../lib/extend":766,"../../plot_api/edit_types":809,"../../plots/cartesian/axis_format_attributes":830,"../../plots/domain":855,"../../plots/font_attributes":856}],1321:[function(t,e,r){"use strict";var n=t("../../plots/get_data").getModuleCalcData,i=t("./plot");r.name="table",r.plot=function(t){var e=n(t.calcdata,"table")[0];e.length&&i(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has("table"),a=e._has&&e._has("table");i&&!a&&n._paperdiv.selectAll(".table").remove()}},{"../../plots/get_data":864,"./plot":1328}],1322:[function(t,e,r){"use strict";var n=t("../../lib/gup").wrap;e.exports=function(){return n({})}},{"../../lib/gup":773}],1323:[function(t,e,r){"use strict";e.exports={cellPad:8,columnExtentOffset:10,columnTitleOffset:28,emptyHeaderHeight:16,latexCheck:/^\$.*\$$/,goldenRatio:1.618,lineBreaker:"<br>",maxDimensionCount:60,overdrag:45,releaseTransitionDuration:120,releaseTransitionEase:"cubic-out",scrollbarCaptureWidth:18,scrollbarHideDelay:1e3,scrollbarHideDuration:1e3,scrollbarOffset:5,scrollbarWidth:8,transitionDuration:100,transitionEase:"cubic-out",uplift:5,wrapSpacer:" ",wrapSplitCharacter:" ",cn:{table:"table",tableControlView:"table-control-view",scrollBackground:"scroll-background",yColumn:"y-column",columnBlock:"column-block",scrollAreaClip:"scroll-area-clip",scrollAreaClipRect:"scroll-area-clip-rect",columnBoundary:"column-boundary",columnBoundaryClippath:"column-boundary-clippath",columnBoundaryRect:"column-boundary-rect",columnCells:"column-cells",columnCell:"column-cell",cellRect:"cell-rect",cellText:"cell-text",cellTextHolder:"cell-text-holder",scrollbarKit:"scrollbar-kit",scrollbar:"scrollbar",scrollbarSlider:"scrollbar-slider",scrollbarGlyph:"scrollbar-glyph",scrollbarCaptureZone:"scrollbar-capture-zone"}}},{}],1324:[function(t,e,r){"use strict";var n=t("./constants"),i=t("../../lib/extend").extendFlat,a=t("fast-isnumeric");function o(t){if(Array.isArray(t)){for(var e=0,r=0;r<t.length;r++)e=Math.max(e,o(t[r]));return e}return t}function s(t,e){return t+e}function l(t){var e,r=t.slice(),n=1/0,i=0;for(e=0;e<r.length;e++)Array.isArray(r[e])||(r[e]=[r[e]]),n=Math.min(n,r[e].length),i=Math.max(i,r[e].length);if(n!==i)for(e=0;e<r.length;e++){var a=i-r[e].length;a&&(r[e]=r[e].concat(c(a)))}return r}function c(t){for(var e=new Array(t),r=0;r<t;r++)e[r]="";return e}function u(t){return t.calcdata.columns.reduce((function(e,r){return r.xIndex<t.xIndex?e+r.columnWidth:e}),0)}function f(t,e){return Object.keys(t).map((function(r){return i({},t[r],{auxiliaryBlocks:e})}))}function h(t,e){for(var r,n={},i=0,a=0,o={firstRowIndex:null,lastRowIndex:null,rows:[]},s=0,l=0,c=0;c<t.length;c++)r=t[c],o.rows.push({rowIndex:c,rowHeight:r}),((a+=r)>=e||c===t.length-1)&&(n[i]=o,o.key=l++,o.firstRowIndex=s,o.lastRowIndex=c,o={firstRowIndex:null,lastRowIndex:null,rows:[]},i+=a,s=c+1,a=0);return n}e.exports=function(t,e){var r=l(e.cells.values),p=function(t){return t.slice(e.header.values.length,t.length)},d=l(e.header.values);d.length&&!d[0].length&&(d[0]=[""],d=l(d));var 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L=Math.max(o(e.header.line.width),o(e.cells.line.width)),C={key:e.uid+t._context.staticPlot,translateX:g.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-g.y[1]),size:t._fullLayout._size,width:v,maxLineWidth:L,height:y,columnOrder:M,groupHeight:y,rowBlocks:k,headerRowBlocks:T,scrollY:0,cells:i({},e.cells,{values:r}),headerCells:i({},e.header,{values:m}),gdColumns:m.map((function(t){return t[0]})),gdColumnsOriginalOrder:m.map((function(t){return t[0]})),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:m.map((function(t,e){var r=A[t];return A[t]=(r||0)+1,{key:t+"__"+A[t],label:t,specIndex:e,xIndex:M[e],xScale:u,x:void 0,calcdata:void 0,columnWidth:S[e]}}))};return C.columns.forEach((function(t){t.calcdata=C,t.x=u(t)})),C}},{"../../lib/extend":766,"./constants":1323,"fast-isnumeric":242}],1325:[function(t,e,r){"use strict";var n=t("../../lib/extend").extendFlat;r.splitToPanels=function(t){var e=[0,0],r=n({},t,{key:"header",type:"header",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!0,values:t.calcdata.headerCells.values[t.specIndex],rowBlocks:t.calcdata.headerRowBlocks,calcdata:n({},t.calcdata,{cells:t.calcdata.headerCells})});return[n({},t,{key:"cells1",type:"cells",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),n({},t,{key:"cells2",type:"cells",page:1,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),r]},r.splitToCells=function(t){var e=function(t){var e=t.rowBlocks[t.page],r=e?e.rows[0].rowIndex:0,n=e?r+e.rows.length:0;return[r,n]}(t);return(t.values||[]).slice(e[0],e[1]).map((function(r,n){return{keyWithinBlock:n+("string"==typeof r&&r.match(/[<$&> 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a.indexOf(t)})),s=o.length;s<n;s++)o.push(s);e("columnorder",o)}(e,s),s("cells.values"),s("cells.format"),s("cells.align"),s("cells.prefix"),s("cells.suffix"),s("cells.height"),s("cells.line.width"),s("cells.line.color"),s("cells.fill.color"),n.coerceFont(s,"cells.font",n.extendFlat({},o.font)),e._length=null}},{"../../lib":776,"../../plots/domain":855,"./attributes":1320}],1327:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),plot:t("./plot"),moduleType:"trace",name:"table",basePlotModule:t("./base_plot"),categories:["noOpacity"],meta:{}}},{"./attributes":1320,"./base_plot":1321,"./calc":1322,"./defaults":1326,"./plot":1328}],1328:[function(t,e,r){"use strict";var n=t("./constants"),i=t("@plotly/d3"),a=t("../../lib").numberFormat,o=t("../../lib/gup"),s=t("../../components/drawing"),l=t("../../lib/svg_text_utils"),c=t("../../lib").raiseToTop,u=t("../../lib").strTranslate,f=t("../../lib").cancelTransition,h=t("./data_preparation_helper"),p=t("./data_split_helpers"),d=t("../../components/color");function m(t){return Math.ceil(t.calcdata.maxLineWidth/2)}function g(t,e){return"clip"+t._fullLayout._uid+"_scrollAreaBottomClip_"+e.key}function v(t,e){return"clip"+t._fullLayout._uid+"_columnBoundaryClippath_"+e.calcdata.key+"_"+e.specIndex}function y(t){return[].concat.apply([],t.map((function(t){return t}))).map((function(t){return t.__data__}))}function x(t,e,r){var a=t.selectAll("."+n.cn.scrollbarKit).data(o.repeat,o.keyFun);a.enter().append("g").classed(n.cn.scrollbarKit,!0).style("shape-rendering","geometricPrecision"),a.each((function(t){var e=t.scrollbarState;e.totalHeight=function(t){var e=t.rowBlocks;return D(e,e.length-1)+(e.length?R(e[e.length-1],1/0):1)}(t),e.scrollableAreaHeight=t.groupHeight-S(t),e.currentlyVisibleHeight=Math.min(e.totalHeight,e.scrollableAreaHeight),e.ratio=e.currentlyVisibleHeight/e.totalHeight,e.barLength=Math.max(e.ratio*e.currentlyVisibleHeight,n.goldenRatio*n.scrollbarWidth),e.barWiggleRoom=e.currentlyVisibleHeight-e.barLength,e.wiggleRoom=Math.max(0,e.totalHeight-e.scrollableAreaHeight),e.topY=0===e.barWiggleRoom?0:t.scrollY/e.wiggleRoom*e.barWiggleRoom,e.bottomY=e.topY+e.barLength,e.dragMultiplier=e.wiggleRoom/e.barWiggleRoom})).attr("transform",(function(t){var e=t.width+n.scrollbarWidth/2+n.scrollbarOffset;return u(e,S(t))}));var s=a.selectAll("."+n.cn.scrollbar).data(o.repeat,o.keyFun);s.enter().append("g").classed(n.cn.scrollbar,!0);var l=s.selectAll("."+n.cn.scrollbarSlider).data(o.repeat,o.keyFun);l.enter().append("g").classed(n.cn.scrollbarSlider,!0),l.attr("transform",(function(t){return u(0,t.scrollbarState.topY||0)}));var c=l.selectAll("."+n.cn.scrollbarGlyph).data(o.repeat,o.keyFun);c.enter().append("line").classed(n.cn.scrollbarGlyph,!0).attr("stroke","black").attr("stroke-width",n.scrollbarWidth).attr("stroke-linecap","round").attr("y1",n.scrollbarWidth/2),c.attr("y2",(function(t){return t.scrollbarState.barLength-n.scrollbarWidth/2})).attr("stroke-opacity",(function(t){return t.columnDragInProgress||!t.scrollbarState.barWiggleRoom||r?0:.4})),c.transition().delay(0).duration(0),c.transition().delay(n.scrollbarHideDelay).duration(n.scrollbarHideDuration).attr("stroke-opacity",0);var f=s.selectAll("."+n.cn.scrollbarCaptureZone).data(o.repeat,o.keyFun);f.enter().append("line").classed(n.cn.scrollbarCaptureZone,!0).attr("stroke","white").attr("stroke-opacity",.01).attr("stroke-width",n.scrollbarCaptureWidth).attr("stroke-linecap","butt").attr("y1",0).on("mousedown",(function(r){var n=i.event.y,a=this.getBoundingClientRect(),o=r.scrollbarState,s=n-a.top,l=i.scale.linear().domain([0,o.scrollableAreaHeight]).range([0,o.totalHeight]).clamp(!0);o.topY<=s&&s<=o.bottomY||L(e,t,null,l(s-o.barLength/2))(r)})).call(i.behavior.drag().origin((function(t){return i.event.stopPropagation(),t.scrollbarState.scrollbarScrollInProgress=!0,t})).on("drag",L(e,t)).on("dragend",(function(){}))),f.attr("y2",(function(t){return t.scrollbarState.scrollableAreaHeight})),e._context.staticPlot&&(c.remove(),f.remove())}function b(t,e,r,a){var l=function(t){var e=t.selectAll("."+n.cn.columnCell).data(p.splitToCells,(function(t){return t.keyWithinBlock}));return e.enter().append("g").classed(n.cn.columnCell,!0),e.exit().remove(),e}(function(t){var e=t.selectAll("."+n.cn.columnCells).data(o.repeat,o.keyFun);return e.enter().append("g").classed(n.cn.columnCells,!0),e.exit().remove(),e}(r));!function(t){t.each((function(t,e){var 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================================================
FILE: 2023年/2023.08.05 局部细节放大图/局部细节放大图.R
================================================
# =============== 局部细节放大图 ===============
library(ggplot2)
library(patchwork)
library(tidyverse)
# 1. 生成模拟数据
com_battery = data.frame("Time" = 1:30,"True" = cumsum(abs(rnorm(30,2,0.4))),
                          "Proposed" = cumsum(abs(rnorm(30,2,0.5))),
                          "Linear" = cumsum(abs(rnorm(30,2,0.4))), 
                          "Power" = cumsum(abs(rnorm(30,2,0.1))),
                          "Exp" = cumsum(abs(rnorm(30,2,0.3))))

cols <- c("black","#85BA8F", "#A3C8DC","#349839","#EA5D2D","#EABB77","#F09594")
# 2.基础绘图
p = com_battery %>% pivot_longer(cols = !Time, names_to = "Model", values_to = "Value") %>%
  mutate(Model = factor(Model, levels = c("True", "Proposed", "Linear","Power","Exp"))) %>% 
  ggplot(aes(Time,Value,col = Model,shape = Model)) + 
  geom_line() + geom_point(size=1.5,alpha=0.8) + 
  # scale_color_viridis(discrete = T) +
  scale_color_manual(values = cols) +
  theme_bw() + theme(panel.grid = element_blank()) + #,legend.title=element_text(size=12), legend.text=element_text(size=11) +
  xlab("Time") + ylab("Rate(%)")

## 方法一：ggforce
library(ggforce) 
p + facet_zoom(xlim = c(18, 24),ylim = c(40, 43), zoom.size = 0.4)

## 方法二：patchwork
library(patchwork)
# 绘制放大图
ppp = p + xlim(5,10) + ylim(10,20) + theme(legend.position = 'none') +
  xlab("") + ylab("") 
ppp 
# 添加到原图中
p +   
  geom_rect(aes(xmin = 5, xmax = 10, ymin = 10, ymax = 20),
            fill = "transparent", color = "black", alpha = 0, 
            linetype = "dashed", linewidth =0.2) + #添加选择放大位置的框
  theme(legend.position = c(0.9,.2),legend.background = element_rect(fill = 'white', colour = 'black')) + #修改图例位置
  geom_segment(aes(x = 7, xend = 10, y = 20, yend = 38.3), 
               col = "gray60", linewidth =0.2,linetype = "dashed",
               arrow = arrow(length = unit(0.2, "cm"), type = "closed")) + # 添加指向箭头
  inset_element(ppp, 0.01, 0.6, 0.6, 0.95, on_top = TRUE)









================================================
FILE: 2023年/2023.08.24 绘制足球数据/ggsoccer.R
================================================
library(ggsoccer)

# 最基础 ====
ggplot() +
  annotate_pitch() +
  theme_pitch() 

# 投射路线
pass_data <- data.frame(x = c(24, 18, 64, 78, 53),
                        y = c(43, 55, 88, 18, 44),
                        x2 = c(34, 44, 81, 85, 64),
                        y2 = c(40, 62, 89, 44, 28))

## 版本一
ggplot(pass_data) +
  annotate_pitch(colour = "white",
                 fill = "#3ab54a") +
  geom_segment(aes(x = x, y = y, xend = x2, yend = y2),
               arrow = arrow(length = unit(0.25, "cm"),
                             type = "closed")) +
  theme_pitch() +
  direction_label() #+
  # ggtitle("Simple passmap", 
  #         "ggsoccer example")


## 版本二（半场）
ggplot(pass_data) +
  annotate_pitch(colour = "white",
                 fill = "#3ab54a") +
  geom_segment(aes(x = x, y = y, xend = x2, yend = y2),
               arrow = arrow(length = unit(0.25, "cm"),
                             type = "closed")) +
  theme_pitch() +
  # direction_label() +
  # ggtitle("Simple passmap", 
  # "ggsoccer example") +
  coord_flip(xlim = c(49, 101)) +
  scale_y_reverse() 


# 投射情况 =======
df <- data.frame(x = rnorm(20, 80, 10), 
                 y = rnorm(20, 50, 20),
                 Shot = sample(c("In", "Out"),
                               40, replace = TRUE))

library(viridis)
ggplot(df) +
  annotate_pitch(colour = "white",
                 fill = "#3ab54a") +
  geom_point(aes(x = x, y = y, fill = Shot),
             shape = 21,
             size = 4) +
  coord_cartesian(xlim = c(45, 105))+ 
  scale_fill_viridis(discrete=T) +
  theme_pitch() +
  theme(panel.background = element_rect(fill = "#3ab54a")) 


# ggplot 版本复现=======

ggplot()+
  geom_rect(aes(xmin = -10, xmax = 100, ymin= -10, ymax = 130), fill = "#3ab54a", colour = "#FFFFFF", size = 0.5)+ ### Background outline
  geom_rect(aes(xmin = 0, xmax = 90, ymin= 0, ymax = 120), fill = "#3ab54a", colour = "#FFFFFF", size = 0.5)+ ###Sideline, Endline
  geom_rect(aes(xmin = 0, xmax = 90, ymin= 0, ymax = 60), fill = NA , colour = "#FFFFFF", size = 0.5)+ #Halfway
  geom_rect(aes(xmin = 24.85, xmax = 65.15, ymin= 0, ymax = 16.5), fill = "#3ab54a", colour = "#FFFFFF", size = 0.5)+ #lower 18 yard box
  geom_rect(aes(xmin = 35.85, xmax = 54.15, ymin= 0, ymax = 5.5), fill = "#3ab54a", colour = "#FFFFFF", size = 0.5)+ # Lower 5 yard box
  geom_rect(aes(xmin = 24.85, xmax = 65.15, ymin= 103.5, ymax = 120), fill = "#3ab54a", colour = "#FFFFFF", size = 0.5)+ #upper 18 yard box
  geom_rect(aes(xmin = 35.85, xmax = 54.15, ymin= 114.5, ymax = 120), fill = "#3ab54a", colour = "#FFFFFF", size = 0.5)+ #upper 5 yard box
  geom_curve(aes(x = 35.85, y = 16.5, xend = 54.15, yend = 16.5), curvature = -0.6, colour = "#FFFFFF")+ #lower D
  geom_curve(aes(x = 35.85, y = 103.5, xend = 54.15, yend = 103.5), curvature = .6, colour = "#FFFFFF")+ #upper D
  ggforce::geom_circle(aes(x0=45, y0=60, r= 9.15), colour = "#FFFFFF")+ ##Halfway circle 
  # coord_flip(xlim = c(49, 101)) +
  coord_flip() +
  scale_y_reverse() +
  theme(rect = element_blank(),
        line = element_blank(),
        text = element_blank())


================================================
FILE: 2023年/2023.10.08 灯芯柱状图/.Rhistory
================================================
scale_fill_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_alpha_manual(values = c(.25, .4), guide = "none") +
theme(
plot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = "black"),
plot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),
panel.grid.major = element_blank(),
axis.text.x = element_blank()
)
df_animals_sum %>%
ggplot(aes(cal_year, n)) +
geom_col(aes(fill = factor(cal_year)), width = .85) +
geom_col(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(alpha = cal_year == 2020), # 这里有细节！
fill = "white", width = .5 # 宽度和透明度设置
) +
geom_text( #这里的数据处理：添加文本+加入rescues
data = df_animals_sum %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
aes(label = n_lab), size = 4.3, lineheight = .8,
nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))),
aes(label = n_lab),
color = "white", lineheight = .8, size = 4.3,
nudge_y = 12, vjust = 0, fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(y = -15, label = cal_year, color = factor(cal_year)),
size = 6, hjust = .5, vjust = 1
) +
coord_cartesian(clip = "off") #这想干嘛？
df_animals_sum %>%
ggplot(aes(cal_year, n)) +
geom_col(aes(fill = factor(cal_year)), width = .85) +
geom_col(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(alpha = cal_year == 2020), # 这里有细节！
fill = "white", width = .5 # 宽度和透明度设置
) +
geom_text( #这里的数据处理：添加文本+加入rescues
data = df_animals_sum %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
aes(label = n_lab), size = 4.3, lineheight = .8,
nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))),
aes(label = n_lab),
color = "white", lineheight = .8, size = 4.3,
nudge_y = 12, vjust = 0, fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(y = -15, label = cal_year, color = factor(cal_year)),
size = 6, hjust = .5, vjust = 1
) +
coord_cartesian(clip = "off") +#这想干嘛？
scale_y_continuous(limits = c(-15, NA)) +
scale_color_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_fill_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_alpha_manual(values = c(.25, .4), guide = "none") +
theme(
plot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = "black"),
plot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),
panel.grid.major = element_blank(),
axis.text.x = element_blank()
)
df_animals_sum %>%
ggplot(aes(cal_year, n)) +
geom_col(aes(fill = factor(cal_year)), width = .85) +
geom_col(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(alpha = cal_year == 2020), # 这里有细节！
fill = "white", width = .5 # 宽度和透明度设置
) +
geom_text( #这里的数据处理：添加文本+加入rescues
data = df_animals_sum %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
aes(label = n_lab), size = 4.3, lineheight = .8,
nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))),
aes(label = n_lab),
color = "white", lineheight = .8, size = 4.3,
nudge_y = 12, vjust = 0, fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(y = -15, label = cal_year, color = factor(cal_year)),
size = 6, hjust = .5, vjust = 1
) +
coord_cartesian(clip = "off") +#这想干嘛？
scale_y_continuous(limits = c(-15, NA)) +
scale_color_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_fill_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_alpha_manual(values = c(.25, .4), guide = "none") +
theme(
plot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = "black"),
plot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),
panel.grid.major = element_blank(),
axis.text.x = element_blank()
)
df_animals_sum %>%
ggplot(aes(cal_year, n)) +
geom_col(aes(fill = factor(cal_year)), width = .85) +
geom_col(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(alpha = cal_year == 2020), # 这里有细节！
fill = "white", width = .5 # 宽度和透明度设置
) +
geom_text( #这里的数据处理：添加文本+加入rescues
data = df_animals_sum %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
aes(label = n_lab), size = 4.3, lineheight = .8,
nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))),
aes(label = n_lab),
color = "white", lineheight = .8, size = 4.3,
nudge_y = 12, vjust = 0, fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(y = -15, label = cal_year, color = factor(cal_year)),
size = 6, hjust = .5, vjust = 1
) +
# coord_cartesian(clip = "off") +#这想干嘛？
scale_y_continuous(limits = c(-15, NA)) +
scale_color_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_fill_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_alpha_manual(values = c(.25, .4), guide = "none") +
theme(
plot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = "black"),
plot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),
panel.grid.major = element_blank(),
axis.text.x = element_blank()
)
df_animals_sum %>%
ggplot(aes(cal_year, n)) +
geom_col(aes(fill = factor(cal_year)), width = .85) +
geom_col(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(alpha = cal_year == 2020), # 这里有细节！
fill = "white", width = .5 # 宽度和透明度设置
) +
geom_text( #这里的数据处理：添加文本+加入rescues
data = df_animals_sum %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
aes(label = n_lab), size = 4.3, lineheight = .8,
nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))),
aes(label = n_lab),
color = "white", lineheight = .8, size = 4.3,
nudge_y = 12, vjust = 0, fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(y = -15, label = cal_year, color = factor(cal_year)),
size = 6, hjust = .5, vjust = 1
) +
coord_cartesian(clip = "off") +#这想干嘛？
scale_y_continuous(limits = c(-15, NA)) +
scale_color_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_fill_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_alpha_manual(values = c(.25, .4), guide = "none") +
theme(
plot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = "black"),
plot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),
panel.grid.major = element_blank(),
axis.text.x = element_blank()
)
df_animals_sum %>%
ggplot(aes(cal_year, n)) +
geom_col(aes(fill = factor(cal_year)), width = .85) +
geom_col(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(alpha = cal_year == 2020), # 这里有细节！
fill = "white", width = .5 # 宽度和透明度设置
) +
geom_text( #这里的数据处理：添加文本+加入rescues
data = df_animals_sum %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
aes(label = n_lab), size = 4.3, lineheight = .8,
nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))),
aes(label = n_lab),
color = "white", lineheight = .8, size = 4.3,
nudge_y = 12, vjust = 0, fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(y = -15, label = cal_year, color = factor(cal_year)),
size = 6, hjust = .5, vjust = 1
) +
coord_cartesian(clip = "off") +#这想干嘛？
scale_y_continuous(limits = c(-15, NA)) #+
df_animals_sum %>%
ggplot(aes(cal_year, n)) +
geom_col(aes(fill = factor(cal_year)), width = .85) +
geom_col(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(alpha = cal_year == 2020), # 这里有细节！
fill = "white", width = .5 # 宽度和透明度设置
) +
geom_text( #这里的数据处理：添加文本+加入rescues
data = df_animals_sum %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
aes(label = n_lab), size = 4.3, lineheight = .8,
nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))),
aes(label = n_lab),
color = "white", lineheight = .8, size = 4.3,
nudge_y = 12, vjust = 0, fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(y = -15, label = cal_year, color = factor(cal_year)),
size = 6, hjust = .5, vjust = 1
) +
coord_cartesian(clip = "off") +#这想干嘛？
scale_y_continuous(limits = c(-15, NA)) +
scale_color_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_fill_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_alpha_manual(values = c(.25, .4), guide = "none") +
theme(
plot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = "black"),
plot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),
panel.grid.major = element_blank(),
axis.text.x = element_blank()
)
df_animals_sum %>%
ggplot(aes(cal_year, n)) +
geom_col(aes(fill = factor(cal_year)), width = .85) +
geom_col(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(alpha = cal_year == 2020), # 这里有细节！
fill = "white", width = .5 # 宽度和透明度设置
) +
geom_text( #这里的数据处理：添加文本+加入rescues
data = df_animals_sum %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
aes(label = n_lab), size = 4.3, lineheight = .8,
nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))),
aes(label = n_lab),
color = "white", lineheight = .8, size = 4.3,
nudge_y = 12, vjust = 0, fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(y = -15, label = cal_year, color = factor(cal_year)),
size = 6, hjust = .5, vjust = 1
) +
coord_cartesian(clip = "off") +#这想干嘛？
scale_y_continuous(limits = c(-15, NA)) #+
df_animals_sum %>%
ggplot(aes(cal_year, n)) +
geom_col(aes(fill = factor(cal_year)), width = .85) +
geom_col(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(alpha = cal_year == 2020), # 这里有细节！
fill = "white", width = .5 # 宽度和透明度设置
) +
geom_text( #这里的数据处理：添加文本+加入rescues
data = df_animals_sum %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
aes(label = n_lab), size = 4.3, lineheight = .8,
nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))),
aes(label = n_lab),
color = "white", lineheight = .8, size = 4.3,
nudge_y = 12, vjust = 0, fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(y = -15, label = cal_year, color = factor(cal_year)),
size = 6, hjust = .5, vjust = 1
) +
coord_cartesian(clip = "off")# +#这想干嘛？
df_animals_sum %>%
ggplot(aes(cal_year, n)) +
geom_col(aes(fill = factor(cal_year)), width = .85) +
geom_col(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(alpha = cal_year == 2020), # 这里有细节！
fill = "white", width = .5 # 宽度和透明度设置
) +
geom_text( #这里的数据处理：添加文本+加入rescues
data = df_animals_sum %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
aes(label = n_lab), size = 4.3, lineheight = .8,
nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))),
aes(label = n_lab),
color = "white", lineheight = .8, size = 4.3,
nudge_y = 12, vjust = 0, fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(y = -15, label = cal_year, color = factor(cal_year)),
size = 6, hjust = .5, vjust = 1
) +
coord_cartesian(clip = "off") +#这想干嘛？
# scale_y_continuous(limits = c(-15, NA)) +
scale_color_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_fill_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_alpha_manual(values = c(.25, .4), guide = "none") +
theme(
plot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = "black"),
plot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),
panel.grid.major = element_blank(),
axis.text.x = element_blank()
)
df_animals_sum %>%
ggplot(aes(cal_year, n)) +
geom_col(aes(fill = factor(cal_year)), width = .85) +
geom_col(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(alpha = cal_year == 2020), # 这里有细节！
fill = "white", width = .5 # 宽度和透明度设置
) +
geom_text( #这里的数据处理：添加文本+加入rescues
data = df_animals_sum %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
aes(label = n_lab), size = 4.3, lineheight = .8,
nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))),
aes(label = n_lab),
color = "white", lineheight = .8, size = 4.3,
nudge_y = 12, vjust = 0, fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(y = -15, label = cal_year, color = factor(cal_year)),
size = 6, hjust = .5, vjust = 1
) +
coord_cartesian(clip = "off") +#这想干嘛？
# scale_y_continuous(limits = c(-15, NA)) +
scale_color_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_fill_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_alpha_manual(values = c(.25, .4), guide = "none") #+
df_animals_sum %>%
ggplot(aes(cal_year, n)) +
geom_col(aes(fill = factor(cal_year)), width = .85) +
geom_col(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(alpha = cal_year == 2020), # 这里有细节！
fill = "white", width = .5 # 宽度和透明度设置
) +
geom_text( #这里的数据处理：添加文本+加入rescues
data = df_animals_sum %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
aes(label = n_lab), size = 4.3, lineheight = .8,
nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))),
aes(label = n_lab),
color = "white", lineheight = .8, size = 4.3,
nudge_y = 12, vjust = 0, fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(y = -15, label = cal_year, color = factor(cal_year)),
size = 6, hjust = .5, vjust = 1
) +
coord_cartesian(clip = "off") +#这想干嘛？
# scale_y_continuous(limits = c(-15, NA)) +
scale_color_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_fill_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") #+
df_animals_sum %>%
ggplot(aes(cal_year, n)) +
geom_col(aes(fill = factor(cal_year)), width = .85) +
geom_col(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(alpha = cal_year == 2020), # 这里有细节！
fill = "white", width = .5 # 宽度和透明度设置
) +
geom_text( #这里的数据处理：添加文本+加入rescues
data = df_animals_sum %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
aes(label = n_lab), size = 4.3, lineheight = .8,
nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))),
aes(label = n_lab),
color = "white", lineheight = .8, size = 4.3,
nudge_y = 12, vjust = 0, fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(y = -15, label = cal_year, color = factor(cal_year)),
size = 6, hjust = .5, vjust = 1
) +
coord_cartesian(clip = "off") +#这想干嘛？
# scale_y_continuous(limits = c(-15, NA)) +
scale_color_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_fill_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_alpha_manual(values = c(.25, .4), guide = "none") +
theme(
plot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = "black"),
plot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),
panel.grid.major = element_blank(),
axis.text.x = element_blank()
)
df_animals_sum %>%
ggplot(aes(cal_year, n)) +
geom_col(aes(fill = factor(cal_year)), width = .85) +
geom_col(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(alpha = cal_year == 2020), # 这里有细节！
fill = "white", width = .5 # 宽度和透明度设置
) +
geom_text( #这里的数据处理：添加文本+加入rescues
data = df_animals_sum %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
aes(label = n_lab), size = 4.3, lineheight = .8,
nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))),
aes(label = n_lab),
color = "white", lineheight = .8, size = 4.3,
nudge_y = 12, vjust = 0, fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(y = -15, label = cal_year, color = factor(cal_year)),
size = 6, hjust = .5, vjust = 1
) +
coord_cartesian(clip = "off") +#这想干嘛？
# scale_y_continuous(limits = c(-15, NA)) +
scale_color_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_fill_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_alpha_manual(values = c(.25, .4), guide = "none") +
theme(
plot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = "black"),
plot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),
panel.grid.major = element_blank(),
# axis.text.x = element_blank()
)
df_animals_sum %>%
ggplot(aes(cal_year, n)) +
geom_col(aes(fill = factor(cal_year)), width = .85) +
geom_col(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(alpha = cal_year == 2020), # 这里有细节！
fill = "white", width = .5 # 宽度和透明度设置
) +
geom_text( #这里的数据处理：添加文本+加入rescues
data = df_animals_sum %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
aes(label = n_lab), size = 4.3, lineheight = .8,
nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>%
mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))),
aes(label = n_lab),
color = "white", lineheight = .8, size = 4.3,
nudge_y = 12, vjust = 0, fontface = "bold"
) +
geom_text(
data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
aes(y = -15, label = cal_year, color = factor(cal_year)),
size = 6, hjust = .5, vjust = 1
) +
coord_cartesian(clip = "off") +#这想干嘛？
# scale_y_continuous(limits = c(-15, NA)) +
scale_color_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_fill_manual(values = c(rep("grey30", 11), "#b40059", "grey70"), guide = "none") +
scale_alpha_manual(values = c(.25, .4), guide = "none") +
theme(
# plot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = "black"),
# plot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),
panel.grid.major = element_blank(),
axis.text.x = element_blank()
)


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FILE: 2023年/2023.10.08 灯芯柱状图/.Rproj.user/C6560784/sources/session-6c0d0c41/4BEAD114-contents
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# 代码来源于https://github.com/z3tt/TidyTuesday/blob/main/R/2021_27_AnimalRescues.Rmd
# 与源代码有所改动，数据读取代码进行了改动；删除第二幅图代码。
library(tidyverse)
library(ggtext)
library(ggrepel)
library(patchwork)
library(systemfonts)


# 主题设置 ====== 
theme_set(theme_minimal(base_size = 19))

theme_update(
  text = element_text(color = "grey12"),
  axis.title = element_blank(),
  axis.text.x = element_text(),
  axis.text.y = element_blank(),
  panel.grid.major.y = element_blank(),
  panel.grid.minor = element_blank(),
  plot.margin = margin(20, 5, 10, 10),
  plot.subtitle = element_textbox_simple(size = 14,
                                         lineheight = 1.6),
  plot.title.position = "plot",
  plot.caption = element_text( color = "#b40059", hjust = .5,
                              size = 10, margin = margin(35, 0, 0, 0))
)

# 数据读取+处理 ========
df_animals <- readr::read_csv('animal_rescues.txt')

df_animals_agg <-
  df_animals %>% 
  mutate(
    animal_group_aggregated = case_when(
      str_detect(animal_group_parent, "Domestic|Livestock|Farm|Horse|Cow|Sheep|Goat|Lamb|Bull") ~ "Other Domestic Animals",
      animal_group_parent %in% c("Cat", "cat") ~ "Cats",
      animal_group_parent %in% c("Bird", "Budgie") ~ "Birds",
      animal_group_parent == "Dog" ~ "Dogs",
      animal_group_parent == "Fox" ~ "Foxes",
      TRUE ~ "Other Wild Animals"
    )
  ) %>% 
  count(cal_year, animal_group_aggregated) %>% 
  group_by(animal_group_aggregated) %>% 
  mutate(
    total = sum(n),
    current = n[which(cal_year == 2021)]
  ) %>% 
  ungroup() %>% 
  mutate(
    animal_group_aggregated = fct_reorder(animal_group_aggregated, total),
    animal_group_aggregated = fct_relevel(animal_group_aggregated, "Other Domestic Animals", after = 0),
    animal_group_aggregated = fct_relevel(animal_group_aggregated, "Other Wild Animals", after = 0)
  )

df_animals_labs <-
  df_animals_agg %>% 
  filter(cal_year == 2016) %>% 
  group_by(animal_group_aggregated) %>% 
  mutate(n = case_when(
    animal_group_aggregated == "Cats" ~ 320,
    animal_group_aggregated %in% c("Birds", "Dogs") ~ 135,
    TRUE ~ 55
  ))

df_animals_annotate <-
  df_animals_agg %>% 
  mutate(label = "\n\n← Number of Rescues in 2021 so far.") %>% 
  filter(cal_year == 2021 & animal_group_aggregated == "Cats")

df_animals_sum <-
  df_animals_agg %>% 
  filter(cal_year < 2021) %>% 
  group_by(cal_year) %>% 
  summarize(n = sum(n))

# write_csv(df_animals_sum,"df_animals_sum.csv")
# df_animals_sum = read.csv("df_animals_sum.csv")
## 绘图 ==============

  df_animals_sum %>% 
  ggplot(aes(cal_year, n)) +
  geom_col(aes(fill = factor(cal_year)), width = .85) +
  geom_col(
    data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
    aes(alpha = cal_year == 2020), # 这里有细节！
    fill = "white", width = .5 # 宽度和透明度设置
  ) +
  geom_text( #这里的数据处理：添加文本+加入rescues
    data = df_animals_sum %>% 
      mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
    aes(label = n_lab), size = 4.3, lineheight = .8, 
    nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
  ) +
  geom_text( #添加文本+加入cats
    data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>% 
      mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))), 
    aes(label = n_lab), 
    color = "white", lineheight = .8, size = 4.3, 
    nudge_y = 12, vjust = 0, fontface = "bold"
  ) +
  geom_text( # 手动添加年份标签
    data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
    aes(y = -15, label = cal_year, color = factor(cal_year)), 
    size = 6, hjust = .5, vjust = 1
  ) +
  coord_cartesian(clip = "off") +#这想干嘛？
  scale_y_continuous(limits = c(-15, NA)) +
  scale_color_manual(values = c(rep("grey30", 11), "#b40059"), guide = "none") +
  scale_fill_manual(values = c(rep("grey30", 11), "#b40059"), guide = "none") +
  scale_alpha_manual(values = c(.25, .4), guide = "none") +
  theme(
    # plot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = "black"),
    # plot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),
    panel.grid.major = element_blank(),
    axis.text.x = element_blank()
  )





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================================================
FILE: 2023年/2023.10.08 灯芯柱状图/animal_rescues.txt
================================================
incident_number,date_time_of_call,cal_year,fin_year,type_of_incident,pump_count,pump_hours_total,hourly_notional_cost,incident_notional_cost,final_description,animal_group_parent,originof_call,property_type,property_category,special_service_type_category,special_service_type,ward_code,ward,borough_code,borough,stn_ground_name,uprn,street,usrn,postcode_district,easting_m,northing_m,easting_rounded,northing_rounded,latitude,longitude
139091,01/01/2009 03:01,2009,2008/09,Special Service,1,2,255,510,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011467,Crystal Palace & Upper Norwood,E09000008,Croydon,Norbury,NULL,Waddington Way,20500146,SE19,NULL,NULL,532350,170050,NULL,NULL
275091,01/01/2009 08:51,2009,2008/09,Special Service,1,1,255,255,Redacted,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000169,Woodside,E09000008,Croydon,Woodside,NULL,Grasmere Road,NULL,SE25,534785,167546,534750,167550,51.39095371,-0.064166887
2075091,04/01/2009 10:07,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000558,Carshalton Central,E09000029,Sutton,Wallington,NULL,Mill Lane,NULL,SM5,528041,164923,528050,164950,51.36894086,-0.161985191
2872091,05/01/2009 12:27,2009,2008/09,Special Service,1,1,255,255,Redacted,Horse,Person (mobile),"Intensive Farming Sheds (chickens, pigs etc)",Non Residential,Animal rescue from water,Animal rescue from water - Farm animal,E05000330,Harefield,E09000017,Hillingdon,Ruislip,1.00021E+11,Park Lane,21401484,UB9,504689,190685,504650,190650,51.60528344,-0.489683853
3553091,06/01/2009 15:23,2009,2008/09,Special Service,1,1,255,255,Redacted,Rabbit,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Swindon Lane,21300122,RM3,NULL,NULL,554650,192350,NULL,NULL
3742091,06/01/2009 19:30,2009,2008/09,Special Service,1,1,255,255,Redacted,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000027,Alibon,E09000002,Barking and Dagenham,Dagenham,NULL,Rogers Road,19900321,RM10,NULL,NULL,549350,184950,NULL,NULL
4011091,07/01/2009 06:29,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,NULL,Holloway Road,NULL,E11,539013,186162,539050,186150,51.55722086,0.003880198
4211091,07/01/2009 11:55,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000515,Wanstead,E09000026,Redbridge,Leytonstone,NULL,Aldersbrook Road,NULL,E12,541327,186654,541350,186650,51.56106688,0.037433776
4306091,07/01/2009 13:48,2009,2008/09,Special Service,1,1,255,255,Redacted,Squirrel,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05011470,New Addington North,E09000008,Croydon,Addington,NULL,Brockham Crescent,20501673,CR0,NULL,NULL,538750,163350,NULL,NULL
4715091,07/01/2009 21:24,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Stoke Newington,NULL,Southwold Road,NULL,E5,535425,186743,535450,186750,51.56331352,-0.047620955
5186091,08/01/2009 14:34,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000115,Cray Valley West,E09000006,Bromley,Orpington,1.0002E+11,Tilbury Close,20301320,BR5,546981,169297,546950,169250,51.4036626,0.111722339
5363091,08/01/2009 19:23,2009,2008/09,Special Service,1,2,255,510,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000620,Queenstown,E09000032,Wandsworth,Clapham,NULL,Battersea Park,NULL,SW11,528666,177290,528650,177250,51.47994253,-0.148525691
5682091,09/01/2009 11:01,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000535,Camberwell Green,E09000028,Southwark,Peckham,NULL,Valmar Road,NULL,SE5,532478,176515,532450,176550,51.47209876,-0.093952949
5688091,09/01/2009 11:18,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,100090039,Rainham Road North,19900293,RM10,549923,186441,549950,186450,51.55693599,0.161258357
5724091,09/01/2009 12:40,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000528,South Richmond,E09000027,Richmond upon Thames,Richmond,10002260585,Church Road,22405449,TW10,518497,174567,518450,174550,51.45768783,-0.29580467
5770091,09/01/2009 13:43,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011489,Woodside,E09000008,Croydon,Woodside,1.00021E+11,Belmont Road,20500674,SE25,534865,167671,534850,167650,51.39205798,-0.062970325
5789091,09/01/2009 14:30,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Wasteland,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,10023230026,Bessingham Walk,22006012,SE4,535913,174968,535950,174950,51.45738228,-0.045120782
5797091,09/01/2009 14:39,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Ambulance,Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000431,Streatham South,E09000022,Lambeth,Norbury,NULL,Streatham Common South,NULL,SW16,530995,170839,530950,170850,51.42143443,-0.117391508
6259091,10/01/2009 09:35,2009,2008/09,Special Service,1,1,255,255,Redacted,Unknown - Domestic Animal Or Pet,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000540,East Walworth,E09000028,Southwark,Old Kent Road,NULL,Balfour Street,NULL,SE17,532595,178874,532550,178850,51.49327092,-0.091384632
6270091,10/01/2009 10:09,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000159,Purley,E09000008,Croydon,Purley,NULL,Brighton Road,NULL,CR8,531679,161982,531650,161950,51.34167993,-0.11084037
6317091,10/01/2009 11:27,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000540,East Walworth,E09000028,Southwark,Old Kent Road,NULL,Balfour Street,NULL,SE17,532594,178879,532550,178850,51.49331609,-0.091397151
6353091,10/01/2009 12:26,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,Brownfield Street,22700211,E14,NULL,NULL,538150,181250,NULL,NULL
6355091,10/01/2009 12:32,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),Road surface/pavement,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000288,Greenhill,E09000015,Harrow,Harrow,NULL,Roxborough Avenue,NULL,HA1,515074,187728,515050,187750,51.57668207,-0.340758362
6378091,10/01/2009 13:05,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000039,Thames,E09000002,Barking and Dagenham,Barking,100089001,Stern Close,19903819,IG11,547091,183173,547050,183150,51.52831647,0.119072708
6400091,10/01/2009 13:28,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000055,Hendon,E09000003,Barnet,Hendon,NULL,Green Walk,NULL,NW4,524023,188969,524050,188950,51.58594049,-0.211240794
6530091,10/01/2009 16:38,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009321,Bromley North,E09000030,Tower Hamlets,Poplar,NULL,Hancock Road,NULL,E3,538165,182731,538150,182750,51.52659798,-0.009689766
6970091,11/01/2009 10:11,2009,2008/09,Special Service,2,3,255,765,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000637,Knightsbridge and Belgravia,E09000033,Westminster,Kensington,NULL,Hyde Park,NULL,SW7,527257,180039,527250,180050,51.50496632,-0.167814607
7051091,11/01/2009 12:32,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000315,Hylands,E09000016,Havering,Hornchurch,1.00021E+11,Albany Road,21300936,RM12,552339,186735,552350,186750,51.55893097,0.196208829
7613091,12/01/2009 13:10,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000031,Eastbury,E09000002,Barking and Dagenham,Barking,NULL,Canonsleigh Road,19900436,RM9,NULL,NULL,546950,184150,NULL,NULL
7720091,12/01/2009 16:39,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000032,Gascoigne,E09000002,Barking and Dagenham,Barking,NULL,Tomlins Orchard,NULL,IG11,544212,183641,544250,183650,51.5332637,0.077790345
8648091,14/01/2009 06:38,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000454,Whitefoot,E09000023,Lewisham,Bromley,NULL,Shroffold Road,22003952,BR1,NULL,NULL,539750,172050,NULL,NULL
9252091,15/01/2009 12:38,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (mobile),Wasteland,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000425,Larkhall,E09000022,Lambeth,Clapham,NULL,Deeley Road,NULL,SW8,529741,176685,529750,176650,51.47426021,-0.133275614
10299091,17/01/2009 11:40,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Public toilets,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000348,Chiswick Homefields,E09000018,Hounslow,Chiswick,NULL,Dan Mason Drive,NULL,W4,521444,176789,521450,176750,51.47703452,-0.252645628
10470091,17/01/2009 18:08,2009,2008/09,Special Service,1,2,255,510,Redacted,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000186,Norwood Green,E09000009,Ealing,Southall,NULL,Armstrong Way,NULL,UB2,513501,179808,513550,179850,51.50581677,-0.366000236
10829091,18/01/2009 11:08,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000136,Haverstock,E09000007,Camden,Kentish Town,NULL,Malden Place,20400336,NW5,NULL,NULL,528050,185150,NULL,NULL
10976091,18/01/2009 16:19,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Swindon Lane,NULL,RM3,554742,192355,554750,192350,51.60877075,0.23331818
12317091,21/01/2009 00:43,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000040,Valence,E09000002,Barking and Dagenham,Dagenham,NULL,Greenway,19900158,RM8,NULL,NULL,547450,186950,NULL,NULL
12451091,21/01/2009 10:33,2009,2008/09,Special Service,2,3,255,765,Redacted,Fox,Person (land line),Beach,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000427,Prince's,E09000022,Lambeth,Lambeth,NULL,Albert Embankment,NULL,SE1,530526,178612,530550,178650,51.49139735,-0.121267747
12533091,21/01/2009 14:04,2009,2008/09,Special Service,1,2,255,510,Redacted,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Kingston,NULL,Ham Gate Avenue,NULL,TW10,518938,171828,518950,171850,51.43297839,-0.290379808
12770091,21/01/2009 21:55,2009,2008/09,Special Service,1,1,255,255,Redacted,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000411,St. James,E09000021,Kingston upon Thames,New Malden,NULL,Welbeck Close,NULL,KT3,NULL,NULL,521550,167750,NULL,NULL
13245091,22/01/2009 21:35,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000287,Edgware,E09000015,Harrow,Stanmore,NULL,Broomgrove Gardens,21202323,HA8,NULL,NULL,519350,190750,NULL,NULL
14002091,24/01/2009 10:31,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Tenement Building,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000516,Barnes,E09000027,Richmond upon Thames,Hammersmith,NULL,Lonsdale Road,22404716,SW13,NULL,NULL,521950,177550,NULL,NULL
14652091,25/01/2009 10:26,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Railway,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000374,Hillrise,E09000019,Islington,Holloway,NULL,Fairbridge Road,NULL,N19,529970,186960,529950,186950,51.56654715,-0.12619315
14691091,25/01/2009 12:11,2009,2008/09,Special Service,2,3,255,765,Redacted,Dog,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000364,Syon,E09000018,Hounslow,Heston,NULL,Commerce Road,NULL,TW8,517085,177478,517050,177450,51.48414487,-0.315155902
14784091,25/01/2009 15:27,2009,2008/09,Special Service,1,1,255,255,Redacted,Unknown - Domestic Animal Or Pet,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011489,Woodside,E09000008,Croydon,Woodside,1.00021E+11,Belmont Road,20500674,SE25,534865,167671,534850,167650,51.39205798,-0.062970325
14796091,25/01/2009 16:01,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000564,Sutton Central,E09000029,Sutton,Sutton,NULL,Carshalton Road,NULL,SM1,526028,164010,526050,164050,51.36118516,-0.191210825
15664091,27/01/2009 10:49,2009,2008/09,Special Service,1,1,255,255,Redacted,Squirrel,Person (land line),Vehicle Repair Workshop,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000449,New Cross,E09000023,Lewisham,Deptford,2.00001E+11,Creekside,22004382,SE8,537492,177108,537450,177150,51.47623195,-0.021576172
15682091,27/01/2009 11:21,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Royal College Street,20400512,NW1,NULL,NULL,529450,183750,NULL,NULL
15865091,27/01/2009 17:18,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Football stadium,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000219,Eltham South,E09000011,Greenwich,Eltham,NULL,Footscray Road,NULL,SE9,543250,174250,543250,174250,51.44912339,0.060121979
16245091,28/01/2009 13:40,2009,2008/09,Special Service,1,2,255,510,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000590,Cann Hall,E09000031,Waltham Forest,Leytonstone,1.00023E+11,Ferndale Road,22836050,E11,539647,186846,539650,186850,51.56321059,0.013290743
16271091,28/01/2009 14:41,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000123,Penge and Cator,E09000006,Bromley,Beckenham,NULL,Queen Adelaide Road,20302300,SE20,NULL,NULL,535450,170450,NULL,NULL
17262091,30/01/2009 10:43,2009,2008/09,Special Service,1,1,255,255,Redacted,Unknown - Heavy Livestock Animal,Person (land line),Other Dwelling,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000483,Forest Gate North,E09000025,Newham,Stratford,NULL,Maryland Square,22200086,E15,NULL,NULL,539250,185250,NULL,NULL
17297091,30/01/2009 12:17,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011102,Faraday,E09000028,Southwark,Old Kent Road,NULL,Thurlow Street,22502507,SE17,NULL,NULL,532950,178350,NULL,NULL
17633091,30/01/2009 23:36,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000075,Erith,E09000004,Bexley,Erith,NULL,Erith High Street,NULL,DA8,551609,178303,551650,178350,51.48336535,0.182061343
17783091,31/01/2009 09:47,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,2.00002E+11,St. James Lane,21106430,N10,529026,189379,529050,189350,51.58850236,-0.138917727
18369091,01/02/2009 08:48,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000333,Ickenham,E09000017,Hillingdon,Hillingdon,NULL,Long Lane,21401178,UB10,NULL,NULL,507850,185550,NULL,NULL
18386091,01/02/2009 09:30,2009,2008/09,Special Service,1,1,255,255,Redacted,Fox,Person (mobile),Park,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000044,Burnt Oak,E09000003,Barnet,Mill Hill,NULL,The Greenway,NULL,NW9,520785,190200,520750,190250,51.59770605,-0.257531516
20027091,04/02/2009 00:25,2009,2008/09,Special Service,1,1,255,255,Redacted,Fox,Person (land line),Wasteland,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000252,Avonmore and Brook Green,E09000013,Hammersmith and Fulham,Hammersmith,34057075,Avonmore Road,21000081,W14,524639,178810,524650,178850,51.49450385,-0.205949974
20379091,04/02/2009 17:47,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009390,Campden,E09000020,Kensington and Chelsea,Kensington,NULL,Bedford Gardens,21700696,W8,NULL,NULL,525350,180150,NULL,NULL
21166091,06/02/2009 09:08,2009,2008/09,Special Service,2,3,255,765,Redacted,Dog,Other FRS,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05004162,Loughton Forest,E07000072,Epping Forest,Essex,NULL,Warren Hill,NULL,IG10,540410,195273,540450,195250,51.63874348,0.027660488
21319091,06/02/2009 14:42,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,NULL,Gipsy Hill,NULL,SE19,533337,171017,533350,171050,51.42248815,-0.083662056
21401091,06/02/2009 17:14,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,St. John's Crescent,21901271,SW9,NULL,NULL,531350,175850,NULL,NULL
21458091,06/02/2009 19:29,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,St. John's Crescent,21901271,SW9,NULL,NULL,531350,175850,NULL,NULL
21760091,07/02/2009 11:12,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000644,St. James's,E09000033,Westminster,Soho,NULL,Lisle Street,NULL,WC2H,529769,180812,529750,180850,51.51134253,-0.131356592
21807091,07/02/2009 12:43,2009,2008/09,Special Service,1,1,255,255,Redacted,Unknown - Heavy Livestock Animal,Police,Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000405,Chessington South,E09000021,Kingston upon Thames,Surbiton,NULL,Fair Oak Lane,NULL,KT9,517179,161575,517150,161550,51.34119192,-0.319051309
22115091,08/02/2009 00:59,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Other Residential Home,Other Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000379,St. Mary's,E09000019,Islington,Islington,10012787326,Upper Street,21606199,N1,531616,184385,531650,184350,51.54302515,-0.103421503
22276091,08/02/2009 12:08,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Other FRS,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Rangers Road,NULL,E4,539784,194599,539750,194550,51.63284278,0.018351358
26040091,14/02/2009 11:38,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,10003976332,Park Road,21103726,N8,529321,189057,529350,189050,51.58554114,-0.134780349
26715091,15/02/2009 15:53,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009330,St. Katharine's & Wapping,E09000030,Tower Hamlets,Shadwell,6009847,Porters Walk,22701920,E1W,534781,180604,534750,180650,51.50830086,-0.059254177
27109091,16/02/2009 11:13,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000159,Purley,E09000008,Croydon,Purley,NULL,High Street,NULL,CR8,531368,161544,531350,161550,51.33781546,-0.115464278
27805091,17/02/2009 13:23,2009,2008/09,Special Service,2,5,255,1275,Redacted,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000075,Erith,E09000004,Bexley,Erith,NULL,Macarthur Close,NULL,DA8,551276,178506,551250,178550,51.48527845,0.17735639
27898091,17/02/2009 16:13,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,NULL,Great West Road,NULL,TW7,516420,177339,516450,177350,51.48303265,-0.32477449
28382091,18/02/2009 13:22,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Bridge Road,NULL,NW10,520994,184849,520950,184850,51.54957001,-0.256357552
29403091,20/02/2009 09:35,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Bradwell Avenue,19900051,RM10,NULL,NULL,549250,186850,NULL,NULL
30133091,21/02/2009 14:08,2009,2008/09,Special Service,1,1,255,255,Redacted,Fox,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000416,Bishop's,E09000022,Lambeth,Lambeth,2E+11,Upper Ground,21901574,SE1,531012,180382,531050,180350,51.50719166,-0.1136144
30194091,21/02/2009 16:12,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000060,Underhill,E09000003,Barnet,Barnet,200082080,Milton Avenue,20029780,EN5,524699,195738,524650,195750,51.64662325,-0.199082475
30836091,22/02/2009 13:17,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000456,Cannon Hill,E09000024,Merton,New Malden,NULL,Cannon Hill Lane,NULL,SW20,523933,168582,523950,168550,51.40273602,-0.219693097
30981091,22/02/2009 17:22,2009,2008/09,Special Service,1,1,255,255,Redacted,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Swiftsden Way,22001946,BR1,NULL,NULL,539450,170750,NULL,NULL
169092,23/02/2009 15:28,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Woodgrange Road,NULL,E7,NULL,NULL,540550,185150,NULL,NULL
31317091,23/02/2009 16:56,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Woodford,NULL,Hale End Road,22842150,E4,NULL,NULL,538750,191550,NULL,NULL
32555091,25/02/2009 22:18,2009,2008/09,Special Service,1,2,255,510,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000229,Woolwich Common,E09000011,Greenwich,Plumstead,NULL,Nightingale Place,20800938,SE18,NULL,NULL,543450,178050,NULL,NULL
33526091,27/02/2009 16:35,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000039,Thames,E09000002,Barking and Dagenham,Barking,NULL,Shaw Avenue,19900574,IG11,NULL,NULL,548150,183450,NULL,NULL
34598091,01/03/2009 08:31,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000130,Camden Town with Primrose Hill,E09000007,Camden,Euston,NULL,Fitzroy Road,NULL,NW1,528043,183876,528050,183850,51.53927199,-0.155102008
35246091,02/03/2009 14:29,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000475,Beckton,E09000025,Newham,East Ham,NULL,Robin Crescent,22207863,E6,NULL,NULL,542050,181750,NULL,NULL
35738091,03/03/2009 11:05,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000481,East Ham North,E09000025,Newham,East Ham,NULL,Edith Road,22207542,E6,NULL,NULL,541750,184250,NULL,NULL
35781091,03/03/2009 12:54,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (mobile),Railway building - other,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000478,Canning Town South,E09000025,Newham,Plaistow,NULL,Silvertown Way,NULL,E16,539504,181475,539550,181450,51.51498237,0.009102484
36590091,04/03/2009 18:36,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000474,Wimbledon Park,E09000024,Merton,Wimbledon,NULL,Weir Road,NULL,SW19,525885,171885,525850,171850,51.43199153,-0.19047348
36694091,04/03/2009 21:44,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009333,Spitalfields & Banglatown,E09000030,Tower Hamlets,Whitechapel,NULL,Brick Lane,22700191,E1,NULL,NULL,533850,181850,NULL,NULL
36729091,04/03/2009 23:35,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000447,Lee Green,E09000023,Lewisham,Lee Green,NULL,Weardale Road,NULL,SE13,538844,175164,538850,175150,51.45843297,-0.002884196
37589091,06/03/2009 15:22,2009,2008/09,Special Service,1,2,255,510,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000485,Green Street East,E09000025,Newham,East Ham,46023983,Elizabeth Road,22200586,E6,541831,184038,541850,184050,51.53743364,0.043645966
37752091,06/03/2009 20:25,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009388,Abingdon,E09000020,Kensington and Chelsea,Kensington,NULL,South Edwardes Square,NULL,W8,524987,179079,524950,179050,51.49684467,-0.20084439
38115091,07/03/2009 15:00,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000331,Heathrow Villages,E09000017,Hillingdon,Hayes,NULL,West End Lane,NULL,UB3,NULL,NULL,508450,177350,NULL,NULL
38412091,07/03/2009 22:52,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011108,Nunhead & Queen's Road,E09000028,Southwark,New Cross,NULL,Hooks Close,22501286,SE15,NULL,NULL,534750,176550,NULL,NULL
39040091,08/03/2009 23:54,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000112,Clock House,E09000006,Bromley,Beckenham,1.00024E+11,Elmers End Road,20300747,BR3,535275,169192,535250,169150,51.40562868,-0.056501617
39688091,10/03/2009 10:18,2009,2008/09,Special Service,1,2,255,510,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000497,Bridge,E09000026,Redbridge,Woodford,NULL,Underwood Road,NULL,IG8,541613,191143,541650,191150,51.60133166,0.043365143
40866091,12/03/2009 11:51,2009,2008/09,Special Service,2,3,255,765,Redacted,Horse,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000030,Eastbrook,E09000002,Barking and Dagenham,Dagenham,100007496,The Chase,19900828,RM7,551413,186198,551450,186150,51.55435513,0.182630569
40929091,12/03/2009 13:51,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),"Nurseries, market garden",Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000503,Fairlop,E09000026,Redbridge,Hainault,NULL,Inverness Drive,NULL,IG6,545379,191578,545350,191550,51.60428185,0.097883827
40946091,12/03/2009 14:36,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000477,Canning Town North,E09000025,Newham,Plaistow,NULL,Grange Road,22201429,E13,NULL,NULL,539950,182650,NULL,NULL
42324091,14/03/2009 20:06,2009,2008/09,Special Service,1,2,255,510,Redacted,Bird,Person (land line),Other retail,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000418,Clapham Common,E09000022,Lambeth,Clapham,1.00023E+11,Abbeville Road,21900061,SW4,529250,174250,529250,174250,51.45248937,-0.141230246
42388091,14/03/2009 21:29,2009,2008/09,Special Service,1,1,255,255,Redacted,Unknown - Heavy Livestock Animal,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000111,Chislehurst,E09000006,Bromley,Eltham,1.0002E+11,Barham Road,20302299,BR7,543609,171318,543650,171350,51.42268621,0.064096003
42771091,15/03/2009 14:59,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000342,West Drayton,E09000017,Hillingdon,Hayes,NULL,Station Road,21401864,UB7,NULL,NULL,506350,179650,NULL,NULL
42805091,15/03/2009 15:55,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),Park,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000073,Danson Park,E09000004,Bexley,Bexley,NULL,Danson Park,NULL,DA6,547276,175179,547250,175150,51.45643893,0.118404478
43668091,16/03/2009 19:56,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000599,High Street,E09000031,Waltham Forest,Walthamstow,NULL,Hawarden Road,22843850,E17,NULL,NULL,535850,189150,NULL,NULL
44528091,18/03/2009 09:37,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,NULL,Tollet Street,22701221,E1,NULL,NULL,535650,182250,NULL,NULL
44576091,18/03/2009 11:23,2009,2008/09,Special Service,1,2,255,510,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Westbury Road,22200283,E7,NULL,NULL,540850,184950,NULL,NULL
44593091,18/03/2009 11:46,2009,2008/09,Special Service,1,1,255,255,Redacted,Sheep,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Lea Valley Road,NULL,E4,537512,195033,537550,195050,51.63730176,-0.014284793
45058091,18/03/2009 22:07,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000529,South Twickenham,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Strawberry Vale,22403855,TW1,516044,172402,516050,172450,51.43873711,-0.331805652
46051091,20/03/2009 13:35,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011468,Fairfield,E09000008,Croydon,Croydon,NULL,Coombe Road,20502601,CR0,NULL,NULL,532450,164850,NULL,NULL
46168091,20/03/2009 16:36,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000195,Chase,E09000010,Enfield,Enfield,207116149,Elm Gardens,20702659,EN2,532650,198384,532650,198350,51.66858263,-0.083228566
46614091,21/03/2009 09:22,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000138,Holborn and Covent Garden,E09000007,Camden,Soho,NULL,Northington Street,20401017,WC1N,NULL,NULL,530750,182050,NULL,NULL
46818091,21/03/2009 16:01,2009,2008/09,Special Service,1,1,255,255,Redacted,Unknown - Heavy Livestock Animal,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000282,West Green,E09000014,Haringey,Hornsey,NULL,Langham Close,21103517,N15,NULL,NULL,531550,189650,NULL,NULL
46950091,21/03/2009 18:24,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05011110,Peckham,E09000028,Southwark,Old Kent Road,2.00003E+11,Hoyland Close,22501310,SE15,534615,177301,534650,177350,51.47865805,-0.062904025
49666091,25/03/2009 19:53,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05009329,St. Dunstan's,E09000030,Tower Hamlets,Bethnal Green,6136084,Eastfield Street,22702396,E14,536442,181589,536450,181550,51.51675404,-0.034953717
49798091,25/03/2009 23:32,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Warehouse,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000094,Kilburn,E09000005,Brent,West Hampstead,NULL,Priory Park Road,NULL,NW6,525021,183897,525050,183850,51.54013744,-0.198646295
50107091,26/03/2009 16:04,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000428,St. Leonard's,E09000022,Lambeth,Norbury,NULL,Lewin Road,21900869,SW16,NULL,NULL,529850,170950,NULL,NULL
50224091,26/03/2009 19:39,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000033,Goresbrook,E09000002,Barking and Dagenham,Dagenham,100000157,Lullington Road,19900003,RM9,548250,184167,548250,184150,51.53694489,0.136186442
50969091,28/03/2009 03:28,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),Private garage,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000317,Pettits,E09000016,Havering,Romford,1.00021E+11,Collier Row Lane,21301094,RM5,550223,190138,550250,190150,51.59007418,0.167161739
51076091,28/03/2009 10:51,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011485,South Norwood,E09000008,Croydon,Woodside,NULL,Farnley Road,20500936,SE25,NULL,NULL,532950,168050,NULL,NULL
51218091,28/03/2009 16:00,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (mobile),Multi-Storey car park,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,128011094,Lady Booth Road,21800579,KT1,518241,169196,518250,169150,51.40946896,-0.301280159
51295091,28/03/2009 18:12,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000365,Turnham Green,E09000018,Hounslow,Chiswick,NULL,Wellesley Road,21501177,W4,NULL,NULL,520150,178250,NULL,NULL
51306091,28/03/2009 18:38,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Chalton Street,20400794,NW1,NULL,NULL,529550,183150,NULL,NULL
51613091,29/03/2009 11:37,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Chalton Street,NULL,NW1,529876,182723,529850,182750,51.52849185,-0.129111558
52735091,31/03/2009 07:40,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),"Takeaway, fast food",Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000107,Biggin Hill,E09000006,Bromley,Biggin Hill,1.00023E+11,Main Road,20302882,TN16,542161,158562,542150,158550,51.30842551,0.038190634
53242091,31/03/2009 21:00,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Police,Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Kingston,NULL,Petersham Road,NULL,TW10,518275,173281,518250,173250,51.44617602,-0.299427934
53328091,31/03/2009 23:41,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000476,Boleyn,E09000025,Newham,Plaistow,NULL,Boundary Road,22200398,E13,NULL,NULL,541550,182950,NULL,NULL
53426091,01/04/2009 07:01,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Kingston,NULL,Petersham Road,NULL,TW10,518215,173325,518250,173350,51.44658399,-0.300276241
54193091,02/04/2009 11:59,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000517,East Sheen,E09000027,Richmond upon Thames,Richmond,NULL,Fife Road,NULL,SW14,520423,174506,520450,174550,51.45673375,-0.268116991
54586091,03/04/2009 01:27,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000463,Lavender Fields,E09000024,Merton,Mitcham,NULL,Victoria Road,NULL,CR4,527135,170076,527150,170050,51.41545484,-0.173149512
56140091,05/04/2009 11:35,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000140,Kilburn,E09000007,Camden,West Hampstead,5021535,Kingsgate Road,20400550,NW6,525284,183950,525250,183950,51.5405555,-0.194837117
56826091,06/04/2009 12:12,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000054,Hale,E09000003,Barnet,Mill Hill,NULL,Banstock Road,20002240,HA8,NULL,NULL,520050,191650,NULL,NULL
57003091,06/04/2009 16:20,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Other retail,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000437,Bellingham,E09000023,Lewisham,Forest Hill,NULL,Holmshaw Close,NULL,SE26,536484,171829,536450,171850,51.42903652,-0.038116382
58297091,08/04/2009 14:31,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011484,South Croydon,E09000008,Croydon,Croydon,1.00021E+11,St. Peters Road,20502342,CR0,532752,164426,532750,164450,51.36339425,-0.094533415
59370091,10/04/2009 04:39,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011489,Woodside,E09000008,Croydon,Woodside,NULL,Woodside Road,20501593,SE25,NULL,NULL,534750,167150,NULL,NULL
59435091,10/04/2009 09:58,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000429,Stockwell,E09000022,Lambeth,Brixton,NULL,Burnley Road,21900263,SW9,NULL,NULL,530750,176350,NULL,NULL
60105091,11/04/2009 11:47,2009,2009/10,Special Service,1,1,260,260,Redacted,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009395,Earl's Court,E09000020,Kensington and Chelsea,Kensington,NULL,Earls Court Road,21700166,SW5,NULL,NULL,525650,178450,NULL,NULL
60185091,11/04/2009 15:03,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000366,Barnsbury,E09000019,Islington,Islington,NULL,Liverpool Road,21606676,N1,NULL,NULL,531350,184050,NULL,NULL
60188091,11/04/2009 15:08,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000564,Sutton Central,E09000029,Sutton,Sutton,5870048525,Glena Mount,22602333,SM1,526316,164883,526350,164850,51.36896711,-0.18676661
60418091,11/04/2009 23:32,2009,2009/10,Special Service,1,2,260,520,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000290,Harrow Weald,E09000015,Harrow,Stanmore,NULL,Kenton Lane,21201479,HA3,NULL,NULL,515750,191450,NULL,NULL
60935091,12/04/2009 22:40,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000328,Charville,E09000017,Hillingdon,Hillingdon,1.00021E+11,Grosvenor Avenue,21400854,UB4,509634,183116,509650,183150,51.53631224,-0.420667426
61258091,13/04/2009 14:10,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000622,St. Mary's Park,E09000032,Wandsworth,Battersea,NULL,Searles Close,22904532,SW11,NULL,NULL,527350,177050,NULL,NULL
61338091,13/04/2009 17:15,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000343,West Ruislip,E09000017,Hillingdon,Ruislip,NULL,Sharps Lane,21401730,HA4,NULL,NULL,508650,187550,NULL,NULL
62970091,16/04/2009 13:27,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000481,East Ham North,E09000025,Newham,East Ham,NULL,Shakespeare Crescent,22200245,E12,NULL,NULL,542950,184750,NULL,NULL
63575091,17/04/2009 16:12,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Police,House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,NULL,Manor Road,19900861,RM10,NULL,NULL,550550,184550,NULL,NULL
63759091,17/04/2009 22:04,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000306,Brooklands,E09000016,Havering,Romford,NULL,Riverside Close,NULL,RM1,550769,189275,550750,189250,51.58217448,0.174666936
64532091,19/04/2009 09:25,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000380,St. Peter's,E09000019,Islington,Islington,NULL,Sudeley Street,NULL,N1,NULL,NULL,531850,183250,NULL,NULL
64561091,19/04/2009 11:10,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000636,Hyde Park,E09000033,Westminster,Paddington,NULL,Westbourne Terrace,8400878,W2,NULL,NULL,526450,181150,NULL,NULL
64780091,19/04/2009 20:05,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Kitchener Road,22207776,E7,NULL,NULL,540750,184650,NULL,NULL
65083091,20/04/2009 11:27,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000050,Edgware,E09000003,Barnet,Mill Hill,NULL,Penshurst Gardens,20034160,HA8,NULL,NULL,519750,192250,NULL,NULL
65250091,20/04/2009 15:43,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000479,Custom House,E09000025,Newham,Plaistow,NULL,King George Avenue,NULL,E16,541587,181218,541550,181250,51.51215481,0.038998078
66330091,22/04/2009 07:12,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000317,Pettits,E09000016,Havering,Romford,NULL,Heather Drive,21301721,RM1,NULL,NULL,551150,190450,NULL,NULL
66477091,22/04/2009 13:05,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Police,Van,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000272,Highgate,E09000014,Haringey,Hornsey,NULL,Stormont Road,NULL,N6,527547,187711,527550,187750,51.57384876,-0.160860203
66704091,22/04/2009 18:11,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,NULL,Reed Road,21106602,N17,NULL,NULL,533950,190150,NULL,NULL
67069091,23/04/2009 10:34,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000487,Little Ilford,E09000025,Newham,Ilford,NULL,Warrior Square,22200278,E12,NULL,NULL,543050,185950,NULL,NULL
67368091,23/04/2009 19:36,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011488,West Thornton,E09000008,Croydon,Norbury,NULL,Canterbury Road,20500756,CR0,NULL,NULL,530950,166850,NULL,NULL
67495091,23/04/2009 22:55,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009386,Victoria,E09000012,Hackney,Bethnal Green,NULL,Victoria Park Road,20901033,E9,NULL,NULL,535250,183850,NULL,NULL
67718091,24/04/2009 10:23,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000482,East Ham South,E09000025,Newham,East Ham,NULL,Monmouth Road,22200865,E6,NULL,NULL,542650,182550,NULL,NULL
67891091,24/04/2009 14:47,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000206,Ponders End,E09000010,Enfield,Edmonton,NULL,Southfield Road,NULL,EN3,535170,195390,535150,195350,51.64107805,-0.047968305
68399091,25/04/2009 10:41,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000567,Sutton West,E09000029,Sutton,Sutton,NULL,Fairholme Road,22603018,SM1,NULL,NULL,524750,163850,NULL,NULL
68438091,25/04/2009 12:32,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Wasteland,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009376,Homerton,E09000012,Hackney,Homerton,1.00023E+11,Lower Clapton Road,20900640,E5,535144,185361,535150,185350,51.55096175,-0.052202746
68463091,25/04/2009 13:25,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Wasteland,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009376,Homerton,E09000012,Hackney,Homerton,1.00023E+11,Lower Clapton Road,20900640,E5,535144,185361,535150,185350,51.55096175,-0.052202746
69109091,26/04/2009 10:51,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,34038663,Stanlake Villas,21000775,W12,523047,180128,523050,180150,51.50669755,-0.228411641
69428091,26/04/2009 16:22,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011489,Woodside,E09000008,Croydon,Woodside,NULL,Dundee Road,20500883,SE25,NULL,NULL,534850,167750,NULL,NULL
69718091,26/04/2009 23:00,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,NULL,High Road,NULL,E11,539108,185976,539150,185950,51.55552606,0.005176124
70888091,29/04/2009 02:27,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,6138601,Printers Mews,22702385,E3,536341,183625,536350,183650,51.53507423,-0.03562075
70939091,29/04/2009 06:57,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000039,Thames,E09000002,Barking and Dagenham,Barking,NULL,Farr Avenue,NULL,IG11,546128,183247,546150,183250,51.52923128,0.105230888
71406091,29/04/2009 19:45,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Private garage,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000261,Ravenscourt Park,E09000013,Hammersmith and Fulham,Hammersmith,34007375,Verbena Gardens,21000845,W6,522081,178294,522050,178250,51.49042384,-0.242957984
72306091,01/05/2009 10:15,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000401,Berrylands,E09000021,Kingston upon Thames,Surbiton,NULL,Surbiton Hill Park,21800924,KT5,NULL,NULL,519350,167950,NULL,NULL
72385091,01/05/2009 13:03,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000418,Clapham Common,E09000022,Lambeth,Clapham,NULL,St. Alphonsus Road,21901259,SW4,NULL,NULL,529650,175050,NULL,NULL
72856091,01/05/2009 23:19,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Hornsey Road,21603387,N7,NULL,NULL,530650,186350,NULL,NULL
73167091,02/05/2009 13:52,2009,2009/10,Special Service,1,1,260,260,Redacted,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000322,Squirrel's Heath,E09000016,Havering,Harold Hill,NULL,Belgrave Avenue,NULL,RM2,553486,189963,553450,189950,51.58762293,0.214149429
73250091,02/05/2009 16:00,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000419,Clapham Town,E09000022,Lambeth,Clapham,NULL,Grafton Square,21900630,SW4,NULL,NULL,529350,175550,NULL,NULL
73338091,02/05/2009 18:12,2009,2009/10,Special Service,1,2,260,520,Redacted,Dog,Person (mobile),Other industrial manufacturing facility,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000206,Ponders End,E09000010,Enfield,Enfield,NULL,Wharf Road,NULL,EN3,536167,195414,536150,195450,51.64105284,-0.033559972
73771091,03/05/2009 09:37,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000198,Enfield Highway,E09000010,Enfield,Enfield,NULL,Exeter Road,20702225,EN3,NULL,NULL,535850,196650,NULL,NULL
74585091,04/05/2009 16:19,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000380,St. Peter's,E09000019,Islington,Islington,NULL,Britannia Row,NULL,N1,532113,183709,532150,183750,51.53683414,-0.096512325
74705091,04/05/2009 19:27,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009376,Homerton,E09000012,Hackney,Homerton,1.00021E+11,Collent Street,20900264,E9,535464,184570,535450,184550,51.54377698,-0.047894229
75025091,05/05/2009 11:06,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000630,Abbey Road,E09000033,Westminster,Paddington,NULL,Hill Road,8400370,NW8,NULL,NULL,526350,183050,NULL,NULL
75774091,06/05/2009 13:34,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000400,Alexandra,E09000021,Kingston upon Thames,Surbiton,NULL,Greenfield Avenue,21800469,KT5,NULL,NULL,520050,167150,NULL,NULL
75866091,06/05/2009 16:30,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000311,Hacton,E09000016,Havering,Hornchurch,1.00021E+11,Plumpton Avenue,21300541,RM12,554521,186029,554550,186050,51.55199456,0.227349917
75921091,06/05/2009 18:12,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000111,Chislehurst,E09000006,Bromley,Sidcup,NULL,Mead Road,20302425,BR7,NULL,NULL,544150,171050,NULL,NULL
76285091,07/05/2009 10:46,2009,2009/10,Special Service,1,2,260,520,Redacted,Horse,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Harbet Road,NULL,N18,535997,191703,535950,191750,51.60774677,-0.037455336
77153091,08/05/2009 20:14,2009,2009/10,Special Service,2,3,260,780,Redacted,Deer,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000345,Yiewsley,E09000017,Hillingdon,Hayes,NULL,Iron Bridge Road,NULL,UB7,507229,179972,507250,179950,51.50851513,-0.456284776
77871091,09/05/2009 20:35,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000517,East Sheen,E09000027,Richmond upon Thames,Richmond,1.00023E+11,Portman Avenue,22404893,SW14,520725,175579,520750,175550,51.46631319,-0.263406575
78016091,09/05/2009 23:37,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000460,Figge's Marsh,E09000024,Merton,Mitcham,NULL,Acacia Road,22100045,CR4,NULL,NULL,528750,169250,NULL,NULL
78207091,10/05/2009 08:09,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Fraser Road,20704236,N9,NULL,NULL,534450,193250,NULL,NULL
78277091,10/05/2009 11:30,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Other FRS,Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000200,Grange,E09000010,Enfield,Enfield,207173552,The Town,20703007,EN2,532891,196563,532850,196550,51.65216199,-0.080437331
78838091,11/05/2009 07:45,2009,2009/10,Special Service,3,7,260,1820,Redacted,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Dod Street,NULL,E14,536750,181250,536750,181250,51.5136333,-0.030649027
78877091,11/05/2009 09:34,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Stanlake Villas,21000775,W12,NULL,NULL,523050,180150,NULL,NULL
78887091,11/05/2009 09:57,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000057,Mill Hill,E09000003,Barnet,Mill Hill,NULL,Milespit Hill,NULL,NW7,522930,191874,522950,191850,51.61228715,-0.225992137
79115091,11/05/2009 16:06,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000606,Markhouse,E09000031,Waltham Forest,Walthamstow,2.00001E+11,Priory Avenue,22866350,E17,537324,188885,537350,188850,51.58210313,-0.019404797
79225091,11/05/2009 18:06,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000060,Underhill,E09000003,Barnet,Barnet,NULL,Dollis Valley Way,20012860,EN5,NULL,NULL,524750,195450,NULL,NULL
79685091,12/05/2009 11:51,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009401,Queen's Gate,E09000020,Kensington and Chelsea,Kensington,NULL,Palace Gate,21700390,W8,NULL,NULL,526150,179650,NULL,NULL
79935091,12/05/2009 17:47,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009391,Chelsea Riverside,E09000020,Kensington and Chelsea,Chelsea,NULL,Beaufort Street,21700031,SW3,NULL,NULL,526850,177550,NULL,NULL
79987091,12/05/2009 18:55,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009373,Hackney Downs,E09000012,Hackney,Stoke Newington,NULL,Stellman Close,20900972,E5,NULL,NULL,534050,186050,NULL,NULL
80034091,12/05/2009 19:45,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000520,Hampton,E09000027,Richmond upon Thames,Twickenham,NULL,Linden Road,22400961,TW12,NULL,NULL,513050,169950,NULL,NULL
80571091,13/05/2009 16:36,2009,2009/10,Special Service,1,1,260,260,Redacted,Squirrel,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,Hampstead Way,NULL,NW11,524933,188618,524950,188650,51.58258503,-0.198238142
80721091,13/05/2009 19:22,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000259,Palace Riverside,E09000013,Hammersmith and Fulham,Fulham,NULL,Stevenage Road,NULL,SW6,NULL,NULL,523550,176750,NULL,NULL
80733091,13/05/2009 19:44,2009,2009/10,Special Service,2,3,260,780,Redacted,Fox,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Agnes Street,22700051,E14,NULL,NULL,536850,181450,NULL,NULL
81137091,14/05/2009 15:16,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000204,Lower Edmonton,E09000010,Enfield,Edmonton,NULL,Hendon Road,20704328,N9,NULL,NULL,534450,193950,NULL,NULL
81584091,15/05/2009 09:56,2009,2009/10,Special Service,1,2,260,520,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000266,Alexandra,E09000014,Haringey,Hornsey,1.00021E+11,Dukes Avenue,21101214,N10,529189,189965,529150,189950,51.5937312,-0.136350772
81713091,15/05/2009 14:53,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000172,Dormers Wells,E09000009,Ealing,Southall,NULL,Fleming Road,20600676,UB1,NULL,NULL,514150,181050,NULL,NULL
81770091,15/05/2009 16:50,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Gunmakers Lane,NULL,E3,536431,183664,536450,183650,51.53540298,-0.034308843
82208091,16/05/2009 13:20,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Police,Car,Road Vehicle,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000088,Dollis Hill,E09000005,Brent,West Hampstead,NULL,Edgware Road,NULL,NW2,523093,186704,523050,186750,51.5657882,-0.225450969
82239091,16/05/2009 14:28,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000122,Orpington,E09000006,Bromley,Orpington,NULL,Eton Road,NULL,BR6,546760,164562,546750,164550,51.36117455,0.10659316
82314091,16/05/2009 17:09,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011235,Barkingside,E09000026,Redbridge,Hainault,1.00022E+11,Glenthorne Gardens,22302878,IG6,543552,189468,543550,189450,51.58579059,0.07065814
82769091,17/05/2009 09:54,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011470,New Addington North,E09000008,Croydon,Addington,NULL,Castle Hill Avenue,20501679,CR0,NULL,NULL,538050,162850,NULL,NULL
83410091,18/05/2009 13:44,2009,2009/10,Special Service,1,4,260,1040,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011241,Cranbrook,E09000026,Redbridge,Ilford,NULL,Wanstead Park Road,22303358,IG1,NULL,NULL,542550,187150,NULL,NULL
83903091,19/05/2009 10:38,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Multi-Storey car park,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,1.00023E+11,Belmont Road,21400145,UB8,505250,184250,505250,184250,51.54733916,-0.483512375
83922091,19/05/2009 11:35,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000199,Enfield Lock,E09000010,Enfield,Enfield,207174568,South Ordnance Road,20706877,EN3,537163,198347,537150,198350,51.66716695,-0.018026026
84125091,19/05/2009 17:46,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000192,Walpole,E09000009,Ealing,Ealing,12068057,Camborne Avenue,20600303,W13,516987,179766,516950,179750,51.50472921,-0.315808596
84176091,19/05/2009 19:07,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Vehicle Repair Workshop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000331,Heathrow Villages,E09000017,Hillingdon,Heathrow,10022798455,Northern Perimeter Road,21401419,TW6,507059,176863,507050,176850,51.48060266,-0.459676129
84307091,20/05/2009 00:18,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000133,Frognal and Fitzjohns,E09000007,Camden,West Hampstead,NULL,Fitzjohns Avenue,20400156,NW3,NULL,NULL,526550,185050,NULL,NULL
84523091,20/05/2009 11:36,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000033,Goresbrook,E09000002,Barking and Dagenham,Dagenham,NULL,Hedgemans Way,19900490,RM9,NULL,NULL,548250,184450,NULL,NULL
85725091,22/05/2009 10:30,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000060,Underhill,E09000003,Barnet,Barnet,NULL,Dollis Valley Way,20012860,EN5,NULL,NULL,524750,195450,NULL,NULL
85961091,22/05/2009 16:24,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,NULL,Clarence Street,NULL,KT1,517750,169250,517750,169250,51.41005638,-0.308318681
86422091,23/05/2009 06:21,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011223,Crook Log,E09000004,Bexley,Bexley,1.0002E+11,Dallin Road,20100417,DA6,547912,175328,547950,175350,51.45761208,0.12761399
87224091,24/05/2009 10:18,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000203,Jubilee,E09000010,Enfield,Edmonton,NULL,Cuckoo Hall Lane,20703243,N9,NULL,NULL,535650,194850,NULL,NULL
87402091,24/05/2009 14:53,2009,2009/10,Special Service,1,2,260,520,Redacted,Horse,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000607,Valley,E09000031,Waltham Forest,Walthamstow,NULL,Harbet Road,NULL,E4,536306,191889,536350,191850,51.60934349,-0.032923514
87493091,24/05/2009 16:46,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Other FRS,Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Rangers Road,NULL,E4,540223,194887,540250,194850,51.63532158,0.024805463
89326091,27/05/2009 08:42,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000057,Mill Hill,E09000003,Barnet,Finchley,NULL,Stockford Avenue,20041050,NW7,NULL,NULL,523950,191350,NULL,NULL
89957091,28/05/2009 11:11,2009,2009/10,Special Service,1,1,260,260,Redacted,Horse,Police,"Grassland, pasture, grazing etc",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000607,Valley,E09000031,Waltham Forest,Walthamstow,NULL,Harbet Road,NULL,E4,536288,191773,536250,191750,51.60830546,-0.033228363
90144091,28/05/2009 17:20,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000028,Becontree,E09000002,Barking and Dagenham,Dagenham,NULL,Waldegrave Road,19900744,RM8,NULL,NULL,547150,186850,NULL,NULL
90706091,29/05/2009 12:24,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000223,Kidbrooke with Hornfair,E09000011,Greenwich,East Greenwich,NULL,Chichester Close,20800330,SE3,NULL,NULL,541150,177050,NULL,NULL
90806091,29/05/2009 15:19,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Swiftsden Way,22001946,BR1,NULL,NULL,539450,170750,NULL,NULL
92294091,31/05/2009 14:28,2009,2009/10,Special Service,1,1,260,260,Redacted,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000614,Fairfield,E09000032,Wandsworth,Wandsworth,NULL,Wandsworth High Street,NULL,SW18,NULL,NULL,525250,174750,NULL,NULL
92335091,31/05/2009 15:52,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000466,Merton Park,E09000024,Merton,Wimbledon,NULL,Kenley Road,22103553,SW19,NULL,NULL,525050,169150,NULL,NULL
92987091,01/06/2009 14:14,2009,2009/10,Special Service,1,1,260,260,Redacted,Squirrel,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,Chingford Avenue,22825100,E4,NULL,NULL,537750,193450,NULL,NULL
93306091,01/06/2009 19:49,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000198,Enfield Highway,E09000010,Enfield,Enfield,NULL,The Sunny Road,NULL,EN3,535909,197600,535950,197650,51.66075919,-0.036437145
93409091,01/06/2009 21:43,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Wasteland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,NULL,Martha Street,NULL,E1,535106,181051,535150,181050,51.51224017,-0.054402715
93474091,01/06/2009 23:58,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000066,Blackfen and Lamorbey,E09000004,Bexley,Eltham,NULL,Raeburn Road,NULL,DA15,545353,174226,545350,174250,51.44837222,0.090354776
93633091,02/06/2009 10:12,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009373,Hackney Downs,E09000012,Hackney,Stoke Newington,1.00023E+11,Evering Road,20900387,N16,534014,186173,534050,186150,51.55852818,-0.068182007
93664091,02/06/2009 11:15,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000125,Plaistow and Sundridge,E09000006,Bromley,Bromley,NULL,London Lane,20302952,BR1,NULL,NULL,540350,170350,NULL,NULL
93755091,02/06/2009 13:28,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000192,Walpole,E09000009,Ealing,Ealing,12068057,Camborne Avenue,20600303,W13,516996,179757,516950,179750,51.50464646,-0.315681966
93771091,02/06/2009 13:49,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000439,Brockley,E09000023,Lewisham,Greenwich,NULL,Oscar Street,NULL,SE8,537152,176583,537150,176550,51.47159661,-0.026672594
93925091,02/06/2009 16:50,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000049,East Finchley,E09000003,Barnet,Finchley,NULL,Manor Park Road,20028300,N2,NULL,NULL,526550,189750,NULL,NULL
94505091,03/06/2009 11:03,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000125,Plaistow and Sundridge,E09000006,Bromley,Bromley,1.0002E+11,London Lane,20302952,BR1,540372,170382,540350,170350,51.41508507,0.017201125
94526091,03/06/2009 12:03,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000592,Chapel End,E09000031,Waltham Forest,Walthamstow,NULL,Aveling Park Road,22812350,E17,NULL,NULL,537350,190350,NULL,NULL
94847091,03/06/2009 21:28,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000342,West Drayton,E09000017,Hillingdon,Hayes,1.00022E+11,Thornton Avenue,21401990,UB7,506893,179212,506850,179250,51.50174767,-0.461354654
95289091,04/06/2009 16:17,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011479,Selhurst,E09000008,Croydon,Croydon,NULL,Gloucester Road,20500980,CR0,NULL,NULL,533050,167250,NULL,NULL
95595091,04/06/2009 21:37,2009,2009/10,Special Service,1,1,260,260,Redacted,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Animal rescue from water,Wild animal rescue from water or mud,E05000105,Willesden Green,E09000005,Brent,Willesden,NULL,Robson Avenue,20202225,NW10,NULL,NULL,522450,184250,NULL,NULL
96023091,05/06/2009 17:39,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000260,Parsons Green and Walham,E09000013,Hammersmith and Fulham,Fulham,NULL,Delvino Road,21000270,SW6,NULL,NULL,525250,176650,NULL,NULL
96063091,05/06/2009 18:10,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,NULL,Adams Walk,NULL,KT1,518193,169280,518150,169250,51.41023391,-0.301942003
96083091,05/06/2009 18:37,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,NULL,Adams Walk,NULL,KT1,518193,169280,518150,169250,51.41023391,-0.301942003
96737091,06/06/2009 19:29,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011244,Goodmayes,E09000026,Redbridge,Ilford,NULL,Airthrie Road,22301793,IG3,NULL,NULL,546650,186850,NULL,NULL
96751091,06/06/2009 20:03,2009,2009/10,Special Service,1,1,260,260,Redacted,Unknown - Heavy Livestock Animal,Person (land line),Fire station,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011487,Waddon,E09000008,Croydon,Croydon,2.00001E+11,Old Town,20501261,CR0,531995,165038,531950,165050,51.36907055,-0.105173804
97181091,07/06/2009 10:24,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Launcelot Road,22003472,BR1,NULL,NULL,540550,171750,NULL,NULL
97816091,08/06/2009 14:25,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000200,Grange,E09000010,Enfield,Enfield,207173553,The Town,20703007,EN2,532877,196570,532850,196550,51.6522282,-0.080636921
98231091,09/06/2009 08:37,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Belgrave Road,22200367,E13,NULL,NULL,541050,182450,NULL,NULL
98345091,09/06/2009 13:26,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,NULL,Mill Street,22501722,SE1,NULL,NULL,533850,179750,NULL,NULL
98378091,09/06/2009 14:19,2009,2009/10,Special Service,1,3,260,780,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Ruislip Road,20602098,UB5,NULL,NULL,512550,182850,NULL,NULL
99402091,11/06/2009 13:28,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011487,Waddon,E09000008,Croydon,Croydon,NULL,Hyrstdene,20502743,CR2,NULL,NULL,531950,164550,NULL,NULL
100131091,12/06/2009 15:08,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000131,Cantelowes,E09000007,Camden,Kentish Town,NULL,Stratford Villas,20400668,NW1,NULL,NULL,529550,184350,NULL,NULL
100378091,12/06/2009 21:31,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000493,Wall End,E09000025,Newham,East Ham,NULL,Lathom Road,22200795,E6,NULL,NULL,542950,184250,NULL,NULL
100421091,12/06/2009 22:31,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000059,Totteridge,E09000003,Barnet,Finchley,NULL,Brook Meadow,20005320,N12,NULL,NULL,525650,193050,NULL,NULL
100673091,13/06/2009 10:47,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Richmond,NULL,Richmond Park,NULL,TW10,518545,173686,518550,173650,51.44975962,-0.29540893
100748091,13/06/2009 12:51,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000559,Carshalton South and Clockhouse,E09000029,Sutton,Purley,NULL,Lloyd Avenue,22602437,CR5,NULL,NULL,528450,160150,NULL,NULL
101149091,13/06/2009 22:20,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000209,Southgate Green,E09000010,Enfield,Southgate,NULL,Ulleswater Road,20704867,N14,NULL,NULL,530550,193150,NULL,NULL
101172091,13/06/2009 23:08,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Police,Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000051,Finchley Church End,E09000003,Barnet,Finchley,NULL,Church Crescent,20008600,N3,NULL,NULL,524950,190750,NULL,NULL
101506091,14/06/2009 14:28,2009,2009/10,Special Service,1,1,260,260,Redacted,Snake,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011486,Thornton Heath,E09000008,Croydon,Norbury,NULL,Parchmore Road,20501277,CR7,NULL,NULL,532250,168650,NULL,NULL
101787091,14/06/2009 21:32,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000606,Markhouse,E09000031,Waltham Forest,Walthamstow,NULL,Lorne Road,NULL,E17,537104,188705,537150,188750,51.58053917,-0.022648253
102083091,15/06/2009 08:50,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,St. Leonards Road,NULL,E14,538209,181339,538250,181350,51.5140784,-0.009601643
102278091,15/06/2009 15:13,2009,2009/10,Special Service,2,3,260,780,Redacted,Horse,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,NULL,Billet Road,NULL,E17,537190,190998,537150,190950,51.60112257,-0.020513151
103485091,16/06/2009 20:17,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000603,Lea Bridge,E09000031,Waltham Forest,Leyton,NULL,Markhouse Road,22856450,E17,NULL,NULL,536850,187750,NULL,NULL
103550091,16/06/2009 22:19,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009400,Pembridge,E09000020,Kensington and Chelsea,Kensington,NULL,Ladbroke Road,NULL,W11,525088,180506,525050,180550,51.5096471,-0.198884303
104724091,18/06/2009 20:36,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011485,South Norwood,E09000008,Croydon,Woodside,NULL,Hambledon Gardens,20501016,SE25,NULL,NULL,533750,168650,NULL,NULL
104744091,18/06/2009 20:52,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000172,Dormers Wells,E09000009,Ealing,Southall,12015882,Fleming Road,20600676,UB1,514197,181099,514150,181050,51.51728004,-0.355558207
104839091,18/06/2009 23:43,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000448,Lewisham Central,E09000023,Lewisham,Lee Green,10091837631,Lee High Road,22004135,SE13,538749,175299,538750,175250,51.45966936,-0.004197807
104860091,19/06/2009 00:45,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,Northumberland Park,21104859,N17,NULL,NULL,534350,191450,NULL,NULL
104961091,19/06/2009 08:47,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011464,Bensham Manor,E09000008,Croydon,Norbury,NULL,Ecclesbourne Road,20500893,CR7,NULL,NULL,532350,167950,NULL,NULL
105152091,19/06/2009 15:26,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Church/Chapel,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000434,Thurlow Park,E09000022,Lambeth,West Norwood,1.00022E+11,Robson Road,21901163,SE27,532202,172317,532250,172350,51.43443673,-0.099491724
105158091,19/06/2009 15:43,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000446,Ladywell,E09000023,Lewisham,Lewisham,NULL,Loampit Hill,22000636,SE13,NULL,NULL,537550,176050,NULL,NULL
105324091,19/06/2009 18:07,2009,2009/10,Special Service,1,2,260,520,Redacted,Fox,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000637,Knightsbridge and Belgravia,E09000033,Westminster,Kensington,NULL,Ennismore Gardens,8401318,SW7,NULL,NULL,527050,179450,NULL,NULL
105494091,19/06/2009 21:27,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000447,Lee Green,E09000023,Lewisham,Lee Green,NULL,Taunton Road,NULL,SE12,539697,174860,539650,174850,51.45549166,0.009265702
105589091,20/06/2009 00:06,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000267,Bounds Green,E09000014,Haringey,Hornsey,NULL,Nightingale Road,NULL,N22,530716,191077,530750,191050,51.60337209,-0.113905662
105830091,20/06/2009 09:21,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000482,East Ham South,E09000025,Newham,East Ham,NULL,Saxon Road,22201036,E6,NULL,NULL,542550,182250,NULL,NULL
105902091,20/06/2009 12:13,2009,2009/10,Special Service,1,2,260,520,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000127,West Wickham,E09000006,Bromley,Beckenham,1.0002E+11,North Road,20301772,BR4,537894,166191,537850,166150,51.3780314,-0.02003555
106013091,20/06/2009 15:05,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000558,Carshalton Central,E09000029,Sutton,Wallington,NULL,Mill Lane,NULL,SM5,NULL,NULL,528150,164950,NULL,NULL
106536091,21/06/2009 09:50,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000604,Leyton,E09000031,Waltham Forest,Leyton,NULL,High Road Leyton,22844900,E10,NULL,NULL,538050,186550,NULL,NULL
106585091,21/06/2009 11:55,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Wild animal rescue from height,E05011467,Crystal Palace & Upper Norwood,E09000008,Croydon,Norbury,1.00021E+11,Glenhurst Rise,20500063,SE19,532303,170121,532350,170150,51.41467829,-0.098858661
106628091,21/06/2009 13:36,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011469,Kenley,E09000008,Croydon,Purley,1.00021E+11,Godstone Road,20503071,CR8,532409,160217,532450,160250,51.32564869,-0.101021031
106786091,21/06/2009 18:22,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000043,Brunswick Park,E09000003,Barnet,Southgate,NULL,Marshalls Close,NULL,N11,528762,193022,528750,193050,51.62130078,-0.141389907
106858091,21/06/2009 19:38,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000277,St. Ann's,E09000014,Haringey,Tottenham,NULL,Roslyn Road,21103823,N15,NULL,NULL,532950,188950,NULL,NULL
107370091,22/06/2009 13:43,2009,2009/10,Special Service,1,1,260,260,Redacted,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000183,North Greenford,E09000009,Ealing,Northolt,NULL,Hutton Close,20600930,UB6,NULL,NULL,514650,185450,NULL,NULL
107995091,23/06/2009 09:10,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000636,Hyde Park,E09000033,Westminster,Paddington,NULL,St. Michaels Street,8401928,W2,NULL,NULL,527050,181450,NULL,NULL
108839091,24/06/2009 14:26,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000108,Bromley Common and Keston,E09000006,Bromley,Orpington,NULL,Holwood Park Avenue,20303006,BR6,NULL,NULL,542850,164750,NULL,NULL
109011091,24/06/2009 18:29,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000149,Broad Green,E09000008,Croydon,Croydon,NULL,Wentworth Road,NULL,CR0,531262,166822,531250,166850,51.38527269,-0.115039284
109020091,24/06/2009 18:41,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000452,Sydenham,E09000023,Lewisham,Forest Hill,1.00022E+11,Queensthorpe Road,22001820,SE26,535602,171699,535650,171650,51.42807985,-0.050845312
110129091,25/06/2009 10:02,2009,2009/10,Special Service,1,1,260,260,KITTEN WITH HEAD STUCK IN JAR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Keedonwood Road,22005125,BR1,NULL,NULL,539350,171450,NULL,NULL
110478091,25/06/2009 19:32,2009,2009/10,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000278,Seven Sisters,E09000014,Haringey,Tottenham,1.00021E+11,Manchester Road,21103584,N15,532983,188176,532950,188150,51.57677212,-0.082288888
110527091,25/06/2009 20:47,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN DRAINPIPE,Bird,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000077,Lesnes Abbey,E09000004,Bexley,Erith,NULL,New Road,NULL,SE2,547709,178623,547750,178650,51.48727187,0.126071312
110531091,25/06/2009 20:56,2009,2009/10,Special Service,1,1,260,260,DEER STUCK IN FENCE,Deer,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000339,Townfield,E09000017,Hillingdon,Hillingdon,1.00023E+11,Wood End,21402199,UB3,509511,181260,509550,181250,51.51965437,-0.423016711
110594091,25/06/2009 22:31,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED ON FENCE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000092,Kensal Green,E09000005,Brent,Willesden,NULL,Bramston Road,20200419,NW10,NULL,NULL,522350,183550,NULL,NULL
111619091,27/06/2009 08:31,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED UNDER BUILDING FOUNDATIONS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Cranley Gardens,21100200,N10,NULL,NULL,528850,189150,NULL,NULL
113037091,28/06/2009 01:18,2009,2009/10,Special Service,1,1,260,260,INJURED PIGEON TRAPPED ON LEDGE,Bird,Person (land line),Police station,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011246,Ilford Town,E09000026,Redbridge,Ilford,10034922099,High Road,22306010,IG1,544309,186651,544350,186650,51.56028527,0.08042085
113265091,28/06/2009 13:01,2009,2009/10,Special Service,1,1,260,260,DOG LOCKED IN CAR,Dog,Person (mobile),DIY Warehouse,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000532,West Twickenham,E09000027,Richmond upon Thames,Twickenham,1.00023E+11,Sixth Cross Road,22401847,TW2,514667,172042,514650,172050,51.43578182,-0.351724788
113497091,28/06/2009 18:45,2009,2009/10,Special Service,1,1,260,260,DUCK TRAPPED BY FISHING LINE IN CENTRE OF LAKE,Bird,Person (land line),Park,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,1.00023E+11,Thicket Road,20302199,SE20,534576,170498,534550,170450,51.41753138,-0.066049971
113582091,28/06/2009 20:23,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRDS TRAPPED IN FISHING LINE ON POND,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000418,Clapham Common,E09000022,Lambeth,Clapham,NULL,Clapham Common South Side,NULL,SW4,528972,174604,528950,174650,51.45573409,-0.145100062
113630091,28/06/2009 21:37,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN NEETING,Bird,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000175,East Acton,E09000009,Ealing,Acton,12151266,East Acton Lane,20602069,W3,521167,180417,521150,180450,51.50970059,-0.255387834
113669091,28/06/2009 22:52,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009333,Spitalfields & Banglatown,E09000030,Tower Hamlets,Whitechapel,NULL,Vallance Road,22701259,E1,NULL,NULL,534450,182050,NULL,NULL
114153091,29/06/2009 16:31,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED UNDER VAN WHEELARCH,Dog,Police,Car,Road Vehicle,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000054,Hale,E09000003,Barnet,Mill Hill,NULL,Elmgate Gardens,NULL,HA8,520574,192594,520550,192550,51.61926622,-0.259755242
114400091,29/06/2009 21:37,2009,2009/10,Special Service,1,2,260,520,INJURED CAT IN PRECARIOUS POSITION,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000649,West End,E09000033,Westminster,Soho,NULL,North Audley Street,8401474,W1K,NULL,NULL,528250,180950,NULL,NULL
115111091,30/06/2009 16:49,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN GUTTERING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000306,Brooklands,E09000016,Havering,Dagenham,NULL,Rush Green Gardens,21301569,RM7,NULL,NULL,550550,187450,NULL,NULL
115812091,01/07/2009 13:21,2009,2009/10,Special Service,1,2,260,520,CROW STUCK IN WIRING,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011482,Shirley North,E09000008,Croydon,Woodside,1.00023E+11,Orchard Way,20500526,CR0,536578,166770,536550,166750,51.38355185,-0.038710578
115841091,01/07/2009 14:19,2009,2009/10,Special Service,1,1,260,260,UNKNOWN SNAKE IN HOUSE,Snake,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000366,Barnsbury,E09000019,Islington,Islington,NULL,Brayfield Terrace,21604331,N1,NULL,NULL,531150,184050,NULL,NULL
116595091,02/07/2009 08:46,2009,2009/10,Special Service,1,1,260,260,FOX TRAPPED IN FENCE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000329,Eastcote and East Ruislip,E09000017,Hillingdon,Ruislip,NULL,Bridle Road,21400247,HA5,NULL,NULL,511350,188450,NULL,NULL
116616091,02/07/2009 09:35,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011108,Nunhead & Queen's Road,E09000028,Southwark,New Cross,NULL,Lanvanor Road,22501479,SE15,NULL,NULL,535050,176250,NULL,NULL
116790091,02/07/2009 15:30,2009,2009/10,Special Service,1,1,260,260,CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000571,Wandle Valley,E09000029,Sutton,Wallington,NULL,Corbet Close,22605443,SM6,NULL,NULL,528150,165850,NULL,NULL
117217091,02/07/2009 23:59,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN CELLAR,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000424,Knight's Hill,E09000022,Lambeth,West Norwood,NULL,Broxholm Road,21900257,SE27,NULL,NULL,531250,172250,NULL,NULL
117355091,03/07/2009 06:20,2009,2009/10,Special Service,1,2,260,520,CAT TRAPPED IN CAR BONNET,Cat,Person (land line),Car,Road Vehicle,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000546,Peckham,E09000028,Southwark,Peckham,NULL,Sumner Road,NULL,SE15,533909,177153,533950,177150,51.47749554,-0.073120332
117460091,03/07/2009 10:39,2009,2009/10,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000347,Brentford,E09000018,Hounslow,Chiswick,NULL,Hope Close,NULL,TW8,518038,178098,518050,178050,51.48951951,-0.301230311
117546091,03/07/2009 13:34,2009,2009/10,Special Service,1,1,260,260,RUNNING CALL TO DOG IN PRECARIOUS POSITION,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000379,St. Mary's,E09000019,Islington,Islington,NULL,Crane Grove,21604621,N7,NULL,NULL,531450,184750,NULL,NULL
117580091,03/07/2009 14:12,2009,2009/10,Special Service,1,1,260,260,PET LIZARD TRAPPED BEHIND RADIATOR,Lizard,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000044,Burnt Oak,E09000003,Barnet,Mill Hill,NULL,Deansbrook Road,20011980,HA8,NULL,NULL,520350,191450,NULL,NULL
117790091,03/07/2009 18:04,2009,2009/10,Special Service,1,2,260,520,Redacted,Snake,Person (land line),Nursing/Care Home/Hospice,Other Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000365,Turnham Green,E09000018,Hounslow,Chiswick,1.00022E+11,Clifton Gardens,21500282,W4,520814,178685,520850,178650,51.49420973,-0.26106487
118265091,04/07/2009 10:29,2009,2009/10,Special Service,1,1,260,260,DOG IN WATER,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000474,Wimbledon Park,E09000024,Merton,Wimbledon,NULL,Havelock Road,NULL,SW19,526216,171229,526250,171250,51.42602232,-0.185947876
118289091,04/07/2009 11:15,2009,2009/10,Special Service,1,1,260,260,DOG STUCK UNDER JETTY IN LAKE,Dog,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from water,Animal rescue from water - Domestic pet,E05000030,Eastbrook,E09000002,Barking and Dagenham,Dagenham,NULL,The Chase,NULL,RM7,550970,186014,550950,186050,51.55282046,0.176166741
119148091,05/07/2009 09:25,2009,2009/10,Special Service,NULL,NULL,260,NULL,CAT TRAPPED UNDER FLOOR BOARDS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011485,South Norwood,E09000008,Croydon,Woodside,NULL,Regina Road,20501335,SE25,NULL,NULL,534250,168650,NULL,NULL
119220091,05/07/2009 11:50,2009,2009/10,Special Service,1,2,260,520,DEER TRAPPED IN DITCH,Deer,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000107,Biggin Hill,E09000006,Bromley,Biggin Hill,NULL,Oaklands Lane,NULL,TN16,541262,159497,541250,159450,51.31705164,0.025671909
119274091,05/07/2009 13:05,2009,2009/10,Special Service,1,1,260,260,CAT ON WINDOW LEDGE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009379,King's Park,E09000012,Hackney,Homerton,NULL,Nye Bevan Estate,20950152,E5,NULL,NULL,535850,186050,NULL,NULL
119988091,06/07/2009 11:08,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN KITCHEN CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000042,Whalebone,E09000002,Barking and Dagenham,Dagenham,NULL,Heath Road,19900168,RM6,NULL,NULL,547950,187650,NULL,NULL
120163091,06/07/2009 16:17,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN FENCE BEHIND,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000346,Bedfont,E09000018,Hounslow,Feltham,NULL,New Road,NULL,TW14,509091,173878,509050,173850,51.45338397,-0.431345303
120711091,07/07/2009 15:29,2009,2009/10,Special Service,1,1,260,260,DOG ON WINDOW LEDGE,Dog,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009371,De Beauvoir,E09000012,Hackney,Shoreditch,NULL,St. Peter's Way,20900960,N1,NULL,NULL,533450,184150,NULL,NULL
121132091,07/07/2009 19:29,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED ON WALL BY NETTING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Hatherley Gardens,NULL,N8,NULL,NULL,530150,188250,NULL,NULL
121370091,08/07/2009 06:33,2009,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED IN PEN,Cat,Other FRS,House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000405,Chessington South,E09000021,Kingston upon Thames,Surbiton,NULL,Woodview,21801080,KT9,NULL,NULL,517050,161550,NULL,NULL
121400091,08/07/2009 07:50,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,1.00022E+11,Sydenham Hill,22005552,SE26,534452,172089,534450,172050,51.43185854,-0.067229066
121763091,08/07/2009 18:48,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN DRAIN,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009327,Mile End,E09000030,Tower Hamlets,Bethnal Green,NULL,Copperfield Road,22700356,E3,NULL,NULL,536350,182050,NULL,NULL
122010091,09/07/2009 06:42,2009,2009/10,Special Service,1,1,260,260,DOG STUCK UNDER SHED,Dog,Person (mobile),Other private non-residential building,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000477,Canning Town North,E09000025,Newham,Plaistow,46016969,Clifton Road,22201355,E16,539488,181730,539450,181750,51.51727783,0.008972957
122114091,09/07/2009 11:10,2009,2009/10,Special Service,1,2,260,520,CAT STUCK IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000423,Herne Hill,E09000022,Lambeth,Brixton,1.00023E+11,Luxor Street,21900905,SE5,532028,175994,532050,175950,51.46752178,-0.100622633
122734091,10/07/2009 09:49,2009,2009/10,Special Service,1,1,260,260,DISTRESSED KITTEN STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000134,Gospel Oak,E09000007,Camden,Kentish Town,5028919,Lawn Road,20400310,NW3,527542,185149,527550,185150,51.5508257,-0.161860922
122755091,10/07/2009 10:23,2009,2009/10,Special Service,1,1,260,260,PIGEON TRAPPED IN FISHING LINE AND HANGING FROM TREE,Bird,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000149,Broad Green,E09000008,Croydon,Croydon,NULL,Oakfield Road,NULL,CR0,532212,166438,532250,166450,51.38160165,-0.101538053
122958091,10/07/2009 15:38,2009,2009/10,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT TRAPPED UNDER FLOORBOARDIS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000051,Finchley Church End,E09000003,Barnet,Finchley,NULL,Regents Park Road,20036360,N3,NULL,NULL,524850,189750,NULL,NULL
123495091,11/07/2009 11:26,2009,2009/10,Special Service,1,1,260,260,HORSE COLLAPSED,Horse,Police,Other road vehicle,Road Vehicle,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000516,Barnes,E09000027,Richmond upon Thames,Hammersmith,NULL,Lonsdale Road,NULL,SW13,522727,177812,522750,177850,51.48595225,-0.233825239
123496091,11/07/2009 11:26,2009,2009/10,Special Service,1,1,260,260,INJURED CAT STUCK AT TOP OF TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Lee Green,NULL,Casterbridge Road,NULL,SE3,NULL,NULL,540450,175650,NULL,NULL
123501091,11/07/2009 11:31,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN SKY LIGHT,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000623,Shaftesbury,E09000032,Wandsworth,Clapham,NULL,Stormont Road,22904998,SW11,NULL,NULL,528250,175350,NULL,NULL
124094091,12/07/2009 11:47,2009,2009/10,Special Service,1,1,260,260,CAT STUCK IN BUILDING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000613,East Putney,E09000032,Wandsworth,Wandsworth,NULL,Laker Place,22906531,SW15,NULL,NULL,524450,174450,NULL,NULL
124719091,13/07/2009 12:27,2009,2009/10,Special Service,1,1,260,260,"CAT TRAPPED UNDER OBJECT,",Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000627,Wandsworth Common,E09000032,Wandsworth,Tooting,NULL,College Gardens,22901019,SW17,NULL,NULL,527250,172650,NULL,NULL
124774091,13/07/2009 14:24,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN LEAD,Dog,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000366,Barnsbury,E09000019,Islington,Islington,NULL,Liverpool Road,21606676,N1,NULL,NULL,531350,183850,NULL,NULL
124937091,13/07/2009 18:17,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN BARS OF GATE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000113,Copers Cope,E09000006,Bromley,Beckenham,NULL,Hackington Crescent,20302253,BR3,NULL,NULL,537450,170850,NULL,NULL
125073091,13/07/2009 22:33,2009,2009/10,Special Service,1,2,260,520,DOG STRANDED IN POND,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000438,Blackheath,E09000023,Lewisham,Lee Green,NULL,South Row,NULL,SE3,539873,176419,539850,176450,51.46945755,0.012413146
126901091,16/07/2009 18:57,2009,2009/10,Special Service,1,1,260,260,CAT STUCK IN LOFT SPACE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011481,Selsdon Vale & Forestdale,E09000008,Croydon,Addington,NULL,Pixton Way,20501806,CR0,NULL,NULL,536550,163050,NULL,NULL
126989091,16/07/2009 20:52,2009,2009/10,Special Service,1,1,260,260,ANIMAL TRAPPED IN CHIMNEY STACK,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000598,Hatch Lane,E09000031,Waltham Forest,Woodford,NULL,Forest Glade,22837100,E4,NULL,NULL,539150,192050,NULL,NULL
127086091,16/07/2009 22:55,2009,2009/10,Special Service,1,2,260,520,KITTEN TRAPPED BEHIND PIPING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011110,Peckham,E09000028,Southwark,Peckham,NULL,Goldsmith Road,22501075,SE15,NULL,NULL,534250,176850,NULL,NULL
127265091,17/07/2009 08:12,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000610,Balham,E09000032,Wandsworth,Tooting,NULL,Ormeley Road,22903710,SW12,NULL,NULL,528950,173450,NULL,NULL
127275091,17/07/2009 08:26,2009,2009/10,Special Service,1,1,260,260,CAT PAW TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000210,Town,E09000010,Enfield,Enfield,NULL,Rochester Close,20702932,EN1,NULL,NULL,533450,197650,NULL,NULL
128121091,18/07/2009 17:34,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000063,Woodhouse,E09000003,Barnet,Finchley,NULL,Mayfield Avenue,20028980,N12,NULL,NULL,526750,192750,NULL,NULL
128281091,18/07/2009 21:33,2009,2009/10,Special Service,1,1,260,260,INJURED DOG TRAPPED BEHIND FENCE,Dog,Person (mobile),Roadside vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000260,Parsons Green and Walham,E09000013,Hammersmith and Fulham,Fulham,34073354,Maynard Close,21000546,SW6,525958,177069,525950,177050,51.47856524,-0.187580134
128667091,19/07/2009 13:10,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH DOG STRANDED ON ROOF,Dog,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000372,Highbury East,E09000019,Islington,Stoke Newington,NULL,Riversdale Road,21603742,N5,NULL,NULL,532050,186250,NULL,NULL
129393091,20/07/2009 16:12,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA  GET CAT FROM TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000182,Northfield,E09000009,Ealing,Ealing,12082274,Creighton Road,20600470,W5,517601,179240,517650,179250,51.49987445,-0.30714137
129850091,21/07/2009 08:57,2009,2009/10,Special Service,1,1,260,260,FOX TRAPPED IN METAL GATE,Fox,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000616,Graveney,E09000032,Wandsworth,Tooting,NULL,Eswyn Road,NULL,SW17,527714,171620,527750,171650,51.42920122,-0.164271938
130051091,21/07/2009 15:47,2009,2009/10,Special Service,1,1,260,260,CAT FALLEN DOWN HOLE,Cat,Person (land line),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Ironmonger Row,NULL,EC1V,532375,182625,532350,182650,51.52703135,-0.093144201
130149091,21/07/2009 18:18,2009,2009/10,Special Service,1,1,260,260,PIDGEON TRAPPED ON BALCONY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009324,Island Gardens,E09000030,Tower Hamlets,Millwall,NULL,Crews Street,22700379,E14,NULL,NULL,537150,178850,NULL,NULL
130265091,21/07/2009 21:20,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000277,St. Ann's,E09000014,Haringey,Tottenham,NULL,Green Lanes,21106543,N4,NULL,NULL,531850,188550,NULL,NULL
130592091,22/07/2009 12:34,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000618,Nightingale,E09000032,Wandsworth,Tooting,NULL,Gosberton Road,22902080,SW12,NULL,NULL,528250,173550,NULL,NULL
130701091,22/07/2009 16:38,2009,2009/10,Special Service,1,1,260,260,DOG IN PRECAROUS POSITION,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011235,Barkingside,E09000026,Redbridge,Hainault,NULL,Longwood Gardens,22303025,IG5,NULL,NULL,543650,189650,NULL,NULL
131215091,23/07/2009 12:31,2009,2009/10,Special Service,1,1,260,260,DOG WITH LEG TRAPPED BEHIND RADIATOR,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000629,West Putney,E09000032,Wandsworth,Wandsworth,NULL,Larpent Avenue,22902819,SW15,NULL,NULL,523050,174950,NULL,NULL
131792091,24/07/2009 11:42,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011095,Borough & Bankside,E09000028,Southwark,Dowgate,2.00003E+11,Southwark Street,22502304,SE1,532215,180267,532250,180250,51.50587829,-0.096333455
131967091,24/07/2009 15:47,2009,2009/10,Special Service,1,1,260,260,TWO DOGS IN TOILET   ELDERLY LADY INVOLVED,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000108,Bromley Common and Keston,E09000006,Bromley,Orpington,NULL,Holwood Park Avenue,20303006,BR6,NULL,NULL,542850,164750,NULL,NULL
132513091,25/07/2009 11:17,2009,2009/10,Special Service,NULL,NULL,260,NULL,KITTEN TRAPPED IN GUTTERING ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000446,Ladywell,E09000023,Lewisham,Lewisham,NULL,Sandrock Road,22000900,SE13,NULL,NULL,537450,176050,NULL,NULL
132530091,25/07/2009 11:36,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000054,Hale,E09000003,Barnet,Mill Hill,200102230,Selvage Lane,20039020,NW7,520680,192603,520650,192650,51.61932444,-0.258221717
132594091,25/07/2009 13:38,2009,2009/10,Special Service,1,1,260,260,GOOSE TRAPPED IN FISHING WIRE,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000418,Clapham Common,E09000022,Lambeth,Clapham,NULL,Clapham Common South Side,NULL,SW4,529176,174853,529150,174850,51.45792538,-0.142074729
132939091,25/07/2009 22:09,2009,2009/10,Special Service,1,1,260,260,CAT STUCK IN A CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000498,Chadwell,E09000026,Redbridge,Ilford,NULL,Somerville Road,NULL,RM6,547329,188628,547350,188650,51.57726886,0.124783183
133347091,26/07/2009 16:03,2009,2009/10,Special Service,1,1,260,260,DOG IN THE RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Wandsworth Bridge Road,NULL,SW6,525905,175719,525950,175750,51.46644439,-0.188823202
134133091,27/07/2009 22:53,2009,2009/10,Special Service,1,1,260,260,KITTEN STUCK BEHINED KITCHEN UNITS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000566,Sutton South,E09000029,Sutton,Sutton,NULL,Worcester Road,22605696,SM2,NULL,NULL,525750,163450,NULL,NULL
135294091,29/07/2009 19:55,2009,2009/10,Special Service,1,1,260,260,PERSON LOCKED OUT DANGER OF FIRE,Unknown - Heavy Livestock Animal,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Elmington Road,22500901,SE5,NULL,NULL,532650,176950,NULL,NULL
135342091,29/07/2009 22:21,2009,2009/10,Special Service,1,1,260,260,ASSIST MEMBER OF PUBLIC,Unknown - Heavy Livestock Animal,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Elmington Road,22500901,SE5,NULL,NULL,532650,176950,NULL,NULL
135439091,30/07/2009 07:04,2009,2009/10,Special Service,1,1,260,260,CAT STUCK BETWEEN WALLS,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000254,Fulham Broadway,E09000013,Hammersmith and Fulham,Fulham,NULL,Ongar Road,21000614,SW6,NULL,NULL,525250,177850,NULL,NULL
135716091,30/07/2009 17:37,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BEHIND KITCHEN UNITS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Wallwood Street,22701288,E14,NULL,NULL,536950,181550,NULL,NULL
136236091,31/07/2009 14:22,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN RAILINGS,Bird,Person (mobile),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000368,Caledonian,E09000019,Islington,Holloway,NULL,Burness Close,NULL,N7,530730,184768,530750,184750,51.54667291,-0.116048453
136460091,31/07/2009 18:41,2009,2009/10,Special Service,1,1,260,260,DOG WITH LEG TRAPPED IN METAL TABLE LEG,Dog,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000135,Hampstead Town,E09000007,Camden,West Hampstead,NULL,Willoughby Road,20400173,NW3,NULL,NULL,526750,185650,NULL,NULL
136604091,31/07/2009 21:45,2009,2009/10,Special Service,1,1,260,260,ANIMAL TRAPPED BEHIND  FIREPLACE,Unknown - Wild Animal,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000372,Highbury East,E09000019,Islington,Stoke Newington,NULL,Highbury Quadrant,21603362,N5,NULL,NULL,532250,186250,NULL,NULL
136894091,01/08/2009 08:56,2009,2009/10,Special Service,1,1,260,260,FOX AND CAT TRAPPED IN BASEMENT AREA,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000261,Ravenscourt Park,E09000013,Hammersmith and Fulham,Hammersmith,NULL,King Street,21000478,W6,NULL,NULL,522250,178650,NULL,NULL
136955091,01/08/2009 11:53,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL CAUGHT IN FENCE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000356,Heston East,E09000018,Hounslow,Heston,NULL,Great West Road,21505052,TW5,NULL,NULL,513250,176750,NULL,NULL
137177091,01/08/2009 18:29,2009,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED IN PIPE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Sylvan Road,22207917,E7,NULL,NULL,540550,184850,NULL,NULL
137344091,01/08/2009 22:57,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED BETWEEN FENCE AND VAN,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000110,Chelsfield and Pratts Bottom,E09000006,Bromley,Orpington,NULL,Southfleet Road,NULL,BR6,NULL,NULL,545350,165050,NULL,NULL
137500091,02/08/2009 08:51,2009,2009/10,Special Service,1,1,260,260,CAT STUCK IN FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000038,River,E09000002,Barking and Dagenham,Dagenham,NULL,Tilney Road,19900830,RM9,NULL,NULL,548750,184550,NULL,NULL
137525091,02/08/2009 11:38,2009,2009/10,Special Service,1,5,260,1300,HORSE STUCK IN CANAL,Horse,Police,River/canal,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Harbet Road,NULL,N18,535750,192250,535750,192250,51.6127218,-0.04080847
137549091,02/08/2009 12:55,2009,2009/10,Special Service,1,1,260,260,FOX STUCK ON ROOF,Fox,Person (mobile),Shopping Centre,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000460,Figge's Marsh,E09000024,Merton,Mitcham,48103248,Majestic Way,22104137,CR4,527882,169132,527850,169150,51.40680309,-0.16275333
138096091,03/08/2009 10:04,2009,2009/10,Special Service,1,1,260,260,CAT IN PRECARIOUS POSITION,Cat,Police,Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000368,Caledonian,E09000019,Islington,Euston,NULL,Northdown Street,NULL,N1,NULL,NULL,530550,183050,NULL,NULL
138743091,04/08/2009 08:19,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN BARBED WIRE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000476,Boleyn,E09000025,Newham,East Ham,NULL,Hatherley Gardens,22200698,E6,NULL,NULL,541850,182750,NULL,NULL
138786091,04/08/2009 10:20,2009,2009/10,Special Service,1,2,260,520,PIGEON TANGLED IN WIRE AT FIRST FLOOR LEVEL,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000199,Enfield Lock,E09000010,Enfield,Enfield,NULL,Raynton Road,20702375,EN3,NULL,NULL,535850,198850,NULL,NULL
139219091,04/08/2009 22:27,2009,2009/10,Special Service,1,1,260,260,KITTEN IN DISTRESS ON ROOF OF FLATS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009396,Golborne,E09000020,Kensington and Chelsea,North Kensington,NULL,Manchester Drive,21700938,W10,NULL,NULL,524150,182150,NULL,NULL
141662091,08/08/2009 16:33,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH PIDGEON TRAPPED IN WIRE IN TREE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000059,Totteridge,E09000003,Barnet,Barnet,NULL,Dollis Valley Way,20012860,EN5,NULL,NULL,524750,195250,NULL,NULL
141814091,08/08/2009 20:18,2009,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED ON ROOF AND IN DISTRESS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,NULL,Anerley Hill,20302140,SE19,NULL,NULL,533950,170650,NULL,NULL
141954091,08/08/2009 23:47,2009,2009/10,Special Service,1,2,260,520,DOG TRAPPED UNDER BUS,Dog,Person (land line),Bus/coach,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000082,St. Michael's,E09000004,Bexley,Bexley,NULL,Elsa Road,NULL,DA16,546772,176434,546750,176450,51.46784649,0.111676447
142184091,09/08/2009 11:54,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED ON WINDOW SILL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009396,Golborne,E09000020,Kensington and Chelsea,North Kensington,NULL,Golborne Road,21700877,W10,NULL,NULL,524550,182050,NULL,NULL
142266091,09/08/2009 14:18,2009,2009/10,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK IN BASEMENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000636,Hyde Park,E09000033,Westminster,Paddington,NULL,Westbourne Terrace,8400878,W2,NULL,NULL,526450,181050,NULL,NULL
142528091,09/08/2009 21:14,2009,2009/10,Special Service,1,1,260,260,CAT IN STUCK IN BIN CHUTE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000418,Clapham Common,E09000022,Lambeth,Clapham,NULL,Oaklands Estate,21901112,SW4,NULL,NULL,529250,174150,NULL,NULL
142558091,09/08/2009 22:14,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED UNDER BATH,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000043,Brunswick Park,E09000003,Barnet,Southgate,NULL,Falkland Avenue,20015300,N11,NULL,NULL,528550,192950,NULL,NULL
142845091,10/08/2009 11:18,2009,2009/10,Special Service,1,1,260,260,PIGEON HANGING FROM WIRING,Bird,Person (land line),Cables,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000153,Fairfield,E09000008,Croydon,Croydon,NULL,Church Street,NULL,CR9,531927,165532,531950,165550,51.37352584,-0.105966985
143598091,11/08/2009 15:27,2009,2009/10,Special Service,1,1,260,260,CAT UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000455,Abbey,E09000024,Merton,Wimbledon,48050228,Mill Road,22104413,SW19,526396,170068,526350,170050,51.41554799,-0.183773793
144140091,12/08/2009 11:47,2009,2009/10,Special Service,1,2,260,520,ASSIST RSPCA WITH HEDGEHOG STUCK IN DRAIN,Hedgehog,Person (land line),Hotel/motel,Other Residential,Animal rescue from below ground,Wild animal rescue from below ground,E05000357,Heston West,E09000018,Hounslow,Hayes,1.00024E+11,High Street,21500608,TW5,510762,177536,510750,177550,51.48593874,-0.406161445
144194091,12/08/2009 13:23,2009,2009/10,Special Service,1,1,260,260,PIGEON STUCK ON ROOF AREA,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000216,Charlton,E09000011,Greenwich,East Greenwich,NULL,Wyndcliff Road,20801658,SE7,NULL,NULL,540750,177550,NULL,NULL
144229091,12/08/2009 14:39,2009,2009/10,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,Wiltshire Road,21901503,SW9,NULL,NULL,531350,175850,NULL,NULL
144791091,13/08/2009 12:05,2009,2009/10,Special Service,1,3,260,780,Redacted,Horse,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,NULL,Ferry Lane,NULL,N17,534971,189377,534950,189350,51.58709199,-0.053156734
146772091,16/08/2009 11:45,2009,2009/10,Special Service,1,1,260,260,DOG IN DISTRESS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000290,Harrow Weald,E09000015,Harrow,Harrow,NULL,Boxtree Lane,21201442,HA3,NULL,NULL,514750,191150,NULL,NULL
146886091,16/08/2009 15:52,2009,2009/10,Special Service,1,2,260,520,DOG WITH HEAD STUCK IN BASKET,Dog,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000443,Evelyn,E09000023,Lewisham,Deptford,NULL,Evelyn Street,NULL,SE8,536614,178043,536650,178050,51.48484688,-0.03384924
147019091,16/08/2009 18:56,2009,2009/10,Special Service,1,1,260,260,ASSIST POLICE WITH DUCKLINGS IN DRAIN,Bird,Police,Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000649,West End,E09000033,Westminster,Soho,NULL,Mount Row,NULL,W1K,528569,180689,528550,180650,51.51051119,-0.148684313
147111091,16/08/2009 21:00,2009,2009/10,Special Service,1,1,260,260,ASSIST POLICE AT SCENE OF PREVIOUS INCIDENT,Unknown - Heavy Livestock Animal,Police,Pipe or drain,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000649,West End,E09000033,Westminster,Soho,NULL,Mount Row,NULL,W1K,528610,180753,528650,180750,51.51107703,-0.148070488
147549091,17/08/2009 14:11,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN CUPBOARD,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Clonmore Street,22900991,SW18,NULL,NULL,524950,173050,NULL,NULL
147848091,17/08/2009 20:50,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN BARBED WIRE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000087,Brondesbury Park,E09000005,Brent,West Hampstead,NULL,Cavendish Road,NULL,NW6,NULL,NULL,524650,184550,NULL,NULL
147930091,17/08/2009 22:31,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011110,Peckham,E09000028,Southwark,Peckham,NULL,Cator Street,22500466,SE15,NULL,NULL,533850,177150,NULL,NULL
148936091,19/08/2009 15:57,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN TUNNEL,Dog,Person (mobile),"Tunnel, subway",Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Wood Vale,NULL,N10,529108,188786,529150,188750,51.58315455,-0.137952552
149261091,19/08/2009 22:58,2009,2009/10,Special Service,1,1,260,260,INJURED CYGENT STUCK IN MUD,Bird,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000364,Syon,E09000018,Hounslow,Chiswick,NULL,Pump Alley,NULL,TW8,517924,177495,517950,177450,51.48412358,-0.303072753
149275091,19/08/2009 23:12,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Cat,Person (mobile),Private garage,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000029,Chadwell Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Padnall Road,NULL,RM6,547869,189245,547850,189250,51.58267167,0.132829952
150546091,21/08/2009 18:52,2009,2009/10,Special Service,1,1,260,260,WHITE CAT TRAPPED ON TOP OF BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Coventry Road,22700372,E1,NULL,NULL,534850,182250,NULL,NULL
150926091,22/08/2009 11:09,2009,2009/10,Special Service,1,1,260,260,CAT LOCKED IN VACANT FLAT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000275,Noel Park,E09000014,Haringey,Hornsey,NULL,Bury Road,21104308,N22,NULL,NULL,531350,190050,NULL,NULL
151097091,22/08/2009 16:26,2009,2009/10,Special Service,2,3,260,780,YOUNG DEER TRAPPED IN RIVER,Deer,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000209,Southgate Green,E09000010,Enfield,Southgate,NULL,Powys Lane,NULL,N13,529888,192479,529850,192450,51.61616261,-0.125335542
151098091,22/08/2009 16:26,2009,2009/10,Special Service,1,1,260,260,DOG STUCK IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000642,Queen's Park,E09000033,Westminster,North Kensington,NULL,Ilbert Street,NULL,W10,524424,182623,524450,182650,51.52881952,-0.207700271
151712091,23/08/2009 14:51,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN FENCE AND BRICK WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Grosvenor Crescent,21400855,UB10,NULL,NULL,507750,184050,NULL,NULL
151862091,23/08/2009 18:08,2009,2009/10,Special Service,1,1,260,260,DOG STUCK IN FENCE,Dog,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000073,Danson Park,E09000004,Bexley,Bexley,NULL,Beechwood Crescent,NULL,DA7,548011,175889,548050,175850,51.46262703,0.129272714
151966091,23/08/2009 20:39,2009,2009/10,Special Service,1,1,260,260,CAT WITH PAW TRAPPED IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011469,Kenley,E09000008,Croydon,Purley,1.00021E+11,Valley Road,20502396,CR8,532961,159601,532950,159650,51.319984,-0.093332773
151984091,23/08/2009 20:55,2009,2009/10,Special Service,1,1,260,260,HORSE IN DISTRESS,Horse,Person (land line),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05011232,Thamesmead East,E09000004,Bexley,Erith,1.0002E+11,Coralline Walk,20100359,SE2,547374,179464,547350,179450,51.49491581,0.121600833
152122091,24/08/2009 00:14,2009,2009/10,Special Service,1,1,260,260,CAT WITH HEAD STUCK IN WINDOW,Cat,Person (land line),Tenement Building,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Eversholt Street,20400856,NW1,NULL,NULL,529650,182650,NULL,NULL
153215091,25/08/2009 13:54,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED INSIDE VEHICLE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000450,Perry Vale,E09000023,Lewisham,Forest Hill,NULL,Perry Vale,NULL,SE23,535536,172993,535550,172950,51.43972422,-0.05129941
153614091,25/08/2009 23:15,2009,2009/10,Special Service,1,1,260,260,PUPPY STUCK BEHIND RADIATOR,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009369,Clissold,E09000012,Hackney,Stoke Newington,NULL,Clissold Crescent,20900257,N16,NULL,NULL,532650,185950,NULL,NULL
153698091,26/08/2009 01:55,2009,2009/10,Special Service,1,1,260,260,DEER TRAPPED IN GATES,Deer,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000211,Turkey Street,E09000010,Enfield,Enfield,NULL,Winnington Road,NULL,EN3,535138,198728,535150,198750,51.6710814,-0.047141127
153742091,26/08/2009 06:44,2009,2009/10,Special Service,1,1,260,260,CAT STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000037,Parsloes,E09000002,Barking and Dagenham,Dagenham,NULL,Raydons Road,19900296,RM9,NULL,NULL,548650,185450,NULL,NULL
154250091,26/08/2009 19:46,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011479,Selhurst,E09000008,Croydon,Norbury,NULL,Kemp Gardens,20501089,CR0,NULL,NULL,532050,167050,NULL,NULL
155237091,28/08/2009 11:13,2009,2009/10,Special Service,1,1,260,260,HOUSE MARTIN TAPPED IN GRATE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000085,Alperton,E09000005,Brent,Wembley,NULL,Egerton Road,20201459,HA0,NULL,NULL,518550,184250,NULL,NULL
155475091,28/08/2009 18:21,2009,2009/10,Special Service,1,1,260,260,DOG IN CANAL,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009381,London Fields,E09000012,Hackney,Bethnal Green,NULL,Mare Street,NULL,E8,534810,183540,534850,183550,51.53467762,-0.057713672
156878091,30/08/2009 19:43,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED UNDER FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Abdale Road,21000025,W12,NULL,NULL,522950,180250,NULL,NULL
157140091,31/08/2009 03:55,2009,2009/10,Special Service,1,1,260,260,DOG THROWN INTO ELECTRICITY SUB STATION,Dog,Police,Other outdoor equipment/machinery,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000352,Feltham West,E09000018,Hounslow,Feltham,NULL,Ellington Road,NULL,TW13,509781,171828,509750,171850,51.43482548,-0.422053816
157711091,31/08/2009 21:28,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN BASEMENT,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000138,Holborn and Covent Garden,E09000007,Camden,Euston,NULL,Millman Street,20401009,WC1N,NULL,NULL,530650,182150,NULL,NULL
157740091,31/08/2009 22:14,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN CURTAIN RAIL AND WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000571,Wandle Valley,E09000029,Sutton,Wallington,NULL,Mullards Close,22600835,CR4,NULL,NULL,527950,166350,NULL,NULL
157744091,31/08/2009 22:16,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED UNDER PORTAKABIN,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011222,Crayford,E09000004,Bexley,Bexley,NULL,Halcot Avenue,20100654,DA6,NULL,NULL,550150,174850,NULL,NULL
157746091,31/08/2009 22:20,2009,2009/10,Special Service,1,1,260,260,DOG IN PRECARIOUS POSITION ON BALCONY,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009383,Springfield,E09000012,Hackney,Stoke Newington,NULL,Portland Avenue,20900813,N16,NULL,NULL,534050,187550,NULL,NULL
157949091,01/09/2009 10:28,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN SHUTTERS,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000036,Mayesbrook,E09000002,Barking and Dagenham,Barking,100010525,Gale Street,19900144,RM9,547608,184601,547650,184650,51.54101257,0.127118175
158794091,02/09/2009 10:57,2009,2009/10,Special Service,2,5,260,1300,HORSE STUCK IN MUD,Horse,Police,Wasteland,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000079,North End,E09000004,Bexley,Erith,NULL,Manor Road,NULL,DA8,552859,177622,552850,177650,51.47691035,0.199754957
158888091,02/09/2009 13:49,2009,2009/10,Special Service,1,1,260,260,PIGEON TRAPPED BEHIND SHOP SIGN,Bird,Person (mobile),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000449,New Cross,E09000023,Lewisham,New Cross,2.00001E+11,New Cross Road,22005521,SE14,536135,176932,536150,176950,51.4749784,-0.041171635
159023091,02/09/2009 18:15,2009,2009/10,Special Service,1,3,260,780,HORSE IMPAILED ON RAILINGS - LARGE ANIMAL RESCUE,Horse,Person (mobile),Scrub land,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000506,Hainault,E09000026,Redbridge,Hainault,NULL,Five Oaks Lane,NULL,IG7,548230,191870,548250,191850,51.60616354,0.139144946
159599091,03/09/2009 18:05,2009,2009/10,Special Service,1,2,260,520,CAT TRAPPED IN CAR BONNET,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000098,Queens Park,E09000005,Brent,West Hampstead,202070563,Winchester Avenue,20201918,NW6,524377,184063,524350,184050,51.54177145,-0.207868936
159971091,04/09/2009 09:59,2009,2009/10,Special Service,1,1,260,260,RUNNING CALL TO ASSIST RSPCA WITH TRAPPED GULL IN NETTING,Bird,Person (land line),DIY Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000411,St. James,E09000021,Kingston upon Thames,New Malden,48130762,Burlington Road,22100936,KT3,522013,168011,522050,168050,51.39802007,-0.247477991
161542091,06/09/2009 13:20,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05011253,Valentines,E09000026,Redbridge,Ilford,1.00022E+11,Auckland Road,22302592,IG1,543979,187521,543950,187550,51.56818687,0.076019598
161584091,06/09/2009 13:59,2009,2009/10,Special Service,1,4,260,1040,CAT STUCK BETWEEN TWO WALLS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000381,Tollington,E09000019,Islington,Holloway,NULL,Hornsey Road,21603387,N19,NULL,NULL,530350,186850,NULL,NULL
161897091,06/09/2009 22:49,2009,2009/10,Special Service,1,1,260,260,CAT STUCK UNDER SINK,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011244,Goodmayes,E09000026,Redbridge,Ilford,NULL,Express Drive,22302040,IG3,NULL,NULL,546650,187250,NULL,NULL
161935091,07/09/2009 00:33,2009,2009/10,Special Service,1,1,260,260,KITTENS STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011244,Goodmayes,E09000026,Redbridge,Dagenham,NULL,Mayfield Road,22302272,RM8,NULL,NULL,547150,187150,NULL,NULL
162044091,07/09/2009 06:59,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN DERELICT HOUSE BASEMENT - NORMANSFIELD HOUSE,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000522,Hampton Wick,E09000027,Richmond upon Thames,Twickenham,NULL,Langdon Park,22407115,TW11,NULL,NULL,517250,170150,NULL,NULL
162829091,08/09/2009 11:01,2009,2009/10,Special Service,1,1,260,260,CAT STUCK BEHIND PIPES IN BATHROOM,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009389,Brompton & Hans Town,E09000020,Kensington and Chelsea,Chelsea,NULL,Clabon Mews,21700103,SW1X,NULL,NULL,527750,178850,NULL,NULL
163350091,08/09/2009 22:08,2009,2009/10,Special Service,1,2,260,520,ANIMAL IN DISTRESS AT REAR OF PROPERTY,Unknown - Domestic Animal Or Pet,Person (land line),"Grassland, pasture, grazing etc",Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000479,Custom House,E09000025,Newham,East Ham,NULL,Allhallows Road,NULL,E6,542018,181658,542050,181650,51.51600046,0.04538193
163980091,09/09/2009 22:08,2009,2009/10,Special Service,1,2,260,520,RUNNING CALL TO CAT IN DISTRESS,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000301,Roxbourne,E09000015,Harrow,Northolt,NULL,Drinkwater Road,NULL,HA2,513810,186859,513850,186850,51.56912796,-0.359272599
164819091,11/09/2009 08:13,2009,2009/10,Special Service,1,1,260,260,CAT LOCKED IN GARAGE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000029,Chadwell Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Millbrook Gardens,19900793,RM6,NULL,NULL,548450,188450,NULL,NULL
165020091,11/09/2009 15:41,2009,2009/10,Special Service,1,1,260,260,DOG STUCK ON ROOF,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011472,Norbury & Pollards Hill,E09000008,Croydon,Norbury,NULL,London Road,20502653,SW16,NULL,NULL,530650,169550,NULL,NULL
166508091,13/09/2009 11:28,2009,2009/10,Special Service,1,1,260,260,CAT LOCKED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000191,Southfield,E09000009,Ealing,Chiswick,12097296,Ravenscroft Road,20601427,W4,520373,178805,520350,178850,51.49538217,-0.26737346
166566091,13/09/2009 13:14,2009,2009/10,Special Service,2,5,260,1300,HORSE STUCK IN RESERVOIR,Horse,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000206,Ponders End,E09000010,Enfield,Enfield,NULL,Wharf Road,NULL,EN3,536484,195509,536450,195550,51.64182959,-0.02894472
167858091,15/09/2009 10:25,2009,2009/10,Special Service,1,1,260,260,DEER TRAPPED UNDER BUS,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000057,Mill Hill,E09000003,Barnet,Mill Hill,NULL,Wills Grove,NULL,NW7,522626,192433,522650,192450,51.61737705,-0.230185062
168066091,15/09/2009 16:16,2009,2009/10,Special Service,1,1,260,260,DOG WITH PAW STUCK IN DOOR STOP,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000031,Eastbury,E09000002,Barking and Dagenham,Barking,NULL,Stamford Road,19900586,RM9,NULL,NULL,547050,184050,NULL,NULL
168084091,15/09/2009 16:58,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN METAL RAILINGS,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,NULL,East Hall Lane,NULL,RM13,NULL,NULL,553650,181250,NULL,NULL
168706091,16/09/2009 14:53,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN WINDOW FRAME,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000643,Regent's Park,E09000033,Westminster,Paddington,NULL,Allitsen Road,8400134,NW8,NULL,NULL,527050,183250,NULL,NULL
169159091,17/09/2009 10:16,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000061,West Finchley,E09000003,Barnet,Finchley,NULL,Burnbrae Close,NULL,N12,525907,191621,525950,191650,51.60935593,-0.183111199
169251091,17/09/2009 13:59,2009,2009/10,Special Service,1,1,260,260,Redacted,cat,Person (mobile),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Animal rescue from water,Animal rescue from water - Domestic pet,E05000520,Hampton,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Cleveland Avenue,22401375,TW12,512755,170394,512750,170350,51.42135336,-0.379743633
169351091,17/09/2009 17:02,2009,2009/10,Special Service,1,1,260,260,CAT IN DISTRESS STUCK ON WALL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000635,Harrow Road,E09000033,Westminster,North Kensington,NULL,Fernleigh Close,8401933,W9,NULL,NULL,524850,182950,NULL,NULL
170011091,18/09/2009 16:57,2009,2009/10,Special Service,2,6,260,1560,ROAD TRAFFIC ACCIDENT,Unknown - Heavy Livestock Animal,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000072,Cray Meadows,E09000004,Bexley,Sidcup,NULL,North Cray Road,NULL,DA14,548540,171687,548550,171650,51.424732,0.135118979
170041091,18/09/2009 17:57,2009,2009/10,Special Service,1,1,260,260,DOG STUCK BETWEEN WINDOW AND FRAME,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000044,Burnt Oak,E09000003,Barnet,Mill Hill,NULL,Homefield Road,20023940,HA8,NULL,NULL,520750,191550,NULL,NULL
171419091,20/09/2009 16:45,2009,2009/10,Special Service,1,1,260,260,CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011219,Bexleyheath,E09000004,Bexley,Bexley,NULL,Lyndhurst Road,20100896,DA7,NULL,NULL,549850,175950,NULL,NULL
171560091,20/09/2009 20:03,2009,2009/10,Special Service,1,1,260,260,CAT STUCK IN HOLE IN GARDEN,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009321,Bromley North,E09000030,Tower Hamlets,Bethnal Green,NULL,Arnold Road,22700089,E3,NULL,NULL,537350,182650,NULL,NULL
171757091,21/09/2009 01:13,2009,2009/10,Special Service,1,1,260,260,DOG FALLEN INTO A PIT - FRU REQUIRED FOR LINE OPS,Dog,Person (land line),Mine or quarry (not above ground building),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Great Central Way,NULL,NW10,520662,185607,520650,185650,51.55645343,-0.260883603
172624091,22/09/2009 13:21,2009,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED IN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Neville Road,22207816,E7,NULL,NULL,540450,184050,NULL,NULL
172627091,22/09/2009 13:24,2009,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000287,Edgware,E09000015,Harrow,Stanmore,NULL,Vancouver Road,21202374,HA8,NULL,NULL,519850,190650,NULL,NULL
173090091,23/09/2009 07:03,2009,2009/10,Special Service,1,1,260,260,FOX WITH TAIL TRAPPED IN FENCE,Fox,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000561,Nonsuch,E09000029,Sutton,Sutton,5870005097,Moreton Road,22601732,KT4,522530,165604,522550,165650,51.37627566,-0.240879147
173361091,23/09/2009 16:40,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH RESCUE OF SWAN FROM SEWAGE WORKS,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000362,Isleworth,E09000018,Hounslow,Heston,2.00004E+11,Mogden Lane,21500768,TW7,515436,174840,515450,174850,51.46077335,-0.3397544
173844091,24/09/2009 11:28,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN CHAIR,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000049,East Finchley,E09000003,Barnet,Finchley,NULL,Juliana Close,20024883,N2,NULL,NULL,525950,189950,NULL,NULL
173903091,24/09/2009 13:42,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN WINDOW,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Ampthill Square,20499150,NW1,NULL,NULL,529350,183050,NULL,NULL
174707091,25/09/2009 18:31,2009,2009/10,Special Service,1,1,260,260,INJURED CAT TRAPPED IN BUILDING,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Dufferin Street,21604739,EC1Y,NULL,NULL,532450,182150,NULL,NULL
175126091,26/09/2009 11:15,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000174,Ealing Common,E09000009,Ealing,Acton,12089284,Almond Avenue,20600049,W5,518023,179392,518050,179350,51.50115276,-0.30101377
175315091,26/09/2009 16:03,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX UNDER DRAIN,Fox,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000536,Cathedrals,E09000028,Southwark,Dowgate,NULL,Southwark Bridge Road,NULL,SE1,532102,179829,532150,179850,51.50196852,-0.098124655
175328091,26/09/2009 16:19,2009,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED IN WINDOW,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,Pembury Road,21104920,N17,NULL,NULL,533850,190450,NULL,NULL
176570091,28/09/2009 08:51,2009,2009/10,Special Service,1,1,260,260,KITTEN IN DISTRESS ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009319,Bow East,E09000030,Tower Hamlets,Homerton,NULL,Jodrell Road,22700683,E3,NULL,NULL,536850,183950,NULL,NULL
176658091,28/09/2009 12:08,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000090,Fryent,E09000005,Brent,Hendon,202037766,Townsend Lane,20201708,NW9,520859,188244,520850,188250,51.58011108,-0.257136481
176888091,28/09/2009 18:53,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009319,Bow East,E09000030,Tower Hamlets,Homerton,NULL,Jodrell Road,22700683,E3,NULL,NULL,536850,183950,NULL,NULL
178189091,30/09/2009 19:53,2009,2009/10,Special Service,1,1,260,260,CAT STUCK IN VENTILATION SHAFT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011105,Newington,E09000028,Southwark,Lambeth,NULL,John Ruskin Street,22501369,SE5,NULL,NULL,531750,177350,NULL,NULL
178709091,01/10/2009 16:16,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009380,Lea Bridge,E09000012,Hackney,Stoke Newington,NULL,Inver Close,20901843,E5,NULL,NULL,535050,186750,NULL,NULL
178916091,01/10/2009 19:21,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED BETWEEN FENCES,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011240,Clementswood,E09000026,Redbridge,Ilford,NULL,Albert Road,22305619,IG1,NULL,NULL,543850,186150,NULL,NULL
178993091,01/10/2009 21:14,2009,2009/10,Special Service,1,1,260,260,INJURED CAT FALLEN ONTO FIRST FLOOR BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000368,Caledonian,E09000019,Islington,Islington,NULL,Carnoustie Drive,21604456,N1,NULL,NULL,530650,184050,NULL,NULL
179878091,03/10/2009 07:56,2009,2009/10,Special Service,1,1,260,260,CAT STUCK ON ROOF FOR TWO DAYS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Stoke Newington,NULL,Inver Close,20901843,E5,NULL,NULL,535050,186750,NULL,NULL
180427091,03/10/2009 22:34,2009,2009/10,Special Service,1,2,260,520,Redacted,Horse,Other FRS,Wasteland,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000331,Heathrow Villages,E09000017,Hillingdon,Heathrow,NULL,Southern Perimeter Road,NULL,TW6,508684,174752,508650,174750,51.46131801,-0.436932413
180663091,04/10/2009 10:00,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE - CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000162,Selsdon and Ballards,E09000008,Croydon,Addington,NULL,Benhurst Gardens,NULL,CR2,NULL,NULL,535450,162350,NULL,NULL
180981091,04/10/2009 18:55,2009,2009/10,Special Service,1,1,260,260,RUNNING CALL TO STRAY HORSE IN ROADWAY,Horse,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000337,Pinkwell,E09000017,Hillingdon,Hayes,NULL,Shepiston Lane,NULL,UB3,507941,178612,507950,178650,51.49615568,-0.446445506
181318091,05/10/2009 09:58,2009,2009/10,Special Service,1,3,260,780,SMALL ANIMALS IN DISTRESS,Unknown - Wild Animal,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000195,Chase,E09000010,Enfield,Enfield,207178584,Forty Hill,20702687,EN2,533705,198583,533750,198550,51.67012104,-0.067906257
181457091,05/10/2009 14:26,2009,2009/10,Special Service,1,3,260,780,ASSIST RSPCA WITH HORSE STUCK IN WATER,Horse,Person (running call),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000331,Heathrow Villages,E09000017,Hillingdon,Hayes,NULL,Wilton Close,NULL,UB7,505839,177661,505850,177650,51.48800492,-0.476999219
181559091,05/10/2009 17:49,2009,2009/10,Special Service,1,1,260,260,DEER TRAPPED IN FENCE,Deer,Other FRS,Secondary school,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,1.00024E+11,Settle Road,21301584,RM3,555153,192679,555150,192650,51.61156883,0.239392339
183265091,08/10/2009 12:50,2009,2009/10,Special Service,1,2,260,520,DOG TRAPPED IN HOLE IN CHURCH YARD,Dog,Person (land line),Cemetery,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009376,Homerton,E09000012,Hackney,Homerton,10008463064,Homerton High Street,20900533,E9,535758,185118,535750,185150,51.54863095,-0.043445945
183345091,08/10/2009 15:23,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED ON ROOF OF HOUSE,Dog,Person (mobile),House in Multiple Occupation - Up to 2 storeys (not known if licensed),Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000359,Hounslow Heath,E09000018,Hounslow,Heston,NULL,Grove Road,21500530,TW3,NULL,NULL,513350,175350,NULL,NULL
183610091,08/10/2009 20:35,2009,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND RADIATOR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011239,Clayhall,E09000026,Redbridge,Ilford,NULL,Margaret Way,22303051,IG4,NULL,NULL,542250,188450,NULL,NULL
185248091,11/10/2009 14:05,2009,2009/10,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Longfield Street,22903013,SW18,NULL,NULL,525150,173650,NULL,NULL
185339091,11/10/2009 16:36,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009395,Earl's Court,E09000020,Kensington and Chelsea,Kensington,NULL,Warwick Road,NULL,SW5,525176,178528,525150,178550,51.49185093,-0.198318469
185828091,12/10/2009 13:32,2009,2009/10,Special Service,1,3,260,780,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000438,Blackheath,E09000023,Lewisham,Greenwich,NULL,Oakcroft Road,22000759,SE13,NULL,NULL,538650,176050,NULL,NULL
185873091,12/10/2009 15:24,2009,2009/10,Special Service,1,1,260,260,CAT STUCK UNDER SHED,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000121,Mottingham and Chislehurst North,E09000006,Bromley,Eltham,1.0002E+11,Framlingham Crescent,20302308,SE9,542562,171672,542550,171650,51.42613117,0.049190501
186283091,13/10/2009 07:42,2009,2009/10,Special Service,1,1,260,260,DOG STUCK IN FENCE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000343,West Ruislip,E09000017,Hillingdon,Ruislip,NULL,Midcroft,NULL,HA4,509304,187387,509350,187350,51.57476446,-0.424096794
186290091,13/10/2009 08:09,2009,2009/10,Special Service,1,1,260,260,CAT STUCK BETWEEN WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Mint Close,21402248,UB10,NULL,NULL,507850,182750,NULL,NULL
186316091,13/10/2009 09:41,2009,2009/10,Special Service,1,1,260,260,DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000132,Fortune Green,E09000007,Camden,West Hampstead,NULL,Fordwych Road,NULL,NW2,524735,184983,524750,184950,51.54996072,-0.202383282
186872091,14/10/2009 05:37,2009,2009/10,Special Service,1,1,260,260,PUPPY TRAPPED BETWEEN WALL,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000494,West Ham,E09000025,Newham,Stratford,NULL,Redriffe Road,22201565,E13,NULL,NULL,539850,183550,NULL,NULL
188219091,16/10/2009 08:31,2009,2009/10,Special Service,1,1,260,260,FOX STUCK IN SWIMMING POOL,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Wild animal rescue from water or mud,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,NULL,Lambs Lane South,21320023,RM13,NULL,NULL,553250,182150,NULL,NULL
189058091,17/10/2009 13:30,2009,2009/10,Special Service,1,1,260,260,DISTRESSED CAT STUCK UP TREE,Cat,Person (land line),Heathland,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011219,Bexleyheath,E09000004,Bexley,Bexley,1.0002E+11,Martens Avenue,20100936,DA7,550435,174969,550450,174950,51.45372216,0.163748267
189627091,18/10/2009 00:38,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN BUSHES,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000352,Feltham West,E09000018,Hounslow,Feltham,NULL,Westmacott Drive,NULL,TW14,509641,173204,509650,173250,51.44721988,-0.423641504
191318091,20/10/2009 14:39,2009,2009/10,Special Service,2,7,260,1820,HORSE TRAPPED IN MUD AND WATER,Horse,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000331,Heathrow Villages,E09000017,Hillingdon,Hayes,NULL,High Street,NULL,UB7,505695,177756,505650,177750,51.48888573,-0.479044044
191753091,21/10/2009 10:15,2009,2009/10,Special Service,1,3,260,780,CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011484,South Croydon,E09000008,Croydon,Croydon,NULL,Mansfield Road,20502209,CR2,NULL,NULL,532750,163550,NULL,NULL
192366091,22/10/2009 12:14,2009,2009/10,Special Service,1,1,260,260,CAT STUCK INSIDE BUILDING,Cat,Person (land line),Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009371,De Beauvoir,E09000012,Hackney,Shoreditch,1.00021E+11,De Beauvoir Road,20900318,N1,533197,184026,533150,184050,51.53942802,-0.080772022
192841091,23/10/2009 07:27,2009,2009/10,Special Service,1,1,260,260,RUNNING CALL KITTEN WITH HEAD STUCK IN BONGO DRUMS,Cat,Person (land line),Fire station,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000484,Forest Gate South,E09000025,Newham,Stratford,46099182,Romford Road,22208081,E15,539571,184672,539550,184650,51.54369413,0.011333185
192957091,23/10/2009 12:39,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN BARBED WIRE IN WOODS,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000563,Stonecot,E09000029,Sutton,Sutton,48083368,London Road,22103999,SM4,524750,166750,524750,166750,51.3860923,-0.208596798
193474091,24/10/2009 06:51,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BEHIND FITTED CUPBOARD,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000645,Tachbrook,E09000033,Westminster,Lambeth,NULL,Rampayne Street,8400468,SW1V,NULL,NULL,529650,178450,NULL,NULL
194165091,25/10/2009 08:46,2009,2009/10,Special Service,1,1,260,260,CAT LOCKED IN BUILDING UNDER RENOVATION,Cat,Person (land line),Bungalow - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011227,Longlands,E09000004,Bexley,Sidcup,NULL,Woodside Crescent,20101615,DA15,NULL,NULL,545350,172250,NULL,NULL
194175091,25/10/2009 09:35,2009,2009/10,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,Crouch End,E09000014,Haringey,Hornsey,10003973644,Clifton Road,21100175,N8,529742,188220,529750,188250,51.57792269,-0.129016105
194827091,26/10/2009 11:15,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000273,Hornsey,E09000014,Haringey,Hornsey,NULL,Temple Road,21103974,N8,NULL,NULL,530650,189250,NULL,NULL
195730091,27/10/2009 16:29,2009,2009/10,Special Service,1,1,260,260,CAT  STUCK IN DRAINPIPE,Cat,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000530,Teddington,E09000027,Richmond upon Thames,Twickenham,1.00023E+11,Broom Road,22403325,TW11,516784,171324,516750,171350,51.42889646,-0.32151899
196171091,28/10/2009 09:52,2009,2009/10,Special Service,1,1,260,260,POSSIBLE CAT STUCK BEHIND FIRE PLACE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000211,Turkey Street,E09000010,Enfield,Enfield,NULL,Hoe Lane,20702753,EN3,NULL,NULL,535150,198150,NULL,NULL
196332091,28/10/2009 14:36,2009,2009/10,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN FENCE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000314,Heaton,E09000016,Havering,Harold Hill,NULL,Keats Avenue,NULL,RM3,552504,190993,552550,190950,51.59714399,0.200433719
197054091,29/10/2009 17:10,2009,2009/10,Special Service,1,1,260,260,INJURED CAT TRAPPED IN DERELICT BUILDING,Cat,Person (mobile),Pre School/nursery,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011104,London Bridge & West Bermondsey,E09000028,Southwark,Dockhead,2.00003E+11,Stevens Street,22502387,SE1,533444,179440,533450,179450,51.49815774,-0.078948355
197069091,29/10/2009 17:30,2009,2009/10,Special Service,1,1,260,260,DOG LOCKED IN BEDROOM,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Central Street,21604474,EC1V,NULL,NULL,532150,182350,NULL,NULL
197435091,30/10/2009 13:09,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL POSSIBLY TRAPPED BEHIND WALL,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011237,Chadwell,E09000026,Redbridge,Dagenham,NULL,Percival Gardens,22302340,RM6,NULL,NULL,547650,188350,NULL,NULL
198919091,01/11/2009 13:08,2009,2009/10,Special Service,1,2,260,520,CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009369,Clissold,E09000012,Hackney,Stoke Newington,NULL,Clissold Road,NULL,N16,532912,186018,532950,186050,51.55739613,-0.084127508
199999091,03/11/2009 13:42,2009,2009/10,Special Service,1,1,260,260,CAT STUCK UP CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000452,Sydenham,E09000023,Lewisham,Forest Hill,NULL,Sydenham Road,22001951,SE26,NULL,NULL,535750,171450,NULL,NULL
200399091,04/11/2009 08:21,2009,2009/10,Special Service,1,1,260,260,THREE KITTENS STUCK UP CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000640,Maida Vale,E09000033,Westminster,Paddington,NULL,Randolph Avenue,8401514,W9,NULL,NULL,525850,182950,NULL,NULL
201050091,05/11/2009 15:38,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA OFFICER WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000480,East Ham Central,E09000025,Newham,East Ham,NULL,Lathom Road,22200795,E6,NULL,NULL,542450,184150,NULL,NULL
201406091,05/11/2009 20:08,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED BETWEEN TOILET AND WALL,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000592,Chapel End,E09000031,Waltham Forest,Walthamstow,NULL,Aveling Park Road,22812350,E17,NULL,NULL,537450,190450,NULL,NULL
203387091,08/11/2009 15:25,2009,2009/10,Special Service,1,1,260,260,RUNNING CALL TO CAT TRAPPED IN BUILDING UNDER REFURBISHMENT,Cat,Person (land line),Private Summer house,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009391,Chelsea Riverside,E09000020,Kensington and Chelsea,Chelsea,217064858,Paultons Square,21700394,SW3,526862,177759,526850,177750,51.48456449,-0.174321982
204325091,10/11/2009 09:35,2009,2009/10,Special Service,1,1,260,260,CAT STUCK UNDERNEATH FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000417,Brixton Hill,E09000022,Lambeth,West Norwood,NULL,Doverfield Road,21900472,SW2,NULL,NULL,530350,173950,NULL,NULL
205487091,12/11/2009 10:56,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000201,Haselbury,E09000010,Enfield,Edmonton,NULL,Lancing Gardens,NULL,N9,533900,194257,533950,194250,51.63120031,-0.066744016
205974091,13/11/2009 08:41,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN BARS OF STAIRCASE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,NULL,Dallas Road,22002331,SE26,NULL,NULL,534750,171950,NULL,NULL
206274091,13/11/2009 19:03,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000042,Whalebone,E09000002,Barking and Dagenham,Dagenham,100019872,Kings Avenue,19900236,RM6,548877,188303,548850,188350,51.57394284,0.146968736
206288091,13/11/2009 19:31,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (land line),Nursing/Care Home/Hospice,Other Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000063,Woodhouse,E09000003,Barnet,Barnet,NULL,High Road,NULL,N12,526290,193009,526250,193050,51.6217437,-0.177083554
206935091,14/11/2009 16:00,2009,2009/10,Special Service,1,1,260,260,DEER TRAPPED IN RAILINGS,Deer,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000162,Selsdon and Ballards,E09000008,Croydon,Addington,NULL,Quail Gardens,NULL,CR2,536153,162239,536150,162250,51.34293642,-0.046548067
207988091,16/11/2009 09:49,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALL AND SHED,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000259,Palace Riverside,E09000013,Hammersmith and Fulham,Fulham,NULL,Stevenage Road,21000786,SW6,NULL,NULL,523850,176450,NULL,NULL
208015091,16/11/2009 10:55,2009,2009/10,Special Service,1,1,260,260,TO ASSIST RSPCA WITH CAT ON HOUSE ROOF STUCK BETWEEN CHIMNEY POTS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,NULL,Railway Road,22403758,TW11,NULL,NULL,515550,171550,NULL,NULL
208537091,17/11/2009 12:07,2009,2009/10,Special Service,1,3,260,780,DOG TRAPPED DOWN HOLE,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000110,Chelsfield and Pratts Bottom,E09000006,Bromley,Orpington,NULL,Goddington Lane,NULL,BR6,547318,164861,547350,164850,51.36371683,0.114725626
208605091,17/11/2009 14:38,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Multi-Storey car park,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,NULL,Fairfield Road,NULL,KT1,518250,169250,518250,169250,51.40995241,-0.301132808
208663091,17/11/2009 16:16,2009,2009/10,Special Service,NULL,NULL,260,NULL,Redacted,Horse,Person (land line),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Freezeland Way,NULL,UB10,507714,184730,507750,184750,51.55118871,-0.447845025
209647091,19/11/2009 13:33,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN DERELICT PUB,Cat,Person (land line),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000026,Abbey,E09000002,Barking and Dagenham,Barking,NULL,Ripple Road,NULL,IG11,544508,183943,544550,183950,51.53590173,0.082178672
209722091,19/11/2009 15:59,2009,2009/10,Special Service,1,1,260,260,CAT STUCK BETWEEN WALL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000273,Hornsey,E09000014,Haringey,Hornsey,NULL,South View Road,21103882,N8,NULL,NULL,530050,189650,NULL,NULL
210177091,20/11/2009 14:04,2009,2009/10,Special Service,1,1,260,260,CAT STUCK BEHIND BATH,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000460,Figge's Marsh,E09000024,Merton,Mitcham,NULL,Tamworth Lane,22106216,CR4,NULL,NULL,528450,169150,NULL,NULL
211205091,22/11/2009 09:58,2009,2009/10,Special Service,1,1,260,260,KITTEN WITH TRAPPED IN CAVITY BEHIND KITCHEN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000112,Clock House,E09000006,Bromley,Beckenham,NULL,Clement Road,20301621,BR3,NULL,NULL,535850,169150,NULL,NULL
211435091,22/11/2009 16:55,2009,2009/10,Special Service,1,1,260,260,DOG STUCK ON RIVER BANK OF RIVER THAMES,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000525,Mortlake and Barnes Common,E09000027,Richmond upon Thames,Richmond,NULL,The Terrace,NULL,SW13,521449,176273,521450,176250,51.47239588,-0.252750773
211780091,23/11/2009 10:03,2009,2009/10,Special Service,1,1,260,260,DISTRESSED PARROT STUCK IN TREE,Bird,Person (land line),Wasteland,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000606,Markhouse,E09000031,Waltham Forest,Walthamstow,NULL,Gosport Road,NULL,E17,536786,188781,536750,188750,51.58129928,-0.027205447
212340091,24/11/2009 14:21,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED BETWEEN RAILINGS,Dog,Person (mobile),Other outdoor equipment/machinery,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000635,Harrow Road,E09000033,Westminster,North Kensington,NULL,Saltram Crescent,NULL,W9,525061,182597,525050,182550,51.52844528,-0.198531394
212840091,25/11/2009 12:57,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Shelter,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,NULL,Wheatfield Way,NULL,KT1,518261,169314,518250,169350,51.41052532,-0.300953375
214680091,28/11/2009 13:52,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE - CAT TRAPPED BY NETTING IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000131,Cantelowes,E09000007,Camden,Kentish Town,5034208,Rochester Square,20400662,NW1,529442,184378,529450,184350,51.54346467,-0.134756324
214917091,28/11/2009 21:00,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL TRAPPED BEHIND BOX,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000045,Childs Hill,E09000003,Barnet,Hendon,NULL,Ridge Hill,20036540,NW11,NULL,NULL,524350,187350,NULL,NULL
216000091,30/11/2009 17:52,2009,2009/10,Special Service,1,2,260,520,CAT STUCK BETWEEN TWO WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000312,Harold Wood,E09000016,Havering,Harold Hill,NULL,Gooshays Drive,21301233,RM3,NULL,NULL,554550,191450,NULL,NULL
216709091,01/12/2009 19:06,2009,2009/10,Special Service,1,1,260,260,DOG WITH LEG STUCK IN CAGE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000107,Biggin Hill,E09000006,Bromley,Biggin Hill,NULL,Jail Lane,20301511,TN16,NULL,NULL,541850,159550,NULL,NULL
217135091,02/12/2009 14:45,2009,2009/10,Special Service,1,1,260,260,CAT FALLEN FROM ROOF ONTO SPIKE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000452,Sydenham,E09000023,Lewisham,Beckenham,NULL,Venner Road,NULL,SE26,NULL,NULL,535350,171450,NULL,NULL
217651091,03/12/2009 13:09,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,Jelf Road,21900759,SW2,NULL,NULL,531350,174950,NULL,NULL
217775091,03/12/2009 17:39,2009,2009/10,Special Service,1,1,260,260,CAT POSSIBLY WITH FOOT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000043,Brunswick Park,E09000003,Barnet,Southgate,NULL,Brunswick Crescent,20005820,N11,NULL,NULL,528350,193350,NULL,NULL
217837091,03/12/2009 19:48,2009,2009/10,Special Service,1,1,260,260,DOG FALLEN INTO CANAL,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000084,Thamesmead East,E09000004,Bexley,Plumstead,NULL,Kinder Close,NULL,SE28,547534,180704,547550,180750,51.50601577,0.12442243
219412091,06/12/2009 18:59,2009,2009/10,Special Service,1,3,260,780,CAT LOCKED IN AT SCENE OF PREVIOUS FIRE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000612,Earlsfield,E09000032,Wandsworth,Wandsworth,NULL,Willow Tree Close,22906203,SW18,NULL,NULL,526050,173350,NULL,NULL
219738091,07/12/2009 12:02,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Cowdray Road,21400498,UB10,NULL,NULL,508350,183650,NULL,NULL
219840091,07/12/2009 15:12,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Multi-Storey car park,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,NULL,Wheatfield Way,NULL,KT1,518262,169291,518250,169250,51.4103184,-0.300946673
220308091,08/12/2009 12:49,2009,2009/10,Special Service,1,1,260,260,ASSIST POLICE TO RESCUE DOG FALLEN INTO PIT,Dog,Police,Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000328,Charville,E09000017,Hillingdon,Hillingdon,1.00021E+11,Fairholme Crescent,21400705,UB4,509970,182496,509950,182450,51.53067436,-0.416018295
223140091,13/12/2009 12:51,2009,2009/10,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000628,West Hill,E09000032,Wandsworth,Wandsworth,1.00023E+11,Victoria Drive,22905825,SW19,524092,172792,524050,172750,51.44053809,-0.215935787
224109091,15/12/2009 08:41,2009,2009/10,Special Service,1,1,260,260,LARGE ANIMAL RESCUE -  INFIRMED HORSE THAT HAS FALLEN,Horse,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000221,Glyndon,E09000011,Greenwich,Plumstead,1.00023E+11,Chestnut Rise,20800328,SE18,544978,178059,544950,178050,51.48291011,0.086533228
224156091,15/12/2009 10:49,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA OFFICER WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,NULL,Lewisham Park,NULL,SE13,538033,174453,538050,174450,51.4522418,-0.014827122
225454091,17/12/2009 12:56,2009,2009/10,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000443,Evelyn,E09000023,Lewisham,Deptford,10023231343,Scawen Road,22000907,SE8,536274,178345,536250,178350,51.48764279,-0.038626364
225555091,17/12/2009 16:22,2009,2009/10,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN GATE RAILINGS,Dog,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000598,Hatch Lane,E09000031,Waltham Forest,Woodford,2.00001E+11,Henrys Avenue,22844450,IG8,539563,192075,539550,192050,51.61021754,0.01415559
226460091,19/12/2009 00:09,2009,2009/10,Special Service,1,1,260,260,FOX WITH FOOT CAUGHT IN WIRE FENCING,Fox,Person (land line),Railings,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000073,Danson Park,E09000004,Bexley,Bexley,NULL,Northdown Road,NULL,DA16,547131,176018,547150,176050,51.4640154,0.116668056
226648091,19/12/2009 09:36,2009,2009/10,Special Service,1,1,260,260,DOG IN LAKE,Dog,Other FRS,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000195,Chase,E09000010,Enfield,Enfield,NULL,Forty Hill,NULL,EN2,533723,198599,533750,198550,51.67026053,-0.067640008
226755091,19/12/2009 12:15,2009,2009/10,Special Service,1,1,260,260,CAT LOCKED IN NEXT DOOR NEIGHBOURS FLAT,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000253,College Park and Old Oak,E09000013,Hammersmith and Fulham,North Kensington,NULL,Trenmar Gardens,21000827,NW10,NULL,NULL,522650,182750,NULL,NULL
227042091,19/12/2009 22:07,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN LOFT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000215,Blackheath Westcombe,E09000011,Greenwich,Lee Green,NULL,Chiswell Square,20800332,SE3,NULL,NULL,540850,176350,NULL,NULL
227400091,20/12/2009 13:50,2009,2009/10,Special Service,1,1,260,260,GULL TRAPPED IN NETTING,Bird,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000286,Canons,E09000015,Harrow,Stanmore,NULL,Winton Gardens,NULL,HA8,518895,191330,518850,191350,51.60826279,-0.284423844
227404091,20/12/2009 13:58,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN BUILDINGS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000360,Hounslow South,E09000018,Hounslow,Heston,NULL,Shirley Drive,21501016,TW3,NULL,NULL,514050,174650,NULL,NULL
227698091,20/12/2009 22:19,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED UNDER DECKING,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011481,Selsdon Vale & Forestdale,E09000008,Croydon,Addington,NULL,Bardolph Avenue,20501657,CR0,NULL,NULL,536650,162750,NULL,NULL
227975091,21/12/2009 13:03,2009,2009/10,Special Service,1,1,260,260,DOG FALLEN THROUGH ICE IN LAKE,Dog,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000195,Chase,E09000010,Enfield,Enfield,207178584,Forty Hill,20702687,EN2,533639,198561,533650,198550,51.66993903,-0.068868485
228616091,22/12/2009 10:06,2009,2009/10,Special Service,1,1,260,260,RUNNING CALL TO KITTEN TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Richmond Avenue,21401627,UB10,NULL,NULL,507850,184550,NULL,NULL
228746091,22/12/2009 15:05,2009,2009/10,Special Service,1,1,260,260,DOG IN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000356,Heston East,E09000018,Hounslow,Heston,2.00004E+11,Osterley Park,21500647,TW7,513750,176750,513750,176750,51.47828145,-0.363398753
229229091,23/12/2009 12:00,2009,2009/10,Special Service,1,1,260,260,BIRD WITH WINGS TRAPPED IN NETTING,Bird,Person (mobile),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,128019778,Wheatfield Way,21801044,KT1,518247,169248,518250,169250,51.40993506,-0.301176591
229900091,24/12/2009 15:02,2009,2009/10,Special Service,2,3,260,780,DOG FALLEN INTO CANAL,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000200,Grange,E09000010,Enfield,Enfield,NULL,Cecil Road,NULL,EN2,532455,196459,532450,196450,51.65133022,-0.086775248
230352091,25/12/2009 11:28,2009,2009/10,Special Service,1,1,260,260,DOG STUCK IN LAKE,Dog,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05011226,Falconwood & Welling,E09000004,Bexley,Bexley,10011845449,Danson Park,20100426,DA6,547521,174952,547550,174950,51.4543355,0.12183349
230868091,26/12/2009 11:23,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000610,Balham,E09000032,Wandsworth,Tooting,NULL,Laitwood Road,22902761,SW12,NULL,NULL,528950,173350,NULL,NULL
230891091,26/12/2009 12:38,2009,2009/10,Special Service,1,2,260,520,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009396,Golborne,E09000020,Kensington and Chelsea,North Kensington,217080482,St. Ervans Road,21701009,W10,524510,181918,524550,181950,51.5224646,-0.206710241
231397091,27/12/2009 16:59,2009,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED UNDER DECKING,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000280,Tottenham Green,E09000014,Haringey,Tottenham,NULL,Kirkton Road,21103498,N15,NULL,NULL,533150,189050,NULL,NULL
231733091,28/12/2009 11:52,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011484,South Croydon,E09000008,Croydon,Croydon,NULL,Brighton Road,20501945,CR2,NULL,NULL,532450,163050,NULL,NULL
231762091,28/12/2009 13:43,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN EXTRACTOR FAN,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,The Ridgeway,22870050,E4,NULL,NULL,538350,194250,NULL,NULL
233284091,31/12/2009 18:09,2009,2009/10,Special Service,1,1,260,260,CAT STUCK UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Boleyn Road,22208058,E7,NULL,NULL,540550,184150,NULL,NULL
248101,01/01/2010 09:12,2010,2009/10,Special Service,1,2,260,520,CAT TRAPPED BEHIND BEDROOM WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Boleyn Road,22208058,E7,NULL,NULL,540550,184150,NULL,NULL
413101,01/01/2010 16:27,2010,2009/10,Special Service,1,3,260,780,PIGEON TRAPPED IN MESH,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000629,West Putney,E09000032,Wandsworth,Wandsworth,NULL,Tildesley Road,22905407,SW15,NULL,NULL,523050,174350,NULL,NULL
1409101,03/01/2010 15:01,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000039,Thames,E09000002,Barking and Dagenham,Barking,NULL,Curzon Crescent,NULL,IG11,545778,182999,545750,182950,51.5270933,0.100086349
1886101,04/01/2010 12:02,2010,2009/10,Special Service,1,1,260,260,HORSE STUCK IN ICE,Horse,Person (mobile),Wasteland,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000214,Abbey Wood,E09000011,Greenwich,Plumstead,NULL,Sewell Road,NULL,SE2,547278,180043,547250,180050,51.50014323,0.120460558
3215101,06/01/2010 10:09,2010,2009/10,Special Service,2,5,260,1300,Redacted,Dog,Police,Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Kingston,NULL,Richmond Park,NULL,KT2,519261,170438,519250,170450,51.42041796,-0.28620326
3223101,06/01/2010 10:22,2010,2009/10,Special Service,1,1,260,260,DOG FALLEN THROUGH ICE ON BOATING POND,Dog,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000036,Mayesbrook,E09000002,Barking and Dagenham,Barking,NULL,Lodge Avenue,NULL,RM8,546374,184522,546350,184550,51.54062387,0.109304088
3814101,07/01/2010 10:21,2010,2009/10,Special Service,1,1,260,260,DOG FALLEN INTO FROZEN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000620,Queenstown,E09000032,Wandsworth,Battersea,121016626,Battersea Park,22900030,SW11,527546,177314,527550,177350,51.4804116,-0.164636837
3975101,07/01/2010 15:04,2010,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE - CAT TRAPPED UNDER RUBBLE PILE,Cat,Other FRS,Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000409,Norbiton,E09000021,Kingston upon Thames,Kingston,1.00022E+11,Cambridge Road,21800199,KT1,519460,169055,519450,169050,51.40794635,-0.283808522
4416101,08/01/2010 09:35,2010,2009/10,Special Service,1,1,260,260,DOG IN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000315,Hylands,E09000016,Havering,Hornchurch,1.00023E+11,Hornchurch Road,21300425,RM11,553152,187026,553150,187050,51.5613255,0.208053787
4496101,08/01/2010 12:57,2010,2009/10,Special Service,1,1,260,260,DOG STUCK IN FROZEN POND,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000611,Bedford,E09000032,Wandsworth,Tooting,NULL,Doctor Johnson Avenue,NULL,SW17,528746,172278,528750,172250,51.4348816,-0.149196207
4669101,08/01/2010 18:18,2010,2009/10,Special Service,1,2,260,520,CAT STUCK BETWEEN TWO WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011226,Falconwood & Welling,E09000004,Bexley,Eltham,NULL,Sutherland Avenue,20101404,DA16,NULL,NULL,545550,175050,NULL,NULL
4716101,08/01/2010 19:25,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED UNDER DECKING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000374,Hillrise,E09000019,Islington,Holloway,NULL,Ashley Road,21606730,N19,NULL,NULL,530150,187450,NULL,NULL
5172101,09/01/2010 14:20,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN DIVAN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Bramshaw Road,20900162,E9,NULL,NULL,535850,184550,NULL,NULL
5250101,09/01/2010 16:20,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN BARBED WIRE,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000226,Plumstead,E09000011,Greenwich,Plumstead,NULL,Kentmere Road,NULL,SE18,545342,178620,545350,178650,51.48785766,0.092002502
5871101,10/01/2010 15:45,2010,2009/10,Special Service,3,2,260,520,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000195,Chase,E09000010,Enfield,Enfield,207178584,Forty Hill,20702687,EN2,533730,198596,533750,198550,51.67023191,-0.067539992
6179101,11/01/2010 02:11,2010,2009/10,Special Service,1,1,260,260,CAT WITH FOOT TRAPPED BETWEEN GRATE AND DOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000353,Hanworth,E09000018,Hounslow,Feltham,NULL,Hounslow Road,21500632,TW13,NULL,NULL,512050,172450,NULL,NULL
6277101,11/01/2010 09:24,2010,2009/10,Special Service,1,1,260,260,DOG IN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Woodford,NULL,The Charter Road,NULL,IG8,539750,191750,539750,191750,51.60725082,0.016724981
6311101,11/01/2010 10:34,2010,2009/10,Special Service,1,1,260,260,DOG STUCK IN POND,Dog,Police,Park,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000406,Coombe Hill,E09000021,Kingston upon Thames,New Malden,NULL,Kingston Vale,NULL,SW15,521312,172276,521350,172250,51.43650188,-0.256091194
6323101,11/01/2010 11:12,2010,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN FOXHOLE,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000516,Barnes,E09000027,Richmond upon Thames,Hammersmith,1.00022E+11,Madrid Road,22404740,SW13,522360,177137,522350,177150,51.47996511,-0.239341861
6350101,11/01/2010 12:22,2010,2009/10,Special Service,1,1,260,260,DOG IN LAKE,Dog,Other FRS,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000195,Chase,E09000010,Enfield,Enfield,207178584,Forty Hill,20702687,EN2,533639,198561,533650,198550,51.66993903,-0.068868485
6386101,11/01/2010 13:17,2010,2009/10,Special Service,1,1,260,260,PERSON AND DOG STUCK IN LAKE,Dog,Person (mobile),Park,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000611,Bedford,E09000032,Wandsworth,Tooting,NULL,Tooting Bec Road,NULL,SW17,528250,172250,528250,172250,51.43474229,-0.156338076
6453101,11/01/2010 15:18,2010,2009/10,Special Service,1,2,260,520,DOG TRAPPED IN  ICE  IN LAKE,Dog,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000620,Queenstown,E09000032,Wandsworth,Battersea,121016626,Battersea Park,22900030,SW11,527546,177314,527550,177350,51.4804116,-0.164636837
6543101,11/01/2010 18:14,2010,2009/10,Special Service,1,1,260,260,CALLED BY RSPCA CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000494,West Ham,E09000025,Newham,Stratford,46035206,Hartland Road,22201440,E15,539621,184279,539650,184250,51.54015031,0.011897974
6547101,11/01/2010 18:18,2010,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Woodford,NULL,The Charter Road,NULL,IG8,539250,191750,539250,191750,51.60737457,0.009509364
6548101,11/01/2010 18:19,2010,2009/10,Special Service,1,1,260,260,ANIMAL TRAPPED IN WIRE FENCING REAR OF,Unknown - Heavy Livestock Animal,Person (land line),Railway trackside vegetation,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000377,Mildmay,E09000019,Islington,Islington,NULL,Harecourt Road,NULL,N1,532141,184915,532150,184950,51.54766527,-0.095656309
6881101,12/01/2010 09:27,2010,2009/10,Special Service,1,1,260,260,DOG FALLEN THROUGH ICE INTO LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000317,Pettits,E09000016,Havering,Romford,NULL,Main Road,NULL,RM2,551890,189810,551850,189850,51.58668065,0.191064525
6909101,12/01/2010 10:44,2010,2009/10,Special Service,2,3,260,780,DOG IN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Settle Road,NULL,RM3,554890,192718,554850,192750,51.61199159,0.235614326
7223101,12/01/2010 21:25,2010,2009/10,Special Service,1,1,260,260,DOG FALLEN THROUGH ICE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009383,Springfield,E09000012,Hackney,Stoke Newington,NULL,Springfield,NULL,E5,534750,187250,534750,187250,51.56803113,-0.057159313
7565101,13/01/2010 14:49,2010,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000376,Junction,E09000019,Islington,Kentish Town,5300051397,Huddleston Road,21600471,N7,529365,186059,529350,186050,51.55858918,-0.135248222
7588101,13/01/2010 15:44,2010,2009/10,Special Service,1,1,260,260,DOG STUCK IN FENCE,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000376,Junction,E09000019,Islington,Kentish Town,NULL,Huddleston Road,21600471,N7,NULL,NULL,529350,186050,NULL,NULL
7644101,13/01/2010 17:10,2010,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Surbiton,NULL,High Street,NULL,KT1,517818,169056,517850,169050,51.40829864,-0.307405787
7933101,14/01/2010 08:47,2010,2009/10,Special Service,1,1,260,260,DOG IN FROZEN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000333,Ickenham,E09000017,Hillingdon,Hillingdon,NULL,Swakeleys Drive,NULL,UB10,507303,185620,507350,185650,51.55926654,-0.453499072
8079101,14/01/2010 14:16,2010,2009/10,Special Service,1,1,260,260,DOG IN WATER,Dog,Person (mobile),Park,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000529,South Twickenham,E09000027,Richmond upon Thames,Twickenham,NULL,Grove Road,NULL,TW2,514808,172133,514850,172150,51.43657116,-0.349667735
8478101,15/01/2010 10:12,2010,2009/10,Special Service,1,1,260,260,FOX TRAPPED IN GARDEN,Fox,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000420,Coldharbour,E09000022,Lambeth,Brixton,1.00023E+11,Gresham Road,21900641,SW9,531472,175569,531450,175550,51.46383175,-0.108780336
9188101,16/01/2010 16:42,2010,2009/10,Special Service,1,1,260,260,RUNNING CALL TO BIRD TRAPPED IN TREE,Bird,Person (land line),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000169,Woodside,E09000008,Croydon,Woodside,NULL,Spring Lane,NULL,SE25,534764,167233,534750,167250,51.38814595,-0.064587215
9574101,17/01/2010 10:16,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000409,Norbiton,E09000021,Kingston upon Thames,Kingston,NULL,Neville Road,21800695,KT1,NULL,NULL,519450,169350,NULL,NULL
9596101,17/01/2010 11:26,2010,2009/10,Special Service,1,1,260,260,PIGEON STUCK IN NETTING,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011246,Ilford Town,E09000026,Redbridge,Ilford,10034913606,Hainault Street,22302904,IG1,544035,186621,544050,186650,51.56008569,0.076458672
10111101,18/01/2010 09:59,2010,2009/10,Special Service,1,1,260,260,CAT FALLEN FROM BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Stainsby Road,22701152,E14,NULL,NULL,537150,181350,NULL,NULL
10239101,18/01/2010 14:41,2010,2009/10,Special Service,1,1,260,260,DOG TRAPPED UNDER SHED,Dog,Person (mobile),Private Garden Shed,Non Residential,Animal rescue from water,Animal rescue from water - Domestic pet,E05000327,Cavendish,E09000017,Hillingdon,Northolt,1.00021E+11,St. Peters Close,21401843,HA4,511541,186810,511550,186850,51.56914065,-0.392012833
10650101,19/01/2010 07:38,2010,2009/10,Special Service,1,1,260,260,DOGS TRAPPED IN LEAD,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000108,Bromley Common and Keston,E09000006,Bromley,Bromley,NULL,Whitebeam Avenue,20300693,BR2,NULL,NULL,543150,166850,NULL,NULL
10676101,19/01/2010 09:00,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED BEHIND WASHING MACHINE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000170,Acton Central,E09000009,Ealing,Acton,NULL,Burlington Gardens,20600290,W3,NULL,NULL,520550,180150,NULL,NULL
10941101,19/01/2010 18:56,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED BEHIND SHED,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05011103,Goose Green,E09000028,Southwark,Peckham,2.00003E+11,Upland Road,22502589,SE22,534265,174924,534250,174950,51.45738003,-0.068843058
11350101,20/01/2010 16:09,2010,2009/10,Special Service,1,1,260,260,ANIMAL STUCK IN CHIMNEY,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05009335,Weavers,E09000030,Tower Hamlets,Shoreditch,NULL,Ravenscroft Street,22700996,E2,NULL,NULL,533850,183050,NULL,NULL
11924101,21/01/2010 17:57,2010,2009/10,Special Service,1,1,260,260,DOG TRAPPED UNDER SHED,Dog,Person (land line),Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,1.00023E+11,Dells Close,22831325,E4,537709,194950,537750,194950,51.63650779,-0.011472553
11975101,21/01/2010 19:28,2010,2009/10,Special Service,1,1,260,260,CAT UP TREE,Cat,Person (land line),Cemetery,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009385,Stoke Newington,E09000012,Hackney,Stoke Newington,10008244248,Stoke Newington High Street,20901507,N16,533623,186710,533650,186750,51.56344668,-0.07361513
12658101,23/01/2010 00:18,2010,2009/10,Special Service,1,1,260,260,CAT STUCK BETWEEN GARAGE AND FLAT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000028,Becontree,E09000002,Barking and Dagenham,Dagenham,NULL,Haydon Road,19900170,RM8,NULL,NULL,547450,186650,NULL,NULL
12811101,23/01/2010 10:12,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN SHED,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000109,Bromley Town,E09000006,Bromley,Bromley,NULL,Blyth Road,20301843,BR1,NULL,NULL,539850,169550,NULL,NULL
12883101,23/01/2010 13:27,2010,2009/10,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),Bungalow - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,NULL,St. Marys Crescent,21500986,TW7,NULL,NULL,515150,177350,NULL,NULL
14274101,26/01/2010 10:01,2010,2009/10,Special Service,1,2,260,520,HORSE IN RIVER,Horse,Person (land line),Wasteland,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000206,Ponders End,E09000010,Enfield,Enfield,207167256,Wharf Road,20702448,EN3,536356,195438,536350,195450,51.64122266,-0.030821026
14471101,26/01/2010 16:29,2010,2009/10,Special Service,1,1,260,260,CAT STUCK UNDER BRIDGE,Cat,Person (land line),"Tunnel, subway",Outdoor Structure,Animal rescue from water,Animal rescue from water - Domestic pet,E05000525,Mortlake and Barnes Common,E09000027,Richmond upon Thames,Richmond,NULL,Woodlands Road,NULL,SW13,521648,175736,521650,175750,51.46752692,-0.250071538
14918101,27/01/2010 13:13,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,Finchley Road,NULL,NW11,524895,188569,524850,188550,51.58215308,-0.198803721
15165101,27/01/2010 22:39,2010,2009/10,Special Service,1,1,260,260,CAT STUCK INSIDE WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000282,West Green,E09000014,Haringey,Tottenham,NULL,Moira Close,21104815,N17,NULL,NULL,533050,190450,NULL,NULL
15786101,29/01/2010 08:30,2010,2009/10,Special Service,1,1,260,260,DOG WITH PAW STUCK IN GRATING,Dog,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,NULL,Auckland Hill,NULL,SE27,532476,171637,532450,171650,51.42826175,-0.095806331
15868101,29/01/2010 11:44,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON TOP OF BUILDING,Cat,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000270,Fortis Green,E09000014,Haringey,Hornsey,NULL,Twyford Avenue,NULL,N2,528017,189704,528050,189750,51.5916529,-0.153355886
15908101,29/01/2010 13:20,2010,2009/10,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000309,Emerson Park,E09000016,Havering,Hornchurch,1.00021E+11,Ernest Road,21300344,RM11,554903,188006,554950,188050,51.5696525,0.233724821
16354101,30/01/2010 09:26,2010,2009/10,Special Service,1,2,260,520,DEER TRAPPED IN FENCE,Deer,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000162,Selsdon and Ballards,E09000008,Croydon,Addington,NULL,Quail Gardens,NULL,CR2,536153,162239,536150,162250,51.34293642,-0.046548067
16515101,30/01/2010 14:51,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (land line),Bungalow - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000202,Highlands,E09000010,Enfield,Southgate,NULL,Linkside Close,20702809,EN2,NULL,NULL,531050,196650,NULL,NULL
16889101,31/01/2010 00:35,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX TRAPPED BETWEEN GATES,Fox,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000627,Wandsworth Common,E09000032,Wandsworth,Wandsworth,NULL,Quarry Road,22904103,SW18,NULL,NULL,526450,174450,NULL,NULL
17049101,31/01/2010 11:48,2010,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000595,Forest,E09000031,Waltham Forest,Leytonstone,NULL,Whipps Cross Road,NULL,E11,539248,188748,539250,188750,51.58040012,0.00829114
17119101,31/01/2010 13:45,2010,2009/10,Special Service,1,1,260,260,DUCK HANGING FROM TREE WITH FISHING LINE,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000611,Bedford,E09000032,Wandsworth,Tooting,NULL,Tooting Bec Road,NULL,SW16,528250,172250,528250,172250,51.43474229,-0.156338076
17235101,31/01/2010 18:12,2010,2009/10,Special Service,1,2,260,520,DOG IN LAKE UNDER PONTOON,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000611,Bedford,E09000032,Wandsworth,Tooting,NULL,Doctor Johnson Avenue,NULL,SW17,529150,172072,529150,172050,51.43293843,-0.143462422
17481101,01/02/2010 07:53,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN DUCTING,Cat,Other FRS,Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Kingston,NULL,Willow Bank,22406125,TW10,NULL,NULL,516950,172450,NULL,NULL
17566101,01/02/2010 11:49,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED FOX,Fox,Person (land line),Road surface/pavement,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05009391,Chelsea Riverside,E09000020,Kensington and Chelsea,Chelsea,NULL,Justice Walk,NULL,SW3,527065,177683,527050,177650,51.48383595,-0.171427281
18061101,02/02/2010 10:28,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN HEATING PIPE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Kingston,NULL,Willow Bank,22406125,TW10,NULL,NULL,516950,172450,NULL,NULL
19467101,05/02/2010 09:44,2010,2009/10,Special Service,1,2,260,520,RUNNING CALL TO CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011254,Wanstead Park,E09000026,Redbridge,Leytonstone,NULL,Belgrave Road,22305986,E11,NULL,NULL,540250,187150,NULL,NULL
19519101,05/02/2010 11:54,2010,2009/10,Special Service,1,1,260,260,RUNNING CALL TO ASSIST RSPCA WITH RELEASE OF BIRD,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000182,Northfield,E09000009,Ealing,Ealing,NULL,Sterling Place,20602244,W5,NULL,NULL,518150,178850,NULL,NULL
19547101,05/02/2010 13:01,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,Willifield Way,NULL,NW11,525107,188906,525150,188950,51.58513469,-0.19562558
20305101,06/02/2010 16:12,2010,2009/10,Special Service,1,3,260,780,DOG TRAPPED IN HOLE UNDER STAIRCASE,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,2.00001E+11,Ledrington Road,20302181,SE19,534250,171250,534250,171250,51.42436661,-0.070450735
22085101,10/02/2010 13:13,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN ROOF,Cat,Person (mobile),Factory,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000443,Evelyn,E09000023,Lewisham,Deptford,NULL,Rolt Street,NULL,SE8,536518,177740,536550,177750,51.48214717,-0.035348086
22340101,10/02/2010 22:23,2010,2009/10,Special Service,1,1,260,260,KITTEN STUCK UNDER FLOOR BOARD,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000608,William Morris,E09000031,Waltham Forest,Walthamstow,NULL,Cottenham Road,22829450,E17,NULL,NULL,536850,189450,NULL,NULL
22442101,11/02/2010 05:20,2010,2009/10,Special Service,1,1,260,260,DEER TRAPPED IN FENCE,Deer,Police,Park,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Whitchurch Road,NULL,RM3,554750,192250,554750,192250,51.60782516,0.233387301
22493101,11/02/2010 08:44,2010,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000275,Noel Park,E09000014,Haringey,Hornsey,NULL,High Road,NULL,N22,531046,190276,531050,190250,51.5960973,-0.109442285
22580101,11/02/2010 12:24,2010,2009/10,Special Service,1,1,260,260,CAT STUCK UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000468,Ravensbury,E09000024,Merton,Mitcham,NULL,Riverside Drive,22105424,CR4,NULL,NULL,527450,167850,NULL,NULL
23023101,12/02/2010 09:54,2010,2009/10,Special Service,1,1,260,260,DOG STUCK IN COFFEE TABLE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000097,Preston,E09000005,Brent,Wembley,NULL,Elmstead Avenue,20200435,HA9,NULL,NULL,518350,187150,NULL,NULL
23132101,12/02/2010 13:55,2010,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,NULL,Uxbridge Road,20602315,W13,NULL,NULL,516950,180550,NULL,NULL
23276101,12/02/2010 19:33,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED BELOW GROUND,Cat,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000633,Churchill,E09000033,Westminster,Lambeth,2.00002E+11,Churchill Gardens,8401852,SW1V,529165,177950,529150,177950,51.48576026,-0.141102776
23565101,13/02/2010 11:50,2010,2009/10,Special Service,1,1,260,260,PIGEON TRAPPED IN WIRE ON BALCONY,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011483,Shirly South,E09000008,Croydon,Addington,NULL,Bramble Close,20501669,CR0,NULL,NULL,537450,164850,NULL,NULL
23620101,13/02/2010 13:38,2010,2009/10,Special Service,1,1,260,260,CAT STUCK BETWEEN KITCHEN CUPBOARDS AND WALL,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011104,London Bridge & West Bermondsey,E09000028,Southwark,Old Kent Road,NULL,Woods Place,22502735,SE1,NULL,NULL,533250,179250,NULL,NULL
25056101,16/02/2010 10:44,2010,2009/10,Special Service,1,2,260,520,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000538,College,E09000028,Southwark,Forest Hill,NULL,Lordship Lane,NULL,SE22,534154,173565,534150,173550,51.44519347,-0.070954563
25863101,17/02/2010 20:32,2010,2009/10,Special Service,1,4,260,1040,DOG TAPPED IN RAILINGS,Dog,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Ion Square,NULL,E2,534350,182841,534350,182850,51.52850593,-0.064608431
26036101,18/02/2010 02:36,2010,2009/10,Special Service,1,1,260,260,CAT STUCK UNDER FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000285,Belmont,E09000015,Harrow,Stanmore,NULL,Weston Drive,21202529,HA7,NULL,NULL,516950,190850,NULL,NULL
26068101,18/02/2010 06:20,2010,2009/10,Special Service,1,1,260,260,HORSE WITH LEG TRAPPED UNDER STABLE DOOR,Horse,Person (mobile),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000405,Chessington South,E09000021,Kingston upon Thames,Surbiton,128032260,Barwell Lane,21800115,KT9,516963,163096,516950,163050,51.3549067,-0.32165141
26735101,19/02/2010 12:53,2010,2009/10,Special Service,1,1,260,260,CAT STUCK ON HOOK,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009381,London Fields,E09000012,Hackney,Shoreditch,10091775400,Kingsland Road,20900581,E8,533510,184081,533550,184050,51.53984825,-0.076240615
28318101,22/02/2010 14:17,2010,2009/10,Special Service,1,1,260,260,KITTEN STUCK BEHIND BOILER,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,NULL,Priors Field,20601392,UB5,NULL,NULL,512350,184550,NULL,NULL
29013101,23/02/2010 21:48,2010,2009/10,Special Service,1,1,260,260,PUPPY STUCK ON ROOF,Dog,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000217,Coldharbour and New Eltham,E09000011,Greenwich,Eltham,NULL,Southwood Road,20801384,SE9,NULL,NULL,544050,172850,NULL,NULL
29201101,24/02/2010 09:37,2010,2009/10,Special Service,1,1,260,260,FOX WITH HEAD TRAPPED IN IRON GATE,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000035,Longbridge,E09000002,Barking and Dagenham,Barking,100019626,Lyndhurst Gardens,19900231,IG11,545386,184685,545350,184650,51.54234371,0.095134895
29349101,24/02/2010 15:36,2010,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011223,Crook Log,E09000004,Bexley,Bexley,NULL,Cannon Road,20100258,DA7,NULL,NULL,548550,176650,NULL,NULL
30019101,25/02/2010 21:41,2010,2009/10,Special Service,1,1,260,260,DOG STUCK IN CHAIR,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Farm animal,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,The Bishops Avenue,20003740,N2,NULL,NULL,527250,189050,NULL,NULL
30735101,27/02/2010 08:17,2010,2009/10,Special Service,1,1,260,260,PIGEON TRAPPED ON BEHIND FIREPLACE,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000306,Brooklands,E09000016,Havering,Romford,NULL,Norfolk Road,21301453,RM7,NULL,NULL,550350,188250,NULL,NULL
30868101,27/02/2010 14:01,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000613,East Putney,E09000032,Wandsworth,Wandsworth,NULL,Strathan Close,NULL,SW18,524513,174251,524550,174250,51.45355845,-0.20936883
31007101,27/02/2010 18:48,2010,2009/10,Special Service,1,1,260,260,PERSONS LOCKED OUT OF CAR,Unknown - Heavy Livestock Animal,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000434,Thurlow Park,E09000022,Lambeth,West Norwood,NULL,Thurlow Park Road,NULL,SE21,531769,173053,531750,173050,51.44115187,-0.105443615
31381101,28/02/2010 11:58,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH DUCK HANGING FROM TREE OVER LAKE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000466,Merton Park,E09000024,Merton,Wimbledon,NULL,Cannon Hill Lane,NULL,SW20,524495,169365,524450,169350,51.40965009,-0.211343201
33402101,04/03/2010 10:07,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA,Unknown - Domestic Animal Or Pet,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000104,Wembley Central,E09000005,Brent,Wembley,202090471,Harley Close,20201668,HA0,517721,184942,517750,184950,51.55109705,-0.30350882
33920101,05/03/2010 08:30,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000333,Ickenham,E09000017,Hillingdon,Hillingdon,NULL,Thornhill Road,21401989,UB10,NULL,NULL,507150,186050,NULL,NULL
24109,06/03/2010 14:58,2010,2009/10,Special Service,1,1,260,260,NULL,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000572,Worcester Park,E09000029,Sutton,Sutton,5870015588,Amesbury Close,22601220,KT4,523396,166276,523350,166250,51.38212795,-0.228209764
34886101,06/03/2010 20:51,2010,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN RAILINGS,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Elbe Street,NULL,SW6,526169,176395,526150,176350,51.47246097,-0.184783626
35862101,08/03/2010 14:56,2010,2009/10,Special Service,1,1,260,260,DOG TRAPPED UNDER BUS,Dog,Person (mobile),Bus/coach,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000144,Swiss Cottage,E09000007,Camden,West Hampstead,NULL,Fairfax Road,NULL,NW6,526386,184246,526350,184250,51.54297024,-0.178848953
36306101,09/03/2010 13:10,2010,2009/10,Special Service,1,2,260,520,ASSIST RSPCA OFFICER WITH CAT UP TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011230,Sidcup,E09000004,Bexley,Sidcup,1.0002E+11,Ladbrooke Crescent,20100810,DA14,547846,172118,547850,172150,51.42878609,0.125324192
36783101,10/03/2010 10:42,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000435,Tulse Hill,E09000022,Lambeth,Brixton,1.00022E+11,Helix Road,21900694,SW2,530974,174374,530950,174350,51.45320784,-0.116387167
37009101,10/03/2010 18:29,2010,2009/10,Special Service,1,1,260,260,HAMSTER TRAPPED UNDER FLOORBOARDS,Hamster,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000373,Highbury West,E09000019,Islington,Islington,NULL,Roseleigh Avenue,21603757,N5,NULL,NULL,531850,185650,NULL,NULL
37310101,11/03/2010 08:58,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000039,Thames,E09000002,Barking and Dagenham,Barking,NULL,Atlantis Close,NULL,IG11,546649,182515,546650,182550,51.52251898,0.112431931
37604101,11/03/2010 19:27,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000039,Thames,E09000002,Barking and Dagenham,Barking,NULL,Atlantis Close,NULL,IG11,546647,182514,546650,182550,51.52251051,0.112402708
37935101,12/03/2010 12:40,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING UNDER RAILWAY ARCHES,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,NULL,Norwood High Street,NULL,SE27,532012,171998,532050,171950,51.43161422,-0.102342257
37981101,12/03/2010 14:46,2010,2009/10,Special Service,1,2,260,520,Redacted,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011487,Waddon,E09000008,Croydon,Croydon,1.00021E+11,Warham Road,20502408,CR2,532103,164298,532150,164250,51.36239515,-0.103897722
38325101,13/03/2010 07:53,2010,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED IN WALL CAVITY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009386,Victoria,E09000012,Hackney,Bethnal Green,NULL,Tudor Grove,20901015,E9,NULL,NULL,535050,184050,NULL,NULL
38567101,13/03/2010 16:42,2010,2009/10,Special Service,1,1,260,260,TWO CATS TRAPPED IN PIT,cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000270,Fortis Green,E09000014,Haringey,Hornsey,1.00021E+11,Muswell Hill Broadway,21106387,N10,528723,189860,528750,189850,51.59289414,-0.143112801
39175101,14/03/2010 17:24,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN  WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,NULL,Hengrave Road,22001546,SE23,NULL,NULL,535550,174050,NULL,NULL
39481101,15/03/2010 09:30,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN LOWER LEVEL OF PROPERTY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000141,King's Cross,E09000007,Camden,Euston,NULL,Mecklenburgh Square,20499101,WC1N,NULL,NULL,530550,182450,NULL,NULL
39548101,15/03/2010 11:42,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED UNDER FENCE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,1.00022E+11,Thornford Road,22001969,SE13,538353,174633,538350,174650,51.45378133,-0.010154398
39641101,15/03/2010 14:45,2010,2009/10,Special Service,1,1,260,260,CAT POSSIBLY TRAPPED UP CHIMNEY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000191,Southfield,E09000009,Ealing,Chiswick,NULL,Church Path,20600383,W4,NULL,NULL,520350,179250,NULL,NULL
39769101,15/03/2010 18:09,2010,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN PARK RAILINGS,Dog,Person (mobile),Infant/Primary school,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000338,South Ruislip,E09000017,Hillingdon,Ruislip,1.00023E+11,Cedar Avenue,21400329,HA4,511334,184924,511350,184950,51.55222997,-0.395595185
40284101,16/03/2010 14:40,2010,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000558,Carshalton Central,E09000029,Sutton,Wallington,NULL,Salisbury Road,22602589,SM5,NULL,NULL,527850,164050,NULL,NULL
40297101,16/03/2010 15:15,2010,2009/10,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN METAL CAGE BARS,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000530,Teddington,E09000027,Richmond upon Thames,Twickenham,NULL,Grove Gardens,22403524,TW11,NULL,NULL,516250,171650,NULL,NULL
40735101,17/03/2010 09:36,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000225,Peninsula,E09000011,Greenwich,East Greenwich,NULL,Pelton Road,20801159,SE10,NULL,NULL,539150,178350,NULL,NULL
40823101,17/03/2010 13:39,2010,2009/10,Special Service,1,1,260,260,RUNNING CALL TO BIRD CAUGHT IN MESH,Bird,Person (land line),Converted office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000649,West End,E09000033,Westminster,Soho,10033593404,Greek Street,8401382,W1D,529761,181159,529750,181150,51.51446281,-0.131344143
40961101,17/03/2010 17:24,2010,2009/10,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000601,Hoe Street,E09000031,Waltham Forest,Walthamstow,NULL,Aubrey Road,22812200,E17,NULL,NULL,537450,189550,NULL,NULL
41403101,18/03/2010 12:52,2010,2009/10,Special Service,1,1,260,260,DOG STUCK IN HOLE,Dog,Person (mobile),Scrub land,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000340,Uxbridge North,E09000017,Hillingdon,Hillingdon,1.00023E+11,Court Drive,21400492,UB10,506843,183663,506850,183650,51.54176374,-0.460726184
41562101,18/03/2010 17:59,2010,2009/10,Special Service,2,1,260,260,CAT WEDGED BETWEEN GARAGES,Cat,Person (land line),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,12035502,Sandown Way,20601508,UB5,512462,184979,512450,184950,51.55250074,-0.379315035
43123101,21/03/2010 15:57,2010,2009/10,Special Service,1,1,260,260,CAT WITH HEAD TRAPPED IN FENCING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Elmington Road,22500901,SE5,NULL,NULL,532850,177050,NULL,NULL
43647101,22/03/2010 14:18,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000296,Marlborough,E09000015,Harrow,Harrow,NULL,Radnor Road,21201691,HA1,NULL,NULL,515050,188750,NULL,NULL
44319101,23/03/2010 20:32,2010,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND BOILER,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000277,St. Ann's,E09000014,Haringey,Tottenham,NULL,Kerswell Close,21103484,N15,NULL,NULL,533050,188550,NULL,NULL
44588101,24/03/2010 12:20,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALL & PIPE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000477,Canning Town North,E09000025,Newham,Plaistow,NULL,Gage Road,22201413,E16,NULL,NULL,539450,181750,NULL,NULL
44602101,24/03/2010 12:44,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRD IN TREE,Bird,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Eglington Road,NULL,E4,538812,194898,538850,194850,51.6357698,0.004434796
44611101,24/03/2010 13:00,2010,2009/10,Special Service,1,1,260,260,CAT WITH LEG TRAPPED IN CAT FLAP,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Crooms Hill,20800415,SE10,NULL,NULL,538450,177350,NULL,NULL
44843101,24/03/2010 19:40,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN PIPES AND WATER TANK,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011238,Churchfields,E09000026,Redbridge,Woodford,NULL,Liston Way,22305479,IG8,NULL,NULL,541250,191150,NULL,NULL
45166101,25/03/2010 14:32,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED PIGEON,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011464,Bensham Manor,E09000008,Croydon,Norbury,NULL,Langdale Road,20501119,CR7,NULL,NULL,531250,168150,NULL,NULL
45245101,25/03/2010 17:26,2010,2009/10,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN WOODEN FENCE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000457,Colliers Wood,E09000024,Merton,Mitcham,48001205,Alexandra Road,22100090,CR4,527406,170209,527450,170250,51.41658939,-0.169206783
45955101,27/03/2010 01:18,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN CEILING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009401,Queen's Gate,E09000020,Kensington and Chelsea,Kensington,NULL,Cromwell Road,21700138,SW7,NULL,NULL,525850,178950,NULL,NULL
46188101,27/03/2010 13:39,2010,2009/10,Special Service,1,1,260,260,CROW TRAPPED IN FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000431,Streatham South,E09000022,Lambeth,Norbury,NULL,Green Lane,21900636,SW16,NULL,NULL,530550,170150,NULL,NULL
46694101,28/03/2010 16:57,2010,2009/10,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000455,Abbey,E09000024,Merton,Wimbledon,NULL,Granville Road,22102928,SW19,NULL,NULL,525250,170150,NULL,NULL
48658101,01/04/2010 14:24,2010,2010/11,Special Service,1,3,260,780,RUNNING CALL TO SICK ANIMAL LOCKED IN VAN,Unknown - Heavy Livestock Animal,Person (land line),Van,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000482,East Ham South,E09000025,Newham,East Ham,NULL,Johnstone Road,NULL,E6,542904,182578,542950,182550,51.52404411,0.058514732
49059101,02/04/2010 11:12,2010,2010/11,Special Service,1,1,260,260,CAT STUCK INBETWEEN WALLS,Cat,Person (land line),Outdoor storage,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,34076146,Byam Street,21000177,SW6,526113,176176,526150,176150,51.47050526,-0.185667529
49100101,02/04/2010 12:56,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011224,East Wickham,E09000004,Bexley,Eltham,1.0002E+11,Ashmore Grove,20100060,DA16,544813,175799,544850,175750,51.46264485,0.083232931
49164101,02/04/2010 15:04,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000569,Wallington North,E09000029,Sutton,Wallington,NULL,Park Road,NULL,SM6,528982,164382,528950,164350,51.36386631,-0.148670825
49165101,02/04/2010 15:11,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND BOARDING IN CELLAR,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000619,Northcote,E09000032,Wandsworth,Battersea,NULL,Bolingbroke Grove,22900545,SW11,NULL,NULL,527150,174850,NULL,NULL
49244101,02/04/2010 18:13,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000569,Wallington North,E09000029,Sutton,Wallington,NULL,Park Road,NULL,SM6,528984,164341,528950,164350,51.36349738,-0.148656972
50175101,04/04/2010 19:28,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Oakdene Road,21401442,UB10,NULL,NULL,507750,183450,NULL,NULL
50581101,05/04/2010 15:18,2010,2010/11,Special Service,1,1,260,260,DOG STUCK DOWN FOXHOLE,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,10070709920,Richmond Road,22403771,TW1,516750,173750,516750,173750,51.45070742,-0.321207963
50676101,05/04/2010 18:54,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED UNDER PILE OF TIMBER IN SHED,Cat,Person (land line),Nursing/Care Home/Hospice,Other Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000044,Burnt Oak,E09000003,Barnet,Stanmore,200035739,East Road,20013460,HA8,520052,190797,520050,190750,51.60322764,-0.267905616
50742101,05/04/2010 20:14,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UNDER BONNET OF CAR,Cat,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000141,King's Cross,E09000007,Camden,Euston,5048946,Sidmouth Street,20400898,WC1H,530586,182614,530550,182650,51.52734872,-0.118922573
51775101,07/04/2010 17:15,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED  BETWEED BED AND WALL,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011104,London Bridge & West Bermondsey,E09000028,Southwark,Dockhead,NULL,Druid Street,22500807,SE1,NULL,NULL,533750,179650,NULL,NULL
52086101,08/04/2010 11:26,2010,2010/11,Special Service,1,1,260,260,HERON TRAPPED IN NETTING IN POND,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,NULL,Erlanger Road,NULL,SE14,535775,176381,535750,176350,51.47011332,-0.046563615
52296101,08/04/2010 18:33,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (mobile),Swimming Pool,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05009376,Homerton,E09000012,Hackney,Homerton,1.00023E+11,Lower Clapton Road,20900640,E5,535128,185405,535150,185450,51.55136098,-0.052416501
52614101,09/04/2010 08:42,2010,2010/11,Special Service,1,4,260,1040,HORSE FALLEN BETWEEN STABLES,Horse,Person (mobile),Animal boarding/breeding establishment - cats,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,1.00021E+11,Noak Hill Road,21301452,RM3,554187,193530,554150,193550,51.61948023,0.225827382
52701101,09/04/2010 12:20,2010,2010/11,Special Service,1,2,260,520,CAT TRAPPED IN AIR VENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,NULL,Greystead Road,22001515,SE23,NULL,NULL,535250,173850,NULL,NULL
52726101,09/04/2010 13:30,2010,2010/11,Special Service,1,2,260,520,DEER WITH HEAD STUCK IN RAILINGS,Deer,Other FRS,Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Dagnam Park Drive,NULL,RM3,555393,192127,555350,192150,51.60654314,0.242611004
53037101,09/04/2010 20:34,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN GROUNDS OF WAREHOUSE,Cat,Person (mobile),Converted office,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,6021826,Hollybush Place,22700647,E2,534912,182771,534950,182750,51.52774289,-0.056538408
54290101,11/04/2010 18:44,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN FENCE,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,6210797,Agnes Street,22700051,E14,536845,181457,536850,181450,51.5154705,-0.02920048
54936101,12/04/2010 19:33,2010,2010/11,Special Service,1,3,260,780,CAT TRAPPED UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011111,Peckham Rye,E09000028,Southwark,Forest Hill,NULL,Colyton Road,22500560,SE22,NULL,NULL,534850,174550,NULL,NULL
54953101,12/04/2010 19:55,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,10033525852,Northumberland Avenue,8401479,WC2N,530125,180375,530150,180350,51.50733349,-0.12639059
55016101,12/04/2010 21:02,2010,2010/11,Special Service,1,1,260,260,DOGS TRAPPED INSIDE CAR  WITH FUMES,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000536,Cathedrals,E09000028,Southwark,Lambeth,NULL,Short Street,NULL,SE1,531495,179884,531450,179850,51.50260419,-0.106844419
56052101,14/04/2010 18:33,2010,2010/11,Special Service,1,1,260,260,PIDGEON TRAPPED IN AIR VENT,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000283,White Hart Lane,E09000014,Haringey,Tottenham,NULL,Topham Square,21105162,N17,NULL,NULL,532350,190750,NULL,NULL
56094101,14/04/2010 19:25,2010,2010/11,Special Service,1,2,260,520,KITTEN TRAPPED BEHIND TOILET,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,Canterbury Crescent,21900284,SW9,NULL,NULL,531250,175550,NULL,NULL
56117101,14/04/2010 19:59,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED UNDERNEATH FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011486,Thornton Heath,E09000008,Croydon,Norbury,NULL,Bulganak Road,20500737,CR7,NULL,NULL,532250,168450,NULL,NULL
56200101,14/04/2010 22:32,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND SINK,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000174,Ealing Common,E09000009,Ealing,Acton,NULL,Willow Road,20601910,W5,NULL,NULL,518350,179650,NULL,NULL
56454101,15/04/2010 13:05,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED IN MIDDLE OF RAILWAY BRIDGE,Bird,Person (land line),Railway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000569,Wallington North,E09000029,Sutton,Wallington,NULL,Woodcote Road,NULL,SM6,528750,164250,528750,164250,51.36273253,-0.15204916
56469101,15/04/2010 13:44,2010,2010/11,Special Service,1,4,260,1040,CAT TRAPPED,Cat,Person (land line),Outdoor storage,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05011251,Seven Kings,E09000026,Redbridge,Ilford,1.00022E+11,Elgin Road,22302025,IG3,545509,187831,545550,187850,51.57057981,0.098207401
56674101,15/04/2010 19:33,2010,2010/11,Special Service,1,1,260,260,PUPPY STUCK BETWEEN TWO WALLS,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000358,Hounslow Central,E09000018,Hounslow,Heston,10001271387,Herbert Place,21590269,TW7,514673,176456,514650,176450,51.47545277,-0.350208875
57142101,16/04/2010 16:06,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000175,East Acton,E09000009,Ealing,Acton,NULL,Third Avenue,20601749,W3,NULL,NULL,521650,180150,NULL,NULL
57253101,16/04/2010 19:03,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011477,Purley Oaks & Riddlesdown,E09000008,Croydon,Purley,NULL,Norman Avenue,20502236,CR2,NULL,NULL,532450,162450,NULL,NULL
57584101,17/04/2010 09:51,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BETWEEN FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000225,Peninsula,E09000011,Greenwich,East Greenwich,NULL,Old Woolwich Road,20801109,SE10,NULL,NULL,538950,178050,NULL,NULL
58332101,18/04/2010 11:29,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000215,Blackheath Westcombe,E09000011,Greenwich,East Greenwich,NULL,Stratheden Road,20801429,SE3,NULL,NULL,540250,177150,NULL,NULL
58367101,18/04/2010 12:49,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000196,Cockfosters,E09000010,Enfield,Barnet,NULL,Old Orchard Close,20706106,EN4,NULL,NULL,526850,198150,NULL,NULL
58438101,18/04/2010 14:59,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING NEAR SHOPS,Bird,Person (land line),Single shop,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000048,East Barnet,E09000003,Barnet,Barnet,200034934,East Barnet Road,20013380,EN4,526726,196048,526750,196050,51.64895631,-0.169689366
58746101,18/04/2010 23:51,2010,2010/11,Special Service,1,1,260,260,HAMSTER TRAPPED IN DISABLED LIFT,Hamster,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Lee Green,NULL,Sheldon Close,20801338,SE12,NULL,NULL,540650,174650,NULL,NULL
58835101,19/04/2010 04:03,2010,2010/11,Special Service,1,1,260,260,DEER TRAPPED IN FENCE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000513,Snaresbrook,E09000026,Redbridge,Leytonstone,NULL,High Street Wanstead,NULL,E11,540661,188358,540650,188350,51.57654492,0.028513822
58950101,19/04/2010 09:52,2010,2010/11,Special Service,1,1,260,260,PREGNANT DOG WITH HEAD TRAPPED IN CAT FLAP,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000613,East Putney,E09000032,Wandsworth,Wandsworth,NULL,Keswick Road,22902627,SW15,NULL,NULL,524350,174450,NULL,NULL
59132101,19/04/2010 16:34,2010,2010/11,Special Service,1,1,260,260,CAT STUCK BETWEEN WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000528,South Richmond,E09000027,Richmond upon Thames,Richmond,NULL,Sheen Road,22405917,TW9,NULL,NULL,518750,174950,NULL,NULL
59248101,19/04/2010 19:50,2010,2010/11,Special Service,1,1,260,260,SEAGULL TRAPPED BEHIND SCAFFOLDING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000375,Holloway,E09000019,Islington,Holloway,NULL,Biddestone Road,NULL,N7,NULL,NULL,530650,185650,NULL,NULL
59503101,20/04/2010 08:35,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN BARBED WIRE,Dog,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Anthony Way,NULL,N18,535750,191750,535750,191750,51.6082287,-0.041001921
59530101,20/04/2010 10:00,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND SECURITY GATE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000638,Lancaster Gate,E09000033,Westminster,Kensington,NULL,Pembridge Square,8400629,W2,NULL,NULL,525450,180750,NULL,NULL
60136101,21/04/2010 11:41,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA OFFICER TO RELEASE KITTEN IN TOILET PIPE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,Cricklewood Broadway,20200487,NW2,NULL,NULL,524150,185350,NULL,NULL
60261101,21/04/2010 15:33,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED BETWEEN SHOP SIGN,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000048,East Barnet,E09000003,Barnet,Barnet,200035153,East Barnet Road,20013380,EN4,527133,195404,527150,195450,51.64307706,-0.164043589
60354101,21/04/2010 18:08,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO PIGEON TRAPPED,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Park Avenue South,21100674,N8,NULL,NULL,529750,189050,NULL,NULL
60892101,22/04/2010 16:03,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRDS,Bird,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000298,Pinner South,E09000015,Harrow,Harrow,NULL,Chapel Lane,NULL,HA5,512034,189470,512050,189450,51.5929514,-0.384054247
62023101,24/04/2010 06:03,2010,2010/11,Special Service,1,1,260,260,CAT WITH LEG TRAPPED IN DOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009399,Notting Dale,E09000020,Kensington and Chelsea,North Kensington,NULL,Hurstway Walk,21701277,W11,NULL,NULL,523850,180850,NULL,NULL
62095101,24/04/2010 10:13,2010,2010/11,Special Service,1,1,260,260,FOX WITH HEAD TRAPPED IN FENCE,Fox,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000105,Willesden Green,E09000005,Brent,Willesden,202130170,Hawthorn Road,20200106,NW10,522436,184458,522450,184450,51.54574541,-0.235706516
62267101,24/04/2010 13:38,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000417,Brixton Hill,E09000022,Lambeth,Brixton,NULL,Mauleverer Road,21900932,SW2,NULL,NULL,530250,174750,NULL,NULL
62726101,24/04/2010 23:51,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN GRATE,Cat,Person (mobile),Railings,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000138,Holborn and Covent Garden,E09000007,Camden,Soho,5055562,Princeton Street,20401141,WC1R,530716,181744,530750,181750,51.51950016,-0.117372256
62815101,25/04/2010 01:20,2010,2010/11,Special Service,1,1,260,260,FOX TRAPPED BY GLASS AT REAR OF PROPERTY,Fox,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000170,Acton Central,E09000009,Ealing,Acton,NULL,Chaucer Road,20600357,W3,NULL,NULL,520350,180350,NULL,NULL
63606101,26/04/2010 12:38,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000428,St. Leonard's,E09000022,Lambeth,Tooting,NULL,Abbotswood Road,21900063,SW16,NULL,NULL,529550,172250,NULL,NULL
63612101,26/04/2010 12:53,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND WARDROBE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000458,Cricket Green,E09000024,Merton,Mitcham,NULL,London Road,22103995,CR4,NULL,NULL,527350,168350,NULL,NULL
63812101,26/04/2010 19:47,2010,2010/11,Special Service,1,1,260,260,PARROT CAUGHT IN ROOF TILE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000452,Sydenham,E09000023,Lewisham,Forest Hill,NULL,Spring Hill,22001911,SE26,NULL,NULL,535350,171650,NULL,NULL
64167101,27/04/2010 13:25,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UP CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011477,Purley Oaks & Riddlesdown,E09000008,Croydon,Purley,NULL,Brancaster Lane,20501939,CR8,NULL,NULL,532350,161350,NULL,NULL
64323101,27/04/2010 17:48,2010,2010/11,Special Service,1,1,260,260,ANIMAL TRAPPED IN CHIMNEY,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000286,Canons,E09000015,Harrow,Stanmore,NULL,Wychwood Close,21202535,HA8,NULL,NULL,517750,191650,NULL,NULL
64396101,27/04/2010 19:12,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN HOUSE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000145,West Hampstead,E09000007,Camden,West Hampstead,NULL,Mill Lane,20400242,NW6,NULL,NULL,525350,185250,NULL,NULL
64733101,28/04/2010 12:38,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Train station - elsewhere,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,Kilburn High Road,NULL,NW6,524588,184643,524550,184650,51.54693752,-0.204622646
65157101,29/04/2010 02:24,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN WINDOW FRAME,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000494,West Ham,E09000025,Newham,Stratford,NULL,Paul Street,22201551,E15,NULL,NULL,539150,183950,NULL,NULL
65274101,29/04/2010 10:40,2010,2010/11,Special Service,1,1,260,260,ASSISTING RSPCA WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000131,Cantelowes,E09000007,Camden,Kentish Town,NULL,Patshull Road,20400509,NW5,NULL,NULL,528950,184750,NULL,NULL
65431101,29/04/2010 16:09,2010,2010/11,Special Service,1,1,260,260,PIGEON STUCK IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000403,Canbury,E09000021,Kingston upon Thames,Kingston,NULL,Brunswick Road,21800179,KT2,NULL,NULL,519150,169850,NULL,NULL
65446101,29/04/2010 16:38,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000615,Furzedown,E09000032,Wandsworth,Tooting,1.00023E+11,Welham Road,22906027,SW16,528856,170853,528850,170850,51.42204994,-0.148132378
65462101,29/04/2010 17:00,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN RAILINGS,Dog,Other FRS,Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000079,North End,E09000004,Bexley,Erith,NULL,Hollywood Way,NULL,DA8,552644,176714,552650,176750,51.46881023,0.196269169
67819101,02/05/2010 22:18,2010,2010/11,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED UNDER GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000036,Mayesbrook,E09000002,Barking and Dagenham,Barking,NULL,Porters Avenue,19900284,RM9,NULL,NULL,546750,184750,NULL,NULL
68283101,03/05/2010 20:12,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED BEHIND METAL FENCE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000572,Worcester Park,E09000029,Sutton,Sutton,NULL,Buckland Way,NULL,KT4,523009,166164,523050,166150,51.38120525,-0.233806795
69271101,05/05/2010 17:59,2010,2010/11,Special Service,1,1,260,260,CAT FALLEN INTO RIVER,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000461,Graveney,E09000024,Merton,Mitcham,NULL,Seely Road,NULL,SW17,528831,170543,528850,170550,51.41926959,-0.148604304
69604101,06/05/2010 11:19,2010,2010/11,Special Service,1,1,260,260,PIDGEON TRAPPED IN SASH WINDOW,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000381,Tollington,E09000019,Islington,Holloway,NULL,Hornsey Road,NULL,N19,530455,186743,530450,186750,51.56448516,-0.119280134
69934101,06/05/2010 20:05,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRD,Bird,Person (land line),Railway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000087,Brondesbury Park,E09000005,Brent,West Hampstead,202079063,Kilburn High Road,20202523,NW6,524755,184458,524750,184450,51.54523804,-0.202281067
70019101,06/05/2010 22:49,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN DRAIN,Dog,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,Dartmouth Road,NULL,NW2,523913,184830,523950,184850,51.54876661,-0.214286344
70335101,07/05/2010 15:40,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000423,Herne Hill,E09000022,Lambeth,Brixton,1.00022E+11,Lowden Road,21900899,SE24,532091,175189,532050,175150,51.46027274,-0.100016667
70477101,07/05/2010 19:39,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN BASEMENT,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000145,West Hampstead,E09000007,Camden,West Hampstead,NULL,Broadhurst Gardens,20400410,NW6,NULL,NULL,525650,184550,NULL,NULL
70766101,08/05/2010 09:37,2010,2010/11,Special Service,1,1,260,260,CAT STUCK BETWEEN WALLS OF ROOM,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000601,Hoe Street,E09000031,Waltham Forest,Walthamstow,NULL,Grosvenor Park Road,22841250,E17,NULL,NULL,537750,188650,NULL,NULL
70860101,08/05/2010 13:16,2010,2010/11,Special Service,1,1,260,260,DOG WITH HEAD STUCK OUT OF WINDOW,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000230,Woolwich Riverside,E09000011,Greenwich,East Greenwich,NULL,Ogilby Street,20801103,SE18,NULL,NULL,542750,178650,NULL,NULL
70935101,08/05/2010 16:09,2010,2010/11,Special Service,1,3,260,780,CAT TRAPPED BEHIND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000601,Hoe Street,E09000031,Waltham Forest,Walthamstow,NULL,Grosvenor Park Road,22841250,E17,NULL,NULL,537750,188650,NULL,NULL
70978101,08/05/2010 17:20,2010,2010/11,Special Service,1,1,260,260,KITTEN STUCK IN TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000330,Harefield,E09000017,Hillingdon,Ruislip,NULL,Sanctuary Close,NULL,UB9,505333,191075,505350,191050,51.60866891,-0.480271017
71260101,09/05/2010 05:35,2010,2010/11,Special Service,1,1,260,260,DEER STUCK IN LAKE,Deer,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000195,Chase,E09000010,Enfield,Enfield,NULL,Tingeys Top Lane,NULL,EN2,531305,199496,531350,199450,51.67889115,-0.102248994
71601101,09/05/2010 21:28,2010,2010/11,Special Service,1,1,260,260,FOX CUB TRAPPED IN FENCE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000210,Town,E09000010,Enfield,Enfield,NULL,Hallside Road,20702728,EN1,NULL,NULL,533550,198050,NULL,NULL
71770101,10/05/2010 06:40,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN MUD,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009391,Chelsea Riverside,E09000020,Kensington and Chelsea,Chelsea,NULL,Cheyne Walk,NULL,SW10,526631,177256,526650,177250,51.48009572,-0.177827129
71912101,10/05/2010 12:43,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000369,Canonbury,E09000019,Islington,Islington,NULL,Willow Bridge Road,NULL,N1,532103,184481,532150,184450,51.54377406,-0.096366834
72228101,10/05/2010 23:26,2010,2010/11,Special Service,2,3,260,780,LAMB IN STREAM,Lamb,Other FRS,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000203,Jubilee,E09000010,Enfield,Chingford,NULL,Lea Valley Road,NULL,E4,537359,194968,537350,194950,51.636755,-0.016519754
72442101,11/05/2010 13:50,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000298,Pinner South,E09000015,Harrow,Harrow,2.00004E+11,Pembroke Avenue,21202620,HA5,512006,187469,512050,187450,51.57497172,-0.385096366
72956101,12/05/2010 11:56,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Train station - elsewhere,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000469,Raynes Park,E09000024,Merton,New Malden,48081513,Grand Drive,22102916,SW20,523204,169290,523250,169250,51.40925784,-0.229922539
73131101,12/05/2010 16:54,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011489,Woodside,E09000008,Croydon,Woodside,1.00023E+11,Howard Road,20501062,SE25,534389,167693,534350,167650,51.39236872,-0.069799461
73444101,13/05/2010 09:29,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED UNDER DECKING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011099,Dulwich Hill,E09000028,Southwark,Forest Hill,NULL,Dunstans Road,22500836,SE22,NULL,NULL,534350,173950,NULL,NULL
73471101,13/05/2010 10:05,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Heathland,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000163,Shirley,E09000008,Croydon,Addington,NULL,Bramble Close,NULL,CR0,537562,164757,537550,164750,51.36522549,-0.025357425
73572101,13/05/2010 13:31,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK IN LOFT,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000256,Hammersmith Broadway,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Hammersmith Grove,21000410,W6,NULL,NULL,523050,179350,NULL,NULL
74238101,14/05/2010 15:56,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000135,Hampstead Town,E09000007,Camden,West Hampstead,NULL,Frognal Rise,NULL,NW3,526218,185979,526250,185950,51.55858229,-0.180648982
74378101,14/05/2010 19:58,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN DRAIN,Cat,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000408,Grove,E09000021,Kingston upon Thames,Surbiton,NULL,Mill Street,NULL,KT1,518469,168768,518450,168750,51.40557454,-0.298146071
74480101,14/05/2010 23:27,2010,2010/11,Special Service,1,1,260,260,IGUANA TRAPPED BEHIND HEATING PIPES,Lizard,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000345,Yiewsley,E09000017,Hillingdon,Hillingdon,NULL,Peggotty Way,21401507,UB8,NULL,NULL,507950,181250,NULL,NULL
74722101,15/05/2010 11:47,2010,2010/11,Special Service,1,1,260,260,CAT  TRAPPED IN ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000626,Tooting,E09000032,Wandsworth,Tooting,NULL,Cowick Road,NULL,SW17,527813,171754,527850,171750,51.43038322,-0.16280033
74801101,15/05/2010 14:02,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED SQUIRREL IN DRAINPIPE,Squirrel,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011105,Newington,E09000028,Southwark,Old Kent Road,NULL,Carter Street,22500455,SE17,NULL,NULL,532050,178050,NULL,NULL
75830101,17/05/2010 08:56,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN HOLE,Dog,Person (land line),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000117,Darwin,E09000006,Bromley,Biggin Hill,NULL,Shire Lane,NULL,BR6,544040,162292,544050,162250,51.34147219,0.066635966
75871101,17/05/2010 10:54,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000532,West Twickenham,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Lisbon Avenue,22401630,TW2,514096,172520,514050,172550,51.44019322,-0.359781932
75971101,17/05/2010 14:52,2010,2010/11,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000053,Golders Green,E09000003,Barnet,West Hampstead,200023491,Claremont Road,20009220,NW2,523634,186707,523650,186750,51.56569694,-0.217648823
76439101,18/05/2010 10:53,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011476,Purley & Woodcote,E09000008,Croydon,Purley,1.00021E+11,Promenade de Verdun,20502565,CR8,529943,161471,529950,161450,51.33748612,-0.135936602
76723101,18/05/2010 20:03,2010,2010/11,Special Service,1,1,260,260,CAT FALLEN DOWN CHUTE - POSSIBLY TRAPPED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000228,Thamesmead Moorings,E09000011,Greenwich,Plumstead,NULL,Attlee Road,20800096,SE28,NULL,NULL,546850,180750,NULL,NULL
76958101,19/05/2010 10:26,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN DRAIN,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000216,Charlton,E09000011,Greenwich,East Greenwich,NULL,Fairlawn,20800552,SE7,NULL,NULL,541150,177550,NULL,NULL
77521101,20/05/2010 08:38,2010,2010/11,Special Service,1,1,260,260,TWO KITTENS STUCK IN OVEN,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000214,Abbey Wood,E09000011,Greenwich,Plumstead,NULL,Church Manorway,20800336,SE2,NULL,NULL,546150,179450,NULL,NULL
77957101,20/05/2010 21:35,2010,2010/11,Special Service,1,1,260,260,RUBBISH ALIGHT IN GREEN PARK,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009389,Brompton & Hans Town,E09000020,Kensington and Chelsea,Chelsea,NULL,Sloane Street,21700497,SW1X,NULL,NULL,527850,179150,NULL,NULL
78181101,21/05/2010 09:46,2010,2010/11,Special Service,1,1,260,260,SQUIRREL TRAPPED BETWEEN CABLE AND WALL,Squirrel,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000138,Holborn and Covent Garden,E09000007,Camden,Shoreditch,NULL,Hatton Garden,NULL,EC1N,531359,181842,531350,181850,51.52023186,-0.108073553
78352101,21/05/2010 15:04,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000365,Turnham Green,E09000018,Hounslow,Chiswick,NULL,Marlborough Road,21500739,W4,NULL,NULL,520150,178350,NULL,NULL
78429101,21/05/2010 16:52,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000636,Hyde Park,E09000033,Westminster,Paddington,NULL,Hyde Park Crescent,8400829,W2,NULL,NULL,527150,181150,NULL,NULL
78510101,21/05/2010 18:47,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Grosvenor Road,22403520,TW1,516084,173541,516050,173550,51.44896586,-0.330856942
79464101,23/05/2010 00:30,2010,2010/11,Special Service,1,1,260,260,CAT FALLEN & INJURED CALLER UNABLE TO REACH,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000425,Larkhall,E09000022,Lambeth,Clapham,NULL,Union Road,NULL,SW4,NULL,NULL,529850,176150,NULL,NULL
79596101,23/05/2010 08:00,2010,2010/11,Special Service,1,1,260,260,KITTEN STUCK BEHIND KITCHEN UNIT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000552,Surrey Docks,E09000028,Southwark,Deptford,NULL,Globe Pond Road,NULL,SE16,NULL,NULL,536050,180250,NULL,NULL
80517101,24/05/2010 13:25,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000374,Hillrise,E09000019,Islington,Holloway,NULL,Hazellville Road,NULL,N19,529871,187528,529850,187550,51.57167431,-0.127411032
80519101,24/05/2010 13:25,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED ON WINDOWSILL,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000279,Stroud Green,E09000014,Haringey,Holloway,NULL,Upper Tollington Park,21104024,N4,NULL,NULL,531250,187450,NULL,NULL
80554101,24/05/2010 14:30,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED UNDER BUILDERS MACHINERY,Cat,Person (mobile),Other outdoor equipment/machinery,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05009378,Hoxton West,E09000012,Hackney,Islington,NULL,Cropley Street,NULL,N1,532663,183254,532650,183250,51.5326163,-0.088758164
80593101,24/05/2010 15:30,2010,2010/11,Special Service,1,1,260,260,CAT UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000374,Hillrise,E09000019,Islington,Holloway,NULL,Hazellville Road,NULL,N19,529871,187528,529850,187550,51.57167431,-0.127411032
80648101,24/05/2010 16:34,2010,2010/11,Special Service,1,1,260,260,FOX WITH HEAD STUCK IN CAR WHEEL,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000028,Becontree,E09000002,Barking and Dagenham,Dagenham,NULL,Crossway,19900101,RM8,NULL,NULL,547150,186150,NULL,NULL
80730101,24/05/2010 17:40,2010,2010/11,Special Service,1,1,260,260,KITTEN STUCK BEHINED PIPE WORK,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000128,Belsize,E09000007,Camden,Kentish Town,NULL,Primrose Hill Road,20400446,NW3,NULL,NULL,527550,184450,NULL,NULL
81218101,25/05/2010 10:26,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL TRAPPED BEHIND GAS FIRE,Unknown - Heavy Livestock Animal,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000426,Oval,E09000022,Lambeth,Lambeth,NULL,Dorset Road,21900471,SW8,NULL,NULL,530750,177250,NULL,NULL
81267101,25/05/2010 11:48,2010,2010/11,Special Service,1,1,260,260,DOG IN PRECARIOUS POSITION,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000272,Highgate,E09000014,Haringey,Kentish Town,NULL,Highgate Hill,21106146,N6,NULL,NULL,528850,187250,NULL,NULL
81518101,25/05/2010 18:13,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000329,Eastcote and East Ruislip,E09000017,Hillingdon,Ruislip,NULL,Campbell Close,NULL,HA4,510322,188104,510350,188150,51.58101098,-0.409187808
81828101,26/05/2010 07:38,2010,2010/11,Special Service,1,1,260,260,FOX ENTANGLED IN NETTING,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,NULL,Clifden Road,22403392,TW1,NULL,NULL,515850,173150,NULL,NULL
81930101,26/05/2010 11:58,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000571,Wandle Valley,E09000029,Sutton,Mitcham,5870112655,Lyle Close,22600753,CR4,528131,166840,528150,166850,51.38614872,-0.160002221
82540101,27/05/2010 11:33,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UNDER STORAGE UNIT,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000125,Plaistow and Sundridge,E09000006,Bromley,Bromley,1.0002E+11,Pike Close,20302078,BR1,540583,171191,540550,171150,51.42230265,0.020554012
82542101,27/05/2010 11:35,2010,2010/11,Special Service,1,3,260,780,ASSIST RSPCA TO GAIN ENTRY,Unknown - Heavy Livestock Animal,Police,Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Wick Road,20901074,E9,NULL,NULL,536050,184750,NULL,NULL
83720101,29/05/2010 09:31,2010,2010/11,Special Service,1,1,260,260,ANIMAL TRAPPED IN CHIMNEY,Unknown - Heavy Livestock Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000479,Custom House,E09000025,Newham,Plaistow,NULL,Varley Road,22201175,E16,NULL,NULL,540950,181350,NULL,NULL
84519101,30/05/2010 18:44,2010,2010/11,Special Service,1,1,260,260,BABY BIRD TRAPPED IN METAL POST OF PARK GATE,Bird,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,Peel Close,NULL,E4,537570,193709,537550,193750,51.62539042,-0.013966943
84569101,30/05/2010 19:44,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000434,Thurlow Park,E09000022,Lambeth,West Norwood,1.00023E+11,Norwood Road,21901036,SE24,531786,173170,531750,173150,51.44219937,-0.105155644
84915101,31/05/2010 13:31,2010,2010/11,Special Service,2,3,260,780,CYGNETS TRAPPED,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009330,St. Katharine's & Wapping,E09000030,Tower Hamlets,Shadwell,6026299,Glamis Road,22700541,E1W,535250,180750,535250,180750,51.50950086,-0.052444265
85446101,01/06/2010 11:55,2010,2010/11,Special Service,1,1,260,260,DUCKLINGS STUCK IN DRAIN,Bird,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000283,White Hart Lane,E09000014,Haringey,Tottenham,NULL,Creighton Road,NULL,N17,533235,191420,533250,191450,51.605864,-0.077424536
85448101,01/06/2010 12:07,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX STUCK IN TREE,Fox,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05009392,Colville,E09000020,Kensington and Chelsea,North Kensington,NULL,Powis Square,21700974,W11,NULL,NULL,524850,181150,NULL,NULL
85659101,01/06/2010 18:54,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000134,Gospel Oak,E09000007,Camden,Kentish Town,NULL,Kingsford Street,NULL,NW5,NULL,NULL,527750,185250,NULL,NULL
86323101,02/06/2010 20:57,2010,2010/11,Special Service,1,1,260,260,DOG SHUT IN LIFT - NON EMERGENCY,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000438,Blackheath,E09000023,Lewisham,Greenwich,NULL,Lethbridge Close,22004663,SE13,NULL,NULL,538150,176450,NULL,NULL
86786101,03/06/2010 16:46,2010,2010/11,Special Service,1,1,260,260,ASSISTS RSPCA WITH BIRD STUCK IN GUTTERING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000097,Preston,E09000005,Brent,Wembley,NULL,Carlton Avenue East,20201332,HA9,NULL,NULL,517950,187050,NULL,NULL
86997101,03/06/2010 20:59,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND KITCHEN UNIT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000271,Harringay,E09000014,Haringey,Hornsey,NULL,Denmark Road,21103188,N8,NULL,NULL,531050,189350,NULL,NULL
88642101,06/06/2010 09:57,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED IN FISHING LINE,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000456,Cannon Hill,E09000024,Merton,Wimbledon,NULL,Martin Way,NULL,SW20,524261,168785,524250,168750,51.40448874,-0.214909046
88800101,06/06/2010 16:22,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON WINDOWSILL,Cat,Police,Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000617,Latchmere,E09000032,Wandsworth,Battersea,NULL,Wye Street,22906361,SW11,NULL,NULL,526850,175950,NULL,NULL
88816101,06/06/2010 17:01,2010,2010/11,Special Service,1,2,260,520,PIGEON STUCK IN A TREE,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000456,Cannon Hill,E09000024,Merton,Wimbledon,NULL,Martin Way,NULL,SW20,524261,168785,524250,168750,51.40448874,-0.214909046
89282101,07/06/2010 11:38,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Train station - platform (at ground level or elevated),Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000036,Mayesbrook,E09000002,Barking and Dagenham,Barking,10090633312,Gale Street,19900144,RM9,547629,184529,547650,184550,51.54036014,0.127390614
89323101,07/06/2010 13:01,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Wadeson Street,22701281,E2,NULL,NULL,535050,183450,NULL,NULL
89353101,07/06/2010 13:46,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000569,Wallington North,E09000029,Sutton,Wallington,5870078866,Park Lane,22605913,SM6,528505,164019,528550,164050,51.36071184,-0.155649689
89432101,07/06/2010 16:19,2010,2010/11,Special Service,1,1,260,260,DOG IN DISTRESS IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05009392,Colville,E09000020,Kensington and Chelsea,North Kensington,NULL,Lancaster Road,NULL,W11,524750,181250,524750,181250,51.51640824,-0.203489105
89514101,07/06/2010 18:33,2010,2010/11,Special Service,1,1,260,260,CROW TRAPPED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Eltham,10010221623,Queenscroft Road,20801032,SE9,541806,174461,541850,174450,51.45138262,0.039440163
90438101,09/06/2010 15:49,2010,2010/11,Special Service,1,1,260,260,KITTEN STUCK IN DRAIN,Cat,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Hanford Close,NULL,SW18,525285,173377,525250,173350,51.44553346,-0.19857294
90448101,09/06/2010 16:25,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000441,Crofton Park,E09000023,Lewisham,Lewisham,NULL,Marnock Road,22004584,SE4,NULL,NULL,536650,174650,NULL,NULL
91207101,10/06/2010 21:24,2010,2010/11,Special Service,1,1,260,260,CAT SHUT IN EMPTY PROPERTY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000424,Knight's Hill,E09000022,Lambeth,West Norwood,NULL,Chichester Mews,21901720,SE27,NULL,NULL,531350,171950,NULL,NULL
91792101,11/06/2010 23:44,2010,2010/11,Special Service,1,1,260,260,CAT WITH LEG TRAPPED IN CHAIR OS,Cat,Person (mobile),Garden equipment,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05009402,Redcliffe,E09000020,Kensington and Chelsea,Chelsea,217092286,Westgate Terrace,21700584,SW10,525808,177990,525850,177950,51.48687573,-0.189411354
91980101,12/06/2010 11:21,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN GRILL OF WINDOW,Bird,Person (mobile),Community centre/Hall,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000381,Tollington,E09000019,Islington,Holloway,5300041297,Hatchard Road,21606443,N19,529948,186846,529950,186850,51.56552774,-0.126552477
93111101,14/06/2010 09:46,2010,2010/11,Special Service,1,1,260,260,DEER TRAPPED IN FENCE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000329,Eastcote and East Ruislip,E09000017,Hillingdon,Ruislip,NULL,Field End Road,NULL,HA5,511103,187742,511150,187750,51.57760408,-0.398035334
93658101,15/06/2010 09:38,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000435,Tulse Hill,E09000022,Lambeth,West Norwood,NULL,Arlingford Road,21900109,SW2,NULL,NULL,531250,174350,NULL,NULL
93813101,15/06/2010 16:24,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN MESH,Bird,Person (land line),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Greenwood Road,NULL,E8,534376,184591,534350,184550,51.54422563,-0.06356643
93843101,15/06/2010 17:34,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND SHED,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000170,Acton Central,E09000009,Ealing,Acton,12101494,Alfred Road,20600036,W3,520399,180209,520350,180250,51.50799514,-0.266520181
94209101,16/06/2010 10:42,2010,2010/11,Special Service,1,1,260,260,CAT WITH PAW TRAPPED IN BACK DOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Creswick Walk,22702077,E3,NULL,NULL,537050,182950,NULL,NULL
94233101,16/06/2010 11:45,2010,2010/11,Special Service,1,1,260,260,FOX TRAPPED BETWEEN POST AND FENCE,Fox,Person (mobile),Other outdoor sporting venue,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000273,Hornsey,E09000014,Haringey,Hornsey,NULL,Rectory Gardens,NULL,N8,530309,189353,530350,189350,51.5879737,-0.120418399
94513101,16/06/2010 19:43,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL TRAPPED BEHIND GAS FIRE,Unknown - Heavy Livestock Animal,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000191,Southfield,E09000009,Ealing,Chiswick,NULL,Somerset Road,20601561,W4,NULL,NULL,520750,179350,NULL,NULL
94576101,16/06/2010 21:11,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN GUTTER ON ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000430,Streatham Hill,E09000022,Lambeth,West Norwood,NULL,Telford Avenue,21901352,SW2,NULL,NULL,529950,173150,NULL,NULL
94956101,17/06/2010 15:04,2010,2010/11,Special Service,1,1,260,260,DUCK AND DUCKLINGS STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000523,Heathfield,E09000027,Richmond upon Thames,Twickenham,NULL,Percy Road,22401740,TW2,NULL,NULL,513950,173150,NULL,NULL
95038101,17/06/2010 17:11,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA TO RECOVER ANIMAL FROM BALCONY,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000612,Earlsfield,E09000032,Wandsworth,Tooting,NULL,Strathdon Drive,22905023,SW17,NULL,NULL,526650,172150,NULL,NULL
95128101,17/06/2010 19:37,2010,2010/11,Special Service,1,1,260,260,ASSIST ELDERLY PERSON WITH DISTRESSED DOG,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000368,Caledonian,E09000019,Islington,Islington,NULL,Treaty Street,21606165,N1,NULL,NULL,530550,183650,NULL,NULL
95514101,18/06/2010 13:16,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA TO RESCUE MALLARD DUCK AND CHICKS FROM TOP OF DRAINPIPE,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011117,Surrey Docks,E09000028,Southwark,Deptford,2.00003E+11,Brunswick Quay,22500361,SE16,535963,179145,535950,179150,51.49490678,-0.042794826
95570101,18/06/2010 15:26,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA,Unknown - Domestic Animal Or Pet,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000626,Tooting,E09000032,Wandsworth,Tooting,1.00023E+11,Fountain Road,22901761,SW17,526887,171551,526850,171550,51.4287666,-0.176186377
96006101,19/06/2010 11:57,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING  UNDER BRIDGE,Bird,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000349,Chiswick Riverside,E09000018,Hounslow,Chiswick,NULL,Chiswick Roundabout,NULL,W4,519250,178250,519250,178250,51.4906317,-0.283730286
96413101,19/06/2010 21:50,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000166,Upper Norwood,E09000008,Croydon,Norbury,NULL,Glenhurst Rise,NULL,SE19,532327,170125,532350,170150,51.41470863,-0.098512258
96549101,20/06/2010 05:42,2010,2010/11,Special Service,2,3,260,780,SMALL ANIMAL RESCUE,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Wesley Road,NULL,NW10,520418,183856,520450,183850,51.54076847,-0.265000624
96600101,20/06/2010 09:17,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND WASHING MACHINE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000256,Hammersmith Broadway,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Fulham Palace Road,21000356,W6,NULL,NULL,523350,178250,NULL,NULL
96641101,20/06/2010 11:30,2010,2010/11,Special Service,1,1,260,260,DOG LOCKED IN ROOM,Dog,Person (land line),Hospital,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000628,West Hill,E09000032,Wandsworth,Wandsworth,121004807,West Hill,22906045,SW15,524218,174097,524250,174050,51.45223909,-0.213666357
97096101,21/06/2010 00:31,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED UNDER BATH,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009330,St. Katharine's & Wapping,E09000030,Tower Hamlets,Shadwell,NULL,Garnet Street,22700529,E1W,NULL,NULL,535050,180450,NULL,NULL
408102,21/06/2010 16:17,2010,2010/11,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Pre School/nursery,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000105,Willesden Green,E09000005,Brent,Willesden,202104266,Doyle Gardens,20201769,NW10,522651,183733,522650,183750,51.53918296,-0.232860104
97326101,22/06/2010 11:57,2010,2010/11,Special Service,1,2,260,520,CAT TRAPPED NEAR RIVER UNDER RAILINGS,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Windsor Wharf,NULL,E9,536956,185021,536950,185050,51.54747034,-0.0262165
97443101,22/06/2010 14:18,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Balvernie Grove,22900265,SW18,NULL,NULL,525050,173650,NULL,NULL
97555101,22/06/2010 16:52,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000553,The Lane,E09000028,Southwark,Peckham,NULL,Costa Street,NULL,SE15,534143,176041,534150,176050,51.46744701,-0.070174601
98110101,23/06/2010 12:17,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Standen Road,22904920,SW18,NULL,NULL,525050,173450,NULL,NULL
98136101,23/06/2010 12:40,2010,2010/11,Special Service,1,1,260,260,DOG STUCK ON RIVER BANK,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000455,Abbey,E09000024,Merton,Wimbledon,NULL,Wandle Bank,NULL,SW19,526583,170225,526550,170250,51.41691733,-0.18103009
98594101,23/06/2010 21:34,2010,2010/11,Special Service,1,1,260,260,CAT  TRAPPED UNDER CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000350,Cranford,E09000018,Hounslow,Heathrow,1.00022E+11,Avenue Gardens,21500061,TW5,510537,177160,510550,177150,51.48260313,-0.409518181
98661101,23/06/2010 22:34,2010,2010/11,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN METAL GATE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000373,Highbury West,E09000019,Islington,Holloway,5300028570,Drayton Park,21603141,N5,531421,185870,531450,185850,51.55641561,-0.105677568
98674101,23/06/2010 23:07,2010,2010/11,Special Service,1,1,260,260,RABBIT STUCK INBETWEEN FENCE AND WALL,Rabbit,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011239,Clayhall,E09000026,Redbridge,Hainault,NULL,Stradbroke Grove,22303282,IG5,NULL,NULL,542550,190050,NULL,NULL
98740101,24/06/2010 00:33,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED UNDER CUPBOARD,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009384,Stamford Hill West,E09000012,Hackney,Stoke Newington,NULL,Lordship Park,20900636,N16,NULL,NULL,532350,186750,NULL,NULL
99084101,24/06/2010 16:11,2010,2010/11,Special Service,1,1,260,260,DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05009318,Blackwall & Cubitt Town,E09000030,Tower Hamlets,Millwall,6061900,East Ferry Road,22700444,E14,537991,179127,537950,179150,51.49425381,-0.013606633
99291101,24/06/2010 20:30,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN GUTTERING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000138,Holborn and Covent Garden,E09000007,Camden,Soho,1.00023E+11,Chancery Lane,8100118,WC2A,530875,181625,530850,181650,51.51839395,-0.115126079
99545101,25/06/2010 09:00,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND GARAGE,Cat,Person (mobile),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000504,Fullwell,E09000026,Redbridge,Hainault,NULL,Sydney Road,NULL,IG6,544277,190048,544250,190050,51.59081719,0.081353631
99846101,25/06/2010 16:08,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN PIPEWORK IN BATHROOM,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000342,West Drayton,E09000017,Hillingdon,Hayes,NULL,Lily Drive,21402817,UB7,NULL,NULL,505750,178850,NULL,NULL
99960101,25/06/2010 18:29,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN PIPE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000227,Shooters Hill,E09000011,Greenwich,Plumstead,1.00021E+11,Voce Road,20801553,SE18,544843,177107,544850,177150,51.47439037,0.084200227
100001101,25/06/2010 19:19,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO BIRD STUCK IN TREE,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009393,Courtfield,E09000020,Kensington and Chelsea,Kensington,NULL,Courtfield Gardens,NULL,SW5,NULL,NULL,525850,178750,NULL,NULL
100395101,26/06/2010 10:39,2010,2010/11,Special Service,1,2,260,520,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000532,West Twickenham,E09000027,Richmond upon Thames,Twickenham,NULL,Fulwell Park Avenue,22401495,TW2,NULL,NULL,514050,172650,NULL,NULL
100611101,26/06/2010 15:29,2010,2010/11,Special Service,3,8,260,2080,CAT TRAPPED IN BIN CHUTE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000642,Queen's Park,E09000033,Westminster,North Kensington,NULL,Sixth Avenue,NULL,W10,NULL,NULL,524050,182650,NULL,NULL
100615101,26/06/2010 15:31,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED ON BUILDING SITE,Dog,Person (land line),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009376,Homerton,E09000012,Hackney,Homerton,NULL,Berger Road,NULL,E9,535807,184884,535850,184850,51.54651639,-0.042829892
100633101,26/06/2010 15:59,2010,2010/11,Special Service,1,1,260,260,DUCKLING STUCK IN DRAIN,Bird,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Windsor Street,NULL,UB8,505521,184070,505550,184050,51.54567065,-0.479659599
100733101,26/06/2010 17:56,2010,2010/11,Special Service,1,1,260,260,DUCKLING TRAPPED IN DRAINAGE DITCH BY LAKES,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000135,Hampstead Town,E09000007,Camden,Kentish Town,NULL,Parliament Hill,NULL,NW3,527304,185711,527350,185750,51.55593,-0.16508846
100859101,26/06/2010 20:26,2010,2010/11,Special Service,1,1,260,260,DUCKLINGS STUCK DOWN DRAIN,Bird,Person (mobile),Other retail,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000050,Edgware,E09000003,Barnet,Mill Hill,200221604,Barnet Way,20030684,NW7,520252,193926,520250,193950,51.6313061,-0.263947154
101157101,27/06/2010 07:49,2010,2010/11,Special Service,1,2,260,520,KITTEN TRAPPED IN WASHING MACHINE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011489,Woodside,E09000008,Croydon,Woodside,NULL,Rees Gardens,20501333,CR0,NULL,NULL,533850,167250,NULL,NULL
101238101,27/06/2010 12:01,2010,2010/11,Special Service,1,2,260,520,CAT TRAPPED BETWEEN TWO GARAGE WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000602,Larkswood,E09000031,Waltham Forest,Chingford,NULL,Palace View Road,22864150,E4,NULL,NULL,537850,192350,NULL,NULL
101492101,27/06/2010 17:12,2010,2010/11,Special Service,1,1,260,260,DOG IN PRECARIOUS POSITION,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000127,West Wickham,E09000006,Bromley,Woodside,NULL,High Street,20301748,BR4,NULL,NULL,537950,166050,NULL,NULL
101869101,27/06/2010 22:52,2010,2010/11,Special Service,1,1,260,260,KITTEN FALLEN ONTO ROOF,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000047,Coppetts,E09000003,Barnet,Finchley,200129129,Woodhouse Road,20047200,N12,527583,192020,527550,192050,51.61256448,-0.158775862
102221101,28/06/2010 12:47,2010,2010/11,Special Service,1,2,260,520,TO ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000116,Crystal Palace,E09000006,Bromley,West Norwood,1.0002E+11,Belvedere Road,20302135,SE19,533623,170416,533650,170450,51.41701984,-0.079777516
103128101,29/06/2010 09:25,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011245,Hainault,E09000026,Redbridge,Hainault,NULL,Five Oaks Lane,22304587,IG7,NULL,NULL,548350,192150,NULL,NULL
103894101,30/06/2010 11:29,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO ASSIST RSPCA,Unknown - Domestic Animal Or Pet,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,High Road,NULL,N17,533787,190037,533750,190050,51.59330524,-0.069985331
104048101,30/06/2010 15:41,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON A ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011482,Shirley North,E09000008,Croydon,Woodside,NULL,The Glade,20500570,CR0,NULL,NULL,536150,167150,NULL,NULL
104212101,30/06/2010 18:57,2010,2010/11,Special Service,1,1,260,260,DOG FALLEN INTO SHAFT,Dog,Person (mobile),"Tunnel, subway",Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009379,King's Park,E09000012,Hackney,Homerton,NULL,Millfields Road,NULL,E5,535872,186192,535850,186150,51.55825479,-0.041388455
104525101,01/07/2010 07:41,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BETWEEN FENCE AND BUILDING,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011217,Barnehurst,E09000004,Bexley,Bexley,NULL,Cumbrian Avenue,20100406,DA7,NULL,NULL,551250,176450,NULL,NULL
105169101,01/07/2010 22:09,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011097,Champion Hill,E09000028,Southwark,Peckham,NULL,Grove Lane,22501157,SE5,NULL,NULL,533050,176050,NULL,NULL
105429101,02/07/2010 08:31,2010,2010/11,Special Service,1,1,260,260,HORSE STUCK IN DITCH,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000065,Belvedere,E09000004,Bexley,Erith,NULL,Anderson Way,NULL,DA17,549983,179865,549950,179850,51.49783354,0.159326523
105684101,02/07/2010 15:40,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Stephendale Road,21000783,SW6,NULL,NULL,525950,176050,NULL,NULL
106296101,03/07/2010 12:12,2010,2010/11,Special Service,1,2,260,520,CAT TRAPPED IN GAP BETWEEN TWO WALLS,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,NULL,Queens Road,22403749,TW1,NULL,NULL,516050,173450,NULL,NULL
106365101,03/07/2010 13:26,2010,2010/11,Special Service,1,1,260,260,KITTEN IN CONTAINED AREA,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000123,Penge and Cator,E09000006,Bromley,Beckenham,NULL,Densole Close,20301882,BR3,NULL,NULL,536150,169950,NULL,NULL
106377101,03/07/2010 13:36,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA TO CAT IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000130,Camden Town with Primrose Hill,E09000007,Camden,Euston,5108232,Ainger Road,20400706,NW3,527795,184051,527750,184050,51.54090085,-0.158612499
106783101,03/07/2010 20:54,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED UP A TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000358,Hounslow Central,E09000018,Hounslow,Heston,1.00022E+11,Myrtle Road,21500782,TW3,514098,176053,514050,176050,51.47194678,-0.358614634
106824101,03/07/2010 21:36,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,NULL,Queens Road,22403749,TW1,NULL,NULL,516050,173450,NULL,NULL
108173101,05/07/2010 11:18,2010,2010/11,Special Service,1,1,260,260,TO ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000446,Ladywell,E09000023,Lewisham,Lewisham,NULL,Slagrove Place,NULL,SE13,537511,174806,537550,174850,51.45554082,-0.022197768
108334101,05/07/2010 14:57,2010,2010/11,Special Service,1,1,260,260,HORSE TRAPPED ON GATE,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011229,St. Mary's & St. James,E09000004,Bexley,Sidcup,1.0002E+11,Parsonage Lane,20101117,DA14,548885,171633,548850,171650,51.42415633,0.140054795
109050101,06/07/2010 09:58,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN SASH WINDOW,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000279,Stroud Green,E09000014,Haringey,Hornsey,NULL,Ferme Park Road,21106156,N8,NULL,NULL,530650,188250,NULL,NULL
109356101,06/07/2010 18:07,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000028,Becontree,E09000002,Barking and Dagenham,Barking,NULL,Fitzstephen Road,19900133,RM8,NULL,NULL,546950,185450,NULL,NULL
109997101,07/07/2010 15:12,2010,2010/11,Special Service,1,1,260,260,BABY SEAGULL TRAPPED ON SPIKE,Bird,Person (land line),Converted office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,NULL,Maiden Lane,NULL,WC2E,530335,180721,530350,180750,51.51039458,-0.123238552
110211101,07/07/2010 19:34,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN MUD,Dog,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009318,Blackwall & Cubitt Town,E09000030,Tower Hamlets,Poplar,NULL,Orchard Place,NULL,E14,539012,180818,539050,180850,51.50919957,0.001757646
110217101,07/07/2010 19:41,2010,2010/11,Special Service,1,1,260,260,DEER  STUCK IN RAILINGS  NEAR RAILWAY LINE,Deer,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000198,Enfield Highway,E09000010,Enfield,Enfield,NULL,Eastfield Road,NULL,EN3,536370,198126,536350,198150,51.66537414,-0.029571393
110849101,08/07/2010 16:55,2010,2010/11,Special Service,1,1,260,260,BIRD STUCK IN FIRE PLACE / CHIMNEY,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000271,Harringay,E09000014,Haringey,Hornsey,NULL,Burgoyne Road,21106779,N4,NULL,NULL,531550,188250,NULL,NULL
110956101,08/07/2010 18:39,2010,2010/11,Special Service,1,2,260,520,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000379,St. Mary's,E09000019,Islington,Islington,NULL,Islington Green,21605183,N1,NULL,NULL,531650,183650,NULL,NULL
110963101,08/07/2010 18:54,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN DRAINPIPE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000376,Junction,E09000019,Islington,Kentish Town,NULL,St. John's Grove,21600814,N19,NULL,NULL,529550,186550,NULL,NULL
110986101,08/07/2010 19:15,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA PIGEON IMPALED ON TV AERIAL ON ROOF,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000603,Lea Bridge,E09000031,Waltham Forest,Leyton,NULL,Rochdale Road,22870650,E17,NULL,NULL,537150,187850,NULL,NULL
112055101,09/07/2010 20:50,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND RADIATOR,Cat,Person (mobile),Call Centre,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000418,Clapham Common,E09000022,Lambeth,Clapham,2E+11,Crescent Lane,21900408,SW4,529591,174985,529550,174950,51.4590169,-0.13605642
113121101,10/07/2010 19:16,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN GATE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000283,White Hart Lane,E09000014,Haringey,Tottenham,NULL,White Hart Lane,21106111,N17,NULL,NULL,532450,191450,NULL,NULL
113351101,10/07/2010 23:45,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO FOX IN PRECARIOUS POSITION,Fox,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000455,Abbey,E09000024,Merton,Wimbledon,48065645,Southey Road,22105907,SW19,525429,170149,525450,170150,51.41649044,-0.197643557
114151101,11/07/2010 23:43,2010,2010/11,Special Service,1,1,260,260,CAT FALLEN BETWEEN WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000286,Canons,E09000015,Harrow,Stanmore,NULL,High Street,21202607,HA8,NULL,NULL,519050,191850,NULL,NULL
114353101,12/07/2010 11:10,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN WALL SPACE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000439,Brockley,E09000023,Lewisham,Greenwich,NULL,Deals Gateway,22006140,SE13,NULL,NULL,537450,176750,NULL,NULL
114393101,12/07/2010 12:32,2010,2010/11,Special Service,1,1,260,260,PERSON LOCKED OUT - COOKER BELIEVED TO BE LEFT ON,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,Endlebury Road,22834200,E4,NULL,NULL,538550,193550,NULL,NULL
114476101,12/07/2010 14:42,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT TRAPPED BEHIND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Roman Road,22701028,E3,NULL,NULL,536350,183250,NULL,NULL
114825101,12/07/2010 19:04,2010,2010/11,Special Service,1,1,260,260,CAT AND KITTEN TRAAPED UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000437,Bellingham,E09000023,Lewisham,Forest Hill,NULL,Burford Road,22001297,SE6,NULL,NULL,536850,172650,NULL,NULL
114832101,12/07/2010 19:15,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED UNDER A BRIDGE,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,White Hart Lane,NULL,N17,533607,191364,533650,191350,51.60527261,-0.072077343
115623101,13/07/2010 21:53,2010,2010/11,Special Service,1,1,260,260,PIGEON STUCK IN NETTING,Bird,Person (mobile),Other building/use not known,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,Whitehall Street,NULL,N17,533750,191235,533750,191250,51.60407944,-0.070062827
116271101,15/07/2010 06:16,2010,2010/11,Special Service,1,2,260,520,CAT IN PRECARIOUS POSITION,Cat,Person (land line),Wasteland,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000118,Farnborough and Crofton,E09000006,Bromley,Orpington,NULL,York Rise,NULL,BR6,545312,166023,545350,166050,51.37467435,0.086406343
116813101,15/07/2010 23:36,2010,2010/11,Special Service,1,1,260,260,CAT STUCK BEHIND WALL,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05009381,London Fields,E09000012,Hackney,Homerton,1.00021E+11,Mare Street,20900658,E8,534933,184323,534950,184350,51.54168443,-0.055641527
117301101,16/07/2010 17:33,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000043,Brunswick Park,E09000003,Barnet,Southgate,NULL,Ferney Road,20015620,EN4,NULL,NULL,528250,194650,NULL,NULL
117461101,16/07/2010 21:07,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND KITCHEN UNIT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000289,Harrow on the Hill,E09000015,Harrow,Northolt,NULL,Greenford Road,21201219,HA1,NULL,NULL,515650,185950,NULL,NULL
117523101,16/07/2010 22:26,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON BALCONY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05009398,Norland,E09000020,Kensington and Chelsea,North Kensington,NULL,Elgin Crescent,21700866,W11,NULL,NULL,524350,180950,NULL,NULL
117542101,16/07/2010 23:01,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000321,South Hornchurch,E09000016,Havering,Wennington,NULL,Grove Park Road,21300795,RM13,NULL,NULL,552350,183850,NULL,NULL
117544101,16/07/2010 23:08,2010,2010/11,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Scrub land,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Princes Road,NULL,N18,535228,192920,535250,192950,51.61886816,-0.048084728
117546101,16/07/2010 23:08,2010,2010/11,Special Service,1,1,260,260,CAT FALLEN ONTO BALCONY AND INJURED,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05009400,Pembridge,E09000020,Kensington and Chelsea,Kensington,NULL,Kensington Park Road,21700905,W11,NULL,NULL,525150,180650,NULL,NULL
117703101,17/07/2010 06:52,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009369,Clissold,E09000012,Hackney,Stoke Newington,NULL,Howard Road,20900539,N16,NULL,NULL,533050,185450,NULL,NULL
117804101,17/07/2010 11:18,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000368,Caledonian,E09000019,Islington,Islington,5300087625,Stanmore Street,21606051,N1,530644,183899,530650,183850,51.53888335,-0.117610431
117819101,17/07/2010 11:49,2010,2010/11,Special Service,1,2,260,520,RUNNING CALL TO ASSIST WITH ANIMALS IN DISTRESS,Unknown - Heavy Livestock Animal,Person (land line),Canal/riverbank vegetation,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009396,Golborne,E09000020,Kensington and Chelsea,North Kensington,217106411,Kensal Road,21700902,W10,524552,182258,524550,182250,51.525511,-0.205985016
118253101,17/07/2010 21:24,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN PRICKLY BUSHES,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000489,Plaistow North,E09000025,Newham,Plaistow,NULL,North Street,NULL,E13,540611,183119,540650,183150,51.52948087,0.025700736
118454101,18/07/2010 08:13,2010,2010/11,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN GATES,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000314,Heaton,E09000016,Havering,Harold Hill,NULL,Masefield Crescent,21300098,RM3,NULL,NULL,553150,190650,NULL,NULL
118571101,18/07/2010 13:46,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009383,Springfield,E09000012,Hackney,Stoke Newington,NULL,Mount Pleasant Lane,20900713,E5,NULL,NULL,534750,186850,NULL,NULL
119188101,19/07/2010 05:32,2010,2010/11,Special Service,1,1,260,260,KITTENS TRAPPED IN NETTING,Cat,Person (mobile),Refuse/rubbish tip,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,6085968,Gillender Street,22700538,E14,538250,182250,538250,182250,51.52225487,-0.008653906
119823101,19/07/2010 20:22,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN WALL CAVITY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,Loughborough Estate,21900895,SW9,NULL,NULL,531650,176050,NULL,NULL
120568101,20/07/2010 17:36,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Railway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000338,South Ruislip,E09000017,Hillingdon,Ruislip,NULL,Station Approach,NULL,HA4,511114,185402,511150,185450,51.55656957,-0.398616075
121120101,21/07/2010 07:14,2010,2010/11,Special Service,1,1,260,260,FOX TRAPPED BEHIND TICKET BOOTH,Fox,Person (land line),Railway building - other,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000428,St. Leonard's,E09000022,Lambeth,Norbury,2E+11,Estreham Road,21900528,SW16,529689,170520,529650,170550,51.41886771,-0.136280312
121273101,21/07/2010 13:19,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN WINDOW FRAME,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,NULL,Kingsman Street,20800867,SE18,NULL,NULL,542950,178950,NULL,NULL
122019101,22/07/2010 05:21,2010,2010/11,Special Service,1,1,260,260,CAT IN PRECARIOUS POSITION,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000145,West Hampstead,E09000007,Camden,West Hampstead,NULL,West End Lane,20499056,NW6,NULL,NULL,525450,185150,NULL,NULL
122107101,22/07/2010 10:27,2010,2010/11,Special Service,1,3,260,780,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (land line),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000606,Markhouse,E09000031,Waltham Forest,Walthamstow,1.00023E+11,Gosport Road,22840200,E17,536784,188602,536750,188650,51.57969128,-0.02730388
122528101,22/07/2010 22:45,2010,2010/11,Special Service,1,1,260,260,CAT FALLEN BEHIND BUILT IN CUPBOARD,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009384,Stamford Hill West,E09000012,Hackney,Stoke Newington,NULL,Dunsmure Road,20900349,N16,NULL,NULL,533350,187450,NULL,NULL
122530101,22/07/2010 23:08,2010,2010/11,Special Service,1,3,260,780,DOG TRAPPED IN RABBIT HOLE,Dog,Person (land line),Tree scrub,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000517,East Sheen,E09000027,Richmond upon Thames,Richmond,10024344879,Richmond Park,22407048,TW10,520681,173226,520650,173250,51.44517473,-0.264841368
123241101,23/07/2010 21:39,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN BUSH AFTER FALLING FROM WINDOW,Cat,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05011244,Goodmayes,E09000026,Redbridge,Dagenham,1.00022E+11,Armstrong Close,22301811,RM8,547553,187549,547550,187550,51.56751532,0.127560848
123273101,23/07/2010 22:57,2010,2010/11,Special Service,1,1,260,260,DOG  TRAPPED IN NETTLES,Dog,Person (mobile),Heathland,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000202,Highlands,E09000010,Enfield,Southgate,NULL,Lowther Drive,NULL,EN2,530363,196305,530350,196350,51.65043493,-0.117054331
123783101,24/07/2010 16:10,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED IN ROOF,Bird,Person (mobile),Town Hall,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000480,East Ham Central,E09000025,Newham,East Ham,10012839270,Barking Road,22207589,E6,542650,183540,542650,183550,51.53275251,0.055245138
123880101,24/07/2010 17:33,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED ON A LEDGE,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000056,High Barnet,E09000003,Barnet,Barnet,NULL,Rockways,20036920,EN5,NULL,NULL,521950,195350,NULL,NULL
124199101,24/07/2010 21:07,2010,2010/11,Special Service,1,1,260,260,ABANDONED CALL TRACED AS COMING FROM PRIVATE SUB,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000351,Feltham North,E09000018,Hounslow,Feltham,NULL,Burns Avenue,21500182,TW14,NULL,NULL,510550,174250,NULL,NULL
124697101,25/07/2010 14:12,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000619,Northcote,E09000032,Wandsworth,Battersea,NULL,Wakehurst Road,NULL,SW11,527776,174822,527750,174850,51.45796417,-0.162225941
125476101,26/07/2010 13:39,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA  REP WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000303,Stanmore Park,E09000015,Harrow,Stanmore,1.00021E+11,Bridges Road,21202175,HA7,515772,192200,515750,192250,51.61673275,-0.329214813
125537101,26/07/2010 15:13,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON THE ROOF,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000252,Avonmore and Brook Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Maclise Road,21000529,W14,NULL,NULL,524150,179150,NULL,NULL
125540101,26/07/2010 15:16,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000261,Ravenscourt Park,E09000013,Hammersmith and Fulham,Hammersmith,34128676,Ravenscourt Park,21000675,W6,522458,179079,522450,179050,51.49739756,-0.237258242
126024101,27/07/2010 03:02,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,128004630,Clarence Street,21800255,KT1,518336,169437,518350,169450,51.41161515,-0.299834423
126116101,27/07/2010 08:05,2010,2010/11,Special Service,1,1,260,260,CROW TRAPPED ON FLAG POLE,Bird,Person (land line),Temple,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000431,Streatham South,E09000022,Lambeth,Norbury,1.00023E+11,Colmer Road,21900371,SW16,530365,169951,530350,169950,51.41359923,-0.126773166
126254101,27/07/2010 12:47,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000174,Ealing Common,E09000009,Ealing,Ealing,NULL,Webster Gardens,20601847,W5,NULL,NULL,517950,180250,NULL,NULL
126259101,27/07/2010 13:03,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011221,Blendon & Penhill,E09000004,Bexley,Sidcup,NULL,Harcourt Avenue,20100669,DA15,NULL,NULL,547250,173750,NULL,NULL
126830101,28/07/2010 11:05,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UNDER CAR BONNET,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000607,Valley,E09000031,Waltham Forest,Chingford,NULL,Orchard Close,NULL,E4,537213,192535,537250,192550,51.61492821,-0.019580353
126895101,28/07/2010 12:43,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL KITTEN STUCK IN TREE,Cat,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000543,Livesey,E09000028,Southwark,Old Kent Road,NULL,Rotherhithe New Road,NULL,SE16,534521,178074,534550,178050,51.48562702,-0.063962559
127410101,28/07/2010 21:38,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011108,Nunhead & Queen's Road,E09000028,Southwark,New Cross,NULL,Hollydale Road,22501269,SE15,NULL,NULL,535150,176250,NULL,NULL
127800101,29/07/2010 13:30,2010,2010/11,Special Service,1,1,260,260,DOG WITH PAW TRAPPED IN MANHOLE COVER,Dog,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000158,Norbury,E09000008,Croydon,Norbury,NULL,Elgar Avenue,NULL,SW16,530219,168974,530250,168950,51.40485248,-0.129229831
128604101,30/07/2010 13:08,2010,2010/11,Special Service,1,1,260,260,SQUIRREL INJURED ON PHONE POLE,Squirrel,Person (land line),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,NULL,Rosebery Avenue,NULL,N17,534336,190412,534350,190450,51.59654435,-0.061921232
128625101,30/07/2010 13:45,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,NULL,Belvedere Road,20302135,SE19,NULL,NULL,533950,170050,NULL,NULL
128740101,30/07/2010 16:30,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,NULL,Victoria Embankment,NULL,WC2R,530679,180682,530650,180650,51.50996472,-0.118298749
128803101,30/07/2010 17:40,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED SINK CUPBOARD,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000452,Sydenham,E09000023,Lewisham,Forest Hill,NULL,Laurel Grove,22001631,SE26,NULL,NULL,536050,171550,NULL,NULL
129048101,30/07/2010 21:44,2010,2010/11,Special Service,1,1,260,260,FISH IN DANGER OF DYING IN POND,Fish,Person (land line),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000567,Sutton West,E09000029,Sutton,Sutton,5870036768,Sherwood Park Road,22602599,SM1,525474,164195,525450,164150,51.3629704,-0.199098566
129068101,30/07/2010 22:20,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011113,Rye Lane,E09000028,Southwark,Peckham,NULL,Galatea Square,22501014,SE15,NULL,NULL,534650,175850,NULL,NULL
129261101,31/07/2010 06:15,2010,2010/11,Special Service,2,5,260,1300,"FISH IN DISTRESS, HVPU REQUIRED",Fish,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000195,Chase,E09000010,Enfield,Enfield,207178584,Forty Hill,20702687,EN2,533639,198561,533650,198550,51.66993903,-0.068868485
129276101,31/07/2010 07:11,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000641,Marylebone High Street,E09000033,Westminster,Soho,NULL,Wimpole Street,8400289,W1G,NULL,NULL,528650,181450,NULL,NULL
129472101,31/07/2010 13:39,2010,2010/11,Special Service,1,1,260,260,CAT WEDGED BETWEEN WALL AND SHED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000475,Beckton,E09000025,Newham,East Ham,NULL,Yarrow Crescent,22201249,E6,NULL,NULL,542050,181850,NULL,NULL
129960101,31/07/2010 22:44,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000316,Mawneys,E09000016,Havering,Romford,1.00021E+11,Dunster Close,21300004,RM5,550070,190097,550050,190050,51.58974651,0.1649373
130577101,01/08/2010 17:21,2010,2010/11,Special Service,1,1,260,260,BIRD IN DISTRESS,Bird,Other FRS,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000075,Erith,E09000004,Bexley,Erith,NULL,Erith High Street,NULL,DA8,551434,178144,551450,178150,51.48198358,0.179474875
130721101,01/08/2010 19:24,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000441,Crofton Park,E09000023,Lewisham,Forest Hill,NULL,Ewart Road,NULL,SE23,535876,173567,535850,173550,51.44480106,-0.046190511
130810101,01/08/2010 21:14,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011104,London Bridge & West Bermondsey,E09000028,Southwark,Dockhead,2.00003E+11,Tyers Gate,22502569,SE1,533207,179735,533250,179750,51.50086467,-0.082249296
131013101,02/08/2010 10:12,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED UNDER BED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011111,Peckham Rye,E09000028,Southwark,New Cross,NULL,Stuart Road,22502403,SE15,NULL,NULL,535450,175050,NULL,NULL
131115101,02/08/2010 12:35,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000290,Harrow Weald,E09000015,Harrow,Harrow,1.00021E+11,Long Elmes,21201136,HA3,514240,190760,514250,190750,51.60410305,-0.35180198
131165101,02/08/2010 14:16,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON WINDOW LEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000412,St. Mark's,E09000021,Kingston upon Thames,Surbiton,NULL,Claremont Road,21800253,KT6,NULL,NULL,518050,167450,NULL,NULL
132244101,03/08/2010 17:42,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN HOLE BEHIND A KITCHEN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009395,Earl's Court,E09000020,Kensington and Chelsea,Kensington,NULL,Philbeach Gardens,21700663,SW5,NULL,NULL,525250,178350,NULL,NULL
133381101,05/08/2010 14:31,2010,2010/11,Special Service,1,1,260,260,ASSIST THE RSPCA WITH TRAPPED PIDGEONS,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000260,Parsons Green and Walham,E09000013,Hammersmith and Fulham,Fulham,34044126,Grimston Road,21000394,SW6,524632,176143,524650,176150,51.47053639,-0.206990913
133966101,06/08/2010 10:45,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009395,Earl's Court,E09000020,Kensington and Chelsea,Kensington,NULL,Hogarth Road,21700260,SW5,NULL,NULL,525550,178650,NULL,NULL
134701101,07/08/2010 10:39,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011482,Shirley North,E09000008,Croydon,Woodside,NULL,The Glade,20500570,CR0,NULL,NULL,535750,167650,NULL,NULL
134823101,07/08/2010 14:53,2010,2010/11,Special Service,1,1,260,260,BIRD STUCK IN TREE,Bird,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000380,St. Peter's,E09000019,Islington,Islington,NULL,Colebrooke Row,NULL,N1,531609,183121,531650,183150,51.53166771,-0.103994716
135035101,07/08/2010 20:54,2010,2010/11,Special Service,1,1,260,260,PIGEON STUCK IN WINDOW,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009335,Weavers,E09000030,Tower Hamlets,Shoreditch,6005161,Redchurch Street,22701001,E2,533783,182413,533750,182450,51.52479443,-0.07293985
135823101,09/08/2010 02:23,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (running call),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000649,West End,E09000033,Westminster,Soho,NULL,Duke Street,8400398,W1K,NULL,NULL,528350,181050,NULL,NULL
135844101,09/08/2010 02:58,2010,2010/11,Special Service,1,1,260,260,DEER STUCK ON FENCING,Deer,Police,"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Fairford Way,NULL,RM3,555610,191744,555650,191750,51.60304219,0.245572324
135930101,09/08/2010 09:20,2010,2010/11,Special Service,1,1,260,260,BIRD WITH FOOT STUCK IN ROOF TILES,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000287,Edgware,E09000015,Harrow,Stanmore,NULL,Broomgrove Gardens,21202323,HA8,NULL,NULL,519450,190650,NULL,NULL
137073101,11/08/2010 00:33,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND FIREPLACE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000300,Rayners Lane,E09000015,Harrow,Harrow,NULL,Rayners Lane,21202081,HA5,NULL,NULL,512950,187850,NULL,NULL
137082101,11/08/2010 01:00,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND FIREPLACE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000329,Eastcote and East Ruislip,E09000017,Hillingdon,Ruislip,NULL,The Close,21401939,HA5,NULL,NULL,511150,187850,NULL,NULL
137703101,12/08/2010 08:31,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009402,Redcliffe,E09000020,Kensington and Chelsea,Chelsea,NULL,Redcliffe Square,21700454,SW10,NULL,NULL,525750,178050,NULL,NULL
137899101,12/08/2010 16:52,2010,2010/11,Special Service,1,1,260,260,DOG WITH NECK STUCK IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000315,Hylands,E09000016,Havering,Hornchurch,1.00023E+11,Hornchurch Road,21300425,RM11,553152,187026,553150,187050,51.5613255,0.208053787
138210101,13/08/2010 07:15,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000091,Harlesden,E09000005,Brent,Park Royal,NULL,St. Marys Road,20202537,NW10,NULL,NULL,521450,183850,NULL,NULL
138463101,13/08/2010 17:12,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN FENCE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Kingston,NULL,Ham Street,NULL,TW10,517286,172821,517250,172850,51.44224725,-0.313805479
139163101,14/08/2010 18:57,2010,2010/11,Special Service,1,4,260,1040,KITTEN TRAPPED UNDER FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000493,Wall End,E09000025,Newham,East Ham,NULL,Southend Road,22201066,E6,NULL,NULL,542650,184250,NULL,NULL
140157101,16/08/2010 13:58,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED INJURED GULL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000205,Palmers Green,E09000010,Enfield,Southgate,NULL,Aldermans Hill,20703847,N13,NULL,NULL,530950,192650,NULL,NULL
140766101,17/08/2010 12:13,2010,2010/11,Special Service,1,1,260,260,HORSE TRAPPED IN STABLE DOOR,Horse,Person (mobile),Barn,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011480,Selsdon & Addington Village,E09000008,Croydon,Croydon,1.00023E+11,Coombe Lane,20501992,CR0,532750,164750,532750,164750,51.36630647,-0.094441367
140977101,17/08/2010 19:27,2010,2010/11,Special Service,1,1,260,260,CAT UP TREE,Cat,Person (land line),Club/night club,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Kingston,NULL,Parkleys,NULL,TW10,517876,171688,517850,171650,51.43194195,-0.305697126
141020101,17/08/2010 20:29,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO ASSIST INJURED CAT,Cat,Person (land line),Railway trackside vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000526,North Richmond,E09000027,Richmond upon Thames,Richmond,1.00022E+11,Manor Grove,22405753,TW9,519316,175511,519350,175550,51.46600036,-0.283703981
141331101,18/08/2010 10:51,2010,2010/11,Special Service,1,1,260,260,ASSISTING RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000229,Woolwich Common,E09000011,Greenwich,Plumstead,NULL,Ordnance Road,20801117,SE18,NULL,NULL,543250,177550,NULL,NULL
141958101,19/08/2010 13:37,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009388,Abingdon,E09000020,Kensington and Chelsea,Kensington,NULL,Stratford Road,21700527,W8,NULL,NULL,525450,179050,NULL,NULL
142028101,19/08/2010 15:57,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BY COLLAR  ON TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000176,Elthorne,E09000009,Ealing,Ealing,12070339,Boston Road,20602241,W7,516073,179253,516050,179250,51.5003067,-0.329140491
142243101,19/08/2010 21:48,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN RUBBISH CHUTE,Cat,Police,Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000419,Clapham Town,E09000022,Lambeth,Clapham,NULL,Macaulay Road,21900911,SW4,NULL,NULL,528950,175550,NULL,NULL
142434101,20/08/2010 10:48,2010,2010/11,Special Service,1,2,260,520,CAT TRAPPED UNDER JETTY,Cat,Person (land line),Barge,Boat,Animal rescue from water,Animal rescue from water - Domestic pet,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,NULL,Woolwich High Street,NULL,SE18,543339,179251,543350,179250,51.49403846,0.063429055
143104101,21/08/2010 12:10,2010,2010/11,Special Service,1,2,260,520,MAGPIE STUCK IN BUILDING,Bird,Person (land line),Other indoor sporting venue,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05009387,Woodberry Down,E09000012,Hackney,Stoke Newington,10008241969,Green Lanes,20900468,N4,532311,186881,532350,186850,51.56529283,-0.092467492
143684101,22/08/2010 05:42,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Library,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000480,East Ham Central,E09000025,Newham,East Ham,10008995498,High Street South,22200716,E6,542665,183494,542650,183450,51.53233538,0.055442633
143724101,22/08/2010 08:33,2010,2010/11,Special Service,1,1,260,260,CAT SHUT IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011242,Fairlop,E09000026,Redbridge,Hainault,1.00022E+11,Kelsie Way,22304638,IG6,545203,191539,545250,191550,51.60397678,0.095328095
144084101,22/08/2010 20:28,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN CAVITY WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,NULL,Exeter Road,19900478,RM10,NULL,NULL,549850,184750,NULL,NULL
144631101,23/08/2010 20:20,2010,2010/11,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05011462,Addiscombe East,E09000008,Croydon,Woodside,10014052812,Shirley Road,20500552,CR9,534873,166649,534850,166650,51.38287195,-0.063243189
145674101,25/08/2010 18:26,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),"Tunnel, subway",Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000145,West Hampstead,E09000007,Camden,West Hampstead,NULL,Kilburn High Road,NULL,NW6,524677,184637,524650,184650,51.54686396,-0.20334192
145807101,25/08/2010 21:47,2010,2010/11,Special Service,1,1,260,260,CAT IN PRECARIOUS POSITION,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,6028943,Tarling Street,22701188,E1,535137,181069,535150,181050,51.51239452,-0.053949357
146509101,27/08/2010 02:24,2010,2010/11,Special Service,1,1,260,260,FOX STUCK IN BASEMENT AREA OUTSIDE,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000418,Clapham Common,E09000022,Lambeth,Clapham,NULL,Shandon Road,21901225,SW4,NULL,NULL,529450,174350,NULL,NULL
146732101,27/08/2010 15:02,2010,2010/11,Special Service,2,3,260,780,KITTEN STUCK INSIDE RECLINER CHAIR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000306,Brooklands,E09000016,Havering,Romford,NULL,Drummond Road,21301151,RM7,NULL,NULL,550650,189150,NULL,NULL
146953101,27/08/2010 22:19,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND CABINET,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000194,Bush Hill Park,E09000010,Enfield,Edmonton,NULL,Park Avenue,20707523,EN1,NULL,NULL,533350,195150,NULL,NULL
147305101,28/08/2010 13:58,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN GARDEN,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000644,St. James's,E09000033,Westminster,Lambeth,NULL,Great Peter Street,8401375,SW1P,NULL,NULL,530150,179250,NULL,NULL
147438101,28/08/2010 17:47,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000415,Tudor,E09000021,Kingston upon Thames,Kingston,1.00022E+11,Aragon Road,21800070,KT2,518013,171382,518050,171350,51.42916325,-0.303829153
147864101,29/08/2010 11:00,2010,2010/11,Special Service,1,1,260,260,CAT STUCK BEHIND BOARDS,Cat,Person (land line),Wasteland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Penpoll Road,NULL,E8,534751,184785,534750,184750,51.54587959,-0.058087753
148673101,30/08/2010 18:41,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN LAKE,Dog,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000540,East Walworth,E09000028,Southwark,Old Kent Road,NULL,Cobourg Road,NULL,SE5,533598,178124,533550,178150,51.48629505,-0.077228935
149174101,31/08/2010 15:44,2010,2010/11,Special Service,1,1,260,260,CROW TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000402,Beverley,E09000021,Kingston upon Thames,New Malden,NULL,Potters Grove,21800775,KT3,NULL,NULL,520250,168250,NULL,NULL
150566101,02/09/2010 20:21,2010,2010/11,Special Service,1,1,260,260,INJURED CAT STUCK IN TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000170,Acton Central,E09000009,Ealing,Acton,NULL,Horn Lane,NULL,W3,520052,180223,520050,180250,51.50819469,-0.271513023
150741101,03/09/2010 04:15,2010,2010/11,Special Service,1,1,260,260,CAT WITH HEAD TRAPPED UNDER FENCE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000196,Cockfosters,E09000010,Enfield,Southgate,NULL,Gloucester Gardens,20701065,EN4,NULL,NULL,528350,195850,NULL,NULL
150842101,03/09/2010 10:25,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000455,Abbey,E09000024,Merton,Wimbledon,NULL,Granville Road,22102928,SW19,NULL,NULL,525250,170150,NULL,NULL
151078101,03/09/2010 17:37,2010,2010/11,Special Service,1,1,260,260,SQUIRREL STUCK IN BOILER,Squirrel,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011472,Norbury & Pollards Hill,E09000008,Croydon,Norbury,NULL,Norbury Crescent,20501230,SW16,NULL,NULL,530850,169450,NULL,NULL
151656101,04/09/2010 17:14,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000372,Highbury East,E09000019,Islington,Islington,10012787400,Highbury Grove,21605117,N5,531967,185406,531950,185450,51.55211837,-0.097980233
151960101,05/09/2010 03:20,2010,2010/11,Special Service,1,1,260,260,CAT WITH HEAD STUCK IN CAN,Cat,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,NULL,Melon Road,NULL,E11,539094,186232,539050,186250,51.55782991,0.005075484
152001101,05/09/2010 08:30,2010,2010/11,Special Service,1,1,260,260,GULL TRAPPED IN GUTTERING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011487,Waddon,E09000008,Croydon,Croydon,NULL,Kemble Road,20501087,CR0,NULL,NULL,531550,165450,NULL,NULL
152182101,05/09/2010 16:15,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT TRAPPED IN BILLBOARD,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000473,West Barnes,E09000024,Merton,New Malden,NULL,Burlington Road,NULL,KT3,522440,168203,522450,168250,51.39965376,-0.241276831
152210101,05/09/2010 17:03,2010,2010/11,Special Service,1,1,260,260,KITTEN STUCK IN BUILDING,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000178,Greenford Green,E09000009,Ealing,Northolt,12134615,Greenford Road,20602353,UB6,514750,183750,514750,183750,51.54099471,-0.346728568
152337101,05/09/2010 20:52,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000434,Thurlow Park,E09000022,Lambeth,West Norwood,NULL,Chestnut Road,21900332,SE27,NULL,NULL,532350,172350,NULL,NULL
152516101,06/09/2010 09:47,2010,2010/11,Special Service,2,3,260,780,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,NULL,Alderney Gardens,NULL,UB5,512583,184317,512550,184350,51.54652667,-0.377782122
153370101,07/09/2010 21:23,2010,2010/11,Special Service,1,1,260,260,CAT STUCK BETWEEN KITCHEN UNITS AND WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000091,Harlesden,E09000005,Brent,Willesden,NULL,Church Road,20200352,NW10,NULL,NULL,521350,184650,NULL,NULL
153576101,08/09/2010 08:56,2010,2010/11,Special Service,1,2,260,520,ASSIST RSPCA WITH HORSE TRAPPED IN FENCE ***SILENT APPROACH REQUESTED****,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000357,Heston West,E09000018,Hounslow,Feltham,NULL,Brabazon Road,NULL,TW5,511155,177271,511150,177250,51.48347985,-0.400586934
153798101,08/09/2010 17:05,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED ON LEDGE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000421,Ferndale,E09000022,Lambeth,Clapham,NULL,Britannia Close,21900235,SW4,NULL,NULL,529950,175150,NULL,NULL
155918101,12/09/2010 10:30,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN GARDEN GATE,Dog,Person (land line),Garden equipment,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000490,Plaistow South,E09000025,Newham,Plaistow,46069619,St. Quintin Road,22207902,E13,540784,182964,540750,182950,51.52804493,0.028131124
156017101,12/09/2010 14:05,2010,2010/11,Special Service,1,2,260,520,DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000162,Selsdon and Ballards,E09000008,Croydon,Addington,NULL,Quail Gardens,NULL,CR2,536153,162239,536150,162250,51.34293642,-0.046548067
156108101,12/09/2010 17:10,2010,2010/11,Special Service,1,1,260,260,HORSE IN RIVER,Horse,Other FRS,Park,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000321,South Hornchurch,E09000016,Havering,Hornchurch,NULL,South End Road,NULL,RM13,552915,183939,552950,183950,51.53365336,0.203298178
156117101,12/09/2010 17:37,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON SCAFFOLDING,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000261,Ravenscourt Park,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Goldhawk Road,21000380,W12,NULL,NULL,522750,179450,NULL,NULL
156680101,13/09/2010 17:13,2010,2010/11,Special Service,1,1,260,260,DISTRESSED CAT TRAPPED IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009373,Hackney Downs,E09000012,Hackney,Homerton,10008239760,Clarence Road,20900248,E5,534849,185816,534850,185850,51.55512109,-0.056280803
157014101,14/09/2010 09:39,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000326,Brunel,E09000017,Hillingdon,Hillingdon,1.00021E+11,Brambles Farm Drive,21400225,UB10,507531,182542,507550,182550,51.53155726,-0.451152068
157241101,14/09/2010 16:56,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BETWEEN FENCE AND WALL,Cat,Person (land line),Wasteland,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000423,Herne Hill,E09000022,Lambeth,Brixton,1.00022E+11,Redan Terrace,21901148,SE5,531964,176247,531950,176250,51.46981036,-0.101449112
157392101,14/09/2010 20:02,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN WELL IN BASEMENT AREA,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009405,Stanley,E09000020,Kensington and Chelsea,Chelsea,NULL,Elm Park Gardens,21700188,SW10,NULL,NULL,526650,178050,NULL,NULL
160182101,19/09/2010 19:15,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED FOX,Fox,Person (mobile),Self contained Sheltered Housing,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000648,Westbourne,E09000033,Westminster,North Kensington,NULL,Great Western Road,8401380,W11,NULL,NULL,524950,181650,NULL,NULL
161076101,21/09/2010 02:41,2010,2010/11,Special Service,1,1,260,260,DOG FALLEN DOWN  INTO DITCH,Dog,Person (land line),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,1.00023E+11,Crystal Palace Station Road,20302353,SE19,534119,170557,534150,170550,51.41816986,-0.072595726
161146101,21/09/2010 08:42,2010,2010/11,Special Service,1,1,260,260,FOX IMPALED ON SPIKED FENCE,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000570,Wallington South,E09000029,Sutton,Wallington,NULL,Cranley Gardens,NULL,SM6,529035,163370,529050,163350,51.35475923,-0.148276762
161329101,21/09/2010 15:15,2010,2010/11,Special Service,1,1,260,260,PIGEON STUCK IN NET,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000195,Chase,E09000010,Enfield,Enfield,NULL,Holbrook Close,20702754,EN1,NULL,NULL,534050,198050,NULL,NULL
161330101,21/09/2010 15:17,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED IN LOCKED WASHING MACHINE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000320,St. Andrew's,E09000016,Havering,Hornchurch,NULL,Maywin Drive,21300493,RM11,NULL,NULL,554850,187250,NULL,NULL
161392101,21/09/2010 16:32,2010,2010/11,Special Service,1,1,260,260,SQUIRREL TRAPPED IN METAL FENCING,Squirrel,Person (land line),Railings,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000370,Clerkenwell,E09000019,Islington,Shoreditch,5300011059,Bowling Green Lane,21604316,EC1R,531407,182325,531450,182350,51.5245613,-0.107202071
161434101,21/09/2010 17:46,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN BARBED WIRE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Harold Road,22207747,E13,NULL,NULL,540850,183650,NULL,NULL
939102,21/09/2010 23:17,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN CONSTRUCTION HOLE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000380,St. Peter's,E09000019,Islington,Islington,NULL,Packington Square,21606921,N1,NULL,NULL,532250,183450,NULL,NULL
161827101,22/09/2010 09:22,2010,2010/11,Special Service,1,2,260,520,HORSE COLLAPSED BEHIND STABLE DOORS,Horse,Person (land line),Barn,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,1.00021E+11,Noak Hill Road,21301452,RM3,554187,193530,554150,193550,51.61948023,0.225827382
161886101,22/09/2010 11:22,2010,2010/11,Special Service,1,1,260,260,KITTEN STUCK AT REAR OF ELECTRICY CIRCUIT BOX,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000105,Willesden Green,E09000005,Brent,Willesden,NULL,Chapter Road,20201486,NW2,NULL,NULL,522750,184950,NULL,NULL
162959101,24/09/2010 10:50,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN BARBED WIRE IN WOODLAND,Dog,Person (mobile),Park,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000195,Chase,E09000010,Enfield,Enfield,NULL,Cooks Hole Road,NULL,EN2,531804,198391,531850,198350,51.6688445,-0.095452158
162969101,24/09/2010 11:14,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000190,Southall Green,E09000009,Ealing,Southall,NULL,Norwood Road,20602330,UB2,NULL,NULL,512450,179050,NULL,NULL
163121101,24/09/2010 16:43,2010,2010/11,Special Service,1,1,260,260,DOG IN RIVER,Dog,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000513,Snaresbrook,E09000026,Redbridge,Leytonstone,NULL,Hermon Hill,NULL,E11,540384,188837,540350,188850,51.58091804,0.024710319
164149101,26/09/2010 10:20,2010,2010/11,Special Service,1,1,260,260,KITTEN WITH PAW TRAPPED IN DOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000590,Cann Hall,E09000031,Waltham Forest,Leytonstone,NULL,Matcham Road,22857250,E11,NULL,NULL,539650,186150,NULL,NULL
164185101,26/09/2010 11:22,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000424,Knight's Hill,E09000022,Lambeth,West Norwood,NULL,Selsdon Road,21901222,SE27,NULL,NULL,531550,171950,NULL,NULL
164237101,26/09/2010 13:21,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000640,Maida Vale,E09000033,Westminster,Paddington,1.00023E+11,Elgin Avenue,8400921,W9,525685,182637,525650,182650,51.52866636,-0.189526437
164837101,27/09/2010 15:43,2010,2010/11,Special Service,1,2,260,520,HORSE TRAPPED IN RESERVOIR,Horse,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,NULL,Harbet Road,NULL,E4,536198,191815,536150,191850,51.60870464,-0.034510989
165209101,28/09/2010 10:46,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BETWEEN CUPBOARD AND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000088,Dollis Hill,E09000005,Brent,Willesden,NULL,Hawarden Hill,20202540,NW2,NULL,NULL,522350,186150,NULL,NULL
165357101,28/09/2010 15:32,2010,2010/11,Special Service,1,1,260,260,DISTRESSED DOGS LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000614,Fairfield,E09000032,Wandsworth,Battersea,NULL,Nantes Close,NULL,SW18,526557,175237,526550,175250,51.46196733,-0.179614115
165500101,28/09/2010 19:17,2010,2010/11,Special Service,1,3,260,780,LARGE BULL IN DISTRESS,Bull,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000290,Harrow Weald,E09000015,Harrow,Harrow,NULL,West Drive,NULL,HA3,514616,191859,514650,191850,51.61390438,-0.346015882
166316101,30/09/2010 09:35,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN FENCE,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009380,Lea Bridge,E09000012,Hackney,Homerton,NULL,Fletching Road,20900413,E5,NULL,NULL,535350,186150,NULL,NULL
166468101,30/09/2010 14:52,2010,2010/11,Special Service,1,1,260,260,PUPPY TRAPPED BETWEEN TWO BUILDINGS,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000058,Oakleigh,E09000003,Barnet,Barnet,NULL,Richmond Road,20036500,EN5,NULL,NULL,526350,195750,NULL,NULL
166714101,30/09/2010 23:46,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT TRAPPED IN A BIN,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009379,King's Park,E09000012,Hackney,Homerton,NULL,Rushmore Road,NULL,E5,535919,185883,535950,185850,51.55546674,-0.040830133
167163101,01/10/2010 19:50,2010,2010/11,Special Service,1,1,260,260,Redacted,cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000052,Garden Suburb,E09000003,Barnet,West Hampstead,NULL,Ingram Avenue,20024620,NW11,NULL,NULL,526150,187650,NULL,NULL
167408101,02/10/2010 07:50,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BETWEEN IRON BAR AND STEPS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009380,Lea Bridge,E09000012,Hackney,Homerton,NULL,Mildenhall Road,20900691,E5,NULL,NULL,535250,186050,NULL,NULL
168970101,04/10/2010 13:06,2010,2010/11,Special Service,1,1,260,260,CAT UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011116,South Bermondsey,E09000028,Southwark,Old Kent Road,2.00003E+11,Beatrice Road,22500183,SE1,534495,178539,534450,178550,51.48981195,-0.064159714
170525101,07/10/2010 02:15,2010,2010/11,Special Service,1,1,260,260,HORSE STUCK IN RAILINGS,Horse,Police,"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000071,Crayford,E09000004,Bexley,Bexley,NULL,Thames Road,NULL,DA1,552670,175375,552650,175350,51.4567727,0.196065531
171408101,08/10/2010 18:11,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000298,Pinner South,E09000015,Harrow,Harrow,NULL,Marsh Road,NULL,HA5,512146,189485,512150,189450,51.59306395,-0.382433287
172942101,11/10/2010 07:06,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED IN COURT YARD,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000632,Bryanston and Dorset Square,E09000033,Westminster,Paddington,NULL,Montagu Square,8401432,W1H,NULL,NULL,527750,181450,NULL,NULL
173382101,11/10/2010 23:17,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN WIRE IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000055,Hendon,E09000003,Barnet,Mill Hill,NULL,Watford Way,NULL,NW4,522250,190250,522250,190250,51.59784015,-0.236373124
173608101,12/10/2010 12:54,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011237,Chadwell,E09000026,Redbridge,Ilford,1.00022E+11,Dunmow Close,22302002,RM6,547158,188837,547150,188850,51.57919139,0.122404717
173702101,12/10/2010 16:38,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA OFFICER WITH CAT UP TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,NULL,Alderney Gardens,NULL,UB5,512683,184259,512650,184250,51.54598544,-0.376359118
174234101,13/10/2010 17:00,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN RABBIT HOLE,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000517,East Sheen,E09000027,Richmond upon Thames,Richmond,NULL,Richmond Park,NULL,TW10,520681,173226,520650,173250,51.44517473,-0.264841368
174255101,13/10/2010 17:30,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN RABBIT HOLE,Dog,Person (land line),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000517,East Sheen,E09000027,Richmond upon Thames,Richmond,NULL,Richmond Park,NULL,TW10,520681,173226,520650,173250,51.44517473,-0.264841368
174599101,14/10/2010 08:12,2010,2010/11,Special Service,1,1,260,260,DOG STUCK ON ROOF OF BUNGALOW,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000351,Feltham North,E09000018,Hounslow,Feltham,NULL,Tachbrook Road,21501108,TW14,NULL,NULL,509950,173850,NULL,NULL
174735101,14/10/2010 12:59,2010,2010/11,Special Service,1,1,260,260,CAT IN DISTRESS,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000286,Canons,E09000015,Harrow,Stanmore,1.00021E+11,Handel Way,21200896,HA8,519178,191366,519150,191350,51.60852661,-0.280326808
174979101,14/10/2010 21:11,2010,2010/11,Special Service,1,1,260,260,CAT IN DISTRESS,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000569,Wallington North,E09000029,Sutton,Wallington,5870110475,Bute Court,22605499,SM6,529120,164335,529150,164350,51.36341262,-0.146706757
174999101,14/10/2010 21:59,2010,2010/11,Special Service,1,1,260,260,CAT IN DISTRESS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Oakridge Road,22001754,BR1,NULL,NULL,539250,171650,NULL,NULL
175252101,15/10/2010 12:44,2010,2010/11,Special Service,1,1,260,260,INJURED CAT UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000479,Custom House,E09000025,Newham,Plaistow,NULL,Baxter Road,NULL,E16,541491,181452,541450,181450,51.51428155,0.037709449
175348101,15/10/2010 16:15,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000446,Ladywell,E09000023,Lewisham,Lewisham,1.00022E+11,Adelaide Avenue,22005430,SE4,537264,174995,537250,174950,51.45729913,-0.025677258
176054101,16/10/2010 18:23,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND WARDROBE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000178,Greenford Green,E09000009,Ealing,Northolt,NULL,Whitton Avenue West,20601899,UB6,NULL,NULL,515050,185050,NULL,NULL
176610101,17/10/2010 16:24,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED BETWEEN TWO WINDOW PANES,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000528,South Richmond,E09000027,Richmond upon Thames,Richmond,NULL,Kingsmead,22403590,TW10,NULL,NULL,518750,174150,NULL,NULL
177051101,18/10/2010 10:41,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN METAL RAILINGS IN STREAM,Dog,Person (mobile),Park,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000048,East Barnet,E09000003,Barnet,Barnet,200022527,Church Hill Road,20008700,EN4,527202,195265,527250,195250,51.64181232,-0.163097421
177056101,18/10/2010 10:53,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA - CAT ON LEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000027,Alibon,E09000002,Barking and Dagenham,Dagenham,NULL,St. Mark's Place,19901091,RM10,NULL,NULL,549550,184850,NULL,NULL
177119101,18/10/2010 13:17,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED ON ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,Addison Way,20000260,NW11,NULL,NULL,524950,189250,NULL,NULL
177212101,18/10/2010 16:20,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN PIPES,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000598,Hatch Lane,E09000031,Waltham Forest,Chingford,NULL,Chingdale Road,NULL,E4,539488,193261,539450,193250,51.62089326,0.0135448
177228101,18/10/2010 17:01,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000230,Woolwich Riverside,E09000011,Greenwich,East Greenwich,NULL,Leda Road,20800899,SE18,NULL,NULL,542750,179150,NULL,NULL
177767101,19/10/2010 17:45,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,NULL,Marden Square,22501655,SE16,NULL,NULL,534750,179050,NULL,NULL
177859101,19/10/2010 20:34,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN SINK UNIT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000219,Eltham South,E09000011,Greenwich,Eltham,NULL,Keightley Drive,20800835,SE9,NULL,NULL,544550,173050,NULL,NULL
178150101,20/10/2010 12:29,2010,2010/11,Special Service,1,1,260,260,INJURED CAT FALEN DOWN BASEMENT AREA,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000170,Acton Central,E09000009,Ealing,Acton,NULL,Horn Lane,20602383,W3,NULL,NULL,520150,180750,NULL,NULL
178742101,21/10/2010 12:53,2010,2010/11,Special Service,1,1,260,260,DOG IN RIVER ALONG TOW PATH,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Ferry Lane,NULL,TW9,518820,177706,518850,177750,51.48583275,-0.29010391
180508101,24/10/2010 06:13,2010,2010/11,Special Service,1,2,260,520,CAT TRAPPED BEHIND WARDROBE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011223,Crook Log,E09000004,Bexley,Bexley,NULL,Paddock Road,20101092,DA6,NULL,NULL,548250,175150,NULL,NULL
180586101,24/10/2010 11:33,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Romford Road,22201573,E7,NULL,NULL,540550,184950,NULL,NULL
180721101,24/10/2010 16:22,2010,2010/11,Special Service,1,1,260,260,PIGEON STUCK IN WIRE,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000364,Syon,E09000018,Hounslow,Heston,1.00023E+11,London Road,21500707,TW7,516580,176902,516550,176950,51.47907203,-0.322615354
181358101,25/10/2010 18:15,2010,2010/11,Special Service,1,1,260,260,INJURED CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000620,Queenstown,E09000032,Wandsworth,Battersea,NULL,Warriner Gardens,22905967,SW11,NULL,NULL,528050,176650,NULL,NULL
181407101,25/10/2010 19:06,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN-BETWEEN FALSE CEILING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000189,Southall Broadway,E09000009,Ealing,Southall,NULL,St. Josephs Drive,20601600,UB1,NULL,NULL,512450,180150,NULL,NULL
181727101,26/10/2010 10:59,2010,2010/11,Special Service,1,1,260,260,CAT STUCK BEHIND METAL FRAMEWORK,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Goldington Street,20400800,NW1,NULL,NULL,529750,183350,NULL,NULL
181782101,26/10/2010 12:53,2010,2010/11,Special Service,1,1,260,260,FOX TRAPPED BETWEEN  BORDED UP AREA,Fox,Person (mobile),Wasteland,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Brady Street,NULL,E1,534708,182146,534750,182150,51.52217519,-0.05971633
182228101,27/10/2010 09:25,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN BARBED WIRE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000428,St. Leonard's,E09000022,Lambeth,Tooting,1.00022E+11,Abbotswood Road,21900063,SW16,529591,172596,529550,172550,51.43754704,-0.136930342
182984101,28/10/2010 15:33,2010,2010/11,Special Service,1,3,260,780,HORSE FALLEN IN RIVER,Horse,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000307,Cranham,E09000016,Havering,Hornchurch,NULL,Hall Lane,NULL,RM14,556571,188757,556550,188750,51.57593971,0.25810753
183102101,28/10/2010 19:18,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND FENCE,Cat,Person (land line),Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011252,South Woodford,E09000026,Redbridge,Leytonstone,1.00022E+11,Cadogan Gardens,22302674,E18,540986,189566,540950,189550,51.58731833,0.033684964
183890101,30/10/2010 09:46,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED UNDERGROUND,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,10010216723,Greenwich Park,20800172,SE10,539250,176750,539250,176750,51.47258539,0.003579963
184497101,31/10/2010 08:03,2010,2010/11,Special Service,1,1,260,260,CAT CAUGHT BETWEEN WINDOW PANE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009379,King's Park,E09000012,Hackney,Homerton,NULL,Baslow Walk,20900111,E5,NULL,NULL,535850,185850,NULL,NULL
184650101,31/10/2010 13:48,2010,2010/11,Special Service,1,1,260,260,TO ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000565,Sutton North,E09000029,Sutton,Sutton,NULL,All Saints Road,NULL,SM1,526176,165392,526150,165350,51.37357273,-0.188596227
184731101,31/10/2010 16:32,2010,2010/11,Special Service,1,1,260,260,KITTENS POSSIBLY TRAPPED BEHIND COOKER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000174,Ealing Common,E09000009,Ealing,Acton,NULL,Hanger Lane,20602048,W5,NULL,NULL,518650,180750,NULL,NULL
185448101,01/11/2010 21:05,2010,2010/11,Special Service,1,1,260,260,CAT BELIEVED TO BE INJURED ON ROOF OF BLOCK OF FLATS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000609,Wood Street,E09000031,Waltham Forest,Walthamstow,NULL,Stocksfield Road,22877000,E17,NULL,NULL,538450,189650,NULL,NULL
186795101,04/11/2010 13:17,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Scrub land,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000609,Wood Street,E09000031,Waltham Forest,Walthamstow,1.00023E+11,Hylands Road,22846900,E17,538933,190033,538950,190050,51.59202436,0.004255643
187375101,05/11/2010 14:38,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN RAIL,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000331,Heathrow Villages,E09000017,Hillingdon,Heathrow,1.00024E+11,Bath Road,21402329,UB3,507870,176980,507850,176950,51.48150021,-0.447966119
188198101,06/11/2010 09:52,2010,2010/11,Special Service,1,2,260,520,SICK COW STUCK AND UNABLE TO BE RAISED,Cow,Person (land line),Barn,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000134,Gospel Oak,E09000007,Camden,Kentish Town,5023224,Cressfield Close,20401332,NW5,528364,185356,528350,185350,51.5524998,-0.149936393
189072101,07/11/2010 14:34,2010,2010/11,Special Service,1,1,260,260,DOG STUCK DOWN HOLE IN GARDEN,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000275,Noel Park,E09000014,Haringey,Hornsey,NULL,Bury Road,21104308,N22,NULL,NULL,531350,190050,NULL,NULL
189099101,07/11/2010 15:03,2010,2010/11,Special Service,1,1,260,260,KITTEN POSSIBLY TRAPPED IN WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000450,Perry Vale,E09000023,Lewisham,Forest Hill,NULL,Inglemere Road,22001583,SE23,NULL,NULL,535650,172350,NULL,NULL
189713101,08/11/2010 14:45,2010,2010/11,Special Service,1,4,260,1040,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000042,Whalebone,E09000002,Barking and Dagenham,Dagenham,100013067,High Road,19900165,RM6,547795,187922,547750,187950,51.57080353,0.131206591
190694101,10/11/2010 14:23,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Selsey Street,22701078,E14,NULL,NULL,537250,181650,NULL,NULL
191667101,12/11/2010 01:25,2010,2010/11,Special Service,1,1,260,260,DOG WITH CAN TRAPPED ON JAW,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000316,Mawneys,E09000016,Havering,Romford,NULL,Marlborough Road,21301397,RM7,NULL,NULL,550050,189250,NULL,NULL
191993101,12/11/2010 18:34,2010,2010/11,Special Service,1,1,260,260,INJURED DOG TRAPPED BETWEEN SHED AND FENCE,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000477,Canning Town North,E09000025,Newham,Plaistow,46058887,Pretoria Road,22207844,E16,539557,182418,539550,182450,51.52344323,0.010238996
192533101,13/11/2010 17:37,2010,2010/11,Special Service,1,1,260,260,DOG IN RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000209,Southgate Green,E09000010,Enfield,Southgate,NULL,Powys Lane,NULL,N13,530218,192198,530250,192150,51.61356123,-0.120676367
192955101,14/11/2010 13:23,2010,2010/11,Special Service,1,1,260,260,GULL TRAPPED IN WIRE,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000611,Bedford,E09000032,Wandsworth,Tooting,NULL,Tooting Bec Road,NULL,SW16,529082,172094,529050,172050,51.43315162,-0.144432095
193901101,16/11/2010 09:33,2010,2010/11,Special Service,1,1,260,260,KITTENS STUCK UNDER FLOORBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,Park Lane,21104898,N17,NULL,NULL,533950,191050,NULL,NULL
193975101,16/11/2010 12:16,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN ROOF GUTTERING,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000621,Roehampton and Putney Heath,E09000032,Wandsworth,Wandsworth,10024086666,Norstead Place,22903562,SW15,522143,172825,522150,172850,51.44125772,-0.243952581
194569101,17/11/2010 14:45,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED UNDERNEATH CAR BONNET,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05009327,Mile End,E09000030,Tower Hamlets,Bethnal Green,NULL,English Street,NULL,E3,536726,182227,536750,182250,51.52241866,-0.030615749
194813101,17/11/2010 23:23,2010,2010/11,Special Service,1,2,260,520,DOG TRAPPED BETWEEN BUILDINGS,Dog,Person (mobile),Other industrial manufacturing facility,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,2E+11,Martell Road,21900929,SE21,532476,172275,532450,172250,51.43399532,-0.095567956
195192101,18/11/2010 18:15,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND BATH,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000211,Turkey Street,E09000010,Enfield,Enfield,NULL,Stoneleigh Avenue,20702978,EN1,NULL,NULL,534850,198050,NULL,NULL
195387101,19/11/2010 02:36,2010,2010/11,Special Service,1,1,260,260,DOG FALLEN IN RIVER AND TRAPPED,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000344,Yeading,E09000017,Hillingdon,Southall,NULL,Tollgate Drive,NULL,UB4,511855,180747,511850,180750,51.51458448,-0.389409353
195502101,19/11/2010 11:31,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000210,Town,E09000010,Enfield,Enfield,NULL,Parsonage Lane,20702893,EN2,NULL,NULL,532650,197150,NULL,NULL
198196101,24/11/2010 10:03,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011476,Purley & Woodcote,E09000008,Croydon,Purley,NULL,Northwood Avenue,20502239,CR8,NULL,NULL,531650,160650,NULL,NULL
199442101,26/11/2010 16:00,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED BETWEEN ROOF TILES,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000349,Chiswick Riverside,E09000018,Hounslow,Chiswick,1.00022E+11,Chiswick Quay,21501385,W4,520156,176602,520150,176650,51.47562841,-0.271246741
200369101,28/11/2010 01:04,2010,2010/11,Special Service,1,1,260,260,PERSONS SHUT IN LIFT EMERGENCY,Unknown - Heavy Livestock Animal,Person (land line),Purpose built office,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05009305,Farringdon Without,E09000001,City of London,Dowgate,1.00023E+11,Giltspur Street,8100228,EC1A,531798,181481,531750,181450,51.51688551,-0.101884777
200664101,28/11/2010 13:27,2010,2010/11,Special Service,1,1,260,260,GOOSE STUCK IN ICE ON POND,Bird,Other FRS,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000195,Chase,E09000010,Enfield,Enfield,207175098,Whitewebbs Lane,20703047,EN2,533123,199933,533150,199950,51.68239033,-0.075803298
200780101,28/11/2010 15:59,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000354,Hanworth Park,E09000018,Hounslow,Feltham,NULL,Pevensey Road,NULL,TW13,511984,173268,511950,173250,51.44733735,-0.389919566
202363101,01/12/2010 05:06,2010,2010/11,Special Service,1,1,260,260,RABBIT UNDER FLOORBOARDS,Rabbit,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000108,Bromley Common and Keston,E09000006,Bromley,Bromley,NULL,Birch Row,20300258,BR2,NULL,NULL,543350,166950,NULL,NULL
202573101,01/12/2010 13:48,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED ON TREE BRANCH,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,NULL,Globe Road,NULL,E2,535245,182850,535250,182850,51.52837313,-0.051710598
202749101,01/12/2010 19:40,2010,2010/11,Special Service,1,1,260,260,BIRDS STUCK IN ICE ON ROOF,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011463,Addiscombe West,E09000008,Croydon,Woodside,NULL,Warren Road,20501538,CR0,NULL,NULL,533450,166250,NULL,NULL
202941101,02/12/2010 08:16,2010,2010/11,Special Service,1,1,260,260,HORSE STUCK ON GATE,Horse,Person (mobile),Railings,Outdoor Structure,Animal rescue from water,Animal rescue from water - Farm animal,E05000405,Chessington South,E09000021,Kingston upon Thames,Surbiton,NULL,Barwell Lane,NULL,KT9,516984,163067,516950,163050,51.35464173,-0.321359497
203054101,02/12/2010 11:26,2010,2010/11,Special Service,4,4,260,1040,DOG STUCK IN POND,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000200,Grange,E09000010,Enfield,Enfield,NULL,Cecil Road,NULL,EN2,532429,196560,532450,196550,51.65224395,-0.087112641
203134101,02/12/2010 13:38,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Wick Road,20901074,E9,NULL,NULL,536350,184750,NULL,NULL
204291101,04/12/2010 11:04,2010,2010/11,Special Service,1,1,260,260,CAT  STUCK IN BIN CHUTE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Gosfield Road,19900150,RM8,NULL,NULL,549450,186950,NULL,NULL
205017101,05/12/2010 13:07,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN ICE,Dog,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000137,Highgate,E09000007,Camden,West Hampstead,NULL,Hampstead Lane,NULL,NW3,527087,187418,527050,187450,51.57131936,-0.167600351
205123101,05/12/2010 15:58,2010,2010/11,Special Service,1,1,260,260,CAT SHUT IN DERELICT BUILDING,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,NULL,Devonshire Road,22001409,SE23,NULL,NULL,535350,173150,NULL,NULL
205621101,06/12/2010 13:52,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011464,Bensham Manor,E09000008,Croydon,Norbury,NULL,Torridge Road,20501496,CR7,NULL,NULL,531950,167850,NULL,NULL
205682101,06/12/2010 15:46,2010,2010/11,Special Service,1,1,260,260,DOG STUCK ON ROOF,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,NULL,Sunbury Street,20801435,SE18,NULL,NULL,542950,179050,NULL,NULL
206017101,07/12/2010 08:16,2010,2010/11,Special Service,1,2,260,520,CAT STUCK ON A CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011238,Churchfields,E09000026,Redbridge,Woodford,NULL,Eagle Terrace,22304953,IG8,NULL,NULL,540750,191250,NULL,NULL
206385101,07/12/2010 20:43,2010,2010/11,Special Service,1,1,260,260,DOG IN RIVER LEE,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Stoke Newington,NULL,Riverside Close,NULL,E5,535437,186887,535450,186850,51.56460467,-0.047392545
207392101,09/12/2010 17:36,2010,2010/11,Special Service,3,6,260,1560,Redacted,Cow,Other FRS,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000323,Upminster,E09000016,Havering,Hornchurch,1.00021E+11,St. Marys Lane,21320207,RM14,559664,187062,559650,187050,51.55984466,0.301935469
207711101,10/12/2010 08:23,2010,2010/11,Special Service,1,1,260,260,KITTEN FALLEN INTO BUILDING FOOTINGS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000103,Welsh Harp,E09000005,Brent,Willesden,NULL,Harp Island Close,NULL,NW10,NULL,NULL,520750,186650,NULL,NULL
207767101,10/12/2010 10:29,2010,2010/11,Special Service,1,1,260,260,TWO CATS STUCK IN TREE,cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,NULL,Jerningham Road,22000563,SE14,NULL,NULL,536050,176250,NULL,NULL
208421101,11/12/2010 14:24,2010,2010/11,Special Service,1,1,260,260,DOG WITH FOOT TRAPPED IN SHOWER CHAIR,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000254,Fulham Broadway,E09000013,Hammersmith and Fulham,Fulham,NULL,Clem Attlee Estate,21000509,SW6,NULL,NULL,524750,177450,NULL,NULL
209524101,13/12/2010 16:22,2010,2010/11,Special Service,1,1,260,260,DOG FALLEN THROUGH ICE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000605,Leytonstone,E09000031,Waltham Forest,Leytonstone,NULL,Whipps Cross Road,NULL,E11,539750,187750,539750,187750,51.57130821,0.015134742
210029101,14/12/2010 14:28,2010,2010/11,Special Service,1,2,260,520,HORSE SLINGS REQUIRED TO LIFT HORSE IN FIELD,Horse,Other FRS,"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000405,Chessington South,E09000021,Kingston upon Thames,Surbiton,128032246,Barwell Lane,21800115,KT9,517047,163136,517050,163150,51.35524892,-0.320432473
210464101,15/12/2010 10:06,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Roadside vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000563,Stonecot,E09000029,Sutton,Sutton,NULL,Henley Avenue,NULL,SM3,524165,165657,524150,165650,51.37639722,-0.217381009
210725101,15/12/2010 19:13,2010,2010/11,Special Service,1,1,260,260,DOG IN CANAL,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009375,Haggerston,E09000012,Hackney,Shoreditch,NULL,Baltic Place,NULL,N1,533455,183757,533450,183750,51.53694971,-0.077155969
211037101,16/12/2010 13:14,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND KITCHEN UNITS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000194,Bush Hill Park,E09000010,Enfield,Edmonton,NULL,Rowantree Road,20704689,N21,NULL,NULL,532850,194450,NULL,NULL
212157101,18/12/2010 11:32,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND BATH PANEL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000463,Lavender Fields,E09000024,Merton,Mitcham,NULL,Wilson Avenue,22106741,CR4,NULL,NULL,527450,169950,NULL,NULL
212258101,18/12/2010 13:42,2010,2010/11,Special Service,1,1,260,260,DOG IN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000349,Chiswick Riverside,E09000018,Hounslow,Chiswick,1.00023E+11,Burlington Lane,21500176,W4,520934,177319,520950,177350,51.48190705,-0.259804588
212264101,18/12/2010 13:49,2010,2010/11,Special Service,1,1,260,260,DOG IN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000611,Bedford,E09000032,Wandsworth,Tooting,NULL,Doctor Johnson Avenue,NULL,SW17,528746,172278,528750,172250,51.4348816,-0.149196207
212716101,19/12/2010 01:35,2010,2010/11,Special Service,1,1,260,260,HAMSTER TRAPPED BEHIND SINK UNIT,Hamster,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000045,Childs Hill,E09000003,Barnet,West Hampstead,NULL,Handley Grove,20020807,NW2,NULL,NULL,523950,186150,NULL,NULL
212826101,19/12/2010 08:13,2010,2010/11,Special Service,1,1,260,260,DOG FALLEN THROUGH ICE  ON LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000620,Queenstown,E09000032,Wandsworth,Battersea,10091499918,Battersea Park,22900030,SW11,527750,177250,527750,177250,51.47979045,-0.161723808
212932101,19/12/2010 12:29,2010,2010/11,Special Service,1,1,260,260,DOG IN LAKE,Dog,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05011223,Crook Log,E09000004,Bexley,Bexley,1.00023E+11,Danson Road,20101842,DA5,547524,174571,547550,174550,51.45091127,0.121717744
212938101,19/12/2010 12:35,2010,2010/11,Special Service,1,1,260,260,DOGS IN CANAL,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Twyford Abbey Road,NULL,NW10,519670,183209,519650,183250,51.53511239,-0.276001153
213149101,19/12/2010 19:28,2010,2010/11,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN GATE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000030,Eastbrook,E09000002,Barking and Dagenham,Dagenham,NULL,Eastbrook Drive,NULL,RM7,551329,186553,551350,186550,51.55756735,0.18157242
213213101,19/12/2010 21:25,2010,2010/11,Special Service,1,2,260,520,DOG TRAPPED UNDER DECKING,Dog,Other FRS,House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011466,Coulsdon Town,E09000008,Croydon,Purley,NULL,Rickman Hill,20502506,CR5,NULL,NULL,528850,158850,NULL,NULL
213464101,20/12/2010 09:31,2010,2010/11,Special Service,1,1,260,260,SEAGULL HANGING FROM WING FROM CABLE,Bird,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009324,Island Gardens,E09000030,Tower Hamlets,Millwall,NULL,Pier Street,NULL,E14,538406,178917,538450,178950,51.49226535,-0.007714839
213525101,20/12/2010 11:29,2010,2010/11,Special Service,1,1,260,260,DOG IN WATER,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000637,Knightsbridge and Belgravia,E09000033,Westminster,Soho,NULL,Hyde Park,NULL,W2,527421,180188,527450,180150,51.50626848,-0.165399101
214208101,21/12/2010 08:31,2010,2010/11,Special Service,1,1,260,260,DOG IN POND,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000456,Cannon Hill,E09000024,Merton,Wimbledon,NULL,Cannon Hill Lane,NULL,SW20,524317,168669,524350,168650,51.40343394,-0.214144959
214242101,21/12/2010 09:41,2010,2010/11,Special Service,1,1,260,260,DOG IN POND,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Grove Road,NULL,E3,535925,183375,535950,183350,51.53292782,-0.041711347
214289101,21/12/2010 10:57,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000374,Hillrise,E09000019,Islington,Holloway,5300042128,Hazellville Road,21606446,N19,529569,187603,529550,187650,51.57241773,-0.131738589
214330101,21/12/2010 12:30,2010,2010/11,Special Service,1,2,260,520,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000073,Danson Park,E09000004,Bexley,Bexley,NULL,Danson Park,NULL,DA6,547507,175180,547550,175150,51.45638782,0.121727235
214391101,21/12/2010 14:25,2010,2010/11,Special Service,1,1,260,260,DOG IN POND,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000054,Hale,E09000003,Barnet,Mill Hill,NULL,Kenilworth Road,NULL,HA8,520076,192995,520050,192950,51.62297636,-0.266808077
214925101,22/12/2010 10:15,2010,2010/11,Special Service,1,1,260,260,DOG TAPPED IN FENCE,Dog,Police,Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05011465,Broad Green,E09000008,Croydon,Norbury,1.00021E+11,Constance Road,20500812,CR0,531819,166930,531850,166950,51.3861144,-0.106999456
215280101,22/12/2010 23:04,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED,Cat,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000379,St. Mary's,E09000019,Islington,Islington,NULL,Hawes Street,NULL,N1,531773,184032,531750,184050,51.53981627,-0.10129091
216079101,24/12/2010 12:59,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND CHIMNEY  BREAST INJURED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011222,Crayford,E09000004,Bexley,Bexley,NULL,Mill Place,20100980,DA1,NULL,NULL,552550,175250,NULL,NULL
216187101,24/12/2010 16:55,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT IN PRECARIOUS POSITION,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000208,Southgate,E09000010,Enfield,Southgate,NULL,Bourne Avenue,20703943,N14,NULL,NULL,530050,193950,NULL,NULL
216289101,24/12/2010 19:47,2010,2010/11,Special Service,1,1,260,260,PERSON LOCKED IN PARK WITH DOG,Dog,Police,Park,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000315,Hylands,E09000016,Havering,Hornchurch,NULL,Osborne Road,NULL,RM11,552840,188221,552850,188250,51.57214697,0.204075301
216541101,25/12/2010 09:42,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,North Circular Road,20201638,NW10,NULL,NULL,520450,184650,NULL,NULL
217168101,26/12/2010 13:49,2010,2010/11,Special Service,1,1,260,260,DOG IN WATER WAY,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000530,Teddington,E09000027,Richmond upon Thames,Twickenham,NULL,Bushy Park,NULL,TW11,516059,169509,516050,169550,51.4127326,-0.332536785
217392101,26/12/2010 20:26,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO KITTEN IN TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000560,Cheam,E09000029,Sutton,Sutton,5870022418,Chelsea Gardens,22602908,SM3,524473,164851,524450,164850,51.36908609,-0.213239807
217490101,26/12/2010 23:51,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN RAILINGS,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Stanhope Street,NULL,NW1,528996,182535,528950,182550,51.52700381,-0.141859183
218152101,28/12/2010 10:45,2010,2010/11,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009383,Springfield,E09000012,Hackney,Stoke Newington,NULL,Springfield,NULL,E5,534751,187318,534750,187350,51.56864196,-0.057118868
218261101,28/12/2010 14:37,2010,2010/11,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Rangers Road,NULL,E4,539689,194767,539650,194750,51.63437593,0.017046618
218847101,29/12/2010 15:20,2010,2010/11,Special Service,2,3,260,780,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Rangers Road,NULL,E4,539871,194590,539850,194550,51.63274032,0.019603981
219023101,29/12/2010 20:10,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN SHUTTERS,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,NULL,Commercial Road,NULL,E1,534666,181299,534650,181250,51.51457379,-0.060644895
219734101,31/12/2010 10:53,2010,2010/11,Special Service,1,1,260,260,AFA ACTUATING,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000285,Belmont,E09000015,Harrow,Stanmore,NULL,Bromefield,21202482,HA7,NULL,NULL,517450,190850,NULL,NULL
219745101,31/12/2010 11:31,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000285,Belmont,E09000015,Harrow,Stanmore,NULL,Bromefield,21202482,HA7,NULL,NULL,517450,190850,NULL,NULL
219835101,31/12/2010 14:39,2010,2010/11,Special Service,1,1,260,260,CAT ON TOP OF CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000567,Sutton West,E09000029,Sutton,Sutton,NULL,Alberta Avenue,22602811,SM1,NULL,NULL,524650,164450,NULL,NULL
312111,01/01/2011 13:21,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED BETWEEN STAIRS PANEL,Cat,Person (land line),Self contained Sheltered Housing,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000450,Perry Vale,E09000023,Lewisham,Forest Hill,NULL,Siddons Road,22001894,SE23,NULL,NULL,536050,172550,NULL,NULL
336111,01/01/2011 14:09,2011,2010/11,Special Service,1,2,260,520,BIRD TRAPPED IN NETTING,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000552,Surrey Docks,E09000028,Southwark,Deptford,NULL,Cunard Walk,NULL,SE16,536116,178960,536150,178950,51.49320749,-0.040663524
678111,02/01/2011 08:56,2011,2010/11,Special Service,1,1,260,260,FOX STUCK BETWEEN WALL AND GARAGE,Fox,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000277,St. Ann's,E09000014,Haringey,Tottenham,1.00023E+11,Conway Road,21103140,N15,532158,188844,532150,188850,51.58296906,-0.093936269
882111,02/01/2011 16:13,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN A CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,NULL,Haven Green,20600845,W5,NULL,NULL,517750,181050,NULL,NULL
1790111,04/01/2011 17:30,2011,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000115,Cray Valley West,E09000006,Bromley,Orpington,NULL,Millfield Cottages,20304051,BR5,NULL,NULL,546950,168550,NULL,NULL
1842111,04/01/2011 18:56,2011,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000115,Cray Valley West,E09000006,Bromley,Orpington,2.00002E+11,Millfield Close,20301217,BR5,546881,168369,546850,168350,51.39535016,0.109902056
2067111,05/01/2011 09:24,2011,2010/11,Special Service,1,1,260,260,CAT STUCK ON SMALL LEDGE ABOVE WATER,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000103,Welsh Harp,E09000005,Brent,Willesden,202083269,Harp Island Close,20200936,NW10,520780,186635,520750,186650,51.56566724,-0.258829183
2106111,05/01/2011 11:16,2011,2010/11,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000115,Cray Valley West,E09000006,Bromley,Orpington,NULL,Millfield Cottages,20304051,BR5,NULL,NULL,546950,168550,NULL,NULL
2741111,06/01/2011 20:41,2011,2010/11,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN GATE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,NULL,Melville Road,21300838,RM13,NULL,NULL,552450,182250,NULL,NULL
3496111,08/01/2011 10:21,2011,2010/11,Special Service,1,1,260,260,DOG TRAPPED UNDER CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000353,Hanworth,E09000018,Hounslow,Feltham,NULL,Hounslow Road,NULL,TW13,511875,172019,511850,172050,51.43613309,-0.39188165
3678111,08/01/2011 17:00,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRD TRAPPED IN TREE,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009391,Chelsea Riverside,E09000020,Kensington and Chelsea,Chelsea,NULL,Lots Road,NULL,SW10,526163,177054,526150,177050,51.4783848,-0.184635031
3680111,08/01/2011 17:03,2011,2010/11,Special Service,1,2,260,520,DOG WITH HEAD TRAPPED IN METAL FENCE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000417,Brixton Hill,E09000022,Lambeth,Clapham,NULL,Saxby Road,21901216,SW2,NULL,NULL,530250,173950,NULL,NULL
3699111,08/01/2011 17:42,2011,2010/11,Special Service,1,1,260,260,FOX TRAPPED IN HOLE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011472,Norbury & Pollards Hill,E09000008,Croydon,Norbury,NULL,Norbury Crescent,20501230,SW16,NULL,NULL,531450,169150,NULL,NULL
3992111,09/01/2011 08:52,2011,2010/11,Special Service,1,2,260,520,ASSIST RSPCA RETRIEVE A CAT,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000053,Golders Green,E09000003,Barnet,Finchley,200200344,Ashbourne Avenue,20001520,NW11,524676,188983,524650,188950,51.58592221,-0.201815711
5524111,12/01/2011 12:54,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000434,Thurlow Park,E09000022,Lambeth,West Norwood,NULL,Norwood Road,21901036,SE24,NULL,NULL,531750,173250,NULL,NULL
5838111,13/01/2011 00:52,2011,2010/11,Special Service,1,1,260,260,CAT STUCK UNDER FLOOR,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000639,Little Venice,E09000033,Westminster,Paddington,NULL,Warrington Crescent,8400690,W9,NULL,NULL,526050,182350,NULL,NULL
6039111,13/01/2011 12:39,2011,2010/11,Special Service,1,1,260,260,DOG STUCK IN RABBIT HOLE,Dog,Person (land line),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Kingston,NULL,Meadlands Drive,NULL,TW10,517596,172744,517550,172750,51.44149096,-0.309372564
6212111,13/01/2011 18:12,2011,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED IN WALL CAVITY,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000639,Little Venice,E09000033,Westminster,Paddington,NULL,Warrington Crescent,8400690,W9,NULL,NULL,526050,182350,NULL,NULL
6236111,13/01/2011 19:10,2011,2010/11,Special Service,1,1,260,260,FOX STUCK BETWEEN FENCE PANELS,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000648,Westbourne,E09000033,Westminster,North Kensington,1.00023E+11,Leamington Road Villas,8400535,W11,524925,181521,524950,181550,51.51880514,-0.200872308
8013111,17/01/2011 07:24,2011,2010/11,Special Service,1,1,260,260,DEER TRAPPED IN FENCE,Deer,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000057,Mill Hill,E09000003,Barnet,Mill Hill,NULL,The Ridgeway,NULL,NW7,522249,193072,522250,193050,51.62320161,-0.235404973
9070111,19/01/2011 09:48,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN GUTTERING,Cat,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Corker Walk,NULL,N7,530915,186639,530950,186650,51.56344396,-0.112686218
9540111,20/01/2011 09:01,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH REMOVAL OF CAT FROM TREE,Cat,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000557,Belmont,E09000029,Sutton,Sutton,5870028202,Kingswood Drive,22602417,SM2,525708,162810,525750,162850,51.35047125,-0.196228217
12290111,25/01/2011 16:47,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND SHED,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000521,Hampton North,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Stevens Close,22401889,TW12,512515,170965,512550,170950,51.42653316,-0.383012663
12690111,26/01/2011 16:44,2011,2010/11,Special Service,1,1,260,260,KITTEN STUCK UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000035,Longbridge,E09000002,Barking and Dagenham,Barking,NULL,Woodbridge Road,19900391,IG11,NULL,NULL,545750,185050,NULL,NULL
13146111,27/01/2011 16:43,2011,2010/11,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011095,Borough & Bankside,E09000028,Southwark,Dowgate,2.00003E+11,Copperfield Street,22500603,SE1,532158,179964,532150,179950,51.50316864,-0.097267753
14131111,29/01/2011 12:15,2011,2010/11,Special Service,1,1,260,260,TO REMOVE LOCK FROM GARAGES - CAT INSIDE,Cat,Person (land line),Vehicle Repair Workshop,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000131,Cantelowes,E09000007,Camden,Kentish Town,5071843,Camden Mews,20400538,NW1,529615,184661,529650,184650,51.54596829,-0.132158786
14408111,29/01/2011 20:22,2011,2010/11,Special Service,1,1,260,260,KITTEN STUCK IN VENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Petworth Gardens,21401525,UB10,NULL,NULL,508250,183650,NULL,NULL
15686111,01/02/2011 11:30,2011,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED IN A TREE,Cat,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000559,Carshalton South and Clockhouse,E09000029,Sutton,Wallington,NULL,Grosvenor Avenue,NULL,SM5,528218,163835,528250,163850,51.3591229,-0.159835861
16711111,03/02/2011 10:50,2011,2010/11,Special Service,1,1,260,260,DOGTRAPPED UNDER DECKING,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000372,Highbury East,E09000019,Islington,Islington,NULL,Highbury Place,21605119,N5,NULL,NULL,531650,184850,NULL,NULL
17182111,04/02/2011 07:40,2011,2010/11,Special Service,1,2,260,520,HORSE COLLAPSED,Horse,Person (land line),Barn,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000059,Totteridge,E09000003,Barnet,Finchley,200075806,Lullington Garth,20027560,N12,525250,192250,525250,192250,51.61515507,-0.192369619
17261111,04/02/2011 11:30,2011,2010/11,Special Service,2,3,260,780,Redacted,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000607,Valley,E09000031,Waltham Forest,Chingford,NULL,Hall Lane,NULL,E4,536284,192312,536250,192350,51.61314996,-0.033076692
18520111,06/02/2011 11:00,2011,2010/11,Special Service,1,1,260,260,DOG TRAPPED ON RIVERBANK  WATER LEVEL ONE IMPLEMENTED,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000446,Ladywell,E09000023,Lewisham,Lewisham,NULL,Malyons Road,NULL,SE13,537483,174439,537450,174450,51.45224964,-0.022742987
19256111,07/02/2011 17:53,2011,2010/11,Special Service,1,1,260,260,DOG WITH JAW TRAPPED IN FENCING,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000378,St. George's,E09000019,Islington,Holloway,5300092653,Tufnell Park Road,21606493,N7,530233,185970,530250,185950,51.55758978,-0.122767119
19581111,08/02/2011 10:25,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN BATHROOM PIPE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011104,London Bridge & West Bermondsey,E09000028,Southwark,Dockhead,NULL,Tower Bridge Road,22502529,SE1,NULL,NULL,533650,179750,NULL,NULL
20199111,09/02/2011 14:39,2011,2010/11,Special Service,1,1,260,260,DOG IN CANAL,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000322,Squirrel's Heath,E09000016,Havering,Hornchurch,NULL,Slewins Close,NULL,RM11,553524,188749,553550,188750,51.57670512,0.214167528
20451111,10/02/2011 00:42,2011,2010/11,Special Service,1,2,260,520,CAT IMPALED ON RAILINGS,Cat,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000423,Herne Hill,E09000022,Lambeth,Brixton,2E+11,Flaxman Road,21900566,SE5,532014,176231,532050,176250,51.46965491,-0.100735643
21058111,11/02/2011 11:12,2011,2010/11,Special Service,1,1,260,260,CAT STUCK UP INSIDE THE CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000517,East Sheen,E09000027,Richmond upon Thames,Richmond,NULL,Lodge Avenue,22406866,SW14,NULL,NULL,521050,175650,NULL,NULL
21535111,12/02/2011 10:10,2011,2010/11,Special Service,1,1,260,260,CAT STUCK ON A ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000435,Tulse Hill,E09000022,Lambeth,Brixton,NULL,Appach Road,21900099,SW2,NULL,NULL,531050,174450,NULL,NULL
22134111,13/02/2011 13:42,2011,2010/11,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000198,Enfield Highway,E09000010,Enfield,Enfield,207161278,Celadon Close,20702172,EN3,536143,196803,536150,196850,51.65354049,-0.033366267
22236111,13/02/2011 18:24,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND CUPBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000381,Tollington,E09000019,Islington,Holloway,NULL,Wray Crescent,21604047,N4,NULL,NULL,530550,186750,NULL,NULL
22865111,15/02/2011 08:52,2011,2010/11,Special Service,1,1,260,260,FOX TRAPPED BETWEEN TWO FENCES,Fox,Person (mobile),Railings,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000313,Havering Park,E09000016,Havering,Romford,1.00021E+11,Ashvale Gardens,21300966,RM5,550786,192089,550750,192050,51.60745371,0.176120774
24455111,18/02/2011 15:36,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN GAP BETWEEN EXTENSIONS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000036,Mayesbrook,E09000002,Barking and Dagenham,Barking,NULL,Lodge Avenue,19900241,RM8,NULL,NULL,546650,184850,NULL,NULL
25514111,20/02/2011 15:47,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009381,London Fields,E09000012,Hackney,Homerton,NULL,Martello Street,NULL,E8,534758,184309,534750,184350,51.54160043,-0.058168844
26038111,21/02/2011 17:24,2011,2010/11,Special Service,1,2,260,520,DOG STUCK IN FENCE   IN REAR GARDEN,Dog,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000105,Willesden Green,E09000005,Brent,Willesden,NULL,Chapter Road,20201486,NW2,NULL,NULL,522450,185050,NULL,NULL
26137111,21/02/2011 19:53,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN GUTTER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000138,Holborn and Covent Garden,E09000007,Camden,Euston,NULL,Brownlow Mews,20401021,WC1N,NULL,NULL,530850,182150,NULL,NULL
26639111,22/02/2011 20:44,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED ON FENCE,Cat,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Bridge Road,NULL,NW10,521066,184557,521050,184550,51.54693026,-0.255420093
27880111,25/02/2011 10:12,2011,2010/11,Special Service,1,1,260,260,A NUMBER OF DOGS LOCKED IN VEHICLE,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,Temple Fortune Hill,NULL,NW11,525396,188655,525350,188650,51.58281473,-0.19154619
29404111,28/02/2011 11:51,2011,2010/11,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000060,Underhill,E09000003,Barnet,Barnet,NULL,Wood Street,20046960,EN5,NULL,NULL,524550,196450,NULL,NULL
30147111,01/03/2011 22:45,2011,2010/11,Special Service,1,1,260,260,CAT STUCK ON ROOF BELIEVED TO BE INJURED,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000476,Boleyn,E09000025,Newham,East Ham,NULL,Victoria Avenue,22201179,E6,NULL,NULL,541650,183850,NULL,NULL
30449111,02/03/2011 16:44,2011,2010/11,Special Service,2,3,260,780,Redacted,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000611,Bedford,E09000032,Wandsworth,Tooting,NULL,Doctor Johnson Avenue,NULL,SW17,529105,172084,529150,172050,51.43305651,-0.144105048
30782111,03/03/2011 11:56,2011,2010/11,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000144,Swiss Cottage,E09000007,Camden,West Hampstead,NULL,Greencroft Gardens,20400420,NW6,NULL,NULL,526150,184450,NULL,NULL
30890111,03/03/2011 15:57,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED BETWEEN SHED & FENCE,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000218,Eltham North,E09000011,Greenwich,Eltham,1.00021E+11,Greenvale Road,20800683,SE9,542832,175165,542850,175150,51.45745092,0.054479912
31280111,04/03/2011 11:48,2011,2010/11,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,2.00003E+11,Avondale Square,22500119,SE1,534077,178126,534050,178150,51.48619972,-0.070333565
31739111,05/03/2011 05:56,2011,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000062,West Hendon,E09000003,Barnet,Hendon,NULL,Woolmead Avenue,20047600,NW9,NULL,NULL,522150,187550,NULL,NULL
31985111,05/03/2011 16:20,2011,2010/11,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000060,Underhill,E09000003,Barnet,Barnet,200156486,Hammond Close,20020640,EN5,524441,195633,524450,195650,51.64573674,-0.202846772
32093111,05/03/2011 19:34,2011,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000043,Brunswick Park,E09000003,Barnet,Southgate,NULL,Hampden Square,20020700,N14,NULL,NULL,528550,194250,NULL,NULL
32125111,05/03/2011 20:35,2011,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT TRAPPED IN GARAGE,Cat,Person (mobile),Other education establishment,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000419,Clapham Town,E09000022,Lambeth,Clapham,2E+11,Belmont Road,21900167,SW4,529473,175582,529450,175550,51.46440903,-0.137535716
32145111,05/03/2011 21:18,2011,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED ON LEDGE BETWEEN TWO BUILDINGS,Cat,Person (mobile),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000380,St. Peter's,E09000019,Islington,Islington,5300095158,Upper Street,21606199,N1,531629,183540,531650,183550,51.53542845,-0.103549982
32405111,06/03/2011 13:30,2011,2010/11,Special Service,1,1,260,260,CAT STUCK BETWEEN CEMENT AND SKIRTING BOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009375,Haggerston,E09000012,Hackney,Shoreditch,NULL,Cester Street,20900230,E2,NULL,NULL,534050,183650,NULL,NULL
32631111,06/03/2011 21:17,2011,2010/11,Special Service,1,1,260,260,DOG TRAPPED UNDER BOARDS IN EXTENSION,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000352,Feltham West,E09000018,Hounslow,Feltham,NULL,Cranleigh Road,21500317,TW13,NULL,NULL,509550,171650,NULL,NULL
32671111,06/03/2011 22:41,2011,2010/11,Special Service,1,1,260,260,DOG STUCK IN DECKING,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000121,Mottingham and Chislehurst North,E09000006,Bromley,Eltham,NULL,Framlingham Crescent,20302308,SE9,NULL,NULL,542350,171950,NULL,NULL
33179111,07/03/2011 20:57,2011,2010/11,Special Service,1,1,260,260,FOX TRAPPED IN WIRE,Fox,Person (mobile),Garden equipment,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000489,Plaistow North,E09000025,Newham,Plaistow,46070667,Stirling Road,22207905,E13,540674,183160,540650,183150,51.5298336,0.026624686
33505111,08/03/2011 13:36,2011,2010/11,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000572,Worcester Park,E09000029,Sutton,Sutton,NULL,Hill Crescent,22601578,KT4,NULL,NULL,523450,165650,NULL,NULL
33518111,08/03/2011 14:15,2011,2010/11,Special Service,1,1,260,260,DOG STUCK IN MUD ON LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,6057757,Victoria Park,22700893,E9,535708,183586,535750,183550,51.53487601,-0.044756699
34099111,09/03/2011 15:06,2011,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Cat,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000131,Cantelowes,E09000007,Camden,Kentish Town,5078715,Bartholomew Road,20400384,NW5,529128,184658,529150,184650,51.54605285,-0.139179131
34100111,09/03/2011 15:11,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH SEAGULL TRAPPED UP TREE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Eastmead Avenue,20600594,UB6,NULL,NULL,513650,182750,NULL,NULL
34588111,10/03/2011 14:53,2011,2010/11,Special Service,1,1,260,260,DOG STUCK ON GARAGE ROOF,Dog,Person (mobile),Private garage,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000280,Tottenham Green,E09000014,Haringey,Tottenham,1.00021E+11,Hale Gardens,21103364,N17,534191,189483,534150,189450,51.58823081,-0.064368191
34910111,11/03/2011 05:13,2011,2010/11,Special Service,1,1,260,260,CAT WITH FOOT TRAPPED IN SECURITY BARS ON WINDOW,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000128,Belsize,E09000007,Camden,West Hampstead,NULL,Quickswood,20400588,NW3,NULL,NULL,527450,184250,NULL,NULL
36312111,13/03/2011 17:47,2011,2010/11,Special Service,1,1,260,260,BIRD TRAPPED BEHIND PANEL,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000414,Tolworth and Hook Rise,E09000021,Kingston upon Thames,Surbiton,NULL,Fullers Avenue,21800424,KT6,NULL,NULL,518650,165650,NULL,NULL
36607111,14/03/2011 09:44,2011,2010/11,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000569,Wallington North,E09000029,Sutton,Wallington,5870077757,Westcroft Road,22605657,SM5,528375,164768,528350,164750,51.36747262,-0.157245672
37066111,15/03/2011 02:27,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN SCAFFOLDING,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000114,Cray Valley East,E09000006,Bromley,Orpington,NULL,Mountfield Way,NULL,BR5,NULL,NULL,547250,168150,NULL,NULL
37164111,15/03/2011 09:47,2011,2010/11,Special Service,1,1,260,260,DOG STUCK IN MUD ON CANAL BANK,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000176,Elthorne,E09000009,Ealing,Ealing,NULL,Green Lane,NULL,W7,515079,179665,515050,179650,51.50421271,-0.343320137
37368111,15/03/2011 17:37,2011,2010/11,Special Service,1,1,260,260,CAT STUCK IN GUTTER,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,NULL,Bermondsey Wall West,22500227,SE16,NULL,NULL,534150,179750,NULL,NULL
38722111,18/03/2011 12:22,2011,2010/11,Special Service,2,3,260,780,DOG STUCK IN STREAM,Dog,Person (mobile),Other outdoor location,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000060,Underhill,E09000003,Barnet,Barnet,NULL,Barnet Lane,NULL,EN5,525017,195237,525050,195250,51.64205028,-0.194667623
39081111,19/03/2011 03:27,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOOR BOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000427,Prince's,E09000022,Lambeth,Lambeth,NULL,Marylee Way,21900931,SE11,NULL,NULL,530950,178550,NULL,NULL
39473111,19/03/2011 16:42,2011,2010/11,Special Service,1,1,260,260,BIRD TRAPPED BEHING MESH,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000223,Kidbrooke with Hornfair,E09000011,Greenwich,East Greenwich,NULL,Centurion Square,20803005,SE18,NULL,NULL,542150,176950,NULL,NULL
39510111,19/03/2011 17:40,2011,2010/11,Special Service,1,2,260,520,CAT STUCK IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000189,Southall Broadway,E09000009,Ealing,Southall,NULL,Avenue Road,NULL,UB1,512819,180109,512850,180150,51.50865861,-0.375726553
39944111,20/03/2011 13:07,2011,2010/11,Special Service,1,1,260,260,SWAN TRAPPED UNDER PONTOON,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009323,Canary Wharf,E09000030,Tower Hamlets,Millwall,6061889,Westferry Road,22701325,E14,537256,179121,537250,179150,51.49437874,-0.024189882
39965111,20/03/2011 13:50,2011,2010/11,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Osborne Road,22207832,E7,NULL,NULL,540750,185250,NULL,NULL
40006111,20/03/2011 15:19,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED UNDER FRIDGE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011113,Rye Lane,E09000028,Southwark,Peckham,NULL,Nutbrook Street,22501823,SE15,NULL,NULL,534150,175750,NULL,NULL
40523111,21/03/2011 13:17,2011,2010/11,Special Service,1,1,260,260,CAT STUCK ON WINDOW LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000251,Askew,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Becklow Road,21000108,W12,NULL,NULL,522050,179850,NULL,NULL
40652111,21/03/2011 17:41,2011,2010/11,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Other FRS,Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,10009141891,Cart Lane,22822700,E4,539038,194461,539050,194450,51.6317873,0.007524848
41510111,23/03/2011 11:09,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Licensed House in Multiple Occupation - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000375,Holloway,E09000019,Islington,Holloway,NULL,Camden Road,21606413,N7,NULL,NULL,530450,185750,NULL,NULL
42011111,24/03/2011 09:54,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH THRUSH TANGLED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009335,Weavers,E09000030,Tower Hamlets,Bethnal Green,NULL,Ramsey Street,NULL,E2,534377,182370,534350,182350,51.52426698,-0.064399048
42622111,25/03/2011 09:32,2011,2010/11,Special Service,1,1,260,260,CAT STUCK ON  TELEGRAPH POLE,Cat,Person (land line),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000033,Goresbrook,E09000002,Barking and Dagenham,Dagenham,NULL,Hatfield Road,NULL,RM9,548394,184250,548350,184250,51.53765289,0.138296138
42671111,25/03/2011 12:00,2011,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED UNDER FENCE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Keedonwood Road,22005125,BR1,NULL,NULL,539750,171350,NULL,NULL
42723111,25/03/2011 14:06,2011,2010/11,Special Service,1,1,260,260,DOG STUCK UNDER DECKING,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000113,Copers Cope,E09000006,Bromley,Beckenham,NULL,The Drive,20301800,BR3,NULL,NULL,537250,169750,NULL,NULL
42739111,25/03/2011 14:43,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000348,Chiswick Homefields,E09000018,Hounslow,Chiswick,NULL,Swanscombe Road,21501102,W4,NULL,NULL,521250,178350,NULL,NULL
42770111,25/03/2011 15:30,2011,2010/11,Special Service,1,1,260,260,PUPPY  IN DISTRESS LOCKED IN CAR,Dog,Person (land line),Car,Road Vehicle,Animal rescue from height,Animal rescue from height - Domestic pet,E05000049,East Finchley,E09000003,Barnet,Finchley,NULL,Old Farm Road,NULL,N2,526754,190806,526750,190850,51.60184173,-0.171180504
43265111,26/03/2011 09:53,2011,2010/11,Special Service,NULL,NULL,260,NULL,DOG IN DISTRESS IN RIVER,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000416,Bishop's,E09000022,Lambeth,Lambeth,NULL,Albert Embankment,NULL,SE1,530485,179007,530450,179050,51.49495659,-0.12171203
44012111,27/03/2011 17:16,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000560,Cheam,E09000029,Sutton,Sutton,NULL,Cuddington Way,22602955,SM2,NULL,NULL,524050,161750,NULL,NULL
44188111,27/03/2011 22:55,2011,2010/11,Special Service,1,1,260,260,BIRDS TRAPPED IN NETTING,Bird,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000409,Norbiton,E09000021,Kingston upon Thames,Kingston,NULL,Coombe Road,NULL,KT2,519273,169547,519250,169550,51.41240758,-0.286330534
44559111,28/03/2011 18:12,2011,2010/11,Special Service,1,2,260,520,CAT STUCK IN BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009387,Woodberry Down,E09000012,Hackney,Stoke Newington,NULL,Amhurst Park,20900061,N16,NULL,NULL,533250,187950,NULL,NULL
44894111,29/03/2011 10:23,2011,2010/11,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000111,Chislehurst,E09000006,Bromley,Bromley,NULL,Empress Drive,NULL,BR7,543606,170853,543650,170850,51.41850861,0.063864411
44942111,29/03/2011 12:37,2011,2010/11,Special Service,1,1,260,260,INJURED CAT IN BASEMENT AREA,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000649,West End,E09000033,Westminster,Soho,NULL,Brown Hart Gardens,8401078,W1K,NULL,NULL,528350,181050,NULL,NULL
45073111,29/03/2011 17:05,2011,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT TRAPPED IN TREE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009396,Golborne,E09000020,Kensington and Chelsea,North Kensington,NULL,Portobello Road,21700970,W10,NULL,NULL,524150,181950,NULL,NULL
45294111,30/03/2011 00:03,2011,2010/11,Special Service,1,1,260,260,CAT FALLEN INTO WATER,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000454,Whitefoot,E09000023,Lewisham,Lewisham,NULL,Allerford Road,NULL,SE6,537946,172079,537950,172050,51.43092955,-0.017002403
45430111,30/03/2011 10:07,2011,2010/11,Special Service,1,1,260,260,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Churston Avenue,22200476,E13,NULL,NULL,540850,183850,NULL,NULL
45531111,30/03/2011 14:24,2011,2010/11,Special Service,1,1,260,260,PERSON IN PRECARIOUS POSITION,Unknown - Domestic Animal Or Pet,Police,House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000103,Welsh Harp,E09000005,Brent,Willesden,NULL,North Circular Road,20201063,NW2,NULL,NULL,521150,186150,NULL,NULL
45959111,31/03/2011 09:04,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN DOUBLE GLAZED WINDOW,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000494,West Ham,E09000025,Newham,Stratford,NULL,Park Road,22200936,E15,NULL,NULL,540050,183850,NULL,NULL
46132111,31/03/2011 16:18,2011,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000187,Perivale,E09000009,Ealing,Wembley,NULL,Alperton Lane,NULL,UB6,517298,182863,517250,182850,51.53249966,-0.310299286
46177111,31/03/2011 18:15,2011,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000258,North End,E09000013,Hammersmith and Fulham,Fulham,NULL,North End Road,NULL,W14,NULL,NULL,524750,178150,NULL,NULL
46389111,01/04/2011 03:48,2011,2011/12,Special Service,1,1,260,260,FOX TRAPPED BY CABLE,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,Whitley Road,21105234,N17,NULL,NULL,533450,190350,NULL,NULL
46433111,01/04/2011 07:20,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000619,Northcote,E09000032,Wandsworth,Clapham,NULL,Canford Road,22900786,SW11,NULL,NULL,528050,174950,NULL,NULL
46502111,01/04/2011 09:33,2011,2011/12,Special Service,1,3,260,780,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000557,Belmont,E09000029,Sutton,Sutton,5870028202,Kingswood Drive,22602417,SM2,525708,162810,525750,162850,51.35047125,-0.196228217
47328111,02/04/2011 19:32,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000462,Hillside,E09000024,Merton,Wimbledon,48001256,Alexandra Road,22100093,SW19,524793,170796,524750,170750,51.42244547,-0.206557312
47368111,02/04/2011 20:02,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN CHOKE LEAD,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Seaton Street,20704711,N18,NULL,NULL,534350,192150,NULL,NULL
47758111,03/04/2011 14:42,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX CUB TRAPPED IN DRAIN,Fox,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000176,Elthorne,E09000009,Ealing,Ealing,NULL,Green Man Lane,NULL,W13,516427,180518,516450,180550,51.51160347,-0.323625299
48824111,05/04/2011 11:40,2011,2011/12,Special Service,1,1,260,260,TO ASSIST RSPCA WITH TRAPPED GULL,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000138,Holborn and Covent Garden,E09000007,Camden,Soho,5088624,Bedford Row,20401148,WC1R,530800,181743,530850,181750,51.51947175,-0.116162632
49019111,05/04/2011 19:37,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Romford Road,22208081,E15,NULL,NULL,539750,184750,NULL,NULL
49189111,06/04/2011 02:03,2011,2011/12,Special Service,3,9,260,2340,Redacted,Horse,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,NULL,Sandpiper Close,NULL,E17,535938,191035,535950,191050,51.60175833,-0.03856555
49295111,06/04/2011 10:39,2011,2011/12,Special Service,1,1,260,260,DOG WITH FOOT TRAPPED IN METAL CRATE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000108,Bromley Common and Keston,E09000006,Bromley,Bromley,NULL,Southlands Road,20300621,BR2,NULL,NULL,541850,168250,NULL,NULL
49539111,06/04/2011 18:50,2011,2011/12,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000380,St. Peter's,E09000019,Islington,Islington,5300052653,Islington Green,21605183,N1,531642,183551,531650,183550,51.53552427,-0.103358545
50170111,07/04/2011 17:30,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN WIRE FENCE,Bird,Person (land line),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000376,Junction,E09000019,Islington,Holloway,5300030632,Elthorne Road,21600283,N19,529550,186801,529550,186850,51.56521484,-0.132307906
50249111,07/04/2011 19:59,2011,2011/12,Special Service,1,1,260,260,DOG STUCK UNDER SHED,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000607,Valley,E09000031,Waltham Forest,Chingford,1.00023E+11,Bateman Road,22813200,E4,537293,192015,537250,192050,51.61023604,-0.018629105
50623111,08/04/2011 11:30,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000421,Ferndale,E09000022,Lambeth,Brixton,1.00023E+11,Acre Lane,21900069,SW2,530447,175091,530450,175050,51.45977304,-0.123703249
51657111,09/04/2011 20:58,2011,2011/12,Special Service,1,1,260,260,CAT LOCKED IN A BEDROOM,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000416,Bishop's,E09000022,Lambeth,Lambeth,NULL,Stamford Street,21901292,SE1,NULL,NULL,531250,180350,NULL,NULL
51906111,10/04/2011 09:33,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000306,Brooklands,E09000016,Havering,Dagenham,NULL,Lilliput Road,NULL,RM7,550848,187723,550850,187750,51.5682086,0.175140577
52137111,10/04/2011 17:05,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN DRAIN INJURED,Cat,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000321,South Hornchurch,E09000016,Havering,Wennington,NULL,Rainham Road,NULL,RM13,552250,182750,552250,182750,51.52314992,0.193205084
52625111,11/04/2011 09:23,2011,2011/12,Special Service,1,2,260,520,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000376,Junction,E09000019,Islington,Kentish Town,5300092364,Tremlett Grove,21600876,N19,529130,186416,529150,186450,51.56185119,-0.138505293
52764111,11/04/2011 13:35,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000179,Hanger Hill,E09000009,Ealing,Park Royal,NULL,Abbeyfields Close,20600004,NW10,NULL,NULL,519250,183050,NULL,NULL
52959111,11/04/2011 17:39,2011,2011/12,Special Service,1,1,260,260,CAT STUCK BETWEEN BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Burgess Street,22700227,E14,NULL,NULL,537050,181750,NULL,NULL
53406111,12/04/2011 12:59,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN ALLOY WHEEL,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011245,Hainault,E09000026,Redbridge,Hainault,NULL,Huntsman Road,22304632,IG6,NULL,NULL,546650,192050,NULL,NULL
53552111,12/04/2011 17:06,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000375,Holloway,E09000019,Islington,Holloway,NULL,Cottage Road,NULL,N7,530689,185099,530650,185050,51.549657,-0.116516507
53637111,12/04/2011 18:55,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000413,Surbiton Hill,E09000021,Kingston upon Thames,Surbiton,1.00022E+11,Ditton Road,21800332,KT6,518180,166164,518150,166150,51.38223011,-0.303164177
54660111,14/04/2011 12:00,2011,2011/12,Special Service,1,1,260,260,Redacted,Bird,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000527,St. Margarets and North Twickenham,E09000027,Richmond upon Thames,Twickenham,NULL,Langhorn Drive,NULL,TW2,515250,173750,515250,173750,51.45101457,-0.342785509
55494111,15/04/2011 16:18,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX TRAPPED BETWEEN GARAGE AND WALL,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000326,Brunel,E09000017,Hillingdon,Hillingdon,NULL,Uxbridge Road,21402681,UB10,NULL,NULL,508250,182050,NULL,NULL
55505111,15/04/2011 16:38,2011,2011/12,Special Service,1,1,260,260,KITTEN FALLEN INTO WALLED GARDEN,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009393,Courtfield,E09000020,Kensington and Chelsea,Chelsea,NULL,Roland Gardens,21700466,SW7,NULL,NULL,526450,178350,NULL,NULL
56214111,16/04/2011 19:57,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER CONTAINER,Cat,Person (land line),Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000482,East Ham South,E09000025,Newham,East Ham,46053643,Newham Way,22208162,E6,542556,182109,542550,182150,51.51991765,0.053312419
56393111,17/04/2011 01:16,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN HOLE,Dog,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000440,Catford South,E09000023,Lewisham,Lewisham,NULL,Culverley Road,NULL,SE6,537836,173433,537850,173450,51.44312371,-0.018057323
56552111,17/04/2011 10:07,2011,2011/12,Special Service,1,1,260,260,PIGEON STUCK IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000604,Leyton,E09000031,Waltham Forest,Leyton,NULL,Villiers Close,22884200,E10,NULL,NULL,537350,186950,NULL,NULL
56769111,17/04/2011 17:35,2011,2011/12,Special Service,1,2,260,520,CROW TRAPPED IN NETTING,Bird,Person (land line),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000423,Herne Hill,E09000022,Lambeth,Brixton,1.00023E+11,Denmark Hill,21900453,SE5,532511,175964,532550,175950,51.46713935,-0.093684469
57137111,18/04/2011 11:18,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000419,Clapham Town,E09000022,Lambeth,Clapham,NULL,Heather Close,21900691,SW8,NULL,NULL,528650,175850,NULL,NULL
57712111,19/04/2011 09:44,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER SHED,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000086,Barnhill,E09000005,Brent,Stanmore,202137854,Tookey Close,20204049,HA3,518942,187928,518950,187950,51.5776778,-0.284897421
57771111,19/04/2011 11:59,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO PARROT IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000549,Rotherhithe,E09000028,Southwark,Dockhead,NULL,Abbeyfield Estate,NULL,SE16,535057,178877,535050,178850,51.49271549,-0.055940526
57945111,19/04/2011 17:08,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER SHED,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000086,Barnhill,E09000005,Brent,Stanmore,202137854,Tookey Close,20204049,HA3,518942,187928,518950,187950,51.5776778,-0.284897421
58088111,19/04/2011 19:59,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000343,West Ruislip,E09000017,Hillingdon,Ruislip,NULL,Reservoir Road,21401623,HA4,NULL,NULL,508450,188950,NULL,NULL
58127111,19/04/2011 20:42,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK ON LEDGE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000051,Finchley Church End,E09000003,Barnet,Finchley,NULL,Arden Road,20001200,N3,NULL,NULL,524650,189950,NULL,NULL
58182111,19/04/2011 21:42,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN  ROOF,Cat,Person (land line),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011480,Selsdon & Addington Village,E09000008,Croydon,Addington,2.00001E+11,Addington Village Road,20501645,CR0,537048,163923,537050,163950,51.35785456,-0.033058089
58211111,19/04/2011 22:29,2011,2011/12,Special Service,1,1,260,260,CAT FALLEN FROM FLATS BELIEVED INJURED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Wandsworth Bridge Road,21000856,SW6,NULL,NULL,525650,176250,NULL,NULL
58568111,20/04/2011 13:59,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000401,Berrylands,E09000021,Kingston upon Thames,Surbiton,1.00022E+11,South Bank,21800867,KT6,518385,167278,518350,167250,51.39219993,-0.299849034
58930111,20/04/2011 22:47,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN EAVES,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Park Road,21103726,N8,NULL,NULL,529450,189050,NULL,NULL
59329111,21/04/2011 16:49,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000509,Monkhams,E09000026,Redbridge,Woodford,NULL,The Pines,NULL,IG8,540048,193382,540050,193350,51.62184166,0.021677015
59873111,22/04/2011 11:59,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000455,Abbey,E09000024,Merton,Wimbledon,NULL,Granville Road,22102928,SW19,NULL,NULL,525350,170150,NULL,NULL
59927111,22/04/2011 13:23,2011,2011/12,Special Service,1,1,260,260,CAT ON TOP OF CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000487,Little Ilford,E09000025,Newham,Ilford,NULL,Fifth Avenue,22200168,E12,NULL,NULL,542550,185750,NULL,NULL
59941111,22/04/2011 13:36,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN TWO BUILDINGS,Cat,Person (land line),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000380,St. Peter's,E09000019,Islington,Islington,5300014896,Camden Passage,21604427,N1,531631,183531,531650,183550,51.5353471,-0.103524526
59967111,22/04/2011 14:12,2011,2011/12,Special Service,1,2,260,520,CAT TRAPPED UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000270,Fortis Green,E09000014,Haringey,Hornsey,1.00023E+11,Fortis Green,21106130,N10,528250,189750,528250,189750,51.59201335,-0.149977421
59992111,22/04/2011 14:50,2011,2011/12,Special Service,1,1,260,260,TO ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011477,Purley Oaks & Riddlesdown,E09000008,Croydon,Croydon,10014048741,Sanderstead Road,20502318,CR2,532803,162650,532850,162650,51.34742184,-0.094463366
59993111,22/04/2011 14:50,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000255,Fulham Reach,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Greyhound Road,21000393,W6,NULL,NULL,524150,177850,NULL,NULL
60257111,22/04/2011 18:29,2011,2011/12,Special Service,1,1,260,260,CAT LOCKED IN GARDEN IN DISTRESS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009319,Bow East,E09000030,Tower Hamlets,Bethnal Green,NULL,Garrison Road,22702435,E3,NULL,NULL,537050,183550,NULL,NULL
60766111,23/04/2011 11:03,2011,2011/12,Special Service,1,1,260,260,INJURED CAT STUCK UP TREE AT REAR,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000454,Whitefoot,E09000023,Lewisham,Lee Green,1.00022E+11,Evans Road,22001448,SE6,539454,172556,539450,172550,51.43484741,0.004863815
60959111,23/04/2011 16:09,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN GUTTERING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000113,Copers Cope,E09000006,Bromley,Beckenham,NULL,Hackington Crescent,20302253,BR3,NULL,NULL,537450,170850,NULL,NULL
61046111,23/04/2011 17:11,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009321,Bromley North,E09000030,Tower Hamlets,Poplar,NULL,Rainhill Way,NULL,E3,537682,182519,537650,182550,51.52481074,-0.016730666
61849111,24/04/2011 17:36,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011219,Bexleyheath,E09000004,Bexley,Bexley,NULL,Highland Road,20100719,DA6,NULL,NULL,549350,174850,NULL,NULL
62424111,25/04/2011 16:55,2011,2011/12,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT STUCK ON CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,Greyhound Road,21103355,N17,NULL,NULL,533550,189850,NULL,NULL
62805111,26/04/2011 07:40,2011,2011/12,Special Service,1,1,260,260,INJURED CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000219,Eltham South,E09000011,Greenwich,Eltham,NULL,Ladysmith Road,20800878,SE9,NULL,NULL,543350,174050,NULL,NULL
63138111,26/04/2011 19:33,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000401,Berrylands,E09000021,Kingston upon Thames,Surbiton,NULL,Christ Church Road,NULL,KT5,518906,166972,518950,166950,51.38934083,-0.292466624
63427111,27/04/2011 12:11,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN OVERFLOW PIPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Prince Edward Road,20900820,E9,NULL,NULL,536950,184650,NULL,NULL
64026111,28/04/2011 11:41,2011,2011/12,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Wembley,NULL,Mead Plat,20200474,NW10,NULL,NULL,520450,184850,NULL,NULL
64269111,28/04/2011 19:05,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN WALL,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000371,Finsbury Park,E09000019,Islington,Holloway,10001295364,Seven Sisters Road,21603790,N4,531220,186568,531250,186550,51.56273502,-0.108315098
64398111,28/04/2011 22:59,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009333,Spitalfields & Banglatown,E09000030,Tower Hamlets,Shoreditch,NULL,Commercial Street,22700347,E1,NULL,NULL,533550,182050,NULL,NULL
64656111,29/04/2011 14:46,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA ON SCENE WITH CAT ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Margery Park Road,22207803,E7,NULL,NULL,540150,184550,NULL,NULL
64762111,29/04/2011 18:07,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000380,St. Peter's,E09000019,Islington,Islington,NULL,Duncan Terrace,NULL,N1,531560,183141,531550,183150,51.53185885,-0.104693263
64851111,29/04/2011 21:06,2011,2011/12,Special Service,1,3,260,780,PUPPIES POSSIBLY TRAPPED UNDER GRILL,Dog,Person (mobile),Bridge,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000544,Newington,E09000028,Southwark,Old Kent Road,NULL,Pelier Street,NULL,SE17,532281,177812,532250,177850,51.48380056,-0.096302632
65017111,30/04/2011 08:59,2011,2011/12,Special Service,1,1,260,260,DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000156,Kenley,E09000008,Croydon,Purley,NULL,Bourne View,NULL,CR8,532944,159791,532950,159750,51.32169552,-0.09350582
65060111,30/04/2011 11:33,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN UNDERSTAIRS CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000061,West Finchley,E09000003,Barnet,Finchley,NULL,Courthouse Gardens,20010540,N3,NULL,NULL,525450,191750,NULL,NULL
65215111,30/04/2011 16:01,2011,2011/12,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000571,Wandle Valley,E09000029,Sutton,Mitcham,5870068833,Thornton Road,22601078,SM5,527243,166532,527250,166550,51.38358024,-0.172866954
65291111,30/04/2011 18:30,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND WALL,Cat,Person (mobile),Factory,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,207052032,Leeds Street,20704430,N18,534147,192288,534150,192250,51.6134475,-0.063931317
65718111,01/05/2011 11:00,2011,2011/12,Special Service,1,1,260,260,FOX TRAPPED IN SHED,Fox,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000042,Whalebone,E09000002,Barking and Dagenham,Dagenham,100033146,Winifred Road,19900390,RM8,548459,187143,548450,187150,51.56363005,0.140451933
65804111,01/05/2011 12:37,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000275,Noel Park,E09000014,Haringey,Hornsey,1.00023E+11,High Road,21104652,N22,531183,190044,531150,190050,51.59398057,-0.107552187
65902111,01/05/2011 15:36,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009394,Dalgarno,E09000020,Kensington and Chelsea,North Kensington,217059344,Notting Barn Road,21700956,W10,523657,182044,523650,182050,51.52378421,-0.218954737
65916111,01/05/2011 16:11,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO DOG TRAPPED IN KENNEL,Dog,Person (land line),Recycling plant,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Rainham Road North,NULL,RM10,549561,186667,549550,186650,51.55906258,0.156136425
66736111,02/05/2011 17:15,2011,2011/12,Special Service,1,1,260,260,PUPPY ON ROOF,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000250,Addison,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Cromwell Grove,21000251,W6,NULL,NULL,523350,179450,NULL,NULL
66969111,02/05/2011 22:21,2011,2011/12,Special Service,1,4,260,1040,BABY DEER STUCK BETWEEN TWO HOUSES,Deer,Police,Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000200,Grange,E09000010,Enfield,Edmonton,NULL,Old Park Ridings,NULL,N21,531992,195259,531950,195250,51.64065537,-0.093916484
67459111,03/05/2011 19:21,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED IN  WHEEL,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,Elmhurst Road,NULL,N17,533534,190323,533550,190350,51.59593531,-0.073526734
67535111,03/05/2011 21:08,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000145,West Hampstead,E09000007,Camden,West Hampstead,5112216,Inglewood Road,20400261,NW6,525442,185080,525450,185050,51.55067588,-0.19215735
67885111,04/05/2011 13:40,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN UP A TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000460,Figge's Marsh,E09000024,Merton,Mitcham,NULL,Acacia Road,NULL,CR4,528638,169240,528650,169250,51.40760298,-0.151850703
68468111,05/05/2011 12:57,2011,2011/12,Special Service,1,1,260,260,LARGE BIRD TRAPPED IN BUSHES,Bird,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000046,Colindale,E09000003,Barnet,Hendon,NULL,Colindale Avenue,NULL,NW9,521422,189813,521450,189850,51.59409144,-0.248472738
68478111,05/05/2011 13:17,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000254,Fulham Broadway,E09000013,Hammersmith and Fulham,Fulham,NULL,Seagrave Road,21000720,SW6,NULL,NULL,525350,177750,NULL,NULL
68681111,05/05/2011 18:00,2011,2011/12,Special Service,1,4,260,1040,CAT STUCK IN THE CHIMNEY,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Grosvenor Road,NULL,E7,540790,184627,540750,184650,51.54298697,0.028881817
68685111,05/05/2011 18:02,2011,2011/12,Special Service,1,1,260,260,BIRD ENTWINED IN WIRE STUCK IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Palace Road,NULL,N8,529989,188810,529950,188850,51.58316788,-0.125235525
69093111,06/05/2011 14:02,2011,2011/12,Special Service,1,1,260,260,FOX CUB WITH HEAD STUCK IN RAILINGS,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05009379,King's Park,E09000012,Hackney,Homerton,NULL,Roding Road,NULL,E5,536039,185389,536050,185350,51.55099867,-0.039291041
69292111,06/05/2011 19:01,2011,2011/12,Special Service,1,2,260,520,CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000356,Heston East,E09000018,Hounslow,Southall,1.00022E+11,Jessop Avenue,21501548,UB2,512676,178630,512650,178650,51.49539387,-0.37825823
69424111,06/05/2011 21:33,2011,2011/12,Special Service,1,1,260,260,CROW TRAPPED IN TV AERIAL,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000228,Thamesmead Moorings,E09000011,Greenwich,Plumstead,NULL,Owen Close,20801126,SE28,NULL,NULL,547150,180350,NULL,NULL
70038111,07/05/2011 19:09,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Devonshire Drive,NULL,SE10,NULL,NULL,537950,176950,NULL,NULL
70115111,07/05/2011 21:25,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED ON LEDGE ON TOP OF SHOP,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,1.00023E+11,Lewisham High Street,22004177,SE13,538331,175439,538350,175450,51.46102959,-0.010155936
70895111,09/05/2011 06:30,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN HOLE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011230,Sidcup,E09000004,Bexley,Sidcup,NULL,Mallard Walk,20100909,DA14,NULL,NULL,547250,170950,NULL,NULL
70941111,09/05/2011 08:41,2011,2011/12,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000379,St. Mary's,E09000019,Islington,Islington,NULL,Moon Street,21605461,N1,NULL,NULL,531550,183850,NULL,NULL
71173111,09/05/2011 16:43,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO DOG STUCK IN CHAIN,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000317,Pettits,E09000016,Havering,Romford,NULL,Beauly Way,21300998,RM1,NULL,NULL,551450,190650,NULL,NULL
71417111,09/05/2011 23:17,2011,2011/12,Special Service,1,1,260,260,CAT STUCK BEHIND FENCE POSSIBLY INJURED,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000446,Ladywell,E09000023,Lewisham,Lewisham,1.00022E+11,Kneller Road,22000588,SE4,536379,175376,536350,175350,51.46093671,-0.038260516
71740111,10/05/2011 15:23,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN THICK UNDERGROWTH,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000360,Hounslow South,E09000018,Hounslow,Heston,1.00022E+11,Catherine Gardens,21500215,TW3,514690,175113,514650,175150,51.46337855,-0.350399499
72131111,11/05/2011 08:06,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK IN WELL,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000477,Canning Town North,E09000025,Newham,Plaistow,NULL,Barking Road,22208079,E13,NULL,NULL,540250,182250,NULL,NULL
72217111,11/05/2011 11:35,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011473,Norbury Park,E09000008,Croydon,Norbury,NULL,Virginia Road,20500145,CR7,NULL,NULL,531650,169850,NULL,NULL
72277111,11/05/2011 13:13,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO DOG IN PRECARIOUS POSITION,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,NULL,Globe Road,22700551,E2,NULL,NULL,535250,182850,NULL,NULL
72500111,11/05/2011 20:54,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011111,Peckham Rye,E09000028,Southwark,New Cross,NULL,Rye Road,22502174,SE15,NULL,NULL,535550,175050,NULL,NULL
72544111,11/05/2011 22:37,2011,2011/12,Special Service,1,1,260,260,INJURED KITTEN STUCK IN NEIGHBOURING GARDEN,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000433,Thornton,E09000022,Lambeth,Tooting,NULL,Atkins Road,21900121,SW12,NULL,NULL,529150,173750,NULL,NULL
72730111,12/05/2011 09:56,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,Kings Road,NULL,E4,538614,194204,538650,194250,51.62958242,0.001301014
73174111,12/05/2011 21:55,2011,2011/12,Special Service,1,2,260,520,KITTEN STUCK INSIDE CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05011233,West Heath,E09000004,Bexley,Bexley,1.0002E+11,Shakespeare Road,20101275,DA7,548277,176796,548250,176750,51.47070718,0.133479428
73453111,13/05/2011 12:45,2011,2011/12,Special Service,1,1,260,260,CAT FALLEN INTO UPPER BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,NULL,Reedham Close,21103792,N17,NULL,NULL,534650,189350,NULL,NULL
73455111,13/05/2011 12:46,2011,2011/12,Special Service,1,1,260,260,SQUIRREL STUCK IN TOILET,Squirrel,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,NULL,Vicarage Road,21105184,N17,NULL,NULL,534150,190850,NULL,NULL
73655111,13/05/2011 18:50,2011,2011/12,Special Service,1,1,260,260,CAT STUCK ON LEDGE BY CANAL,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000634,Church Street,E09000033,Westminster,Paddington,NULL,Lisson Grove,NULL,NW8,527032,182294,527050,182250,51.52528269,-0.170242371
73926111,14/05/2011 01:18,2011,2011/12,Special Service,1,1,260,260,DOG BELEIVED TRAPPED,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000055,Hendon,E09000003,Barnet,Hendon,NULL,Sunningfields Road,NULL,NW4,523031,189617,523050,189650,51.59198151,-0.225324552
74114111,14/05/2011 11:14,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRDS,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000649,West End,E09000033,Westminster,Soho,1.00023E+11,Lower John Street,8400181,W1F,529375,180875,529350,180850,51.51199896,-0.137008
74146111,14/05/2011 12:27,2011,2011/12,Special Service,1,1,260,260,ANIMAL TRAPPED BEHIND CHIMNEY,Unknown - Heavy Livestock Animal,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Rolls Road,22502100,SE1,NULL,NULL,533850,178350,NULL,NULL
74421111,14/05/2011 19:57,2011,2011/12,Special Service,1,1,260,260,PIGEON WITH WING TRAPPED IN GUTTERING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000464,Longthornton,E09000024,Merton,Norbury,NULL,Hemlock Close,22103260,SW16,NULL,NULL,529550,169450,NULL,NULL
74594111,14/05/2011 23:29,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BY WINDOW LEDGE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000229,Woolwich Common,E09000011,Greenwich,Plumstead,NULL,Sandy Hill Road,20801323,SE18,NULL,NULL,543750,178350,NULL,NULL
74766111,15/05/2011 09:50,2011,2011/12,Special Service,1,1,260,260,HERON WITH SUSPECTED BROKEN LEG,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000347,Brentford,E09000018,Hounslow,Ealing,1.00022E+11,Clitherow Road,21500285,TW8,517052,178306,517050,178350,51.49159367,-0.315356649
74851111,15/05/2011 11:41,2011,2011/12,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,NULL,Warley Street,22701296,E2,NULL,NULL,535650,182750,NULL,NULL
74994111,15/05/2011 14:30,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011108,Nunhead & Queen's Road,E09000028,Southwark,New Cross,NULL,Montpelier Road,22501739,SE15,NULL,NULL,534850,176850,NULL,NULL
75005111,15/05/2011 14:45,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED IN BUS ENGINE,Cat,Person (land line),Bus/coach,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Cambridge Heath Road,NULL,E2,534871,183325,534850,183350,51.53273102,-0.056917017
75211111,15/05/2011 19:58,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000423,Herne Hill,E09000022,Lambeth,Brixton,NULL,Lilford Road,NULL,SE5,532250,176250,532250,176250,51.46977058,-0.097332809
76046111,17/05/2011 09:16,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,The Ridgeway,NULL,E4,538397,194313,538350,194350,51.63061519,-0.001789062
76055111,17/05/2011 09:45,2011,2011/12,Special Service,1,2,260,520,CAT STUCK BETWEEN TWO WALLS,Cat,Other FRS,Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000404,Chessington North and Hook,E09000021,Kingston upon Thames,Surbiton,1.00022E+11,Somerset Avenue,21800861,KT9,517853,164792,517850,164750,51.36996654,-0.308314911
76111111,17/05/2011 12:22,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER DRAWER AND INJURED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000043,Brunswick Park,E09000003,Barnet,Southgate,NULL,Crown Lane,20701467,N14,NULL,NULL,529350,194350,NULL,NULL
76214111,17/05/2011 16:45,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN CAR SEAT,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000104,Wembley Central,E09000005,Brent,Wembley,202089574,Harrow Road,20202039,HA0,517683,185013,517650,185050,51.55174311,-0.30403294
76409111,17/05/2011 22:12,2011,2011/12,Special Service,NULL,NULL,260,NULL,RUNNING CALL TO ANMINAL TRAPPED IN FENCE,Unknown - Heavy Livestock Animal,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000351,Feltham North,E09000018,Hounslow,Feltham,1.00023E+11,Faggs Road,21500428,TW14,510292,174713,510250,174750,51.46065666,-0.41380766
77553111,19/05/2011 22:03,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN BEDROOM,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Thane Villas,21603913,N7,NULL,NULL,530950,186350,NULL,NULL
78364111,21/05/2011 12:17,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA  WITH CAT TRAPPED ON CANAL  BANK,Cat,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000643,Regent's Park,E09000033,Westminster,Paddington,1.00023E+11,Lisson Grove,8400993,NW8,526935,182426,526950,182450,51.52649077,-0.171592307
78888111,21/05/2011 23:20,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN GATE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000123,Penge and Cator,E09000006,Bromley,Beckenham,NULL,Ivychurch Close,NULL,SE20,535016,170203,535050,170250,51.41477577,-0.059838506
79282111,22/05/2011 15:53,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000608,William Morris,E09000031,Waltham Forest,Walthamstow,1.00023E+11,Brookdale Road,22819650,E17,537080,189590,537050,189550,51.58849742,-0.022649597
79490111,22/05/2011 20:46,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED ON BALCONY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000545,Nunhead,E09000028,Southwark,New Cross,NULL,Queens Road,NULL,SE15,NULL,NULL,534950,176750,NULL,NULL
79755111,23/05/2011 10:47,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000611,Bedford,E09000032,Wandsworth,Tooting,NULL,Hillbury Road,NULL,SW17,528791,172274,528750,172250,51.43483543,-0.148550636
80721111,24/05/2011 18:13,2011,2011/12,Special Service,1,2,260,520,CAT TRAPPED ON LEDGE OVER STREAM,Cat,Person (land line),River/canal,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000362,Isleworth,E09000018,Hounslow,Heston,NULL,Braddock Close,NULL,TW7,515897,176005,515850,176050,51.47115008,-0.332740075
81098111,25/05/2011 09:30,2011,2011/12,Special Service,1,2,260,520,DEER STUCK IN RAILINGS,Deer,Police,Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000152,Croham,E09000008,Croydon,Croydon,NULL,Wisborough Road,NULL,CR2,533887,162732,533850,162750,51.34790456,-0.078876678
81574111,25/05/2011 19:30,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALLS IN GARDEN,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000301,Roxbourne,E09000015,Harrow,Northolt,1.00021E+11,Rosebery Avenue,21201953,HA2,512549,185886,512550,185850,51.56063549,-0.377770694
81750111,25/05/2011 23:59,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN LIFT SHAFT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011110,Peckham,E09000028,Southwark,Peckham,NULL,Blakes Road,22500261,SE15,NULL,NULL,533450,177150,NULL,NULL
81954111,26/05/2011 12:19,2011,2011/12,Special Service,1,3,260,780,HORSE STUCK IN A DITCH,Horse,Person (mobile),Other outdoor location,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Bury Road,NULL,E4,539376,194813,539350,194850,51.63486678,0.012545269
82278111,26/05/2011 21:30,2011,2011/12,Special Service,1,1,260,260,CAT IN PRECARIOUS POSITION,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000343,West Ruislip,E09000017,Hillingdon,Ruislip,1.00021E+11,Standale Grove,21401849,HA4,508448,188637,508450,188650,51.58616486,-0.436058375
82423111,27/05/2011 06:50,2011,2011/12,Special Service,3,9,260,2340,Redacted,Horse,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,NULL,Sandpiper Close,NULL,E17,535940,191003,535950,191050,51.6014703,-0.03854908
82679111,27/05/2011 16:28,2011,2011/12,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000188,South Acton,E09000009,Ealing,Acton,NULL,Bollo Bridge Road,20600203,W3,NULL,NULL,520050,179550,NULL,NULL
83122111,28/05/2011 10:50,2011,2011/12,Special Service,1,1,260,260,CAT FALLEN TO LOWER LEVEL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009393,Courtfield,E09000020,Kensington and Chelsea,Chelsea,NULL,Cranley Place,21700129,SW7,NULL,NULL,526650,178650,NULL,NULL
83207111,28/05/2011 13:39,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009369,Clissold,E09000012,Hackney,Stoke Newington,NULL,Allen Road,NULL,N16,533042,185793,533050,185750,51.55534351,-0.082338372
83583111,28/05/2011 23:33,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED IN REAR OF FRIDGE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000479,Custom House,E09000025,Newham,Plaistow,NULL,St. Michaels Close,22207901,E16,NULL,NULL,541650,181550,NULL,NULL
83739111,29/05/2011 10:24,2011,2011/12,Special Service,1,1,260,260,CAT IN PRECARIOUS POSITION ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Kildare Walk,22702109,E14,NULL,NULL,536950,181150,NULL,NULL
83841111,29/05/2011 14:28,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Indoor Market,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000476,Boleyn,E09000025,Newham,Plaistow,10009002891,Queens Market,22208088,E13,541172,183689,541150,183650,51.53446279,0.034010859
84387111,30/05/2011 13:22,2011,2011/12,Special Service,1,1,260,260,TO ASSIST THE RSCPA WITH CAT STUCK ON WINDOW LEDGE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000640,Maida Vale,E09000033,Westminster,Paddington,NULL,Randolph Avenue,8401514,W9,NULL,NULL,525850,182950,NULL,NULL
84904111,31/05/2011 08:46,2011,2011/12,Special Service,NULL,NULL,260,NULL,DUCKLINGS DOWN STORM DRAIN,Bird,Person (land line),"Tunnel, subway",Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05009318,Blackwall & Cubitt Town,E09000030,Tower Hamlets,Millwall,NULL,Friars Mead,NULL,E14,538310,179118,538350,179150,51.49409505,-0.009017979
84941111,31/05/2011 10:52,2011,2011/12,Special Service,1,1,260,260,DOG STRUGGLING IN POND,Dog,Person (mobile),Park,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000028,Becontree,E09000002,Barking and Dagenham,Dagenham,NULL,Green Lane,NULL,RM8,546985,187025,546950,187050,51.56295514,0.119152713
85459111,01/06/2011 07:56,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN VENT,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000378,St. George's,E09000019,Islington,Holloway,NULL,Parkhurst Road,21603373,N7,NULL,NULL,530350,185950,NULL,NULL
85501111,01/06/2011 09:33,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN GARDEN,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000219,Eltham South,E09000011,Greenwich,Eltham,1.00021E+11,Restons Crescent,20801259,SE9,545130,174062,545150,174050,51.44695572,0.087080691
85777111,01/06/2011 19:07,2011,2011/12,Special Service,1,1,260,260,CAT FALLEN FROM UPPER FLOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000622,St. Mary's Park,E09000032,Wandsworth,Battersea,NULL,Surrey Lane,22905114,SW11,NULL,NULL,527150,176750,NULL,NULL
86151111,02/06/2011 11:33,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH DUCKLING TRAPPED IN DRAIN,Bird,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Cambridge Gate Mews,NULL,NW1,528813,182435,528850,182450,51.52614684,-0.144532273
87221111,03/06/2011 19:19,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000451,Rushey Green,E09000023,Lewisham,Forest Hill,NULL,Blythe Vale,22004624,SE6,NULL,NULL,536850,173150,NULL,NULL
87238111,03/06/2011 19:47,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000257,Munster,E09000013,Hammersmith and Fulham,Fulham,NULL,Colehill Lane,21000227,SW6,NULL,NULL,524450,176750,NULL,NULL
87636111,04/06/2011 10:44,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE - CAT FALLEN INTO ROOF GUTTERING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Lichfield Road,22405720,TW9,NULL,NULL,519050,176750,NULL,NULL
87750111,04/06/2011 13:38,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA TO CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011100,Dulwich Village,E09000028,Southwark,West Norwood,NULL,Croxted Road,22500662,SE21,NULL,NULL,532550,173250,NULL,NULL
87997111,04/06/2011 19:15,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA FOR SWAN TRAPPED IN WIRE ON POND,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000540,East Walworth,E09000028,Southwark,Old Kent Road,NULL,Albany Road,NULL,SE5,533498,178006,533450,178050,51.48525823,-0.078712897
89162111,06/06/2011 19:30,2011,2011/12,Special Service,1,2,260,520,DOG STRANDED ON ISLAND,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000200,Grange,E09000010,Enfield,Enfield,NULL,Cecil Road,NULL,EN2,532375,196298,532350,196250,51.64990226,-0.087991832
90016111,08/06/2011 11:05,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN WINDOW,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009393,Courtfield,E09000020,Kensington and Chelsea,Chelsea,NULL,Roland Gardens,21700466,SW7,NULL,NULL,526450,178250,NULL,NULL
90649111,09/06/2011 16:54,2011,2011/12,Special Service,1,1,260,260,KITTEN UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011472,Norbury & Pollards Hill,E09000008,Croydon,Norbury,NULL,Melrose Avenue,20501194,SW16,NULL,NULL,531250,168950,NULL,NULL
90700111,09/06/2011 18:52,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN BOILER AND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000092,Kensal Green,E09000005,Brent,North Kensington,NULL,Harrow Road,20201241,NW10,NULL,NULL,522650,182950,NULL,NULL
90830111,09/06/2011 21:41,2011,2011/12,Special Service,1,1,260,260,CAT LOCKED IN CHURCH,Cat,Person (mobile),Church/Chapel,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011488,West Thornton,E09000008,Croydon,Norbury,2.00001E+11,Thornton Road,20502655,CR0,530862,167084,530850,167050,51.38771948,-0.120687671
91020111,10/06/2011 07:01,2011,2011/12,Special Service,2,4,260,1040,SWAN TRAPPED IN GATE - WATER RESCUE LEVEL TWO,Bird,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000203,Jubilee,E09000010,Enfield,Edmonton,207178165,Picketts Lock Lane,20705131,N9,536277,193753,536250,193750,51.62610041,-0.032617754
91105111,10/06/2011 11:26,2011,2011/12,Special Service,2,3,260,780,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000630,Abbey Road,E09000033,Westminster,West Hampstead,NULL,Henstridge Place,8400120,NW8,NULL,NULL,527050,183550,NULL,NULL
91317111,10/06/2011 19:06,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN CHMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000407,Coombe Vale,E09000021,Kingston upon Thames,New Malden,NULL,Traps Lane,21800979,KT3,NULL,NULL,521050,169750,NULL,NULL
91631111,11/06/2011 10:54,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011241,Cranbrook,E09000026,Redbridge,Ilford,NULL,Exeter Gardens,22302833,IG1,NULL,NULL,542650,187550,NULL,NULL
92154111,12/06/2011 07:34,2011,2011/12,Special Service,1,1,260,260,CAT WITH NECK STUCK IN GARDEN SEAT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Hollybush Street,22207756,E13,NULL,NULL,541050,183050,NULL,NULL
92664111,13/06/2011 11:38,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011108,Nunhead & Queen's Road,E09000028,Southwark,New Cross,NULL,Kirkwood Road,22501432,SE15,NULL,NULL,534850,176350,NULL,NULL
93036111,14/06/2011 00:35,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND MESH WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Goswell Road,NULL,EC1V,NULL,NULL,531950,182550,NULL,NULL
93313111,14/06/2011 14:42,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP A TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000528,South Richmond,E09000027,Richmond upon Thames,Richmond,1.00022E+11,Friars Lane,22403490,TW9,517515,174814,517550,174850,51.46011227,-0.309850557
93338111,14/06/2011 15:30,2011,2011/12,Special Service,1,2,260,520,DOG WITH HEAD STUCK IN BRIDGE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000126,Shortlands,E09000006,Bromley,Beckenham,NULL,Mays Hill Road,NULL,BR2,539604,169006,539650,169050,51.40290951,0.005623159
93365111,14/06/2011 16:16,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000474,Wimbledon Park,E09000024,Merton,Wandsworth,NULL,Revelstoke Road,22105361,SW18,NULL,NULL,525050,172650,NULL,NULL
93473111,14/06/2011 20:01,2011,2011/12,Special Service,1,1,260,260,SQUIRREL TRAPPED IN GUTTER,Squirrel,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Cadogan Terrace,20900196,E9,NULL,NULL,536650,184550,NULL,NULL
93532111,14/06/2011 22:01,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO CAT ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009321,Bromley North,E09000030,Tower Hamlets,Bethnal Green,NULL,Bow Road,22700175,E3,NULL,NULL,537350,182850,NULL,NULL
94116111,15/06/2011 23:26,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000198,Enfield Highway,E09000010,Enfield,Enfield,NULL,Exeter Road,20702225,EN3,NULL,NULL,535850,196650,NULL,NULL
94248111,16/06/2011 09:33,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO BIRD STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000188,South Acton,E09000009,Ealing,Acton,NULL,King Edwards Gardens,20600982,W3,NULL,NULL,519350,180150,NULL,NULL
94430111,16/06/2011 17:17,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000364,Syon,E09000018,Hounslow,Heston,NULL,Hornbeam Crescent,21500624,TW8,NULL,NULL,516950,177450,NULL,NULL
95088111,17/06/2011 23:15,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN CONTAINER,Cat,Person (land line),"Large refuse/rubbish container (eg skip, paladin)",Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000445,Grove Park,E09000023,Lewisham,Eltham,NULL,Mirror Path,NULL,SE9,541420,172163,541450,172150,51.43082919,0.032971641
95105111,18/06/2011 00:02,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000358,Hounslow Central,E09000018,Hounslow,Heston,NULL,Alderwick Drive,21500023,TW3,NULL,NULL,514650,175950,NULL,NULL
95113111,18/06/2011 01:03,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN DUCTING,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Camberwell Church Street,22500395,SE5,NULL,NULL,532950,176750,NULL,NULL
95210111,18/06/2011 08:45,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH DUCKLINGS TRAPPED IN DRAIN,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000569,Wallington North,E09000029,Sutton,Wallington,NULL,Butter Hill,NULL,SM6,528512,164927,528550,164950,51.36887064,-0.155221322
95350111,18/06/2011 12:16,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER SINK,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Chalton Street,20400794,NW1,NULL,NULL,529850,182850,NULL,NULL
95507111,18/06/2011 16:58,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011247,Loxford,E09000026,Redbridge,Ilford,NULL,Medway Close,22306129,IG1,NULL,NULL,544450,185150,NULL,NULL
95839111,19/06/2011 11:56,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN WALL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000180,Hobbayne,E09000009,Ealing,Ealing,NULL,Greatdown Road,20600754,W7,NULL,NULL,515650,181950,NULL,NULL
95966111,19/06/2011 16:55,2011,2011/12,Special Service,1,1,260,260,DOG IN DISTRESS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000113,Copers Cope,E09000006,Bromley,Beckenham,NULL,Overbrae,20302297,BR3,NULL,NULL,537350,170950,NULL,NULL
96063111,19/06/2011 21:13,2011,2011/12,Special Service,1,1,260,260,DOG IN POND,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000442,Downham,E09000023,Lewisham,Bromley,10023230395,Beckenham Place Park,22007038,BR3,538659,171094,538650,171050,51.42190432,-0.007137475
96161111,20/06/2011 05:02,2011,2011/12,Special Service,1,1,260,260,FERRET TRAPPED BEHIND KITCHEN UNIT,Ferret,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009389,Brompton & Hans Town,E09000020,Kensington and Chelsea,Chelsea,NULL,Hans Crescent,21700243,SW1X,NULL,NULL,527750,179450,NULL,NULL
96639111,21/06/2011 01:06,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED BEHIND WARDROBE,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000471,Trinity,E09000024,Merton,Wimbledon,NULL,Haydons Road,22103210,SW19,NULL,NULL,526050,170950,NULL,NULL
97137111,22/06/2011 00:32,2011,2011/12,Special Service,1,1,260,260,FOX TRAPPED IN HOLE,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000619,Northcote,E09000032,Wandsworth,Battersea,NULL,Webb's Road,22905994,SW11,NULL,NULL,527850,174450,NULL,NULL
97409111,22/06/2011 16:37,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING UNDER RAILWAY BRIDGE,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000338,South Ruislip,E09000017,Hillingdon,Ruislip,NULL,Station Approach,NULL,HA4,511114,185402,511150,185450,51.55656957,-0.398616075
97466111,22/06/2011 18:50,2011,2011/12,Special Service,1,1,260,260,DOG WITH PAW TRAPPED IN PAVEMENT,Dog,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000647,Warwick,E09000033,Westminster,Lambeth,NULL,Churton Street,NULL,SW1V,529330,178617,529350,178650,51.49171683,-0.138483596
97530111,22/06/2011 20:42,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Licensed House in Multiple Occupation - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009367,Brownswood,E09000012,Hackney,Holloway,NULL,Finsbury Park Road,20900408,N4,NULL,NULL,531850,186550,NULL,NULL
97547111,22/06/2011 21:26,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN TREE,Cat,Person (land line),Retirement/Old Persons Home,Other Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000314,Heaton,E09000016,Havering,Harold Hill,1.00021E+11,North Hill Drive,21300103,RM3,553670,192583,553650,192550,51.61111291,0.217950246
97930111,24/06/2011 13:23,2011,2011/12,Special Service,1,4,260,1040,CAT TRAPPED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Friary Estate,22501485,SE15,NULL,NULL,534150,177650,NULL,NULL
97932111,24/06/2011 13:24,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000381,Tollington,E09000019,Islington,Holloway,5300091439,Tollington Park,21603933,N4,530858,187085,530850,187050,51.5674652,-0.113342272
97935111,24/06/2011 13:33,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED INSIDE CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000198,Enfield Highway,E09000010,Enfield,Enfield,NULL,Orchardleigh Avenue,20702356,EN3,NULL,NULL,535050,197250,NULL,NULL
98379111,25/06/2011 07:38,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK ON SCAFFOLDING,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Coopers Road,22500590,SE1,NULL,NULL,533950,178350,NULL,NULL
98498111,25/06/2011 12:27,2011,2011/12,Special Service,1,2,260,520,DEER STUCK ON WOOD IN CANAL WATER RESCUE LEVEL ONE IMPLEMENTED,Deer,Police,River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05009326,Limehouse,E09000030,Tower Hamlets,Poplar,NULL,Newell Street,NULL,E14,536672,181060,536650,181050,51.51194475,-0.031846037
98564111,25/06/2011 14:08,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL TRAPPED IN CHIMNEY,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000298,Pinner South,E09000015,Harrow,Harrow,NULL,Ellement Close,21201857,HA5,NULL,NULL,511950,188750,NULL,NULL
98593111,25/06/2011 15:12,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Friary Estate,22501485,SE15,NULL,NULL,534150,177650,NULL,NULL
99675111,26/06/2011 23:22,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN HEATING DUCT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000417,Brixton Hill,E09000022,Lambeth,West Norwood,NULL,Redlands Way,21901149,SW2,NULL,NULL,530550,173750,NULL,NULL
100182111,27/06/2011 17:57,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000641,Marylebone High Street,E09000033,Westminster,Euston,10033583997,Marylebone High Street,8401405,W1U,528309,181734,528350,181750,51.51996161,-0.152048634
100425111,27/06/2011 23:22,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011237,Chadwell,E09000026,Redbridge,Ilford,NULL,Norwich Crescent,22306357,RM6,NULL,NULL,547050,188650,NULL,NULL
100612111,28/06/2011 10:41,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX TRAPPED IN FENCE,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000559,Carshalton South and Clockhouse,E09000029,Sutton,Purley,5870073935,Grove Lane,22602358,CR5,528795,160043,528750,160050,51.32491293,-0.15292353
101026111,28/06/2011 17:52,2011,2011/12,Special Service,1,2,260,520,ASSIST RSPCA,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000376,Junction,E09000019,Islington,Kentish Town,NULL,Bickerton Road,21600078,N19,NULL,NULL,529150,186550,NULL,NULL
101143111,28/06/2011 21:25,2011,2011/12,Special Service,1,1,260,260,PIGEON STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000316,Mawneys,E09000016,Havering,Romford,NULL,Linley Crescent,21301362,RM7,NULL,NULL,550150,189850,NULL,NULL
101370111,29/06/2011 09:20,2011,2011/12,Special Service,1,1,260,260,BIRD IN DISTRESS  HANGING FROM TREE,Bird,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000354,Hanworth Park,E09000018,Hounslow,Feltham,NULL,Victoria Road,NULL,TW13,510724,173056,510750,173050,51.44567931,-0.408109769
101467111,29/06/2011 13:09,2011,2011/12,Special Service,1,1,260,260,DOG IN DISTRESS,Dog,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000370,Clerkenwell,E09000019,Islington,Islington,NULL,Rosebery Avenue,NULL,EC1R,531182,182319,531150,182350,51.52455963,-0.110445711
101755111,29/06/2011 21:04,2011,2011/12,Special Service,3,9,260,2340,Redacted,Deer,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Thomas Road,NULL,E14,537117,181441,537150,181450,51.51526084,-0.025289165
102265111,30/06/2011 16:02,2011,2011/12,Special Service,1,1,260,260,DOG IN DISTRESS ON WINDOW LEDGE,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009398,Norland,E09000020,Kensington and Chelsea,North Kensington,NULL,Ladbroke Grove,21700912,W11,NULL,NULL,524450,181050,NULL,NULL
102981111,01/07/2011 17:54,2011,2011/12,Special Service,1,1,260,260,DOG IN RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000622,St. Mary's Park,E09000032,Wandsworth,Battersea,NULL,Bridges Court,NULL,SW11,526514,175953,526550,175950,51.46841173,-0.179976883
103355111,02/07/2011 06:00,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN GUTTER,Cat,Person (land line),Self contained Sheltered Housing,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009386,Victoria,E09000012,Hackney,Bethnal Green,NULL,Mare Street,20900658,E8,NULL,NULL,534950,184050,NULL,NULL
103407111,02/07/2011 09:13,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED BETWEEN DOOR OF LIFT,Dog,Police,Nursing/Care Home/Hospice,Other Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,2.00003E+11,Aspinden Road,22500102,SE16,534902,178797,534950,178750,51.49203356,-0.058202438
103458111,02/07/2011 11:29,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000635,Harrow Road,E09000033,Westminster,North Kensington,NULL,Ashmore Road,8400332,W9,NULL,NULL,524750,182350,NULL,NULL
104190111,03/07/2011 09:54,2011,2011/12,Special Service,1,1,260,260,TO ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011246,Ilford Town,E09000026,Redbridge,Ilford,10034922123,High Road,22306010,IG1,544374,186725,544350,186750,51.56093357,0.081388218
104229111,03/07/2011 11:46,2011,2011/12,Special Service,1,2,260,520,CAT STUCK ON ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009401,Queen's Gate,E09000020,Kensington and Chelsea,Kensington,NULL,Cornwall Gardens,21700116,SW7,NULL,NULL,525850,179150,NULL,NULL
104367111,03/07/2011 15:03,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER JETTY,Cat,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000286,Canons,E09000015,Harrow,Stanmore,NULL,Honeypot Lane,NULL,HA7,517992,190795,517950,190750,51.60364431,-0.297637445
104609111,03/07/2011 20:19,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP CHIMNEY,Cat,Police,House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009329,St. Dunstan's,E09000030,Tower Hamlets,Bethnal Green,NULL,Duckett Street,22700429,E1,NULL,NULL,536050,181850,NULL,NULL
104654111,03/07/2011 21:13,2011,2011/12,Special Service,1,1,260,260,PIGEON STUCK IN TOWER,Bird,Person (land line),Church/Chapel,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05009328,Poplar,E09000030,Tower Hamlets,Poplar,6064299,Woodstock Terrace,22701364,E14,537797,180949,537750,180950,51.51067425,-0.015687723
104926111,04/07/2011 10:22,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED IN WATER,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Fore Street,NULL,N18,534046,192490,534050,192450,51.61528679,-0.065311972
105043111,04/07/2011 14:38,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON STUCK IN NETTING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000209,Southgate Green,E09000010,Enfield,Southgate,NULL,Aldermans Hill,20703847,N13,NULL,NULL,530650,192950,NULL,NULL
105683111,05/07/2011 15:33,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN BATHROOM,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,NULL,Thicket Road,20302199,SE20,NULL,NULL,534850,170650,NULL,NULL
105919111,05/07/2011 21:51,2011,2011/12,Special Service,1,2,260,520,PIGEON STUCK IN NETTING UNDER BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000644,St. James's,E09000033,Westminster,Soho,10033534302,Embankment Place,8401314,WC2N,530406,180379,530450,180350,51.50730472,-0.122342442
106042111,06/07/2011 08:02,2011,2011/12,Special Service,1,2,260,520,PREGNANT MARE FALLEN INTO DITCH,Horse,Person (land line),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000495,Aldborough,E09000026,Redbridge,Ilford,NULL,Billet Road,NULL,RM6,546750,189250,546750,189250,51.58300851,0.116693197
106053111,06/07/2011 08:37,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER DOOR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011473,Norbury Park,E09000008,Croydon,Norbury,NULL,Green Lane,20503000,CR7,NULL,NULL,531850,169350,NULL,NULL
106060111,06/07/2011 09:14,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Underground car park,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000638,Lancaster Gate,E09000033,Westminster,Paddington,10033542018,Queensway,8401512,W2,525913,180671,525950,180650,51.51094693,-0.186943574
106220111,06/07/2011 15:19,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000417,Brixton Hill,E09000022,Lambeth,Brixton,NULL,St. Saviour's Road,21901287,SW2,NULL,NULL,530650,174550,NULL,NULL
106256111,06/07/2011 16:20,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED INCHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009387,Woodberry Down,E09000012,Hackney,Stoke Newington,NULL,Woodberry Down Estate,20950238,N4,NULL,NULL,532750,187750,NULL,NULL
106411111,06/07/2011 21:19,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN RAILINGS,Dog,Person (mobile),Warehouse,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,Brantwood Road,NULL,N17,533965,191661,533950,191650,51.60785644,-0.066797592
106492111,07/07/2011 00:30,2011,2011/12,Special Service,1,2,260,520,CAT STUCK IN BASEMENT AREA,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009405,Stanley,E09000020,Kensington and Chelsea,Chelsea,NULL,Bury Walk,21700053,SW3,NULL,NULL,527150,178450,NULL,NULL
106606111,07/07/2011 08:36,2011,2011/12,Special Service,1,1,260,260,DOG IN CANAL,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000176,Elthorne,E09000009,Ealing,Ealing,NULL,Green Lane,NULL,W7,515079,179665,515050,179650,51.50421271,-0.343320137
106696111,07/07/2011 11:57,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000043,Brunswick Park,E09000003,Barnet,Southgate,NULL,Knoll Drive,20025520,N14,NULL,NULL,528550,194550,NULL,NULL
106920111,07/07/2011 19:59,2011,2011/12,Special Service,1,1,260,260,BIRD STUCK IN  TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011466,Coulsdon Town,E09000008,Croydon,Purley,1.00021E+11,Chipstead Valley Road,20502600,CR5,529208,159465,529250,159450,51.31962485,-0.147208741
107150111,08/07/2011 10:40,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Licensed House in Multiple Occupation - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Toucan Close,20204163,NW10,NULL,NULL,519350,182950,NULL,NULL
107355111,08/07/2011 17:58,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011116,South Bermondsey,E09000028,Southwark,Old Kent Road,NULL,Mason Close,22502807,SE16,NULL,NULL,534450,178450,NULL,NULL
107462111,08/07/2011 21:31,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000606,Markhouse,E09000031,Waltham Forest,Walthamstow,10091777532,Markhouse Road,22856450,E17,536592,188195,536550,188150,51.57608048,-0.030231159
107712111,09/07/2011 10:05,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000469,Raynes Park,E09000024,Merton,New Malden,NULL,Cottenham Park Road,22101632,SW20,NULL,NULL,522750,169950,NULL,NULL
108094111,09/07/2011 21:39,2011,2011/12,Special Service,1,1,260,260,CAT FALLEN INTO BASEMENT,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000260,Parsons Green and Walham,E09000013,Hammersmith and Fulham,Fulham,NULL,Coniger Road,21000233,SW6,NULL,NULL,525250,176250,NULL,NULL
108228111,10/07/2011 02:13,2011,2011/12,Special Service,1,1,260,260,FOX TRAPPED IN BASEMENT,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009405,Stanley,E09000020,Kensington and Chelsea,Chelsea,NULL,Elm Park Gardens,21700188,SW10,NULL,NULL,526650,177950,NULL,NULL
108316111,10/07/2011 08:52,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN WIRE AT ROOF LEVEL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Ruislip Road East,NULL,UB6,NULL,NULL,514450,182250,NULL,NULL
108373111,10/07/2011 11:33,2011,2011/12,Special Service,1,1,260,260,HORSE TRAPPED ON FENCE,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000154,Fieldway,E09000008,Croydon,Addington,NULL,Lodge Lane,NULL,CR0,537814,162997,537850,162950,51.34934848,-0.022421741
108460111,10/07/2011 13:56,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN HOLE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000451,Rushey Green,E09000023,Lewisham,Lewisham,NULL,Thomas Lane,22001968,SE6,NULL,NULL,537550,173850,NULL,NULL
108538111,10/07/2011 16:38,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000644,St. James's,E09000033,Westminster,Lambeth,NULL,Old Pye Street,8400146,SW1P,NULL,NULL,529750,179250,NULL,NULL
108683111,10/07/2011 20:11,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO BIRD IN NETTING,Bird,Person (land line),Swimming Pool,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,NULL,Lewisham High Street,NULL,SE13,538250,175750,538250,175750,51.46384407,-0.01119965
108685111,10/07/2011 20:12,2011,2011/12,Special Service,2,3,260,780,HORSE IN CANAL,Horse,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from water,Animal rescue from water - Farm animal,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Harbet Road,NULL,E4,536209,191844,536250,191850,51.60896258,-0.034340972
108739111,10/07/2011 22:16,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH  INJURED CAT FALLEN FROM ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000449,New Cross,E09000023,Lewisham,Deptford,NULL,Deptford High Street,22000328,SE8,NULL,NULL,537150,177350,NULL,NULL
108951111,11/07/2011 09:09,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED INSIDE GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000608,William Morris,E09000031,Waltham Forest,Walthamstow,1.00023E+11,Pembar Avenue,22864850,E17,536040,189664,536050,189650,51.58941416,-0.037624361
109052111,11/07/2011 13:23,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN NETTING,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000188,South Acton,E09000009,Ealing,Acton,NULL,Brouncker Road,20600274,W3,NULL,NULL,520150,179650,NULL,NULL
110206111,13/07/2011 12:08,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA CAT STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Rockingham Parade,21401643,UB8,NULL,NULL,505150,183950,NULL,NULL
110219111,13/07/2011 12:55,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED FOX,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000485,Green Street East,E09000025,Newham,East Ham,NULL,Marlborough Road,22207804,E7,NULL,NULL,541150,184450,NULL,NULL
110278111,13/07/2011 15:13,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000320,St. Andrew's,E09000016,Havering,Hornchurch,1.00023E+11,Diban Avenue,21300320,RM12,552640,185847,552650,185850,51.55087105,0.200162648
111131111,14/07/2011 22:22,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND SHED,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000044,Burnt Oak,E09000003,Barnet,Mill Hill,NULL,Barnfield Road,NULL,HA8,520267,190613,520250,190650,51.60152828,-0.264865466
111448111,15/07/2011 11:51,2011,2011/12,Special Service,2,3,260,780,HORSE STUCK IN DITCH,Horse,Person (land line),Other outdoor location,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000607,Valley,E09000031,Waltham Forest,Walthamstow,NULL,Harbet Road,NULL,E4,536288,191773,536250,191750,51.60830546,-0.033228363
111558111,15/07/2011 14:33,2011,2011/12,Special Service,1,1,260,260,INJURED FOX FALLEN THROUGH ROOF OF BUILDING,Fox,Person (land line),Hospital,Non Residential,Animal rescue from below ground,Wild animal rescue from below ground,E05009336,Whitechapel,E09000030,Tower Hamlets,Whitechapel,6014098,Stepney Way,22701162,E1,534604,181593,534650,181550,51.51723055,-0.061425634
111840111,15/07/2011 20:58,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING ON BRIDGE,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,Kilburn High Road,NULL,NW6,524588,184643,524550,184650,51.54693752,-0.204622646
111842111,15/07/2011 21:02,2011,2011/12,Special Service,1,1,260,260,FOX TRAPPED BETWEEN WALLS,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000278,Seven Sisters,E09000014,Haringey,Tottenham,NULL,Hermitage Road,21103408,N4,NULL,NULL,532450,188150,NULL,NULL
112187111,16/07/2011 12:29,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN STUCK UP A TREE,Cat,Other FRS,Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000323,Upminster,E09000016,Havering,Hornchurch,1.00021E+11,Howard Road,21301828,RM14,556246,186725,556250,186750,51.55777322,0.252519046
112460111,16/07/2011 20:02,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN PIPE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011109,Old Kent Road,E09000028,Southwark,Peckham,NULL,Lindley Estate,22501931,SE15,NULL,NULL,534250,177250,NULL,NULL
112480111,16/07/2011 20:29,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND BATH,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011109,Old Kent Road,E09000028,Southwark,Peckham,NULL,Lindley Estate,22501931,SE15,NULL,NULL,534250,177250,NULL,NULL
112710111,17/07/2011 09:44,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000608,William Morris,E09000031,Waltham Forest,Walthamstow,NULL,Fleeming Road,22836450,E17,NULL,NULL,536950,190350,NULL,NULL
113013111,17/07/2011 20:39,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO BIRD TRAPPED IN TREE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000273,Hornsey,E09000014,Haringey,Hornsey,NULL,Boyton Road,NULL,N8,NULL,NULL,530250,189650,NULL,NULL
113396111,18/07/2011 17:25,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000481,East Ham North,E09000025,Newham,East Ham,NULL,Stanley Road,22200257,E12,NULL,NULL,542250,185050,NULL,NULL
113720111,19/07/2011 11:04,2011,2011/12,Special Service,1,1,260,260,ASSIST LAS WITH CAT IN TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000515,Wanstead,E09000026,Redbridge,Leytonstone,NULL,Blake Hall Crescent,NULL,E11,540485,187190,540450,187150,51.56609358,0.025509385
113916111,19/07/2011 17:26,2011,2011/12,Special Service,1,1,260,260,TWO KITTENS TRAPPED IN WASHROOM,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000433,Thornton,E09000022,Lambeth,Tooting,NULL,Poynders Gardens,21902132,SW4,NULL,NULL,529350,173950,NULL,NULL
113956111,19/07/2011 18:47,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND WALL IN HOUSE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Friary Estate,22501485,SE15,NULL,NULL,534150,177650,NULL,NULL
114234111,20/07/2011 08:18,2011,2011/12,Special Service,1,1,260,260,SQUIRELL TRAPPED BEHIND RADIATOR,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,NULL,Granleigh Road,22840650,E11,NULL,NULL,539150,186750,NULL,NULL
114525111,20/07/2011 18:40,2011,2011/12,Special Service,1,2,260,520,ASSIST RSPCA WITH BIRD OF PREY STUCK IN TREE,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000322,Squirrel's Heath,E09000016,Havering,Harold Hill,1.00021E+11,Amery Gardens,21300946,RM2,553577,189879,553550,189850,51.58684343,0.215425195
115121111,21/07/2011 18:36,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,NULL,Brightside Road,22001270,SE13,NULL,NULL,538950,174350,NULL,NULL
115135111,21/07/2011 19:04,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED  IN BUSHES,Cat,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009399,Notting Dale,E09000020,Kensington and Chelsea,North Kensington,217079792,St. Anns Road,21701005,W11,523820,180690,523850,180650,51.51157976,-0.217081948
115364111,22/07/2011 08:55,2011,2011/12,Special Service,1,1,260,260,Redacted,Bird,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from water,Animal rescue from water - Bird,E05000506,Hainault,E09000026,Redbridge,Hainault,NULL,Peregrine Road,NULL,IG6,546861,192100,546850,192150,51.60858828,0.119486693
115409111,22/07/2011 10:18,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED UNDER FENCE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Cowley Road,NULL,UB8,505135,183645,505150,183650,51.54192261,-0.485350818
115622111,22/07/2011 17:52,2011,2011/12,Special Service,1,1,260,260,Redacted,Unknown - Domestic Animal Or Pet,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000192,Walpole,E09000009,Ealing,Ealing,12003057,Sherwood Close,20601548,W13,516614,180231,516650,180250,51.50898548,-0.321026675
115829111,23/07/2011 00:46,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN HOLE IN REAR GARDEN,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009329,St. Dunstan's,E09000030,Tower Hamlets,Bethnal Green,NULL,White Horse Lane,22701335,E1,NULL,NULL,535850,181850,NULL,NULL
116051111,23/07/2011 13:01,2011,2011/12,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Other indoor sporting venue,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000430,Streatham Hill,E09000022,Lambeth,West Norwood,NULL,Blairderry Road,NULL,SW2,530223,172839,530250,172850,51.43958602,-0.127753622
116078111,23/07/2011 13:48,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED UNDER GUTTERING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Locarno Road,20601074,UB6,NULL,NULL,514550,182450,NULL,NULL
116826111,24/07/2011 19:39,2011,2011/12,Special Service,1,1,260,260,FERRETT TRAPPED BETWEEN TWO BRICK WALLS,Ferret,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05011487,Waddon,E09000008,Croydon,Croydon,NULL,Warrington Road,20501539,CR0,NULL,NULL,531650,165150,NULL,NULL
116835111,24/07/2011 19:49,2011,2011/12,Special Service,1,1,260,260,HORSE IN WATERWAY,Horse,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Advent Way,NULL,N18,535843,192412,535850,192450,51.61415513,-0.03940338
117319111,25/07/2011 16:35,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN DUCTING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009371,De Beauvoir,E09000012,Hackney,Shoreditch,NULL,St. Peter's Way,20900960,N1,NULL,NULL,533450,184250,NULL,NULL
117331111,25/07/2011 16:54,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),"Takeaway, fast food",Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,NULL,Clarence Street,NULL,KT1,517750,169250,517750,169250,51.41005638,-0.308318681
118503111,27/07/2011 14:28,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND FIREPLACE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000298,Pinner South,E09000015,Harrow,Harrow,NULL,West End Avenue,21201906,HA5,NULL,NULL,511950,189250,NULL,NULL
118813111,27/07/2011 23:09,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000438,Blackheath,E09000023,Lewisham,Greenwich,NULL,Eliot Park,22005585,SE13,NULL,NULL,538450,175950,NULL,NULL
118971111,28/07/2011 07:29,2011,2011/12,Special Service,1,1,260,260,Redacted,Dog,Police,Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000203,Jubilee,E09000010,Enfield,Edmonton,207097515,Lingfield Gardens,20704440,N9,534845,194856,534850,194850,51.63635749,-0.052868051
119421111,28/07/2011 20:26,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN GUTTERING OF HOUSE,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000252,Avonmore and Brook Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Edith Road,21000296,W14,NULL,NULL,524350,178550,NULL,NULL
119475111,28/07/2011 21:32,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEADTRAPPED IN RAILINGS,Dog,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Eltham,NULL,Middle Park Avenue,NULL,SE9,542034,173616,542050,173650,51.44373246,0.042380415
120356111,30/07/2011 09:45,2011,2011/12,Special Service,1,2,260,520,DOG TRAPPED UNDER CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000049,East Finchley,E09000003,Barnet,Finchley,NULL,Brackenbury Road,NULL,N2,526274,189786,526250,189750,51.59278292,-0.178474307
121066111,31/07/2011 11:12,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Stacey Street,NULL,N7,531132,186120,531150,186150,51.55872952,-0.109750714
121425111,31/07/2011 21:33,2011,2011/12,Special Service,1,1,260,260,INJURED CAT IN PRECARIOUS POSITION,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000376,Junction,E09000019,Islington,Kentish Town,NULL,Holloway Road,21606449,N19,NULL,NULL,529550,186650,NULL,NULL
122415111,02/08/2011 10:51,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP A TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000275,Noel Park,E09000014,Haringey,Hornsey,1.00021E+11,Alexandra Road,21104163,N8,531155,189727,531150,189750,51.59113839,-0.108074469
122826111,02/08/2011 22:10,2011,2011/12,Special Service,NULL,NULL,260,NULL,FOX WITH HEAD STUCK IN WIRE FENCING,Fox,Person (land line),Heathland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Chingford,1.00023E+11,Long Leys,22853650,E4,537923,191703,537950,191750,51.60727858,-0.009659447
123097111,03/08/2011 10:42,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRD,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000202,Highlands,E09000010,Enfield,Enfield,NULL,Chasewood Avenue,20702592,EN2,NULL,NULL,531650,197550,NULL,NULL
123936111,04/08/2011 13:52,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011480,Selsdon & Addington Village,E09000008,Croydon,Addington,1.00021E+11,Croham Valley Road,20502623,CR2,534512,163424,534550,163450,51.35397575,-0.06964627
124103111,04/08/2011 19:29,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED ON EAVES OF HOUSE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000217,Coldharbour and New Eltham,E09000011,Greenwich,Eltham,NULL,Clayfarm Road,20800346,SE9,NULL,NULL,544250,172650,NULL,NULL
124193111,04/08/2011 22:14,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN FOOTINGS DITCH,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000352,Feltham West,E09000018,Hounslow,Feltham,1.00022E+11,Southcote Avenue,21501033,TW13,510003,172693,510050,172650,51.44255695,-0.418593064
124499111,05/08/2011 14:54,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000297,Pinner,E09000015,Harrow,Harrow,NULL,Chapel Lane,NULL,HA5,511750,189750,511750,189750,51.59552446,-0.388063255
124950111,06/08/2011 03:54,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND RAFTERS,Cat,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,10023599451,Heathway,19900172,RM10,549069,184707,549050,184750,51.54158157,0.148214917
124988111,06/08/2011 06:13,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN ROOF,Cat,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,10023599451,Heathway,19900172,RM10,549069,184707,549050,184750,51.54158157,0.148214917
125145111,06/08/2011 13:06,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Wyatt Road,22207969,E7,NULL,NULL,540350,184650,NULL,NULL
125959111,07/08/2011 11:17,2011,2011/12,Special Service,1,2,260,520,CAT STUCK UP A TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000043,Brunswick Park,E09000003,Barnet,Southgate,NULL,Cambstone Close,NULL,N11,528419,193689,528450,193650,51.62737313,-0.146097301
126149111,07/08/2011 16:29,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED BEHIND CHIMNEY,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000345,Yiewsley,E09000017,Hillingdon,Hillingdon,NULL,Violet Avenue,21402055,UB8,NULL,NULL,506550,181550,NULL,NULL
126248111,07/08/2011 19:59,2011,2011/12,Special Service,1,1,260,260,DOG ON WINDOW LEDGE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009321,Bromley North,E09000030,Tower Hamlets,Poplar,NULL,Bruce Road,22700213,E3,NULL,NULL,537850,182750,NULL,NULL
126528111,08/08/2011 00:56,2011,2011/12,Special Service,1,2,260,520,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (land line),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000371,Finsbury Park,E09000019,Islington,Holloway,5300083326,Sonderburg Road,21603814,N7,530968,186516,530950,186550,51.56232631,-0.1119678
126897111,08/08/2011 15:46,2011,2011/12,Special Service,1,1,260,260,CAT STUCK BEHIND WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Redhill Street,20400768,NW1,NULL,NULL,528850,183050,NULL,NULL
126926111,08/08/2011 16:39,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND RADIATOR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000098,Queens Park,E09000005,Brent,North Kensington,NULL,Hartland Road,20202446,NW6,NULL,NULL,524650,183450,NULL,NULL
127320111,08/08/2011 20:16,2011,2011/12,Special Service,1,1,260,260,PUPPY WITH LEG TRAPPED IN GARDEN SEAT,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,NULL,Epsom Close,20600632,UB5,NULL,NULL,512750,185050,NULL,NULL
129497111,09/08/2011 17:06,2011,2011/12,Special Service,1,2,260,520,CAT STUCK BEHIND GAS FIRE,Cat,Person (land line),Retirement/Old Persons Home,Other Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000454,Whitefoot,E09000023,Lewisham,Bromley,10070773801,Reigate Road,22003617,BR1,539750,172250,539750,172250,51.43202481,0.008998771
131161111,10/08/2011 22:59,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011102,Faraday,E09000028,Southwark,Old Kent Road,NULL,Hopwood Road,22501291,SE17,NULL,NULL,532850,177950,NULL,NULL
131582111,11/08/2011 17:14,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA INSPECTOR WITH CAT STUCK ON BALCONY,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000128,Belsize,E09000007,Camden,West Hampstead,NULL,Belsize Park,20400424,NW3,NULL,NULL,526850,184750,NULL,NULL
131625111,11/08/2011 19:00,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011102,Faraday,E09000028,Southwark,Old Kent Road,NULL,Beaconsfield Road,22500180,SE17,NULL,NULL,533050,178050,NULL,NULL
131765111,11/08/2011 23:31,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN SHUTTERS,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000182,Northfield,E09000009,Ealing,Ealing,12070254,Salisbury Road,20601502,W13,516612,179627,516650,179650,51.5035573,-0.321255143
133497111,14/08/2011 21:27,2011,2011/12,Special Service,1,1,260,260,FOX STUCK IN BASEMENT AREA   RSPCA IN ATTENDANCE,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05011097,Champion Hill,E09000028,Southwark,Peckham,NULL,Grove Park,22501164,SE5,NULL,NULL,533550,176050,NULL,NULL
133783111,15/08/2011 12:23,2011,2011/12,Special Service,2,5,260,1300,Redacted,Horse,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Old Mill Lane,NULL,UB8,504970,181403,504950,181450,51.52180139,-0.488397217
134060111,15/08/2011 21:19,2011,2011/12,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Benbow Waye,NULL,UB8,505106,181807,505150,181850,51.5254074,-0.486317223
134327111,16/08/2011 12:12,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000411,St. James,E09000021,Kingston upon Thames,New Malden,NULL,South Lane,21800871,KT3,NULL,NULL,520950,167150,NULL,NULL
134386111,16/08/2011 14:27,2011,2011/12,Special Service,1,2,260,520,HORSE TRAPPED IN HORSEBOX,Horse,Person (mobile),Other road vehicle,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000117,Darwin,E09000006,Bromley,Biggin Hill,1.0002E+11,Buckhurst Road,20301435,TN16,543923,158209,543950,158250,51.30481082,0.063309827
134717111,17/08/2011 00:11,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO FOX STUCK BEHIND FRIDGE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,College Gardens,22827900,E4,NULL,NULL,538050,194450,NULL,NULL
134913111,17/08/2011 11:46,2011,2011/12,Special Service,1,2,260,520,PUPPY WITH PAW TRAPPED IN SINK PLUG HOLE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000053,Golders Green,E09000003,Barnet,Hendon,NULL,Woodlands,20047240,NW11,NULL,NULL,524150,188550,NULL,NULL
135157111,17/08/2011 19:02,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN WINE RACK,Dog,Person (land line),Fire station,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000317,Pettits,E09000016,Havering,Romford,1.00023E+11,Pettits Lane North,21301497,RM1,551176,190578,551150,190550,51.59377304,0.181097417
135436111,18/08/2011 10:45,2011,2011/12,Special Service,1,1,260,260,CAT WITH HEAD TRAPPED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000256,Hammersmith Broadway,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Queen Caroline Street,21000660,W6,NULL,NULL,523050,178150,NULL,NULL
135671111,18/08/2011 17:46,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000595,Forest,E09000031,Waltham Forest,Leyton,NULL,Belmont Park Road,22815400,E10,NULL,NULL,538050,188150,NULL,NULL
135706111,18/08/2011 18:41,2011,2011/12,Special Service,1,1,260,260,CAT IN RIVER,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000461,Graveney,E09000024,Merton,Mitcham,NULL,Seely Road,NULL,SW17,528168,170798,528150,170750,51.42171137,-0.158041903
135743111,18/08/2011 19:47,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN NETTING,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000603,Lea Bridge,E09000031,Waltham Forest,Leyton,NULL,Lea Hall Road,22850450,E10,NULL,NULL,537350,187350,NULL,NULL
136502111,20/08/2011 06:18,2011,2011/12,Special Service,1,1,260,260,YOUNG CAT STUCK BEHIND WASHING MACHINE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000253,College Park and Old Oak,E09000013,Hammersmith and Fulham,Acton,NULL,Henchman Street,21000433,W12,NULL,NULL,521750,181350,NULL,NULL
136585111,20/08/2011 11:03,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN SHOP SHUTTERS,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05009376,Homerton,E09000012,Hackney,Homerton,1.00023E+11,Morning Lane,20900706,E9,535564,184753,535550,184750,51.5453975,-0.046382631
136602111,20/08/2011 11:53,2011,2011/12,Special Service,1,1,260,260,DOG WITH LEG STUCK IN IRON ORNAMENT,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000028,Becontree,E09000002,Barking and Dagenham,Dagenham,NULL,Stevens Road,19900311,RM8,NULL,NULL,546950,186350,NULL,NULL
137564111,22/08/2011 07:13,2011,2011/12,Special Service,1,2,260,520,CAT STUCK ON WASHING LINE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000033,Goresbrook,E09000002,Barking and Dagenham,Dagenham,NULL,Goresbrook Road,19900853,RM9,NULL,NULL,548350,183850,NULL,NULL
137685111,22/08/2011 12:15,2011,2011/12,Special Service,1,1,260,260,BABY FOX TRAPPED BEHIND SHED AND WALL,Fox,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000604,Leyton,E09000031,Waltham Forest,Leyton,10009145394,Beaumont Road,22813550,E10,537999,187802,537950,187850,51.5722067,-0.010093733
137725111,22/08/2011 13:35,2011,2011/12,Special Service,1,2,260,520,RUNNING CALL TO CAT TRAPPED IN CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000482,East Ham South,E09000025,Newham,East Ham,NULL,Lonsdale Avenue,NULL,E6,542191,182374,542150,182350,51.52239078,0.048162048
137793111,22/08/2011 16:45,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRD TRAPPED IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000300,Rayners Lane,E09000015,Harrow,Harrow,NULL,Rayners Lane,NULL,HA5,512958,187606,512950,187650,51.57601322,-0.371320384
137892111,22/08/2011 19:18,2011,2011/12,Special Service,1,1,260,260,PERSON SHUT IN LIFT EMERGENCY,Unknown - Heavy Livestock Animal,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000647,Warwick,E09000033,Westminster,Chelsea,1.00023E+11,Eccleston Place,8401172,SW1W,528716,178865,528750,178850,51.49408557,-0.147232491
138096111,23/08/2011 00:19,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN PIPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009333,Spitalfields & Banglatown,E09000030,Tower Hamlets,Whitechapel,NULL,Hanbury Street,22700597,E1,NULL,NULL,534350,181950,NULL,NULL
138428111,23/08/2011 19:11,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN PROPERTY BENEATH,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011488,West Thornton,E09000008,Croydon,Norbury,NULL,Brigstock Road,20500721,CR7,NULL,NULL,531650,167950,NULL,NULL
138462111,23/08/2011 20:46,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UNDER HOUSE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000451,Rushey Green,E09000023,Lewisham,Lewisham,NULL,Silvermere Road,22001896,SE6,NULL,NULL,537550,174150,NULL,NULL
138710111,24/08/2011 11:55,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011489,Woodside,E09000008,Croydon,Woodside,NULL,Belmont Road,20500674,SE25,NULL,NULL,534850,167650,NULL,NULL
138712111,24/08/2011 12:00,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000366,Barnsbury,E09000019,Islington,Islington,5300006446,Barnsbury Park,21604200,N1,531232,184355,531250,184350,51.54284494,-0.108966932
138769111,24/08/2011 14:30,2011,2011/12,Special Service,1,1,260,260,DOG STUCK IN FENCE,Dog,Person (mobile),Wasteland,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000343,West Ruislip,E09000017,Hillingdon,Ruislip,NULL,Larkspur Close,NULL,HA4,508047,187866,508050,187850,51.57931199,-0.442081029
138840111,24/08/2011 16:55,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA  WITH  CAT   IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000199,Enfield Lock,E09000010,Enfield,Enfield,207129172,Park Road,20702363,EN3,536386,199398,536350,199350,51.67680053,-0.028844069
138855111,24/08/2011 17:29,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000049,East Finchley,E09000003,Barnet,Finchley,NULL,East End Road,20013420,N2,NULL,NULL,526350,189650,NULL,NULL
138992111,24/08/2011 23:08,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN BRANCH OF TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000171,Cleveland,E09000009,Ealing,Ealing,12072192,Glasse Close,20600722,W13,516102,180955,516150,180950,51.51559791,-0.328162673
139277111,25/08/2011 16:00,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000452,Sydenham,E09000023,Lewisham,Forest Hill,NULL,Queensthorpe Road,22001820,SE26,NULL,NULL,535650,171750,NULL,NULL
139322111,25/08/2011 17:08,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN METAL BARS OF CHILDS SWING,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000114,Cray Valley East,E09000006,Bromley,Orpington,NULL,Crowhurst Way,NULL,BR5,547454,167710,547450,167750,51.38928033,0.117859006
139930111,26/08/2011 20:21,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED UNDER PORTAKABIN,Dog,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000549,Rotherhithe,E09000028,Southwark,Deptford,NULL,Gomm Road,NULL,SE16,535323,179204,535350,179250,51.49559048,-0.05198591
140197111,27/08/2011 11:39,2011,2011/12,Special Service,1,1,260,260,FOX  LOCKED IN COURTYARD,Fox,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,6033588,Roman Road,22701028,E2,535441,182899,535450,182850,51.52876654,-0.048867994
140677111,28/08/2011 07:29,2011,2011/12,Special Service,1,1,260,260,HAMSTER TRAPPED UNDER FLOORBOARD,Hamster,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000452,Sydenham,E09000023,Lewisham,Forest Hill,NULL,Sydenham Park,22002956,SE26,NULL,NULL,535150,172050,NULL,NULL
140714111,28/08/2011 09:40,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND WARDROBE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011484,South Croydon,E09000008,Croydon,Croydon,NULL,Birdhurst Rise,20501922,CR2,NULL,NULL,533050,164250,NULL,NULL
140876111,28/08/2011 15:46,2011,2011/12,Special Service,1,1,260,260,DOG STUCK IN STREAM,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000461,Graveney,E09000024,Merton,Mitcham,NULL,Rural Way,NULL,SW16,528902,170491,528950,170450,51.41878616,-0.147602676
141320111,29/08/2011 14:11,2011,2011/12,Special Service,1,1,260,260,KITTEN WITH HEAD TRAPPED IN FENCE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000336,Northwood Hills,E09000017,Hillingdon,Ruislip,NULL,Hilliard Road,21400980,HA6,NULL,NULL,509850,190950,NULL,NULL
141796111,30/08/2011 10:15,2011,2011/12,Special Service,1,1,260,260,BUDGIE TRAPPED BEHIND WALL UNIT,Budgie,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011232,Thamesmead East,E09000004,Bexley,Plumstead,NULL,St. Georges Close,20101791,SE28,NULL,NULL,547950,181350,NULL,NULL
141815111,30/08/2011 11:02,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON WINDOW LEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011247,Loxford,E09000026,Redbridge,Ilford,NULL,Buttsbury Road,22302672,IG1,NULL,NULL,544350,185050,NULL,NULL
141926111,30/08/2011 15:25,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED BETWEEN WALL AND SHED,Dog,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,207048371,Somerset Road,20704746,N18,533625,192330,533650,192350,51.61394908,-0.071449794
141934111,30/08/2011 15:47,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN SHED AND FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000381,Tollington,E09000019,Islington,Holloway,NULL,Mitford Road,21603566,N19,NULL,NULL,530350,186750,NULL,NULL
141999111,30/08/2011 18:53,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALL AND FENCE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000423,Herne Hill,E09000022,Lambeth,Brixton,1.00022E+11,Redan Terrace,21901148,SE5,531964,176247,531950,176250,51.46981036,-0.101449112
143432111,02/09/2011 11:37,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000059,Totteridge,E09000003,Barnet,Finchley,200106214,St. Andrew's Close,20037920,N12,526088,192839,526050,192850,51.62026123,-0.180061064
144141111,03/09/2011 14:13,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN GUTTERING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000625,Thamesfield,E09000032,Wandsworth,Wandsworth,NULL,Sefton Street,22904553,SW15,NULL,NULL,523250,175950,NULL,NULL
144171111,03/09/2011 15:48,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING UNDER RAILWAY BRIDGE,Bird,Person (mobile),Railway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000126,Shortlands,E09000006,Bromley,Beckenham,1.00023E+11,Shortlands Road,20302018,BR2,539334,169330,539350,169350,51.40588729,0.001871381
144317111,03/09/2011 20:53,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED BETWEEN WALL  AND GARAGE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011223,Crook Log,E09000004,Bexley,Bexley,NULL,Gipsy Road,20100597,DA16,NULL,NULL,547550,176150,NULL,NULL
144578111,04/09/2011 10:53,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN GUTTERING,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009381,London Fields,E09000012,Hackney,Bethnal Green,NULL,Mare Street,20900658,E8,NULL,NULL,534850,183550,NULL,NULL
144750111,04/09/2011 16:36,2011,2011/12,Special Service,1,1,260,260,DOG IN PRECARIOUS POSITION,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009321,Bromley North,E09000030,Tower Hamlets,Poplar,NULL,Bruce Road,22700213,E3,NULL,NULL,537850,182750,NULL,NULL
145548111,06/09/2011 09:19,2011,2011/12,Special Service,1,1,260,260,PIGEON ENTANGLED IN NETTING,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009293,Bread Street,E09000001,City of London,Dowgate,95503563,Cheapside,8100124,EC2V,532126,181233,532150,181250,51.51458024,-0.097253259
146417111,07/09/2011 20:59,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO CAT UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000303,Stanmore Park,E09000015,Harrow,Stanmore,1.00023E+11,Hardwick Close,21202225,HA7,517199,192269,517150,192250,51.6170577,-0.308590074
147948111,10/09/2011 13:43,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED IN WASHING MACHINE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009329,St. Dunstan's,E09000030,Tower Hamlets,Bethnal Green,NULL,White Horse Lane,NULL,E1,NULL,NULL,535750,181950,NULL,NULL
147980111,10/09/2011 14:37,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED UNDER FLOOR IN BATHROOM,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000424,Knight's Hill,E09000022,Lambeth,West Norwood,NULL,Canterbury Grove,21900286,SE27,NULL,NULL,531450,172150,NULL,NULL
148042111,10/09/2011 17:07,2011,2011/12,Special Service,1,3,260,780,KITTEN STUCK IN VENT - DISTRESSED,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000230,Woolwich Riverside,E09000011,Greenwich,East Greenwich,NULL,Tivoli Gardens,20801502,SE18,NULL,NULL,542250,178850,NULL,NULL
148636111,11/09/2011 20:33,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED IN RAZOR WIRE,Cat,Person (mobile),Other private non-residential building,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011468,Fairfield,E09000008,Croydon,Croydon,10014050277,Sydenham Road,20501457,CR0,532662,166379,532650,166350,51.38096656,-0.095097529
148729111,12/09/2011 00:35,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN FENCE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011228,Northumberland Heath,E09000004,Bexley,Erith,NULL,Frinsted Road,20100579,DA8,NULL,NULL,550750,177250,NULL,NULL
148932111,12/09/2011 13:07,2011,2011/12,Special Service,1,1,260,260,FOX WITH SEVERE LEG INJURIES,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000122,Orpington,E09000006,Bromley,Orpington,NULL,Avalon Road,20300005,BR6,NULL,NULL,547050,165750,NULL,NULL
149329111,13/09/2011 08:48,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN LIFT AND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,NULL,St. Andrew's Road,NULL,E17,NULL,NULL,536150,190150,NULL,NULL
149895111,14/09/2011 09:51,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED PIGEON,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000202,Highlands,E09000010,Enfield,Enfield,NULL,Windmill Hill,NULL,EN2,532188,196734,532150,196750,51.65386421,-0.090528527
149899111,14/09/2011 10:12,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN CONCRETE TUNNEL,Dog,Person (mobile),Wasteland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000171,Cleveland,E09000009,Ealing,Ealing,NULL,Argyle Road,NULL,W13,516290,182445,516250,182450,51.52895099,-0.324962907
149998111,14/09/2011 13:55,2011,2011/12,Special Service,1,1,260,260,CAT WITH LEG TRAPPED BEHIND RADIATOR,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000433,Thornton,E09000022,Lambeth,Clapham,NULL,Moberly Road,21900972,SW4,NULL,NULL,529750,173950,NULL,NULL
150645111,15/09/2011 15:19,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000054,Hale,E09000003,Barnet,Mill Hill,NULL,Fairmead Crescent,20015180,HA8,NULL,NULL,520250,193050,NULL,NULL
151026111,16/09/2011 09:28,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN TOP OF SPIKY FENCE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009378,Hoxton West,E09000012,Hackney,Shoreditch,NULL,Underwood Street,20901023,N1,NULL,NULL,532550,182950,NULL,NULL
151278111,16/09/2011 18:42,2011,2011/12,Special Service,1,1,260,260,BIRD STUCK BETWEEN WALLS,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000036,Mayesbrook,E09000002,Barking and Dagenham,Barking,NULL,Lodge Avenue,19900241,RM8,NULL,NULL,546650,184750,NULL,NULL
151524111,17/09/2011 06:18,2011,2011/12,Special Service,1,1,260,260,FOX TRAPPED IN BASEMENT LIGHTWELL,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05009405,Stanley,E09000020,Kensington and Chelsea,Chelsea,NULL,Elm Park Gardens,21700188,SW10,NULL,NULL,526650,177950,NULL,NULL
151873111,17/09/2011 19:59,2011,2011/12,Special Service,1,4,260,1040,PUPPY WITH HEAD STUCK BETWEEN CONCRETE POSTS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000039,Thames,E09000002,Barking and Dagenham,Barking,NULL,Shaw Gardens,19900575,IG11,NULL,NULL,548150,183350,NULL,NULL
152127111,18/09/2011 11:11,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED IN BASEMENT,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000138,Holborn and Covent Garden,E09000007,Camden,Soho,NULL,Northington Street,20401017,WC1N,NULL,NULL,530750,182050,NULL,NULL
152249111,18/09/2011 16:08,2011,2011/12,Special Service,1,1,260,260,CAT CAUGHT BETWEEN WALL OUTSIDE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,NULL,King Street,22403584,TW1,NULL,NULL,516150,173150,NULL,NULL
152551111,19/09/2011 06:03,2011,2011/12,Special Service,1,1,260,260,FOX TRAPPED IN GATE,Fox,Person (land line),Garden equipment,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,1.00022E+11,Warkworth Gardens,21501169,TW7,516195,177259,516150,177250,51.48235983,-0.32803963
152619111,19/09/2011 10:25,2011,2011/12,Special Service,1,1,260,260,INJURED PARROT STUCK IN TREE,Bird,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000217,Coldharbour and New Eltham,E09000011,Greenwich,Eltham,1.00021E+11,Kingsley Wood Drive,20800865,SE9,542966,172225,542950,172250,51.43099875,0.055220543
152635111,19/09/2011 10:57,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH  CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000606,Markhouse,E09000031,Waltham Forest,Walthamstow,NULL,Clacton Road,NULL,E17,536350,188518,536350,188550,51.57904147,-0.033596225
152827111,19/09/2011 18:51,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN POND,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000603,Lea Bridge,E09000031,Waltham Forest,Leyton,NULL,Markmanor Avenue,NULL,E17,536542,187653,536550,187650,51.57122209,-0.031162577
152861111,19/09/2011 19:36,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,NULL,King Street,22403584,TW1,NULL,NULL,516150,173150,NULL,NULL
152912111,19/09/2011 21:38,2011,2011/12,Special Service,1,1,260,260,DOG FALLEN DOWN STORM DRAIN,Dog,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000565,Sutton North,E09000029,Sutton,Sutton,NULL,Grennell Road,NULL,SM1,526380,165422,526350,165450,51.37379702,-0.185656256
153173111,20/09/2011 11:53,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO ASSIST RSPCA WITH BIRDS IN DISTRESS,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000475,Beckton,E09000025,Newham,Barking,NULL,Jenkins Lane,NULL,IG11,544456,182324,544450,182350,51.52136739,0.080766184
153602111,21/09/2011 07:55,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP CHIMNEY,Cat,Police,Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000381,Tollington,E09000019,Islington,Holloway,NULL,Sussex Way,21606975,N19,NULL,NULL,530250,186750,NULL,NULL
154104111,22/09/2011 08:38,2011,2011/12,Special Service,1,1,260,260,PUPPY STUCK UNDER DECKING,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000433,Thornton,E09000022,Lambeth,West Norwood,NULL,Parkthorne Road,21901070,SW12,NULL,NULL,529750,173650,NULL,NULL
154310111,22/09/2011 17:28,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN FIRE PLACE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000425,Larkhall,E09000022,Lambeth,Brixton,NULL,Hemberton Road,21900697,SW9,NULL,NULL,530250,175850,NULL,NULL
154534111,22/09/2011 23:19,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND RADIATOR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000428,St. Leonard's,E09000022,Lambeth,Tooting,NULL,Mount Ephraim Road,21900992,SW16,NULL,NULL,529850,172450,NULL,NULL
155304111,24/09/2011 08:15,2011,2011/12,Special Service,1,2,260,520,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000321,South Hornchurch,E09000016,Havering,Wennington,NULL,Avelon Road,NULL,RM13,552561,184014,552550,184050,51.53442292,0.198230978
156058111,25/09/2011 13:13,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN SEWER PIPE,Cat,Police,House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,NULL,Panmure Road,22004856,SE26,NULL,NULL,534650,172150,NULL,NULL
156357111,25/09/2011 21:52,2011,2011/12,Special Service,1,2,260,520,HORSES LOOSE IN ROADWAY,Horse,Person (land line),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,NULL,East Hall Lane,NULL,RM13,553512,181054,553550,181050,51.5075704,0.21064327
156406111,25/09/2011 23:44,2011,2011/12,Special Service,1,1,260,260,CAT STUCK BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000322,Squirrel's Heath,E09000016,Havering,Hornchurch,NULL,Heath Park Road,21300409,RM2,NULL,NULL,552350,188850,NULL,NULL
156554111,26/09/2011 10:45,2011,2011/12,Special Service,1,1,260,260,ASSIST RSCPA,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000105,Willesden Green,E09000005,Brent,Willesden,NULL,Strode Road,NULL,NW10,522425,184571,522450,184550,51.54676336,-0.235825765
156731111,26/09/2011 16:44,2011,2011/12,Special Service,1,1,260,260,DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000062,West Hendon,E09000003,Barnet,Hendon,200222074,Prince Charles Drive,20034920,NW4,522963,187848,522950,187850,51.576098,-0.226925119
157758111,28/09/2011 10:50,2011,2011/12,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000275,Noel Park,E09000014,Haringey,Hornsey,NULL,Coleraine Road,21103124,N8,NULL,NULL,531250,189750,NULL,NULL
157763111,28/09/2011 10:55,2011,2011/12,Special Service,1,3,260,780,HORSE STUCK IN RIVER,Horse,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000309,Emerson Park,E09000016,Havering,Hornchurch,1.00021E+11,Wingletye Lane,21300661,RM11,555415,188368,555450,188350,51.57276421,0.241266584
157873111,28/09/2011 15:14,2011,2011/12,Special Service,1,1,260,260,HAMPSTER STUCK IN CAR ENGINE,Hamster,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000328,Charville,E09000017,Hillingdon,Hillingdon,1.00021E+11,Woodstock Gardens,21402215,UB4,509542,181882,509550,181850,51.52523885,-0.422376831
157969111,28/09/2011 18:32,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED ON LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000114,Cray Valley East,E09000006,Bromley,Orpington,NULL,Mountfield Way,20301224,BR5,NULL,NULL,547250,168150,NULL,NULL
158705111,29/09/2011 21:41,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED IN HEDGE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009333,Spitalfields & Banglatown,E09000030,Tower Hamlets,Whitechapel,6014538,Hobsons Place,22701982,E1,534142,181914,534150,181950,51.52022505,-0.067957984
158724111,29/09/2011 22:16,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN DRAIN PIPE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011483,Shirly South,E09000008,Croydon,Addington,NULL,Broom Gardens,20500409,CR0,NULL,NULL,537350,165150,NULL,NULL
159671111,01/10/2011 11:09,2011,2011/12,Special Service,1,1,260,260,CAT LOCKED IN CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000267,Bounds Green,E09000014,Haringey,Southgate,NULL,Woodfield Way,NULL,N11,529526,191315,529550,191350,51.60578568,-0.130990772
159970111,01/10/2011 18:36,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK BETWEEN OUTDOOR STORAGE UNITS,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000482,East Ham South,E09000025,Newham,East Ham,46026688,Folkestone Road,22200619,E6,543465,182947,543450,182950,51.52721775,0.066745717
160387111,02/10/2011 08:32,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000330,Harefield,E09000017,Hillingdon,Ruislip,NULL,Dovedale Close,21400612,UB9,NULL,NULL,505050,190150,NULL,NULL
160437111,02/10/2011 10:45,2011,2011/12,Special Service,1,1,260,260,DOG LOCKED IN CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000556,Beddington South,E09000029,Sutton,Wallington,5870087480,Stratton Avenue,22602644,SM6,529702,162613,529750,162650,51.3478044,-0.138978905
160557111,02/10/2011 14:34,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN AN IRON GATE,Dog,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000164,South Norwood,E09000008,Croydon,Woodside,NULL,Ross Road,NULL,SE25,533420,169083,533450,169050,51.40508867,-0.083196278
160797111,02/10/2011 20:00,2011,2011/12,Special Service,1,2,260,520,CAT STUCK IN CAR WHEEL,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000141,King's Cross,E09000007,Camden,Euston,NULL,Cromer Street,NULL,WC1H,530550,182706,530550,182750,51.52818383,-0.11940715
161580111,03/10/2011 20:23,2011,2011/12,Special Service,1,1,260,260,CAT STUCK BETWEEN HOUSE AND DRAINING AREA,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000165,Thornton Heath,E09000008,Croydon,Norbury,NULL,Grange Road,NULL,CR7,NULL,NULL,532750,168450,NULL,NULL
161690111,04/10/2011 00:08,2011,2011/12,Special Service,1,1,260,260,"INJURED CAT, TANGLED UP IN TREE",Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009384,Stamford Hill West,E09000012,Hackney,Stoke Newington,1.00021E+11,Bethune Road,20900135,N16,533196,187373,533150,187350,51.56950573,-0.079520845
162371111,05/10/2011 06:37,2011,2011/12,Special Service,1,1,260,260,FOX TRAPPED IN GATE,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05009373,Hackney Downs,E09000012,Hackney,Stoke Newington,1.00021E+11,Northwold Road,20900753,E5,534385,186636,534350,186650,51.56260061,-0.062656792
163506111,07/10/2011 07:25,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000510,Newbury,E09000026,Redbridge,Ilford,NULL,Aldborough Road South,NULL,IG3,545604,188572,545650,188550,51.57721342,0.099883968
164133111,08/10/2011 09:36,2011,2011/12,Special Service,1,4,260,1040,HORSE IN DITCH,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000405,Chessington South,E09000021,Kingston upon Thames,Surbiton,128032260,Barwell Lane,21800115,KT9,516963,163096,516950,163050,51.3549067,-0.32165141
164240111,08/10/2011 13:34,2011,2011/12,Special Service,1,1,260,260,HAMSTER BELIEED TRAPPED BEHIND KITCHEN SINK,Hamster,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,NULL,Drayton Green Road,20602212,W13,NULL,NULL,516750,180650,NULL,NULL
164921111,09/10/2011 15:22,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED BETWEEN WALL AND FENCE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000208,Southgate,E09000010,Enfield,Southgate,NULL,Corfield Road,20706133,N21,NULL,NULL,530550,195950,NULL,NULL
165312111,10/10/2011 09:22,2011,2011/12,Special Service,1,1,260,260,HORSE TRAPPED IN STABLE DOORS,Horse,Person (mobile),Barn,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Woodford,1.00023E+11,Frinton Drive,22838400,IG8,538729,191091,538750,191050,51.60158145,0.001730321
165561111,10/10/2011 16:46,2011,2011/12,Special Service,1,2,260,520,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000335,Northwood,E09000017,Hillingdon,Ruislip,NULL,Rofant Road,21401648,HA6,NULL,NULL,509250,191750,NULL,NULL
165693111,10/10/2011 19:49,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO TRAPPED BIRD,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000170,Acton Central,E09000009,Ealing,Acton,NULL,Twyford Avenue,20601782,W3,NULL,NULL,519350,180850,NULL,NULL
166070111,11/10/2011 16:06,2011,2011/12,Special Service,1,1,260,260,DISTRESSED CAT TRAPPED IN LOCKUP GARAGE,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000139,Kentish Town,E09000007,Camden,Kentish Town,5034883,Falkland Road,20400365,NW5,529270,185288,529250,185250,51.55168209,-0.136901078
166170111,11/10/2011 18:56,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND SHEDS,Cat,Person (mobile),Garden equipment,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000375,Holloway,E09000019,Islington,Holloway,5300075952,Prospect Place,21610015,N7,530257,185557,530250,185550,51.55387274,-0.122573983
167033111,13/10/2011 12:24,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP A TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000171,Cleveland,E09000009,Ealing,Ealing,12074497,Cavendish Avenue,20600336,W13,516006,181667,516050,181650,51.52201688,-0.32931127
167641111,14/10/2011 13:22,2011,2011/12,Special Service,1,1,260,260,DOG STUCK ON BALCONY,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000358,Hounslow Central,E09000018,Hounslow,Heston,NULL,Prince Regent Road,21500896,TW3,NULL,NULL,513950,175850,NULL,NULL
167687111,14/10/2011 14:42,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000329,Eastcote and East Ruislip,E09000017,Hillingdon,Ruislip,NULL,Broadwood Avenue,21400255,HA4,NULL,NULL,509150,188350,NULL,NULL
167894111,14/10/2011 19:29,2011,2011/12,Special Service,1,2,260,520,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000114,Cray Valley East,E09000006,Bromley,Orpington,NULL,Hood Avenue,20301156,BR5,NULL,NULL,546750,167750,NULL,NULL
167915111,14/10/2011 19:52,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Heathland,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000430,Streatham Hill,E09000022,Lambeth,West Norwood,1.00022E+11,Tenham Avenue,21901354,SW2,529744,173012,529750,173050,51.44125062,-0.134578076
168388111,15/10/2011 14:48,2011,2011/12,Special Service,1,1,260,260,BIRDS TRAPPED BEHIND WIRE MESH,Bird,Person (land line),Hotel/motel,Other Residential,Animal rescue from height,Animal rescue from height - Bird,E05000175,East Acton,E09000009,Ealing,Acton,12096646,Western Avenue,20602102,W3,519520,182315,519550,182350,51.52710928,-0.278466052
168440111,15/10/2011 16:23,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000647,Warwick,E09000033,Westminster,Chelsea,NULL,Winchester Street,8400290,SW1V,NULL,NULL,528850,178350,NULL,NULL
168552111,15/10/2011 19:29,2011,2011/12,Special Service,1,1,260,260,DOG WITH PAW STUCK IN BENCH,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000031,Eastbury,E09000002,Barking and Dagenham,Barking,100045801,Movers Lane,19900540,IG11,545750,183750,545750,183750,51.53384858,0.099993441
168975111,16/10/2011 13:09,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN BASEMENT AREA,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Cranbury Road,21000244,SW6,NULL,NULL,525750,176250,NULL,NULL
169000111,16/10/2011 13:54,2011,2011/12,Special Service,1,1,260,260,CAT UP TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000336,Northwood Hills,E09000017,Hillingdon,Ruislip,NULL,Selway Close,21401724,HA5,NULL,NULL,510850,189550,NULL,NULL
169037111,16/10/2011 15:20,2011,2011/12,Special Service,1,1,260,260,SQUIRREL TRAPPED BETWEEN WALL AND DRAINPIPE,Squirrel,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000210,Town,E09000010,Enfield,Enfield,NULL,Tenniswood Road,20702989,EN1,NULL,NULL,533050,197750,NULL,NULL
169615111,17/10/2011 15:55,2011,2011/12,Special Service,1,1,260,260,TWO DOGS TRAPPED,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Swindon Lane,21300122,RM3,NULL,NULL,554750,192350,NULL,NULL
170149111,18/10/2011 17:16,2011,2011/12,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000631,Bayswater,E09000033,Westminster,Paddington,NULL,Porchester Road,8400189,W2,NULL,NULL,525850,181250,NULL,NULL
170312111,18/10/2011 21:45,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN SOFA,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000436,Vassall,E09000022,Lambeth,Brixton,NULL,Caldwell Street,21900272,SW9,NULL,NULL,531050,177050,NULL,NULL
170618111,19/10/2011 14:12,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH SWAN TRAPPED BEHIND WIRE FENCE,Bird,Person (mobile),Heathland,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000085,Alperton,E09000005,Brent,Wembley,NULL,Bridgewater Road,NULL,HA0,517608,184147,517650,184150,51.54397539,-0.305403341
171429111,20/10/2011 21:15,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,10009805290,Ossory Road,22501843,SE1,534122,177866,534150,177850,51.48385255,-0.069784557
171719111,21/10/2011 14:05,2011,2011/12,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000131,Cantelowes,E09000007,Camden,Kentish Town,5049795,Camden Terrace,20400531,NW1,529755,184675,529750,184650,51.54606196,-0.13013578
171759111,21/10/2011 15:42,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED ON SHOP SIGN,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000104,Wembley Central,E09000005,Brent,Wembley,202102272,Ealing Road,20200486,HA0,518179,184225,518150,184250,51.54455734,-0.297146894
172157111,22/10/2011 10:57,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BY FENCE,Cat,Person (mobile),Wasteland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000097,Preston,E09000005,Brent,Wembley,NULL,Elmstead Avenue,NULL,HA9,518701,186847,518750,186850,51.56801332,-0.288738891
172300111,22/10/2011 15:21,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009374,Hackney Wick,E09000012,Hackney,Homerton,1.00021E+11,Wick Road,20901074,E9,536014,184760,536050,184750,51.54535234,-0.039894308
172709111,23/10/2011 09:10,2011,2011/12,Special Service,1,1,260,260,CAT STUCK BETWEEN WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Bridgwater Road,NULL,RM3,NULL,NULL,553550,192150,NULL,NULL
172822111,23/10/2011 14:22,2011,2011/12,Special Service,1,2,260,520,Redacted,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000073,Danson Park,E09000004,Bexley,Bexley,NULL,Danson Park,NULL,DA6,547521,174952,547550,174950,51.4543355,0.12183349
172960111,23/10/2011 18:39,2011,2011/12,Special Service,1,1,260,260,CAT LOCKED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000111,Chislehurst,E09000006,Bromley,Sidcup,1.0002E+11,Royal Parade,20302443,BR7,544425,170184,544450,170150,51.41228933,0.075361791
173067111,23/10/2011 21:33,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN FENCE AND SHED,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000627,Wandsworth Common,E09000032,Wandsworth,Wandsworth,NULL,Bassingham Road,22900298,SW18,NULL,NULL,526350,173850,NULL,NULL
173472111,24/10/2011 17:56,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED INCAR  DOOR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000120,Kelsey and Eden Park,E09000006,Bromley,Beckenham,NULL,Eden Park Avenue,NULL,BR3,537309,168012,537350,168050,51.39453656,-0.027733103
173496111,24/10/2011 18:16,2011,2011/12,Special Service,1,2,260,520,CAT STUCK IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000328,Charville,E09000017,Hillingdon,Hillingdon,NULL,Weald Way,21402095,UB4,NULL,NULL,509150,182950,NULL,NULL
174017111,25/10/2011 17:42,2011,2011/12,Special Service,1,2,260,520,DOG DOWN BADGER HOLE,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000153,Fairfield,E09000008,Croydon,Croydon,NULL,Coombe Road,NULL,CR0,534101,164716,534150,164750,51.36568367,-0.075058645
174036111,25/10/2011 18:29,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN DRAIN,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000270,Fortis Green,E09000014,Haringey,Finchley,10022937575,Coppetts Road,21107082,N10,527773,191065,527750,191050,51.60393915,-0.156381079
174525111,26/10/2011 19:13,2011,2011/12,Special Service,1,2,260,520,DOG WEDGED IN BETWEEN TWO BUILDINGS,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000322,Squirrel's Heath,E09000016,Havering,Hornchurch,NULL,Northumberland Avenue,21300001,RM11,NULL,NULL,553450,189050,NULL,NULL
176174111,29/10/2011 19:03,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED BETWEEN TWO WALLS,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011219,Bexleyheath,E09000004,Bexley,Bexley,NULL,Springfield Road,20101322,DA7,NULL,NULL,549850,175450,NULL,NULL
176514111,30/10/2011 10:21,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN RAILINGS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,NULL,Melville Road,21300838,RM13,NULL,NULL,552450,182250,NULL,NULL
176526111,30/10/2011 10:54,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN BAG ON FLAT ROOF,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009386,Victoria,E09000012,Hackney,Homerton,NULL,Loddiges Road,20900628,E9,NULL,NULL,535050,184350,NULL,NULL
177260111,31/10/2011 11:30,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011240,Clementswood,E09000026,Redbridge,Ilford,NULL,Richmond Road,22303183,IG1,NULL,NULL,544150,186450,NULL,NULL
178539111,02/11/2011 16:19,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000204,Lower Edmonton,E09000010,Enfield,Edmonton,NULL,Richmond Crescent,20704665,N9,NULL,NULL,534450,194350,NULL,NULL
178853111,03/11/2011 08:27,2011,2011/12,Special Service,1,2,260,520,CAT TRAPPED ON EMBANKMENT BY CANAL,Cat,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Cowley Mill Road,NULL,UB8,504742,183403,504750,183450,51.53982041,-0.491087746
179019111,03/11/2011 16:11,2011,2011/12,Special Service,1,1,260,260,DOG IN RIVER,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000307,Cranham,E09000016,Havering,Hornchurch,NULL,Hall Lane,NULL,RM14,556571,188757,556550,188750,51.57593971,0.25810753
180002111,05/11/2011 11:50,2011,2011/12,Special Service,1,2,260,520,DOG TRAPPED IN POND,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000054,Hale,E09000003,Barnet,Mill Hill,NULL,Barnet Way,NULL,NW7,520030,194741,520050,194750,51.63867829,-0.266873647
180176111,05/11/2011 17:18,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO BIRD CAUGHT IN TREE,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011250,Newbury,E09000026,Redbridge,Ilford,1.00022E+11,Aldborough Road South,22301800,IG3,545241,187554,545250,187550,51.56815993,0.094228811
180844111,06/11/2011 11:56,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING AT FOURTH FLOOR LEVEL,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009405,Stanley,E09000020,Kensington and Chelsea,Chelsea,NULL,Elm Park Gardens,21700188,SW10,NULL,NULL,526650,178050,NULL,NULL
181451111,07/11/2011 09:27,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED ON BALCONY,Dog,Police,Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Burdett Road,22700226,E14,NULL,NULL,536950,181450,NULL,NULL
182155111,08/11/2011 16:26,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN DOOR,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000111,Chislehurst,E09000006,Bromley,Bromley,NULL,Sylvester Avenue,20302244,BR7,NULL,NULL,542650,170550,NULL,NULL
182955111,10/11/2011 06:36,2011,2011/12,Special Service,1,1,260,260,DOG HANGING FROM TREE,Dog,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000167,Waddon,E09000008,Croydon,Croydon,NULL,Duppas Hill Road,NULL,CR0,531559,164969,531550,164950,51.36855152,-0.11145912
182959111,10/11/2011 06:46,2011,2011/12,Special Service,1,1,260,260,HORSE STUCK IN A DITCH,Horse,Police,Heathland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000571,Wandle Valley,E09000029,Sutton,Wallington,5870083134,London Road,22602441,CR4,528333,166524,528350,166550,51.38326328,-0.157215018
183038111,10/11/2011 10:27,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN FENCE,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000462,Hillside,E09000024,Merton,Wimbledon,48047271,Mansel Road,22104188,SW19,524527,170744,524550,170750,51.42203656,-0.210399303
183663111,11/11/2011 15:30,2011,2011/12,Special Service,2,3,260,780,BIRD TRAPPED BY STRING HANGING FROM ROOF,Bird,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000185,Northolt West End,E09000009,Ealing,Southall,12013776,Canberra Drive,20600310,UB5,511493,182652,511450,182650,51.53177802,-0.394021716
183746111,11/11/2011 18:31,2011,2011/12,Special Service,1,2,260,520,SWAN WITH HEAD STUCK IN RAILINGS,Bird,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05011117,Surrey Docks,E09000028,Southwark,Deptford,2.00003E+11,Plough Way,22501989,SE16,536029,178890,536050,178850,51.49259938,-0.041942931
184047111,12/11/2011 10:00,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000038,River,E09000002,Barking and Dagenham,Dagenham,NULL,School Road,NULL,RM10,549336,183929,549350,183950,51.53452051,0.151732675
184431111,13/11/2011 00:49,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED BETWEEN FLOORING OF KENNEL,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000320,St. Andrew's,E09000016,Havering,Hornchurch,NULL,Victor Gardens,21300638,RM12,NULL,NULL,553850,187150,NULL,NULL
184634111,13/11/2011 13:08,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA RELEASE BIRD TRAPPED IN GUTTERING,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,Walm Lane,20202085,NW2,NULL,NULL,524050,185250,NULL,NULL
185029111,14/11/2011 10:38,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009373,Hackney Downs,E09000012,Hackney,Homerton,1.00021E+11,Queensdown Road,20900834,E5,534726,185748,534750,185750,51.5545394,-0.058079912
185415111,15/11/2011 07:40,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Police,Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000504,Fullwell,E09000026,Redbridge,Hainault,NULL,Cranbrook Road,NULL,IG6,544156,189551,544150,189550,51.5863823,0.079404148
185457111,15/11/2011 09:45,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000128,Belsize,E09000007,Camden,West Hampstead,NULL,Belsize Square,20400430,NW3,NULL,NULL,527050,184750,NULL,NULL
186435111,17/11/2011 12:25,2011,2011/12,Special Service,1,1,260,260,ASSIST WITH HORSE TRAPPED IN STALL,Horse,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Farm animal,E05000218,Eltham North,E09000011,Greenwich,Eltham,NULL,Glenesk Road,NULL,SE9,543671,174569,543650,174550,51.45188323,0.066305761
186588111,17/11/2011 17:52,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN PIPE IN FLAT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Stanhope Street,20400774,NW1,NULL,NULL,529050,182950,NULL,NULL
186652111,17/11/2011 19:40,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN BASEMENT,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009334,Stepney Green,E09000030,Tower Hamlets,Shadwell,NULL,Jamaica Street,22700675,E1,NULL,NULL,535350,181450,NULL,NULL
186863111,18/11/2011 09:21,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Town Hall,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000118,Farnborough and Crofton,E09000006,Bromley,Orpington,NULL,Ladycroft Way,NULL,BR6,544191,164474,544150,164450,51.36104113,0.069684907
187153111,18/11/2011 18:46,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED PIGEON,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,Redbridge,Woodford,2.00003E+11,The Broadway,22306023,IG8,540903,191851,540950,191850,51.60787139,0.033404634
187819111,19/11/2011 22:14,2011,2011/12,Special Service,1,1,260,260,DOG FALLEN IN RIVER,Dog,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009309,Queenhithe,E09000001,City of London,Dowgate,NULL,Queen Victoria Street,NULL,EC4V,532099,180905,532050,180950,51.51163891,-0.097764989
188858111,22/11/2011 03:30,2011,2011/12,Special Service,1,2,260,520,HORSE STUCK IN DITCH,Horse,Person (land line),"Grassland, pasture, grazing etc",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000571,Wandle Valley,E09000029,Sutton,Mitcham,NULL,London Road,NULL,CR4,528255,166632,528250,166650,51.38425147,-0.158296285
189442111,23/11/2011 11:39,2011,2011/12,Special Service,1,1,260,260,DOG IN RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Bushwood Road,NULL,TW9,519333,177528,519350,177550,51.48412507,-0.282779136
189460111,23/11/2011 12:24,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN FENCE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000493,Wall End,E09000025,Newham,East Ham,46043555,Langdon Crescent,22200790,E6,543442,183447,543450,183450,51.53171641,0.066617792
189831111,24/11/2011 08:55,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000207,Southbury,E09000010,Enfield,Enfield,NULL,Larksfield Grove,NULL,EN1,534846,197601,534850,197650,51.66102436,-0.0517955
189949111,24/11/2011 14:36,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,1.00023E+11,Clarence Street,21800255,KT1,518206,169370,518250,169350,51.41104008,-0.301725172
190170111,24/11/2011 22:19,2011,2011/12,Special Service,1,1,260,260,DEER WITH HEAD STUCK IN RAILINGS,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Dagnam Park Drive,NULL,RM3,554342,192357,554350,192350,51.60889849,0.227547407
190269111,25/11/2011 07:07,2011,2011/12,Special Service,1,1,260,260,CAT   TRAPPED IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000198,Enfield Highway,E09000010,Enfield,Enfield,207031702,Addison Road,20702096,EN3,535725,197760,535750,197750,51.66224146,-0.039033582
191670111,27/11/2011 16:57,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED BETWEEN TWO HOUSES,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000480,East Ham Central,E09000025,Newham,East Ham,NULL,Wakefield Street,22201187,E6,NULL,NULL,542150,183650,NULL,NULL
191969111,28/11/2011 08:49,2011,2011/12,Special Service,1,2,260,520,HORSE STUCK IN RIVER - LEVEL ONE WATER RESCUE,Horse,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000309,Emerson Park,E09000016,Havering,Hornchurch,1.00021E+11,Wingletye Lane,21300661,RM11,555415,188368,555450,188350,51.57276421,0.241266584
192003111,28/11/2011 10:46,2011,2011/12,Special Service,1,1,260,260,BIRD STUCK BEHIND GAS FIRE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011111,Peckham Rye,E09000028,Southwark,New Cross,NULL,Rye Hill Park,22502162,SE15,NULL,NULL,535050,175150,NULL,NULL
192368111,29/11/2011 00:46,2011,2011/12,Special Service,1,1,260,260,INJURED CAT TRAPPED ON FENCE,Cat,Person (land line),Secondary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000273,Hornsey,E09000014,Haringey,Hornsey,10003976087,High Street,21106115,N8,530501,189292,530550,189250,51.58738112,-0.117671242
192560111,29/11/2011 13:10,2011,2011/12,Special Service,1,2,260,520,Redacted,Bird,Person (land line),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000130,Camden Town with Primrose Hill,E09000007,Camden,Kentish Town,5131040,Chalk Farm Road,20400624,NW1,528250,184250,528250,184250,51.54258617,-0.151982666
192615111,29/11/2011 15:11,2011,2011/12,Special Service,1,1,260,260,CAT FALLEN THROUGH ROOF OF GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000354,Hanworth Park,E09000018,Hounslow,Feltham,1.00024E+11,High Street,21500609,TW13,510061,172281,510050,172250,51.43884274,-0.417886764
192790111,29/11/2011 21:41,2011,2011/12,Special Service,1,1,260,260,DEER WITH HEAD TRAPPED IN METAL FENCING,Deer,Police,House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Tring Gardens,NULL,RM3,NULL,NULL,554450,192750,NULL,NULL
194264111,03/12/2011 02:20,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UNDER CAR ENGINE,Cat,Person (land line),Van,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000113,Copers Cope,E09000006,Bromley,Beckenham,NULL,Abbey Lane,NULL,BR3,537435,170628,537450,170650,51.41801435,-0.024910616
194387111,03/12/2011 10:57,2011,2011/12,Special Service,1,1,260,260,DOG IN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000137,Highgate,E09000007,Camden,West Hampstead,NULL,Hampstead Lane,NULL,NW3,527087,187418,527050,187450,51.57131936,-0.167600351
194397111,03/12/2011 11:39,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND FRIDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000227,Shooters Hill,E09000011,Greenwich,Eltham,NULL,Shrewsbury Lane,20801354,SE18,NULL,NULL,543850,177050,NULL,NULL
194986111,04/12/2011 12:39,2011,2011/12,Special Service,NULL,NULL,260,NULL,HORSE WITH NECK STUCK IN HORSE BOX,Horse,Other FRS,Other road vehicle,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000207,Southbury,E09000010,Enfield,Enfield,NULL,Cattlegate Road,NULL,EN2,534836,197513,534850,197550,51.66023599,-0.051973898
195000111,04/12/2011 13:06,2011,2011/12,Special Service,1,1,260,260,KITTEN IN DISTRESS STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,1.00023E+11,Old Church Road,22863050,E4,537588,193832,537550,193850,51.62649127,-0.013658806
195063111,04/12/2011 15:29,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER RAILWAY BRIDGE,Cat,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000253,College Park and Old Oak,E09000013,Hammersmith and Fulham,Acton,NULL,Erconwald Street,NULL,W12,521697,181202,521650,181250,51.51664198,-0.247483497
195914111,06/12/2011 11:37,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000470,St. Helier,E09000024,Merton,Mitcham,NULL,Central Road,22101299,SM4,NULL,NULL,525850,167850,NULL,NULL
196048111,06/12/2011 16:40,2011,2011/12,Special Service,1,1,260,260,PUPPY WITH HEAD STUCK IN PIPE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000477,Canning Town North,E09000025,Newham,Plaistow,NULL,Balaam Street,22200346,E13,NULL,NULL,540450,182550,NULL,NULL
196068111,06/12/2011 17:22,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRD TRAPPED ON WALL,Bird,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000632,Bryanston and Dorset Square,E09000033,Westminster,Paddington,NULL,Thornton Place,NULL,W1H,527754,181833,527750,181850,51.52097699,-0.160007659
196485111,07/12/2011 13:01,2011,2011/12,Special Service,1,1,260,260,HORSE STUCK IN FEEDER,Horse,Person (land line),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000559,Carshalton South and Clockhouse,E09000029,Sutton,Wallington,NULL,Telegraph Track,NULL,SM5,528533,161624,528550,161650,51.33918096,-0.15611196
197524111,09/12/2011 10:53,2011,2011/12,Special Service,1,2,260,520,Redacted,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000552,Surrey Docks,E09000028,Southwark,Deptford,NULL,Rope Street,NULL,SE16,536205,179026,536250,179050,51.49377917,-0.039356834
198693111,11/12/2011 12:43,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05011489,Woodside,E09000008,Croydon,Woodside,NULL,Portland Road,20501311,SE25,NULL,NULL,534550,167950,NULL,NULL
199772111,13/12/2011 13:58,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND WARDROBE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000626,Tooting,E09000032,Wandsworth,Tooting,NULL,Tooting Grove,22905473,SW17,NULL,NULL,527150,171250,NULL,NULL
201204111,16/12/2011 12:45,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN TRAPPED UNDER CAR,Cat,Person (mobile),Car,Road Vehicle,Animal rescue from height,Animal rescue from height - Domestic pet,E05000098,Queens Park,E09000005,Brent,North Kensington,NULL,Wakeman Road,NULL,NW10,523574,182785,523550,182750,51.53046193,-0.219890643
201866111,17/12/2011 14:19,2011,2011/12,Special Service,1,2,260,520,ASSIST  RSPCA WITH SEAGULL TRAPPED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000364,Syon,E09000018,Hounslow,Heston,NULL,Amhurst Gardens,NULL,TW7,516213,176385,516250,176350,51.47450072,-0.32806767
201900111,17/12/2011 15:35,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED BETWEEN WALLS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000343,West Ruislip,E09000017,Hillingdon,Ruislip,NULL,Arlington Drive,21400057,HA4,NULL,NULL,508550,188250,NULL,NULL
201961111,17/12/2011 17:36,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK BEHIND FRIDGE,Cat,Person (land line),Loose refuse,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,NULL,Martha Street,NULL,E1,535096,181023,535050,181050,51.51199095,-0.054557461
201991111,17/12/2011 18:25,2011,2011/12,Special Service,1,2,260,520,CAT STUCK UNDER LIFT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011110,Peckham,E09000028,Southwark,Peckham,NULL,Blakes Road,22500261,SE15,NULL,NULL,533450,177150,NULL,NULL
202543111,18/12/2011 19:20,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD STUCK BEHIND WASHING MACHINE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011231,Slade Green & Northend,E09000004,Bexley,Erith,NULL,Mariners Walk,20101643,DA8,NULL,NULL,551950,177750,NULL,NULL
202839111,19/12/2011 09:49,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA OFFICER WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000435,Tulse Hill,E09000022,Lambeth,Brixton,1.00023E+11,Endymion Road,21900523,SW2,530842,174066,530850,174050,51.45047038,-0.118399603
202919111,19/12/2011 12:36,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA TO RELEASE FOX IN BASEMENT,Fox,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Uxbridge Road,21000835,W12,NULL,NULL,522950,180050,NULL,NULL
202958111,19/12/2011 13:47,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED BETWEEN TWO FENCES,Dog,Person (mobile),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000049,East Finchley,E09000003,Barnet,Finchley,NULL,Elmshurst Crescent,NULL,N2,526565,189306,526550,189350,51.58840393,-0.174448693
203380111,20/12/2011 10:17,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000435,Tulse Hill,E09000022,Lambeth,Brixton,1.00023E+11,Endymion Road,21900523,SW2,530842,174066,530850,174050,51.45047038,-0.118399603
203386111,20/12/2011 10:30,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN ENGINE BAY,Cat,Person (land line),Van,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000449,New Cross,E09000023,Lewisham,Deptford,1.00023E+11,Amersham Vale,22000036,SE14,536788,177108,536750,177150,51.47640255,-0.031706903
204381111,22/12/2011 11:47,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Wat Tyler Road,NULL,SE3,538750,176750,538750,176750,51.47270806,-0.00361435
204851111,23/12/2011 10:21,2011,2011/12,Special Service,1,2,260,520,BIRD TRAPPED IN WIRE,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009303,Dowgate,E09000001,City of London,Dowgate,NULL,College Street,NULL,EC4R,532526,180834,532550,180850,51.51090094,-0.091641986
205399111,24/12/2011 14:08,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN LOFT,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000433,Thornton,E09000022,Lambeth,Tooting,NULL,Rosethorn Close,21901179,SW12,NULL,NULL,529550,173650,NULL,NULL
205418111,24/12/2011 14:44,2011,2011/12,Special Service,1,1,260,260,CAT STUCK BEHIND KITCHEN CORNER UNIT,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000191,Southfield,E09000009,Ealing,Acton,NULL,Valetta Road,20601798,W3,NULL,NULL,521350,179850,NULL,NULL
205721111,25/12/2011 09:38,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,NULL,Millais Road,22858750,E11,NULL,NULL,538550,185850,NULL,NULL
206172111,26/12/2011 12:50,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN GATE AND WALL,Cat,Person (mobile),Railings,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000194,Bush Hill Park,E09000010,Enfield,Edmonton,207112198,Village Park Close,20706108,EN1,533141,195361,533150,195350,51.64130135,-0.077283265
206177111,26/12/2011 12:58,2011,2011/12,Special Service,1,1,260,260,PIGEONS TRAPPED UNDER NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000209,Southgate Green,E09000010,Enfield,Southgate,NULL,Palmers Road,20705024,N11,NULL,NULL,529250,192150,NULL,NULL
206266111,26/12/2011 16:21,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN VENT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000601,Hoe Street,E09000031,Waltham Forest,Walthamstow,NULL,First Avenue,22836300,E17,NULL,NULL,537550,188950,NULL,NULL
206614111,27/12/2011 12:14,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND FRIDGE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000321,South Hornchurch,E09000016,Havering,Wennington,NULL,Danbury Road,21300746,RM13,NULL,NULL,551650,183850,NULL,NULL
206637111,27/12/2011 13:12,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000103,Welsh Harp,E09000005,Brent,Stanmore,202069460,Meadowbank Road,20202641,NW9,520565,187812,520550,187850,51.57629131,-0.26152585
206999111,28/12/2011 09:32,2011,2011/12,Special Service,1,1,260,260,UNKNOWN ANIMAL STUCK IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000340,Uxbridge North,E09000017,Hillingdon,Hillingdon,NULL,Stuart Close,21401886,UB10,NULL,NULL,507350,184750,NULL,NULL
207037111,28/12/2011 11:52,2011,2011/12,Special Service,1,2,260,520,HORSE STUCK IN DITCH,Horse,Police,Roadside vegetation,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000114,Cray Valley East,E09000006,Bromley,Orpington,NULL,Waldens Road,NULL,BR5,548427,167277,548450,167250,51.38513632,0.131651702
207043111,28/12/2011 12:28,2011,2011/12,Special Service,1,1,260,260,ANIMAL OR BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000340,Uxbridge North,E09000017,Hillingdon,Hillingdon,NULL,Stuart Close,21401886,UB10,NULL,NULL,507350,184750,NULL,NULL
207242111,28/12/2011 20:23,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED IN HOLE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000029,Chadwell Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Nash Road,19900270,RM6,NULL,NULL,547650,189450,NULL,NULL
207430111,29/12/2011 10:16,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN CAR ENGINE COMPARTMENT,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000599,High Street,E09000031,Waltham Forest,Walthamstow,1.00024E+11,High Street,22845050,E17,536475,188908,536450,188950,51.5825158,-0.031642043
207652111,29/12/2011 17:56,2011,2011/12,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000306,Brooklands,E09000016,Havering,Romford,NULL,Cromer Road,21301117,RM7,NULL,NULL,550350,188250,NULL,NULL
208046111,30/12/2011 16:54,2011,2011/12,Special Service,2,3,260,780,DEER TRAPPED UNDER CAR,Deer,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000314,Heaton,E09000016,Havering,Romford,NULL,Lower Bedfords Road,NULL,RM1,552587,192071,552550,192050,51.60680725,0.202099996
310121,01/01/2012 13:39,2012,2011/12,Special Service,1,3,260,780,CAT STUCK BETWEEN TWO WALLS,Cat,Person (mobile),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011252,South Woodford,E09000026,Redbridge,Woodford,1.00022E+11,High View Road,22300483,E18,539554,190026,539550,190050,51.59180798,0.013211406
654121,02/01/2012 09:26,2012,2011/12,Special Service,1,1,260,260,CAT IN DISTRESS,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000134,Gospel Oak,E09000007,Camden,Kentish Town,NULL,Wellesley Road,NULL,NW5,528185,185191,528150,185150,51.55105761,-0.152576839
716121,02/01/2012 13:04,2012,2011/12,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Park Avenue North,21100673,N8,NULL,NULL,529850,189550,NULL,NULL
1433121,03/01/2012 20:13,2012,2011/12,Special Service,2,3,260,780,Redacted,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009322,Bromley South,E09000030,Tower Hamlets,Poplar,NULL,Violet Road,NULL,E3,537692,181859,537650,181850,51.51887743,-0.016844426
1765121,04/01/2012 15:30,2012,2011/12,Special Service,1,1,260,260,INJURED DOG BEHIND FENCE BY RAILWAY LINE,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000075,Erith,E09000004,Bexley,Erith,NULL,James Watt Way,NULL,DA8,551448,177721,551450,177750,51.47817917,0.179494985
2149121,05/01/2012 13:11,2012,2011/12,Special Service,1,2,260,520,INCOMPLETE CALL DOG TRAPPED UNDER TREE,Dog,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000414,Tolworth and Hook Rise,E09000021,Kingston upon Thames,Surbiton,1.00022E+11,Hook Rise North,21860007,KT6,519431,165428,519450,165450,51.37535346,-0.285443139
2658121,06/01/2012 13:25,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN IN TREE,Cat,Person (land line),Wasteland,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000201,Haselbury,E09000010,Enfield,Edmonton,NULL,Silver Street,NULL,N18,532622,192653,532650,192650,51.61708887,-0.085805107
3348121,07/01/2012 22:03,2012,2011/12,Special Service,1,1,260,260,CAT STUCK IN GARAGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Plender Street,20400747,NW1,NULL,NULL,529250,183650,NULL,NULL
3374121,07/01/2012 22:52,2012,2011/12,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (land line),Railings,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Defoe Avenue,NULL,TW9,519379,177147,519350,177150,51.48069102,-0.282245578
3513121,08/01/2012 09:58,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA TO RESCUE CAT IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000437,Bellingham,E09000023,Lewisham,Beckenham,1.00022E+11,Oakview Road,22001756,SE6,537692,171355,537650,171350,51.42448515,-0.020934939
3652121,08/01/2012 15:43,2012,2011/12,Special Service,1,1,260,260,DOG WITH JAW TRAPPED IN LIFT GRILL,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000135,Hampstead Town,E09000007,Camden,West Hampstead,NULL,East Heath Road,20400070,NW3,NULL,NULL,526850,186150,NULL,NULL
3721121,08/01/2012 17:46,2012,2011/12,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Wandsworth Bridge Road,21000856,SW6,NULL,NULL,525850,175850,NULL,NULL
3880121,09/01/2012 01:05,2012,2011/12,Special Service,1,1,260,260,DOG TRAPPED BEHIND SHED,Dog,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000345,Yiewsley,E09000017,Hillingdon,Hillingdon,1.00022E+11,Castle Avenue,21400320,UB7,506650,180754,506650,180750,51.51565343,-0.464388078
4037121,09/01/2012 13:42,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED IN WIRING,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000049,East Finchley,E09000003,Barnet,Finchley,NULL,Trinity Road,20043280,N2,NULL,NULL,526550,189850,NULL,NULL
4293121,09/01/2012 21:36,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000344,Yeading,E09000017,Hillingdon,Southall,NULL,Kittiwake Way,21401109,UB4,NULL,NULL,511750,181650,NULL,NULL
4996121,11/01/2012 11:27,2012,2011/12,Special Service,1,1,260,260,CAT STUCK UNDER FLOORBOARDS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011111,Peckham Rye,E09000028,Southwark,Forest Hill,NULL,Marmora Road,22501666,SE22,NULL,NULL,535150,174350,NULL,NULL
5139121,11/01/2012 16:24,2012,2011/12,Special Service,1,1,260,260,RUNNING CALL TO KITTEN ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000055,Hendon,E09000003,Barnet,Hendon,NULL,Heriot Road,20022000,NW4,NULL,NULL,523350,188850,NULL,NULL
48122,12/01/2012 12:57,2012,2011/12,Special Service,1,2,260,520,HORSE IN DITCH,Horse,Person (land line),Heathland,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000571,Wandle Valley,E09000029,Sutton,Mitcham,NULL,Seymour Road,NULL,CR4,528292,166619,528250,166650,51.38412629,-0.157769571
88122,12/01/2012 14:40,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000179,Hanger Hill,E09000009,Ealing,Park Royal,NULL,Cleveley Crescent,NULL,W5,518497,183049,518450,183050,51.53392141,-0.292958997
135122,12/01/2012 16:36,2012,2011/12,Special Service,1,1,260,260,SQUIRREL TRAPPED ON ROOF,Squirrel,Person (land line),Vehicle sales building,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000449,New Cross,E09000023,Lewisham,Deptford,1.00022E+11,Creekside,22004382,SE8,537439,177125,537450,177150,51.47639759,-0.022332238
1184122,14/01/2012 20:50,2012,2011/12,Special Service,1,1,260,260,RCA TO CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000210,Town,E09000010,Enfield,Enfield,NULL,Lavender Hill,20705086,EN2,NULL,NULL,532250,197850,NULL,NULL
1454122,15/01/2012 12:05,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED PIGEON,Bird,Person (mobile),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000275,Noel Park,E09000014,Haringey,Hornsey,NULL,High Road,NULL,N22,531113,190103,531150,190150,51.59452706,-0.108540124
2327122,17/01/2012 00:42,2012,2011/12,Special Service,1,1,260,260,FOX TRAPPED BETWEEN FENCES,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000051,Finchley Church End,E09000003,Barnet,Finchley,200138721,Hendon Lane,20021800,N3,524662,190301,524650,190350,51.59777019,-0.201549635
2677122,17/01/2012 15:45,2012,2011/12,Special Service,2,3,260,780,Redacted,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000213,Winchmore Hill,E09000010,Enfield,Southgate,NULL,Broad Walk,NULL,N21,531149,194416,531150,194450,51.63327694,-0.106407658
5808121,19/01/2012 08:52,2012,2011/12,Special Service,1,1,260,260,BIRD TRAPPED DOWN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009369,Clissold,E09000012,Hackney,Stoke Newington,NULL,Howard Road,20900539,N16,NULL,NULL,533050,185450,NULL,NULL
6451121,20/01/2012 18:33,2012,2011/12,Special Service,1,1,260,260,PONY STUCK ON A FENCE,Horse,Person (mobile),Wasteland,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000072,Cray Meadows,E09000004,Bexley,Sidcup,NULL,Bunkers Hill,NULL,DA14,548834,172137,548850,172150,51.42869842,0.139533387
6762121,21/01/2012 11:22,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND SHED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000479,Custom House,E09000025,Newham,Plaistow,NULL,Garvary Road,22207723,E16,NULL,NULL,540950,181250,NULL,NULL
6852121,21/01/2012 14:24,2012,2011/12,Special Service,1,1,260,260,INJURED BIRD ON ROOF OF HOUSE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000201,Haselbury,E09000010,Enfield,Edmonton,NULL,Tranmere Road,20704859,N9,NULL,NULL,533850,194450,NULL,NULL
7003121,21/01/2012 19:52,2012,2011/12,Special Service,1,1,260,260,CAR ALIGHT FOLLOWING RTA,Unknown - Domestic Animal Or Pet,Police,Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011487,Waddon,E09000008,Croydon,Croydon,NULL,Warham Road,20502408,CR2,NULL,NULL,532350,164350,NULL,NULL
7303121,22/01/2012 12:37,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000452,Sydenham,E09000023,Lewisham,Forest Hill,NULL,Hazel Grove,22001536,SE26,NULL,NULL,535950,171550,NULL,NULL
7422121,22/01/2012 17:12,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALL AND SHED,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000098,Queens Park,E09000005,Brent,North Kensington,202118264,Chamberlayne Road,20202065,NW10,523449,183467,523450,183450,51.53661857,-0.221452742
7999121,23/01/2012 23:04,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER BASEMENT GRILL,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009336,Whitechapel,E09000030,Tower Hamlets,Whitechapel,6013053,New Road,22700873,E1,534571,181429,534550,181450,51.51576464,-0.061963546
8336121,24/01/2012 16:52,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN VENT,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000609,Wood Street,E09000031,Waltham Forest,Walthamstow,2.00001E+11,Wood Street,22888350,E17,538337,189636,538350,189650,51.58860371,-0.004498563
8660121,25/01/2012 11:23,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN WIRE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000116,Crystal Palace,E09000006,Bromley,West Norwood,NULL,Fox Hill,20303952,SE19,NULL,NULL,533850,170150,NULL,NULL
10039121,28/01/2012 00:39,2012,2011/12,Special Service,1,1,260,260,CAT STUCK UNDER FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000058,Oakleigh,E09000003,Barnet,Southgate,NULL,Manor Drive,20028180,N20,NULL,NULL,527450,193350,NULL,NULL
10197121,28/01/2012 11:10,2012,2011/12,Special Service,1,1,260,260,DOG TRAPPED UNDER SHED,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009387,Woodberry Down,E09000012,Hackney,Stoke Newington,NULL,Denver Road,20900324,N16,NULL,NULL,533150,187850,NULL,NULL
10711121,29/01/2012 09:48,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA INSPECTOR WITH SWAN ON ROOF,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000252,Avonmore and Brook Green,E09000013,Hammersmith and Fulham,Hammersmith,34062433,Maclise Road,21000529,W14,524194,179146,524150,179150,51.49762141,-0.212238703
10839121,29/01/2012 15:09,2012,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,128039317,Castle Street,21800212,KT1,518205,169375,518250,169350,51.41108523,-0.301737877
11585121,30/01/2012 23:42,2012,2011/12,Special Service,1,1,260,260,FOX TRAPPED BETWEEN WALLS,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000095,Mapesbury,E09000005,Brent,West Hampstead,202152295,Keyes Road,20200117,NW2,524008,185385,524050,185350,51.55373371,-0.212721536
11872121,31/01/2012 11:41,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000477,Canning Town North,E09000025,Newham,Plaistow,46026584,Florence Street,22201407,E16,539756,182245,539750,182250,51.52183943,0.013036929
12147121,31/01/2012 20:46,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN GUTTERING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009402,Redcliffe,E09000020,Kensington and Chelsea,Chelsea,NULL,Bramham Gardens,21700043,SW5,NULL,NULL,525850,178450,NULL,NULL
12387121,01/02/2012 10:54,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN BARBED WIRE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000494,West Ham,E09000025,Newham,Stratford,NULL,Meeson Road,NULL,E15,NULL,NULL,539650,184050,NULL,NULL
12541121,01/02/2012 16:06,2012,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Other FRS,Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,10022941117,Worcester Avenue,21105271,N17,533750,191250,533750,191250,51.60421424,-0.070057111
13055121,02/02/2012 11:29,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP A TREE,Cat,Person (mobile),Heathland,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000621,Roehampton and Putney Heath,E09000032,Wandsworth,Wandsworth,NULL,Telegraph Road,NULL,SW15,523342,173690,523350,173650,51.44877243,-0.226408784
13699121,03/02/2012 12:42,2012,2011/12,Special Service,1,1,260,260,RUNNING CALL TO CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009386,Victoria,E09000012,Hackney,Homerton,NULL,Victoria Park Road,20901033,E9,NULL,NULL,535950,184050,NULL,NULL
13836121,03/02/2012 17:02,2012,2011/12,Special Service,1,1,260,260,ANIMAL TRAPPED IN BAG IN TREE,Unknown - Wild Animal,Police,Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Bath Street,21604222,EC1V,NULL,NULL,532550,182550,NULL,NULL
14549121,04/02/2012 16:12,2012,2011/12,Special Service,1,1,260,260,CAT  TRAPPED IN FIREPLACE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000425,Larkhall,E09000022,Lambeth,Brixton,NULL,Prideaux Road,21901118,SW9,NULL,NULL,530350,175850,NULL,NULL
14627121,04/02/2012 17:51,2012,2011/12,Special Service,1,1,260,260,SWANS TRAPPED IN POND,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000552,Surrey Docks,E09000028,Southwark,Deptford,NULL,Russell Place,NULL,SE16,536182,179212,536150,179250,51.49545618,-0.03961618
14911121,05/02/2012 02:14,2012,2011/12,Special Service,1,1,260,260,DOG IN POND  WATER RESCUE LEVEL TWO,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000111,Chislehurst,E09000006,Bromley,Sidcup,NULL,Green Lane,NULL,BR7,544044,170821,544050,170850,51.41811004,0.070145956
15154121,05/02/2012 13:21,2012,2011/12,Special Service,1,1,260,260,DOG IN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000073,Danson Park,E09000004,Bexley,Bexley,NULL,Danson Park,NULL,DA6,547750,175250,547750,175250,51.45695349,0.125251397
15166121,05/02/2012 13:46,2012,2011/12,Special Service,1,2,260,520,DOG IN CANAL UNDER ICE - LEVEL TWO WATER RESCUE IMPLEMENTED,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000178,Greenford Green,E09000009,Ealing,Northolt,NULL,Oldfield Lane North,NULL,UB6,514885,184497,514850,184450,51.54768113,-0.344539258
15358121,05/02/2012 19:28,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN BIN CHUTE ROOM,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000419,Clapham Town,E09000022,Lambeth,Clapham,NULL,Nelsons Row,NULL,SW4,529710,175317,529750,175350,51.46197331,-0.134222926
16209121,07/02/2012 14:44,2012,2011/12,Special Service,2,3,260,780,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000611,Bedford,E09000032,Wandsworth,Tooting,10025174808,Doctor Johnson Avenue,22901279,SW17,528750,171750,528750,171750,51.43013547,-0.149330437
16571121,08/02/2012 09:39,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA KITTEN STUCK IN TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000556,Beddington South,E09000029,Sutton,Purley,NULL,Great Woodcote Park,22604963,CR8,NULL,NULL,530150,162450,NULL,NULL
16702121,08/02/2012 14:51,2012,2011/12,Special Service,1,1,260,260,ASSIST  RSPCA  WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000340,Uxbridge North,E09000017,Hillingdon,Hillingdon,NULL,Holm Grove,21401007,UB10,NULL,NULL,507450,184250,NULL,NULL
16870121,08/02/2012 21:57,2012,2011/12,Special Service,1,1,260,260,KITTEN STUCK UNDER FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011237,Chadwell,E09000026,Redbridge,Ilford,NULL,Haywards Close,22302130,RM6,NULL,NULL,547050,188650,NULL,NULL
17531121,10/02/2012 10:32,2012,2011/12,Special Service,2,3,260,780,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000515,Wanstead,E09000026,Redbridge,Leytonstone,NULL,Northumberland Avenue,NULL,E12,541255,187124,541250,187150,51.56530819,0.036584474
18054121,11/02/2012 10:50,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000206,Ponders End,E09000010,Enfield,Enfield,NULL,Percy Gardens,NULL,EN3,535794,195860,535750,195850,51.645151,-0.038773882
18130121,11/02/2012 12:49,2012,2011/12,Special Service,1,1,260,260,DOG IN RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000353,Hanworth,E09000018,Hounslow,Feltham,NULL,Ellerman Avenue,NULL,TW2,513176,172882,513150,172850,51.44363131,-0.372897318
18181121,11/02/2012 13:38,2012,2011/12,Special Service,1,2,260,520,DOG UNDER ICE IN PARK,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Lee Green,NULL,Kidbrooke Park Road,NULL,SE3,540859,174903,540850,174950,51.45559055,0.025996182
18186121,11/02/2012 13:44,2012,2011/12,Special Service,1,1,260,260,RUNNING CALL TO DOG TRAPPED IN CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000132,Fortune Green,E09000007,Camden,West Hampstead,NULL,Fortune Green Road,NULL,NW6,525250,185250,525250,185250,51.55224631,-0.194864627
18213121,11/02/2012 14:18,2012,2011/12,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000620,Queenstown,E09000032,Wandsworth,Battersea,NULL,Battersea Park,NULL,SW11,528483,177258,528450,177250,51.4796965,-0.151171155
18366121,11/02/2012 17:35,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA,Unknown - Heavy Livestock Animal,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000046,Colindale,E09000003,Barnet,Mill Hill,NULL,Little Strand,20027060,NW9,NULL,NULL,521750,190450,NULL,NULL
19912121,13/02/2012 22:24,2012,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000530,Teddington,E09000027,Richmond upon Thames,Twickenham,NULL,Park Road,22403720,TW11,NULL,NULL,515850,170750,NULL,NULL
20018121,14/02/2012 07:59,2012,2011/12,Special Service,1,3,260,780,HORSE STUCK IN DITCH,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000312,Harold Wood,E09000016,Havering,Harold Hill,NULL,Shepherds Hill,NULL,RM3,555438,190419,555450,190450,51.59118514,0.242504548
20131121,14/02/2012 14:06,2012,2011/12,Special Service,1,1,260,260,Redacted,Dog,Other FRS,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Rangers Road,NULL,E4,539788,194780,539750,194750,51.63446819,0.018481342
20209121,14/02/2012 16:50,2012,2011/12,Special Service,1,3,260,780,HORSE IN DITCH,Horse,Person (mobile),Other outdoor location,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000079,North End,E09000004,Bexley,Erith,NULL,Ray Lamb Way,NULL,DA8,552750,177750,552750,177750,51.47808986,0.198241941
20818121,15/02/2012 21:28,2012,2011/12,Special Service,2,3,260,780,DOG TRAPPED UNDER SHED,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000427,Prince's,E09000022,Lambeth,Lambeth,NULL,Newburn Street,21901016,SE11,NULL,NULL,530850,178250,NULL,NULL
21119121,16/02/2012 14:06,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN HOUSES,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000476,Boleyn,E09000025,Newham,East Ham,NULL,Blenheim Road,22200387,E6,NULL,NULL,541550,182950,NULL,NULL
22297121,18/02/2012 16:08,2012,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000279,Stroud Green,E09000014,Haringey,Holloway,NULL,Scarborough Road,21103849,N4,NULL,NULL,531350,187450,NULL,NULL
22572121,19/02/2012 11:01,2012,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND CABINET,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011242,Fairlop,E09000026,Redbridge,Hainault,NULL,Baron Gardens,22302607,IG6,NULL,NULL,544450,189950,NULL,NULL
22577121,19/02/2012 11:23,2012,2011/12,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011237,Chadwell,E09000026,Redbridge,Dagenham,1.00022E+11,Norbury Gardens,22302307,RM6,547673,188782,547650,188750,51.57856275,0.129808614
23211121,20/02/2012 15:35,2012,2011/12,Special Service,1,1,260,260,CAT IN PRECARIOUS POSTION REQ BY RSPCA,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000569,Wallington North,E09000029,Sutton,Wallington,5870082311,Caraway Place,22600393,SM6,528756,165336,528750,165350,51.37249116,-0.151570068
23525121,21/02/2012 09:46,2012,2011/12,Special Service,1,2,260,520,ELDERLY HORSE FALLEN AND IN DISTRESS,Horse,Person (mobile),Barn,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,1.00021E+11,Noak Hill Road,21301452,RM3,554187,193530,554150,193550,51.61948023,0.225827382
23610121,21/02/2012 13:11,2012,2011/12,Special Service,1,1,260,260,DOG STUCK ON LEDGE OF ROOF,Dog,Police,Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,NULL,Lewisham High Street,22004177,SE13,NULL,NULL,538150,175150,NULL,NULL
23785121,21/02/2012 17:14,2012,2011/12,Special Service,1,2,260,520,BIRD STUCK IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Corker Walk,21606796,N7,NULL,NULL,530950,186650,NULL,NULL
24077121,22/02/2012 08:00,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALL  AND WARDROBE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011464,Bensham Manor,E09000008,Croydon,Norbury,NULL,Wiltshire Road,20501575,CR7,NULL,NULL,531350,168450,NULL,NULL
24379121,22/02/2012 19:15,2012,2011/12,Special Service,1,1,260,260,PUPPY TRAPPED BEHIND SINK,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000045,Childs Hill,E09000003,Barnet,West Hampstead,NULL,Granville Road,20018860,NW2,NULL,NULL,524650,186850,NULL,NULL
24413121,22/02/2012 19:55,2012,2011/12,Special Service,1,1,260,260,PARROT TRAPPED BEHIND FIREPLACE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000378,ST. GEORGE'S,E09000019,ISLINGTON,Holloway,NULL,DALMENY AVENUE,21606427,N7,NULL,NULL,529950,185450,NULL,NULL
24462121,22/02/2012 22:18,2012,2011/12,Special Service,1,1,260,260,DOG WITH PAW CAUGHT IN RAILINGS,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Gee Street,21604937,EC1V,NULL,NULL,532150,182350,NULL,NULL
25181121,24/02/2012 10:36,2012,2011/12,Special Service,1,2,260,520,HORSE UNABLE TO GET UP,Horse,Person (mobile),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000111,Chislehurst,E09000006,Bromley,Eltham,NULL,Slades Drive,NULL,BR7,544056,171975,544050,171950,51.42847648,0.07078766
25206121,24/02/2012 11:53,2012,2011/12,Special Service,1,1,260,260,PIDGEON TRAPPED  IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000136,Haverstock,E09000007,Camden,Kentish Town,NULL,Hartland Road,NULL,NW1,528592,184293,528550,184250,51.54289487,-0.147037979
25990121,25/02/2012 18:26,2012,2011/12,Special Service,1,1,260,260,BIRD ENTANGLED IN STRING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000121,Mottingham and Chislehurst North,E09000006,Bromley,Eltham,NULL,Prestbury Square,20302083,SE9,NULL,NULL,542850,171850,NULL,NULL
27917121,29/02/2012 14:10,2012,2011/12,Special Service,1,2,260,520,CAT STUCK IN CHIMNEY FLUE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000470,St. Helier,E09000024,Merton,Mitcham,NULL,Blanchland Road,22100654,SM4,NULL,NULL,525850,167950,NULL,NULL
28436121,01/03/2012 13:10,2012,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000556,Beddington South,E09000029,Sutton,Purley,5870095118,Great Woodcote Park,22604963,CR8,530166,162463,530150,162450,51.34635049,-0.132374858
28448121,01/03/2012 13:29,2012,2011/12,Special Service,1,1,260,260,TO ASSIST RSPCA,Bird,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000250,Addison,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Shepherds Bush Road,NULL,W6,523455,179132,523450,179150,51.49765729,-0.222884228
28703121,01/03/2012 20:46,2012,2011/12,Special Service,1,1,260,260,KITTEN BEHIND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,NULL,Elf Row,22700459,E1W,NULL,NULL,535450,180850,NULL,NULL
28988121,02/03/2012 14:25,2012,2011/12,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000625,Thamesfield,E09000032,Wandsworth,Wandsworth,1.00023E+11,Ashlone Road,22900158,SW15,523483,175923,523450,175950,51.46881071,-0.223602068
29146121,02/03/2012 19:38,2012,2011/12,Special Service,1,1,260,260,SEAGULL TRAPPED IN TREE,Bird,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000649,West End,E09000033,Westminster,Soho,10033571000,Park Lane,8400585,W1K,528454,180126,528450,180150,51.50547766,-0.15054557
29909121,04/03/2012 10:35,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000180,Hobbayne,E09000009,Ealing,Ealing,NULL,Church Road,NULL,W7,515489,180564,515450,180550,51.51220918,-0.33712111
30103121,04/03/2012 20:51,2012,2011/12,Special Service,1,1,260,260,CAT STUCK UNDERNEATH FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Montagu Gardens,20704506,N18,NULL,NULL,534850,192750,NULL,NULL
30697121,06/03/2012 06:45,2012,2011/12,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011102,Faraday,E09000028,Southwark,Old Kent Road,NULL,Thurlow Street,22502507,SE17,NULL,NULL,532950,178350,NULL,NULL
32116121,09/03/2012 07:46,2012,2011/12,Special Service,1,1,260,260,KITTENS TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011482,Shirley North,E09000008,Croydon,Woodside,NULL,Winchet Walk,20502807,CR0,NULL,NULL,535150,167250,NULL,NULL
32316121,09/03/2012 16:37,2012,2011/12,Special Service,1,2,260,520,CAT TRAPPED BETWEEN FENCES,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000614,Fairfield,E09000032,Wandsworth,Wandsworth,1.00023E+11,Santos Road,22904521,SW18,525057,174858,525050,174850,51.45889405,-0.201329153
33391121,11/03/2012 15:29,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN HOUSES,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000346,Bedfont,E09000018,Hounslow,Feltham,NULL,Whitebridge Close,21501197,TW14,NULL,NULL,509750,174150,NULL,NULL
33936121,12/03/2012 14:22,2012,2011/12,Special Service,1,1,260,260,DEER TRAPPED BETWEEN METAL AND WOODEN FENC E,Deer,Person (land line),Scrub land,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000338,South Ruislip,E09000017,Hillingdon,Ruislip,NULL,Lea Crescent,NULL,HA4,509644,185566,509650,185550,51.55833114,-0.419760122
34403121,13/03/2012 12:57,2012,2011/12,Special Service,1,1,260,260,CAT STUCK UNDER FLOOR BOARD,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,NULL,Corfton Road,20600441,W5,NULL,NULL,518150,181650,NULL,NULL
34431121,13/03/2012 14:04,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UNDER FLOOR BOARDS,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,NULL,Corfton Road,20600441,W5,NULL,NULL,518150,181650,NULL,NULL
34917121,14/03/2012 11:11,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000257,Munster,E09000013,Hammersmith and Fulham,Fulham,NULL,Bishops Road,21000124,SW6,NULL,NULL,524650,177050,NULL,NULL
34931121,14/03/2012 11:54,2012,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000595,Forest,E09000031,Waltham Forest,Leyton,1.00023E+11,Leyton Green Road,22852400,E10,538125,188236,538150,188250,51.57607574,-0.008106373
35391121,15/03/2012 07:48,2012,2011/12,Special Service,1,1,260,260,DEER TRAPPED IN FENCE,Deer,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Tring Close,NULL,RM3,554511,192703,554550,192750,51.6119609,0.230138519
35453121,15/03/2012 10:32,2012,2011/12,Special Service,NULL,NULL,260,NULL,CAT INJURED UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009400,Pembridge,E09000020,Kensington and Chelsea,Kensington,217066036,Pembridge Villas,21700757,W11,525250,180750,525250,180750,51.51180412,-0.196464488
35473121,15/03/2012 11:10,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000374,Hillrise,E09000019,Islington,Hornsey,NULL,Ella Road,NULL,N8,530406,187900,530450,187950,51.57489391,-0.11955788
35944121,16/03/2012 09:18,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000478,Canning Town South,E09000025,Newham,Plaistow,NULL,Ling Road,22200812,E16,NULL,NULL,540450,181950,NULL,NULL
36004121,16/03/2012 12:05,2012,2011/12,Special Service,1,2,260,520,ASSIST RSPCA WITH HERON TRAPPED IN TREE WATER LEVEL RESCUE TWO,Bird,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000345,Yiewsley,E09000017,Hillingdon,Hillingdon,NULL,Packet Boat Lane,NULL,UB8,505339,181208,505350,181250,51.51997999,-0.48313884
36042121,16/03/2012 14:16,2012,2011/12,Special Service,1,1,260,260,TWO CATS IN A BAG FLOATING IN CANAL,cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009377,Hoxton East & Shoreditch,E09000012,Hackney,Shoreditch,NULL,Whitmore Road,NULL,N1,533141,183674,533150,183650,51.53627805,-0.081712082
36064121,16/03/2012 15:21,2012,2011/12,Special Service,1,1,260,260,CAT STUCK ON ROOF OF VICTORIAN TERRACE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000455,Abbey,E09000024,Merton,Wimbledon,NULL,Bournemouth Road,22100732,SW19,NULL,NULL,525450,169750,NULL,NULL
36628121,17/03/2012 17:58,2012,2011/12,Special Service,1,1,260,260,DOG TRAPPED DOWN FOX HOLE IN GARDEN,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000185,Northolt West End,E09000009,Ealing,Northolt,12034372,Parkfield Road,20601360,UB5,512085,183299,512050,183250,51.53747604,-0.385284846
36953121,18/03/2012 08:55,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011480,Selsdon & Addington Village,E09000008,Croydon,Addington,NULL,Wyncote Way,20501877,CR2,NULL,NULL,535650,162550,NULL,NULL
36979121,18/03/2012 10:49,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP A TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000104,Wembley Central,E09000005,Brent,Wembley,NULL,Ecclestone Place,NULL,HA9,518839,185297,518850,185250,51.5540537,-0.287271981
36995121,18/03/2012 11:39,2012,2011/12,Special Service,NULL,NULL,260,NULL,PONY COLLASPED IN STABLE,Horse,Person (land line),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011482,Shirley North,E09000008,Croydon,Woodside,1.00021E+11,Orchard Way,20500526,CR0,536525,167298,536550,167250,51.38830928,-0.039269103
37444121,19/03/2012 09:33,2012,2011/12,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000354,Hanworth Park,E09000018,Hounslow,Feltham,NULL,Queens Road,21500912,TW13,NULL,NULL,511050,173350,NULL,NULL
38006121,20/03/2012 11:17,2012,2011/12,Special Service,1,1,260,260,INJURED SQUIRREL IN TREE,Squirrel,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Wild animal rescue from height,E05009384,Stamford Hill West,E09000012,Hackney,Stoke Newington,NULL,Durley Road,NULL,N16,533186,187648,533150,187650,51.57197936,-0.079560997
38312121,20/03/2012 22:07,2012,2011/12,Special Service,1,1,260,260,KITTEN STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000201,Haselbury,E09000010,Enfield,Edmonton,207096415,Wyldfield Gardens,20704962,N9,533791,193841,533750,193850,51.62748792,-0.068476945
38636121,21/03/2012 14:26,2012,2011/12,Special Service,2,3,260,780,DOG TRAPPED UNDER CABIN,Dog,Person (land line),Temporary office (eg portacabin),Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000227,Shooters Hill,E09000011,Greenwich,Eltham,1.00023E+11,Shooters Hill,20801346,DA16,544523,176322,544550,176350,51.46741841,0.079274952
40271121,24/03/2012 13:46,2012,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000607,Valley,E09000031,Waltham Forest,Chingford,1.00023E+11,Mandeville Court,22855325,E4,536410,192538,536450,192550,51.61515031,-0.031170109
40322121,24/03/2012 15:33,2012,2011/12,Special Service,1,1,260,260,CAT STUCK IN CHIMNEY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,Exeter Road,20200226,NW2,NULL,NULL,524350,184850,NULL,NULL
40488121,24/03/2012 21:08,2012,2011/12,Special Service,1,1,260,260,CAT STUCK IN HOLLOW ON ROOF,Cat,Person (mobile),Vehicle Repair Workshop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000603,Lea Bridge,E09000031,Waltham Forest,Leyton,2.00001E+11,Warley Close,22885110,E10,536940,187482,536950,187450,51.5695891,-0.025489691
40728121,25/03/2012 11:38,2012,2011/12,Special Service,1,2,260,520,KITTENS STUCK IN WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011476,Purley & Woodcote,E09000008,Croydon,Purley,NULL,Hartley Old Road,20502122,CR8,NULL,NULL,530750,160250,NULL,NULL
41492121,26/03/2012 20:22,2012,2011/12,Special Service,1,1,260,260,SWAN TRAPPED IN WIRE FENCE,Bird,Police,Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Woodford,NULL,Armstrong Avenue,NULL,IG8,539184,191802,539150,191850,51.60785813,0.008577519
41499121,26/03/2012 20:36,2012,2011/12,Special Service,1,1,260,260,KITTEN STUCK ON SCAFFOLDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,St. Giles Road,22502337,SE5,NULL,NULL,533050,177050,NULL,NULL
41831121,27/03/2012 12:31,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000428,St. Leonard's,E09000022,Lambeth,Tooting,NULL,Whinfell Close,21901490,SW16,NULL,NULL,529650,171450,NULL,NULL
41860121,27/03/2012 13:21,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000301,Roxbourne,E09000015,Harrow,Northolt,NULL,Eastway Crescent,NULL,HA2,513655,186980,513650,186950,51.57024673,-0.361468932
41877121,27/03/2012 14:00,2012,2011/12,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009403,Royal Hospital,E09000020,Kensington and Chelsea,Chelsea,NULL,Draycott Place,21700154,SW3,NULL,NULL,527750,178650,NULL,NULL
41926121,27/03/2012 15:48,2012,2011/12,Special Service,1,1,260,260,CAT STUCK BETWEEN TWO WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011229,St. Mary's & St. James,E09000004,Bexley,Bexley,NULL,Glenhurst Avenue,20100604,DA5,NULL,NULL,548950,173350,NULL,NULL
42096121,27/03/2012 19:48,2012,2011/12,Special Service,1,1,260,260,DOG TRAPPED ON MUD BANK OF RIVER THAMES,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000362,Isleworth,E09000018,Hounslow,Heston,NULL,Swan Street,NULL,TW7,516631,175811,516650,175850,51.46925578,-0.322240867
42312121,28/03/2012 09:37,2012,2011/12,Special Service,1,1,260,260,INJURED CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000623,Shaftesbury,E09000032,Wandsworth,Clapham,NULL,Eversleigh Road,22901568,SW11,NULL,NULL,528350,176250,NULL,NULL
42996121,29/03/2012 08:14,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Broomhouse Lane,21000798,SW6,NULL,NULL,525150,176050,NULL,NULL
43603121,30/03/2012 01:29,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN STAIRWELL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000131,Cantelowes,E09000007,Camden,Kentish Town,NULL,Cliff Road,20400536,NW1,NULL,NULL,529850,184850,NULL,NULL
43747121,30/03/2012 10:10,2012,2011/12,Special Service,1,1,260,260,SPARROW HAWK CAUGHT IN NETTING,Bird,Person (mobile),Other entertainment venue,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,10033549112,Whitehall,8400881,SW1A,530171,180017,530150,180050,51.50410562,-0.125860229
44012121,30/03/2012 18:39,2012,2011/12,Special Service,1,1,260,260,DOG STUCK IN METAL FENCE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000194,Bush Hill Park,E09000010,Enfield,Edmonton,NULL,Wellington Road,NULL,EN1,533293,194390,533250,194350,51.63253957,-0.075458048
44345121,31/03/2012 11:22,2012,2011/12,Special Service,1,2,260,520,DEER STUCK IN RAILINGS,Deer,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000162,Selsdon and Ballards,E09000008,Croydon,Addington,NULL,Kingswood Way,NULL,CR2,535726,161571,535750,161550,51.33703541,-0.052929189
45055121,01/04/2012 14:38,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND UNIT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000092,Kensal Green,E09000005,Brent,North Kensington,NULL,Purves Road,20202226,NW10,NULL,NULL,522950,183050,NULL,NULL
45321121,01/04/2012 20:21,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000174,Ealing Common,E09000009,Ealing,Ealing,NULL,The Grove,NULL,W5,NULL,NULL,517850,180450,NULL,NULL
45682121,02/04/2012 14:09,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000463,Lavender Fields,E09000024,Merton,Mitcham,NULL,Eveline Road,22102499,CR4,NULL,NULL,527650,169550,NULL,NULL
46231121,03/04/2012 14:00,2012,2012/13,Special Service,1,1,260,260,PIGEON TRAPPED IN TREE,Bird,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009335,Weavers,E09000030,Tower Hamlets,Bethnal Green,NULL,Bethnal Green Road,NULL,E2,534202,182598,534250,182550,51.52635748,-0.066833253
46668121,04/04/2012 09:51,2012,2012/13,Special Service,1,1,260,260,BIRD WITH LEG TRAPPED IN WIRE,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000160,Sanderstead,E09000008,Croydon,Croydon,NULL,Sanderstead Road,NULL,CR2,533189,162108,533150,162150,51.34246079,-0.089126623
47833121,06/04/2012 11:38,2012,2012/13,Special Service,1,1,260,260,TO ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000216,Charlton,E09000011,Greenwich,East Greenwich,NULL,Limekiln Drive,20800917,SE7,NULL,NULL,540950,177750,NULL,NULL
48014121,06/04/2012 17:05,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN ARCHWAY,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000412,St. Mark's,E09000021,Kingston upon Thames,Surbiton,NULL,Maple Road,NULL,KT6,517886,167576,517850,167550,51.39498215,-0.306919076
48139121,06/04/2012 20:19,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN TREE BY STRING,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000051,Finchley Church End,E09000003,Barnet,Hendon,NULL,Windsor Road,NULL,N3,524223,190220,524250,190250,51.59713921,-0.207913015
48395121,07/04/2012 09:03,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN GENERATOR IN ALLEYWAY REAR OF,Cat,Person (land line),Electricity power station,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Trowbridge Road,NULL,RM3,553971,191924,553950,191950,51.60510968,0.222004183
48512121,07/04/2012 15:28,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN GUTTERING,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000315,Hylands,E09000016,Havering,Hornchurch,NULL,Albany Road,21300936,RM12,NULL,NULL,552350,186650,NULL,NULL
48600121,07/04/2012 18:40,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRD,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000368,Caledonian,E09000019,Islington,Islington,NULL,Havelock Street,21605077,N1,NULL,NULL,530450,183750,NULL,NULL
48948121,08/04/2012 14:11,2012,2012/13,Special Service,1,1,260,260,CROW TRAPPED IN TREE,Bird,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011236,Bridge,E09000026,Redbridge,Woodford,1.00023E+11,Prospect Road,22305259,IG8,541296,192045,541250,192050,51.60951625,0.039154238
48990121,08/04/2012 15:55,2012,2012/13,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Other outdoor equipment/machinery,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009370,Dalston,E09000012,Hackney,Stoke Newington,NULL,St. Mark's Rise,NULL,E8,533861,185023,533850,185050,51.54823016,-0.070824604
49034121,08/04/2012 17:29,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL STUCK IN CHIMNEY,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000108,Bromley Common and Keston,E09000006,Bromley,Bromley,NULL,Southborough Lane,20300617,BR2,NULL,NULL,543750,167550,NULL,NULL
49084121,08/04/2012 18:52,2012,2012/13,Special Service,1,2,260,520,DUCK STUCK IN PIPEWORK,Bird,Person (land line),Sports Hall,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,10001273301,MacFarlane Lane,21590134,TW7,515892,177809,515850,177850,51.48736527,-0.332220888
49188121,08/04/2012 22:28,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN BASEMENT GAP,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000129,Bloomsbury,E09000007,Camden,Soho,NULL,Windmill Street,20401070,W1T,NULL,NULL,529550,181650,NULL,NULL
50721121,12/04/2012 10:30,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000480,East Ham Central,E09000025,Newham,East Ham,NULL,Friars Road,22207584,E6,NULL,NULL,541950,183550,NULL,NULL
50804121,12/04/2012 14:20,2012,2012/13,Special Service,1,2,260,520,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000131,Cantelowes,E09000007,Camden,Kentish Town,NULL,St. Augustines Road,20400671,NW1,NULL,NULL,529850,184650,NULL,NULL
50808121,12/04/2012 14:29,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON STUCK IN BRANCHES,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,NULL,Bancroft Road,NULL,E1,535571,182608,535550,182650,51.52612043,-0.047107022
50809121,12/04/2012 14:33,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN WIRE,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000084,Thamesmead East,E09000004,Bexley,Erith,NULL,Yarnton Way,NULL,SE2,547492,179648,547450,179650,51.49653834,0.123376326
50845121,12/04/2012 15:55,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000294,Kenton East,E09000015,Harrow,Stanmore,NULL,D'Arcy Gardens,21201544,HA3,NULL,NULL,518050,189350,NULL,NULL
50867121,12/04/2012 16:33,2012,2012/13,Special Service,1,2,260,520,DOG TRAPPED IN CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Richmond,1.00023E+11,Richmond Hill,22403768,TW10,518448,173845,518450,173850,51.45120893,-0.296751036
50900121,12/04/2012 17:55,2012,2012/13,Special Service,1,1,260,260,DOVE TRAPPED IN GUTTERING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000333,Ickenham,E09000017,Hillingdon,Ruislip,NULL,Milton Road,21401300,UB10,NULL,NULL,507650,185950,NULL,NULL
50982121,12/04/2012 19:38,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN STORAGE CONTAINER,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000414,Tolworth and Hook Rise,E09000021,Kingston upon Thames,Surbiton,1.00022E+11,Verona Drive,21800997,KT6,518277,165571,518250,165550,51.37688012,-0.301968066
51014121,12/04/2012 21:28,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000140,Kilburn,E09000007,Camden,West Hampstead,NULL,Kilburn High Road,20400395,NW6,NULL,NULL,525150,184050,NULL,NULL
51097121,13/04/2012 03:40,2012,2012/13,Special Service,1,2,260,520,CAT TRAPPED DOWN HOLE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009375,Haggerston,E09000012,Hackney,Shoreditch,NULL,Kingsland Road,20900581,E8,NULL,NULL,533550,183850,NULL,NULL
51412121,13/04/2012 20:21,2012,2012/13,Special Service,NULL,NULL,260,NULL,INJURED CAT IN DITCH (UNACCESSABLE TO INFORMANTS),Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Dalston Lane,NULL,E8,534539,185138,534550,185150,51.54910234,-0.061008473
51715121,14/04/2012 13:17,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX  TRAPPED IN FENCE,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000306,Brooklands,E09000016,Havering,Dagenham,NULL,Meadow Road,NULL,RM7,549822,187430,549850,187450,51.56584909,0.160223418
52160121,15/04/2012 09:16,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UP A TELEGRAPH POLE,Cat,Person (land line),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000496,Barkingside,E09000026,Redbridge,Ilford,NULL,Roll Gardens,NULL,IG2,543284,188743,543250,188750,51.57934419,0.066496909
52423121,15/04/2012 18:49,2012,2012/13,Special Service,1,2,260,520,CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000289,Harrow on the Hill,E09000015,Harrow,Northolt,2E+11,South Hill Avenue,21201256,HA1,514421,186279,514450,186250,51.56379155,-0.350649328
52773121,16/04/2012 12:14,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOORBOARDS IN HALLWAY AREA,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Haringey Park,21103376,N8,NULL,NULL,530350,188350,NULL,NULL
52782121,16/04/2012 12:23,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000566,Sutton South,E09000029,Sutton,Sutton,5870045799,Brighton Road,22605685,SM2,526057,163273,526050,163250,51.35455509,-0.191055453
53006121,16/04/2012 19:10,2012,2012/13,Special Service,1,2,260,520,DOG STUCK UNDER CONCRETE BASE IN PARK,Dog,Police,Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000457,Colliers Wood,E09000024,Merton,Wimbledon,NULL,Byegrove Road,NULL,SW19,526805,170534,526850,170550,51.41964485,-0.177728831
53077121,16/04/2012 20:46,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED BETWEEN WALL AND RAILWAY TRACK,Cat,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,10010229460,Greenwich High Road,20800685,SE10,538063,177332,538050,177350,51.47810592,-0.013271984
53299121,17/04/2012 10:56,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Crooms Hill,NULL,SE10,538735,177045,538750,177050,51.47536266,-0.003714395
53839121,18/04/2012 17:06,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000617,Latchmere,E09000032,Wandsworth,Battersea,NULL,Culvert Road,22901100,SW11,NULL,NULL,527950,176350,NULL,NULL
54335121,19/04/2012 19:27,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000250,Addison,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Cromwell Grove,21000251,W6,NULL,NULL,523450,179450,NULL,NULL
54541121,20/04/2012 10:10,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRD,Bird,Person (land line),Other Dwelling,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Old Kent Road,22501843,SE15,NULL,NULL,534750,177550,NULL,NULL
55173121,21/04/2012 16:41,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN FENCE,Cat,Person (mobile),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000091,Harlesden,E09000005,Brent,Park Royal,202127816,Bramshill Road,20200105,NW10,521533,183193,521550,183150,51.53457121,-0.249159283
55467121,22/04/2012 10:23,2012,2012/13,Special Service,1,1,260,260,FOX CUB TRAPPED IN FENCE,Fox,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000298,Pinner South,E09000015,Harrow,Harrow,NULL,Cannonbury Avenue,NULL,HA5,511928,188208,511950,188250,51.58162944,-0.385986056
55924121,23/04/2012 12:10,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRD CAUGHT IN NETTING,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,1.00023E+11,London Road,22403635,TW1,516161,173602,516150,173650,51.44949833,-0.329729315
56045121,23/04/2012 16:39,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN HOLE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000285,Belmont,E09000015,Harrow,Stanmore,NULL,Beverley Gardens,21202479,HA7,NULL,NULL,516250,190650,NULL,NULL
56307121,24/04/2012 09:55,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN MESH,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000567,Sutton West,E09000029,Sutton,Sutton,NULL,Mulgrave Road,22605508,SM2,NULL,NULL,525650,163650,NULL,NULL
56393121,24/04/2012 14:18,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED BEHIND FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000124,Petts Wood and Knoll,E09000006,Bromley,Orpington,NULL,Heath Side,20300415,BR5,NULL,NULL,544550,166550,NULL,NULL
56563121,24/04/2012 17:28,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN HOLE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000255,Fulham Reach,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Rainville Road,21000669,W6,NULL,NULL,523450,177450,NULL,NULL
57403121,26/04/2012 11:07,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH RESCUE OF CAT,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Park Road,NULL,N8,530049,188465,530050,188450,51.5800537,-0.124497644
57478121,26/04/2012 14:45,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN FOXHOLE,Dog,Person (mobile),Scrub land,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009394,Dalgarno,E09000020,Kensington and Chelsea,North Kensington,NULL,Dalgarno Gardens,NULL,W10,523118,181789,523150,181750,51.52161006,-0.226809188
57692121,26/04/2012 21:59,2012,2012/13,Special Service,1,1,260,260,PIGEON STUCK IN GATE FRAME,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000430,Streatham Hill,E09000022,Lambeth,West Norwood,NULL,Bushell Close,21900267,SW2,NULL,NULL,530950,172950,NULL,NULL
57871121,27/04/2012 11:37,2012,2012/13,Special Service,1,1,260,260,HORSE TRAPPED IN MUD,Horse,Person (mobile),Park,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,NULL,Ferry Lane,NULL,RM13,551477,180682,551450,180650,51.50477667,0.181183537
57881121,27/04/2012 12:09,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER UNITS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000622,St. Mary's Park,E09000032,Wandsworth,Battersea,NULL,Surrey Lane,22905114,SW11,NULL,NULL,527250,176850,NULL,NULL
58084121,27/04/2012 19:54,2012,2012/13,Special Service,1,1,260,260,PIGEON CAUGHT IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000607,Valley,E09000031,Waltham Forest,Chingford,NULL,Chingford Mount Road,22826000,E4,NULL,NULL,537350,192050,NULL,NULL
58131121,27/04/2012 21:27,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON WINDOWLEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000115,Cray Valley West,E09000006,Bromley,Orpington,NULL,Barnesdale Crescent,20300918,BR5,NULL,NULL,546350,167550,NULL,NULL
58375121,28/04/2012 10:49,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON THIRD FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Wembley,NULL,Rainborough Close,20200763,NW10,NULL,NULL,520250,184850,NULL,NULL
59007121,29/04/2012 12:35,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011230,Sidcup,E09000004,Bexley,Sidcup,1.0002E+11,Tarling Close,20101418,DA14,546874,172115,546850,172150,51.42901166,0.111351514
59242121,29/04/2012 19:36,2012,2012/13,Special Service,1,1,260,260,CAT POSSIBLY TRAPPED UNDER FALLEN TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,Westholm,20045820,NW11,NULL,NULL,525550,189150,NULL,NULL
59442121,30/04/2012 09:45,2012,2012/13,Special Service,1,1,260,260,LARGE ANIMAL RESCUE,Unknown - Animal rescue from water - Farm animal,Person (land line),"Grassland, pasture, grazing etc",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05004081,Warley,E07000068,Brentwood,Essex,NULL,Codham Hall Lane,NULL,CM13,558421,189162,558450,189150,51.57906201,0.284965091
59539121,30/04/2012 13:47,2012,2012/13,Special Service,1,4,260,1040,CAT TRAPPED BEWTEEN TWO WALLS,Cat,Person (land line),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000526,North Richmond,E09000027,Richmond upon Thames,Richmond,1.00022E+11,Bicester Road,22405345,TW9,519522,175708,519550,175750,51.46772753,-0.280673298
60358121,01/05/2012 21:18,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN BASEMENT,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000366,Barnsbury,E09000019,Islington,Islington,NULL,Barnsbury Square,21604202,N1,NULL,NULL,531050,184150,NULL,NULL
60816121,02/05/2012 22:37,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000599,High Street,E09000031,Waltham Forest,Walthamstow,NULL,Erskine Road,NULL,E17,536901,189186,536950,189150,51.58491066,-0.025389104
61179121,03/05/2012 19:52,2012,2012/13,Special Service,1,2,260,520,RUNNING CALL TO DEER TRAPPED IN WOODED AREA,Deer,Person (mobile),Scrub land,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000117,Darwin,E09000006,Bromley,Orpington,NULL,Shire Lane,NULL,BR6,545125,164023,545150,164050,51.35675098,0.08290678
62143121,05/05/2012 19:50,2012,2012/13,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Retirement/Old Persons Home,Other Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000100,Stonebridge,E09000005,Brent,Park Royal,202121832,Barry Road,20202130,NW10,520502,184425,520550,184450,51.54586443,-0.26359519
62406121,06/05/2012 08:58,2012,2012/13,Special Service,1,2,260,520,HORSE IN PRECARIOUS POSITION,Horse,Person (mobile),Barn,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000111,Chislehurst,E09000006,Bromley,Sidcup,1.00023E+11,Kemnal Road,20303635,BR7,544623,171629,544650,171650,51.42522311,0.078796617
62512121,06/05/2012 14:11,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Uxbridge Road,21000835,W12,NULL,NULL,522750,180150,NULL,NULL
63057121,07/05/2012 20:32,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000285,Belmont,E09000015,Harrow,Stanmore,NULL,Abercorn Road,21202472,HA7,NULL,NULL,517150,191250,NULL,NULL
63417121,08/05/2012 18:06,2012,2012/13,Special Service,1,1,260,260,DOG IN RIVER,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000620,Queenstown,E09000032,Wandsworth,Battersea,NULL,Battersea Park,NULL,SW11,528483,177258,528450,177250,51.4796965,-0.151171155
64409121,10/05/2012 17:07,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000342,West Drayton,E09000017,Hillingdon,Hayes,NULL,Pippins Close,21401546,UB7,NULL,NULL,505950,179350,NULL,NULL
64764121,11/05/2012 13:10,2012,2012/13,Special Service,1,4,260,1040,DOG TRAPPED IN BADGER SETT,Dog,Person (mobile),Wasteland,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000150,Coulsdon East,E09000008,Croydon,Purley,NULL,Mead Way,NULL,CR5,530695,158065,530650,158050,51.30670397,-0.126394083
64940121,11/05/2012 19:12,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON  ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Melliss Avenue,22407046,TW9,NULL,NULL,519650,176950,NULL,NULL
65398121,12/05/2012 18:04,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN NEETING,Bird,Police,Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011238,Churchfields,E09000026,Redbridge,Woodford,NULL,Churchfields,22304896,E18,NULL,NULL,540550,190750,NULL,NULL
65432121,12/05/2012 19:03,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER STEPS OF BUILDING,Dog,Person (mobile),Other outdoor sporting venue,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,2.00001E+11,Ledrington Road,20302181,SE19,534453,170657,534450,170650,51.41898943,-0.067757494
65475121,12/05/2012 20:58,2012,2012/13,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000131,Cantelowes,E09000007,Camden,Kentish Town,NULL,St. Augustines Road,20400671,NW1,NULL,NULL,529850,184550,NULL,NULL
65961121,13/05/2012 20:21,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN DRAINPIPE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000037,Parsloes,E09000002,Barking and Dagenham,Dagenham,NULL,Groveway,19903732,RM8,NULL,NULL,547750,185850,NULL,NULL
65971121,13/05/2012 20:38,2012,2012/13,Special Service,1,1,260,260,CAT WITH PAW STUCK IN WINDOW HINGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000629,West Putney,E09000032,Wandsworth,Wandsworth,NULL,William Gardens,22906198,SW15,NULL,NULL,522850,174950,NULL,NULL
65974121,13/05/2012 20:53,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,NULL,Vicarage Road,NULL,RM10,549566,184371,549550,184350,51.53843119,0.155233812
66179121,14/05/2012 07:37,2012,2012/13,Special Service,1,1,260,260,DEER TRAPPED IN METAL FENCE,Deer,Police,Park,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000052,Garden Suburb,E09000003,Barnet,West Hampstead,NULL,Ingram Avenue,NULL,NW11,526215,187657,526250,187650,51.573663,-0.18009031
66332121,14/05/2012 14:28,2012,2012/13,Special Service,1,1,260,260,BIRDS CAUGHT IN NETTING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000649,West End,E09000033,Westminster,Soho,1.00024E+11,Grosvenor Square,8400101,W1K,528483,180848,528450,180850,51.51195966,-0.149864979
66426121,14/05/2012 18:09,2012,2012/13,Special Service,1,1,260,260,DOG IN PRECARIOUS POSITION,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000491,Royal Docks,E09000025,Newham,Plaistow,NULL,Silverland Street,22207887,E16,NULL,NULL,542950,180050,NULL,NULL
66434121,14/05/2012 18:31,2012,2012/13,Special Service,1,1,260,260,KITTEN STUCK BEHIND CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000213,Winchmore Hill,E09000010,Enfield,Southgate,NULL,Lynwood Grove,20704463,N21,NULL,NULL,531350,194350,NULL,NULL
67296121,16/05/2012 18:04,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN RECLINING CHAIR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011467,Crystal Palace & Upper Norwood,E09000008,Croydon,West Norwood,NULL,Queen Mary Road,20500111,SE19,NULL,NULL,531950,170650,NULL,NULL
67387121,16/05/2012 20:52,2012,2012/13,Special Service,1,1,260,260,DOG STUCK IN HOLE,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000152,Croham,E09000008,Croydon,Croydon,NULL,Selsdon Road,NULL,CR2,533514,163242,533550,163250,51.35257543,-0.084038277
67402121,16/05/2012 21:09,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON LEDGE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Pulborough Road,22904009,SW18,NULL,NULL,524850,173550,NULL,NULL
67760121,17/05/2012 17:25,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Person (mobile),Heathland,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000101,Sudbury,E09000005,Brent,Wembley,NULL,Eton Avenue,NULL,HA0,517446,185663,517450,185650,51.55763452,-0.307233101
68097121,18/05/2012 11:57,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN SLIDING DOORS,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05011110,Peckham,E09000028,Southwark,Peckham,NULL,Diamond Street,22500768,SE15,NULL,NULL,533450,177150,NULL,NULL
68347121,18/05/2012 21:00,2012,2012/13,Special Service,1,1,260,260,DOG STUCK IN METAL RAILINGS,Dog,Person (mobile),Roadside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Crooms Hill,NULL,SE10,538589,177088,538550,177050,51.47578481,-0.00579839
68384121,18/05/2012 22:18,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN BRANCH OF TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000433,Thornton,E09000022,Lambeth,Tooting,1.00022E+11,Atkins Road,21900121,SW12,529135,173782,529150,173750,51.44830968,-0.143054988
68450121,19/05/2012 01:24,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER GRILL,Cat,Person (mobile),Other outdoor equipment/machinery,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009333,Spitalfields & Banglatown,E09000030,Tower Hamlets,Whitechapel,6002083,Pomell Way,22700956,E1,533822,181430,533850,181450,51.51595151,-0.07275119
68494121,19/05/2012 03:38,2012,2012/13,Special Service,1,1,260,260,INJURED DOG TRAPPED BEHIND FENCE,Dog,Person (mobile),Scrub land,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000325,Botwell,E09000017,Hillingdon,Hayes,NULL,Dawley Road,NULL,UB3,508858,180097,508850,180050,51.50932738,-0.432783176
68589121,19/05/2012 09:55,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000120,Kelsey and Eden Park,E09000006,Bromley,Beckenham,1.0002E+11,Greenways,20301738,BR3,537398,168878,537350,168850,51.40229714,-0.026119727
68627121,19/05/2012 11:34,2012,2012/13,Special Service,1,1,260,260,SQUIRREL TRAPPED IN WIRE,Squirrel,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000462,Hillside,E09000024,Merton,Wimbledon,48024296,Edge Hill,22102334,SW19,523686,170286,523650,170250,51.41810429,-0.22264834
69062121,20/05/2012 04:14,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN BED,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000425,Larkhall,E09000022,Lambeth,Clapham,NULL,Clapham Road,21900345,SW9,NULL,NULL,530250,176050,NULL,NULL
69112121,20/05/2012 09:42,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Bus/coach station/garage,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011116,South Bermondsey,E09000028,Southwark,Old Kent Road,2.00003E+11,Mandela Way,22501644,SE1,533481,178797,533450,178750,51.49237063,-0.078658734
69362121,20/05/2012 19:59,2012,2012/13,Special Service,1,1,260,260,RUNNING CALL TO DOG TRAPPED BEHIND WALL,Dog,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011467,Crystal Palace & Upper Norwood,E09000008,Croydon,West Norwood,2.00001E+11,Central Hill,20500023,SE19,532588,170889,532550,170850,51.42151349,-0.094475856
69600121,21/05/2012 12:48,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN DRAIN,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05011468,Fairfield,E09000008,Croydon,Croydon,NULL,Keens Road,20502164,CR0,NULL,NULL,532350,164850,NULL,NULL
70277121,22/05/2012 18:03,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000489,Plaistow North,E09000025,Newham,Stratford,NULL,Cecil Road,22200450,E13,NULL,NULL,540450,183650,NULL,NULL
71319121,24/05/2012 11:21,2012,2012/13,Special Service,1,1,260,260,FOX STUCK BETWEEN TWO FENCES,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving wild animal,E05000592,Chapel End,E09000031,Waltham Forest,Walthamstow,NULL,Thorpe Road,22881850,E17,NULL,NULL,538050,190250,NULL,NULL
71434121,24/05/2012 14:38,2012,2012/13,Special Service,1,1,260,260,PIGEON STUCK BEHIND CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000360,Hounslow South,E09000018,Hounslow,Heston,NULL,Dalmeny Crescent,21500335,TW3,NULL,NULL,514750,175350,NULL,NULL
3293122,24/05/2012 21:07,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000419,Clapham Town,E09000022,Lambeth,Clapham,NULL,Cedars Road,21900308,SW4,NULL,NULL,528650,175650,NULL,NULL
3354122,24/05/2012 22:46,2012,2012/13,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK IN TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05011471,New Addington South,E09000008,Croydon,Addington,1.00021E+11,North Downs Road,20501792,CR0,538018,162330,538050,162350,51.34330521,-0.019752656
3651122,25/05/2012 12:18,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000108,Bromley Common and Keston,E09000006,Bromley,Bromley,NULL,Knowle Road,NULL,BR2,542524,165866,542550,165850,51.37396958,0.046315254
3782122,25/05/2012 15:48,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRD,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000604,Leyton,E09000031,Waltham Forest,Leyton,NULL,Lea Hall Road,22850450,E10,NULL,NULL,537450,187350,NULL,NULL
4156122,26/05/2012 01:58,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER GATE,Dog,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Lever Street,NULL,EC1V,532139,182597,532150,182550,51.52683498,-0.096554751
4233122,26/05/2012 06:01,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN FENCE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011112,Rotherhithe,E09000028,Southwark,Dockhead,10009807682,Gomm Road,22501079,SE16,535165,179147,535150,179150,51.49511603,-0.054282408
4584122,26/05/2012 16:19,2012,2012/13,Special Service,1,1,260,260,HORSE FALLEN OVER WITH LEG TRAPPED IN TRAILER,Horse,Person (mobile),Other road vehicle,Road Vehicle,Other animal assistance,Assist  trapped livestock animal,E05000118,Farnborough and Crofton,E09000006,Bromley,Orpington,NULL,Tubbenden Lane,NULL,BR6,545532,165623,545550,165650,51.37102385,0.089401407
4638122,26/05/2012 17:00,2012,2012/13,Special Service,1,2,260,520,Redacted,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000282,West Green,E09000014,Haringey,Tottenham,NULL,Penniston Close,NULL,N17,532229,190321,532250,190350,51.59622523,-0.092356506
4763122,26/05/2012 19:47,2012,2012/13,Special Service,1,2,260,520,KITTEN TRAPPED UNDERNEATH KITCHEN CABINET,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Fore Street,20704226,N18,NULL,NULL,534050,192050,NULL,NULL
5126122,27/05/2012 09:28,2012,2012/13,Special Service,1,1,260,260,RUNNING CALL PIGEON TRAPPED ON BALCONY RAILINGS,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000526,North Richmond,E09000027,Richmond upon Thames,Richmond,NULL,Orchard Road,NULL,TW9,NULL,NULL,519250,175650,NULL,NULL
5485122,27/05/2012 19:28,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN AIR VENT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011103,Goose Green,E09000028,Southwark,Peckham,NULL,Solway Road,22502266,SE22,NULL,NULL,534350,175350,NULL,NULL
6638122,29/05/2012 10:27,2012,2012/13,Special Service,1,1,260,260,MAGPIE TRAPPED IN UPSTAIRS WINDOW,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000623,Shaftesbury,E09000032,Wandsworth,Battersea,NULL,Eversleigh Road,22901568,SW11,NULL,NULL,528050,176050,NULL,NULL
6915122,29/05/2012 18:17,2012,2012/13,Special Service,1,1,260,260,INJURED CROW STUCK IN AIREY/LIGHTWELL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal harm involving wild animal,E05000370,Clerkenwell,E09000019,Islington,Islington,NULL,Yardley Street,21606383,WC1X,NULL,NULL,531150,182450,NULL,NULL
6978122,29/05/2012 20:28,2012,2012/13,Special Service,1,1,260,260,DOG STUCK IN HOLE,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000376,Junction,E09000019,Islington,Kentish Town,NULL,Holloway Road,21606449,N19,NULL,NULL,529450,186750,NULL,NULL
7429122,30/05/2012 16:04,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000516,Barnes,E09000027,Richmond upon Thames,Hammersmith,NULL,Boileau Road,NULL,SW13,522509,177587,522550,177550,51.48397727,-0.237041378
7906122,31/05/2012 12:17,2012,2012/13,Special Service,1,1,260,260,ASSIST  RSPCA WITH RECOVERY OF DUCKLINGS,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000192,Walpole,E09000009,Ealing,Ealing,12148352,Melbourne Avenue,20601160,W13,516334,180277,516350,180250,51.50945656,-0.325044347
7999122,31/05/2012 15:50,2012,2012/13,Special Service,1,3,260,780,DUCKLINGS TRAPPED UNDER MANHOLE COVER,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Other animal assistance,Assist trapped wild animal,E05009330,St. Katharine's & Wapping,E09000030,Tower Hamlets,Shadwell,NULL,Vaughan Way,NULL,E1W,534432,180217,534450,180250,51.5049062,-0.06442752
8224122,31/05/2012 22:24,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN PIPE IN KITCHEN,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Broomhouse Lane,21000798,SW6,NULL,NULL,525250,175950,NULL,NULL
8997122,02/06/2012 14:22,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Lee Green,NULL,Eltham Road,NULL,SE9,541438,174591,541450,174550,51.45264276,0.034199504
9052122,02/06/2012 16:32,2012,2012/13,Special Service,1,1,260,260,DUCKLING FALLEN DOWN DRAIN,Bird,Person (land line),Road surface/pavement,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Richmond,NULL,Petersham Road,NULL,TW10,518211,173775,518250,173750,51.45062926,-0.30018357
9136122,02/06/2012 19:04,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED ON ROOF,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000599,High Street,E09000031,Waltham Forest,Walthamstow,NULL,Ickworth Park Road,22846950,E17,NULL,NULL,536350,189350,NULL,NULL
9508122,03/06/2012 12:49,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000220,Eltham West,E09000011,Greenwich,Lee Green,NULL,Eltham Road,NULL,SE9,541162,174630,541150,174650,51.45306202,0.030245505
9975122,04/06/2012 10:55,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000489,Plaistow North,E09000025,Newham,Plaistow,NULL,Credon Road,22207671,E13,NULL,NULL,541150,183250,NULL,NULL
10009122,04/06/2012 12:43,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000220,Eltham West,E09000011,Greenwich,Lee Green,NULL,Eltham Road,NULL,SE9,541165,174631,541150,174650,51.45307025,0.03028905
10017122,04/06/2012 13:03,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011218,Belvedere,E09000004,Bexley,Erith,NULL,Grosvenor Road,20100641,DA17,NULL,NULL,549050,177950,NULL,NULL
10041122,04/06/2012 14:11,2012,2012/13,Special Service,2,3,260,780,Redacted,Horse,Police,Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy wild animal,E05000607,Valley,E09000031,Waltham Forest,Walthamstow,NULL,Shadbolt Avenue,NULL,E4,536352,192119,536350,192150,51.61139917,-0.032170205
10097122,04/06/2012 17:07,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND BOILER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000354,Hanworth Park,E09000018,Hounslow,Feltham,NULL,Castle Way,21500214,TW13,NULL,NULL,511150,171850,NULL,NULL
10129122,04/06/2012 18:19,2012,2012/13,Special Service,1,2,260,520,HORSE TRAPPED UNDER TREE,Horse,Other FRS,Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011229,St. Mary's & St. James,E09000004,Bexley,Bexley,1.0002E+11,Monterey Close,20100992,DA5,550314,172547,550350,172550,51.43199214,0.16098005
10161122,04/06/2012 20:13,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000477,Canning Town North,E09000025,Newham,Plaistow,46004757,Beckton Road,22200364,E16,539853,181746,539850,181750,51.51733135,0.014236252
10310122,05/06/2012 01:25,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UNDER FLOORBOARDS,Cat,Police,House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Glenparke Road,22207729,E7,NULL,NULL,540750,184850,NULL,NULL
10333122,05/06/2012 02:08,2012,2012/13,Special Service,1,1,260,260,DOG STUCK IN IRON GATE,Dog,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000209,Southgate Green,E09000010,Enfield,Southgate,207045458,Shakespeare Avenue,20701888,N11,529444,192236,529450,192250,51.61408109,-0.131834375
10362122,05/06/2012 03:16,2012,2012/13,Special Service,1,1,260,260,ASSISTING POLICE WITH CAT STUCK UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000220,Eltham West,E09000011,Greenwich,Lee Green,NULL,Embry Road,NULL,SE9,541250,174750,541250,174750,51.45411841,0.031559018
10449122,05/06/2012 10:47,2012,2012/13,Special Service,1,2,260,520,BIRD STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000291,Hatch End,E09000015,Harrow,Harrow,NULL,Evelyn Drive,21201116,HA5,NULL,NULL,511650,190950,NULL,NULL
10469122,05/06/2012 12:16,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000322,Squirrel's Heath,E09000016,Havering,Harold Hill,NULL,Compton Avenue,21301099,RM2,NULL,NULL,553150,189550,NULL,NULL
10557122,05/06/2012 16:33,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN CHASSIS OF CAR OUTSIDE,Cat,Person (land line),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000549,Rotherhithe,E09000028,Southwark,Deptford,NULL,Abbeyfield Road,NULL,SE16,535277,178756,535250,178750,51.49157556,-0.052819827
10616122,05/06/2012 18:53,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000374,Hillrise,E09000019,Islington,Holloway,5300049348,Hornsey Lane,21600464,N6,529329,187515,529350,187550,51.57168197,-0.135232082
10657122,05/06/2012 20:41,2012,2012/13,Special Service,1,1,260,260,FOX STUCK IN FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011235,Barkingside,E09000026,Redbridge,Ilford,NULL,Icknield Drive,22302961,IG2,NULL,NULL,543950,188750,NULL,NULL
11035122,06/06/2012 17:34,2012,2012/13,Special Service,1,1,260,260,CAT WEDGED BETWEEN TWO WALLS,Cat,Police,Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000087,Brondesbury Park,E09000005,Brent,West Hampstead,NULL,Cavendish Road,20202448,NW6,NULL,NULL,524650,184450,NULL,NULL
11384122,07/06/2012 08:40,2012,2012/13,Special Service,1,1,260,260,DUCKLINGS TRAPPED IN DRAIN,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,NULL,Waterway Avenue,NULL,SE13,538003,175622,538050,175650,51.46275403,-0.014802933
11597122,07/06/2012 16:02,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000201,Haselbury,E09000010,Enfield,Edmonton,NULL,Haselbury Road,20704311,N18,NULL,NULL,533350,192950,NULL,NULL
11716122,07/06/2012 20:32,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000612,Earlsfield,E09000032,Wandsworth,Wandsworth,NULL,Twilley Street,22905634,SW18,NULL,NULL,525850,173850,NULL,NULL
12092122,08/06/2012 14:51,2012,2012/13,Special Service,1,1,260,260,KITTENS TRAPPED BENEATH LIFT CAR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011225,Erith,E09000004,Bexley,Erith,NULL,Winifred Road,20101605,DA8,NULL,NULL,551050,178350,NULL,NULL
12123122,08/06/2012 15:39,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000561,Nonsuch,E09000029,Sutton,Sutton,NULL,Braemar Road,NULL,KT4,522622,165351,522650,165350,51.37398197,-0.239645107
13591122,11/06/2012 10:10,2012,2012/13,Special Service,1,1,260,260,CROW TRAPPED IN NETTING,Bird,Person (mobile),Telephone exchange,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05011225,Erith,E09000004,Bexley,Erith,10011862607,Viking Way,20101683,DA8,550519,179176,550550,179150,51.49150039,0.16674822
13679122,11/06/2012 13:15,2012,2012/13,Special Service,1,1,260,260,BIRD POSSIBLY TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000339,Townfield,E09000017,Hillingdon,Hayes,NULL,Hemmen Lane,21400942,UB3,NULL,NULL,510050,181050,NULL,NULL
13723122,11/06/2012 14:44,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN FENCE AND HIGH BUSHES,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000027,Alibon,E09000002,Barking and Dagenham,Dagenham,100106144,Heathway,19900172,RM9,549031,184719,549050,184750,51.54169941,0.147672433
13875122,11/06/2012 20:05,2012,2012/13,Special Service,1,1,260,260,DOG IN DITCH,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Ferry Lane,NULL,TW9,518152,177176,518150,177150,51.48120893,-0.299897256
14190122,12/06/2012 14:08,2012,2012/13,Special Service,1,1,260,260,KITTEN STUCK BEHINED CAR EXHAUST,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000546,Peckham,E09000028,Southwark,Peckham,NULL,Cator Street,NULL,SE15,533808,177250,533850,177250,51.47839113,-0.074537108
14336122,12/06/2012 20:03,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000149,Broad Green,E09000008,Croydon,Croydon,NULL,Cairo New Road,NULL,CR0,531914,165729,531950,165750,51.37529926,-0.106080617
14530122,13/06/2012 08:04,2012,2012/13,Special Service,1,1,260,260,CAT WITH LEG TRAPPED IN GUTTERING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000290,Harrow Weald,E09000015,Harrow,Stanmore,NULL,College Road,21201461,HA3,NULL,NULL,515150,190950,NULL,NULL
15380122,14/06/2012 20:42,2012,2012/13,Special Service,1,4,260,1040,FOX CUB STUCK BETWEEN TWO WALLS,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011252,South Woodford,E09000026,Redbridge,Woodford,NULL,Byron Avenue,22300147,E18,NULL,NULL,539650,190050,NULL,NULL
15469122,15/06/2012 02:03,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON FENCE,Cat,Person (land line),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000489,Plaistow North,E09000025,Newham,Plaistow,NULL,Eastern Road,NULL,E13,540771,183358,540750,183350,51.53158862,0.028101158
17100122,18/06/2012 09:45,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER BUS,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000506,Hainault,E09000026,Redbridge,Hainault,NULL,Huntsman Road,NULL,IG6,546856,192475,546850,192450,51.61195906,0.11957153
17745122,19/06/2012 15:26,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN GARAGE ROOF,Cat,Person (land line),Private garage,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000602,Larkswood,E09000031,Waltham Forest,Chingford,1.00023E+11,Cranston Gardens,22829900,E4,537689,191986,537650,191950,51.6098788,-0.01292534
18177122,20/06/2012 11:12,2012,2012/13,Special Service,2,3,260,780,CAT STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000433,Thornton,E09000022,Lambeth,Tooting,NULL,Poynders Gardens,21902132,SW4,NULL,NULL,529350,173950,NULL,NULL
18251122,20/06/2012 14:24,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN TRAPPED IN METAL DRUM,Cat,Person (land line),"Large refuse/rubbish container (eg skip, paladin)",Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000266,Alexandra,E09000014,Haringey,Hornsey,1.00023E+11,Alexandra Park Road,21106086,N10,529009,190471,529050,190450,51.59831961,-0.138762055
18325122,20/06/2012 17:10,2012,2012/13,Special Service,NULL,NULL,260,NULL,DOG WITH HEAD STUCK IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000437,Bellingham,E09000023,Lewisham,Beckenham,NULL,Lushington Road,NULL,SE6,537620,171450,537650,171450,51.42535631,-0.021933009
18627122,21/06/2012 05:05,2012,2012/13,Special Service,1,5,260,1300,HORSE STUCK ON SOAK AWAY,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000206,Ponders End,E09000010,Enfield,Enfield,NULL,Wharf Road,NULL,EN3,536228,195385,536250,195350,51.64077745,-0.032690281
18983122,21/06/2012 22:15,2012,2012/13,Special Service,1,1,260,260,DOG STUCK IN RAILINGS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Crooms Hill,20800415,SE10,NULL,NULL,538450,177450,NULL,NULL
20803122,25/06/2012 11:18,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED BETWEEN  FENCE AND WALL,Dog,Police,Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Gooshays Drive,NULL,RM3,554117,192242,554150,192250,51.60792689,0.224250363
21099122,25/06/2012 21:02,2012,2012/13,Special Service,1,1,260,260,BIRD CAUGHT IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000421,Ferndale,E09000022,Lambeth,Brixton,NULL,Brixton Road,NULL,SW9,531024,175476,531050,175450,51.4630998,-0.115260147
21239122,26/06/2012 04:48,2012,2012/13,Special Service,1,2,260,520,DOG WITH HEAD STUCK IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000342,West Drayton,E09000017,Hillingdon,Hayes,NULL,Church Close,NULL,UB7,506048,179403,506050,179450,51.5036235,-0.47346664
21311122,26/06/2012 10:20,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000615,Furzedown,E09000032,Wandsworth,Tooting,NULL,Chillerton Road,NULL,SW17,528521,171241,528550,171250,51.42561289,-0.152807227
21637122,26/06/2012 22:25,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN HOLE BEHIND TOILET,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000423,Herne Hill,E09000022,Lambeth,Brixton,NULL,Robert Burns Mews,21902302,SE24,NULL,NULL,531850,174750,NULL,NULL
22083122,27/06/2012 19:27,2012,2012/13,Special Service,1,1,260,260,PIGEON TRAPPED IN WIRE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011241,Cranbrook,E09000026,Redbridge,Ilford,1.00023E+11,Clarence Avenue,22302721,IG2,543093,188312,543050,188350,51.57551988,0.063566864
22468122,28/06/2012 12:36,2012,2012/13,Special Service,1,3,260,780,HORSE CAST IN DITCH,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000331,Heathrow Villages,E09000017,Hillingdon,Heathrow,NULL,Colnbrook by Pass,NULL,UB7,505307,177206,505350,177250,51.4840144,-0.48479438
22697122,28/06/2012 18:00,2012,2012/13,Special Service,1,1,260,260,DOG FALLEN DOWN DRAIN,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011234,Aldborough,E09000026,Redbridge,Ilford,NULL,Applegarth Drive,22301809,IG2,NULL,NULL,545850,189250,NULL,NULL
22715122,28/06/2012 18:18,2012,2012/13,Special Service,1,1,260,260,DOG STUCK ON ROOF,Dog,Person (mobile),House in Multiple Occupation - 3 or more storeys (not known if licensed),Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000063,Woodhouse,E09000003,Barnet,Finchley,NULL,High Road,20022400,N12,NULL,NULL,526450,192050,NULL,NULL
23232122,29/06/2012 14:55,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRD TRAPPED IN TREE BY WIRE,Bird,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000230,Woolwich Riverside,E09000011,Greenwich,East Greenwich,NULL,Venus Road,NULL,SE18,542720,179182,542750,179150,51.49357494,0.0544905
24156122,01/07/2012 11:50,2012,2012/13,Special Service,1,1,260,260,CAT IMPALED ON RAILING,Cat,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000644,St. James's,E09000033,Westminster,Lambeth,NULL,Great Peter Street,NULL,SW1P,530135,179211,530150,179250,51.4968705,-0.126675815
24193122,01/07/2012 13:32,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Bird,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000438,Blackheath,E09000023,Lewisham,Lee Green,NULL,South Row,NULL,SE3,539798,176409,539750,176450,51.46938619,0.011330123
24750122,02/07/2012 15:54,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Cemetery,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009402,Redcliffe,E09000020,Kensington and Chelsea,Fulham,217060697,Old Brompton Road,21700372,SW5,525488,178099,525450,178050,51.48792635,-0.193979074
24990122,02/07/2012 23:37,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN GATE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000253,College Park and Old Oak,E09000013,Hammersmith and Fulham,Acton,NULL,Wulfstan Street,NULL,W12,521998,181140,521950,181150,51.51601994,-0.243169051
25223122,03/07/2012 14:58,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000482,East Ham South,E09000025,Newham,East Ham,NULL,Edwin Avenue,22200578,E6,NULL,NULL,543250,182950,NULL,NULL
25565122,04/07/2012 10:13,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000205,Palmers Green,E09000010,Enfield,Edmonton,207004258,North Circular Road,20707474,N13,531373,192276,531350,192250,51.61399391,-0.103975839
71657121,05/07/2012 15:00,2012,2012/13,Special Service,1,1,260,260,HORSE TRAPPED BY TREE TRUNK,Horse,Other FRS,Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011474,Old Coulsdon,E09000008,Croydon,Purley,1.00021E+11,Stites Hill Road,20500340,CR5,532030,157160,532050,157150,51.29826323,-0.107586803
71786121,05/07/2012 19:25,2012,2012/13,Special Service,1,3,260,780,HORSE TRAPPED IN DITCH,Horse,Other FRS,Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000110,Chelsfield and Pratts Bottom,E09000006,Bromley,Orpington,1.0002E+11,Skeet Hill Lane,20301291,BR5,549047,165483,549050,165450,51.36885459,0.139803182
71789121,05/07/2012 19:28,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN BASEMENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009381,London Fields,E09000012,Hackney,Shoreditch,NULL,Kingsland Road,20900581,E8,NULL,NULL,533550,184150,NULL,NULL
71889121,05/07/2012 22:19,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN DITCH,Cat,Person (mobile),Hedge,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000423,Herne Hill,E09000022,Lambeth,Brixton,10023853329,Cambria Road,21900277,SE5,532121,175817,532150,175850,51.46590943,-0.099350639
72050121,06/07/2012 09:33,2012,2012/13,Special Service,1,1,260,260,NEW BORN KITTENS STUCK IN HOLE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000198,Enfield Highway,E09000010,Enfield,Enfield,NULL,Carterhatch Road,20702166,EN3,NULL,NULL,536150,197350,NULL,NULL
72247121,06/07/2012 18:00,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000445,Grove Park,E09000023,Lewisham,Lee Green,NULL,Baring Road,NULL,SE12,540160,173450,540150,173450,51.44270703,0.015367382
72312121,06/07/2012 20:15,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000478,Canning Town South,E09000025,Newham,Plaistow,NULL,Forty Acre Lane,22200623,E16,NULL,NULL,540050,181650,NULL,NULL
72569121,07/07/2012 10:59,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX TRAPPED IN GARAGE,Fox,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped wild animal,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,NULL,Digby Street,NULL,E2,535373,182750,535350,182750,51.52744389,-0.049904918
73064121,08/07/2012 10:29,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN BARS,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000613,East Putney,E09000032,Wandsworth,Wandsworth,1.00023E+11,Putney Hill,22904077,SW15,523907,174889,523950,174850,51.45942517,-0.217862827
73085121,08/07/2012 10:54,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH SEGULLS TRAPPED DOWN A WELL,Bird,Person (land line),Converted office,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Bird,E05009305,Farringdon Without,E09000001,City of London,Soho,1.00023E+11,Bream's Buildings,8100080,EC4A,531176,181357,531150,181350,51.51591576,-0.110890077
73826121,09/07/2012 16:55,2012,2012/13,Special Service,1,1,260,260,ANIMAL TRAPPED BEHIND CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000029,Chadwell Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Thatches Grove,19900833,RM6,NULL,NULL,548350,189450,NULL,NULL
74235121,10/07/2012 11:58,2012,2012/13,Special Service,1,2,260,520,PREGNANT CAT TRAPPED UNDER FLOORBOARDS OF CELLAR,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000623,Shaftesbury,E09000032,Wandsworth,Battersea,NULL,Grayshott Road,22902114,SW11,NULL,NULL,528050,175850,NULL,NULL
74239121,10/07/2012 12:08,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRD TRAPPED IN NETTING,Bird,Person (mobile),Restaurant/cafe,Non Residential,Other animal assistance,Assist trapped wild animal,E05000528,South Richmond,E09000027,Richmond upon Thames,Richmond,1.00023E+11,The Quadrant,22406165,TW9,517980,175089,517950,175050,51.46248728,-0.303068732
74342121,10/07/2012 16:04,2012,2012/13,Special Service,1,2,260,520,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011111,Peckham Rye,E09000028,Southwark,Forest Hill,NULL,Therapia Road,22502490,SE22,NULL,NULL,535050,174250,NULL,NULL
74668121,11/07/2012 10:53,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011096,Camberwell Green,E09000028,Southwark,Peckham,NULL,Wyndham Road,22500231,SE5,NULL,NULL,532150,177450,NULL,NULL
75143121,12/07/2012 09:13,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER FENCE,Dog,Person (land line),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Defoe Avenue,NULL,TW9,519398,177172,519350,177150,51.48091172,-0.281963656
76278121,14/07/2012 12:48,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN RAILINGS,Dog,Police,Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Crooms Hill,NULL,SE10,538435,177495,538450,177450,51.47947986,-0.007854896
76376121,14/07/2012 16:28,2012,2012/13,Special Service,1,1,260,260,DEER TRAPPED ON RAILWAY LINE,Deer,Coastguard,Railway trackside vegetation,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000120,Kelsey and Eden Park,E09000006,Bromley,Beckenham,1.0002E+11,Hawksbrook Lane,20301744,BR3,537739,167290,537750,167250,51.38794459,-0.021836012
76410121,14/07/2012 17:31,2012,2012/13,Special Service,1,1,260,260,DUCKLINGS TRAPPED IN LOCK,Bird,Person (land line),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009328,Poplar,E09000030,Tower Hamlets,Poplar,NULL,Leamouth Road,NULL,E14,538959,181091,538950,181050,51.51166589,0.001102031
76467121,14/07/2012 20:20,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Unlicensed House in Multiple Occupation - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000279,Stroud Green,E09000014,Haringey,Holloway,NULL,Marquis Road,21103599,N4,NULL,NULL,530950,187350,NULL,NULL
76750121,15/07/2012 12:47,2012,2012/13,Special Service,1,1,260,260,TO ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000615,Furzedown,E09000032,Wandsworth,Tooting,1.00023E+11,North Drive,22903564,SW16,529169,171792,529150,171750,51.43041772,-0.143291249
76771121,15/07/2012 13:28,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED DOWN DISUSED TOILET IN DERELICT BUILDING,Cat,Person (mobile),Purpose built office,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000178,Greenford Green,E09000009,Ealing,Northolt,12066276,Greenford Road,20602353,UB6,515219,185069,515250,185050,51.55275418,-0.339537489
77051121,15/07/2012 23:15,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009335,Weavers,E09000030,Tower Hamlets,Shoreditch,NULL,Virginia Road,22702359,E2,NULL,NULL,533550,182550,NULL,NULL
77201121,16/07/2012 08:07,2012,2012/13,Special Service,1,1,260,260,DOG IN CANAL,Dog,Person (land line),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Lawrence Avenue,NULL,NW10,520635,183803,520650,183850,51.54024587,-0.261891214
77800121,17/07/2012 13:33,2012,2012/13,Special Service,1,2,260,520,DEER TRAPPED IN FENCE,Deer,Police,Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,NULL,Blandford Road,NULL,TW11,514934,171015,514950,171050,51.42649733,-0.348218207
78010121,17/07/2012 19:52,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN TREE,Cat,Person (mobile),Heathland,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000052,Garden Suburb,E09000003,Barnet,West Hampstead,NULL,Hampstead Way,NULL,NW11,525949,187472,525950,187450,51.57205988,-0.183992941
78383121,18/07/2012 15:45,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000207,Southbury,E09000010,Enfield,Edmonton,NULL,Landseer Road,20702786,EN1,NULL,NULL,534050,195650,NULL,NULL
78823121,19/07/2012 13:58,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Crooms Hill,NULL,SE10,538648,177077,538650,177050,51.47567152,-0.004953721
78905121,19/07/2012 17:11,2012,2012/13,Special Service,1,1,260,260,CAT WITH HEAD STUCK IN HOLE,Cat,Person (land line),Small refuse/rubbish container,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000449,New Cross,E09000023,Lewisham,Deptford,1.00022E+11,Sterling Gardens,22000975,SE14,536106,177476,536150,177450,51.47987401,-0.041379415
78996121,19/07/2012 20:10,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000098,Queens Park,E09000005,Brent,North Kensington,10091060895,Harvist Road,20201304,NW6,524031,183105,524050,183150,51.53323775,-0.213193078
79108121,20/07/2012 01:07,2012,2012/13,Special Service,1,1,260,260,CAT WITH PAW TRAPPED IN WINDOW MECHANISM,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000124,Petts Wood and Knoll,E09000006,Bromley,Orpington,NULL,Station Square,20300632,BR5,NULL,NULL,544450,167650,NULL,NULL
79527121,20/07/2012 20:15,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN GARDEN FURNITURE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000564,Sutton Central,E09000029,Sutton,Sutton,NULL,Gauntlett Road,22602329,SM1,NULL,NULL,526650,164050,NULL,NULL
79765121,21/07/2012 11:27,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED  IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000427,Prince's,E09000022,Lambeth,Lambeth,NULL,Walcot Square,21901452,SE11,NULL,NULL,531350,178950,NULL,NULL
79774121,21/07/2012 11:39,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN DRAIN,Cat,Person (land line),Church/Chapel,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000487,Little Ilford,E09000025,Newham,Ilford,46254140,Romford Road,22208082,E12,542597,185811,542550,185850,51.55317242,0.055400669
79976121,21/07/2012 18:41,2012,2012/13,Special Service,1,1,260,260,BIRDS TRAPPED IN WIRE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Hollybush Place,NULL,E2,534888,182830,534850,182850,51.52827881,-0.056861589
79993121,21/07/2012 19:10,2012,2012/13,Special Service,1,1,260,260,BIRDS TRAPPED IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000469,Raynes Park,E09000024,Merton,New Malden,NULL,Station Approach,NULL,SW20,522884,168974,522850,168950,51.40648719,-0.234630679
80604121,22/07/2012 18:13,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRDS IN NETTING,Bird,Person (land line),Other transport building,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Bethnal Green Road,NULL,E2,534953,182724,534950,182750,51.52731074,-0.055965725
80622121,22/07/2012 18:46,2012,2012/13,Special Service,1,1,260,260,BIRDS TRAPPED IN WIRE MESH,Bird,Person (mobile),Train station - concourse,Non Residential,Other animal assistance,Assist trapped wild animal,E05000469,Raynes Park,E09000024,Merton,New Malden,NULL,Grand Drive,NULL,SW20,523204,169290,523250,169250,51.40925784,-0.229922539
80938121,23/07/2012 10:17,2012,2012/13,Special Service,1,1,260,260,DOG STUCK IN FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal harm involving domestic animal,E05011480,Selsdon & Addington Village,E09000008,Croydon,Addington,2.00001E+11,Queenhill Road,20502285,CR2,534715,162461,534750,162450,51.3452737,-0.067097135
81659121,24/07/2012 12:56,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011483,Shirly South,E09000008,Croydon,Addington,NULL,Shirley Hills Road,20501831,CR0,NULL,NULL,535750,164450,NULL,NULL
81911121,24/07/2012 17:57,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011099,Dulwich Hill,E09000028,Southwark,Forest Hill,NULL,Friern Road,22501002,SE22,NULL,NULL,534150,173850,NULL,NULL
82233121,25/07/2012 06:09,2012,2012/13,Special Service,1,1,260,260,HORSE IN OVERFLOW CANAL,Horse,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000203,Jubilee,E09000010,Enfield,Chingford,NULL,Lea Valley Road,NULL,E4,537359,194968,537350,194950,51.636755,-0.016519754
82267121,25/07/2012 08:28,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011482,Shirley North,E09000008,Croydon,Woodside,NULL,Wickham Road,20500586,CR0,NULL,NULL,535750,165750,NULL,NULL
82342121,25/07/2012 11:17,2012,2012/13,Special Service,1,1,260,260,ELDERLY PERSON LOCKED IN,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011114,St. George's,E09000028,Southwark,Lambeth,NULL,Hayles Street,22501221,SE11,NULL,NULL,531650,178950,NULL,NULL
82564121,25/07/2012 18:09,2012,2012/13,Special Service,1,1,260,260,CROW TRAPPED ON AERIAL,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Surbiton,128035648,High Street,21800505,KT1,517796,168855,517750,168850,51.40649659,-0.307788557
82578121,25/07/2012 18:25,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN SASH WINDOW,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000251,Askew,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Roxwell Road,21000705,W12,NULL,NULL,522250,179850,NULL,NULL
82934121,26/07/2012 07:54,2012,2012/13,Special Service,1,1,260,260,DUCKLINGS STUCK IN LOCKED DRAIN,Bird,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000515,Wanstead,E09000026,Redbridge,Leytonstone,NULL,Wanstead Park,NULL,E11,541657,187443,541650,187450,51.56807379,0.042508679
83003121,26/07/2012 11:11,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000428,St. Leonard's,E09000022,Lambeth,Tooting,NULL,Broadlands Avenue,NULL,SW16,530185,172596,530150,172550,51.43741093,-0.128389292
83649121,27/07/2012 08:15,2012,2012/13,Special Service,1,2,260,520,TWO DUCKLINGS STUCK IN GRID,Bird,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000515,Wanstead,E09000026,Redbridge,Leytonstone,NULL,Wanstead Park,NULL,E11,541657,187443,541650,187450,51.56807379,0.042508679
83806121,27/07/2012 14:11,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX STUCK BETWEEN TWO HOUSES,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011243,Fullwell,E09000026,Redbridge,Hainault,NULL,Kirkland Avenue,22305121,IG5,NULL,NULL,543450,190250,NULL,NULL
84143121,27/07/2012 21:46,2012,2012/13,Special Service,1,1,260,260,DOG FALLEN DOWN DRAIN SHAFT,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000487,Little Ilford,E09000025,Newham,Ilford,NULL,Fourth Avenue,NULL,E12,542494,185722,542450,185750,51.55239869,0.053880045
84159121,27/07/2012 22:17,2012,2012/13,Special Service,1,1,260,260,CAT STUCK DOWN HOLE IN GARDEN,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000088,Dollis Hill,E09000005,Brent,Willesden,NULL,Gladstone Park Gardens,20201385,NW2,NULL,NULL,523150,186450,NULL,NULL
84391121,28/07/2012 12:15,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000380,St. Peter's,E09000019,Islington,Islington,NULL,Laundry Lane,NULL,N1,532135,184011,532150,184050,51.53954292,-0.096081996
84624121,28/07/2012 19:01,2012,2012/13,Special Service,1,1,260,260,PIGEON TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,St. Gabriels Road,20202433,NW2,NULL,NULL,523950,185150,NULL,NULL
84688121,28/07/2012 20:22,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND SINK,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,NULL,Reed Road,21106602,N17,NULL,NULL,533850,189950,NULL,NULL
85085121,29/07/2012 14:32,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN STUCK IN TREE - NOT TRAPPED,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000320,St. Andrew's,E09000016,Havering,Hornchurch,1.00021E+11,Abbs Cross Gardens,21300186,RM12,553417,187019,553450,187050,51.56119061,0.211870572
85257121,29/07/2012 20:33,2012,2012/13,Special Service,1,1,260,260,PIGEON TRAPPED IN WIRE,Bird,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped wild animal,E05009385,Stoke Newington,E09000012,Hackney,Stoke Newington,NULL,Wilmer Place,NULL,N16,533510,186575,533550,186550,51.56226029,-0.075295543
85461121,30/07/2012 10:09,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED INBETWEEN WALL AND SHED,Dog,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011233,West Heath,E09000004,Bexley,Bexley,1.0002E+11,Brixham Road,20100203,DA16,547702,177025,547750,177050,51.47291509,0.125302578
85686121,30/07/2012 18:24,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN IN BUSHES,Dog,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000078,Longlands,E09000004,Bexley,Sidcup,NULL,Sydney Road,NULL,DA14,545599,171720,545550,171750,51.42579106,0.092862183
86011121,31/07/2012 12:19,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000456,Cannon Hill,E09000024,Merton,Wimbledon,NULL,Cannon Hill Lane,NULL,SW20,524248,168582,524250,168550,51.40266715,-0.215166869
86236121,31/07/2012 21:58,2012,2012/13,Special Service,1,2,260,520,CAT STUCK IN TREE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000424,Knight's Hill,E09000022,Lambeth,West Norwood,1.00023E+11,Knollys Road,21900820,SW16,531396,172455,531350,172450,51.43586433,-0.111028993
87148121,02/08/2012 20:53,2012,2012/13,Special Service,1,1,260,260,DISTRESSED KITTEN TRAPPED IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000126,Shortlands,E09000006,Bromley,Bromley,NULL,Broadoaks Way,20301570,BR2,NULL,NULL,539650,167850,NULL,NULL
87173121,02/08/2012 21:39,2012,2012/13,Special Service,1,1,260,260,PIGEON TRAPPED BEHIND GAS FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000297,Pinner,E09000015,Harrow,Harrow,NULL,Norman Crescent,21201793,HA5,NULL,NULL,511550,190750,NULL,NULL
87286121,03/08/2012 08:35,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN OLYMPIC ADVERTISING BANNER ON LAMPOST,Bird,Person (land line),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05009393,Courtfield,E09000020,Kensington and Chelsea,Kensington,NULL,Cromwell Road,NULL,SW7,525920,178827,525950,178850,51.49437306,-0.187500749
87359121,03/08/2012 12:03,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRD RESCUE,Bird,Person (mobile),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000616,Graveney,E09000032,Wandsworth,Tooting,NULL,Longley Road,NULL,SW17,527241,170921,527250,170950,51.42302536,-0.17132299
87447121,03/08/2012 15:00,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED BEHIND WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000620,Queenstown,E09000032,Wandsworth,Clapham,NULL,St. Philip Street,22904897,SW8,NULL,NULL,528750,176250,NULL,NULL
87529121,03/08/2012 17:44,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011100,Dulwich Village,E09000028,Southwark,Brixton,2.00003E+11,Casino Avenue,22500457,SE24,532527,174938,532550,174950,51.4579152,-0.093838466
88614121,05/08/2012 18:52,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN FENCE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000175,East Acton,E09000009,Ealing,Park Royal,NULL,Park Royal Road,NULL,NW10,520398,182176,520350,182150,51.52567377,-0.265863009
88921121,06/08/2012 13:09,2012,2012/13,Special Service,1,1,260,260,CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000401,Berrylands,E09000021,Kingston upon Thames,Surbiton,NULL,Avenue South,NULL,KT5,519074,166896,519050,166850,51.38862256,-0.290078721
88969121,06/08/2012 14:56,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN FALSE CEILING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000033,Goresbrook,E09000002,Barking and Dagenham,Dagenham,NULL,Coombes Road,19900453,RM9,NULL,NULL,548950,183850,NULL,NULL
89115121,06/08/2012 21:36,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000496,Barkingside,E09000026,Redbridge,Hainault,NULL,Cranbrook Road,NULL,IG6,543751,189073,543750,189050,51.58219059,0.073366851
89272121,07/08/2012 09:40,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN STUCK ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000450,Perry Vale,E09000023,Lewisham,Forest Hill,NULL,Stanstead Road,22004078,SE23,NULL,NULL,535550,173150,NULL,NULL
90200121,09/08/2012 08:16,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND BATH PANEL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011218,Belvedere,E09000004,Bexley,Erith,NULL,Tunstock Way,20101485,DA17,NULL,NULL,548550,179150,NULL,NULL
90319121,09/08/2012 13:40,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN BARBED WIRE,Cat,Person (mobile),Pre School/nursery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000311,Hacton,E09000016,Havering,Hornchurch,NULL,Winifred Avenue,NULL,RM12,553961,185944,553950,185950,51.55138386,0.219242186
90361121,09/08/2012 15:35,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN RAILING,Dog,Police,Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Alfred Street,NULL,E3,537026,182820,537050,182850,51.52767486,-0.02606372
90385121,09/08/2012 16:38,2012,2012/13,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000039,Thames,E09000002,Barking and Dagenham,Barking,100043997,Kingsbridge Road,19900516,IG11,544866,182952,544850,182950,51.52690542,0.086929536
90972121,10/08/2012 17:41,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UP  CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000430,Streatham Hill,E09000022,Lambeth,West Norwood,NULL,Normanhurst Road,21901026,SW2,NULL,NULL,530750,172950,NULL,NULL
91320121,11/08/2012 09:24,2012,2012/13,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000622,St. Mary's Park,E09000032,Wandsworth,Battersea,NULL,Surrey Lane,NULL,SW11,NULL,NULL,527150,176750,NULL,NULL
91465121,11/08/2012 15:23,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (mobile),Hedge,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000371,Finsbury Park,E09000019,Islington,Holloway,5300081977,Seven Sisters Road,21603790,N7,531005,186442,531050,186450,51.56165271,-0.111461867
91685121,11/08/2012 20:45,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000622,St. Mary's Park,E09000032,Wandsworth,Battersea,NULL,Inworth Street,22902538,SW11,NULL,NULL,527350,176350,NULL,NULL
92168121,12/08/2012 18:39,2012,2012/13,Special Service,1,2,260,520,ASSIST RSPCA RESCUING CAT,Unknown - Domestic Animal Or Pet,Police,Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000418,Clapham Common,E09000022,Lambeth,Clapham,NULL,St. Alphonsus Road,21901259,SW4,NULL,NULL,529550,175050,NULL,NULL
92368121,13/08/2012 02:33,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Caraway Close,22200443,E13,NULL,NULL,540750,181950,NULL,NULL
92423121,13/08/2012 07:21,2012,2012/13,Special Service,1,5,260,1300,RUNNING CALL TO SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,East India Dock Road,22700445,E14,NULL,NULL,537850,181050,NULL,NULL
92492121,13/08/2012 10:25,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN SHUTTERS,Cat,Person (mobile),"Takeaway, fast food",Non Residential,Other animal assistance,Assist trapped domestic animal,E05000229,Woolwich Common,E09000011,Greenwich,Plumstead,1.00023E+11,Woolwich New Road,20801651,SE18,543634,178703,543650,178750,51.4890395,0.067452771
92526121,13/08/2012 11:38,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED IN FLAT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000418,Clapham Common,E09000022,Lambeth,Clapham,NULL,St. Alphonsus Road,21901259,SW4,NULL,NULL,529550,175050,NULL,NULL
92598121,13/08/2012 14:29,2012,2012/13,Special Service,1,1,260,260,CAT FALLEN FROM WINDOW INTO SUB LEVEL RECESS,Cat,Person (land line),Hedge,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,6211693,Meath Crescent,22702470,E2,535809,182738,535850,182750,51.52723152,-0.043628241
92681121,13/08/2012 17:10,2012,2012/13,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000365,Turnham Green,E09000018,Hounslow,Chiswick,1.00022E+11,Chiswick High Road,21502481,W4,520931,178515,520950,178550,51.49265688,-0.259438533
92806121,13/08/2012 20:47,2012,2012/13,Special Service,1,1,260,260,DOG WITH LEG TRAPPED IN DRAIN,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000162,Selsdon and Ballards,E09000008,Croydon,Addington,NULL,Ingham Road,NULL,CR2,535451,162788,535450,162750,51.34803751,-0.056411718
93072121,14/08/2012 11:13,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN DRAIN,Cat,Person (land line),Single shop,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009374,Hackney Wick,E09000012,Hackney,Homerton,1.00023E+11,Well Street,20901058,E9,535538,184578,535550,184550,51.54383113,-0.046824665
93077121,14/08/2012 11:34,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND BOILER,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000038,River,E09000002,Barking and Dagenham,Dagenham,NULL,Beam Avenue,19900424,RM10,NULL,NULL,549950,183550,NULL,NULL
93080121,14/08/2012 11:59,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH  BIRD TRAPPED NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000353,Hanworth,E09000018,Hounslow,Feltham,NULL,Towfield Road,21501127,TW13,NULL,NULL,512650,172750,NULL,NULL
93362121,14/08/2012 22:12,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALL AND LEAN-TO OF NEIGHBOURING HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000415,Tudor,E09000021,Kingston upon Thames,Kingston,NULL,Tudor Drive,21800983,KT2,NULL,NULL,518350,171250,NULL,NULL
93748121,15/08/2012 15:30,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER SHED,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000226,Plumstead,E09000011,Greenwich,Plumstead,NULL,Riverdale Road,20801275,SE18,NULL,NULL,545550,178350,NULL,NULL
94553121,17/08/2012 08:06,2012,2012/13,Special Service,1,1,260,260,ASSIST  RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000474,Wimbledon Park,E09000024,Merton,Wandsworth,NULL,Haslemere Avenue,22103141,SW18,NULL,NULL,525750,172850,NULL,NULL
95272121,18/08/2012 13:31,2012,2012/13,Special Service,1,1,260,260,ASSIST  RSCPA  WITH CAT  STUCK  IN TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009375,Haggerston,E09000012,Hackney,Shoreditch,NULL,Haggerston Road,NULL,E8,533857,183697,533850,183650,51.53631528,-0.071385972
95544121,18/08/2012 22:15,2012,2012/13,Special Service,1,2,260,520,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Union Road,20601785,UB5,NULL,NULL,513350,183250,NULL,NULL
95952121,19/08/2012 17:05,2012,2012/13,Special Service,1,1,260,260,LAMB TRAPPED IN WIRE FENCING,Lamb,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000203,Jubilee,E09000010,Enfield,Chingford,NULL,Lea Valley Road,NULL,E4,537359,194968,537350,194950,51.636755,-0.016519754
95971121,19/08/2012 17:48,2012,2012/13,Special Service,1,1,260,260,PREGNANT CAT TRAPPED BEHIND SHED,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000440,Catford South,E09000023,Lewisham,Lee Green,1.00022E+11,Minard Road,22001714,SE6,539108,173123,539150,173150,51.44002751,0.000111879
95982121,19/08/2012 18:16,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN CHIMNEY POT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000125,Plaistow and Sundridge,E09000006,Bromley,Bromley,NULL,Portland Road,20302076,BR1,NULL,NULL,541450,171350,NULL,NULL
96290121,20/08/2012 06:48,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN PASSAGEWAY,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000089,Dudden Hill,E09000005,Brent,Willesden,NULL,Ellesmere Road,20201822,NW10,NULL,NULL,522550,185250,NULL,NULL
96940121,21/08/2012 07:52,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN SKYLIGHT ON ROOF,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000623,Shaftesbury,E09000032,Wandsworth,Battersea,NULL,Elspeth Road,22901486,SW11,NULL,NULL,527850,175350,NULL,NULL
96977121,21/08/2012 10:05,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009375,Haggerston,E09000012,Hackney,Shoreditch,1.00023E+11,Haggerston Road,20900480,E8,533857,183697,533850,183650,51.53631528,-0.071385972
97409121,21/08/2012 19:12,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER MANHOLE COVER,Cat,Person (land line),Road surface/pavement,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000633,Churchill,E09000033,Westminster,Lambeth,NULL,Churchill Gardens,NULL,SW1V,529035,178036,529050,178050,51.48656278,-0.142942642
97468121,21/08/2012 20:16,2012,2012/13,Special Service,1,1,260,260,DOG WITH LEG TRAPPED IN BABY GATE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000623,Shaftesbury,E09000032,Wandsworth,Clapham,NULL,Ashley Crescent,22900155,SW11,NULL,NULL,528450,175850,NULL,NULL
97734121,22/08/2012 09:11,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRD TANGLED IN WIRE,Bird,Person (land line),Indoor Market,Non Residential,Other animal assistance,Animal harm involving wild animal,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Hertslet Road,NULL,N7,530655,185937,530650,185950,51.55719568,-0.116695526
97777121,22/08/2012 10:51,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED GULL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000632,Bryanston and Dorset Square,E09000033,Westminster,Paddington,NULL,Edgware Road,8400919,W2,NULL,NULL,527150,181650,NULL,NULL
98023121,22/08/2012 19:55,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN CHAIR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011236,Bridge,E09000026,Redbridge,Woodford,NULL,West Grove,22305438,IG8,NULL,NULL,541350,192050,NULL,NULL
98483121,23/08/2012 18:45,2012,2012/13,Special Service,1,1,260,260,SQUIRREL STUCK  INBETWEEN DRAINPIPE & HOUSE,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000194,Bush Hill Park,E09000010,Enfield,Edmonton,NULL,Third Avenue,20703011,EN1,NULL,NULL,533750,195650,NULL,NULL
98536121,23/08/2012 20:07,2012,2012/13,Special Service,1,1,260,260,SEAGULL TRAPPED,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000643,Regent's Park,E09000033,Westminster,Paddington,NULL,Allsop Place,NULL,NW1,NULL,NULL,527850,182150,NULL,NULL
99021121,24/08/2012 18:54,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000150,Coulsdon East,E09000008,Croydon,Purley,NULL,Chaldon Way,NULL,CR5,530144,158675,530150,158650,51.3123121,-0.134072369
99327121,25/08/2012 12:50,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Pasteur Gardens,20704600,N18,NULL,NULL,532050,192050,NULL,NULL
99536121,25/08/2012 16:05,2012,2012/13,Special Service,1,2,260,520,HORSE STUCK IN WATER,Horse,Other FRS,Woodland/forest - conifers/softwood,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000312,Harold Wood,E09000016,Havering,Harold Hill,1.00021E+11,Harold Court Road,21301257,RM3,555956,190963,555950,190950,51.59592972,0.250217786
99912121,26/08/2012 00:02,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN BASEMENT,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Stanhope Street,20400774,NW1,NULL,NULL,529050,182550,NULL,NULL
100125121,26/08/2012 12:28,2012,2012/13,Special Service,1,1,260,260,COW IN DISTRESS IN GRAND UNION CANAL,Cow,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000330,Harefield,E09000017,Hillingdon,Ruislip,NULL,Canal Side,NULL,UB9,504060,191306,504050,191350,51.61098161,-0.498579108
100608121,27/08/2012 10:35,2012,2012/13,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Avenue Road,21100037,N6,NULL,NULL,529550,187950,NULL,NULL
100952121,27/08/2012 23:54,2012,2012/13,Special Service,1,1,260,260,CAT WITH HEAD STUCK IN RAILINGS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011244,Goodmayes,E09000026,Redbridge,Ilford,NULL,Green Lane,22306487,IG3,NULL,NULL,546050,186950,NULL,NULL
101699121,29/08/2012 16:20,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN PIPE,Cat,Person (mobile),Pre School/nursery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000617,Latchmere,E09000032,Wandsworth,Battersea,10033238938,Winstanley Road,22906291,SW11,526778,175685,526750,175650,51.46594417,-0.176274112
101960121,30/08/2012 08:55,2012,2012/13,Special Service,1,1,260,260,PIDEON TRAPPED IN SHOP,Bird,Person (land line),Large supermarket,Non Residential,Other animal assistance,Assist trapped wild animal,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,1.00023E+11,Syon Lane,21501106,TW7,516041,177649,516050,177650,51.48589669,-0.330128386
102177121,30/08/2012 17:17,2012,2012/13,Special Service,1,1,260,260,FERRETT STUCK IN CAGE,Ferret,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,2.00001E+11,The Ridgeway,22870050,E4,538189,194183,538150,194150,51.62949812,-0.004843574
102198121,30/08/2012 18:06,2012,2012/13,Special Service,1,1,260,260,FOX TRAPPED BEHIND WALL,Fox,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05009372,Hackney Central,E09000012,Hackney,Homerton,10008239077,Ashanti Mews,20901862,E8,535019,185165,535050,185150,51.54923035,-0.054079613
102218121,30/08/2012 18:48,2012,2012/13,Special Service,2,3,260,780,BIRD WITH WING TRAPPED IN FISHING LINE WATER RESCUE LEVEL TWO IMPLEMENTED,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000062,West Hendon,E09000003,Barnet,Hendon,NULL,Cool Oak Lane,NULL,NW9,522250,187750,522250,187750,51.57537212,-0.237243029
102652121,31/08/2012 14:51,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BY LEAD,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011468,Fairfield,E09000008,Croydon,Croydon,NULL,Lansdowne Road,20501121,CR0,NULL,NULL,532850,166350,NULL,NULL
102665121,31/08/2012 15:21,2012,2012/13,Special Service,1,1,260,260,KITTEN WITH LEG TRAPPED IN WINDON,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,NULL,Turnham Road,22001986,SE4,NULL,NULL,536150,175150,NULL,NULL
103437121,01/09/2012 19:11,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER BONNET OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Tunmarsh Lane,NULL,E13,541354,182588,541350,182550,51.52452382,0.036191684
103477121,01/09/2012 20:34,2012,2012/13,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000325,Botwell,E09000017,Hillingdon,Hayes,NULL,Printing House Lane,NULL,UB3,509476,179983,509450,179950,51.50818341,-0.423917159
104094121,03/09/2012 01:31,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED ON BRANCH OF TREE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011240,Clementswood,E09000026,Redbridge,Ilford,NULL,Woodlands Road,22303408,IG1,NULL,NULL,544150,185950,NULL,NULL
104314121,03/09/2012 14:06,2012,2012/13,Special Service,1,2,260,520,Redacted,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000488,Manor Park,E09000025,Newham,Stratford,46033810,Halley Road,22208155,E12,542058,185054,542050,185050,51.54650607,0.047326338
104557121,03/09/2012 22:25,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000254,Fulham Broadway,E09000013,Hammersmith and Fulham,Fulham,NULL,Farm Lane,NULL,SW6,525240,177302,525250,177350,51.4808185,-0.197831484
104904121,04/09/2012 16:33,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND KITCHEN UNIT,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000377,Mildmay,E09000019,Islington,Islington,NULL,Mildmay Grove North,21606886,N1,NULL,NULL,533050,185050,NULL,NULL
105551121,05/09/2012 20:09,2012,2012/13,Special Service,1,1,260,260,BIRD OF PREY TRAPPED IN TREE - CALLED BY RSPCA,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000111,Chislehurst,E09000006,Bromley,Sidcup,1.00023E+11,Perry Street,20303525,BR7,545117,170752,545150,170750,51.41721653,0.085537778
105750121,06/09/2012 09:08,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000122,Orpington,E09000006,Bromley,Orpington,NULL,High Street,20300931,BR6,NULL,NULL,546150,166050,NULL,NULL
105768121,06/09/2012 10:09,2012,2012/13,Special Service,2,3,260,780,HORSE SINKING IN MUD,Horse,Person (mobile),Scrub land,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000075,Erith,E09000004,Bexley,Erith,NULL,Crabtree Manorway North,NULL,DA17,550152,179587,550150,179550,51.49529082,0.161640941
106438121,07/09/2012 10:28,2012,2012/13,Special Service,2,3,260,780,PONY WITH LEG TRAPPED IN TRACTOR,Horse,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000111,Chislehurst,E09000006,Bromley,Eltham,NULL,Slades Drive,NULL,BR7,544396,171908,544350,171950,51.42778797,0.075647602
106522121,07/09/2012 13:55,2012,2012/13,Special Service,1,3,260,780,HORSE STUCK IN DITCH,Horse,Person (mobile),Hedge,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000065,Belvedere,E09000004,Bexley,Erith,NULL,Norman Road,NULL,DA17,549402,180088,549450,180050,51.49999085,0.15105733
107164121,08/09/2012 14:49,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH INJURED SWAN IN CANAL,Bird,Person (land line),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,NULL,Ferry Lane,NULL,N17,534905,189709,534950,189750,51.59009115,-0.053981415
107338121,08/09/2012 18:56,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN HEDGE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal harm involving domestic animal,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,NULL,Pincott Place,NULL,SE4,535963,175418,535950,175450,51.4614142,-0.044228706
107731121,09/09/2012 08:59,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED CROW OVER FISHING LAKE,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,NULL,Crystal Palace Park Road,NULL,SE26,534650,171184,534650,171150,51.42367864,-0.064726111
107753121,09/09/2012 09:57,2012,2012/13,Special Service,1,1,260,260,BIRDS TRAPPED IN DRAIN,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000443,Evelyn,E09000023,Lewisham,Deptford,NULL,Barfleur Lane,22006126,SE8,NULL,NULL,536750,178450,NULL,NULL
108338121,10/09/2012 09:24,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON GLASS ROOF,Cat,Person (mobile),Medical/health centre,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000491,Royal Docks,E09000025,Newham,Plaistow,10008991177,Saville Road,22201035,E16,542243,180126,542250,180150,51.50217764,0.048004636
108559121,10/09/2012 16:47,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,Blair Street,22700165,E14,NULL,NULL,538750,181250,NULL,NULL
108845121,11/09/2012 06:27,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000460,Figge's Marsh,E09000024,Merton,Mitcham,NULL,Holborn Way,22103358,CR4,NULL,NULL,527750,169150,NULL,NULL
108850121,11/09/2012 07:17,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN MESH FENCE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,1.00021E+11,East Close,21300758,RM13,552779,182281,552750,182250,51.51879347,0.200621065
109474121,12/09/2012 13:10,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN BARS OF CAT CAGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000058,Oakleigh,E09000003,Barnet,Southgate,NULL,Oakleigh Road North,20032580,N20,NULL,NULL,527650,193450,NULL,NULL
109514121,12/09/2012 14:44,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,Loughborough Park,NULL,SW9,531826,175402,531850,175450,51.46224867,-0.103749566
109568121,12/09/2012 16:27,2012,2012/13,Special Service,1,3,260,780,ASSIST RSPCA WITH COLLAPSED HORSE,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000114,Cray Valley East,E09000006,Bromley,Sidcup,NULL,Old Maidstone Road,NULL,DA14,548987,170136,548950,170150,51.41067821,0.140892105
109597121,12/09/2012 17:44,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN WIRE,Bird,Person (land line),Converted office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000375,Holloway,E09000019,Islington,Holloway,5300067545,North Road,21600648,N7,530421,185043,530450,185050,51.54921572,-0.12040028
109822121,13/09/2012 07:36,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN WALL VIA AIR VENT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000270,Fortis Green,E09000014,Haringey,Hornsey,NULL,Birchwood Avenue,21100080,N10,NULL,NULL,528350,189450,NULL,NULL
110007121,13/09/2012 16:07,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000324,Barnhill,E09000017,Hillingdon,Southall,1.00021E+11,Uxbridge Road,21402032,UB4,510285,181258,510250,181250,51.5194859,-0.411866336
110154121,13/09/2012 20:26,2012,2012/13,Special Service,1,1,260,260,BIRD STUCK DOWN DRAINPIPE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000050,Edgware,E09000003,Barnet,Mill Hill,NULL,St. Margarets Road,20038180,HA8,NULL,NULL,519650,192150,NULL,NULL
110512121,14/09/2012 14:14,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Other FRS,Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000414,Tolworth and Hook Rise,E09000021,Kingston upon Thames,Surbiton,NULL,Lenelby Road,NULL,KT6,NULL,NULL,519250,166150,NULL,NULL
111672121,16/09/2012 01:52,2012,2012/13,Special Service,1,1,260,260,DOG STUCK BETWEEN WALLS OF FLAT,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009391,Chelsea Riverside,E09000020,Kensington and Chelsea,Chelsea,NULL,Justice Walk,21700281,SW3,NULL,NULL,527050,177650,NULL,NULL
111736121,16/09/2012 08:22,2012,2012/13,Special Service,1,1,260,260,CAT FALLEN FROM UPPER FLOOR,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000138,Holborn and Covent Garden,E09000007,Camden,Soho,NULL,Rosebery Avenue,20401034,EC1R,NULL,NULL,531050,182150,NULL,NULL
111844121,16/09/2012 13:09,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000275,Noel Park,E09000014,Haringey,Tottenham,1.00021E+11,Moselle Avenue,21104826,N22,531319,190372,531350,190350,51.59689642,-0.105467418
112090121,16/09/2012 21:10,2012,2012/13,Special Service,1,1,260,260,KITTEN STUCK IN HOLE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011255,Wanstead Village,E09000026,Redbridge,Leytonstone,NULL,Voluntary Place,22303350,E11,NULL,NULL,540150,188350,NULL,NULL
113311121,18/09/2012 22:10,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Sunnyside Road South,20704799,N9,NULL,NULL,534050,193050,NULL,NULL
113544121,19/09/2012 12:54,2012,2012/13,Special Service,1,1,260,260,COW STUCK IN MUD,Cow,Police,Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,NULL,Coldharbour Lane,NULL,RM13,551394,180894,551350,180850,51.50670375,0.180079624
113655121,19/09/2012 16:01,2012,2012/13,Special Service,1,4,260,1040,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,Oakwood Road,NULL,NW11,525673,188851,525650,188850,51.58451447,-0.187480356
114125121,20/09/2012 11:19,2012,2012/13,Special Service,1,2,260,520,Redacted,cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000364,Syon,E09000018,Hounslow,Heston,NULL,High Street,21500607,TW8,NULL,NULL,517450,177350,NULL,NULL
114351121,20/09/2012 19:12,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN RUBBISH CHUTE,Cat,Police,Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009391,Chelsea Riverside,E09000020,Kensington and Chelsea,Chelsea,NULL,World's End Estate,21701274,SW10,NULL,NULL,526650,177350,NULL,NULL
114508121,21/09/2012 04:29,2012,2012/13,Special Service,1,1,260,260,FOX STUCK ON THE THAMES,Fox,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped wild animal,E05011117,Surrey Docks,E09000028,Southwark,Deptford,2.00003E+11,Rotherhithe Street,22502127,SE16,536589,180263,536550,180250,51.5048027,-0.03335001
115373121,22/09/2012 18:30,2012,2012/13,Special Service,1,1,260,260,BIRD WITH FOOT TRAPPED IN WIRE,Bird,Person (land line),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000200,Grange,E09000010,Enfield,Enfield,207199200,The Town,20703007,EN2,532851,196541,532850,196550,51.65197374,-0.081023528
115879121,23/09/2012 16:07,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED UNDER LIFT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011110,Peckham,E09000028,Southwark,Peckham,NULL,Pioneer Street,22502794,SE15,NULL,NULL,534050,176850,NULL,NULL
115904121,23/09/2012 16:57,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA OFFICER WITH CAT STUCK IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000204,Lower Edmonton,E09000010,Enfield,Edmonton,NULL,Grosvenor Road,20704287,N9,NULL,NULL,534850,194250,NULL,NULL
115929121,23/09/2012 18:03,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011465,Broad Green,E09000008,Croydon,Norbury,NULL,Westcombe Avenue,20501554,CR0,NULL,NULL,530650,166850,NULL,NULL
116304121,24/09/2012 13:09,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN A FALSE CEILING,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,1.00023E+11,High Road,21106680,N17,533815,190182,533850,190150,51.59460158,-0.069526123
116414121,24/09/2012 16:51,2012,2012/13,Special Service,1,1,260,260,BIRD WITH LEG TRAPPED IN STREET FURNITURE,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000443,Evelyn,E09000023,Lewisham,Deptford,NULL,Rolt Street,NULL,SE8,536488,177834,536450,177850,51.48299913,-0.035743532
116554121,24/09/2012 22:49,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN CABLING,Cat,Person (mobile),Warehouse,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000558,Carshalton Central,E09000029,Sutton,Sutton,5870047176,Westmead Road,22602747,SM1,526772,164600,526750,164650,51.36632222,-0.180320259
117173121,26/09/2012 10:00,2012,2012/13,Special Service,1,1,260,260,KITTEN STUCK UNDER FLOOR,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,NULL,Connaught Road,22401387,TW11,NULL,NULL,514950,171350,NULL,NULL
117257121,26/09/2012 13:41,2012,2012/13,Special Service,1,1,260,260,DOG FALLEN INTO DISUSED BASEMENT AREA,Dog,Person (mobile),Other building/use not known,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,6047613,Commercial Road,22700346,E14,536778,181154,536750,181150,51.51276384,-0.030283005
117404121,26/09/2012 20:23,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000121,Mottingham and Chislehurst North,E09000006,Bromley,Eltham,NULL,Dorset Road,20302278,SE9,NULL,NULL,542150,172750,NULL,NULL
117418121,26/09/2012 20:51,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000264,Town,E09000013,Hammersmith and Fulham,Fulham,34073015,Jerdan Place,21000466,SW6,525204,177272,525250,177250,51.48055684,-0.198360258
117611121,27/09/2012 10:33,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN FENCE,Dog,Person (mobile),Tennis Courts,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000189,Southall Broadway,E09000009,Ealing,Southall,NULL,Villiers Road,NULL,UB1,512901,180028,512950,180050,51.50791422,-0.374571379
117627121,27/09/2012 10:58,2012,2012/13,Special Service,1,1,260,260,FOX TRAPPED BETWEEN FENCE POSTS,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011254,Wanstead Park,E09000026,Redbridge,Leytonstone,NULL,Dover Road,22302792,E12,NULL,NULL,541250,186850,NULL,NULL
118613121,29/09/2012 12:19,2012,2012/13,Special Service,NULL,NULL,260,NULL,CAT FALLEN INTO RIVER THAMES,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000552,Surrey Docks,E09000028,Southwark,Dockhead,NULL,Princes Riverside Road,NULL,SE16,535703,180317,535750,180350,51.50550126,-0.046087185
119784121,01/10/2012 18:53,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALL AND FITTED WARDROBE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,White Hart Lane,21106111,N17,NULL,NULL,533850,191450,NULL,NULL
119874121,01/10/2012 22:22,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN DRAIN,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000299,Queensbury,E09000015,Harrow,Stanmore,NULL,Streatfield Road,NULL,HA3,518087,189941,518050,189950,51.59594907,-0.296553655
120103121,02/10/2012 15:18,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND SKIRTING BOARD,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000095,Mapesbury,E09000005,Brent,Willesden,NULL,Tracey Avenue,20201824,NW2,NULL,NULL,523050,185350,NULL,NULL
120933121,04/10/2012 09:33,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011470,New Addington North,E09000008,Croydon,Addington,NULL,North Walk,20502760,CR0,NULL,NULL,537950,163850,NULL,NULL
121132121,04/10/2012 18:00,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN DITCH,Dog,Police,Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000156,Kenley,E09000008,Croydon,Purley,NULL,Gomshall Gardens,NULL,CR8,533039,159779,533050,159750,51.32156549,-0.092147742
121168121,04/10/2012 19:13,2012,2012/13,Special Service,1,1,260,260,CATS TRAPPED UNDER CUPBOARD,cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000431,Streatham South,E09000022,Lambeth,Norbury,NULL,Tankerville Road,21901346,SW16,NULL,NULL,529950,170450,NULL,NULL
121355121,05/10/2012 07:15,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000032,Gascoigne,E09000002,Barking and Dagenham,Barking,NULL,Wedderburn Road,19900623,IG11,NULL,NULL,545050,183650,NULL,NULL
121840121,06/10/2012 04:20,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Lee Green,NULL,Boyd Way,20803094,SE3,NULL,NULL,540750,175250,NULL,NULL
121929121,06/10/2012 10:01,2012,2012/13,Special Service,1,1,260,260,CAT STUCK DOWN DRAIN,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000290,Harrow Weald,E09000015,Harrow,Stanmore,1.00021E+11,Dromey Gardens,21201466,HA3,515693,191288,515650,191250,51.60855211,-0.33065631
122571121,07/10/2012 17:34,2012,2012/13,Special Service,1,1,260,260,DOG LOCKED IN BATHROOM,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,Aberfeldy Street,22700041,E14,NULL,NULL,538550,181150,NULL,NULL
122937121,08/10/2012 14:09,2012,2012/13,Special Service,1,1,260,260,COW STUCK IN RIVER,Cow,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000206,Ponders End,E09000010,Enfield,Enfield,NULL,East Duck Lees Lane,NULL,EN3,536351,195932,536350,195950,51.64566304,-0.030700798
123112121,08/10/2012 21:51,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED INSIDE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009388,Abingdon,E09000020,Kensington and Chelsea,Kensington,NULL,Pembroke Gardens,21701294,W8,NULL,NULL,524850,179050,NULL,NULL
123271121,09/10/2012 10:21,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000614,Fairfield,E09000032,Wandsworth,Battersea,NULL,Harbut Road,22902213,SW11,NULL,NULL,526650,175150,NULL,NULL
123383121,09/10/2012 15:18,2012,2012/13,Special Service,1,1,260,260,FOX TRAPPED IN BASEMENT,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Clova Road,22207664,E7,NULL,NULL,540350,185050,NULL,NULL
123598121,09/10/2012 22:53,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Stephendale Road,21000783,SW6,NULL,NULL,526050,176350,NULL,NULL
123775121,10/10/2012 12:40,2012,2012/13,Special Service,NULL,NULL,260,NULL,KITTEN TRAPPED IN BUSHES,Cat,Person (land line),Hedge,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000032,Gascoigne,E09000002,Barking and Dagenham,Barking,100091372,London Road,19900631,IG11,543805,184044,543850,184050,51.53698859,0.072090995
124173121,11/10/2012 11:01,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011238,Churchfields,E09000026,Redbridge,Woodford,1.00022E+11,High Road,22305476,IG8,539853,191379,539850,191350,51.60389156,0.018063566
124494121,11/10/2012 22:54,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN WATER PIPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009386,Victoria,E09000012,Hackney,Bethnal Green,NULL,Tudor Grove,20901015,E9,NULL,NULL,535050,184050,NULL,NULL
125183121,13/10/2012 13:07,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UNDER CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Bruce Road,NULL,NW10,520891,184038,520850,184050,51.54230323,-0.258120917
125232121,13/10/2012 14:47,2012,2012/13,Special Service,2,3,260,780,Redacted,Horse,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Old Mill Lane,NULL,UB8,504922,181891,504950,181850,51.52619663,-0.488943561
125244121,13/10/2012 15:12,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN EXTRACTOR FAN,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000294,Kenton East,E09000015,Harrow,Stanmore,NULL,Hunters Grove,21201554,HA3,NULL,NULL,517450,189350,NULL,NULL
125255121,13/10/2012 15:26,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000425,Larkhall,E09000022,Lambeth,Clapham,NULL,Larkhall Lane,21900847,SW4,NULL,NULL,529950,176350,NULL,NULL
125973121,14/10/2012 22:31,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON WINDOW LEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,Broadwater Road,21104283,N17,NULL,NULL,533350,190450,NULL,NULL
126146121,15/10/2012 10:44,2012,2012/13,Special Service,1,1,260,260,HORSE STUCK IN RESERVOIR,Horse,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,NULL,Folly Lane,NULL,E17,536276,190978,536250,190950,51.60116449,-0.033710226
126448121,15/10/2012 20:49,2012,2012/13,Special Service,1,1,260,260,CAT IN RAVENSBOURNE RIVER IN REAR GARDEN OF,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000440,Catford South,E09000023,Lewisham,Lewisham,NULL,Barmeston Road,NULL,SE6,537569,172932,537550,172950,51.4386864,-0.022091169
126503121,16/10/2012 00:51,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,Broadwater Road,21104283,N17,NULL,NULL,533350,190450,NULL,NULL
126607121,16/10/2012 10:00,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA OFFICER WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000153,Fairfield,E09000008,Croydon,Croydon,NULL,Robert Street,NULL,CR0,532398,165289,532350,165250,51.37123253,-0.09929444
127470121,18/10/2012 00:37,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000216,Charlton,E09000011,Greenwich,East Greenwich,NULL,Calydon Road,20800268,SE7,NULL,NULL,540850,178050,NULL,NULL
127622121,18/10/2012 12:28,2012,2012/13,Special Service,1,1,260,260,UNABLE TO GAIN ACCESS TO HORSE BOX,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving livestock,NULL,NULL,E07000095,Broxbourne,Hertfordshire,148040938,Bulls Cross Ride,4802548,EN7,534365,200488,534350,200450,51.687082,-0.057635883
127750121,18/10/2012 18:04,2012,2012/13,Special Service,1,2,260,520,Redacted,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009334,Stepney Green,E09000030,Tower Hamlets,Shadwell,NULL,Mile End Road,22700818,E1,NULL,NULL,535050,181850,NULL,NULL
128097121,19/10/2012 14:41,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA ACCESS TO GAIN ENTRY,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009334,Stepney Green,E09000030,Tower Hamlets,Shadwell,NULL,Mile End Road,22700818,E1,NULL,NULL,535050,181850,NULL,NULL
128690121,20/10/2012 17:25,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN  GARDEN WALL,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000314,Heaton,E09000016,Havering,Harold Hill,1.00021E+11,Heaton Avenue,21301268,RM3,552743,191328,552750,191350,51.60008916,0.204027218
128993121,21/10/2012 08:52,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011223,Crook Log,E09000004,Bexley,Bexley,NULL,Bristow Road,20100202,DA7,NULL,NULL,548550,176550,NULL,NULL
130010121,23/10/2012 11:34,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED BETWEEN GARAGES,Dog,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000484,Forest Gate South,E09000025,Newham,Stratford,46017134,Clova Road,22207664,E7,539883,185010,539850,185050,51.54665412,0.015963572
131389121,26/10/2012 08:43,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN ROOM POSSIBLY BADLY INJURED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000180,Hobbayne,E09000009,Ealing,Ealing,NULL,Cuckoo Lane,NULL,W7,NULL,NULL,515350,180850,NULL,NULL
131703121,26/10/2012 22:35,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER KITCHEN UNITS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000451,Rushey Green,E09000023,Lewisham,Forest Hill,NULL,Catford Hill,22005976,SE6,NULL,NULL,536850,172950,NULL,NULL
132770121,28/10/2012 22:00,2012,2012/13,Special Service,1,1,260,260,KITTEN WITH HEAD TRAPPED IN CHAIR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000362,Isleworth,E09000018,Hounslow,Heston,NULL,Twickenham Road,21501137,TW7,NULL,NULL,516050,174850,NULL,NULL
133226121,29/10/2012 20:39,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED BETWEEN RADIATOR AND CUPBOARD,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000314,Heaton,E09000016,Havering,Harold Hill,NULL,Keats Avenue,21301316,RM3,NULL,NULL,552550,191050,NULL,NULL
133461121,30/10/2012 12:26,2012,2012/13,Special Service,1,1,260,260,HORSE STUCK IN TRENCH,Horse,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,1.00021E+11,The Green,21300794,RM13,554148,180847,554150,180850,51.50553744,0.219709628
133466121,30/10/2012 12:51,2012,2012/13,Special Service,1,1,260,260,FOX ON ROOF,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000230,Woolwich Riverside,E09000011,Greenwich,East Greenwich,NULL,Little Heath,20800925,SE7,NULL,NULL,542350,178150,NULL,NULL
133514121,30/10/2012 14:58,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER CAR,Dog,Person (mobile),Car,Road Vehicle,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000151,Coulsdon West,E09000008,Croydon,Purley,NULL,Woodcote Grove Road,NULL,CR5,529719,159834,529750,159850,51.32282504,-0.139745874
133589121,30/10/2012 17:33,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED ON ROOF,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000340,Uxbridge North,E09000017,Hillingdon,Hillingdon,NULL,High Street,NULL,UB8,505508,184218,505550,184250,51.54700336,-0.479802652
133941121,31/10/2012 14:18,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN GATE,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05011229,St. Mary's & St. James,E09000004,Bexley,Sidcup,10011862998,Davis Way,20101866,DA14,548068,170876,548050,170850,51.41756806,0.127996566
134541121,01/11/2012 16:19,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED IN TREE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000059,Totteridge,E09000003,Barnet,Barnet,200071628,Lawrence Campe Close,20026120,N20,526801,193427,526850,193450,51.62538529,-0.169554809
135120121,02/11/2012 20:35,2012,2012/13,Special Service,1,1,260,260,ADULT STUCK UP A TREE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009397,Holland,E09000020,Kensington and Chelsea,Hammersmith,NULL,Warwick Road,NULL,W14,NULL,NULL,524850,178750,NULL,NULL
136023121,04/11/2012 10:34,2012,2012/13,Special Service,1,1,260,260,KITTEN WITH HEAD TRAPPED IN BOTTLE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000356,Heston East,E09000018,Hounslow,Southall,NULL,The Alders,21500021,TW5,NULL,NULL,512650,177950,NULL,NULL
136243121,04/11/2012 18:09,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN BASEMENT WELL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000366,Barnsbury,E09000019,Islington,Islington,NULL,Cloudesley Road,21604548,N1,NULL,NULL,531250,183750,NULL,NULL
136472121,05/11/2012 09:05,2012,2012/13,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT TRAPPED IN CARE ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000149,Broad Green,E09000008,Croydon,Croydon,NULL,Onslow Road,NULL,CR0,531366,166316,531350,166350,51.38070135,-0.113732631
137065121,06/11/2012 02:28,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN RAILINGS,Dog,Police,Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011222,Crayford,E09000004,Bexley,Bexley,1.0002E+11,Old Road,20101070,DA1,551045,175066,551050,175050,51.45443147,0.172562285
137710121,07/11/2012 14:10,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN BETWEEN TWO WALLS,Dog,Person (land line),Wasteland,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000309,Emerson Park,E09000016,Havering,Hornchurch,1.00021E+11,Hazelmere Gardens,21300407,RM11,553013,188586,553050,188550,51.57537951,0.206728334
137713121,07/11/2012 14:35,2012,2012/13,Special Service,1,2,260,520,HORSE COLLAPSED,Horse,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,1.00021E+11,The Green,21300794,RM13,554148,180847,554150,180850,51.50553744,0.219709628
137858121,07/11/2012 19:57,2012,2012/13,Special Service,1,1,260,260,CAT STUCK BEHIND DISHWASHER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000314,Heaton,E09000016,Havering,Harold Hill,NULL,Sheridan Close,21301595,RM3,NULL,NULL,553050,191450,NULL,NULL
138275121,08/11/2012 16:29,2012,2012/13,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000333,Ickenham,E09000017,Hillingdon,Ruislip,1.00021E+11,Bushey Road,21400286,UB10,507477,186675,507450,186650,51.56871599,-0.450667806
138355121,08/11/2012 19:08,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON SCAFFOLDING,Cat,Person (land line),Outdoor storage,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05011238,Churchfields,E09000026,Redbridge,Woodford,1.00023E+11,Navestock Crescent,22305207,IG8,541283,190977,541250,190950,51.59992281,0.038536711
138358121,08/11/2012 19:19,2012,2012/13,Special Service,1,1,260,260,KITTEN STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000626,Tooting,E09000032,Wandsworth,Tooting,NULL,Smallwood Road,22904658,SW17,NULL,NULL,526550,171650,NULL,NULL
138799121,09/11/2012 20:27,2012,2012/13,Special Service,1,1,260,260,KITTEN STUCK BEHIND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000423,Herne Hill,E09000022,Lambeth,Brixton,NULL,Chaucer Road,21900327,SE24,NULL,NULL,531450,174650,NULL,NULL
139066121,10/11/2012 10:45,2012,2012/13,Special Service,1,1,260,260,PIGEON TRAPPED BEHIND FIREPLACE,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000608,William Morris,E09000031,Waltham Forest,Walthamstow,NULL,Forest Road,22837350,E17,NULL,NULL,536550,189650,NULL,NULL
139139121,10/11/2012 13:51,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA GAIN ENTRY,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000489,Plaistow North,E09000025,Newham,Stratford,NULL,Pelly Road,22200947,E13,NULL,NULL,540250,183750,NULL,NULL
139915121,11/11/2012 21:07,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND DASHBOARD OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000040,Valence,E09000002,Barking and Dagenham,Dagenham,NULL,Bentry Road,NULL,RM8,548580,186735,548550,186750,51.55993232,0.142024102
140533121,13/11/2012 09:06,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED ON LEDGE,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000226,Plumstead,E09000011,Greenwich,Plumstead,NULL,Alliance Road,NULL,SE18,546087,177572,546050,177550,51.47824908,0.102291898
141756121,15/11/2012 20:44,2012,2012/13,Special Service,1,1,260,260,PUPPY TRAPPED IN WIRE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000111,Chislehurst,E09000006,Bromley,Bromley,NULL,Oakleigh Park Avenue,20300531,BR7,NULL,NULL,543450,169550,NULL,NULL
142049121,16/11/2012 14:14,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED BY WIRE,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000547,Peckham Rye,E09000028,Southwark,New Cross,NULL,Solomons Passage,NULL,SE15,534774,175351,534750,175350,51.46109638,-0.061358794
142068121,16/11/2012 15:09,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000626,Tooting,E09000032,Wandsworth,Tooting,NULL,Smallwood Road,22904658,SW17,NULL,NULL,526550,171650,NULL,NULL
142404121,17/11/2012 09:40,2012,2012/13,Special Service,1,1,260,260,CAT LOCKED IN SHED,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011111,Peckham Rye,E09000028,Southwark,New Cross,10009791949,Ivydale Road,22501337,SE15,535719,175600,535750,175650,51.46310829,-0.047668989
142459121,17/11/2012 12:27,2012,2012/13,Special Service,1,1,260,260,CAT LOCKED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,34101973,Godolphin Road,21000378,W12,522825,179620,522850,179650,51.5021802,-0.231785642
142587121,17/11/2012 17:23,2012,2012/13,Special Service,1,3,260,780,CAT TRAPPED IN  VOID BETWEEN TWO EXTENSIONS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000206,Ponders End,E09000010,Enfield,Edmonton,NULL,Clarence Road,20702604,EN3,NULL,NULL,535050,195650,NULL,NULL
142980121,18/11/2012 15:01,2012,2012/13,Special Service,1,1,260,260,DOG STUCK IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000424,Knight's Hill,E09000022,Lambeth,West Norwood,NULL,Winton Way,NULL,SW16,531289,171140,531250,171150,51.42407145,-0.113054288
143190121,18/11/2012 22:36,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER SHED,Dog,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000412,St. Mark's,E09000021,Kingston upon Thames,Surbiton,1.00023E+11,St. Andrews Square,21800889,KT6,517535,167481,517550,167450,51.39420104,-0.311993321
143916121,20/11/2012 15:56,2012,2012/13,Special Service,1,1,260,260,PUPPY STUCK DOWN HOLE IN GARDEN,Dog,Person (land line),Bungalow - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000284,Woodside,E09000014,Haringey,Tottenham,NULL,Saxon Road,21105019,N22,NULL,NULL,531850,190850,NULL,NULL
144036121,20/11/2012 21:48,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN PRECARIOUS POSITION,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,207103072,Claremont Street,20704054,N18,534324,191927,534350,191950,51.61016124,-0.061514697
144415121,21/11/2012 19:54,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN ELECTRICAL SUBSTATION,Cat,Police,Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000101,Sudbury,E09000005,Brent,Wembley,NULL,The Boltons,NULL,HA0,515931,185792,515950,185750,51.55910685,-0.329034789
144959121,22/11/2012 18:51,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND WALL - POSSIBLY INJURED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000601,Hoe Street,E09000031,Waltham Forest,Walthamstow,NULL,West Avenue,22886250,E17,NULL,NULL,537650,188950,NULL,NULL
145090121,22/11/2012 22:48,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN WIRE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000182,Northfield,E09000009,Ealing,Ealing,NULL,Little Ealing Lane,20601069,W5,NULL,NULL,517450,178950,NULL,NULL
145274121,23/11/2012 09:42,2012,2012/13,Special Service,1,1,260,260,DOG STUCK ON ROOF OF BUILDING,Dog,Police,Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000489,Plaistow North,E09000025,Newham,Plaistow,NULL,Pelly Road,NULL,E13,540446,183323,540450,183350,51.53135508,0.023404968
145335121,23/11/2012 12:54,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA GAIN ACCESS TO GATED AREA,Unknown - Domestic Animal Or Pet,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05009335,Weavers,E09000030,Tower Hamlets,Bethnal Green,6020121,Voss Street,22701278,E2,534265,182571,534250,182550,51.52609987,-0.065935928
145568121,23/11/2012 21:47,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN WINDOW,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009371,De Beauvoir,E09000012,Hackney,Shoreditch,NULL,De Beauvoir Road,20950061,N1,NULL,NULL,533250,183950,NULL,NULL
145576121,23/11/2012 22:15,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN GUTTERING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Shellness Road,20900912,E5,NULL,NULL,534850,185350,NULL,NULL
145595121,23/11/2012 23:09,2012,2012/13,Special Service,1,1,260,260,RUNNING CALL TO DOG TRAPPED,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Denman Road,22500744,SE15,NULL,NULL,533650,176550,NULL,NULL
145854121,24/11/2012 14:23,2012,2012/13,Special Service,1,1,260,260,ASSSIST RSPCA WITH SWANS TRAPPED IN NETTING  ON CANAL,Bird,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000331,Heathrow Villages,E09000017,Hillingdon,Heathrow,NULL,Sandringham Road,NULL,TW6,506049,174716,506050,174750,51.46149431,-0.474858539
146657121,25/11/2012 23:51,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN HOLE UNDER STAIRS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000253,College Park and Old Oak,E09000013,Hammersmith and Fulham,Acton,NULL,Erconwald Street,21000318,W12,NULL,NULL,521750,181350,NULL,NULL
147695121,28/11/2012 11:00,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009377,Hoxton East & Shoreditch,E09000012,Hackney,Shoreditch,1.00023E+11,King John Court,20900573,EC2A,533354,182353,533350,182350,51.52435671,-0.079142793
147709121,28/11/2012 11:21,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Dulverton Road,NULL,RM3,NULL,NULL,554050,191650,NULL,NULL
148126121,29/11/2012 11:10,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED SEAGULL,Bird,Person (mobile),Wasteland,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000190,Southall Green,E09000009,Ealing,Southall,NULL,The Common,NULL,UB2,512027,178587,512050,178550,51.49513629,-0.387616963
148371121,29/11/2012 20:35,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED DOWN DRAIN,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000271,Harringay,E09000014,Haringey,Hornsey,NULL,Endymion Road,NULL,N4,531702,187985,531750,187950,51.57535649,-0.10083541
148438121,29/11/2012 23:39,2012,2012/13,Special Service,1,1,260,260,CAT ON ROOF IN DANGER OF FALLING,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,New Road,21103670,N8,NULL,NULL,530050,188650,NULL,NULL
148786121,30/11/2012 16:21,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED BETWEEN SHED AND FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000602,Larkswood,E09000031,Waltham Forest,Chingford,NULL,Heaton Close,22844260,E4,NULL,NULL,538450,193050,NULL,NULL
149783121,02/12/2012 15:26,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN A TREE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000048,East Barnet,E09000003,Barnet,Barnet,NULL,Castlewood Road,NULL,EN4,527048,196844,527050,196850,51.65603716,-0.164748995
149804121,02/12/2012 16:05,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009403,Royal Hospital,E09000020,Kensington and Chelsea,Chelsea,NULL,Elystan Place,21700194,SW3,NULL,NULL,527450,178450,NULL,NULL
150271121,03/12/2012 15:04,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000037,Parsloes,E09000002,Barking and Dagenham,Dagenham,100016786,Keppel Road,19900204,RM9,548080,185739,548050,185750,51.55111426,0.134397651
150631121,04/12/2012 10:48,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA  WITH CAT STUCK IN TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011228,Northumberland Heath,E09000004,Bexley,Erith,1.0002E+11,Silver Spring Close,20101306,DA8,549977,177918,549950,177950,51.48034087,0.158412862
150930121,04/12/2012 23:36,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN KITCHEN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000485,Green Street East,E09000025,Newham,Stratford,NULL,Halley Road,22207740,E7,NULL,NULL,541150,184750,NULL,NULL
151022121,05/12/2012 07:55,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN OVERHEAD WIRE,Bird,Person (land line),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Bodney Road,NULL,E8,534563,185248,534550,185250,51.55008512,-0.060620544
151839121,06/12/2012 17:02,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN RUBBISH CHUTE,Cat,Person (mobile),Small refuse/rubbish container,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000115,Cray Valley West,E09000006,Bromley,Sidcup,1.0002E+11,Leith Hill,20301187,BR5,546058,169622,546050,169650,51.40682129,0.098595563
152080121,07/12/2012 07:30,2012,2012/13,Special Service,1,1,260,260,CAT WITH HEAD STUCK IN FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011233,West Heath,E09000004,Bexley,Plumstead,NULL,First Avenue,20100554,DA7,NULL,NULL,547450,177250,NULL,NULL
152564121,08/12/2012 06:35,2012,2012/13,Special Service,1,1,260,260,DOG IN CANAL,Dog,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000176,Elthorne,E09000009,Ealing,Ealing,NULL,Billets Hart Close,NULL,W7,515248,179567,515250,179550,51.50329751,-0.340918266
153143121,09/12/2012 09:22,2012,2012/13,Special Service,2,5,260,1300,HORSE STUCK IN DITCH,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000309,Emerson Park,E09000016,Havering,Hornchurch,1.00021E+11,Wingletye Lane,21300661,RM11,555476,188420,555450,188450,51.5732146,0.242169101
153320121,09/12/2012 16:02,2012,2012/13,Special Service,NULL,NULL,260,NULL,Redacted,Dog,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000320,St. Andrew's,E09000016,Havering,Hornchurch,1.00023E+11,North Street,21300513,RM11,554003,187415,554050,187450,51.56458893,0.220490683
153577121,10/12/2012 06:40,2012,2012/13,Special Service,1,1,260,260,CAT WITH LEG TRAPPED IN POOL TABLE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Thane Villas,21603913,N7,NULL,NULL,530950,186250,NULL,NULL
154633121,12/12/2012 10:11,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Shellness Road,20900912,E5,NULL,NULL,534850,185350,NULL,NULL
154832121,12/12/2012 16:36,2012,2012/13,Special Service,1,1,260,260,SWAN TRAPPED IN ICED LAKE,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000620,Queenstown,E09000032,Wandsworth,Battersea,NULL,Prince of Wales Drive,NULL,SW11,528359,176985,528350,176950,51.47727119,-0.153054864
155428121,13/12/2012 19:41,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BY HEAD BETWEEN WALL AND FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000176,Elthorne,E09000009,Ealing,Ealing,NULL,York Avenue,20601977,W7,NULL,NULL,515450,180250,NULL,NULL
155491121,13/12/2012 22:30,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN WIRE FENCING,Cat,Person (mobile),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000203,Jubilee,E09000010,Enfield,Edmonton,NULL,Meridian Way,NULL,N9,535927,194326,535950,194350,51.63133409,-0.037448703
155638121,14/12/2012 07:13,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UP CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,NULL,Connaught Road,22401387,TW11,NULL,NULL,514650,171450,NULL,NULL
155797121,14/12/2012 13:36,2012,2012/13,Special Service,1,2,260,520,CAT STUCK BETWEEN WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Shellness Road,20900912,E5,NULL,NULL,534850,185350,NULL,NULL
155904121,14/12/2012 17:04,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED ON CHIMNEY BREAST,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000469,Raynes Park,E09000024,Merton,Wimbledon,NULL,Durrington Avenue,22102238,SW20,NULL,NULL,523250,170050,NULL,NULL
155999121,14/12/2012 20:26,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Shellness Road,20900912,E5,NULL,NULL,534850,185350,NULL,NULL
156719121,16/12/2012 11:06,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN WALL CAVITY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000378,St. George's,E09000019,Islington,Holloway,NULL,Dalmeny Avenue,21606427,N7,NULL,NULL,529950,185550,NULL,NULL
156832121,16/12/2012 15:36,2012,2012/13,Special Service,1,1,260,260,KITTEN UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000331,Heathrow Villages,E09000017,Hillingdon,Heathrow,1.00021E+11,Caroline Place,21400318,UB3,509103,177070,509150,177050,51.48207253,-0.430189138
157048121,17/12/2012 00:08,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000258,North End,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Vereker Road,NULL,W14,524496,178118,524450,178150,51.48831614,-0.208252736
157207121,17/12/2012 13:50,2012,2012/13,Special Service,1,1,260,260,HAWK TRAPPED IN TREE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000044,Burnt Oak,E09000003,Barnet,Mill Hill,200118832,Trevor Road,20043200,HA8,520775,190778,520750,190750,51.60290274,-0.257477302
157209121,17/12/2012 13:56,2012,2012/13,Special Service,1,1,260,260,DOG WITH LEG STUCK IN METAL BASKET,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000061,West Finchley,E09000003,Barnet,Finchley,NULL,Wentworth Avenue,20045100,N3,NULL,NULL,525550,191450,NULL,NULL
157325121,17/12/2012 17:05,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND BOILER,Cat,Police,Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000059,Totteridge,E09000003,Barnet,Barnet,NULL,Green Road,20019240,N20,NULL,NULL,526450,193450,NULL,NULL
157499121,17/12/2012 21:58,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000329,Eastcote and East Ruislip,E09000017,Hillingdon,Ruislip,NULL,Dormywood,21400605,HA4,NULL,NULL,509750,188550,NULL,NULL
157530121,17/12/2012 23:12,2012,2012/13,Special Service,1,1,260,260,ASSIST POLICE IN GAINING ENTRY,Unknown - Domestic Animal Or Pet,Police,Vehicle Repair Workshop,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000449,New Cross,E09000023,Lewisham,Deptford,NULL,Amersham Vale,NULL,SE14,536767,177446,536750,177450,51.47944503,-0.031878261
157657121,18/12/2012 10:31,2012,2012/13,Special Service,1,1,260,260,INJURED DOG STUCK IN MUD ON FORESHORE,Dog,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000228,Thamesmead Moorings,E09000011,Greenwich,Plumstead,NULL,Ware Point Drive,NULL,SE28,544758,180031,544750,180050,51.5006859,0.084175445
158637121,20/12/2012 13:04,2012,2012/13,Special Service,1,1,260,260,CAT STUCK BETWEEN CAVITY WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000472,Village,E09000024,Merton,Wandsworth,NULL,Bathgate Road,22100444,SW19,NULL,NULL,523950,172450,NULL,NULL
159392121,21/12/2012 23:08,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011217,Barnehurst,E09000004,Bexley,Bexley,NULL,Manor Way,20100920,DA7,NULL,NULL,550650,175750,NULL,NULL
159615121,22/12/2012 12:56,2012,2012/13,Special Service,1,1,260,260,CAT UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000037,Parsloes,E09000002,Barking and Dagenham,Dagenham,NULL,Linkway,NULL,RM8,547464,185958,547450,185950,51.5532431,0.12561122
160224121,23/12/2012 15:23,2012,2012/13,Special Service,1,1,260,260,ANIMALS LOCKED IN ROOM,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000336,Northwood Hills,E09000017,Hillingdon,Ruislip,NULL,Woodside Road,21402213,HA6,NULL,NULL,509950,191550,NULL,NULL
160571121,24/12/2012 12:07,2012,2012/13,Special Service,1,1,260,260,KITTEN STUCK IN TV STAND,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000410,Old Malden,E09000021,Kingston upon Thames,New Malden,NULL,Percy Gardens,21800762,KT4,NULL,NULL,520950,166350,NULL,NULL
161316121,25/12/2012 22:57,2012,2012/13,Special Service,1,1,260,260,ONE SHEEP TRAPPED IN FENCING,Sheep,Other FRS,Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000195,Chase,E09000010,Enfield,Enfield,NULL,East Lodge Lane,NULL,EN2,529893,199380,529850,199350,51.67817701,-0.122702983
161649121,26/12/2012 23:22,2012,2012/13,Special Service,1,1,260,260,ANIMAL TRAPPED IN CHIMNEY,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000255,Fulham Reach,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Claybrook Road,21000220,W6,NULL,NULL,523850,177950,NULL,NULL
161832121,27/12/2012 13:16,2012,2012/13,Special Service,1,1,260,260,DOG STUCK IN LIFT EMERGENCY,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000426,Oval,E09000022,Lambeth,Lambeth,NULL,Richborne Terrace,21901154,SW8,NULL,NULL,530650,177350,NULL,NULL
161849121,27/12/2012 14:14,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN METAL PANELLING,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Goldington Street,20400800,NW1,NULL,NULL,529750,183350,NULL,NULL
162647121,29/12/2012 17:14,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011234,Aldborough,E09000026,Redbridge,Ilford,NULL,Ashurst Drive,22302589,IG6,NULL,NULL,544150,188850,NULL,NULL
163205121,31/12/2012 07:06,2012,2012/13,Special Service,1,1,260,260,CAT WITH LEG TRAPPED IN FENCE NEXT TO DISUSED INDIAN TAKEAWAY SHOP,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000450,Perry Vale,E09000023,Lewisham,Forest Hill,NULL,Stanstead Road,NULL,SE23,535543,173228,535550,173250,51.44183438,-0.051108879
295131,01/01/2013 14:22,2013,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),Purpose built office,Non Residential,Other animal assistance,Animal harm involving wild animal,E05000457,Colliers Wood,E09000024,Merton,Wimbledon,48082272,High Street Colliers Wood,22103303,SW19,526768,170279,526750,170250,51.41736135,-0.178351767
380131,01/01/2013 18:29,2013,2012/13,Special Service,1,1,260,260,DOG TRAPPED BETWEEN METAL CHAIRS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000436,Vassall,E09000022,Lambeth,Brixton,NULL,Frederick Crescent,NULL,SW9,531627,177060,531650,177050,51.47719501,-0.105995713
436131,01/01/2013 21:03,2013,2012/13,Special Service,1,1,260,260,INJURED DOG TRAPPED ON RAILWAY LINE,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000254,Fulham Broadway,E09000013,Hammersmith and Fulham,Fulham,34071759,Brompton Park Crescent,21000159,SW6,525602,177658,525650,177650,51.48393773,-0.192494735
953131,03/01/2013 11:17,2013,2012/13,Special Service,1,1,260,260,FOX TRAPPED IN FENCING,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,1.00021E+11,Spencer Road,21105063,N17,534327,190622,534350,190650,51.59843357,-0.061970756
1065131,03/01/2013 15:57,2013,2012/13,Special Service,1,1,260,260,CAT FALLEN FROM SIXTH FLOOR TO FIRST FLOOR,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Ackroyd Drive,22700042,E3,NULL,NULL,536950,181850,NULL,NULL
1491131,04/01/2013 16:53,2013,2012/13,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,6210084,Tarling Street,22701188,E1,535129,181179,535150,181150,51.51338493,-0.054022422
1751131,05/01/2013 09:45,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOORBOARDS AND SINK,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011245,Hainault,E09000026,Redbridge,Hainault,NULL,Burrow Close,22304539,IG7,NULL,NULL,545750,192450,NULL,NULL
2077131,05/01/2013 23:19,2013,2012/13,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,Denman Drive North,20012240,NW11,NULL,NULL,525350,188950,NULL,NULL
2194131,06/01/2013 10:17,2013,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER DECKING,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000303,Stanmore Park,E09000015,Harrow,Stanmore,NULL,Gordon Avenue,21202215,HA7,NULL,NULL,516750,191650,NULL,NULL
2602131,07/01/2013 11:45,2013,2012/13,Special Service,NULL,NULL,260,NULL,CAT STUCK BETWEEN CUPBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000349,Chiswick Riverside,E09000018,Hounslow,Chiswick,NULL,Hartington Road,NULL,W4,NULL,NULL,520050,177050,NULL,NULL
2666131,07/01/2013 14:58,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000040,Valence,E09000002,Barking and Dagenham,Dagenham,NULL,Morris Road,19900257,RM8,NULL,NULL,548950,186650,NULL,NULL
3020131,08/01/2013 12:35,2013,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN ROOM,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000613,East Putney,E09000032,Wandsworth,Wandsworth,NULL,Buttermere Drive,22900741,SW15,NULL,NULL,524550,174650,NULL,NULL
3089131,08/01/2013 15:34,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN UP TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000545,Nunhead,E09000028,Southwark,New Cross,NULL,Clifton Crescent,NULL,SE15,535097,177257,535050,177250,51.47814777,-0.055984289
3213131,08/01/2013 21:27,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009379,King's Park,E09000012,Hackney,Homerton,NULL,Glyn Road,20900450,E5,NULL,NULL,535850,185650,NULL,NULL
3993131,10/01/2013 19:18,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000292,Headstone North,E09000015,Harrow,Harrow,NULL,Manor Way,21201337,HA2,NULL,NULL,513550,189350,NULL,NULL
4260131,11/01/2013 13:28,2013,2012/13,Special Service,1,1,260,260,CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000478,Canning Town South,E09000025,Newham,Plaistow,NULL,New Barn Street,22200883,E13,NULL,NULL,540550,182050,NULL,NULL
5122131,13/01/2013 14:08,2013,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000419,Clapham Town,E09000022,Lambeth,Clapham,NULL,Rectory Grove,21901147,SW4,NULL,NULL,529250,175850,NULL,NULL
5528131,14/01/2013 13:49,2013,2012/13,Special Service,1,2,260,520,DOG TRAPPED IN SWING DOOR,Dog,Person (land line),Purpose built office,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000649,West End,E09000033,Westminster,Soho,10033595937,Hanover Square,8401494,W1S,528951,181019,528950,181050,51.51338991,-0.143062044
5610131,14/01/2013 17:19,2013,2012/13,Special Service,1,1,260,260,CAT STUCK BEHIND CHAIR LIFT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011245,Hainault,E09000026,Redbridge,Hainault,NULL,Sylvester Gardens,22304737,IG6,NULL,NULL,546950,192150,NULL,NULL
5930131,15/01/2013 14:09,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN GARAGE,Cat,Police,Private garage,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000155,Heathfield,E09000008,Croydon,Addington,NULL,Linton Glade,NULL,CR0,536230,162545,536250,162550,51.34566781,-0.04532615
6036131,15/01/2013 18:31,2013,2012/13,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000417,Brixton Hill,E09000022,Lambeth,Brixton,NULL,St. Saviour's Road,21901287,SW2,NULL,NULL,530650,174550,NULL,NULL
6072131,15/01/2013 19:44,2013,2012/13,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000520,Hampton,E09000027,Richmond upon Thames,Twickenham,NULL,Plevna Road,NULL,TW12,NULL,NULL,513750,169550,NULL,NULL
6138131,15/01/2013 23:07,2013,2012/13,Special Service,1,1,260,260,DOG STUCK BEHIND FENCE,Dog,Person (land line),Secondary school,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000188,South Acton,E09000009,Ealing,Acton,12141198,Twyford Crescent,20601783,W3,519635,180375,519650,180350,51.50964912,-0.277467292
6849131,17/01/2013 11:37,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN SHED AND WALL,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000370,Clerkenwell,E09000019,Islington,Islington,5300078415,River Street,21605813,EC1R,531265,182846,531250,182850,51.52927637,-0.109053695
7147131,17/01/2013 21:37,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND SHUTTER,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,1.00023E+11,Thomas Street,20801490,SE18,543564,178883,543550,178850,51.49067468,0.066518372
7290131,18/01/2013 09:13,2013,2012/13,Special Service,1,1,260,260,DOG ON SILTBANK IN MIDDLE OF CANAL,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Wyvern Way,NULL,UB8,504952,184007,504950,184050,51.54521043,-0.48788083
7377131,18/01/2013 11:45,2013,2012/13,Special Service,1,1,260,260,CAT WITH HEAD TRAPPED BETWEEN RSJ AND WINDOW FRAME,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000412,St. Mark's,E09000021,Kingston upon Thames,Surbiton,NULL,Addison Gardens,21800036,KT5,NULL,NULL,518950,168050,NULL,NULL
8241131,20/01/2013 11:55,2013,2012/13,Special Service,2,3,260,780,"DOG FALLEN THROUGH ICE ON POND, WATER RESCUE LEVEL TWO",Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000643,Regent's Park,E09000033,Westminster,Paddington,NULL,Regents Park,NULL,NW1,528023,182218,528050,182250,51.52437612,-0.155992735
8446131,20/01/2013 20:36,2013,2012/13,Special Service,1,2,260,520,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000266,Alexandra,E09000014,Haringey,Hornsey,NULL,Dukes Avenue,NULL,N10,529462,190120,529450,190150,51.59506149,-0.13235481
8688131,21/01/2013 11:33,2013,2012/13,Special Service,1,1,260,260,DOG FALLEN THROUGH ICE INTO POND,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000325,Botwell,E09000017,Hillingdon,Hayes,1.00024E+11,Stockley Park,21400150,UB11,508060,180516,508050,180550,51.51324649,-0.444148973
9119131,22/01/2013 09:05,2013,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000056,High Barnet,E09000003,Barnet,Barnet,NULL,Bakers Hill,NULL,EN5,526273,197148,526250,197150,51.65894366,-0.175836913
9393131,22/01/2013 19:16,2013,2012/13,Special Service,1,1,260,260,DOG FALLEN INTO THE CANAL,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000344,Yeading,E09000017,Hillingdon,Southall,NULL,Brookside Road,NULL,UB4,511633,181196,511650,181150,51.51866398,-0.392465263
9641131,23/01/2013 14:00,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN BACK OF CHEST DRAWER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000063,Woodhouse,E09000003,Barnet,Finchley,NULL,Friern Watch Avenue,20016820,N12,NULL,NULL,526550,192750,NULL,NULL
9973131,24/01/2013 10:40,2013,2012/13,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000485,Green Street East,E09000025,Newham,Stratford,10008997118,Sandringham Road,22207876,E7,541182,185020,541150,185050,51.54642045,0.034688211
10004131,24/01/2013 12:17,2013,2012/13,Special Service,1,3,260,780,DOG STUCK IN BADGER HOLE,Dog,Person (mobile),Heathland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000137,Highgate,E09000007,Camden,West Hampstead,10091822697,Hampstead Lane,21106630,NW3,527087,187418,527050,187450,51.57131936,-0.167600351
10028131,24/01/2013 12:51,2013,2012/13,Special Service,1,1,260,260,ASSIST RSCPA WITH PIGEON TRAPPED OS,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009306,Langbourn,E09000001,City of London,Dowgate,95506892,Gracechurch Street,8100241,EC3V,532962,180985,532950,180950,51.51215556,-0.085305891
10142131,24/01/2013 17:54,2013,2012/13,Special Service,1,1,260,260,TO ASSIST RSPCA WITH PIGEON TRAPPED ON BUILDING,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000564,Sutton Central,E09000029,Sutton,Sutton,5870035554,High Street,22605497,SM1,525920,164118,525950,164150,51.36217973,-0.192723011
10585131,25/01/2013 16:26,2013,2012/13,Special Service,1,2,260,520,Redacted,Dog,Police,Lake/pond/reservoir,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000345,Yiewsley,E09000017,Hillingdon,Hillingdon,NULL,Wraysbury Drive,NULL,UB7,505643,180463,505650,180450,51.51322694,-0.478982129
10698131,25/01/2013 21:37,2013,2012/13,Special Service,1,1,260,260,DOG TRAPPED INBETWEEN WALLS,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000630,Abbey Road,E09000033,Westminster,West Hampstead,1.00023E+11,Clifton Hill,8400800,NW8,526212,183593,526250,183550,51.53714058,-0.181590818
11674131,28/01/2013 09:36,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN SHED,Cat,Person (land line),Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000308,Elm Park,E09000016,Havering,Hornchurch,1.00021E+11,Kilmartin Way,21300447,RM12,552761,185101,552750,185150,51.54413555,0.20158308
11697131,28/01/2013 10:32,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000522,Hampton Wick,E09000027,Richmond upon Thames,Twickenham,NULL,Crieff Court,22406634,TW11,NULL,NULL,517150,170150,NULL,NULL
12083131,29/01/2013 09:32,2013,2012/13,Special Service,1,1,260,260,FOX TRAPPED IN FENCE,Fox,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000349,Chiswick Riverside,E09000018,Hounslow,Chiswick,1.00023E+11,Burlington Lane,21500176,W4,520405,177266,520450,177250,51.48154339,-0.267437096
12521131,30/01/2013 09:51,2013,2012/13,Special Service,1,1,260,260,COCKER SPANIEL CAUGHT IN BARBED WIRE IN POND,Dog,Police,Lake/pond/reservoir,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000303,Stanmore Park,E09000015,Harrow,Stanmore,NULL,Goodhall Close,NULL,HA7,516607,191873,516650,191850,51.61362146,-0.31726855
13412131,01/02/2013 08:55,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN PIT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000648,Westbourne,E09000033,Westminster,Paddington,NULL,Elmfield Way,8401841,W9,NULL,NULL,525250,181950,NULL,NULL
13639131,01/02/2013 18:20,2013,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN BETWEEN WALL AND IVY,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011107,North Walworth,E09000028,Southwark,Old Kent Road,NULL,Elsted Street,22500908,SE17,NULL,NULL,532850,178650,NULL,NULL
13709131,01/02/2013 20:23,2013,2012/13,Special Service,1,1,260,260,CAT LOCKED INSIDED CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000203,Jubilee,E09000010,Enfield,Edmonton,NULL,Granary Close,20703354,N9,NULL,NULL,535150,194650,NULL,NULL
13735131,01/02/2013 21:48,2013,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND AIR BRICK,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000431,Streatham South,E09000022,Lambeth,Norbury,NULL,Abercairn Road,21900064,SW16,NULL,NULL,529150,170050,NULL,NULL
14624131,03/02/2013 17:25,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDERNEATH BATH,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000639,Little Venice,E09000033,Westminster,Paddington,NULL,Sutherland Avenue,8401592,W9,NULL,NULL,525950,182450,NULL,NULL
15359131,05/02/2013 12:54,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED UP DOMESTIC INTERNAL PIPES,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Althorne Way,19900019,RM10,NULL,NULL,549150,186850,NULL,NULL
15848131,06/02/2013 14:50,2013,2012/13,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK OVER FENCE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000434,Thurlow Park,E09000022,Lambeth,West Norwood,NULL,Harpenden Road,NULL,SE27,NULL,NULL,531650,172650,NULL,NULL
15933131,06/02/2013 18:06,2013,2012/13,Special Service,1,1,260,260,SMALL ANIMAL TRAPPED IN LOFT,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,NULL,Melon Road,22858225,E11,NULL,NULL,539050,186250,NULL,NULL
16216131,07/02/2013 14:07,2013,2012/13,Special Service,1,1,260,260,SMALL ANIMAL TRAPPED  IN LOFT AREA,Unknown - Domestic Animal Or Pet,Person (land line),Veterinary surgery,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000647,Warwick,E09000033,Westminster,Lambeth,10033550691,Hugh Street,8400826,SW1V,528904,178741,528950,178750,51.4929284,-0.144571129
16283131,07/02/2013 17:05,2013,2012/13,Special Service,1,1,260,260,RUNNING CALL TO PIGEON TRAPPED BEHIND GLASS,Bird,Person (land line),Post office (purpose built),Non Residential,Other animal assistance,Assist trapped domestic animal,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,Kerbey Street,NULL,E14,537844,181109,537850,181150,51.51210062,-0.014948327
16906131,08/02/2013 22:28,2013,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN BETWEEN WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Bradstock Road,20900160,E9,NULL,NULL,535950,184650,NULL,NULL
17505131,10/02/2013 08:33,2013,2012/13,Special Service,1,2,260,520,ASSIST RSPCA WITH INJURED PIGEON,Bird,Person (land line),Hospital,Non Residential,Other animal assistance,Assist trapped wild animal,E05000562,St. Helier,E09000029,Sutton,Sutton,5870054822,Wrythe Lane,22605653,SM5,526535,166124,526550,166150,51.38007156,-0.183180799
17781131,10/02/2013 21:46,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000112,Clock House,E09000006,Bromley,Beckenham,NULL,Elmers End Road,20303586,SE20,NULL,NULL,535150,169350,NULL,NULL
18081131,11/02/2013 16:16,2013,2012/13,Special Service,1,1,260,260,PIGEON STUCK ON ROOF,Bird,Other FRS,House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011244,Goodmayes,E09000026,Redbridge,Ilford,NULL,Milverton Gardens,22302286,IG3,NULL,NULL,545750,186850,NULL,NULL
18859131,13/02/2013 15:00,2013,2012/13,Special Service,1,1,260,260,DOG STUCK IN DITCH,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000331,Heathrow Villages,E09000017,Hillingdon,Hayes,NULL,Sipson Lane,NULL,UB3,508231,178204,508250,178250,51.49243302,-0.442394666
19657131,15/02/2013 09:36,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011252,South Woodford,E09000026,Redbridge,Woodford,NULL,Woodford Road,22305452,E18,NULL,NULL,540250,189750,NULL,NULL
19830131,15/02/2013 17:22,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER RUBBLE,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000485,Green Street East,E09000025,Newham,East Ham,NULL,Prestbury Road,NULL,E7,541427,184398,541450,184350,51.54076991,0.037969337
20590131,17/02/2013 11:53,2013,2012/13,Special Service,1,2,260,520,DOG TRAPPED ON RAILWAY LINE,Dog,Person (land line),Railway,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011116,South Bermondsey,E09000028,Southwark,Dockhead,2.00003E+11,Enid Street,22500914,SE16,533891,179403,533850,179450,51.49771958,-0.07252663
20695131,17/02/2013 15:46,2013,2012/13,Special Service,1,1,260,260,DOG HEAD STUCK IN RAILINGS,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,1.00023E+11,Valognes Avenue,22883550,E17,536206,190916,536250,190950,51.60062427,-0.034744407
20700131,17/02/2013 15:59,2013,2012/13,Special Service,1,1,260,260,CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011235,Barkingside,E09000026,Redbridge,Ilford,NULL,Inglehurst Gardens,22302967,IG4,NULL,NULL,542650,188650,NULL,NULL
21506131,19/02/2013 11:15,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000451,Rushey Green,E09000023,Lewisham,Lewisham,1.00022E+11,Honley Road,22001571,SE6,537891,173919,537850,173950,51.44747768,-0.017077312
21512131,19/02/2013 11:31,2013,2012/13,Special Service,1,1,260,260,ASSIST POLICE,Unknown - Domestic Animal Or Pet,Police,Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011095,Borough & Bankside,E09000028,Southwark,Dowgate,NULL,Weller Street,22502673,SE1,NULL,NULL,532250,179750,NULL,NULL
21657131,19/02/2013 17:26,2013,2012/13,Special Service,1,1,260,260,CAT STUCK UNDER FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000087,Brondesbury Park,E09000005,Brent,Willesden,NULL,Sidmouth Road,20201521,NW2,NULL,NULL,523250,184150,NULL,NULL
22531131,21/02/2013 13:16,2013,2012/13,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000135,Hampstead Town,E09000007,Camden,West Hampstead,NULL,Heath Street,20499121,NW3,NULL,NULL,526350,185850,NULL,NULL
23104131,22/02/2013 16:33,2013,2012/13,Special Service,1,1,260,260,PUPPY WITH HEAD TRAPPED IN WALL,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000616,Graveney,E09000032,Wandsworth,Tooting,1.00023E+11,Bickley Street,22900494,SW17,527516,171246,527550,171250,51.4258845,-0.167253037
23221131,22/02/2013 20:26,2013,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,NULL,Craven Passage,NULL,WC2N,530213,180385,530250,180350,51.50740311,-0.125119613
23669131,23/02/2013 17:18,2013,2012/13,Special Service,1,1,260,260,CAT STUCK  BEHIND CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000217,Coldharbour and New Eltham,E09000011,Greenwich,Eltham,NULL,Littlemede,20800929,SE9,NULL,NULL,543050,172350,NULL,NULL
23991131,24/02/2013 11:21,2013,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000217,Coldharbour and New Eltham,E09000011,Greenwich,Eltham,NULL,Littlemede,20800929,SE9,NULL,NULL,542950,172350,NULL,NULL
24099131,24/02/2013 14:58,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000029,Chadwell Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Sheepcotes Road,NULL,RM6,548048,189284,548050,189250,51.58297522,0.135428001
24108131,24/02/2013 15:15,2013,2012/13,Special Service,1,1,260,260,BIRDS TRAPPED IN NETTING NEAR,Bird,Person (mobile),Railway,Outdoor,Other animal assistance,Assist trapped wild animal,E05000253,College Park and Old Oak,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Wood Lane,NULL,W12,523323,180729,523350,180750,51.51203884,-0.224226669
25398131,27/02/2013 13:33,2013,2012/13,Special Service,1,1,260,260,CAT STUCK ON TELEPHONE POLE,Cat,Person (land line),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000199,Enfield Lock,E09000010,Enfield,Enfield,NULL,Park Road,NULL,EN3,536363,199169,536350,199150,51.67474831,-0.029265833
26016131,28/02/2013 19:38,2013,2012/13,Special Service,1,1,260,260,CAT STUCK IN AIR VENT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009323,Canary Wharf,E09000030,Tower Hamlets,Millwall,NULL,Marsh Wall,22700793,E14,NULL,NULL,537250,179950,NULL,NULL
26341131,01/03/2013 13:38,2013,2012/13,Special Service,1,1,260,260,TO ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011465,Broad Green,E09000008,Croydon,Croydon,1.00021E+11,Euston Road,20500922,CR0,531168,166190,531150,166150,51.37961472,-0.116622569
26533131,01/03/2013 20:57,2013,2012/13,Special Service,1,1,260,260,TWO FOXES TRAPPED IN BASEMENT AREA,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009381,London Fields,E09000012,Hackney,Shoreditch,NULL,Queensbridge Road,20900836,E8,NULL,NULL,533950,184250,NULL,NULL
27174131,03/03/2013 10:26,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011485,South Norwood,E09000008,Croydon,Woodside,NULL,St. Dunstans Road,20501408,SE25,NULL,NULL,533850,168450,NULL,NULL
27736131,04/03/2013 14:51,2013,2012/13,Special Service,1,1,260,260,PIGEON STUCK IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000450,Perry Vale,E09000023,Lewisham,Forest Hill,NULL,Colfe Road,NULL,SE23,536004,173201,536050,173250,51.4414813,-0.044490298
28295131,05/03/2013 18:14,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON WINDOW SILL,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Felstead Street,NULL,E9,NULL,NULL,536950,184650,NULL,NULL
28532131,06/03/2013 10:12,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000457,Colliers Wood,E09000024,Merton,Wimbledon,NULL,Boundary Road,22100726,SW19,NULL,NULL,526650,170650,NULL,NULL
28767131,06/03/2013 19:03,2013,2012/13,Special Service,1,2,260,520,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Grand Avenue,21100352,N10,NULL,NULL,528350,189350,NULL,NULL
28952131,07/03/2013 08:20,2013,2012/13,Special Service,1,1,260,260,FOX WITH HEAD TRAPPED,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000205,Palmers Green,E09000010,Enfield,Edmonton,NULL,Arnold Gardens,20703872,N13,NULL,NULL,531550,192450,NULL,NULL
29215131,07/03/2013 19:38,2013,2012/13,Special Service,1,1,260,260,KITTEN WITH HEAD TRAPPED IN HOLE IN CHAIR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000370,Clerkenwell,E09000019,Islington,Islington,NULL,Margery Street,21605383,WC1X,NULL,NULL,531150,182650,NULL,NULL
29377131,08/03/2013 09:06,2013,2012/13,Special Service,1,1,260,260,SMALL ANIMAL TRAPPED,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000640,Maida Vale,E09000033,Westminster,Paddington,NULL,Widley Road,8400708,W9,NULL,NULL,525350,182450,NULL,NULL
30349131,10/03/2013 06:22,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND SCAFFOLDNG,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011112,Rotherhithe,E09000028,Southwark,Deptford,NULL,Rotherhithe Old Road,22502124,SE16,NULL,NULL,535550,178850,NULL,NULL
30817131,11/03/2013 09:33,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009397,Holland,E09000020,Kensington and Chelsea,Kensington,217039505,Holland Park Road,21700644,W14,524841,179307,524850,179350,51.49892599,-0.202865901
30893131,11/03/2013 12:43,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN GATE,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Christie Road,NULL,E9,536115,184565,536150,184550,51.54357571,-0.038514001
31320131,12/03/2013 10:04,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Crouch Hall Road,NULL,N8,529887,188376,529850,188350,51.57929122,-0.126867151
31393131,12/03/2013 12:43,2013,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED UNDER FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000555,Beddington North,E09000029,Sutton,Mitcham,NULL,Rosemary Close,22605577,CR0,NULL,NULL,530150,166950,NULL,NULL
31958131,13/03/2013 18:20,2013,2012/13,Special Service,1,1,260,260,TO ASSIST RSPCA WITH TRAPPED FOX,Fox,Person (land line),College/University,Non Residential,Animal rescue from below ground,Wild animal rescue from below ground,E05009305,Farringdon Without,E09000001,City of London,Soho,95509816,Chancery Lane,8100118,WC2A,531158,181254,531150,181250,51.5149943,-0.111187648
32372131,14/03/2013 15:32,2013,2012/13,Special Service,1,2,260,520,DOG TRAPPED IN BADGER SETT,Dog,Person (land line),Golf course (not building on course),Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000335,Northwood,E09000017,Hillingdon,Ruislip,1.00023E+11,The Drive,21401951,HA6,509356,190370,509350,190350,51.60156642,-0.422418058
33271131,16/03/2013 15:30,2013,2012/13,Special Service,1,2,260,520,TO ASSIST RSPCA WITH CAT UP A TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009385,Stoke Newington,E09000012,Hackney,Stoke Newington,1.00021E+11,Walford Road,20901038,N16,533398,185854,533350,185850,51.55580758,-0.077183355
33438131,16/03/2013 23:17,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000640,Maida Vale,E09000033,Westminster,Paddington,NULL,Kilburn Park Road,8400177,NW6,NULL,NULL,525350,182750,NULL,NULL
34196131,18/03/2013 18:50,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON STUCK ON TV ARIEL,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000533,Whitton,E09000027,Richmond upon Thames,Twickenham,NULL,Hazel Close,22401563,TW2,NULL,NULL,514550,173850,NULL,NULL
34221131,18/03/2013 19:41,2013,2012/13,Special Service,1,1,260,260,DOG WITH LEG TRAPPED IN FURNITURE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05009374,Hackney Wick,E09000012,Hackney,Homerton,1.00023E+11,Mabley Street,20900646,E9,536365,185041,536350,185050,51.54779292,-0.034726737
34628131,19/03/2013 18:41,2013,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Bradstock Road,20900160,E9,NULL,NULL,535950,184650,NULL,NULL
34659131,19/03/2013 19:44,2013,2012/13,Special Service,1,2,260,520,ANIMINAL ON ROADWAY,Unknown - Heavy Livestock Animal,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000337,Pinkwell,E09000017,Hillingdon,Hayes,NULL,Shepiston Lane,NULL,UB3,507941,178612,507950,178650,51.49615568,-0.446445506
35048131,20/03/2013 18:11,2013,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Bird,Person (land line),Bank/Building Society,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,12083650,New Broadway,20601231,W5,517629,180736,517650,180750,51.51331421,-0.306239389
35285131,21/03/2013 11:48,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000060,Underhill,E09000003,Barnet,Barnet,NULL,Windsor Road,20046720,EN5,NULL,NULL,523950,195350,NULL,NULL
35821131,22/03/2013 15:14,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN BRANCHES OF TREE,Cat,Police,Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009382,Shacklewell,E09000012,Hackney,Stoke Newington,1.00023E+11,Downs Park Road,20901783,E8,534033,185388,534050,185350,51.55146933,-0.068206712
35931131,22/03/2013 19:21,2013,2012/13,Special Service,1,1,260,260,CAT STUCK BEHIND BOILER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000527,St. Margarets and North Twickenham,E09000027,Richmond upon Thames,Heston,NULL,Broadway Avenue,22403320,TW1,NULL,NULL,516650,174250,NULL,NULL
36614131,24/03/2013 13:25,2013,2012/13,Special Service,1,1,260,260,INJURED CAT TRAPPED IN KITCHEN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,NULL,Arlington Road,22403262,TW1,NULL,NULL,517050,174350,NULL,NULL
36795131,24/03/2013 20:18,2013,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN TUNNEL UNDER SHED,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000601,Hoe Street,E09000031,Waltham Forest,Walthamstow,NULL,Summit Road,22878650,E17,NULL,NULL,537950,189050,NULL,NULL
37256131,25/03/2013 19:54,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN ENGINE OF VAN,Cat,Person (land line),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,Forest Gardens,NULL,N17,533702,190387,533750,190350,51.59647057,-0.071078425
37483131,26/03/2013 08:31,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN GARDEN,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000258,North End,E09000013,Hammersmith and Fulham,Fulham,NULL,Chesson Road,21000211,W14,NULL,NULL,524750,177850,NULL,NULL
38755131,28/03/2013 23:23,2013,2012/13,Special Service,1,1,260,260,TO ASSIST RSPCA WITH INJURED DEER,Deer,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving wild animal,E05011252,South Woodford,E09000026,Redbridge,Woodford,NULL,Elmhurst Drive,22304959,E18,NULL,NULL,540350,190450,NULL,NULL
38756131,28/03/2013 23:26,2013,2012/13,Special Service,1,2,260,520,Redacted,Cat,Person (land line),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000305,West Harrow,E09000015,Harrow,Harrow,1.00021E+11,The Drive,21202021,HA2,513414,187651,513450,187650,51.57632616,-0.364728255
39037131,29/03/2013 15:32,2013,2012/13,Special Service,1,2,260,520,CAT STUCK IN TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000118,Farnborough and Crofton,E09000006,Bromley,Orpington,NULL,Wyndham Close,NULL,BR6,544232,166115,544250,166150,51.37577614,0.070937874
39741131,31/03/2013 02:33,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000620,Queenstown,E09000032,Wandsworth,Battersea,NULL,Warriner Gardens,22905967,SW11,NULL,NULL,528050,176650,NULL,NULL
39809131,31/03/2013 09:44,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000267,Bounds Green,E09000014,Haringey,Southgate,NULL,Whittington Road,21106553,N22,NULL,NULL,530150,191250,NULL,NULL
39892131,31/03/2013 14:20,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP A TREE,Cat,Person (mobile),Railway trackside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000101,Sudbury,E09000005,Brent,Wembley,NULL,The Croft,NULL,HA0,517187,185332,517150,185350,51.55471332,-0.311077494
39962131,31/03/2013 16:35,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED IN BUILDING,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000405,Chessington South,E09000021,Kingston upon Thames,Surbiton,NULL,May Close,21800658,KT9,NULL,NULL,518850,163850,NULL,NULL
40010131,31/03/2013 17:34,2013,2012/13,Special Service,1,1,260,260,SQUIRREL TRAPPED BETWEEN DRAIN PIPE AND WALL,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000309,Emerson Park,E09000016,Havering,Hornchurch,NULL,Kinfauns Avenue,21300448,RM11,NULL,NULL,553250,188350,NULL,NULL
40271131,01/04/2013 12:04,2013,2013/14,Special Service,1,1,290,290,CAT STUCK ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000267,Bounds Green,E09000014,Haringey,Southgate,NULL,Whittington Road,21106553,N22,NULL,NULL,530150,191250,NULL,NULL
40634131,02/04/2013 08:10,2013,2013/14,Special Service,1,1,290,290,CAT FALLEN ONTO BALCONY - INJURED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,NULL,Oakwood Drive,21901044,SE19,NULL,NULL,532950,170850,NULL,NULL
41742131,04/04/2013 15:29,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED BETWEEN WALLS,Fox,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009400,Pembridge,E09000020,Kensington and Chelsea,Kensington,NULL,Pembridge Crescent,21700964,W11,NULL,NULL,525150,180750,NULL,NULL
42495131,06/04/2013 11:45,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN TWO WALLS,Cat,Police,House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000350,Cranford,E09000018,Hounslow,Heathrow,NULL,Clevedon Gardens,21500276,TW5,NULL,NULL,510650,176450,NULL,NULL
42614131,06/04/2013 16:23,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED UNDER A TREE LOG,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000062,West Hendon,E09000003,Barnet,Hendon,NULL,Marriotts Close,NULL,NW9,521741,188032,521750,188050,51.57801645,-0.244486891
42677131,06/04/2013 18:48,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN BUSHES,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000443,Evelyn,E09000023,Lewisham,Deptford,NULL,Foreshore,NULL,SE8,536834,178513,536850,178550,51.48901731,-0.030500659
42687131,06/04/2013 19:07,2013,2013/14,Special Service,1,1,290,290,BIRDS HEAD TRAPPED IN CAGE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Farmfield Road,22001461,BR1,NULL,NULL,539150,171250,NULL,NULL
42720131,06/04/2013 19:46,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000104,Wembley Central,E09000005,Brent,Wembley,NULL,Station Grove,NULL,HA0,518299,184834,518250,184850,51.55000568,-0.295212575
42963131,07/04/2013 08:22,2013,2013/14,Special Service,1,1,290,290,PIGEON WITH WING TRAPPED,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011464,Bensham Manor,E09000008,Croydon,Norbury,NULL,Pawsons Road,20502843,CR0,NULL,NULL,532650,167850,NULL,NULL
42994131,07/04/2013 10:02,2013,2013/14,Special Service,1,1,290,290,CAT IN BASE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000220,Eltham West,E09000011,Greenwich,Lee Green,1.00021E+11,Birdbrook Road,20800169,SE3,541407,175744,541450,175750,51.46301127,0.03421412
43149131,07/04/2013 16:41,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT TRAPPED IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000619,Northcote,E09000032,Wandsworth,Clapham,NULL,Clapham Common West Side,NULL,SW4,528303,174670,528350,174650,51.45647909,-0.154699711
44170131,09/04/2013 22:49,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN SOFA,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011218,Belvedere,E09000004,Bexley,Erith,NULL,Tunstock Way,20101485,DA17,NULL,NULL,548550,179150,NULL,NULL
44333131,10/04/2013 12:17,2013,2013/14,Special Service,1,1,290,290,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000333,Ickenham,E09000017,Hillingdon,Hillingdon,NULL,Warren Road,21402077,UB10,NULL,NULL,506650,185650,NULL,NULL
44841131,11/04/2013 16:06,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL STUCK IN CHIMNEY,Unknown - Wild Animal,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011101,Dulwich Wood,E09000028,Southwark,West Norwood,NULL,Baird Gardens,22500130,SE19,NULL,NULL,533350,171650,NULL,NULL
44985131,11/04/2013 21:34,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Mary Datchelor Close,22501674,SE5,NULL,NULL,532850,176950,NULL,NULL
45540131,13/04/2013 08:54,2013,2013/14,Special Service,1,1,290,290,CAT FALLEN INTO BASEMENT AREA OF UNOCCUPIED FLAT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009400,Pembridge,E09000020,Kensington and Chelsea,Kensington,NULL,Linden Gardens,21700734,W2,NULL,NULL,525450,180650,NULL,NULL
45633131,13/04/2013 13:33,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Kensington Road,NULL,UB5,513133,183505,513150,183550,51.53911872,-0.37011425
46071131,14/04/2013 11:19,2013,2013/14,Special Service,1,1,290,290,INJURED CAT TRAPPED BEHIND METAL GATE,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000112,Clock House,E09000006,Bromley,Woodside,NULL,Arrol Road,NULL,BR3,535444,168908,535450,168950,51.40303617,-0.054181685
46165131,14/04/2013 15:00,2013,2013/14,Special Service,1,2,290,580,CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000048,East Barnet,E09000003,Barnet,Barnet,NULL,Alexander Close,20000680,EN4,NULL,NULL,526750,196150,NULL,NULL
46261131,14/04/2013 18:06,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,1.00023E+11,Coniston Road,21104393,N17,534130,191565,534150,191550,51.60695448,-0.064452993
46588131,15/04/2013 11:27,2013,2013/14,Special Service,2,3,290,870,LARGE ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (mobile),Other Dwelling,Dwelling,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000111,Chislehurst,E09000006,Bromley,Eltham,NULL,Slades Drive,NULL,BR7,NULL,NULL,544350,171750,NULL,NULL
46694131,15/04/2013 16:40,2013,2013/14,Special Service,1,1,290,290,FOUR WEEK OLD KITTEN TRAPPED IN TOY CAR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000103,Welsh Harp,E09000005,Brent,Willesden,NULL,Village Way,20202335,NW10,NULL,NULL,521050,185850,NULL,NULL
47050131,16/04/2013 14:54,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,NULL,Cockspur Street,NULL,SW1Y,529897,180430,529850,180450,51.50788017,-0.129653768
47080131,16/04/2013 16:18,2013,2013/14,Special Service,1,1,290,290,DISTRESSED CAT UP A TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000602,Larkswood,E09000031,Waltham Forest,Chingford,NULL,New Road,22860850,E4,NULL,NULL,537950,192850,NULL,NULL
47667131,17/04/2013 17:12,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009404,St. Helen's,E09000020,Kensington and Chelsea,North Kensington,NULL,Finstock Road,21700873,W10,NULL,NULL,523650,181350,NULL,NULL
47799131,17/04/2013 20:52,2013,2013/14,Special Service,1,2,290,580,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000446,Ladywell,E09000023,Lewisham,Lewisham,NULL,Chudleigh Road,22001343,SE4,NULL,NULL,537450,174850,NULL,NULL
48360131,19/04/2013 07:01,2013,2013/14,Special Service,2,12,290,3480,FOAL IN RIVER WATER LEVEL ONE IMPLEMENTED,Horse,Police,River/canal,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000607,Valley,E09000031,Waltham Forest,Chingford,NULL,Lower Hall Lane,NULL,E4,536288,192441,536250,192450,51.6143082,-0.032968828
48896131,20/04/2013 12:10,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK BETWEEN FENCE POST,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000592,Chapel End,E09000031,Waltham Forest,Walthamstow,NULL,Forest View Road,22837700,E17,NULL,NULL,538350,190850,NULL,NULL
49030131,20/04/2013 17:00,2013,2013/14,Special Service,1,1,290,290,SQUIRREL STUCK IN PIPE,Squirrel,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000441,Crofton Park,E09000023,Lewisham,Forest Hill,NULL,Codrington Hill,22001355,SE23,NULL,NULL,536650,174050,NULL,NULL
49236131,20/04/2013 21:40,2013,2013/14,Special Service,1,1,290,290,BAT TRAPPPED IN HOUSE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,NULL,Longfield Avenue,20601079,W5,NULL,NULL,517550,180750,NULL,NULL
49542131,21/04/2013 15:01,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Cemetery,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000523,Heathfield,E09000027,Richmond upon Thames,Twickenham,1.00023E+11,Hospital Bridge Road,22401591,TW2,513705,172994,513750,172950,51.44453203,-0.365252995
49598131,21/04/2013 17:20,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000644,St. James's,E09000033,Westminster,Lambeth,NULL,Old Pye Street,8400146,SW1P,NULL,NULL,529750,179250,NULL,NULL
49600131,21/04/2013 17:26,2013,2013/14,Special Service,1,1,290,290,FOX CUB RUN OVER BY MOTOR VEHICLE,Fox,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000252,Avonmore and Brook Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Brook Green,NULL,W6,523743,178858,523750,178850,51.49513186,-0.218833452
49656131,21/04/2013 19:12,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN NETTING,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000451,Rushey Green,E09000023,Lewisham,Lewisham,NULL,Carswell Road,NULL,SE6,NULL,NULL,538250,173850,NULL,NULL
50911131,24/04/2013 10:19,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000180,Hobbayne,E09000009,Ealing,Ealing,12061258,Hillyard Road,20600896,W7,515339,181815,515350,181850,51.52348351,-0.338872468
51128131,24/04/2013 18:45,2013,2013/14,Special Service,2,1,290,290,DOG STUCK IN MUD ALONG RIVERBANK,Dog,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000427,Prince's,E09000022,Lambeth,Lambeth,2E+11,Albert Embankment,21900079,SE1,530317,178147,530350,178150,51.48726664,-0.124447991
51240131,24/04/2013 22:52,2013,2013/14,Special Service,1,1,290,290,Redacted,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000612,Earlsfield,E09000032,Wandsworth,Tooting,NULL,Garratt Lane,NULL,SW18,526041,172977,526050,172950,51.44177106,-0.187842262
51596131,25/04/2013 17:19,2013,2013/14,Special Service,1,1,290,290,Redacted,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000649,West End,E09000033,Westminster,Soho,NULL,Wardour Street,8400688,W1F,NULL,NULL,529650,180850,NULL,NULL
52258131,27/04/2013 02:30,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BETWEEN TWO WALLS,Cat,Person (land line),Common external bin storage area,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000560,Cheam,E09000029,Sutton,Sutton,5870117065,High Street,22605727,SM3,524336,163612,524350,163650,51.35798086,-0.215639544
52391131,27/04/2013 12:34,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN TWO GARAGES,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000089,Dudden Hill,E09000005,Brent,Willesden,202113970,The Circle,20201991,NW2,521324,186229,521350,186250,51.56190186,-0.251124394
52515131,27/04/2013 17:35,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA TO RESCUE KITTENS UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011237,Chadwell,E09000026,Redbridge,Ilford,NULL,Chadwell Heath Lane,22301932,RM6,NULL,NULL,547250,188350,NULL,NULL
52537131,27/04/2013 18:32,2013,2013/14,Special Service,1,2,290,580,HORSE IN CANAL  WATER RESCUE LEVEL ONE IMPLEMENTED,Horse,Police,Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000607,Valley,E09000031,Waltham Forest,Walthamstow,NULL,Shadbolt Avenue,NULL,E4,536392,191867,536350,191850,51.60912498,-0.031690845
52892131,28/04/2013 12:51,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000111,Chislehurst,E09000006,Bromley,Bromley,NULL,Newton Park Place,20304176,BR7,NULL,NULL,542750,170350,NULL,NULL
53190131,28/04/2013 21:22,2013,2013/14,Special Service,1,1,290,290,CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000381,Tollington,E09000019,Islington,Holloway,NULL,Kingsdown Road,NULL,N19,530286,186720,530250,186750,51.56431751,-0.121725434
53474131,29/04/2013 14:58,2013,2013/14,Special Service,1,1,290,290,KITTEN FALLEN DOWN HOLE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000615,Furzedown,E09000032,Wandsworth,Tooting,NULL,Crowborough Road,22901088,SW17,NULL,NULL,528450,171250,NULL,NULL
53499131,29/04/2013 15:55,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000267,Bounds Green,E09000014,Haringey,Hornsey,NULL,High Road,21104652,N22,NULL,NULL,530850,191150,NULL,NULL
54340131,01/05/2013 10:39,2013,2013/14,Special Service,2,3,290,870,Redacted,Horse,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Stonehill Business Park,NULL,N18,536006,191991,536050,191950,51.6103326,-0.03721381
54436131,01/05/2013 14:07,2013,2013/14,Special Service,1,1,290,290,RUNNING CALL TO CAT STUCK ON LEDGE,Cat,Person (land line),Other outdoor equipment/machinery,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05011487,Waddon,E09000008,Croydon,Croydon,1.00021E+11,Duppas Hill Terrace,20500889,CR0,531898,165062,531850,165050,51.36930875,-0.106557572
54971131,02/05/2013 13:04,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED BEHIND FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000317,Pettits,E09000016,Havering,Romford,NULL,Dee Way,21301134,RM1,NULL,NULL,551250,190850,NULL,NULL
55094131,02/05/2013 17:14,2013,2013/14,Special Service,1,3,290,870,Redacted,Horse,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000607,Valley,E09000031,Waltham Forest,Walthamstow,NULL,Harbet Road,NULL,E4,536288,191773,536250,191750,51.60830546,-0.033228363
55521131,03/05/2013 12:20,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED IN CANAL,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05009329,St. Dunstan's,E09000030,Tower Hamlets,Bethnal Green,NULL,Carr Street,NULL,E14,536405,181697,536450,181650,51.51773348,-0.035444848
55544131,03/05/2013 13:03,2013,2013/14,Special Service,1,1,290,290,BIRD STUCK IN GUTTERING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000345,Yiewsley,E09000017,Hillingdon,Hillingdon,NULL,Edgar Road,21400647,UB7,NULL,NULL,506450,180350,NULL,NULL
55648131,03/05/2013 16:37,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT TRAPPED ON SCHOOL ROOF,Cat,Person (mobile),Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000302,Roxeth,E09000015,Harrow,Northolt,10002294644,Wyvenhoe Road,21202151,HA2,514024,185979,514050,185950,51.56117549,-0.356471549
55994131,04/05/2013 08:37,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Gosfield Road,19900150,RM8,NULL,NULL,549350,186950,NULL,NULL
56009131,04/05/2013 09:33,2013,2013/14,Special Service,1,1,290,290,DOG WITH PAW TRAPPED IN CRATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000493,Wall End,E09000025,Newham,East Ham,NULL,Ranelagh Road,22200983,E6,NULL,NULL,543150,183550,NULL,NULL
56016131,04/05/2013 09:47,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN WELL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000618,Nightingale,E09000032,Wandsworth,Tooting,NULL,Balham High Road,22900217,SW12,NULL,NULL,528450,173150,NULL,NULL
56349131,04/05/2013 21:40,2013,2013/14,Special Service,1,2,290,580,DOG TRAPPED BEHIND FENCE,Dog,Other FRS,Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000353,Hanworth,E09000018,Hounslow,Feltham,NULL,Main Street,NULL,TW13,511648,170608,511650,170650,51.4234958,-0.395590266
56516131,05/05/2013 10:04,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000111,Chislehurst,E09000006,Bromley,Bromley,NULL,Lower Camden,20303012,BR7,NULL,NULL,542850,169950,NULL,NULL
56532131,05/05/2013 10:37,2013,2013/14,Special Service,1,1,290,290,DUCKLINGS TRAPPED IN STORM DRAIN,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011251,Seven Kings,E09000026,Redbridge,Ilford,10034932271,Barley Lane,22301832,IG3,546412,188816,546450,188850,51.57919668,0.111637569
56541131,05/05/2013 10:58,2013,2013/14,Special Service,1,2,290,580,CAT STUCK IN FOLD UP BED IN WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011486,Thornton Heath,E09000008,Croydon,Norbury,NULL,Burton Close,20502994,CR7,NULL,NULL,532750,168550,NULL,NULL
56545131,05/05/2013 11:04,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009390,Campden,E09000020,Kensington and Chelsea,Kensington,NULL,Hornton Street,21700720,W8,NULL,NULL,525350,179850,NULL,NULL
56827131,05/05/2013 20:43,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER VAN,Cat,Person (mobile),Van,Road Vehicle,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000544,Newington,E09000028,Southwark,Lambeth,NULL,Doddington Grove,NULL,SE17,531816,178043,531850,178050,51.48598495,-0.102909401
57741131,07/05/2013 11:39,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000335,Northwood,E09000017,Hillingdon,Ruislip,1.00021E+11,Copse Wood Way,21400472,HA6,508366,191213,508350,191250,51.60933443,-0.436446372
57750131,07/05/2013 11:56,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD TRAPPED IN WALL,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000604,Leyton,E09000031,Waltham Forest,Leyton,NULL,Capworth Street,22822200,E10,NULL,NULL,537650,187650,NULL,NULL
58275131,08/05/2013 13:11,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN TREE - TO ASSIST RSPCA,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000479,Custom House,E09000025,Newham,Plaistow,NULL,Goldwing Close,22207734,E16,NULL,NULL,540450,181250,NULL,NULL
58549131,08/05/2013 21:47,2013,2013/14,Special Service,1,1,290,290,CAT STUCK UP CHIMNEY,Cat,Police,House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000530,Teddington,E09000027,Richmond upon Thames,Twickenham,NULL,Avenue Road,22403273,TW11,NULL,NULL,516050,170450,NULL,NULL
58953131,09/05/2013 17:27,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009367,Brownswood,E09000012,Hackney,Stoke Newington,NULL,Queens Drive,20900835,N4,NULL,NULL,532250,186450,NULL,NULL
59048131,09/05/2013 19:40,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000639,Little Venice,E09000033,Westminster,Paddington,NULL,Sutherland Avenue,8401592,W9,NULL,NULL,525950,182450,NULL,NULL
59852131,11/05/2013 14:32,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA TO RETRIEVE FOX CUB FALLEN INTO BASEMENT,Fox,Person (land line),Other outdoor location,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05009327,Mile End,E09000030,Tower Hamlets,Bethnal Green,6050035,Brokesley Street,22700204,E3,536777,182447,536750,182450,51.52438329,-0.029795714
60652131,13/05/2013 13:42,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000611,Bedford,E09000032,Wandsworth,Tooting,121019077,Elmfield Road,22901466,SW17,528628,172908,528650,172950,51.44057026,-0.150664214
60701131,13/05/2013 15:56,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000264,Town,E09000013,Hammersmith and Fulham,Fulham,NULL,Shottendane Road,NULL,SW6,525122,176888,525150,176850,51.47712389,-0.199676306
60771131,13/05/2013 19:02,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED BY NETTING,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000221,Glyndon,E09000011,Greenwich,Plumstead,10010213101,Erebus Drive,20802018,SE28,544339,179499,544350,179450,51.49601267,0.07792551
61804131,16/05/2013 08:40,2013,2013/14,Special Service,1,3,290,870,RUNNING CALL TO GOOSE TRAPPED IN FENCING,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000509,Monkhams,E09000026,Redbridge,Woodford,NULL,Links Road,NULL,IG8,540127,192235,540150,192250,51.61151535,0.022359222
62136131,17/05/2013 06:45,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Lee Green,1.00021E+11,Casterbridge Road,20800294,SE3,540336,175703,540350,175750,51.46290908,0.018790747
62176131,17/05/2013 10:25,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Bungalow - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000323,Upminster,E09000016,Havering,Hornchurch,NULL,Cedar Gardens,21301759,RM14,NULL,NULL,556350,186450,NULL,NULL
62188131,17/05/2013 11:03,2013,2013/14,Special Service,1,1,290,290,DOG STUCK IN TREE,Dog,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000532,West Twickenham,E09000027,Richmond upon Thames,Twickenham,NULL,Mill Road,NULL,TW2,514358,172805,514350,172850,51.44270192,-0.355922037
62234131,17/05/2013 13:14,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000400,Alexandra,E09000021,Kingston upon Thames,Surbiton,1.00022E+11,Southwood Drive,21800881,KT5,520444,166619,520450,166650,51.38584414,-0.270492616
62246131,17/05/2013 14:18,2013,2013/14,Special Service,1,2,290,580,PONY IN RELIEF CANAL,Horse,Person (land line),River/canal,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000607,Valley,E09000031,Waltham Forest,Chingford,NULL,Hall Lane,NULL,E4,536284,192312,536250,192350,51.61314996,-0.033076692
62320131,17/05/2013 16:40,2013,2013/14,Special Service,1,2,290,580,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011465,Broad Green,E09000008,Croydon,Norbury,NULL,Mitcham Road,20502543,CR0,NULL,NULL,530650,166950,NULL,NULL
62347131,17/05/2013 18:10,2013,2013/14,Special Service,1,1,290,290,TO ASSIST RSPCA WITH CAT STUCK BETWEEN FENCE AND WALL,Cat,Person (land line),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,12127766,Drayton Green Road,20602212,W13,516732,180647,516750,180650,51.51270003,-0.319189441
62434131,17/05/2013 21:18,2013,2013/14,Special Service,1,1,290,290,KITTENS STUCK BETWEEN WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009368,Cazenove,E09000012,Hackney,Stoke Newington,NULL,Upper Clapton Road,20901025,E5,NULL,NULL,534550,186750,NULL,NULL
62619131,18/05/2013 10:46,2013,2013/14,Special Service,1,1,290,290,TO ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000571,Wandle Valley,E09000029,Sutton,Mitcham,NULL,Bisham Close,NULL,SM5,527705,166525,527750,166550,51.3834137,-0.166234069
62798131,18/05/2013 16:35,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (land line),Other outdoor location,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000379,St. Mary's,E09000019,Islington,Islington,5300025063,Crane Grove,21604621,N7,531386,184801,531350,184850,51.54681714,-0.106581045
63017131,19/05/2013 00:29,2013,2013/14,Special Service,1,1,290,290,INJURED CAT TRAPPED UNDER KITCHEN SINK,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000488,Manor Park,E09000025,Newham,Stratford,NULL,Sherrard Road,22208154,E12,NULL,NULL,542050,185150,NULL,NULL
63209131,19/05/2013 13:12,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH TRAPPED PIGEONS,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000595,Forest,E09000031,Waltham Forest,Leyton,NULL,Abbotts Park Road,22800150,E10,NULL,NULL,538250,187750,NULL,NULL
63281131,19/05/2013 16:34,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN PIPE IN BATHROOM,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000406,Coombe Hill,E09000021,Kingston upon Thames,Kingston,NULL,Kingston Hill,21800559,KT2,NULL,NULL,519650,170150,NULL,NULL
63411131,19/05/2013 19:38,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED BEHIND CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000086,Barnhill,E09000005,Brent,Hendon,NULL,Rossdale Drive,20200267,NW9,NULL,NULL,520150,187250,NULL,NULL
63575131,20/05/2013 05:56,2013,2013/14,Special Service,1,1,290,290,BABY FOX TRAPPED IN WIRE FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving wild animal,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Antlers Hill,22811100,E4,NULL,NULL,538050,195650,NULL,NULL
63686131,20/05/2013 12:33,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN WIRE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000156,Kenley,E09000008,Croydon,Purley,NULL,Stoats Nest Road,NULL,CR5,530471,160197,530450,160150,51.32591576,-0.128827151
63864131,20/05/2013 18:38,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000193,Bowes,E09000010,Enfield,Edmonton,NULL,Beale Close,NULL,N13,531806,192090,531850,192050,51.61222123,-0.097795711
64153131,21/05/2013 09:42,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000630,Abbey Road,E09000033,Westminster,Paddington,NULL,Hamilton Terrace,8400431,NW8,NULL,NULL,526150,182950,NULL,NULL
64159131,21/05/2013 10:08,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH BIRDS TRAPPED BEHIND BOILER,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000047,Coppetts,E09000003,Barnet,Finchley,NULL,Poplar Grove,20034620,N11,NULL,NULL,528050,191550,NULL,NULL
64287131,21/05/2013 15:27,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN WIRE UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000156,Kenley,E09000008,Croydon,Purley,NULL,Stoats Nest Road,NULL,CR5,530479,160173,530450,160150,51.32569824,-0.128721179
64358131,21/05/2013 17:45,2013,2013/14,Special Service,1,3,290,870,HORSE FALLEN OVER AND UNABLE TO STAND,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000111,Chislehurst,E09000006,Bromley,Eltham,NULL,Slades Drive,NULL,BR7,544056,171975,544050,171950,51.42847648,0.07078766
64417131,21/05/2013 19:08,2013,2013/14,Special Service,1,1,290,290,THREE HORSES STUCK IN RIVER,Horse,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000607,Valley,E09000031,Waltham Forest,Walthamstow,NULL,Harbet Road,NULL,E4,536330,191767,536350,191750,51.60824138,-0.032624529
64435131,21/05/2013 19:36,2013,2013/14,Special Service,1,1,290,290,ANIMAL TRAPPED BEHIND CHIMNEY,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011247,Loxford,E09000026,Redbridge,Ilford,NULL,Thornton Road,22303311,IG1,NULL,NULL,543550,185750,NULL,NULL
64806131,22/05/2013 17:25,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED BETWEEN TWO WALLS,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000340,Uxbridge North,E09000017,Hillingdon,Hillingdon,NULL,Vine Lane,21402051,UB10,NULL,NULL,506950,183050,NULL,NULL
64903131,22/05/2013 21:18,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER CUPBOARD,Cat,Person (mobile),Self contained Sheltered Housing,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000314,Heaton,E09000016,Havering,Harold Hill,NULL,Widecombe Close,21301699,RM3,NULL,NULL,553650,190750,NULL,NULL
65231131,23/05/2013 17:49,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN EXTRACTOR FAN,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000331,Heathrow Villages,E09000017,Hillingdon,Hayes,NULL,Byron Way,21400290,UB7,NULL,NULL,506550,178750,NULL,NULL
65643131,24/05/2013 15:29,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05011489,Woodside,E09000008,Croydon,Woodside,1.00021E+11,Brocklesby Road,20500725,SE25,534650,168059,534650,168050,51.39559581,-0.065911578
66053131,25/05/2013 13:24,2013,2013/14,Special Service,1,1,290,290,INJURED PEACOCK ON ROOF OF HOUSE,Bird,Police,House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000563,Stonecot,E09000029,Sutton,Sutton,NULL,Morley Road,22601733,SM3,NULL,NULL,524750,166150,NULL,NULL
66465131,26/05/2013 12:40,2013,2013/14,Special Service,1,1,290,290,CAT INJURED  - STUCK UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000373,Highbury West,E09000019,Islington,Holloway,5300004578,Avenell Road,21602868,N5,531582,186231,531550,186250,51.55962221,-0.103221672
66542131,26/05/2013 15:13,2013,2013/14,Special Service,1,1,290,290,BIRD STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,NULL,Whitton Avenue West,20602462,UB5,NULL,NULL,514050,185150,NULL,NULL
66584131,26/05/2013 17:08,2013,2013/14,Special Service,1,1,290,290,THREE HORSES IN RIVER,Horse,Person (land line),River/canal,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000607,Valley,E09000031,Waltham Forest,Walthamstow,NULL,Harbet Road,NULL,E4,536288,191773,536250,191750,51.60830546,-0.033228363
66863131,27/05/2013 09:03,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05011463,Addiscombe West,E09000008,Croydon,Woodside,NULL,Northway Road,20501243,CR0,NULL,NULL,533850,166850,NULL,NULL
66868131,27/05/2013 09:25,2013,2013/14,Special Service,1,1,290,290,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05011479,Selhurst,E09000008,Croydon,Norbury,NULL,Northbrook Road,20501241,CR0,NULL,NULL,532650,167750,NULL,NULL
66955131,27/05/2013 13:51,2013,2013/14,Special Service,1,1,290,290,CAT STUCK UP TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000433,Thornton,E09000022,Lambeth,Tooting,NULL,Emmanuel Road,NULL,SW12,529454,173075,529450,173050,51.4418831,-0.138725318
67128131,27/05/2013 18:54,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000561,Nonsuch,E09000029,Sutton,Sutton,NULL,Wickham Avenue,22603452,SM3,NULL,NULL,523350,164250,NULL,NULL
67346131,28/05/2013 07:51,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED IN SPEAKER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000104,Wembley Central,E09000005,Brent,Wembley,NULL,Copland Road,20201240,HA0,NULL,NULL,518150,184950,NULL,NULL
67367131,28/05/2013 09:17,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BEHIND GRILL IN BASEMENT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011111,Peckham Rye,E09000028,Southwark,Forest Hill,NULL,Mundania Road,22501751,SE22,NULL,NULL,535150,174550,NULL,NULL
67627131,28/05/2013 21:40,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN BASEMENT,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000483,Forest Gate North,E09000025,Newham,Stratford,NULL,Sebert Road,22207878,E7,NULL,NULL,540750,185450,NULL,NULL
67888131,29/05/2013 14:25,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000433,Thornton,E09000022,Lambeth,Tooting,NULL,Emmanuel Road,NULL,SW12,529309,173075,529350,173050,51.44191619,-0.140810478
68291131,30/05/2013 12:11,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND WASHING MACHINE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000063,Woodhouse,E09000003,Barnet,Finchley,NULL,High Road,20022400,N12,NULL,NULL,526350,192450,NULL,NULL
68318131,30/05/2013 14:24,2013,2013/14,Special Service,1,1,290,290,PIGEON TANGLED IN TELEPHONE WIRE,Bird,Person (mobile),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Boleyn Road,NULL,E7,541038,184223,541050,184250,51.53929475,0.032293897
69052131,01/06/2013 05:42,2013,2013/14,Special Service,1,2,290,580,BABY DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Westminster Gardens,NULL,E4,539445,194022,539450,194050,51.62774203,0.013226802
69103131,01/06/2013 10:43,2013,2013/14,Special Service,1,1,290,290,BIRD STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000440,Catford South,E09000023,Lewisham,Lewisham,NULL,Daneby Road,22001393,SE6,NULL,NULL,538450,172650,NULL,NULL
69201131,01/06/2013 14:56,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN TRAMPOLINE,Bird,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000432,Streatham Wells,E09000022,Lambeth,Norbury,10091845968,Albert Carr Gardens,21900078,SW16,530166,171140,530150,171150,51.42433036,-0.129196926
69567131,02/06/2013 10:18,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Crouch Hall Road,NULL,N8,530037,188337,530050,188350,51.57890619,-0.124718058
69573131,02/06/2013 11:07,2013,2013/14,Special Service,1,1,290,290,ASSISST RSPCA,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009370,Dalston,E09000012,Hackney,Homerton,1.00023E+11,Ramsgate Street,20900089,E8,533865,184857,533850,184850,51.54673747,-0.070830001
69576131,02/06/2013 11:18,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000555,Beddington North,E09000029,Sutton,Wallington,NULL,Bloxworth Close,22600334,SM6,NULL,NULL,529250,165050,NULL,NULL
69590131,02/06/2013 11:55,2013,2013/14,Special Service,1,1,290,290,INJURED CAT TRAPPED BEHIND A WOODEN FENCE,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000179,Hanger Hill,E09000009,Ealing,Park Royal,12096869,Iveagh Avenue,20600941,NW10,519173,183200,519150,183250,51.53513648,-0.283166421
69682131,02/06/2013 16:08,2013,2013/14,Special Service,1,1,290,290,PUPPY TRAPPED UNDER CAR SEAT,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000467,Pollards Hill,E09000024,Merton,Norbury,NULL,Shropshire Close,NULL,CR4,530342,168126,530350,168150,51.39720332,-0.127773939
70658131,04/06/2013 14:54,2013,2013/14,Special Service,1,1,290,290,CAT STUCK UNDER CELLAR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000417,Brixton Hill,E09000022,Lambeth,Clapham,NULL,Lyham Road,21900909,SW2,NULL,NULL,530350,173950,NULL,NULL
70843131,04/06/2013 22:16,2013,2013/14,Special Service,1,1,290,290,SQUIRREL TRAPPED BEHIND KITCHEN UNIT,Squirrel,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000368,Caledonian,E09000019,Islington,Islington,NULL,Earlsferry Way,21604767,N1,NULL,NULL,530450,183950,NULL,NULL
71352131,05/06/2013 21:21,2013,2013/14,Special Service,1,1,290,290,HORSE TRAPPED IN HORSE BOX,Horse,Person (mobile),Trailer (not attached to tractor unit),Road Vehicle,Other animal assistance,Assist  trapped livestock animal,E05004175,Waltham Abbey Honey Lane,E07000072,Epping Forest,Essex,NULL,Roundhills,NULL,EN9,538828,199885,538850,199850,51.68057949,0.006644869
71410131,05/06/2013 22:57,2013,2013/14,Special Service,1,1,290,290,ASSIS RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000037,Parsloes,E09000002,Barking and Dagenham,Dagenham,NULL,Wood Lane,19900414,RM8,NULL,NULL,548650,186150,NULL,NULL
71659131,06/06/2013 13:23,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA FALLEN CAT,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000433,Thornton,E09000022,Lambeth,Clapham,1.00022E+11,Tilson Gardens,21901383,SW2,530115,173850,530150,173850,51.44869663,-0.128935333
72292131,07/06/2013 17:59,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000251,Askew,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Becklow Gardens,21000108,W12,NULL,NULL,522150,179950,NULL,NULL
72310131,07/06/2013 18:45,2013,2013/14,Special Service,1,1,290,290,SQUIRREL TRAPPED BEHIND WARDROBE,Squirrel,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000140,Kilburn,E09000007,Camden,West Hampstead,NULL,Priory Road,20400412,NW6,NULL,NULL,525650,183950,NULL,NULL
73139131,09/06/2013 11:02,2013,2013/14,Special Service,1,1,290,290,BABY SEAGULL STUCK ON SCAFFOLDING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000367,Bunhill,E09000019,Islington,Islington,NULL,Malta Street,21605367,EC1V,NULL,NULL,531850,182450,NULL,NULL
73434131,09/06/2013 22:23,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN ENGINE OF CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000570,Wallington South,E09000029,Sutton,Wallington,5870089253,Francis Road,22602319,SM6,529373,163771,529350,163750,51.35828643,-0.143279728
74152131,11/06/2013 11:21,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA,Fox,Person (land line),Secondary school,Non Residential,Animal rescue from height,Wild animal rescue from height,E05011102,Faraday,E09000028,Southwark,Old Kent Road,2.00003E+11,Shorncliffe Road,22502241,SE1,533441,178209,533450,178250,51.48709596,-0.079456666
74424131,11/06/2013 23:22,2013,2013/14,Special Service,1,1,290,290,DOG STUCK IN HOLE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000172,Dormers Wells,E09000009,Ealing,Southall,12132075,Greenford Road,20602352,UB1,514247,180661,514250,180650,51.51333326,-0.35497982
75084131,13/06/2013 13:33,2013,2013/14,Special Service,1,1,290,290,ASSIST WITH KITTEN TRAPPED BETWEEN WALLS,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000361,Hounslow West,E09000018,Hounslow,Feltham,1.00022E+11,Rosemary Avenue,21500960,TW4,511946,176037,511950,176050,51.47223259,-0.38959065
75537131,14/06/2013 12:40,2013,2013/14,Special Service,1,1,290,290,INJURED CAT TRAPPED IN GARDEN,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000175,East Acton,E09000009,Ealing,Park Royal,12109289,Old Oak Lane,20601301,NW10,521580,182727,521550,182750,51.53037297,-0.248642818
75592131,14/06/2013 15:01,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH DUCK TRAPPED BETWEEN WALLS,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000637,Knightsbridge and Belgravia,E09000033,Westminster,Chelsea,NULL,Eaton Terrace,8400822,SW1W,NULL,NULL,528350,178750,NULL,NULL
75619131,14/06/2013 16:01,2013,2013/14,Special Service,1,1,290,290,DEER TRAPPED BETWEEN WALL AND FENCE,Deer,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011236,Bridge,E09000026,Redbridge,Woodford,1.00022E+11,Wallers Close,22306075,IG8,542729,191884,542750,191850,51.60770857,0.059769271
75873131,14/06/2013 23:38,2013,2013/14,Special Service,1,1,290,290,BADLY INJURED CAT STUCK ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000528,South Richmond,E09000027,Richmond upon Thames,Richmond,NULL,Reynolds Place,22403767,TW10,NULL,NULL,518650,174050,NULL,NULL
76720131,16/06/2013 18:38,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000035,Longbridge,E09000002,Barking and Dagenham,Barking,NULL,Hepworth Gardens,19900176,IG11,NULL,NULL,546350,185150,NULL,NULL
76741131,16/06/2013 19:12,2013,2013/14,Special Service,1,1,290,290,TWO CATS TRAPPED ON FLAT ROOF,cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000412,St. Mark's,E09000021,Kingston upon Thames,Surbiton,NULL,St. Andrews Road,21800888,KT6,NULL,NULL,517750,167350,NULL,NULL
77118131,17/06/2013 16:58,2013,2013/14,Special Service,1,1,290,290,KITTEN WITH HEAD TRAPPED IN GATE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011476,Purley & Woodcote,E09000008,Croydon,Purley,NULL,Famet Close,20502065,CR8,NULL,NULL,532150,160550,NULL,NULL
77204131,17/06/2013 20:02,2013,2013/14,Special Service,1,2,290,580,FOX CUBS TRAPPED IN PIT,Fox,Person (land line),Other outdoor location,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05011107,North Walworth,E09000028,Southwark,Old Kent Road,2.00003E+11,Brandon Street,22500317,SE17,532441,178763,532450,178750,51.49230946,-0.093643288
77421131,18/06/2013 10:52,2013,2013/14,Special Service,1,2,290,580,ASSIST RSPCA RESCUE CAT,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000598,Hatch Lane,E09000031,Waltham Forest,Woodford,10009143365,The Avenue,22812500,E4,538614,191833,538650,191850,51.60827715,0.000363801
77510131,18/06/2013 13:28,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000096,Northwick Park,E09000005,Brent,Wembley,NULL,Watford Road,20201453,HA1,NULL,NULL,516450,186850,NULL,NULL
77615131,18/06/2013 18:13,2013,2013/14,Special Service,1,1,290,290,ANIMAL TRAPPED BEHIND FIREPLACE,Unknown - Wild Animal,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000140,Kilburn,E09000007,Camden,West Hampstead,NULL,Mortimer Crescent,20400688,NW6,NULL,NULL,525850,183550,NULL,NULL
77619131,18/06/2013 18:18,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED BETWEEN FENCE AND WALL AT REAR OFF,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,NULL,Ryecroft Road,NULL,SE13,538481,174830,538450,174850,51.45552037,-0.008236348
78416131,20/06/2013 06:07,2013,2013/14,Special Service,1,1,290,290,DEER STUCK IN FENCE,Deer,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000329,Eastcote and East Ruislip,E09000017,Hillingdon,Ruislip,NULL,Elmbridge Drive,21400673,HA4,NULL,NULL,509950,188550,NULL,NULL
78452131,20/06/2013 08:57,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000358,Hounslow Central,E09000018,Hounslow,Heston,NULL,The Drive,21500367,TW7,NULL,NULL,514750,176250,NULL,NULL
78460131,20/06/2013 09:10,2013,2013/14,Special Service,1,1,290,290,CAT  FALLEN ON TO FIRST FLOOR BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Don Phelan Close,22500789,SE5,NULL,NULL,532750,176950,NULL,NULL
78640131,20/06/2013 15:32,2013,2013/14,Special Service,1,3,290,870,RUNNING CALL TO BIRD OF PREY TRAPPED ON ROOF,Bird,Person (land line),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000351,Feltham North,E09000018,Hounslow,Feltham,2.00002E+11,Armadale Road,21501321,TW14,510391,174861,510350,174850,51.46196764,-0.412337062
78664131,20/06/2013 16:30,2013,2013/14,Special Service,1,1,290,290,CAT STUCK DOWN DUSTCHUTE,Cat,Person (mobile),Small refuse/rubbish container,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000115,Cray Valley West,E09000006,Bromley,Sidcup,1.0002E+11,Leith Hill,20301187,BR5,546058,169622,546050,169650,51.40682129,0.098595563
78686131,20/06/2013 17:17,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA IN RELEASING TRAPPED PIDGEON,Pigeon,Person (land line),Large supermarket,Non Residential,Other animal assistance,Animal harm involving wild animal,E05000218,Eltham North,E09000011,Greenwich,Eltham,NULL,Eltham High Street,NULL,SE9,542865,174457,542850,174450,51.45108066,0.054668844
78944131,21/06/2013 08:02,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BEHIND CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000044,Burnt Oak,E09000003,Barnet,Mill Hill,NULL,Montrose Avenue,20030060,HA8,NULL,NULL,520850,190950,NULL,NULL
79003131,21/06/2013 12:07,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN FENCE AND WALL,Cat,Person (land line),Self contained Sheltered Housing,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000219,Eltham South,E09000011,Greenwich,Eltham,NULL,Enslin Road,20800536,SE9,NULL,NULL,543250,173950,NULL,NULL
79527131,22/06/2013 12:53,2013,2013/14,Special Service,1,1,290,290,BIRD LOCKED IN PARK SHED,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05009387,Woodberry Down,E09000012,Hackney,Stoke Newington,NULL,Green Lanes,NULL,N4,532066,187530,532050,187550,51.5711825,-0.095756609
79941131,23/06/2013 10:59,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN WALLS,Cat,Police,Pre School/nursery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05009319,Bow East,E09000030,Tower Hamlets,Bethnal Green,6076499,Parnell Road,22700917,E3,537008,183478,537050,183450,51.53359209,-0.026067201
80094131,23/06/2013 17:13,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA INSPECTOR WITH CAT IN TREE,Cat,Person (mobile),Hedge,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Clifton Road,NULL,N8,529671,188438,529650,188450,51.57989809,-0.129959755
80424131,24/06/2013 12:40,2013,2013/14,Special Service,2,3,290,870,CAT WITH HEAD TRAPPED IN RADIATOR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000195,Chase,E09000010,Enfield,Enfield,NULL,Linden Gardens,20702806,EN1,NULL,NULL,534350,197850,NULL,NULL
80771131,25/06/2013 07:47,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN BASEMENT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011246,Ilford Town,E09000026,Redbridge,Ilford,NULL,Stanley Road,22303272,IG1,NULL,NULL,544650,186550,NULL,NULL
81303131,26/06/2013 08:22,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN WINDOW,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000250,Addison,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Sinclair Road,21000737,W14,NULL,NULL,524250,179250,NULL,NULL
81662131,26/06/2013 21:37,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000344,Yeading,E09000017,Hillingdon,Southall,NULL,Yeading Lane,21402234,UB4,NULL,NULL,511250,182450,NULL,NULL
82186131,27/06/2013 22:32,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000217,Coldharbour and New Eltham,E09000011,Greenwich,Eltham,NULL,Woodcroft,NULL,SE9,542907,172374,542950,172350,51.43235252,0.054432519
82215131,28/06/2013 00:43,2013,2013/14,Special Service,1,1,290,290,CAT FALLEN FROM FIFTH FLOOR POSSIBLY INJURED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,North Kensington,NULL,Queensdale Crescent,21000663,W11,NULL,NULL,523750,180250,NULL,NULL
82343131,28/06/2013 09:57,2013,2013/14,Special Service,1,2,290,580,TO ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000217,Coldharbour and New Eltham,E09000011,Greenwich,Eltham,NULL,Woodcroft,NULL,SE9,542778,172369,542750,172350,51.43234009,0.052576058
82622131,28/06/2013 20:30,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000542,Grange,E09000028,Southwark,Old Kent Road,NULL,Pages Walk,NULL,SE1,533331,179054,533350,179050,51.49471556,-0.080721073
82669131,28/06/2013 22:44,2013,2013/14,Special Service,1,1,290,290,CAT UNDER FLOORBOARDS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000250,Addison,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Sinclair Road,21000737,W14,NULL,NULL,524050,179350,NULL,NULL
82832131,29/06/2013 10:14,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN ENGINE COMPARTMENT OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000032,Gascoigne,E09000002,Barking and Dagenham,Barking,NULL,Dovehouse Mead,NULL,IG11,544641,183369,544650,183350,51.53071001,0.083859427
83203131,29/06/2013 22:02,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BETWEEN WALL AND FENCE,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,1.00021E+11,Cascade Avenue,21100135,N10,529094,189338,529050,189350,51.58811836,-0.137951777
84016131,01/07/2013 10:28,2013,2013/14,Special Service,1,1,290,290,CAT IN PRECARIOUS POSITION,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000145,West Hampstead,E09000007,Camden,West Hampstead,NULL,Sumatra Road,20400253,NW6,NULL,NULL,525150,184950,NULL,NULL
84059131,01/07/2013 13:02,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD TRAPPED IN GATE,Dog,Person (land line),Bungalow - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000321,South Hornchurch,E09000016,Havering,Wennington,NULL,Askwith Road,21300703,RM13,NULL,NULL,550750,183050,NULL,NULL
84353131,01/07/2013 21:15,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA OFFICER WITH CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000475,Beckton,E09000025,Newham,East Ham,NULL,Tollgate Road,NULL,E6,541977,181738,541950,181750,51.51672963,0.044823649
84386131,01/07/2013 22:41,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED ON FENCING,Cat,Person (mobile),Fire station,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,6025102,Cable Street,22700241,E1,535007,180901,535050,180950,51.51091588,-0.055885932
84956131,03/07/2013 00:11,2013,2013/14,Special Service,1,2,290,580,CAT FALLEN NEAR TO RAILWAY LINES,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009397,Holland,E09000020,Kensington and Chelsea,Hammersmith,NULL,Russell Gardens Mews,21700668,W14,NULL,NULL,524150,179450,NULL,NULL
85212131,03/07/2013 14:43,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000265,Wormholt and White City,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Adelaide Grove,21000030,W12,NULL,NULL,522450,180250,NULL,NULL
85226131,03/07/2013 15:20,2013,2013/14,Special Service,1,1,290,290,TO ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Homerton,NULL,Lea Bridge Road,20900602,E5,NULL,NULL,535550,186550,NULL,NULL
85453131,03/07/2013 23:31,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN BASEMENT,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000368,Caledonian,E09000019,Islington,Islington,5300079183,Roman Way,21605828,N7,530915,184396,530950,184350,51.54328699,-0.113520319
85589131,04/07/2013 10:50,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000040,Valence,E09000002,Barking and Dagenham,Dagenham,NULL,Lynett Road,19903743,RM8,NULL,NULL,548050,187050,NULL,NULL
86083131,05/07/2013 10:44,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA ON SCENE,Unknown - Wild Animal,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000378,St. George's,E09000019,Islington,Holloway,5300048438,Holloway Road,21606449,N7,530229,186201,530250,186250,51.55966662,-0.12273931
86294131,05/07/2013 17:31,2013,2013/14,Special Service,1,1,290,290,TO ASSIST RSPCA WITH SNAKE ON ROOF,Snake,Person (land line),Mosque,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000142,Regent's Park,E09000007,Camden,Euston,5005910,North Gower Street,20400840,NW1,529297,182572,529250,182550,51.52726756,-0.137509019
86634131,06/07/2013 07:53,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN SOFA,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000648,Westbourne,E09000033,Westminster,Paddington,NULL,Alfred Road,8400328,W2,NULL,NULL,525450,181850,NULL,NULL
86662131,06/07/2013 09:02,2013,2013/14,Special Service,1,1,290,290,CAT LOCKED IN CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000472,Village,E09000024,Merton,Wimbledon,NULL,Lancaster Gardens,NULL,SW19,524131,171481,524150,171450,51.42874703,-0.215833843
86856131,06/07/2013 15:04,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER COOKER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000493,Wall End,E09000025,Newham,East Ham,NULL,Skeffington Road,22201058,E6,NULL,NULL,542650,183950,NULL,NULL
87047131,06/07/2013 19:36,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED ON LEDGE ABOVE THE BETTING SHOP,Dog,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000598,Hatch Lane,E09000031,Waltham Forest,Chingford,1.00023E+11,Hatch Lane,22843600,E4,538823,192972,538850,192950,51.61846054,0.003830702
87139131,06/07/2013 21:30,2013,2013/14,Special Service,1,1,290,290,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Jenkins Road,22200749,E13,NULL,NULL,540750,182150,NULL,NULL
87142131,06/07/2013 21:38,2013,2013/14,Special Service,1,2,290,580,CAT STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,NULL,Foxwell Mews,22005374,SE4,NULL,NULL,536250,175750,NULL,NULL
87347131,07/07/2013 08:40,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000366,Barnsbury,E09000019,Islington,Euston,NULL,Priory Green,21605823,N1,NULL,NULL,530850,183250,NULL,NULL
87493131,07/07/2013 14:00,2013,2013/14,Special Service,1,1,290,290,DOG LOCKED OUT ON BALCONY,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000609,Wood Street,E09000031,Waltham Forest,Walthamstow,NULL,Wood Street,22888350,E17,NULL,NULL,538750,188950,NULL,NULL
87525131,07/07/2013 14:43,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000469,Raynes Park,E09000024,Merton,Wimbledon,NULL,Amity Grove,22100168,SW20,NULL,NULL,523150,169350,NULL,NULL
87643131,07/07/2013 17:33,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN RAILINGS,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000620,Queenstown,E09000032,Wandsworth,Battersea,121022314,Battersea Park,22900030,SW11,528483,177258,528450,177250,51.4796965,-0.151171155
88156131,08/07/2013 10:21,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN BETWEEN WINDOW AND WINDOW FRAME,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000267,Bounds Green,E09000014,Haringey,Hornsey,NULL,Commerce Road,21104385,N22,NULL,NULL,530750,190950,NULL,NULL
88775131,09/07/2013 11:00,2013,2013/14,Special Service,1,1,290,290,PUPPY WITH HEAD STUCK IN FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,1.00021E+11,Huntland Close,21300430,RM13,552958,181555,552950,181550,51.51222205,0.202884147
88930131,09/07/2013 15:36,2013,2013/14,Special Service,1,2,290,580,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000328,Charville,E09000017,Hillingdon,Hillingdon,NULL,Grosvenor Avenue,21400854,UB4,NULL,NULL,509650,183150,NULL,NULL
89838131,10/07/2013 19:56,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000196,Cockfosters,E09000010,Enfield,Southgate,NULL,South Lodge Drive,20702961,N14,NULL,NULL,529850,195750,NULL,NULL
89922131,10/07/2013 23:04,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED BEHIND SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,Warren Road,22885300,E4,NULL,NULL,538250,194050,NULL,NULL
90415131,11/07/2013 20:41,2013,2013/14,Special Service,1,1,290,290,Redacted,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000416,Bishop's,E09000022,Lambeth,Lambeth,NULL,The Cut,21901359,SE1,NULL,NULL,531450,179850,NULL,NULL
90516131,11/07/2013 23:49,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND COOKER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000207,Southbury,E09000010,Enfield,Enfield,NULL,Fotheringham Road,20702689,EN1,NULL,NULL,533550,196250,NULL,NULL
90711131,12/07/2013 12:29,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA,Unknown - Wild Animal,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000595,Forest,E09000031,Waltham Forest,Leyton,NULL,Abbotts Park Road,22800150,E10,NULL,NULL,538350,187850,NULL,NULL
90805131,12/07/2013 14:59,2013,2013/14,Special Service,1,1,290,290,RUNNING CALL TO PIGEON TRAPPED IN TV AERIAL,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Kingston,NULL,Beaufort Road,22403287,TW10,NULL,NULL,517150,171550,NULL,NULL
90822131,12/07/2013 15:25,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH KITTEN UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000144,Swiss Cottage,E09000007,Camden,West Hampstead,5019782,St. Marys Mews,20401345,NW6,525699,184051,525650,184050,51.54137102,-0.188820101
90880131,12/07/2013 17:05,2013,2013/14,Special Service,2,3,290,870,PIGEON TRAPPED ON AERIAL,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Kingston,NULL,Beaufort Road,22403287,TW10,NULL,NULL,517150,171550,NULL,NULL
90983131,12/07/2013 18:39,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000314,Heaton,E09000016,Havering,Harold Hill,1.00021E+11,Heaton Avenue,21301268,RM3,552793,191315,552750,191350,51.59995879,0.20474287
91277131,13/07/2013 07:01,2013,2013/14,Special Service,2,3,290,870,KITTEN STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000144,Swiss Cottage,E09000007,Camden,West Hampstead,5048239,St. Marys Mews,20401345,NW6,525706,184078,525750,184050,51.54161211,-0.188709576
91283131,13/07/2013 07:37,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000059,Totteridge,E09000003,Barnet,Barnet,NULL,Longland Drive,NULL,N20,525712,193394,525750,193350,51.6253329,-0.185290417
91396131,13/07/2013 13:31,2013,2013/14,Special Service,1,1,290,290,INJURED KITTEN TRAPPED ON ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Barking Road,22208079,E13,NULL,NULL,541050,182950,NULL,NULL
91455131,13/07/2013 13:56,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH SNAKE STUCK IN WALL PANEL,Snake,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000638,Lancaster Gate,E09000033,Westminster,Paddington,NULL,Porchester Terrace,8400631,W2,NULL,NULL,526050,180750,NULL,NULL
91494131,13/07/2013 15:28,2013,2013/14,Special Service,1,1,290,290,PUPPY POSSIBLY TRAPPED IN DRAIN,Dog,Person (land line),Pipe or drain,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,1.00022E+11,Lausanne Road,22000604,SE15,535381,176607,535350,176650,51.47223865,-0.052146156
92049131,14/07/2013 11:49,2013,2013/14,Special Service,1,1,290,290,KITTEN UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000053,Golders Green,E09000003,Barnet,Finchley,NULL,Courtleigh Gardens,20010600,NW11,NULL,NULL,524250,189050,NULL,NULL
92051131,14/07/2013 11:55,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER CUPBOARD,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000226,Plumstead,E09000011,Greenwich,Plumstead,1.00023E+11,Plumstead High Street,20801187,SE18,545085,178811,545050,178850,51.48963983,0.088381957
92516131,14/07/2013 21:22,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000620,Queenstown,E09000032,Wandsworth,Clapham,NULL,Patmore Estate,22903805,SW8,NULL,NULL,529350,176950,NULL,NULL
92737131,15/07/2013 07:19,2013,2013/14,Special Service,1,1,290,290,CAT STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000145,West Hampstead,E09000007,Camden,West Hampstead,NULL,West End Lane,20499056,NW6,NULL,NULL,525450,185150,NULL,NULL
92854131,15/07/2013 11:36,2013,2013/14,Special Service,1,2,290,580,DOG STUCK RAILWAY BRIDGE,Dog,Person (mobile),Railway,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000026,Abbey,E09000002,Barking and Dagenham,Barking,NULL,Salisbury Avenue,NULL,IG11,544559,184275,544550,184250,51.53887188,0.083049647
92910131,15/07/2013 12:54,2013,2013/14,Special Service,1,3,290,870,PUPPY IN PRECARIOUS POSITION,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009387,Woodberry Down,E09000012,Hackney,Holloway,NULL,Portland Rise,20900814,N4,NULL,NULL,532050,187250,NULL,NULL
93368131,15/07/2013 22:35,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN GATE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000098,Queens Park,E09000005,Brent,North Kensington,NULL,Wakeman Road,20201535,NW10,NULL,NULL,523650,182850,NULL,NULL
93404131,16/07/2013 00:19,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD STUCK IN FENCE,Dog,Police,Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000155,Heathfield,E09000008,Croydon,Addington,NULL,Tedder Road,NULL,CR2,535529,163143,535550,163150,51.3512091,-0.055157234
93588131,16/07/2013 11:41,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,The Broadway,20705014,N9,NULL,NULL,534350,193450,NULL,NULL
93722131,16/07/2013 15:00,2013,2013/14,Special Service,1,1,290,290,DEHYDRATED CAT IN DISTRESS,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000221,Glyndon,E09000011,Greenwich,Plumstead,NULL,Hudson Place,NULL,SE18,544340,178313,544350,178350,51.48535549,0.077455217
93733131,16/07/2013 15:12,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD TRAPPED UNDER FENCE,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Portia Way,22700962,E3,NULL,NULL,536650,181950,NULL,NULL
93807131,16/07/2013 17:07,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN DRAIN PIPE,Cat,Police,House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000379,St. Mary's,E09000019,Islington,Islington,NULL,Crossley Street,21604641,N7,NULL,NULL,531050,184850,NULL,NULL
93959131,16/07/2013 19:28,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN STEEL GIRDERS,Bird,Person (land line),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000271,Harringay,E09000014,Haringey,Holloway,NULL,Stroud Green Road,NULL,N4,531448,186858,531450,186850,51.56528802,-0.104919527
94104131,16/07/2013 22:51,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN NETTING O.S STATION,Bird,Person (mobile),Train station - elsewhere,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000647,Warwick,E09000033,Westminster,Lambeth,1.00023E+11,Victoria Arcade,8401626,SW1E,528953,179126,528950,179150,51.49637721,-0.143725076
94111131,16/07/2013 23:01,2013,2013/14,Special Service,1,1,290,290,SMALL DEER TRAPPED IN GATE,Deer,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000303,Stanmore Park,E09000015,Harrow,Stanmore,10000001962,Dennis Lane,21200883,HA7,516824,192852,516850,192850,51.62237547,-0.313809687
94146131,17/07/2013 00:06,2013,2013/14,Special Service,1,1,290,290,HAMSTER TRAPPED BEHIND BOILER,Hamster,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000288,Greenhill,E09000015,Harrow,Harrow,NULL,Headstone Road,NULL,HA1,NULL,NULL,515050,188350,NULL,NULL
94261131,17/07/2013 08:56,2013,2013/14,Special Service,1,1,290,290,KITTEN IN DISTRESS ON ROOF,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000569,Wallington North,E09000029,Sutton,Wallington,5870091465,Clifton Road,22602212,SM6,529142,164292,529150,164250,51.36302118,-0.146406532
94623131,17/07/2013 16:59,2013,2013/14,Special Service,1,1,290,290,TO ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000562,St. Helier,E09000029,Sutton,Sutton,NULL,Green Lane,NULL,SM4,525922,166694,525950,166650,51.38533044,-0.191782568
95439131,18/07/2013 16:09,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05009380,Lea Bridge,E09000012,Hackney,Homerton,1.00021E+11,Mayola Road,20900676,E5,535381,185917,535350,185950,51.5559014,-0.048572876
95637131,18/07/2013 19:00,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009329,St. Dunstan's,E09000030,Tower Hamlets,Bethnal Green,NULL,Galsworthy Avenue,22702379,E14,NULL,NULL,536350,181450,NULL,NULL
95789131,18/07/2013 21:55,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND SHED,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,6041105,Patriot Square,22700922,E2,535076,183147,535050,183150,51.53108247,-0.054031534
95937131,19/07/2013 03:12,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000226,Plumstead,E09000011,Greenwich,Plumstead,1.00023E+11,Plumstead High Street,20801187,SE18,545056,178759,545050,178750,51.48918002,0.087943188
95944131,19/07/2013 03:37,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000226,Plumstead,E09000011,Greenwich,Plumstead,1.00023E+11,Plumstead High Street,20801187,SE18,545056,178759,545050,178750,51.48918002,0.087943188
96041131,19/07/2013 10:16,2013,2013/14,Special Service,1,1,290,290,ROAD TRAFFIC COLLISION,Unknown - Domestic Animal Or Pet,Person (land line),Caravan on tow,Road Vehicle,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000307,Cranham,E09000016,Havering,Hornchurch,NULL,Folkes Lane,NULL,RM14,558677,188034,558650,188050,51.56885573,0.288147558
96119131,19/07/2013 11:27,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED  DOWN SIDE OF HOUSE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000058,Oakleigh,E09000003,Barnet,Barnet,NULL,The Fairway,20015220,EN5,NULL,NULL,525950,195450,NULL,NULL
96504131,19/07/2013 17:29,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER KITCHEN SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000047,Coppetts,E09000003,Barnet,Southgate,NULL,East Crescent,20013400,N11,NULL,NULL,527850,192950,NULL,NULL
96606131,19/07/2013 19:21,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN CUPBOARD,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000559,Carshalton South and Clockhouse,E09000029,Sutton,Wallington,NULL,Pine Walk,22602544,SM5,NULL,NULL,526750,162050,NULL,NULL
96677131,19/07/2013 20:44,2013,2013/14,Special Service,1,1,290,290,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Bushey Road,22207639,E13,NULL,NULL,541250,183250,NULL,NULL
96807131,19/07/2013 22:51,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000074,East Wickham,E09000004,Bexley,Plumstead,NULL,Hill View Drive,NULL,DA16,545076,176269,545050,176250,51.4668008,0.08720898
97043131,20/07/2013 10:41,2013,2013/14,Special Service,1,1,290,290,CAT STUCK UNDER FLOORBOARDS,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000047,Coppetts,E09000003,Barnet,Southgate,NULL,East Crescent,20013400,N11,NULL,NULL,527850,192950,NULL,NULL
97089131,20/07/2013 12:01,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN DITCH,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000131,Cantelowes,E09000007,Camden,Kentish Town,NULL,Camden Park Road,20400532,NW1,NULL,NULL,529750,184850,NULL,NULL
97318131,20/07/2013 19:40,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN CEILING,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000031,Eastbury,E09000002,Barking and Dagenham,Barking,NULL,Meadow Road,19900530,IG11,NULL,NULL,545950,184050,NULL,NULL
97610131,21/07/2013 07:42,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED BEHIND OVEN,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000202,Highlands,E09000010,Enfield,Enfield,NULL,Comreddy Close,20702615,EN2,NULL,NULL,531650,197950,NULL,NULL
97719131,21/07/2013 12:22,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN BARBED WIRE,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000330,Harefield,E09000017,Hillingdon,Ruislip,1.00021E+11,High Street,21400961,UB9,505196,190516,505150,190550,51.60367006,-0.48241621
97739131,21/07/2013 12:42,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN ATENNA ON ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000029,Chadwell Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Uplands Road,19900620,RM6,NULL,NULL,547650,189550,NULL,NULL
98192131,21/07/2013 23:32,2013,2013/14,Special Service,1,1,290,290,CAT STUCK ON LEDGE OVER WATERWAY,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000362,Isleworth,E09000018,Hounslow,Heston,NULL,Pankhurst Close,NULL,TW7,515887,175968,515850,175950,51.47081957,-0.332896102
98204131,21/07/2013 23:55,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN TWO BUILDINGS,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009335,Weavers,E09000030,Tower Hamlets,Bethnal Green,6211060,Barnet Grove,22700122,E2,534229,182635,534250,182650,51.52668355,-0.066430182
98399131,22/07/2013 12:00,2013,2013/14,Special Service,1,1,290,290,DOG IN PRECARIOUS POSITION,Dog,Person (land line),Single shop,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000598,Hatch Lane,E09000031,Waltham Forest,Chingford,1.00023E+11,Hatch Lane,22843600,E4,538831,192974,538850,192950,51.61847654,0.003946973
99399131,23/07/2013 14:01,2013,2013/14,Special Service,1,1,290,290,PUPPY FALLEN DOWN EMBANKMENT,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Ferry Lane,NULL,TW9,518441,177481,518450,177450,51.48388987,-0.295635218
100087131,24/07/2013 16:16,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED BEHIND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000429,Stockwell,E09000022,Lambeth,Clapham,NULL,Belmore Street,21900168,SW8,NULL,NULL,529850,176850,NULL,NULL
100135131,24/07/2013 17:12,2013,2013/14,Special Service,1,1,290,290,TWO BIRDS TRAPPED IN NETTING UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Station Place,NULL,N4,531341,186764,531350,186750,51.56446822,-0.106497405
100547131,25/07/2013 04:59,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009382,Shacklewell,E09000012,Hackney,Stoke Newington,NULL,Prince George Road,20900821,N16,NULL,NULL,533450,185650,NULL,NULL
100577131,25/07/2013 07:45,2013,2013/14,Special Service,1,1,290,290,DOG LOCKED IN ROOM,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000475,Beckton,E09000025,Newham,East Ham,NULL,Pepper Close,22200951,E6,NULL,NULL,542950,181750,NULL,NULL
100609131,25/07/2013 09:45,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED BY LEG,Bird,Person (land line),Church/Chapel,Non Residential,Animal rescue from height,Wild animal rescue from height,E05011467,Crystal Palace & Upper Norwood,E09000008,Croydon,West Norwood,1.00023E+11,Westow Street,20500150,SE19,533496,170469,533450,170450,51.41752605,-0.081582824
101731131,27/07/2013 04:16,2013,2013/14,Special Service,1,1,290,290,FOX WITH HEAD TRAPPED IN FENCE,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05009387,Woodberry Down,E09000012,Hackney,Holloway,1.00023E+11,Portland Rise,20900814,N4,531927,187223,531950,187250,51.56845621,-0.097876151
101758131,27/07/2013 05:39,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED NEAR CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000485,Green Street East,E09000025,Newham,East Ham,NULL,South Esk Road,22207889,E7,NULL,NULL,541250,184550,NULL,NULL
101796131,27/07/2013 09:12,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED IN GATE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,Elsden Road,21104501,N17,NULL,NULL,533650,190550,NULL,NULL
101862131,27/07/2013 11:57,2013,2013/14,Special Service,1,1,290,290,DOG STUCK IN RIVER,Dog,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Ferry Lane,NULL,TW9,518222,177275,518250,177250,51.48208413,-0.298856576
102039131,27/07/2013 16:23,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000421,Ferndale,E09000022,Lambeth,Brixton,NULL,Tunstall Road,21901410,SW9,NULL,NULL,530950,175450,NULL,NULL
102619131,28/07/2013 18:19,2013,2013/14,Special Service,1,1,290,290,TWO PIGEONS STUCK IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000373,Highbury West,E09000019,Islington,Holloway,NULL,St. Thomas's Road,NULL,N4,531402,186665,531450,186650,51.56356434,-0.105654816
102897131,29/07/2013 08:58,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED AT ROOF LEVEL,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011464,Bensham Manor,E09000008,Croydon,Norbury,NULL,Elliott Road,20500907,CR7,NULL,NULL,531950,168350,NULL,NULL
103087131,29/07/2013 16:13,2013,2013/14,Special Service,1,1,290,290,Redacted,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000208,Southgate,E09000010,Enfield,Southgate,NULL,The Bourne,NULL,N14,530147,194196,530150,194150,51.63153252,-0.120958737
103440131,30/07/2013 11:14,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT TRAPPED BETWEEN WALL,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000372,Highbury East,E09000019,Islington,Stoke Newington,NULL,Highbury Grange,NULL,N5,532105,185676,532150,185650,51.55451241,-0.095889742
103613131,30/07/2013 18:18,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000107,Biggin Hill,E09000006,Bromley,Biggin Hill,1.0002E+11,Sunningvale Avenue,20303649,TN16,541632,158560,541650,158550,51.30853932,0.030605492
103634131,30/07/2013 18:51,2013,2013/14,Special Service,1,1,290,290,CAT FALLEN FROM BALCONY INTO BUSH,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000376,Junction,E09000019,Islington,Kentish Town,5300051386,Huddleston Road,21600471,N7,529360,185989,529350,185950,51.55796124,-0.135346045
104299131,01/08/2013 07:35,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Homerton,NULL,Lea Bridge Road,20900602,E5,NULL,NULL,535550,186550,NULL,NULL
104331131,01/08/2013 09:31,2013,2013/14,Special Service,1,1,290,290,DOG STUCK UNDERNEATH FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000608,William Morris,E09000031,Waltham Forest,Walthamstow,NULL,Forest Road,NULL,E17,536516,189633,536550,189650,51.5890206,-0.030769324
104348131,01/08/2013 10:28,2013,2013/14,Special Service,1,1,290,290,PUPPY TRAPPED UNDER SHED,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Claremont Road,22403387,TW1,516998,174008,516950,174050,51.4529751,-0.317555274
104541131,01/08/2013 14:51,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND LIFT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011110,Peckham,E09000028,Southwark,Peckham,NULL,Gatonby Street,22502994,SE15,NULL,NULL,533850,176850,NULL,NULL
105214131,02/08/2013 14:09,2013,2013/14,Special Service,1,1,290,290,HAMSTER TRAPPED IN HOLE,Hamster,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000380,St. Peter's,E09000019,Islington,Islington,NULL,Elia Street,21604792,N1,NULL,NULL,531750,183150,NULL,NULL
105585131,03/08/2013 02:17,2013,2013/14,Special Service,1,1,290,290,KITTEN WITH HEAD TRAPPED IN METAL BARS ACROSS CAGE,Cat,Person (land line),Veterinary surgery,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000455,Abbey,E09000024,Merton,Wimbledon,48083695,Merton High Street,22104371,SW19,526278,170138,526250,170150,51.41620337,-0.185444865
105913131,03/08/2013 19:21,2013,2013/14,Special Service,1,1,290,290,CAT STUCK ON FENCE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05009319,Bow East,E09000030,Tower Hamlets,Bethnal Green,NULL,Lefevre Walk,22702079,E3,NULL,NULL,537050,183450,NULL,NULL
105925131,03/08/2013 19:38,2013,2013/14,Special Service,1,1,290,290,CAT WITH ITS HEAD STUCK IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000223,Kidbrooke with Hornfair,E09000011,Greenwich,Eltham,NULL,Flintmill Crescent,20800579,SE3,NULL,NULL,542250,176050,NULL,NULL
106048131,03/08/2013 23:59,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN PIPE WORK IN KITCHEN,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000278,Seven Sisters,E09000014,Haringey,Tottenham,NULL,Vale Terrace,21104028,N4,NULL,NULL,532150,188050,NULL,NULL
106319131,04/08/2013 15:31,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN CHAIR,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000370,Clerkenwell,E09000019,Islington,Islington,NULL,Margery Street,21605383,WC1X,NULL,NULL,531150,182650,NULL,NULL
106423131,04/08/2013 17:46,2013,2013/14,Special Service,1,1,290,290,CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000402,Beverley,E09000021,Kingston upon Thames,New Malden,1.00022E+11,Franks Avenue,21800420,KT3,520435,168142,520450,168150,51.39953438,-0.270106082
106427131,04/08/2013 17:50,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA TO GAIN ACCESS TO TRAPPED PIGEON,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000595,Forest,E09000031,Waltham Forest,Leyton,NULL,Abbotts Park Road,22800150,E10,NULL,NULL,538250,187750,NULL,NULL
106527131,04/08/2013 20:31,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Seven Sisters Road,NULL,N4,531337,186696,531350,186650,51.56385806,-0.106580443
107182131,06/08/2013 08:39,2013,2013/14,Special Service,1,2,290,580,DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000503,Fairlop,E09000026,Redbridge,Woodford,NULL,Regents Drive,NULL,IG8,542969,191343,542950,191350,51.60278659,0.063012321
107328131,06/08/2013 16:53,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED IN BASEMENT,Fox,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000380,St. Peter's,E09000019,Islington,Islington,NULL,St. Peter's Street,21606037,N1,NULL,NULL,531950,183550,NULL,NULL
107334131,06/08/2013 17:09,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED UNDER CONCRETE SLOPE,Dog,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Plender Street,NULL,NW1,529342,183669,529350,183650,51.53711587,-0.136457965
107506131,06/08/2013 22:44,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND SINK,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000229,Woolwich Common,E09000011,Greenwich,Plumstead,NULL,Sandy Hill Road,20801323,SE18,NULL,NULL,543650,178450,NULL,NULL
107640131,07/08/2013 07:59,2013,2013/14,Special Service,1,1,290,290,CAT WITH LEG TRAPPED IN WINDOW,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000201,Haselbury,E09000010,Enfield,Edmonton,NULL,Hewish Road,20704336,N18,NULL,NULL,533150,192750,NULL,NULL
107719131,07/08/2013 12:13,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN RAILINGS ON BALCONY,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011218,Belvedere,E09000004,Bexley,Erith,10011844455,Ambrook Road,20100036,DA17,549311,179064,549350,179050,51.49081401,0.149314033
108013131,07/08/2013 22:07,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN FENCE,Dog,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000226,Plumstead,E09000011,Greenwich,Plumstead,NULL,Viewland Road,NULL,SE18,545522,178148,545550,178150,51.48357024,0.0943988
108171131,08/08/2013 08:12,2013,2013/14,Special Service,1,1,290,290,SQUIRREL STUCK IN METAL FENCE,Squirrel,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011219,Bexleyheath,E09000004,Bexley,Bexley,1.00023E+11,Broadway,20100205,DA6,548833,175385,548850,175350,51.45788304,0.140884372
108216131,08/08/2013 10:17,2013,2013/14,Special Service,1,1,290,290,CAT WITH HEAD TRAPPED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Shroffold Road,22003952,BR1,NULL,NULL,539650,171950,NULL,NULL
108271131,08/08/2013 12:36,2013,2013/14,Special Service,1,1,290,290,CAT LOCKED IN,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000114,Cray Valley East,E09000006,Bromley,Orpington,NULL,Sanderstead Road,20301276,BR5,NULL,NULL,546750,167050,NULL,NULL
108521131,08/08/2013 20:37,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED BEHIND SHED,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000058,Oakleigh,E09000003,Barnet,Barnet,NULL,Church Way,20008980,N20,NULL,NULL,527050,193350,NULL,NULL
108766131,09/08/2013 11:07,2013,2013/14,Special Service,1,2,290,580,CAT TRAPPED BETWEEN FENCES,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011233,West Heath,E09000004,Bexley,Bexley,1.0002E+11,Penshurst Road,20101134,DA7,548796,176907,548750,176950,51.47156841,0.140992886
108911131,09/08/2013 16:45,2013,2013/14,Special Service,1,2,290,580,CAT WITH PAW TRAPPED,Cat,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000136,Haverstock,E09000007,Camden,Kentish Town,5048295,Ferdinand Street,20400626,NW1,528426,184398,528450,184350,51.54387625,-0.14939213
109348131,10/08/2013 09:22,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN WIRE,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000151,Coulsdon West,E09000008,Croydon,Purley,NULL,Stoats Nest Road,NULL,CR5,530481,160165,530450,160150,51.32562588,-0.128695418
109388131,10/08/2013 11:28,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN TREE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000282,West Green,E09000014,Haringey,Tottenham,2.00003E+11,Adams Road,21106976,N17,532776,190279,532750,190250,51.59571913,-0.084480042
109850131,11/08/2013 00:20,2013,2013/14,Special Service,1,2,290,580,DOG STUCK BETWEEN WALL AND CONVERSATORY,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009323,Canary Wharf,E09000030,Tower Hamlets,Millwall,NULL,Claire Place,22700317,E14,NULL,NULL,537250,179250,NULL,NULL
110192131,11/08/2013 17:27,2013,2013/14,Special Service,1,1,290,290,BIRD WITH LEG TRAPPED IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000508,Mayfield,E09000026,Redbridge,Ilford,NULL,South Park Drive,NULL,IG3,545236,186549,545250,186550,51.55913098,0.093742016
110374131,12/08/2013 00:01,2013,2013/14,Special Service,1,1,290,290,INJURED DOG TRAPPED BEHIND FENCE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000223,Kidbrooke with Hornfair,E09000011,Greenwich,East Greenwich,NULL,Whetstone Road,20801603,SE3,NULL,NULL,541450,176550,NULL,NULL
110491131,12/08/2013 08:46,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED ON LEDGE NEAR CANAL,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,6168476,Ursula Gould Way,22702630,E14,537284,181607,537250,181650,51.51671207,-0.022819283
111060131,13/08/2013 10:27,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED ON LEDGE ON FOURTH FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000133,Frognal and Fitzjohns,E09000007,Camden,West Hampstead,NULL,Redington Road,20400055,NW3,NULL,NULL,525950,185750,NULL,NULL
111327131,13/08/2013 19:39,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED IN BASEMENT AREA,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000374,Hillrise,E09000019,Islington,Holloway,NULL,Miranda Road,21600604,N19,NULL,NULL,529450,187150,NULL,NULL
111351131,13/08/2013 20:01,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN BARBED WIRE FENCE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000033,Goresbrook,E09000002,Barking and Dagenham,Barking,NULL,Gale Street,19900144,RM9,NULL,NULL,547650,184250,NULL,NULL
112696131,16/08/2013 14:04,2013,2013/14,Special Service,1,1,290,290,ASSIST PARKS POLICE,Unknown - Wild Animal,Police,Park,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Kingston,10002252044,Richmond Park,22407048,TW10,518960,172804,518950,172850,51.44174558,-0.289735623
112849131,16/08/2013 20:07,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BETWEEN WALL AND SHED,Cat,Person (land line),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000380,St. Peter's,E09000019,Islington,Islington,5300065937,New North Road,21605530,N1,532420,183886,532450,183850,51.53835281,-0.092021888
113122131,17/08/2013 10:10,2013,2013/14,Special Service,1,1,290,290,CAT FALLEN AND TRAPPED IN BASEMENT AREA,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009389,Brompton & Hans Town,E09000020,Kensington and Chelsea,Chelsea,NULL,Ovington Square,21700387,SW3,NULL,NULL,527550,179150,NULL,NULL
113188131,17/08/2013 12:36,2013,2013/14,Special Service,1,1,290,290,CAT ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000417,Brixton Hill,E09000022,Lambeth,Brixton,NULL,St. Saviour's Road,21901287,SW2,NULL,NULL,530650,174550,NULL,NULL
114399131,19/08/2013 20:10,2013,2013/14,Special Service,1,1,290,290,CAT LOCKED IN HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000207,Southbury,E09000010,Enfield,Edmonton,NULL,Main Avenue,20702825,EN1,NULL,NULL,533750,195850,NULL,NULL
114815131,20/08/2013 16:05,2013,2013/14,Special Service,1,1,290,290,CAT UP TREE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000640,Maida Vale,E09000033,Westminster,Paddington,NULL,Randolph Gardens,8401516,NW6,NULL,NULL,525650,183150,NULL,NULL
114847131,20/08/2013 16:51,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED IN BOILER,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000097,Preston,E09000005,Brent,Wembley,NULL,Oakington Avenue,20202423,HA9,NULL,NULL,518950,186350,NULL,NULL
115506131,21/08/2013 19:07,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED BEHIND WALLS,Bird,Person (land line),Other indoor sporting venue,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000601,Hoe Street,E09000031,Waltham Forest,Walthamstow,1.00023E+11,Hoe Street,22845700,E17,537377,189695,537350,189650,51.58936866,-0.018324013
116281131,23/08/2013 08:03,2013,2013/14,Special Service,1,2,290,580,FOX TRAPPED IN WOOD POST,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000459,Dundonald,E09000024,Merton,Wimbledon,48048396,Mayfield Road,22104293,SW19,524935,169888,524950,169850,51.41425372,-0.204835775
116980131,24/08/2013 14:37,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH DOG TRAPPED ON BALCONY ON FIRST FLOOR,Dog,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000207,Southbury,E09000010,Enfield,Edmonton,NULL,Ayley Croft,20702490,EN1,NULL,NULL,534250,195550,NULL,NULL
117361131,24/08/2013 19:50,2013,2013/14,Special Service,1,2,290,580,CAT TRAPPED BETWEEN GARAGE AND NEIGHBOURING HOUSE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000323,Upminster,E09000016,Havering,Hornchurch,NULL,Springfield Gardens,21301896,RM14,NULL,NULL,556450,186150,NULL,NULL
117401131,24/08/2013 20:58,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED IN ON SECOND FLOOR,Cat,Person (land line),Hospital,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000135,Hampstead Town,E09000007,Camden,Kentish Town,5087826,Pond Street,20400297,NW3,527297,185410,527250,185450,51.55322653,-0.165298226
117615131,25/08/2013 12:47,2013,2013/14,Special Service,1,1,290,290,SQUIRELL ON ROOF - RSPCA ON SCENE,Squirrel,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000458,Cricket Green,E09000024,Merton,Mitcham,NULL,Miles Road,22104407,CR4,NULL,NULL,527150,168850,NULL,NULL
117981131,25/08/2013 23:36,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN KITCHEN,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000188,South Acton,E09000009,Ealing,Acton,NULL,Newport Road,20604685,W3,NULL,NULL,520050,179650,NULL,NULL
118035131,26/08/2013 02:13,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN HOSPITAL GROUNDS,Cat,Person (land line),Hospital,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000135,Hampstead Town,E09000007,Camden,Kentish Town,5087826,Pond Street,20400297,NW3,527297,185410,527250,185450,51.55322653,-0.165298226
118471131,26/08/2013 23:07,2013,2013/14,Special Service,1,2,290,580,CAT TRAPPED ON BUILDING,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,NULL,Victoria Park Square,22701269,E2,NULL,NULL,535050,182950,NULL,NULL
118680131,27/08/2013 11:13,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT TRAPPED ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000642,Queen's Park,E09000033,Westminster,North Kensington,NULL,Briar Walk,8400753,W10,NULL,NULL,524250,182450,NULL,NULL
119867131,29/08/2013 18:45,2013,2013/14,Special Service,1,1,290,290,BIRDS TRAPPED IN WIRE,Bird,Person (mobile),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,1.00023E+11,Eden Walk,21820013,KT1,518105,169217,518150,169250,51.40968602,-0.3032277
120113131,30/08/2013 09:17,2013,2013/14,Special Service,1,1,290,290,PUPPY WITH HEAD STUCK IN FENCE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000101,Sudbury,E09000005,Brent,Wembley,NULL,Fernbank Avenue,20200263,HA0,NULL,NULL,515650,185550,NULL,NULL
120447131,30/08/2013 19:59,2013,2013/14,Special Service,1,2,290,580,CAT TRAPPED IN AWNING,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000118,Farnborough and Crofton,E09000006,Bromley,Orpington,1.00024E+11,Crofton Road,20302866,BR6,543218,165228,543250,165250,51.36806226,0.056022528
120528131,30/08/2013 22:06,2013,2013/14,Special Service,1,1,290,290,CAT IN PRECARIOUS POSITION,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000098,Queens Park,E09000005,Brent,North Kensington,202088739,Harvist Road,20201304,NW6,524382,183187,524350,183150,51.53389756,-0.20810629
120636131,31/08/2013 04:01,2013,2013/14,Special Service,1,1,290,290,YOUNG DOG WITH HEAD TRAPPED IN BED RAILINGS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000101,Sudbury,E09000005,Brent,Wembley,NULL,Fernbank Avenue,20200263,HA0,NULL,NULL,515650,185550,NULL,NULL
120660131,31/08/2013 05:33,2013,2013/14,Special Service,1,1,290,290,CAT WITH PAW STUCK IN WINDOW,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009318,Blackwall & Cubitt Town,E09000030,Tower Hamlets,Millwall,NULL,Stewart Street,22701165,E14,NULL,NULL,538350,179450,NULL,NULL
121050131,31/08/2013 17:18,2013,2013/14,Special Service,1,1,290,290,PARROT STUCK IN TREE BY ITS HARNESS,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000301,Roxbourne,E09000015,Harrow,Northolt,NULL,Ravenswood Crescent,NULL,HA2,512688,186641,512650,186650,51.56739373,-0.375524427
121141131,31/08/2013 20:02,2013,2013/14,Special Service,1,1,290,290,PIDGEON TRAPPED IN NETTING,Bird,Person (land line),Bus/coach station/garage,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000633,Churchill,E09000033,Westminster,Chelsea,10033531076,Buckingham Palace Road,8400915,SW1W,528644,178672,528650,178650,51.49236746,-0.148339393
121485131,01/09/2013 12:52,2013,2013/14,Special Service,1,1,290,290,CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000214,Abbey Wood,E09000011,Greenwich,Plumstead,NULL,Smithies Road,20801372,SE2,NULL,NULL,546650,178750,NULL,NULL
121494131,01/09/2013 13:02,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN TREE,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000355,Heston Central,E09000018,Hounslow,Heston,NULL,Sutton Lane,NULL,TW3,512960,176567,512950,176550,51.47679485,-0.374828163
121689131,01/09/2013 17:14,2013,2013/14,Special Service,1,1,290,290,KITTEN STUCK IN CAR BEHIND GLOVE BOX,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Walthamstow,2.00001E+11,Lamorna Close,22849350,E17,538515,190503,538550,190550,51.59635053,-0.001589304
122447131,02/09/2013 23:13,2013,2013/14,Special Service,1,1,290,290,ASSIST POLICE WITH DOG UNDER BUS,Dog,Police,Bus/coach,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000050,Edgware,E09000003,Barnet,Mill Hill,NULL,Edgwarebury Lane,NULL,HA8,519389,192312,519350,192350,51.61698404,-0.276959606
122580131,03/09/2013 09:13,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN DOOR GRILL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000095,Mapesbury,E09000005,Brent,Willesden,NULL,Blenheim Gardens,20202394,NW2,NULL,NULL,523350,185050,NULL,NULL
122892131,03/09/2013 20:23,2013,2013/14,Special Service,1,1,290,290,CAT WITH NECK STUCK IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000368,Caledonian,E09000019,Islington,Holloway,NULL,Roman Way,21605828,N7,NULL,NULL,530750,184750,NULL,NULL
123278131,04/09/2013 14:54,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000543,Livesey,E09000028,Southwark,New Cross,NULL,Drovers Place,NULL,SE15,535008,177317,535050,177350,51.4787082,-0.057242171
123310131,04/09/2013 16:00,2013,2013/14,Special Service,1,2,290,580,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000306,Brooklands,E09000016,Havering,Romford,NULL,Mawney Road,21301408,RM7,NULL,NULL,550350,188950,NULL,NULL
123314131,04/09/2013 16:03,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER SHED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000104,Wembley Central,E09000005,Brent,Wembley,NULL,Wembley Hill Road,NULL,HA9,NULL,NULL,518850,185250,NULL,NULL
123748131,05/09/2013 10:45,2013,2013/14,Special Service,1,1,290,290,DOG IN LAKE,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000036,Mayesbrook,E09000002,Barking and Dagenham,Barking,NULL,Lodge Avenue,NULL,RM8,546643,184678,546650,184650,51.54195577,0.113245084
124099131,05/09/2013 20:40,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED IN FENCE,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000569,Wallington North,E09000029,Sutton,Wallington,5870078870,Park Lane,22605913,SM6,528496,164057,528450,164050,51.36105538,-0.155765166
124740131,07/09/2013 03:54,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN METAL CAGE OS,Dog,Person (mobile),Church/Chapel,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009405,Stanley,E09000020,Kensington and Chelsea,Chelsea,217085319,Sydney Street,21700534,SW3,527204,178342,527250,178350,51.4897272,-0.169189306
124906131,07/09/2013 14:13,2013,2013/14,Special Service,1,2,290,580,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000483,Forest Gate North,E09000025,Newham,Stratford,10014033106,Lorne Road,22207794,E7,540992,185866,540950,185850,51.55406996,0.032288746
125682131,08/09/2013 21:51,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND  PIPE IN KITCHEN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000604,Leyton,E09000031,Waltham Forest,Leyton,NULL,Goldsmith Road,22839800,E10,NULL,NULL,537550,187150,NULL,NULL
125904131,09/09/2013 11:55,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED BEHIND WASHING MACHINE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000122,Orpington,E09000006,Bromley,Orpington,NULL,Lamberhurst Close,20301179,BR5,NULL,NULL,547750,166150,NULL,NULL
126092131,09/09/2013 19:54,2013,2013/14,Special Service,1,2,290,580,DOG TRAPPED UNDER SUMMERHOUSE,Dog,Person (mobile),Outdoor storage,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011221,Blendon & Penhill,E09000004,Bexley,Bexley,1.0002E+11,South View Close,20101318,DA5,548648,174191,548650,174150,51.44720316,0.137721878
126886131,11/09/2013 18:11,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000476,Boleyn,E09000025,Newham,East Ham,NULL,Compton Avenue,22200498,E6,NULL,NULL,541750,183150,NULL,NULL
128200131,14/09/2013 08:21,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN GRAND UNION CANAL,Dog,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000186,Norwood Green,E09000009,Ealing,Southall,NULL,Harewood Terrace,NULL,UB2,512914,178834,512950,178850,51.49717997,-0.374766031
128279131,14/09/2013 11:27,2013,2013/14,Special Service,1,1,290,290,HORSE COLLAPSED IN FIELD,Horse,Person (mobile),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000114,Cray Valley East,E09000006,Bromley,Sidcup,1.0002E+11,Cookham Road,20302992,BR8,549570,169348,549550,169350,51.40344436,0.148936733
129213131,16/09/2013 15:18,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER GRATING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011102,Faraday,E09000028,Southwark,Old Kent Road,NULL,Boyson Road,22500309,SE17,NULL,NULL,532550,177750,NULL,NULL
129820131,17/09/2013 20:23,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000478,Canning Town South,E09000025,Newham,Plaistow,NULL,Jutland Road,22200758,E13,NULL,NULL,540350,182250,NULL,NULL
130134131,18/09/2013 15:37,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED UNDER CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000449,New Cross,E09000023,Lewisham,New Cross,NULL,New Cross Road,NULL,SE14,536306,176996,536350,176950,51.47551237,-0.038686271
130400131,19/09/2013 07:50,2013,2013/14,Special Service,1,1,290,290,FOX STUCK BETWEEN FENCES,Fox,Person (land line),Fence,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05011482,Shirley North,E09000008,Croydon,Woodside,1.00021E+11,Swinburne Crescent,20500566,CR0,535240,166966,535250,166950,51.38563324,-0.057851966
130852131,20/09/2013 10:25,2013,2013/14,Special Service,1,1,290,290,KITTENS STUCK IN TREES,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000549,Rotherhithe,E09000028,Southwark,Deptford,NULL,Southwark Park,NULL,SE16,535192,179036,535150,179050,51.49411208,-0.053936212
130984131,20/09/2013 16:58,2013,2013/14,Special Service,1,1,290,290,CAT WITH HEAD STUCK IN FOOD CAN,Cat,Police,Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000616,Graveney,E09000032,Wandsworth,Tooting,NULL,Coteford Street,NULL,SW17,528125,171477,528150,171450,51.42782341,-0.158414733
131135131,20/09/2013 21:29,2013,2013/14,Special Service,1,1,290,290,SQUIRREL TRAPPED BEHIND GAS HEATER,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000346,Bedfont,E09000018,Hounslow,Feltham,NULL,Stanwell Road,21501060,TW14,NULL,NULL,508450,173750,NULL,NULL
131346131,21/09/2013 11:44,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000294,Kenton East,E09000015,Harrow,Stanmore,NULL,Malvern Gardens,21201565,HA3,NULL,NULL,518450,189450,NULL,NULL
131504131,21/09/2013 18:09,2013,2013/14,Special Service,1,1,290,290,ZEBRA FINCH BIRD TRAPPED BEHIND RADIATOR,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011230,Sidcup,E09000004,Bexley,Sidcup,NULL,Melville Road,20100963,DA14,NULL,NULL,547350,172550,NULL,NULL
131970131,22/09/2013 16:34,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000319,Romford Town,E09000016,Havering,Romford,10091833603,South Street,21301604,RM1,551236,188724,551250,188750,51.57709881,0.181164824
131982131,22/09/2013 17:14,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN METER CUPBOARD OUTSIDE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009404,St. Helen's,E09000020,Kensington and Chelsea,North Kensington,NULL,Cambridge Gardens,21700828,W10,NULL,NULL,524150,181350,NULL,NULL
132004131,22/09/2013 17:53,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER COOKER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,NULL,Rushgrove Street,20801304,SE18,NULL,NULL,542950,178550,NULL,NULL
132223131,23/09/2013 07:51,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED ON SECOND FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000357,Heston West,E09000018,Hounslow,Feltham,NULL,Norman Crescent,21500807,TW5,NULL,NULL,511650,177050,NULL,NULL
132427131,23/09/2013 14:06,2013,2013/14,Special Service,1,1,290,290,CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011476,Purley & Woodcote,E09000008,Croydon,Purley,1.00021E+11,Briar Hill,20501944,CR8,530392,161768,530350,161750,51.34005275,-0.129385853
132493131,23/09/2013 16:53,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED BETWEEN SHED AND FENCE,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000411,St. James,E09000021,Kingston upon Thames,New Malden,1.00022E+11,George Road,21800435,KT3,521918,168069,521950,168050,51.39856176,-0.248823027
132844131,24/09/2013 13:29,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED IN CABLE,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving wild animal,E05011113,Rye Lane,E09000028,Southwark,Peckham,2.00003E+11,Highshore Road,22501242,SE15,534005,176470,534050,176450,51.47133495,-0.071997602
133069131,24/09/2013 21:16,2013,2013/14,Special Service,1,1,290,290,Redacted,Bird,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000166,Upper Norwood,E09000008,Croydon,Norbury,NULL,The Lawns,NULL,SE19,532860,169614,532850,169650,51.4099919,-0.091043687
133182131,25/09/2013 07:13,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED IN FENCE IN REAR GARDEN,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000380,St. Peter's,E09000019,Islington,Islington,5300070556,Packington Square,21606921,N1,532085,183520,532050,183550,51.53514223,-0.096986682
133227131,25/09/2013 09:50,2013,2013/14,Special Service,1,2,290,580,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000174,Ealing Common,E09000009,Ealing,Ealing,NULL,The Common,NULL,W5,518333,180340,518350,180350,51.50960833,-0.296231735
134107131,27/09/2013 01:40,2013,2013/14,Special Service,1,1,290,290,DEER TRAPPED IN FENCING,Deer,Police,House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011466,Coulsdon Town,E09000008,Croydon,Purley,NULL,Magpie Close,20502492,CR5,NULL,NULL,529350,158050,NULL,NULL
134157131,27/09/2013 07:08,2013,2013/14,Special Service,1,1,290,290,HAMSTER TRAPPED UNDER FLOORBOARDS,Hamster,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000045,Childs Hill,E09000003,Barnet,West Hampstead,NULL,Golders Green Road,20018180,NW11,NULL,NULL,524950,187450,NULL,NULL
134391131,27/09/2013 18:33,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPED IN HOLE OF FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000454,Whitefoot,E09000023,Lewisham,Bromley,NULL,Cranmore Road,22001376,BR1,NULL,NULL,539450,172250,NULL,NULL
134782131,28/09/2013 16:17,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN CAR MECHANICS,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05000172,Dormers Wells,E09000009,Ealing,Southall,NULL,Shackleton Road,NULL,UB1,512873,180673,512850,180650,51.51371703,-0.374768288
135986131,01/10/2013 10:00,2013,2013/14,Special Service,1,2,290,580,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000344,Yeading,E09000017,Hillingdon,Southall,NULL,Pikestone Close,21401532,UB4,NULL,NULL,512150,182150,NULL,NULL
136065131,01/10/2013 13:13,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000488,Manor Park,E09000025,Newham,Stratford,NULL,Claremont Road,22207659,E7,NULL,NULL,541450,185350,NULL,NULL
136116131,01/10/2013 15:43,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER BOILER,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011486,Thornton Heath,E09000008,Croydon,Norbury,NULL,St. Pauls Road,20501422,CR7,NULL,NULL,532350,168650,NULL,NULL
137273131,04/10/2013 00:28,2013,2013/14,Special Service,1,1,290,290,CAT IN PRECARIOUS POSITION  ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Maidenstone Hill,20800959,SE10,NULL,NULL,538150,176750,NULL,NULL
137888131,05/10/2013 08:59,2013,2013/14,Special Service,2,3,290,870,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000259,Palace Riverside,E09000013,Hammersmith and Fulham,Fulham,NULL,Millshott Close,NULL,SW6,523510,176957,523550,176950,51.4780978,-0.222852562
138071131,05/10/2013 15:36,2013,2013/14,Special Service,1,4,290,1160,DOG TRAPPED IN STORM WATER PIPE,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000055,Hendon,E09000003,Barnet,Mill Hill,NULL,Watford Way,NULL,NW4,522478,190416,522450,190450,51.59928255,-0.233025122
138463131,06/10/2013 08:50,2013,2013/14,Special Service,1,1,290,290,CAT BEHIND CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,Pioneer Close,22702451,E14,NULL,NULL,537550,181750,NULL,NULL
138560131,06/10/2013 13:19,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED  IN DRAIN,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011479,Selhurst,E09000008,Croydon,Croydon,NULL,Elmwood Road,20500912,CR0,NULL,NULL,531950,166850,NULL,NULL
139155131,07/10/2013 18:13,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD IN RAILINGS,Dog,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000136,Haverstock,E09000007,Camden,Kentish Town,NULL,Ferdinand Street,NULL,NW1,528434,184353,528450,184350,51.54347002,-0.149293243
139492131,08/10/2013 12:34,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK BEHIND A WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000090,Fryent,E09000005,Brent,Stanmore,NULL,Burgess Avenue,20202560,NW9,NULL,NULL,520550,188250,NULL,NULL
139939131,09/10/2013 11:12,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Wisdons Close,NULL,RM10,549845,187032,549850,187050,51.56226691,0.160385577
139994131,09/10/2013 14:01,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000284,Woodside,E09000014,Haringey,Tottenham,NULL,Sandford Avenue,21105015,N22,NULL,NULL,532050,190950,NULL,NULL
140048131,09/10/2013 16:30,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000284,Woodside,E09000014,Haringey,Tottenham,NULL,Sandford Avenue,21105015,N22,NULL,NULL,532050,190950,NULL,NULL
140588131,10/10/2013 14:57,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND BOILER,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000367,Bunhill,E09000019,Islington,Islington,NULL,Percival Street,21605667,EC1V,NULL,NULL,531650,182450,NULL,NULL
140590131,10/10/2013 15:01,2013,2013/14,Special Service,1,2,290,580,ASSIST RSPCA OFFICER WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009369,Clissold,E09000012,Hackney,Stoke Newington,NULL,Carysfort Road,20900204,N16,NULL,NULL,532650,186050,NULL,NULL
141747131,12/10/2013 20:02,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BEHIND CUPBOARD,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,NULL,Belvedere Road,20302135,SE19,NULL,NULL,533950,170050,NULL,NULL
141976131,13/10/2013 07:28,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER HOUSE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011117,Surrey Docks,E09000028,Southwark,Deptford,NULL,Russia Dock Road,22502158,SE16,NULL,NULL,536250,180050,NULL,NULL
142362131,14/10/2013 07:42,2013,2013/14,Special Service,1,1,290,290,RUNNING CALL TO CAT STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Kingston,NULL,Parkleys,22403727,TW10,NULL,NULL,517850,171750,NULL,NULL
142691131,14/10/2013 22:02,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED BEHIND RADIATOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000566,Sutton South,E09000029,Sutton,Sutton,NULL,Ambleside Gardens,22602067,SM2,NULL,NULL,526350,163650,NULL,NULL
143033131,15/10/2013 18:06,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BEHIND TOILET PIPE,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011242,Fairlop,E09000026,Redbridge,Hainault,1.00023E+11,New North Road,22304684,IG6,544871,191437,544850,191450,51.60314568,0.090495412
143969131,17/10/2013 20:47,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD TRAPPED UNDER IRON BAR,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000128,Belsize,E09000007,Camden,West Hampstead,5017447,Belsize Park Gardens,20400436,NW3,527378,184727,527350,184750,51.5470702,-0.164377681
144013131,17/10/2013 22:19,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011475,Park Hill & Whitgift,E09000008,Croydon,Croydon,1.00021E+11,Rutland Gardens,20502313,CR0,533453,164714,533450,164750,51.36581827,-0.084362251
144650131,19/10/2013 12:45,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED BETWEEN TWO WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000284,Woodside,E09000014,Haringey,Tottenham,NULL,Dunbar Road,21104475,N22,NULL,NULL,531450,190950,NULL,NULL
144767131,19/10/2013 17:23,2013,2013/14,Special Service,1,2,290,580,HORSE TRAPPED IN FENCE,Horse,Police,Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000342,West Drayton,E09000017,Hillingdon,Hayes,NULL,Wise Lane,NULL,UB7,505728,178726,505750,178750,51.4975983,-0.478278351
145925131,22/10/2013 13:57,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000319,Romford Town,E09000016,Havering,Romford,10091576954,High Street,21301276,RM1,551137,188821,551150,188850,51.57799687,0.17977893
146533131,23/10/2013 17:30,2013,2013/14,Special Service,1,1,290,290,PIGEONS TRAPPED IN NETTING,Bird,Person (land line),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000325,Botwell,E09000017,Hillingdon,Hayes,1.00024E+11,Judge Heath Lane,21401064,UB8,508317,181035,508350,181050,51.51786213,-0.44028763
146770131,24/10/2013 04:49,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BETWEEN HARDBOARD AND WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Seven Sisters Road,21603790,N7,NULL,NULL,530850,186350,NULL,NULL
147059131,24/10/2013 14:59,2013,2013/14,Special Service,1,1,290,290,KITTEN  STUCK IN ENGINE COMPARTMENT  OF  CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000260,Parsons Green and Walham,E09000013,Hammersmith and Fulham,Fulham,NULL,Avalon Road,NULL,SW6,525782,176916,525750,176950,51.47722933,-0.190167599
147453131,25/10/2013 09:41,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED UNDER HEATER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000566,Sutton South,E09000029,Sutton,Sutton,NULL,Ambleside Gardens,22602067,SM2,NULL,NULL,526350,163650,NULL,NULL
147969131,26/10/2013 12:02,2013,2013/14,Special Service,1,2,290,580,DOG DOWN FOXHOLE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000124,Petts Wood and Knoll,E09000006,Bromley,Orpington,NULL,Broomhill Road,NULL,BR6,546136,166693,546150,166650,51.38048328,0.098511635
147981131,26/10/2013 12:22,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,Kellett Road,21900773,SW2,NULL,NULL,531150,175150,NULL,NULL
148355131,27/10/2013 09:05,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000048,East Barnet,E09000003,Barnet,Barnet,NULL,Welbeck Road,20044920,EN4,NULL,NULL,527050,195350,NULL,NULL
148486131,27/10/2013 14:18,2013,2013/14,Special Service,1,1,290,290,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000278,Seven Sisters,E09000014,Haringey,Tottenham,NULL,Richmond Road,NULL,N15,533185,188212,533150,188250,51.57704792,-0.079361963
148504131,27/10/2013 15:05,2013,2013/14,Special Service,1,2,290,580,TO ASSIST RSPCA JILL WITH CAT STUCK UP TREE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000270,Fortis Green,E09000014,Haringey,Hornsey,1.00021E+11,Midhurst Avenue,21100589,N10,528018,189604,528050,189650,51.590754,-0.153377897
149325131,28/10/2013 10:14,2013,2013/14,Special Service,1,1,290,290,DOG ON ROOF,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000465,Lower Morden,E09000024,Merton,Sutton,NULL,Tudor Drive,22106354,SM4,NULL,NULL,524450,166750,NULL,NULL
150207131,30/10/2013 00:18,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000213,Winchmore Hill,E09000010,Enfield,Edmonton,NULL,Highfield Road,20704341,N21,NULL,NULL,532050,193950,NULL,NULL
150293131,30/10/2013 08:50,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED BETWEEN FENCES,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000470,St. Helier,E09000024,Merton,Sutton,NULL,Garendon Gardens,22102784,SM4,NULL,NULL,525550,166950,NULL,NULL
150426131,30/10/2013 14:04,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN SHED AND WALL,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000214,Abbey Wood,E09000011,Greenwich,Plumstead,1.00021E+11,Rochdale Road,20801281,SE2,546537,178727,546550,178750,51.48851098,0.109246338
150920131,31/10/2013 18:09,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED ON ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000370,Clerkenwell,E09000019,Islington,Islington,NULL,Northampton Square,21605564,EC1V,NULL,NULL,531750,182650,NULL,NULL
150976131,31/10/2013 20:09,2013,2013/14,Special Service,1,1,290,290,DOG FALLEN DOWN EMBANKMENT BY RIVER,Dog,Police,River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05009318,Blackwall & Cubitt Town,E09000030,Tower Hamlets,Millwall,NULL,Capstan Square,NULL,E14,538366,179652,538350,179650,51.49887997,-0.008002436
151172131,01/11/2013 07:52,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05009376,Homerton,E09000012,Hackney,Homerton,NULL,Morning Lane,NULL,E9,535343,184922,535350,184950,51.54696913,-0.049502902
151259131,01/11/2013 11:18,2013,2013/14,Special Service,1,1,290,290,BLACKBIRD TRAPPED IN TREE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000296,Marlborough,E09000015,Harrow,Stanmore,1.00021E+11,Peel Road,21201687,HA3,515673,189687,515650,189650,51.59416666,-0.331472797
151448131,01/11/2013 17:28,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN WALL CAVITY,Cat,Person (land line),Bakery,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011113,Rye Lane,E09000028,Southwark,Peckham,2.00003E+11,Bellenden Road,22500202,SE15,533965,176017,533950,176050,51.46727348,-0.072744703
151451131,01/11/2013 17:34,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER BRANCHES AND RUBBISH,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000464,Longthornton,E09000024,Merton,Norbury,48042957,Lilian Road,22103903,SW16,529201,169687,529250,169650,51.4114926,-0.143597763
151827131,02/11/2013 14:18,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000634,Church Street,E09000033,Westminster,Paddington,NULL,Ashbridge Street,8400327,NW8,NULL,NULL,527250,182050,NULL,NULL
152319131,03/11/2013 09:43,2013,2013/14,Special Service,1,1,290,290,DOG STUCK UNDER TREE IN GARDEN,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000227,Shooters Hill,E09000011,Greenwich,Eltham,1.00021E+11,Red Lion Lane,20801247,SE18,543131,176695,543150,176650,51.47112362,0.059399602
152362131,03/11/2013 12:07,2013,2013/14,Special Service,1,1,290,290,TO ASSIST RSPCA WITH FOX TRAPPED BETWEEN TWO GARAGES,Fox,Person (land line),Private Summer house,Non Residential,Other animal assistance,Assist trapped wild animal,E05000058,Oakleigh,E09000003,Barnet,Southgate,200106405,St. James Close,20038060,N20,527255,193049,527250,193050,51.62188593,-0.163136922
152391131,03/11/2013 13:07,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED BETWEEN LIFT DOORS,Bird,Person (mobile),Mosque,Non Residential,Other animal assistance,Assist trapped wild animal,E05000288,Greenhill,E09000015,Harrow,Harrow,1.00023E+11,Station Road,21201079,HA1,515635,189125,515650,189150,51.58912329,-0.33220625
152433131,03/11/2013 14:26,2013,2013/14,Special Service,1,1,290,290,PARROT TRAPPED IN GOLF NETTING,Bird,Person (mobile),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000348,Chiswick Homefields,E09000018,Hounslow,Chiswick,2.00004E+11,Dan Mason Drive,21500503,W4,520676,176258,520650,176250,51.47242618,-0.263880159
152434131,03/11/2013 14:26,2013,2013/14,Special Service,1,2,290,580,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000096,Northwick Park,E09000005,Brent,Wembley,202098179,The Fairway,20201761,HA0,516922,186924,516950,186950,51.56907664,-0.314369602
152949131,04/11/2013 14:49,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000280,Tottenham Green,E09000014,Haringey,Tottenham,NULL,Tottenham Green East,21104004,N15,NULL,NULL,533750,189250,NULL,NULL
153950131,06/11/2013 13:10,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000536,Cathedrals,E09000028,Southwark,Dowgate,NULL,Redcross Way,NULL,SE1,532472,180217,532450,180250,51.50536883,-0.092651366
154129131,06/11/2013 19:40,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN CEILING VOID,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000601,Hoe Street,E09000031,Waltham Forest,Walthamstow,1.00023E+11,Hoe Street,22845700,E17,537437,188522,537450,188550,51.57881376,-0.017916672
154461131,07/11/2013 15:46,2013,2013/14,Special Service,2,7,290,2030,DOG TRAPPED BETWEEN TWO HOUSE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011220,Blackfen & Lamorbey,E09000004,Bexley,Sidcup,NULL,Beverley Avenue,20100135,DA15,NULL,NULL,545550,173650,NULL,NULL
154957131,08/11/2013 17:51,2013,2013/14,Special Service,1,2,290,580,HORSE FALLEN INTO LAKE,Horse,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000330,Harefield,E09000017,Hillingdon,Ruislip,NULL,Moorhall Road,NULL,UB9,504910,188621,504950,188650,51.58669051,-0.487110359
155355131,09/11/2013 14:50,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,1.00023E+11,Thicket Road,20302199,SE20,534474,170871,534450,170850,51.42090759,-0.06737457
156209131,11/11/2013 11:43,2013,2013/14,Special Service,1,1,290,290,CAT ON ROOF,Cat,Person (land line),Private garage,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000267,Bounds Green,E09000014,Haringey,Hornsey,1.00021E+11,Commerce Road,21104385,N22,530758,190932,530750,190950,51.60205931,-0.113353549
157858131,15/11/2013 10:25,2013,2013/14,Special Service,1,1,290,290,TO ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Cemetery,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000175,East Acton,E09000009,Ealing,Park Royal,NULL,Chase Road,NULL,NW10,520828,182083,520850,182050,51.52474623,-0.259699501
157865131,15/11/2013 10:49,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED IN BASEMENT AREA,Fox,Person (mobile),Outdoor storage,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000461,Graveney,E09000024,Merton,Mitcham,48128999,London Road,22103996,SW17,527943,170569,527950,170550,51.41970404,-0.161358741
157913131,15/11/2013 12:39,2013,2013/14,Special Service,1,2,290,580,CAT TRAPPED BEHIND BOXED IN CISTERN,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000623,Shaftesbury,E09000032,Wandsworth,Battersea,NULL,Clapham Common North Side,22900961,SW4,NULL,NULL,527750,175150,NULL,NULL
158360131,16/11/2013 10:56,2013,2013/14,Special Service,1,1,290,290,HORSE TRAPPED ON FENCE,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000111,Chislehurst,E09000006,Bromley,Sidcup,1.0002E+11,Kemnal Road,20303635,BR7,544622,171679,544650,171650,51.42567265,0.078802657
158861131,17/11/2013 10:26,2013,2013/14,Special Service,1,1,290,290,REQ FOR ASSISTANCE FROM RSPCA,Unknown - Domestic Animal Or Pet,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000057,Mill Hill,E09000003,Barnet,Mill Hill,200081491,Milespit Hill,20029600,NW7,522921,192182,522950,192150,51.61505707,-0.226014176
159099131,17/11/2013 19:33,2013,2013/14,Special Service,1,1,290,290,CAT STUCK ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000443,Evelyn,E09000023,Lewisham,Deptford,NULL,Barfleur Lane,22006126,SE8,NULL,NULL,536750,178450,NULL,NULL
159428131,18/11/2013 16:31,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN CAGE,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011097,Champion Hill,E09000028,Southwark,Brixton,NULL,Denmark Hill Estate,22500847,SE5,NULL,NULL,532850,175350,NULL,NULL
159535131,18/11/2013 19:40,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND FREEZER,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000434,Thurlow Park,E09000022,Lambeth,West Norwood,NULL,Norwood Road,NULL,SE24,531817,173253,531850,173250,51.44293807,-0.104678983
160062131,19/11/2013 18:59,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND BOILER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000493,Wall End,E09000025,Newham,East Ham,NULL,Burges Road,22200424,E6,NULL,NULL,542650,184250,NULL,NULL
160143131,19/11/2013 21:52,2013,2013/14,Special Service,1,1,290,290,INJURED CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000354,Hanworth Park,E09000018,Hounslow,Feltham,1.00023E+11,Victoria Road,21501253,TW13,510724,173056,510750,173050,51.44567931,-0.408109769
160751131,21/11/2013 10:36,2013,2013/14,Special Service,1,2,290,580,Redacted,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000612,Earlsfield,E09000032,Wandsworth,Tooting,NULL,Trewint Street,NULL,SW18,525915,172666,525950,172650,51.43900398,-0.189764761
161464131,22/11/2013 20:24,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009367,Brownswood,E09000012,Hackney,Holloway,NULL,Alexandra Grove,NULL,N4,531981,187026,531950,187050,51.56667323,-0.097171343
162280131,24/11/2013 15:41,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Retirement/Old Persons Home,Other Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000335,Northwood,E09000017,Hillingdon,Ruislip,1.00021E+11,Murray Road,21401342,HA6,509159,191122,509150,191150,51.60836377,-0.425027391
163060131,26/11/2013 12:34,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000352,Feltham West,E09000018,Hounslow,Feltham,NULL,Ennerdale Close,NULL,TW14,509542,173251,509550,173250,51.44766145,-0.425051001
163186131,26/11/2013 16:59,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA PIGEON TRAPPED IN NETTING,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000564,Sutton Central,E09000029,Sutton,Sutton,5870034546,St. Nicholas Way,22602630,SM1,525782,164052,525750,164050,51.36161713,-0.194727431
163998131,28/11/2013 11:31,2013,2013/14,Special Service,1,1,290,290,HORSE STUCK IN WATER - LEVEL TWO WATER IMPLEMENTED,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist trapped domestic animal,E05000079,North End,E09000004,Bexley,Erith,NULL,Ray Lamb Way,NULL,DA8,553469,177526,553450,177550,51.47588275,0.208490418
164862131,30/11/2013 08:20,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN WIRE MESH NEAR PLATFORM ONE,Bird,Person (mobile),"Tunnel, subway",Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000475,Beckton,E09000025,Newham,East Ham,NULL,Cyprus Place,NULL,E6,543321,180858,543350,180850,51.50848313,0.063822568
165496131,01/12/2013 13:30,2013,2013/14,Special Service,1,1,290,290,PIEGEON TRAPPED IN WIRING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000639,Little Venice,E09000033,Westminster,Paddington,NULL,Hall Place,8400423,W2,NULL,NULL,526650,182050,NULL,NULL
165727131,01/12/2013 20:54,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BETWEEN TWO WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000372,Highbury East,E09000019,Islington,Stoke Newington,NULL,Highbury Grange,21603356,N5,NULL,NULL,531950,185750,NULL,NULL
166619131,03/12/2013 19:22,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000331,Heathrow Villages,E09000017,Hillingdon,Heathrow,1.00021E+11,Doghurst Avenue,21400599,UB3,507912,177034,507950,177050,51.48197757,-0.447345044
166897131,04/12/2013 12:28,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000046,Colindale,E09000003,Barnet,Mill Hill,NULL,Corner Mead,NULL,NW9,521699,190850,521650,190850,51.60335142,-0.244116771
167793131,06/12/2013 10:35,2013,2013/14,Special Service,1,1,290,290,TWO BIRDS TRAPPED BEHIND GAS FIRE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000026,Abbey,E09000002,Barking and Dagenham,Barking,NULL,Park Avenue,19900716,IG11,NULL,NULL,544550,184750,NULL,NULL
167809131,06/12/2013 11:07,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN CHIMNEY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011101,Dulwich Wood,E09000028,Southwark,Forest Hill,NULL,Lordship Lane,22501557,SE22,NULL,NULL,534150,173450,NULL,NULL
168069131,06/12/2013 22:31,2013,2013/14,Special Service,1,1,290,290,DOG IMPALED ON METAL FENCE,Dog,Police,Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000048,East Barnet,E09000003,Barnet,Barnet,NULL,Approach Road,NULL,EN4,526535,196099,526550,196050,51.64945762,-0.17243015
168345131,07/12/2013 14:25,2013,2013/14,Special Service,1,1,290,290,CAT ON ROOF OF TERRACE HOUSE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000373,Highbury West,E09000019,Islington,Holloway,NULL,Elfort Road,21603170,N5,NULL,NULL,531450,185850,NULL,NULL
169274131,09/12/2013 16:58,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD STUCK IN DOOR,Dog,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,1.00022E+11,Durban Road,21900486,SE27,532589,171902,532550,171950,51.43061683,-0.094082798
170069131,11/12/2013 13:23,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000091,Harlesden,E09000005,Brent,Park Royal,NULL,Craven Park Road,NULL,NW10,521491,183632,521450,183650,51.53852571,-0.249613097
170952131,13/12/2013 11:33,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Bird in Bush Road,22500242,SE15,NULL,NULL,534350,177350,NULL,NULL
171012131,13/12/2013 14:26,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000616,Graveney,E09000032,Wandsworth,Tooting,1.00023E+11,Laurel Close,22902835,SW17,527402,171346,527450,171350,51.42680883,-0.168855964
171760131,15/12/2013 10:37,2013,2013/14,Special Service,1,3,290,870,HORSE STUCK IN DITCH,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000333,Ickenham,E09000017,Hillingdon,Hillingdon,1.00023E+11,Long Lane,21401178,UB10,508051,185283,508050,185250,51.55609477,-0.442816765
171915131,15/12/2013 15:58,2013,2013/14,Special Service,1,2,290,580,KITTEN TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000407,Coombe Vale,E09000021,Kingston upon Thames,New Malden,NULL,Orme Road,21800732,KT1,NULL,NULL,519950,169150,NULL,NULL
172120131,16/12/2013 04:40,2013,2013/14,Special Service,1,2,290,580,INJURED CAT TRAPPED BEHIND LIFT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011110,Peckham,E09000028,Southwark,Peckham,NULL,Gatonby Street,22502994,SE15,NULL,NULL,533850,176850,NULL,NULL
172696131,17/12/2013 11:58,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED UNDER CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Radnor Street,NULL,EC1V,532463,182575,532450,182550,51.52656139,-0.091895183
173432131,19/12/2013 07:43,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Train station - concourse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000475,Beckton,E09000025,Newham,East Ham,NULL,Cyprus Place,NULL,E6,543304,180942,543350,180950,51.50924224,0.063611898
173518131,19/12/2013 11:28,2013,2013/14,Special Service,1,1,290,290,DOG STUCK UNDER STOARGE UNIT,Dog,Person (mobile),Outdoor storage,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,NULL,High Street,NULL,TW12,514419,171026,514450,171050,51.42670036,-0.355618864
174029131,20/12/2013 12:23,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Lynhurst Crescent,NULL,UB10,NULL,NULL,508650,184150,NULL,NULL
174040131,20/12/2013 12:49,2013,2013/14,Special Service,2,3,290,870,HORSE TRAPPED IN TREE TRUNKS ON STEEP INCLINE,Horse,Person (land line),Wasteland,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000071,Crayford,E09000004,Bexley,Bexley,NULL,Maiden Lane,NULL,DA1,552329,175374,552350,175350,51.45685538,0.191160727
174592131,21/12/2013 16:56,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000370,Clerkenwell,E09000019,Islington,Islington,NULL,Claremont Close,21606789,N1,NULL,NULL,531350,183050,NULL,NULL
174832131,22/12/2013 10:38,2013,2013/14,Special Service,1,1,290,290,KITTEN STUCK UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000340,Uxbridge North,E09000017,Hillingdon,Hillingdon,NULL,Croft Close,21400524,UB10,NULL,NULL,507050,184450,NULL,NULL
174841131,22/12/2013 11:03,2013,2013/14,Special Service,1,1,290,290,PERSONS LOCKED OUT,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000174,Ealing Common,E09000009,Ealing,Ealing,NULL,Grange Road,20600751,W5,NULL,NULL,517850,180250,NULL,NULL
174957131,22/12/2013 15:44,2013,2013/14,Special Service,1,2,290,580,PUPPY TRAPPED BEHIND A FENCE,Dog,Other FRS,Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000199,Enfield Lock,E09000010,Enfield,Enfield,NULL,George Lovell Drive,NULL,EN3,537409,198783,537450,198750,51.67102495,-0.014299835
174961131,22/12/2013 15:53,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN TREE,Cat,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000484,Forest Gate South,E09000025,Newham,Stratford,10009018024,Romford Road,22201573,E7,540802,185073,540850,185050,51.54699166,0.029233017
175275131,23/12/2013 10:21,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000479,Custom House,E09000025,Newham,Plaistow,NULL,St. Michaels Close,22207901,E16,NULL,NULL,541650,181550,NULL,NULL
177130131,24/12/2013 18:46,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPEDN IN HEDGING,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Malmesbury Road,NULL,E3,537149,183075,537150,183050,51.52993651,-0.024192481
177406131,25/12/2013 10:36,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011229,St. Mary's & St. James,E09000004,Bexley,Sidcup,NULL,Ellenborough Road,20100490,DA14,NULL,NULL,547950,171150,NULL,NULL
177808131,26/12/2013 11:12,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD TRAPPED,Dog,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000312,Harold Wood,E09000016,Havering,Harold Hill,1.00021E+11,Sussex Avenue,21301631,RM3,554949,191214,554950,191250,51.59846241,0.235801456
177814131,26/12/2013 11:33,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN BASEMENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Hampstead Road,20400780,NW1,NULL,NULL,529150,182750,NULL,NULL
178314131,27/12/2013 14:45,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING UNDERNEATH BRIDGE,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000178,Greenford Green,E09000009,Ealing,Northolt,NULL,Lyon Way,NULL,UB6,515312,183580,515350,183550,51.5393524,-0.338683824
178712131,28/12/2013 17:07,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA,Unknown - Wild Animal,Person (mobile),Sports pavilion/shower block/changing facility,Non Residential,Other animal assistance,Assist trapped wild animal,E05000347,Brentford,E09000018,Hounslow,Acton,2.00004E+11,Gunnersbury Park,20602411,W3,519046,179236,519050,179250,51.49953645,-0.286334863
179109131,29/12/2013 14:35,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH SWAN STUCK BEHIND FENCE,Bird,Person (land line),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05000603,Lea Bridge,E09000031,Waltham Forest,Leyton,1.00023E+11,Lea Bridge Road,22850300,E10,535630,186783,535650,186750,51.5636238,-0.044649735
179803131,31/12/2013 09:26,2013,2013/14,Special Service,1,1,290,290,DOG UNABLE TO LEAVE RAILWAY EMBANKMENT NEAR,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000208,Southgate,E09000010,Enfield,Southgate,NULL,Oakwood Park Road,NULL,N14,529694,195315,529650,195350,51.64169291,-0.127085516
236141,01/01/2014 11:58,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Fitzwygram Close,22401481,TW12,514158,171065,514150,171050,51.4271035,-0.35935876
955141,03/01/2014 11:31,2014,2013/14,Special Service,1,5,290,1450,ASSIST POLICE WITH HORSE INJURED IN RTC,Horse,Police,Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Rangers Road,NULL,E4,539722,194772,539750,194750,51.63441268,0.017525125
1557141,04/01/2014 23:43,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH FOX STUCK IN RAILING,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000045,Childs Hill,E09000003,Barnet,West Hampstead,NULL,Ranulf Road,20035940,NW2,NULL,NULL,524850,185850,NULL,NULL
1636141,05/01/2014 04:34,2014,2013/14,Special Service,1,1,290,290,CAT STUCK IN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000631,Bayswater,E09000033,Westminster,Paddington,NULL,Northumberland Place,8401480,W2,NULL,NULL,525250,181250,NULL,NULL
1724141,05/01/2014 12:04,2014,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED BETWEEN CABINETS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000311,Hacton,E09000016,Havering,Hornchurch,NULL,Ascot Gardens,21300204,RM12,NULL,NULL,554150,185850,NULL,NULL
1742141,05/01/2014 12:56,2014,2013/14,Special Service,1,1,290,290,DOG WITH HEAD STUCK IN GATE,Dog,Person (mobile),Animal boarding/breeding establishment - dogs,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000309,Emerson Park,E09000016,Havering,Hornchurch,1.00021E+11,Wingletye Lane,21300661,RM11,554939,189052,554950,189050,51.57904051,0.234704421
2981141,08/01/2014 15:10,2014,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING ABOVE,Bird,Person (land line),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,1.00023E+11,York Street,22403983,TW1,516327,173388,516350,173350,51.44754091,-0.327411803
3044141,08/01/2014 17:33,2014,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN BRAMBLE BUSHES,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000352,Feltham West,E09000018,Hounslow,Feltham,NULL,Vernon Road,21501149,TW13,NULL,NULL,509850,172650,NULL,NULL
3317141,09/01/2014 11:07,2014,2013/14,Special Service,1,1,290,290,FOX STUCK IN GARDEN,Fox,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05009323,Canary Wharf,E09000030,Tower Hamlets,Millwall,NULL,Manilla Street,22700776,E14,NULL,NULL,537150,179850,NULL,NULL
3828141,10/01/2014 16:06,2014,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Other office/call centre type building,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000485,Green Street East,E09000025,Newham,East Ham,10008996681,Plashet Grove,22200963,E6,541355,183966,541350,183950,51.53690607,0.036758561
4101141,11/01/2014 09:21,2014,2013/14,Special Service,1,1,290,290,KITTENS TRAPPED IN WALL,Cat,Person (mobile),Vehicle Repair Workshop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,10003973600,Maple Place,21106933,N17,534466,191019,534450,191050,51.60196788,-0.059813075
4334141,11/01/2014 20:31,2014,2013/14,Special Service,1,1,290,290,DOG FALLEN IN RIVER THAMES   UNABLE TO GET OUT UNASSISTED,Dog,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000255,Fulham Reach,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Rainville Road,NULL,W6,523387,177633,523350,177650,51.48420007,-0.224386993
4556141,12/01/2014 11:44,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN REAR GARDEN,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,Colville,E09000020,Kensington and Chelsea,North Kensington,NULL,Portobello Road,21700970,W11,NULL,NULL,524550,181350,NULL,NULL
4640141,12/01/2014 15:35,2014,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009398,Norland,E09000020,Kensington and Chelsea,North Kensington,NULL,Cornwall Crescent,21700850,W11,NULL,NULL,524250,181150,NULL,NULL
4920141,13/01/2014 10:27,2014,2013/14,Special Service,NULL,NULL,290,NULL,PIDGEON TRAPPED IN WIRE NETTING,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05009405,Stanley,E09000020,Kensington and Chelsea,Chelsea,NULL,Dovehouse Street,NULL,SW3,527090,178146,527050,178150,51.48799135,-0.170900926
5311141,14/01/2014 09:11,2014,2013/14,Special Service,1,3,290,870,DOG STUCK IN HOLE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000528,South Richmond,E09000027,Richmond upon Thames,Richmond,NULL,Richmond Park,NULL,TW10,519156,173962,519150,173950,51.45211212,-0.286527366
5375141,14/01/2014 11:32,2014,2013/14,Special Service,1,1,290,290,Redacted,Bird,Person (land line),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000534,Brunswick Park,E09000028,Southwark,Peckham,NULL,McNeil Road,NULL,SE5,533107,176307,533150,176350,51.47008199,-0.084980296
5710141,15/01/2014 09:16,2014,2013/14,Special Service,1,1,290,290,RUNNING CALL TO CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000603,Lea Bridge,E09000031,Waltham Forest,Leyton,NULL,Marsh Lane,NULL,E10,537067,187003,537050,187050,51.56525395,-0.023844915
5752141,15/01/2014 11:00,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000057,Mill Hill,E09000003,Barnet,Mill Hill,200090205,Page Street,20033240,NW7,522423,191098,522450,191050,51.60542364,-0.233581234
5805141,15/01/2014 13:03,2014,2013/14,Special Service,1,1,290,290,ASSIST  RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011233,West Heath,E09000004,Bexley,Erith,1.00023E+11,Hurst Lane,20100755,SE2,547613,178035,547650,178050,51.48201353,0.124443937
5823141,15/01/2014 13:41,2014,2013/14,Special Service,1,1,290,290,ASSIST IN GAINING ENTRY,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,NULL,Victoria Park Square,22701269,E2,NULL,NULL,535050,182950,NULL,NULL
5887141,15/01/2014 16:50,2014,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED IN DISUSED GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,10022800410,Waterloo Road,21402084,UB8,504958,183645,504950,183650,51.54195551,-0.487902207
5910141,15/01/2014 18:00,2014,2013/14,Special Service,1,1,290,290,PIGEONS TRAPPED IN NETTING,Bird,Person (mobile),Temple,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000359,Hounslow Heath,E09000018,Hounslow,Heston,2.00004E+11,Alice Way,21501576,TW3,513540,175170,513550,175150,51.46412256,-0.366928509
6117141,16/01/2014 10:32,2014,2013/14,Special Service,1,2,290,580,Redacted,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000075,Erith,E09000004,Bexley,Erith,NULL,Wharfside Close,NULL,DA8,551685,178078,551650,178050,51.48132337,0.183058414
6308141,16/01/2014 18:10,2014,2013/14,Special Service,1,1,290,290,CAT STUCK IN CEILING,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,1.00023E+11,Electric Avenue,21900507,SW9,531150,175409,531150,175450,51.46246852,-0.113472238
6594141,17/01/2014 13:30,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN DRAIN,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Hatherley Gardens,21106534,N8,NULL,NULL,530150,188250,NULL,NULL
6602141,17/01/2014 13:54,2014,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED BEHIND WALL,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,1.00021E+11,Woolwich New Road,20801651,SE18,543795,178880,543750,178850,51.49058907,0.069842179
6645141,17/01/2014 15:55,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN WALL AND FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000555,Beddington North,E09000029,Sutton,Mitcham,NULL,Rosemary Close,22605577,CR0,NULL,NULL,530150,166950,NULL,NULL
7077141,18/01/2014 17:06,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH PIGEONS TRAPPED IN NETTING,Bird,Person (mobile),Other retail,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000358,Hounslow Central,E09000018,Hounslow,Heston,1.00023E+11,High Street,21500610,TW3,513612,175574,513650,175550,51.46773923,-0.36576278
7443141,19/01/2014 15:01,2014,2013/14,Special Service,1,1,290,290,DOG WITH HEAD STUCK IN RAILINGS,Dog,Person (land line),Warehouse,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000030,Eastbrook,E09000002,Barking and Dagenham,Dagenham,100025153,Rainham Road South,19900295,RM10,549815,185629,549850,185650,51.54966866,0.159356262
7894141,20/01/2014 18:16,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA,Unknown - Wild Animal,Person (mobile),House in Multiple Occupation - 3 or more storeys (not known if licensed),Dwelling,Other animal assistance,Assist trapped wild animal,E05000098,Queens Park,E09000005,Brent,North Kensington,NULL,Chamberlayne Road,20202065,NW10,NULL,NULL,523650,183050,NULL,NULL
8850141,23/01/2014 09:17,2014,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED UNDER CAR,Cat,Person (mobile),Car,Road Vehicle,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Farrer Road,NULL,N8,529534,189167,529550,189150,51.58648077,-0.131667204
8851141,23/01/2014 09:17,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH DOG FALLEN FROM ROOF,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000436,Vassall,E09000022,Lambeth,Brixton,1.00022E+11,Langton Road,21900840,SW9,531795,177151,531750,177150,51.47797372,-0.103544076
8864141,23/01/2014 09:52,2014,2013/14,Special Service,1,1,290,290,FOX STUCK IN BARBED WIRE,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000203,Jubilee,E09000010,Enfield,Edmonton,NULL,Woodpecker Close,NULL,N9,534647,195326,534650,195350,51.64062847,-0.055546344
9427141,24/01/2014 19:08,2014,2013/14,Special Service,1,1,290,290,CAT AND KITTENS WEDGED IN CAR BONNET,Cat,Person (land line),Underground car park,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,10009148338,Hillyfield,22819150,E17,536137,190003,536150,190050,51.59243694,-0.036093754
9689141,25/01/2014 12:59,2014,2013/14,Special Service,1,1,290,290,BIRD TRAPPED BY WIRE ON ROOF,Bird,Person (mobile),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000104,Wembley Central,E09000005,Brent,Wembley,NULL,High Road,NULL,HA0,517929,185045,517950,185050,51.55197943,-0.300475803
10561141,27/01/2014 08:21,2014,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN HOLE,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000560,Cheam,E09000029,Sutton,Sutton,5870000623,Burdon Lane,22605695,SM2,524552,162982,524550,162950,51.35227155,-0.21275927
11133141,28/01/2014 18:31,2014,2013/14,Special Service,1,1,290,290,Redacted,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000080,Northumberland Heath,E09000004,Bexley,Erith,NULL,Hurst Road,NULL,DA8,550374,177016,550350,177050,51.47213094,0.1637416
11144141,28/01/2014 18:48,2014,2013/14,Special Service,1,1,290,290,CAT WITH LEG IMPALED ON RAILINGS,Cat,Person (mobile),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,Beaufoy Road,NULL,N17,533605,191286,533650,191250,51.60457217,-0.072135895
11205141,28/01/2014 22:39,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN WALL AND DECKING,Cat,Person (land line),Other building/use not known,Non Residential,Animal rescue from water,Animal rescue from water - Domestic pet,E05000431,Streatham South,E09000022,Lambeth,Norbury,1.00022E+11,Guildersfield Road,21900649,SW16,530138,170159,530150,170150,51.41552062,-0.129959193
4149,29/01/2014 15:03,2014,2013/14,Special Service,1,3,290,870,NULL,Unknown - Animal rescue from water - Farm animal,Not known,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000071,Crayford,E09000004,Bexley,Bexley,NULL,Thames Road,NULL,DA1,552838,175418,552850,175450,51.45711382,0.198500308
12729141,01/02/2014 12:47,2014,2013/14,Special Service,1,2,290,580,SMALL ANIMAL RESCUE FOLLOWING COLLAPSE OF WALL,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000145,West Hampstead,E09000007,Camden,West Hampstead,NULL,Holmdale Road,20400258,NW6,NULL,NULL,525250,185150,NULL,NULL
12791141,01/02/2014 14:45,2014,2013/14,Special Service,1,1,290,290,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000456,Cannon Hill,E09000024,Merton,Wimbledon,NULL,Cannon Hill Lane,22101155,SW20,NULL,NULL,524150,168550,NULL,NULL
13164141,02/02/2014 13:43,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA RESCUE DOG FROM THIRD FLOOR WINDOW,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011116,South Bermondsey,E09000028,Southwark,Old Kent Road,NULL,Stubbs Drive,22502405,SE16,NULL,NULL,534750,178350,NULL,NULL
13245141,02/02/2014 16:05,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN DRAIN PIPE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011488,West Thornton,E09000008,Croydon,Norbury,NULL,Goldwell Road,20500983,CR7,NULL,NULL,531050,168150,NULL,NULL
13270141,02/02/2014 16:56,2014,2013/14,Special Service,1,1,290,290,DOG WITH HEAD STUCK IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Daling Way,NULL,E3,536333,183440,536350,183450,51.53341371,-0.035807604
13326141,02/02/2014 19:08,2014,2013/14,Special Service,1,1,290,290,PERSON LOCED OUT DANGER OF FIRE,Unknown - Domestic Animal Or Pet,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000434,Thurlow Park,E09000022,Lambeth,West Norwood,NULL,Harpenden Road,21900674,SE27,NULL,NULL,531650,172650,NULL,NULL
13739141,03/02/2014 19:33,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN FENCES,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011242,Fairlop,E09000026,Redbridge,Hainault,NULL,Genas Close,22306511,IG6,NULL,NULL,543950,190650,NULL,NULL
14100141,04/02/2014 17:05,2014,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN RABBIT HOLE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000155,Heathfield,E09000008,Croydon,Addington,NULL,Court Wood Lane,NULL,CR0,536777,161830,536750,161850,51.33911127,-0.037751661
14493141,05/02/2014 15:08,2014,2013/14,Special Service,1,1,290,290,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000400,Alexandra,E09000021,Kingston upon Thames,Surbiton,NULL,Alexandra Drive,NULL,KT5,519226,166751,519250,166750,51.38728744,-0.287943853
15251141,07/02/2014 11:59,2014,2013/14,Special Service,1,1,290,290,KITTEN STUCK BEHINED WALL,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Kenton Road,20900566,E9,NULL,NULL,535750,184650,NULL,NULL
15916141,08/02/2014 19:51,2014,2013/14,Special Service,1,1,290,290,BIRDS TRAPPED IN WIRE MESH,Bird,Person (mobile),Other outdoor equipment/machinery,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000176,Elthorne,E09000009,Ealing,Ealing,12059025,Church Road,20600387,W7,515688,180328,515650,180350,51.5100474,-0.334332098
15976141,09/02/2014 00:04,2014,2013/14,Special Service,1,1,290,290,PIGEONS TRAPPED IN WIRE MESH UNDER RAILWAY BRIDGE,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000180,Hobbayne,E09000009,Ealing,Ealing,NULL,Church Road,NULL,W7,515494,180554,515450,180550,51.51211829,-0.337052364
16196141,09/02/2014 14:11,2014,2013/14,Special Service,1,1,290,290,DOG STUCK UNDER ELECTRIC CHAIR,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000038,River,E09000002,Barking and Dagenham,Dagenham,NULL,Broad Street,19900439,RM10,NULL,NULL,549150,184350,NULL,NULL
16667141,10/02/2014 17:08,2014,2013/14,Special Service,1,1,290,290,HORSE STUCK IN HAY CONTAINER,Horse,Person (mobile),Agricultural equipment,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000117,Darwin,E09000006,Bromley,Biggin Hill,1.0002E+11,Jail Lane,20301511,TN16,542935,158831,542950,158850,51.31064916,0.049395278
17302141,12/02/2014 10:54,2014,2013/14,Special Service,1,1,290,290,CAT WITH HEAD TRAPPED BETWEEN DOOR AND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000203,Jubilee,E09000010,Enfield,Edmonton,NULL,Elmcroft Avenue,20702660,N9,NULL,NULL,535050,195250,NULL,NULL
17398141,12/02/2014 14:33,2014,2013/14,Special Service,1,4,290,1160,CAT STUCK IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000457,Colliers Wood,E09000024,Merton,Wimbledon,NULL,Singleton Close,NULL,SW17,527637,170496,527650,170450,51.41911685,-0.165783406
17397141,12/02/2014 14:34,2014,2013/14,Special Service,1,1,290,290,CAT UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,NULL,Malvern Road,21103582,N17,NULL,NULL,534050,189850,NULL,NULL
17407141,12/02/2014 14:48,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA TO RESCUE CAT FROM ROOF OF HOUSE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000458,Cricket Green,E09000024,Merton,Mitcham,NULL,Thornville Grove,22106775,CR4,NULL,NULL,526550,169150,NULL,NULL
17823141,13/02/2014 13:34,2014,2013/14,Special Service,1,1,290,290,CATS HEAD TRAPPED AROUND EXHAUST PIPE OF CAR,cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000216,Charlton,E09000011,Greenwich,East Greenwich,1.00021E+11,Leila Parnell Place,20800903,SE7,541150,177608,541150,177650,51.47982512,0.031260684
17915141,13/02/2014 17:45,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN NETTING UNDER BRIDGE,Cat,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000140,Kilburn,E09000007,Camden,West Hampstead,NULL,Abbey Road,NULL,NW6,525712,183937,525750,183950,51.5403436,-0.188673445
18160141,14/02/2014 11:23,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000109,Bromley Town,E09000006,Bromley,Bromley,NULL,Ravensbourne Avenue,NULL,BR2,539063,169628,539050,169650,51.40863156,-0.001905441
19347141,15/02/2014 18:01,2014,2013/14,Special Service,1,2,290,580,ASSIST RSCPA OFFICER WITH TRAPPED PIGEON,Bird,Person (land line),Indoor Market,Non Residential,Other animal assistance,Assist trapped wild animal,E05000130,Camden Town with Primrose Hill,E09000007,Camden,Kentish Town,5167971,Chalk Farm Road,20400624,NW1,528482,184222,528450,184250,51.54228183,-0.148649242
19517141,16/02/2014 00:55,2014,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE - SMALL KITTEN TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000106,Bickley,E09000006,Bromley,Bromley,NULL,Sundridge Avenue,20303578,BR7,NULL,NULL,542550,170150,NULL,NULL
19747141,16/02/2014 15:19,2014,2013/14,Special Service,1,1,290,290,DOG IN LAKE,Dog,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000502,Cranbrook,E09000026,Redbridge,Ilford,NULL,Emerson Road,NULL,IG1,543324,188003,543350,188050,51.5726847,0.066772076
20564141,18/02/2014 10:10,2014,2013/14,Special Service,1,1,290,290,DOG FALLEN IN POND,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,34110376,Devonport Road,21000272,W12,522905,179646,522950,179650,51.50239651,-0.230624568
20918141,19/02/2014 06:20,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN HATCH,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011116,South Bermondsey,E09000028,Southwark,Dockhead,NULL,Amina Way,22502887,SE16,NULL,NULL,534150,179050,NULL,NULL
21030141,19/02/2014 13:33,2014,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN RAILINGS,Dog,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011218,Belvedere,E09000004,Bexley,Erith,1.0002E+11,Edwards Road,20100488,DA17,549195,179002,549150,179050,51.49028751,0.147618239
21056141,19/02/2014 14:16,2014,2013/14,Special Service,1,1,290,290,DOG IN RIVER,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000520,Hampton,E09000027,Richmond upon Thames,Twickenham,NULL,High Street,NULL,TW12,513974,169561,513950,169550,51.41362266,-0.362487454
21250141,19/02/2014 21:18,2014,2013/14,Special Service,1,1,290,290,RABBIT STUCK UNDER DECKING,Rabbit,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000612,Earlsfield,E09000032,Wandsworth,Tooting,NULL,Wimbledon Road,22906250,SW17,NULL,NULL,526450,171950,NULL,NULL
21491141,20/02/2014 13:59,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED BY LEG IN SPRINGS OF BED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000180,Hobbayne,E09000009,Ealing,Ealing,NULL,Campbell Road,20600307,W7,NULL,NULL,515450,180550,NULL,NULL
21842141,21/02/2014 11:32,2014,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING UNDER ARCHWAY,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009336,Whitechapel,E09000030,Tower Hamlets,Whitechapel,NULL,Pinchin Street,NULL,E1,534398,180949,534350,180950,51.51149234,-0.064638225
21971141,21/02/2014 17:34,2014,2013/14,Special Service,1,1,290,290,PONY WITH HEAD STUCK IN FENCE,Horse,Person (mobile),Scrub land,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000361,Hounslow West,E09000018,Hounslow,Feltham,NULL,Cardington Square,NULL,TW4,511907,175256,511950,175250,51.46522054,-0.390398928
22647141,23/02/2014 09:30,2014,2013/14,Special Service,2,7,290,2030,HORSE WEDGED IN STABLE,Horse,Person (mobile),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000111,Chislehurst,E09000006,Bromley,Eltham,NULL,Slades Drive,NULL,BR7,544333,172016,544350,172050,51.42877446,0.074786037
23199141,24/02/2014 16:36,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH GULL TRAPPED IN TREE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000645,Tachbrook,E09000033,Westminster,Lambeth,NULL,Grosvenor Road,NULL,SW1V,529625,177909,529650,177950,51.48528665,-0.134496441
23656141,25/02/2014 17:39,2014,2013/14,Special Service,1,1,290,290,CAT STUCK ON WINDOW LEDGE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Stanlake Villas,21000775,W12,NULL,NULL,523050,180150,NULL,NULL
24228141,27/02/2014 05:34,2014,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000253,College Park and Old Oak,E09000013,Hammersmith and Fulham,North Kensington,NULL,Du Cane Road,NULL,W12,522185,181144,522150,181150,51.51601553,-0.240473975
24375141,27/02/2014 15:59,2014,2013/14,Special Service,1,2,290,580,CAT   TRAPPED IN TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000510,Newbury,E09000026,Redbridge,Ilford,NULL,Perth Road,NULL,IG2,543675,188127,543650,188150,51.5737096,0.071884024
24484141,27/02/2014 21:30,2014,2013/14,Special Service,1,1,290,290,DOG FALLEN IN RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000427,Prince's,E09000022,Lambeth,Lambeth,NULL,Albert Embankment,NULL,SE1,530523,178781,530550,178750,51.49291681,-0.121248485
25114141,01/03/2014 13:03,2014,2013/14,Special Service,1,1,290,290,DOG STUCK IN BABY GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000126,Shortlands,E09000006,Bromley,Bromley,NULL,Newbury Road,20300517,BR2,NULL,NULL,540250,168550,NULL,NULL
25368141,01/03/2014 22:34,2014,2013/14,Special Service,1,1,290,290,BIRD STUCK UP CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000110,Chelsfield and Pratts Bottom,E09000006,Bromley,Orpington,NULL,Repton Road,20303572,BR6,NULL,NULL,546950,164250,NULL,NULL
25849141,03/03/2014 08:33,2014,2013/14,Special Service,1,1,290,290,DOG TRAPPED BENEATH CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000516,Barnes,E09000027,Richmond upon Thames,Hammersmith,NULL,Rocks Lane,NULL,SW13,522998,176690,522950,176650,51.47580956,-0.230314423
26466141,04/03/2014 15:58,2014,2013/14,Special Service,1,1,290,290,Redacted,Bird,Police,Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,1.00023E+11,Molesworth Street,22004181,SE13,538084,175354,538050,175350,51.46032599,-0.013742251
26479141,04/03/2014 16:19,2014,2013/14,Special Service,1,1,290,290,HORSES LOOSE ON ROADWAY,Horse,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving livestock,E05000330,Harefield,E09000017,Hillingdon,Ruislip,NULL,Breakspear Road North,NULL,UB9,506449,189733,506450,189750,51.59639698,-0.464568083
26486141,04/03/2014 16:42,2014,2013/14,Special Service,1,1,290,290,PEREGRINE FALCON STUCK IN NETTING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,1.00023E+11,Molesworth Street,22004181,SE13,538084,175354,538050,175350,51.46032599,-0.013742251
26536141,04/03/2014 18:26,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Bird,Person (mobile),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000623,Shaftesbury,E09000032,Wandsworth,Battersea,1.00023E+11,Lavender Hill,22902838,SW11,527567,175640,527550,175650,51.46536265,-0.164937921
26724141,05/03/2014 05:47,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000477,Canning Town North,E09000025,Newham,Plaistow,NULL,Whitwell Road,22201218,E13,NULL,NULL,540250,182550,NULL,NULL
26878141,05/03/2014 13:40,2014,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN GUTTERING,Bird,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped wild animal,E05000572,Worcester Park,E09000029,Sutton,Sutton,5870121012,Central Road,22601354,KT4,522708,165691,522750,165650,51.37701918,-0.238292936
27013141,05/03/2014 18:54,2014,2013/14,Special Service,1,1,290,290,DOG WITH PAW TRAPPED IN METAL FENCE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011100,Dulwich Village,E09000028,Southwark,West Norwood,10090287157,Gallery Road,22501015,SE21,532861,173179,532850,173150,51.44202926,-0.089693767
27206141,06/03/2014 07:41,2014,2013/14,Special Service,1,1,290,290,BIRD STUCK IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,The Broadway,20705014,N9,NULL,NULL,534350,193450,NULL,NULL
27591141,07/03/2014 01:02,2014,2013/14,Special Service,1,4,290,1160,ASSIST VET WITH COLLAPSED HORSE IN STABLE,Horse,Person (mobile),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05011254,Wanstead Park,E09000026,Redbridge,Leytonstone,1.00024E+11,Empress Avenue,22302824,E12,541924,187071,541950,187050,51.564664,0.046208294
27879141,07/03/2014 17:59,2014,2013/14,Special Service,1,2,290,580,DOG TRAPPED IN HOLLOW TREE,Dog,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Kingston,10002251968,Richmond Park,22407048,TW10,518839,171707,518850,171750,51.43191167,-0.291843938
28193141,08/03/2014 12:55,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000195,Chase,E09000010,Enfield,Enfield,207162441,Bowles Green,20702523,EN1,534625,199071,534650,199050,51.67428667,-0.054422869
28262141,08/03/2014 16:35,2014,2013/14,Special Service,NULL,NULL,290,NULL,CAT TRAPPED ON BARBED WIRE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal harm involving domestic animal,E05009321,Bromley North,E09000030,Tower Hamlets,Bethnal Green,6074888,Tidworth Road,22701213,E3,537240,182506,537250,182550,51.52480134,-0.023102993
28384141,08/03/2014 20:36,2014,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN PLASTIC BIN LID,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000257,Munster,E09000013,Hammersmith and Fulham,Fulham,NULL,Fulham Palace Road,21000356,SW6,NULL,NULL,524150,176550,NULL,NULL
28634141,09/03/2014 12:45,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Mornington Terrace,20400729,NW1,NULL,NULL,528850,183550,NULL,NULL
28706141,09/03/2014 14:11,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK IN PIPE ON ROOF,Cat,Person (land line),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,1.00023E+11,Paxton Place,21901077,SE27,533053,171662,533050,171650,51.4283513,-0.087502236
28795141,09/03/2014 17:46,2014,2013/14,Special Service,1,1,290,290,CAT STUCK UNDER CAR BONNET,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000279,Stroud Green,E09000014,Haringey,Hornsey,NULL,Highbank Way,NULL,N8,531152,188311,531150,188350,51.5784143,-0.10864594
29496141,11/03/2014 11:18,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT TRAPPED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000030,Eastbrook,E09000002,Barking and Dagenham,Dagenham,100064644,Hardie Road,19900799,RM10,550132,186250,550150,186250,51.55516432,0.164189481
29581141,11/03/2014 14:51,2014,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN NETTING,Bird,Person (land line),Furniture warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Chingford,1.00023E+11,Jubilee Avenue,22847785,E4,538422,191882,538450,191850,51.60876464,-0.002387751
29668141,11/03/2014 18:16,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA - TRAPPED PIGEON,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000599,High Street,E09000031,Waltham Forest,Walthamstow,1.00023E+11,Blackhorse Road,22816400,E17,536287,188811,536250,188850,51.5816896,-0.034391404
30027141,12/03/2014 14:42,2014,2013/14,Special Service,1,1,290,290,CAT STUCK IN DRAIN,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Shenley Road,22502236,SE5,NULL,NULL,533350,176650,NULL,NULL
30073141,12/03/2014 16:13,2014,2013/14,Special Service,1,1,290,290,DOG WITH HEAD TRAPPED IN FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000122,Orpington,E09000006,Bromley,Orpington,1.00023E+11,Goddington Lane,20300903,BR6,547228,165031,547250,165050,51.36526767,0.113504097
30079141,12/03/2014 16:25,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER DECKING,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,1.00021E+11,Grand Avenue,21100352,N10,528496,189403,528450,189450,51.588839,-0.146555088
30429141,13/03/2014 09:26,2014,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Other retail,Non Residential,Other animal assistance,Animal harm involving wild animal,E05011249,Monkhams,E09000026,Redbridge,Woodford,10034920926,The Broadway,22306023,IG8,540896,191874,540850,191850,51.60807981,0.03331285
32417141,17/03/2014 01:54,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN RAIN DUCT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000267,Bounds Green,E09000014,Haringey,Hornsey,NULL,Morant Place,NULL,N22,NULL,NULL,530750,190950,NULL,NULL
32680141,17/03/2014 16:22,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER FLOOR BOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011248,Mayfield,E09000026,Redbridge,Ilford,NULL,South Park Drive,22302417,IG3,NULL,NULL,545250,186550,NULL,NULL
33319141,18/03/2014 22:21,2014,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING ON BALCONY,Bird,Person (land line),Bingo Hall,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000170,Acton Central,E09000009,Ealing,Acton,12145563,High Street,20602317,W3,520485,180099,520450,180050,51.50698821,-0.265319146
33530141,19/03/2014 12:53,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH PIGEON TRAPPED UNDER RAILWAY BRIDGE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000057,Mill Hill,E09000003,Barnet,Finchley,NULL,Bittacy Road,NULL,NW7,524003,191512,524050,191550,51.60879877,-0.210631472
34115141,20/03/2014 17:48,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000558,Carshalton Central,E09000029,Sutton,Wallington,5870060965,Banstead Road,22602087,SM5,527407,163876,527450,163850,51.35967352,-0.171462795
34602141,21/03/2014 21:16,2014,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000356,Heston East,E09000018,Hounslow,Southall,NULL,Burket Close,21501741,UB2,NULL,NULL,512550,178650,NULL,NULL
34979141,22/03/2014 16:54,2014,2013/14,Special Service,1,1,290,290,CATS TRAPPED IN WALL,cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009379,King's Park,E09000012,Hackney,Homerton,NULL,Hazlewood Close,20900498,E5,NULL,NULL,536050,186050,NULL,NULL
36130141,25/03/2014 10:13,2014,2013/14,Special Service,1,1,290,290,RUNNING CALL TO BIRD TRAPPED BEHIND FIRE PLACE,Bird,Person (land line),Licensed House in Multiple Occupation - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000083,Sidcup,E09000004,Bexley,Sidcup,NULL,Manor Road,NULL,DA15,NULL,NULL,546150,172350,NULL,NULL
36781141,26/03/2014 18:56,2014,2013/14,Special Service,1,2,290,580,CAT STUCK IN CHIMNEY BREAST,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000446,Ladywell,E09000023,Lewisham,Lewisham,NULL,Kneller Road,22000588,SE4,NULL,NULL,536350,175350,NULL,NULL
36976141,27/03/2014 10:01,2014,2013/14,Special Service,1,2,290,580,CAT STUCK IN FIRE APPLIANCE,Cat,Person (land line),Lorry/HGV,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011113,Rye Lane,E09000028,Southwark,Peckham,2.00003E+11,Peckham Road,22501935,SE5,533584,176740,533550,176750,51.47386086,-0.077953398
37065141,27/03/2014 14:44,2014,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000446,Ladywell,E09000023,Lewisham,Lewisham,NULL,Kneller Road,22000588,SE4,NULL,NULL,536350,175350,NULL,NULL
37383141,28/03/2014 08:50,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000562,St. Helier,E09000029,Sutton,Sutton,5870042244,Glastonbury Road,22600576,SM4,525585,166254,525550,166250,51.38145064,-0.19677838
37700141,28/03/2014 19:41,2014,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),River/canal,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000440,Catford South,E09000023,Lewisham,Lewisham,1.00022E+11,Barmeston Road,22001200,SE6,537569,172932,537550,172950,51.4386864,-0.022091169
37799141,29/03/2014 00:30,2014,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN COOKER HOOD EXTRACTOR,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009377,Hoxton East & Shoreditch,E09000012,Hackney,Shoreditch,NULL,Hoxton Square,20900542,N1,NULL,NULL,533150,182650,NULL,NULL
37913141,29/03/2014 09:19,2014,2013/14,Special Service,1,3,290,870,DEER STUCK IN RAILINGS,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000202,Highlands,E09000010,Enfield,Southgate,NULL,Worlds End Lane,NULL,EN2,531129,196376,531150,196350,51.65089493,-0.10596204
39164141,31/03/2014 18:14,2014,2013/14,Special Service,1,1,290,290,PIGEON IN NETTING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000061,West Finchley,E09000003,Barnet,Finchley,NULL,Avondale Avenue,20002000,N12,NULL,NULL,525750,192250,NULL,NULL
39247141,31/03/2014 22:00,2014,2013/14,Special Service,1,1,290,290,CAT WITH HEAD TRAPPED IN STAIRWAY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011112,Rotherhithe,E09000028,Southwark,Deptford,NULL,Hawkstone Road,22501219,SE16,NULL,NULL,535550,178850,NULL,NULL
39659141,01/04/2014 20:25,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON GATED LEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011098,Chaucer,E09000028,Southwark,Dockhead,NULL,Cluny Estate,22500218,SE1,NULL,NULL,533250,179350,NULL,NULL
40665141,04/04/2014 04:51,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE - CAT TRAPPED BEHIND FITTED WARDROBE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000035,Longbridge,E09000002,Barking and Dagenham,Barking,NULL,Longbridge Road,19900226,IG11,NULL,NULL,545450,185150,NULL,NULL
40800141,04/04/2014 13:47,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000435,Tulse Hill,E09000022,Lambeth,West Norwood,NULL,Papworth Way,NULL,SW2,531290,173709,531250,173750,51.44715843,-0.112088458
41086141,05/04/2014 00:49,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009398,Norland,E09000020,Kensington and Chelsea,North Kensington,NULL,Elgin Crescent,21700866,W11,NULL,NULL,524350,180950,NULL,NULL
41293141,05/04/2014 13:45,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),"Grassland, pasture, grazing etc",Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000050,Edgware,E09000003,Barnet,Stanmore,200015610,Brockley Hill,20005240,HA7,517389,194173,517350,194150,51.63413066,-0.305209374
41658141,06/04/2014 10:32,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN CHINMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000137,Highgate,E09000007,Camden,Kentish Town,NULL,Lissenden Gardens,20400214,NW5,NULL,NULL,528350,185850,NULL,NULL
43051141,09/04/2014 13:37,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK ON CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000482,East Ham South,E09000025,Newham,East Ham,NULL,Denbigh Road,22200534,E6,NULL,NULL,541650,182550,NULL,NULL
43175141,09/04/2014 18:13,2014,2014/15,Special Service,1,2,295,590,CAT TRAPPED IN PARTITION WALL,Cat,Person (land line),Nursing/Care Home/Hospice,Other Residential,Other animal assistance,Assist trapped domestic animal,E05000280,Tottenham Green,E09000014,Haringey,Tottenham,10003981417,Rangemoor Road,21106599,N15,533870,188917,533850,188950,51.58322096,-0.069214429
43574141,10/04/2014 16:15,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT TRAPPED IN VENT NEAR TO CINEWORLD,Cat,Person (land line),Cinema,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011246,Ilford Town,E09000026,Redbridge,Ilford,10034911858,Clements Road,22305994,IG1,543882,186343,543850,186350,51.55762675,0.074139455
43645141,10/04/2014 18:27,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH SWAN TRAPPED BEHIND FENCE,Bird,Person (mobile),Scrub land,Outdoor,Other animal assistance,Assist trapped wild animal,E05000275,Noel Park,E09000014,Haringey,Hornsey,NULL,Western Road,NULL,N22,530616,190176,530650,190150,51.59529851,-0.11568375
44042141,11/04/2014 14:52,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN FENCE,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000050,Edgware,E09000003,Barnet,Mill Hill,200057510,Harrowes Meade,20021040,HA8,519451,193390,519450,193350,51.62665939,-0.27569729
44529141,12/04/2014 15:34,2014,2014/15,Special Service,1,1,295,295,DOG IN PRECARIOUS POSITION,Dog,Other FRS,Estate Agent,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,1.00023E+11,Stanley Road,22876500,E4,538915,194455,538950,194450,51.63176372,0.005746469
44622141,12/04/2014 19:05,2014,2014/15,Special Service,1,1,295,295,DOG FALLEN ONTO RIVER BANK,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009318,Blackwall & Cubitt Town,E09000030,Tower Hamlets,Poplar,NULL,Orchard Place,NULL,E14,539437,180723,539450,180750,51.50824119,0.007840151
44924141,13/04/2014 14:17,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER DECKING,Dog,Police,Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000036,Mayesbrook,E09000002,Barking and Dagenham,Barking,100066294,Sheppey Road,19900819,RM9,547314,184406,547350,184450,51.53933719,0.122800303
44966141,13/04/2014 15:38,2014,2014/15,Special Service,1,1,295,295,BIRDS TRAPPED BEHIND WIRE,Bird,Person (land line),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,10033604168,Craven Passage,8401684,WC2N,530213,180385,530250,180350,51.50740311,-0.125119613
45075141,13/04/2014 19:14,2014,2014/15,Special Service,1,1,295,295,CAT STUCK REAR OF TOILET,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000048,East Barnet,E09000003,Barnet,Barnet,NULL,East Barnet Road,20013380,EN4,NULL,NULL,526950,195650,NULL,NULL
45293141,14/04/2014 09:37,2014,2014/15,Special Service,1,1,295,295,CAT STUCK BETWEEN TWO HOUSES,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Greenwich South Street,20800688,SE10,NULL,NULL,537950,176850,NULL,NULL
45880141,15/04/2014 16:15,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH INJURED CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011222,Crayford,E09000004,Bexley,Bexley,1.0002E+11,Heath Road,20100697,DA1,551991,174365,551950,174350,51.44788013,0.185866308
45885141,15/04/2014 16:16,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED BETWEEN TWO WALLS,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000563,Stonecot,E09000029,Sutton,Sutton,5870014648,Staines Avenue,22601901,SM3,523938,165664,523950,165650,51.37650967,-0.220638441
46117141,16/04/2014 01:27,2014,2014/15,Special Service,1,2,295,590,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000226,Plumstead,E09000011,Greenwich,Plumstead,NULL,Bassant Road,20800124,SE18,NULL,NULL,545550,177750,NULL,NULL
46842141,17/04/2014 13:48,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009330,St. Katharine's & Wapping,E09000030,Tower Hamlets,Whitechapel,6008456,St. Anthony's Close,22701949,E1W,534173,180388,534150,180350,51.5065044,-0.068092021
46854141,17/04/2014 14:04,2014,2014/15,Special Service,1,1,295,295,CAT ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009396,Golborne,E09000020,Kensington and Chelsea,North Kensington,NULL,Appleford Road,21700797,W10,NULL,NULL,524350,182050,NULL,NULL
46939141,17/04/2014 17:37,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN ELECTICAL CUPBOARD,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011218,Belvedere,E09000004,Bexley,Erith,NULL,Upper Abbey Road,20101494,DA17,NULL,NULL,548950,178850,NULL,NULL
47176141,18/04/2014 09:23,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO CAT STUCK IN BATHROOM WALL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000419,Clapham Town,E09000022,Lambeth,Clapham,NULL,Grafton Square,21900630,SW4,NULL,NULL,529450,175650,NULL,NULL
47220141,18/04/2014 11:10,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN CHIMNEY POT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000626,Tooting,E09000032,Wandsworth,Tooting,NULL,Selkirk Road,22904558,SW17,NULL,NULL,527250,171750,NULL,NULL
47391141,18/04/2014 16:56,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000642,Queen's Park,E09000033,Westminster,North Kensington,NULL,Beethoven Street,8401106,W10,NULL,NULL,524450,182850,NULL,NULL
47676141,19/04/2014 09:16,2014,2014/15,Special Service,1,1,295,295,CAT STUCK UP TREE RUNNING CALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000569,Wallington North,E09000029,Sutton,Wallington,NULL,Clifton Road,NULL,SM6,NULL,NULL,529150,164250,NULL,NULL
47878141,19/04/2014 17:11,2014,2014/15,Special Service,1,1,295,295,YOUNG DEER TRAPPED BEHIND SHED,Deer,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000333,Ickenham,E09000017,Hillingdon,Ruislip,NULL,Hoylake Crescent,21401023,UB10,NULL,NULL,507550,186750,NULL,NULL
47971141,19/04/2014 20:02,2014,2014/15,Special Service,1,1,295,295,BABY DUCKS TRAPPED ON ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000028,Becontree,E09000002,Barking and Dagenham,Barking,NULL,Academy Way,NULL,RM8,NULL,NULL,546550,185550,NULL,NULL
48236141,20/04/2014 12:38,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000286,Canons,E09000015,Harrow,Stanmore,NULL,Cheyneys Avenue,NULL,HA8,517907,191765,517950,191750,51.61238012,-0.298538237
48640141,21/04/2014 11:56,2014,2014/15,Special Service,1,1,295,295,TWO DOGS LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000346,Bedfont,E09000018,Hounslow,Feltham,NULL,Bedfont Road,NULL,TW14,508863,172778,508850,172750,51.44354083,-0.434963185
48774141,21/04/2014 16:46,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND SHED,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000469,Raynes Park,E09000024,Merton,New Malden,48057323,Polesden Gardens,22105127,SW20,522683,169059,522650,169050,51.40729462,-0.23748976
48797141,21/04/2014 17:59,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000485,Green Street East,E09000025,Newham,Stratford,NULL,Strone Road,22207910,E7,NULL,NULL,541550,184750,NULL,NULL
49045141,22/04/2014 08:02,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN BANISTER,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000316,Mawneys,E09000016,Havering,Romford,NULL,Linley Crescent,21301362,RM7,NULL,NULL,550150,189850,NULL,NULL
49076141,22/04/2014 09:54,2014,2014/15,Special Service,2,9,295,2655,KITTEN TRAPPED IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000350,Cranford,E09000018,Hounslow,Feltham,NULL,Salisbury Road,21500996,TW4,NULL,NULL,511450,175750,NULL,NULL
49254141,22/04/2014 17:42,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON LEDGE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000135,Hampstead Town,E09000007,Camden,West Hampstead,NULL,Flask Walk,20400159,NW3,NULL,NULL,526450,185750,NULL,NULL
49339141,22/04/2014 20:49,2014,2014/15,Special Service,1,1,295,295,DOG LOCKED IN GARAGE,Dog,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011465,Broad Green,E09000008,Croydon,Norbury,1.00021E+11,Cameron Road,20500750,CR0,531866,167067,531850,167050,51.3873347,-0.106273554
49492141,23/04/2014 09:59,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT UP  TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000122,Orpington,E09000006,Bromley,Orpington,NULL,Park Avenue,20300947,BR6,NULL,NULL,546450,165550,NULL,NULL
49528141,23/04/2014 11:25,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND RAILINGS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011241,Cranbrook,E09000026,Redbridge,Ilford,NULL,Wanstead Lane,22303356,IG1,NULL,NULL,542150,188050,NULL,NULL
49831141,23/04/2014 23:08,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN SHAFT,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000250,Addison,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Shepherds Bush Road,21000727,W6,NULL,NULL,523450,179150,NULL,NULL
49945141,24/04/2014 09:46,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA RESCUE PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000438,Blackheath,E09000023,Lewisham,Greenwich,NULL,Lethbridge Close,22004663,SE13,NULL,NULL,538250,176550,NULL,NULL
50071141,24/04/2014 16:12,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER SHED,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011217,Barnehurst,E09000004,Bexley,Erith,NULL,Colyers Lane,20100348,DA8,NULL,NULL,550750,176950,NULL,NULL
50345141,25/04/2014 09:42,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009387,Woodberry Down,E09000012,Hackney,Stoke Newington,1.00023E+11,Cranwich Road,20900285,N16,533008,187810,533050,187850,51.5734772,-0.082066676
50406141,25/04/2014 12:46,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000452,Sydenham,E09000023,Lewisham,Forest Hill,NULL,Westwood Hill,22004717,SE26,NULL,NULL,534950,171750,NULL,NULL
50722141,26/04/2014 09:02,2014,2014/15,Special Service,1,1,295,295,HORSE STUCK OVER FENCE,Horse,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist  trapped livestock animal,E05007402,Tatsfield and Titsey,E07000215,Tandridge,Surrey,1.00062E+11,Ricketts Hill Road,39500869,TN16,541911,158227,541950,158250,51.30547736,0.034473086
51030141,26/04/2014 20:41,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000596,Grove Green,E09000031,Waltham Forest,Leyton,NULL,Murchison Road,22860300,E10,NULL,NULL,538150,186750,NULL,NULL
51787141,28/04/2014 17:47,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN TREE BY WIRE,Bird,Person (mobile),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000603,Lea Bridge,E09000031,Waltham Forest,Walthamstow,NULL,Boundary Road,NULL,E17,537140,187975,537150,187950,51.5739707,-0.022413507
51805141,28/04/2014 18:35,2014,2014/15,Special Service,1,2,295,590,CAT IN PRECARIOUS POSITION ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000373,Highbury West,E09000019,Islington,Islington,NULL,Coach House Lane,21603055,N5,NULL,NULL,531550,185450,NULL,NULL
52072141,29/04/2014 10:25,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000039,Thames,E09000002,Barking and Dagenham,Barking,100088907,Sovereign Road,19903818,IG11,547119,182963,547150,182950,51.52642226,0.119388468
53006141,30/04/2014 23:11,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO CAT IN PRECARIOUS POSITION,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Heather Close,21610008,N7,NULL,NULL,530850,186450,NULL,NULL
53105141,01/05/2014 08:00,2014,2014/15,Special Service,2,5,295,1475,SMALL ANIMAL RESCUE,Cat,Person (land line),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Ursula Gould Way,NULL,E14,537298,181558,537250,181550,51.51626834,-0.022636724
53212141,01/05/2014 12:55,2014,2014/15,Special Service,1,1,295,295,DOG WITH LEG CAUGHT IN TABLE LEG,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000358,Hounslow Central,E09000018,Hounslow,Heston,NULL,Pears Road,21500870,TW3,NULL,NULL,514750,175850,NULL,NULL
53448141,01/05/2014 20:58,2014,2014/15,Special Service,1,1,295,295,RABBIT TRAPPED IN FIREPLACE,Rabbit,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000455,Abbey,E09000024,Merton,Wimbledon,NULL,Griffiths Road,22102985,SW19,NULL,NULL,525450,170350,NULL,NULL
53659141,02/05/2014 09:31,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH KITTEN STUCK UP A TREE,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000523,Heathfield,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Sheringham Avenue,22401837,TW2,513045,173074,513050,173050,51.44538311,-0.374720192
53660141,02/05/2014 09:31,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT IMPALED ON TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000306,Brooklands,E09000016,Havering,Romford,NULL,Burlington Avenue,NULL,RM7,549968,188360,549950,188350,51.57416647,0.162724676
53864141,02/05/2014 17:40,2014,2014/15,Special Service,1,1,295,295,SMALL BIRD TRAPPED UNDER FLOOR BOARD,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011235,Barkingside,E09000026,Redbridge,Ilford,NULL,Campbell Avenue,22302679,IG6,NULL,NULL,544150,189050,NULL,NULL
53868141,02/05/2014 17:44,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO CAT STUCK ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Heather Close,21610008,N7,NULL,NULL,530850,186450,NULL,NULL
53889141,02/05/2014 18:30,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Fire station,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000219,Eltham South,E09000011,Greenwich,Eltham,2.00001E+11,Eltham High Street,20800526,SE9,543207,174425,543250,174450,51.45070677,0.059574378
54895141,04/05/2014 16:20,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000220,Eltham West,E09000011,Greenwich,Eltham,NULL,Keynsham Road,20800848,SE9,NULL,NULL,541950,174750,NULL,NULL
55314141,05/05/2014 10:30,2014,2014/15,Special Service,1,1,295,295,PUPPYS PAW TRAPPED IN CAGE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000344,Yeading,E09000017,Hillingdon,Southall,NULL,Brazier Crescent,21402892,UB5,NULL,NULL,512650,182350,NULL,NULL
55347141,05/05/2014 11:05,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT TRAPPED IN LOCK UP GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,NULL,Pitsea Street,NULL,E1,535786,181159,535750,181150,51.51304781,-0.044567853
55365141,05/05/2014 11:40,2014,2014/15,Special Service,1,1,295,295,DOG WITH HEAD TRAPPED IN FENCE,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011480,Selsdon & Addington Village,E09000008,Croydon,Addington,1.00021E+11,Heather Way,20501743,CR2,535820,162915,535850,162950,51.34909079,-0.051068101
55371141,05/05/2014 11:54,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN LETTER BOX,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000359,Hounslow Heath,E09000018,Hounslow,Feltham,NULL,Wellington Road South,21501181,TW4,NULL,NULL,513050,174550,NULL,NULL
55883141,06/05/2014 11:58,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Train station - elsewhere,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,Redbridge,Woodford,10034920911,Snakes Lane,22306023,IG8,540952,191777,540950,191750,51.6071942,0.034082045
56219141,07/05/2014 07:37,2014,2014/15,Special Service,1,1,295,295,FOX ON PONTOON,Fox,Person (land line),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,2.00003E+11,Shad Thames,22502224,SE1,533879,179813,533850,179850,51.50140689,-0.072543881
56326141,07/05/2014 13:30,2014,2014/15,Special Service,1,1,295,295,TO ASSIST RSPCA ON SCENE WITH CAT UP TREE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000204,Lower Edmonton,E09000010,Enfield,Edmonton,NULL,Barrowfield Close,20703899,N9,NULL,NULL,534950,193250,NULL,NULL
56904141,08/05/2014 20:27,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Leander Road,20601041,UB5,NULL,NULL,513050,183150,NULL,NULL
58159141,11/05/2014 17:34,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED IN NETTING UNDERNEATH BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000219,Eltham South,E09000011,Greenwich,Eltham,NULL,Court Road,NULL,SE9,542458,173285,542450,173250,51.44065175,0.048344167
58236141,11/05/2014 20:28,2014,2014/15,Special Service,1,1,295,295,CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000264,Town,E09000013,Hammersmith and Fulham,Fulham,34069734,Fulham Road,21000359,SW6,525243,176921,525250,176950,51.47739372,-0.197923181
59076141,13/05/2014 20:54,2014,2014/15,Special Service,1,1,295,295,DANGEROUS ELECTRICS,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000255,Fulham Reach,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Colwith Road,21000230,W6,NULL,NULL,523450,177750,NULL,NULL
59268141,14/05/2014 11:08,2014,2014/15,Special Service,1,1,295,295,ASSIST THE RSPCA WITH A CAT ON A ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000091,Harlesden,E09000005,Brent,Willesden,NULL,Essex Road,20200214,NW10,NULL,NULL,521550,184450,NULL,NULL
59289141,14/05/2014 12:21,2014,2014/15,Special Service,1,2,295,590,ASSIST RSPCA WITH DOG STUCK DOWN DRAIN,Dog,Person (land line),Pipe or drain,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Sheffield Drive,NULL,RM3,555502,192447,555550,192450,51.60938816,0.244325617
59778141,15/05/2014 14:10,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000357,Heston West,E09000018,Hounslow,Heston,NULL,The Croft,21500322,TW5,NULL,NULL,512450,177550,NULL,NULL
60017141,15/05/2014 21:14,2014,2014/15,Special Service,1,1,295,295,CAT STUCK BEHIND WATER HEATER,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009393,Courtfield,E09000020,Kensington and Chelsea,Chelsea,NULL,Stanhope Gardens,21700522,SW7,NULL,NULL,526350,178750,NULL,NULL
60420141,16/05/2014 18:48,2014,2014/15,Special Service,1,1,295,295,BIRD POSSIBLY TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000196,Cockfosters,E09000010,Enfield,Southgate,NULL,Trent Gardens,20701999,N14,NULL,NULL,528950,194950,NULL,NULL
61582141,18/05/2014 16:10,2014,2014/15,Special Service,1,1,295,295,CAT WITH HEAD STUCK IN DRAIN,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,NULL,Devonshire Road,22001409,SE23,NULL,NULL,535450,173150,NULL,NULL
61924141,18/05/2014 23:43,2014,2014/15,Special Service,1,1,295,295,FOX TRAPPED UNDER BEDROOM FURNITURE,Fox,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000413,Surbiton Hill,E09000021,Kingston upon Thames,Surbiton,NULL,Ewell Road,21800386,KT6,NULL,NULL,518550,166950,NULL,NULL
62526141,19/05/2014 21:46,2014,2014/15,Special Service,1,1,295,295,DOG STUCK ON  WINDOW LEDGE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011224,East Wickham,E09000004,Bexley,Plumstead,NULL,Burnell Avenue,20100233,DA16,NULL,NULL,546350,176250,NULL,NULL
62798141,20/05/2014 14:45,2014,2014/15,Special Service,1,3,295,885,HORSE COLLAPSED  UNABLE TO STAND WITHOUT ASSISTANCE,Horse,Person (land line),Other animal boarding/breeding establishment,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000134,Gospel Oak,E09000007,Camden,Kentish Town,5023224,Cressfield Close,20401332,NW5,528359,185349,528350,185350,51.55243802,-0.150011023
63236141,21/05/2014 08:01,2014,2014/15,Special Service,1,2,295,590,FOX TRAPPED IN FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011250,Newbury,E09000026,Redbridge,Ilford,NULL,Leicester Gardens,22302225,IG3,NULL,NULL,545450,187950,NULL,NULL
63244141,21/05/2014 08:36,2014,2014/15,Special Service,1,1,295,295,DISTRESSED DOG STUCK ON ROOF,Dog,Police,Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000616,Graveney,E09000032,Wandsworth,Tooting,NULL,Mitcham Road,22903376,SW17,NULL,NULL,527750,171150,NULL,NULL
63301141,21/05/2014 11:12,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH BIRD STUCK IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009335,Weavers,E09000030,Tower Hamlets,Bethnal Green,NULL,Gibraltar Walk,22702188,E2,NULL,NULL,533950,182550,NULL,NULL
63340141,21/05/2014 13:32,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA TO GAIN ENTRY,Unknown - Domestic Animal Or Pet,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000482,East Ham South,E09000025,Newham,East Ham,NULL,Charlemont Road,22200458,E6,NULL,NULL,543150,182750,NULL,NULL
63613141,21/05/2014 22:08,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN TURNSTILE,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000474,Wimbledon Park,E09000024,Merton,Wandsworth,NULL,Wimbledon Park,NULL,SW19,524785,172469,524750,172450,51.43748313,-0.20608373
63914141,22/05/2014 16:02,2014,2014/15,Special Service,1,1,295,295,INJURED SWAN IN  TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000515,Wanstead,E09000026,Redbridge,Leytonstone,NULL,Northumberland Avenue,NULL,E12,541004,187082,541050,187050,51.5649936,0.032948869
64024141,22/05/2014 19:34,2014,2014/15,Special Service,1,2,295,590,PUPPY TRAPPED DOWN MANHOLE,Dog,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000344,Yeading,E09000017,Hillingdon,Southall,NULL,Friar Road,NULL,UB4,511678,182176,511650,182150,51.52746325,-0.391506455
64111141,23/05/2014 00:17,2014,2014/15,Special Service,1,1,295,295,CAT STUCK UNDER FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000053,Golders Green,E09000003,Barnet,Finchley,NULL,Courtleigh Gardens,20010600,NW11,NULL,NULL,524250,189050,NULL,NULL
64181141,23/05/2014 07:16,2014,2014/15,Special Service,1,1,295,295,FOX TRAPPED IN FOOTBALL NETTING,Fox,Other FRS,House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000561,Nonsuch,E09000029,Sutton,Sutton,NULL,The Mount,22603393,KT4,NULL,NULL,522850,164650,NULL,NULL
64546141,23/05/2014 21:22,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER DECK,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,NULL,Honor Oak Road,22001574,SE23,NULL,NULL,535350,173950,NULL,NULL
64779141,24/05/2014 11:54,2014,2014/15,Special Service,1,1,295,295,BIRD STUCK BETWEEN TWO WALLS,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009367,Brownswood,E09000012,Hackney,Holloway,NULL,Finsbury Park Road,20900408,N4,NULL,NULL,531950,186550,NULL,NULL
65192141,25/05/2014 10:47,2014,2014/15,Special Service,1,1,295,295,DOG BELEIVED TRAPPED,Dog,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist trapped domestic animal,E05000029,Chadwell Heath,E09000002,Barking and Dagenham,Ilford,NULL,Billet Road,NULL,RM6,547482,189693,547450,189650,51.58679837,0.127436576
65225141,25/05/2014 12:43,2014,2014/15,Special Service,1,1,295,295,CAT  TRAPPED IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000590,Cann Hall,E09000031,Waltham Forest,Leytonstone,1.00023E+11,Ferndale Road,22836050,E11,539545,186830,539550,186850,51.56309206,0.011813872
65303141,25/05/2014 13:45,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER SHED,Dog,Person (mobile),Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Connaught Road,22401387,TW11,514761,171386,514750,171350,51.42986683,-0.350585392
65431141,25/05/2014 19:57,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Church/Chapel,Non Residential,Other animal assistance,Assist trapped wild animal,E05000348,Chiswick Homefields,E09000018,Hounslow,Chiswick,2.00004E+11,Chiswick Mall,21500250,W4,521612,177811,521650,177850,51.48618378,-0.249876237
65690141,26/05/2014 10:01,2014,2014/15,Special Service,1,1,295,295,TO ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000445,Grove Park,E09000023,Lewisham,Eltham,NULL,Clayhill Crescent,22003801,SE9,NULL,NULL,541750,171850,NULL,NULL
65715141,26/05/2014 11:36,2014,2014/15,Special Service,1,1,295,295,BIRD STUCK IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009369,Clissold,E09000012,Hackney,Stoke Newington,NULL,Lordship Park,20900636,N16,NULL,NULL,532750,186850,NULL,NULL
65722141,26/05/2014 11:57,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN PITT,Dog,Police,Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000049,East Finchley,E09000003,Barnet,Finchley,NULL,Brighton Road,NULL,N2,526274,190038,526250,190050,51.59504762,-0.17838365
65994141,27/05/2014 05:52,2014,2014/15,Special Service,NULL,NULL,295,NULL,CAT STUCK ON ROOF TRAPPED BEHIND LOCKED DOOR,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000433,Thornton,E09000022,Lambeth,Tooting,NULL,Poynders Gardens,21902132,SW4,NULL,NULL,529350,173950,NULL,NULL
66115141,27/05/2014 13:51,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED IN WHEEL ARCH,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Todds Walk,NULL,N7,530688,186584,530650,186550,51.56300236,-0.115979599
66264141,27/05/2014 18:53,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON ROOF,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Stanlake Road,21000774,W12,NULL,NULL,523050,180250,NULL,NULL
66267141,27/05/2014 19:00,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN A DRAIN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009330,St. Katharine's & Wapping,E09000030,Tower Hamlets,Shadwell,NULL,Telfords Yard,22701933,E1W,NULL,NULL,534350,180650,NULL,NULL
66303141,27/05/2014 20:07,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN DRAIN HOPPER OF BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009330,St. Katharine's & Wapping,E09000030,Tower Hamlets,Shadwell,NULL,Telfords Yard,22701933,E1W,NULL,NULL,534350,180650,NULL,NULL
66513141,28/05/2014 10:09,2014,2014/15,Special Service,1,1,295,295,SMALL BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,NULL,Thornbury Road,21501119,TW7,NULL,NULL,515050,176550,NULL,NULL
66595141,28/05/2014 13:52,2014,2014/15,Special Service,1,2,295,590,CAT STUCK ON TOP OF WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000134,Gospel Oak,E09000007,Camden,Kentish Town,NULL,Byron Mews,20401428,NW3,NULL,NULL,527450,185550,NULL,NULL
67041141,29/05/2014 12:43,2014,2014/15,Special Service,1,2,295,590,CAT TRAPPED IN WALL CAVITY,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,10010202712,Powis Street,20801201,SE18,543660,178936,543650,178950,51.49112655,0.067921758
67462141,30/05/2014 10:01,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000060,Underhill,E09000003,Barnet,Barnet,NULL,Bedford Avenue,20002880,EN5,NULL,NULL,524750,196150,NULL,NULL
67639141,30/05/2014 19:00,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,1.00022E+11,Canonbie Road,22001315,SE23,535094,173757,535050,173750,51.44669552,-0.057363573
67671141,30/05/2014 20:09,2014,2014/15,Special Service,1,1,295,295,BIRDS TRAPPED IN NETTING,Bird,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,12142215,Mount Park Road,20601211,W5,517728,181725,517750,181750,51.5221824,-0.304483129
67788141,31/05/2014 00:29,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,NULL,Drakefell Road,22000345,SE14,NULL,NULL,535550,176050,NULL,NULL
67935141,31/05/2014 12:10,2014,2014/15,Special Service,1,1,295,295,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,207192527,Watermill Lane,20704891,N18,533190,192296,533150,192250,51.61374664,-0.077741512
67966141,31/05/2014 13:29,2014,2014/15,Special Service,1,1,295,295,CAT STUCK UP TREE - ASSIST RSPCA OFFICER,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000058,Oakleigh,E09000003,Barnet,Barnet,NULL,Greenhill Park,20019420,EN5,NULL,NULL,525750,195750,NULL,NULL
68446141,01/06/2014 12:45,2014,2014/15,Special Service,1,2,295,590,CROW STUCK ON ROOF,Bird,Other FRS,Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000406,Coombe Hill,E09000021,Kingston upon Thames,Kingston,NULL,Kingsnympton Park,21800556,KT2,NULL,NULL,519750,170650,NULL,NULL
68470141,01/06/2014 13:44,2014,2014/15,Special Service,1,1,295,295,KITTEN  UNDER FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000445,Grove Park,E09000023,Lewisham,Bromley,NULL,Amblecote Road,22003110,SE12,NULL,NULL,540850,172050,NULL,NULL
68546141,01/06/2014 16:19,2014,2014/15,Special Service,1,1,295,295,KITTEN STUCK IN HOLE UNDER FLOOR,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009379,King's Park,E09000012,Hackney,Homerton,NULL,Mandeville Street,20900651,E5,NULL,NULL,536050,185950,NULL,NULL
68927141,02/06/2014 13:53,2014,2014/15,Special Service,1,1,295,295,CAT STRANDED ON WINDOW LEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000643,Regent's Park,E09000033,Westminster,Paddington,NULL,Prince Albert Road,8400534,NW8,NULL,NULL,527250,183050,NULL,NULL
69194141,02/06/2014 23:58,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE - INJURED CAT TRAPPED ON ROOF,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000205,Palmers Green,E09000010,Enfield,Edmonton,NULL,Felstead Close,20706992,N13,NULL,NULL,531050,192250,NULL,NULL
69630141,04/06/2014 01:59,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000301,Roxbourne,E09000015,Harrow,Northolt,NULL,Alexandra Avenue,NULL,HA2,513328,186361,513350,186350,51.56474892,-0.366384682
69742141,04/06/2014 10:50,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000080,Northumberland Heath,E09000004,Bexley,Bexley,NULL,Brook Street,NULL,DA8,550120,177019,550150,177050,51.47222529,0.1600886
69923141,04/06/2014 18:51,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000308,Elm Park,E09000016,Havering,Hornchurch,NULL,Bader Way,21300706,RM13,NULL,NULL,552450,184650,NULL,NULL
69953141,04/06/2014 20:09,2014,2014/15,Special Service,1,1,295,295,FOX STUCK  IN BASEMENT AREA,Fox,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009398,Norland,E09000020,Kensington and Chelsea,North Kensington,NULL,Lansdowne Crescent,21700922,W11,NULL,NULL,524550,180750,NULL,NULL
70683141,06/06/2014 13:54,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05009375,Haggerston,E09000012,Hackney,Shoreditch,10008229917,Moye Close,20901800,E2,534358,183463,534350,183450,51.53409345,-0.064255952
71132141,07/06/2014 08:55,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER RLY BRIDGE,Cat,Person (land line),Railway building - other,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000423,Herne Hill,E09000022,Lambeth,Brixton,NULL,Padfield Road,NULL,SE5,532051,175846,532050,175850,51.46618638,-0.100346943
71404141,07/06/2014 19:29,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND WALL IN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Mingard Walk,21606902,N7,NULL,NULL,530850,186550,NULL,NULL
71620141,08/06/2014 07:13,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED IN TREE,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000170,Acton Central,E09000009,Ealing,Acton,12103714,Northfields Road,20601272,W3,520149,181856,520150,181850,51.52285072,-0.269559653
72214141,09/06/2014 10:11,2014,2014/15,Special Service,2,4,295,1180,GOAT TRAPPED BELOW GROUND LEVEL ON LEDGE,Goat,Person (land line),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000203,Jubilee,E09000010,Enfield,Chingford,NULL,Lea Valley Road,NULL,E4,537359,194968,537350,194950,51.636755,-0.016519754
72239141,09/06/2014 10:57,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN VENT ON ROOF,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000039,Thames,E09000002,Barking and Dagenham,Barking,NULL,Galleons Drive,19903950,IG11,NULL,NULL,546450,182550,NULL,NULL
72369141,09/06/2014 17:25,2014,2014/15,Special Service,1,1,295,295,INJURED CAT TRAPPED IN BUSES,Cat,Person (land line),Canal/riverbank vegetation,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000221,Glyndon,E09000011,Greenwich,Plumstead,10010212913,Erebus Drive,20802018,SE28,544497,179633,544450,179650,51.4971764,0.080254867
72418141,09/06/2014 18:55,2014,2014/15,Special Service,1,1,295,295,ANIMAL TRAPPED BETWEEN WALLS,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000521,Hampton North,E09000027,Richmond upon Thames,Twickenham,NULL,Queenswood Avenue,22401772,TW12,NULL,NULL,513750,170750,NULL,NULL
73017141,10/06/2014 20:16,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND BOILER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Aldenham Street,20400793,NW1,NULL,NULL,529550,183150,NULL,NULL
74797141,13/06/2014 20:07,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN DRAINPIPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000136,Haverstock,E09000007,Camden,Kentish Town,NULL,Harmood Street,20400488,NW1,NULL,NULL,528550,184450,NULL,NULL
74802141,13/06/2014 20:11,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED IN FENCE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009322,Bromley South,E09000030,Tower Hamlets,Poplar,NULL,Gale Street,22700525,E3,NULL,NULL,537350,181850,NULL,NULL
75043141,14/06/2014 08:29,2014,2014/15,Special Service,1,1,295,295,FLEDGLING GULL TRAPPED IN GUTTER,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000633,Churchill,E09000033,Westminster,Chelsea,NULL,Ormonde Place,8400862,SW1W,NULL,NULL,528250,178550,NULL,NULL
75395141,14/06/2014 21:21,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN LARGE HOLE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000427,Prince's,E09000022,Lambeth,Lambeth,NULL,Kennington Lane,21900787,SE11,NULL,NULL,531250,178350,NULL,NULL
75401141,14/06/2014 21:33,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN CONSERVATORY AND FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000445,Grove Park,E09000023,Lewisham,Eltham,NULL,Mirror Path,22006110,SE9,NULL,NULL,541450,172150,NULL,NULL
75729141,15/06/2014 13:58,2014,2014/15,Special Service,1,1,295,295,PIDGEON TRAPPED IN NETTING ON BALCONY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011096,Camberwell Green,E09000028,Southwark,Brixton,NULL,Denmark Road,22500754,SE5,NULL,NULL,532250,176350,NULL,NULL
75738141,15/06/2014 14:27,2014,2014/15,Special Service,1,1,295,295,MAGPIE TRAPPED IN TREE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000186,Norwood Green,E09000009,Ealing,Southall,NULL,Tentelow Lane,20601705,UB2,NULL,NULL,514050,179550,NULL,NULL
75839141,15/06/2014 18:05,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND FIREPLACE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009368,Cazenove,E09000012,Hackney,Stoke Newington,NULL,Rossendale Street,20900869,E5,NULL,NULL,534550,186650,NULL,NULL
75877141,15/06/2014 19:20,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND PIPES IN AIRING CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009328,Poplar,E09000030,Tower Hamlets,Poplar,NULL,Poplar High Street,22700960,E14,NULL,NULL,537550,180750,NULL,NULL
75906141,15/06/2014 20:35,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000458,Cricket Green,E09000024,Merton,Mitcham,NULL,London Road,22103995,CR4,NULL,NULL,527550,168550,NULL,NULL
75983141,15/06/2014 23:14,2014,2014/15,Special Service,1,2,295,590,CAT STUCK BETWEEN WALLS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,NULL,James Voller Way,22702472,E1,NULL,NULL,535050,181050,NULL,NULL
76012141,16/06/2014 01:00,2014,2014/15,Special Service,1,1,295,295,KITTEN STUCK UNDER STAIR LIFT,Cat,Ambulance,House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011108,Nunhead & Queen's Road,E09000028,Southwark,New Cross,NULL,Hathorne Close,22501225,SE15,NULL,NULL,535050,176350,NULL,NULL
76059141,16/06/2014 06:37,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED BETWEEN FENCE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,NULL,James Voller Way,22702472,E1,NULL,NULL,535050,181050,NULL,NULL
76380141,16/06/2014 19:30,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN NETTING,Bird,Person (land line),Law Courts,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011463,Addiscombe West,E09000008,Croydon,Croydon,1.00023E+11,Altyre Road,20500623,CR9,532890,165536,532850,165550,51.37333743,-0.092138081
76465141,16/06/2014 21:38,2014,2014/15,Special Service,1,1,295,295,DOG WITH REAR PAWS CAUGHT IN WROUGHT IRON GARDEN FURNITURE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Geraint Road,22003348,BR1,NULL,NULL,540450,171750,NULL,NULL
76497141,16/06/2014 23:28,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN SCHOOL GROUNDS,Dog,Person (land line),Secondary school,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011217,Barnehurst,E09000004,Bexley,Erith,1.00023E+11,Colyers Lane,20100348,DA8,550938,176943,550950,176950,51.47132499,0.171824553
76500141,17/06/2014 00:15,2014,2014/15,Special Service,1,1,295,295,FOX IN BEDROOM,Fox,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011472,Norbury & Pollards Hill,E09000008,Croydon,Norbury,NULL,London Road,20502653,SW16,NULL,NULL,530850,169350,NULL,NULL
77113141,18/06/2014 05:46,2014,2014/15,Special Service,1,1,295,295,FOX TRAPPED BETWEEN CUPBOARD AND WALL,Fox,Police,House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011235,Barkingside,E09000026,Redbridge,Ilford,NULL,Gantshill Crescent,22302867,IG2,NULL,NULL,543350,188850,NULL,NULL
77464141,18/06/2014 21:26,2014,2014/15,Special Service,1,1,295,295,KITTEN WITH HEAD STUCK HOLE UNDER TV SET,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000216,Charlton,E09000011,Greenwich,East Greenwich,NULL,Highcombe,20800768,SE7,NULL,NULL,540850,177750,NULL,NULL
77642141,19/06/2014 09:29,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE FROM WATER,Unknown - Domestic Animal Or Pet,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Moree Way,NULL,N18,534017,192670,534050,192650,51.61691123,-0.065661821
77706141,19/06/2014 11:23,2014,2014/15,Special Service,1,2,295,590,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,1.00022E+11,Eliot Bank,22002391,SE23,534951,173026,534950,173050,51.4401604,-0.059698493
77708141,19/06/2014 11:25,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000439,Brockley,E09000023,Lewisham,Greenwich,NULL,Ship Street,22004556,SE8,NULL,NULL,537250,176650,NULL,NULL
77839141,19/06/2014 15:47,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED BEHIND AIR VENTS,Bird,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped wild animal,E05009385,Stoke Newington,E09000012,Hackney,Stoke Newington,NULL,Lawrence Buildings,NULL,N16,533698,186435,533650,186450,51.56095763,-0.072638167
77903141,19/06/2014 18:33,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Menotti Street,22700810,E2,NULL,NULL,534450,182350,NULL,NULL
78125141,20/06/2014 07:44,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED BY WIRE IN A TREE,Bird,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000565,Sutton North,E09000029,Sutton,Sutton,5870053300,All Saints Road,22605398,SM1,526047,165284,526050,165250,51.37263073,-0.190486852
78132141,20/06/2014 08:35,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000458,Cricket Green,E09000024,Merton,Mitcham,NULL,London Road,22103995,CR4,NULL,NULL,527250,168250,NULL,NULL
78221141,20/06/2014 13:32,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Pigeon,Person (land line),Other transport building,Non Residential,Other animal assistance,Assist trapped wild animal,E05011250,Newbury,E09000026,Redbridge,Ilford,10090506655,Ley Street,22303015,IG2,544465,187740,544450,187750,51.57003044,0.083116746
78432141,20/06/2014 20:06,2014,2014/15,Special Service,1,1,295,295,BIRD STUCK UNDER NETTING,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,10033572395,Jermyn Street,8400480,SW1Y,529539,180533,529550,180550,51.50888791,-0.134771581
78674141,21/06/2014 05:43,2014,2014/15,Special Service,1,1,295,295,FOX TRAPPED NEAR TO RIVER BANK,Fox,Person (land line),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000552,Surrey Docks,E09000028,Southwark,Dockhead,NULL,Rotherhithe Street,NULL,SE16,535687,180267,535650,180250,51.50505578,-0.046336812
79233141,22/06/2014 02:29,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED BETWEEN TWO SHEDS,Dog,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Woodford,1.00023E+11,Hale End Road,22842250,IG8,538585,191023,538550,191050,51.60100585,-0.000374335
79378141,22/06/2014 09:45,2014,2014/15,Special Service,1,1,295,295,CROW STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000050,Edgware,E09000003,Barnet,Mill Hill,NULL,Meadfield,20029120,HA8,NULL,NULL,519750,193850,NULL,NULL
79396141,22/06/2014 10:20,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN OPENING OF WINDOW,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000492,Stratford and New Town,E09000025,Newham,Stratford,NULL,Whitear Walk,22201696,E15,NULL,NULL,538850,184950,NULL,NULL
79564141,22/06/2014 15:22,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN RESTAURANT DUCTING,Cat,Person (land line),Restaurant/cafe,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000428,St. Leonard's,E09000022,Lambeth,Tooting,1.00023E+11,Streatham High Road,21901326,SW16,530180,172485,530150,172450,51.43641453,-0.128501949
79639141,22/06/2014 17:16,2014,2014/15,Special Service,1,1,295,295,PIGEON IMPALED ON TOP OF A LAMPOST,Bird,Person (land line),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,10033544649,The Piazza,8401623,WC2E,530291,180917,530250,180950,51.51216614,-0.123799838
79759141,22/06/2014 20:15,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000215,Blackheath Westcombe,E09000011,Greenwich,East Greenwich,NULL,Westcombe Hill,20801590,SE3,NULL,NULL,540350,177850,NULL,NULL
79795141,22/06/2014 21:14,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN PIPEWORK,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Kingsland Road,22200779,E13,NULL,NULL,541250,182650,NULL,NULL
80601141,24/06/2014 08:48,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN WIRING,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000413,Surbiton Hill,E09000021,Kingston upon Thames,Surbiton,NULL,Glenbuck Road,21800446,KT6,NULL,NULL,518150,167250,NULL,NULL
80639141,24/06/2014 11:24,2014,2014/15,Special Service,2,3,295,885,SMALL ANIMAL RESCUE,Cat,Police,Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000558,Carshalton Central,E09000029,Sutton,Sutton,NULL,Orchard Way,NULL,SM1,526801,164890,526850,164850,51.36892207,-0.179800571
81770141,26/06/2014 11:24,2014,2014/15,Special Service,NULL,NULL,295,NULL,HORSE FALLEN INTO DITCH,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000155,Heathfield,E09000008,Croydon,Addington,NULL,Kent Gate Way,NULL,CR0,537037,163850,537050,163850,51.3572012,-0.033244088
81820141,26/06/2014 12:42,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000435,Tulse Hill,E09000022,Lambeth,Brixton,NULL,Endymion Road,21900523,SW2,NULL,NULL,530750,174250,NULL,NULL
81823141,26/06/2014 12:46,2014,2014/15,Special Service,1,2,295,590,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Other FRS,Cycle path/public footpath/bridleway,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000075,Erith,E09000004,Bexley,Erith,NULL,Wharfside Close,NULL,DA8,551605,178082,551650,178050,51.48138074,0.181908914
82345141,27/06/2014 15:08,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000457,Colliers Wood,E09000024,Merton,Mitcham,NULL,Arnold Road,22100204,SW17,NULL,NULL,527750,170450,NULL,NULL
82992141,28/06/2014 15:13,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED AT BACK OF KITCHEN CABINETS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000336,Northwood Hills,E09000017,Hillingdon,Ruislip,NULL,Tolcarne Drive,21401999,HA5,NULL,NULL,510950,189650,NULL,NULL
83131141,28/06/2014 19:44,2014,2014/15,Special Service,1,1,295,295,DOG STUCK UNDER RADIATOR,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000257,Munster,E09000013,Hammersmith and Fulham,Fulham,NULL,Lambrook Terrace,21000487,SW6,NULL,NULL,524050,176850,NULL,NULL
83138141,28/06/2014 20:06,2014,2014/15,Special Service,1,1,295,295,DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000209,Southgate Green,E09000010,Enfield,Southgate,NULL,Aldermans Hill,NULL,N13,530706,192854,530750,192850,51.61934327,-0.113387955
83401141,29/06/2014 11:39,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN TREE,Bird,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000039,Thames,E09000002,Barking and Dagenham,Barking,100043506,Julia Gardens,19900507,IG11,547817,183476,547850,183450,51.53084946,0.129658062
83522141,29/06/2014 16:41,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000051,Finchley Church End,E09000003,Barnet,Finchley,NULL,Station Road,NULL,N3,NULL,NULL,525350,190550,NULL,NULL
83876141,30/06/2014 14:17,2014,2014/15,Special Service,1,1,295,295,DEER TRAPPED IN RAILINGS,Deer,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000199,Enfield Lock,E09000010,Enfield,Enfield,NULL,Ramney Drive,NULL,EN3,536472,199050,536450,199050,51.67365251,-0.027736919
83984141,30/06/2014 17:52,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN BIKE SHED,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Bridgeway Street,20400795,NW1,NULL,NULL,529450,183150,NULL,NULL
84292141,01/07/2014 09:44,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO ANIMAL TRAPPED IN BATH,Unknown - Domestic Animal Or Pet,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009385,Stoke Newington,E09000012,Hackney,Stoke Newington,NULL,Northwold Road,20900753,N16,NULL,NULL,533650,186650,NULL,NULL
84382141,01/07/2014 14:08,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011223,Crook Log,E09000004,Bexley,Bexley,NULL,Dunwich Road,20100468,DA7,NULL,NULL,548650,176850,NULL,NULL
84389141,01/07/2014 14:11,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON WINDOWLEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000370,Clerkenwell,E09000019,Islington,Shoreditch,NULL,Farringdon Lane,21604841,EC1R,NULL,NULL,531450,182250,NULL,NULL
84500141,01/07/2014 17:18,2014,2014/15,Special Service,1,1,295,295,ANIMAL TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009336,Whitechapel,E09000030,Tower Hamlets,Whitechapel,NULL,Settles Street,22701081,E1,NULL,NULL,534350,181550,NULL,NULL
84526141,01/07/2014 18:33,2014,2014/15,Special Service,1,1,295,295,CAT LOCKED IN GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000185,Northolt West End,E09000009,Ealing,Southall,NULL,Brett Close,NULL,UB5,511510,182875,511550,182850,51.53377897,-0.393706202
84765141,02/07/2014 08:11,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND WASHING MACHINE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011245,Hainault,E09000026,Redbridge,Hainault,NULL,Covert Road,22304561,IG6,NULL,NULL,545650,191950,NULL,NULL
84847141,02/07/2014 12:33,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN SATALITE DISH,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000346,Bedfont,E09000018,Hounslow,Feltham,NULL,Sandy Drive,21501400,TW14,NULL,NULL,509250,173250,NULL,NULL
85114141,02/07/2014 20:51,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH TRAPPED BIRD,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009326,Limehouse,E09000030,Tower Hamlets,Poplar,NULL,Island Row,22700672,E14,NULL,NULL,536550,180950,NULL,NULL
85380141,03/07/2014 09:51,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN FENCE AND GARAGE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000220,Eltham West,E09000011,Greenwich,Lee Green,NULL,Bayfield Road,20800129,SE9,NULL,NULL,541550,175150,NULL,NULL
85425141,03/07/2014 11:43,2014,2014/15,Special Service,1,1,295,295,FOX TRAPPED IN FRONT BASEMENT WELL OF HOUSE,Fox,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000379,St. Mary's,E09000019,Islington,Islington,NULL,Cross Street,21604640,N1,NULL,NULL,531850,183950,NULL,NULL
85630141,03/07/2014 17:35,2014,2014/15,Special Service,NULL,NULL,295,NULL,DOG ON WINDOW LEDGE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000201,Haselbury,E09000010,Enfield,Edmonton,NULL,Tillotson Road,20704845,N9,NULL,NULL,533550,193850,NULL,NULL
85702141,03/07/2014 19:01,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN BUILDING,Cat,Person (mobile),Doctors surgery,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000060,Underhill,E09000003,Barnet,Barnet,200019907,Cattley Close,20007210,EN5,524316,196398,524350,196350,51.65263936,-0.204380872
85753141,03/07/2014 20:13,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN WATER,Cat,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000362,Isleworth,E09000018,Hounslow,Heston,NULL,Shire Horse Way,NULL,TW7,515951,175885,515950,175850,51.47006048,-0.332002258
86032141,04/07/2014 09:53,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN FENCING,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,1.00021E+11,Broadwater Road,21104283,N17,533408,190201,533450,190250,51.59486886,-0.075391032
86868141,05/07/2014 20:32,2014,2014/15,Special Service,1,1,295,295,PARROT TRAPPED IN SPIKED BALCONY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000634,Church Street,E09000033,Westminster,Paddington,NULL,Lisson Grove,8400993,NW1,NULL,NULL,527350,181850,NULL,NULL
87145141,06/07/2014 10:41,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED ON AERIAL,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Merton Road,22903296,SW18,NULL,NULL,525350,172950,NULL,NULL
87491141,07/07/2014 01:46,2014,2014/15,Special Service,1,1,295,295,CAT WITH HEAD STUCK IN RAILINGS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009389,Brompton & Hans Town,E09000020,Kensington and Chelsea,Chelsea,NULL,Wiltshire Close,21701166,SW3,NULL,NULL,527550,178650,NULL,NULL
87609141,07/07/2014 12:18,2014,2014/15,Special Service,1,2,295,590,ASSIST RSPCA WITH DUCKLING STUCK IN DRAIN,Bird,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000454,Whitefoot,E09000023,Lewisham,Lewisham,NULL,Conisborough Crescent,NULL,SE6,538391,172066,538350,172050,51.43070439,-0.010610221
87671141,07/07/2014 14:37,2014,2014/15,Special Service,1,2,295,590,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000257,Munster,E09000013,Hammersmith and Fulham,Fulham,NULL,Salisbury Mews,21001228,SW6,NULL,NULL,524650,177250,NULL,NULL
87829141,07/07/2014 19:27,2014,2014/15,Special Service,1,1,295,295,BIRD STUCK BEHIND FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000414,Tolworth and Hook Rise,E09000021,Kingston upon Thames,Surbiton,NULL,Brook Road,21800175,KT6,NULL,NULL,518150,165650,NULL,NULL
87951141,08/07/2014 00:51,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN METAL GATE,Cat,Police,House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011478,Sanderstead,E09000008,Croydon,Addington,NULL,Norfolk Avenue,20502235,CR2,NULL,NULL,534450,162250,NULL,NULL
88059141,08/07/2014 08:08,2014,2014/15,Special Service,1,1,295,295,CAT  TRAPPED IN TILTING WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009375,Haggerston,E09000012,Hackney,Bethnal Green,NULL,Pownall Road,20900817,E8,NULL,NULL,534350,183650,NULL,NULL
88255141,08/07/2014 16:06,2014,2014/15,Special Service,1,1,295,295,DOG WITH HEAD STUCK IN SOFA,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000412,St. Mark's,E09000021,Kingston upon Thames,Surbiton,NULL,Avenue Elmers,21800091,KT6,NULL,NULL,518350,167850,NULL,NULL
89693141,11/07/2014 11:14,2014,2014/15,Special Service,1,1,295,295,TO ASSIST RSPCA WITH CAT ON WINDOW LEDGE,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000432,Streatham Wells,E09000022,Lambeth,Tooting,1.00023E+11,Streatham High Road,21901326,SW16,530158,171902,530150,171950,51.43118022,-0.129032296
89808141,11/07/2014 16:47,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN WINDOW SHUTTER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,NULL,Rothbury Gardens,21500965,TW7,NULL,NULL,516350,177250,NULL,NULL
89828141,11/07/2014 17:35,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED  BETWEEN WALL AND KITCHEN UNITS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,NULL,Rosendale Road,21901178,SE21,NULL,NULL,532650,172250,NULL,NULL
90330141,12/07/2014 16:35,2014,2014/15,Special Service,1,1,295,295,CAT STUCK UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000319,Romford Town,E09000016,Havering,Hornchurch,NULL,Melton Gardens,21301418,RM1,NULL,NULL,551650,187750,NULL,NULL
90335141,12/07/2014 16:44,2014,2014/15,Special Service,1,1,295,295,RABBIT  STUCK BETWEEN FENCE  AND WALL,Rabbit,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05011221,Blendon & Penhill,E09000004,Bexley,Bexley,NULL,Thistlefield Close,20101820,DA5,NULL,NULL,547950,173250,NULL,NULL
90768141,13/07/2014 08:54,2014,2014/15,Special Service,1,1,295,295,SQUIRREL STUCK BETWEEN SCAFFOLD BOARDS,Squirrel,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05011108,Nunhead & Queen's Road,E09000028,Southwark,New Cross,NULL,Pomeroy Street,22503094,SE14,NULL,NULL,535350,176950,NULL,NULL
91409141,14/07/2014 16:23,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009336,Whitechapel,E09000030,Tower Hamlets,Whitechapel,NULL,Varden Street,22701260,E1,NULL,NULL,534750,181450,NULL,NULL
91410141,14/07/2014 16:25,2014,2014/15,Special Service,1,1,295,295,CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000120,Kelsey and Eden Park,E09000006,Bromley,Beckenham,10013152190,Creswell Drive,20304074,BR3,537774,167752,537750,167750,51.39208764,-0.021154458
91619141,15/07/2014 00:12,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Blackheath Road,20800175,SE10,NULL,NULL,537750,176750,NULL,NULL
91839141,15/07/2014 12:02,2014,2014/15,Special Service,1,1,295,295,PIGEON STUCK IN BOILER,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Cavendish Road,20704019,N18,NULL,NULL,534950,192450,NULL,NULL
92090141,15/07/2014 19:03,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN WIRE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000094,Kilburn,E09000005,Brent,West Hampstead,202094782,Glengall Road,20201848,NW6,524883,183790,524850,183750,51.53920633,-0.200673086
92117141,15/07/2014 19:49,2014,2014/15,Special Service,1,1,295,295,CAT WITH LEG TRAPPED IN WINDOW,Cat,Police,Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000638,Lancaster Gate,E09000033,Westminster,Paddington,NULL,Cleveland Square,8400938,W2,NULL,NULL,526250,181050,NULL,NULL
92168141,15/07/2014 21:32,2014,2014/15,Special Service,1,1,295,295,INJURED CAT STUCK UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000133,Frognal and Fitzjohns,E09000007,Camden,West Hampstead,5066027,Finchley Road,20400045,NW3,525235,185773,525250,185750,51.55694992,-0.194894915
92367141,16/07/2014 09:47,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER FLOOR IN PIPE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000273,Hornsey,E09000014,Haringey,Hornsey,NULL,Rathcoole Gardens,21103786,N8,NULL,NULL,530850,188750,NULL,NULL
92724141,16/07/2014 20:47,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND WATER TANK,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011485,South Norwood,E09000008,Croydon,Woodside,NULL,Albert Road,20500610,SE25,NULL,NULL,534350,168450,NULL,NULL
92750141,16/07/2014 21:18,2014,2014/15,Special Service,1,1,295,295,BIRD STUCK IN EAVES OF HOUSE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000327,Cavendish,E09000017,Hillingdon,Ruislip,NULL,Hoylake Gardens,21401024,HA4,NULL,NULL,510950,187350,NULL,NULL
92896141,17/07/2014 01:31,2014,2014/15,Special Service,1,1,295,295,CAT FALLEN INJURED AND TRAPPED ON LEDGE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009403,Royal Hospital,E09000020,Kensington and Chelsea,Chelsea,NULL,Cliveden Place,21700106,SW1W,NULL,NULL,528150,178850,NULL,NULL
93292141,17/07/2014 19:02,2014,2014/15,Special Service,1,1,295,295,KITTEN STUCK UNDER FLOORBOARDS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000273,Hornsey,E09000014,Haringey,Hornsey,NULL,Rathcoole Gardens,21103786,N8,NULL,NULL,530850,188750,NULL,NULL
94149141,18/07/2014 21:41,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Alston Road,20703857,N18,NULL,NULL,534750,192050,NULL,NULL
94156141,18/07/2014 21:53,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH PIDGEON TRAPPED IN NETTING,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000087,Brondesbury Park,E09000005,Brent,West Hampstead,NULL,Kilburn High Road,NULL,NW6,524759,184460,524750,184450,51.54525513,-0.202222704
94249141,19/07/2014 02:20,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000623,Shaftesbury,E09000032,Wandsworth,Battersea,NULL,Eccles Road,22901397,SW11,NULL,NULL,527650,175350,NULL,NULL
94395141,19/07/2014 10:46,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000623,Shaftesbury,E09000032,Wandsworth,Battersea,NULL,Eccles Road,22901397,SW11,NULL,NULL,527650,175350,NULL,NULL
94491141,19/07/2014 14:21,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN SHED,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011477,Purley Oaks & Riddlesdown,E09000008,Croydon,Purley,1.00021E+11,Kingsdown Avenue,20502175,CR2,531977,162485,531950,162450,51.3461313,-0.106378144
94680141,19/07/2014 20:30,2014,2014/15,Special Service,1,1,295,295,CAT STUCK BEHIND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Whyteville Road,22207952,E7,NULL,NULL,540850,184750,NULL,NULL
94913141,20/07/2014 11:44,2014,2014/15,Special Service,1,1,295,295,BIRD WITH WING TRAPPED IN GRATE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000570,Wallington South,E09000029,Sutton,Wallington,NULL,Blenheim Gardens,22602125,SM6,NULL,NULL,529250,163450,NULL,NULL
95200141,20/07/2014 20:29,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN FISHING WIRE,Bird,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05009369,Clissold,E09000012,Hackney,Stoke Newington,NULL,Howard Road,NULL,N16,533175,185514,533150,185550,51.5528049,-0.080526559
95423141,21/07/2014 09:32,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000436,Vassall,E09000022,Lambeth,Brixton,NULL,Hackford Road,NULL,SW9,530945,176760,530950,176750,51.4746571,-0.115921658
95724141,21/07/2014 21:26,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO BIRD TRAPPED X WIRE IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000552,Surrey Docks,E09000028,Southwark,Deptford,NULL,South Sea Street,NULL,SE16,536580,179151,536550,179150,51.49481203,-0.033909999
96253141,22/07/2014 21:45,2014,2014/15,Special Service,1,2,295,590,BIRD TRAPPED IN BRANCH OF TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000346,Bedfont,E09000018,Hounslow,Feltham,NULL,Staines Road,NULL,TW14,509712,174112,509750,174150,51.45536735,-0.422339108
96866141,24/07/2014 00:30,2014,2014/15,Special Service,1,1,295,295,CAT FALLEN AND INJURED ONTO METAL BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Little Cottage Place,20803007,SE10,NULL,NULL,537950,177450,NULL,NULL
97602141,25/07/2014 11:08,2014,2014/15,Special Service,1,2,295,590,KITTEN TRAPPED IN TOILET PIPE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000037,Parsloes,E09000002,Barking and Dagenham,Dagenham,NULL,Groveway,19903732,RM8,NULL,NULL,547550,186050,NULL,NULL
97738141,25/07/2014 14:23,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CLOSED IN ROOF AREA,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000204,Lower Edmonton,E09000010,Enfield,Edmonton,NULL,Huntingdon Road,20703422,N9,NULL,NULL,535350,194050,NULL,NULL
97885141,25/07/2014 19:19,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED BEHIND FITTED WARDROBE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000289,Harrow on the Hill,E09000015,Harrow,Northolt,NULL,Brooke Avenue,21201201,HA2,NULL,NULL,514450,186450,NULL,NULL
98163141,26/07/2014 11:18,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED IN SHOP,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000641,Marylebone High Street,E09000033,Westminster,Soho,1.00023E+11,Wigmore Street,8400283,W1U,528439,181292,528450,181250,51.51595986,-0.150336981
98265141,26/07/2014 15:13,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED BETWEEN TWO GLASS WINDOWS,Bird,Person (mobile),Health spa/farm,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000427,Prince's,E09000022,Lambeth,Lambeth,1.00023E+11,Glasshouse Walk,21900610,SE11,530477,178337,530450,178350,51.48893727,-0.122074695
98448141,26/07/2014 20:33,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000472,Village,E09000024,Merton,Wimbledon,NULL,Church Road,22101434,SW19,NULL,NULL,524250,171450,NULL,NULL
98821141,27/07/2014 15:02,2014,2014/15,Special Service,2,3,295,885,ABANDONED CALL TO FIRE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving wild animal,E05011253,Valentines,E09000026,Redbridge,Ilford,NULL,Wanstead Park Road,22303358,IG1,NULL,NULL,543050,186550,NULL,NULL
98980141,27/07/2014 19:24,2014,2014/15,Special Service,1,1,295,295,ASSIST LAS WITH CAT ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011102,Faraday,E09000028,Southwark,Old Kent Road,NULL,Bagshot Street,22500129,SE17,NULL,NULL,533250,178150,NULL,NULL
99011141,27/07/2014 19:54,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH TRAPPED CAT,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000290,Harrow Weald,E09000015,Harrow,Harrow,NULL,Courtenay Gardens,NULL,HA3,514302,190367,514350,190350,51.60055821,-0.351035131
99187141,28/07/2014 01:52,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER DECKING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Richmond Road,22869850,E4,NULL,NULL,538750,194250,NULL,NULL
99469141,28/07/2014 14:22,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT TRAPPED ON WINDOW LEDGE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000134,Gospel Oak,E09000007,Camden,Kentish Town,NULL,Constantine Road,20400194,NW3,NULL,NULL,527650,185650,NULL,NULL
100132141,29/07/2014 19:31,2014,2014/15,Special Service,1,1,295,295,SMALL DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Pole Hill Road,NULL,UB10,507991,182555,507950,182550,51.53158637,-0.444519058
100471141,30/07/2014 13:30,2014,2014/15,Special Service,NULL,NULL,295,NULL,SMALL ANIMAL RESCUE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011229,St. Mary's & St. James,E09000004,Bexley,Sidcup,NULL,Davis Way,20101866,DA14,NULL,NULL,548050,170950,NULL,NULL
100687141,30/07/2014 20:20,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN MUD,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000075,Erith,E09000004,Bexley,Erith,NULL,Macarthur Close,NULL,DA8,551159,178491,551150,178450,51.48517493,0.175666238
100774141,30/07/2014 21:59,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN NETTING,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011104,London Bridge & West Bermondsey,E09000028,Southwark,Dockhead,NULL,Grange Walk,22501108,SE1,NULL,NULL,533350,179250,NULL,NULL
100997141,31/07/2014 11:28,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN BROKEN ROOF SLATES,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000421,Ferndale,E09000022,Lambeth,Brixton,NULL,Gateley Road,21900590,SW9,NULL,NULL,530950,175750,NULL,NULL
101256141,31/07/2014 22:01,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000264,Town,E09000013,Hammersmith and Fulham,Fulham,NULL,Fulham Road,NULL,SW6,524917,177055,524950,177050,51.47867,-0.202567731
101689141,01/08/2014 18:20,2014,2014/15,Special Service,1,1,295,295,ASSISTING RSPCA WITH RESCUE OF GULL,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009300,Cordwainer,E09000001,City of London,Dowgate,1.00023E+11,Queen Street,8100440,EC4N,532441,181061,532450,181050,51.51296082,-0.092780905
102115141,02/08/2014 14:38,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CAR AIR VENT,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000174,Ealing Common,E09000009,Ealing,Acton,NULL,Carbery Avenue,NULL,W3,518993,179711,518950,179750,51.50381672,-0.286937912
102339141,02/08/2014 22:10,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER DEBRIS,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000193,Bowes,E09000010,Enfield,Edmonton,NULL,Beale Close,NULL,N13,NULL,NULL,531850,192150,NULL,NULL
102362141,02/08/2014 22:46,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CAR MECHANISM,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000131,Cantelowes,E09000007,Camden,Kentish Town,NULL,St. Pancras Way,NULL,NW1,529166,184392,529150,184350,51.54365365,-0.138729019
102543141,03/08/2014 08:38,2014,2014/15,Special Service,1,1,295,295,CAT AND WOMAN STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000048,East Barnet,E09000003,Barnet,Barnet,NULL,Albert Road,20000520,EN4,NULL,NULL,526550,196250,NULL,NULL
102973141,03/08/2014 21:44,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED IN CAR BONNETT,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000536,Cathedrals,E09000028,Southwark,Dowgate,NULL,Scovell Road,NULL,SE1,532058,179616,532050,179650,51.50006462,-0.09883791
103225141,04/08/2014 13:03,2014,2014/15,Special Service,1,1,295,295,ASSIST POLICE WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Police,Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000483,Forest Gate North,E09000025,Newham,Stratford,NULL,Bramall Close,NULL,E15,539556,185365,539550,185350,51.54992506,0.011391629
103292141,04/08/2014 15:21,2014,2014/15,Special Service,1,1,295,295,FOX CUB WITH HEAD TRAPPED UNDER METAL FENCE,Fox,Person (land line),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000375,Holloway,E09000019,Islington,Holloway,NULL,Goodinge Close,NULL,N7,530076,184972,530050,184950,51.54865722,-0.125399402
103429141,04/08/2014 19:07,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN POLE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000094,Kilburn,E09000005,Brent,North Kensington,NULL,Albert Road,20201431,NW6,NULL,NULL,524850,183250,NULL,NULL
103785141,05/08/2014 10:18,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN WASHING MACHINE AND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000349,Chiswick Riverside,E09000018,Hounslow,Chiswick,NULL,Park Road,21501255,W4,NULL,NULL,520750,177850,NULL,NULL
104450141,06/08/2014 13:15,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000060,Underhill,E09000003,Barnet,Barnet,NULL,Dollis Valley Way,20012860,EN5,NULL,NULL,524750,195450,NULL,NULL
104586141,06/08/2014 19:31,2014,2014/15,Special Service,1,1,295,295,SEAGULL TRAPPED IN WIRE IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000090,Fryent,E09000005,Brent,Stanmore,NULL,Highfield Avenue,NULL,NW9,520303,188850,520350,188850,51.58567605,-0.26494949
104602141,06/08/2014 20:03,2014,2014/15,Special Service,1,2,295,590,CAT TRAPPED BEHIND PARTITION WALL,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,2E+11,Electric Avenue,21900507,SW9,531094,175398,531050,175350,51.46238263,-0.114281961
104973141,07/08/2014 15:30,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000400,Alexandra,E09000021,Kingston upon Thames,Surbiton,NULL,Alexandra Drive,NULL,KT5,519226,166751,519250,166750,51.38728744,-0.287943853
105034141,07/08/2014 17:16,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN FENCING,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000027,Alibon,E09000002,Barking and Dagenham,Dagenham,100013832,Heathway,19900172,RM10,549059,184810,549050,184850,51.54250969,0.148114382
105544141,08/08/2014 16:01,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH BIRD TRAPPED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009318,Blackwall & Cubitt Town,E09000030,Tower Hamlets,Millwall,NULL,Capstan Square,NULL,E14,538344,179582,538350,179550,51.49825632,-0.00834662
105716141,08/08/2014 21:42,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED IN TELEPHONE WIRE OS,Bird,Person (mobile),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000047,Coppetts,E09000003,Barnet,Southgate,NULL,Parkhurst Road,NULL,N11,528279,192451,528250,192450,51.61627965,-0.148571955
106125141,09/08/2014 20:07,2014,2014/15,Special Service,1,1,295,295,DOG IN PRECARIOUS POSITION ON LEDGE,Dog,Police,Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000378,St. George's,E09000019,Islington,Holloway,NULL,Carleton Road,21606415,N7,NULL,NULL,529950,185650,NULL,NULL
106416141,10/08/2014 10:22,2014,2014/15,Special Service,1,1,295,295,DOG STUCK IN HOLE,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011247,Loxford,E09000026,Redbridge,Ilford,NULL,Natal Road,22303092,IG1,NULL,NULL,543850,185650,NULL,NULL
106755141,10/08/2014 14:10,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000280,Tottenham Green,E09000014,Haringey,Tottenham,NULL,Mansfield Avenue,21103590,N15,NULL,NULL,532750,189250,NULL,NULL
106776141,10/08/2014 14:35,2014,2014/15,Special Service,1,1,295,295,BIRDS STUCK IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000548,Riverside,E09000028,Southwark,Dockhead,NULL,Spa Road,NULL,SE16,534217,179250,534250,179250,51.49626737,-0.067891225
107101141,11/08/2014 06:17,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN IRON BAR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000572,Worcester Park,E09000029,Sutton,Sutton,NULL,Cheam Common Road,22601362,KT4,NULL,NULL,522750,165750,NULL,NULL
107329141,11/08/2014 17:18,2014,2014/15,Special Service,1,1,295,295,DOG IN PRECARIOUS POSITION,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000207,Southbury,E09000010,Enfield,Enfield,NULL,Great Cambridge Road,20705613,EN1,NULL,NULL,534450,197550,NULL,NULL
107607141,12/08/2014 10:04,2014,2014/15,Special Service,1,2,295,590,"CAT STUCK ON ROOF, LEVEL ONE LINE OPS IMPLEMENTED",Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000370,Clerkenwell,E09000019,Islington,Shoreditch,NULL,Kingsway Place,21610048,EC1R,NULL,NULL,531450,182350,NULL,NULL
107914141,12/08/2014 21:05,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON PIGEON SPIKES,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000626,Tooting,E09000032,Wandsworth,Tooting,NULL,Tooting High Street,22905477,SW17,NULL,NULL,527250,171250,NULL,NULL
108042141,13/08/2014 08:03,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009319,Bow East,E09000030,Tower Hamlets,Bethnal Green,NULL,Saxon Road,22701065,E3,NULL,NULL,536750,183250,NULL,NULL
108075141,13/08/2014 10:32,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH RESCUE OF PIGEON WITH INJURY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000353,Hanworth,E09000018,Hounslow,Twickenham,NULL,Swan Road,21501100,TW13,NULL,NULL,512250,171650,NULL,NULL
108276141,13/08/2014 18:13,2014,2014/15,Special Service,1,1,295,295,CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000069,Christchurch,E09000004,Bexley,Bexley,NULL,Freta Road,NULL,DA6,548576,174964,548550,174950,51.45416768,0.137011161
108531141,14/08/2014 08:40,2014,2014/15,Special Service,1,1,295,295,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000139,Kentish Town,E09000007,Camden,Kentish Town,NULL,Prince of Wales Road,20400460,NW5,NULL,NULL,528650,184750,NULL,NULL
109460141,15/08/2014 22:06,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA TO RELEASE TRAPPED FOX,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000125,Plaistow and Sundridge,E09000006,Bromley,Bromley,1.0002E+11,Palace Road,20300550,BR1,540623,169719,540650,169750,51.40906519,0.020545144
109613141,16/08/2014 09:23,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000171,Cleveland,E09000009,Ealing,Ealing,NULL,Copley Close,NULL,W7,NULL,NULL,515950,181250,NULL,NULL
109791141,16/08/2014 16:36,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN GRATE,Cat,Person (mobile),Church/Chapel,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000271,Harringay,E09000014,Haringey,Hornsey,1.00024E+11,Frobisher Road,21103314,N8,531595,189258,531550,189250,51.58682118,-0.101902203
109866141,16/08/2014 18:51,2014,2014/15,Special Service,1,1,295,295,DOG STUCK ON BALCONY - UNABLE TO REACH HIM FROM WINDOW,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000137,Highgate,E09000007,Camden,Kentish Town,NULL,Carrol Close,20401317,NW5,NULL,NULL,528550,185650,NULL,NULL
110127141,17/08/2014 09:50,2014,2014/15,Special Service,1,1,295,295,Redacted,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009377,Hoxton East & Shoreditch,E09000012,Hackney,Shoreditch,NULL,Shoreditch High Street,20900920,E1,NULL,NULL,533450,182450,NULL,NULL
110581141,18/08/2014 11:02,2014,2014/15,Special Service,1,1,295,295,Redacted,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000055,Hendon,E09000003,Barnet,Hendon,200059798,Heriot Road,20022000,NW4,523303,188862,523350,188850,51.58513682,-0.221665158
111062141,19/08/2014 12:27,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND PANELLING IN KITCHEN,Cat,Police,Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011231,Slade Green & Northend,E09000004,Bexley,Bexley,NULL,Sun Court,20101396,DA8,NULL,NULL,552050,176350,NULL,NULL
111505141,20/08/2014 11:32,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED BEHIND FENCING,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000610,Balham,E09000032,Wandsworth,Tooting,NULL,Bolingbroke Grove,NULL,SW11,527729,173772,527750,173750,51.4485383,-0.163280721
112083141,21/08/2014 15:39,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED INSIDE DERELICT BUILDING,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Bethnal Green,NULL,Mile End Road,NULL,E3,NULL,NULL,536450,182550,NULL,NULL
112334141,22/08/2014 04:13,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED BEHIND KITCHEN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000223,Kidbrooke with Hornfair,E09000011,Greenwich,East Greenwich,NULL,Portway Gardens,20801198,SE18,NULL,NULL,541850,176950,NULL,NULL
112376141,22/08/2014 08:25,2014,2014/15,Special Service,1,3,295,885,COW  IN WATER,Cow,Police,River/canal,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000206,Ponders End,E09000010,Enfield,Enfield,NULL,Alma Road,NULL,EN3,535969,196414,535950,196450,51.65008701,-0.036030991
112427141,22/08/2014 11:11,2014,2014/15,Special Service,1,1,295,295,SEAGULL TRAPPED IN NETTING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000366,Barnsbury,E09000019,Islington,Islington,10010438509,Upper Street,21606199,N1,531356,183589,531350,183550,51.53593234,-0.107465554
112452141,22/08/2014 12:17,2014,2014/15,Special Service,1,3,295,885,CAT TRAPPED BETWEEN WALLS,Cat,Other FRS,Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000199,Enfield Lock,E09000010,Enfield,Enfield,207035723,Park Road,20702363,EN3,536440,199591,536450,199550,51.67852174,-0.027988234
112475141,22/08/2014 13:09,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN BARBED WIRE ON A GARAGE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000296,Marlborough,E09000015,Harrow,Stanmore,1.00021E+11,Warham Road,21201711,HA3,515841,189985,515850,189950,51.59681054,-0.328950202
112815141,23/08/2014 07:21,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED ON LEDGE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000139,Kentish Town,E09000007,Camden,Kentish Town,NULL,Ospringe Road,20400232,NW5,NULL,NULL,529250,185550,NULL,NULL
112896141,23/08/2014 11:52,2014,2014/15,Special Service,1,1,295,295,KITTEN STUCK ON CHIMNEY POT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000346,Bedfont,E09000018,Hounslow,Feltham,NULL,Imperial Road,21500636,TW14,NULL,NULL,509450,173750,NULL,NULL
113122141,23/08/2014 19:12,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON LEDGE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000471,Trinity,E09000024,Merton,Wimbledon,NULL,Wycliffe Road,22106897,SW19,NULL,NULL,525950,170550,NULL,NULL
113346141,24/08/2014 09:38,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED UNDER CAR BONNET,Cat,Police,Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,2.00001E+11,Ledrington Road,20302181,SE19,534453,170657,534450,170650,51.41898943,-0.067757494
113559141,24/08/2014 17:59,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED BETWEEN TWO SHEDS,Dog,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000221,Glyndon,E09000011,Greenwich,Plumstead,1.00021E+11,Leghorn Road,20800902,SE18,544932,178213,544950,178250,51.48430567,0.085934391
113850141,25/08/2014 11:12,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011097,Champion Hill,E09000028,Southwark,Peckham,NULL,Champion Hill,22500477,SE5,NULL,NULL,532850,175850,NULL,NULL
114188141,25/08/2014 21:07,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009375,Haggerston,E09000012,Hackney,Bethnal Green,NULL,Hackney Road,20900479,E2,NULL,NULL,534450,183550,NULL,NULL
114759141,27/08/2014 12:23,2014,2014/15,Special Service,1,1,295,295,PUPPY STUCK BETWEEN SHED AND BRICK WALL,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000085,Alperton,E09000005,Brent,Wembley,NULL,Mount Pleasant,20202568,HA0,NULL,NULL,518950,184050,NULL,NULL
114787141,27/08/2014 13:48,2014,2014/15,Special Service,1,1,295,295,FUNERAL HORSE COLLAPSED IN ROADWAY,Horse,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000315,Hylands,E09000016,Havering,Hornchurch,NULL,Hornchurch Road,NULL,RM11,552602,187428,552650,187450,51.56508645,0.200300004
114824141,27/08/2014 15:43,2014,2014/15,Special Service,1,2,295,590,CAT TRAPPED IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000488,Manor Park,E09000025,Newham,Stratford,46015474,Claremont Road,22207659,E7,541286,185317,541250,185350,51.5490632,0.036306148
114829141,27/08/2014 16:00,2014,2014/15,Special Service,1,2,295,590,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011468,Fairfield,E09000008,Croydon,Croydon,NULL,St. Andrews Road,20502337,CR0,NULL,NULL,532250,164850,NULL,NULL
115716141,29/08/2014 14:55,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN SOFA BY LEG,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000322,Squirrel's Heath,E09000016,Havering,Harold Hill,NULL,Ferguson Avenue,21301199,RM2,NULL,NULL,553750,190150,NULL,NULL
116082141,30/08/2014 10:45,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER VAN,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Animal harm involving domestic animal,E05000489,Plaistow North,E09000025,Newham,Plaistow,NULL,Queens Road West,NULL,E13,540684,183530,540650,183550,51.53315589,0.026916399
116198141,30/08/2014 16:28,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000118,Farnborough and Crofton,E09000006,Bromley,Orpington,NULL,Lovibonds Avenue,20303011,BR6,NULL,NULL,544050,165350,NULL,NULL
116515141,31/08/2014 11:00,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,Heathcote Grove,22844200,E4,NULL,NULL,538150,193650,NULL,NULL
117127141,01/09/2014 17:50,2014,2014/15,Special Service,1,1,295,295,TO ASSIST RSPCA WITH SEAGULL WEDGED IN GUTTERING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000207,Southbury,E09000010,Enfield,Enfield,NULL,Lincoln Road,20702802,EN1,NULL,NULL,533550,196050,NULL,NULL
117151141,01/09/2014 18:46,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN NETTING ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000121,Mottingham and Chislehurst North,E09000006,Bromley,Eltham,NULL,Dunkery Road,20302283,SE9,NULL,NULL,542150,172150,NULL,NULL
117156141,01/09/2014 18:53,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED IN GUTTERING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000186,Norwood Green,E09000009,Ealing,Southall,NULL,Harewood Terrace,20600828,UB2,NULL,NULL,512950,178850,NULL,NULL
118469141,04/09/2014 18:20,2014,2014/15,Special Service,1,2,295,590,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011098,Chaucer,E09000028,Southwark,Dockhead,2.00003E+11,Hankey Place,22501189,SE1,532753,179603,532750,179650,51.49978517,-0.088835919
118534141,04/09/2014 19:54,2014,2014/15,Special Service,1,2,295,590,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000280,Tottenham Green,E09000014,Haringey,Tottenham,NULL,Stamford Road,21103923,N15,NULL,NULL,534150,189050,NULL,NULL
119056141,05/09/2014 21:33,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN BARBED WIRE FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Granby Terrace,NULL,NW1,529127,183032,529150,183050,51.53144039,-0.139789701
119359141,06/09/2014 13:20,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH PIGEON TRAPPED AT TOP OF TELEGRAPH POLE,Bird,Person (land line),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000031,Eastbury,E09000002,Barking and Dagenham,Barking,NULL,Surrey Road,NULL,IG11,545075,184010,545050,184050,51.53635848,0.090375544
119540141,06/09/2014 20:14,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND KITCHEN CUPBOARDS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000050,Edgware,E09000003,Barnet,Mill Hill,NULL,Station Road,20040860,HA8,NULL,NULL,519350,191850,NULL,NULL
119654141,07/09/2014 02:14,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH DEER TRAPPED IN FENCE,Deer,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000590,Cann Hall,E09000031,Waltham Forest,Leytonstone,1.00023E+11,Cann Hall Road,22822050,E11,539733,186150,539750,186150,51.55693516,0.014254251
120105141,08/09/2014 00:23,2014,2014/15,Special Service,1,1,295,295,DOG WITH FOOT TRAPPED IN ELECTRIC FAN,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,Oban Street,22700888,E14,NULL,NULL,538950,181450,NULL,NULL
120229141,08/09/2014 08:51,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Underground car park,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000425,Larkhall,E09000022,Lambeth,Brixton,1.00023E+11,Landor Road,21900834,SW9,530194,175679,530150,175650,51.46511551,-0.127126674
120480141,08/09/2014 17:10,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN SHED AND FENCE,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009336,Whitechapel,E09000030,Tower Hamlets,Shadwell,6172618,Martineau Square,22702656,E1,534508,180991,534550,180950,51.5118436,-0.063038009
120635141,08/09/2014 21:52,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO PIGEON TRAPPED,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000570,Wallington South,E09000029,Sutton,Wallington,NULL,Woodcote Road,NULL,SM6,528891,163965,528850,163950,51.36013926,-0.150128217
120810141,09/09/2014 10:21,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000637,Knightsbridge and Belgravia,E09000033,Westminster,Kensington,10033534401,Kensington Gardens,8400731,W2,526760,180006,526750,180050,51.50478133,-0.174983574
121283141,10/09/2014 10:29,2014,2014/15,Special Service,1,1,295,295,KITTEN STUCK IN RADIATOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Bath Street,21604222,EC1V,NULL,NULL,532550,182550,NULL,NULL
121790141,11/09/2014 12:31,2014,2014/15,Special Service,1,2,295,590,FOX WITH HEAD STUCK BETWEEN POSTS,Fox,Person (mobile),Pub/wine bar/bar,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000400,Alexandra,E09000021,Kingston upon Thames,Surbiton,128002630,Tolworth Broadway,21800172,KT6,519664,166019,519650,166050,51.38061627,-0.281898378
122055141,12/09/2014 01:08,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH TRAPPED KITTEN,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000617,Latchmere,E09000032,Wandsworth,Battersea,1.00023E+11,Rowditch Lane,22904454,SW11,528180,176406,528150,176450,51.47210829,-0.155840698
122157141,12/09/2014 10:20,2014,2014/15,Special Service,1,1,295,295,DOG LOCKED IN CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000061,West Finchley,E09000003,Barnet,Finchley,NULL,Derby Avenue,NULL,N12,526287,192240,526250,192250,51.61483362,-0.177403765
122170141,12/09/2014 10:50,2014,2014/15,Special Service,1,2,295,590,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011474,Old Coulsdon,E09000008,Croydon,Purley,NULL,Placehouse Lane,20500312,CR5,NULL,NULL,530950,157950,NULL,NULL
122305141,12/09/2014 16:14,2014,2014/15,Special Service,1,1,295,295,DOG FALLEN DOWN WELL,Dog,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,1.00022E+11,Trelawn Road,21901397,SW2,531267,174776,531250,174750,51.45675274,-0.112023733
122355141,12/09/2014 17:35,2014,2014/15,Special Service,1,1,295,295,DOG WITH LEG TRAPPED IN CAR DOOR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000359,Hounslow Heath,E09000018,Hounslow,Feltham,NULL,Raglan Close,NULL,TW4,512486,174793,512450,174750,51.4609444,-0.382214513
122493141,12/09/2014 22:43,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000605,Leytonstone,E09000031,Waltham Forest,Leytonstone,NULL,Leyspring Road,22851600,E11,NULL,NULL,539950,187350,NULL,NULL
122597141,13/09/2014 08:01,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE - CAT WITH LEG TRAPPED IN ELECTRIC GATE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000103,Welsh Harp,E09000005,Brent,Willesden,NULL,North Circular Road,20201638,NW10,NULL,NULL,521250,185550,NULL,NULL
122601141,13/09/2014 08:24,2014,2014/15,Special Service,1,2,295,590,ASSIST RSPCA WITH BIRD WITH LEG TRAPPED IN FLOODLIGHT,Bird,Person (land line),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011465,Broad Green,E09000008,Croydon,Croydon,2.00001E+11,Volta Way,20502799,CR0,530664,166207,530650,166250,51.37988353,-0.123854075
122684141,13/09/2014 11:31,2014,2014/15,Special Service,1,1,295,295,CAT STUCK BETWEEN WALL AND LAMP POST,Cat,Person (land line),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000039,Thames,E09000002,Barking and Dagenham,Barking,NULL,Farr Avenue,NULL,IG11,546056,183218,546050,183250,51.52898932,0.104181663
122716141,13/09/2014 12:32,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000365,Turnham Green,E09000018,Hounslow,Chiswick,2.00003E+11,Marlborough Road,21500739,W4,520105,178372,520150,178350,51.49154747,-0.271379476
122768141,13/09/2014 14:35,2014,2014/15,Special Service,1,3,295,885,FOX  TRAPPED BETWEEN KIOSK & WALL,Fox,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000416,Bishop's,E09000022,Lambeth,Lambeth,NULL,Westminster Bridge Road,NULL,SE1,530634,179886,530650,179850,51.50282162,-0.119241565
122821141,13/09/2014 16:19,2014,2014/15,Special Service,1,1,295,295,CAT STUCK BEHIND WASHING MACHINE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000097,Preston,E09000005,Brent,Wembley,NULL,Elliot Close,20202339,HA9,NULL,NULL,519050,186350,NULL,NULL
122915141,13/09/2014 19:07,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000282,West Green,E09000014,Haringey,Hornsey,NULL,Carlingford Road,21103058,N15,NULL,NULL,531750,189550,NULL,NULL
123179141,14/09/2014 10:16,2014,2014/15,Special Service,1,1,295,295,CAT STUCK BEHIND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Dance Square,21610129,EC1V,NULL,NULL,532050,182450,NULL,NULL
123198141,14/09/2014 11:20,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000417,Brixton Hill,E09000022,Lambeth,West Norwood,NULL,Holmewood Gardens,21901553,SW2,NULL,NULL,530650,173650,NULL,NULL
123443141,14/09/2014 18:50,2014,2014/15,Special Service,2,3,295,885,CAT TRAPPED IN CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000361,Hounslow West,E09000018,Hounslow,Feltham,NULL,Manor Avenue,NULL,TW4,511805,176060,511850,176050,51.47246717,-0.391612636
123770141,15/09/2014 13:48,2014,2014/15,Special Service,1,1,295,295,TO ASSIST RSPCA ON SCENE WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Royal College Street,NULL,NW1,529494,183538,529450,183550,51.53590379,-0.134315741
123910141,15/09/2014 19:05,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000061,West Finchley,E09000003,Barnet,Finchley,NULL,Argyle Road,20001240,N12,NULL,NULL,525550,192150,NULL,NULL
124514141,16/09/2014 21:33,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Atherton Mews,22207603,E7,NULL,NULL,540050,184950,NULL,NULL
124538141,16/09/2014 22:55,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED BEHIND CUPBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000402,Beverley,E09000021,Kingston upon Thames,New Malden,NULL,Kingston Road,21800563,KT3,NULL,NULL,521250,168250,NULL,NULL
125508141,18/09/2014 22:41,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA TO RETRIEVE KITTEN STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000037,Parsloes,E09000002,Barking and Dagenham,Dagenham,NULL,Ravensfield Close,19900893,RM9,NULL,NULL,547950,185850,NULL,NULL
125741141,19/09/2014 11:53,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000121,Mottingham and Chislehurst North,E09000006,Bromley,Eltham,NULL,Dunkery Road,20302283,SE9,NULL,NULL,542150,172150,NULL,NULL
126118141,19/09/2014 17:53,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000265,Wormholt and White City,E09000013,Hammersmith and Fulham,Hammersmith,NULL,White City Estate,21000077,W12,NULL,NULL,522850,180950,NULL,NULL
126177141,19/09/2014 19:17,2014,2014/15,Special Service,1,1,295,295,KITTENS HANGING OFF BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,NULL,Roberts Place,19901083,RM10,NULL,NULL,549450,184750,NULL,NULL
126251141,19/09/2014 21:29,2014,2014/15,Special Service,1,2,295,590,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Ismailia Road,22207763,E7,NULL,NULL,540650,184450,NULL,NULL
126265141,19/09/2014 21:49,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON FENCE IN PARK,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000102,Tokyngton,E09000005,Brent,Wembley,NULL,Oakington Manor Drive,NULL,HA9,519491,185179,519450,185150,51.55285564,-0.277912146
126379141,20/09/2014 03:34,2014,2014/15,Special Service,1,1,295,295,RETRIEVAL OF INJURED KITTEN,Cat,Person (mobile),Licensed House in Multiple Occupation - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009380,Lea Bridge,E09000012,Hackney,Homerton,NULL,Glenarm Road,20901685,E5,NULL,NULL,535450,185550,NULL,NULL
126665141,20/09/2014 15:34,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000215,Blackheath Westcombe,E09000011,Greenwich,Lee Green,1.00021E+11,Bennett Park,20800150,SE3,539734,175991,539750,175950,51.4656458,0.010244249
127090141,21/09/2014 12:18,2014,2014/15,Special Service,1,1,295,295,BIRD STUCK IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000063,Woodhouse,E09000003,Barnet,Finchley,NULL,Christchurch Avenue,20008520,N12,NULL,NULL,526150,191650,NULL,NULL
127279141,21/09/2014 18:43,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000061,West Finchley,E09000003,Barnet,Finchley,NULL,Strathmore Gardens,20041220,N3,NULL,NULL,525650,190550,NULL,NULL
127315141,21/09/2014 19:34,2014,2014/15,Special Service,1,1,295,295,CAT AND KITTENS STUCK BETWEEN WALLS,Cat,Person (land line),Common external bin storage area,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Galahad Road,NULL,N9,534263,193553,534250,193550,51.62478748,-0.061772591
128258141,23/09/2014 18:39,2014,2014/15,Special Service,2,3,295,885,Redacted,Bird,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped wild animal,E05000331,Heathrow Villages,E09000017,Hillingdon,Heathrow,NULL,Bath Road,NULL,UB7,505038,176823,505050,176850,51.48062175,-0.488780768
128360141,23/09/2014 22:02,2014,2014/15,Special Service,1,1,295,295,CAT STUCK TRAPPED BY WIRE,Cat,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Wilton Way,NULL,E8,534659,184750,534650,184750,51.54558702,-0.059427081
128887141,25/09/2014 06:08,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND CHEST OF DRAWERS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000378,St George's,E09000019,Islington,Holloway,NULL,Holloway Road,21606449,N19,NULL,NULL,530050,186450,NULL,NULL
129342141,26/09/2014 08:24,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED INSIDE VAN ENGINE COMPARTMENT,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,100039830,Crown Street,19900463,RM10,550005,184554,550050,184550,51.53995913,0.16163693
130084141,27/09/2014 17:51,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED,Cat,Person (mobile),Fence,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,10009142024,Orange Grove,22863675,E11,539005,186360,539050,186350,51.55900204,0.003843078
130124141,27/09/2014 18:42,2014,2014/15,Special Service,1,1,295,295,KITTEN STUCK ON NETTING OUTSIDE WINDOW OF FLAT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009326,Limehouse,E09000030,Tower Hamlets,Poplar,NULL,Gill Street,22700537,E14,NULL,NULL,536950,180850,NULL,NULL
130198141,27/09/2014 20:23,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN COURTYARD,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009384,Stamford Hill West,E09000012,Hackney,Stoke Newington,NULL,Amhurst Park,20900061,N16,NULL,NULL,533250,187950,NULL,NULL
130657141,28/09/2014 18:46,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000618,Nightingale,E09000032,Wandsworth,Tooting,1.00023E+11,Hendham Road,22902331,SW17,527529,172805,527550,172850,51.43989269,-0.166505364
131522141,30/09/2014 15:34,2014,2014/15,Special Service,1,2,295,590,KITTEN TRAPPED INSIDE CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000161,Selhurst,E09000008,Croydon,Croydon,NULL,Holmesdale Road,NULL,CR0,532808,167838,532850,167850,51.39404405,-0.092455489
131805141,01/10/2014 07:56,2014,2014/15,Special Service,1,1,295,295,ASSIST RCPCA OFFICER WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000122,Orpington,E09000006,Bromley,Orpington,1.0002E+11,The Avenue,20300979,BR6,545859,165615,545850,165650,51.37086815,0.094092498
132003141,01/10/2014 16:52,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND FENCE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000308,Elm Park,E09000016,Havering,Hornchurch,1.00021E+11,Bader Way,21300706,RM13,552435,184672,552450,184650,51.54036906,0.1967002
132116141,01/10/2014 21:02,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER FLOOR BOARD,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000599,High Street,E09000031,Waltham Forest,Walthamstow,NULL,Forest Road,22837350,E17,NULL,NULL,536450,189550,NULL,NULL
132281141,02/10/2014 08:13,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER WHEEL ARCH OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000530,Teddington,E09000027,Richmond upon Thames,Twickenham,NULL,Church Road,NULL,TW11,515722,171039,515750,171050,51.42655273,-0.336881314
132359141,02/10/2014 11:25,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO CAT TRAPPED BEHIND WALL,Cat,Person (land line),Pipe or drain,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Church Street,NULL,N9,534266,193590,534250,193550,51.62511925,-0.061715101
132910141,03/10/2014 14:24,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000400,Alexandra,E09000021,Kingston upon Thames,Surbiton,NULL,Southwood Drive,NULL,KT5,520461,166774,520450,166750,51.38723364,-0.270195939
133149141,04/10/2014 00:23,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000449,New Cross,E09000023,Lewisham,New Cross,NULL,Casella Road,22000200,SE14,NULL,NULL,535650,177050,NULL,NULL
133222141,04/10/2014 06:54,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN ROOF,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000045,Childs Hill,E09000003,Barnet,West Hampstead,200198007,Cricklewood Lane,20011060,NW2,524990,186430,524950,186450,51.56290876,-0.198193878
133863141,05/10/2014 14:00,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN WINDOW,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000357,Heston West,E09000018,Hounslow,Feltham,NULL,Browning Way,21500168,TW5,NULL,NULL,511750,176750,NULL,NULL
134352141,06/10/2014 15:25,2014,2014/15,Special Service,1,1,295,295,SQUIRREL TRAPPED ON SATELLITE DISH,Squirrel,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000620,Queenstown,E09000032,Wandsworth,Clapham,NULL,Radcliffe Path,22906571,SW8,NULL,NULL,528950,176150,NULL,NULL
134701141,07/10/2014 10:47,2014,2014/15,Special Service,1,1,295,295,ASSIST RCPCA OFFICER WITH CAT STUCK INSIDE PART OF CAR ENGINE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Philpot Square,21001054,SW6,NULL,NULL,525650,175650,NULL,NULL
134953141,07/10/2014 19:23,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000352,Feltham West,E09000018,Hounslow,Feltham,NULL,High Street,21500609,TW13,NULL,NULL,510450,172950,NULL,NULL
135254141,08/10/2014 14:42,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN LOFT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011238,Churchfields,E09000026,Redbridge,Woodford,NULL,Lilian Gardens,22306099,IG8,NULL,NULL,540850,190850,NULL,NULL
135605141,09/10/2014 13:00,2014,2014/15,Special Service,1,1,295,295,TO ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,NULL,Brights Avenue,21300722,RM13,NULL,NULL,552950,182250,NULL,NULL
135664141,09/10/2014 15:18,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN IRON GATE,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Park Road,21103726,N8,NULL,NULL,530050,188550,NULL,NULL
135862141,10/10/2014 01:28,2014,2014/15,Special Service,1,1,295,295,DOG WITH PAW TRAPPED IN METAL CAGE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000568,The Wrythe,E09000029,Sutton,Wallington,NULL,Arlington Drive,22600274,SM5,NULL,NULL,527750,165850,NULL,NULL
136481141,11/10/2014 13:31,2014,2014/15,Special Service,1,2,295,590,BUZZARD STUCK IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000230,Woolwich Riverside,E09000011,Greenwich,East Greenwich,NULL,Samuel Street,NULL,SE18,542501,178805,542550,178850,51.49024251,0.051185802
136553141,11/10/2014 16:10,2014,2014/15,Special Service,1,1,295,295,DOG IN RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000474,Wimbledon Park,E09000024,Merton,Wimbledon,NULL,Plough Lane,NULL,SW19,526071,171477,526050,171450,51.42828341,-0.18794424
136581141,11/10/2014 16:57,2014,2014/15,Special Service,1,1,295,295,Redacted,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000643,Regent's Park,E09000033,Westminster,Paddington,NULL,Regents Park,NULL,NW1,527791,182549,527750,182550,51.52740331,-0.159215045
136921141,12/10/2014 11:35,2014,2014/15,Special Service,1,1,295,295,FOX TRAPPED BY LEG,Fox,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,NULL,Wincanton Crescent,20601917,UB5,NULL,NULL,513550,185250,NULL,NULL
137435141,13/10/2014 13:25,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT WEDGED IN WINDOW,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009375,Haggerston,E09000012,Hackney,Shoreditch,NULL,Livermere Road,20900626,E8,NULL,NULL,533750,183950,NULL,NULL
138812141,16/10/2014 08:57,2014,2014/15,Special Service,1,1,295,295,KITTEN STUCK UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000119,Hayes and Coney Hall,E09000006,Bromley,Addington,NULL,Chestnut Avenue,20303031,BR4,NULL,NULL,539450,164550,NULL,NULL
139077141,16/10/2014 21:39,2014,2014/15,Special Service,1,1,295,295,CAT FALLEN ONTO UNOCCUPIED BALCONY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009393,Courtfield,E09000020,Kensington and Chelsea,Kensington,NULL,Courtfield Road,21700125,SW7,NULL,NULL,526150,178750,NULL,NULL
140335141,19/10/2014 15:57,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN DRAIN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000476,Boleyn,E09000025,Newham,East Ham,NULL,Castle Street,22200447,E6,NULL,NULL,541550,183350,NULL,NULL
140540141,19/10/2014 23:14,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER GRATE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011485,South Norwood,E09000008,Croydon,Woodside,NULL,Prince Road,20501314,SE25,NULL,NULL,533150,167950,NULL,NULL
140635141,20/10/2014 08:16,2014,2014/15,Special Service,1,1,295,295,CATS TRAPPED IN GRATE,cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011485,South Norwood,E09000008,Croydon,Woodside,NULL,Prince Road,20501314,SE25,NULL,NULL,533150,167950,NULL,NULL
140731141,20/10/2014 10:59,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN FENCE,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011469,Kenley,E09000008,Croydon,Purley,NULL,Kenley Lane,NULL,CR8,532983,158885,532950,158850,51.31354411,-0.093283862
140851141,20/10/2014 16:23,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000555,Beddington North,E09000029,Sutton,Wallington,5870094344,Rectory Lane,22602563,SM6,529459,165019,529450,165050,51.36948273,-0.141591317
141817141,22/10/2014 17:39,2014,2014/15,Special Service,1,2,295,590,HORSE TRAPPED IN GATE,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011229,St. Mary's & St. James,E09000004,Bexley,Bexley,NULL,Monterey Close,NULL,DA5,550293,172604,550250,172650,51.43250988,0.160702353
142056141,23/10/2014 07:17,2014,2014/15,Special Service,1,2,295,590,CAT TRAPPED BETWEEN CEILING AND FLOORBOARDS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009330,St Katharine's & Wapping,E09000030,Tower Hamlets,Shadwell,NULL,Telfords Yard,22701933,E1W,NULL,NULL,534350,180650,NULL,NULL
142139141,23/10/2014 10:52,2014,2014/15,Special Service,1,1,295,295,INJURED SWAN IN ROADWAY,Bird,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000599,High Street,E09000031,Waltham Forest,Walthamstow,NULL,Forest Road,NULL,E17,535764,189396,535750,189350,51.58707244,-0.041709593
142999141,24/10/2014 19:39,2014,2014/15,Special Service,1,2,295,590,ASSIST RSPCA TO RELEASE FOX STUCK IN STORM DRAIN,Fox,Person (land line),Road surface/pavement,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05011248,Mayfield,E09000026,Redbridge,Ilford,NULL,Mortlake Road,NULL,IG1,545267,185985,545250,185950,51.55405528,0.0939562
143184141,25/10/2014 07:46,2014,2014/15,Special Service,1,1,295,295,FOX WITH LEG TRAPPED IN GATE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000298,Pinner South,E09000015,Harrow,Harrow,NULL,East Towers,21201853,HA5,NULL,NULL,511750,188550,NULL,NULL
145232141,29/10/2014 08:07,2014,2014/15,Special Service,1,1,295,295,HAMPSTER TRAPPED IN FIRE PLACE,Hamster,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000595,Forest,E09000031,Waltham Forest,Leytonstone,NULL,Forest Drive East,22836950,E11,NULL,NULL,538750,187950,NULL,NULL
145391141,29/10/2014 16:16,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000196,Cockfosters,E09000010,Enfield,Southgate,207123768,Prince George Avenue,20702906,N14,529644,195837,529650,195850,51.64639539,-0.127614458
145430141,29/10/2014 17:32,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL STUK IN LOFT,Unknown - Wild Animal,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000621,Roehampton and Putney Heath,E09000032,Wandsworth,Wandsworth,NULL,Roma Read Close,22906576,SW15,NULL,NULL,522550,173550,NULL,NULL
145560141,29/10/2014 21:39,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN NEIGHBOURING GARDEN,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011240,Clementswood,E09000026,Redbridge,Ilford,NULL,Hampton Road,22302910,IG1,NULL,NULL,544250,185750,NULL,NULL
145593141,30/10/2014 00:01,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN ROOFING AREA,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,1.00021E+11,Bruce Grove,21104298,N17,533431,190559,533450,190550,51.59808047,-0.074923187
145952141,30/10/2014 19:21,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Police,House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000208,Southgate,E09000010,Enfield,Southgate,NULL,Dalrymple Close,20701473,N14,NULL,NULL,529750,195150,NULL,NULL
148995141,05/11/2014 10:30,2014,2014/15,Special Service,1,2,295,590,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Reginald Road,NULL,E7,540059,184504,540050,184550,51.54206362,0.018298833
149708141,06/11/2014 14:17,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000378,St George's,E09000019,Islington,Holloway,NULL,Tufnell Park Road,NULL,N7,NULL,NULL,529750,185950,NULL,NULL
149863141,06/11/2014 19:36,2014,2014/15,Special Service,1,2,295,590,CAT TRAPPED UNDER FLOOR BOARDS IN BATHROOM,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011485,South Norwood,E09000008,Croydon,Woodside,NULL,Clifton Road,20500797,SE25,NULL,NULL,532950,168250,NULL,NULL
150226141,07/11/2014 14:32,2014,2014/15,Special Service,1,1,295,295,FOX TRAPPED BETWEEN SHED AND BRICK WALL,Fox,Person (land line),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011234,Aldborough,E09000026,Redbridge,Ilford,1.00022E+11,Ashurst Drive,22302589,IG2,544100,188760,544150,188750,51.57928902,0.07827197
151090141,09/11/2014 09:51,2014,2014/15,Special Service,1,1,295,295,CAT IN DISTRESS ON SCAFFOLDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000125,Plaistow and Sundridge,E09000006,Bromley,Bromley,NULL,Babbacombe Road,20302903,BR1,NULL,NULL,540450,169850,NULL,NULL
151144141,09/11/2014 11:49,2014,2014/15,Special Service,1,4,295,1180,HORSE IN DITCH,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000309,Emerson Park,E09000016,Havering,Hornchurch,1.00021E+11,Wingletye Lane,21300661,RM11,555476,188420,555450,188450,51.5732146,0.242169101
151174141,09/11/2014 12:56,2014,2014/15,Special Service,1,1,295,295,FOX TRAPPED BETWEEN TWO WALLS,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000102,Tokyngton,E09000005,Brent,Wembley,202149501,Engineers Way,20201887,HA9,519480,185827,519450,185850,51.55868192,-0.277850906
152103141,11/11/2014 14:55,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000446,Ladywell,E09000023,Lewisham,Lewisham,NULL,Algernon Road,22001161,SE13,NULL,NULL,537650,174950,NULL,NULL
152973141,13/11/2014 15:52,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO CAT STUCK IN TREE,Cat,Person (land line),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,NULL,Church Road,NULL,TW11,515538,171427,515550,171450,51.43007749,-0.339400287
153140141,14/11/2014 00:45,2014,2014/15,Special Service,1,1,295,295,DOG FALLEN DOWN MANHOLE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000219,Eltham South,E09000011,Greenwich,Eltham,NULL,Restons Crescent,NULL,SE9,544942,174303,544950,174350,51.44916937,0.084475905
153277141,14/11/2014 09:48,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER DECKING,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000431,Streatham South,E09000022,Lambeth,Norbury,NULL,Heybridge Avenue,21900709,SW16,NULL,NULL,530450,170450,NULL,NULL
153384141,14/11/2014 14:46,2014,2014/15,Special Service,1,1,295,295,PIGEON STUCK UP CHIMNEY,Bird,Person (land line),Licensed House in Multiple Occupation - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000618,Nightingale,E09000032,Wandsworth,Tooting,NULL,Trinity Road,22906604,SW17,NULL,NULL,527750,172650,NULL,NULL
154054141,15/11/2014 20:42,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN FENCE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000560,Cheam,E09000029,Sutton,Sutton,NULL,Peaches Close,22603252,SM2,NULL,NULL,524150,163050,NULL,NULL
154321141,16/11/2014 13:52,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN RAILINGS,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal harm involving wild animal,E05000451,Rushey Green,E09000023,Lewisham,Lewisham,NULL,Albacore Crescent,NULL,SE13,537826,174423,537850,174450,51.45202256,-0.017815992
155135141,18/11/2014 14:48,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000595,Forest,E09000031,Waltham Forest,Leyton,NULL,Belmont Park Road,22815400,E10,NULL,NULL,538050,188250,NULL,NULL
155308141,18/11/2014 21:08,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000430,Streatham Hill,E09000022,Lambeth,West Norwood,NULL,Barcombe Avenue,21900139,SW2,NULL,NULL,530450,172750,NULL,NULL
155488141,19/11/2014 09:07,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN BETWEEN WALLS,Cat,Person (land line),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000602,Larkswood,E09000031,Waltham Forest,Chingford,1.00023E+11,Larkswood Road,22849800,E4,537700,192624,537750,192650,51.61560909,-0.012516181
156690141,21/11/2014 23:21,2014,2014/15,Special Service,1,1,295,295,DOGS MOUTH TRAPPED IN CAGE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000377,Mildmay,E09000019,Islington,Stoke Newington,NULL,St. Jude Street,21603842,N16,NULL,NULL,533250,185050,NULL,NULL
156770141,22/11/2014 05:28,2014,2014/15,Special Service,1,1,295,295,DOG FALLEN FROM BALCONY,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009404,St. Helen's,E09000020,Kensington and Chelsea,North Kensington,NULL,St. Charles Square,21701008,W10,NULL,NULL,524050,181650,NULL,NULL
157399141,23/11/2014 11:50,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN THORN BUSHES,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000220,Eltham West,E09000011,Greenwich,Eltham,NULL,Elmbrook Gardens,NULL,SE9,542417,175322,542450,175350,51.4589662,0.048573871
157478141,23/11/2014 14:18,2014,2014/15,Special Service,1,1,295,295,DOG IN RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000451,Rushey Green,E09000023,Lewisham,Forest Hill,NULL,Catford Bridge,NULL,SE6,537332,173573,537350,173550,51.44450408,-0.025250472
158868141,26/11/2014 13:08,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT IN PRECARIOUS POSITION,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000377,Mildmay,E09000019,Islington,Islington,NULL,King Henrys Walk,21605227,N1,NULL,NULL,533150,184950,NULL,NULL
159728141,28/11/2014 12:04,2014,2014/15,Special Service,1,1,295,295,CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000415,Tudor,E09000021,Kingston upon Thames,Kingston,128008767,Tudor Drive,21800983,KT2,518401,171071,518450,171050,51.42628724,-0.298354422
159900141,28/11/2014 19:33,2014,2014/15,Special Service,1,1,295,295,CAT IN PRECARIOUS POSITION,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Devonshire Drive,NULL,SE10,NULL,NULL,537850,176950,NULL,NULL
160205141,29/11/2014 13:19,2014,2014/15,Special Service,1,1,295,295,BIRD WITH WING TRAPPED IN WIRE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011217,Barnehurst,E09000004,Bexley,Erith,NULL,Stuart Mantle Way,20101365,DA8,NULL,NULL,551050,177150,NULL,NULL
161277141,01/12/2014 19:49,2014,2014/15,Special Service,1,1,295,295,INJURED CAT TRAPPED BEHIND FENCE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000304,Wealdstone,E09000015,Harrow,Stanmore,NULL,The Meadow Way,21202458,HA3,NULL,NULL,515150,190550,NULL,NULL
161638141,02/12/2014 18:01,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED  IN ROOM,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000283,White Hart Lane,E09000014,Haringey,Tottenham,NULL,Daubeney Gardens,21104439,N17,NULL,NULL,532350,191250,NULL,NULL
162376141,04/12/2014 11:02,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER WARDROBE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000057,Mill Hill,E09000003,Barnet,Mill Hill,NULL,Watford Way,20044760,NW7,NULL,NULL,522250,190750,NULL,NULL
162464141,04/12/2014 15:01,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE - CAT TRAPPED IN BASEMENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000369,Canonbury,E09000019,Islington,Islington,NULL,Downham Road,21604726,N1,NULL,NULL,532850,184050,NULL,NULL
162556141,04/12/2014 19:06,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN GATE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000649,West End,E09000033,Westminster,Soho,NULL,Green Street,8401383,W1K,NULL,NULL,528150,180950,NULL,NULL
162799141,05/12/2014 09:16,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED BEHIND WALL IN KITCHEN,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000139,Kentish Town,E09000007,Camden,Kentish Town,NULL,Leverton Street,20400230,NW5,NULL,NULL,529050,185550,NULL,NULL
163419141,06/12/2014 14:25,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009404,St. Helen's,E09000020,Kensington and Chelsea,North Kensington,217052368,Latimer Road,21700930,W10,523195,181527,523150,181550,51.51923862,-0.22579147
164334141,08/12/2014 13:14,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA TO RESCUE BIRD,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000027,Alibon,E09000002,Barking and Dagenham,Dagenham,NULL,Spurling Road,19900710,RM9,NULL,NULL,548850,184850,NULL,NULL
164352141,08/12/2014 14:13,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000185,Northolt West End,E09000009,Ealing,Southall,NULL,Aspen Lane,NULL,UB5,512356,182603,512350,182650,51.53116669,-0.381600795
165190141,10/12/2014 11:41,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009331,St Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Patriot Square,22700922,E2,NULL,NULL,535050,183150,NULL,NULL
165199141,10/12/2014 11:58,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN PIPE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000596,Grove Green,E09000031,Waltham Forest,Leyton,NULL,Murchison Road,22860300,E10,NULL,NULL,538350,186950,NULL,NULL
165307141,10/12/2014 17:10,2014,2014/15,Special Service,1,1,295,295,CAT UNDER FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011236,Bridge,E09000026,Redbridge,Woodford,NULL,Finchingfield Avenue,22304988,IG8,NULL,NULL,541450,191350,NULL,NULL
165659141,11/12/2014 12:11,2014,2014/15,Special Service,1,1,295,295,PIDGEON TRAPPED IN NETTING,Bird,Person (land line),Petrol station,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000371,Finsbury Park,E09000019,Islington,Holloway,5300050471,Hornsey Road,21603387,N7,530646,186415,530650,186450,51.56149336,-0.116647877
166062141,12/12/2014 09:49,2014,2014/15,Special Service,1,1,295,295,ANIMAL STUCK BEHIND CHIMNEY,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000326,Brunel,E09000017,Hillingdon,Hillingdon,NULL,Hillingdon Road,21400984,UB10,NULL,NULL,505950,183350,NULL,NULL
167663141,15/12/2014 19:00,2014,2014/15,Special Service,2,1,295,295,DOG WITH HEAD TRAPPED IN DOOR,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,NULL,Mount Avenue,20601209,W5,NULL,NULL,517350,181650,NULL,NULL
167894141,16/12/2014 11:31,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH KITTEN,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009321,Bromley North,E09000030,Tower Hamlets,Bethnal Green,NULL,British Street,NULL,E3,536896,182433,536850,182450,51.5242287,-0.028086894
168459141,17/12/2014 17:09,2014,2014/15,Special Service,1,1,295,295,ASSIST  RSPCA WITH PIGEON TRAPPED  BEHIND SIGN,Bird,Person (mobile),Other retail,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000358,Hounslow Central,E09000018,Hounslow,Heston,1.00023E+11,High Street,21500610,TW3,513937,175752,513950,175750,51.46927381,-0.361028613
168710141,18/12/2014 11:25,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED BEHIND SUNBED,Dog,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000220,Eltham West,E09000011,Greenwich,Lee Green,1.00021E+11,Eltham Green Road,20800525,SE9,541335,175223,541350,175250,51.45834757,0.032970349
169524141,20/12/2014 08:39,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Ravensbury Road,22904209,SW18,NULL,NULL,525650,172950,NULL,NULL
169546141,20/12/2014 10:27,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED UNDER FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000028,Becontree,E09000002,Barking and Dagenham,Dagenham,NULL,Lichfield Road,19900221,RM8,NULL,NULL,547050,186150,NULL,NULL
169799141,20/12/2014 23:09,2014,2014/15,Special Service,1,1,295,295,CAT FALLEN INTO BASEMENT AREA,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Homerton,NULL,Lea Bridge Road,20900602,E5,NULL,NULL,534950,186250,NULL,NULL
169868141,21/12/2014 03:35,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN MATTRESS SPRINGS,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000376,Junction,E09000019,Islington,Kentish Town,NULL,Foxham Road,21600321,N19,NULL,NULL,529550,186150,NULL,NULL
169973141,21/12/2014 12:56,2014,2014/15,Special Service,1,1,295,295,FOX WITH HEAD TRAPPED IN BUCKET ON ROOF OF SHED,Fox,Person (land line),Scrub land,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000483,Forest Gate North,E09000025,Newham,Stratford,46074215,Thorogood Gardens,22200101,E15,539367,185023,539350,185050,51.54689859,0.008532269
170522141,22/12/2014 19:12,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000098,Queens Park,E09000005,Brent,North Kensington,202090245,Harrow Road,20201241,NW10,523750,182592,523750,182550,51.52868888,-0.217422448
170565141,22/12/2014 21:01,2014,2014/15,Special Service,2,5,295,1475,TO ASSIST RSPCA WITH KITTEN TRAPPED BETWEEN FIRE ESCAPE AND WALL,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000409,Norbiton,E09000021,Kingston upon Thames,Kingston,128037500,Coombe Road,21800283,KT2,519207,169532,519250,169550,51.41228664,-0.287284167
171288141,24/12/2014 14:00,2014,2014/15,Special Service,1,1,295,295,CAT STUCK BEHIND COOKER,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000379,St Mary's,E09000019,Islington,Islington,NULL,College Cross,21604568,N1,NULL,NULL,531450,184350,NULL,NULL
171286141,24/12/2014 14:01,2014,2014/15,Special Service,1,1,295,295,DOG WITH JAW TRAPPED IN FENCE,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05009367,Brownswood,E09000012,Hackney,Holloway,1.00021E+11,Queens Drive,20900835,N4,531875,186685,531850,186650,51.56363364,-0.098827488
171323141,24/12/2014 15:30,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN RABBIT HOLE,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,6158164,Southern Grove,22701117,E3,536748,182373,536750,182350,51.52372533,-0.030242189
171741141,25/12/2014 17:23,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN METAL GATE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000276,Northumberland Park,E09000014,Haringey,Edmonton,1.00021E+11,Meridian Walk,21106478,N17,533470,191699,533450,191650,51.60831548,-0.073927035
172002141,26/12/2014 12:41,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND KITCHEN UNIT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011116,South Bermondsey,E09000028,Southwark,Dockhead,NULL,Dunlop Place,22500834,SE16,NULL,NULL,533950,179150,NULL,NULL
172364141,27/12/2014 11:27,2014,2014/15,Special Service,1,3,295,885,DOG TRAPPED UNDER METAL CONTAINER,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011096,Camberwell Green,E09000028,Southwark,Old Kent Road,NULL,New Church Road,NULL,SE5,532783,177656,532750,177650,51.48228112,-0.089135794
172911141,28/12/2014 17:58,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN AWNING,Cat,Person (mobile),"Takeaway, fast food",Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000649,West End,E09000033,Westminster,Euston,10033548014,New Cavendish Street,8400597,W1W,529175,181824,529150,181850,51.52057325,-0.139540856
173182141,29/12/2014 10:34,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT TRAPPED IN BASEMENT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000612,Earlsfield,E09000032,Wandsworth,Wandsworth,NULL,Lydden Grove,NULL,SW18,NULL,NULL,525750,173550,NULL,NULL
173242141,29/12/2014 13:56,2014,2014/15,Special Service,1,1,295,295,ANIMAL TRAPPED BEHIND WALL,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000115,Cray Valley West,E09000006,Bromley,Sidcup,NULL,Wisley Road,20302480,BR5,NULL,NULL,546450,170250,NULL,NULL
173832141,30/12/2014 19:32,2014,2014/15,Special Service,1,2,295,590,CAT TRAPPED  IN PARTITION  WALL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000336,Northwood Hills,E09000017,Hillingdon,Ruislip,NULL,Woodgate Crescent,21402205,HA6,NULL,NULL,510450,191550,NULL,NULL
304151,01/01/2015 13:45,2015,2014/15,Special Service,1,1,295,295,TERRIER IN BURROW,Dog,Person (mobile),Heathland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000520,Hampton,E09000027,Richmond upon Thames,Twickenham,10070720201,Hampton Court Road,22406635,KT8,516325,169113,516350,169150,51.40911893,-0.328843467
468151,01/01/2015 21:22,2015,2014/15,Special Service,1,1,295,295,PERSON STUCK UP TREE,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011098,Chaucer,E09000028,Southwark,Dockhead,2.00003E+11,Staple Street,22502372,SE1,532836,179512,532850,179550,51.4989479,-0.087675106
1074151,03/01/2015 10:47,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH KITTEN IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000210,Town,E09000010,Enfield,Enfield,NULL,Lavender Hill,20705086,EN2,NULL,NULL,532250,197850,NULL,NULL
1528151,04/01/2015 14:12,2015,2014/15,Special Service,1,2,295,590,Redacted,Dog,Person (mobile),Hedge,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011469,Kenley,E09000008,Croydon,Purley,NULL,Godstone Road,NULL,CR8,531920,160611,531950,160650,51.32930299,-0.107889502
2217151,06/01/2015 10:17,2015,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000105,Willesden Green,E09000005,Brent,Willesden,NULL,Villiers Road,20202068,NW2,NULL,NULL,522450,184850,NULL,NULL
2270151,06/01/2015 12:39,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Stoke Newington,1.00021E+11,Cleveleys Road,20900253,E5,534978,186490,534950,186450,51.56114702,-0.054162955
2291151,06/01/2015 13:40,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011226,Falconwood & Welling,E09000004,Bexley,Bexley,NULL,South Gipsy Road,20101316,DA16,NULL,NULL,547550,175750,NULL,NULL
2815151,07/01/2015 22:06,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN WASHING MACHINE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000530,Teddington,E09000027,Richmond upon Thames,Twickenham,NULL,Sutherland Grove,22403864,TW11,NULL,NULL,515550,171150,NULL,NULL
3211151,08/01/2015 21:57,2015,2014/15,Special Service,1,1,295,295,CAT LOCKED IN SHED,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011470,New Addington North,E09000008,Croydon,Addington,NULL,Fieldway,20501718,CR0,NULL,NULL,537950,163550,NULL,NULL
3409151,09/01/2015 13:06,2015,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000326,Brunel,E09000017,Hillingdon,Hillingdon,NULL,Little London Close,NULL,UB8,507800,181735,507850,181750,51.52425258,-0.447522188
4360151,11/01/2015 14:08,2015,2014/15,Special Service,1,1,295,295,DOG WITH PAW STUCK DOWN PLUGHOLE,Dog,Police,House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000119,Hayes and Coney Hall,E09000006,Bromley,Addington,NULL,Chestnut Avenue,20303031,BR4,NULL,NULL,539450,164550,NULL,NULL
4390151,11/01/2015 16:16,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND ADVERTISING BOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000572,Worcester Park,E09000029,Sutton,Sutton,NULL,Central Road,22601354,KT4,NULL,NULL,522550,165850,NULL,NULL
4484151,11/01/2015 20:19,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND AND UNDER SINK,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011471,New Addington South,E09000008,Croydon,Addington,NULL,Calley Down Crescent,20501677,CR0,NULL,NULL,538950,161950,NULL,NULL
4551151,12/01/2015 01:49,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA TO GAIN ENTRY,Unknown - Domestic Animal Or Pet,Person (mobile),Other car park structure,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000251,Askew,E09000013,Hammersmith and Fulham,Hammersmith,34149446,Goldhawk Road,21000380,W12,522634,179482,522650,179450,51.50098137,-0.234584045
4678151,12/01/2015 12:07,2015,2014/15,Special Service,1,1,295,295,HARRIS HAWK IMPAILED ON ANTI PIGEON SPIKE,Bird,Person (land line),Art Gallery,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,St James's,E09000033,Westminster,Soho,1.00023E+11,Trafalgar Square,8400264,WC2N,529997,180557,529950,180550,51.50899852,-0.12816685
4693151,12/01/2015 12:51,2015,2014/15,Special Service,1,1,295,295,CAT ON RAILWAY LINE,Cat,Person (land line),Railway trackside vegetation,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000204,Lower Edmonton,E09000010,Enfield,Edmonton,NULL,Bury Street,NULL,N9,534049,194378,534050,194350,51.63225219,-0.064546206
5489151,14/01/2015 14:26,2015,2014/15,Special Service,1,1,295,295,KITTEN IN PRECARIOUS POSITION,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011113,Rye Lane,E09000028,Southwark,Peckham,NULL,Waghorn Street,22502636,SE15,NULL,NULL,534150,175850,NULL,NULL
5842151,15/01/2015 10:26,2015,2014/15,Special Service,1,1,295,295,CAT WITH HEAD TRAPPED BEHIND PIPE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000031,Eastbury,E09000002,Barking and Dagenham,Barking,NULL,Lambourne Road,19900527,IG11,NULL,NULL,545450,184150,NULL,NULL
6442151,16/01/2015 18:39,2015,2014/15,Special Service,1,1,295,295,DOG TRAPPED BETWEEN TWO FENCES,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000106,Bickley,E09000006,Bromley,Bromley,NULL,Stoneleigh Road,NULL,BR1,543680,168578,543650,168550,51.39804753,0.064007054
6714151,17/01/2015 13:29,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED  IN THE BASEMENT,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,34032546,Uxbridge Road,21000835,W12,523610,179924,523650,179950,51.50474142,-0.220375016
7148151,18/01/2015 12:58,2015,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER DECKING,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011466,Coulsdon Town,E09000008,Croydon,Purley,1.00021E+11,Woodplace Lane,20502536,CR5,529446,157377,529450,157350,51.30080578,-0.144551503
7492151,19/01/2015 08:19,2015,2014/15,Special Service,1,2,295,590,HORSE STUCK IN A DITCH,Horse,Person (land line),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011218,Belvedere,E09000004,Bexley,Erith,NULL,St. Thomas Road,NULL,DA17,550117,179667,550150,179650,51.49601893,0.161171192
7611151,19/01/2015 13:36,2015,2014/15,Special Service,1,1,295,295,SQUIRREL TRAPPED IN HOLE,Squirrel,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000615,Furzedown,E09000032,Wandsworth,Tooting,1.00023E+11,Freshwater Road,22901806,SW17,528395,171030,528350,171050,51.42374511,-0.154694938
7898151,20/01/2015 04:08,2015,2014/15,Special Service,1,1,295,295,DOG WITH HEAD TRAPPED IN FENCE,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000302,Roxeth,E09000015,Harrow,Northolt,NULL,Tregenna Avenue,NULL,HA2,513432,185887,513450,185850,51.56046776,-0.365037756
8063151,20/01/2015 13:32,2015,2014/15,Special Service,1,1,295,295,FOX TRAPPED,Fox,Person (land line),Day care/Drop in centre,Non Residential,Other animal assistance,Assist trapped wild animal,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,6027320,Glamis Road,22700541,E1W,535420,180901,535450,180950,51.51081711,-0.04993811
8071151,20/01/2015 13:47,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000209,Southgate Green,E09000010,Enfield,Southgate,NULL,Townsend Avenue,20704855,N14,NULL,NULL,529950,192950,NULL,NULL
8731151,22/01/2015 03:10,2015,2014/15,Special Service,1,1,295,295,FOX TRAPPED BY ITS HIPS IN WROUGHT IRON GATE,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000250,Addison,E09000013,Hammersmith and Fulham,Hammersmith,34033154,Addison Gardens,21000029,W14,523782,179497,523750,179450,51.50086623,-0.21804784
9318151,23/01/2015 08:12,2015,2014/15,Special Service,2,3,295,885,HORSE WITH LEG TRAPPED IN HARROW,Horse,Person (mobile),Agricultural equipment,Outdoor Structure,Other animal assistance,Assist  trapped livestock animal,E05000119,Hayes and Coney Hall,E09000006,Bromley,Addington,NULL,Gates Green Road,NULL,BR4,540586,164653,540550,164650,51.3635522,0.018011771
9641151,23/01/2015 21:42,2015,2014/15,Special Service,1,2,295,590,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009328,Poplar,E09000030,Tower Hamlets,Poplar,NULL,Cottage Street,22700369,E14,NULL,NULL,537950,180950,NULL,NULL
9717151,24/01/2015 00:15,2015,2014/15,Special Service,1,2,295,590,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009328,Poplar,E09000030,Tower Hamlets,Poplar,NULL,Cottage Street,22700369,E14,NULL,NULL,537950,180950,NULL,NULL
10144151,25/01/2015 00:28,2015,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000046,Colindale,E09000003,Barnet,Hendon,NULL,Sheaveshill Avenue,NULL,NW9,521497,189759,521450,189750,51.59359,-0.247409176
11062151,27/01/2015 09:35,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000488,Manor Park,E09000025,Newham,Stratford,NULL,Carlyle Road,22200136,E12,NULL,NULL,542050,185650,NULL,NULL
11479151,28/01/2015 09:25,2015,2014/15,Special Service,1,1,295,295,CAT STUCK BEHIND WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000201,Haselbury,E09000010,Enfield,Edmonton,NULL,Hydefield Court,20704361,N9,NULL,NULL,533350,193850,NULL,NULL
11649151,28/01/2015 17:28,2015,2014/15,Special Service,1,4,295,1180,DOG TRAPPED IN FOX HOLE AT THE SIDE OF PUB HSE,Dog,Other FRS,Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000405,Chessington South,E09000021,Kingston upon Thames,Surbiton,NULL,Moor Lane,NULL,KT9,519243,163990,519250,163950,51.36246843,-0.288624467
11892151,29/01/2015 09:29,2015,2014/15,Special Service,1,1,295,295,ANIMAL IN PRECARIOUS POSITION,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000032,Gascoigne,E09000002,Barking and Dagenham,Barking,100055986,Greatfields Road,19900681,IG11,544893,183306,544850,183350,51.53007938,0.08746393
11907151,29/01/2015 10:08,2015,2014/15,Special Service,2,7,295,2065,HORSE STUCK IN MUD,Horse,Other FRS,"Grassland, pasture, grazing etc",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05011231,Slade Green & Northend,E09000004,Bexley,Erith,1.00023E+11,Moat Lane,20100987,DA8,552723,176642,552750,176650,51.46814204,0.197374549
13074151,01/02/2015 10:39,2015,2014/15,Special Service,1,1,295,295,CROW TRAPPED IN NETTING,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000129,Bloomsbury,E09000007,Camden,Euston,5107265,Huntley Street,20400955,WC1E,529535,182078,529550,182050,51.52277356,-0.134261639
13558151,02/02/2015 10:51,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA INSPECTOR WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Eltham,1.00021E+11,Churchbury Road,20800337,SE9,541500,173758,541550,173750,51.44514199,0.034758477
13585151,02/02/2015 11:56,2015,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN FIRE PLACE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000354,Hanworth Park,E09000018,Hounslow,Feltham,NULL,Elmwood Avenue,21500403,TW13,NULL,NULL,511150,172150,NULL,NULL
14102151,03/02/2015 13:57,2015,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Bird,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,NULL,Southwark Park Road,NULL,SE16,534736,179437,534750,179450,51.49782445,-0.060347832
14425151,04/02/2015 10:06,2015,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05009386,Victoria,E09000012,Hackney,Homerton,NULL,Lauriston Road,NULL,E9,535506,184259,535550,184250,51.54097218,-0.04740852
15164151,05/02/2015 21:11,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN GARAGE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000035,Longbridge,E09000002,Barking and Dagenham,Barking,NULL,Westrow Drive,19900382,IG11,NULL,NULL,546050,185450,NULL,NULL
15387151,06/02/2015 14:17,2015,2014/15,Special Service,1,1,295,295,DOG IN PRECARIOUS POSITON,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000376,Junction,E09000019,Islington,Kentish Town,NULL,Hornsey Lane,21606907,N6,NULL,NULL,528950,187250,NULL,NULL
15734151,07/02/2015 08:44,2015,2014/15,Special Service,1,1,295,295,BIRD OF PREY SNAGGED IN TV CABLE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Greenhill Gardens,20600774,UB5,NULL,NULL,512650,183150,NULL,NULL
16278151,08/02/2015 13:12,2015,2014/15,Special Service,1,2,295,590,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000111,Chislehurst,E09000006,Bromley,Bromley,NULL,Waratah Drive,NULL,BR7,543073,171058,543050,171050,51.42048528,0.05628742
16842151,09/02/2015 16:39,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000621,Roehampton and Putney Heath,E09000032,Wandsworth,Wandsworth,NULL,Foxcombe Road,NULL,SW15,522334,173377,522350,173350,51.44617768,-0.241015281
17603151,11/02/2015 13:17,2015,2014/15,Special Service,1,1,295,295,REQUEST TO ASSIST RSPCA WITH CAT TRAPPED IN TREE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,10002255588,Melliss Avenue,22407046,TW9,519801,177002,519850,177050,51.4792987,-0.276220509
17917151,12/02/2015 09:46,2015,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000031,Eastbury,E09000002,Barking and Dagenham,Barking,NULL,Lambourne Road,19900527,IG11,NULL,NULL,545450,184050,NULL,NULL
18008151,12/02/2015 13:36,2015,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000451,Rushey Green,E09000023,Lewisham,Lewisham,NULL,Carswell Road,NULL,SE6,538241,173803,538250,173850,51.44635008,-0.012089197
18410151,13/02/2015 15:27,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA TO RESCUE CAT IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011245,Hainault,E09000026,Redbridge,Hainault,1.00022E+11,Tine Road,22304748,IG7,545304,192308,545350,192350,51.61086059,0.097103972
18454151,13/02/2015 16:55,2015,2014/15,Special Service,1,1,295,295,SQUIRELL TRAPPED IN BIN,Squirrel,Person (mobile),Small refuse/rubbish container,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000628,West Hill,E09000032,Wandsworth,Wandsworth,10033241589,West Hill,22906045,SW15,524336,174218,524350,174250,51.4533007,-0.211926524
18554151,13/02/2015 20:00,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011247,Loxford,E09000026,Redbridge,Ilford,NULL,Roman Road,22304210,IG1,NULL,NULL,544050,184950,NULL,NULL
18824151,14/02/2015 13:54,2015,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009402,Redcliffe,E09000020,Kensington and Chelsea,Chelsea,NULL,Redcliffe Gardens,21700450,SW5,NULL,NULL,525850,178250,NULL,NULL
18990151,14/02/2015 22:51,2015,2014/15,Special Service,1,1,295,295,Redacted,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011102,Faraday,E09000028,Southwark,Old Kent Road,NULL,Dawes Street,NULL,SE17,532795,178433,532750,178450,51.48926092,-0.088671152
19171151,15/02/2015 12:52,2015,2014/15,Special Service,1,1,295,295,FOX IN PRECARIOUS POSITION,Fox,Other FRS,Single shop,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,128035984,Adams Walk,21820112,KT1,518216,169262,518250,169250,51.41006734,-0.301617452
19218151,15/02/2015 15:46,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000609,Wood Street,E09000031,Waltham Forest,Walthamstow,NULL,Fyfield Road,22838600,E17,NULL,NULL,538650,189950,NULL,NULL
19265151,15/02/2015 17:11,2015,2014/15,Special Service,1,1,295,295,DOG STUCK IN TREE,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000044,Burnt Oak,E09000003,Barnet,Mill Hill,NULL,Blundell Road,NULL,HA8,521334,191347,521350,191350,51.60789653,-0.249213191
20423151,18/02/2015 11:06,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH TRAPPED FOX,Fox,Person (land line),Purpose built office,Non Residential,Other animal assistance,Assist trapped wild animal,E05000370,Clerkenwell,E09000019,Islington,Shoreditch,10090551507,Cowcross Street,21604616,EC1M,531651,181854,531650,181850,51.5202718,-0.103862859
20664151,18/02/2015 20:22,2015,2014/15,Special Service,1,1,295,295,CAT STUCK IN CULVERT,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000454,Whitefoot,E09000023,Lewisham,Lewisham,NULL,Waterbank Road,NULL,SE6,538015,172043,538050,172050,51.43058927,-0.016024477
21137151,20/02/2015 07:41,2015,2014/15,Special Service,1,2,295,590,KITTEN STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000618,Nightingale,E09000032,Wandsworth,Tooting,NULL,Trinity Road,22906604,SW17,NULL,NULL,527650,172950,NULL,NULL
22217151,22/02/2015 15:52,2015,2014/15,Special Service,1,1,295,295,ASSIST MOP WITH INJURED DOG,Dog,Person (land line),Fire station,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000320,St Andrew's,E09000016,Havering,Hornchurch,1.00023E+11,North Street,21300513,RM11,554003,187415,554050,187450,51.56458893,0.220490683
22630151,23/02/2015 18:41,2015,2014/15,Special Service,1,1,295,295,CAT STUCK ON WINDOW LEDGE UNABLE TO GET DOWN,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000494,West Ham,E09000025,Newham,Stratford,NULL,Redriffe Road,22201565,E13,NULL,NULL,539850,183550,NULL,NULL
22710151,23/02/2015 23:38,2015,2014/15,Special Service,1,1,295,295,CAT IN THE RIVER,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000362,Isleworth,E09000018,Hounslow,Heston,NULL,Pankhurst Close,NULL,TW7,515880,175981,515850,175950,51.47093784,-0.332992584
24550151,28/02/2015 15:34,2015,2014/15,Special Service,1,1,295,295,CAT UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,1.00023E+11,Valley Side,22883450,E4,537400,194193,537450,194150,51.629781,-0.016231551
24859151,01/03/2015 11:16,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN WALL RECESS,Cat,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000362,Isleworth,E09000018,Hounslow,Heston,1.00022E+11,Pankhurst Close,21501694,TW7,515908,175973,515950,175950,51.47086021,-0.33259225
24944151,01/03/2015 14:05,2015,2014/15,Special Service,1,1,295,295,BIRDS TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000438,Blackheath,E09000023,Lewisham,Greenwich,NULL,Lethbridge Close,22004663,SE13,NULL,NULL,538150,176550,NULL,NULL
24960151,01/03/2015 14:43,2015,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN FIREPLACE,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000612,Earlsfield,E09000032,Wandsworth,Wandsworth,NULL,St. John's Drive,22904856,SW18,NULL,NULL,525750,173350,NULL,NULL
25022151,01/03/2015 17:03,2015,2014/15,Special Service,1,1,295,295,CAT STUCK BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011476,Purley & Woodcote,E09000008,Croydon,Purley,NULL,Partridge Knoll,20502261,CR8,NULL,NULL,531850,160950,NULL,NULL
25078151,01/03/2015 19:12,2015,2014/15,Special Service,1,1,295,295,PUPPY WITH HEAD TRAPPED IN RAILINGS,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009402,Redcliffe,E09000020,Kensington and Chelsea,Chelsea,NULL,Redcliffe Place,21700452,SW10,NULL,NULL,526050,177750,NULL,NULL
25686151,03/03/2015 09:54,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT UP A TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000331,Heathrow Villages,E09000017,Hillingdon,Hayes,1.00021E+11,Shortlands,21401750,UB3,508821,177620,508850,177650,51.48707052,-0.434079377
26576151,05/03/2015 10:12,2015,2014/15,Special Service,NULL,NULL,295,NULL,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000334,Manor,E09000017,Hillingdon,Ruislip,NULL,Bessingby Road,NULL,HA4,510774,186703,510750,186750,51.56832999,-0.403108673
26999151,06/03/2015 10:08,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH INJURED HERON,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving wild animal,E05000261,Ravenscourt Park,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Black Lion Mews,21001189,W6,NULL,NULL,522050,178550,NULL,NULL
27632151,07/03/2015 17:59,2015,2014/15,Special Service,1,1,295,295,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000058,Oakleigh,E09000003,Barnet,Barnet,NULL,Loring Road,20027400,N20,NULL,NULL,527050,193650,NULL,NULL
29906151,12/03/2015 20:04,2015,2014/15,Special Service,1,1,295,295,CAT WITH HEAD STUCK IN CHAIR,Cat,Police,Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000284,Woodside,E09000014,Haringey,Tottenham,NULL,Olympus Grove,21107058,N22,NULL,NULL,531350,190750,NULL,NULL
30334151,13/03/2015 18:04,2015,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN BRANCHES IN REAR GARDEN,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000105,Willesden Green,E09000005,Brent,Willesden,NULL,Chapter Road,20201486,NW2,NULL,NULL,522850,184950,NULL,NULL
31411151,16/03/2015 09:03,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH KITTEN IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,1.00023E+11,Ommaney Road,22000770,SE14,535973,176485,535950,176450,51.47100038,-0.043674722
31852151,17/03/2015 10:56,2015,2014/15,Special Service,1,2,295,590,HORSE TRAPPED IN HORSEBOX,Horse,Person (mobile),Agricultural vehicle,Road Vehicle,Other animal assistance,Assist  trapped livestock animal,E05000108,Bromley Common and Keston,E09000006,Bromley,Biggin Hill,1.0002E+11,Jackass Lane,20300016,BR2,541322,163884,541350,163850,51.35645973,0.028271547
32395151,18/03/2015 16:18,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT UP A TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011484,South Croydon,E09000008,Croydon,Croydon,NULL,Campion Close,NULL,CR0,533340,164615,533350,164650,51.3649551,-0.086021581
33118151,20/03/2015 09:25,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA FOR TRAPPED BIRDS,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000218,Eltham North,E09000011,Greenwich,Eltham,NULL,Well Hall Road,NULL,SE9,542592,174942,542550,174950,51.45550756,0.05093801
33665151,21/03/2015 16:40,2015,2014/15,Special Service,1,2,295,590,DOG TRAPPED DOWN HOLE,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000171,Cleveland,E09000009,Ealing,Ealing,12074960,Kent Avenue,20600973,W13,516585,181781,516550,181750,51.52292241,-0.32093185
33908151,22/03/2015 10:48,2015,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER STONE SHED,Dog,Person (mobile),Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000562,St Helier,E09000029,Sutton,Sutton,5870041665,Love Lane,22600746,SM4,525625,166807,525650,166850,51.38641175,-0.196008539
33997151,22/03/2015 14:58,2015,2014/15,Special Service,1,1,295,295,TRAPPED BIRD,Bird,Person (land line),Hotel/motel,Other Residential,Animal rescue from height,Animal rescue from height - Bird,E05000472,Village,E09000024,Merton,Wimbledon,48072645,West Side Common,22106645,SW19,523233,170939,523250,170950,51.42407158,-0.228932728
34269151,23/03/2015 07:13,2015,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN CHMINEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,NULL,Romborough Gardens,22001854,SE13,NULL,NULL,538150,174750,NULL,NULL
34492151,23/03/2015 17:51,2015,2014/15,Special Service,1,1,295,295,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Homerton,NULL,Fletching Road,20900413,E5,NULL,NULL,535250,186150,NULL,NULL
34499151,23/03/2015 18:12,2015,2014/15,Special Service,1,1,295,295,CAT STUCK BETWEEN WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Greenwich South Street,20800688,SE10,NULL,NULL,537950,176850,NULL,NULL
34789151,24/03/2015 13:51,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND GARAGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000427,Prince's,E09000022,Lambeth,Lambeth,NULL,Lambeth Walk,21900831,SE11,NULL,NULL,530850,178750,NULL,NULL
34920151,24/03/2015 19:02,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER DASH BOARD OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000171,Cleveland,E09000009,Ealing,Ealing,NULL,Pitshanger Lane,NULL,W5,516990,182111,516950,182150,51.52580473,-0.31498735
35101151,25/03/2015 07:28,2015,2014/15,Special Service,1,1,295,295,DUCKLINGS FALLEN INTO DRAIN,Bird,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009323,Canary Wharf,E09000030,Tower Hamlets,Millwall,NULL,Old Bellgate Place,NULL,E14,537144,179084,537150,179050,51.49407342,-0.025816608
35341151,25/03/2015 18:23,2015,2014/15,Special Service,1,1,295,295,DUCKLINGS IN DISTRESS,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05009323,Canary Wharf,E09000030,Tower Hamlets,Millwall,NULL,Westferry Road,NULL,E14,537053,180032,537050,180050,51.50261456,-0.026758469
35594151,26/03/2015 10:12,2015,2014/15,Special Service,1,1,295,295,CAT POSSIBLY TRAPPED BEHIND SINK,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011107,North Walworth,E09000028,Southwark,Old Kent Road,NULL,Barlow Street,22500153,SE17,NULL,NULL,532850,178750,NULL,NULL
35603151,26/03/2015 10:53,2015,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000032,Gascoigne,E09000002,Barking and Dagenham,Barking,NULL,Sutton Road,19900602,IG11,NULL,NULL,545350,183550,NULL,NULL
36503151,28/03/2015 14:30,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA BIRD STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000275,Noel Park,E09000014,Haringey,Tottenham,NULL,Farrant Avenue,21104519,N22,NULL,NULL,531550,190350,NULL,NULL
36539151,28/03/2015 15:20,2015,2014/15,Special Service,1,1,295,295,DOG LOCKED IN BEDROOM,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000372,Highbury East,E09000019,Islington,Stoke Newington,NULL,Riversdale Road,21603742,N5,NULL,NULL,532050,186150,NULL,NULL
36718151,28/03/2015 22:46,2015,2014/15,Special Service,1,1,295,295,CAT STUCK ON WINDOW SILL AT THIRD FLOOR LEVEL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000623,Shaftesbury,E09000032,Wandsworth,Clapham,NULL,Nansen Road,22903466,SW11,NULL,NULL,528350,175550,NULL,NULL
37273151,30/03/2015 09:24,2015,2014/15,Special Service,1,1,295,295,ANIMAL TRAPPED IN FIRE BREAST,Unknown - Wild Animal,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000605,Leytonstone,E09000031,Waltham Forest,Leytonstone,NULL,Grove Green Road,22841400,E11,NULL,NULL,538850,187150,NULL,NULL
37876151,31/03/2015 14:11,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN FIREPLACE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000207,Southbury,E09000010,Enfield,Enfield,NULL,Queens Road,20702914,EN1,NULL,NULL,533450,196450,NULL,NULL
37998151,31/03/2015 17:46,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,2.00003E+11,Southwark Park Road,22502293,SE16,534791,178829,534750,178850,51.49234759,-0.059788129
38268151,01/04/2015 11:28,2015,2015/16,Special Service,1,1,298,298,Redacted,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000258,North End,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Perham Road,21000637,W14,NULL,NULL,524450,178050,NULL,NULL
38468151,01/04/2015 17:07,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000113,Copers Cope,E09000006,Bromley,Beckenham,NULL,Springpark Drive,20302029,BR3,NULL,NULL,538550,168850,NULL,NULL
38539151,01/04/2015 19:13,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN TREE,Bird,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,NULL,New Place Square,NULL,SE16,534718,179451,534750,179450,51.49795455,-0.060601642
38795151,02/04/2015 09:22,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT TRAPPED UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000175,East Acton,E09000009,Ealing,Acton,NULL,Davis Road,20600514,W3,NULL,NULL,521750,179950,NULL,NULL
38881151,02/04/2015 13:55,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON MIDDLE BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,NULL,Royal Road,22401806,TW11,NULL,NULL,514850,171450,NULL,NULL
39056151,02/04/2015 20:23,2015,2015/16,Special Service,1,1,298,298,CAT UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000207,Southbury,E09000010,Enfield,Enfield,NULL,Queens Road,20702914,EN1,NULL,NULL,533450,196450,NULL,NULL
39445151,03/04/2015 17:20,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON ROOF - SUSPECTED BROKEN LEG,Cat,Person (land line),Common external bin storage area,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,12046702,Sussex Crescent,20601670,UB5,513237,184780,513250,184750,51.55055727,-0.368205608
39471151,03/04/2015 18:15,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN VENT  ONE TL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000648,Westbourne,E09000033,Westminster,Paddington,NULL,Lord Hills Road,8400851,W2,NULL,NULL,525850,181850,NULL,NULL
40295151,05/04/2015 15:59,2015,2015/16,Special Service,1,2,298,596,CAT TRAPPED IN BRAMBLE BUSH,Cat,Person (mobile),Scrub land,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000467,Pollards Hill,E09000024,Merton,Norbury,NULL,Commonside East,NULL,CR4,529301,168252,529350,168250,51.39857337,-0.142683438
40589151,06/04/2015 09:50,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND BATHTUB,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000284,Woodside,E09000014,Haringey,Tottenham,NULL,Ellenborough Road,21104494,N22,NULL,NULL,532050,190750,NULL,NULL
40953151,07/04/2015 09:08,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000275,Noel Park,E09000014,Haringey,Tottenham,NULL,Willingdon Road,21105242,N22,NULL,NULL,531650,190050,NULL,NULL
41008151,07/04/2015 12:02,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED UNDER SHED,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000045,Childs Hill,E09000003,Barnet,West Hampstead,NULL,Westcroft Close,20400111,NW2,NULL,NULL,524250,185750,NULL,NULL
41545151,08/04/2015 17:05,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN RAILINGS,Dog,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000058,Oakleigh,E09000003,Barnet,Barnet,200115352,The Fairway,20015220,EN5,525913,195507,525950,195550,51.64427697,-0.18162866
42072151,09/04/2015 19:37,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON SCAFFOLDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,Orchard Place,21104873,N17,NULL,NULL,533750,191050,NULL,NULL
42095151,09/04/2015 20:22,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND THE INSIDE WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011233,West Heath,E09000004,Bexley,Erith,NULL,Oakhurst Avenue,20101054,DA7,NULL,NULL,548250,177050,NULL,NULL
42356151,10/04/2015 12:55,2015,2015/16,Special Service,1,1,298,298,PIGEON TRAPPED IN NETTING UNDER RAILWAY BRIDGE,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000109,Bromley Town,E09000006,Bromley,Bromley,NULL,Palace View,NULL,BR1,540560,168819,540550,168850,51.40099341,0.019283498
42369151,10/04/2015 13:30,2015,2015/16,Special Service,1,3,298,894,Redacted,Horse,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05011218,Belvedere,E09000004,Bexley,Erith,NULL,Crabtree Manorway North,NULL,DA17,550176,179738,550150,179750,51.49664124,0.162050703
42685151,11/04/2015 06:43,2015,2015/16,Special Service,1,1,298,298,CAT STUCK DOWN HOLE,Cat,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000423,Herne Hill,E09000022,Lambeth,West Norwood,NULL,Effra Road,NULL,SW2,531345,174405,531350,174450,51.45340054,-0.111039348
43549151,12/04/2015 20:36,2015,2015/16,Special Service,1,1,298,298,CAT FALLEN AND TRAPPED BETWEEN WALL AND HEDGE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009396,Golborne,E09000020,Kensington and Chelsea,North Kensington,NULL,Portobello Road,21700970,W10,NULL,NULL,524250,181750,NULL,NULL
43845151,13/04/2015 15:07,2015,2015/16,Special Service,1,1,298,298,BIRD WITH LEG TRAPPED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05000559,Carshalton South and Clockhouse,E09000029,Sutton,Wallington,NULL,Kenny Drive,NULL,SM5,528152,162335,528150,162350,51.34565681,-0.161323007
44818151,15/04/2015 11:47,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000331,Heathrow Villages,E09000017,Hillingdon,Surrey,NULL,Spout Lane North,NULL,TW19,504706,174740,504750,174750,51.4619599,-0.49417632
45198151,15/04/2015 21:53,2015,2015/16,Special Service,1,1,298,298,ABANDONED CALL TO FIRE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000196,Cockfosters,E09000010,Enfield,Barnet,NULL,Cockfosters Road,20701010,EN4,NULL,NULL,528150,196250,NULL,NULL
45671151,16/04/2015 18:48,2015,2015/16,Special Service,1,1,298,298,INJURED CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009323,Canary Wharf,E09000030,Tower Hamlets,Millwall,6062865,The Quarterdeck,22702098,E14,537179,179628,537150,179650,51.49895351,-0.025101313
46011151,17/04/2015 13:41,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000612,Earlsfield,E09000032,Wandsworth,Tooting,NULL,Garratt Lane,NULL,SW18,526031,173008,526050,173050,51.44205189,-0.187975048
46042151,17/04/2015 14:31,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000060,Underhill,E09000003,Barnet,Mill Hill,NULL,Hyver Hill,20024560,NW7,NULL,NULL,520950,194750,NULL,NULL
46655151,18/04/2015 17:06,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009400,Pembridge,E09000020,Kensington and Chelsea,Kensington,217013002,Chepstow Crescent,21700705,W11,525119,180844,525150,180850,51.51267792,-0.198317929
47869151,20/04/2015 23:10,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN ALLEWAY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011486,Thornton Heath,E09000008,Croydon,Norbury,NULL,Annsworthy Crescent,20503127,SE25,NULL,NULL,532850,169050,NULL,NULL
48401151,21/04/2015 23:33,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED UNDER BATH,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Coleman Road,22500540,SE5,NULL,NULL,533150,177350,NULL,NULL
48648151,22/04/2015 14:44,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN NETTING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,NULL,Orange Grove,NULL,E11,NULL,NULL,539050,186350,NULL,NULL
48706151,22/04/2015 16:55,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009383,Springfield,E09000012,Hackney,Stoke Newington,NULL,Warwick Grove,NULL,E5,534645,187017,534650,187050,51.5659624,-0.058762469
48988151,23/04/2015 08:17,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH KITTEN STUCK IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000598,Hatch Lane,E09000031,Waltham Forest,Woodford,1.00023E+11,Balliol Avenue,22812900,E4,539021,192766,539050,192750,51.61656068,0.006607139
49145151,23/04/2015 13:14,2015,2015/16,Special Service,1,2,298,596,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000532,West Twickenham,E09000027,Richmond upon Thames,Twickenham,NULL,Chertsey Road,22406590,TW2,NULL,NULL,514250,173250,NULL,NULL
49438151,23/04/2015 21:08,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED ON RAILWAY,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011476,Purley & Woodcote,E09000008,Croydon,Purley,1.00021E+11,Beaumont Road,20501910,CR8,531232,161051,531250,161050,51.33341619,-0.117596988
49827151,24/04/2015 17:26,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000037,Parsloes,E09000002,Barking and Dagenham,Dagenham,NULL,Bonham Road,19900048,RM8,NULL,NULL,547850,186150,NULL,NULL
49874151,24/04/2015 18:40,2015,2015/16,Special Service,1,2,298,596,CAT TRAPPED IN BETWEEN BUILDINGS,Cat,Person (land line),Pre School/nursery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000032,Gascoigne,E09000002,Barking and Dagenham,Barking,100102851,The Coverdales,19900678,IG11,544450,183508,544450,183550,51.53200784,0.081164761
49954151,24/04/2015 21:01,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Fore Street,NULL,N18,NULL,NULL,534050,192050,NULL,NULL
50155151,25/04/2015 08:03,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BETWEEN BUILDINGS,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000178,Greenford Green,E09000009,Ealing,Northolt,12054301,Castle Road,20600331,UB5,514107,184549,514150,184550,51.54830611,-0.355737881
50218151,25/04/2015 11:20,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND WALL,Cat,Person (land line),Pre School/nursery,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000032,Gascoigne,E09000002,Barking and Dagenham,Barking,100102851,The Coverdales,19900678,IG11,544450,183508,544450,183550,51.53200784,0.081164761
50447151,25/04/2015 20:36,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN CHIMNEY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000090,Fryent,E09000005,Brent,Stanmore,NULL,Church Lane,20200700,NW9,NULL,NULL,520550,187950,NULL,NULL
50849151,27/04/2015 00:28,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN ROOF SPACE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000040,Valence,E09000002,Barking and Dagenham,Dagenham,NULL,Chittys Lane,19900082,RM8,NULL,NULL,547550,187150,NULL,NULL
51015151,27/04/2015 15:28,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN GATE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011470,New Addington North,E09000008,Croydon,Addington,1.00021E+11,Bygrove,20502685,CR0,537451,163537,537450,163550,51.35428881,-0.027422536
51389151,28/04/2015 11:54,2015,2015/16,Special Service,2,3,298,894,HORSE STUCK UP SIDE DOWN IN DITCH,Horse,Person (mobile),Hedge,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000297,Pinner,E09000015,Harrow,Harrow,10000001399,Woodhall Road,21201827,HA5,511790,191712,511750,191750,51.61315119,-0.386860853
51957151,29/04/2015 11:53,2015,2015/16,Special Service,1,1,298,298,CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,NULL,Kitchener Road,19900509,RM10,NULL,NULL,549950,184850,NULL,NULL
52040151,29/04/2015 15:10,2015,2015/16,Special Service,1,2,298,596,SHEEP TRAPPED INTO RIVER LEA,Sheep,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000206,Ponders End,E09000010,Enfield,Enfield,NULL,Wharf Road,NULL,EN3,536306,195443,536350,195450,51.64127972,-0.031541196
52059151,29/04/2015 15:57,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN MESH AND NETTING UP TREE,Cat,Police,Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000376,Junction,E09000019,Islington,Kentish Town,NULL,Larch Close,NULL,N19,529177,186618,529150,186650,51.56365575,-0.137753482
52172151,29/04/2015 20:42,2015,2015/16,Special Service,1,1,298,298,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Train station - elsewhere,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000416,Bishop's,E09000022,Lambeth,Lambeth,10000444312,Waterloo Station,21902368,SE1,531050,179932,531050,179950,51.50313881,-0.113234309
52605151,30/04/2015 16:19,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA,Unknown - Wild Animal,Person (land line),TV/film/music/art studio,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,1.00023E+11,West Cross Way,21501481,TW8,516515,177579,516550,177550,51.48517021,-0.323327869
52928151,01/05/2015 09:30,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH TRAPPED BIRD,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000109,Bromley Town,E09000006,Bromley,Bromley,NULL,Ethelbert Road,NULL,BR1,540265,168955,540250,168950,51.40228841,0.015099277
53373151,02/05/2015 08:53,2015,2015/16,Special Service,1,2,298,596,PROTECTED BIRD STUCK IN NETTING,Bird,Person (mobile),Sports pavilion/shower block/changing facility,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,10002167250,Wood Lane,19900414,RM8,549547,187053,549550,187050,51.56253454,0.156098585
53466151,02/05/2015 13:50,2015,2015/16,Special Service,1,1,298,298,INCOMPLETE CALL TO BIN ALIGHT,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009329,St Dunstan's,E09000030,Tower Hamlets,Shadwell,NULL,York Square,NULL,E14,536150,181221,536150,181250,51.51351745,-0.039301561
53629151,02/05/2015 19:04,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND OVEN,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000331,Heathrow Villages,E09000017,Hillingdon,Hayes,NULL,Cranford Lane,21400509,UB3,NULL,NULL,508750,177650,NULL,NULL
53675151,02/05/2015 20:33,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON ROOF UNDER TILE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,NULL,Clarence Road,22826950,E17,NULL,NULL,535950,190350,NULL,NULL
53821151,03/05/2015 06:29,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED INSIDE POOL TABLE,Cat,Person (mobile),Pub/wine bar/bar,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000425,Larkhall,E09000022,Lambeth,Clapham,1.00023E+11,Pensbury Street,21901091,SW8,529427,176351,529450,176350,51.47133044,-0.137916424
53869151,03/05/2015 10:03,2015,2015/16,Special Service,1,1,298,298,FOX TRAPPED IN GATE,Fox,Person (land line),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000312,Harold Wood,E09000016,Havering,Harold Hill,1.00021E+11,Kersey Gardens,21301321,RM3,554635,191063,554650,191050,51.59719194,0.23120501
54077151,03/05/2015 19:09,2015,2015/16,Special Service,1,1,298,298,KITTEN WITH PAW STUCK IN WINDOW FRAME,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000132,Fortune Green,E09000007,Camden,West Hampstead,NULL,Fordwych Road,20400109,NW2,NULL,NULL,524550,184950,NULL,NULL
54525151,04/05/2015 19:18,2015,2015/16,Special Service,1,1,298,298,PIDGEON TRAPPED IN CHIMNEY BREAST,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000120,Kelsey and Eden Park,E09000006,Bromley,Beckenham,NULL,Uplands,20301812,BR3,NULL,NULL,537250,168950,NULL,NULL
55583151,07/05/2015 08:30,2015,2015/16,Special Service,1,1,298,298,DEER TRAPPED IN METAL FENCE,Deer,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000117,Darwin,E09000006,Bromley,Biggin Hill,1.00023E+11,Single Street,20301537,TN16,543600,159928,543650,159950,51.32033994,0.059371536
56158151,08/05/2015 11:51,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED UNDER THE RADIATOR,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000320,St Andrew's,E09000016,Havering,Hornchurch,NULL,High Street,21300416,RM12,NULL,NULL,553650,187250,NULL,NULL
56234151,08/05/2015 14:40,2015,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011487,Waddon,E09000008,Croydon,Croydon,NULL,Old Town,20501261,CR0,NULL,NULL,531850,165250,NULL,NULL
56241151,08/05/2015 15:08,2015,2015/16,Special Service,1,1,298,298,BIRD CAUGHT IN WIRE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000377,Mildmay,E09000019,Islington,Islington,NULL,Pyrland Road,21603706,N5,NULL,NULL,532550,185250,NULL,NULL
56385151,08/05/2015 20:27,2015,2015/16,Special Service,1,2,298,596,Redacted,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,NULL,Wellington Street,NULL,SE18,543556,178810,543550,178850,51.49002075,0.066373554
56615151,09/05/2015 12:11,2015,2015/16,Special Service,2,1,298,298,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Fortis Green Road,21100314,N10,NULL,NULL,528450,189550,NULL,NULL
56791151,09/05/2015 18:41,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED UNDER ELECTRIC SOFA,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000309,Emerson Park,E09000016,Havering,Hornchurch,NULL,Rutland Drive,21300572,RM11,NULL,NULL,555050,188850,NULL,NULL
56884151,09/05/2015 20:51,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN HOLE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000479,Custom House,E09000025,Newham,Plaistow,NULL,Cundy Road,NULL,E16,NULL,NULL,541150,181050,NULL,NULL
57182151,10/05/2015 13:41,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000057,Mill Hill,E09000003,Barnet,Finchley,NULL,Bittacy Hill,NULL,NW7,524126,191404,524150,191450,51.6078011,-0.208894315
57527151,11/05/2015 08:55,2015,2015/16,Special Service,1,1,298,298,MAN AND DOG STUCK IN BROOK,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000059,Totteridge,E09000003,Barnet,Barnet,NULL,Macaret Close,NULL,N20,525712,194739,525750,194750,51.63742007,-0.184807771
58117151,12/05/2015 13:25,2015,2015/16,Special Service,1,1,298,298,DOG IN PRECARIOUS POSITION,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000308,Elm Park,E09000016,Havering,Hornchurch,NULL,Mungo Park Road,21300841,RM13,NULL,NULL,552350,184350,NULL,NULL
58129151,12/05/2015 13:35,2015,2015/16,Special Service,1,1,298,298,DOG COLLAR STUCK AROUND LEG AND NECK,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000209,Southgate Green,E09000010,Enfield,Southgate,NULL,Betstyle Road,20701335,N11,NULL,NULL,529050,192550,NULL,NULL
58293151,12/05/2015 18:54,2015,2015/16,Special Service,1,1,298,298,PIDGEON TRAPPED IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000087,Brondesbury Park,E09000005,Brent,West Hampstead,NULL,Brondesbury Park,NULL,NW6,524776,184460,524750,184450,51.54525137,-0.201977675
58870151,13/05/2015 20:43,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND PIPES,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Fortune Place,22503247,SE1,NULL,NULL,533950,178250,NULL,NULL
58891151,13/05/2015 21:46,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000350,Cranford,E09000018,Hounslow,Feltham,1.00022E+11,Burnham Gardens,21500181,TW4,510730,176578,510750,176550,51.47733429,-0.406922035
58912151,13/05/2015 22:33,2015,2015/16,Special Service,1,1,298,298,KITTENS STUCK DOWN HOLE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Fortune Place,22503247,SE1,NULL,NULL,533950,178250,NULL,NULL
58913151,13/05/2015 22:35,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND BOILER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000419,Clapham Town,E09000022,Lambeth,Clapham,NULL,Tessa Sanderson Place,21901978,SW8,NULL,NULL,528750,175950,NULL,NULL
59068151,14/05/2015 10:06,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000063,Woodhouse,E09000003,Barnet,Barnet,200141012,High Road,20022420,N20,526350,193143,526350,193150,51.62293445,-0.176169012
59441151,15/05/2015 02:36,2015,2015/16,Special Service,1,1,298,298,BABY FOX IN DISTRESS,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Grove Road,NULL,E3,NULL,NULL,536150,182950,NULL,NULL
59509151,15/05/2015 08:30,2015,2015/16,Special Service,1,1,298,298,Redacted,Unknown - Wild Animal,Person (land line),Lake/pond/reservoir,Outdoor,Other animal assistance,Assist trapped wild animal,E05000211,Turkey Street,E09000010,Enfield,Enfield,207155981,Turkey Street,20703019,EN3,535290,198834,535250,198850,51.67199734,-0.044903451
59560151,15/05/2015 10:47,2015,2015/16,Special Service,1,1,298,298,KITTEN STUCK IN LOFT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000314,Heaton,E09000016,Havering,Harold Hill,NULL,Heaton Avenue,21301268,RM3,NULL,NULL,552950,191250,NULL,NULL
59967151,16/05/2015 09:41,2015,2015/16,Special Service,1,1,298,298,FOX CUB TRAPPED IN NETTING,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011233,West Heath,E09000004,Bexley,Plumstead,NULL,Plymstock Road,20101156,DA16,NULL,NULL,547350,177150,NULL,NULL
60034151,16/05/2015 13:10,2015,2015/16,Special Service,1,1,298,298,BIRD TANGLED UP IN NETTING,Bird,Person (mobile),Library,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,1.00024E+11,Garfield Road,22403495,TW1,516305,173387,516350,173350,51.44753644,-0.32772858
60157151,16/05/2015 17:12,2015,2015/16,Special Service,1,1,298,298,CAT STUCK UP TREE WITH LEAD CAUGHT ROUND BRANCHES,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000185,Northolt West End,E09000009,Ealing,Northolt,NULL,Laughton Road,NULL,UB5,511821,183574,511850,183550,51.54000004,-0.389002521
60206151,16/05/2015 18:58,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011249,Monkhams,E09000026,Redbridge,Woodford,NULL,The Broadway,22306023,IG8,NULL,NULL,540950,191850,NULL,NULL
60725151,17/05/2015 20:24,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN NETTING,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000218,Eltham North,E09000011,Greenwich,Eltham,NULL,Well Hall Road,NULL,SE9,542593,174930,542550,174950,51.45539948,0.050947559
60894151,18/05/2015 08:27,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000557,Belmont,E09000029,Sutton,Sutton,NULL,Hadleigh Drive,22602363,SM2,NULL,NULL,525450,162550,NULL,NULL
61114151,18/05/2015 17:14,2015,2015/16,Special Service,1,1,298,298,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05011249,Monkhams,E09000026,Redbridge,Woodford,NULL,The Broadway,NULL,IG8,540911,191851,540950,191850,51.60786939,0.033520084
61151151,18/05/2015 19:18,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN ROOF,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,Bromley Road,21104285,N17,NULL,NULL,534150,190950,NULL,NULL
61162151,18/05/2015 19:41,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN FOOT CANAL,Cat,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Elgar Avenue,NULL,NW10,520725,184936,520750,184950,51.55040943,-0.260205489
61518151,19/05/2015 18:36,2015,2015/16,Special Service,1,1,298,298,ANIMAL TRAPPED ON ROOF,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,Upper North Street,22701256,E14,NULL,NULL,537450,181250,NULL,NULL
61608151,19/05/2015 23:10,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED INSIDE INSIDE CHAIR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Eltham,NULL,Queenscroft Road,20801224,SE9,NULL,NULL,541950,174250,NULL,NULL
62262151,21/05/2015 13:50,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000464,Longthornton,E09000024,Merton,Norbury,NULL,Oakleigh Way,22104836,CR4,NULL,NULL,528850,169650,NULL,NULL
62643151,22/05/2015 09:25,2015,2015/16,Special Service,1,1,298,298,TO ASSIST RSPCA WITH FOX CUB STUCK IN CHIMNEY,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000465,Lower Morden,E09000024,Merton,Sutton,NULL,Aragon Road,22100186,SM4,NULL,NULL,524250,166550,NULL,NULL
62700151,22/05/2015 11:34,2015,2015/16,Special Service,2,10,298,2980,HORSE FALLEN INTO SWIMMING  POOL,Horse,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from water,Animal rescue from water - Farm animal,E05000117,Darwin,E09000006,Bromley,Biggin Hill,1.0002E+11,Cudham Lane South,20302994,TN14,544910,158822,544950,158850,51.3100692,0.077708109
62874151,22/05/2015 17:03,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000520,Hampton,E09000027,Richmond upon Thames,Twickenham,NULL,Chestnut Avenue,22401361,TW12,NULL,NULL,513250,170250,NULL,NULL
63402151,23/05/2015 18:57,2015,2015/16,Special Service,1,1,298,298,DOG STUCK IN GATE,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Heathway,NULL,RM9,548930,186115,548950,186150,51.55426942,0.146807173
63740151,24/05/2015 13:20,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Dog,Police,Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000218,Eltham North,E09000011,Greenwich,Eltham,1.00023E+11,Well Hall Road,20801573,SE9,542498,175088,542450,175050,51.45684315,0.049644779
63750151,24/05/2015 13:44,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000208,Southgate,E09000010,Enfield,Southgate,NULL,Pennington Drive,20706126,N21,NULL,NULL,530450,195850,NULL,NULL
63868151,24/05/2015 18:15,2015,2015/16,Special Service,1,1,298,298,"PIGEON STUCK IN NETTING, ASSIST RSPCA",Bird,Person (mobile),Mosque,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000288,Greenhill,E09000015,Harrow,Harrow,1.00023E+11,Station Road,21201079,HA1,515635,189125,515650,189150,51.58912329,-0.33220625
64161151,25/05/2015 12:17,2015,2015/16,Special Service,1,1,298,298,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Dental surgery,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000106,Bickley,E09000006,Bromley,Bromley,1.0002E+11,Southborough Lane,20300617,BR2,542720,167656,542750,167650,51.39000475,0.04984696
64323151,25/05/2015 19:25,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000356,Heston East,E09000018,Hounslow,Southall,NULL,Fern Lane,21500440,TW5,NULL,NULL,512750,178050,NULL,NULL
64543151,26/05/2015 08:48,2015,2015/16,Special Service,1,1,298,298,DOG WITH KARABINER STUCK IN MOUTH,Dog,Person (land line),Veterinary surgery,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,12047902,Mandeville Road,20601113,UB5,513023,184175,513050,184150,51.54516254,-0.37148485
64558151,26/05/2015 09:51,2015,2015/16,Special Service,1,1,298,298,RUNNING CALL TO CAT STUCK ON ROOF,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000264,Town,E09000013,Hammersmith and Fulham,Fulham,NULL,Bishops Road,21000124,SW6,NULL,NULL,524850,177050,NULL,NULL
64581151,26/05/2015 11:11,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED TUNNEL BEHIND BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000181,Lady Margaret,E09000009,Ealing,Southall,NULL,Allenby Road,20600040,UB1,NULL,NULL,513550,181850,NULL,NULL
64717151,26/05/2015 16:05,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN TRAMPOLINE SPRINGS,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000562,St Helier,E09000029,Sutton,Mitcham,5870070009,Green Wrythe Lane,22600593,SM5,527236,166513,527250,166550,51.38341105,-0.172974292
65406151,28/05/2015 00:34,2015,2015/16,Special Service,1,2,298,596,CAT TRAPPED BETWEEN WALLS,Cat,Police,Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Kingston,1.00022E+11,Perryfield Way,22403728,TW10,516905,172622,516950,172650,51.44053744,-0.319350843
65444151,28/05/2015 02:36,2015,2015/16,Special Service,1,2,298,596,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000519,"Ham, Petersham and Richmond Riverside",E09000027,Richmond upon Thames,Kingston,1.00022E+11,Perryfield Way,22403728,TW10,516905,172622,516950,172650,51.44053744,-0.319350843
66034151,29/05/2015 09:19,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN NETTING AT HEIGHT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011247,Loxford,E09000026,Redbridge,Ilford,NULL,Medway Close,22306129,IG1,NULL,NULL,544450,185150,NULL,NULL
66184151,29/05/2015 15:33,2015,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05011247,Loxford,E09000026,Redbridge,Ilford,NULL,Roden Street,NULL,IG1,543410,186078,543450,186050,51.55536568,0.067227654
66205151,29/05/2015 16:19,2015,2015/16,Special Service,1,1,298,298,ABANDONED CALL VERIFIED AS COMING FROM PRIV SUB,Unknown - Domestic Animal Or Pet,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,1.00021E+11,Newbury Gardens,21300162,RM3,553657,192016,553650,192050,51.60602205,0.21751414
66385151,29/05/2015 22:07,2015,2015/16,Special Service,1,1,298,298,FOX WITH HEAD STUCK IN WALL,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000557,Belmont,E09000029,Sutton,Sutton,NULL,Holland Avenue,22602396,SM2,NULL,NULL,525150,162950,NULL,NULL
66396151,29/05/2015 22:37,2015,2015/16,Special Service,1,1,298,298,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Common external bin storage area,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000171,Cleveland,E09000009,Ealing,Ealing,12018312,Copley Close,20600438,W7,515930,181218,515950,181250,51.51799696,-0.330553877
66728151,30/05/2015 17:46,2015,2015/16,Special Service,1,3,298,894,HORSE IN PRECARIOUS POSITION,Horse,Police,Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy wild animal,E05004176,Waltham Abbey North East,E07000072,Epping Forest,Essex,1.00091E+11,Sewardstone Road,13801212,EN9,538224,200728,538250,200750,51.68830354,-0.001752217
66802151,30/05/2015 19:12,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN TRAMPOLINE,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000441,Crofton Park,E09000023,Lewisham,Forest Hill,1.00022E+11,Crofton Park Road,22001382,SE4,536741,174310,536750,174350,51.45126989,-0.033464738
67456151,01/06/2015 10:12,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED  BETWEEN SPIKES,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000487,Little Ilford,E09000025,Newham,Ilford,NULL,Grantham Road,22200183,E12,NULL,NULL,543150,186050,NULL,NULL
68719151,04/06/2015 00:24,2015,2015/16,Special Service,1,1,298,298,INJURED CAT TRAPPED IN BASEMENT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009390,Campden,E09000020,Kensington and Chelsea,Kensington,NULL,Campden Hill,21700632,W8,NULL,NULL,525050,179850,NULL,NULL
68816151,04/06/2015 07:14,2015,2015/16,Special Service,1,1,298,298,FOX CAUGHT WITHIN FENCING,Fox,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000598,Hatch Lane,E09000031,Waltham Forest,Woodford,1.00023E+11,Nightingale Avenue,22861250,E4,539070,192312,539050,192350,51.61246903,0.007134444
68904151,04/06/2015 12:15,2015,2015/16,Special Service,1,2,298,596,ASSIST RSPCA WITH  CAT STUCK ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000216,Charlton,E09000011,Greenwich,East Greenwich,NULL,Floyd Road,20800581,SE7,NULL,NULL,541250,178350,NULL,NULL
69015151,04/06/2015 15:57,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,Kilburn High Road,NULL,NW6,524679,184642,524650,184650,51.54690846,-0.203311321
69594151,05/06/2015 15:12,2015,2015/16,Special Service,1,1,298,298,DOG IN PRECARIOUS POSITION ON ROOF,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011224,East Wickham,E09000004,Bexley,Plumstead,NULL,Lovel Avenue,20100881,DA16,NULL,NULL,546350,176350,NULL,NULL
69618151,05/06/2015 16:07,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011223,Crook Log,E09000004,Bexley,Bexley,1.0002E+11,Devonshire Road,20100446,DA6,548356,175303,548350,175350,51.45727135,0.133989412
69630151,05/06/2015 16:28,2015,2015/16,Special Service,1,1,298,298,DISABLED CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000614,Fairfield,E09000032,Wandsworth,Battersea,NULL,Nantes Close,NULL,SW18,526384,175174,526350,175150,51.46143974,-0.182125603
70098151,06/06/2015 13:26,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000250,Addison,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Lakeside Road,21000485,W14,NULL,NULL,523750,179750,NULL,NULL
70177151,06/06/2015 15:50,2015,2015/16,Special Service,1,1,298,298,DOG  TRAPPED  NEAR   RAILWAY  LINE,Dog,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist trapped domestic animal,E05000484,Forest Gate South,E09000025,Newham,Stratford,46034335,Hampton Road,22207742,E7,540848,185419,540850,185450,51.55008928,0.030034329
70697151,07/06/2015 13:46,2015,2015/16,Special Service,1,1,298,298,KITTEN STUCK ON CHIMNEY STACK,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000189,Southall Broadway,E09000009,Ealing,Southall,NULL,Bankside,20600112,UB1,NULL,NULL,511750,180550,NULL,NULL
72062151,09/06/2015 21:37,2015,2015/16,Special Service,1,1,298,298,MUNTJAC  DEER STUCK IN CANAL,Deer,Person (land line),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000345,Yiewsley,E09000017,Hillingdon,Hillingdon,NULL,St. Stephens Road,NULL,UB7,505908,180376,505950,180350,51.51239537,-0.475190822
72135151,10/06/2015 01:37,2015,2015/16,Special Service,1,1,298,298,RUNNING CALL TO CAT STUCK UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009331,St Peter's,E09000030,Tower Hamlets,Bethnal Green,6040385,Approach Road,22700080,E2,535216,183125,535250,183150,51.53085126,-0.052022869
72237151,10/06/2015 10:23,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000326,Brunel,E09000017,Hillingdon,Hillingdon,1.00021E+11,St. Helens Close,21401819,UB8,505797,181657,505750,181650,51.52393015,-0.476405674
72256151,10/06/2015 11:14,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009331,St Peter's,E09000030,Tower Hamlets,Bethnal Green,6040380,Approach Road,22700080,E2,535246,183112,535250,183150,51.53072725,-0.051595625
72572151,10/06/2015 20:25,2015,2015/16,Special Service,1,1,298,298,DOG WITH LEG TRAPPED IN WIRE BASKET,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000108,Bromley Common and Keston,E09000006,Bromley,Bromley,NULL,Crown Lane,20300141,BR2,NULL,NULL,541950,167550,NULL,NULL
72730151,11/06/2015 02:26,2015,2015/16,Special Service,1,1,298,298,FOX WITH HEAD STUCK IN PLASTIC BOX,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000305,West Harrow,E09000015,Harrow,Northolt,1.00021E+11,Twyford Road,21202027,HA2,513731,187606,513750,187650,51.57585787,-0.360170211
72985151,11/06/2015 14:54,2015,2015/16,Special Service,1,1,298,298,BIRD WITH LEG TRAPPED IN TREE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,NULL,Harrison Road,19900495,RM10,NULL,NULL,549550,184650,NULL,NULL
73316151,11/06/2015 23:20,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED INSIDE KITCHEN UNIT,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000631,Bayswater,E09000033,Westminster,North Kensington,NULL,Westbourne Grove,8400743,W11,NULL,NULL,525250,181150,NULL,NULL
73809151,12/06/2015 20:16,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BETWEEN WALL AND FENCE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000616,Graveney,E09000032,Wandsworth,Tooting,NULL,Trevelyan Road,22905561,SW17,NULL,NULL,527350,170950,NULL,NULL
73913151,13/06/2015 02:09,2015,2015/16,Special Service,1,1,298,298,FOX WITH HEAD TRAPPED,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000305,West Harrow,E09000015,Harrow,Northolt,NULL,Twyford Road,21202027,HA2,NULL,NULL,513750,187650,NULL,NULL
74120151,13/06/2015 15:03,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT TRAPPED BETWEEN WALL AND SHED,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009371,De Beauvoir,E09000012,Hackney,Islington,NULL,Ardleigh Road,20900079,N1,NULL,NULL,533050,184450,NULL,NULL
74134151,13/06/2015 15:39,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED BETWEEN WALL AND NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,207185363,Church Street,20704052,N9,534309,193547,534350,193550,51.62472259,-0.061110776
74194151,13/06/2015 17:19,2015,2015/16,Special Service,1,1,298,298,CAT FALLEN AND TRAPPED BETWEEN WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009375,Haggerston,E09000012,Hackney,Shoreditch,NULL,Weymouth Terrace,20901068,E2,NULL,NULL,533750,183150,NULL,NULL
74524151,14/06/2015 10:42,2015,2015/16,Special Service,1,1,298,298,CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000440,Catford South,E09000023,Lewisham,Lewisham,NULL,Braidwood Road,22001262,SE6,NULL,NULL,538750,173150,NULL,NULL
74587151,14/06/2015 13:44,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH PIGEON IN TREE,Bird,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009330,St Katharine's & Wapping,E09000030,Tower Hamlets,Shadwell,NULL,Benson Quay,NULL,E1W,535089,180587,535050,180550,51.50807458,-0.054825267
74755151,14/06/2015 20:26,2015,2015/16,Special Service,1,1,298,298,PIGEON TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000031,Eastbury,E09000002,Barking and Dagenham,Barking,NULL,Keir Hardie Way,19900508,IG11,NULL,NULL,546350,184050,NULL,NULL
74900151,15/06/2015 07:32,2015,2015/16,Special Service,1,1,298,298,FOX CAUGHT IN NETTING,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011488,West Thornton,E09000008,Croydon,Norbury,NULL,Ashley Road,20500643,CR7,NULL,NULL,530950,168050,NULL,NULL
75167151,15/06/2015 17:38,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED ON CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000599,High Street,E09000031,Waltham Forest,Walthamstow,NULL,Northcote Road,22862000,E17,NULL,NULL,536350,189150,NULL,NULL
75521151,16/06/2015 12:43,2015,2015/16,Special Service,1,1,298,298,PARROT TRAPPED IN EXCAVATION,Bird,Person (land line),Other Dwelling,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000637,Knightsbridge and Belgravia,E09000033,Westminster,Chelsea,NULL,Ennismore Gardens Mews,NULL,SW7,NULL,NULL,527150,179350,NULL,NULL
75557151,16/06/2015 14:15,2015,2015/16,Special Service,1,1,298,298,BIRD STUCK IN GUTTERING,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000528,South Richmond,E09000027,Richmond upon Thames,Richmond,NULL,Marlborough Road,22403656,TW10,NULL,NULL,518550,174250,NULL,NULL
75618151,16/06/2015 15:57,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN SHUTTERS,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000328,Charville,E09000017,Hillingdon,Hillingdon,1.00023E+11,Kingshill Avenue,21401102,UB4,509691,182664,509650,182650,51.53223858,-0.419986597
454152,18/06/2015 11:29,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,NULL,Osprey Close,22864025,E17,NULL,NULL,536350,190950,NULL,NULL
76309151,18/06/2015 18:29,2015,2015/16,Special Service,1,1,298,298,CAT  STUCK IN RAILINGS,Cat,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,1.00022E+11,Benton's Rise,21900175,SE27,532589,171392,532550,171350,51.42603358,-0.094273481
76462151,18/06/2015 23:49,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED UNDER FLOORBOARD,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000260,Parsons Green and Walham,E09000013,Hammersmith and Fulham,Fulham,NULL,Harwood Road,21000422,SW6,NULL,NULL,525550,177050,NULL,NULL
76536151,19/06/2015 04:32,2015,2015/16,Special Service,1,3,298,894,KITTEN TRAPPED UNDER FLOOR BOARDS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000260,Parsons Green and Walham,E09000013,Hammersmith and Fulham,Fulham,NULL,Harwood Road,21000422,SW6,NULL,NULL,525550,177050,NULL,NULL
76975151,19/06/2015 22:39,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Dog,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000446,Ladywell,E09000023,Lewisham,Lewisham,1.00022E+11,Phoebeth Road,22001803,SE4,537411,174862,537450,174850,51.45606832,-0.023614399
77276151,20/06/2015 15:40,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH BIRD RESCUE,Bird,Person (land line),Hotel/motel,Other Residential,Animal rescue from height,Animal rescue from height - Bird,E05000428,St Leonard's,E09000022,Lambeth,Norbury,2E+11,Stanthorpe Road,21901295,SW16,530190,171523,530150,171550,51.42776684,-0.128711366
77381151,20/06/2015 17:56,2015,2015/16,Special Service,1,1,298,298,FOX TRAPPED BETWEEN WALL AND FENCE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000285,Belmont,E09000015,Harrow,Stanmore,NULL,Lyon Meade,21202510,HA7,NULL,NULL,517550,190750,NULL,NULL
77509151,20/06/2015 22:52,2015,2015/16,Special Service,1,1,298,298,DOG ON RIVERBANK,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000259,Palace Riverside,E09000013,Hammersmith and Fulham,Fulham,NULL,Willow Bank,NULL,SW6,524339,175791,524350,175750,51.46743721,-0.211330927
77683151,21/06/2015 12:04,2015,2015/16,Special Service,1,2,298,596,TWO CATS STUCK IN WALL CAVITY,cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000181,Lady Margaret,E09000009,Ealing,Southall,NULL,Fermoy Road,20600664,UB6,NULL,NULL,513750,181950,NULL,NULL
78282151,22/06/2015 18:13,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN ROOF,Bird,Person (land line),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009385,Stoke Newington,E09000012,Hackney,Stoke Newington,1.00021E+11,Stoke Newington High Street,20900977,N16,533575,186225,533550,186250,51.55909964,-0.074491119
78334151,22/06/2015 20:11,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011245,Hainault,E09000026,Redbridge,Hainault,NULL,The Lowe,22304744,IG7,NULL,NULL,546150,192150,NULL,NULL
78581151,23/06/2015 12:03,2015,2015/16,Special Service,1,1,298,298,PIGEON WITH LEGS TRAPPED IN NETTING,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,NULL,Ness Street,NULL,SE16,534167,179263,534150,179250,51.49639606,-0.068606134
79068151,24/06/2015 11:17,2015,2015/16,Special Service,1,1,298,298,CAT LOCKED IN UNOCCUPIED FLAT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000259,Palace Riverside,E09000013,Hammersmith and Fulham,Fulham,NULL,Fulham Palace Road,21000356,SW6,NULL,NULL,523950,176850,NULL,NULL
79690151,25/06/2015 15:48,2015,2015/16,Special Service,1,1,298,298,DOG STUCK BETWEEN SEATS IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Eastmead Avenue,NULL,UB6,513666,182721,513650,182750,51.53196536,-0.362684847
79954151,25/06/2015 22:24,2015,2015/16,Special Service,1,2,298,596,ASSIST RSPCA WITH SEAGULL IN NETTING,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000130,Camden Town with Primrose Hill,E09000007,Camden,Euston,NULL,Regents Park Road,NULL,NW1,527591,183819,527550,183850,51.53886197,-0.161636575
79960151,25/06/2015 22:42,2015,2015/16,Special Service,1,1,298,298,TORTOISE TRAPPED,Tortoise,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000411,St James,E09000021,Kingston upon Thames,New Malden,1.00022E+11,Blakes Avenue,21800146,KT3,521833,167461,521850,167450,51.39311556,-0.250252708
80063151,26/06/2015 00:59,2015,2015/16,Special Service,1,1,298,298,DOG STUCK ON BAY WINDOW,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000256,Hammersmith Broadway,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Carthew Road,21000187,W6,NULL,NULL,522750,179250,NULL,NULL
80434151,26/06/2015 16:26,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN UNDERGROWTH/BRAMBLES,Dog,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000215,Blackheath Westcombe,E09000011,Greenwich,East Greenwich,NULL,Combe Mews,NULL,SE3,539786,177276,539750,177250,51.47718008,0.011500176
80630151,26/06/2015 21:00,2015,2015/16,Special Service,1,1,298,298,DOG WITH HEAD STUCK IN WINE WRACK,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,NULL,Church Road,22403381,TW11,NULL,NULL,515450,171450,NULL,NULL
80823151,27/06/2015 06:53,2015,2015/16,Special Service,1,1,298,298,FOX TRAPPED BY METAL RAILING,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000428,St Leonard's,E09000022,Lambeth,Norbury,1.00022E+11,Hambro Road,21900658,SW16,529889,171091,529850,171050,51.42395352,-0.133196661
81064151,27/06/2015 16:38,2015,2015/16,Special Service,1,1,298,298,DOG IN PRECARIOUS POSITION,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Stoke Newington,NULL,Inver Close,20901843,E5,NULL,NULL,535050,186750,NULL,NULL
81487151,28/06/2015 06:42,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Euston Street,NULL,NW1,529454,182559,529450,182550,51.5271148,-0.135251843
81634151,28/06/2015 11:44,2015,2015/16,Special Service,1,1,298,298,RUNNING CALL TO CAT IN TREE,Cat,Person (land line),Hedge,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,1.00021E+11,Grosvenor Crescent,21400855,UB10,507730,184144,507750,184150,51.54591854,-0.447793805
81679151,28/06/2015 13:36,2015,2015/16,Special Service,1,1,298,298,CAT ON ROOF,Cat,Person (mobile),Gym,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000136,Haverstock,E09000007,Camden,Kentish Town,5134816,Chalk Farm Road,20400624,NW1,528273,184399,528250,184350,51.54392,-0.151596905
81701151,28/06/2015 14:36,2015,2015/16,Special Service,1,1,298,298,SQUIRRELL TRAPPED IN GUTTER,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000311,Hacton,E09000016,Havering,Hornchurch,NULL,Laburnum Walk,21300049,RM12,NULL,NULL,553150,185750,NULL,NULL
81712151,28/06/2015 15:14,2015,2015/16,Special Service,1,1,298,298,INJURED CAT ON OTHER SIDE OF FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal harm involving domestic animal,E05000038,River,E09000002,Barking and Dagenham,Dagenham,100100925,Chequers Lane,19900655,RM9,549067,183484,549050,183450,51.53059305,0.147669046
81769151,28/06/2015 18:00,2015,2015/16,Special Service,1,1,298,298,RAVEN TRAPPED IN FENCING,Bird,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000256,Hammersmith Broadway,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Banim Street,NULL,W6,522868,178838,522850,178850,51.49514274,-0.231438641
81799151,28/06/2015 19:14,2015,2015/16,Special Service,1,1,298,298,PIGEON CAUGHT IN NETTING,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05011116,South Bermondsey,E09000028,Southwark,Dockhead,NULL,Spa Road,NULL,SE16,534069,179266,534050,179250,51.49644626,-0.070015908
82173151,29/06/2015 14:19,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED ON  LEDGE,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Crouch End Hill,21106570,N8,NULL,NULL,530050,188250,NULL,NULL
82302151,29/06/2015 17:39,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON HOUSE ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,NULL,Argyle Road,20602281,W13,NULL,NULL,516450,181450,NULL,NULL
82434151,29/06/2015 20:43,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED DOWN FOX HOLE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011245,Hainault,E09000026,Redbridge,Hainault,1.00022E+11,Romford Road,22304722,IG7,546737,192748,546750,192750,51.61444301,0.117968359
82803151,30/06/2015 14:49,2015,2015/16,Special Service,1,1,298,298,CAT IN RIVER,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000362,Isleworth,E09000018,Hounslow,Heston,NULL,Pankhurst Close,NULL,TW7,515804,175985,515850,175950,51.47098934,-0.334085006
82911151,30/06/2015 17:07,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BETWEEN FENCE AND HOUSE,Cat,Other FRS,House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011224,East Wickham,E09000004,Bexley,Plumstead,NULL,Glenmore Road,20100605,DA16,NULL,NULL,545850,176750,NULL,NULL
84450151,02/07/2015 17:24,2015,2015/16,Special Service,1,1,298,298,DOG STUCK ON ROOF,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000411,St James,E09000021,Kingston upon Thames,New Malden,NULL,Malden Road,21800637,KT3,NULL,NULL,521650,167150,NULL,NULL
84516151,02/07/2015 18:38,2015,2015/16,Special Service,1,1,298,298,DOG IN DISTRESS IN POND,Dog,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000456,Cannon Hill,E09000024,Merton,New Malden,NULL,Cannon Hill Lane,NULL,SW20,524082,168487,524050,168450,51.40184967,-0.217585305
85251151,03/07/2015 20:47,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000063,Woodhouse,E09000003,Barnet,Finchley,NULL,Torrington Park,20043000,N12,NULL,NULL,526950,192450,NULL,NULL
85293151,03/07/2015 22:08,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN GUTTER,Cat,Person (land line),Pipe or drain,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000516,Barnes,E09000027,Richmond upon Thames,Hammersmith,1.00022E+11,Nassau Road,22404814,SW13,521871,176570,521850,176550,51.47497457,-0.246575519
85632151,04/07/2015 09:31,2015,2015/16,Special Service,1,1,298,298,DUCKLINGS STUCK IN DRAIN PIPE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000133,Frognal and Fitzjohns,E09000007,Camden,West Hampstead,NULL,Buckland Crescent,20400428,NW3,NULL,NULL,526750,184550,NULL,NULL
85925151,04/07/2015 17:40,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000134,Gospel Oak,E09000007,Camden,Kentish Town,NULL,Constantine Road,20400194,NW3,NULL,NULL,527450,185550,NULL,NULL
86195151,04/07/2015 21:55,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000273,Hornsey,E09000014,Haringey,Hornsey,NULL,North View Road,NULL,N8,529850,189611,529850,189650,51.59039812,-0.126944673
86335151,05/07/2015 00:23,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN RAILINGS,Cat,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000174,Ealing Common,E09000009,Ealing,Ealing,12090082,Ranelagh Road,20601422,W5,518019,179832,518050,179850,51.50510817,-0.300924222
86474151,05/07/2015 08:25,2015,2015/16,Special Service,1,2,298,596,KITTEN TRAPPED IN RUBBISH,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011229,St. Mary's & St. James,E09000004,Bexley,Sidcup,NULL,Ellenborough Road,NULL,DA14,547886,171016,547850,171050,51.41887352,0.125439539
87185151,06/07/2015 16:51,2015,2015/16,Special Service,1,1,298,298,CAT WITH PAW TRAPPED IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000286,Canons,E09000015,Harrow,Stanmore,NULL,Rusper Close,21200918,HA7,NULL,NULL,517350,192550,NULL,NULL
87525151,07/07/2015 07:41,2015,2015/16,Special Service,1,1,298,298,SMALL DEER STUCK IN FENCING,Deer,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000195,Chase,E09000010,Enfield,Enfield,207164248,Theobalds Park Road,20703010,EN2,531624,199680,531650,199650,51.68046997,-0.09756852
87605151,07/07/2015 11:47,2015,2015/16,Special Service,1,1,298,298,DOG STUCK BEHIND SINK,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000251,Askew,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Kelmscott Gardens,21000468,W12,NULL,NULL,522250,179450,NULL,NULL
87640151,07/07/2015 12:57,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON ROOF LEDGE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Manbey Park Road,22201497,E15,NULL,NULL,539250,184950,NULL,NULL
87643151,07/07/2015 13:06,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN CAVITY WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Rowcross Street,22502146,SE1,NULL,NULL,533850,178350,NULL,NULL
87666151,07/07/2015 14:03,2015,2015/16,Special Service,1,1,298,298,CAT ON TOP OF ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Waterloo Road,21402084,UB8,NULL,NULL,505050,183250,NULL,NULL
87918151,07/07/2015 19:20,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN HOUSE GUTTERING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000028,Becontree,E09000002,Barking and Dagenham,Dagenham,NULL,Crossway,19900101,RM8,NULL,NULL,547150,186150,NULL,NULL
87944151,07/07/2015 19:57,2015,2015/16,Special Service,1,1,298,298,SMALL DOG TRAPPED ON LEDGE,Dog,Police,Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009395,Earl's Court,E09000020,Kensington and Chelsea,Kensington,NULL,Kenway Road,21700298,SW5,NULL,NULL,525550,178750,NULL,NULL
88082151,07/07/2015 22:41,2015,2015/16,Special Service,1,1,298,298,CHICKEN IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000429,Stockwell,E09000022,Lambeth,Lambeth,NULL,Dorset Road,NULL,SW8,530581,177278,530550,177250,51.4793963,-0.120968698
88197151,08/07/2015 08:06,2015,2015/16,Special Service,1,1,298,298,CAT WITH INJURY STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000429,Stockwell,E09000022,Lambeth,Clapham,NULL,Larkhall Lane,21900847,SW4,NULL,NULL,530150,176650,NULL,NULL
88357151,08/07/2015 14:50,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011465,Broad Green,E09000008,Croydon,Croydon,NULL,Chapman Road,NULL,CR0,531107,166453,531150,166450,51.38199231,-0.117401578
88419151,08/07/2015 16:46,2015,2015/16,Special Service,1,1,298,298,PIGEON STUCK UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000469,Raynes Park,E09000024,Merton,New Malden,48072152,West Barnes Lane,22106627,SW20,522847,169231,522850,169250,51.40880499,-0.235073489
89099151,09/07/2015 19:24,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000418,Clapham Common,E09000022,Lambeth,Clapham,NULL,Oaklands Estate,21901167,SW4,NULL,NULL,529550,174050,NULL,NULL
89212151,09/07/2015 22:00,2015,2015/16,Special Service,1,1,298,298,KITTEN STUCK UNDER HOUSE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Naylor Road,22501758,SE15,NULL,NULL,534550,177350,NULL,NULL
89445151,10/07/2015 09:56,2015,2015/16,Special Service,1,2,298,596,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000207,Southbury,E09000010,Enfield,Enfield,NULL,Broadfield Square,20702530,EN1,NULL,NULL,534750,196950,NULL,NULL
89682151,10/07/2015 16:28,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Bird,Person (land line),Vehicle sales building,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000125,Plaistow and Sundridge,E09000006,Bromley,Bromley,1.00023E+11,Burnt Ash Lane,20302356,BR1,540710,171176,540750,171150,51.42213639,0.022373383
90176151,11/07/2015 06:39,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000527,St Margarets and North Twickenham,E09000027,Richmond upon Thames,Twickenham,NULL,Hill View Road,22403562,TW1,NULL,NULL,516150,174150,NULL,NULL
90253151,11/07/2015 10:13,2015,2015/16,Special Service,1,1,298,298,DISTRESSED DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000052,Garden Suburb,E09000003,Barnet,West Hampstead,200086166,North End Road,20031620,NW11,525496,187362,525450,187350,51.5711723,-0.190565333
90602151,11/07/2015 17:59,2015,2015/16,Special Service,1,1,298,298,DOG STUCK IN RESERVOIR,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Ardra Road,NULL,N9,535751,193419,535750,193450,51.6232263,-0.040341545
90847151,11/07/2015 23:53,2015,2015/16,Special Service,1,1,298,298,POSSIBLY INJURED CAT ON STUCK ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Astonville Street,22900173,SW18,NULL,NULL,525150,173350,NULL,NULL
90951151,12/07/2015 07:47,2015,2015/16,Special Service,1,1,298,298,BIRD STUCK IN NETTING,Bird,Person (land line),Bank/Building Society,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,Redbridge,Woodford,10034920945,The Broadway,22306023,IG8,540957,191847,540950,191850,51.60782195,0.034182318
91139151,12/07/2015 16:23,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000437,Bellingham,E09000023,Lewisham,Lewisham,NULL,Swallands Road,22001945,SE6,NULL,NULL,537450,172050,NULL,NULL
91583151,13/07/2015 17:08,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN QUAGGY,Dog,Person (mobile),Mine or quarry (not above ground building),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Lee Green,NULL,Eltham Palace Road,NULL,SE9,541262,174156,541250,174150,51.44877776,0.031494726
91801151,14/07/2015 06:20,2015,2015/16,Special Service,1,1,298,298,BIRD STUCK ON LAMP POST - HANGING FROM STRING,Bird,Person (mobile),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,The Broadway,NULL,UB6,514240,182329,514250,182350,51.5283264,-0.354540141
92111151,14/07/2015 20:18,2015,2015/16,Special Service,1,1,298,298,FALCON TRAPPED IN NETTING ON CANOPY,Bird,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,2.00001E+11,Molesworth Street,22004181,SE13,538198,175663,538150,175650,51.46307495,-0.011981701
92363151,15/07/2015 10:53,2015,2015/16,Special Service,1,1,298,298,KITTEN STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000604,Leyton,E09000031,Waltham Forest,Leyton,NULL,Clyde Place,22827550,E10,NULL,NULL,537650,187550,NULL,NULL
92529151,15/07/2015 18:29,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN HOLE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000200,Grange,E09000010,Enfield,Southgate,207013027,Waverley Road,20703037,EN2,531866,196584,531850,196550,51.65259183,-0.095236949
92554151,15/07/2015 19:17,2015,2015/16,Special Service,1,1,298,298,BIRD IN PRECARIOUS POSITION,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000649,West End,E09000033,Westminster,Soho,1.00023E+11,Warwick Street,8401043,W1B,529340,180749,529350,180750,51.51087462,-0.137558294
92817151,16/07/2015 10:46,2015,2015/16,Special Service,1,1,298,298,INJURED SEAGULL RESCUED,Bird,Person (land line),Fire station,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,1.00023E+11,London Road,21500707,TW7,515166,176444,515150,176450,51.47524488,-0.343117224
93151151,16/07/2015 22:00,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN TREE,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000329,Eastcote and East Ruislip,E09000017,Hillingdon,Ruislip,NULL,Pinn Way,NULL,HA4,509284,188108,509250,188150,51.58124881,-0.424161122
93295151,17/07/2015 03:52,2015,2015/16,Special Service,1,1,298,298,KITTEN STUCK IN WALL,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000216,Charlton,E09000011,Greenwich,East Greenwich,NULL,Elliscombe Road,20800517,SE7,NULL,NULL,541250,177850,NULL,NULL
93368151,17/07/2015 08:46,2015,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000280,Tottenham Green,E09000014,Haringey,Tottenham,NULL,Seven Sisters Road,NULL,N15,NULL,NULL,533450,188750,NULL,NULL
93378151,17/07/2015 09:45,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN A TREE  RSPCA OFFICER ON SCENE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000251,Askew,E09000013,Hammersmith and Fulham,Hammersmith,34110102,Coningham Road,21000235,W12,522600,179985,522650,179950,51.50550939,-0.234898885
93645151,17/07/2015 17:33,2015,2015/16,Special Service,1,1,298,298,FERRET STUCK IN LIFT SHAFT,Ferret,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000409,Norbiton,E09000021,Kingston upon Thames,Kingston,NULL,Willingham Way,21801048,KT1,NULL,NULL,519150,169050,NULL,NULL
93873151,17/07/2015 23:54,2015,2015/16,Special Service,1,1,298,298,CAT IN PRECARIOUS POSITION,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011116,South Bermondsey,E09000028,Southwark,Old Kent Road,NULL,Welsford Street,22502678,SE1,NULL,NULL,534150,178550,NULL,NULL
94730151,19/07/2015 12:53,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000251,Askew,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Coningham Road,NULL,W12,522580,180011,522550,180050,51.50574739,-0.235177879
94775151,19/07/2015 14:25,2015,2015/16,Special Service,1,1,298,298,INJURED DOG,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000455,Abbey,E09000024,Merton,Wimbledon,NULL,Kingston Road,NULL,SW19,525447,170016,525450,170050,51.41529116,-0.197431868
95290151,20/07/2015 09:45,2015,2015/16,Special Service,1,1,298,298,BIRD STUCK IN NETTING,Bird,Person (land line),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,Redbridge,Woodford,NULL,The Broadway,NULL,IG8,540909,191823,540950,191850,51.60761829,0.033479979
95362151,20/07/2015 12:38,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN WELL,Cat,Person (land line),Purpose built office,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011095,Borough & Bankside,E09000028,Southwark,Dowgate,2.00003E+11,Union Street,22502587,SE1,532047,179969,532050,179950,51.5032395,-0.098864219
95780151,21/07/2015 08:32,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN CAR SEAT,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000096,Northwick Park,E09000005,Brent,Wembley,NULL,Littleton Road,NULL,HA1,515999,186549,515950,186550,51.56589655,-0.32780485
95816151,21/07/2015 10:18,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH FOX IMPALED ON WIRE,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000087,Brondesbury Park,E09000005,Brent,Willesden,202136167,All Souls Avenue,20202012,NW10,522957,183603,522950,183650,51.53794811,-0.228495477
95912151,21/07/2015 12:34,2015,2015/16,Special Service,1,2,298,596,CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009371,De Beauvoir,E09000012,Hackney,Shoreditch,NULL,Whitmore Road,NULL,N1,533136,183753,533150,183750,51.53698915,-0.081754272
96926151,22/07/2015 21:10,2015,2015/16,Special Service,1,2,298,596,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009375,Haggerston,E09000012,Hackney,Bethnal Green,NULL,Goldsmiths Row,NULL,E2,534318,183363,534350,183350,51.53320435,-0.064870438
97144151,23/07/2015 09:38,2015,2015/16,Special Service,1,1,298,298,DOG WITH LEG TRAPPED IN COLLAR,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000640,Maida Vale,E09000033,Westminster,Paddington,NULL,Carlton Vale,8400641,NW6,NULL,NULL,525750,183150,NULL,NULL
97855151,24/07/2015 12:16,2015,2015/16,Special Service,1,1,298,298,PIGEON TRAPPED IN CONFINED ROOF SPACE OF WAREHOUSE,Bird,Person (mobile),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,10010227714,Seymour Street,20801860,SE18,544277,179046,544250,179050,51.49195801,0.076847884
98158151,24/07/2015 22:26,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011116,South Bermondsey,E09000028,Southwark,Old Kent Road,NULL,Chaucer Drive,22500491,SE1,NULL,NULL,533850,178550,NULL,NULL
98359151,25/07/2015 10:50,2015,2015/16,Special Service,1,1,298,298,DOG STUCK IN RESERVOIR,Dog,Person (mobile),"Tunnel, subway",Outdoor Structure,Animal rescue from water,Animal rescue from water - Domestic pet,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Stonehill Business Park,NULL,N18,536073,192057,536050,192050,51.61090951,-0.036221193
98437151,25/07/2015 13:45,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011105,Newington,E09000028,Southwark,Lambeth,2.00003E+11,John Ruskin Street,22501369,SE5,532095,177685,532050,177650,51.48270269,-0.099027228
98450151,25/07/2015 14:13,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN WINDOW,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,Gresham Road,21901684,SW9,NULL,NULL,531350,175650,NULL,NULL
98474151,25/07/2015 15:05,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WAS FOX TRAPPED IN BARBED WIRE,Fox,Person (land line),Park,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000379,St Mary's,E09000019,Islington,Islington,NULL,Madras Place,NULL,N7,531309,184958,531350,184950,51.54824595,-0.107632306
98679151,25/07/2015 22:09,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH PIGEON HANGING ON ROOF AERIAL,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011471,New Addington South,E09000008,Croydon,Addington,NULL,Cator Crescent,20501681,CR0,NULL,NULL,538950,161750,NULL,NULL
99212151,27/07/2015 00:45,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009319,Bow East,E09000030,Tower Hamlets,Bethnal Green,NULL,Vernon Road,22701264,E3,NULL,NULL,536850,183350,NULL,NULL
100321151,29/07/2015 12:38,2015,2015/16,Special Service,1,1,298,298,SQUIRREL TRAPPED IN BOILER,Squirrel,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000144,Swiss Cottage,E09000007,Camden,West Hampstead,NULL,Goldhurst Terrace,20400421,NW6,NULL,NULL,526250,184350,NULL,NULL
100841151,30/07/2015 13:13,2015,2015/16,Special Service,1,1,298,298,FOX TRAPPED BETWEEN SHED AND FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Davis Street,22207680,E13,NULL,NULL,540950,183050,NULL,NULL
101024151,30/07/2015 19:25,2015,2015/16,Special Service,1,1,298,298,BIRDS TRAPPED NETTING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,NULL,Orange Grove,22863675,E11,NULL,NULL,539050,186350,NULL,NULL
101530151,31/07/2015 19:41,2015,2015/16,Special Service,1,1,298,298,CAT STUCK BEHIND CHIMNEY BREAST,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011484,South Croydon,E09000008,Croydon,Croydon,NULL,Selsdon Road,20502323,CR2,NULL,NULL,532650,164250,NULL,NULL
101652151,31/07/2015 23:19,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN WINDOW,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009378,Hoxton West,E09000012,Hackney,Shoreditch,NULL,Provost Estate,20950175,N1,NULL,NULL,532650,183050,NULL,NULL
101699151,01/08/2015 01:54,2015,2015/16,Special Service,1,1,298,298,PIDGEON WITH LEG TRAPPED IN MESHING ON BALCONY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011112,Rotherhithe,E09000028,Southwark,Dockhead,NULL,Canada Estate,22502073,SE16,NULL,NULL,535350,179450,NULL,NULL
101762151,01/08/2015 06:44,2015,2015/16,Special Service,1,1,298,298,FOX CUB TRAPPED IN FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000455,Abbey,E09000024,Merton,Wimbledon,NULL,Laburnum Road,22103687,SW19,NULL,NULL,526450,170350,NULL,NULL
101777151,01/08/2015 07:37,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009385,Stoke Newington,E09000012,Hackney,Stoke Newington,NULL,Yoakley Road,20901100,N16,NULL,NULL,533150,186750,NULL,NULL
101802151,01/08/2015 09:53,2015,2015/16,Special Service,1,2,298,596,ASSIST VET WITH HORSE,Horse,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000111,Chislehurst,E09000006,Bromley,Sidcup,1.00023E+11,Hawkwood Lane,20300397,BR7,544209,169505,544250,169550,51.40624305,0.071982122
101862151,01/08/2015 13:08,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000604,Leyton,E09000031,Waltham Forest,Leyton,NULL,Clyde Place,22827550,E10,NULL,NULL,537750,187550,NULL,NULL
101876151,01/08/2015 13:25,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CROW STUCK IN HIGH WIRE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Wild animal rescue from height,E05011245,Hainault,E09000026,Redbridge,Hainault,NULL,Fox Burrow Road,NULL,IG7,547963,192913,547950,192950,51.61560518,0.135732164
102180151,01/08/2015 23:40,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED UNDER FLOOR BOARD,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000306,Brooklands,E09000016,Havering,Romford,NULL,Mawney Road,21301408,RM7,NULL,NULL,550550,188850,NULL,NULL
102494151,02/08/2015 16:10,2015,2015/16,Special Service,1,1,298,298,PIGEONS TRAPPED BEHIND NETTING,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000111,Chislehurst,E09000006,Bromley,Bromley,NULL,Chislehurst Road,NULL,BR7,543099,169579,543050,169550,51.40718878,0.056064275
103004151,03/08/2015 12:13,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND THE UNIT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009324,Island Gardens,E09000030,Tower Hamlets,Millwall,NULL,Glengarnock Avenue,NULL,E14,NULL,NULL,538450,178550,NULL,NULL
104532151,06/08/2015 09:10,2015,2015/16,Special Service,NULL,NULL,298,NULL,DOG IN WATER,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000416,Bishop's,E09000022,Lambeth,Lambeth,NULL,Westminster Bridge Road,NULL,SE1,530534,179412,530550,179450,51.49858495,-0.120856827
104878151,06/08/2015 21:23,2015,2015/16,Special Service,1,1,298,298,TWO KITTENS STUCK IN FENCE,Cat,Person (land line),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000356,Heston East,E09000018,Hounslow,Heston,NULL,Winchester Avenue,NULL,TW5,513148,177970,513150,177950,51.48936754,-0.37167322
105060151,07/08/2015 09:14,2015,2015/16,Special Service,2,4,298,1192,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,Sheldon Road,NULL,NW2,523854,185501,523850,185550,51.55481004,-0.214900866
105257151,07/08/2015 15:23,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000413,Surbiton Hill,E09000021,Kingston upon Thames,Surbiton,1.00022E+11,Ditton Road,21800332,KT6,518265,166232,518250,166250,51.38282361,-0.301920739
105504151,07/08/2015 22:40,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009401,Queen's Gate,E09000020,Kensington and Chelsea,Kensington,NULL,Stanford Road,NULL,W8,NULL,NULL,525850,179250,NULL,NULL
105668151,08/08/2015 10:01,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000610,Balham,E09000032,Wandsworth,Tooting,10033237072,Balham High Road,22900217,SW12,528612,173430,528650,173450,51.44526515,-0.150704828
105728151,08/08/2015 12:59,2015,2015/16,Special Service,1,1,298,298,DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000191,Southfield,E09000009,Ealing,Chiswick,NULL,Acton Lane,NULL,W4,520473,179572,520450,179550,51.50225434,-0.26567188
106044151,08/08/2015 20:50,2015,2015/16,Special Service,1,1,298,298,PARROT TRAPPED IN NETTING,Bird,Person (land line),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000367,Bunhill,E09000019,Islington,Shoreditch,5300067252,Norman Street,21605557,EC1V,532229,182465,532250,182450,51.52562769,-0.095307657
106153151,09/08/2015 01:18,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON WALL NEAR RAILWAY LINE,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Cherrywood Close,NULL,E3,536325,182855,536350,182850,51.52815875,-0.036149212
107372151,11/08/2015 11:34,2015,2015/16,Special Service,2,3,298,894,BIRD TRAPPED IN AERIAL         AL REQUESTED FROM INCIDENT,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000363,OSTERLEY AND SPRING GROVE,E09000018,HOUNSLOW,Heston,NULL,SPRING GROVE ROAD,NULL,TW7,NULL,NULL,514650,176550,NULL,NULL
107541151,11/08/2015 17:23,2015,2015/16,Special Service,1,1,298,298,PIGEONS TRAPPED UNDER NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000634,Church Street,E09000033,Westminster,Paddington,NULL,Jerome Crescent,NULL,NW8,NULL,NULL,527050,182450,NULL,NULL
107581151,11/08/2015 19:01,2015,2015/16,Special Service,1,1,298,298,CAT STUCK BEHIND SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009399,Notting Dale,E09000020,Kensington and Chelsea,North Kensington,NULL,Silchester Road,21700998,W10,NULL,NULL,523850,181150,NULL,NULL
107655151,11/08/2015 21:03,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011479,Selhurst,E09000008,Croydon,Croydon,NULL,Saxon Road,20501365,SE25,NULL,NULL,532950,167650,NULL,NULL
107882151,12/08/2015 10:40,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED NEAR RAILWAY LINE,Dog,Person (mobile),Railway,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000347,Brentford,E09000018,Hounslow,Chiswick,NULL,Green Dragon Lane,NULL,TW8,518331,178160,518350,178150,51.4900156,-0.296991309
108145151,12/08/2015 20:27,2015,2015/16,Special Service,1,1,298,298,DOG IN CANAL,Dog,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000186,Norwood Green,E09000009,Ealing,Southall,NULL,Tentelow Lane,NULL,UB2,514072,179584,514050,179550,51.50368864,-0.35784892
108828151,14/08/2015 07:13,2015,2015/16,Special Service,1,1,298,298,Redacted,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009377,Hoxton East & Shoreditch,E09000012,Hackney,Shoreditch,10008293621,Clere Street,20900615,EC2A,532976,182321,532950,182350,51.52415827,-0.084600358
108909151,14/08/2015 10:35,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED ON ROOF,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000563,Stonecot,E09000029,Sutton,Sutton,NULL,Henley Avenue,22601567,SM3,NULL,NULL,524050,165250,NULL,NULL
109772151,15/08/2015 23:40,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON LEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000640,Maida Vale,E09000033,Westminster,Paddington,NULL,Delaware Road,8400064,W9,NULL,NULL,525650,182350,NULL,NULL
109907151,16/08/2015 09:39,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN DRAIN,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000417,Brixton Hill,E09000022,Lambeth,West Norwood,1.00022E+11,Brixton Hill,21900236,SW2,530457,173942,530450,173950,51.44944478,-0.123982689
110054151,16/08/2015 16:28,2015,2015/16,Special Service,1,2,298,596,CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,NULL,PENWITH ROAD,NULL,SW18,NULL,NULL,525750,173050,NULL,NULL
110849151,18/08/2015 06:50,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BETWEEN WINDOW AND METAL SHUTTERS OF SHOP,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05009335,WEAVERS,E09000030,TOWER HAMLETS,Shoreditch,6089472,HACKNEY ROAD,NULL,E2,533515,182699,533550,182650,51.52742796,-0.076692333
110872151,18/08/2015 08:32,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED UP CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000439,BROCKLEY,E09000023,LEWISHAM,Greenwich,NULL,WICKHAM ROAD,NULL,SE4,NULL,NULL,536950,176150,NULL,NULL
110878151,18/08/2015 08:48,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED UNDER CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000278,SEVEN SISTERS,E09000014,HARINGEY,Tottenham,1.00021E+11,ALBERT ROAD,NULL,N15,533158,188253,533150,188250,51.57742274,-0.079735847
110903151,18/08/2015 09:42,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000110,CHELSFIELD AND PRATTS BOTTOM,E09000006,BROMLEY,Orpington,NULL,SEVENOAKS ROAD,NULL,BR6,NULL,NULL,547250,162650,NULL,NULL
110963151,18/08/2015 12:30,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000444,FOREST HILL,E09000023,LEWISHAM,Forest Hill,NULL,DEVONSHIRE ROAD,NULL,SE23,NULL,NULL,535550,173450,NULL,NULL
111247151,18/08/2015 23:43,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN GARAGE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011482,Shirley North,E09000008,CROYDON,Woodside,NULL,LONGHEATH GARDENS,NULL,CR0,NULL,NULL,535550,167850,NULL,NULL
111887151,20/08/2015 13:00,2015,2015/16,Special Service,1,1,298,298,FOX WITH BODY TRAPPED IN CONCRETE IN BASEMENT,Fox,Other FRS,Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000192,WALPOLE,E09000009,EALING,Ealing,NULL,LAMMAS PARK ROAD,NULL,W5,NULL,NULL,517450,179950,NULL,NULL
112003151,20/08/2015 18:23,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN NEIGHBOURS SHED,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,1.00022E+11,LEAHURST ROAD,NULL,SE13,538963,174651,538950,174650,51.45379392,-0.001373742
112363151,21/08/2015 14:09,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT HANGING FROM WINDOW,Cat,Police,House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000480,EAST HAM CENTRAL,E09000025,NEWHAM,East Ham,NULL,POULETT ROAD,NULL,E6,NULL,NULL,542750,183450,NULL,NULL
112482151,21/08/2015 18:23,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED UNDER SOLAR PANELS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011244,Goodmayes,E09000026,REDBRIDGE,Dagenham,NULL,GIBSON ROAD,NULL,RM8,NULL,NULL,547350,187450,NULL,NULL
112765151,22/08/2015 09:42,2015,2015/16,Special Service,1,1,298,298,DOG LOCKED IN CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000193,BOWES,E09000010,ENFIELD,Edmonton,207187990,NORFOLK AVENUE,NULL,N13,531803,191730,531850,191750,51.60898683,-0.097974226
112829151,22/08/2015 12:24,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Bungalow - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000335,NORTHWOOD,E09000017,HILLINGDON,Ruislip,NULL,MURRAY ROAD,NULL,HA6,NULL,NULL,509150,191150,NULL,NULL
113399151,23/08/2015 12:51,2015,2015/16,Special Service,1,2,298,596,ASSIST RSPCA OFFICER WITH CAT IN A TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000125,PLAISTOW AND SUNDRIDGE,E09000006,BROMLEY,Bromley,10003621525,HARLEYFORD,NULL,BR1,540967,169871,540950,169850,51.41034579,0.025548275
113442151,23/08/2015 14:47,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED BEHIND PIPES,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000359,HOUNSLOW HEATH,E09000018,HOUNSLOW,Heston,NULL,BENSON CLOSE,NULL,TW3,NULL,NULL,513150,175350,NULL,NULL
113787151,24/08/2015 09:33,2015,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000125,PLAISTOW AND SUNDRIDGE,E09000006,BROMLEY,Bromley,10003621563,HARLEYFORD,NULL,BR1,541017,169876,541050,169850,51.4103783,0.0262687
113863151,24/08/2015 13:17,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,NULL,GOLDFINCH ROAD,NULL,SE28,NULL,NULL,544950,179450,NULL,NULL
114558151,26/08/2015 02:18,2015,2015/16,Special Service,1,1,298,298,DEER STUCK IN METAL GATE - NOW OUT,Deer,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist  trapped livestock animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,DAGNAM PARK DRIVE,NULL,RM3,555390,192166,555350,192150,51.60689437,0.242584985
114644151,26/08/2015 09:38,2015,2015/16,Special Service,1,1,298,298,KITTENS STUCK UP TREE - BEEN THERE SOME TIME,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011251,Seven Kings,E09000026,REDBRIDGE,Ilford,1.00022E+11,BARLEY LANE,NULL,IG3,546567,188442,546550,188450,51.57579589,0.113716951
114694151,26/08/2015 12:10,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH BIRD HANGING FROM WIRE ON ROOF,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000562,ST. HELIER,E09000029,SUTTON,Sutton,NULL,WESTMINSTER ROAD,NULL,SM1,NULL,NULL,526550,165950,NULL,NULL
114768151,26/08/2015 13:43,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN CAGE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000340,UXBRIDGE NORTH,E09000017,HILLINGDON,Hillingdon,NULL,SAUNDERS ROAD,NULL,UB10,NULL,NULL,506750,183950,NULL,NULL
115200151,26/08/2015 18:38,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND KITCHEN CUPBOARD,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,NULL,COLNEY HATCH LANE,NULL,N10,NULL,NULL,528550,190450,NULL,NULL
115244151,26/08/2015 19:47,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN BIN,Cat,Person (mobile),Wheelie bin (domestic size),Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,207088523,BOUNCES ROAD,NULL,N9,534886,193995,534850,193950,51.62861048,-0.052607923
115521151,27/08/2015 15:42,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN TREE,Cat,Person (land line),Roadside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000622,ST. MARY'S PARK,E09000032,WANDSWORTH,Battersea,1.00023E+11,BATTERSEA HIGH STREET,NULL,SW11,526968,176540,526950,176550,51.47358555,-0.173233649
115869151,28/08/2015 08:29,2015,2015/16,Special Service,1,2,298,596,PONY CAUGHT IN CABLING,Horse,Person (mobile),Golf course (not building on course),Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011232,Thamesmead East,E09000004,BEXLEY,Plumstead,1.0002E+11,FAIRWAY DRIVE,NULL,SE28,548098,181210,548050,181250,51.51041491,0.132755493
116020151,28/08/2015 16:50,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000381,TOLLINGTON,E09000019,ISLINGTON,Holloway,NULL,HANLEY ROAD,NULL,N4,NULL,NULL,530550,187350,NULL,NULL
116036151,28/08/2015 17:23,2015,2015/16,Special Service,1,1,298,298,DOG IN PRECARIOUS POSITION ON ROOF OF HOUSE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000314,HEATON,E09000016,HAVERING,Harold Hill,NULL,KEATS AVENUE,NULL,RM3,NULL,NULL,552650,191050,NULL,NULL
116055151,28/08/2015 18:52,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON WINDOW LEDGE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,NULL,NAGS HEAD ROAD,NULL,EN3,NULL,NULL,535750,196150,NULL,NULL
116072151,28/08/2015 19:48,2015,2015/16,Special Service,1,1,298,298,CAT IN DISTRESS IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217050518,LANCASTER ROAD,NULL,W11,524762,181452,524750,181450,51.51822101,-0.203244772
116948151,30/08/2015 18:25,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN WIRING,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000479,CUSTOM HOUSE,E09000025,NEWHAM,East Ham,46083846,GLENCAIRNE CLOSE,NULL,E16,541798,181916,541750,181950,51.51837405,0.042317201
117449151,31/08/2015 20:28,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND FACIA,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000085,ALPERTON,E09000005,BRENT,Wembley,NULL,DOUGLAS AVENUE,NULL,HA0,NULL,NULL,518350,184550,NULL,NULL
117465151,31/08/2015 21:20,2015,2015/16,Special Service,1,1,298,298,DOG STUCK IN HOLE BEHIND SHED,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,1.00021E+11,LEGHORN ROAD,NULL,SE18,544916,178174,544950,178150,51.48395933,0.085688124
117644151,01/09/2015 10:26,2015,2015/16,Special Service,1,1,298,298,DOG LOCKED IN CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,1.00023E+11,CAPWORTH STREET,NULL,E10,537324,187427,537350,187450,51.56900162,-0.019973846
117797151,01/09/2015 15:54,2015,2015/16,Special Service,1,1,298,298,CAT UP A TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000409,NORBITON,E09000021,KINGSTON UPON THAMES,Kingston,128036972,FRANKLIN CLOSE,NULL,KT1,519106,168974,519150,168950,51.40729274,-0.288923069
117839151,01/09/2015 17:47,2015,2015/16,Special Service,1,1,298,298,KITTENS TRAPPED IN WALL VOID,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000284,WOODSIDE,E09000014,HARINGEY,Tottenham,NULL,SAXON ROAD,NULL,N22,NULL,NULL,531850,190850,NULL,NULL
117994151,02/09/2015 00:19,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Plumstead,NULL,WOOLWICH NEW ROAD,NULL,SE18,NULL,NULL,543550,178650,NULL,NULL
118271151,02/09/2015 17:14,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE    TRAPPED BIRD,Bird,Person (mobile),Post office (purpose built),Non Residential,Other animal assistance,Assist trapped wild animal,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Kingston,128006223,ASHDOWN ROAD,NULL,KT1,518098,169121,518050,169150,51.40882466,-0.303360263
118798151,03/09/2015 20:43,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT FALLEN INTO BASEMENT,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009368,CAZENOVE,E09000012,HACKNEY,Stoke Newington,NULL,UPPER CLAPTON ROAD,NULL,E5,NULL,NULL,534450,187250,NULL,NULL
120553151,07/09/2015 14:04,2015,2015/16,Special Service,1,1,298,298,PIGEON STUCK IN WIRE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000185,NORTHOLT WEST END,E09000009,EALING,Northolt,NULL,CHURCH ROAD,NULL,UB5,NULL,NULL,511750,183250,NULL,NULL
120557151,07/09/2015 14:22,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011489,Woodside,E09000008,CROYDON,Woodside,NULL,CROWTHER ROAD,NULL,SE25,NULL,NULL,534350,167950,NULL,NULL
120905151,08/09/2015 08:50,2015,2015/16,Special Service,1,1,298,298,KITTEN STUCK IN ENGINE OF CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011236,Bridge,E09000026,REDBRIDGE,Woodford,1.00022E+11,FINCHINGFIELD AVENUE,NULL,IG8,541419,191195,541450,191150,51.6018476,0.040586836
121386151,09/09/2015 08:45,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN MOBILITY SCOOTER,Cat,Person (land line),Motorcycle,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009321,BROMLEY NORTH,E09000030,TOWER HAMLETS,Poplar,6072746,BROMLEY HIGH STREET,NULL,E3,537772,182851,537750,182850,51.52777221,-0.015304411
122639151,11/09/2015 19:12,2015,2015/16,Special Service,1,2,298,596,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011241,Cranbrook,E09000026,REDBRIDGE,Ilford,NULL,CLARENDON GARDENS,NULL,IG1,NULL,NULL,543150,187550,NULL,NULL
122906151,12/09/2015 11:50,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,SELBORNE ROAD,NULL,N14,NULL,NULL,530350,193450,NULL,NULL
123445151,13/09/2015 12:19,2015,2015/16,Special Service,1,1,298,298,SQUIRREL TRAPPED IN BATHROOM POSSIBLY INJURED,Squirrel,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000432,STREATHAM WELLS,E09000022,LAMBETH,Norbury,NULL,RUTFORD ROAD,NULL,SW16,NULL,NULL,530250,171350,NULL,NULL
123683151,13/09/2015 22:06,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000207,SOUTHBURY,E09000010,ENFIELD,Enfield,207122657,LINCOLN WAY,NULL,EN1,534699,195899,534650,195850,51.64576516,-0.054574671
123880151,14/09/2015 10:19,2015,2015/16,Special Service,1,1,298,298,CAT WITH HEAD STUCK BEHIND PIPING,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,REDCLIFFE SQUARE,NULL,SW10,NULL,NULL,525750,178150,NULL,NULL
123978151,14/09/2015 14:10,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT STUCK IN GULLY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Soho,NULL,EARLHAM STREET,NULL,WC2H,NULL,NULL,530050,181050,NULL,NULL
124052151,14/09/2015 17:18,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN GUTTERING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000270,FORTIS GREEN,E09000014,HARINGEY,Finchley,NULL,OSIER CRESCENT,NULL,N10,NULL,NULL,527850,190850,NULL,NULL
124129151,14/09/2015 20:01,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009371,DE BEAUVOIR,E09000012,HACKNEY,Shoreditch,NULL,LANCASTER CLOSE,NULL,N1,NULL,NULL,533350,184050,NULL,NULL
124298151,15/09/2015 09:42,2015,2015/16,Special Service,1,1,298,298,CAT WITH BROKEN LEG IN PIT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000137,HIGHGATE,E09000007,CAMDEN,Kentish Town,NULL,BALMORE STREET,NULL,N19,NULL,NULL,528850,186650,NULL,NULL
124673151,16/09/2015 05:19,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND FRIDGE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011240,Clementswood,E09000026,REDBRIDGE,Ilford,NULL,KINGSTON ROAD,NULL,IG1,NULL,NULL,544450,185950,NULL,NULL
124778151,16/09/2015 12:10,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,200053519,GROSVENOR ROAD,NULL,N3,524964,191332,524950,191350,51.60696875,-0.196824952
126220151,19/09/2015 16:55,2015,2015/16,Special Service,1,1,298,298,CAT IN RIVER - CAT NOW OUT OF RIVER,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009326,LIMEHOUSE,E09000030,TOWER HAMLETS,Poplar,6043002,THREE COLT STREET,NULL,E14,536828,180723,536850,180750,51.50887863,-0.029730135
126599151,20/09/2015 08:37,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED  BEHIND BRICKWORK IN CELLAR,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000026,ABBEY,E09000002,BARKING AND DAGENHAM,Barking,NULL,PARK AVENUE,NULL,IG11,NULL,NULL,544250,184850,NULL,NULL
126740151,20/09/2015 14:29,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Bird,Person (land line),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000446,LADYWELL,E09000023,LEWISHAM,Lewisham,1.00024E+11,BROCKLEY ROAD,NULL,SE4,536688,175085,536650,175050,51.45824715,-0.033927839
127159151,21/09/2015 11:44,2015,2015/16,Special Service,1,1,298,298,DOG WITH HEAD STUCK IN GATE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011225,Erith,E09000004,BEXLEY,Erith,NULL,PARK CRESCENT,NULL,DA8,NULL,NULL,550650,177750,NULL,NULL
127186151,21/09/2015 12:58,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist  trapped livestock animal,E05000267,BOUNDS GREEN,E09000014,HARINGEY,Southgate,NULL,RING WAY,NULL,N11,NULL,NULL,529350,191750,NULL,NULL
127215151,21/09/2015 13:58,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON THE ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000596,GROVE GREEN,E09000031,WALTHAM FOREST,Leyton,NULL,ST GEORGES ROAD,NULL,E10,NULL,NULL,538250,186350,NULL,NULL
127670151,22/09/2015 13:39,2015,2015/16,Special Service,1,2,298,596,CAT STUCK IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009383,SPRINGFIELD,E09000012,HACKNEY,Stoke Newington,10008328867,BROADWAY MEWS,NULL,E5,533712,187726,533750,187750,51.57255575,-0.071946074
129183151,25/09/2015 19:59,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE - CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000176,ELTHORNE,E09000009,EALING,Ealing,NULL,ALEXANDRIA ROAD,NULL,W13,NULL,NULL,516450,180550,NULL,NULL
129485151,26/09/2015 13:04,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Electricity power station,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000375,HOLLOWAY,E09000019,ISLINGTON,Holloway,10001298528,STOCK ORCHARD CRESCENT,NULL,N7,530695,185372,530650,185350,51.55210897,-0.116328662
130006151,27/09/2015 13:43,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009405,STANLEY,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,SYDNEY STREET,NULL,SW3,NULL,NULL,527050,178450,NULL,NULL
130059151,27/09/2015 15:56,2015,2015/16,Special Service,1,1,298,298,DOG FALLEN DOWN LIGHTWELL,Dog,Person (mobile),Church/Chapel,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009405,STANLEY,E09000020,KENSINGTON AND CHELSEA,Chelsea,217085319,SYDNEY STREET,NULL,SW3,527204,178342,527250,178350,51.4897272,-0.169189306
130393151,28/09/2015 10:20,2015,2015/16,Special Service,1,3,298,894,KITTEN TRAPPED ON CHIMNEY BREAST ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,COLLEGE PLACE,20400753,NW1,NULL,NULL,529350,183750,NULL,NULL
130441151,28/09/2015 13:13,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH PIGEON STUCK IN NETTING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000561,NONSUCH,E09000029,SUTTON,Sutton,NULL,CHURCH HILL ROAD,22602924,SM3,NULL,NULL,523850,164850,NULL,NULL
130996151,29/09/2015 16:23,2015,2015/16,Special Service,NULL,NULL,298,NULL,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,COLLEGE PLACE,20400753,NW1,NULL,NULL,529350,183750,NULL,NULL
131750151,01/10/2015 06:34,2015,2015/16,Special Service,1,1,298,298,CAT WITH HEAD STUCK IN TIN,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05011488,West Thornton,E09000008,CROYDON,Norbury,1.00021E+11,CECIL ROAD,20500766,CR0,530526,167120,530550,167150,51.38812023,-0.125500468
131795151,01/10/2015 09:02,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN EQUIPMENT AND INJURED,Cat,Person (mobile),Loose refuse,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000041,VILLAGE,E09000002,BARKING AND DAGENHAM,Dagenham,100090325,CHURCH STREET,19900760,RM10,549959,184470,549950,184450,51.53921658,0.160938392
132902151,03/10/2015 13:24,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000035,LONGBRIDGE,E09000002,BARKING AND DAGENHAM,Barking,NULL,LONGBRIDGE ROAD,19900226,IG11,NULL,NULL,545350,185150,NULL,NULL
133023151,03/10/2015 17:41,2015,2015/16,Special Service,1,1,298,298,CAT STUCK BETWEEN TWO WALLS,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,46098316,WOODGRANGE ROAD,22200290,E7,540484,185415,540450,185450,51.55014408,0.024786518
133579151,04/10/2015 18:42,2015,2015/16,Special Service,1,1,298,298,PIGEON STUCK IN NETTING,Bird,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Kentish Town,5132322,CLARENCE WAY,20400631,NW1,528874,184480,528850,184450,51.54451117,-0.142905265
133653151,04/10/2015 21:02,2015,2015/16,Special Service,1,1,298,298,HORSE TRAPPED IN A PIT,Horse,Police,Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000340,UXBRIDGE NORTH,E09000017,HILLINGDON,Hillingdon,768011217,LONG LANE SERVICE ROAD O/S HILLINGDON STATION,21402626,UB10,507674,184906,507650,184950,51.55277827,-0.448367867
133658151,04/10/2015 21:09,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000359,HOUNSLOW HEATH,E09000018,HOUNSLOW,Heston,NULL,NEW ROAD,21500795,TW3,NULL,NULL,513750,175050,NULL,NULL
133816151,05/10/2015 10:12,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011113,Rye Lane,E09000028,SOUTHWARK,Peckham,2.00003E+11,VIVIAN SQUARE,22502634,SE15,534607,175783,534650,175750,51.46501832,-0.06359688
134223151,06/10/2015 07:11,2015,2015/16,Special Service,1,1,298,298,DOG WITH MOUTH TRAPPED IN CAGE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000058,OAKLEIGH,E09000003,BARNET,Barnet,NULL,GLOUCESTER ROAD,20018000,EN5,NULL,NULL,526350,195550,NULL,NULL
134438151,06/10/2015 16:51,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,207089309,WESTMINSTER ROAD,20704914,N9,534894,194005,534850,194050,51.62869842,-0.052488562
134460151,06/10/2015 18:08,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000468,RAVENSBURY,E09000024,MERTON,Mitcham,48058951,RAVENSBURY GROVE,22105319,CR4,526558,168228,526550,168250,51.39897538,-0.182101254
134713151,07/10/2015 10:49,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011486,Thornton Heath,E09000008,CROYDON,Norbury,1.00021E+11,SPA HILL,20500122,SE19,532602,169594,532650,169550,51.40987253,-0.094758565
135255151,08/10/2015 14:50,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000287,EDGWARE,E09000015,HARROW,Stanmore,1.00021E+11,STAG LANE,21202393,HA8,519953,190379,519950,190350,51.59949206,-0.269476992
135259151,08/10/2015 14:58,2015,2015/16,Special Service,1,1,298,298,PIGEON TRAPPED  IN TREE,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011483,Shirly South,E09000008,CROYDON,Woodside,1.00021E+11,SHIRLEY CHURCH ROAD,20500549,CR0,536196,165223,536150,165250,51.36974152,-0.044789209
135983151,09/10/2015 22:47,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05011115,St. Giles,E09000028,SOUTHWARK,Peckham,NULL,ELMINGTON ROAD,22500901,SE5,NULL,NULL,532850,177050,NULL,NULL
135996151,09/10/2015 23:32,2015,2015/16,Special Service,1,1,298,298,CAT POSSIBLY TRAPPED IN SCAFFOLDING NET,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000565,SUTTON NORTH,E09000029,SUTTON,Sutton,NULL,CHAUCER GARDENS,22602196,SM1,NULL,NULL,525350,165050,NULL,NULL
136529151,11/10/2015 09:46,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BETWEEN SHED AND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000172,DORMERS WELLS,E09000009,EALING,Southall,NULL,ST GEORGES AVENUE,20601594,UB1,NULL,NULL,512650,180550,NULL,NULL
137141151,12/10/2015 16:07,2015,2015/16,Special Service,3,6,298,1788,Redacted,Bird,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,10022942449,FERRY LANE,21103287,N17,534742,189492,534750,189450,51.58818019,-0.056416095
137226151,12/10/2015 18:51,2015,2015/16,Special Service,1,2,298,596,PARROT TRAPPED IN TREE BY HARNESS,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011487,Waddon,E09000008,CROYDON,Croydon,1.00023E+11,PURLEY WAY,20502609,CR0,531129,163115,531150,163150,51.35198919,-0.118315448
137259151,12/10/2015 19:33,2015,2015/16,Special Service,1,1,298,298,RUNNING CALL TO PARROT STUCK IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011244,Goodmayes,E09000026,REDBRIDGE,Ilford,1.00022E+11,GREEN LANE,22306487,IG3,545486,186761,545450,186750,51.56097148,0.097433418
137772151,13/10/2015 20:42,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN WALLSPACE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000462,HILLSIDE,E09000024,MERTON,Wimbledon,NULL,DELAMERE ROAD,22102019,SW20,NULL,NULL,523750,169650,NULL,NULL
137965151,14/10/2015 10:48,2015,2015/16,Special Service,1,1,298,298,CAT UP TREE - REQUESTED BY RSPCA,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000293,HEADSTONE SOUTH,E09000015,HARROW,Harrow,10002293474,PINNER ROAD,21201353,HA1,514633,188310,514650,188350,51.58200278,-0.346929697
138240151,14/10/2015 21:46,2015,2015/16,Special Service,1,1,298,298,KITTEN STUCK IN ROOF VENTILATION,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000358,HOUNSLOW CENTRAL,E09000018,HOUNSLOW,Heston,NULL,STIRLING GROVE,21501633,TW3,NULL,NULL,514350,176350,NULL,NULL
138559151,15/10/2015 16:33,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING UNDER RLY BRIDGE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000099,QUEENSBURY,E09000005,BRENT,Stanmore,202142989,QUEENSBURY STATION PARADE,20200704,HA8,518863,189745,518850,189750,51.59402454,-0.285422
138824151,16/10/2015 10:52,2015,2015/16,Special Service,1,1,298,298,ASSIST RSCPA WITH BIRD TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000378,ST. GEORGE'S,E09000019,ISLINGTON,Kentish Town,NULL,BRECKNOCK ROAD,21606410,N19,NULL,NULL,529550,185550,NULL,NULL
138880151,16/10/2015 13:31,2015,2015/16,Special Service,1,1,298,298,KITTEN WITH HEAD STUCK IN CHAIR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000115,CRAY VALLEY WEST,E09000006,BROMLEY,Orpington,NULL,GREENLEIGH AVENUE,20301141,BR5,NULL,NULL,546750,168450,NULL,NULL
139035151,16/10/2015 19:48,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN BUSH,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000524,KEW,E09000027,RICHMOND UPON THAMES,Richmond,10002255387,MELLISS AVENUE,22407046,TW9,519717,176796,519750,176750,51.47746498,-0.277499265
139607151,18/10/2015 01:03,2015,2015/16,Special Service,1,1,298,298,DOG STUCK BETWEEN SHED AND WALL,Dog,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000423,HERNE HILL,E09000022,LAMBETH,Brixton,1.00022E+11,FERNDENE ROAD,21900555,SE24,532423,175310,532450,175350,51.4612826,-0.095195335
139924151,18/10/2015 19:00,2015,2015/16,Special Service,1,1,298,298,ANIMAL POSSIBLY STUCK IN CHIMNEY,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,SWINGATE LANE,20801447,SE18,NULL,NULL,545350,177750,NULL,NULL
140409151,19/10/2015 21:07,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT TRAPPED IN ENGINE BAY,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000558,CARSHALTON CENTRAL,E09000029,SUTTON,Wallington,5870063899,BEYNON ROAD,22602119,SM5,527730,164189,527750,164150,51.36241411,-0.166713878
140633151,20/10/2015 12:17,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000118,FARNBOROUGH AND CROFTON,E09000006,BROMLEY,Orpington,1.0002E+11,BROADWATER GARDENS,20300056,BR6,544188,164891,544150,164850,51.3647889,0.069810604
140694151,20/10/2015 14:25,2015,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,NULL,MIDDLETON ROAD,22600797,SM5,NULL,NULL,527650,166750,NULL,NULL
141227151,21/10/2015 16:54,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA OFFICER  WITH KITTEN STUCK IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000192,WALPOLE,E09000009,EALING,Ealing,12073474,BONCHURCH ROAD,20600205,W13,516591,180042,516550,180050,51.50729154,-0.321420416
141363151,21/10/2015 21:48,2015,2015/16,Special Service,1,1,298,298,CAT STUCK AT THE BACK OF KITCHEN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011229,St. Mary's & St. James,E09000004,BEXLEY,Sidcup,NULL,ELLENBOROUGH ROAD,20100490,DA14,NULL,NULL,547950,171050,NULL,NULL
141975151,23/10/2015 12:37,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED BETWEEN FENCES,Cat,Person (mobile),Fence,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000031,EASTBURY,E09000002,BARKING AND DAGENHAM,Barking,100050186,SURREY ROAD,19900597,IG11,545202,184132,545250,184150,51.53742209,0.092255623
142147151,23/10/2015 18:22,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN FLAT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000279,STROUD GREEN,E09000014,HARINGEY,Holloway,NULL,CARLTON ROAD,21103060,N4,NULL,NULL,531250,187650,NULL,NULL
142384151,24/10/2015 09:35,2015,2015/16,Special Service,1,1,298,298,SEAGULL TRAPPED ON WIRE,Bird,Person (land line),Secondary school,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000349,CHISWICK RIVERSIDE,E09000018,HOUNSLOW,Chiswick,1.00023E+11,BURLINGTON LANE,21500176,W4,520934,177319,520950,177350,51.48190705,-0.259804588
142683151,24/10/2015 23:29,2015,2015/16,Special Service,1,1,298,298,DOG STUCK BEHIND THE FIRE PLACE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000470,ST. HELIER,E09000024,MERTON,Sutton,NULL,RUSTINGTON WALK,22105577,SM4,NULL,NULL,524950,166950,NULL,NULL
143465151,26/10/2015 15:42,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH PIGEON TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000175,EAST ACTON,E09000009,EALING,Acton,NULL,DUNCAN GROVE,20600573,W3,NULL,NULL,521150,181350,NULL,NULL
143492151,26/10/2015 16:30,2015,2015/16,Special Service,1,2,298,596,INJURED CAT ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009323,CANARY WHARF,E09000030,TOWER HAMLETS,Millwall,NULL,MILLHARBOUR,22700822,E14,NULL,NULL,537650,179550,NULL,NULL
143691151,27/10/2015 02:00,2015,2015/16,Special Service,1,1,298,298,DOG BELIEVED TRAPPED NEAR RAILWAY LINE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000612,EARLSFIELD,E09000032,WANDSWORTH,Wandsworth,10070247664,GROTON ROAD,22902169,SW18,525971,172960,525950,172950,51.44163382,-0.188854978
144454151,28/10/2015 16:40,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN WINDOW,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000279,STROUD GREEN,E09000014,HARINGEY,Holloway,NULL,BEATRICE ROAD,21102955,N4,NULL,NULL,531350,187850,NULL,NULL
144795151,29/10/2015 12:42,2015,2015/16,Special Service,1,1,298,298,ANIMAL IN DISTRESS,Unknown - Domestic Animal Or Pet,Person (land line),Recycling plant,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,2.00002E+11,TROUT LANE,21402018,UB7,505331,180691,505350,180650,51.51533451,-0.483408516
144805151,29/10/2015 13:42,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH TRAPPED MAGPIE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000410,OLD MALDEN,E09000021,KINGSTON UPON THAMES,New Malden,NULL,SHEEPHOUSE WAY,21800852,KT3,NULL,NULL,520850,166450,NULL,NULL
145206151,30/10/2015 10:31,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000435,TULSE HILL,E09000022,LAMBETH,West Norwood,2E+11,CROSBY WALK,21901740,SW2,531293,173823,531250,173850,51.44818223,-0.112003058
145811151,31/10/2015 13:31,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN RAILINGS BY HEAD,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000429,STOCKWELL,E09000022,LAMBETH,Clapham,10001114164,PARADISE ROAD,21901067,SW4,530196,176427,530150,176450,51.47183719,-0.126822793
145879151,31/10/2015 16:03,2015,2015/16,Special Service,1,1,298,298,DOG STUCK ON A ROOF,Dog,Police,Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011113,Rye Lane,E09000028,SOUTHWARK,Peckham,2.00003E+11,RYE LANE,22502166,SE15,534330,176322,534350,176350,51.4699279,-0.067377406
146647151,01/11/2015 21:19,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN CAR ENGINE,Cat,Police,Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009382,SHACKLEWELL,E09000012,HACKNEY,Stoke Newington,10008311432,SOMERFORD GROVE,20900933,N16,533678,185622,533650,185650,51.55365642,-0.073235002
146799151,02/11/2015 10:41,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH SEAGULL TRAPPED BY WING,Bird,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000054,HALE,E09000003,BARNET,Mill Hill,200152271,BARNET WAY,20002480,NW7,520722,194830,520750,194850,51.63933009,-0.256847824
147513151,03/11/2015 22:00,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED BETWEEN TWO WALLS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,NULL,DIMSDALE DRIVE,20704150,EN1,NULL,NULL,534050,195050,NULL,NULL
148019151,05/11/2015 09:56,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000462,HILLSIDE,E09000024,MERTON,Wimbledon,48024296,EDGE HILL,22102334,SW19,523685,170279,523650,170250,51.4180416,-0.222665155
148200151,05/11/2015 16:53,2015,2015/16,Special Service,1,1,298,298,DOG STUCK IN RAILING,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,207192937,LENA CRESCENT,20706956,N9,535342,193621,535350,193650,51.62514006,-0.046168397
148698151,06/11/2015 14:27,2015,2015/16,Special Service,1,1,298,298,DOG STUCK IN MUDDY PART OF A RIVER,Dog,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000311,HACTON,E09000016,HAVERING,Hornchurch,10070703899,NEWMARKET WAY,21300511,RM12,554386,185559,554350,185550,51.54780868,0.225198409
149275151,07/11/2015 10:40,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN ROLLER BLIND,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05009335,WEAVERS,E09000030,TOWER HAMLETS,Bethnal Green,6019275,BETHNAL GREEN ROAD,22700150,E2,534305,182628,534350,182650,51.52660257,-0.065337944
150060151,08/11/2015 17:42,2015,2015/16,Special Service,1,1,298,298,DOG WITH HEAD TRAPPED IN GATE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000343,WEST RUISLIP,E09000017,HILLINGDON,Ruislip,1.00023E+11,WHITEHEATH AVENUE,21402149,HA4,508219,188082,508250,188050,51.58122044,-0.439533287
151013151,10/11/2015 15:16,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN BOILER ROOM,Cat,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,202131480,BARRY ROAD,20202130,NW10,520412,184403,520450,184450,51.54568589,-0.264900013
151214151,10/11/2015 22:52,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND WALL IN CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,NULL,EUESDEN CLOSE,20706955,N9,NULL,NULL,534650,193350,NULL,NULL
152051151,12/11/2015 12:08,2015,2015/16,Special Service,1,2,298,596,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000029,CHADWELL HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,100022009,NEWHOUSE AVENUE,19900260,RM6,547776,189579,547750,189550,51.58569715,0.131629183
152072151,12/11/2015 12:57,2015,2015/16,Special Service,1,1,298,298,DOG STUCK IN GATE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,1.00021E+11,WAVERLEY ROAD,21105207,N17,534501,191205,534550,191250,51.60363095,-0.05923676
152185151,12/11/2015 17:13,2015,2015/16,Special Service,1,1,298,298,CAT STUCK BETWEEN GARAGE AND WALL,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000322,SQUIRREL'S HEATH,E09000016,HAVERING,Hornchurch,2.00003E+11,STATION ROAD,21300068,RM2,552985,189291,552950,189250,51.58172138,0.206631222
152205151,12/11/2015 17:36,2015,2015/16,Special Service,1,1,298,298,CAT STUCK BETWEEN GARAGE AND WALL,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000322,SQUIRREL'S HEATH,E09000016,HAVERING,Hornchurch,1.00021E+11,STATION ROAD,21300068,RM2,552991,189291,552950,189250,51.58171975,0.206717748
152339151,12/11/2015 23:03,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED BEHIND WALL IN BATHROOM,Cat,Other FRS,Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist  trapped livestock animal,E05000566,SUTTON SOUTH,E09000029,SUTTON,Sutton,NULL,CAVENDISH ROAD,22602184,SM2,NULL,NULL,526350,163550,NULL,NULL
NA,18/11/2015 16:02,2015,2015/16,Special Service,1,1,298,298,Redacted,Fox,Person (land line),Hotel/motel,Other Residential,Animal rescue from height,Wild animal rescue from height,E05009312,WALBROOK,E09000001,CITY OF LONDON,Dowgate,1.00023E+11,ST SWITHIN'S LANE,8100511,EC4N,532692,180957,532650,180950,51.51196737,-0.089205015
NA,18/11/2015 19:02,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN VAN ENGINE  THIS IS A WHITE VAN,Cat,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000277,ST. ANN'S,E09000014,HARINGEY,Tottenham,10003972743,ASCOT ROAD,21102916,N15,532644,188702,532650,188750,51.58157882,-0.086979667
NA,20/11/2015 15:58,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON WINDOW LEDGE - ONE STOREY HIGH,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009321,BROMLEY NORTH,E09000030,TOWER HAMLETS,Poplar,NULL,BROMLEY HIGH STREET,22700207,E3,NULL,NULL,537750,182850,NULL,NULL
NA,20/11/2015 18:52,2015,2015/16,Special Service,1,1,298,298,PARROT TRAPPED BETWEEN FENCES,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000275,NOEL PARK,E09000014,HARINGEY,Tottenham,NULL,FARRANT AVENUE,21104519,N22,NULL,NULL,531750,190550,NULL,NULL
NA,21/11/2015 13:17,2015,2015/16,Special Service,1,1,298,298,CAT UNDER DECKING,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000283,WHITE HART LANE,E09000014,HARINGEY,Tottenham,1.00021E+11,GREAT CAMBRIDGE ROAD,21106423,N17,532692,191691,532650,191650,51.60842751,-0.085158422
NA,21/11/2015 18:01,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED BEHIND GAS FIRE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000629,WEST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,CORTIS ROAD,22901049,SW15,NULL,NULL,522850,174350,NULL,NULL
NA,23/11/2015 11:05,2015,2015/16,Special Service,1,2,298,596,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000285,BELMONT,E09000015,HARROW,Stanmore,1.00021E+11,VERNON DRIVE,21202526,HA7,516497,190802,516450,190850,51.60401833,-0.319212318
NA,23/11/2015 17:26,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN FENCE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000305,WEST HARROW,E09000015,HARROW,Harrow,NULL,SPRINGWAY,21202034,HA1,NULL,NULL,514750,187750,NULL,NULL
NA,24/11/2015 22:24,2015,2015/16,Special Service,1,1,298,298,DISTRESSED DOG TRAPPED IN GARDEN OF ABANDONED HOUSE,Dog,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000252,AVONMORE AND BROOK GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34056267,EDITH ROAD,21000296,W14,524413,178572,524450,178550,51.4924146,-0.209287698
NA,25/11/2015 08:10,2015,2015/16,Special Service,1,2,298,596,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000280,TOTTENHAM GREEN,E09000014,HARINGEY,Tottenham,NULL,SEAFORD ROAD,21106587,N15,NULL,NULL,532850,188950,NULL,NULL
NA,25/11/2015 09:51,2015,2015/16,Special Service,1,1,298,298,DEER TRAPPED IN RAILINGS POLICE ATTENDING,Deer,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011483,Shirly South,E09000008,CROYDON,Addington,1.00021E+11,BISHOPS WALK,20501664,CR0,535503,164068,535550,164050,51.35952772,-0.055178236
NA,26/11/2015 10:55,2015,2015/16,Special Service,1,2,298,596,CAT STUCK IN STORM DRAIN,Cat,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009380,LEA BRIDGE,E09000012,HACKNEY,Stoke Newington,1.00021E+11,RIVERSIDE CLOSE,20900860,E5,535357,186958,535350,186950,51.56526187,-0.048518775
NA,26/11/2015 22:33,2015,2015/16,Special Service,1,1,298,298,MONKJACK DEER TRAPPED IN METAL GATE  RSPCA IN ATTENDANCE,Deer,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000329,EASTCOTE AND EAST RUISLIP,E09000017,HILLINGDON,Ruislip,NULL,PARK AVENUE,21401482,HA4,NULL,NULL,509850,188550,NULL,NULL
NA,28/11/2015 12:05,2015,2015/16,Special Service,1,2,298,596,DOG TRAPPED IN PIPE  ACCESS VIA WATERS EDGE PUBLIC HOUSE   CALLER WILL MEET YOU THERE,Dog,Person (mobile),Tree scrub,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000343,WEST RUISLIP,E09000017,HILLINGDON,Ruislip,10091098645,RESERVOIR ROAD,21401623,HA4,508833,189500,508850,189550,51.59384758,-0.430236208
NA,28/11/2015 13:15,2015,2015/16,Special Service,1,1,298,298,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal harm involving wild animal,E05009390,CAMPDEN,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,VICARAGE GATE,21700772,W8,NULL,NULL,525550,179950,NULL,NULL
NA,30/11/2015 11:01,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN TREE CALLED BY RSPCA     RSPCA ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011478,Sanderstead,E09000008,CROYDON,Purley,NULL,DOWNSWAY,20502039,CR2,NULL,NULL,533350,161750,NULL,NULL
NA,30/11/2015 22:15,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000177,GREENFORD BROADWAY,E09000009,EALING,Southall,12045051,FISHER CLOSE,20600674,UB6,513144,182945,513150,182950,51.53408336,-0.370135419
NA,01/12/2015 10:55,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN TREE IN FRONT OF CHAPEL CALLER ON WAY TO MEET BRIGADE,Bird,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,1.00024E+11,PRINCES AVENUE,21100705,N10,528639,189592,528650,189550,51.59050489,-0.144422871
NA,02/12/2015 14:07,2015,2015/16,Special Service,1,2,298,596,DOG TRAPPED IN FOX HOLE  OPP THE BRIDGE NEAR PAGODA   ACCESS VIA CROWN GATE WEST,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009331,ST. PETER'S,E09000030,TOWER HAMLETS,Bethnal Green,6651787,APPROACH ROAD,22700080,E2,535529,183486,535550,183450,51.53402032,-0.047374368
NA,05/12/2015 11:52,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,1.00023E+11,BEDFORD HILL,22900404,SW12,529021,172328,529050,172350,51.43526847,-0.145223977
NA,06/12/2015 13:16,2015,2015/16,Special Service,1,1,298,298,CAT WITH HEAD STUCK IN RAILINGS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011116,South Bermondsey,E09000028,SOUTHWARK,Dockhead,NULL,DRAPPERS WAY,22502965,SE16,NULL,NULL,534450,178950,NULL,NULL
NA,08/12/2015 11:11,2015,2015/16,Special Service,1,2,298,596,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009377,HOXTON EAST & SHOREDITCH,E09000012,HACKNEY,Shoreditch,NULL,ORSMAN ROAD,20900769,N1,NULL,NULL,533250,183750,NULL,NULL
NA,09/12/2015 21:33,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED UNDER DECKING,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000320,ST. ANDREW'S,E09000016,HAVERING,Hornchurch,NULL,BURNWAY,21300256,RM11,NULL,NULL,554350,187550,NULL,NULL
NA,10/12/2015 11:14,2015,2015/16,Special Service,1,1,298,298,PUPPY TRAPPED DOWN PIPE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000377,MILDMAY,E09000019,ISLINGTON,Islington,NULL,MILDMAY STREET,21605429,N1,NULL,NULL,532850,184850,NULL,NULL
NA,12/12/2015 12:47,2015,2015/16,Special Service,3,6,298,1788,HORSE TRAPPED IN DITCH LEVEL ONE HAZMAT FOUR PUMP SPECIAL SERVICE,Horse,Person (land line),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist trapped domestic animal,E05000335,NORTHWOOD,E09000017,HILLINGDON,Ruislip,10022800461,DUCKS HILL ROAD,21400619,HA6,507554,190631,507550,190650,51.60425861,-0.448346369
NA,12/12/2015 15:40,2015,2015/16,Special Service,1,1,298,298,DOG WITH HEAD STUCK IN CABINET UNIT,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,NULL,RICHMOND PARK ROAD,22403043,SW14,NULL,NULL,520450,175050,NULL,NULL
NA,13/12/2015 13:58,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED UNDER CAR SEAT,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000336,NORTHWOOD HILLS,E09000017,HILLINGDON,Ruislip,1.00021E+11,CHIPPENHAM CLOSE,21400388,HA5,509958,189380,509950,189350,51.59255101,-0.414039555
NA,13/12/2015 18:18,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BETWEEN GARAGE AND BRICK WALL,Cat,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Assist trapped wild animal,E05000131,CANTELOWES,E09000007,CAMDEN,Kentish Town,5061339,ST PAUL'S CRESCENT,20400541,NW1,529873,184442,529850,184450,51.54394092,-0.128520977
NA,15/12/2015 22:18,2015,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,SALISBURY STREET,8401547,NW8,NULL,NULL,527050,182150,NULL,NULL
NA,16/12/2015 23:06,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED BEHIND BOILER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009380,LEA BRIDGE,E09000012,HACKNEY,Stoke Newington,NULL,DETMOLD ROAD,20900328,E5,NULL,NULL,535050,186750,NULL,NULL
NA,17/12/2015 22:50,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN PLASTIC STAND,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000309,EMERSON PARK,E09000016,HAVERING,Hornchurch,NULL,ELM GROVE,21300336,RM11,NULL,NULL,554450,188550,NULL,NULL
NA,18/12/2015 10:31,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE - CAT TRAPPED BETWEEN SHED AND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000430,STREATHAM HILL,E09000022,LAMBETH,West Norwood,NULL,LIMETREE CLOSE,21900877,SW2,NULL,NULL,530950,173250,NULL,NULL
NA,19/12/2015 21:09,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH INJURERD KITTEN STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000314,HEATON,E09000016,HAVERING,Harold Hill,NULL,WIDECOMBE CLOSE,21301699,RM3,NULL,NULL,553650,190650,NULL,NULL
NA,22/12/2015 12:42,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN TREE  RSPCA ON SCENE,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000349,CHISWICK RIVERSIDE,E09000018,HOUNSLOW,Chiswick,2.00003E+11,PARK ROAD,21501255,W4,520593,177575,520550,177550,51.4842806,-0.264625659
NA,25/12/2015 12:38,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND IRON GRILL IN BASEMENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000425,LARKHALL,E09000022,LAMBETH,Clapham,NULL,LARKHALL RISE,21900848,SW4,NULL,NULL,529750,176150,NULL,NULL
NA,26/12/2015 14:21,2015,2015/16,Special Service,1,1,298,298,LEA VALLEY PARK NEAR LAKE AND MARSHLAND BEHIND THE ICE RINK   SWAN TRAPPED IN BUSHES  CALLER WILL WA,Bird,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,10091184362,LEA BRIDGE ROAD,22850300,E10,535722,187036,535750,187050,51.56587523,-0.043225687
NA,27/12/2015 10:12,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE  CAT TRAPPED IN BUSHES,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,1.00022E+11,MOUNT PLEASANT ROAD,22001729,SE13,538116,174373,538150,174350,51.45150269,-0.013664564
NA,27/12/2015 12:45,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN STORM DRAIN   CHINGFORD PLAINES - CALLER WILL MEET YOU,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000593,CHINGFORD GREEN,E09000031,WALTHAM FOREST,Chingford,2.00001E+11,BURY ROAD,22821000,E4,539377,194868,539350,194850,51.63536075,0.012581595
NA,27/12/2015 15:02,2015,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (land line),Hostel (e.g. for homeless people),Other Residential,Other animal assistance,Assist trapped domestic animal,E05011098,Chaucer,E09000028,SOUTHWARK,Dowgate,2.00003E+11,TABARD STREET,22502459,SE1,532557,179675,532550,179650,51.50047816,-0.091630949
NA,28/12/2015 11:23,2015,2015/16,Special Service,1,1,298,298,DOG STUCK ON ISLAND WITHIN LAKE  CALLER WILL MEET YOU ON COLLEGE ROAD,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05011100,Dulwich Village,E09000028,SOUTHWARK,West Norwood,10090287587,COLLEGE ROAD,22500543,SE21,533474,173534,533450,173550,51.44507549,-0.080745179
NA,29/12/2015 08:16,2015,2015/16,Special Service,1,1,298,298,DISTRESSED CAT ON BALCONY RESIDENTS AWAY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,NULL,MILE END ROAD,22700818,E1,NULL,NULL,534950,181950,NULL,NULL
NA,30/12/2015 12:57,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED UNDER FLOORBOARDS IN LOFT CONVERSION,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000216,CHARLTON,E09000011,GREENWICH,East Greenwich,NULL,SANDTOFT ROAD,20801321,SE7,NULL,NULL,540750,177850,NULL,NULL
NA,31/12/2015 09:02,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON BALCONY,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009388,ABINGDON,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,ADAM AND EVE MEWS,21700003,W8,NULL,NULL,525450,179350,NULL,NULL
NA,31/12/2015 16:46,2015,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009383,SPRINGFIELD,E09000012,HACKNEY,Stoke Newington,NULL,SPRINGFIELD,20900946,E5,NULL,NULL,534750,187250,NULL,NULL
NA,02/01/2016 11:03,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON ROOF  THREE STOREY BUILDING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000191,SOUTHFIELD,E09000009,EALING,Chiswick,NULL,DISRAELI CLOSE,20602336,W4,NULL,NULL,520550,178950,NULL,NULL
NA,02/01/2016 14:32,2016,2015/16,Special Service,1,1,298,298,BIRD STUCK IN LIFT SHAFT,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000190,SOUTHALL GREEN,E09000009,EALING,Southall,NULL,CANALSIDE GARDENS,20604849,UB2,NULL,NULL,512250,178650,NULL,NULL
NA,03/01/2016 06:59,2016,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE DOG FALLEN FROM FIRST FLOOR BALCONY,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000601,HOE STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,HOE STREET,22845700,E17,NULL,NULL,537250,189450,NULL,NULL
NA,03/01/2016 11:37,2016,2015/16,Special Service,1,1,298,298,DOG STUCK DOWN RABBIT HOLE LIONS GATE ENTRANCE TO BUSHY PARK,Dog,Person (land line),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000520,HAMPTON,E09000027,RICHMOND UPON THAMES,Twickenham,1.00023E+11,HAMPTON COURT ROAD,22406635,KT8,516248,168938,516250,168950,51.40756178,-0.330007353
NA,04/01/2016 07:56,2016,2015/16,Special Service,1,1,298,298,DOG WITH LEG STUCK IN DRAIN OPPOSITE JEFFREY COURT,Dog,Person (mobile),Pipe or drain,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000217,COLDHARBOUR AND NEW ELTHAM,E09000011,GREENWICH,Eltham,10010196669,MOTTINGHAM ROAD,20801816,SE9,542381,172608,542350,172650,51.43458756,0.046964988
NA,05/01/2016 21:44,2016,2015/16,Special Service,1,1,298,298,CAT FALLEN OUT OF WINDOW ONTO NETTING CALLER HAS RANG BACK TO CANCEL - THEY HAVE MANAGED TO GET THE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000370,CLERKENWELL,E09000019,ISLINGTON,Shoreditch,NULL,BAKER'S ROW,21604177,EC1R,NULL,NULL,531250,182250,NULL,NULL
NA,06/01/2016 11:24,2016,2015/16,Special Service,1,1,298,298,RUNNING CALL FROM RSPCA TO CAT STUCK ON BALCONY  RSPCA IN ATTENDANCE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,NULL,LANSDOWNE ROAD,21104730,N17,NULL,NULL,534550,190750,NULL,NULL
NA,07/01/2016 13:50,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA TO RETRIEVE AN OWL FROM A TREE,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000325,BOTWELL,E09000017,HILLINGDON,Hillingdon,1.00021E+11,NORMANDY DRIVE,21401404,UB3,508445,181256,508450,181250,51.51982394,-0.438375766
NA,07/01/2016 15:30,2016,2015/16,Special Service,1,1,298,298,DOG FALLEN INTO RIVERL,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000446,LADYWELL,E09000023,LEWISHAM,Lewisham,10090785155,MALYONS ROAD,22001682,SE13,537527,174424,537550,174450,51.45210417,-0.022115978
NA,07/01/2016 16:38,2016,2015/16,Special Service,1,1,298,298,DOG STUCK INBETWEEN IRON RAILINGS,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011254,Wanstead Park,E09000026,REDBRIDGE,Leytonstone,1.00022E+11,ALDERSBROOK ROAD,22302576,E12,541099,186703,541050,186750,51.56156426,0.034166553
NA,08/01/2016 12:37,2016,2015/16,Special Service,1,1,298,298,CAT STUCK ON ROOF  RSPCA IN ATTENDANCE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,NULL,LANSDOWNE ROAD,22849650,E17,NULL,NULL,537150,188450,NULL,NULL
NA,08/01/2016 17:05,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011479,Selhurst,E09000008,CROYDON,Croydon,1.00021E+11,SELHURST PLACE,20501371,SE25,533163,167128,533150,167150,51.38758042,-0.087622152
NA,09/01/2016 10:59,2016,2015/16,Special Service,2,4,298,1192,HORSE FALLEN ONTO SIDE AND STUCK IN WATER,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,2.00002E+11,WILLOW AVENUE,21402165,UB7,506942,180802,506950,180850,51.5160297,-0.460167047
NA,10/01/2016 21:35,2016,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,NULL,SOUTHBROOK ROAD,22001902,SE12,NULL,NULL,539850,174350,NULL,NULL
NA,11/01/2016 23:12,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED INSIDE STORAGE ROOM AT REAR OF CAFE,Cat,Person (mobile),Restaurant/cafe,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,1.00023E+11,LEWISHAM HIGH STREET,22004177,SE13,538023,174917,538050,174950,51.45641385,-0.01479011
NA,12/01/2016 15:52,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH FOX TRAPPED,Fox,Person (mobile),Infant/Primary school,Non Residential,Animal rescue from below ground,Wild animal rescue from below ground,E05009386,VICTORIA,E09000012,HACKNEY,Bethnal Green,1.00023E+11,RUTLAND ROAD,20900881,E9,535618,183791,535650,183750,51.53673978,-0.04597462
NA,13/01/2016 08:59,2016,2015/16,Special Service,1,1,298,298,DOG WITH HEAD STUCK IN METAL GATE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,207107778,SHIRLEY GROVE,20703657,N9,535466,194666,535450,194650,51.63450071,-0.043973761
NA,14/01/2016 13:12,2016,2015/16,Special Service,1,1,298,298,PIDGEON CAUGHT IN NETTING  CALL FOR ASSISTANCE VIA RSPCA,Bird,Person (land line),Electricity power station,Non Residential,Other animal assistance,Assist trapped wild animal,E05000460,FIGGE'S MARSH,E09000024,MERTON,Mitcham,48063159,SANDY LANE,22105754,CR4,528287,169694,528250,169650,51.41176257,-0.156730473
NA,15/01/2016 09:30,2016,2015/16,Special Service,1,2,298,596,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000107,BIGGIN HILL,E09000006,BROMLEY,Biggin Hill,1.0002E+11,FLAMBOROUGH CLOSE,20301503,TN16,540864,157837,540850,157850,51.30223244,0.019308996
NA,17/01/2016 10:06,2016,2015/16,Special Service,1,1,298,298,Redacted,Dog,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011249,Monkhams,E09000026,REDBRIDGE,Woodford,1.00023E+11,SNAKES LANE WEST,22305313,IG8,540223,192067,540250,192050,51.60998186,0.023677611
NA,17/01/2016 17:15,2016,2015/16,Special Service,1,1,298,298,HORSE TRAPPED IN CHAINS CALLER STATES CHAIN IS ROUND THE HORSES NECK AND IT IS UNABLE TO BREATH,Horse,Person (mobile),Heathland,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000356,HESTON EAST,E09000018,HOUNSLOW,Heston,1.00022E+11,FERRARO CLOSE,21501368,TW5,513408,177953,513450,177950,51.48916268,-0.367935429
NA,20/01/2016 19:26,2016,2015/16,Special Service,1,1,298,298,DOG TRAPPED UNDER BALCONY,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,NULL,TOLSFORD ROAD,20901007,E5,NULL,NULL,534750,185250,NULL,NULL
NA,21/01/2016 15:40,2016,2015/16,Special Service,1,1,298,298,SWAN TRAPPED BEHIND FENCING   LOCATION IS ACTUALLY - RIGHT UP THE CANAL PATH AND BY THE BRIDGE  OPPO,Bird,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05000085,ALPERTON,E09000005,BRENT,Wembley,202207951,HATTON ROAD,20204264,HA0,518226,183592,518250,183550,51.53885838,-0.296681975
NA,23/01/2016 08:47,2016,2015/16,Special Service,2,3,298,894,CAT STUCK UP TREE  RSPCA IN ATTENDANCE  REQUESTING ATTENDING OF BRIGADE,Cat,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000265,WORMHOLT AND WHITE CITY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34018649,AUSTRALIA ROAD,21000077,W12,522834,180857,522850,180850,51.51329561,-0.231225322
NA,23/01/2016 16:26,2016,2015/16,Special Service,1,2,298,596,HORSE TRAPPED BETWEEN TWO FENCING GATES   WHELANS FARM - WHITE SIGN AT THE GATE   HORSE IS BLEEDING,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000107,BIGGIN HILL,E09000006,BROMLEY,Biggin Hill,1.0002E+11,LUSTED HALL LANE,20301522,TN16,541084,157656,541050,157650,51.30055143,0.022391198
NA,23/01/2016 18:13,2016,2015/16,Special Service,1,1,298,298,DOG STUCK DOWN MANHOLE IN REAR GARDEN   STAFFORDSHIRE BULL TERRIER,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000121,MOTTINGHAM AND CHISLEHURST NORTH,E09000006,BROMLEY,Eltham,NULL,KERSEY GARDENS,20302550,SE9,NULL,NULL,542150,171850,NULL,NULL
NA,23/01/2016 22:59,2016,2015/16,Special Service,1,1,298,298,Redacted,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,MARLBOROUGH ROAD,20704475,N9,NULL,NULL,534050,194050,NULL,NULL
NA,25/01/2016 18:03,2016,2015/16,Special Service,1,2,298,596,"CAT STUCK INBETWEEN WALLS    CALLER HAS SPOKEN TO RSPCA  - AND NOW RSPCA HAS REQUESTED ATTENDANCE, A",Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,NA,NA,NA,NA,Essex,1.0009E+11,COOLGARDIE AVENUE,13801656,IG7,543261,193093,543250,193050,51.61843716,0.067940207
NA,27/01/2016 10:31,2016,2015/16,Special Service,NULL,NULL,298,NULL,Redacted,Horse,Person (land line),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05011229,St. Mary's & St. James,E09000004,BEXLEY,Bexley,10011847600,VICARAGE ROAD,20101516,DA5,549830,173127,549850,173150,51.43733172,0.154268014
NA,28/01/2016 23:11,2016,2015/16,Special Service,1,1,298,298,CAT STUCK IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving wild animal - Other action,E05000132,FORTUNE GREEN,E09000007,CAMDEN,West Hampstead,10015406363,FORDWYCH ROAD,20400109,NW2,524594,184996,524550,184950,51.55010867,-0.204411196
NA,30/01/2016 20:59,2016,2015/16,Special Service,1,1,298,298,DOG TRAPPED UNDER DECKING,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000487,LITTLE ILFORD,E09000025,NEWHAM,Ilford,46039882,JACK CORNWELL STREET,22200193,E12,543209,185644,543250,185650,51.55151695,0.064153881
NA,04/02/2016 06:31,2016,2015/16,Special Service,1,2,298,596,CAT TRAPPED BEHIND OVEN,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000047,COPPETTS,E09000003,BARNET,Finchley,NULL,FERNCROFT AVENUE,20015600,N12,NULL,NULL,527450,191850,NULL,NULL
NA,05/02/2016 00:52,2016,2015/16,Special Service,1,2,298,596,CAT TRAPPED IN METAL GATE ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,NULL,AXMINSTER ROAD,21602871,N7,NULL,NULL,530650,186250,NULL,NULL
NA,06/02/2016 09:47,2016,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009390,CAMPDEN,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,CAMPDEN HILL,21700632,W8,NULL,NULL,525050,179850,NULL,NULL
NA,06/02/2016 11:52,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH A CAT TRAPPED BETWEEN SHED AND RAILINGS,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000590,CANN HALL,E09000031,WALTHAM FOREST,Leytonstone,1.00023E+11,VANSITTART ROAD,22883600,E7,540141,185902,540150,185950,51.55460547,0.020036794
NA,07/02/2016 15:33,2016,2015/16,Special Service,1,1,298,298,Redacted,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000520,HAMPTON,E09000027,RICHMOND UPON THAMES,Twickenham,1.00023E+11,HAMPTON COURT ROAD,22406635,KT8,514958,168955,514950,168950,51.40797731,-0.348540705
NA,07/02/2016 21:18,2016,2015/16,Special Service,1,1,298,298,DEER TRAPPED BETWEEN FENCES    NEXT TO FISHING LAKE BY RIVERSIDE HOTEL,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,1.00022E+11,TROUT ROAD,21402019,UB7,505445,180611,505450,180650,51.51459418,-0.481790149
NA,09/02/2016 00:23,2016,2015/16,Special Service,1,2,298,596,CAT TRAPPED UNDER FLOOR BOARDS,Cat,Person (mobile),Sports/Social club,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,1.00023E+11,PALL MALL,8401211,SW1Y,529520,180203,529550,180250,51.50592659,-0.135166367
NA,09/02/2016 18:04,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN SCAFFOLDING,Cat,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009331,ST. PETER'S,E09000030,TOWER HAMLETS,Bethnal Green,6198162,CAMBRIDGE HEATH ROAD,22700252,E2,534886,183107,534850,183150,51.53076845,-0.056784358
NA,10/02/2016 14:23,2016,2015/16,Special Service,1,1,298,298,CAT SPEARED IN TREE DOWN LITTLE ALLEY OFF OF SOUTH TERRACED CALLER IS DISTRESSED CAT IS STILL ALIVE,Cat,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal harm involving domestic animal,E05000401,BERRYLANDS,E09000021,KINGSTON UPON THAMES,Surbiton,128038863,SOUTH TERRACE FOOTPATH (OPP 7 SOUTH TERRACE-SOUTH BANK CARPARK R/O SURBITON STATION),21820088,KT6,518363,167443,518350,167450,51.39368753,-0.300110214
NA,11/02/2016 11:09,2016,2015/16,Special Service,1,1,298,298,PARROT TRAPPED BY LEG IN TREE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,5300100516,ARUNDEL SQUARE,21604148,N7,531174,184617,531150,184650,51.54521291,-0.109705223
NA,13/02/2016 10:12,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,10003642540,UPCHURCH CLOSE,20302611,SE20,534969,170186,534950,170150,51.41463419,-0.060520417
NA,14/02/2016 08:27,2016,2015/16,Special Service,1,1,298,298,GOOSE WITH LEG TRAPPED IN WIRE ON MILL POND      GOOSE IS ON THE POND UNABLE TO MOVE D,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05011101,Dulwich Wood,E09000028,SOUTHWARK,West Norwood,10000815058,COLLEGE ROAD,22500543,SE21,533272,173119,533250,173150,51.44139355,-0.083806205
NA,14/02/2016 08:31,2016,2015/16,Special Service,1,1,298,298,HORSE STUCK IN HOLE IN HORSE BOX PAST TOTTENHAM HOTSPUR TRAINING GROUND ON RIGHT HAND SIDE OF ROAD -,Horse,Person (mobile),Trailer (not attached to tractor unit),Road Vehicle,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000195,CHASE,E09000010,ENFIELD,Enfield,207166636,WHITEWEBBS LANE,20703047,EN2,533190,199796,533150,199750,51.68114337,-0.074886946
NA,15/02/2016 15:17,2016,2015/16,Special Service,1,2,298,596,ASSIST RSPCA WITH CAT TRAPPED IN WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000295,KENTON WEST,E09000015,HARROW,Stanmore,NULL,ALICIA GARDENS,21201593,HA3,NULL,NULL,517250,189150,NULL,NULL
NA,17/02/2016 08:12,2016,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN TREE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011096,Camberwell Green,E09000028,SOUTHWARK,Old Kent Road,NULL,URLWIN STREET,22502602,SE5,NULL,NULL,532250,177550,NULL,NULL
NA,17/02/2016 19:39,2016,2015/16,Special Service,1,1,298,298,KITTEN IN TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011230,Sidcup,E09000004,BEXLEY,Sidcup,1.0002E+11,BIRKBECK ROAD,20100150,DA14,546423,172045,546450,172050,51.42849927,0.104839899
NA,19/02/2016 09:58,2016,2015/16,Special Service,1,2,298,596,ASSIST RSPCA  KITTEN FALLEN DOWN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011462,Addiscombe East,E09000008,CROYDON,Woodside,NULL,SUNDRIDGE ROAD,20501449,CR0,NULL,NULL,534050,166350,NULL,NULL
NA,19/02/2016 10:15,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,NULL,GOUGH WALK,22702107,E14,NULL,NULL,537250,181150,NULL,NULL
NA,20/02/2016 11:26,2016,2015/16,Special Service,1,2,298,596,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011462,Addiscombe East,E09000008,CROYDON,Woodside,NULL,SUNDRIDGE ROAD,20501449,CR0,NULL,NULL,534050,166350,NULL,NULL
NA,20/02/2016 22:11,2016,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (mobile),Shopping Centre,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000263,SHEPHERD'S BUSH GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34158749,ARIEL WAY,21000056,W12,523373,180286,523350,180250,51.50804655,-0.22366146
NA,22/02/2016 07:03,2016,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN RUBBLE  THIS IS A CONSTRUCTION SITE CAN HEAR BARKING COMING FROM UNDER THE RUBBLE  CA,Dog,Person (mobile),Warehouse,Non Residential,Animal rescue from below ground,Wild animal rescue from below ground,E05009380,LEA BRIDGE,E09000012,HACKNEY,Homerton,10008234220,CHATSWORTH ROAD,20900224,E5,535462,186421,535450,186450,51.56041107,-0.047211326
NA,24/02/2016 14:22,2016,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000430,STREATHAM HILL,E09000022,LAMBETH,West Norwood,1.00022E+11,CRIFFEL AVENUE,21900413,SW2,530103,173043,530150,173050,51.44144691,-0.129404271
NA,24/02/2016 16:24,2016,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE - DOG STUCK UNDER SHOWER BLOCK IN PERIVALE PARK CALLER WILL MEET CREW AT ENTRANC,Dog,Person (mobile),Sports pavilion/shower block/changing facility,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000187,PERIVALE,E09000009,EALING,Southall,12052504,HICKS AVENUE,20600878,UB6,514954,183075,514950,183050,51.53488653,-0.344008435
NA,24/02/2016 21:03,2016,2015/16,Special Service,1,1,298,298,DOG WITH FOOT TRAPPED,Dog,Person (land line),Veterinary surgery,Non Residential,Other animal assistance,Assist trapped domestic animal,NA,NA,NA,NA,Essex,1.0009E+11,PALMERSTON ROAD,13801587,IG9,541111,194025,541150,194050,51.6273543,0.037280963
NA,25/02/2016 01:09,2016,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE ASSIST RSPCA WITH FOX TRAPPED IN GATE  RSPCA ON SCENE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000058,OAKLEIGH,E09000003,BARNET,Barnet,NULL,TEMPLE AVENUE,20042240,N20,NULL,NULL,526850,194850,NULL,NULL
NA,25/02/2016 10:49,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH TRAPPED CAT,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011469,Kenley,E09000008,CROYDON,Purley,1.00021E+11,CLEMENT CLOSE,20500202,CR8,531567,159073,531550,159050,51.31556234,-0.113520474
NA,26/02/2016 00:40,2016,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000344,YEADING,E09000017,HILLINGDON,Southall,NULL,FAIRCLOUGH CLOSE,21402893,UB5,NULL,NULL,512650,182350,NULL,NULL
NA,27/02/2016 16:36,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN BUILDING SITE CALLER WILL MEET LFB ON SCENE,Cat,Person (mobile),Multi-Storey car park,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,10070014842,SIMPSONS ROAD,20301386,BR1,540323,168645,540350,168650,51.39948845,0.015810059
NA,28/02/2016 08:36,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED BETWEEN WALLS POSSIBLY INJURED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,NULL,COLDBATH STREET,20800354,SE13,NULL,NULL,537850,176350,NULL,NULL
NA,28/02/2016 19:58,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN BASEMENT,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,NULL,FLORENCE STREET,21604885,N1,NULL,NULL,531750,184150,NULL,NULL
NA,29/02/2016 22:43,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000267,BOUNDS GREEN,E09000014,HARINGEY,Hornsey,1.00023E+11,MORANT PLACE,21106461,N22,530816,190940,530850,190950,51.60211774,-0.112513601
NA,29/02/2016 23:39,2016,2015/16,Special Service,1,1,298,298,DOG TRAPPED BEHIND FENCE,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000626,TOOTING,E09000032,WANDSWORTH,Tooting,1.00023E+11,THURSO STREET,22905382,SW17,526752,171723,526750,171750,51.43034259,-0.178065775
NA,04/03/2016 08:48,2016,2015/16,Special Service,1,1,298,298,Redacted,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,HALF MOON STREET,8400107,W1J,NULL,NULL,528850,180250,NULL,NULL
NA,04/03/2016 11:00,2016,2015/16,Special Service,1,2,298,596,DOG STUCK IN HOLE IN GROUND CALLER WILL MEET YOU,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,NULL,CHRISTCHURCH ROAD,22402731,SW14,NULL,NULL,520050,174850,NULL,NULL
NA,04/03/2016 21:27,2016,2015/16,Special Service,1,1,298,298,KITTEN IN DISTRESS UNDER FLOORBOARDS BEHIND BOILER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000115,CRAY VALLEY WEST,E09000006,BROMLEY,Orpington,NULL,PETERSHAM GARDENS,20301248,BR5,NULL,NULL,546050,169050,NULL,NULL
NA,04/03/2016 21:58,2016,2015/16,Special Service,1,1,298,298,PERSON IN PRECARIOUS POSITION ON ROOF - WENT UP TO GET A CAT DOWN,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000142,REGENT'S PARK,E09000007,CAMDEN,Euston,NULL,ARLINGTON ROAD,20400726,NW1,NULL,NULL,529050,183450,NULL,NULL
NA,06/03/2016 12:30,2016,2015/16,Special Service,1,2,298,596,ASSIST RSPCA WITH CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Clapham,1.00023E+11,LURLINE GARDENS,22903101,SW11,528443,176855,528450,176850,51.47608384,-0.151893142
NA,06/03/2016 17:35,2016,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE  CAT TRAPPED IN VOID,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009327,MILE END,E09000030,TOWER HAMLETS,Bethnal Green,NULL,ERIC STREET,22700474,E3,NULL,NULL,536550,182450,NULL,NULL
NA,07/03/2016 10:22,2016,2015/16,Special Service,1,2,298,596,FOAL TRAPPED IN HEDGEROW,Horse,Person (land line),Hedge,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000330,HAREFIELD,E09000017,HILLINGDON,Ruislip,1.00021E+11,SPRINGWELL LANE,21401802,UB9,504996,192041,504950,192050,51.61741448,-0.484846694
NA,07/03/2016 15:37,2016,2015/16,Special Service,1,2,298,596,Redacted,Bird,Person (mobile),Multi-Storey car park,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000090,FRYENT,E09000005,BRENT,Stanmore,202098984,EDGWARE ROAD,20201631,NW9,520856,189464,520850,189450,51.59107628,-0.256759974
NA,08/03/2016 17:07,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011113,Rye Lane,E09000028,SOUTHWARK,Peckham,NULL,FENWICK ROAD,22500954,SE15,NULL,NULL,534250,175550,NULL,NULL
NA,09/03/2016 15:26,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN KITCHEN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000494,WEST HAM,E09000025,NEWHAM,Stratford,NULL,REDRIFFE ROAD,22201565,E13,NULL,NULL,539850,183550,NULL,NULL
NA,12/03/2016 11:30,2016,2015/16,Special Service,1,1,298,298,Redacted,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Plumstead,2.00002E+11,WELLINGTON STREET,20801579,SE18,543580,178797,543550,178750,51.48989785,0.066713723
NA,14/03/2016 16:31,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT UP TREE ENTER VIA HEATH ROAD TO THE DEAD END OF DALEY THOMPSON WAY,Cat,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000419,CLAPHAM TOWN,E09000022,LAMBETH,Clapham,1.00022E+11,DALEY THOMPSON WAY,21900432,SW8,528820,175968,528850,175950,51.46802683,-0.14679042
NA,15/03/2016 07:48,2016,2015/16,Special Service,1,2,298,596,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011227,Longlands,E09000004,BEXLEY,Sidcup,NULL,WOODSIDE ROAD,20101618,DA15,NULL,NULL,545450,172350,NULL,NULL
NA,15/03/2016 21:23,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH INJURED FOX BEHIND FENCE,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,202131511,BARRETTS GREEN ROAD,20201454,NW10,520532,183036,520550,183050,51.53337445,-0.263638185
NA,16/03/2016 18:08,2016,2015/16,Special Service,1,1,298,298,DOG TRAPPED BETWEEN SHED AND FENCE,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000297,PINNER,E09000015,HARROW,Harrow,10070269582,CAULFIELD GARDENS,21202812,HA5,511228,190161,511250,190150,51.59932185,-0.395465962
NA,18/03/2016 09:09,2016,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE CAT STUCK IN RIVER  RSPCA IN ATTENDANCE,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000199,ENFIELD LOCK,E09000010,ENFIELD,Enfield,207039016,SALISBURY ROAD,20702387,EN3,536894,198588,536850,198550,51.66939824,-0.021818846
NA,19/03/2016 09:28,2016,2015/16,Special Service,1,2,298,596,CAT TRAPPED IN ROOF VOID  GROUND FLOOR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000342,WEST DRAYTON,E09000017,HILLINGDON,Hayes,NULL,CHERRY ORCHARD,21400367,UB7,NULL,NULL,506150,179950,NULL,NULL
NA,21/03/2016 19:26,2016,2015/16,Special Service,1,1,298,298,Redacted,Unknown - Wild Animal,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000622,ST. MARY'S PARK,E09000032,WANDSWORTH,Battersea,10008158694,BATTERSEA PARK ROAD,22900337,SW11,527258,176270,527250,176250,51.47109397,-0.169157406
NA,22/03/2016 12:01,2016,2015/16,Special Service,1,1,298,298,CAT STUCK BEHIND WALL CALLED BY RSPCA,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000047,COPPETTS,E09000003,BARNET,Finchley,NULL,LABURNUM CLOSE,20025540,N11,NULL,NULL,528350,191550,NULL,NULL
NA,25/03/2016 19:28,2016,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED UNDER FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000210,TOWN,E09000010,ENFIELD,Enfield,NULL,INVERNESS AVENUE,20702766,EN1,NULL,NULL,533350,197750,NULL,NULL
NA,26/03/2016 12:21,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH TRAPPED BIRD,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000369,CANONBURY,E09000019,ISLINGTON,Islington,NULL,YEATE STREET,21606385,N1,NULL,NULL,532650,184150,NULL,NULL
NA,27/03/2016 10:01,2016,2015/16,Special Service,1,1,298,298,CAT IN PRECARIOUS POSITION IN FIR TREE - REQUESTED BY RSPCA,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Lee Green,NULL,COURTLANDS AVENUE,20800397,SE12,NULL,NULL,540550,175050,NULL,NULL
NA,27/03/2016 14:32,2016,2015/16,Special Service,1,1,298,298,SNAKE LOOSE IN FLAT - RSPCA TOLD CALLER THEY WERE TOO BUSY TO ATTEND,Snake,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000375,HOLLOWAY,E09000019,ISLINGTON,Holloway,NULL,WIDDENHAM ROAD,21604016,N7,NULL,NULL,530650,185550,NULL,NULL
NA,31/03/2016 14:15,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH TRAPPED FOX ON BALCONY,Fox,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000425,LARKHALL,E09000022,LAMBETH,Clapham,NULL,STEWART'S ROAD,21901302,SW8,NULL,NULL,529550,176450,NULL,NULL
NA,01/04/2016 12:23,2016,2016/17,Special Service,1,3,326,978,Redacted,Horse,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000119,HAYES AND CONEY HALL,E09000006,BROMLEY,Biggin Hill,10003628732,NASH LANE,20300763,BR2,540691,163271,540650,163250,51.35110779,0.018973548
NA,02/04/2016 09:55,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK UP TREE  RSPCA OFFICER ADVISED THE ROAD IS VERY NARROW,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000207,SOUTHBURY,E09000010,ENFIELD,Enfield,NULL,FALMER ROAD,20702673,EN1,NULL,NULL,533450,196250,NULL,NULL
NA,02/04/2016 14:13,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN CAR ENGINE  CAR IS LOCATED IN CAR PARK AT SIDE OF COSTA COFFEE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05000558,CARSHALTON CENTRAL,E09000029,SUTTON,Wallington,5870115275,HIGH STREET,22605690,SM5,528140,164601,528150,164650,51.36602471,-0.16067974
NA,03/04/2016 10:46,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN TREE RSPCA IN ATTENDANCE WILL MEET AT ENTRANCE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000283,WHITE HART LANE,E09000014,HARINGEY,Tottenham,1.00024E+11,CHURCH LANE,21104361,N17,533177,191090,533150,191050,51.60291227,-0.078386763
NA,03/04/2016 19:30,2016,2016/17,Special Service,1,2,326,652,BIRD POSSIBLY TRAPPED IN CHIMNEY,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000469,RAYNES PARK,E09000024,MERTON,Wimbledon,NULL,AMITY GROVE,22100168,SW20,NULL,NULL,523050,169350,NULL,NULL
NA,04/04/2016 11:05,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CAR ENGINE/TRANSMISSION BAY - RSPCA ON SCENE - THIS IS OUTSIDE OLYMPIA,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000252,AVONMORE AND BROOK GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34103611,HAMMERSMITH ROAD,21000411,W14,524250,178851,524250,178850,51.49495787,-0.211536215
NA,06/04/2016 06:58,2016,2016/17,Special Service,1,2,326,652,HAMSTER STUCK UNDER FLOORBOARDS,Hamster,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000469,RAYNES PARK,E09000024,MERTON,Wimbledon,NULL,DURRINGTON PARK ROAD,22102241,SW20,NULL,NULL,523250,170050,NULL,NULL
NA,06/04/2016 15:51,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),Sports/Social club,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,10091857858,UPPER MALL,21000834,W6,522375,178316,522350,178350,51.4905581,-0.238717768
NA,06/04/2016 17:22,2016,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011225,Erith,E09000004,BEXLEY,Erith,NULL,TRANQUIL RISE,20101480,DA8,NULL,NULL,551250,178250,NULL,NULL
NA,06/04/2016 17:57,2016,2016/17,Special Service,1,1,326,326,KITTEN IN DISTRESS RUNNING CALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Hayes,NULL,KEATS WAY,21401069,UB7,NULL,NULL,506650,178850,NULL,NULL
NA,07/04/2016 14:09,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED BY WINGS AND FEET IN NETTING  ON SIDE OF BUILDING,Bird,Person (mobile),Train station - elsewhere,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Kentish Town,5087781,KENTISH TOWN ROAD,20400363,NW1,528897,183912,528850,183950,51.53940137,-0.142781689
NA,08/04/2016 15:09,2016,2016/17,Special Service,1,2,326,652,CAT TRAPPED UNDER TIMBER ON RAILWAY LINE,Cat,Person (mobile),Railway,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,1.00023E+11,SHRUBLAND ROAD,22874650,E17,537138,188724,537150,188750,51.58070164,-0.022150443
NA,09/04/2016 19:39,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011247,Loxford,E09000026,REDBRIDGE,Ilford,1.00022E+11,BUTTSBURY ROAD,22302672,IG1,544354,185082,544350,185050,51.5461756,0.080426421
NA,10/04/2016 00:00,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL STUCK IN CHIMNEY,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011241,Cranbrook,E09000026,REDBRIDGE,Ilford,NULL,WANSTEAD PARK ROAD,22303358,IG1,NULL,NULL,542350,187650,NULL,NULL
NA,10/04/2016 12:58,2016,2016/17,Special Service,1,1,326,326,VEHICLE IN PRECARIOUS POSITION,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,NULL,SEVEN KINGS WAY,21880100,KT2,NULL,NULL,518050,169650,NULL,NULL
NA,10/04/2016 16:53,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED UNDER METAL GATE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Clapham,1.00023E+11,CAREY GARDENS,22900794,SW8,529605,176531,529650,176550,51.47290738,-0.135289139
NA,11/04/2016 23:36,2016,2016/17,Special Service,1,1,326,326,CAT STUCK BETWEEN CAVITY WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000306,BROOKLANDS,E09000016,HAVERING,Romford,NULL,BROOKLANDS ROAD,21301021,RM7,NULL,NULL,550650,189150,NULL,NULL
NA,12/04/2016 16:19,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN BATHROOM PIPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,NULL,ELF ROW,22700459,E1W,NULL,NULL,535450,180850,NULL,NULL
NA,13/04/2016 09:25,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009376,HOMERTON,E09000012,HACKNEY,Homerton,NULL,HOMERTON HIGH STREET,20900533,E9,NULL,NULL,535650,185250,NULL,NULL
NA,13/04/2016 23:41,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011253,Valentines,E09000026,REDBRIDGE,Ilford,NULL,COURTLAND AVENUE,22302752,IG1,NULL,NULL,542850,186850,NULL,NULL
NA,14/04/2016 00:52,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009376,HOMERTON,E09000012,HACKNEY,Homerton,NULL,HOMERTON HIGH STREET,20900533,E9,NULL,NULL,535650,185250,NULL,NULL
NA,14/04/2016 17:54,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,ARDSHIEL CLOSE,22900137,SW15,NULL,NULL,523850,175750,NULL,NULL
NA,16/04/2016 02:04,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED BETWEEN HOUSE AND WALL,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011482,Shirley North,E09000008,CROYDON,Woodside,NULL,THE GLADE,20500570,CR0,NULL,NULL,536150,167050,NULL,NULL
NA,16/04/2016 13:26,2016,2016/17,Special Service,1,2,326,652,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000527,ST. MARGARETS AND NORTH TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,1.00022E+11,HILL VIEW ROAD,22403562,TW1,516309,174226,516350,174250,51.4550763,-0.327395376
NA,17/04/2016 11:08,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN PRECARIOUS POSITION,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,PANCRAS ROAD,20400755,NW1,NULL,NULL,529750,183350,NULL,NULL
NA,18/04/2016 09:14,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),Train station - elsewhere,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000253,COLLEGE PARK AND OLD OAK,E09000013,HAMMERSMITH AND FULHAM,Acton,34004343,ERCONWALD STREET,21000318,W12,521702,181205,521750,181250,51.51666787,-0.247410437
NA,18/04/2016 20:21,2016,2016/17,Special Service,1,1,326,326,KITTENS TRAPPED BEHIND KITCHEN UNIT,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000417,BRIXTON HILL,E09000022,LAMBETH,Brixton,NULL,LAMBERT ROAD,21900827,SW2,NULL,NULL,530550,174650,NULL,NULL
NA,19/04/2016 08:30,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN FRIDGE FREEZER AND WALL VOID,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,West Hampstead,NULL,CARLINGFORD ROAD,20400177,NW3,NULL,NULL,526750,185750,NULL,NULL
NA,19/04/2016 10:40,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000476,BOLEYN,E09000025,NEWHAM,East Ham,NULL,ABBOTS ROAD,22200304,E6,NULL,NULL,541950,183450,NULL,NULL
NA,19/04/2016 12:22,2016,2016/17,Special Service,1,1,326,326,Assist rspca with injured pigeon in tree   - RSPCA IN ATTENDANCE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000446,LADYWELL,E09000023,LEWISHAM,Lewisham,NULL,GREENBANKS CLOSE,22007093,SE13,NULL,NULL,537750,175750,NULL,NULL
NA,19/04/2016 16:00,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED BETWEEN DOOR AND WALL,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000487,LITTLE ILFORD,E09000025,NEWHAM,East Ham,NULL,GAINSBOROUGH AVENUE,22200179,E12,NULL,NULL,543250,185150,NULL,NULL
NA,20/04/2016 11:50,2016,2016/17,Special Service,1,2,326,652,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (mobile),Cemetery,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011108,Nunhead & Queen's Road,E09000028,SOUTHWARK,New Cross,2.00003E+11,LINDEN GROVE,22501510,SE15,535326,175699,535350,175650,51.46409203,-0.053284898
NA,21/04/2016 12:14,2016,2016/17,Special Service,1,1,326,326,CAT STUCK BETWEEN TWO WALLS,Cat,Person (land line),Large supermarket,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,6354412,CHAPMAN STREET,22700297,E1,534781,180975,534750,180950,51.5116348,-0.059112409
NA,21/04/2016 20:27,2016,2016/17,Special Service,1,1,326,326,ADULT CAT AND FOUR NEWLY BORN KITTENS TRAPPED BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,NULL,SANDERSTEAD ROAD,22872700,E10,NULL,NULL,536350,187250,NULL,NULL
NA,22/04/2016 15:26,2016,2016/17,Special Service,1,1,326,326,CAT ON ROOF  CALLER STATES AT COMMUNITY CENTRE  RSPCA OFFICER IN ATTENDANCE,Cat,Person (mobile),Community centre/Hall,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011110,Peckham,E09000028,SOUTHWARK,Peckham,10091837469,BULLER CLOSE,22500371,SE15,534258,177188,534250,177150,51.47772738,-0.068084623
NA,23/04/2016 09:17,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN WIRE FENCING CALLER COULD NOT GIVE A HOUSE NUMBER BUT IS WAITING OUTSIDE FOR BRIGADE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009381,LONDON FIELDS,E09000012,HACKNEY,Shoreditch,NULL,ALBION DRIVE,20901765,E8,NULL,NULL,533650,184050,NULL,NULL
NA,23/04/2016 10:12,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT LOCKED IN GARAGE,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000592,CHAPEL END,E09000031,WALTHAM FOREST,Walthamstow,1.00023E+11,MARTEN ROAD,22857150,E17,537487,190487,537450,190450,51.59645851,-0.016427448
NA,23/04/2016 15:39,2016,2016/17,Special Service,1,1,326,326,PIGEON HANGING FROM TELEGRAPH WIRING  RSPCA IN ATTENDANCE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000463,LAVENDER FIELDS,E09000024,MERTON,Mitcham,NULL,BOROUGH ROAD,22100705,CR4,NULL,NULL,527250,169350,NULL,NULL
NA,23/04/2016 16:07,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED UNDER CAR   NEAR HIGH STREET   NR SCOTCH THE BUTCHERS AND NISSAR,Dog,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000568,THE WRYTHE,E09000029,SUTTON,Wallington,5870116940,GREEN WRYTHE LANE,22600593,SM5,527571,165366,527550,165350,51.37302777,-0.168574454
NA,23/04/2016 17:21,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05009374,HACKNEY WICK,E09000012,HACKNEY,Homerton,10008338255,WALLIS ROAD,20901039,E9,537238,184685,537250,184650,51.54438262,-0.022283051
NA,24/04/2016 13:42,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,NULL,HAMILTON ROAD,21900659,SE27,NULL,NULL,532750,171750,NULL,NULL
NA,24/04/2016 15:56,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED BEHIND FIRE MANTEL,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000532,WEST TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,GLEBE WAY,22401508,TW13,NULL,NULL,513250,172250,NULL,NULL
NA,25/04/2016 12:55,2016,2016/17,Special Service,1,1,326,326,CAT UP TREE RSPCA IN ATTENDANCE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000526,NORTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,1.00022E+11,KEW ROAD,22405688,TW9,518493,175644,518450,175650,51.46736843,-0.295501855
NA,26/04/2016 13:41,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH A CROW TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000257,MUNSTER,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,COLEHILL LANE,21000227,SW6,NULL,NULL,524250,176650,NULL,NULL
NA,26/04/2016 20:47,2016,2016/17,Special Service,1,1,326,326,BIRD STUCK CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011098,Chaucer,E09000028,SOUTHWARK,Dockhead,NULL,LONG LANE,22501547,SE1,NULL,NULL,532650,179750,NULL,NULL
NA,27/04/2016 08:36,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND KITCHEN CABINET,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000344,YEADING,E09000017,HILLINGDON,Southall,NULL,FAIRCLOUGH CLOSE,21402893,UB5,NULL,NULL,512650,182350,NULL,NULL
NA,27/04/2016 14:30,2016,2016/17,Special Service,1,1,326,326,CAT STUCK ON ROOF CALL RECIEVED FROM RSPCA,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011476,Purley & Woodcote,E09000008,CROYDON,Purley,NULL,SELCROFT ROAD,20502321,CR8,NULL,NULL,531750,161250,NULL,NULL
NA,27/04/2016 19:20,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON ROOF TRAPPED ON SLOPING ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000251,ASKEW,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,CONINGHAM ROAD,21000235,W12,NULL,NULL,522650,179650,NULL,NULL
NA,28/04/2016 15:00,2016,2016/17,Special Service,1,2,326,652,DOG TRAPPED UNDER BUILDING RODING VALLEY CRICKET CLUB,Dog,Person (mobile),Sports pavilion/shower block/changing facility,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,NA,NA,NA,NA,Essex,10012166383,BRADWELL ROAD,13801513,IG9,542481,194502,542450,194550,51.63129565,0.057253936
NA,29/04/2016 14:00,2016,2016/17,Special Service,1,1,326,326,CAT STUCK BEHIND SHED,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000368,CALEDONIAN,E09000019,ISLINGTON,Euston,NULL,CALSHOT STREET,21604418,N1,NULL,NULL,530650,183250,NULL,NULL
NA,29/04/2016 22:00,2016,2016/17,Special Service,1,1,326,326,HAMSTER STUCK IN CAVITY WALL,Hamster,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000316,MAWNEYS,E09000016,HAVERING,Romford,NULL,BLANDFORD CLOSE,21301022,RM7,NULL,NULL,549650,189250,NULL,NULL
NA,30/04/2016 18:35,2016,2016/17,Special Service,1,1,326,326,CATS STUCK IN ROOF VOID OF FLATS,cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Plumstead,NULL,BARNFIELD GARDENS,20800116,SE18,NULL,NULL,543950,177850,NULL,NULL
NA,01/05/2016 12:49,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL TO CAT UP TREE OUTSIDE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009367,BROWNSWOOD,E09000012,HACKNEY,Holloway,1.00021E+11,QUEENS DRIVE,20900835,N4,531956,186721,531950,186750,51.5639382,-0.097646118
NA,01/05/2016 16:27,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK IN BATHROOM,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,NULL,LADYWELL ROAD,22001623,SE13,NULL,NULL,537950,174850,NULL,NULL
NA,02/05/2016 14:29,2016,2016/17,Special Service,1,1,326,326,CAT UP TREE OUTSIDE RUNNING CALL,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009367,BROWNSWOOD,E09000012,HACKNEY,Holloway,1.00021E+11,QUEENS DRIVE,20900835,N4,531959,186723,531950,186750,51.56395547,-0.097602113
NA,03/05/2016 10:53,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA INSPECTOR WITH CAT TRAPPED ON WINDOW LEDGE,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000645,TACHBROOK,E09000033,WESTMINSTER,Lambeth,NULL,ST GEORGE'S SQUARE,8400197,SW1V,NULL,NULL,529650,178250,NULL,NULL
NA,03/05/2016 17:05,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000190,SOUTHALL GREEN,E09000009,EALING,Southall,12022790,SPENCER STREET,20601577,UB2,511720,179486,511750,179450,51.50327729,-0.391753266
NA,04/05/2016 13:13,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED BY WIRE,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000427,PRINCE'S,E09000022,LAMBETH,Lambeth,NULL,MARYLEE WAY,21900931,SE11,NULL,NULL,531050,178550,NULL,NULL
NA,05/05/2016 17:01,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000597,HALE END AND HIGHAMS PARK,E09000031,WALTHAM FOREST,Woodford,NULL,MAPPERLEY DRIVE,22856000,IG8,NULL,NULL,539150,191350,NULL,NULL
NA,05/05/2016 19:49,2016,2016/17,Special Service,1,2,326,652,CAT STUCK ON ROOF  IT KEEPS SLIDING,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000615,FURZEDOWN,E09000032,WANDSWORTH,Tooting,NULL,EARDLEY ROAD,22901341,SW16,NULL,NULL,529450,171150,NULL,NULL
NA,06/05/2016 10:59,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN NETTING ON BALCONY CALLED BY RSPCA J/O CROWNSTONE ROAD,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000435,TULSE HILL,E09000022,LAMBETH,Brixton,NULL,BRIXTON WATER LANE,21900242,SW2,NULL,NULL,530850,174650,NULL,NULL
NA,06/05/2016 11:14,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,NULL,BOUTON PLACE,21610090,N1,NULL,NULL,531650,184050,NULL,NULL
NA,07/05/2016 12:02,2016,2016/17,Special Service,1,1,326,326,DOG IN PRECARIOUS POSITION   SLIPPED DOWN ROOF TO SECOND FLOOR LEVEL,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011100,Dulwich Village,E09000028,SOUTHWARK,West Norwood,NULL,COURT LANE GARDENS,22500626,SE21,NULL,NULL,533550,173850,NULL,NULL
NA,07/05/2016 19:07,2016,2016/17,Special Service,1,1,326,326,BIRD STUCK IN LOFT,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011250,Newbury,E09000026,REDBRIDGE,Ilford,NULL,COLENSO ROAD,22301952,IG2,NULL,NULL,545050,187350,NULL,NULL
NA,07/05/2016 21:55,2016,2016/17,Special Service,1,1,326,326,CAT WITH PAW TRAPPED IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000029,CHADWELL HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,BILLET ROAD,19900044,RM6,NULL,NULL,547550,189750,NULL,NULL
NA,08/05/2016 12:45,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH TRAPPED FOX CUB,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000439,BROCKLEY,E09000023,LEWISHAM,Greenwich,1.00023E+11,LEWISHAM WAY,22000623,SE4,536994,176364,536950,176350,51.46966684,-0.029030885
NA,08/05/2016 15:47,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED  UNDERNEATH RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000200,GRANGE,E09000010,ENFIELD,Enfield,207170226,WINDMILL HILL,20703056,EN2,532185,196706,532150,196750,51.6536133,-0.09058244
NA,08/05/2016 20:07,2016,2016/17,Special Service,1,2,326,652,BIRD ENTANGLED IN WIRE - TRAPPED IN TREE - TL REQUESTED FROM SCENE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000628,WEST HILL,E09000032,WANDSWORTH,Wandsworth,1.00023E+11,THURSLEY GARDENS,22905379,SW19,523965,172720,523950,172750,51.43991877,-0.217787306
NA,08/05/2016 22:35,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,NULL,LAKEDALE ROAD,20800879,SE18,NULL,NULL,545250,178250,NULL,NULL
NA,09/05/2016 20:39,2016,2016/17,Special Service,1,1,326,326,SQUIRREL TRAPPED IN WALL,Squirrel,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009320,BOW WEST,E09000030,TOWER HAMLETS,Bethnal Green,NULL,WHITTON WALK,22702076,E3,NULL,NULL,537050,182850,NULL,NULL
NA,10/05/2016 09:46,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000439,BROCKLEY,E09000023,LEWISHAM,Greenwich,1.00022E+11,BOLDEN STREET,22000132,SE8,537594,176353,537550,176350,51.46942249,-0.02040235
NA,10/05/2016 12:03,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000215,BLACKHEATH WESTCOMBE,E09000011,GREENWICH,East Greenwich,NULL,FOYLE ROAD,20800590,SE3,NULL,NULL,539650,177750,NULL,NULL
NA,11/05/2016 11:28,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN GUTTERING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000176,ELTHORNE,E09000009,EALING,Ealing,NULL,WALKER CLOSE,20601824,W7,NULL,NULL,515150,179950,NULL,NULL
NA,11/05/2016 14:28,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,NULL,CLIFFORD AVENUE,22405471,SW14,NULL,NULL,519950,175950,NULL,NULL
NA,11/05/2016 17:58,2016,2016/17,Special Service,1,1,326,326,INJURED DOG STUCK IN RAILINGS ADDRESS GIVEN AS BY THE CHILDRENS PLAYGROUND IN MILNE PARK NEXT TO HOM,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011471,New Addington South,E09000008,CROYDON,Addington,2.00001E+11,MILNE PARK EAST,20501785,CR0,539007,161468,539050,161450,51.33531909,-0.005896615
NA,11/05/2016 18:49,2016,2016/17,Special Service,1,2,326,652,DISTRESSED CAT STUCK UP TREE - ANIMAL HOSPITAL STAFF IN ATTENDANCE    ROMFORD CEMETERY,Cat,Person (land line),Cemetery,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000306,BROOKLANDS,E09000016,HAVERING,Dagenham,1.00024E+11,DAGENHAM ROAD,21301125,RM7,550778,187799,550750,187750,51.56891016,0.17416392
NA,12/05/2016 08:01,2016,2016/17,Special Service,1,1,326,326,CAT STUCK ON ROOF CALLED BY RSPCA - RSPCA OFFICER IN ATTENDANCE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011240,Clementswood,E09000026,REDBRIDGE,Ilford,NULL,RICHMOND ROAD,22303183,IG1,NULL,NULL,544450,185650,NULL,NULL
NA,12/05/2016 10:40,2016,2016/17,Special Service,1,1,326,326,PIDGEON TRAPPED BETWEEN WALLS CALLER IS IN THE CAR PARK AND WILL MEET YOU THERE,Bird,Person (mobile),Leisure Centre,Non Residential,Other animal assistance,Assist trapped wild animal,E05000172,DORMERS WELLS,E09000009,EALING,Southall,12042082,DORMERS WELLS LANE,20600548,UB1,513466,181057,513450,181050,51.5170497,-0.366102554
NA,12/05/2016 18:39,2016,2016/17,Special Service,1,1,326,326,RSPCA REQUEST TO ATTEND CAT STUCK IN DRY CANAL RSPCA WILL MEET YOU THERE,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,207048735,POWYS LANE,20704622,N13,530245,192304,530250,192350,51.61450755,-0.120247284
NA,13/05/2016 22:48,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),Temporary office (eg portacabin),Non Residential,Other animal assistance,Assist trapped domestic animal,E05000136,HAVERSTOCK,E09000007,CAMDEN,Kentish Town,5134491,HARMOOD STREET,20400488,NW1,528525,184504,528550,184550,51.54480636,-0.147926585
NA,14/05/2016 16:48,2016,2016/17,Special Service,1,1,326,326,DOG IN POND  GO DOWN PATH BY THE TRAFFIC LIGHT AND FORTY HILL IS THE NAME OF THE PARK,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000195,CHASE,E09000010,ENFIELD,Enfield,207158578,FORTY HILL,20702687,EN2,533610,198287,533650,198250,51.66748374,-0.069392265
NA,14/05/2016 17:28,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED IN DISABLED LIFT SHAFT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000464,LONGTHORNTON,E09000024,MERTON,Norbury,NULL,CAIRNS AVENUE,22100004,SW16,NULL,NULL,529450,168850,NULL,NULL
NA,15/05/2016 17:00,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000285,BELMONT,E09000015,HARROW,Stanmore,1.00021E+11,YORK AVENUE,21202537,HA7,516812,190416,516850,190450,51.60048383,-0.314794415
NA,15/05/2016 20:33,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND FIREPLACE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000418,CLAPHAM COMMON,E09000022,LAMBETH,Clapham,NULL,ELMS ROAD,21900517,SW4,NULL,NULL,529350,174650,NULL,NULL
NA,16/05/2016 11:18,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009331,ST. PETER'S,E09000030,TOWER HAMLETS,Bethnal Green,NULL,COATE STREET,22700332,E2,NULL,NULL,534450,183250,NULL,NULL
NA,16/05/2016 12:20,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN NETTING  RSPCA IN ATTENDANCE BY RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000099,QUEENSBURY,E09000005,BRENT,Stanmore,202142989,QUEENSBURY STATION PARADE,20200704,HA8,518850,189735,518850,189750,51.5939374,-0.28561297
NA,17/05/2016 15:52,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN WIRE,Bird,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000104,WEMBLEY CENTRAL,E09000005,BRENT,Wembley,202063165,NORTON ROAD,20200120,HA0,517779,184562,517750,184550,51.54766965,-0.30279977
NA,17/05/2016 20:59,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE - DISTRESSED CAT POSSIBLY INJURED TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000174,EALING COMMON,E09000009,EALING,Ealing,NULL,GRANGE PARK,20600749,W5,NULL,NULL,518250,180250,NULL,NULL
NA,18/05/2016 12:11,2016,2016/17,Special Service,1,2,326,652,ASSIST RSPCA KITTEN TRAPPED IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000335,NORTHWOOD,E09000017,HILLINGDON,Ruislip,1.00021E+11,KEMPS DRIVE,21401075,HA6,509702,191309,509750,191350,51.60993935,-0.417130651
NA,19/05/2016 17:44,2016,2016/17,Special Service,1,1,326,326,RABBIT TRAPPED IN CAR ENGINE,Rabbit,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05011466,Coulsdon Town,E09000008,CROYDON,Purley,10014050785,WOODFIELD HILL,20502531,CR5,528792,157951,528750,157950,51.30611244,-0.153720812
NA,19/05/2016 20:20,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000322,SQUIRREL'S HEATH,E09000016,HAVERING,Hornchurch,NULL,CLIVE ROAD,21301086,RM2,NULL,NULL,552750,188550,NULL,NULL
NA,20/05/2016 09:20,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011111,Peckham Rye,E09000028,SOUTHWARK,Forest Hill,2.00003E+11,MARMORA ROAD,22501666,SE22,535148,174333,535150,174350,51.45185891,-0.056367208
NA,20/05/2016 10:54,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED INSIDE CAR CHASSIS,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000090,FRYENT,E09000005,BRENT,Stanmore,202088139,HAYDON CLOSE,20202282,NW9,520401,189123,520450,189150,51.58810873,-0.263442032
NA,20/05/2016 11:47,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED UNDER CAR,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000060,UNDERHILL,E09000003,BARNET,Mill Hill,NULL,HYVER HILL,20024560,NW7,NULL,NULL,521050,194650,NULL,NULL
NA,20/05/2016 13:11,2016,2016/17,Special Service,1,1,326,326,CAT STUCK UP TREE RSPCA IN ATTENDANCE - CALLED BY RSPCA,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000198,ENFIELD HIGHWAY,E09000010,ENFIELD,Enfield,207138706,BRIMSDOWN AVENUE,20702145,EN3,536283,197666,536250,197650,51.66126163,-0.031007736
NA,20/05/2016 15:48,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED BEHIND GAS FIRE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist  trapped livestock animal,E05000590,CANN HALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,NAPIER ROAD,22860450,E11,NULL,NULL,539250,186150,NULL,NULL
NA,20/05/2016 21:54,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED UNDER FLOOR BOARDS OR WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000199,ENFIELD LOCK,E09000010,ENFIELD,Enfield,NULL,OSTELL CRESCENT,20706922,EN3,NULL,NULL,537450,198250,NULL,NULL
NA,21/05/2016 10:43,2016,2016/17,Special Service,1,1,326,326,CAT STUCK BETWEEN ROOF TILES AND ROOF TERRACE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000191,SOUTHFIELD,E09000009,EALING,Chiswick,NULL,ACTON LANE,20600011,W4,NULL,NULL,520450,179450,NULL,NULL
NA,21/05/2016 12:04,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD TRAPPED IN TABLE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000353,HANWORTH,E09000018,HOUNSLOW,Feltham,NULL,HAMPTON ROAD WEST,21500550,TW13,NULL,NULL,512150,172450,NULL,NULL
NA,21/05/2016 16:58,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN DECKING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000191,SOUTHFIELD,E09000009,EALING,Chiswick,NULL,ACTON LANE,20600011,W4,NULL,NULL,520450,179450,NULL,NULL
NA,21/05/2016 19:53,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000377,MILDMAY,E09000019,ISLINGTON,Islington,NULL,NEWINGTON GREEN,21603608,N1,NULL,NULL,532850,185250,NULL,NULL
NA,22/05/2016 01:01,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000263,SHEPHERD'S BUSH GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,ST STEPHEN'S AVENUE,21000765,W12,NULL,NULL,522850,179650,NULL,NULL
NA,22/05/2016 12:19,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN STRING OUTSIDE WINDOW FLAT C,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,1.00024E+11,PORTNALL ROAD,8400567,W9,524682,182845,524650,182850,51.53075783,-0.203904353
NA,24/05/2016 08:30,2016,2016/17,Special Service,1,1,326,326,TWO LARGE BLACK DOGS TRAPPED IN CAR WITH WINDOWS SHUT IN DIRECT SUNLIGHT THIS IS A SILVER VOLVO THE,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000519,"HAM, PETERSHAM AND RICHMOND RIVERSIDE",E09000027,RICHMOND UPON THAMES,Kingston,1.00022E+11,CLIFFORD ROAD,22403393,TW10,517924,172704,517950,172750,51.44106331,-0.304668604
NA,24/05/2016 12:44,2016,2016/17,Special Service,1,1,326,326,GUIDE DOG IMPALED ON A BRANCH IN BACK GARDEN,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000285,BELMONT,E09000015,HARROW,Stanmore,1.00021E+11,MOUNTBEL ROAD,21202513,HA7,516189,190485,516150,190450,51.60123281,-0.323762716
NA,24/05/2016 13:56,2016,2016/17,Special Service,1,1,326,326,ASSSIT RSPCA WITH FOX CLUBS  STUCK IN A HOLE,Fox,Person (mobile),Fence,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05009334,STEPNEY GREEN,E09000030,TOWER HAMLETS,Shadwell,6170247,REDMANS ROAD,22701002,E1,535216,181879,535250,181850,51.5196545,-0.052500953
NA,25/05/2016 09:00,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN HOLE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000622,ST. MARY'S PARK,E09000032,WANDSWORTH,Battersea,1.00023E+11,CAMBRIDGE ROAD,22900771,SW11,527527,176503,527550,176550,51.47312744,-0.165202568
NA,26/05/2016 17:42,2016,2016/17,Special Service,1,1,326,326,PIGEON STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,NULL,REDSTON ROAD,21100732,N8,NULL,NULL,529550,189350,NULL,NULL
NA,26/05/2016 19:52,2016,2016/17,Special Service,1,1,326,326,Redacted,Squirrel,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000478,CANNING TOWN SOUTH,E09000025,NEWHAM,Plaistow,NULL,VINCENT STREET,22201651,E16,NULL,NULL,539850,181450,NULL,NULL
NA,26/05/2016 23:03,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000534,BRUNSWICK PARK,E09000028,SOUTHWARK,Peckham,NULL,CROFTON ROAD,22500652,SE5,NULL,NULL,533550,176350,NULL,NULL
NA,27/05/2016 08:02,2016,2016/17,Special Service,1,1,326,326,CAT IN CANAL,Cat,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000635,HARROW ROAD,E09000033,WESTMINSTER,North Kensington,1.00024E+11,HORMEAD ROAD,8400803,W9,524636,182141,524650,182150,51.52444099,-0.20481618
NA,28/05/2016 02:22,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED BETWEEN FENCE AND WALL,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000563,STONECOT,E09000029,SUTTON,Sutton,5870023878,WALTON AVENUE,22601978,SM3,524503,165443,524550,165450,51.3744,-0.212601993
NA,28/05/2016 12:54,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA INSPECTOR WITH DUCKLINGS STUCK ON ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000637,KNIGHTSBRIDGE AND BELGRAVIA,E09000033,WESTMINSTER,Chelsea,NULL,BELGRAVE MEWS NORTH,8401109,SW1X,NULL,NULL,528150,179550,NULL,NULL
NA,28/05/2016 13:51,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE DUCK AND CHICKS IN PRECARIOUS POSITION ON ROOF TERRACE OF PENTHOUSE CALLER WILL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000628,WEST HILL,E09000032,WANDSWORTH,Wandsworth,NULL,WHITELANDS CRESCENT,22906840,SW18,NULL,NULL,524550,173950,NULL,NULL
NA,28/05/2016 14:43,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE DUCK AND CHICKS IN PRECARIOUS POSITION,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000628,WEST HILL,E09000032,WANDSWORTH,Wandsworth,NULL,WHITELANDS CRESCENT,22906840,SW18,NULL,NULL,524550,173950,NULL,NULL
NA,28/05/2016 16:54,2016,2016/17,Special Service,1,1,326,326,CAT STUCK UNDER FLOOR BOARDS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000618,NIGHTINGALE,E09000032,WANDSWORTH,Tooting,NULL,RAMSDEN ROAD,22904179,SW12,NULL,NULL,528350,173750,NULL,NULL
NA,28/05/2016 19:10,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000475,BECKTON,E09000025,NEWHAM,East Ham,NULL,FIREFLY GARDENS,22200612,E6,NULL,NULL,542150,182050,NULL,NULL
NA,29/05/2016 08:32,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CUPBOARD BEHIND PIPES,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000455,ABBEY,E09000024,MERTON,Wimbledon,NULL,HIGH PATH,22103300,SW19,NULL,NULL,525950,169850,NULL,NULL
NA,29/05/2016 20:59,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE - BIRD TRAPPED IN CAVITY WALL RSPCA AND COUNCIL HAVE REFUSED TO ATTEND,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000455,ABBEY,E09000024,MERTON,Wimbledon,NULL,HIGH PATH,22103300,SW19,NULL,NULL,525950,169850,NULL,NULL
NA,29/05/2016 22:43,2016,2016/17,Special Service,1,3,326,978,FOX TRAPPED IN SEWAGE PIPE  RSPCA ON ROUTE,Fox,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,West Hampstead,5040328,GAINSBOROUGH GARDENS,20400084,NW3,526784,186045,526750,186050,51.5590486,-0.172464844
NA,30/05/2016 19:17,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED BEHIND HEATER,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000363,OSTERLEY AND SPRING GROVE,E09000018,HOUNSLOW,Heston,NULL,NORTHUMBERLAND GARDENS,21501433,TW7,NULL,NULL,516350,177250,NULL,NULL
NA,31/05/2016 22:15,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CAVITY WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011228,Northumberland Heath,E09000004,BEXLEY,Erith,NULL,WALSINGHAM WALK,20106303,DA17,NULL,NULL,549350,177850,NULL,NULL
NA,01/06/2016 17:22,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,KINGSWAY,20025360,N12,NULL,NULL,526350,192050,NULL,NULL
NA,03/06/2016 19:07,2016,2016/17,Special Service,1,1,326,326,PIGEON STUCK IN AERIEL ON ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000036,MAYESBROOK,E09000002,BARKING AND DAGENHAM,Barking,NULL,ILCHESTER ROAD,19900189,RM8,NULL,NULL,546750,185350,NULL,NULL
NA,03/06/2016 19:13,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN BUILDING,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000193,BOWES,E09000010,ENFIELD,Southgate,NULL,BOWES ROAD,20705181,N13,NULL,NULL,530350,192050,NULL,NULL
NA,04/06/2016 09:05,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT UP A TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000141,KING'S CROSS,E09000007,CAMDEN,Euston,5105804,LEIGH STREET,20400878,WC1H,530109,182486,530150,182450,51.52630839,-0.125842082
NA,04/06/2016 12:11,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN BUILDING OPPOSITE,Cat,Person (land line),Other building/use not known,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000092,KENSAL GREEN,E09000005,BRENT,North Kensington,34128611,HARROW ROAD,21001204,NW10,522730,182875,522750,182850,51.53145467,-0.232020581
NA,04/06/2016 15:39,2016,2016/17,Special Service,1,1,326,326,DOG STUCK IN GATE,Dog,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000289,HARROW ON THE HILL,E09000015,HARROW,Northolt,1.00021E+11,PORLOCK AVENUE,21201250,HA2,514436,187466,514450,187450,51.57445699,-0.350046601
NA,04/06/2016 18:39,2016,2016/17,Special Service,1,1,326,326,PUPPY TRAPPED BETWEEN GARAGE AND WALL,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000115,CRAY VALLEY WEST,E09000006,BROMLEY,Orpington,NULL,CLARENDON WAY,20300926,BR7,NULL,NULL,545950,168650,NULL,NULL
NA,04/06/2016 18:56,2016,2016/17,Special Service,1,3,326,978,ASSIST RSPCA WITH CROW TRAPPED IN TWINE IN TREE    ALP REQUESTED FROM SCENE  RSPCA INSPECTOR SAYS TH,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34007860,UPPER MALL,21000834,W6,522356,178299,522350,178250,51.49040942,-0.238997191
NA,05/06/2016 21:46,2016,2016/17,Special Service,1,1,326,326,DEER STUCK IN RAILING/FENCE   RUISLIP LIDO PARK     CALLER WILL MEET NR RESVIOR ROAD ENTRANCE,Deer,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05000343,WEST RUISLIP,E09000017,HILLINGDON,Ruislip,10090329361,RESERVOIR ROAD,21401623,HA5,509106,189498,509150,189450,51.59377693,-0.426297312
NA,06/06/2016 09:19,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011235,Barkingside,E09000026,REDBRIDGE,Ilford,1.00022E+11,EASTERN AVENUE,22302011,IG2,543104,188449,543150,188450,51.57674812,0.063781268
NA,06/06/2016 21:28,2016,2016/17,Special Service,1,1,326,326,CAT STUCK UP ON SCAFFOLDING SINCE YESTERDAY,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000257,MUNSTER,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,FULHAM PALACE ROAD,21000356,SW6,NULL,NULL,524250,176350,NULL,NULL
NA,07/06/2016 01:29,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE -CAT WITH PAW TRAPPED IN DOOR MECHANISM,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000439,BROCKLEY,E09000023,LEWISHAM,Greenwich,NULL,MANOR AVENUE,22000667,SE4,NULL,NULL,536950,176350,NULL,NULL
NA,09/06/2016 12:34,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN NETTING OUTSIDE THE RAILWAY STN      CALLER WILL MEET AND DIRECT,Bird,Person (mobile),Train station - concourse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000461,GRAVENEY,E09000024,MERTON,Mitcham,48044464,LONDON ROAD,22103996,SW17,527968,170577,527950,170550,51.41977031,-0.16099651
NA,09/06/2016 18:20,2016,2016/17,Special Service,1,1,326,326,KITTENS STUCK UNDER SINK,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011479,Selhurst,E09000008,CROYDON,Croydon,NULL,STRATHMORE ROAD,20501438,CR0,NULL,NULL,532450,166750,NULL,NULL
NA,10/06/2016 19:17,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN BIKE FRAME,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000048,EAST BARNET,E09000003,BARNET,Barnet,NULL,EAST BARNET ROAD,20013380,EN4,NULL,NULL,526950,195650,NULL,NULL
NA,11/06/2016 14:33,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011249,Monkhams,E09000026,REDBRIDGE,Woodford,NULL,THE BROADWAY,22306023,IG8,NULL,NULL,540850,191850,NULL,NULL
NA,11/06/2016 16:22,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL TO CAT IN PRECARIOUS POSITION,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05009390,CAMPDEN,E09000020,KENSINGTON AND CHELSEA,Kensington,217124087,KENSINGTON HIGH STREET,21700648,W8,525400,179513,525450,179550,51.50065374,-0.194743724
NA,11/06/2016 17:29,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000351,FELTHAM NORTH,E09000018,HOUNSLOW,Feltham,NULL,HOUNSLOW ROAD,21500631,TW14,NULL,NULL,510950,174350,NULL,NULL
NA,11/06/2016 17:50,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED IN ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000355,HESTON CENTRAL,E09000018,HOUNSLOW,Feltham,1.00022E+11,CRANFORD LANE,21500315,TW5,512312,177284,512350,177250,51.48336832,-0.383927217
NA,12/06/2016 11:47,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK UP TREE RSPCA INSPECTORS LADDER ISNT LONG ENOUGH,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000110,CHELSFIELD AND PRATTS BOTTOM,E09000006,BROMLEY,Orpington,NULL,FOXBURY CLOSE,20300907,BR6,NULL,NULL,546250,164050,NULL,NULL
NA,12/06/2016 13:26,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH MUNTJAC DEER STUCK IN IRON GATE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000052,GARDEN SUBURB,E09000003,BARNET,Hendon,200106269,ST EDWARDS CLOSE,20037980,NW11,525083,188054,525050,188050,51.57748307,-0.196275253
NA,12/06/2016 14:52,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL TO DOG ATTACKING CAT,Dog,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000112,CLOCK HOUSE,E09000006,BROMLEY,Beckenham,1.00023E+11,SAMOS ROAD,20302966,SE20,535017,169178,535050,169150,51.40556435,-0.060213992
NA,12/06/2016 15:36,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,121044707,WANDSWORTH HIGH STREET,22905902,SW18,525545,174429,525550,174450,51.45493064,-0.194460661
NA,13/06/2016 09:09,2016,2016/17,Special Service,1,2,326,652,ASSIST RSPCA WITH TRAPPED BIRD TL REQUESTED FROM SCENE,Bird,Person (mobile),Water works,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011468,Fairfield,E09000008,CROYDON,Croydon,2.00001E+11,WATERWORKS YARD,20502803,CR0,532205,165383,532250,165350,51.37212223,-0.102030634
NA,13/06/2016 19:25,2016,2016/17,Special Service,1,1,326,326,CAT STUCK UNDER A CARAVAN,Cat,Person (mobile),Towing caravan (not on tow or on site),Road Vehicle,Other animal assistance,Animal harm involving domestic animal,E05000437,BELLINGHAM,E09000023,LEWISHAM,Forest Hill,1.00022E+11,SOUTHEND LANE,22005190,SE26,536760,171737,536750,171750,51.42814329,-0.034184238
NA,13/06/2016 20:54,2016,2016/17,Special Service,1,2,326,652,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Water works,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011468,Fairfield,E09000008,CROYDON,Croydon,2.00001E+11,WATERWORKS YARD,20502803,CR0,532205,165383,532250,165350,51.37212223,-0.102030634
NA,13/06/2016 22:10,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN HOLE IN THE GROUND CALLER WILL YOU AT THE FRONT ENTRANCE AND DIRECT YOU,Cat,Person (mobile),Large supermarket,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,1.00023E+11,LEWISHAM HIGH STREET,22004177,SE13,538242,175393,538250,175350,51.46063794,-0.011454187
NA,14/06/2016 08:28,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED BEHIND EXTENSION,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000044,BURNT OAK,E09000003,BARNET,Mill Hill,NULL,TREVOR ROAD,20043200,HA8,NULL,NULL,520750,190750,NULL,NULL
NA,14/06/2016 13:12,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,1.00022E+11,SOUTH CROXTED ROAD,21901245,SE21,532828,172215,532850,172250,51.43337374,-0.090529584
NA,14/06/2016 15:20,2016,2016/17,Special Service,1,1,326,326,"ASSIST RSPCA WITH KITTEN STUCK IN DRAINPIPE  RSPCA ON SCENE, STATE BEST ACCESS IS UP RAMP NEXT MULTI",Cat,Person (mobile),Shopping Centre,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,1.00023E+11,LEWISHAM HIGH STREET,22004177,SE13,538252,175394,538250,175350,51.46064448,-0.011309945
NA,14/06/2016 18:11,2016,2016/17,Special Service,1,2,326,652,Redacted,Bird,Person (land line),Water works,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011468,Fairfield,E09000008,CROYDON,Croydon,2.00001E+11,WATERWORKS YARD,20502803,CR0,532205,165383,532250,165350,51.37212223,-0.102030634
NA,15/06/2016 10:47,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE - CHICKEN TRAPPED BETWEEN TWO WALLS,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,NULL,CHARLTON ROAD,20703193,N9,NULL,NULL,535750,195050,NULL,NULL
NA,15/06/2016 19:56,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN BUILDING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000480,EAST HAM CENTRAL,E09000025,NEWHAM,East Ham,NULL,KEMPTON ROAD,22200762,E6,NULL,NULL,542650,183850,NULL,NULL
NA,15/06/2016 22:09,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011243,Fullwell,E09000026,REDBRIDGE,Hainault,NULL,FULLWELL AVENUE,22305008,IG5,NULL,NULL,543550,190650,NULL,NULL
NA,16/06/2016 12:55,2016,2016/17,Special Service,1,1,326,326,KITTENS TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000626,TOOTING,E09000032,WANDSWORTH,Tooting,NULL,COVERTON ROAD,22901060,SW17,NULL,NULL,527250,171350,NULL,NULL
NA,16/06/2016 14:49,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN AERIAL ON ROOF RSPCA ON SCENE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000304,WEALDSTONE,E09000015,HARROW,Stanmore,NULL,SPENCER ROAD,21202451,HA3,NULL,NULL,515350,190050,NULL,NULL
NA,16/06/2016 17:46,2016,2016/17,Special Service,1,2,326,652,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000293,HEADSTONE SOUTH,E09000015,HARROW,Harrow,NULL,SPENCER ROAD,21202451,HA1,NULL,NULL,514650,188950,NULL,NULL
NA,17/06/2016 12:24,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD STUCK IN RAILINGS DOWN ALLEYWAY AT SIDE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000556,BEDDINGTON SOUTH,E09000029,SUTTON,Wallington,5870109238,MOLLISON DRIVE,22605507,SM6,530396,163820,530350,163850,51.35849339,-0.128577529
NA,18/06/2016 09:25,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT TRAPPED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000063,WOODHOUSE,E09000003,BARNET,Finchley,200061015,HIGH ROAD,20022400,N12,526443,191172,526450,191150,51.60520079,-0.175536673
NA,18/06/2016 11:12,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN TWO WALLS   OPPOSITE MAYHEW ANIMAL HOME,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000092,KENSAL GREEN,E09000005,BRENT,North Kensington,NULL,TRENMAR GARDENS,20204129,NW10,NULL,NULL,522750,182950,NULL,NULL
NA,19/06/2016 09:12,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED IN TRAMPOLINE,Fox,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000439,BROCKLEY,E09000023,LEWISHAM,Lewisham,NULL,DRAKE ROAD,22000344,SE4,NULL,NULL,537250,175750,NULL,NULL
NA,19/06/2016 15:28,2016,2016/17,Special Service,1,1,326,326,DUCKLINGS IN DRAIN   CALLER WILL MEET BRIGADE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000605,LEYTONSTONE,E09000031,WALTHAM FOREST,Leytonstone,1.00023E+11,HIGH ROAD LEYTONSTONE,22844950,E11,539464,187559,539450,187550,51.56966275,0.010935064
NA,20/06/2016 06:29,2016,2016/17,Special Service,1,1,326,326,KITTEN STUCK IN TREE IN MILLFIELD PARK CALLER WILL MEET AT ENTRANCE TO PARK AND DIRECT,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,LEA BRIDGE,E09000012,HACKNEY,Stoke Newington,1.00023E+11,SOUTHWOLD ROAD,20900939,E5,534813,186599,534850,186550,51.56216599,-0.056500105
NA,23/06/2016 11:35,2016,2016/17,Special Service,1,3,326,978,ASSIST RSPCA WITH CAT UP TREE BEST ENTRANCE VIA RAINHAM CLOSE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,1.00023E+11,RESTONS CRESCENT,20801259,SE9,545026,174190,545050,174150,51.4481325,0.085637599
NA,23/06/2016 20:19,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN BUILDING MATERIALS,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000198,ENFIELD HIGHWAY,E09000010,ENFIELD,Enfield,207138569,FOURACRES,20702234,EN3,536097,197623,536050,197650,51.66092034,-0.0337119
NA,23/06/2016 21:19,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED UNDER RUBBLE,Cat,Person (mobile),Common external bin storage area,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000195,CHASE,E09000010,ENFIELD,Enfield,207156922,CEDAR ROAD,20702575,EN2,532161,198166,532150,198150,51.66673879,-0.09037781
NA,24/06/2016 14:14,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN RAILINGS ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000474,WIMBLEDON PARK,E09000024,MERTON,Wimbledon,NULL,PLOUGH LANE,22105115,SW19,NULL,NULL,526050,171550,NULL,NULL
NA,24/06/2016 14:21,2016,2016/17,Special Service,1,1,326,326,CAT UP TREE - RSPCA REQUEST ASSISTANCE RAINHAM CLOSE MAIN ENTRANCE,Cat,Person (mobile),Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,1.00023E+11,RESTONS CRESCENT,20801259,SE9,545115,174200,545150,174250,51.44819958,0.086921557
NA,24/06/2016 16:02,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN NETTING UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000099,QUEENSBURY,E09000005,BRENT,Stanmore,202142989,QUEENSBURY STATION PARADE,20200704,HA8,518851,189733,518850,189750,51.59391922,-0.285599217
NA,24/06/2016 16:44,2016,2016/17,Special Service,1,1,326,326,DOGS HEAD STUCK IN BALCONY RAILING,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011101,Dulwich Wood,E09000028,SOUTHWARK,West Norwood,NULL,BOWEN DRIVE,22500298,SE21,NULL,NULL,533350,171650,NULL,NULL
NA,24/06/2016 17:51,2016,2016/17,Special Service,1,1,326,326,DOG STUCK IN BETEEN FENCE POST,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000271,HARRINGAY,E09000014,HARINGEY,Hornsey,NULL,WIGHTMAN ROAD,21106644,N4,NULL,NULL,531450,188150,NULL,NULL
NA,25/06/2016 07:36,2016,2016/17,Special Service,1,1,326,326,FOX WITH HEAD TRAPPED IN FENCE,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000030,EASTBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,100056090,HOOKS HALL DRIVE,19900682,RM10,550460,186083,550450,186050,51.55357652,0.168845752
NA,25/06/2016 09:01,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED UNDER DECKING,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011235,Barkingside,E09000026,REDBRIDGE,Ilford,NULL,ICKNIELD DRIVE,22302961,IG2,NULL,NULL,543950,188750,NULL,NULL
NA,25/06/2016 14:22,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000113,COPERS COPE,E09000006,BROMLEY,Beckenham,1.0002E+11,HOWARDS CREST CLOSE,20302548,BR3,538449,169050,538450,169050,51.40358758,-0.010953264
NA,25/06/2016 17:48,2016,2016/17,Special Service,1,1,326,326,PUPPY STUCK ON FIRST FLOOR WINDOW SILL ABOVE CLAIRES STORE CAUGHT BY COLLAR,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,PUTNEY HIGH STREET,22904067,SW15,NULL,NULL,524050,175350,NULL,NULL
NA,25/06/2016 19:59,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000533,WHITTON,E09000027,RICHMOND UPON THAMES,Twickenham,10091158014,CHERTSEY ROAD,22406590,TW2,514763,173693,514750,173650,51.45060113,-0.349809522
NA,26/06/2016 08:46,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CAVITY WALL,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,NULL,SPARROWS LANE,20801387,SE9,NULL,NULL,544250,173450,NULL,NULL
NA,26/06/2016 13:38,2016,2016/17,Special Service,1,1,326,326,PIDGEON CAUGHT UP IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011484,South Croydon,E09000008,CROYDON,Croydon,NULL,BRIGHTON ROAD,20501945,CR2,NULL,NULL,532450,162850,NULL,NULL
NA,26/06/2016 16:05,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000206,PONDERS END,E09000010,ENFIELD,Edmonton,207119637,HIGH STREET,20705021,EN3,535185,195543,535150,195550,51.64244933,-0.047692521
NA,27/06/2016 15:52,2016,2016/17,Special Service,1,1,326,326,BIRD STUCK IN TREE  RSPCA IN ATTENDANCE,Bird,Person (mobile),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000284,WOODSIDE,E09000014,HARINGEY,Hornsey,1.00021E+11,WHITE HART LANE,21106108,N22,531067,190879,531050,190850,51.60151122,-0.108914316
NA,27/06/2016 19:27,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000431,STREATHAM SOUTH,E09000022,LAMBETH,Norbury,NULL,COVINGTON WAY,21900399,SW16,NULL,NULL,530850,170850,NULL,NULL
NA,28/06/2016 12:00,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED BEHIND WIRE FENCE,Dog,Person (land line),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,1.00021E+11,REDSTON ROAD,21100732,N8,529650,189553,529650,189550,51.58992292,-0.12985145
NA,28/06/2016 16:53,2016,2016/17,Special Service,1,2,326,652,DOG STUCK DOWN RABBIT HOLE FULHAM CEMETARY MUNSTER ROAD ENTRANCE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000257,MUNSTER,E09000013,HAMMERSMITH AND FULHAM,Fulham,34024967,FULHAM PALACE ROAD,21000356,SW6,523939,177215,523950,177250,51.48032283,-0.216587754
NA,30/06/2016 09:45,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD STUCK IN RAILINGS,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000330,HAREFIELD,E09000017,HILLINGDON,Ruislip,1.00021E+11,SPRINGWELL LANE,21401802,UB9,504972,192038,504950,192050,51.61739198,-0.485194113
NA,30/06/2016 13:27,2016,2016/17,Special Service,1,1,326,326,DOG STUCK BETWEEN RAILINGS AND WINDOWS OF SECOND FLOOR NEIGHBOUR WILL MEET YOU,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,NULL,BAILEY CLOSE,20802072,SE28,NULL,NULL,545150,180250,NULL,NULL
NA,30/06/2016 16:12,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000358,HOUNSLOW CENTRAL,E09000018,HOUNSLOW,Heston,NULL,HIGH STREET,21500610,TW3,NULL,NULL,514150,175850,NULL,NULL
NA,30/06/2016 16:55,2016,2016/17,Special Service,1,1,326,326,CAT STUCK BETWEEN METAL GATE AND FRONT DOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009386,VICTORIA,E09000012,HACKNEY,Homerton,NULL,FRAMPTON PARK ROAD,20900423,E9,NULL,NULL,535150,184250,NULL,NULL
NA,30/06/2016 18:17,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009382,SHACKLEWELL,E09000012,HACKNEY,Stoke Newington,NULL,PRINCE GEORGE ROAD,20900821,N16,NULL,NULL,533450,185650,NULL,NULL
NA,30/06/2016 19:30,2016,2016/17,Special Service,2,3,326,978,FOX STUCK ON MUD BANK RIVER WANDLE NEAR TO THAMES   WATER RESCUE LEVEL TWO INCIDENT  ADDITIONAL FRU,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,10090499099,ENTERPRISE WAY,22901532,SW18,525486,175158,525450,175150,51.46149545,-0.195051009
NA,30/06/2016 20:40,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000217,COLDHARBOUR AND NEW ELTHAM,E09000011,GREENWICH,Eltham,NULL,THE KNOLE,20801475,SE9,NULL,NULL,543150,171750,NULL,NULL
NA,01/07/2016 08:33,2016,2016/17,Special Service,1,1,326,326,FOX CUB WITH HEAD STUCK IN WATERING CAN     CUB IS CURRENTLY ASLEEP ON CALLERS LAP,Fox,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped wild animal,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,1.00022E+11,MILTON ROAD,22404788,SW14,520559,175562,520550,175550,51.46619573,-0.265800961
NA,01/07/2016 09:20,2016,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000265,WORMHOLT AND WHITE CITY,E09000013,HAMMERSMITH AND FULHAM,Acton,34142355,WESTWAY,21000877,W12,521700,180880,521750,180850,51.51374738,-0.24755141
NA,01/07/2016 16:52,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED BETWEEN SHEDS,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05011109,Old Kent Road,E09000028,SOUTHWARK,New Cross,2.00003E+11,NAYLOR ROAD,22501758,SE15,534697,177255,534650,177250,51.47822515,-0.061741472
NA,01/07/2016 20:33,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED IN PIPEWORK OF BASEMENT AREA -  CALL PASSED BY RSPCA WHO DO NOT HAVE A REPRESENTATIVE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,GREAT CAMBRIDGE ROAD,20700114,N9,NULL,NULL,532950,193450,NULL,NULL
NA,02/07/2016 14:34,2016,2016/17,Special Service,1,3,326,978,DEER STUCK IN FENCE CALLER SAYS THIS IS A BABY DEER,Deer,Person (mobile),Secondary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00024E+11,SETTLE ROAD,21301584,RM3,555265,192587,555250,192550,51.61071138,0.240967783
NA,03/07/2016 14:42,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED BETWEEN SHED AND FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,WARWICK ROAD,20704889,N18,NULL,NULL,533550,192950,NULL,NULL
NA,03/07/2016 15:28,2016,2016/17,Special Service,1,1,326,326,BIRDS TRAPPED IN NETTING,Bird,Person (mobile),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009333,SPITALFIELDS & BANGLATOWN,E09000030,TOWER HAMLETS,Bethnal Green,6017513,CAMBRIDGE HEATH ROAD,22700252,E1,534826,181992,534850,181950,51.52076315,-0.058075429
NA,03/07/2016 21:19,2016,2016/17,Special Service,1,1,326,326,DEER TRAPPED IN METAL FENCE,Deer,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000211,TURKEY STREET,E09000010,ENFIELD,Enfield,207155346,BROOKSIDE GARDENS,20702537,EN1,535135,199016,535150,199050,51.67367007,-0.047073183
NA,05/07/2016 18:56,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011482,Shirley North,E09000008,CROYDON,Woodside,NULL,DAFFODIL CLOSE,20500430,CR0,NULL,NULL,535850,166350,NULL,NULL
NA,07/07/2016 08:50,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL TO CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000563,STONECOT,E09000029,SUTTON,Sutton,5870040740,SUTTON COMMON ROAD,22601923,SM3,525321,166229,525350,166250,51.38128425,-0.200578775
NA,07/07/2016 11:21,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN RAILINGS ENTRANCE NEAR THE SUN PH,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000558,CARSHALTON CENTRAL,E09000029,SUTTON,Wallington,5870062834,NORTH STREET,22602499,SM5,527895,164680,527850,164650,51.36678981,-0.164168746
NA,07/07/2016 14:29,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011244,Goodmayes,E09000026,REDBRIDGE,Ilford,NULL,FELBRIGGE ROAD,22302051,IG3,NULL,NULL,546150,187050,NULL,NULL
NA,07/07/2016 20:45,2016,2016/17,Special Service,1,1,326,326,DOG WITH PAW TRAPPED IN COLLAR,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000210,TOWN,E09000010,ENFIELD,Enfield,NULL,LAVENDER HILL,20705086,EN2,NULL,NULL,532250,197850,NULL,NULL
NA,08/07/2016 00:50,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD STUCK IN METAL FENCE,Dog,Person (land line),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000322,SQUIRREL'S HEATH,E09000016,HAVERING,Hornchurch,1.00021E+11,CLIVE ROAD,21301086,RM2,552703,188542,552750,188550,51.57506821,0.202239344
NA,09/07/2016 21:13,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN ROOF THIS IS NEXT TO GREENWICH COMMUNITY COLLEGE,Cat,Person (mobile),Art Gallery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,2.00003E+11,GREENWICH HIGH ROAD,20800685,SE10,538188,177305,538150,177350,51.4778328,-0.011483749
NA,10/07/2016 02:01,2016,2016/17,Special Service,1,1,326,326,"ASSIST RSPCA WITH CAT IN CAR ENGINE  RSPCA ON SCENE  CAR PARK NEXT TO SKATE PARK, ALTHOUGH RSPCA OFF",Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000421,FERNDALE,E09000022,LAMBETH,Brixton,2E+11,STOCKWELL PARK WALK,21901315,SW9,530991,175823,530950,175850,51.46622586,-0.115606483
NA,10/07/2016 07:45,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000424,KNIGHT'S HILL,E09000022,LAMBETH,West Norwood,2E+11,WOODVALE WALK,21902038,SE27,532259,171288,532250,171250,51.42517603,-0.099055996
NA,10/07/2016 13:16,2016,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,NULL,NORTH WORPLE WAY,22404835,SW14,NULL,NULL,521150,175750,NULL,NULL
NA,11/07/2016 09:51,2016,2016/17,Special Service,1,1,326,326,DOG STUCK ON THE MUD FLATS,Dog,Person (land line),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05011232,Thamesmead East,E09000004,BEXLEY,Plumstead,10011848055,BELVEDERE ROAD,20100122,SE2,548897,180930,548850,180950,51.50768945,0.144142626
NA,11/07/2016 12:53,2016,2016/17,Special Service,1,2,326,652,CAT UP TREE - CALL FROM RSPCA,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,10010231541,GOLDCREST CLOSE,20800654,SE28,547240,180717,547250,180750,51.50620921,0.120194682
NA,11/07/2016 14:24,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED BY WIRE ASSIST RSPCA,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,10010210624,HILL VIEW DRIVE,20801883,SE28,545508,180211,545550,180250,51.50211086,0.095047436
NA,11/07/2016 22:02,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,NULL,BRETTENHAM ROAD,20703959,N18,NULL,NULL,534750,192550,NULL,NULL
NA,13/07/2016 08:51,2016,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (land line),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000175,EAST ACTON,E09000009,EALING,Acton,12140183,THE VALE,20602316,W3,521658,180031,521650,180050,51.50612609,-0.248449179
NA,13/07/2016 12:01,2016,2016/17,Special Service,1,1,326,326,ALLEY AT BROOKFIELDS AVENUE FOX WITH HEAD STUCK BETWEEN TWO WOODEN POSTS RSPCA ON SCENE,Fox,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000468,RAVENSBURY,E09000024,MERTON,Mitcham,48060157,RIVERSIDE DRIVE,22105424,CR4,527219,167882,527250,167850,51.39571811,-0.172728318
NA,14/07/2016 08:14,2016,2016/17,Special Service,1,1,326,326,ACCESS VIA THE DOWN TO THE VISITORS ENTRANCE PIGEON TRAPPED IN ATRIUM OF ENTRANCE HALL,Bird,Person (land line),Secondary school,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000469,RAYNES PARK,E09000024,MERTON,Wimbledon,48130420,CRESCENT ROAD,22101710,SW20,523716,169960,523750,169950,51.41516789,-0.222330844
NA,15/07/2016 11:39,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000263,SHEPHERD'S BUSH GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34152377,ARIEL WAY,21000056,W12,523348,180283,523350,180250,51.50802504,-0.22402256
NA,15/07/2016 14:57,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Multi-Storey car park,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,10090326477,MONTFICHET ROAD,22208219,E20,538307,184587,538350,184550,51.54324136,-0.006915662
NA,16/07/2016 21:58,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,CAMELFORD WALK,21701176,W11,NULL,NULL,524150,181050,NULL,NULL
NA,16/07/2016 23:05,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000286,CANONS,E09000015,HARROW,Stanmore,2.00004E+11,HOWBERRY ROAD,21202504,HA7,517670,191796,517650,191750,51.61270831,-0.301948966
NA,17/07/2016 18:44,2016,2016/17,Special Service,1,1,326,326,DOG LOCKED  IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000263,SHEPHERD'S BUSH GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34143953,ARIEL WAY,21000056,W12,523371,180294,523350,180250,51.50811889,-0.223687466
NA,18/07/2016 07:43,2016,2016/17,Special Service,1,1,326,326,CAT WITH PAW TRAPPED IN DOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011476,Purley & Woodcote,E09000008,CROYDON,Purley,NULL,RUSSELL GREEN CLOSE,20502309,CR8,NULL,NULL,531250,162150,NULL,NULL
NA,18/07/2016 09:49,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN AIR CONDITIONING UNIT,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000428,ST. LEONARD'S,E09000022,LAMBETH,Tooting,2E+11,STREATHAM HIGH ROAD,21901326,SW16,530140,172135,530150,172150,51.4332783,-0.129205565
NA,18/07/2016 15:46,2016,2016/17,Special Service,1,1,326,326,DUCKLING TRAPPED ON ROOF  REQUESTED BY RSPCA - UNABLE TO ATTEND,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000260,PARSONS GREEN AND WALHAM,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,NEW KING'S ROAD,21000589,SW6,NULL,NULL,525350,176550,NULL,NULL
NA,18/07/2016 15:49,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED INSIDE ROOF AREA,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000564,SUTTON CENTRAL,E09000029,SUTTON,Sutton,5870035727,HIGH STREET,22605497,SM1,525776,164547,525750,164550,51.36606717,-0.194638648
NA,18/07/2016 16:34,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011106,North Bermondsey,E09000028,SOUTHWARK,Dockhead,NULL,MARINE STREET,22501658,SE16,NULL,NULL,534050,179350,NULL,NULL
NA,18/07/2016 19:29,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000287,EDGWARE,E09000015,HARROW,Stanmore,NULL,BACON LANE,21202318,HA8,NULL,NULL,519450,190950,NULL,NULL
NA,18/07/2016 20:30,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED IN METAL HOUSING OF RAILIWAY LINE,Cat,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000423,HERNE HILL,E09000022,LAMBETH,Brixton,1.00022E+11,MILKWOOD ROAD,21900960,SE24,531882,174651,531850,174650,51.45548654,-0.103223721
NA,18/07/2016 20:56,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH BIRD TRAPPED IN TREE,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000401,BERRYLANDS,E09000021,KINGSTON UPON THAMES,Surbiton,1.00023E+11,SURBITON HILL PARK,21800924,KT5,518764,167671,518750,167650,51.39565307,-0.29427311
NA,19/07/2016 12:14,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000271,HARRINGAY,E09000014,HARINGEY,Tottenham,NULL,ST MARGARETS AVENUE,21103910,N15,NULL,NULL,531750,189250,NULL,NULL
NA,19/07/2016 12:44,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH TRAPPED BIRDS,Bird,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009303,DOWGATE,E09000001,CITY OF LONDON,Dowgate,1.00023E+11,BUSH LANE,8100101,EC4R,532684,180855,532650,180850,51.51105261,-0.089358602
NA,20/07/2016 08:43,2016,2016/17,Special Service,1,1,326,326,KITTEN STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000363,OSTERLEY AND SPRING GROVE,E09000018,HOUNSLOW,Heston,NULL,SYON LANE,21501106,TW7,NULL,NULL,515650,177750,NULL,NULL
NA,20/07/2016 10:33,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH DUCKLINGS TRAPPED ON BALCONY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000637,KNIGHTSBRIDGE AND BELGRAVIA,E09000033,WESTMINSTER,Chelsea,NULL,BELGRAVE MEWS NORTH,8401109,SW1X,NULL,NULL,528150,179550,NULL,NULL
NA,20/07/2016 19:12,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000362,ISLEWORTH,E09000018,HOUNSLOW,Heston,1.00022E+11,SUMMERWOOD ROAD,21501326,TW7,515945,174437,515950,174450,51.45704733,-0.33256288
NA,21/07/2016 07:46,2016,2016/17,Special Service,1,1,326,326,PARROT TRAPPED IN TREE - RSPCA REQUESTED MEMBER OF PUBLIC CALL LFB,Bird,Person (mobile),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000491,ROYAL DOCKS,E09000025,NEWHAM,Plaistow,46081188,WINIFRED STREET,22207958,E16,542762,180033,542750,180050,51.50121122,0.055439372
NA,21/07/2016 18:56,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED BEHIND VENT,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000358,HOUNSLOW CENTRAL,E09000018,HOUNSLOW,Heston,NULL,JAMES STREET,21500646,TW3,NULL,NULL,514550,175750,NULL,NULL
NA,21/07/2016 20:42,2016,2016/17,Special Service,1,2,326,652,CAT TRAPPED IN WINDOW  CALLER WILL WAIT FOR BRIGADE AND DIRECT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009378,HOXTON WEST,E09000012,HACKNEY,Shoreditch,NULL,MURRAY GROVE,20900720,N1,NULL,NULL,532550,183050,NULL,NULL
NA,22/07/2016 07:21,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH FOX STUCK BETWEEN TWO FENCEPOSTS   RSPCA ON SCENE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011243,Fullwell,E09000026,REDBRIDGE,Hainault,NULL,RUSHDEN GARDENS,22303205,IG5,NULL,NULL,543150,190050,NULL,NULL
NA,22/07/2016 08:09,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED IN FENCE  RSPCA UNABLE TO ATTEND AT THIS TIME,Fox,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05011241,Cranbrook,E09000026,REDBRIDGE,Ilford,1.00022E+11,ENDSLEIGH GARDENS,22302825,IG1,542758,187041,542750,187050,51.56418406,0.058220086
NA,22/07/2016 18:34,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000297,PINNER,E09000015,HARROW,Harrow,10000001987,WOODHALL ROAD,21201827,HA5,511760,191469,511750,191450,51.61097304,-0.387371391
NA,23/07/2016 10:59,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN GUTTER,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,Kentish Town,NULL,PARLIAMENT HILL,20400088,NW3,NULL,NULL,527450,185850,NULL,NULL
NA,23/07/2016 11:28,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000092,KENSAL GREEN,E09000005,BRENT,North Kensington,NULL,HARROW ROAD,20201241,NW10,NULL,NULL,522750,182850,NULL,NULL
NA,24/07/2016 01:55,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED  IN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Battersea,NULL,RAWSON STREET,22904224,SW11,NULL,NULL,528450,176750,NULL,NULL
NA,25/07/2016 04:01,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,CULFORD GARDENS,21700139,SW3,NULL,NULL,527850,178550,NULL,NULL
NA,25/07/2016 21:00,2016,2016/17,Special Service,1,1,326,326,PIDGEON TRAPPED IN WIRING AND NETTING UNDER BRIDGE -JUNCTION WITH KINGSTON ROAD,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000402,BEVERLEY,E09000021,KINGSTON UPON THAMES,New Malden,128010707,KINGSTON ROAD,21800563,KT3,520574,168364,520550,168350,51.40150016,-0.268033577
NA,26/07/2016 03:23,2016,2016/17,Special Service,1,1,326,326,CAT STUCK UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000145,WEST HAMPSTEAD,E09000007,CAMDEN,West Hampstead,NULL,PANDORA ROAD,20400260,NW6,NULL,NULL,525250,185050,NULL,NULL
NA,26/07/2016 09:46,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000360,HOUNSLOW SOUTH,E09000018,HOUNSLOW,Heston,NULL,WOODLANDS GROVE,21501229,TW7,NULL,NULL,515450,176150,NULL,NULL
NA,26/07/2016 10:37,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA TO GAIN ENTRY FOR DOG REMOVAL,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000056,HIGH BARNET,E09000003,BARNET,Barnet,NULL,STATION ROAD,20040880,EN5,NULL,NULL,525850,195950,NULL,NULL
NA,26/07/2016 21:09,2016,2016/17,Special Service,1,1,326,326,FLAT B CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000377,MILDMAY,E09000019,ISLINGTON,Islington,NULL,BERESFORD ROAD,21602903,N5,NULL,NULL,532550,185150,NULL,NULL
NA,27/07/2016 01:17,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CUPBOARD OUTSIDE FRONT DOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,NULL,TANNOY SQUARE,21903445,SE27,NULL,NULL,532750,171650,NULL,NULL
NA,27/07/2016 16:16,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH FOX TRAPPED BETWEEN WALL AND SHED  RSPCA INSPECTOR IS GILL,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,NULL,WATNEY ROAD,22405230,SW14,NULL,NULL,520050,176050,NULL,NULL
NA,28/07/2016 10:31,2016,2016/17,Special Service,1,1,326,326,CAT LOCKED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000286,CANONS,E09000015,HARROW,Stanmore,1.00021E+11,MONTGOMERY ROAD,21200910,HA8,518935,191542,518950,191550,51.61015971,-0.283774651
NA,28/07/2016 13:49,2016,2016/17,Special Service,3,12,326,3912,CAT STUCK WITHIN WALL SPACE  RSPCA IN ATTENDANCE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000134,GOSPEL OAK,E09000007,CAMDEN,Kentish Town,NULL,SPRING PLACE,20400353,NW5,NULL,NULL,528550,185050,NULL,NULL
NA,28/07/2016 16:51,2016,2016/17,Special Service,1,1,326,326,SIX WEEK OLD KITTEN STUCK BETWEEN METAL AND WOODEN FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000037,PARSLOES,E09000002,BARKING AND DAGENHAM,Dagenham,100023933,PARSLOES AVENUE,19900277,RM9,548036,185527,548050,185550,51.54922092,0.133674408
NA,29/07/2016 08:43,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON BALCONY - TANGLED IN WIRE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011115,St. Giles,E09000028,SOUTHWARK,Peckham,NULL,MARY DATCHELOR CLOSE,22501674,SE5,NULL,NULL,532750,176750,NULL,NULL
NA,29/07/2016 20:23,2016,2016/17,Special Service,1,1,326,326,PUPPY WITH HEAD STUCK IN RAILING  IN SMALL PARK AREA -  OWNER WILL MEET BRIGADE IN BROOKWAY,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000318,RAINHAM AND WENNINGTON,E09000016,HAVERING,Wennington,1.00021E+11,BROOKWAY,21300726,RM13,552570,181649,552550,181650,51.51317151,0.197337951
NA,29/07/2016 22:26,2016,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000347,BRENTFORD,E09000018,HOUNSLOW,Ealing,1.00022E+11,BOSTON MANOR ROAD,21500133,TW8,516537,178744,516550,178750,51.49563655,-0.32262681
NA,31/07/2016 13:19,2016,2016/17,Special Service,1,1,326,326,HAMSTER TRAPPED UNDER FLOORBOARDS,Hamster,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000344,YEADING,E09000017,HILLINGDON,Southall,NULL,BROOKSIDE ROAD,21400262,UB4,NULL,NULL,511450,181050,NULL,NULL
NA,01/08/2016 06:45,2016,2016/17,Special Service,1,1,326,326,DEER TRAPPED IN GATE,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000200,GRANGE,E09000010,ENFIELD,Edmonton,207125219,PARK CRESCENT,20702888,EN2,532889,195973,532850,195950,51.64686056,-0.080690327
NA,01/08/2016 13:18,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED BETWEEN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000362,ISLEWORTH,E09000018,HOUNSLOW,Heston,NULL,TOLSON ROAD,21501125,TW7,NULL,NULL,516250,175650,NULL,NULL
NA,01/08/2016 13:46,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH BIRD TRAPPED IN ROOF OF HOUSE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000027,ALIBON,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,MEADOW ROAD,19900248,RM9,NULL,NULL,548650,184850,NULL,NULL
NA,01/08/2016 14:54,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN DRAIN,Cat,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009380,LEA BRIDGE,E09000012,HACKNEY,Stoke Newington,1.00021E+11,RIVERSIDE CLOSE,20900860,E5,535313,187047,535350,187050,51.56607219,-0.049119019
NA,01/08/2016 15:04,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN FENCING IN REAR GARDEN,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011229,St. Mary's & St. James,E09000004,BEXLEY,Sidcup,1.0002E+11,ELLENBOROUGH ROAD,20100490,DA14,547923,171048,547950,171050,51.41915141,0.1259846
NA,01/08/2016 18:27,2016,2016/17,Special Service,1,2,326,652,KITTEN TRAPPED IN BATHROOM PIPES,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000628,WEST HILL,E09000032,WANDSWORTH,Wandsworth,NULL,STOFORD CLOSE,22904991,SW19,NULL,NULL,524150,173550,NULL,NULL
NA,02/08/2016 15:22,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA OFFICER WITH SMALL ANIMAL RESCUE CAT POSSIBLY INJURED TRAPPED ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000380,ST. PETER'S,E09000019,ISLINGTON,Islington,NULL,ST PETER'S STREET,21606037,N1,NULL,NULL,532050,183350,NULL,NULL
NA,02/08/2016 22:51,2016,2016/17,Special Service,1,1,326,326,CAT WITH TAIL TRAPPED IN RECLINER CHAIR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000177,GREENFORD BROADWAY,E09000009,EALING,Southall,NULL,STANHOPE ROAD,20601626,UB6,NULL,NULL,514250,181850,NULL,NULL
NA,03/08/2016 13:09,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN CEILING AND ROOF AT MEDIVET VETENARY PRACTICE,Cat,Person (land line),Veterinary surgery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000373,HIGHBURY WEST,E09000019,ISLINGTON,Holloway,5300010233,BLACKSTOCK ROAD,21602925,N4,531766,186498,531750,186450,51.56197864,-0.100469048
NA,03/08/2016 16:42,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000372,HIGHBURY EAST,E09000019,ISLINGTON,Islington,NULL,BAALBEC ROAD,21602873,N5,NULL,NULL,531950,185150,NULL,NULL
NA,03/08/2016 16:52,2016,2016/17,Special Service,1,1,326,326,CAT WITH KITTENS IN DISTRESS ON ROOF  (GIVEN BIRTH TO KITTENS ON ROOF),Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000284,WOODSIDE,E09000014,HARINGEY,Tottenham,NULL,TINTERN ROAD,21105160,N22,NULL,NULL,532050,190650,NULL,NULL
NA,04/08/2016 10:50,2016,2016/17,Special Service,1,1,326,326,DOG STUCK ON TRAIN TRACK -JUNCTION WITH RODERICK ROAD,Dog,Person (mobile),Railway,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000134,GOSPEL OAK,E09000007,CAMDEN,Kentish Town,5036994,SAVERNAKE ROAD,20400202,NW3,527747,185732,527750,185750,51.55601872,-0.158694281
NA,05/08/2016 17:28,2016,2016/17,Special Service,1,1,326,326,ASSIST RPSCA WITH CAT IN TREE OPP A WHITE HOUSE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,200063368,HIGHWOOD HILL,20022660,NW7,521907,193422,521950,193450,51.62642105,-0.240221504
NA,05/08/2016 18:08,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN WIRING ABOVE SHOP FANTASTIC FABRICS  NEXT TO TESCOS,Bird,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped wild animal,E05000042,WHALEBONE,E09000002,BARKING AND DAGENHAM,Dagenham,100012952,HIGH ROAD,19900165,RM6,547883,187946,547850,187950,51.57099615,0.132485524
NA,05/08/2016 20:18,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011246,Ilford Town,E09000026,REDBRIDGE,Ilford,10034932839,HIGH ROAD,22306010,IG1,545060,186925,545050,186950,51.56255473,0.091359877
NA,05/08/2016 20:42,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011489,Woodside,E09000008,CROYDON,Woodside,NULL,BROOKLYN ROAD,20500729,SE25,NULL,NULL,534850,168550,NULL,NULL
NA,06/08/2016 02:22,2016,2016/17,Special Service,1,1,326,326,CAT FALLER FROM WINDOW AND TRAPPED ON LEDGE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000434,THURLOW PARK,E09000022,LAMBETH,West Norwood,NULL,LAIRDALE CLOSE,21900823,SE21,NULL,NULL,532150,173550,NULL,NULL
NA,07/08/2016 10:01,2016,2016/17,Special Service,1,2,326,652,CAT FALLEN INTO LOWER BASEMENT AREA POSSIBLY INJURED OWNER IN ATTENDANCE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Euston,NULL,OGLE STREET,8400854,W1W,NULL,NULL,529150,181750,NULL,NULL
NA,08/08/2016 04:03,2016,2016/17,Special Service,1,1,326,326,DEER STUCK IN GATE,Deer,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000605,LEYTONSTONE,E09000031,WALTHAM FOREST,Leytonstone,NULL,HIGH ROAD LEYTONSTONE,22844950,E11,NULL,NULL,539450,187050,NULL,NULL
NA,08/08/2016 17:08,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED UP SCAFFOLDING REQUEST FROM RSPCA FOR ASSISTANCE     RSPCA INSPECTOR SAMANTHA,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009387,WOODBERRY DOWN,E09000012,HACKNEY,Stoke Newington,NULL,WOODBERRY DOWN ESTATE,20950238,N4,NULL,NULL,532350,187550,NULL,NULL
NA,09/08/2016 11:08,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000518,FULWELL AND HAMPTON HILL,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,ROYAL ROAD,22401806,TW11,NULL,NULL,514950,171450,NULL,NULL
NA,09/08/2016 14:02,2016,2016/17,Special Service,1,1,326,326,DOG LEG STUCK IN TRAMPOLINE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,CRESCENT ROAD,20704108,N9,NULL,NULL,534450,194350,NULL,NULL
NA,09/08/2016 16:19,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED ON BALCONY SEEN FROM   NEIGHBOUR REPORTING SMALL DOG LOCKED OUT ON BALCONY ATTEMPTING TO,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000115,CRAY VALLEY WEST,E09000006,BROMLEY,Sidcup,10003619564,COTMANDENE CRESCENT,20300922,BR5,546513,169412,546550,169450,51.404817,0.105046202
NA,09/08/2016 20:55,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED UNDER CAR SEAT,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000114,CRAY VALLEY EAST,E09000006,BROMLEY,Orpington,10070013483,OSWIN CLOSE,20304251,BR5,546669,167554,546650,167550,51.38808208,0.106520419
NA,10/08/2016 12:13,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA CAT STUCK UP TREE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000434,THURLOW PARK,E09000022,LAMBETH,West Norwood,1.00022E+11,SCOLES CRESCENT,21901218,SW2,531529,173280,531550,173250,51.44324763,-0.108810508
NA,11/08/2016 09:52,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN WINDOW,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011225,Erith,E09000004,BEXLEY,Erith,NULL,ERITH ROAD,20101659,DA17,NULL,NULL,550150,178550,NULL,NULL
NA,11/08/2016 17:50,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000185,NORTHOLT WEST END,E09000009,EALING,Northolt,NULL,RUSHDENE CRESCENT,20601493,UB5,NULL,NULL,511150,183150,NULL,NULL
NA,12/08/2016 09:51,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON ROOF - FELL FROM HIGHER FLAT,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000451,RUSHEY GREEN,E09000023,LEWISHAM,Lewisham,NULL,CANADIAN AVENUE,22001314,SE6,NULL,NULL,537550,173450,NULL,NULL
NA,12/08/2016 20:11,2016,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,1.00023E+11,PEMBURY ROAD,21104920,N17,533762,190795,533750,190750,51.60012267,-0.070057304
NA,13/08/2016 08:48,2016,2016/17,Special Service,1,1,326,326,Redacted,Sheep,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000203,JUBILEE,E09000010,ENFIELD,Enfield,207169544,LEA VALLEY ROAD,20703459,E4,536792,195323,536750,195350,51.64008327,-0.024569114
NA,13/08/2016 10:34,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000530,TEDDINGTON,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,HIGH STREET,22403558,TW11,NULL,NULL,516350,171150,NULL,NULL
NA,13/08/2016 19:55,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH BIRD TRAPPED IN TELEPHONE WIRE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000491,ROYAL DOCKS,E09000025,NEWHAM,East Ham,NULL,DOCKLAND STREET,22207978,E16,NULL,NULL,543150,180150,NULL,NULL
NA,13/08/2016 20:29,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN TWO WALLS    RSPCA ON SCENE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000605,LEYTONSTONE,E09000031,WALTHAM FOREST,Leytonstone,NULL,HIGH ROAD LEYTONSTONE,22844950,E11,NULL,NULL,539750,187650,NULL,NULL
NA,15/08/2016 00:45,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN FENCE AND WALL,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000344,YEADING,E09000017,HILLINGDON,Southall,1.00021E+11,KINGSASH DRIVE,21401100,UB4,512026,182345,512050,182350,51.52891332,-0.386438281
NA,15/08/2016 09:08,2016,2016/17,Special Service,1,1,326,326,FOX WITH HEAD TRAPPED IN FENCE,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000030,EASTBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,100056090,HOOKS HALL DRIVE,19900682,RM10,550462,186084,550450,186050,51.55358497,0.168875005
NA,15/08/2016 11:40,2016,2016/17,Special Service,1,2,326,652,DOG STUCK ON ROOF DOG HAS GOT OUT ON ROOF UNABLE TO GET BACK IN AND OCCUPIERS ARE NOT AT HOME - THIS,Dog,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000412,ST. MARK'S,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,CLAREMONT GARDENS,21800252,KT6,NULL,NULL,518250,167950,NULL,NULL
NA,15/08/2016 13:48,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,NULL,STONEBRIDGE PARK,20200729,NW10,NULL,NULL,520750,184250,NULL,NULL
NA,15/08/2016 14:33,2016,2016/17,Special Service,1,1,326,326,INJURED CAT STUCK IN TREE  RSPCA HAVE NO OFFICERS AVAILABLE TO SEND AT PRESENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000139,KENTISH TOWN,E09000007,CAMDEN,Kentish Town,NULL,BURGHLEY ROAD,20400358,NW5,NULL,NULL,528950,185850,NULL,NULL
NA,15/08/2016 22:36,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN WALL AND SHED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000175,EAST ACTON,E09000009,EALING,Acton,NULL,SYCAMORE CLOSE,20601682,W3,NULL,NULL,521350,180150,NULL,NULL
NA,16/08/2016 12:22,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL FROM RSPCA - ANIMAL STUCK UP TREE FOR SEVERAL DAYS REQUEST PUMP LADDER,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,1.00023E+11,PEMBURY ROAD,21104920,N17,533767,190797,533750,190750,51.60013946,-0.069984394
NA,17/08/2016 16:32,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN NETTING RSPCA ON SCENE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011098,Chaucer,E09000028,SOUTHWARK,Old Kent Road,2.00003E+11,DEVERELL STREET,22500763,SE1,532578,179197,532550,179150,51.4961776,-0.091508092
NA,17/08/2016 17:58,2016,2016/17,Special Service,1,1,326,326,"RABBIT TRAPPED IN GAS FIRE FLUE HAS BEEN MISSING FOR APPROX ONE WEEK, OWNER BELIEVES IT MUST HAVE BE",Rabbit,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009400,PEMBRIDGE,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,PEMBRIDGE SQUARE,21700756,W2,NULL,NULL,525250,180750,NULL,NULL
NA,18/08/2016 20:48,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND TOILET,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000297,PINNER,E09000015,HARROW,Harrow,NULL,GREENWAY,21201763,HA5,NULL,NULL,510950,190350,NULL,NULL
NA,20/08/2016 15:13,2016,2016/17,Special Service,1,1,326,326,PUPPY WITH HEAD TRAPPED IN GATE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000177,GREENFORD BROADWAY,E09000009,EALING,Southall,12044112,RUISLIP ROAD,20602431,UB6,513733,182388,513750,182350,51.52895893,-0.361826776
NA,21/08/2016 21:49,2016,2016/17,Special Service,1,1,326,326,CAT STUCK ON TOP OF CHIMNEY RSPCA UNAVAILABLE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000485,GREEN STREET EAST,E09000025,NEWHAM,East Ham,NULL,MARLBOROUGH ROAD,22207804,E7,NULL,NULL,541150,184450,NULL,NULL
NA,22/08/2016 02:24,2016,2016/17,Special Service,1,1,326,326,CAT STUCK BEHINED KITCHEN CUPBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,FRESTON ROAD,21700874,W10,NULL,NULL,523650,180850,NULL,NULL
NA,22/08/2016 08:19,2016,2016/17,Special Service,1,1,326,326,KITTEN STUCK BEHIND SHED  AT REAR OF,Cat,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000260,PARSONS GREEN AND WALHAM,E09000013,HAMMERSMITH AND FULHAM,Fulham,34073923,REGAL PLACE,21001055,SW6,525798,177039,525750,177050,51.47833119,-0.189893596
NA,22/08/2016 11:34,2016,2016/17,Special Service,1,2,326,652,KITTEN TRAPPED IN TOY IN EMPTY PREMISES CALLER CAN SEE THROUGH THE FRONT ROOM WINDOW,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011465,Broad Green,E09000008,CROYDON,Croydon,NULL,WENTWORTH ROAD,20501549,CR0,NULL,NULL,531250,166550,NULL,NULL
NA,23/08/2016 01:24,2016,2016/17,Special Service,1,1,326,326,CAT HANGING FROM EXTERNAL WINDOW  POSSIBLY TRAPPED RSPCA UNABLE TO ATTEND,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000478,CANNING TOWN SOUTH,E09000025,NEWHAM,Plaistow,NULL,THORNE CLOSE,22201627,E16,NULL,NULL,540050,181350,NULL,NULL
NA,23/08/2016 22:46,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000645,TACHBROOK,E09000033,WESTMINSTER,Lambeth,NULL,ST GEORGE'S SQUARE,8400197,SW1V,NULL,NULL,529650,178050,NULL,NULL
NA,24/08/2016 19:09,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON LEDGE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000279,STROUD GREEN,E09000014,HARINGEY,Holloway,NULL,BEATRICE ROAD,21102955,N4,NULL,NULL,531350,187850,NULL,NULL
NA,25/08/2016 15:08,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN CHAIR,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011101,Dulwich Wood,E09000028,SOUTHWARK,West Norwood,NULL,ROUSE GARDENS,22502143,SE21,NULL,NULL,533250,171750,NULL,NULL
NA,26/08/2016 03:25,2016,2016/17,Special Service,1,1,326,326,DEER TRAPPED IN METAL FENCING     BY THE TALLY HO PUBLIC HOUSE,Deer,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped wild animal,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,200220980,HIGH ROAD,20022400,N12,526349,192179,526350,192150,51.61427152,-0.176530747
NA,27/08/2016 23:04,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND PANEL RSPCA UNABLE TO ATTEND,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000435,TULSE HILL,E09000022,LAMBETH,Brixton,NULL,TUDOR CLOSE,21901992,SW2,NULL,NULL,530750,174050,NULL,NULL
NA,28/08/2016 15:11,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000178,GREENFORD GREEN,E09000009,EALING,Northolt,NULL,GREENFORD ROAD,20602353,UB6,NULL,NULL,514950,183750,NULL,NULL
NA,29/08/2016 18:31,2016,2016/17,Special Service,1,1,326,326,PERSON LOCKED IN,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000173,EALING BROADWAY,E09000009,EALING,Ealing,NULL,LANGHAM GARDENS,20601020,W13,NULL,NULL,516950,180850,NULL,NULL
NA,30/08/2016 18:00,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND FRIDGE  -  CASHMERE BUTCHERS,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,5300088345,STROUD GREEN ROAD,21603885,N4,531090,187152,531050,187150,51.5680134,-0.109971988
NA,30/08/2016 20:15,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON BALCONY TWO FLOORS BELOW  .,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,NULL,YORK BUILDINGS,8400662,WC2N,NULL,NULL,530350,180550,NULL,NULL
NA,30/08/2016 23:01,2016,2016/17,Special Service,1,1,326,326,BEAUMONT AVENUE BY WEST KENSINGTON STATION AT THE LILLIE BRIDGE DEPOT CAT TRAPPED IN METAL CONTAINER,Cat,Person (mobile),"Large refuse/rubbish container (eg skip, paladin)",Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000258,NORTH END,E09000013,HAMMERSMITH AND FULHAM,Fulham,34075044,BEAUMONT AVENUE,21000105,W14,524788,178414,524750,178450,51.49091207,-0.203944668
NA,31/08/2016 17:08,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE  BIRD HANGING FROM WIRE AT RSPCA REQUEST AS OFFICER IS DELAYED,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000129,BLOOMSBURY,E09000007,CAMDEN,Euston,5087674,TOTTENHAM COURT ROAD,20400948,W1T,529491,181872,529450,181850,51.52093234,-0.134971173
NA,31/08/2016 20:35,2016,2016/17,Special Service,1,1,326,326,DEER STUCK IN A DITCH  AT THE SIDE OF ROAD    NEAR THE FOOTBRIDGE   NEAR THE JUNCTION OF THE MAIN RO,Deer,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000305,WEST HARROW,E09000015,HARROW,Harrow,2E+11,VAUGHAN ROAD,21202028,HA1,514233,188068,514250,188050,51.57990887,-0.352778937
NA,01/09/2016 02:51,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED DOWN MAN HOLE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000212,UPPER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,BARCLAY ROAD,20703892,N18,NULL,NULL,533050,191850,NULL,NULL
NA,01/09/2016 20:05,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000049,EAST FINCHLEY,E09000003,BARNET,Finchley,200151566,SUMMERLEE AVENUE,20041380,N2,527497,189074,527450,189050,51.58610903,-0.161087014
NA,02/09/2016 09:53,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,CLARE GARDENS,21701124,W11,NULL,NULL,524450,181150,NULL,NULL
NA,02/09/2016 10:23,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN GUTTERING ON ROOFTOP (UNABLE TO TURN AROUND) OPPOSITE  VET IN ATTENDANCE - WILL DIREC,Cat,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000468,RAVENSBURY,E09000024,MERTON,Mitcham,48020534,DEER PARK GARDENS,22102010,CR4,526679,168291,526650,168250,51.39951458,-0.180340322
NA,02/09/2016 14:54,2016,2016/17,Special Service,1,1,326,326,CAT STUCK ON ROOF  RSPCA IN ATTENDANCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000139,KENTISH TOWN,E09000007,CAMDEN,Kentish Town,NULL,ISLIP STREET,20400366,NW5,NULL,NULL,529150,185050,NULL,NULL
NA,02/09/2016 17:29,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011483,Shirly South,E09000008,CROYDON,Addington,NULL,BROOM ROAD,20500410,CR0,NULL,NULL,537350,165150,NULL,NULL
NA,03/09/2016 11:25,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000187,PERIVALE,E09000009,EALING,Wembley,NULL,EMPIRE ROAD,20600626,UB6,NULL,NULL,517350,183950,NULL,NULL
NA,04/09/2016 10:05,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN CAR ENGINE  ENTRANCE IN LOWFT ROAD,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011096,Camberwell Green,E09000028,SOUTHWARK,Peckham,2.00003E+11,DENMARK ROAD,22500754,SE5,532230,176460,532250,176450,51.47166247,-0.097542095
NA,04/09/2016 17:12,2016,2016/17,Special Service,1,1,326,326,KITTEN STUCK ON ROOF OF SCHOOL  CALLER WILL DIRECT YOU,Cat,Person (land line),Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000437,BELLINGHAM,E09000023,LEWISHAM,Beckenham,1.00023E+11,ELFRIDA CRESCENT,22001435,SE6,537498,171846,537450,171850,51.42894447,-0.023533159
NA,05/09/2016 13:59,2016,2016/17,Special Service,1,1,326,326,"ASSIST RSPCA RETRIEVE INJURED CAT  WILL MEET YOU ON ARRIVAL, RSPCA ON SCENE  NR TO BOXING CLUB",Cat,Person (land line),Sports/Social club,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000494,WEST HAM,E09000025,NEWHAM,Stratford,46255479,CHURCH STREET,22201348,E15,539609,183715,539650,183750,51.53508522,0.011501621
NA,06/09/2016 09:17,2016,2016/17,Special Service,NULL,NULL,326,NULL,COW IN DISTRESS IN WATER,Cow,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207184751,WHARF ROAD,20702448,EN3,536249,195594,536250,195550,51.64265046,-0.032305624
NA,07/09/2016 19:21,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED ON ROOF CALLER WILL MEET YOU,Dog,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,10091908052,CHURCH STREET,20704052,N9,534288,193588,534250,193550,51.62509603,-0.061398245
NA,08/09/2016 07:23,2016,2016/17,Special Service,1,1,326,326,CAT STUCK ON ROOF AT SECOND FLOOR LEVEL,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000208,SOUTHGATE,E09000010,ENFIELD,Southgate,NULL,PENNINGTON DRIVE,20706126,N21,NULL,NULL,530550,195750,NULL,NULL
NA,08/09/2016 08:46,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH KITTEN TRAPPED BETWEEN TWO BUILDINGS,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000325,BOTWELL,E09000017,HILLINGDON,Hillingdon,1.00021E+11,WOOD END GREEN ROAD,21402200,UB3,509155,181327,509150,181350,51.52032542,-0.428124845
NA,08/09/2016 09:46,2016,2016/17,Special Service,1,2,326,652,FOX TRAPPED IN FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000183,NORTH GREENFORD,E09000009,EALING,Wembley,NULL,ASHNESS GARDENS,20600082,UB6,NULL,NULL,516850,184650,NULL,NULL
NA,08/09/2016 19:15,2016,2016/17,Special Service,1,1,326,326,CAT STUCK BEHIND WALL   REQUEST FROM RSPCA - ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000471,TRINITY,E09000024,MERTON,Wimbledon,NULL,HAYDONS ROAD,22103210,SW19,NULL,NULL,526150,170850,NULL,NULL
NA,08/09/2016 19:28,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT TRAPPED IN FENCING,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05009380,LEA BRIDGE,E09000012,HACKNEY,Stoke Newington,1.00021E+11,CLEVELEYS ROAD,20900253,E5,535067,186477,535050,186450,51.56100889,-0.052884794
NA,09/09/2016 07:25,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED IN GATE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000381,TOLLINGTON,E09000019,ISLINGTON,Holloway,NULL,THORPEDALE ROAD,21603927,N4,NULL,NULL,530650,187150,NULL,NULL
NA,09/09/2016 17:16,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000639,LITTLE VENICE,E09000033,WESTMINSTER,Paddington,NULL,MAIDA AVENUE,8400116,W2,NULL,NULL,526450,182050,NULL,NULL
NA,11/09/2016 15:50,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT UP TREE  RSPCA ON SCENE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011098,Chaucer,E09000028,SOUTHWARK,Dockhead,2.00003E+11,WESTON STREET,22502690,SE1,533016,179426,533050,179450,51.49813276,-0.085115871
NA,11/09/2016 16:26,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,NULL,HIGH STREET,20300424,BR1,NULL,NULL,540150,169350,NULL,NULL
NA,12/09/2016 08:45,2016,2016/17,Special Service,1,2,326,652,DISTRESSED KITTEN STUCK IN TREE  OWNER IN ATTENDANCE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,46023620,EIDER CLOSE,22207694,E7,539824,185359,539850,185350,51.54980481,0.015251852
NA,12/09/2016 15:04,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN BOILER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,ST ERVANS ROAD,21701009,W10,NULL,NULL,524650,181850,NULL,NULL
NA,12/09/2016 19:16,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN THORNY BUSH,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000556,BEDDINGTON SOUTH,E09000029,SUTTON,Wallington,5870110978,HANDLEY PAGE ROAD,22605760,SM6,530531,163319,530550,163350,51.35395993,-0.126823331
NA,12/09/2016 19:49,2016,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,1.00021E+11,NORTH PARK,20801090,SE9,543035,174288,543050,174250,51.44951917,0.057045436
NA,13/09/2016 02:55,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009336,WHITECHAPEL,E09000030,TOWER HAMLETS,Whitechapel,NULL,DOCK STREET,22700417,E1,NULL,NULL,534150,180750,NULL,NULL
NA,13/09/2016 20:13,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL TO SQUIRREL TRAPPED IN FLAT,Squirrel,Person (land line),Hostel (e.g. for homeless people),Other Residential,Other animal assistance,Assist trapped wild animal,E05000418,CLAPHAM COMMON,E09000022,LAMBETH,Clapham,2E+11,CLARENCE AVENUE,21900348,SW4,529715,174278,529750,174250,51.45263476,-0.134531555
NA,14/09/2016 10:07,2016,2016/17,Special Service,2,3,326,978,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009322,BROMLEY SOUTH,E09000030,TOWER HAMLETS,Poplar,6174040,AQUA VISTA SQUARE,22702674,E3,537391,181619,537350,181650,51.51679393,-0.021273477
NA,14/09/2016 11:21,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000401,BERRYLANDS,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,SURBITON HILL PARK,21800924,KT5,NULL,NULL,518850,167750,NULL,NULL
NA,14/09/2016 12:21,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,OSIERS ROAD,22903717,SW18,NULL,NULL,525250,175150,NULL,NULL
NA,14/09/2016 21:43,2016,2016/17,Special Service,1,1,326,326,PARROT TRAPPED BEHIND KITCHEN UNITS,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011467,Crystal Palace & Upper Norwood,E09000008,CROYDON,West Norwood,NULL,THE WOODLANDS,20500135,SE19,NULL,NULL,532350,170250,NULL,NULL
NA,15/09/2016 06:47,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,NULL,MEATH CRESCENT,22702470,E2,NULL,NULL,535850,182750,NULL,NULL
NA,15/09/2016 20:11,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN FENCING,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000105,WILLESDEN GREEN,E09000005,BRENT,Willesden,10091060040,OSBORNE ROAD,20202183,NW2,522770,184927,522750,184950,51.54988797,-0.230728646
NA,16/09/2016 09:33,2016,2016/17,Special Service,1,2,326,652,HORSE TRAPPED IN DITCH,Horse,Person (mobile),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000030,EASTBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,100007496,THE CHASE,19900828,RM7,551479,186172,551450,186150,51.55410381,0.183570624
NA,16/09/2016 17:42,2016,2016/17,Special Service,1,1,326,326,ANIMAL TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009397,HOLLAND,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,ST MARY ABBOT'S TERRACE,21700675,W14,NULL,NULL,524850,179150,NULL,NULL
NA,17/09/2016 15:55,2016,2016/17,Special Service,1,1,326,326,"PIGEON TRAPPED IN NETTING ON SECOND LEVEL, RSPCA UNABLE TO ATTEND",Bird,Person (land line),Exhibition Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,10003974918,ALEXANDRA PALACE WAY,21106542,N22,529592,190059,529550,190050,51.59448345,-0.130501616
NA,18/09/2016 09:40,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED IN GARAGE THROUGH HOLE IN ROOF - NOW UNABLE TO GET OUT,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000326,BRUNEL,E09000017,HILLINGDON,Hillingdon,1.00021E+11,ST HELEN CLOSE,21401819,UB8,505789,181659,505750,181650,51.52394962,-0.476520341
NA,18/09/2016 10:56,2016,2016/17,Special Service,2,3,326,978,** DO NOT CLOSE - REFER TO OFFICER OF THE WATCH **    ASSIST RSPCA WITH CAT ON ROOF RSPCA OFFICER ON,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011226,Falconwood & Welling,E09000004,BEXLEY,Plumstead,NULL,BELLEGROVE ROAD,20100114,DA16,NULL,NULL,546350,175850,NULL,NULL
NA,18/09/2016 11:58,2016,2016/17,Special Service,1,1,326,326,PUPPY WITH LEG STUCK IN CHAIR,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000378,ST. GEORGE'S,E09000019,ISLINGTON,Kentish Town,NULL,BRECKNOCK ROAD,21606410,N19,NULL,NULL,529550,185550,NULL,NULL
NA,18/09/2016 22:16,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000060,UNDERHILL,E09000003,BARNET,Barnet,NULL,FITZJOHN AVENUE,20016000,EN5,NULL,NULL,524550,196150,NULL,NULL
NA,20/09/2016 08:55,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED UNDER KITCHEN UNITS   PA REQUIRED WITH SAW,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000115,CRAY VALLEY WEST,E09000006,BROMLEY,Orpington,NULL,VERNON CLOSE,20301328,BR5,NULL,NULL,546950,168850,NULL,NULL
NA,20/09/2016 12:56,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD TRAPPED IN STAIR GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Shadwell,NULL,PORTLAND SQUARE,22701923,E1W,NULL,NULL,534650,180250,NULL,NULL
NA,20/09/2016 13:07,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD TRAPPED IN STAIR GATE   SAME AS PREVIOUS CALL - CALLER REQUIRES BRIGADE - THOUGHT DOG,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Shadwell,NULL,PORTLAND SQUARE,22701923,E1W,NULL,NULL,534650,180250,NULL,NULL
NA,20/09/2016 14:29,2016,2016/17,Special Service,1,1,326,326,DOG WITH COLLAR CAUGHT ON RAILLINGS UNABLE TO BE RELEASED,Dog,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000530,TEDDINGTON,E09000027,RICHMOND UPON THAMES,Twickenham,1.00023E+11,PARK ROAD,22403720,TW11,515964,170400,515950,170450,51.42076016,-0.333610964
NA,20/09/2016 15:45,2016,2016/17,Special Service,1,1,326,326,Redacted,Horse,Person (land line),"Grassland, pasture, grazing etc",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207187270,WHARF ROAD,20702448,EN3,536312,195779,536350,195750,51.64429762,-0.031323687
NA,20/09/2016 17:26,2016,2016/17,Special Service,1,1,326,326,TWO KITTENS TRAPPED IN WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000058,OAKLEIGH,E09000003,BARNET,Barnet,NULL,OAKLEIGH PARK NORTH,20032540,N20,NULL,NULL,526950,194650,NULL,NULL
NA,21/09/2016 11:06,2016,2016/17,Special Service,1,2,326,652,CAT TRAPPED UNDER KITCHEN UNIT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011485,South Norwood,E09000008,CROYDON,Woodside,NULL,HUNTLY ROAD,20501068,SE25,NULL,NULL,533450,168450,NULL,NULL
NA,21/09/2016 20:15,2016,2016/17,Special Service,1,2,326,652,ENTRANCE AT J/O THE AVENUE  RSPCA PARKED THERE TO MEET YOU BIRD STUCK IN TREE,Bird,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,10003977015,ALEXANDRA PALACE WAY,21106542,N22,529489,190242,529450,190250,51.59615165,-0.131920286
NA,22/09/2016 17:18,2016,2016/17,Special Service,1,1,326,326,DEER STUCK IN RAILINGS IN CONSIDERABLE DISTRESS CALLER IS ALSO CALLING THE RSPCA,Deer,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist trapped wild animal,E05000353,HANWORTH,E09000018,HOUNSLOW,Feltham,1.00022E+11,BEACH GROVE,21500085,TW13,513320,172537,513350,172550,51.44050173,-0.370936539
NA,22/09/2016 17:32,2016,2016/17,Special Service,1,1,326,326,DOG STUCK UNDER SHED IN REAR GARDEN - DOG TUNNELED UNDER SHED AND NOW CANNOT GET BACK OUT - OWNER ON,Dog,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000420,COLDHARBOUR,E09000022,LAMBETH,Brixton,1.00022E+11,LOUGHBOROUGH PARK,21900894,SW9,531872,175564,531850,175550,51.46369382,-0.103027426
NA,23/09/2016 09:37,2016,2016/17,Special Service,1,1,326,326,Redacted,Horse,Person (land line),Lorry/HGV,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Hayes,10022800980,HORTON ROAD,21402928,SL3,503588,175483,503550,175450,51.46884401,-0.510046384
NA,24/09/2016 19:51,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000365,TURNHAM GREEN,E09000018,HOUNSLOW,Chiswick,2.00003E+11,GROSVENOR ROAD,21500522,W4,519990,178153,519950,178150,51.48960355,-0.273109521
NA,25/09/2016 13:33,2016,2016/17,Special Service,1,1,326,326,PIGEON INJURED AND TRAPPED IN TREE BY KITE CALLER WILL MEET YOU AT THE ENTRANCE TO THE CEMETARY,Bird,Person (mobile),Cemetery,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000043,BRUNSWICK PARK,E09000003,BARNET,Southgate,200017096,BRUNSWICK PARK ROAD,20005900,N11,528395,193269,528350,193250,51.62360424,-0.146597762
NA,26/09/2016 19:26,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN GUTTER ON ROOF OF SECOND FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011239,Clayhall,E09000026,REDBRIDGE,Hainault,NULL,HERENT DRIVE,22302930,IG5,NULL,NULL,542950,189850,NULL,NULL
NA,27/09/2016 10:40,2016,2016/17,Special Service,1,2,326,652,CAT TRAPPED UP TREE RSPCA INSPECTOR ON SCENE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000052,GARDEN SUBURB,E09000003,BARNET,Finchley,200129873,WOODSIDE,20047340,NW11,525315,188940,525350,188950,51.58539404,-0.192612904
NA,27/09/2016 14:30,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN TWO FENCES,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000126,SHORTLANDS,E09000006,BROMLEY,Beckenham,NULL,MALMAINS WAY,20301661,BR3,NULL,NULL,538750,167950,NULL,NULL
NA,28/09/2016 11:00,2016,2016/17,Special Service,1,1,326,326,CAT STUCK WITHIN CAR ENGINE  NEAR ROMFORD CEMETERY,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000306,BROOKLANDS,E09000016,HAVERING,Dagenham,1.00021E+11,CROW LANE,21301120,RM7,550307,187790,550350,187750,51.56895488,0.167369338
NA,30/09/2016 01:51,2016,2016/17,Special Service,1,1,326,326,KITTEN STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011221,Blendon & Penhill,E09000004,BEXLEY,Sidcup,NULL,HURST ROAD,20101715,DA15,NULL,NULL,546450,172750,NULL,NULL
NA,30/09/2016 12:33,2016,2016/17,Special Service,1,1,326,326,ANIMAL STUCK POSSIBLY IN LOFT AREA,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011471,New Addington South,E09000008,CROYDON,Addington,NULL,CHERTSEY CRESCENT,20501684,CR0,NULL,NULL,538250,162050,NULL,NULL
NA,30/09/2016 20:18,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH SMALL ANIMAL RESCUE OFFICER ON SCENE - PIGEON TRAPPED IN NETTING POSSIBLY INJURED,Bird,Person (mobile),Bank/Building Society,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000341,UXBRIDGE SOUTH,E09000017,HILLINGDON,Hillingdon,1.00023E+11,HIGH STREET,21400966,UB8,505657,184001,505650,184050,51.54502502,-0.477719813
NA,30/09/2016 23:05,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED DOWN DRAIN,Cat,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011485,South Norwood,E09000008,CROYDON,Woodside,10014053628,TENNISON ROAD,20501473,SE25,533621,167956,533650,167950,51.39491363,-0.080732458
NA,01/10/2016 19:47,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE - CAT TRAPPED BEHIND PIPEWORK,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009318,BLACKWALL & CUBITT TOWN,E09000030,TOWER HAMLETS,Millwall,NULL,GLENGALL GROVE,22700548,E14,NULL,NULL,538050,179350,NULL,NULL
NA,02/10/2016 22:20,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000113,COPERS COPE,E09000006,BROMLEY,Beckenham,1.00024E+11,COPERS COPE ROAD,20303032,BR3,537196,169902,537150,169950,51.41154804,-0.028626138
NA,02/10/2016 22:35,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH KITTEN ON A ROOF RSPCA ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,CATHALL ROAD,22823150,E11,NULL,NULL,539150,186350,NULL,NULL
NA,03/10/2016 14:04,2016,2016/17,Special Service,1,2,326,652,CAT STUCK UP CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000063,WOODHOUSE,E09000003,BARNET,Finchley,NULL,WOODGRANGE AVENUE,20047180,N12,NULL,NULL,526850,191450,NULL,NULL
NA,03/10/2016 18:43,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN THE CHIMNEY RSPCA ON SCENE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist  trapped livestock animal,E05000111,CHISLEHURST,E09000006,BROMLEY,Sidcup,NULL,POYNTELL CRESCENT,20303018,BR7,NULL,NULL,544850,169850,NULL,NULL
NA,03/10/2016 19:38,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH SEAGULL CAUGHT UP IN ARIEL ON ROOF,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000210,TOWN,E09000010,ENFIELD,Enfield,NULL,CHURCHBURY ROAD,20702603,EN1,NULL,NULL,532950,197450,NULL,NULL
NA,03/10/2016 21:51,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN WIRE FENCE AT COLLINGWOOD RECREATION GROUND - OWNER WILL MEET BRIGADE AT ENTRANCE TO,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000565,SUTTON NORTH,E09000029,SUTTON,Sutton,5870036275,COLLINGWOOD ROAD,22602218,SM1,525220,164805,525250,164850,51.36850866,-0.202530433
NA,06/10/2016 11:02,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED UNDER BATH,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011109,Old Kent Road,E09000028,SOUTHWARK,Old Kent Road,NULL,HAYMERLE ROAD,22501223,SE15,NULL,NULL,534150,177650,NULL,NULL
NA,06/10/2016 13:32,2016,2016/17,Special Service,1,2,326,652,CAT TRAPPED IN WINDOW  RSPCA UNABLE TO ATTEND,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009329,ST. DUNSTAN'S,E09000030,TOWER HAMLETS,Bethnal Green,NULL,ASTON STREET,22700100,E14,NULL,NULL,536250,181450,NULL,NULL
NA,06/10/2016 18:25,2016,2016/17,Special Service,1,2,326,652,CAT STUCK IN VENTILATION SYSTEM,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000451,RUSHEY GREEN,E09000023,LEWISHAM,Lewisham,1.00023E+11,RUSHEY GREEN,22004211,SE6,537710,173865,537750,173850,51.4470364,-0.019701281
NA,08/10/2016 09:31,2016,2016/17,Special Service,1,1,326,326,HAMSTER TRAPPED BEHIND SKIRTING,Hamster,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009395,EARL'S COURT,E09000020,KENSINGTON AND CHELSEA,Fulham,NULL,EARL'S COURT SQUARE,21700167,SW5,NULL,NULL,525550,178250,NULL,NULL
NA,08/10/2016 11:45,2016,2016/17,Special Service,1,2,326,652,DOG TRAPPED IN TREE TRUNK CALLER WILL MEET YOU BY THE PLAYGROUND ON PETERSHAM ROAD,Dog,Person (mobile),Heathland,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000519,"HAM, PETERSHAM AND RICHMOND RIVERSIDE",E09000027,RICHMOND UPON THAMES,Kingston,1.00022E+11,PETERSHAM ROAD,22403729,TW10,518372,173203,518350,173250,51.44545474,-0.298058829
NA,08/10/2016 13:51,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN TREE CALLER STATES LEGS ARE TRAPPED IN WIRE/ROPE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,6176783,BRODLOVE LANE,22700203,E1W,535552,180951,535550,180950,51.5112348,-0.048017903
NA,08/10/2016 15:16,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED UNDER DECKING,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011464,Bensham Manor,E09000008,CROYDON,Norbury,NULL,ECCLESBOURNE ROAD,20500893,CR7,NULL,NULL,532350,168050,NULL,NULL
NA,10/10/2016 08:22,2016,2016/17,Special Service,2,4,326,1304,Redacted,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000139,KENTISH TOWN,E09000007,CAMDEN,Kentish Town,5024751,HIGHGATE ROAD,20400091,NW5,528908,185410,528950,185450,51.55286121,-0.142074657
NA,10/10/2016 12:33,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,10070008362,GLASSMILL LANE,20303922,BR2,539954,169167,539950,169150,51.40427014,0.01071494
NA,10/10/2016 14:07,2016,2016/17,Special Service,1,1,326,326,"DOG TRAPPED IN FENCE CALLER SAYS BEST ACCESS IS VIA KELSEY PARK AVENUE, NEAR AARON COURT",Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,1.0002E+11,KELSEY PARK AVENUE,20301420,BR3,537748,168832,537750,168850,51.401799,-0.021109191
NA,10/10/2016 17:34,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000256,HAMMERSMITH BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,QUEEN CAROLINE STREET,21000660,W6,NULL,NULL,523250,178250,NULL,NULL
NA,10/10/2016 21:41,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN TUMBLE DRYER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000451,RUSHEY GREEN,E09000023,LEWISHAM,Lewisham,NULL,MOUNTFIELD CLOSE,22006038,SE6,NULL,NULL,538650,173850,NULL,NULL
NA,13/10/2016 09:58,2016,2016/17,Special Service,2,5,326,1630,LARGE ANIMAL RESCUE - SEAL TRAPPED ON LAND WATER RESCUE LEVEL TWO IMPLEMENTED PRESS OFFICER REQUESTE,Unknown - Wild Animal,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy wild animal,E05000347,BRENTFORD,E09000018,HOUNSLOW,Chiswick,10091067964,KEW BRIDGE ROAD,21500657,TW8,518975,177883,518950,177850,51.48739104,-0.287813023
NA,13/10/2016 12:50,2016,2016/17,Special Service,1,1,326,326,PUPPY WITH HEAD STUCK IN METAL WINE RACK,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000124,PETTS WOOD AND KNOLL,E09000006,BROMLEY,Orpington,NULL,OATFIELD ROAD,20300954,BR6,NULL,NULL,546050,166250,NULL,NULL
NA,17/10/2016 08:59,2016,2016/17,Special Service,1,1,326,326,DEER WITH ANTLERS TRAPPED IN CRICKET NET - RSPCA ON SCENE,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,1.00023E+11,JAIL LANE,20301511,TN16,542644,159481,542650,159450,51.31656333,0.045483018
NA,17/10/2016 13:28,2016,2016/17,Special Service,2,4,326,1304,Redacted,Fox,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,6129318,COPENHAGEN PLACE,22700355,E14,536685,181119,536650,181150,51.5124718,-0.031635947
NA,18/10/2016 13:30,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND ADVERTISING BOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,CROUCH END,E09000014,HARINGEY,Hornsey,NULL,PARK ROAD,21103726,N8,NULL,NULL,530050,188550,NULL,NULL
NA,19/10/2016 10:09,2016,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000047,COPPETTS,E09000003,BARNET,Southgate,NULL,HILLSIDE AVENUE,20022980,N11,NULL,NULL,527750,191950,NULL,NULL
NA,19/10/2016 12:52,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000325,BOTWELL,E09000017,HILLINGDON,Hayes,1.00024E+11,JUDGE HEATH LANE,21401064,UB8,508322,181028,508350,181050,51.51779826,-0.440217745
NA,20/10/2016 14:32,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE - PET PARROT TRAPPED IN TREE NEAR CHILDRENS PLAYGROUND,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000184,NORTHOLT MANDEVILLE,E09000009,EALING,Northolt,12141341,EASTCOTE LANE,20600590,UB5,512748,184370,512750,184350,51.54697011,-0.375386603
NA,20/10/2016 14:52,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN GATE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,NULL,MORTLAKE HIGH STREET,22404798,SW14,NULL,NULL,520650,175950,NULL,NULL
NA,21/10/2016 12:48,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE - PUPPY TRAPPED IN RAILINGS  HORN PARK,Dog,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Lee Green,10010228426,GAVESTONE ROAD,20800620,SE12,540645,173613,540650,173650,51.44405171,0.022406015
NA,21/10/2016 18:01,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000045,CHILDS HILL,E09000003,BARNET,West Hampstead,200049284,GOLDERS PARK CLOSE,20018220,NW11,525498,187057,525450,187050,51.56843083,-0.190645312
NA,22/10/2016 12:44,2016,2016/17,Special Service,1,1,326,326,PIGEON WITH WING IMAPLED ON SHARP IMPLEMENT  UNDER A BRIDGE NEAR,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000456,CANNON HILL,E09000024,MERTON,Wimbledon,48039776,KINGSTON ROAD,22103623,SW20,524292,169329,524250,169350,51.40937105,-0.214273154
NA,23/10/2016 00:25,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED IN GATE  >,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000056,HIGH BARNET,E09000003,BARNET,Barnet,200072480,LEICESTER ROAD,20026380,EN5,525761,196055,525750,196050,51.64923573,-0.183627455
NA,23/10/2016 08:15,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000358,HOUNSLOW CENTRAL,E09000018,HOUNSLOW,Heston,NULL,HIGH STREET,21500610,TW3,NULL,NULL,514250,175850,NULL,NULL
NA,23/10/2016 09:30,2016,2016/17,Special Service,1,1,326,326,DOG IN RIVER - FRU CARRYING BOAT REQUESTED BY TAR OFFICER  OWNERS ON SCENE  BEHIND TENNIS COURTS,Dog,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000402,BEVERLEY,E09000021,KINGSTON UPON THAMES,New Malden,1.00022E+11,PARK VIEW,21800750,KT3,521965,168527,521950,168550,51.40266798,-0.247990538
NA,23/10/2016 10:06,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN SOFFITS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011231,Slade Green & Northend,E09000004,BEXLEY,Erith,NULL,SANDPIPER DRIVE,20101766,DA8,NULL,NULL,552850,177250,NULL,NULL
NA,23/10/2016 15:37,2016,2016/17,Special Service,1,1,326,326,ASSIST RSCPA WITH CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000097,PRESTON,E09000005,BRENT,Wembley,202061886,OAKINGTON AVENUE,20202423,HA9,519017,186225,519050,186250,51.56235666,-0.284392116
NA,23/10/2016 15:44,2016,2016/17,Special Service,1,1,326,326,Redacted,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011487,Waddon,E09000008,CROYDON,Croydon,10001002174,DUPPAS HILL ROAD,20502044,CR0,531790,164899,531750,164850,51.36786894,-0.108168525
NA,23/10/2016 18:20,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CAR ENGINE  RE-ATTEND AS OWNER NOW ON SCENE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000097,PRESTON,E09000005,BRENT,Wembley,202061886,OAKINGTON AVENUE,20202423,HA9,519015,186226,519050,186250,51.56236607,-0.284420618
NA,23/10/2016 19:36,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011111,Peckham Rye,E09000028,SOUTHWARK,Forest Hill,NULL,THERAPIA ROAD,22502490,SE22,NULL,NULL,534950,174250,NULL,NULL
NA,24/10/2016 11:16,2016,2016/17,Special Service,1,1,326,326,ASSISTING RSPCA OFFICER WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011115,St. Giles,E09000028,SOUTHWARK,Peckham,2.00003E+11,CAMBERWELL GROVE,22500402,SE5,532897,176547,532850,176550,51.47228814,-0.087911768
NA,25/10/2016 03:09,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED IN GATE,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000267,BOUNDS GREEN,E09000014,HARINGEY,Southgate,1.00021E+11,PALMERSTON ROAD,21106101,N22,530595,191250,530550,191250,51.60495481,-0.115587471
NA,25/10/2016 13:15,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH SMALL ANIMAL RESCUE IN RECREATION PARK NEAR PRIMARY SCHOOL,Unknown - Domestic Animal Or Pet,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000459,DUNDONALD,E09000024,MERTON,Wimbledon,48106210,DUNDONALD ROAD,22102193,SW19,524555,170052,524550,170050,51.41581117,-0.210239743
NA,25/10/2016 18:04,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH BIRD TRAPPED IN GUTTERING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000309,EMERSON PARK,E09000016,HAVERING,Hornchurch,NULL,RAYBURN ROAD,21300554,RM11,NULL,NULL,555350,187850,NULL,NULL
NA,25/10/2016 19:07,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN NETTING WILDLIFE CHARITY WORKER ON SCENE,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000568,THE WRYTHE,E09000029,SUTTON,Wallington,NULL,WRYTHE LANE,22605653,SM5,NULL,NULL,527350,165250,NULL,NULL
NA,26/10/2016 08:22,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED BEHIND FREEZER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,NULL,FOREST RIDGE,20300744,BR3,NULL,NULL,537250,168850,NULL,NULL
NA,26/10/2016 09:33,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN NETTING    NEW TIGERS PUB  J.O LEE ROAD  - CALLER ON SCENE WILL MEET BRIGADE CALLER,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Lee Green,NULL,LEE ROAD,22001639,SE3,NULL,NULL,539850,175050,NULL,NULL
NA,26/10/2016 10:10,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN WALL AND GATE,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist  trapped livestock animal,E05000321,SOUTH HORNCHURCH,E09000016,HAVERING,Wennington,1.00021E+11,MANSER ROAD,21300834,RM13,551023,182975,551050,182950,51.52550042,0.175628763
NA,26/10/2016 12:45,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN RAILINGS  IN THE PLAYGROUND AREA,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000118,FARNBOROUGH AND CROFTON,E09000006,BROMLEY,Orpington,10013151964,TUBBENDEN LANE,20303579,BR6,544740,164772,544750,164750,51.36357935,0.077685685
NA,26/10/2016 17:58,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN HOLE CRANE PARK,Dog,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000532,WEST TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,1.00022E+11,HOSPITAL BRIDGE ROAD,22401591,TW2,513784,172619,513750,172650,51.44114575,-0.364237163
NA,27/10/2016 12:25,2016,2016/17,Special Service,2,3,326,978,ASSIST RSPCA WITH CAT STUCK UP TREE.  J/O LONGLEY LANE RVP WITH RSPCA IN CAR PARK,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000214,ABBEY WOOD,E09000011,GREENWICH,Plumstead,10010224990,BOSTALL HILL,20800197,SE2,546677,177873,546650,177850,51.48080116,0.110906796
NA,27/10/2016 13:27,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000476,BOLEYN,E09000025,NEWHAM,East Ham,NULL,ST MARTINS AVENUE,22207900,E6,NULL,NULL,541650,183250,NULL,NULL
NA,27/10/2016 14:52,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Kentish Town,NULL,OVAL ROAD,20400628,NW1,NULL,NULL,528550,184050,NULL,NULL
NA,27/10/2016 19:42,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD TRAPPED IN SOFA,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009379,KING'S PARK,E09000012,HACKNEY,Homerton,NULL,OVERBURY STREET,20900778,E5,NULL,NULL,535850,185950,NULL,NULL
NA,28/10/2016 01:51,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED IN GATE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011235,Barkingside,E09000026,REDBRIDGE,Ilford,NULL,CAMPBELL AVENUE,22302679,IG6,NULL,NULL,544050,189050,NULL,NULL
NA,28/10/2016 14:16,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009369,CLISSOLD,E09000012,HACKNEY,Stoke Newington,NULL,HOWARD ROAD,20900539,N16,NULL,NULL,532950,185550,NULL,NULL
NA,28/10/2016 19:42,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA GAIN ACCESS     RSPCA ON SCENE WITH INJURED GULL,Bird,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000363,OSTERLEY AND SPRING GROVE,E09000018,HOUNSLOW,Heston,1.00023E+11,GREAT WEST ROAD,21505052,TW8,516837,177730,516850,177750,51.48646104,-0.31864259
NA,28/10/2016 20:39,2016,2016/17,Special Service,1,1,326,326,CAT STUCK UNDER KITCHEN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000595,FOREST,E09000031,WALTHAM FOREST,Leytonstone,NULL,HAINAULT ROAD,22842000,E11,NULL,NULL,538850,187850,NULL,NULL
NA,29/10/2016 09:48,2016,2016/17,Special Service,1,1,326,326,CALL TO BEAR TRAPPED IN DOOR - RSPCA REQUESTED - SECURITY WILL MEET LFB ON SCENE,Unknown - Wild Animal,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000325,BOTWELL,E09000017,HILLINGDON,Hayes,NULL,STATION APPROACH,21401857,UB3,NULL,NULL,509850,179450,NULL,NULL
NA,29/10/2016 11:31,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000516,BARNES,E09000027,RICHMOND UPON THAMES,Hammersmith,NULL,NASSAU ROAD,22404814,SW13,NULL,NULL,521950,176450,NULL,NULL
NA,29/10/2016 15:05,2016,2016/17,Special Service,1,1,326,326,PIGEON CAUGHT IN NETTING,Bird,Person (land line),Doctors surgery,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,5870070369,GREEN WRYTHE LANE,22600593,SM5,527092,166673,527050,166650,51.3848812,-0.174985268
NA,29/10/2016 18:11,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON CHIMNEY  .,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000476,BOLEYN,E09000025,NEWHAM,East Ham,NULL,ST MARTINS AVENUE,22207900,E6,NULL,NULL,541650,183250,NULL,NULL
NA,30/10/2016 09:38,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH FOX ON TOP OF CONTAINERS,Fox,Person (mobile),Outdoor storage,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000378,ST. GEORGE'S,E09000019,ISLINGTON,Holloway,10012786743,CARLETON ROAD,21606415,N7,530047,185914,530050,185950,51.5571294,-0.125469337
NA,31/10/2016 11:24,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT ON ROOF RSPCA ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000119,HAYES AND CONEY HALL,E09000006,BROMLEY,Addington,NULL,HARVEST BANK ROAD,20301742,BR4,NULL,NULL,539950,165150,NULL,NULL
NA,31/10/2016 15:36,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED BY LEG INSIDE CAR OWNER ON SCENE,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000028,BECONTREE,E09000002,BARKING AND DAGENHAM,Barking,100027058,STONARD ROAD,19900313,RM8,546871,185640,546850,185650,51.55054028,0.116931532
NA,05/11/2016 04:23,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE J/O DAGNAM PARK DRIVE - BABY DEER TRAPPED IN RAILINGS - CALLER WILL MEET BRIGADE,Deer,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000314,HEATON,E09000016,HAVERING,Harold Hill,10025354015,NORTH HILL DRIVE,21300103,RM3,553713,192766,553750,192750,51.6127454,0.218651008
NA,07/11/2016 07:11,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN A TREE - NEAR THE CHILDRENS PLAYGROUND CALLER WILL WAIT,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011250,Newbury,E09000026,REDBRIDGE,Ilford,10023192709,ALDBOROUGH ROAD SOUTH,22301800,IG3,545944,188157,545950,188150,51.57339663,0.104614809
NA,07/11/2016 20:16,2016,2016/17,Special Service,1,1,326,326,CAT STUCK UNDER FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011248,Mayfield,E09000026,REDBRIDGE,Ilford,NULL,BETCHWORTH ROAD,22301853,IG3,NULL,NULL,545250,186550,NULL,NULL
NA,08/11/2016 06:10,2016,2016/17,Special Service,1,2,326,652,DOG TRAPPED IN WATER  BEHIND LEA VALLEY RIDING CINTRE DOG CAN BE SEEN FROM BRIDGE,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,10009146683,BLACK PATH,22802350,E10,535844,187290,535850,187250,51.56812841,-0.041368508
NA,09/11/2016 07:23,2016,2016/17,Special Service,1,1,326,326,DOG IN RIVER IN CATOR PARK,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,1.0002E+11,ALDERSMEAD ROAD,20302187,BR3,536205,170074,536250,170050,51.41333233,-0.042800809
NA,09/11/2016 22:35,2016,2016/17,Special Service,1,1,326,326,CAT WITH PAW TRAPPED IN PLUGHOLE OF BATH,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000031,EASTBURY,E09000002,BARKING AND DAGENHAM,Barking,NULL,STUART ROAD,19900708,IG11,NULL,NULL,545750,184050,NULL,NULL
NA,10/11/2016 15:11,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN BRICK WALL AND HOUSE         RSPCA IN ATTENDANCE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000372,HIGHBURY EAST,E09000019,ISLINGTON,Stoke Newington,NULL,BETJEMAN MEWS,21610140,N5,NULL,NULL,532050,185750,NULL,NULL
NA,12/11/2016 15:14,2016,2016/17,Special Service,1,1,326,326,HORSE STUCK IN DITCH ON ITS BACK,Horse,Person (land line),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,128014370,PARKFIELDS ROAD,21800751,KT2,518779,171150,518750,171150,51.42691821,-0.292893424
NA,14/11/2016 13:24,2016,2016/17,Special Service,1,2,326,652,ASSIST RSPCA WITH CAT TRAPPED UNDER CAR  NEAR TO PC WORLD AND CURRYS,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011109,Old Kent Road,E09000028,SOUTHWARK,Old Kent Road,2.00003E+11,OLD KENT ROAD,22501843,SE1,534321,177872,534350,177850,51.48385926,-0.066918079
NA,15/11/2016 13:58,2016,2016/17,Special Service,1,1,326,326,SEAGULL TRAPPED ON WIRE,Bird,Person (land line),Cables,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009404,ST. HELEN'S,E09000020,KENSINGTON AND CHELSEA,North Kensington,217059304,NORTH POLE ROAD,21700955,W10,523183,181594,523150,181550,51.51984338,-0.225940924
NA,15/11/2016 14:15,2016,2016/17,Special Service,1,1,326,326,CAT IN TREE RSPCA ON SCENE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011474,Old Coulsdon,E09000008,CROYDON,Purley,1.00021E+11,WESTON CLOSE,20500369,CR5,530972,157070,530950,157050,51.29769841,-0.122786684
NA,17/11/2016 10:38,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,202065635,MANDELA CLOSE,20201158,NW10,520389,184409,520350,184450,51.54574471,-0.26522949
NA,17/11/2016 12:31,2016,2016/17,Special Service,1,1,326,326,BIRD TANGLED IN WIRE CALLER WAS ON A BUS HE STATES THIS IS NEAR THE SCHOOL/COLLEGE THE QIRE GOES ACR,Bird,Person (mobile),Cables,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000096,NORTHWICK PARK,E09000005,BRENT,Wembley,202101930,EAST LANE,20202525,HA0,517527,186208,517550,186250,51.56251597,-0.305883379
NA,17/11/2016 13:37,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL TO CAT IN PRECARIOUS POSITION,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,10012835276,BOTHA ROAD,22200394,E13,541057,181849,541050,181850,51.51795747,0.031617955
NA,19/11/2016 12:17,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000171,CLEVELAND,E09000009,EALING,Ealing,12076582,PITSHANGER LANE,20601378,W5,516995,182121,516950,182150,51.52589357,-0.314911989
NA,19/11/2016 14:10,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED UNDER FRIDGE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000627,WANDSWORTH COMMON,E09000032,WANDSWORTH,Tooting,NULL,FREWIN ROAD,22901808,SW18,NULL,NULL,527050,173350,NULL,NULL
NA,20/11/2016 14:27,2016,2016/17,Special Service,1,1,326,326,OUTSIDE HSBC ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,REDBRIDGE,Woodford,10034920914,THE BROADWAY,22306023,IG8,540893,191828,540850,191850,51.60766722,0.033251086
NA,20/11/2016 17:50,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH BIRD CAUGHT IN TREE,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000104,WEMBLEY CENTRAL,E09000005,BRENT,Wembley,202128050,BRAEMAR AVENUE,20200556,HA0,517959,184133,517950,184150,51.54377643,-0.300348784
NA,21/11/2016 10:07,2016,2016/17,Special Service,1,1,326,326,EXPLOSION IN SIGNAL BOX,Unknown - Wild Animal,Person (land line),Train station - platform (at ground level or elevated),Non Residential,Other animal assistance,Animal harm involving wild animal,E05000140,KILBURN,E09000007,CAMDEN,Paddington,5085366,KILBURN HIGH ROAD,20400395,NW6,525507,183618,525550,183650,51.53752228,-0.191741581
NA,21/11/2016 12:44,2016,2016/17,Special Service,1,1,326,326,DOG STUCK IN FOX HOLE  CALLER IS ON HAMPSTEAD HEATH AND WILL WAVE YOU DOWN,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,West Hampstead,5132281,SPANIARDS ROAD,20400006,NW3,526394,186643,526350,186650,51.56451022,-0.177873011
NA,22/11/2016 15:38,2016,2016/17,Special Service,1,1,326,326,TO ASSIST RSPCA WITH CAT ON THIRD FLOOR,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009395,EARL'S COURT,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,PENYWERN ROAD,21700411,SW5,NULL,NULL,525450,178350,NULL,NULL
NA,24/11/2016 17:28,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000452,SYDENHAM,E09000023,LEWISHAM,Forest Hill,NULL,KIRKDALE,22002606,SE26,NULL,NULL,535150,171750,NULL,NULL
NA,25/11/2016 11:42,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL TO CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000344,YEADING,E09000017,HILLINGDON,Southall,NULL,MARSWORTH CLOSE,21401242,UB4,NULL,NULL,512350,181850,NULL,NULL
NA,26/11/2016 22:02,2016,2016/17,Special Service,1,1,326,326,"DOG IN WATER  - NEAR THE BRIDGE, TRAPPED ON SIDE OF BANK FRU WITH INFLATABLE BOAT REQUESTED FROM INC",Dog,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,10070248483,PUTNEY BRIDGE,22906420,SW15,524224,175721,524250,175750,51.46683331,-0.213010265
NA,26/11/2016 23:38,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED ON LEDGE,Dog,Person (land line),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000647,WARWICK,E09000033,WESTMINSTER,Lambeth,1.00023E+11,ST GEORGE'S DRIVE,8400194,SW1V,529174,178364,529150,178350,51.48947878,-0.140821898
NA,29/11/2016 10:44,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN RAILINGS ORLEANS PLAY PARK,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000531,TWICKENHAM RIVERSIDE,E09000027,RICHMOND UPON THAMES,Twickenham,1.00023E+11,ORLEANS ROAD,22403709,TW1,516978,173485,516950,173450,51.44827867,-0.318015788
NA,30/11/2016 11:05,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH BIRD TRAPPED IN NETTING,Bird,Person (mobile),Hairdresser,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000048,EAST BARNET,E09000003,BARNET,Barnet,200034906,EAST BARNET ROAD,20013380,EN4,527128,195372,527150,195350,51.64279062,-0.164127425
NA,30/11/2016 18:22,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN RECLINER CHAIR,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000105,WILLESDEN GREEN,E09000005,BRENT,Willesden,NULL,KINGS ROAD,20202255,NW10,NULL,NULL,522550,184250,NULL,NULL
NA,01/12/2016 10:07,2016,2016/17,Special Service,1,1,326,326,SMALL DOG STUCK IN FENCE PANEL,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000316,MAWNEYS,E09000016,HAVERING,Romford,NULL,GELSTHORPE ROAD,21301219,RM5,NULL,NULL,549750,191150,NULL,NULL
NA,01/12/2016 10:32,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL TO CAT IN TREE ON POND,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,10070001081,GLASSMILL LANE,20303922,BR2,539925,169022,539950,169050,51.40297431,0.010241131
NA,01/12/2016 15:38,2016,2016/17,Special Service,1,2,326,652,RUNNING CALL TO CAT TRAPPED UP TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,10070011991,GLASSMILL LANE,20303922,BR2,539934,169018,539950,169050,51.40293615,0.010368854
NA,02/12/2016 09:56,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Lake/pond/reservoir,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,2.00003E+11,GLASSMILL LANE,20303922,BR2,540010,169240,540050,169250,51.40491232,0.011548288
NA,02/12/2016 10:40,2016,2016/17,Special Service,2,3,326,978,SMALL WHITE DOG STUCK IN THE MUD  LAST SEEN AT CHELSEA CREEK,Dog,Person (mobile),Electricity power station,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009391,CHELSEA RIVERSIDE,E09000020,KENSINGTON AND CHELSEA,Chelsea,217114091,LOTS ROAD,21700327,SW10,526233,176943,526250,176950,51.47737164,-0.183667164
NA,03/12/2016 08:37,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN WALL,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011217,Barnehurst,E09000004,BEXLEY,Bexley,1.0002E+11,EVERSLEY AVENUE,20100520,DA7,550762,176216,550750,176250,51.4648397,0.168982503
NA,04/12/2016 13:12,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND METAL SHUTTERS THE CALLER HAS LEFT A ROADSIDE ORANGE CONE BY THE DOOR WHERE THE C,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,10008992232,UPTON LANE,22201168,E7,540620,184697,540650,184650,51.54365837,0.026459982
NA,06/12/2016 03:38,2016,2016/17,Special Service,1,1,326,326,TWO FOXES TRAPPED IN LIGHTWELL,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,NULL,CANONBURY SQUARE,21604444,N1,NULL,NULL,531850,184550,NULL,NULL
NA,06/12/2016 11:21,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE PUPPY FALLEN FROM FIRST FLOOR BALCONY POLICE IN ATTENDANCE,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000557,BELMONT,E09000029,SUTTON,Sutton,NULL,BRIGHTON ROAD,22605685,SM2,NULL,NULL,525850,162650,NULL,NULL
NA,07/12/2016 10:39,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,1.0002E+11,KENT HOUSE ROAD,20302948,BR3,536221,170873,536250,170850,51.42050869,-0.04226422
NA,07/12/2016 13:52,2016,2016/17,Special Service,1,1,326,326,ASSIST POLICE  WITH DOG TRAPPED ON WINDOW LEDGE  ABOVE  KFC  JCT  OF GOLDERS GREEN CRESCENT,Dog,Person (land line),"Takeaway, fast food",Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000053,GOLDERS GREEN,E09000003,BARNET,Hendon,200143306,GOLDERS GREEN ROAD,20018180,NW11,523945,188400,523950,188450,51.58084398,-0.212566916
NA,07/12/2016 19:23,2016,2016/17,Special Service,1,1,326,326,DOG STUCK BETWEEN WALL AND FENCE  OCEAN CHILDREN CENTRE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05009329,ST. DUNSTAN'S,E09000030,TOWER HAMLETS,Bethnal Green,6045138,ASTON STREET,22700100,E14,536166,181494,536150,181450,51.51596686,-0.038965698
NA,08/12/2016 19:19,2016,2016/17,Special Service,1,2,326,652,SWAN STUCK BETWEEN TWO FENCES NEAR SHOP CALLED BROWN SUGAR,Bird,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped wild animal,E05000085,ALPERTON,E09000005,BRENT,Wembley,202132569,ATLIP ROAD,20201491,HA0,518190,183716,518150,183750,51.53998036,-0.297159191
NA,10/12/2016 09:16,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,NULL,THE BROADWAY,20005160,NW7,NULL,NULL,521350,192050,NULL,NULL
NA,10/12/2016 11:10,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED ON ROOF OF CAFE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,NULL,THE BROADWAY,20005160,NW7,NULL,NULL,521350,192050,NULL,NULL
NA,11/12/2016 01:51,2016,2016/17,Special Service,1,1,326,326,DEER STUCK IN RAILINGS LAS ON SCENE AND HAVE CALLED RSPCA  NR THE COMMUNITY CENTRE,Deer,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,10024390524,COLLERNE STREET,21320280,RM3,554294,192195,554250,192150,51.60745612,0.226783556
NA,11/12/2016 19:44,2016,2016/17,Special Service,1,1,326,326,CAT FALLEN FROM BALCONY INTO DOWNSTAIRS GARDEN CALLER STATES CAT IS INJURED,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05009320,BOW WEST,E09000030,TOWER HAMLETS,Bethnal Green,6070524,WHITTON WALK,22702076,E3,537054,182949,537050,182950,51.52882729,-0.025610171
NA,11/12/2016 21:08,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED UNDER SHED OWNER IN ATTENDANCE,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011230,Sidcup,E09000004,BEXLEY,Sidcup,1.0002E+11,FARADAY AVENUE,20100537,DA14,547152,172679,547150,172650,51.43400749,0.115581676
NA,11/12/2016 21:08,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED INBETWEEN CUPBOARD AND LEDGE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009394,DALGARNO,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,BREWSTER GARDENS,21700823,W10,NULL,NULL,523150,181650,NULL,NULL
NA,13/12/2016 13:09,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED UNDER RAILWAY BRIDGE  HANGING BY WING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011476,Purley & Woodcote,E09000008,CROYDON,Purley,1.00021E+11,STOATS NEST ROAD,20502353,CR5,530479,160173,530450,160150,51.32569824,-0.128721179
NA,14/12/2016 14:03,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED UNDER DECKING,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000605,LEYTONSTONE,E09000031,WALTHAM FOREST,Leytonstone,NULL,MICHAEL ROAD,22858450,E11,NULL,NULL,539650,187150,NULL,NULL
NA,15/12/2016 15:59,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED UP TREE BEING ATTACKED BY CROWS  RSPCA NOT REQUESTED AT PRESENT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000124,PETTS WOOD AND KNOLL,E09000006,BROMLEY,Orpington,NULL,KINGSWAY,20302877,BR5,NULL,NULL,544750,167850,NULL,NULL
NA,15/12/2016 17:47,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED BETWEEN TWO CONSERVATORIES,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000114,CRAY VALLEY EAST,E09000006,BROMLEY,Orpington,NULL,MAY AVENUE,20301208,BR5,NULL,NULL,546750,167750,NULL,NULL
NA,15/12/2016 21:07,2016,2016/17,Special Service,1,1,326,326,DOG WITH MOUTH TRAPPED IN CAGE,Dog,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,COLVILLE GARDENS,21700840,W11,NULL,NULL,524850,181250,NULL,NULL
NA,16/12/2016 09:55,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN TREE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000458,CRICKET GREEN,E09000024,MERTON,Mitcham,NULL,MITCHAM PARK,22104461,CR4,NULL,NULL,527650,168250,NULL,NULL
NA,16/12/2016 11:04,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011248,Mayfield,E09000026,REDBRIDGE,Ilford,1.00022E+11,CAPEL GARDENS,22301917,IG3,545841,185684,545850,185650,51.5512027,0.102104932
NA,16/12/2016 13:15,2016,2016/17,Special Service,1,1,326,326,DOG IN CANAL ACCESS VIA TUDOR ESTATE,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009394,DALGARNO,E09000020,KENSINGTON AND CHELSEA,North Kensington,217114044,LADBROKE GROVE,21700912,W10,523872,182381,523850,182350,51.52676585,-0.215738802
NA,16/12/2016 16:05,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED OUTSIDE ON DRAINPIPE,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05011230,Sidcup,E09000004,BEXLEY,Sidcup,10023304917,HATHERLEY ROAD,20100686,DA14,546259,171928,546250,171950,51.42749026,0.102434296
NA,17/12/2016 16:24,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON MUD FLATS ON WATERFRONT-CALLED BY RSPCA MEMBERS OF PUBLIC UNABLE TO REACH IT AND TIDE,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05011225,Erith,E09000004,BEXLEY,Erith,1.0002E+11,CHANDLERS DRIVE,20101694,DA8,551047,178673,551050,178650,51.48684015,0.174132362
NA,17/12/2016 22:15,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND WARDROBE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000629,WEST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,SWINBURNE ROAD,22905173,SW15,NULL,NULL,522350,175050,NULL,NULL
NA,23/12/2016 10:42,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CAR SEAT,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000381,TOLLINGTON,E09000019,ISLINGTON,Holloway,5300091627,TOLLINGTON PARK,21603933,N4,530968,187139,530950,187150,51.56792493,-0.111736029
NA,23/12/2016 10:47,2016,2016/17,Special Service,1,1,326,326,Redacted,Unknown - Domestic Animal Or Pet,Person (land line),Factory,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000190,SOUTHALL GREEN,E09000009,EALING,Southall,12150695,INVERNESS ROAD,20600935,UB2,512426,178758,512450,178750,51.49659409,-0.381817292
NA,23/12/2016 12:45,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000632,BRYANSTON AND DORSET SQUARE,E09000033,WESTMINSTER,Paddington,NULL,SHOULDHAM STREET,8400244,W1H,NULL,NULL,527450,181550,NULL,NULL
NA,24/12/2016 14:04,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON TOP OF SHED CALLER SAYS ITS PAWS ARE TRAPPED,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000489,PLAISTOW NORTH,E09000025,NEWHAM,Stratford,NULL,BROOKS ROAD,22200420,E13,NULL,NULL,540150,183550,NULL,NULL
NA,25/12/2016 14:21,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011240,Clementswood,E09000026,REDBRIDGE,Ilford,NULL,MORTLAKE ROAD,22302295,IG1,NULL,NULL,544350,185650,NULL,NULL
NA,25/12/2016 15:48,2016,2016/17,Special Service,1,1,326,326,DOG ON ROOF OF BUILDING,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,LEA BRIDGE,E09000012,HACKNEY,Homerton,NULL,MEDIAN ROAD,20900679,E5,NULL,NULL,535350,185550,NULL,NULL
NA,25/12/2016 16:32,2016,2016/17,Special Service,1,2,326,652,HERON TRAPPED IN FISHING WIRE ON ISLAND ON LAKE     WATER RESCUE LEVEL ONE NEAR THE TENNIS COURTS  C,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,121019179,DR JOHNSON AVENUE,22901279,SW17,528771,172186,528750,172150,51.43404911,-0.148870167
NA,26/12/2016 11:20,2016,2016/17,Special Service,1,1,326,326,PIDGEON TRAPPED BY LEG DANGLING FROM ROOF - RSPCA ON SCENE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Kingston,128018227,THAMES SIDE,21800939,KT1,517787,169627,517750,169650,51.41343703,-0.307661742
NA,26/12/2016 13:30,2016,2016/17,Special Service,1,1,326,326,SEAGULL TRAPPED IN FISHING WIRE ON AN ISLAND IN THE MIDDLE OF LAKE,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,NA,NA,NA,NA,Chingford,10025175175,RANGERS ROAD,13801596,IG9,539878,194818,539850,194850,51.63478732,0.019796097
NA,27/12/2016 11:28,2016,2016/17,Special Service,1,4,326,1304,TWO DEER STUCK IN FENCING RVP OUTSIDE PORTLANDS CUDHAM LANE SOUTH,Deer,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped wild animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,1.00023E+11,CUDHAM LANE SOUTH,20302994,TN14,545341,157793,545350,157750,51.30071251,0.083468219
NA,28/12/2016 01:58,2016,2016/17,Special Service,1,1,326,326,CAT FALLEN OUT WINDOW - CANT BE REACHED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000373,HIGHBURY WEST,E09000019,ISLINGTON,Holloway,NULL,HORNSEY ROAD,21603387,N7,NULL,NULL,530950,185950,NULL,NULL
NA,29/12/2016 11:28,2016,2016/17,Special Service,2,3,326,978,TWO DOGS FALLEN IN ICE ON RESERVOIR - WATER RESCUE LEVEL TWO,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000062,WEST HENDON,E09000003,BARNET,Hendon,200026731,COOL OAK LANE,20010120,NW9,521859,187718,521850,187750,51.57516898,-0.242893745
NA,30/12/2016 17:39,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,1.00023E+11,GADWALL WAY,20800604,SE28,544843,179690,544850,179650,51.49760008,0.085259282
NA,30/12/2016 20:16,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011227,Longlands,E09000004,BEXLEY,Sidcup,NULL,APPLEDORE CRESCENT,20100043,DA14,NULL,NULL,545550,172150,NULL,NULL
NA,01/01/2017 02:21,2017,2016/17,Special Service,1,2,326,652,DOG WITH HEAD STUCK IN RAILINGS CALLED BY OWNER,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,1.0002E+11,RAVENSBOURNE AVENUE,20303571,BR2,538997,169789,538950,169750,51.41009451,-0.0027907
NA,01/01/2017 12:24,2017,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011468,Fairfield,E09000008,CROYDON,Croydon,1.00023E+11,NORTH END,20501238,CR0,532185,165905,532150,165950,51.376818,-0.102123891
NA,01/01/2017 18:48,2017,2016/17,Special Service,1,2,326,652,DOG STUCK IN HULL OF DERELICT BOAT - WATER RESCUE LEVEL ONE CALLER NOT VERY SURE OF EXACT LOCATION H,Dog,Person (mobile),Barge,Boat,Animal rescue from water,Animal rescue from water - Domestic pet,E05000347,BRENTFORD,E09000018,HOUNSLOW,Chiswick,1.00023E+11,HIGH STREET,21500607,TW8,518452,177780,518450,177750,51.48657493,-0.295376666
NA,01/01/2017 22:53,2017,2016/17,Special Service,1,1,326,326,DEER TRAPPED IN RAILINGS JUNCTION WITH DENNIS LANE,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000303,STANMORE PARK,E09000015,HARROW,Stanmore,768007302,VALENCIA ROAD,21200933,HA7,517167,192558,517150,192550,51.61966181,-0.308955482
NA,02/01/2017 14:01,2017,2016/17,Special Service,1,1,326,326,SEAGULL TRAPPED IN WIRING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011109,Old Kent Road,E09000028,SOUTHWARK,Old Kent Road,10000812314,OLMAR STREET,22501859,SE1,534164,177856,534150,177850,51.48375273,-0.06918385
NA,02/01/2017 15:42,2017,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011109,Old Kent Road,E09000028,SOUTHWARK,Old Kent Road,10090284724,OLD KENT ROAD,22501843,SE1,534108,177947,534150,177950,51.48458378,-0.069955311
NA,03/01/2017 10:08,2017,2016/17,Special Service,1,2,326,652,FOX TRAPPED IN RESOVOIR,Fox,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,2.00001E+11,SPARROWS LANE,20801387,SE9,543858,173978,543850,173950,51.44652523,0.06875502
NA,03/01/2017 12:38,2017,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN WIRE HANGING FROM GUTTERING - GILL RSPCA INSPECTOR ON SCENE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000347,BRENTFORD,E09000018,HOUNSLOW,Ealing,NULL,WHITESTILE ROAD,21501201,TW8,NULL,NULL,517650,178550,NULL,NULL
NA,03/01/2017 14:23,2017,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000207,SOUTHBURY,E09000010,ENFIELD,Enfield,NULL,BRIDGE CLOSE,20702526,EN1,NULL,NULL,534850,197450,NULL,NULL
NA,04/01/2017 12:19,2017,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011486,Thornton Heath,E09000008,CROYDON,Norbury,NULL,WESTBROOK ROAD,20501552,CR7,NULL,NULL,532750,169450,NULL,NULL
NA,05/01/2017 12:12,2017,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE - DOG TRAPPED BEHIND SHED,Dog,Person (land line),Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000220,ELTHAM WEST,E09000011,GREENWICH,Eltham,1.00021E+11,KINGSHOLM GARDENS,20800864,SE9,542143,175414,542150,175450,51.45986172,0.044669495
NA,05/01/2017 18:45,2017,2016/17,Special Service,1,1,326,326,CAT STUCK ON APEX OF ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000526,NORTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,NULL,MANOR GROVE,22405753,TW9,NULL,NULL,519050,175450,NULL,NULL
NA,07/01/2017 15:41,2017,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN LOFT,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000059,TOTTERIDGE,E09000003,BARNET,Finchley,NULL,CISSBURY RING SOUTH,20009100,N12,NULL,NULL,524950,192250,NULL,NULL
NA,10/01/2017 12:56,2017,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),Tree scrub,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011478,Sanderstead,E09000008,CROYDON,Addington,1.00021E+11,ADDINGTON ROAD,20502645,CR2,534320,161427,534350,161450,51.336075,-0.073155049
NA,10/01/2017 17:05,2017,2016/17,Special Service,1,1,326,326,KITTENS STUCK IN HOLE IN CUPBOARD,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011488,West Thornton,E09000008,CROYDON,Norbury,NULL,LONDON ROAD,20503034,CR7,NULL,NULL,531450,167550,NULL,NULL
NA,12/01/2017 01:41,2017,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000520,HAMPTON,E09000027,RICHMOND UPON THAMES,Twickenham,10002249748,MOUNT MEWS,22406389,TW12,513958,169591,513950,169550,51.41389552,-0.362707783
NA,12/01/2017 20:42,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000280,TOTTENHAM GREEN,E09000014,HARINGEY,Tottenham,NULL,GROVE PARK ROAD,21103358,N15,NULL,NULL,533250,189150,NULL,NULL
NA,14/01/2017 20:48,2017,2016/17,Special Service,1,1,326,326,Redacted,Fox,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000128,BELSIZE,E09000007,CAMDEN,West Hampstead,NULL,FELLOWS ROAD,20400582,NW3,NULL,NULL,526950,184250,NULL,NULL
NA,15/01/2017 09:38,2017,2016/17,Special Service,1,1,326,326,DOG WITH HEAD TRAPPED IN GATE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,10003626658,LAYHAMS ROAD,20301563,BR2,539887,163152,539850,163150,51.35023659,0.00738892
NA,15/01/2017 13:48,2017,2016/17,Special Service,2,4,326,1304,CANADA GOOSE STUCK BETWEEN LOCK GATES  - WATER RESCUE LEVEL TWO IMPLEMENETED  EAST INDIA DOCK BASIN,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009318,BLACKWALL & CUBITT TOWN,E09000030,TOWER HAMLETS,Poplar,6652305,ORCHARD PLACE,22700900,E14,539126,180835,539150,180850,51.50932428,0.003405978
NA,16/01/2017 12:19,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CELLAR,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,OAKDENE PARK,20032260,N3,NULL,NULL,524950,191450,NULL,NULL
NA,17/01/2017 10:46,2017,2016/17,Special Service,1,2,326,652,BIRD TRAPPED IN TREE TRAPPED IN WIRES     IN BACK GARDEN,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000132,FORTUNE GREEN,E09000007,CAMDEN,West Hampstead,NULL,BERRIDGE MEWS,20401422,NW6,NULL,NULL,525050,185350,NULL,NULL
NA,17/01/2017 12:15,2017,2016/17,Special Service,1,3,326,978,CAT TRAPPED BETWEEN TWO WALLS   RSPCA NOT BEING NOTIFIED UNLESS YOU REQUEST US TO   .,Cat,Person (land line),Other building/use not known,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000188,SOUTH ACTON,E09000009,EALING,Acton,12097990,BERRYMEAD GARDENS,20600167,W3,520230,179855,520250,179850,51.50484949,-0.269074792
NA,18/01/2017 15:11,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN STAIRS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009405,STANLEY,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,KING'S ROAD,21700299,SW10,NULL,NULL,526250,177350,NULL,NULL
NA,18/01/2017 16:31,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH  TWO FOXES TRAPPED IN GUTTER  ONE IS INJURED THE OTHER IS GUARDING IT,Fox,Person (mobile),Large supermarket,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Greenwich,1.00023E+11,LEWISHAM ROAD,22004659,SE13,538127,176049,538150,176050,51.46656094,-0.012852418
NA,20/01/2017 23:14,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,NULL,WYLLEN CLOSE,22701371,E1,NULL,NULL,535050,182050,NULL,NULL
NA,22/01/2017 00:26,2017,2016/17,Special Service,1,1,326,326,RUNNING CALL TO INJURED CAT TRAPPED UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000456,CANNON HILL,E09000024,MERTON,New Malden,NULL,CHERRYWOOD LANE,22101359,SM4,NULL,NULL,524250,168250,NULL,NULL
NA,22/01/2017 13:35,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH PIGEON CAUGHT IN NETTING,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000137,HIGHGATE,E09000007,CAMDEN,Kentish Town,NULL,DARTMOUTH PARK HILL,20400019,N19,NULL,NULL,528950,186650,NULL,NULL
NA,22/01/2017 14:07,2017,2016/17,Special Service,1,1,326,326,ANIMALTRAPPED IN LOFT,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011242,Fairlop,E09000026,REDBRIDGE,Hainault,NULL,WICKETS WAY,22304769,IG6,NULL,NULL,545850,191550,NULL,NULL
NA,23/01/2017 09:43,2017,2016/17,Special Service,1,1,326,326,DOG TRAPPED UNDER ICE VICTORIA PARK,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009386,VICTORIA,E09000012,HACKNEY,Bethnal Green,10008306831,VICTORIA PARK ROAD,20901033,E9,534850,183624,534850,183650,51.5354229,-0.057105168
NA,25/01/2017 14:58,2017,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000461,GRAVENEY,E09000024,MERTON,Mitcham,48055109,PARK AVENUE,22104962,CR4,528643,170229,528650,170250,51.4164902,-0.151420367
NA,26/01/2017 10:57,2017,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (land line),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000438,BLACKHEATH,E09000023,LEWISHAM,Greenwich,10010243667,SHOOTERS HILL ROAD,20801348,SE10,539626,176827,539650,176850,51.47318478,0.009020457
NA,27/01/2017 10:11,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED INSIDE CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000113,COPERS COPE,E09000006,BROMLEY,Beckenham,NULL,HIGH STREET,20301747,BR3,NULL,NULL,537150,169350,NULL,NULL
NA,28/01/2017 03:11,2017,2016/17,Special Service,1,1,326,326,RSPCA CALL IN NEED OF ASSISTANCE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011485,South Norwood,E09000008,CROYDON,Woodside,NULL,HOLMESDALE ROAD,20503079,SE25,NULL,NULL,533450,168350,NULL,NULL
NA,29/01/2017 16:12,2017,2016/17,Special Service,1,1,326,326,DOG FALLEN THROUGH ICE ON LAKE   CONNAUGHT WATERS   - FAR SIDE OF LAKE FROM RANGERS ROAD,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000593,CHINGFORD GREEN,E09000031,WALTHAM FOREST,Chingford,10025293519,RANGERS ROAD,22869100,E4,539875,194824,539850,194850,51.63484198,0.019755173
NA,30/01/2017 15:44,2017,2016/17,Special Service,1,1,326,326,BIRD INJURED AND STUCK ON LEDGE,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000649,WEST END,E09000033,WESTMINSTER,Soho,1.00023E+11,MORTIMER STREET,8401448,W1W,529008,181466,529050,181450,51.51739406,-0.142077537
NA,30/01/2017 18:15,2017,2016/17,Special Service,1,1,326,326,CAT IN PRECARIOUS POSITION ON WINDOW SILL AT AT SECOND FLOOR LEVEL  CALLER STATES ANIMAL APPEARS INJ,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000429,STOCKWELL,E09000022,LAMBETH,Clapham,NULL,STUDLEY ROAD,21901331,SW4,NULL,NULL,530350,176450,NULL,NULL
NA,01/02/2017 06:06,2017,2016/17,Special Service,2,4,326,1304,"HORSE TANGLED IN FENCE OF OLD JAIL PUBLIC HOUSE  OWNERS AND POLICE ON SCENE   ,",Horse,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,1.00023E+11,JAIL LANE,20301511,TN16,543442,159374,543450,159350,51.31540132,0.056882866
NA,01/02/2017 11:28,2017,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011486,Thornton Heath,E09000008,CROYDON,Woodside,1.00021E+11,EGERTON ROAD,20500900,SE25,533172,168528,533150,168550,51.40015948,-0.086967872
NA,02/02/2017 18:35,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000420,COLDHARBOUR,E09000022,LAMBETH,Brixton,NULL,CANTERBURY CRESCENT,21900284,SW9,NULL,NULL,531250,175650,NULL,NULL
NA,03/02/2017 13:15,2017,2016/17,Special Service,1,1,326,326,DOG WITH HEAD STUCK BETWEEN WALL AND METAL GATE   OWNER ON SCENE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000192,WALPOLE,E09000009,EALING,Ealing,12072976,MATTOCK LANE,20601146,W13,516843,180346,516850,180350,51.50997182,-0.317690305
NA,03/02/2017 22:19,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN A SIX FOOT DROP,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000256,HAMMERSMITH BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,SOUTHERTON ROAD,21000745,W6,NULL,NULL,523050,178950,NULL,NULL
NA,04/02/2017 08:30,2017,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000139,KENTISH TOWN,E09000007,CAMDEN,Kentish Town,NULL,BURGHLEY ROAD,20400358,NW5,NULL,NULL,528950,185550,NULL,NULL
NA,04/02/2017 15:26,2017,2016/17,Special Service,1,1,326,326,KITTEN STUCK ON WIRE ON FENCE    RVP OUTSIDE SECURITY GATE  AND  CALLER WILL DIRECT YOU TO INCIDENT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000131,CANTELOWES,E09000007,CAMDEN,Kentish Town,NULL,ST PAUL'S MEWS,20401499,NW1,NULL,NULL,529850,184350,NULL,NULL
NA,05/02/2017 05:36,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,HUTTON GROVE,20024460,N12,NULL,NULL,526150,191950,NULL,NULL
NA,05/02/2017 23:33,2017,2016/17,Special Service,1,1,326,326,CAT POSSIBLY TRAPPED ON ROOF CALLER STATES CAT SOUNDS DISTRESSED,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,NULL,LEONARD ROAD,22207787,E7,NULL,NULL,540150,185650,NULL,NULL
NA,06/02/2017 16:22,2017,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Converted office,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000649,WEST END,E09000033,WESTMINSTER,Soho,1.00023E+11,BERKELEY SQUARE,8400761,W1J,528766,180716,528750,180750,51.51070902,-0.145837232
NA,07/02/2017 12:31,2017,2016/17,Special Service,1,1,326,326,CAT STUCK IN WATERPIPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Chingford,NULL,CHINGDALE ROAD,22825050,E4,NULL,NULL,539150,193150,NULL,NULL
NA,08/02/2017 14:36,2017,2016/17,Special Service,1,3,326,978,CAT TRAPPED BEHIND PIPE - REQUESTED BY RSPCA,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000042,WHALEBONE,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,LIMBOURNE AVENUE,19900223,RM8,NULL,NULL,548850,187750,NULL,NULL
NA,09/02/2017 18:31,2017,2016/17,Special Service,1,1,326,326,SWAN TRAPPED UNDER ROAD IN WATER   SWAN SANCTUARY ALREADY IN ATTENDANCE,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000558,CARSHALTON CENTRAL,E09000029,SUTTON,Wallington,5870078006,HIGH STREET,22605690,SM5,528012,164501,528050,164550,51.36515478,-0.162553373
NA,11/02/2017 16:49,2017,2016/17,Special Service,1,2,326,652,CAT TRAPPED BETWEEN GARAGES,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000178,GREENFORD GREEN,E09000009,EALING,Northolt,12054320,CASTLE ROAD,20600331,UB5,514129,184504,514150,184550,51.54789722,-0.355435302
NA,12/02/2017 07:56,2017,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE - TRAPPED BETWEEN WALL AND FLOORBOARDS,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000519,"HAM, PETERSHAM AND RICHMOND RIVERSIDE",E09000027,RICHMOND UPON THAMES,Kingston,NULL,MEAD ROAD,22403663,TW10,NULL,NULL,517350,172050,NULL,NULL
NA,13/02/2017 14:10,2017,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED IN BRICKWORK  AS SIDE OF CHURCH RSPCA IN ATTENDANCE,Cat,Person (land line),Church/Chapel,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000480,EAST HAM CENTRAL,E09000025,NEWHAM,East Ham,10008995615,PILGRIMS WAY,22202000,E6,542434,183750,542450,183750,51.53469398,0.052218009
NA,14/02/2017 20:54,2017,2016/17,Special Service,1,1,326,326,KITTEN STUCK BEHIND WOOD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Plumstead,NULL,NIGHTINGALE PLACE,20801085,SE18,NULL,NULL,543450,177950,NULL,NULL
NA,15/02/2017 13:25,2017,2016/17,Special Service,1,2,326,652,CAT TRAPPED IN BETWEEN WALLS  CALLER WILL COME OUTSIDE TO MEET YOU,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009370,DALSTON,E09000012,HACKNEY,Homerton,NULL,RAMSGATE STREET,20900838,E8,NULL,NULL,533850,184950,NULL,NULL
NA,15/02/2017 15:15,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CEILING AREA OF LOBBY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000312,HAROLD WOOD,E09000016,HAVERING,Harold Hill,NULL,FIRWOOD LANE,21320239,RM3,NULL,NULL,554250,190250,NULL,NULL
NA,17/02/2017 18:59,2017,2016/17,Special Service,1,1,326,326,DOG WITH HEAD TRAPPED IN A METAL GATE,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000256,HAMMERSMITH BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34035079,HAMMERSMITH GROVE,21000410,W6,523039,179498,523050,179450,51.50103726,-0.22874653
NA,19/02/2017 07:13,2017,2016/17,Special Service,1,1,326,326,DOG STUCK IN RAILINGS,Dog,Person (land line),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05011115,St. Giles,E09000028,SOUTHWARK,Peckham,10009791706,LYNDHURST WAY,22501612,SE15,533718,176239,533750,176250,51.46932691,-0.076214559
NA,19/02/2017 10:57,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT ON A ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000476,BOLEYN,E09000025,NEWHAM,Plaistow,NULL,SELSDON ROAD,22207879,E13,NULL,NULL,541350,183350,NULL,NULL
NA,19/02/2017 13:48,2017,2016/17,Special Service,1,1,326,326,CAT UP TREE     RSPCA IN ATTENDANCE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011252,South Woodford,E09000026,REDBRIDGE,Woodford,1.00022E+11,THE DRIVE,22305366,E18,540127,189603,540150,189650,51.58786494,0.021308956
NA,20/02/2017 09:20,2017,2016/17,Special Service,1,1,326,326,RUNNING CALL TO SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000052,GARDEN SUBURB,E09000003,BARNET,Finchley,NULL,HAMPSTEAD WAY,20020780,NW11,NULL,NULL,525550,187950,NULL,NULL
NA,21/02/2017 12:45,2017,2016/17,Special Service,1,1,326,326,TO ASSIST RSPCA WITH A CAT TRAPPED IN THE ROOFSPACE OF COMMUNITY CENTRE,Cat,Person (mobile),Community centre/Hall,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011110,Peckham,E09000028,SOUTHWARK,Peckham,10009791094,BULLER CLOSE,22500371,SE15,534258,177188,534250,177150,51.47772738,-0.068084623
NA,21/02/2017 14:05,2017,2016/17,Special Service,1,2,326,652,ASSIST RSPCA WITH KITTEN STUCK UP TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,CROUCH END,E09000014,HARINGEY,Hornsey,NULL,CECILE PARK,21103068,N8,NULL,NULL,530450,188250,NULL,NULL
NA,22/02/2017 16:22,2017,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009369,CLISSOLD,E09000012,HACKNEY,Stoke Newington,NULL,HOWARD ROAD,20900539,N16,NULL,NULL,533050,185450,NULL,NULL
NA,26/02/2017 07:19,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN MUD   AT TIDEWAY VILLAGE GANG PLANK,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Clapham,10024085914,TIDEWAY WALK,22906729,SW8,529343,177616,529350,177650,51.482718,-0.138662778
NA,26/02/2017 11:28,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA (ON SCENE) WITH CAT ON ROOF   .,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,MILL PLACE,21800672,KT1,NULL,NULL,518550,168850,NULL,NULL
NA,26/02/2017 11:50,2017,2016/17,Special Service,1,1,326,326,CAT STUCK ON CHIMNEY BREAST RSPCA ON SCENE   .,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000527,ST. MARGARETS AND NORTH TWICKENHAM,E09000027,RICHMOND UPON THAMES,Heston,NULL,HALIBURTON ROAD,22403529,TW1,NULL,NULL,516450,175150,NULL,NULL
NA,26/02/2017 13:03,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT TRAPPED DOWN HOLE  RSPCA UNABLE TO ATTEND AT THIS TIME,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009370,DALSTON,E09000012,HACKNEY,Stoke Newington,NULL,SANDRINGHAM ROAD,20900886,E8,NULL,NULL,533550,185150,NULL,NULL
NA,27/02/2017 08:51,2017,2016/17,Special Service,1,3,326,978,CAT STUCK IN ROOF SPACE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,MILL PLACE,21800672,KT1,NULL,NULL,518550,168850,NULL,NULL
NA,28/02/2017 01:41,2017,2016/17,Special Service,1,1,326,326,CAT FALLEN FROM WINDOW - TRAPPED ON LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011475,Park Hill & Whitgift,E09000008,CROYDON,Croydon,NULL,FAIRFIELD ROAD,20500927,CR0,NULL,NULL,533050,165450,NULL,NULL
NA,01/03/2017 17:03,2017,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000133,FROGNAL AND FITZJOHNS,E09000007,CAMDEN,West Hampstead,5018198,FROGNAL,20400141,NW3,526286,185126,526250,185150,51.5509012,-0.179974524
NA,01/03/2017 20:29,2017,2016/17,Special Service,1,1,326,326,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,NULL,GLENDISH ROAD,21104569,N17,NULL,NULL,534550,190650,NULL,NULL
NA,02/03/2017 19:49,2017,2016/17,Special Service,1,2,326,652,ASSIST RSPCA WITH CAT STUCK UP CHIMNEY    CALLED BY RSPCA INSPECTOR IN ATTENDANCE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000259,PALACE RIVERSIDE,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,QUEENSMILL ROAD,21000665,SW6,NULL,NULL,523750,177150,NULL,NULL
NA,03/03/2017 21:52,2017,2016/17,Special Service,2,3,326,978,POSSIBLY INJURED DOG TRAPPED BEHIND FENCING - CALLER WILL ADVISE ON YOUR ARRIVAL,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,NULL,ROYDENE ROAD,20801301,SE18,NULL,NULL,545150,178150,NULL,NULL
NA,04/03/2017 15:29,2017,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000476,BOLEYN,E09000025,NEWHAM,Plaistow,10091776185,GREEN STREET,22208084,E13,541265,183769,541250,183750,51.53515839,0.035382841
NA,04/03/2017 19:00,2017,2016/17,Special Service,1,2,326,652,CAT TRAPPED IN ROOF VOID,Cat,Person (mobile),Towing caravan/Camper van on site,Other Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,207000348,MERIDIAN WAY,20703519,N9,536036,194437,536050,194450,51.63230515,-0.035831676
NA,05/03/2017 20:15,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED UP CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000374,HILLRISE,E09000019,ISLINGTON,Holloway,NULL,HORNSEY RISE GARDENS,21606454,N19,NULL,NULL,530050,187750,NULL,NULL
NA,06/03/2017 20:47,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND DOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000174,EALING COMMON,E09000009,EALING,Ealing,NULL,HAMILTON ROAD,20600810,W5,NULL,NULL,518450,180850,NULL,NULL
NA,07/03/2017 09:27,2017,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000214,ABBEY WOOD,E09000011,GREENWICH,Plumstead,NULL,MCLEOD ROAD,20801008,SE2,NULL,NULL,546750,178650,NULL,NULL
NA,07/03/2017 16:49,2017,2016/17,Special Service,1,1,326,326,DOG WITH HEAD STUCK IN RAILINGS  IN PARK  CALLER WILL MEET YOU AND DIRECT,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000038,RIVER,E09000002,BARKING AND DAGENHAM,Dagenham,100014398,HEATHWAY,19900172,RM9,549092,184354,549050,184350,51.53840368,0.148397035
NA,07/03/2017 17:09,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN ROOF EXTENSION   NEXT TO,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000437,BELLINGHAM,E09000023,LEWISHAM,Beckenham,NULL,BROOKEHOWSE ROAD,22001288,SE6,NULL,NULL,537750,171750,NULL,NULL
NA,07/03/2017 18:31,2017,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE OPPOSIT THE FUR WREN RSPCA IN ATTENDANCE,Bird,Person (land line),Department Store,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011219,Bexleyheath,E09000004,BEXLEY,Bexley,10011862061,MAYPLACE ROAD WEST,20100957,DA7,549313,175273,549350,175250,51.45675039,0.147740748
NA,07/03/2017 20:06,2017,2016/17,Special Service,1,1,326,326,PUPPY WITH HEAD TRAPPED IN RACK,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000450,PERRY VALE,E09000023,LEWISHAM,Forest Hill,NULL,COLFE ROAD,22001356,SE23,NULL,NULL,536050,173150,NULL,NULL
NA,08/03/2017 10:15,2017,2016/17,Special Service,2,7,326,2282,ASSIST RSPCA WITH CAT IN CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000374,HILLRISE,E09000019,ISLINGTON,Holloway,NULL,HORNSEY RISE GARDENS,21606454,N19,NULL,NULL,530050,187750,NULL,NULL
NA,08/03/2017 17:31,2017,2016/17,Special Service,1,1,326,326,SQUIRREL TRAPPED UNDER STAIRS,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000126,SHORTLANDS,E09000006,BROMLEY,Bromley,NULL,DURHAM ROAD,20302869,BR2,NULL,NULL,539650,168750,NULL,NULL
NA,10/03/2017 15:59,2017,2016/17,Special Service,1,1,326,326,SMALL ANIMAL TRAPPED IN FIRE PLACE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011241,Cranbrook,E09000026,REDBRIDGE,Ilford,NULL,WANSTEAD PARK ROAD,22303358,IG1,NULL,NULL,542350,187650,NULL,NULL
NA,10/03/2017 21:51,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH KITTEN TRAPPED INSIDE ABANDONED BUILDING,Cat,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000308,ELM PARK,E09000016,HAVERING,Dagenham,10091577428,RAINHAM ROAD,21320165,RM13,551703,184805,551750,184850,51.5417611,0.18621058
NA,10/03/2017 22:40,2017,2016/17,Special Service,1,1,326,326,KITTEN ON ROOF OF HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011472,Norbury & Pollards Hill,E09000008,CROYDON,Norbury,NULL,TYLECROFT ROAD,20501510,SW16,NULL,NULL,530250,169250,NULL,NULL
NA,11/03/2017 11:25,2017,2016/17,Special Service,1,1,326,326,DOG STUCK DOWN HOLE CALLER SAYS TO ENTER VIA THE CAMELOT,Dog,Person (mobile),Golf course (not building on course),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011245,Hainault,E09000026,REDBRIDGE,Hainault,10034920494,ROMFORD ROAD,22304722,IG7,547838,191860,547850,191850,51.60617657,0.133484252
NA,12/03/2017 13:07,2017,2016/17,Special Service,1,1,326,326,CAT STUCK ON WALL TIE  THROUGH THE CATS LEG,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011220,Blackfen & Lamorbey,E09000004,BEXLEY,Eltham,NULL,RAEBURN ROAD,20101182,DA15,NULL,NULL,545350,174450,NULL,NULL
NA,12/03/2017 16:11,2017,2016/17,Special Service,1,1,326,326,DOG STUCK ON MARSH LAND  NEAR TO THE YAUGHT CLUB    CALL FROM RSPCA,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011231,Slade Green & Northend,E09000004,BEXLEY,Erith,10011846295,MANOR ROAD,20100917,DA8,553011,177958,553050,177950,51.47988829,0.202087677
NA,13/03/2017 13:28,2017,2016/17,Special Service,1,2,326,652,DOG TRAPPED IN FOX HOLE - MEET THE OWNER AT ROBIN HOOD GATE CAR PARK ACCESS OFF OF ROEHAMPTON VALE O,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000406,COOMBE HILL,E09000021,KINGSTON UPON THAMES,New Malden,128015984,RICHMOND PARK,22407048,SW15,521200,172273,521250,172250,51.43649886,-0.257702753
NA,13/03/2017 20:26,2017,2016/17,Special Service,1,2,326,652,CAT TRAPPED IN ROOF AREA,Cat,Person (land line),Secondary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,10023215180,LIVERPOOL ROAD,21606676,N7,531167,184856,531150,184850,51.54736234,-0.109717065
NA,14/03/2017 15:47,2017,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN NETTING  TALLY HO PUB  -    RSPCA INSPECTOR IN ATTENDANCE  -    VAN PARKED AT SIDE O,Bird,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,200060798,HIGH ROAD,20022400,N12,526357,192154,526350,192150,51.61404506,-0.176424269
NA,15/03/2017 13:28,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000354,HANWORTH PARK,E09000018,HOUNSLOW,Feltham,1.00022E+11,THE HOLLANDS,21501408,TW13,511814,171830,511850,171850,51.43444642,-0.392818458
NA,15/03/2017 14:31,2017,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000450,PERRY VALE,E09000023,LEWISHAM,Forest Hill,NULL,STANSTEAD ROAD,22004078,SE23,NULL,NULL,535650,173250,NULL,NULL
NA,16/03/2017 15:13,2017,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,1.0002E+11,OAKLANDS ROAD,20302295,BR1,539433,170286,539450,170250,51.4144538,0.0036695
NA,16/03/2017 17:37,2017,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE   PARROTT ON ROOF BELIEVED INJURED,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000456,CANNON HILL,E09000024,MERTON,New Malden,NULL,CANNON HILL LANE,22101155,SW20,NULL,NULL,524150,168350,NULL,NULL
NA,17/03/2017 15:00,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH TRAPPED SEAGULL ON BUILDING SITE  NEAR PIRATE CASTLE,Bird,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Kentish Town,5011266,OVAL ROAD,20400628,NW1,528524,184019,528550,184050,51.54044793,-0.148118025
NA,19/03/2017 05:16,2017,2016/17,Special Service,1,1,326,326,THREE CATS TRAPPED UNDER FLOORBOARDS IN BATHROOM,cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000179,HANGER HILL,E09000009,EALING,Ealing,NULL,BRENTHAM WAY,20600240,W5,NULL,NULL,517750,182150,NULL,NULL
NA,19/03/2017 15:18,2017,2016/17,Special Service,2,5,326,1630,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,10091502050,TOOTING BEC ROAD,22905450,SW17,529057,172082,529050,172050,51.43304946,-0.144795907
NA,19/03/2017 16:47,2017,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000521,HAMPTON NORTH,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,BRIAR CLOSE,22401302,TW12,NULL,NULL,512550,171150,NULL,NULL
NA,19/03/2017 18:27,2017,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,128003782,CATHERINE ROAD,21800213,KT6,517728,167888,517750,167850,51.39781919,-0.30908587
NA,19/03/2017 23:02,2017,2016/17,Special Service,1,1,326,326,FOX TRAPPED UNDER BUILDING,Fox,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05011244,Goodmayes,E09000026,REDBRIDGE,Ilford,NULL,KINFAUNS ROAD,22302200,IG3,NULL,NULL,546550,187150,NULL,NULL
NA,20/03/2017 12:45,2017,2016/17,Special Service,1,1,326,326,JACK RUSSELL DOG  STUCK IN FOX HOLE        BONNER GATE     BY THE LAKE   OPPOSITE THE PAGODA,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009320,BOW WEST,E09000030,TOWER HAMLETS,Bethnal Green,10025165381,GORE ROAD,20900454,E9,535684,183528,535650,183550,51.53436057,-0.045124845
NA,22/03/2017 09:55,2017,2016/17,Special Service,1,1,326,326,BADGER TRAPPED IN STORM DRAIN - RSPCA IN CAR PARK TO REAR OF KEW GARDENS - ACCESS VIA FERRY LANE,Unknown - Wild Animal,Person (mobile),Scrub land,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000524,KEW,E09000027,RICHMOND UPON THAMES,Richmond,1.00023E+11,KEW GREEN,22405687,TW9,518255,176938,518250,176950,51.47904833,-0.298494228
NA,22/03/2017 13:39,2017,2016/17,Special Service,1,3,326,978,LARGE ANIMAL RESCUE     HORSE TRAPPED BETWEEN TREES       ADDITIONAL FRU WITH LARGE ANIMAL RESCUE CA,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011230,Sidcup,E09000004,BEXLEY,Sidcup,10023303271,SIDCUP BY PASS,20100002,DA14,546665,170545,546650,170550,51.41495826,0.107697977
NA,23/03/2017 10:18,2017,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000325,BOTWELL,E09000017,HILLINGDON,Hayes,10092980265,DAWLEY ROAD,21400564,UB3,508840,180054,508850,180050,51.50894436,-0.4330557
NA,23/03/2017 17:52,2017,2016/17,Special Service,1,1,326,326,"DOG STUCK UNDER FENCE NEAR RAILWAY LINE CALLER IN FIELD NEAR SCHOOL, HE SAYS ACCESS VIA PERRYMANS FA",Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011234,Aldborough,E09000026,REDBRIDGE,Ilford,10090503199,PERRYMANS FARM ROAD,22303145,IG2,544870,188734,544850,188750,51.5788581,0.089365834
NA,24/03/2017 17:19,2017,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000099,QUEENSBURY,E09000005,BRENT,Stanmore,NULL,BEVERLEY DRIVE,20202003,HA8,NULL,NULL,519450,189850,NULL,NULL
NA,25/03/2017 17:13,2017,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000520,HAMPTON,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,STATION ROAD,22401132,TW12,NULL,NULL,513750,169650,NULL,NULL
NA,25/03/2017 23:34,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH FOX TRAPPED IN WIRE,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05011223,Crook Log,E09000004,BEXLEY,Bexley,1.00023E+11,AVENUE ROAD,20100067,DA7,548256,175975,548250,175950,51.4633357,0.132832936
NA,26/03/2017 13:55,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000283,WHITE HART LANE,E09000014,HARINGEY,Tottenham,1.00021E+11,COURTMAN ROAD,21104409,N17,532427,191142,532450,191150,51.60355645,-0.089190115
NA,26/03/2017 17:50,2017,2016/17,Special Service,1,1,326,326,DISTRESSED DOG IN PRECARIOUS POSITION,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000100,STONEBRIDGE,E09000005,BRENT,Wembley,NULL,LILBURNE WALK,20200785,NW10,NULL,NULL,520350,184750,NULL,NULL
NA,27/03/2017 14:23,2017,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011103,Goose Green,E09000028,SOUTHWARK,Peckham,2.00003E+11,HINDMANS ROAD,22501253,SE22,534175,174697,534150,174650,51.45536139,-0.070223696
NA,27/03/2017 15:45,2017,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000590,CANN HALL,E09000031,WALTHAM FOREST,Leytonstone,1.00023E+11,HIGH ROAD LEYTONSTONE,22844950,E11,539333,186560,539350,186550,51.56071829,0.008650582
NA,29/03/2017 10:35,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED INSIDE CAR ENGINE,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000647,WARWICK,E09000033,WESTMINSTER,Lambeth,10033550691,HUGH STREET,8400826,SW1V,528896,178753,528850,178750,51.49303806,-0.144681921
NA,29/03/2017 11:51,2017,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED BETWEEN FLATS AND GARAGE,Cat,Person (land line),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000086,BARNHILL,E09000005,BRENT,Wembley,202123197,BARNHILL ROAD,20201054,HA9,520356,186298,520350,186250,51.56272894,-0.265059026
NA,31/03/2017 20:08,2017,2016/17,Special Service,2,3,326,978,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011240,Clementswood,E09000026,REDBRIDGE,Ilford,1.00023E+11,ILFORD LANE,22302963,IG1,543760,186039,543750,186050,51.55492624,0.072256712
NA,01/04/2017 16:23,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED BEHIND RADIATOR,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000337,PINKWELL,E09000017,HILLINGDON,Hayes,NULL,CLEAVE AVENUE,21400420,UB3,NULL,NULL,509350,178550,NULL,NULL
NA,01/04/2017 16:44,2017,2017/18,Special Service,1,1,328,328,Redacted,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,1.00021E+11,DICKENS AVENUE,21400596,UB8,507881,181307,507850,181350,51.52039026,-0.446485773
NA,02/04/2017 12:03,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED ON BALCONY    FALLEN TWO FLOORS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000212,UPPER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,FORE STREET,20704226,N18,NULL,NULL,534050,192050,NULL,NULL
NA,02/04/2017 23:22,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH FOX STUCK ON LEDGE    AT SIDE OF STREAM,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000615,FURZEDOWN,E09000032,WANDSWORTH,Mitcham,1.00023E+11,SOUTHCROFT ROAD,22904728,SW16,528744,170625,528750,170650,51.42002626,-0.149825045
NA,03/04/2017 12:37,2017,2017/18,Special Service,1,1,328,328,"ASSIST RSPCA WITH SMALL ANIMAL RESCUE      RSPCA INSPECTOR ON SCENE, ASSIST WITH A DUCK TANGLED IN A",Bird,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,10003627475,MANOR WAY,20303627,BR3,537844,168702,537850,168750,51.40060749,-0.019780463
NA,03/04/2017 19:48,2017,2017/18,Special Service,1,1,328,328,KITTEN STUCK IN CAGE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000486,GREEN STREET WEST,E09000025,NEWHAM,Stratford,NULL,GLENPARKE ROAD,22207729,E7,NULL,NULL,540750,184850,NULL,NULL
NA,04/04/2017 08:52,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA OFFICER IN EXTRACTING CAT FROM CAR ENGINE,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,KIRBY CLOSE,21301332,RM3,555241,191938,555250,191950,51.60488694,0.240334171
NA,04/04/2017 15:06,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL CAT UP TREE,Cat,Person (land line),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000602,LARKSWOOD,E09000031,WALTHAM FOREST,Chingford,10024419667,HICKMAN AVENUE,22844825,E4,538046,191900,538050,191950,51.60901865,-0.007806957
NA,04/04/2017 18:16,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000617,LATCHMERE,E09000032,WANDSWORTH,Battersea,NULL,MCDERMOTT CLOSE,22906545,SW11,NULL,NULL,527050,175950,NULL,NULL
NA,05/04/2017 11:16,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (land line),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,10023220724,UPPER STREET,21606199,N1,531713,184096,531750,184050,51.54040541,-0.102131634
NA,05/04/2017 13:54,2017,2017/18,Special Service,1,1,328,328,CAT STUCK UP A TREE     CALLED FROM RSPCA,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000267,BOUNDS GREEN,E09000014,HARINGEY,Hornsey,1.00021E+11,TRURO ROAD,21105175,N22,530724,191168,530750,191150,51.604188,-0.113756342
NA,05/04/2017 20:33,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL TO CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,1.00021E+11,PLUMSTEAD HIGH STREET,20801187,SE18,545416,178503,545450,178550,51.48678734,0.093019416
NA,06/04/2017 14:20,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011252,South Woodford,E09000026,REDBRIDGE,Woodford,1.00022E+11,COWSLIP ROAD,22302756,E18,540778,190318,540750,190350,51.59412752,0.030985975
NA,07/04/2017 11:51,2017,2017/18,Special Service,1,1,328,328,CAT STUCK IN TREE     RSPCA INSPECTOR ON SCENE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000174,EALING COMMON,E09000009,EALING,Acton,12090202,CREFFIELD ROAD,20602207,W5,518786,180605,518750,180650,51.51189514,-0.289617958
NA,07/04/2017 19:20,2017,2017/18,Special Service,1,1,328,328,DOG IN RIVER THAMES     JO  UPPER MALL,Dog,Person (mobile),Bridge,Outdoor Structure,Animal rescue from water,Animal rescue from water - Domestic pet,E05000516,BARNES,E09000027,RICHMOND UPON THAMES,Hammersmith,10070711720,CASTELNAU,22404217,W6,522916,178019,522950,178050,51.48777165,-0.231032556
NA,08/04/2017 07:21,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED UNDER SINK AND BEHIND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009398,NORLAND,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,NORLAND SQUARE,21700953,W11,NULL,NULL,524350,180150,NULL,NULL
NA,08/04/2017 22:43,2017,2017/18,Special Service,1,1,328,328,DOG WITH HEAD STUCK IN RAILINGS   BY ISLAND GARDENS STATION   OWNER IS ON SCENE,Dog,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist trapped domestic animal,E05009324,ISLAND GARDENS,E09000030,TOWER HAMLETS,Millwall,6651760,MANCHESTER ROAD,22700775,E14,538216,178509,538250,178550,51.4886454,-0.010609613
NA,09/04/2017 16:04,2017,2017/18,Special Service,1,1,328,328,PIGEON CAUGHT ON WIRE       FOUTH FLOOR LEVEL     CHILD CALLER WILL REMAIN ON SCENE TO DIERECT BRIGA,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,NULL,ILBERT STREET,8400459,W10,NULL,NULL,524050,182650,NULL,NULL
NA,09/04/2017 16:06,2017,2017/18,Special Service,1,1,328,328,Redacted,Squirrel,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000296,MARLBOROUGH,E09000015,HARROW,Stanmore,NULL,HERGA ROAD,21201673,HA3,NULL,NULL,515850,189250,NULL,NULL
NA,10/04/2017 09:45,2017,2017/18,Special Service,1,1,328,328,DOG WITH HEAD STUCK IN GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011474,Old Coulsdon,E09000008,CROYDON,Purley,NULL,TOLLERS LANE,20500354,CR5,NULL,NULL,531050,157450,NULL,NULL
NA,10/04/2017 12:55,2017,2017/18,Special Service,1,1,328,328,RCA TO PARROT IN PRECARIOUS POSITION  BELIEVED TO BE INJURED    NO FURTHER BRIGADE ASSISTANCE REQUIR,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000411,ST. JAMES,E09000021,KINGSTON UPON THAMES,New Malden,128016898,SAVILE CLOSE,21800840,KT3,521309,167615,521350,167650,51.39461181,-0.257728052
NA,10/04/2017 17:47,2017,2017/18,Special Service,1,1,328,328,Redacted,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000132,FORTUNE GREEN,E09000007,CAMDEN,West Hampstead,NULL,FORDWYCH ROAD,20400109,NW2,NULL,NULL,524750,184750,NULL,NULL
NA,12/04/2017 13:36,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000369,CANONBURY,E09000019,ISLINGTON,Islington,NULL,ESSEX ROAD,21604817,N1,NULL,NULL,532550,184450,NULL,NULL
NA,12/04/2017 13:58,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT STUCK ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000142,REGENT'S PARK,E09000007,CAMDEN,Euston,NULL,COLOSSEUM TERRACE,20499026,NW1,NULL,NULL,528850,182450,NULL,NULL
NA,13/04/2017 00:06,2017,2017/18,Special Service,1,4,328,1312,Redacted,Horse,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000475,BECKTON,E09000025,NEWHAM,Barking,10025384850,JENKINS LANE,22201743,IG11,544141,182760,544150,182750,51.5253655,0.076407294
NA,13/04/2017 07:58,2017,2017/18,Special Service,1,1,328,328,HORSE TRAPPED ON STABLE DOOR PASSED CALL X KENT FRS,Horse,Person (land line),Other agricultural building,Non Residential,Animal rescue from height,Animal rescue from height - Farm animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,1.0002E+11,JAIL LANE,20301511,TN16,542742,159334,542750,159350,51.31521776,0.046829442
NA,14/04/2017 09:35,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN GUTTERING ON WALL AND ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,LONG LANE,20027220,N3,NULL,NULL,525950,190650,NULL,NULL
NA,14/04/2017 13:23,2017,2017/18,Special Service,2,5,328,1640,CAT CAUGHT IN AXLE OF CAR FRU REQUESTED FROM SCENE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000621,ROEHAMPTON AND PUTNEY HEATH,E09000032,WANDSWORTH,Wandsworth,10033241620,RODWAY ROAD,22904361,SW15,522289,173878,522250,173850,51.45069015,-0.241489599
NA,15/04/2017 09:34,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000477,CANNING TOWN NORTH,E09000025,NEWHAM,Plaistow,NULL,LADYSMITH ROAD,22201474,E16,NULL,NULL,539550,182550,NULL,NULL
NA,15/04/2017 10:30,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,NULL,QUEENS ROAD,22868450,E17,NULL,NULL,536750,188250,NULL,NULL
NA,15/04/2017 22:23,2017,2017/18,Special Service,1,2,328,656,RUNNING CALL TO STRAY MALNOURISHED DOG,Dog,Person (land line),Fire station,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000286,CANONS,E09000015,HARROW,Stanmore,10002294818,HONEYPOT LANE,21200986,HA7,517991,190798,517950,190750,51.60367148,-0.297650869
NA,18/04/2017 14:33,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000253,COLLEGE PARK AND OLD OAK,E09000013,HAMMERSMITH AND FULHAM,Acton,NULL,HEATHSTAN ROAD,21000429,W12,NULL,NULL,522250,181050,NULL,NULL
NA,18/04/2017 18:07,2017,2017/18,Special Service,1,1,328,328,HORSE TRAPPED  INSIDE HORSE BOX TRPPED UNDER PARTITION UNDER ANOTHER HORSE,Horse,Person (mobile),Trailer (not attached to tractor unit),Road Vehicle,Animal rescue from water,Animal rescue from water - Farm animal,E05000286,CANONS,E09000015,HARROW,Stanmore,1.00023E+11,WARREN LANE,21200934,HA7,516548,193853,516550,193850,51.63142938,-0.317461895
NA,20/04/2017 21:22,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011224,East Wickham,E09000004,BEXLEY,Bexley,NULL,LYME ROAD,20100893,DA16,NULL,NULL,546950,176750,NULL,NULL
NA,21/04/2017 11:58,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE      FOX TRAPPED IN BASEMENT   REQUESTED BY RSPCA,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000260,PARSONS GREEN AND WALHAM,E09000013,HAMMERSMITH AND FULHAM,Fulham,34067459,PERRYMEAD STREET,21000639,SW6,525476,176569,525450,176550,51.47417866,-0.194694631
NA,21/04/2017 14:58,2017,2017/18,Special Service,1,1,328,328,DISTRESSED CAT STUCK ON ROOF TWO STOREY BUILDING,Cat,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000258,NORTH END,E09000013,HAMMERSMITH AND FULHAM,Fulham,34050649,BRAMBER ROAD,21000144,W14,524845,177847,524850,177850,51.48580375,-0.203324401
NA,21/04/2017 18:05,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE   BIRD TRAPPED INSIDE BEDROOM OF DISABLED OCCUPIER,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000044,BURNT OAK,E09000003,BARNET,Stanmore,NULL,BURNT OAK FIELDS,20006240,HA8,NULL,NULL,520150,190850,NULL,NULL
NA,21/04/2017 18:10,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON WALL     CALLED BY RSPCA INSPECTOR DEW   WHO IS ON SCENE,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009320,BOW WEST,E09000030,TOWER HAMLETS,Bethnal Green,6211323,COBORN ROAD,22700334,E3,536616,182982,536650,182950,51.52922978,-0.031907613
NA,22/04/2017 19:49,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011476,Purley & Woodcote,E09000008,CROYDON,Purley,1.00021E+11,STOATS NEST ROAD,20502353,CR5,530474,160175,530450,160150,51.32571736,-0.128792167
NA,23/04/2017 09:55,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000087,BRONDESBURY PARK,E09000005,BRENT,Willesden,202118217,CHAMBERLAYNE ROAD,20202065,NW10,523300,183731,523350,183750,51.53902374,-0.223507538
NA,23/04/2017 15:16,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BEHIND WALL,Cat,Person (land line),Veterinary surgery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05009370,DALSTON,E09000012,HACKNEY,Homerton,1.00024E+11,DALSTON LANE,20900310,E8,533918,184768,533950,184750,51.5459251,-0.070099935
NA,24/04/2017 12:45,2017,2017/18,Special Service,1,2,328,656,PUPPY TRAPPED IN DRAIN,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Wild animal rescue from water or mud,E05000445,GROVE PARK,E09000023,LEWISHAM,Lee Green,NULL,HORNCASTLE ROAD,22005217,SE12,NULL,NULL,540550,173950,NULL,NULL
NA,24/04/2017 20:09,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED IN ROOM,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,NULL,BRODLOVE LANE,22700203,E1W,NULL,NULL,535550,180950,NULL,NULL
NA,25/04/2017 11:07,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000170,ACTON CENTRAL,E09000009,EALING,Acton,12116741,BURLINGTON GARDENS,20600290,W3,520523,180268,520550,180250,51.508499,-0.264714143
NA,25/04/2017 13:57,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA - FOX TRYING TO SWIM IN BRENT STREAM AND STRANDED ON BRICKS IN WATER     MEET RSPCA OFF,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000085,ALPERTON,E09000005,BRENT,Wembley,202135837,ALPERTON LANE,20200264,HA0,518103,183030,518150,183050,51.53383306,-0.298643128
NA,26/04/2017 09:01,2017,2017/18,Special Service,1,1,328,328,PUPPY WITH HEAD STUCK IN CHAIR,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000429,STOCKWELL,E09000022,LAMBETH,Clapham,NULL,BINFIELD ROAD,21900185,SW4,NULL,NULL,530350,176550,NULL,NULL
NA,26/04/2017 15:35,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA OFFICER TO RECOVER DUCKLINGS FROM A DRAIN,Bird,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000063,WOODHOUSE,E09000003,BARNET,Finchley,200096048,QUEENS ROAD,20035600,N3,526363,190617,526350,190650,51.60023107,-0.17689101
NA,27/04/2017 14:48,2017,2017/18,Special Service,1,1,328,328,CAT STUCK IN TREE - POSSIBLY INJURED,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000052,GARDEN SUBURB,E09000003,BARNET,West Hampstead,200105307,SPANIARDS CLOSE,20040220,NW11,526531,187424,526550,187450,51.57149826,-0.175616641
NA,27/04/2017 17:08,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH TRAPPED PIDGEON,Pigeon,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011471,New Addington South,E09000008,CROYDON,Addington,NULL,KENNELWOOD CRESCENT,20501755,CR0,NULL,NULL,538650,161650,NULL,NULL
NA,28/04/2017 19:30,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA INSPECTOR WITH A PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000270,FORTIS GREEN,E09000014,HARINGEY,Hornsey,1.00024E+11,DUKES AVENUE,21101214,N10,528823,189740,528850,189750,51.59179292,-0.141713995
NA,29/04/2017 11:16,2017,2017/18,Special Service,1,1,328,328,CAT POSSIBLY TRAPPED BEHIND SHED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000460,FIGGE'S MARSH,E09000024,MERTON,Mitcham,NULL,FRAMFIELD ROAD,22102703,CR4,NULL,NULL,528150,170050,NULL,NULL
NA,29/04/2017 13:21,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED DOWN STORM DRAIN,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,10003641401,KELSEY PARK AVENUE,20301420,BR3,537667,168930,537650,168950,51.4026993,-0.022234892
NA,29/04/2017 16:56,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN AN AREA WITHIN THE SCHOOL CALLER WILL MEET BRIGADE,Cat,Person (land line),Secondary school,Non Residential,Other animal assistance,Assist trapped domestic animal,E05009383,SPRINGFIELD,E09000012,HACKNEY,Stoke Newington,10008229436,AMHURST PARK,20900061,N16,533417,187913,533450,187950,51.57430612,-0.076129355
NA,30/04/2017 14:15,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON ROOF COULD POSSIBLY FALL ONTO RAILINGS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,WESTCROFT SQUARE,21000874,W6,NULL,NULL,521950,178750,NULL,NULL
NA,30/04/2017 14:15,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Heathland,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,Kentish Town,10025356265,SOUTH HILL PARK,20400192,NW3,527213,185991,527250,185950,51.55846683,-0.166299173
NA,01/05/2017 17:05,2017,2017/18,Special Service,1,2,328,656,PIGEON TRAPPED ON BALCONY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000621,ROEHAMPTON AND PUTNEY HEATH,E09000032,WANDSWORTH,Wandsworth,NULL,DANEBURY AVENUE,22901127,SW15,NULL,NULL,522250,173850,NULL,NULL
NA,03/05/2017 18:49,2017,2017/18,Special Service,1,1,328,328,DISTRESSED CAT TRAPPED BEHIND LOCKED DOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist  trapped livestock animal,E05000091,HARLESDEN,E09000005,BRENT,Willesden,NULL,MARIAN WAY,20200900,NW10,NULL,NULL,521650,184150,NULL,NULL
NA,03/05/2017 20:06,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED BEHIND FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000038,RIVER,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,ROWDOWNS ROAD,19900593,RM9,NULL,NULL,548850,184150,NULL,NULL
NA,04/05/2017 03:12,2017,2017/18,Special Service,1,1,328,328,DEER STUCK IN RAILINGS THIS IS AT THE JUNCTION OF ELMFIELD WAY,Deer,Person (mobile),Heathland,Outdoor,Other animal assistance,Assist trapped wild animal,E05011484,South Croydon,E09000008,CROYDON,Croydon,1.00021E+11,WEST HILL,20502415,CR2,533508,163149,533550,163150,51.35174108,-0.084159241
NA,04/05/2017 12:11,2017,2017/18,Special Service,1,1,328,328,RING NECKED PARAKEET TRAPPED IN TREE,Bird,Person (mobile),Community centre/Hall,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000174,EALING COMMON,E09000009,EALING,Ealing,12083992,ST MARYS ROAD,20601608,W5,517794,180051,517750,180050,51.50712334,-0.304091462
NA,04/05/2017 14:59,2017,2017/18,Special Service,1,1,328,328,DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped wild animal,E05000200,GRANGE,E09000010,ENFIELD,Edmonton,207104081,BERKELEY GARDENS,20703920,N21,532606,194966,532650,194950,51.63787801,-0.085159881
NA,05/05/2017 16:36,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN NETTING  ASSISTING RSPCA,Bird,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000438,BLACKHEATH,E09000023,LEWISHAM,Lee Green,1.00022E+11,LEE HIGH ROAD,22004135,SE12,539825,175022,539850,175050,51.45691585,0.01117073
NA,05/05/2017 17:12,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011229,St. Mary's & St. James,E09000004,BEXLEY,Sidcup,NULL,CALVERT CLOSE,20100248,DA14,NULL,NULL,548150,170950,NULL,NULL
NA,07/05/2017 17:25,2017,2017/18,Special Service,1,1,328,328,CAT WITH HEAD  TRAPPED IN FIRST FLOOR WINDOW    REAR OF PREMISES          OCCUPIERS NOT IN   SEEN BY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000356,HESTON EAST,E09000018,HOUNSLOW,Heston,NULL,WHEATLANDS,21501195,TW5,NULL,NULL,513350,177650,NULL,NULL
NA,08/05/2017 10:24,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL TO  CAT STUCK UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000365,TURNHAM GREEN,E09000018,HOUNSLOW,Chiswick,NULL,ARLINGTON GARDENS,21500042,W4,NULL,NULL,520350,178450,NULL,NULL
NA,08/05/2017 10:43,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT STUCK UP TREE - INSPECTOR ON SCENE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000286,CANONS,E09000015,HARROW,Stanmore,2E+11,MORECAMBE GARDENS,21200911,HA7,517838,192605,517850,192650,51.61994419,-0.299252004
NA,09/05/2017 10:57,2017,2017/18,Special Service,1,2,328,656,CAT IN TREE RSPCA ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000286,CANONS,E09000015,HARROW,Stanmore,NULL,MORECAMBE GARDENS,21200911,HA7,NULL,NULL,517850,192550,NULL,NULL
NA,09/05/2017 21:32,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011253,Valentines,E09000026,REDBRIDGE,Ilford,NULL,COURTLAND AVENUE,22302752,IG1,NULL,NULL,542850,186850,NULL,NULL
NA,09/05/2017 22:03,2017,2017/18,Special Service,1,1,328,328,ASSISITNG RSPCA  CAT IN LIGHT WELL,Cat,Person (land line),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,5300082004,SEVEN SISTERS ROAD,21603790,N7,531072,186462,531050,186450,51.56181687,-0.110488432
NA,10/05/2017 12:48,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN GUTTERING THRID FLOOR LEVEL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000029,CHADWELL HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,PEMBERTON GARDENS,19900278,RM6,NULL,NULL,548450,188750,NULL,NULL
NA,10/05/2017 13:18,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000424,KNIGHT'S HILL,E09000022,LAMBETH,West Norwood,NULL,WOODVALE WALK,21902038,SE27,NULL,NULL,532250,171350,NULL,NULL
NA,11/05/2017 11:08,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000145,WEST HAMPSTEAD,E09000007,CAMDEN,West Hampstead,5097057,KYLEMORE ROAD,20400405,NW6,525222,184481,525250,184450,51.54534141,-0.195541793
NA,12/05/2017 07:23,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL TRAPPED IN AIR VENT,Unknown - Wild Animal,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011097,Champion Hill,E09000028,SOUTHWARK,Peckham,NULL,DOMETT CLOSE,22500787,SE5,NULL,NULL,532850,175450,NULL,NULL
NA,13/05/2017 10:22,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN NETTING ON ROOF,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,REDBRIDGE,Woodford,10034920942,THE BROADWAY,22306023,IG8,540937,191836,540950,191850,51.6077281,0.033889274
NA,13/05/2017 11:16,2017,2017/18,Special Service,1,1,328,328,REAR GARDEN CAT STUCK BETWEEN WALLS,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000477,CANNING TOWN NORTH,E09000025,NEWHAM,Plaistow,10008987716,HILDA ROAD,22207548,E16,539376,182273,539350,182250,51.52218494,0.007574459
NA,14/05/2017 06:42,2017,2017/18,Special Service,1,1,328,328,FOX CUB WITH LEG TRAPPED IN WIRE FENCE RSPCA HAVE NO OFFICERS TO HELP WITH THIS INCIDENT UNTIL AFTER,Fox,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000475,BECKTON,E09000025,NEWHAM,East Ham,46056665,PARTRIDGE SQUARE,22201946,E6,542494,181882,542450,181850,51.51789351,0.052327736
NA,16/05/2017 08:24,2017,2017/18,Special Service,1,1,328,328,INJURED CAT IN DISTRESS STUCK IN TREE  LINE RESCUE REQUESTED FROM SCENE,Cat,Person (mobile),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,West Hampstead,5023119,BACK LANE,20400155,NW3,526401,185818,526450,185850,51.55709445,-0.178068364
NA,16/05/2017 08:36,2017,2017/18,Special Service,1,1,328,328,INJURED CAT FALLEN ONTO FLAT ROOF - CALLER WILL MEET CREWS ON WINDMILL COURT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000095,MAPESBURY,E09000005,BRENT,West Hampstead,NULL,MAPESBURY ROAD,20200318,NW2,NULL,NULL,524350,184950,NULL,NULL
NA,16/05/2017 11:29,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000401,BERRYLANDS,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,BERRYLANDS ROAD,21800135,KT5,NULL,NULL,518750,167350,NULL,NULL
NA,17/05/2017 11:19,2017,2017/18,Special Service,2,3,328,984,Redacted,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207169546,LEA VALLEY ROAD,20703459,EN3,536508,195555,536550,195550,51.64223712,-0.02858016
NA,18/05/2017 16:36,2017,2017/18,Special Service,1,1,328,328,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000562,ST. HELIER,E09000029,SUTTON,Sutton,NULL,WELBECK ROAD,22605656,SM1,NULL,NULL,526750,165750,NULL,NULL
NA,18/05/2017 17:39,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000309,EMERSON PARK,E09000016,HAVERING,Harold Hill,NULL,REDDEN COURT ROAD,21300143,RM3,NULL,NULL,554550,189750,NULL,NULL
NA,19/05/2017 00:35,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Railway building - other,Non Residential,Other animal assistance,Assist trapped wild animal,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,10091861579,STRAND,8401576,WC2N,530232,180492,530250,180450,51.50836032,-0.124806493
NA,19/05/2017 17:28,2017,2017/18,Special Service,1,1,328,328,BIRD  TRAPPED IN AIRCONDITIONING UNIT R.O. LIBRARY    CALLER WILL MEET BRIGADE IN CAR PARK AREA,Bird,Person (land line),Fence,Outdoor Structure,Other animal assistance,Animal harm involving livestock,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,1.00024E+11,FALLING LANE,21400712,UB7,505978,180700,505950,180750,51.51529446,-0.474085124
NA,21/05/2017 02:05,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,6652452,COPENHAGEN PLACE,22700355,E14,536819,181328,536850,181350,51.51431755,-0.029625011
NA,21/05/2017 12:41,2017,2017/18,Special Service,1,1,328,328,CAT STUCK BETWEEN  FENCE AND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,NULL,COPENHAGEN PLACE,22700355,E14,NULL,NULL,536850,181350,NULL,NULL
NA,21/05/2017 17:07,2017,2017/18,Special Service,1,1,328,328,Redacted,Fox,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000478,CANNING TOWN SOUTH,E09000025,NEWHAM,Plaistow,46087203,CHANDLER AVENUE,22200454,E16,540041,181828,540050,181850,51.51802163,0.016976485
NA,21/05/2017 20:41,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE - PIGEON CAUGHT IN NET UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000301,ROXBOURNE,E09000015,HARROW,Northolt,10091091519,URBAN FOOTPATH 121A FROM OPPOSITE 73 SHERWOOD ROAD TO 100A ROXETH GREEN AVENUE CHURCHILL COURT,21202771,HA2,514103,186572,514150,186550,51.56648929,-0.355140023
NA,22/05/2017 17:51,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN NETTING  IN UNDERPASS NEAR STATION    CALLER WAITING FOR YOU,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000297,PINNER,E09000015,HARROW,Harrow,10015397137,STATION APPROACH,21201813,HA5,512170,189518,512150,189550,51.59335578,-0.38207642
NA,23/05/2017 00:59,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA INSP TO FREE A DEER STUCK IN WROUGHT IRON GATE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000107,BIGGIN HILL,E09000006,BROMLEY,Biggin Hill,1.0002E+11,APERFIELD ROAD,20301439,TN16,542472,158921,542450,158950,51.31157396,0.042792865
NA,23/05/2017 06:02,2017,2017/18,Special Service,1,1,328,328,FOX CUB  TRAPPED IN FOOTBALL NETTING     RSPCA UNABLE TO ATTEND WITHIN THE HOUR   ORIGIN CALLER TEL,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011217,Barnehurst,E09000004,BEXLEY,Bexley,NULL,EASTLEIGH ROAD,20100481,DA7,NULL,NULL,550450,175950,NULL,NULL
NA,23/05/2017 17:37,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED UNDER DECKING,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000432,STREATHAM WELLS,E09000022,LAMBETH,West Norwood,NULL,LEIGHAM COURT ROAD,21900860,SW16,NULL,NULL,530550,172350,NULL,NULL
NA,24/05/2017 09:52,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA OFFICER WITH CAT TRAPPED BETWEEN GARAGE & WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000097,PRESTON,E09000005,BRENT,Wembley,NULL,CONISTON GARDENS,20200871,HA9,NULL,NULL,517450,187550,NULL,NULL
NA,24/05/2017 11:03,2017,2017/18,Special Service,1,1,328,328,ASSIST POLICE WITH KITTENS STUCK BEHIND WALL,Cat,Person (land line),Police station,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,1.00023E+11,HIGH ROAD,21106680,N17,533808,189769,533850,189750,51.59089195,-0.069784421
NA,24/05/2017 16:46,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED BETWEEN HOUSE AND RETAINING WALL,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05011229,St. Mary's & St. James,E09000004,BEXLEY,Bexley,1.0002E+11,TILE KILN LANE,20101472,DA5,550357,172514,550350,172550,51.43168423,0.161584156
NA,25/05/2017 12:28,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE  - CAT TRAPPED UNDER BATH,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000116,CRYSTAL PALACE,E09000006,BROMLEY,Beckenham,NULL,RIDSDALE ROAD,20303573,SE20,NULL,NULL,534550,170050,NULL,NULL
NA,26/05/2017 15:54,2017,2017/18,Special Service,1,1,328,328,TO ASSIST RSPCA WITH SQUIRREL TRAPPED IN BOTTOM OF DRAIN PIPE,Squirrel,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000494,WEST HAM,E09000025,NEWHAM,Stratford,NULL,OLD BARROWFIELD,22202003,E15,NULL,NULL,539450,183550,NULL,NULL
NA,26/05/2017 18:40,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000192,WALPOLE,E09000009,EALING,Ealing,12073661,BROADWAY,20600261,W13,516382,180311,516350,180350,51.50975227,-0.324341776
NA,27/05/2017 09:28,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN FENCE GATE HINGES - RSCPA ON ROUTE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000184,NORTHOLT MANDEVILLE,E09000009,EALING,Northolt,12054553,LANCASTER ROAD,20601018,UB5,514101,184743,514150,184750,51.55005095,-0.355761562
NA,27/05/2017 10:24,2017,2017/18,Special Service,1,1,328,328,PIGEON STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000182,NORTHFIELD,E09000009,EALING,Ealing,NULL,WOODSTOCK AVENUE,20601959,W13,NULL,NULL,516350,179350,NULL,NULL
NA,27/05/2017 19:58,2017,2017/18,Special Service,1,1,328,328,DOG WITH CHAIN ROUND NECK - STRANGLING THE DOG,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05011221,Blendon & Penhill,E09000004,BEXLEY,Sidcup,1.00023E+11,THE OVAL,20101453,DA15,546192,173835,546150,173850,51.44464309,0.102258368
NA,28/05/2017 09:49,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON BALCONY ON FIRST FLOOR POSSIBLY FALLEN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,CHALTON STREET,20400794,NW1,NULL,NULL,529650,183150,NULL,NULL
NA,28/05/2017 10:45,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON ROOF - RSPCA IN ATTENDANCE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011097,Champion Hill,E09000028,SOUTHWARK,Peckham,NULL,GROVE LANE,22501157,SE5,NULL,NULL,533050,176050,NULL,NULL
NA,28/05/2017 21:40,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN DRAINPIPE ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000230,WOOLWICH RIVERSIDE,E09000011,GREENWICH,East Greenwich,NULL,SAMUEL STREET,20801316,SE18,NULL,NULL,542550,178750,NULL,NULL
NA,29/05/2017 13:54,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN FIRE PLACE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000280,TOTTENHAM GREEN,E09000014,HARINGEY,Tottenham,NULL,STOWE PLACE,21103948,N15,NULL,NULL,533250,189550,NULL,NULL
NA,29/05/2017 22:20,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011221,Blendon & Penhill,E09000004,BEXLEY,Bexley,1.0002E+11,BLENDON DRIVE,20100161,DA5,547840,174088,547850,174050,51.44648898,0.126060231
NA,30/05/2017 02:11,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED UNDER PILE OF RUBBISH,Cat,Person (mobile),Loose refuse,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,46060665,REGINALD ROAD,22207855,E7,540059,184494,540050,184450,51.54197376,0.018294858
NA,31/05/2017 00:07,2017,2017/18,Special Service,1,1,328,328,CAT STUCK IN MUD ON RIVERBANK - TIDE COMING IN AND CALLER DISTRESSED,Cat,Person (mobile),Wasteland,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,1.00023E+11,POINT PLEASANT,22903900,SW18,525164,175282,525150,175250,51.46268107,-0.199639873
NA,01/06/2017 08:55,2017,2017/18,Special Service,1,1,328,328,Redacted,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,10010194134,LAKEDALE ROAD,20800879,SE18,545289,178561,545250,178550,51.48734112,0.091215407
NA,01/06/2017 09:50,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL TO FOX STUCK BEHIND BATH,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,BARTH ROAD,20800121,SE18,NULL,NULL,545450,178650,NULL,NULL
NA,01/06/2017 11:55,2017,2017/18,Special Service,1,1,328,328,FOX STUCK IN CONCRETE     RSPCA ON SCENE,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving wild animal,E05009320,BOW WEST,E09000030,TOWER HAMLETS,Bethnal Green,6144248,KENILWORTH ROAD,22700694,E3,536070,183318,536050,183350,51.53238074,-0.039644154
NA,01/06/2017 13:41,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA INSPECTOR WITH CAT STUCK ON CHIMNEY POT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000423,HERNE HILL,E09000022,LAMBETH,Brixton,NULL,LOWDEN ROAD,21900899,SE24,NULL,NULL,532050,175150,NULL,NULL
NA,01/06/2017 14:51,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN GARDEN,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000632,BRYANSTON AND DORSET SQUARE,E09000033,WESTMINSTER,Paddington,NULL,HAREWOOD AVENUE,8400434,NW1,NULL,NULL,527350,182150,NULL,NULL
NA,02/06/2017 06:58,2017,2017/18,Special Service,1,1,328,328,KITTEN WITH HEAD TRAPPED IN CHAIR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000194,BUSH HILL PARK,E09000010,ENFIELD,Edmonton,NULL,TRINITY AVENUE,20703015,EN1,NULL,NULL,534050,195250,NULL,NULL
NA,02/06/2017 07:17,2017,2017/18,Special Service,1,1,328,328,DOG STUCK BEHIND SCHOOL FENCE,Dog,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000337,PINKWELL,E09000017,HILLINGDON,Hayes,1.00023E+11,PINKWELL LANE,21401541,UB3,508629,178733,508650,178750,51.49711151,-0.436501416
NA,05/06/2017 15:11,2017,2017/18,Special Service,1,1,328,328,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000055,HENDON,E09000003,BARNET,Hendon,200014104,WOODBURN CLOSE,20047040,NW4,523991,188568,523950,188550,51.5823437,-0.211844057
NA,06/06/2017 21:15,2017,2017/18,Special Service,1,1,328,328,FOX STUCK ON SHED ROOF -  REQUESTED BY RSPCA,Fox,Person (land line),Private Garden Shed,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000339,TOWNFIELD,E09000017,HILLINGDON,Hayes,1.00021E+11,COLDHARBOUR LANE,21400437,UB3,510418,180793,510450,180750,51.51528054,-0.410095827
NA,07/06/2017 06:24,2017,2017/18,Special Service,1,1,328,328,DOG STUCK IN POND,Dog,Person (land line),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05009319,BOW EAST,E09000030,TOWER HAMLETS,Homerton,6057757,OLD FORD ROAD,22700893,E9,536487,184109,536450,184150,51.53938833,-0.033329536
NA,07/06/2017 09:53,2017,2017/18,Special Service,1,1,328,328,FOX WEDGE BETWEEN FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,NULL,ALLENBY ROAD,20802006,SE28,NULL,NULL,544450,179350,NULL,NULL
NA,08/06/2017 08:50,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN HOLE IN GROUND,Cat,Person (mobile),Railway trackside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011217,Barnehurst,E09000004,BEXLEY,Bexley,1.0002E+11,CHEVIOT CLOSE,20100303,DA7,551153,176410,551150,176450,51.46647862,0.174689786
NA,09/06/2017 08:27,2017,2017/18,Special Service,1,1,328,328,PIDGEON STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000048,EAST BARNET,E09000003,BARNET,Barnet,NULL,VICTORIA ROAD,20044140,EN4,NULL,NULL,526850,196250,NULL,NULL
NA,09/06/2017 12:11,2017,2017/18,Special Service,1,1,328,328,RESCUE BIRD TRAPPED IN NETTING  LONDON WILD LIFE PROTECTION  REPRESENTATIVE IN ATTENDANCE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,NULL,ALBANY ROAD,22800700,E10,NULL,NULL,537350,187850,NULL,NULL
NA,09/06/2017 22:15,2017,2017/18,Special Service,1,1,328,328,PIDGEON TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,NULL,WISE LANE,20046840,NW7,NULL,NULL,522550,191650,NULL,NULL
NA,10/06/2017 15:34,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN CUPBOARD/BEHIND KITCHEN UNIT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000256,HAMMERSMITH BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,QUEEN CAROLINE STREET,21000660,W6,NULL,NULL,523150,178250,NULL,NULL
NA,11/06/2017 09:59,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN DEEP PIT IN NEIGHBOURS GARDEN,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000250,ADDISON,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34036714,SHEPHERD'S BUSH ROAD,21000727,W6,523499,179095,523450,179050,51.49731516,-0.222263629
NA,11/06/2017 13:32,2017,2017/18,Special Service,1,1,328,328,DUCKLINGS TRAPPED ON BALCONY ABOVE  RSPCA ON SCENE,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000220,ELTHAM WEST,E09000011,GREENWICH,Lee Green,NULL,HANDLEY DRIVE,20802124,SE3,NULL,NULL,540750,175350,NULL,NULL
NA,11/06/2017 18:27,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED BEHIND FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011237,Chadwell,E09000026,REDBRIDGE,Dagenham,NULL,BEDE ROAD,22301844,RM6,NULL,NULL,547350,188250,NULL,NULL
NA,11/06/2017 21:51,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000416,BISHOP'S,E09000022,LAMBETH,Lambeth,1.00022E+11,FRAZIER STREET,21900578,SE1,531358,179557,531350,179550,51.49969734,-0.108938835
NA,12/06/2017 13:34,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED UNDER PATIO,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000200,GRANGE,E09000010,ENFIELD,Edmonton,NULL,RIDGE AVENUE,20704667,N21,NULL,NULL,532750,194750,NULL,NULL
NA,13/06/2017 12:30,2017,2017/18,Special Service,1,1,328,328,CAT WITH HEAD STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000091,HARLESDEN,E09000005,BRENT,Willesden,NULL,CHARLTON ROAD,20201730,NW10,NULL,NULL,521550,183750,NULL,NULL
NA,13/06/2017 14:49,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH PIGEON TRAPPED NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000638,LANCASTER GATE,E09000033,WESTMINSTER,Paddington,NULL,INVERNESS MEWS,8400467,W2,NULL,NULL,525850,180850,NULL,NULL
NA,14/06/2017 20:18,2017,2017/18,Special Service,1,1,328,328,CAT STUCK UP TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000056,HIGH BARNET,E09000003,BARNET,Barnet,200104705,SOUTH CLOSE,20039920,EN5,524896,196673,524850,196650,51.65498231,-0.195903255
NA,15/06/2017 11:14,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000091,HARLESDEN,E09000005,BRENT,Willesden,NULL,CHARLTON ROAD,20201730,NW10,NULL,NULL,521550,183750,NULL,NULL
NA,16/06/2017 21:19,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011116,South Bermondsey,E09000028,SOUTHWARK,Old Kent Road,NULL,OLD KENT ROAD,22501843,SE1,NULL,NULL,533150,178750,NULL,NULL
NA,17/06/2017 17:23,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT AND KITTEN STUCK ABOVE SHOP SIGN,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,46076070,UPTON LANE,22201168,E7,540576,184717,540550,184750,51.54384905,0.025833894
NA,17/06/2017 22:03,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011225,Erith,E09000004,BEXLEY,Erith,10023302034,FOX HOUSE ROAD,20100567,DA17,549956,178655,549950,178650,51.48696852,0.158423631
NA,17/06/2017 22:39,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED IN FENCE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009398,NORLAND,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,CLARENDON ROAD,21700837,W11,NULL,NULL,524350,180450,NULL,NULL
NA,18/06/2017 02:34,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BEHIND RADIATOR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000592,CHAPEL END,E09000031,WALTHAM FOREST,Walthamstow,NULL,BILLET ROAD,22816150,E17,NULL,NULL,537050,190850,NULL,NULL
NA,18/06/2017 18:51,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,DENBIGH ROAD,21700860,W11,NULL,NULL,524950,180950,NULL,NULL
NA,18/06/2017 21:20,2017,2017/18,Special Service,1,1,328,328,FOX CUB TRAPPED BETWEEN WALL AND SHED,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000451,RUSHEY GREEN,E09000023,LEWISHAM,Lewisham,1.00022E+11,BLAGDON ROAD,22001236,SE13,537791,174332,537750,174350,51.45121332,-0.018354783
NA,19/06/2017 01:08,2017,2017/18,Special Service,1,1,328,328,FOX TRAPPED IN FENCE - RSCPA NOT AVAILABLE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000458,CRICKET GREEN,E09000024,MERTON,Mitcham,NULL,HOMEFIELD GARDENS,22103384,CR4,NULL,NULL,526650,169350,NULL,NULL
NA,19/06/2017 14:28,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN CHINMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011227,Longlands,E09000004,BEXLEY,Sidcup,NULL,DULVERTON ROAD,20100467,SE9,NULL,NULL,544450,172850,NULL,NULL
NA,20/06/2017 19:20,2017,2017/18,Special Service,1,2,328,656,FOX TRAPPED IN FENCE,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000044,BURNT OAK,E09000003,BARNET,Mill Hill,200086935,NORWICH WALK,20031980,HA8,520337,191159,520350,191150,51.60642031,-0.263668465
NA,20/06/2017 20:40,2017,2017/18,Special Service,1,1,328,328,DEER CAUGHT IN RAILING   -  RSCPA OFFICER REUQESTING ASSISTANCE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000045,CHILDS HILL,E09000003,BARNET,West Hampstead,200019428,CARLTON CLOSE,20007040,NW3,525314,186869,525350,186850,51.56678218,-0.193365725
NA,21/06/2017 12:23,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Lee Green,NULL,RAVENS WAY,20801239,SE12,NULL,NULL,540250,174950,NULL,NULL
NA,21/06/2017 15:54,2017,2017/18,Special Service,1,1,328,328,PIDGEON STUCK INSIDE SIGN,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,6354417,CHAPMAN STREET,22700297,E1,534833,180980,534850,180950,51.51166733,-0.058361604
NA,21/06/2017 19:42,2017,2017/18,Special Service,1,1,328,328,BIRD STUCK IN CLOSED CHIMNEY FLU,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000572,WORCESTER PARK,E09000029,SUTTON,Sutton,NULL,ST PHILIPS AVENUE,22605530,KT4,NULL,NULL,522850,165950,NULL,NULL
NA,22/06/2017 07:30,2017,2017/18,Special Service,1,1,328,328,FOX TRAPPED IN WOODEN FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000257,MUNSTER,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,HANNELL ROAD,21000413,SW6,NULL,NULL,524250,177350,NULL,NULL
NA,22/06/2017 10:33,2017,2017/18,Special Service,1,1,328,328,"PIDGEON STUCK  ON A TV AERIAL   TWO STOREY HOUSE, ONTOP OF CHIMNEY ALP  REQUESTED  FROM SCENE",Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000183,NORTH GREENFORD,E09000009,EALING,Northolt,NULL,WHITTON AVENUE EAST,20601898,UB6,NULL,NULL,515450,185250,NULL,NULL
NA,22/06/2017 10:50,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Other building/use not known,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,1.00022E+11,HAMILTON ROAD,21900659,SE27,532918,171726,532950,171750,51.42895812,-0.089418955
NA,22/06/2017 17:23,2017,2017/18,Special Service,1,1,328,328,TRAPPED BIRDS UNDER SOLAR PANEL - ASSIST LONDON WILDLIFE PROTECTION,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000185,NORTHOLT WEST END,E09000009,EALING,Southall,NULL,DILSTON CLOSE,20600537,UB5,NULL,NULL,511450,182950,NULL,NULL
NA,23/06/2017 09:38,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (land line),Day care/Drop in centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,REDBRIDGE,Woodford,10023774152,BROADMEAD ROAD,22304845,IG8,540301,191790,540350,191750,51.6074734,0.024692563
NA,23/06/2017 13:06,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000189,SOUTHALL BROADWAY,E09000009,EALING,Southall,12028427,TOWNSEND ROAD,20601764,UB1,512131,180346,512150,180350,51.51092561,-0.385560902
NA,23/06/2017 16:02,2017,2017/18,Special Service,1,1,328,328,CAT STUCK INSIDE BOARDED UP HOUSE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,NULL,BASTABLE AVENUE,19900412,IG11,NULL,NULL,546450,183150,NULL,NULL
NA,25/06/2017 09:43,2017,2017/18,Special Service,1,1,328,328,TO ASSIST RSPCA WITH BIRD TRAPPED BY STRING IN TREE  RSPCA IN ATTENDANCE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000630,ABBEY ROAD,E09000033,WESTMINSTER,West Hampstead,1.00023E+11,NORFOLK ROAD,8401470,NW8,526856,183587,526850,183550,51.53694245,-0.172312436
NA,27/06/2017 06:27,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Gym,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000142,REGENT'S PARK,E09000007,CAMDEN,Euston,5026656,ALBANY STREET,20400765,NW1,528899,182556,528850,182550,51.52721466,-0.143249011
NA,27/06/2017 13:33,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH RESCUING CAT FROM TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000640,MAIDA VALE,E09000033,WESTMINSTER,Paddington,1.00023E+11,LANARK ROAD,8401175,W9,526106,182636,526150,182650,51.5285636,-0.183460952
NA,27/06/2017 13:41,2017,2017/18,Special Service,1,1,328,328,DOG STUCK UNDER BATH,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000195,CHASE,E09000010,ENFIELD,Enfield,NULL,MANOR COURT,20702827,EN1,NULL,NULL,534750,199350,NULL,NULL
NA,27/06/2017 13:53,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,NULL,THORNES CLOSE,20301970,BR3,NULL,NULL,538150,168650,NULL,NULL
NA,27/06/2017 14:04,2017,2017/18,Special Service,1,1,328,328,DUCKLINGS STUCK DOWN MANHOLE,Bird,Person (land line),Pipe or drain,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000052,GARDEN SUBURB,E09000003,BARNET,West Hampstead,200144152,NORTH END ROAD,20031620,NW11,525131,187432,525150,187450,51.5718825,-0.195804347
NA,27/06/2017 19:29,2017,2017/18,Special Service,1,1,328,328,CAT STUCK IN DRAIN PIPE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000449,NEW CROSS,E09000023,LEWISHAM,New Cross,NULL,BROCKLEHURST STREET,22000165,SE14,NULL,NULL,535950,177150,NULL,NULL
NA,29/06/2017 00:20,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON FOURTH FLOOR BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000366,BARNSBURY,E09000019,ISLINGTON,Islington,NULL,BLACKTHORN AVENUE,21610109,N7,NULL,NULL,531150,184550,NULL,NULL
NA,29/06/2017 09:26,2017,2017/18,Special Service,1,1,328,328,HERON STUCK ON BALCONY  CALL FROM RSPCA REQUESTING BRIGADE ASSISTANCE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,NULL,WIMBLEDON PARK ROAD,22906210,SW18,NULL,NULL,524850,173350,NULL,NULL
NA,30/06/2017 08:30,2017,2017/18,Special Service,1,1,328,328,Redacted,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,1.00021E+11,SORREL CLOSE,20801373,SE28,546425,180103,546450,180150,51.50090377,0.108205079
NA,30/06/2017 14:10,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000479,CUSTOM HOUSE,E09000025,NEWHAM,Plaistow,NULL,ALNWICK ROAD,22207597,E16,NULL,NULL,541350,181150,NULL,NULL
NA,30/06/2017 19:37,2017,2017/18,Special Service,1,1,328,328,CAT POSSIBLY  TRAPPED IN CAR - FRU WITH SNAKE EYE CAMERA REQ FROM SCENE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000356,HESTON EAST,E09000018,HOUNSLOW,Heston,1.00022E+11,GREAT WEST ROAD,21500505,TW5,513190,176747,513150,176750,51.47836675,-0.371460131
NA,01/07/2017 10:46,2017,2017/18,Special Service,1,1,328,328,DUCK TRAPPED UNDER A MANHOLE COVER,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011254,Wanstead Park,E09000026,REDBRIDGE,Leytonstone,10023189808,BRADING CRESCENT,22306084,E12,540886,186848,540850,186850,51.56292044,0.031153937
NA,01/07/2017 12:26,2017,2017/18,Special Service,1,1,328,328,CAT BELIEVED TRAPPED IN LOFT,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000561,NONSUCH,E09000029,SUTTON,Sutton,NULL,THE SPINNEY,22603399,SM3,NULL,NULL,523350,164650,NULL,NULL
NA,02/07/2017 08:21,2017,2017/18,Special Service,1,1,328,328,CAT STUCK UP TREE - RSPCA IN ATTENDANCE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000133,FROGNAL AND FITZJOHNS,E09000007,CAMDEN,West Hampstead,NULL,FINCHLEY ROAD,20400045,NW3,NULL,NULL,525850,185250,NULL,NULL
NA,02/07/2017 19:13,2017,2017/18,Special Service,1,1,328,328,DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,200210732,THE RIDGEWAY,20036680,NW7,523459,192105,523450,192150,51.61424747,-0.218274777
NA,03/07/2017 19:05,2017,2017/18,Special Service,1,1,328,328,ROUNDABOUT AT JUNCTION OF GREEN WRYTHE LANE SMALL ANIMAL RESCUE - BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,NULL,MIDDLETON ROAD,22600797,SM5,NULL,NULL,527150,166650,NULL,NULL
NA,03/07/2017 21:03,2017,2017/18,Special Service,1,1,328,328,Redacted,Deer,Person (land line),Hedge,Outdoor,Other animal assistance,Assist trapped wild animal,E05011469,Kenley,E09000008,CROYDON,Purley,1.00021E+11,BOURNE PARK CLOSE,20500182,CR8,533404,159388,533450,159350,51.31796616,-0.087058782
NA,04/07/2017 10:58,2017,2017/18,Special Service,1,1,328,328,CAT IMPALED ON POLE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000355,HESTON CENTRAL,E09000018,HOUNSLOW,Feltham,NULL,SUMMERHOUSE AVENUE,21501079,TW5,NULL,NULL,512150,176950,NULL,NULL
NA,04/07/2017 20:25,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL  TRAPPED UNDER DOOR STEP,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000600,HIGHAM HILL,E09000031,WALTHAM FOREST,Walthamstow,NULL,UNITY PLACE,22883180,E17,NULL,NULL,536150,189850,NULL,NULL
NA,04/07/2017 21:09,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009383,SPRINGFIELD,E09000012,HACKNEY,Stoke Newington,10025166480,CLAPTON COMMON,20900242,N16,534080,187584,534050,187550,51.57119226,-0.066693267
NA,05/07/2017 10:39,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN SHAFT,Cat,Person (mobile),Self contained Sheltered Housing,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000451,RUSHEY GREEN,E09000023,LEWISHAM,Lewisham,NULL,MERRYFIELDS WAY,22005995,SE6,NULL,NULL,537850,173850,NULL,NULL
NA,05/07/2017 21:59,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED UNDER SINK CUPBOARD,Cat,Person (mobile),Self contained Sheltered Housing,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Chiswick,NULL,RYLETT ROAD,21000710,W12,NULL,NULL,522050,179250,NULL,NULL
NA,05/07/2017 22:03,2017,2017/18,Special Service,1,1,328,328,BIRD WITH WINGS TRAPPED IN NETTING        CALLER IS OUTSIDE CLINIC,Bird,Person (land line),Doctors surgery,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,5870070369,GREEN WRYTHE LANE,22600593,SM5,527092,166678,527050,166650,51.38492614,-0.17498348
NA,05/07/2017 23:03,2017,2017/18,Special Service,1,1,328,328,BIRD WITH WINGS TRAPPED IN NETTING GREEN WRYTHE LANE SURGERY,Bird,Person (mobile),Doctors surgery,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,5870070369,GREEN WRYTHE LANE,22600593,SM5,527105,166680,527150,166650,51.3849412,-0.174796047
NA,05/07/2017 23:30,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009386,VICTORIA,E09000012,HACKNEY,Homerton,NULL,WETHERELL ROAD,20901067,E9,NULL,NULL,535950,184050,NULL,NULL
NA,06/07/2017 08:33,2017,2017/18,Special Service,1,1,328,328,FOX TRAPPED BETWEEN BASEMENT AND WALL OPPOSITE JERKEES RESTAURANT,Fox,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000254,FULHAM BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,WALHAM GROVE,21000852,SW6,NULL,NULL,525150,177450,NULL,NULL
NA,06/07/2017 21:53,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED ON ROOF- FOUR FLOORS HIGH,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000089,DUDDEN HILL,E09000005,BRENT,Willesden,NULL,DUDDEN HILL LANE,20200017,NW10,NULL,NULL,521650,185650,NULL,NULL
NA,08/07/2017 17:15,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED IN FOILAGE BEHIND GARDEN SHED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000361,HOUNSLOW WEST,E09000018,HOUNSLOW,Feltham,NULL,HINTON AVENUE,21500611,TW4,NULL,NULL,511750,175350,NULL,NULL
NA,08/07/2017 17:38,2017,2017/18,Special Service,1,1,328,328,CAT FALLEN AND TRAPPED   CALLER SAID THIS IS A LARGE HOLE THAT THEY ARE UNABLE TO GET TO DUE TO IT B,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000063,WOODHOUSE,E09000003,BARNET,Finchley,NULL,HIGH ROAD,20022400,N12,NULL,NULL,526450,191250,NULL,NULL
NA,08/07/2017 21:12,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED ON ROOF BY LEG CALL FROM RSPCA WHO ARE UNABLE TO DEAL,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011488,West Thornton,E09000008,CROYDON,Norbury,NULL,GROVE ROAD,20501013,CR7,NULL,NULL,531150,167950,NULL,NULL
NA,08/07/2017 21:53,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,MARSHALL STREET,8401402,W1F,NULL,NULL,529250,181050,NULL,NULL
NA,09/07/2017 01:18,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN WALL AND GATE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000298,PINNER SOUTH,E09000015,HARROW,Harrow,NULL,MARSH ROAD,21201338,HA5,NULL,NULL,512450,189050,NULL,NULL
NA,09/07/2017 20:01,2017,2017/18,Special Service,1,1,328,328,Redacted,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000144,SWISS COTTAGE,E09000007,CAMDEN,West Hampstead,5138548,BROADHURST GARDENS,20400410,NW6,526301,184545,526350,184550,51.54567637,-0.179966764
NA,10/07/2017 08:14,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05000338,SOUTH RUISLIP,E09000017,HILLINGDON,Ruislip,1.00021E+11,THE POINT,21401974,HA4,510151,185913,510150,185950,51.56135127,-0.412341077
NA,10/07/2017 16:01,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED AND BELIEVED INJURED IN SUSPENDED CEILING  - CAB VISION HEMMING STREET CALLER SAYS TO,Bird,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped wild animal,E05009331,ST. PETER'S,E09000030,TOWER HAMLETS,Bethnal Green,6647489,HEMMING STREET,22700626,E2,534455,182324,534450,182350,51.52383504,-0.06329294
NA,10/07/2017 18:53,2017,2017/18,Special Service,1,1,328,328,CAT  TRAPPED  IN REAR GARDEN    LEG IS CAUGHT IN STRING AND RUBBISH ON SITE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000600,HIGHAM HILL,E09000031,WALTHAM FOREST,Walthamstow,NULL,MILLFIELD AVENUE,22858800,E17,NULL,NULL,536650,190550,NULL,NULL
NA,11/07/2017 11:16,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH DUCKLING TRAPPED IN WELL,Bird,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,1.00023E+11,VICTORIA EMBANKMENT,8400999,WC2R,531116,180789,531150,180750,51.51082518,-0.111965446
NA,11/07/2017 12:59,2017,2017/18,Special Service,1,1,328,328,FOX WITH HEAD TRAPPED BETWEEN WALL AND FENCE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000044,BURNT OAK,E09000003,BARNET,Mill Hill,NULL,MONTROSE AVENUE,20030060,HA8,NULL,NULL,520850,190750,NULL,NULL
NA,12/07/2017 19:15,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED IN ROOF,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000432,STREATHAM WELLS,E09000022,LAMBETH,Norbury,NULL,FARNAN ROAD,21900542,SW16,NULL,NULL,530350,171250,NULL,NULL
NA,13/07/2017 18:30,2017,2017/18,Special Service,1,1,328,328,BIRD STUCK UNDER DRAIN GRILL,Bird,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000049,EAST FINCHLEY,E09000003,BARNET,Finchley,200228520,SIMMS GARDENS,20039690,N2,526447,190271,526450,190250,51.59710276,-0.175803525
NA,13/07/2017 22:05,2017,2017/18,Special Service,1,2,328,656,DOG TRAPPED IN FOX HOLE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000110,CHELSFIELD AND PRATTS BOTTOM,E09000006,BROMLEY,Orpington,NULL,SEVENOAKS ROAD,20301447,BR6,NULL,NULL,545550,164150,NULL,NULL
NA,14/07/2017 14:18,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA TO GAIN ACCESS  RSPCA INSPECTOR ON SCENE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000368,CALEDONIAN,E09000019,ISLINGTON,Islington,NULL,WHEELER GARDENS,21607021,N1,NULL,NULL,530350,183850,NULL,NULL
NA,14/07/2017 15:12,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL TO PIGEON TRAPPED IN WIRE    RSPCA IN ATTENDANCE,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009380,LEA BRIDGE,E09000012,HACKNEY,Stoke Newington,1.00023E+11,UPPER CLAPTON ROAD,20901025,E5,534770,186540,534750,186550,51.56164607,-0.057142643
NA,14/07/2017 19:59,2017,2017/18,Special Service,1,1,328,328,FOX TRAPPED BETWEEN FENCES    RSPCA UNABLE TO ATTEND,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000037,PARSLOES,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,VERNEY GARDENS,19900743,RM9,NULL,NULL,548150,185650,NULL,NULL
NA,15/07/2017 15:31,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,NULL,CHANNELSEA ROAD,22201339,E15,NULL,NULL,538750,183850,NULL,NULL
NA,16/07/2017 09:13,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000347,BRENTFORD,E09000018,HOUNSLOW,Chiswick,NULL,GREEN DRAGON LANE,21500506,TW8,NULL,NULL,518350,178050,NULL,NULL
NA,16/07/2017 10:02,2017,2017/18,Special Service,1,1,328,328,PUPPY   WITH HEAD TRAPPED IN RECLINING SOFA,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000479,CUSTOM HOUSE,E09000025,NEWHAM,Plaistow,NULL,CHURCHILL ROAD,22200475,E16,NULL,NULL,541350,181250,NULL,NULL
NA,16/07/2017 15:11,2017,2017/18,Special Service,1,1,328,328,DOG IN LAKE DOG IN DISTRESS AND CANNOT GET OUT    OWNER SAYS BEST ACCESS IS VIA ROMFORD ROAD,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05011245,Hainault,E09000026,REDBRIDGE,Hainault,10023773354,ROMFORD ROAD,22304722,IG7,547161,193001,547150,193050,51.61660578,0.124193925
NA,16/07/2017 21:47,2017,2017/18,Special Service,1,1,328,328,DUCKLINGS STUCK DOWN DRAIN OUTSIDE  CALLED BY RSPCA WHO CANNOT ATTEND AT PRESENT,Bird,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from water,Animal rescue from water - Bird,E05009385,STOKE NEWINGTON,E09000012,HACKNEY,Stoke Newington,1.00021E+11,MANOR ROAD,20900653,N16,533420,187075,533450,187050,51.5667748,-0.076403704
NA,16/07/2017 21:49,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BEHIND WOODWORK  CALL FROM RSPCA WHO CANNOT ATTEND AT PRESENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000270,FORTIS GREEN,E09000014,HARINGEY,Hornsey,NULL,FORTIS GREEN,21106130,N2,NULL,NULL,527950,189650,NULL,NULL
NA,17/07/2017 13:26,2017,2017/18,Special Service,1,1,328,328,TO ASSIST RSPCA WITH RETRIEVAL OF CAT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011251,Seven Kings,E09000026,REDBRIDGE,Ilford,NULL,CAMERON ROAD,22301914,IG3,NULL,NULL,545450,187150,NULL,NULL
NA,18/07/2017 08:13,2017,2017/18,Special Service,1,1,328,328,MAGPIES TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000524,KEW,E09000027,RICHMOND UPON THAMES,Richmond,NULL,KEW ROAD,22406655,TW9,NULL,NULL,519050,177150,NULL,NULL
NA,18/07/2017 19:13,2017,2017/18,Special Service,1,1,328,328,DOG STUCK BETWEEN WALL AND FENCE   -   OWNER WILL MEET YOU,Dog,Person (land line),Scrub land,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011227,Longlands,E09000004,BEXLEY,Sidcup,10011847898,WOODSIDE ROAD,20101618,DA15,545471,172209,545450,172250,51.43021791,0.09122314
NA,18/07/2017 19:47,2017,2017/18,Special Service,1,1,328,328,DUCKLINGS STUCK IN DRAIN     NIGHT MANAGER WILL MEET AND SHOW YOU WHERE THEY ARE  RSPCA UNABLE TO AT,Bird,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,202102736,GREAT CENTRAL WAY,20201658,NW10,520928,185316,520950,185350,51.55378125,-0.25714848
NA,19/07/2017 11:38,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED IN DRAINAGE AT RESTAURANT   -  CYPRUS MEZET BAR,Cat,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,2.00001E+11,LEA BRIDGE ROAD,22850300,E10,537496,187833,537450,187850,51.57260808,-0.017334994
NA,19/07/2017 17:43,2017,2017/18,Special Service,1,1,328,328,PIGEONS TRAPPED UNDER NETTING   UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000427,PRINCE'S,E09000022,LAMBETH,Lambeth,1.00022E+11,JUXON STREET,21900769,SE11,530847,178947,530850,178950,51.4943338,-0.116522545
NA,19/07/2017 19:02,2017,2017/18,Special Service,1,1,328,328,BIRD WITH LEG CAUGHT IN STRING IN NEST  RSPCA IN ATTENDANCE ANT CORNER WITH DARTMOUTH PARK HILL - TL,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000137,HIGHGATE,E09000007,CAMDEN,Kentish Town,10015354514,DARTMOUTH PARK HILL,21600227,NW5,529046,186058,529050,186050,51.55865316,-0.1398477
NA,19/07/2017 20:13,2017,2017/18,Special Service,2,3,328,984,INJURED SEAGULL IN WATER       NEAR THE CONSTITUTION PH   FRU REQUIRED WITH BOAT CAPABILITIES WATER,Bird,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Kentish Town,5063437,ST PANCRAS WAY,20400656,NW1,529373,184034,529350,184050,51.54038899,-0.135877168
NA,19/07/2017 22:15,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN BIN CHUTE POSSIBLY BEEN THERE A FEW DAYS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000417,BRIXTON HILL,E09000022,LAMBETH,West Norwood,NULL,DEEPDENE GARDENS,21900447,SW2,NULL,NULL,530550,173950,NULL,NULL
NA,19/07/2017 23:30,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH BIRD TRAPPED IN GUTTERING PL REQUESTED,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000425,LARKHALL,E09000022,LAMBETH,Brixton,NULL,ATHERFOLD ROAD,21900118,SW9,NULL,NULL,530250,175950,NULL,NULL
NA,21/07/2017 13:51,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH FLEDGLING SEAGULL IN DISTRESS RSPCA IN ATTENDANCE,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Shoreditch,5087312,KIRBY STREET,20401188,EC1N,531434,181787,531450,181750,51.51972016,-0.107013692
NA,21/07/2017 15:13,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL TO BIRD IN DISTRESS TRAPPED IN NETTING,Bird,Person (land line),Hospital,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000416,BISHOP'S,E09000022,LAMBETH,Lambeth,1.00023E+11,WESTMINSTER BRIDGE ROAD,21901482,SE1,530695,179518,530650,179550,51.49950039,-0.118499475
NA,22/07/2017 13:16,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPC ON SCENE WITH DOG IN PRECARIOUS POSITION,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000188,SOUTH ACTON,E09000009,EALING,Acton,NULL,AMELIA CLOSE,20604257,W3,NULL,NULL,519650,180050,NULL,NULL
NA,22/07/2017 17:26,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED UNDER FLOORING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000615,FURZEDOWN,E09000032,WANDSWORTH,Tooting,NULL,DAHOMEY ROAD,22901117,SW16,NULL,NULL,529150,170750,NULL,NULL
NA,23/07/2017 14:45,2017,2017/18,Special Service,1,1,328,328,BIRD CAUGHT IN NETTING - REQUESTED BY RSPCA,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000060,UNDERHILL,E09000003,BARNET,Barnet,NULL,QUINTA DRIVE,20035700,EN5,NULL,NULL,523150,195650,NULL,NULL
NA,24/07/2017 10:10,2017,2017/18,Special Service,1,1,328,328,BIRD INJURED AND TRAPPED ON ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,NULL,SOMERFIELD STREET,22006156,SE16,NULL,NULL,535650,178450,NULL,NULL
NA,24/07/2017 12:21,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN FENCE   REQUESTED BY RSPCA,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000597,HALE END AND HIGHAMS PARK,E09000031,WALTHAM FOREST,Chingford,NULL,SELWYN AVENUE,22873600,E4,NULL,NULL,538350,191650,NULL,NULL
NA,24/07/2017 17:55,2017,2017/18,Special Service,1,1,328,328,BIRD LOOSE IN LOUNGE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000626,TOOTING,E09000032,WANDSWORTH,Tooting,NULL,BEECHCROFT ROAD,22900430,SW17,NULL,NULL,527650,172450,NULL,NULL
NA,25/07/2017 14:07,2017,2017/18,Special Service,1,2,328,656,CAT ON ROOF - RSPCA ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000428,ST. LEONARD'S,E09000022,LAMBETH,Norbury,NULL,GLENELDON ROAD,21900617,SW16,NULL,NULL,530450,171550,NULL,NULL
NA,25/07/2017 16:11,2017,2017/18,Special Service,1,1,328,328,Redacted,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,NULL,KELSEY SQUARE,20301422,BR3,NULL,NULL,537250,169350,NULL,NULL
NA,25/07/2017 18:07,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL TO DISTRESSED DOG TRAPPED BETWEEN FENCE AND SHED,Dog,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000532,WEST TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,1.00022E+11,TRAFALGAR ROAD,22401932,TW2,514936,172611,514950,172650,51.44084135,-0.347672043
NA,26/07/2017 18:06,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Hendon,NULL,HOLDERS HILL AVENUE,20023380,NW4,NULL,NULL,523850,189950,NULL,NULL
NA,27/07/2017 12:41,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED IN IRON FENCE CALLED X RSPCA ON SCENE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011223,Crook Log,E09000004,BEXLEY,Bexley,1.0002E+11,LAKESIDE CLOSE,20100811,DA15,547001,174541,547050,174550,51.45077761,0.114184149
NA,27/07/2017 14:07,2017,2017/18,Special Service,1,1,328,328,ASSIST THE RSPCA WITH KITTEN TRAPPED IN A DRAIN - RSPCA ON SCENE,Cat,Person (mobile),TV/film/music/art studio,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000253,COLLEGE PARK AND OLD OAK,E09000013,HAMMERSMITH AND FULHAM,North Kensington,34082175,CUMBERLAND PARK,21001287,NW10,522316,182677,522350,182650,51.52976489,-0.238054898
NA,27/07/2017 19:18,2017,2017/18,Special Service,1,1,328,328,BIRD ENTANGLED IN TREE  RSPCA UNABLE TO ATTEND,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009368,CAZENOVE,E09000012,HACKNEY,Stoke Newington,1.00021E+11,CAZENOVE ROAD,20900211,N16,534124,186926,534150,186950,51.56526878,-0.066309429
NA,28/07/2017 10:39,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED IN GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000313,HAVERING PARK,E09000016,HAVERING,Romford,NULL,CLITHEROE ROAD,21301085,RM5,NULL,NULL,550250,192050,NULL,NULL
NA,28/07/2017 19:31,2017,2017/18,Special Service,1,2,328,656,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000368,CALEDONIAN,E09000019,ISLINGTON,Holloway,NULL,BLUNDELL STREET,21604300,N7,NULL,NULL,530450,184550,NULL,NULL
NA,28/07/2017 20:40,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,1.00023E+11,MARKHOUSE ROAD,22856450,E17,536648,187922,536650,187950,51.57361372,-0.029529525
NA,30/07/2017 15:00,2017,2017/18,Special Service,1,1,328,328,DOG LOCKED IN CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000052,GARDEN SUBURB,E09000003,BARNET,West Hampstead,200105335,SPANIARDS ROAD,20040260,NW3,526636,187262,526650,187250,51.57001883,-0.174160682
NA,30/07/2017 20:55,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED ON LIVE WIRES IN REAR GARDEN,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,NULL,DUNHOLME GREEN,20704164,N9,NULL,NULL,533650,193450,NULL,NULL
NA,02/08/2017 10:39,2017,2017/18,Special Service,1,1,328,328,FOX STUCK IN FENCE,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000091,HARLESDEN,E09000005,BRENT,Park Royal,202146561,CREUKHORNE ROAD,20204180,NW10,521082,184315,521050,184350,51.54475187,-0.255272714
NA,05/08/2017 16:31,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON ROOF - STRUCK BY DRONE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009322,BROMLEY SOUTH,E09000030,TOWER HAMLETS,Poplar,NULL,WHITETHORN STREET,22701342,E3,NULL,NULL,537350,181950,NULL,NULL
NA,05/08/2017 17:07,2017,2017/18,Special Service,1,1,328,328,CAT STUCK BETWEEN WALLS OF BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000375,HOLLOWAY,E09000019,ISLINGTON,Holloway,NULL,WIDDENHAM ROAD,21604016,N7,NULL,NULL,530650,185650,NULL,NULL
NA,06/08/2017 16:25,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN FIRE PLACE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000572,WORCESTER PARK,E09000029,SUTTON,Sutton,NULL,CHARMINSTER ROAD,22601355,KT4,NULL,NULL,523650,166250,NULL,NULL
NA,08/08/2017 11:43,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE - PYTHON STUCK BEHIND RADIATOR,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009385,STOKE NEWINGTON,E09000012,HACKNEY,Stoke Newington,NULL,FOULDEN ROAD,20900420,N16,NULL,NULL,533750,185850,NULL,NULL
NA,08/08/2017 13:54,2017,2017/18,Special Service,1,2,328,656,CAT STUCK IN WINDOW VERY DISTRESSED,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011098,Chaucer,E09000028,SOUTHWARK,Old Kent Road,NULL,POTIER STREET,22502006,SE1,NULL,NULL,532950,179150,NULL,NULL
NA,08/08/2017 14:03,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED IN WINDOW FRAME  CALLER WAITING OUTISDE SISKIN HOUSE FOR YOU,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011112,Rotherhithe,E09000028,SOUTHWARK,Deptford,NULL,TAWNY WAY,22502475,SE16,NULL,NULL,535850,178950,NULL,NULL
NA,08/08/2017 19:42,2017,2017/18,Special Service,1,1,328,328,DOG WITH HEAD TRAPPED IN BARS OF CRATE BASEMENT FLAT,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011095,Borough & Bankside,E09000028,SOUTHWARK,Dowgate,NULL,SOUTHWARK BRIDGE ROAD,22502285,SE1,NULL,NULL,531950,179550,NULL,NULL
NA,08/08/2017 22:18,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL TO CAT STUCK IN STORAGE AREA - NO FURTHER BRIGADE ATTENDANCE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,HAZLEWOOD CRESCENT,21700885,W10,NULL,NULL,524550,182150,NULL,NULL
NA,09/08/2017 00:59,2017,2017/18,Special Service,1,1,328,328,INJURED CAT TRAPPED UNDER DECKING - CALLER WILL MEET BRIGADE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,NULL,LADYWELL ROAD,22001623,SE13,NULL,NULL,537750,174950,NULL,NULL
NA,10/08/2017 10:11,2017,2017/18,Special Service,1,1,328,328,DOG IN PRECARIOUS POSITION  ON ROOF OF TWO STOREY HOUSE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,OAKMERE ROAD,20801098,SE2,NULL,NULL,546150,177850,NULL,NULL
NA,10/08/2017 22:26,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED UNDER FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000351,FELTHAM NORTH,E09000018,HOUNSLOW,Feltham,1.00022E+11,SHAKESPEARE AVENUE,21501006,TW14,510041,174137,510050,174150,51.45552826,-0.417598127
NA,11/08/2017 15:49,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN BUILDING,Bird,Person (land line),Day care/Drop in centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,REDBRIDGE,Woodford,10023774152,BROADMEAD ROAD,22304845,IG8,540303,191793,540350,191750,51.60749986,0.024722625
NA,11/08/2017 17:12,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Lambeth,1.00023E+11,HORSEFERRY ROAD,8401767,SW1P,529705,179070,529750,179050,51.49570208,-0.132918645
NA,13/08/2017 14:53,2017,2017/18,Special Service,1,1,328,328,CAT UP TREE FOR TWO DAYS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011237,Chadwell,E09000026,REDBRIDGE,Ilford,NULL,CRUCIBLE CLOSE,22301978,RM6,NULL,NULL,546850,188350,NULL,NULL
NA,13/08/2017 15:25,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE  -  RSPCA INSPECTOR IN ATTENDANCE   -   CROW HANGING BY THE LEG FROM A TV AERIEL,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000528,SOUTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,NULL,OLD PALACE LANE,22403701,TW9,NULL,NULL,517450,174950,NULL,NULL
NA,13/08/2017 15:51,2017,2017/18,Special Service,1,1,328,328,CAT UP A TREE.   CALLED AT THE REQUEST OF THE RSPCA.  OWNER IS IN ATTENDANCE.  CAT IS PREGNANT AND U,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011237,Chadwell,E09000026,REDBRIDGE,Ilford,NULL,CRUCIBLE CLOSE,22301978,RM6,NULL,NULL,546850,188350,NULL,NULL
NA,13/08/2017 16:21,2017,2017/18,Special Service,1,1,328,328,SWAN ENTANGLED IN FISHING LINE WITH HOOK IN CHEST - RSPCA UNABLE TO ATTEND - BIRD IS OUTSIDE SLUG AN,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000528,SOUTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,1.00023E+11,WHITTAKER AVENUE,22403953,TW9,517670,174644,517650,174650,51.45855219,-0.307676958
NA,14/08/2017 08:28,2017,2017/18,Special Service,1,1,328,328,KITTEN WITH HEAD STUCK IN SINK,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000280,TOTTENHAM GREEN,E09000014,HARINGEY,Tottenham,NULL,MANSFIELD AVENUE,21103590,N15,NULL,NULL,532750,189350,NULL,NULL
NA,14/08/2017 15:20,2017,2017/18,Special Service,1,1,328,328,SNAKE STUCK UNDER KITCHEN CABINET,Snake,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000638,LANCASTER GATE,E09000033,WESTMINSTER,Paddington,NULL,PORCHESTER TERRACE,8400631,W2,NULL,NULL,526050,180750,NULL,NULL
NA,15/08/2017 10:22,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Theatre,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000319,ROMFORD TOWN,E09000016,HAVERING,Romford,1.00024E+11,EASTERN ROAD,21301162,RM1,551521,188627,551550,188650,51.57615077,0.185232655
NA,15/08/2017 19:58,2017,2017/18,Special Service,1,1,328,328,DOG WITH HEAD TRAPPED IN FENCE,Dog,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000426,OVAL,E09000022,LAMBETH,Lambeth,2E+11,SOUTH LAMBETH ROAD,21901248,SW8,530506,177687,530550,177650,51.48308917,-0.12189723
NA,15/08/2017 22:55,2017,2017/18,Special Service,1,1,328,328,CAT STUCK IN BASEMENT FLOORING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,PICCADILLY,8400515,W1J,NULL,NULL,529150,180450,NULL,NULL
NA,16/08/2017 12:33,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000301,ROXBOURNE,E09000015,HARROW,Northolt,1.00021E+11,SHERWOOD ROAD,21201260,HA2,514110,186564,514150,186550,51.56641598,-0.355041668
NA,18/08/2017 19:45,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING    RSPCA INSPECTOR IN ATTENDANCE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000253,COLLEGE PARK AND OLD OAK,E09000013,HAMMERSMITH AND FULHAM,Acton,34004351,BEGONIA WALK,21001154,W12,521895,181078,521850,181050,51.51548492,-0.244674176
NA,19/08/2017 13:11,2017,2017/18,Special Service,1,2,328,656,Redacted,Dog,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000341,UXBRIDGE SOUTH,E09000017,HILLINGDON,Hillingdon,10022796765,PACKET BOAT LANE,21401472,UB8,504901,181192,504950,181150,51.51991766,-0.489454178
NA,19/08/2017 16:42,2017,2017/18,Special Service,1,2,328,656,Redacted,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000055,HENDON,E09000003,BARNET,Hendon,10025492304,THE BURROUGHS ACCESS ROAD BETWEEN FIRE STATION AND LIBRARY,20006295,NW4,522740,189271,522750,189250,51.58893537,-0.229644134
NA,20/08/2017 18:08,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000410,OLD MALDEN,E09000021,KINGSTON UPON THAMES,New Malden,NULL,COLUMBIA AVENUE,21800266,KT4,NULL,NULL,521850,166650,NULL,NULL
NA,20/08/2017 20:26,2017,2017/18,Special Service,1,2,328,656,RUNNING CALL TO A CAT STUCK ON A LEDGE,Cat,Person (land line),College/University,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000055,HENDON,E09000003,BARNET,Hendon,10093301515,THE BURROUGHS,20006280,NW4,522689,189289,522650,189250,51.58910824,-0.230373644
NA,20/08/2017 22:08,2017,2017/18,Special Service,1,1,328,328,CAT STUCK IN SUB  BASEMENT VOID,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Kentish Town,NULL,REGENT'S PARK ROAD,20400622,NW1,NULL,NULL,528150,184350,NULL,NULL
NA,22/08/2017 08:08,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000108,BROMLEY COMMON AND KESTON,E09000006,BROMLEY,Bromley,NULL,ERICKSON GARDENS,20304228,BR2,NULL,NULL,542050,167250,NULL,NULL
NA,22/08/2017 14:41,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000145,WEST HAMPSTEAD,E09000007,CAMDEN,West Hampstead,5043672,SPODE WALK,20401325,NW6,525929,185025,525950,185050,51.55007321,-0.185156867
NA,24/08/2017 15:09,2017,2017/18,Special Service,1,1,328,328,PIDGEON TRAPPED IN BUILDING- CALLED BY RSPCA,Bird,Person (mobile),Other building/use not known,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011108,Nunhead & Queen's Road,E09000028,SOUTHWARK,New Cross,2.00003E+11,MEETING HOUSE LANE,22501699,SE15,534821,177100,534850,177150,51.47680271,-0.060016096
NA,24/08/2017 18:09,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000416,BISHOP'S,E09000022,LAMBETH,Lambeth,10000446356,CARLISLE LANE,21900291,SE1,530970,179351,530950,179350,51.49793601,-0.11460187
NA,26/08/2017 18:51,2017,2017/18,Special Service,1,1,328,328,"DISTRESSED DOG TRAPPED IN CAR-IN MAIN CAR PARK , BATTERSEA PARK, NEAR THE RIVER",Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Battersea,121054054,ALBERT BRIDGE ROAD,22900030,SW11,527931,177427,527950,177450,51.48134028,-0.159054742
NA,27/08/2017 03:50,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED AND INJURED - CALLER WILL MEET - THIS IS FLAT ABOVE FUSION,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011247,Loxford,E09000026,REDBRIDGE,Ilford,NULL,ILFORD LANE,22302963,IG1,NULL,NULL,543850,185650,NULL,NULL
NA,27/08/2017 14:31,2017,2017/18,Special Service,1,1,328,328,INJURED PIGEON STUCK IN TREE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000183,NORTH GREENFORD,E09000009,EALING,Wembley,12078508,WHITTON AVENUE EAST,20601898,UB6,516377,184954,516350,184950,51.5514833,-0.322880475
NA,27/08/2017 17:58,2017,2017/18,Special Service,1,1,328,328,DOG WITH HEAD STUCK IN BIN,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000334,MANOR,E09000017,HILLINGDON,Ruislip,NULL,LINDEN AVENUE,21401163,HA4,NULL,NULL,510450,187350,NULL,NULL
NA,27/08/2017 18:47,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,2.00001E+11,MARKHOUSE ROAD,22856450,E17,536592,188186,536550,188150,51.5759996,-0.030234652
NA,27/08/2017 20:24,2017,2017/18,Special Service,1,2,328,656,"RUNNING CALL TO DOG HIT BY CAR, OBSTRUCTION ON ROADWAY",Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,100103668,WESTMINSTER GARDENS,19900624,IG11,545018,183222,545050,183250,51.52929254,0.089230145
NA,27/08/2017 21:35,2017,2017/18,Special Service,1,1,328,328,KITTEN FALLEN FROM BALCONY INTO BOARDED UP UNDERGROUND CAR PARK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000171,CLEVELAND,E09000009,EALING,Ealing,NULL,CHEYNE PATH,20600372,W7,NULL,NULL,515950,181350,NULL,NULL
NA,27/08/2017 23:00,2017,2017/18,Special Service,1,2,328,656,CAT STUCK IN BIN CHUTE,Cat,Person (mobile),Common external bin storage area,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,10023850480,BEETON WAY,21903448,SE27,532610,171955,532650,171950,51.43108822,-0.093761072
NA,28/08/2017 14:56,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011230,Sidcup,E09000004,BEXLEY,Sidcup,NULL,SIDCUP HIGH STREET,20101300,DA14,NULL,NULL,546250,171850,NULL,NULL
NA,28/08/2017 19:41,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED CALLER STATES IN FENCE INSIDE STATION NEXT TO BLUE PLASTIC TENT,Cat,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,10003976820,PARK LANE,21104898,N17,534876,191073,534850,191050,51.60235506,-0.053875975
NA,29/08/2017 22:33,2017,2017/18,Special Service,1,1,328,328,KITTEN STUCK IN ENGINE BAY OF CAR,Cat,Person (mobile),Car,Road Vehicle,Animal rescue from height,Animal rescue from height - Domestic pet,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,46075339,TUNMARSH LANE,22201157,E13,541021,182783,541050,182750,51.52635932,0.031472899
NA,31/08/2017 19:35,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA (ON SCENE) WITH BIRD TRAPPED IN NETTING - CROYDON ATH. F.C.,Bird,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05011488,West Thornton,E09000008,CROYDON,Norbury,2.00001E+11,MAYFIELD ROAD,20502591,CR7,530419,167906,530450,167950,51.39520853,-0.126748594
NA,31/08/2017 23:53,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED IN WHEEL ARCH OF A CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal harm involving domestic animal,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,46075339,TUNMARSH LANE,22201157,E13,541009,182788,541050,182750,51.52640725,0.031302036
NA,01/09/2017 02:31,2017,2017/18,Special Service,1,1,328,328,FEMALE LOCKED IN PARK HER DOG IS IN LABOUR,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,6651762,CAMBRIDGE HEATH ROAD,22700252,E2,535016,182785,535050,182750,51.52784384,-0.055034727
NA,01/09/2017 12:44,2017,2017/18,Special Service,1,1,328,328,KITTEN STUCK  BEHIND THE TOILET,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011472,Norbury & Pollards Hill,E09000008,CROYDON,Norbury,NULL,NEWLANDS ROAD,20501223,SW16,NULL,NULL,530350,169250,NULL,NULL
NA,01/09/2017 17:55,2017,2017/18,Special Service,NULL,NULL,328,NULL,Redacted,Cat,Person (land line),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,207096408,WYLDFIELD GARDENS,20704962,N9,533777,193813,533750,193850,51.62723963,-0.068689782
NA,02/09/2017 13:56,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED UP INSIDE CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011222,Crayford,E09000004,BEXLEY,Bexley,NULL,GREEN WALK,20100633,DA1,NULL,NULL,551750,175050,NULL,NULL
NA,02/09/2017 14:09,2017,2017/18,Special Service,1,1,328,328,CAT ON ROOF SECOND FLOOR  REQUESTED BY RSPCA,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,NULL,BEADON ROAD,20300246,BR2,NULL,NULL,540350,167850,NULL,NULL
NA,02/09/2017 18:06,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED IN CAR ENGINE  RSPCA UNABLE TO ATTEND,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,207002625,LANCING GARDENS,20704412,N9,533911,194235,533950,194250,51.63099999,-0.066593603
NA,02/09/2017 19:18,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN NETTING ON BALCONY - RSPCA ON SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000185,NORTHOLT WEST END,E09000009,EALING,Southall,NULL,CANBERRA DRIVE,20600310,UB5,NULL,NULL,511450,182750,NULL,NULL
NA,03/09/2017 06:35,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000047,COPPETTS,E09000003,BARNET,Finchley,200144333,PINKHAM WAY,20034380,N11,528959,191787,528950,191750,51.61015736,-0.139000204
NA,04/09/2017 17:30,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED INSIDE CHIMNEY,Bird,Person (land line),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000326,BRUNEL,E09000017,HILLINGDON,Hillingdon,NULL,WHITE HEART AVENUE,21402148,UB8,NULL,NULL,508050,181750,NULL,NULL
NA,06/09/2017 20:05,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN BETWEEN TWO WALLS INSIDE HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011489,Woodside,E09000008,CROYDON,Woodside,NULL,REES GARDENS,20501333,CR0,NULL,NULL,533850,167150,NULL,NULL
NA,07/09/2017 12:14,2017,2017/18,Special Service,1,1,328,328,KITTEN STUCK BEHIND FIRE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011465,Broad Green,E09000008,CROYDON,Croydon,NULL,MITCHAM ROAD,20502543,CR0,NULL,NULL,531150,166650,NULL,NULL
NA,07/09/2017 19:04,2017,2017/18,Special Service,1,1,328,328,CAT STUCK IN AIR BRICK,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000532,WEST TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,TWINING AVENUE,22401943,TW2,NULL,NULL,514550,172250,NULL,NULL
NA,09/09/2017 07:48,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011113,Rye Lane,E09000028,SOUTHWARK,Peckham,NULL,NIGEL ROAD,22501803,SE15,NULL,NULL,534350,175950,NULL,NULL
NA,09/09/2017 15:24,2017,2017/18,Special Service,1,1,328,328,CAT STUCK IN BRICKED WALL IN BACK GARDEN,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000622,ST. MARY'S PARK,E09000032,WANDSWORTH,Battersea,NULL,CAMBRIDGE ROAD,22900771,SW11,NULL,NULL,527550,176550,NULL,NULL
NA,09/09/2017 21:00,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN BASEMENT AREA - OWNER ON SCENE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009369,CLISSOLD,E09000012,HACKNEY,Stoke Newington,NULL,NEVILL ROAD,20900729,N16,NULL,NULL,533250,186050,NULL,NULL
NA,10/09/2017 15:45,2017,2017/18,Special Service,1,2,328,656,SMALL ANIMAL RESCUE DOG UNDER ELECTRIC CHAIR,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,NULL,PLUMSTEAD ROAD,20801188,SE18,NULL,NULL,544550,178850,NULL,NULL
NA,11/09/2017 12:10,2017,2017/18,Special Service,1,1,328,328,REQUEST FROM RSPCA  FOX FALLEN THROUGH GARAGE ROOF,Fox,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped wild animal,E05011217,Barnehurst,E09000004,BEXLEY,Erith,1.0002E+11,BIRLING ROAD,20100151,DA8,551061,176953,551050,176950,51.47138205,0.17359836
NA,11/09/2017 12:31,2017,2017/18,Special Service,1,1,328,328,REQUEST FROM RSPCA  CAT STUCK ON THIRD FLOOR ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000095,MAPESBURY,E09000005,BRENT,Willesden,NULL,CHICHELE ROAD,20202353,NW2,NULL,NULL,523750,185450,NULL,NULL
NA,12/09/2017 16:06,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED DOWN DRAIN - OWNER WILL MEET BRIGADE AT THEIR ADDRESS AND DIRECT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,NULL,MUSWELL HILL BROADWAY,21106387,N10,NULL,NULL,528750,189650,NULL,NULL
NA,13/09/2017 18:11,2017,2017/18,Special Service,1,1,328,328,ANIMAL TRAPPED IN CEILING CAVITY,Unknown - Wild Animal,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000367,BUNHILL,E09000019,ISLINGTON,Islington,NULL,OWEN STREET,21605616,EC1V,NULL,NULL,531550,183050,NULL,NULL
NA,14/09/2017 11:18,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000111,CHISLEHURST,E09000006,BROMLEY,Bromley,NULL,BONCHESTER CLOSE,20300433,BR7,NULL,NULL,543350,170050,NULL,NULL
NA,14/09/2017 14:06,2017,2017/18,Special Service,1,1,328,328,DEER STUCK IN METAL RAILINGS,Deer,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Assist trapped wild animal,E05011481,Selsdon Vale & Forestdale,E09000008,CROYDON,Addington,1.00021E+11,QUAIL GARDENS,20501808,CR2,536215,162216,536250,162250,51.3427149,-0.045667273
NA,15/09/2017 13:43,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN WIRE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000114,CRAY VALLEY EAST,E09000006,BROMLEY,Orpington,NULL,EDENBRIDGE CLOSE,20301107,BR5,NULL,NULL,547750,168050,NULL,NULL
NA,16/09/2017 09:18,2017,2017/18,Special Service,1,1,328,328,CAT STUCK UNDER FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,NULL,DUNBAR AVENUE,20301889,BR3,NULL,NULL,536350,168350,NULL,NULL
NA,16/09/2017 11:26,2017,2017/18,Special Service,1,1,328,328,RSPCA REQUEST FOR PIGEON TRAPPED UP HIGH - OPPOSITE THIS ADDRESS,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000409,NORBITON,E09000021,KINGSTON UPON THAMES,Kingston,NULL,LONDON ROAD,21800616,KT2,NULL,NULL,519050,169550,NULL,NULL
NA,16/09/2017 17:12,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED UNDER FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,NULL,DUNBAR AVENUE,20301889,BR3,NULL,NULL,536350,168350,NULL,NULL
NA,17/09/2017 13:50,2017,2017/18,Special Service,1,1,328,328,TWO DOGS RUN INTO BADGER SET UNDERGROUND,Dog,Person (land line),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000520,HAMPTON,E09000027,RICHMOND UPON THAMES,Twickenham,1.00023E+11,HAMPTON COURT ROAD,22406635,KT8,515966,168659,515950,168650,51.40511173,-0.334151092
NA,17/09/2017 19:20,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN SHED AND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011244,Goodmayes,E09000026,REDBRIDGE,Ilford,NULL,NEW ROAD,22302303,IG3,NULL,NULL,545250,186950,NULL,NULL
NA,19/09/2017 15:39,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009384,STAMFORD HILL WEST,E09000012,HACKNEY,Stoke Newington,1.00021E+11,FAIRHOLT ROAD,20900391,N16,532895,187344,532850,187350,51.56931618,-0.083872215
NA,19/09/2017 21:50,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED UNDER STAIRS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000413,SURBITON HILL,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,DITTON ROAD,21800332,KT6,NULL,NULL,518550,166350,NULL,NULL
NA,21/09/2017 15:21,2017,2017/18,Special Service,1,1,328,328,CAT LEG TRAPPED IN ELECTRIC CHAIR,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,NULL,FIFTH AVENUE,8400299,W10,NULL,NULL,524150,182650,NULL,NULL
NA,22/09/2017 20:53,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN WIRE ON TOP OF BUILDING NEXT TO,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,NULL,WAVERLEY CRESCENT,20801567,SE18,NULL,NULL,544650,178250,NULL,NULL
NA,23/09/2017 12:26,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BEHIND KITCHEN CABINET,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000177,GREENFORD BROADWAY,E09000009,EALING,Southall,NULL,APPRENTICE GARDENS,20604821,UB5,NULL,NULL,512650,182650,NULL,NULL
NA,23/09/2017 13:29,2017,2017/18,Special Service,1,1,328,328,ASSITING RSPCA WITH PIGEON STUCK ON BIRD SPIKES,Bird,Person (land line),Hedge,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34004730,RAVENSCOURT PARK,21000675,W6,522316,178686,522350,178650,51.4938962,-0.239439028
NA,24/09/2017 09:10,2017,2017/18,Special Service,1,1,328,328,DISTRESSED CAT STUCK UP TREE - CALLED BY RSPCA WHO ARE NOT ON SCENE TREE IS AT THE REAR OF FLATS AND,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000049,EAST FINCHLEY,E09000003,BARNET,Finchley,200115679,THE GRANGE ESTATE,20022381,N2,526570,190075,526550,190050,51.59531371,-0.17409938
NA,24/09/2017 10:31,2017,2017/18,Special Service,NULL,NULL,328,NULL,Redacted,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000622,ST. MARY'S PARK,E09000032,WANDSWORTH,Battersea,1.00023E+11,BRIDGES COURT ROAD,22900617,SW11,526563,176148,526550,176150,51.47015326,-0.179202095
NA,25/09/2017 15:53,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000046,COLINDALE,E09000003,BARNET,Hendon,200024793,CLOVELLY AVENUE,20009620,NW9,521574,189331,521550,189350,51.5897269,-0.246446186
NA,25/09/2017 23:34,2017,2017/18,Special Service,1,1,328,328,SQUIRREL TRAPPED IN DRAINPIPE,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000220,ELTHAM WEST,E09000011,GREENWICH,Eltham,NULL,APPLETON ROAD,20800079,SE9,NULL,NULL,542150,175550,NULL,NULL
NA,26/09/2017 15:09,2017,2017/18,Special Service,1,1,328,328,CAT  STUCK IN CHEST OF DRAWERS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009385,STOKE NEWINGTON,E09000012,HACKNEY,Stoke Newington,NULL,WALFORD ROAD,20901038,N16,NULL,NULL,533450,185850,NULL,NULL
NA,26/09/2017 17:15,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED UNDER CAR SEAT   OUTSIDE B AND M SHOP,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist  trapped livestock animal,E05000042,WHALEBONE,E09000002,BARKING AND DAGENHAM,Dagenham,100103427,WHALEBONE LANE SOUTH,19900385,RM8,548687,187771,548650,187750,51.56921279,0.144004183
NA,27/09/2017 16:57,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED BEHIND RADIATOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000268,BRUCE GROVE,E09000014,HARINGEY,Tottenham,NULL,BRUCE GROVE,21104298,N17,NULL,NULL,533450,190550,NULL,NULL
NA,29/09/2017 10:37,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,NULL,SCAWEN ROAD,22000907,SE8,NULL,NULL,536150,178350,NULL,NULL
NA,30/09/2017 08:45,2017,2017/18,Special Service,1,3,328,984,CAT TRAPPED IN CHIMNEY  ALP AND FRU CARRYING SNAKE EYE CAMERA REQUESTED FROM SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000450,PERRY VALE,E09000023,LEWISHAM,Forest Hill,NULL,ELSINORE ROAD,22001440,SE23,NULL,NULL,536350,173050,NULL,NULL
NA,30/09/2017 16:39,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON ROOF - RSPCA IN ATTENDANCE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000639,LITTLE VENICE,E09000033,WESTMINSTER,Paddington,NULL,BRISTOL MEWS,8401670,W9,NULL,NULL,525950,182050,NULL,NULL
NA,01/10/2017 09:04,2017,2017/18,Special Service,1,1,328,328,PERSONS LOCKED IN,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009389,BROMPTON & HANS TOWN,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,WALTON STREET,21700573,SW3,NULL,NULL,527550,179150,NULL,NULL
NA,03/10/2017 18:38,2017,2017/18,Special Service,1,2,328,656,Redacted,Deer,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped wild animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,10013152116,CUDHAM LANE NORTH,20303592,TN14,544436,160945,544450,160950,51.32926742,0.071771897
NA,04/10/2017 17:25,2017,2017/18,Special Service,1,1,328,328,SQUIRREL TRAPPED IN ROOF,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011239,Clayhall,E09000026,REDBRIDGE,Hainault,NULL,LONGWOOD GARDENS,22303025,IG5,NULL,NULL,542950,189250,NULL,NULL
NA,05/10/2017 11:02,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN SOME WIRE    RSPCA ON SCENE ASKING FOR ASSIST,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,WYATTS LANE,22889550,E17,NULL,NULL,538250,189850,NULL,NULL
NA,05/10/2017 19:03,2017,2017/18,Special Service,1,2,328,656,DISABLED DOG TRAPPED BEHIND METAL FENCE     NEAR CHINBROOK MEADOWS,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000445,GROVE PARK,E09000023,LEWISHAM,Bromley,10023231386,AMBLECOTE ROAD,22003110,SE12,540943,171969,540950,171950,51.42920458,0.026037452
NA,06/10/2017 07:28,2017,2017/18,Special Service,1,1,328,328,KITTEN WITH HEAD STUCK IN WASHING MACHINE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000172,DORMERS WELLS,E09000009,EALING,Southall,NULL,PALGRAVE AVENUE,20601338,UB1,NULL,NULL,513150,180750,NULL,NULL
NA,06/10/2017 12:00,2017,2017/18,Special Service,1,1,328,328,CAT UP TREE - RSPCA ON SCENE - FOREST AREA ON ROAD - RSPCA OFFICER STATES SHE WILL BE WAITING TO MEE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000602,LARKSWOOD,E09000031,WALTHAM FOREST,Chingford,1.00023E+11,ROLLS PARK ROAD,22870900,E4,537715,192123,537750,192150,51.61110351,-0.012496338
NA,07/10/2017 10:25,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED BETWEEN SHED AND CAR PARK   FRU WITH SNAKE EYE REQUESTED FROM SCENE,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000211,TURKEY STREET,E09000010,ENFIELD,Enfield,207155997,TURKEY STREET,20703019,EN3,535503,198678,535550,198650,51.67054422,-0.041885619
NA,07/10/2017 20:39,2017,2017/18,Special Service,1,2,328,656,KITTEN STUCK BEHIND THE SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,NULL,KINGSBRIDGE DRIVE,20025230,NW7,NULL,NULL,523850,191350,NULL,NULL
NA,08/10/2017 14:19,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000286,CANONS,E09000015,HARROW,Stanmore,NULL,LONDON ROAD,21200906,HA8,NULL,NULL,518250,192750,NULL,NULL
NA,09/10/2017 09:42,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH FOX POSSIBLY TRAPPED UNDER A CAR,Fox,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped wild animal,E05000647,WARWICK,E09000033,WESTMINSTER,Chelsea,10033533675,BULLEID WAY,8400302,SW1W,528766,178695,528750,178650,51.49254641,-0.146574638
NA,12/10/2017 00:37,2017,2017/18,Special Service,1,1,328,328,FOX CAUGHT IN METAL GATE  RSPCA INSPECTOR ON SCENE (UNABLE TO SEPARATE THE BARS OF THE GATE)  .,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011252,South Woodford,E09000026,REDBRIDGE,Woodford,1.00022E+11,THE DRIVE,22305366,E18,540070,189409,540050,189450,51.5861359,0.020409457
NA,13/10/2017 06:04,2017,2017/18,Special Service,1,1,328,328,"FOX TRAPPED IN A GATE DIRECTIONS GIVEN ARE - TURN LEFT OUT OF THE STATION, THEN LEFT AGAIN AT THE BR",Fox,Person (land line),Wasteland,Outdoor,Other animal assistance,Assist trapped wild animal,E05000439,BROCKLEY,E09000023,LEWISHAM,Lewisham,1.00022E+11,COULGATE STREET,22000274,SE4,536465,175806,536450,175850,51.46478017,-0.036857526
NA,14/10/2017 15:39,2017,2017/18,Special Service,1,1,328,328,DOG WITH HEAD TRAPPED BETWEEN RAILINGS AND BRICK WALL  CALLER STATES BEHIND ROMFORD MARKET,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000319,ROMFORD TOWN,E09000016,HAVERING,Romford,1.00023E+11,NORTH STREET,21301455,RM1,551142,188984,551150,188950,51.57946009,0.179921109
NA,14/10/2017 18:51,2017,2017/18,Special Service,1,1,328,328,DOG WITH HEAD STUCK IN RAILINGS - OWNER ON SCENE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Chingford,NULL,NEWGATE STREET,22861050,E4,NULL,NULL,539250,193150,NULL,NULL
NA,14/10/2017 19:30,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT STUCK ON THIRD FLOOR WINDOW,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000279,STROUD GREEN,E09000014,HARINGEY,Hornsey,NULL,STAPLETON HALL ROAD,21103929,N4,NULL,NULL,531050,187950,NULL,NULL
NA,16/10/2017 11:38,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN TWO WALLS CALL FROM RSPCA - THEY ARE NOT ABLE TO ATTEND,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009335,WEAVERS,E09000030,TOWER HAMLETS,Bethnal Green,NULL,BETHNAL GREEN ROAD,22700150,E2,NULL,NULL,534250,182650,NULL,NULL
NA,16/10/2017 12:49,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT STUCK UP A TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000059,TOTTERIDGE,E09000003,BARNET,Finchley,200130243,WOODSIDE GRANGE ROAD,20047380,N12,525997,192914,525950,192950,51.6209556,-0.181347886
NA,16/10/2017 17:45,2017,2017/18,Special Service,1,1,328,328,DOG STUCK BETWEEN FENCES    CALLER IS AT THE BOTTOM OF NORTHWOOD PRIMARY SCHOOL,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011232,Thamesmead East,E09000004,BEXLEY,Erith,1.0002E+11,ASPEN GREEN,20100063,DA18,548677,179163,548650,179150,51.49187046,0.140230387
NA,17/10/2017 19:06,2017,2017/18,Special Service,1,1,328,328,CAT STUCK BEHIND PANEL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000269,CROUCH END,E09000014,HARINGEY,Hornsey,NULL,CLAREMONT ROAD,21100172,N6,NULL,NULL,529250,187950,NULL,NULL
NA,18/10/2017 08:35,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN WASHING MACHINE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000202,HIGHLANDS,E09000010,ENFIELD,Enfield,NULL,BYCULLAH ROAD,20702553,EN2,NULL,NULL,531950,196750,NULL,NULL
NA,18/10/2017 17:57,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000337,PINKWELL,E09000017,HILLINGDON,Hayes,NULL,CARLTON AVENUE,21400313,UB3,NULL,NULL,509050,178550,NULL,NULL
NA,20/10/2017 10:29,2017,2017/18,Special Service,1,1,328,328,"FOX TRAPPED ON PART OF BUILDING SITE - WHERE THE OLD BHS WAS - CALLER IS A BLUE CROSS VET, SHE WILL",Fox,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000599,HIGH STREET,E09000031,WALTHAM FOREST,Walthamstow,2.00001E+11,SELBORNE WALK,22873500,E17,537138,189070,537150,189050,51.58381075,-0.022015577
NA,20/10/2017 12:10,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN WALL AND BIKE SHED,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05009369,CLISSOLD,E09000012,HACKNEY,Stoke Newington,1.00021E+11,LORDSHIP ROAD,20900638,N16,532918,186559,532950,186550,51.56225639,-0.08383687
NA,21/10/2017 13:32,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,1.00022E+11,MICHELDEVER ROAD,22001712,SE12,539821,174504,539850,174550,51.45226205,0.01090871
NA,21/10/2017 14:32,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000185,NORTHOLT WEST END,E09000009,EALING,Southall,NULL,CANBERRA DRIVE,20600310,UB5,NULL,NULL,511450,182750,NULL,NULL
NA,23/10/2017 10:24,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED IN VOID IN WALL,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000033,GORESBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,STARMANS CLOSE,19903778,RM9,NULL,NULL,548450,183750,NULL,NULL
NA,23/10/2017 20:18,2017,2017/18,Special Service,1,1,328,328,CAT POSSIBLY TRAPPED IN CAR ENGINE                 ENTER VIA MAIN GATE   UPPER CAR PARK  BY THE BUS,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000186,NORWOOD GREEN,E09000009,EALING,Southall,12001075,UXBRIDGE ROAD,20602324,UB1,514814,180045,514850,180050,51.50768194,-0.347012919
NA,24/10/2017 08:42,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE  -  CAT FALLEN ONTO BALCONY,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009384,STAMFORD HILL WEST,E09000012,HACKNEY,Stoke Newington,NULL,DUNSMURE ROAD,20900349,N16,NULL,NULL,533550,187450,NULL,NULL
NA,24/10/2017 17:46,2017,2017/18,Special Service,1,1,328,328,SMALL DOG WIUTH PAW CAUGHT IN PLUGHOLE OF BATH,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,NULL,ATHERTON ROAD,22207604,E7,NULL,NULL,539850,184850,NULL,NULL
NA,25/10/2017 12:10,2017,2017/18,Special Service,1,1,328,328,DOGS LEG CAUGHT IN CAR DOOR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000340,UXBRIDGE NORTH,E09000017,HILLINGDON,Hillingdon,1.00021E+11,SWEETCROFT LANE,21401907,UB10,507407,184463,507450,184450,51.54884735,-0.452352525
NA,26/10/2017 11:03,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011225,Erith,E09000004,BEXLEY,Erith,NULL,FRASER ROAD,20100571,DA8,NULL,NULL,550850,178050,NULL,NULL
NA,28/10/2017 18:12,2017,2017/18,Special Service,1,2,328,656,HORSE WITH LEG TRAPPED IN FENCE,Horse,Person (mobile),Other road vehicle,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000288,GREENHILL,E09000015,HARROW,Harrow,10070268594,STATION ROAD,21201079,HA1,515633,188832,515650,188850,51.58649027,-0.332331596
NA,31/10/2017 16:08,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL TO KITTEN TRAPPED IN ENGINE COMPARTMENT OF SMART CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,1.00024E+11,CAMBRIDGE DRIVE,22003186,SE12,540246,174661,540250,174650,51.4535679,0.017083351
NA,31/10/2017 19:06,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA TO RELEASE A CAT,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000200,GRANGE,E09000010,ENFIELD,Southgate,207124301,UPLANDS WAY,20703023,N21,531382,195908,531350,195950,51.64663027,-0.102482848
NA,01/11/2017 17:05,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH  PIDGEON TRAPPED IN NETTING  OPPOSITE TRAIN STATION,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000451,RUSHEY GREEN,E09000023,LEWISHAM,Lewisham,2.00001E+11,STATION APPROACH,22001929,SE6,537357,173564,537350,173550,51.44441715,-0.024894456
NA,01/11/2017 21:54,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED IN ROOF VOID,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000325,BOTWELL,E09000017,HILLINGDON,Hayes,NULL,CLAYTON ROAD,21400418,UB3,NULL,NULL,509550,179650,NULL,NULL
NA,02/11/2017 12:24,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,NULL,ST GEORGES ROAD,21800892,KT2,NULL,NULL,519050,170150,NULL,NULL
NA,03/11/2017 14:09,2017,2017/18,Special Service,1,3,328,984,HORSE WITH LEG STUCK IN HOLE     CALLER WILL MEET LFB AT ORANGE TREE PH,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000313,HAVERING PARK,E09000016,HAVERING,Romford,1.00021E+11,ORANGE TREE HILL,21301469,RM4,551138,192883,551150,192850,51.6144935,0.181542194
NA,03/11/2017 17:09,2017,2017/18,Special Service,1,1,328,328,CAT  STUCK UNDER CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011241,Cranbrook,E09000026,REDBRIDGE,Ilford,1.00022E+11,WANSTEAD PARK ROAD,22303358,IG1,542591,187101,542550,187150,51.56476542,0.055836741
NA,05/11/2017 13:05,2017,2017/18,Special Service,1,3,328,984,HORSE STUCK IN A DITCH TURN ONTO LEA VALLEY ROAD HEADING TOWARDS NAGS HEAD ROAD  JUST BEFORE ROUNDAB,Horse,Person (land line),Canal/riverbank vegetation,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207172899,LEA VALLEY ROAD,20703459,EN3,536416,195504,536450,195550,51.64180117,-0.02992876
NA,05/11/2017 13:18,2017,2017/18,Special Service,1,2,328,656,SMALL ANIMAL RESCUE - PIGEON ENTANGLED IN STRING AROUND BALLOON WHICH ATTACHED TO A TV AERIEL ON ROO,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000273,HORNSEY,E09000014,HARINGEY,Hornsey,NULL,HAWTHORN ROAD,21103396,N8,NULL,NULL,530150,189550,NULL,NULL
NA,05/11/2017 20:04,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN NETTING  - RSPCA REP WILL MEET YOU BY METRO STORE,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Battersea,1.00023E+11,BATTERSEA PARK ROAD,22900337,SW11,528338,176642,528350,176650,51.47419343,-0.153481481
NA,07/11/2017 17:07,2017,2017/18,Special Service,1,2,328,656,FOX TRAPPED IN GENERATOR HOLE,Fox,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,LADBROKE GARDENS,21700911,W11,NULL,NULL,524550,180850,NULL,NULL
NA,08/11/2017 12:52,2017,2017/18,Special Service,1,1,328,328,BIRD TANGLED IN WIRE ON TELEPHONE POST,Bird,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000089,DUDDEN HILL,E09000005,BRENT,Willesden,202203988,HIGH ROAD,20200934,NW10,521638,184804,521650,184850,51.54902734,-0.247089527
NA,08/11/2017 14:09,2017,2017/18,Special Service,1,1,328,328,BIRD STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011239,Clayhall,E09000026,REDBRIDGE,Ilford,NULL,REDBRIDGE LANE EAST,22303176,IG4,NULL,NULL,542650,188950,NULL,NULL
NA,10/11/2017 10:59,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN NETTING- REQUESTED BY RSPCA,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,PALMERS ROAD,20705024,N11,NULL,NULL,529250,192250,NULL,NULL
NA,10/11/2017 12:24,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED UNDER BATH,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000200,GRANGE,E09000010,ENFIELD,Edmonton,NULL,MORTIMER DRIVE,20702851,EN1,NULL,NULL,533150,195550,NULL,NULL
NA,10/11/2017 18:58,2017,2017/18,Special Service,1,1,328,328,DEER TRAPPED IN RAILINGS OPPOSITE IN MILL HILL FIELD,Deer,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,10091041402,THE RIDGEWAY,20036680,NW7,522218,193060,522250,193050,51.62310049,-0.235856773
NA,12/11/2017 10:03,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH DOG IN CANAL   LOCATION OPP ASCOT GDNS,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000195,CHASE,E09000010,ENFIELD,Enfield,207185397,TURKEY STREET,20703019,EN1,534500,199100,534550,199150,51.67457719,-0.056218332
NA,13/11/2017 16:29,2017,2017/18,Special Service,1,1,328,328,PIGEONS LOCKED IN DERELICT FACTORY  NEAR THE WHITE KNIGHT  LAUNDRY SERVICE   THE CALLER WILL MEET YO,Bird,Person (land line),Warehouse,Non Residential,Other animal assistance,Assist trapped wild animal,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217043053,KENSAL ROAD,21700902,W10,524201,182291,524250,182250,51.52588481,-0.211030368
NA,14/11/2017 19:21,2017,2017/18,Special Service,1,1,328,328,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011227,Longlands,E09000004,BEXLEY,Sidcup,NULL,LONGLANDS ROAD,20100873,DA15,NULL,NULL,545950,172550,NULL,NULL
NA,14/11/2017 20:28,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN NEETING  IN COURT YARD,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000095,MAPESBURY,E09000005,BRENT,West Hampstead,NULL,SHOOT UP HILL,20202001,NW2,NULL,NULL,524250,185050,NULL,NULL
NA,18/11/2017 06:17,2017,2017/18,Special Service,1,2,328,656,FOX STUCK IN A GATE,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,1.00022E+11,ADDISON GROVE,21500006,W4,521502,179072,521550,179050,51.49754064,-0.251025964
NA,20/11/2017 14:08,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000362,ISLEWORTH,E09000018,HOUNSLOW,Heston,10093256859,ST JOHNS ROAD,21500981,TW7,515721,176079,515750,176050,51.47185116,-0.335248742
NA,21/11/2017 09:34,2017,2017/18,Special Service,1,1,328,328,PIGEONS LOCKED IN DERELICT FACTORY NEAR THE WHITE KNIGHT LAUNDRY SERVICES,Bird,Person (land line),Warehouse,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217043053,KENSAL ROAD,21700902,W10,524205,182291,524250,182250,51.52588393,-0.210972738
NA,21/11/2017 20:45,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN TWO BRICK WALLS,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,1.00021E+11,SOUTHEND CLOSE,20801378,SE9,543604,174365,543650,174350,51.45006713,0.065259436
NA,22/11/2017 20:48,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH INJURED SEAL ON THE PONTOON NEAR THE RIVERSIDE PUB CALLER WILL MEET,Unknown - Wild Animal,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000347,BRENTFORD,E09000018,HOUNSLOW,Chiswick,10091693258,THE HOLLOWS,21500617,TW8,518703,177896,518750,177850,51.48756496,-0.291724368
NA,24/11/2017 12:37,2017,2017/18,Special Service,1,1,328,328,CAT IN TREE - CALLED X RSPCA,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000453,TELEGRAPH HILL,E09000023,LEWISHAM,New Cross,1.00023E+11,VESTA ROAD,22001072,SE4,536068,176214,536050,176250,51.46854222,-0.042412075
NA,24/11/2017 18:52,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN WIRE ON ROOF,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000593,CHINGFORD GREEN,E09000031,WALTHAM FOREST,Chingford,NULL,STATION ROAD,22876700,E4,NULL,NULL,538850,194450,NULL,NULL
NA,25/11/2017 14:17,2017,2017/18,Special Service,1,1,328,328,CROW STUCK IN NETTING ON ROOF OF HOUSE - CALLED BY PASSERBY - RSPCA BEING CALLED,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000531,TWICKENHAM RIVERSIDE,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,HOLLY ROAD,22403565,TW1,NULL,NULL,516150,173350,NULL,NULL
NA,25/11/2017 14:46,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,NULL,GREENWICH SOUTH STREET,20800688,SE10,NULL,NULL,537950,176850,NULL,NULL
NA,28/11/2017 17:45,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000334,MANOR,E09000017,HILLINGDON,Ruislip,NULL,DARTMOUTH ROAD,21400559,HA4,NULL,NULL,510350,186250,NULL,NULL
NA,28/11/2017 19:22,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE GUINEA PIGS WITH HEADS CAUGHT IN METAL CAGE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000368,CALEDONIAN,E09000019,ISLINGTON,Islington,NULL,THORNHILL SQUARE,21606132,N1,NULL,NULL,530850,183950,NULL,NULL
NA,29/11/2017 07:49,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED HANGING UPSIDE DOWN IN TREE OPP BAKERY,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,202105725,DISRAELI ROAD,20201279,NW10,520535,183199,520550,183150,51.53483877,-0.263539181
NA,29/11/2017 08:06,2017,2017/18,Special Service,1,1,328,328,DEER WITH HEAD STUCK IN THE RAILINGS     NEXT TO BUS STOP DC,Deer,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000314,HEATON,E09000016,HAVERING,Harold Hill,10070702251,HILLDENE AVENUE,21301279,RM3,553165,191892,553150,191850,51.605042,0.210361365
NA,29/11/2017 16:46,2017,2017/18,Special Service,1,1,328,328,DOG STUCK BETWEEN TWO BRICK WALLS OF BUILDING EXTENSION,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000323,UPMINSTER,E09000016,HAVERING,Hornchurch,NULL,CORBETS TEY ROAD,21301755,RM14,NULL,NULL,555950,185950,NULL,NULL
NA,30/11/2017 11:05,2017,2017/18,Special Service,1,1,328,328,TO ASSIST RSPCA WITH DISTRESSED CAT,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000559,CARSHALTON SOUTH AND CLOCKHOUSE,E09000029,SUTTON,Purley,5870073936,GROVE LANE,22602358,CR5,528815,160051,528850,160050,51.3249803,-0.152633767
NA,30/11/2017 17:47,2017,2017/18,Special Service,1,3,328,984,HORSE FALLEN INTO STREAM NEXT TO GRANT AND STONE,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000336,NORTHWOOD HILLS,E09000017,HILLINGDON,Ruislip,1.00024E+11,JOEL STREET,21401058,HA5,510481,189876,510450,189850,51.59690707,-0.40633654
NA,02/12/2017 21:52,2017,2017/18,Special Service,1,2,328,656,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009370,DALSTON,E09000012,HACKNEY,Homerton,NULL,RAMSGATE STREET,20900838,E8,NULL,NULL,533850,184950,NULL,NULL
NA,05/12/2017 11:12,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN BARBED WIRE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,1.00021E+11,CONISTON ROAD,21104393,N17,534121,191602,534150,191650,51.60728911,-0.06456874
NA,05/12/2017 17:22,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED BEHIND BATH PANEL,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000223,KIDBROOKE WITH HORNFAIR,E09000011,GREENWICH,East Greenwich,NULL,WRICKLEMARSH ROAD,20801655,SE3,NULL,NULL,541550,176750,NULL,NULL
NA,06/12/2017 10:20,2017,2017/18,Special Service,1,1,328,328,KITTEN STUCK IN TREE CALLER THINKS THE KITTEN MAY BE STUCK BY HER COLLAR,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000210,TOWN,E09000010,ENFIELD,Enfield,207105187,LADYSMITH ROAD,20702781,EN1,533688,197167,533650,197150,51.65740082,-0.068693069
NA,08/12/2017 10:11,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED BETWEEN TWO FENCES  BEHIND SELF STORGAE,Dog,Person (mobile),Other bulk storage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Woodside,10003629561,TANNERY CLOSE,20302041,BR3,535620,168127,535650,168150,51.39597568,-0.051951203
NA,09/12/2017 14:39,2017,2017/18,Special Service,1,1,328,328,Redacted,Unknown - Domestic Animal Or Pet,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,6006305,DELLOW STREET,22700403,E1,534962,180818,534950,180850,51.51018076,-0.056565763
NA,09/12/2017 17:45,2017,2017/18,Special Service,1,1,328,328,CALLERS SMALL PET TRAPPED BETWEEN WALL,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000275,NOEL PARK,E09000014,HARINGEY,Hornsey,NULL,BURY ROAD,21104308,N22,NULL,NULL,531450,189850,NULL,NULL
NA,10/12/2017 23:31,2017,2017/18,Special Service,1,1,328,328,CAT STUCK UNDER TERRACE FLOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000296,MARLBOROUGH,E09000015,HARROW,Harrow,NULL,HEADSTONE DRIVE,21201672,HA3,NULL,NULL,515350,189650,NULL,NULL
NA,17/12/2017 22:32,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000225,PENINSULA,E09000011,GREENWICH,East Greenwich,NULL,VANBRUGH HILL,20801534,SE10,NULL,NULL,539450,178150,NULL,NULL
NA,18/12/2017 14:39,2017,2017/18,Special Service,1,1,328,328,PUPPY WITH HEAD TRAPPED UNDER WORKTOP,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000406,COOMBE HILL,E09000021,KINGSTON UPON THAMES,New Malden,NULL,WOLSEY CLOSE,21801064,KT2,NULL,NULL,519750,169750,NULL,NULL
NA,18/12/2017 15:01,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED UNDER FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,5870068797,SHREWSBURY ROAD,22601006,SM5,527404,166829,527450,166850,51.38621331,-0.170448139
NA,19/12/2017 16:11,2017,2017/18,Special Service,1,1,328,328,PUPPY WITH LEG TRAPPED IN PUSHCHAIR,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011224,East Wickham,E09000004,BEXLEY,Plumstead,NULL,MILTON ROAD,20100984,DA16,NULL,NULL,545550,176950,NULL,NULL
NA,20/12/2017 09:25,2017,2017/18,Special Service,1,1,328,328,CAT UP TREE REQUEST FROM RSPCA ON SCENE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011236,Bridge,E09000026,REDBRIDGE,Woodford,1.00023E+11,MORETON GARDENS,22305197,IG8,542365,192059,542350,192050,51.60937309,0.054587368
NA,21/12/2017 13:48,2017,2017/18,Special Service,1,1,328,328,FOX TRAPPED ON SIXTH FLOOR - BELIEVED STUCK ON ROOF BY FIRE ESCAPE,Fox,Person (land line),Hospital,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000641,MARYLEBONE HIGH STREET,E09000033,WESTMINSTER,Euston,1.00023E+11,GREAT PORTLAND STREET,8400603,W1W,528881,182113,528850,182150,51.52323756,-0.143670343
NA,23/12/2017 15:59,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009385,STOKE NEWINGTON,E09000012,HACKNEY,Stoke Newington,1.00021E+11,YORKSHIRE CLOSE,20901101,N16,533376,186093,533350,186050,51.55796054,-0.077410016
NA,25/12/2017 16:29,2017,2017/18,Special Service,1,1,328,328,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000030,EASTBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,THORNTONS FARM AVENUE,19900834,RM7,NULL,NULL,550850,186950,NULL,NULL
NA,26/12/2017 16:05,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT STUCK ON ROOF  TWO STORYS HIGH,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000427,PRINCE'S,E09000022,LAMBETH,Lambeth,NULL,DUGARD WAY,21902068,SE11,NULL,NULL,531750,178650,NULL,NULL
NA,27/12/2017 11:52,2017,2017/18,Special Service,1,2,328,656,TWO DOGS STUCK DOWN A HOLE PASSED BY THE GENERAL MANAGER OF THE CLUB VIA THAMES VALLEY CONTROL,Dog,Person (land line),Football stadium,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,1.00023E+11,HORTON ROAD,21401018,UB7,507040,180591,507050,180550,51.51411464,-0.458819284
NA,27/12/2017 15:57,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED BETWEEN TWO WINDOWS,Bird,Person (land line),Other entertainment venue,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,1.00023E+11,MARE STREET,20900658,E8,534981,184998,534950,184950,51.54773872,-0.054691294
NA,29/12/2017 12:37,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED ON BUOY IN RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000528,SOUTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,10070719802,TWICKENHAM BRIDGE,22406563,TW9,517230,174836,517250,174850,51.46036903,-0.31394375
NA,30/12/2017 12:54,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED BY FEET IN TREE - CALLER WILL MEET BRIGADE,Bird,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05000429,STOCKWELL,E09000022,LAMBETH,Brixton,1.00022E+11,STOCKWELL GARDENS,21901308,SW9,530647,176455,530650,176450,51.47198497,-0.120322661
NA,30/12/2017 14:15,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE RSPCA ON SCENE CAT TRAPPED BEHIND INTERNAL WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,VINES AVENUE,20044240,N3,NULL,NULL,525750,190750,NULL,NULL
NA,31/12/2017 10:40,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Shadwell,6652337,GARNET STREET,22700529,E1W,535073,180591,535050,180550,51.50811435,-0.055054146
NA,31/12/2017 12:45,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED IN WASHING MACHINE,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,NULL,GIPSY HILL,21900606,SE19,NULL,NULL,533350,170950,NULL,NULL
NA,31/12/2017 19:49,2017,2017/18,Special Service,1,1,328,328,CAT HANGING FROM SECOND FLOOR BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Shadwell,NULL,FARTHING FIELDS,22700488,E1W,NULL,NULL,534950,180350,NULL,NULL
NA,03/01/2018 08:31,2018,2017/18,Special Service,1,2,328,656,DOG STUCK IN BADGER SET - CALLER WILL MEET YOU THERE IN THE CAR PARK,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000559,CARSHALTON SOUTH AND CLOCKHOUSE,E09000029,SUTTON,Wallington,5870117711,CROYDON LANE,22602248,SM7,527630,161271,527650,161250,51.33621162,-0.16919495
NA,04/01/2018 10:37,2018,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN NETTING - SECOND FLOOR    RSPCA IN ATTENDANCE - WILL MEET YOU,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009405,STANLEY,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,KING'S ROAD,21700299,SW10,NULL,NULL,526450,177450,NULL,NULL
NA,04/01/2018 12:38,2018,2017/18,Special Service,1,1,328,328,ASSIST RSPCA  WITH BIRD TRAPPED  IN KITE WIRE,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011100,Dulwich Village,E09000028,SOUTHWARK,West Norwood,10090287587,COLLEGE ROAD,22500543,SE21,533485,173672,533450,173650,51.44631306,-0.08053499
NA,06/01/2018 01:38,2018,2017/18,Special Service,1,1,328,328,DOG WITH PAW TRAPPED ON GATE,Dog,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,10009142244,ARGALL AVENUE,22811400,E10,536189,187820,536150,187850,51.57280803,-0.036188514
NA,06/01/2018 12:28,2018,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),"Takeaway, fast food",Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000320,ST. ANDREW'S,E09000016,HAVERING,Hornchurch,1.00023E+11,HIGH STREET,21300416,RM11,553552,187252,553550,187250,51.56324733,0.213918127
NA,07/01/2018 16:56,2018,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN ROOF VOID,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000052,GARDEN SUBURB,E09000003,BARNET,Finchley,NULL,HOGARTH HILL,20023280,NW11,NULL,NULL,525050,189150,NULL,NULL
NA,09/01/2018 20:06,2018,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000619,NORTHCOTE,E09000032,WANDSWORTH,Battersea,NULL,BOLINGBROKE GROVE,22900545,SW11,NULL,NULL,527450,174350,NULL,NULL
NA,10/01/2018 14:44,2018,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000136,HAVERSTOCK,E09000007,CAMDEN,Kentish Town,NULL,HARMOOD STREET,20400488,NW1,NULL,NULL,528550,184550,NULL,NULL
NA,10/01/2018 15:16,2018,2017/18,Special Service,1,3,328,984,Redacted,Horse,Person (land line),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207005640,Y109,20706628,EN3,536399,195463,536350,195450,51.64143687,-0.030190262
NA,11/01/2018 11:46,2018,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000424,KNIGHT'S HILL,E09000022,LAMBETH,West Norwood,NULL,BROXHOLM ROAD,21900257,SE27,NULL,NULL,531350,172350,NULL,NULL
NA,11/01/2018 20:24,2018,2017/18,Special Service,1,1,328,328,DOG TRAPPED IN GATE  IN PARK,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000426,OVAL,E09000022,LAMBETH,Lambeth,1.00023E+11,BONNINGTON SQUARE,21900206,SW8,530671,177854,530650,177850,51.4845519,-0.119460617
NA,13/01/2018 10:59,2018,2017/18,Special Service,1,1,328,328,SEAGULL WITH LEG TRAPPED IN LAMP POST,Bird,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000357,HESTON WEST,E09000018,HOUNSLOW,Hayes,1.00024E+11,HAYES ROAD,21500580,UB2,510621,178773,510650,178750,51.49708476,-0.40780426
NA,14/01/2018 15:52,2018,2017/18,Special Service,1,1,328,328,ASSISTING RSPCA TO GAIN ENTRY IN THE CEMETRY TO RESCUE A FOX,Fox,Person (mobile),Cemetery,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000195,CHASE,E09000010,ENFIELD,Enfield,207156532,CEDAR ROAD,20702575,EN2,532063,198153,532050,198150,51.66664499,-0.091798937
NA,15/01/2018 07:56,2018,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000454,WHITEFOOT,E09000023,LEWISHAM,Bromley,1.00022E+11,WHITEFOOT LANE,22002033,BR1,539007,172135,539050,172150,51.4311739,-0.001727736
NA,15/01/2018 17:57,2018,2017/18,Special Service,1,1,328,328,CAT IN WATER  BEHIND THE NORTHERN AND SHELL BUILDING  CALLER SAID CAT HAS MANAGED TO HOLD ON TO SOME,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009318,BLACKWALL & CUBITT TOWN,E09000030,TOWER HAMLETS,Millwall,6061546,SELSDON WAY,22701077,E14,537787,179180,537750,179150,51.4947798,-0.016522647
NA,17/01/2018 17:46,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN TREE IN MIDDLE OF PARK CALLED BY RSPCA,Cat,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000373,HIGHBURY WEST,E09000019,ISLINGTON,Holloway,5300100960,GILLESPIE ROAD,21603259,N4,531409,186182,531450,186150,51.55922221,-0.105734113
NA,18/01/2018 05:44,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED BEHIND BOILER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009404,ST. HELEN'S,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,CAMBRIDGE GARDENS,21700828,W10,NULL,NULL,523950,181350,NULL,NULL
NA,22/01/2018 08:50,2018,2017/18,Special Service,1,1,328,328,SEAGULL TRAPPED IN STRING IN TREE - O/S ENTRANCE TO PARK,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,207002816,HOUNDSFIELD ROAD,20704353,N9,534574,194569,534550,194550,51.63384329,-0.056891969
NA,22/01/2018 11:36,2018,2017/18,Special Service,NULL,NULL,328,NULL,Redacted,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000173,EALING BROADWAY,E09000009,EALING,Ealing,12140744,THE BROADWAY,20601712,W5,517847,180908,517850,180950,51.51481473,-0.303041811
NA,22/01/2018 11:59,2018,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,207192779,HAYNES DRIVE,20706954,N9,534687,193310,534650,193350,51.62250252,-0.055744587
NA,23/01/2018 23:49,2018,2017/18,Special Service,1,1,328,328,CAT STUCK IN THE WALL BEHIND THE TOILET SYSTEM,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,NULL,LEAMINGTON CLOSE,21301347,RM3,NULL,NULL,555050,191950,NULL,NULL
NA,25/01/2018 19:39,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN FABRIC WAREHOUSE RSPCA LADY IS IN LADY MARGARET ROAD,Cat,Person (mobile),Other retail warehouse,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000172,DORMERS WELLS,E09000009,EALING,Southall,12163511,LADY MARGARET ROAD,20601009,UB1,512808,180434,512850,180450,51.51158188,-0.375781062
NA,27/01/2018 12:37,2018,2017/18,Special Service,1,1,328,328,ASSIST RSPCA TO GAIN ACCESS TO BASEMENT TO FREE TRAPPED FOX,Fox,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,PERRERS ROAD,21000638,W6,NULL,NULL,522750,178950,NULL,NULL
NA,28/01/2018 21:35,2018,2017/18,Special Service,1,1,328,328,CAT IN DISTRESS ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000350,CRANFORD,E09000018,HOUNSLOW,Feltham,NULL,LICHFIELD ROAD,21500694,TW4,NULL,NULL,511150,175550,NULL,NULL
NA,29/01/2018 10:31,2018,2017/18,Special Service,1,2,328,656,DOG TRAPPED DOWN RABBIT HOLE  CALLER WILL MEET IN CEMETRY SMALL CAR PARK,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000323,UPMINSTER,E09000016,HAVERING,Hornchurch,1.00024E+11,OCKENDON ROAD,21301872,RM14,556811,185355,556850,185350,51.54530775,0.260052465
NA,29/01/2018 12:08,2018,2017/18,Special Service,1,2,328,656,ASSIST WITH TRAPPED PIDGEON SOMEONE FROM LONDON WILDLIFE PROTECTION WILL BE THERE TO MEET YOU,Bird,Person (mobile),Train station - concourse,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000050,EDGWARE,E09000003,BARNET,Mill Hill,200107865,STATION ROAD,20040860,HA8,519546,191940,519550,191950,51.61360745,-0.274819754
NA,30/01/2018 11:23,2018,2017/18,Special Service,1,1,328,328,BIRD TRAPPED ON THIRD FLOOR - RSPCA ON SCENE,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000192,WALPOLE,E09000009,EALING,Ealing,NULL,MATTOCK LANE,20602459,W5,NULL,NULL,517450,180550,NULL,NULL
NA,30/01/2018 12:00,2018,2017/18,Special Service,1,1,328,328,CAT  FALLEN AND  STUCK ON WINDOW LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000372,HIGHBURY EAST,E09000019,ISLINGTON,Stoke Newington,NULL,RIVERSDALE ROAD,21603742,N5,NULL,NULL,531950,186050,NULL,NULL
NA,01/02/2018 18:11,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN WALL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000593,CHINGFORD GREEN,E09000031,WALTHAM FOREST,Chingford,NULL,EPPING WAY,22834300,E4,NULL,NULL,538050,195250,NULL,NULL
NA,02/02/2018 17:08,2018,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped wild animal,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Kingston,1.00023E+11,THAMES STREET,21800938,KT1,517865,169291,517850,169250,51.410401,-0.306652305
NA,02/02/2018 23:17,2018,2017/18,Special Service,1,1,328,328,LARGE DOG STUCK IN FOX HOLE - CALLED BY RSPCA - OWNER ON SCENE,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000196,COCKFOSTERS,E09000010,ENFIELD,Barnet,207128183,BEECH HILL,20700960,EN4,527352,197936,527350,197950,51.66578195,-0.159959545
NA,03/02/2018 20:45,2018,2017/18,Special Service,1,1,328,328,CAT STUCK ON ROOF - TANGLED IN IVY - REQUESTED FROM RSPCA,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000279,STROUD GREEN,E09000014,HARINGEY,Holloway,NULL,FLORENCE ROAD,21103297,N4,NULL,NULL,530950,187650,NULL,NULL
NA,04/02/2018 16:15,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN SHED AND FENCE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000614,FAIRFIELD,E09000032,WANDSWORTH,Wandsworth,121011165,EAST HILL,22901361,SW18,526060,174740,526050,174750,51.45761142,-0.186941575
NA,06/02/2018 05:37,2018,2017/18,Special Service,1,1,328,328,ASSIST POLICE WITH DEER TRAPPED IN RAILINGS,Deer,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,DAGNAM PARK DRIVE,21300071,RM3,554874,192446,554850,192450,51.60955211,0.235263148
NA,06/02/2018 16:38,2018,2017/18,Special Service,1,1,328,328,TWO PIGEONS TRAPPED IN NETTING ABOVE SHOP SIGN,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000213,WINCHMORE HILL,E09000010,ENFIELD,Edmonton,207176891,GREEN LANES,20704279,N21,531851,194145,531850,194150,51.63067762,-0.096372779
NA,07/02/2018 11:30,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000196,COCKFOSTERS,E09000010,ENFIELD,Barnet,NULL,BEECH HILL,20700960,EN4,NULL,NULL,527050,198050,NULL,NULL
NA,07/02/2018 13:54,2018,2017/18,Special Service,2,4,328,1312,ASSIST RSPCA WITH RESCUE OF CAT TRAPPED BETWEEN TWO BUILDINGS RSPCA OFFICER MICHAEL - ON SCENE- FRU,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000481,EAST HAM NORTH,E09000025,NEWHAM,East Ham,NULL,MILTON AVENUE,22200860,E6,NULL,NULL,542250,184250,NULL,NULL
NA,07/02/2018 14:34,2018,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000452,SYDENHAM,E09000023,LEWISHAM,Forest Hill,1.00024E+11,SYDENHAM HILL,22005552,SE26,533983,171713,533950,171750,51.4285906,-0.074113748
NA,10/02/2018 10:35,2018,2017/18,Special Service,1,3,328,984,DOG TRAPPED BETWEEN RAILINGS,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011106,North Bermondsey,E09000028,SOUTHWARK,Dockhead,2.00003E+11,SOUTHWARK PARK ROAD,22502293,SE16,534851,179085,534850,179050,51.49463382,-0.058826605
NA,10/02/2018 10:50,2018,2017/18,Special Service,1,1,328,328,CAT LOCKED IN BASEMENT,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000638,LANCASTER GATE,E09000033,WESTMINSTER,Paddington,NULL,GLOUCESTER TERRACE,8400410,W2,NULL,NULL,526350,181050,NULL,NULL
NA,11/02/2018 11:57,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN GARAGE AT REAR       POLICE AND RSPCA UNABLE TO ASSIST     UNKNOWN WHO OWNS GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000210,TOWN,E09000010,ENFIELD,Enfield,207147921,PARSONAGE LANE,20702893,EN2,532554,197338,532550,197350,51.65920574,-0.085012195
NA,11/02/2018 22:58,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN BASEMENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009395,EARL'S COURT,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,WARWICK ROAD,21700680,SW5,NULL,NULL,525250,178450,NULL,NULL
NA,15/02/2018 17:54,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED BEHIND PANELS IN KITCHEN,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000145,WEST HAMPSTEAD,E09000007,CAMDEN,West Hampstead,NULL,SUMATRA ROAD,20400253,NW6,NULL,NULL,525250,184850,NULL,NULL
NA,17/02/2018 10:47,2018,2017/18,Special Service,1,1,328,328,DOG WITH HEAD STUCK IN RAILINGS,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009329,ST. DUNSTAN'S,E09000030,TOWER HAMLETS,Shadwell,6029579,STEPNEY HIGH STREET,22701161,E1,535990,181611,535950,181650,51.51706061,-0.041455496
NA,17/02/2018 12:02,2018,2017/18,Special Service,1,1,328,328,PIGEON CAUGHT IN NETTING IN TREE CALLER STATES IN PARK,Bird,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Shadwell,10015487120,CLEGG STREET,22700325,E1W,534973,180318,534950,180350,51.50568495,-0.056598666
NA,17/02/2018 12:51,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN FENCE  BLUE GATES SCHOOL,Cat,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,6024901,KING DAVID LANE,22700701,E1,535097,180910,535050,180950,51.51097525,-0.054586346
NA,18/02/2018 23:55,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED BEHIND SINK,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000271,HARRINGAY,E09000014,HARINGEY,Hornsey,NULL,LAUSANNE ROAD,21103520,N8,NULL,NULL,531450,189350,NULL,NULL
NA,20/02/2018 15:54,2018,2017/18,Special Service,1,1,328,328,ASSISIT RSPCA WITH PIGEON TRAPPED IN TREE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000303,STANMORE PARK,E09000015,HARROW,Stanmore,NULL,ELLIOTT ROAD,21202201,HA7,NULL,NULL,516350,191950,NULL,NULL
NA,21/02/2018 10:26,2018,2017/18,Special Service,1,1,328,328,DOG STUCK IN PIPE    FRU REQUESTED FOR LARGE ANIMAL RESCUE AND SNAKE EYE  BEHIND THE PAVILLION   GO,Dog,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000056,HIGH BARNET,E09000003,BARNET,Barnet,200154392,TUDOR ROAD,20043380,EN5,525465,196825,525450,196850,51.6562217,-0.187627774
NA,21/02/2018 14:11,2018,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN AIR VENT,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000429,STOCKWELL,E09000022,LAMBETH,Clapham,NULL,PRIORY GROVE,21901122,SW8,NULL,NULL,530050,176450,NULL,NULL
NA,22/02/2018 15:00,2018,2017/18,Special Service,1,1,328,328,DOG WITH HEAD TRAPPED IN FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000435,TULSE HILL,E09000022,LAMBETH,West Norwood,1.00022E+11,TULSE HILL,21901408,SW2,531152,173993,531150,173950,51.44974266,-0.113967977
NA,23/02/2018 12:19,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN MOTORBIKE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000320,ST. ANDREW'S,E09000016,HAVERING,Hornchurch,NULL,MINSTER WAY,21300501,RM11,NULL,NULL,554950,187150,NULL,NULL
NA,24/02/2018 17:31,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN VENT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000494,WEST HAM,E09000025,NEWHAM,Stratford,NULL,WEST ROAD,22201663,E15,NULL,NULL,539950,183850,NULL,NULL
NA,26/02/2018 12:43,2018,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT STUCK ON ROOF  RSPCA OFFICER ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000192,WALPOLE,E09000009,EALING,Ealing,NULL,CHURCH LANE,20600382,W5,NULL,NULL,517550,179750,NULL,NULL
NA,28/02/2018 09:57,2018,2017/18,Special Service,1,1,328,328,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000058,OAKLEIGH,E09000003,BARNET,Barnet,10025378536,GREAT NORTH ROAD,20019050,EN5,525825,195487,525850,195450,51.64411692,-0.182906984
NA,28/02/2018 11:04,2018,2017/18,Special Service,1,1,328,328,ASSIST RSPCA - CAT TRAPPED ON ROOF - FOUR FLOORS UP,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000470,ST. HELIER,E09000024,MERTON,Mitcham,NULL,HATFEILD MEAD,22103156,SM4,NULL,NULL,525150,167650,NULL,NULL
NA,28/02/2018 13:50,2018,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED IN CAR ENGINE  RSPCA AND ANIMAL TRUST IN ATTENDANCE,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000449,NEW CROSS,E09000023,LEWISHAM,New Cross,10070773076,LOVELINCH CLOSE,22000644,SE15,535427,177649,535450,177650,51.4815916,-0.051085024
NA,28/02/2018 17:00,2018,2017/18,Special Service,1,2,328,656,SMALL ANIMAL RESCUE DOG WITH LEG STUCK IN RECLINER CHAIR MECHANISM,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011251,Seven Kings,E09000026,REDBRIDGE,Ilford,NULL,BEDDINGTON ROAD,22301843,IG3,NULL,NULL,545650,187950,NULL,NULL
NA,28/02/2018 17:13,2018,2017/18,Special Service,1,1,328,328,MAN STUCK ON ISLAND OF LAKE-OFF KINGSTON ROAD-CALLER IS WAITING AT J/O LANGDON PARK AND KINGSTON ROA,Unknown - Domestic Animal Or Pet,Person (land line),Park,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000522,HAMPTON WICK,E09000027,RICHMOND UPON THAMES,Twickenham,10002255628,LANGDON PARK,22407115,TW11,517176,170383,517150,170350,51.42035823,-0.316193913
NA,28/02/2018 17:34,2018,2017/18,Special Service,1,1,328,328,DOG IN POND - FALLEN THROUGH ICE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000507,LOXFORD,E09000026,REDBRIDGE,Ilford,10034912879,ALDER WALK,22306313,IG1,544515,184988,544550,184950,51.5452898,0.082708071
NA,02/03/2018 12:09,2018,2017/18,Special Service,1,1,328,328,DOG INTO FROZEN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000593,CHINGFORD GREEN,E09000031,WALTHAM FOREST,Chingford,10025293518,WARREN POND ROAD,22885250,E4,539752,194644,539750,194650,51.63325507,0.017907253
NA,02/03/2018 12:21,2018,2017/18,Special Service,1,1,328,328,DOG IN FROZEN LAKE  THIS IS ON WANDSWORTH COMMON,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000618,NIGHTINGALE,E09000032,WANDSWORTH,Tooting,121015137,BELLEVUE ROAD,22900453,SW17,527464,173714,527450,173750,51.44807667,-0.167113079
NA,03/03/2018 11:58,2018,2017/18,Special Service,2,3,328,984,DOG TRAPPED ON ICE DUE TO FROZEN LAKE FRU ACCESS VIA THE FAIRWAY,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000058,OAKLEIGH,E09000003,BARNET,Barnet,200154418,GREAT NORTH ROAD,20019050,EN5,525855,195549,525850,195550,51.64466739,-0.182451363
NA,03/03/2018 12:13,2018,2017/18,Special Service,1,1,328,328,DOG TRAPPED IN HOLE FRU REQUESTED FROM INCIDENT WITH LARGE ANIMAL RESCUE EQUIPMENT AND SNAKE EYE CAM,Dog,Person (mobile),Golf course (not building on course),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000187,PERIVALE,E09000009,EALING,Ealing,12146468,PERIVALE LANE,20601368,UB6,516250,182789,516250,182750,51.53205099,-0.325425766
NA,03/03/2018 16:49,2018,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE KITTEN STUCK IN BUILDING VOID,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000049,EAST FINCHLEY,E09000003,BARNET,Finchley,NULL,SUMMERLEE GARDENS,20041400,N2,NULL,NULL,527550,189250,NULL,NULL
NA,04/03/2018 10:52,2018,2017/18,Special Service,1,1,328,328,PIG STUCK IN RAILINGS,Unknown - Heavy Livestock Animal,Person (mobile),Zoo,Non Residential,Other animal assistance,Assist  trapped livestock animal,E05000479,CUSTOM HOUSE,E09000025,NEWHAM,Plaistow,46042407,KING GEORGE AVENUE,22207984,E16,541737,181253,541750,181250,51.51243173,0.041172259
NA,04/03/2018 17:04,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN BRAMBLES - ELDERLY LADY UNABLE TO REACH CAT,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000059,TOTTERIDGE,E09000003,BARNET,Barnet,200037045,ELKANETTE MEWS,20014090,N20,526143,193560,526150,193550,51.62672831,-0.179007588
NA,05/03/2018 19:52,2018,2017/18,Special Service,1,1,328,328,CAT STUCK IN TREE    RSPCA NOT RESPONDED TO CALLER,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,207055072,HENLEY ROAD,20704329,N18,533169,192856,533150,192850,51.61878395,-0.077832004
NA,06/03/2018 10:11,2018,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000033,GORESBROOK,E09000002,BARKING AND DAGENHAM,Barking,NULL,WOODWARD ROAD,19900629,RM9,NULL,NULL,547650,184350,NULL,NULL
NA,07/03/2018 17:22,2018,2017/18,Special Service,1,1,328,328,CAT STUCK IN CONDEMED BUILDING,Cat,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000319,ROMFORD TOWN,E09000016,HAVERING,Hornchurch,1.00024E+11,ALBERT ROAD,21300937,RM1,551949,188352,551950,188350,51.57356475,0.191285364
NA,08/03/2018 07:44,2018,2017/18,Special Service,1,1,328,328,RUNNING CALL TO CAT ON ROOF,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000252,AVONMORE AND BROOK GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,BROOK GREEN,21000161,W6,NULL,NULL,523550,179050,NULL,NULL
NA,09/03/2018 00:30,2018,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000424,KNIGHT'S HILL,E09000022,LAMBETH,West Norwood,NULL,LEIGHAM COURT ROAD,21900860,SW16,NULL,NULL,531250,171050,NULL,NULL
NA,09/03/2018 04:33,2018,2017/18,Special Service,1,2,328,656,CAT IMPALED ON FENCE RSPCA ON SCENE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000088,DOLLIS HILL,E09000005,BRENT,Willesden,202018795,FLOWERS CLOSE,20201174,NW2,522159,186222,522150,186250,51.56165909,-0.239086812
NA,09/03/2018 07:16,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED ON BARBED WIRE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000253,COLLEGE PARK AND OLD OAK,E09000013,HAMMERSMITH AND FULHAM,Acton,NULL,WESTWAY,21000877,W12,NULL,NULL,522450,180950,NULL,NULL
NA,09/03/2018 09:30,2018,2017/18,Special Service,1,1,328,328,INJURED FOX DUE TO WIRING IN YARD,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,NULL,BROMFIELD STREET,21606765,N1,NULL,NULL,531450,183450,NULL,NULL
NA,09/03/2018 14:30,2018,2017/18,Special Service,2,7,328,2296,LARGE ANIMAL RESCUE STAG TRAPPED IN FENCING,Unknown - Wild Animal,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Assist trapped wild animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,1.00023E+11,BERRYS GREEN ROAD,20301445,TN16,543958,159360,543950,159350,51.31514529,0.064276154
NA,10/03/2018 23:06,2018,2017/18,Special Service,1,1,328,328,PUPPY WITH HEAD TRAPPED IN STAIR BANNISTERS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000090,FRYENT,E09000005,BRENT,Stanmore,NULL,KINGSBURY ROAD,20201608,NW9,NULL,NULL,519950,188750,NULL,NULL
NA,11/03/2018 12:41,2018,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000518,FULWELL AND HAMPTON HILL,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,ANLABY ROAD,22403255,TW11,NULL,NULL,515050,171150,NULL,NULL
NA,12/03/2018 12:33,2018,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE RSPCA ON SCENE,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,128021348,CHAPEL MILL ROAD,21880152,KT1,518937,168569,518950,168550,51.40368803,-0.29148726
NA,13/03/2018 09:51,2018,2017/18,Special Service,1,2,328,656,Redacted,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000273,HORNSEY,E09000014,HARINGEY,Hornsey,1.00021E+11,CAMPSFIELD ROAD,21103048,N8,530359,189634,530350,189650,51.59048735,-0.119592883
NA,13/03/2018 14:08,2018,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT TRAPPED IN DRAIN,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000293,HEADSTONE SOUTH,E09000015,HARROW,Harrow,NULL,BEDFORD ROAD,21201384,HA1,NULL,NULL,514350,188450,NULL,NULL
NA,14/03/2018 15:42,2018,2017/18,Special Service,1,1,328,328,KITTEN STUCK UP TREE.  CALLER ADVISED TO CONTACT LFB BY RSPCA AS IT SOUNDS LIKE ANIMAL IS IN IMMEDIA,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000193,BOWES,E09000010,ENFIELD,Edmonton,207185096,ST PAULS RISE,20705048,N13,531764,191863,531750,191850,51.61019115,-0.098487163
NA,15/03/2018 12:52,2018,2017/18,Special Service,1,2,328,656,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000193,BOWES,E09000010,ENFIELD,Edmonton,207185096,ST PAULS RISE,20705048,N13,531767,191866,531750,191850,51.61021741,-0.098442737
NA,15/03/2018 15:07,2018,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Hospital,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000629,WEST PUTNEY,E09000032,WANDSWORTH,Wandsworth,121052762,ROEHAMPTON LANE,22904379,SW15,522261,174418,522250,174450,51.45554946,-0.24170598
NA,17/03/2018 18:04,2018,2017/18,Special Service,1,1,328,328,TO ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000110,CHELSFIELD AND PRATTS BOTTOM,E09000006,BROMLEY,Orpington,NULL,CROWN ROAD,20300860,BR6,NULL,NULL,546450,164450,NULL,NULL
NA,18/03/2018 07:36,2018,2017/18,Special Service,1,1,328,328,SEAGULL WITH FEET TRAPPED,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000141,KING'S CROSS,E09000007,CAMDEN,Euston,5132408,WREN STREET,20401020,WC1X,530822,182419,530850,182450,51.52554173,-0.115594861
NA,18/03/2018 12:07,2018,2017/18,Special Service,1,3,328,984,TO ASSIST VET IN LARGE ANIMAL RESCUE  LIFTING A FALLEN HORSE ALL APPLIANCES TO ENTER VIA CLOCKHOUSE,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000313,HAVERING PARK,E09000016,HAVERING,Romford,1.00023E+11,LODGE LANE,21300018,RM5,549336,192759,549350,192750,51.6138595,0.155482586
NA,18/03/2018 16:59,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN RECLING CHAIR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000470,ST. HELIER,E09000024,MERTON,Sutton,NULL,GARENDON ROAD,22102787,SM4,NULL,NULL,525450,166850,NULL,NULL
NA,19/03/2018 19:49,2018,2017/18,Special Service,1,1,328,328,RUNNING CALL TO CAT TRAPPED ON SCAFFOLDING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009405,STANLEY,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,CARLYLE SQUARE,21700072,SW3,NULL,NULL,527050,177950,NULL,NULL
NA,20/03/2018 19:15,2018,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN CHIMNEY   ACCESS VIA THE RIDGEWAY,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000472,VILLAGE,E09000024,MERTON,Wimbledon,NULL,LAURISTON ROAD,22103807,SW19,NULL,NULL,523850,170650,NULL,NULL
NA,21/03/2018 16:53,2018,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000472,VILLAGE,E09000024,MERTON,Wimbledon,NULL,LAURISTON ROAD,22103807,SW19,NULL,NULL,523850,170650,NULL,NULL
NA,23/03/2018 21:23,2018,2017/18,Special Service,1,1,328,328,CAT STUCK IN ROOF INSIDE,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000318,RAINHAM AND WENNINGTON,E09000016,HAVERING,Wennington,1.00024E+11,BRIDGE ROAD,21300721,RM13,552087,182410,552050,182450,51.52013904,0.190710911
NA,24/03/2018 03:20,2018,2017/18,Special Service,1,1,328,328,DOG WITH HEAD STUCK IN CAGE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011225,Erith,E09000004,BEXLEY,Erith,NULL,STONEWOOD ROAD,20101359,DA8,NULL,NULL,551250,178250,NULL,NULL
NA,25/03/2018 10:30,2018,2017/18,Special Service,1,1,328,328,CAT ON CHURCH ROOF RSPCA ON SCENE REQUESTING ASSISTANCE   PLEASE GO TO REAR OF CHURCH,Cat,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011466,Coulsdon Town,E09000008,CROYDON,Purley,2.00001E+11,BRIGHTON ROAD,20501946,CR5,530132,159892,530150,159850,51.32325215,-0.133801051
NA,27/03/2018 15:28,2018,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000308,ELM PARK,E09000016,HAVERING,Hornchurch,1.00021E+11,DERWENT WAY,21300316,RM12,552556,185277,552550,185250,51.54577232,0.198705381
NA,27/03/2018 17:04,2018,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN NETTING ON ROOF,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,NULL,ROMFORD ROAD,22201573,E7,NULL,NULL,540450,185050,NULL,NULL
NA,28/03/2018 13:21,2018,2017/18,Special Service,1,1,328,328,FOX TRAPPED IN FALSE CEILING,Fox,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000339,TOWNFIELD,E09000017,HILLINGDON,Hayes,1.00023E+11,COLDHARBOUR LANE,21400437,UB3,510053,180038,510050,180050,51.50856574,-0.415589348
NA,28/03/2018 18:47,2018,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE CAT TRAPPED ON BALCONY,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009384,STAMFORD HILL WEST,E09000012,HACKNEY,Stoke Newington,NULL,DUNSMURE ROAD,20900349,N16,NULL,NULL,533550,187450,NULL,NULL
NA,31/03/2018 13:28,2018,2017/18,Special Service,1,2,328,656,CAT IN DISTRESS INSIDE LOCKED PREMISES  OWNER ON SCENE,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000115,CRAY VALLEY WEST,E09000006,BROMLEY,Orpington,NULL,CLARENDON WAY,20300926,BR5,NULL,NULL,546250,168550,NULL,NULL
NA,01/04/2018 11:36,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000358,HOUNSLOW CENTRAL,E09000018,HOUNSLOW,Heston,1.00023E+11,INWOOD ROAD,21500639,TW3,514093,175482,514050,175450,51.46681564,-0.35887071
NA,02/04/2018 16:38,2018,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN WIRED MESH - OPPOSITE THIS ADDRESS - CALLER WILL MEET YOU THERE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,BRAMHAM GARDENS,21700043,SW5,NULL,NULL,525750,178350,NULL,NULL
NA,03/04/2018 15:51,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN SHOP WHICH HAS BEEN BOARDED UP AND UNABLE TO GET OUT,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000380,ST. PETER'S,E09000019,ISLINGTON,Islington,5300014859,CAMDEN PASSAGE,21604427,N1,531578,183447,531550,183450,51.53460457,-0.104319614
NA,03/04/2018 17:04,2018,2018/19,Special Service,1,1,333,333,CAT FALLEN THROUGH HOLE IN CUPBOARD NOT MOVING - CALLER UNABLE TO REACH IT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000207,SOUTHBURY,E09000010,ENFIELD,Enfield,NULL,LYTCHET WAY,20702314,EN3,NULL,NULL,535050,197650,NULL,NULL
NA,04/04/2018 12:38,2018,2018/19,Special Service,1,1,333,333,TO ASSIST RSPCA WITH BIRD IN NETTING,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000044,BURNT OAK,E09000003,BARNET,Mill Hill,200057019,HANSHAW DRIVE,20020860,HA8,520621,190819,520650,190850,51.60330412,-0.259685863
NA,07/04/2018 07:16,2018,2018/19,Special Service,1,1,333,333,DOG STUCK UNDER SHED,Dog,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000218,ELTHAM NORTH,E09000011,GREENWICH,Eltham,1.00021E+11,GRANBY ROAD,20800663,SE9,542786,176024,542750,176050,51.4651813,0.054164875
NA,07/04/2018 12:33,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000175,EAST ACTON,E09000009,EALING,Acton,12125209,GOLDSMITHS CLOSE,20602060,W3,520954,180396,520950,180350,51.50955743,-0.258462823
NA,07/04/2018 16:11,2018,2018/19,Special Service,1,1,333,333,BIRD  TRAPPED   IN CHINMEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,CALDERON ROAD,22821550,E11,NULL,NULL,538550,186050,NULL,NULL
NA,08/04/2018 10:36,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND FIREPLACE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000605,LEYTONSTONE,E09000031,WALTHAM FOREST,Leytonstone,NULL,SCARBOROUGH ROAD,22873050,E11,NULL,NULL,538850,187250,NULL,NULL
NA,08/04/2018 20:35,2018,2018/19,Special Service,1,1,333,333,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,NULL,SPARROWS LANE,20801387,SE9,NULL,NULL,544250,173450,NULL,NULL
NA,10/04/2018 19:45,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000432,STREATHAM WELLS,E09000022,LAMBETH,Norbury,NULL,VALLEY ROAD,21901431,SW16,NULL,NULL,530650,171150,NULL,NULL
NA,10/04/2018 20:31,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN DERELICT BUILDING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000449,NEW CROSS,E09000023,LEWISHAM,Greenwich,NULL,BAILDON STREET,22000068,SE8,NULL,NULL,536950,177050,NULL,NULL
NA,11/04/2018 21:06,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON PILLAR ON SIDE OF THAMES  CALLER WILL WAIT AND DIRECT FROM SHORE,Cat,Person (land line),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,10010229303,EREBUS DRIVE,20802018,SE28,544237,179594,544250,179550,51.49689232,0.076495963
NA,12/04/2018 18:57,2018,2018/19,Special Service,1,1,333,333,CAT PHYSICALLY TRAPPED BEHIND SHED,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05011241,Cranbrook,E09000026,REDBRIDGE,Ilford,NULL,ENDSLEIGH GARDENS,22302825,IG1,NULL,NULL,542850,187150,NULL,NULL
NA,12/04/2018 19:11,2018,2018/19,Special Service,1,1,333,333,BUDGIE TRAPPED IN TREE - OWNER ON SCENE,Budgie,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000291,HATCH END,E09000015,HARROW,Harrow,1.00021E+11,AUGUSTINE ROAD,21201095,HA3,514147,190456,514150,190450,51.60138956,-0.353243182
NA,13/04/2018 03:01,2018,2018/19,Special Service,1,1,333,333,FOX CUB TRAPPED IN WINDOW,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05011467,Crystal Palace & Upper Norwood,E09000008,CROYDON,Woodside,NULL,CHURCH ROAD,20500027,SE19,NULL,NULL,533450,170250,NULL,NULL
NA,13/04/2018 17:22,2018,2018/19,Special Service,1,2,333,666,ASSIST RSPCA WITH INJURED CAT IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000464,LONGTHORNTON,E09000024,MERTON,Norbury,48103156,LACROSSE WAY,22103690,SW16,529925,169812,529950,169850,51.41245094,-0.133147446
NA,13/04/2018 22:19,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE CAT FALLEN FROM SECOND FLOOR NOW INJURED TRAPPED BEHIND A WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000367,BUNHILL,E09000019,ISLINGTON,Dowgate,NULL,CHARTERHOUSE STREET,21604499,EC1M,NULL,NULL,531850,181850,NULL,NULL
NA,14/04/2018 11:41,2018,2018/19,Special Service,1,2,333,666,CAT TRAPPED BEHIND WALL,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000271,HARRINGAY,E09000014,HARINGEY,Tottenham,NULL,GREEN LANES,21106375,N8,NULL,NULL,531650,189150,NULL,NULL
NA,14/04/2018 22:02,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON APEX OF SECOND FLOOR WINDOW,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011462,Addiscombe East,E09000008,CROYDON,Woodside,NULL,GRANT ROAD,20500997,CR0,NULL,NULL,533750,166350,NULL,NULL
NA,15/04/2018 01:55,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN VAN,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000028,BECONTREE,E09000002,BARKING AND DAGENHAM,Dagenham,100015444,HOLDEN CLOSE,19900186,RM8,546784,186299,546750,186250,51.55648413,0.115952318
NA,15/04/2018 20:44,2018,2018/19,Special Service,1,1,333,333,DOG STUCK IN RAILINGS   NEAR THE LODGE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000466,MERTON PARK,E09000024,MERTON,Wimbledon,48106007,MOSTYN ROAD,22104569,SW19,524827,169316,524850,169350,51.40913678,-0.206589233
NA,16/04/2018 10:16,2018,2018/19,Special Service,NULL,NULL,333,NULL,DOG ON THE FORESHORE FIREBOAT HAS BEEN REQUESTED BY COASTGUARD,Dog,Person (land line),River/canal,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000622,ST. MARY'S PARK,E09000032,WANDSWORTH,Battersea,1.00023E+11,HESTER ROAD,22902356,SW11,527064,177290,527050,177250,51.48030428,-0.171582831
NA,17/04/2018 15:34,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011102,Faraday,E09000028,SOUTHWARK,Old Kent Road,NULL,INVILLE ROAD,22501332,SE17,NULL,NULL,532850,178050,NULL,NULL
NA,17/04/2018 22:56,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED BETWEEN GARAGE WALL AND FENCING,Dog,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000210,TOWN,E09000010,ENFIELD,Enfield,207137894,APPLE GROVE,20702482,EN1,533480,196820,533450,196850,51.65433202,-0.071830559
NA,18/04/2018 15:35,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,BLACKETT STREET,22900508,SW15,NULL,NULL,523550,175550,NULL,NULL
NA,18/04/2018 16:23,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000280,TOTTENHAM GREEN,E09000014,HARINGEY,Tottenham,10003972440,GREENWAY CLOSE,21106896,N15,533843,189361,533850,189350,51.58721726,-0.069434855
NA,18/04/2018 17:55,2018,2018/19,Special Service,1,1,333,333,CATS TRAPPED IN BASEMENT - CALLED X RSPCA ON SCENE,cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011107,North Walworth,E09000028,SOUTHWARK,Old Kent Road,NULL,DAWES STREET,22500720,SE17,NULL,NULL,532750,178450,NULL,NULL
NA,19/04/2018 10:41,2018,2018/19,Special Service,1,3,333,999,ASSIST WITH HORSE COLLAPSED RSPCA AND POLICE ON SCENE,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011230,Sidcup,E09000004,BEXLEY,Sidcup,10011846821,RECTORY LANE,20101196,DA14,547772,171275,547750,171250,51.4212305,0.123909184
NA,19/04/2018 11:33,2018,2018/19,Special Service,1,2,333,666,Redacted,Horse,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000312,HAROLD WOOD,E09000016,HAVERING,Harold Hill,10025145133,HALL LANE,21301816,RM14,556265,189094,556250,189050,51.57905228,0.253844851
NA,19/04/2018 18:09,2018,2018/19,Special Service,NULL,NULL,333,NULL,KITTEN TRAPPED BETWEEN RAFTERS IN ATTIC,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000615,FURZEDOWN,E09000032,WANDSWORTH,Tooting,NULL,CLAIRVIEW ROAD,22900957,SW16,NULL,NULL,528950,171450,NULL,NULL
NA,20/04/2018 15:03,2018,2018/19,Special Service,1,1,333,333,BIRD STUCK IN AIR VENT,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,CROWNDALE ROAD,20400784,NW1,NULL,NULL,529550,183450,NULL,NULL
NA,21/04/2018 13:33,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,NULL,HILLFIELD PARK MEWS,21100430,N10,NULL,NULL,528650,189450,NULL,NULL
NA,21/04/2018 13:38,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000183,NORTH GREENFORD,E09000009,EALING,Northolt,12065603,ENNISMORE AVENUE,20600631,UB6,515347,184915,515350,184950,51.55134393,-0.337742454
NA,21/04/2018 14:22,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT ON ROOF - CALLER WILL MEET YOU,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,NULL,HILLFIELD PARK MEWS,21100430,N10,NULL,NULL,528650,189450,NULL,NULL
NA,22/04/2018 01:34,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN WINDOW FRAME,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,NULL,SIMONS WALK,22200093,E15,NULL,NULL,538750,185050,NULL,NULL
NA,22/04/2018 13:41,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009370,DALSTON,E09000012,HACKNEY,Homerton,NULL,GRAHAM ROAD,20900460,E8,NULL,NULL,534150,184750,NULL,NULL
NA,22/04/2018 15:47,2018,2018/19,Special Service,1,2,333,666,PARROT TRAPPED IN TREE (OWNER ON SCENE - PARROT WAS WEARING A HARNESS WHICH IS NOW TANGLED IN BRANCH,Bird,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011244,Goodmayes,E09000026,REDBRIDGE,Ilford,1.00022E+11,GOODMAYES LANE,22302089,IG3,546298,186242,546250,186250,51.55609817,0.108923301
NA,22/04/2018 16:23,2018,2018/19,Special Service,1,2,333,666,SMALL ANIMAL RESCUE  -  CAT ON A ROOF - AT REQUEST OF RSPCA - RSPCA OFFICER NOT IN ATTENDANCE  - OWN,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,NULL,GREENWOOD ROAD,20900470,E8,NULL,NULL,534350,184850,NULL,NULL
NA,22/04/2018 20:00,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN SURGARD BUILDING ON UXBRIDGE ROAD,Cat,Person (mobile),Warehouse,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000344,YEADING,E09000017,HILLINGDON,Southall,1.00023E+11,UXBRIDGE ROAD,21402032,UB4,511755,180625,511750,180650,51.51350774,-0.390888571
NA,23/04/2018 15:03,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING THREE FLOORS HIGH,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,1.00022E+11,ST GEORGES ROAD,21800892,KT2,519012,170173,519050,170150,51.41809689,-0.289865402
NA,23/04/2018 16:17,2018,2018/19,Special Service,1,1,333,333,CAT IN TREE - REQUESTED X  RSPCA ON SCENE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000193,BOWES,E09000010,ENFIELD,Southgate,207168964,TELFORD ROAD,20705055,N11,529347,192015,529350,192050,51.61211735,-0.133316033
NA,25/04/2018 11:28,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011222,Crayford,E09000004,BEXLEY,Bexley,NULL,LOWER STATION ROAD,20100884,DA1,NULL,NULL,551350,174250,NULL,NULL
NA,25/04/2018 12:12,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT STUCK ON CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,NULL,MANBEY GROVE,22201496,E15,NULL,NULL,539250,184850,NULL,NULL
NA,25/04/2018 16:20,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH TRAPPED KITTENS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000356,HESTON EAST,E09000018,HOUNSLOW,Heston,NULL,CHURCH ROAD,21500259,TW5,NULL,NULL,513050,177350,NULL,NULL
NA,25/04/2018 16:23,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000175,EAST ACTON,E09000009,EALING,Acton,NULL,BEECH AVENUE,20600143,W3,NULL,NULL,521250,180150,NULL,NULL
NA,25/04/2018 20:31,2018,2018/19,Special Service,1,1,333,333,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000455,ABBEY,E09000024,MERTON,Wimbledon,NULL,RODNEY PLACE,22105454,SW19,NULL,NULL,526150,169950,NULL,NULL
NA,25/04/2018 23:07,2018,2018/19,Special Service,1,1,333,333,BIRD IMPAILED ON RAILING  NEAR PIZZA ON DEMAND,Bird,Person (mobile),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000282,WEST GREEN,E09000014,HARINGEY,Tottenham,10022943417,WEST GREEN ROAD,21104071,N15,532091,189225,532050,189250,51.58640857,-0.094759558
NA,26/04/2018 06:33,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED IN WIRE MESHINGS OF FENCE,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Battersea,1.00023E+11,PRINCE OF WALES DRIVE,22903956,SW11,528075,176760,528050,176750,51.47532154,-0.157214752
NA,26/04/2018 08:17,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN BARBED WIRE UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Chingford,1.00023E+11,BLUEHOUSE ROAD,22816700,E4,539434,193549,539450,193550,51.62349497,0.012893627
NA,26/04/2018 22:30,2018,2018/19,Special Service,1,1,333,333,CAT    POSSIBLY TRAPPED ON RAILWAY EMBANKMENT,Cat,Person (land line),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000311,HACTON,E09000016,HAVERING,Hornchurch,1.00021E+11,CRYSTAL AVENUE,21300308,RM12,554121,186280,554150,186250,51.55436736,0.221699354
NA,27/04/2018 15:06,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON ROOF - POSSIBLY INJURED  CALLED BY RSPCA UNABLE TO ATTEND CAT HAS BEEN STRANDED FOR DAY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,LEA BRIDGE,E09000012,HACKNEY,Homerton,NULL,LEA BRIDGE ROAD,20900602,E5,NULL,NULL,535550,186550,NULL,NULL
NA,29/04/2018 10:50,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000621,ROEHAMPTON AND PUTNEY HEATH,E09000032,WANDSWORTH,Wandsworth,NULL,DANEBURY AVENUE,22901127,SW15,NULL,NULL,522250,173850,NULL,NULL
NA,30/04/2018 01:39,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND PIPE IN BATHROOM - RSPCA WILL NOT ATTEND,Cat,Person (land line),Other Dwelling,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000181,LADY MARGARET,E09000009,EALING,Southall,NULL,EVELYN GROVE,20600639,UB1,NULL,NULL,512750,181250,NULL,NULL
NA,01/05/2018 10:24,2018,2018/19,Special Service,1,1,333,333,FOX CUB TRAPPED IN FENCE,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000038,RIVER,E09000002,BARKING AND DAGENHAM,Dagenham,100054514,DOWNING ROAD,19900660,RM9,548827,184194,548850,184150,51.53704196,0.144523551
NA,01/05/2018 18:15,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED IN FENCE  RSPCA EN ROUTE,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000255,FULHAM REACH,E09000013,HAMMERSMITH AND FULHAM,Fulham,34024957,HAWKSMOOR STREET,21000424,W6,523884,177632,523850,177650,51.48408818,-0.217225429
NA,02/05/2018 10:04,2018,2018/19,Special Service,1,1,333,333,Redacted,Fox,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000420,COLDHARBOUR,E09000022,LAMBETH,Brixton,NULL,GRESHAM ROAD,21900641,SW9,NULL,NULL,531450,175550,NULL,NULL
NA,03/05/2018 10:12,2018,2018/19,Special Service,1,1,333,333,FOX WITH HEAD TRAPPED IN RAILINGS     RSPCA ON SCENE,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011115,St. Giles,E09000028,SOUTHWARK,Peckham,2.00003E+11,DATCHELOR PLACE,22500716,SE5,532835,176737,532850,176750,51.47401309,-0.088730524
NA,04/05/2018 00:24,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH PIGEON TRAPPED ON TELEPHONE WIRE,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009379,KING'S PARK,E09000012,HACKNEY,Homerton,NULL,GLYN ROAD,20900450,E5,NULL,NULL,535750,185950,NULL,NULL
NA,04/05/2018 01:16,2018,2018/19,Special Service,1,1,333,333,FERRET ON ROOF/STUCK IN GUTTERING AREA  OWNER ON SCENE,Ferret,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000185,NORTHOLT WEST END,E09000009,EALING,Southall,NULL,LAMB CLOSE,20604333,UB5,NULL,NULL,512250,182750,NULL,NULL
NA,04/05/2018 10:02,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN WALLS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000449,NEW CROSS,E09000023,LEWISHAM,Greenwich,NULL,REGINALD ROAD,22000852,SE8,NULL,NULL,537350,177050,NULL,NULL
NA,04/05/2018 20:49,2018,2018/19,Special Service,1,1,333,333,PIDGEONS TRAPPED IN NETTING BY RAIL BRIDGE NEAR THE TRAIN STATION,Pigeon,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000434,THURLOW PARK,E09000022,LAMBETH,West Norwood,10025189490,THURLOW PARK ROAD,21901381,SE21,531882,173005,531850,173050,51.44069489,-0.103825633
NA,05/05/2018 14:12,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED - CALLED X RSPCA,Fox,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011112,Rotherhithe,E09000028,SOUTHWARK,Dockhead,10015486241,CULLING ROAD,22500683,SE16,535095,179460,535050,179450,51.49795401,-0.055165546
NA,05/05/2018 16:34,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE    KITTEN UNDER PLAY SLIDE,Cat,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05011238,Churchfields,E09000026,REDBRIDGE,Woodford,10034918704,BRAMLEY CLOSE,22306218,IG8,541295,191046,541250,191050,51.60054588,0.038746077
NA,05/05/2018 19:42,2018,2018/19,Special Service,1,1,333,333,SQUIRREL TRAPPED IN WIRES BEHIND WASHING MACHINE,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011226,Falconwood & Welling,E09000004,BEXLEY,Bexley,NULL,PARK VIEW ROAD,20101104,DA16,NULL,NULL,547650,175550,NULL,NULL
NA,05/05/2018 21:34,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED UNDERNEATH FLOORBOARDS,Fox,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Battersea,NULL,PRINCE OF WALES DRIVE,22903956,SW11,NULL,NULL,528050,176750,NULL,NULL
NA,06/05/2018 17:07,2018,2018/19,Special Service,1,1,333,333,DOG IN PRECARIOUS POSITION ON ROOF OF ROW OF TERRACED HOUSE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,NULL,PIGOTT STREET,22700942,E14,NULL,NULL,537050,181150,NULL,NULL
NA,06/05/2018 21:09,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN HOLE  BETWEEN TWO PROPERTY GARDENS,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000474,WIMBLEDON PARK,E09000024,MERTON,Wimbledon,48002857,ARTHUR ROAD,22100225,SW19,524977,172071,524950,172050,51.43387058,-0.203451568
NA,08/05/2018 12:19,2018,2018/19,Special Service,1,2,333,666,DOG TRAPPED UNDER FOUNDATIONS OF A GARAGE,Dog,Person (mobile),Private Summer house,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000049,EAST FINCHLEY,E09000003,BARNET,Finchley,200037865,ELMSHURST CRESCENT,20014500,N2,526500,189346,526550,189350,51.58878418,-0.17536413
NA,08/05/2018 12:39,2018,2018/19,Special Service,1,1,333,333,INJURED CAT FALLEN ONTO FLAT ROOF   TWO FLOORS UP,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000370,CLERKENWELL,E09000019,ISLINGTON,Islington,NULL,ST JOHN STREET,21606010,EC1V,NULL,NULL,531650,182550,NULL,NULL
NA,09/05/2018 11:58,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH FOX TRAPPED IN BASEMENT AREA -  THIS IS FLATS NEXT TO SCOPE HEAD OFFICE  RSPCA INS,Fox,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000375,HOLLOWAY,E09000019,ISLINGTON,Holloway,5300060249,MARKET ROAD,21605388,N7,530511,184798,530550,184750,51.5469932,-0.1191937
NA,09/05/2018 20:38,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009389,BROMPTON & HANS TOWN,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,EATON PLACE,21700173,SW1X,NULL,NULL,528150,178950,NULL,NULL
NA,10/05/2018 11:36,2018,2018/19,Special Service,1,1,333,333,TO ASSIST RSCPA WITH DUCKLING IN DRAIN OUTSIDE TENNIS CLUB AT TRAFFIC LIGHTS,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011254,Wanstead Park,E09000026,REDBRIDGE,Leytonstone,10025578024,BLAKE HALL ROAD,22302635,E11,540439,187085,540450,187050,51.56516324,0.024808941
NA,10/05/2018 12:36,2018,2018/19,Special Service,1,1,333,333,RUNNING CALL TO TEN PIGEONS STUCK IN NETTING,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000434,THURLOW PARK,E09000022,LAMBETH,West Norwood,10025189490,THURLOW PARK ROAD,21901381,SE21,531880,173001,531850,173050,51.44066593,-0.103853728
NA,10/05/2018 15:00,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE -    CAT STUCK BETWEEN WALLS INSIDE PROPERTY,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,1.00022E+11,LEWISHAM HIGH STREET,22004177,SE13,538299,175614,538250,175650,51.46261682,-0.01054505
NA,10/05/2018 21:58,2018,2018/19,Special Service,1,2,333,666,Redacted,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,LONSDALE ROAD,21700934,W11,NULL,NULL,524950,181050,NULL,NULL
NA,11/05/2018 06:32,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BETWEEN WALL AND GATE,Cat,Person (mobile),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,6068907,CELANDINE CLOSE,22700283,E14,537248,181699,537250,181650,51.51755161,-0.02329694
NA,11/05/2018 12:04,2018,2018/19,Special Service,1,1,333,333,DOG WITH HEAD TRAPPED BETWEEN WALL AND POSTBOX,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000429,STOCKWELL,E09000022,LAMBETH,Clapham,2E+11,WANDSWORTH ROAD,21901460,SW8,529933,176855,529950,176850,51.47575104,-0.130436566
NA,11/05/2018 18:12,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN UNDERGROWTH CALLER WILL MEET YOU IN CAR PARK,Cat,Person (mobile),Hedge,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000305,WEST HARROW,E09000015,HARROW,Harrow,1.00021E+11,FORD CLOSE,21202000,HA1,514759,187876,514750,187850,51.57807674,-0.34525021
NA,12/05/2018 09:19,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED UNDER BATH,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000213,WINCHMORE HILL,E09000010,ENFIELD,Southgate,NULL,WADES HILL,20705059,N21,NULL,NULL,531450,194850,NULL,NULL
NA,12/05/2018 12:12,2018,2018/19,Special Service,1,1,333,333,BIRD  TRAPPED  IN TREE ACCESS VIA GRAFTON WAY JUNC WHITFIELD STREET AL REQUESTED FROM INCIDENT,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000129,BLOOMSBURY,E09000007,CAMDEN,Euston,5095417,WHITFIELD STREET,20400941,W1T,529219,182180,529250,182150,51.5237681,-0.138762126
NA,12/05/2018 14:07,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH RESCUING CAT FROM TREE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000437,BELLINGHAM,E09000023,LEWISHAM,Beckenham,1.00022E+11,KING ALFRED AVENUE,22001604,SE6,537429,171770,537450,171750,51.42828026,-0.024551454
NA,12/05/2018 16:13,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000095,MAPESBURY,E09000005,BRENT,West Hampstead,NULL,SHOOT UP HILL,20202001,NW2,NULL,NULL,524250,185050,NULL,NULL
NA,13/05/2018 10:48,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH FOX ON ROOF      NEAR JUNC GREAT GUILDFORD STREET,Fox,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped wild animal,E05011095,Borough & Bankside,E09000028,SOUTHWARK,Dowgate,10090287532,UNION STREET,22502587,SE1,532224,180061,532250,180050,51.50402939,-0.096275591
NA,13/05/2018 11:38,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED UNDER CAR     -   WRAPPED HIMSELF AROUND A CAR PART,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,5870083109,WOLSELEY ROAD,22601161,CR4,528231,166630,528250,166650,51.38424668,-0.158638296
NA,13/05/2018 13:18,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000610,BALHAM,E09000032,WANDSWORTH,Tooting,NULL,OLD DEVONSHIRE ROAD,22903671,SW12,NULL,NULL,528850,173550,NULL,NULL
NA,13/05/2018 15:09,2018,2018/19,Special Service,1,1,333,333,FOX CUB WITH HEAD  STUCK BETWEEN FENCE AND DECKING     RSPCA  INSPECTOR IS ON SCENE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000087,BRONDESBURY PARK,E09000005,BRENT,Willesden,NULL,CHAMBERS LANE,20201261,NW10,NULL,NULL,523050,184150,NULL,NULL
NA,13/05/2018 17:45,2018,2018/19,Special Service,1,1,333,333,PIGEON CAUGHT IN NETTING,Bird,Person (mobile),Unlicensed House in Multiple Occupation - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000175,EAST ACTON,E09000009,EALING,Acton,NULL,BEECH AVENUE,20600143,W3,NULL,NULL,521250,180150,NULL,NULL
NA,13/05/2018 18:42,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN WINDOW FRAME,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011225,Erith,E09000004,BEXLEY,Erith,NULL,SOUTH ROAD,20101317,DA8,NULL,NULL,551550,177350,NULL,NULL
NA,14/05/2018 13:37,2018,2018/19,Special Service,1,1,333,333,Redacted,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000270,FORTIS GREEN,E09000014,HARINGEY,Finchley,10003972055,MARRIOTT ROAD,21106379,N10,527941,190654,527950,190650,51.60021034,-0.154093253
NA,14/05/2018 18:57,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN NEIGHBOURS GARAGE NEIGHBOURS ARE AWAY,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000564,SUTTON CENTRAL,E09000029,SUTTON,Sutton,5870049088,THICKET ROAD,22602696,SM1,526362,164906,526350,164950,51.3691636,-0.1860978
NA,14/05/2018 20:33,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN DRAIN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011105,Newington,E09000028,SOUTHWARK,Lambeth,NULL,ROYAL ROAD,22502147,SE17,NULL,NULL,531750,177650,NULL,NULL
NA,15/05/2018 12:19,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (land line),Railway building - other,Non Residential,Other animal assistance,Assist trapped wild animal,E05009331,ST. PETER'S,E09000030,TOWER HAMLETS,Bethnal Green,6647509,HOLLYBUSH PLACE,22700647,E2,534924,182738,534950,182750,51.52744564,-0.056374268
NA,15/05/2018 14:26,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Soho,NULL,THEOBALD'S ROAD,20499120,WC1X,NULL,NULL,530750,181950,NULL,NULL
NA,15/05/2018 15:47,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING - LONDON WILDLIFE PROTECTION VOLUNTEER IN ATTENDANCE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000367,BUNHILL,E09000019,ISLINGTON,Shoreditch,NULL,RADNOR STREET,21605754,EC1V,NULL,NULL,532350,182550,NULL,NULL
NA,15/05/2018 18:15,2018,2018/19,Special Service,1,1,333,333,BIRDS TRAPPED IN GATE POST    BIRD RESCUE PERSONNEL AT LOCATION,Bird,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011222,Crayford,E09000004,BEXLEY,Bexley,10023304448,BOURNE ROAD,20100173,DA5,550361,174342,550350,174350,51.4481162,0.162432291
NA,15/05/2018 22:03,2018,2018/19,Special Service,1,1,333,333,FOX CUB TRAPPED IN BASEMENT AREA,Fox,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05009386,VICTORIA,E09000012,HACKNEY,Homerton,NULL,AINSWORTH ROAD,20900040,E9,NULL,NULL,535250,184050,NULL,NULL
NA,16/05/2018 11:22,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000052,GARDEN SUBURB,E09000003,BARNET,Hendon,200099509,ROTHERWICK ROAD,20037260,NW11,525407,187743,525450,187750,51.57462265,-0.191698911
NA,16/05/2018 20:28,2018,2018/19,Special Service,1,1,333,333,DEER TRAPPED IN RAILINGS,Deer,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05011238,Churchfields,E09000026,REDBRIDGE,Woodford,1.00023E+11,BROOMHILL WALK,22305623,IG8,540113,191434,540150,191450,51.60432575,0.021847963
NA,17/05/2018 10:53,2018,2018/19,Special Service,1,1,333,333,Redacted,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000367,BUNHILL,E09000019,ISLINGTON,Islington,NULL,PERCIVAL STREET,21605667,EC1V,NULL,NULL,531850,182450,NULL,NULL
NA,17/05/2018 12:10,2018,2018/19,Special Service,1,1,333,333,BIRD TANGLED IN TREE    CALLED BY RSPCA,Bird,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009336,WHITECHAPEL,E09000030,TOWER HAMLETS,Whitechapel,10025376616,WHITECHAPEL ROAD,22701339,E1,534803,181835,534850,181850,51.51936263,-0.058459825
NA,17/05/2018 16:09,2018,2018/19,Special Service,1,3,333,999,Redacted,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009336,WHITECHAPEL,E09000030,TOWER HAMLETS,Whitechapel,6011524,WHITECHAPEL ROAD,22701339,E1,534826,181838,534850,181850,51.51938599,-0.058128869
NA,18/05/2018 10:42,2018,2018/19,Special Service,1,1,333,333,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000614,FAIRFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,CROMFORD ROAD,22901086,SW18,NULL,NULL,524950,174750,NULL,NULL
NA,19/05/2018 11:26,2018,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN CAR ENGINE,Bird,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving wild animal - Other action,E05000043,BRUNSWICK PARK,E09000003,BARNET,Southgate,200199967,DOWD CLOSE,20012957,N11,528252,193839,528250,193850,51.6287647,-0.14844131
NA,19/05/2018 12:42,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011113,Rye Lane,E09000028,SOUTHWARK,Peckham,NULL,DANBY STREET,22500699,SE15,NULL,NULL,533850,175950,NULL,NULL
NA,20/05/2018 19:44,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011112,Rotherhithe,E09000028,SOUTHWARK,Deptford,2.00003E+11,ROTHERHITHE NEW ROAD,22502114,SE16,535640,178754,535650,178750,51.49147877,-0.047582615
NA,20/05/2018 21:34,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - KITTEN FALLEN TWO FLOORS TO FIRST FLOOR LEVEL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011102,Faraday,E09000028,SOUTHWARK,Old Kent Road,NULL,QUEENS ROW,22502034,SE17,NULL,NULL,532550,177950,NULL,NULL
NA,22/05/2018 13:56,2018,2018/19,Special Service,1,1,333,333,Redacted,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000365,TURNHAM GREEN,E09000018,HOUNSLOW,Chiswick,NULL,CHISWICK HIGH ROAD,21502481,W4,NULL,NULL,520950,178550,NULL,NULL
NA,22/05/2018 18:21,2018,2018/19,Special Service,1,1,333,333,PIGEON STUCK IN CHIMNEY RSPCA INFORMED CALLER TO RING THE FIRE BRIGADE,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000528,SOUTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,NULL,LANCASTER PARK,22403605,TW10,NULL,NULL,518150,174550,NULL,NULL
NA,23/05/2018 11:03,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED WITHIN CAR      CALLER BELIEVES SHE HAS BEEN DRIVING WITH THE CAT TRAPPED WITHIN THE MEC,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011219,Bexleyheath,E09000004,BEXLEY,Bexley,1.0002E+11,MARTENS CLOSE,20100937,DA7,550069,175153,550050,175150,51.45547287,0.158572209
NA,23/05/2018 11:05,2018,2018/19,Special Service,1,1,333,333,DOG STUCK BETWEEN WALL AND SUMMER HOUSE,Dog,Person (mobile),Private Summer house,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000220,ELTHAM WEST,E09000011,GREENWICH,Lee Green,1.00021E+11,KIMBELL PLACE,20801711,SE3,541245,175229,541250,175250,51.4584306,0.0316862
NA,23/05/2018 16:48,2018,2018/19,Special Service,1,1,333,333,BIRD OF PREY TRAPPED IN PIGEON NETTING   NR COLUMBION ROASTERIE AND FLAT IRON SQUARE  RSPCA IN ATTEN,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011095,Borough & Bankside,E09000028,SOUTHWARK,Dowgate,2.00003E+11,UNION STREET,22502587,SE1,532332,180050,532350,180050,51.50390457,-0.094716239
NA,25/05/2018 18:05,2018,2018/19,Special Service,1,1,333,333,ANIMAL / BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000561,NONSUCH,E09000029,SUTTON,Sutton,NULL,THE SPINNEY,22603399,SM3,NULL,NULL,523350,164650,NULL,NULL
NA,25/05/2018 20:00,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN RIVER RSPCA ON WAY,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000600,HIGHAM HILL,E09000031,WALTHAM FOREST,Walthamstow,1.00023E+11,TRAVERS CLOSE,22882325,E17,535763,190830,535750,190850,51.59996384,-0.041160214
NA,26/05/2018 12:46,2018,2018/19,Special Service,1,1,333,333,CAT POSSIBLY TRAPPED IN ROOF VOID CALLER WILL MEET OUTSIDE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000188,SOUTH ACTON,E09000009,EALING,Acton,NULL,HIGH STREET,20602317,W3,NULL,NULL,519750,180250,NULL,NULL
NA,28/05/2018 08:12,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000418,CLAPHAM COMMON,E09000022,LAMBETH,Clapham,10025167195,CLAPHAM COMMON SOUTH SIDE,21900340,SW4,528967,174636,528950,174650,51.45602509,-0.145154301
NA,28/05/2018 12:21,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000637,KNIGHTSBRIDGE AND BELGRAVIA,E09000033,WESTMINSTER,Chelsea,NULL,EATON PLACE,8400819,SW1X,NULL,NULL,528350,179350,NULL,NULL
NA,28/05/2018 14:43,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000045,CHILDS HILL,E09000003,BARNET,West Hampstead,200026500,COMPTON CLOSE,20009970,NW11,523912,186339,523950,186350,51.56233215,-0.213765723
NA,28/05/2018 15:23,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED UP GARDEN TREE SINCE SATURDAY AND IS IN DISTRESS - RSPCA UNABLE TO ATTEND AT PRESENT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011464,Bensham Manor,E09000008,CROYDON,Norbury,NULL,STRATHYRE AVENUE,20501439,SW16,NULL,NULL,531250,168750,NULL,NULL
NA,28/05/2018 21:12,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED BEHIND FIREPLACE,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000558,CARSHALTON CENTRAL,E09000029,SUTTON,Wallington,NULL,NORTH STREET,22602499,SM5,NULL,NULL,527850,164850,NULL,NULL
NA,29/05/2018 06:52,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000047,COPPETTS,E09000003,BARNET,Finchley,NULL,CROMWELL ROAD,20011240,N10,NULL,NULL,528450,191350,NULL,NULL
NA,29/05/2018 11:52,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,NULL,SHEEN LANE,22405019,SW14,NULL,NULL,520550,175450,NULL,NULL
NA,29/05/2018 14:22,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000627,WANDSWORTH COMMON,E09000032,WANDSWORTH,Battersea,NULL,NORTH SIDE WANDSWORTH COMMON,22903584,SW18,NULL,NULL,526750,174850,NULL,NULL
NA,30/05/2018 00:54,2018,2018/19,Special Service,1,1,333,333,CAT FALLEN FROM BALCONY ONTO LEDGE POSSIBLY INJURED - OWNER WILL MEET BRIGADE AT ASDA CAR PARK SIDE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000352,FELTHAM WEST,E09000018,HOUNSLOW,Feltham,NULL,HIGH STREET,21500609,TW13,NULL,NULL,510450,173050,NULL,NULL
NA,30/05/2018 14:42,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011484,South Croydon,E09000008,CROYDON,Croydon,NULL,REDSAN CLOSE,20503113,CR2,NULL,NULL,532550,163150,NULL,NULL
NA,30/05/2018 18:28,2018,2018/19,Special Service,1,2,333,666,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Woodside,NULL,AVIEMORE WAY,20301754,BR3,NULL,NULL,536250,167550,NULL,NULL
NA,30/05/2018 20:19,2018,2018/19,Special Service,1,3,333,999,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,NULL,MERRYWEATHER PLACE,20803082,SE10,NULL,NULL,537550,177150,NULL,NULL
NA,31/05/2018 01:06,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000441,CROFTON PARK,E09000023,LEWISHAM,Forest Hill,NULL,BEARSTEAD RISE,22001209,SE4,NULL,NULL,536650,174450,NULL,NULL
NA,31/05/2018 21:49,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN CEILING CAVITY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011486,Thornton Heath,E09000008,CROYDON,Norbury,NULL,MERSHAM ROAD,20501196,CR7,NULL,NULL,532950,169250,NULL,NULL
NA,01/06/2018 16:15,2018,2018/19,Special Service,NULL,NULL,333,NULL,DOG WITH LEG TRAPPED IN CAGE,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000214,ABBEY WOOD,E09000011,GREENWICH,Plumstead,10010210198,LODGE HILL,20800933,SE2,547142,177759,547150,177750,51.47965683,0.117559186
NA,01/06/2018 18:34,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,MYRTLEDENE ROAD,20801069,SE2,NULL,NULL,546150,178450,NULL,NULL
NA,02/06/2018 13:46,2018,2018/19,Special Service,1,2,333,666,BAT TRAPPED BEHIND MESH    RSPCA ON SCENE,Bird,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000116,CRYSTAL PALACE,E09000006,BROMLEY,Beckenham,10025181173,CRYSTAL PALACE PARK ROAD,20302172,SE26,534343,171232,534350,171250,51.4241845,-0.06910657
NA,02/06/2018 16:52,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009400,PEMBRIDGE,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,CLANRICARDE GARDENS,21700709,W2,NULL,NULL,525450,180550,NULL,NULL
NA,03/06/2018 18:31,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000608,WILLIAM MORRIS,E09000031,WALTHAM FOREST,Walthamstow,NULL,FOREST ROAD,22837350,E17,NULL,NULL,536750,189650,NULL,NULL
NA,03/06/2018 23:25,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON ROOF OF TYRED OUT   CAT HAS FALLEN OUT OF A WINDOW ONTO THE FLAT ROOF BELOW AND IS IN,Cat,Person (mobile),Vehicle Repair Workshop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000439,BROCKLEY,E09000023,LEWISHAM,New Cross,1.00023E+11,ENDWELL ROAD,22000380,SE4,536446,175851,536450,175850,51.465189,-0.037113
NA,04/06/2018 12:56,2018,2018/19,Special Service,1,1,333,333,Redacted,Lizard,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Eltham,1.00021E+11,ACADEMY PLACE,20800038,SE18,543116,176725,543150,176750,51.47140204,0.059210105
NA,05/06/2018 10:52,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN FIREPLACE   RSPCA ON SCENE,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000639,LITTLE VENICE,E09000033,WESTMINSTER,Paddington,NULL,WARWICK AVENUE,8400268,W9,NULL,NULL,525850,182150,NULL,NULL
NA,05/06/2018 16:01,2018,2018/19,Special Service,1,1,333,333,NULL,Unknown - Wild Animal,Not known,Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000199,ENFIELD LOCK,E09000010,ENFIELD,Enfield,207016224,LEA ROAD,4810028,EN9,537501,199679,537550,199650,51.67905963,-0.012614195
NA,06/06/2018 00:55,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED UNDER BED,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,NULL,TRIM STREET,22006052,SE14,NULL,NULL,536750,177550,NULL,NULL
NA,06/06/2018 06:30,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BETWEEN TWO CONSERVATORY WALLS AND FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05011224,East Wickham,E09000004,BEXLEY,Plumstead,NULL,ANTHONY ROAD,20100040,DA16,NULL,NULL,546450,176550,NULL,NULL
NA,06/06/2018 09:00,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN BASEMENT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000646,VINCENT SQUARE,E09000033,WESTMINSTER,Lambeth,NULL,PONSONBY TERRACE,8400188,SW1P,NULL,NULL,529950,178350,NULL,NULL
NA,06/06/2018 12:56,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000337,PINKWELL,E09000017,HILLINGDON,Hayes,1.00023E+11,WALTHAM AVENUE,21402071,UB3,508975,179272,508950,179250,51.50189701,-0.431351136
NA,06/06/2018 17:40,2018,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000301,ROXBOURNE,E09000015,HARROW,Northolt,NULL,MAPLE AVENUE,21201945,HA2,NULL,NULL,513850,186450,NULL,NULL
NA,06/06/2018 20:15,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000273,HORNSEY,E09000014,HARINGEY,Hornsey,NULL,OAKLEY GARDENS,21103699,N8,NULL,NULL,530650,188850,NULL,NULL
NA,06/06/2018 20:53,2018,2018/19,Special Service,1,1,333,333,FOX WITH HEAD TRAPPED BETWEEN TWO WALLS,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011233,West Heath,E09000004,BEXLEY,Bexley,1.0002E+11,CRANBROOK ROAD,20100378,DA7,549008,176878,549050,176850,51.4712521,0.1440309
NA,07/06/2018 13:37,2018,2018/19,Special Service,1,1,333,333,DOG HEAD STUCK IN IRON GATE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011472,Norbury & Pollards Hill,E09000008,CROYDON,Norbury,1.00021E+11,CROINDENE ROAD,20500830,SW16,530452,169578,530450,169550,51.41022704,-0.125651292
NA,08/06/2018 18:41,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED BETWEEN METAL BARS IN PARK ACCESS VIA NARROW STREET,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05009326,LIMEHOUSE,E09000030,TOWER HAMLETS,Poplar,6647100,BRIGHTLINGSEA PLACE,22700197,E14,536569,180850,536550,180850,51.51008915,-0.033405447
NA,09/06/2018 07:45,2018,2018/19,Special Service,1,1,333,333,PIGEONS TRAPPED IN NETTING   THIS IS THE BUILDING OPP TRAVEL LODGE ON SECOND FLOOR . CALLER WILL MEE,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Kingston,1.00024E+11,CLARENCE STREET,21800255,KT1,518299,169374,518250,169350,51.411057,-0.300387
NA,09/06/2018 08:58,2018,2018/19,Special Service,1,1,333,333,KITTEN CAUGHT UNDER CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011104,London Bridge & West Bermondsey,E09000028,SOUTHWARK,Dockhead,2.00003E+11,GRANGE WALK,22501108,SE1,533346,179294,533350,179250,51.49686974,-0.08041351
NA,10/06/2018 15:52,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN DRAINAGE PIPE - CALLER WILL FLAG YOU DOWN,Cat,Person (mobile),Pipe or drain,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000427,PRINCE'S,E09000022,LAMBETH,Lambeth,1.00024E+11,MARYLEE WAY,21900931,SE11,530972,178543,530950,178550,51.49068088,-0.114862387
NA,10/06/2018 22:31,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000436,VASSALL,E09000022,LAMBETH,Brixton,NULL,FOUNTAIN PLACE,21900573,SW9,NULL,NULL,531550,176650,NULL,NULL
NA,11/06/2018 11:05,2018,2018/19,Special Service,1,2,333,666,SEAGULL TRAPPED ON METAL POLE AT SIDE OF BUILDING,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000358,HOUNSLOW CENTRAL,E09000018,HOUNSLOW,Heston,1.00023E+11,HIGH STREET,21500610,TW3,513684,175618,513650,175650,51.46812678,-0.364709766
NA,11/06/2018 12:24,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN TREE  RSPCA IN ATTENDANCE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,46055112,OLIVE ROAD,22200917,E13,541465,182839,541450,182850,51.52675359,0.037895556
NA,11/06/2018 14:12,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED UNDER SHED,Dog,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000115,CRAY VALLEY WEST,E09000006,BROMLEY,Orpington,1.0002E+11,ARCHER ROAD,20301001,BR5,546110,167512,546150,167550,51.38785448,0.09848517
NA,12/06/2018 12:54,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN CHIMNEY - RSPCA ON SCENE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000291,HATCH END,E09000015,HARROW,Harrow,NULL,ROWLANDS AVENUE,21201156,HA5,NULL,NULL,513550,191650,NULL,NULL
NA,12/06/2018 16:51,2018,2018/19,Special Service,1,1,333,333,KITTENS TRAPPED UNDER HOME,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,NULL,CROUCH AVENUE,19900461,IG11,NULL,NULL,546750,183150,NULL,NULL
NA,14/06/2018 14:30,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN RABBIT HOLE - CALLER WILL MEET YOU,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000144,SWISS COTTAGE,E09000007,CAMDEN,Paddington,5151233,RUDGWICK TERRACE,20499145,NW8,527272,183709,527250,183750,51.53794915,-0.166259734
NA,14/06/2018 17:51,2018,2018/19,Special Service,1,1,333,333,DOGS LOCKED IN VAN,Dog,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000380,ST. PETER'S,E09000019,ISLINGTON,Islington,10010437636,ELIA STREET,21604792,N1,531807,183115,531850,183150,51.53157522,-0.101139595
NA,14/06/2018 20:46,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN CHIMNEY FRONT DOOR ON SIDE OF HOUSE IN INGLEBERT STREET,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000370,CLERKENWELL,E09000019,ISLINGTON,Islington,NULL,MYDDELTON SQUARE,21605497,EC1R,NULL,NULL,531250,182950,NULL,NULL
NA,15/06/2018 09:16,2018,2018/19,Special Service,1,1,333,333,BIRD STUCK IN FIRE PLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000562,ST. HELIER,E09000029,SUTTON,Sutton,NULL,WESTMINSTER ROAD,22601134,SM1,NULL,NULL,526650,165850,NULL,NULL
NA,15/06/2018 15:51,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BETWEEN FENCE AND SHED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000566,SUTTON SOUTH,E09000029,SUTTON,Sutton,NULL,LESLIE GARDENS,22602429,SM2,NULL,NULL,525550,163450,NULL,NULL
NA,15/06/2018 22:02,2018,2018/19,Special Service,1,1,333,333,CAT STUCK UNDERNEATH CUPBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000370,CLERKENWELL,E09000019,ISLINGTON,Islington,NULL,AMWELL STREET,21604116,EC1R,NULL,NULL,531150,182950,NULL,NULL
NA,16/06/2018 01:33,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000623,SHAFTESBURY,E09000032,WANDSWORTH,Battersea,NULL,LONGBEACH ROAD,22903012,SW11,NULL,NULL,528050,175450,NULL,NULL
NA,16/06/2018 11:29,2018,2018/19,Special Service,1,1,333,333,DOG ON ROOF OF TWO STOREY HOUSE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000411,ST. JAMES,E09000021,KINGSTON UPON THAMES,New Malden,NULL,MALDEN ROAD,21800637,KT3,NULL,NULL,521650,167150,NULL,NULL
NA,17/06/2018 12:42,2018,2018/19,Special Service,1,1,333,333,DEER TRAPPED IN FENCE,Deer,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,200001187,ABBEY VIEW,20000020,NW7,521798,193238,521750,193250,51.62479274,-0.241853699
NA,17/06/2018 17:03,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED IN BRAMBLES,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,CROWNFIELD ROAD,22830500,E15,NULL,NULL,538850,185550,NULL,NULL
NA,18/06/2018 06:22,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA  WITH DEER TRAPPED IN FENCING  - INSPECTOR ON SCENE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000196,COCKFOSTERS,E09000010,ENFIELD,Southgate,207142817,RESERVOIR ROAD,20701193,N14,529149,195801,529150,195850,51.64618857,-0.134767832
NA,19/06/2018 09:23,2018,2018/19,Special Service,1,1,333,333,POSSIBLE ANIMAL TRAPPED UNDER BATH,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000350,CRANFORD,E09000018,HOUNSLOW,Feltham,NULL,ARUNDEL ROAD,21500046,TW4,NULL,NULL,511250,175850,NULL,NULL
NA,19/06/2018 15:17,2018,2018/19,Special Service,1,2,333,666,Redacted,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011468,Fairfield,E09000008,CROYDON,Croydon,1.00021E+11,FRITH ROAD,20500961,CR0,532143,165615,532150,165650,51.37422702,-0.102830458
NA,20/06/2018 18:08,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON LEDGE CAT HAS BEEN TRAPPED A COUPLE OF DAYS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,CAMBRIDGE GARDENS,21700828,W10,NULL,NULL,524350,181450,NULL,NULL
NA,21/06/2018 10:53,2018,2018/19,Special Service,1,1,333,333,DOG IN DISTRESS LOCKED IN CAR - POLICE WILL TRY TO GET DOG UNIT TO ATTEND ASAP,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000032,GASCOIGNE,E09000002,BARKING AND DAGENHAM,Barking,100057493,KING EDWARDS ROAD,19900704,IG11,544955,183267,544950,183250,51.52972065,0.088353431
NA,21/06/2018 12:22,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND FENCE NEAR RAILWAY TRACK,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000196,COCKFOSTERS,E09000010,ENFIELD,Southgate,NULL,LAKENHEATH,20701119,N14,NULL,NULL,529550,195650,NULL,NULL
NA,22/06/2018 08:41,2018,2018/19,Special Service,1,3,333,999,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000090,FRYENT,E09000005,BRENT,Stanmore,NULL,HAWTHORNE GROVE,20200929,NW9,NULL,NULL,520350,187950,NULL,NULL
NA,22/06/2018 16:14,2018,2018/19,Special Service,1,1,333,333,KITTENS TRAPPED UNDER PAVING,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009369,CLISSOLD,E09000012,HACKNEY,Stoke Newington,NULL,MILTON GARDENS ESTATE,20950139,N16,NULL,NULL,533150,185550,NULL,NULL
NA,22/06/2018 19:01,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN UNDERGROWTH    NEAR THE POND NEXT TO THE CHILDRENS PLAYGROUND      ACCESS VIA TULSE H,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000423,HERNE HILL,E09000022,LAMBETH,West Norwood,10025181107,BROCKWELL PARK INTERNAL ROADS,21901542,SW2,531345,174123,531350,174150,51.45086659,-0.111132912
NA,23/06/2018 19:36,2018,2018/19,Special Service,1,2,333,666,KITTEN TRAPPED IN LOCKED CAR  WHITE PRIUS,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000177,GREENFORD BROADWAY,E09000009,EALING,Southall,12033332,INVICTA GROVE,20600936,UB5,512529,182732,512550,182750,51.53229738,-0.379056388
NA,24/06/2018 15:58,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - MUNTJAC DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Golf course (not building on course),Outdoor,Other animal assistance,Assist trapped wild animal,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,200199927,FRITH LANE,20016860,NW7,524599,191642,524550,191650,51.60984119,-0.201976949
NA,25/06/2018 19:28,2018,2018/19,Special Service,1,1,333,333,CAT FALLEN INTO LOWER GROUND FLOOR    INJURED   OWNER ON SCENE,Cat,Person (mobile),Retirement/Old Persons Home,Other Residential,Other animal assistance,Assist trapped domestic animal,E05000058,OAKLEIGH,E09000003,BARNET,Barnet,200088004,OAKLEIGH PARK NORTH,20032540,N20,526877,194426,526850,194450,51.63435253,-0.168086597
NA,26/06/2018 08:18,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND KENNEL   ALL CREATURES VETERINARY SURGERY,Cat,Person (land line),Veterinary surgery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000623,SHAFTESBURY,E09000032,WANDSWORTH,Clapham,121020566,LAVENDER HILL,22902838,SW11,528273,175683,528250,175650,51.46559,-0.154764
NA,26/06/2018 09:20,2018,2018/19,Special Service,2,3,333,999,CAT TRAPPED IN BARBED WIRE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,WESTCROFT SQUARE,21000874,W6,NULL,NULL,521950,178650,NULL,NULL
NA,26/06/2018 10:29,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000375,HOLLOWAY,E09000019,ISLINGTON,Holloway,NULL,WIDDENHAM ROAD,21604016,N7,NULL,NULL,530650,185650,NULL,NULL
NA,26/06/2018 21:52,2018,2018/19,Special Service,1,1,333,333,INJURED BIRD STUCK IN TREE,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000174,EALING COMMON,E09000009,EALING,Ealing,12144737,GRANGE ROAD,20600751,W5,518247,180434,518250,180450,51.51047417,-0.297432204
NA,27/06/2018 16:55,2018,2018/19,Special Service,1,2,333,666,Redacted,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000189,SOUTHALL BROADWAY,E09000009,EALING,Southall,10025575642,UXBRIDGE ROAD,20602324,UB1,513248,180301,513250,180350,51.51030441,-0.369473531
NA,28/06/2018 07:40,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000455,ABBEY,E09000024,MERTON,Wimbledon,NULL,KINGSTON ROAD,22103622,SW19,NULL,NULL,525350,170050,NULL,NULL
NA,28/06/2018 14:05,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING - ASSIST WILDLIFE PROTECTION,Bird,Person (mobile),Estate Agent,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,REDBRIDGE,Woodford,10093039469,THE BROADWAY,22306023,IG8,540902,191851,540950,191850,51.60787787,0.033394814
NA,28/06/2018 15:20,2018,2018/19,Special Service,1,1,333,333,BIRD STUCK IN THE CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000486,GREEN STREET WEST,E09000025,NEWHAM,Stratford,NULL,THORNGROVE ROAD,22207990,E13,NULL,NULL,541050,183850,NULL,NULL
NA,28/06/2018 15:55,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA INSPECTOR WITH BIRD RESCUE,Bird,Person (land line),Converted office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,REDBRIDGE,Woodford,2.00003E+11,THE BROADWAY,22306023,IG8,540901,191851,540950,191850,51.60787379,0.033387883
NA,28/06/2018 21:50,2018,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009386,VICTORIA,E09000012,HACKNEY,Bethnal Green,NULL,RUTLAND ROAD,20900881,E9,NULL,NULL,535550,183750,NULL,NULL
NA,29/06/2018 12:02,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN AIR VENT OUTSIDE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000033,GORESBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,WALLERS CLOSE,19900614,RM9,NULL,NULL,548550,183650,NULL,NULL
NA,30/06/2018 08:29,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN TREE REQUESTED BY RSPCA,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011489,Woodside,E09000008,CROYDON,Woodside,NULL,ANTHONY ROAD,20500630,SE25,NULL,NULL,534250,167050,NULL,NULL
NA,30/06/2018 14:08,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED UNDER KITCHEN CUPBOARD,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000523,HEATHFIELD,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,ARDEN CLOSE,22407174,TW2,NULL,NULL,512850,173550,NULL,NULL
NA,30/06/2018 15:37,2018,2018/19,Special Service,1,1,333,333,POSSIBLY ANIMAL TRAPPED IN CHIMNEY,Unknown - Wild Animal,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,NULL,RUSSETT WAY,20801306,SE13,NULL,NULL,537950,176350,NULL,NULL
NA,30/06/2018 21:08,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN FENCE NEAR TO SWINSON HOUSE-CALLER WAITING FOR YOU,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,HIGHVIEW GARDENS,20701609,N11,NULL,NULL,529250,192150,NULL,NULL
NA,01/07/2018 03:32,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED INBETWEEN TWO BRICK WALLS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009376,HOMERTON,E09000012,HACKNEY,Homerton,NULL,LOWER CLAPTON ROAD,20900640,E5,NULL,NULL,535150,185450,NULL,NULL
NA,01/07/2018 08:51,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,PALMERS ROAD,20705024,N11,NULL,NULL,529250,192250,NULL,NULL
NA,01/07/2018 12:34,2018,2018/19,Special Service,1,1,333,333,RSPCA REQUESTING ASSISTANCE  FOX TRAPPED IN CONSERVATORY OF EMPTY PROPERTY,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000477,CANNING TOWN NORTH,E09000025,NEWHAM,Plaistow,NULL,SOLENT RISE,22201956,E13,NULL,NULL,540350,182650,NULL,NULL
NA,01/07/2018 16:39,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011108,Nunhead & Queen's Road,E09000028,SOUTHWARK,New Cross,NULL,MORTLOCK CLOSE,22503072,SE15,NULL,NULL,534650,176550,NULL,NULL
NA,01/07/2018 17:28,2018,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN EXTRACTOR FAN,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,CHURCH STREET,8400794,NW8,NULL,NULL,527050,182050,NULL,NULL
NA,02/07/2018 03:18,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN WINDOWFRAME,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009329,ST. DUNSTAN'S,E09000030,TOWER HAMLETS,Bethnal Green,NULL,HARFORD STREET,22700605,E1,NULL,NULL,536150,181950,NULL,NULL
NA,02/07/2018 11:03,2018,2018/19,Special Service,1,1,333,333,DEER STUCK BETWEEN TWO WOODEN FENCES AND A BRICK WALL - ASSIST RSPCA,Deer,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,1.00022E+11,LOWER KINGS ROAD,21880098,KT2,518145,169997,518150,169950,51.41668945,-0.302388586
NA,02/07/2018 12:45,2018,2018/19,Special Service,1,1,333,333,CAT STUCK BETWEEN FLOOR AND CEILING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000407,COOMBE VALE,E09000021,KINGSTON UPON THAMES,New Malden,NULL,KINGSCOTE ROAD,21800551,KT3,NULL,NULL,520850,168550,NULL,NULL
NA,02/07/2018 14:28,2018,2018/19,Special Service,1,1,333,333,CAT STUCK UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000523,HEATHFIELD,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,SPRINGFIELD ROAD,22401864,TW2,NULL,NULL,513350,173350,NULL,NULL
NA,02/07/2018 18:35,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN GUTTER,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009395,EARL'S COURT,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,PENYWERN ROAD,21700411,SW5,NULL,NULL,525450,178350,NULL,NULL
NA,02/07/2018 22:56,2018,2018/19,Special Service,1,2,333,666,CAT TRAPPED BY RUBBISH CHUTE    THIRD FLOOR,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,GREENWICH WEST,E09000011,GREENWICH,Deptford,NULL,MCMILLAN STREET,20801009,SE8,NULL,NULL,537350,177750,NULL,NULL
NA,04/07/2018 16:20,2018,2018/19,Special Service,1,1,333,333,SEAGULL TRAPPED IN NETTING ON TOP OF BUILDING,Bird,Person (mobile),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000090,FRYENT,E09000005,BRENT,Stanmore,202099002,EDGWARE ROAD,20201631,NW9,520744,189456,520750,189450,51.591028,-0.258379
NA,04/07/2018 19:27,2018,2018/19,Special Service,1,1,333,333,PUPPY TRAPPED IN FENCE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000336,NORTHWOOD HILLS,E09000017,HILLINGDON,Ruislip,NULL,PINNER ROAD,21401544,HA6,NULL,NULL,510350,190450,NULL,NULL
NA,05/07/2018 06:37,2018,2018/19,Special Service,1,1,333,333,INJURED CAT STUCK IN TREE - BEEN ATTACKED BY FOX  THIS IS AT THE REAR OF THE FIRE STATION - CALLER W,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,46074805,TORRENS SQUARE,22201632,E15,539537,184895,539550,184850,51.54571103,0.010936647
NA,05/07/2018 17:39,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED ON RIVER BANK REQUEST OF RSPCA GO DOWN MERBURY ROAD GO ACROSS BRIDGE OVER RIVER  FOOTPAT,Fox,Person (land line),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,1.00021E+11,RIDGE CLOSE,20801703,SE28,545055,179893,545050,179850,51.49937852,0.088396198
NA,06/07/2018 07:52,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN BICYCLE WHEEL - RSPCA UNABLE TO ATTEND,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011487,Waddon,E09000008,CROYDON,Croydon,NULL,THEOBALD ROAD,20501485,CR0,NULL,NULL,531750,165850,NULL,NULL
NA,06/07/2018 08:17,2018,2018/19,Special Service,1,1,333,333,DUCKLING FALLEN DOWN DRAIN  J/O BARNES HIGH STREET.   CALLER WILL MEET BRIGADE,Bird,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,10070719691,SWAN PLACE,22405134,SW13,521780,176403,521750,176450,51.47349655,-0.247932398
NA,06/07/2018 19:01,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011244,Goodmayes,E09000026,REDBRIDGE,Ilford,NULL,NUTFIELD GARDENS,22302316,IG3,NULL,NULL,546050,186750,NULL,NULL
NA,06/07/2018 19:54,2018,2018/19,Special Service,1,1,333,333,CAT STUCK BETWEEN BUILDING AND FENCE     THERE FOR THREE DAYS,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000465,LOWER MORDEN,E09000024,MERTON,Sutton,48012549,CARDINAL AVENUE,22101173,SM4,524165,167114,524150,167150,51.38949665,-0.216864079
NA,07/07/2018 10:44,2018,2018/19,Special Service,1,1,333,333,TRAPPED SWIFT HANGING- CAUGHT IN WIRES   RSPCA IN ATTENDANCE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011103,Goose Green,E09000028,SOUTHWARK,Peckham,NULL,BAWDALE ROAD,22500179,SE22,NULL,NULL,533850,174750,NULL,NULL
NA,08/07/2018 12:58,2018,2018/19,Special Service,1,1,333,333,HORSE STUCK IN RIVER    NEAR CARE MANAGEMENT GROUP AT TOP OF THE LANE - CALLER IS AT THE BOTTOM OF T,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000309,EMERSON PARK,E09000016,HAVERING,Hornchurch,10025149987,FOOTPATH 273,21302223,RM11,555643,188107,555650,188150,51.57036296,0.244445568
NA,08/07/2018 14:22,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND OVEN,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000058,OAKLEIGH,E09000003,BARNET,Southgate,NULL,MANOR DRIVE,20028180,N20,NULL,NULL,527450,193350,NULL,NULL
NA,08/07/2018 17:10,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000649,WEST END,E09000033,WESTMINSTER,Soho,10033547810,HEDDON STREET,8400982,W1B,529183,180790,529150,180750,51.51128521,-0.139799856
NA,09/07/2018 07:48,2018,2018/19,Special Service,1,1,333,333,FOX TANGLED UP IN FOOTBALL NET,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011474,Old Coulsdon,E09000008,CROYDON,Purley,NULL,COURT AVENUE,20500209,CR5,NULL,NULL,531250,158150,NULL,NULL
NA,09/07/2018 08:35,2018,2018/19,Special Service,2,8,333,2664,HORSE TRAPPED IN DOORWAY ADDITIONAL FRU REQUESTED FROM SCENE - SILENT APPROACH,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000328,CHARVILLE,E09000017,HILLINGDON,Hillingdon,1.00023E+11,MEAD HOUSE LANE,21401259,UB4,508932,182202,508950,182250,51.52824002,-0.431062535
NA,09/07/2018 14:20,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000335,NORTHWOOD,E09000017,HILLINGDON,Ruislip,NULL,FRITHWOOD AVENUE,21400773,HA6,NULL,NULL,509650,191950,NULL,NULL
NA,09/07/2018 17:23,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON CHIMNEY   RSPCA REQUESTED BRIGADE ATTEND,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,HUXLEY ROAD,20704356,N18,NULL,NULL,533150,192950,NULL,NULL
NA,09/07/2018 18:04,2018,2018/19,Special Service,1,1,333,333,DOG IN PRECARIOUS POSITION  ON THIRD FLOOR OF BLOCK OF FLATS,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000199,ENFIELD LOCK,E09000010,ENFIELD,Enfield,NULL,TYSOE AVENUE,20702432,EN3,NULL,NULL,536450,199150,NULL,NULL
NA,09/07/2018 19:29,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED BETWEEN CAMERA AND LAMP POST,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011479,Selhurst,E09000008,CROYDON,Croydon,1.00021E+11,WHITEHORSE ROAD,20501565,CR0,532463,166767,532450,166750,51.38450191,-0.097796662
NA,10/07/2018 10:23,2018,2018/19,Special Service,1,1,333,333,Redacted,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011464,Bensham Manor,E09000008,CROYDON,Norbury,10014048833,GOODMAN CRESCENT,20503063,CR0,532061,167288,532050,167250,51.38928243,-0.103381488
NA,11/07/2018 16:35,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN AIR VENT,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000621,ROEHAMPTON AND PUTNEY HEATH,E09000032,WANDSWORTH,Wandsworth,1.00023E+11,ROEHAMPTON HIGH STREET,22904376,SW15,522465,173877,522450,173850,51.45064675,-0.238954852
NA,11/07/2018 16:49,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,NULL,DONGOLA ROAD,22200550,E13,NULL,NULL,540750,182650,NULL,NULL
NA,12/07/2018 12:21,2018,2018/19,Special Service,NULL,NULL,333,NULL,DOG TRAPPED IN LAKE CALLER RANG BACK AND CANCELLED,Dog,Person (mobile),Park,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000518,FULWELL AND HAMPTON HILL,E09000027,RICHMOND UPON THAMES,Twickenham,10024348514,CRICKET LANE,22407167,TW12,514645,170856,514650,170850,51.42513281,-0.352412104
NA,12/07/2018 19:33,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,128008067,GROVE ROAD,21800477,KT6,517796,167822,517750,167850,51.39721877,-0.308127488
NA,13/07/2018 07:52,2018,2018/19,Special Service,1,1,333,333,SQUIRREL TRAPPED IN GUTTER OF PROPERTY,Squirrel,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000321,SOUTH HORNCHURCH,E09000016,HAVERING,Wennington,NULL,CHERRY TREE LANE,21300734,RM13,NULL,NULL,551650,182950,NULL,NULL
NA,13/07/2018 11:11,2018,2018/19,Special Service,1,1,333,333,"CAT CRAWLED THROUGH HOLE UNDER SINK, NOW STUCK IN PIPE",Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Plumstead,NULL,BARNFIELD ROAD,20800117,SE18,NULL,NULL,543850,177750,NULL,NULL
NA,13/07/2018 23:32,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED UNDER FLOORING,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000113,COPERS COPE,E09000006,BROMLEY,Beckenham,NULL,SHORTLANDS GROVE,20302017,BR2,NULL,NULL,538850,168950,NULL,NULL
NA,14/07/2018 15:07,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000286,CANONS,E09000015,HARROW,Stanmore,1.00021E+11,COPLEY ROAD,21200876,HA7,517507,192431,517550,192450,51.61845808,-0.304087905
NA,15/07/2018 03:30,2018,2018/19,Special Service,1,1,333,333,DEER STUCK IN FENCE  O.S. REDBRIDGE COLLEGE,Deer,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05011251,Seven Kings,E09000026,REDBRIDGE,Ilford,10034932270,BARLEY LANE,22301832,IG3,546647,188888,546650,188850,51.57978376,0.115063354
NA,15/07/2018 18:50,2018,2018/19,Special Service,1,1,333,333,PARROTS WINGS TRAPPED IN A TREE,Bird,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,10012835891,HORACE ROAD,22207759,E7,540512,185708,540550,185750,51.55277839,0.025319923
NA,15/07/2018 20:23,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000195,CHASE,E09000010,ENFIELD,Enfield,NULL,CHANTRY CLOSE,20702579,EN2,NULL,NULL,532150,198050,NULL,NULL
NA,16/07/2018 05:38,2018,2018/19,Special Service,1,1,333,333,DOG WITH PAW CAUGHT IN GRILL OF OVEN,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011228,Northumberland Heath,E09000004,BEXLEY,Bexley,NULL,DALMENY ROAD,20100418,DA8,NULL,NULL,549750,176850,NULL,NULL
NA,16/07/2018 13:18,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING    RSPCA IN ATTENDANCE,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,128030554,CHAPEL MILL ROAD,21880152,KT1,519001,168498,519050,168450,51.40303859,-0.29057762
NA,16/07/2018 18:07,2018,2018/19,Special Service,1,1,333,333,SEAGULL TRAPPED BEHIND DOOR,Bird,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Assist trapped wild animal,E05009296,CANDLEWICK,E09000001,CITY OF LONDON,Dowgate,95513232,MARTIN LANE,8100347,EC4R,532820,180811,532850,180850,51.5106317,-0.087407891
NA,17/07/2018 08:59,2018,2018/19,Special Service,1,1,333,333,REQUEST RSPCA FOR DOG AT STATION,Dog,Person (land line),Fire station,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011470,New Addington North,E09000008,CROYDON,Addington,10093049356,LODGE LANE,20501776,CR0,537693,163276,537650,163250,51.35188605,-0.024042189
NA,17/07/2018 13:33,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN METAL RAILINGS,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000179,HANGER HILL,E09000009,EALING,Acton,NULL,LINKS ROAD,20601066,W3,NULL,NULL,519150,181350,NULL,NULL
NA,17/07/2018 15:45,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000621,ROEHAMPTON AND PUTNEY HEATH,E09000032,WANDSWORTH,Wandsworth,NULL,DANEBURY AVENUE,22901127,SW15,NULL,NULL,522250,173750,NULL,NULL
NA,17/07/2018 16:16,2018,2018/19,Special Service,2,5,333,1665,PUPPY TRAPPED BETWEEN TWO HOUSES  FRU REQ FROM SCENE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,CHURCH LANE,20704048,N9,NULL,NULL,533950,193850,NULL,NULL
NA,17/07/2018 19:35,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING,Bird,Person (land line),Hotel/motel,Other Residential,Other animal assistance,Assist trapped wild animal,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Heathrow,1.00023E+11,BATH ROAD,21402329,TW6,508042,176872,508050,176850,51.48050489,-0.4455109
NA,17/07/2018 20:09,2018,2018/19,Special Service,1,1,333,333,CAT STUCK UP CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000254,FULHAM BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,RYLSTON ROAD,21000711,SW6,NULL,NULL,524750,177350,NULL,NULL
NA,18/07/2018 17:12,2018,2018/19,Special Service,1,1,333,333,DOG STUCK IN FENCE    OWNER ON SCENE,Dog,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000599,HIGH STREET,E09000031,WALTHAM FOREST,Walthamstow,1.00023E+11,PRETORIA AVENUE,22865950,E17,536180,189268,536150,189250,51.58582713,-0.035757144
NA,19/07/2018 07:09,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED IN NETTING IN SCHOOL FIELD,Fox,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05011464,Bensham Manor,E09000008,CROYDON,Norbury,10014056249,ATTLEE CLOSE,20502859,CR7,532175,167548,532150,167550,51.39159288,-0.101653411
NA,19/07/2018 08:32,2018,2018/19,Special Service,1,2,333,666,DEER STUCK IN MOAT - WATER OPERATIONS LEVEL ONE,Deer,Person (land line),Other public building,Non Residential,Other animal assistance,Assist trapped wild animal,E05000210,TOWN,E09000010,ENFIELD,Enfield,207005373,SILVER STREET,20705135,EN1,532884,196873,532850,196850,51.65495137,-0.080412968
NA,19/07/2018 11:27,2018,2018/19,Special Service,1,1,333,333,RSPCA REQUEST FROM SCENE WITH CAT STUCK UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011226,Falconwood & Welling,E09000004,BEXLEY,Plumstead,1.00023E+11,BELLEGROVE ROAD,20100114,DA16,546195,175858,546150,175850,51.46282439,0.103144147
NA,19/07/2018 11:36,2018,2018/19,Special Service,1,1,333,333,INJURED CAT TRAPPED BEHIND FENCE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000191,SOUTHFIELD,E09000009,EALING,Chiswick,NULL,ACTON LANE,20600011,W4,NULL,NULL,520550,179250,NULL,NULL
NA,20/07/2018 06:54,2018,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED ON SECOND FLOOR LOBBY,Bird,Person (land line),Medical/health centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,10070005345,CROYDON ROAD,20302868,SE20,535364,169774,535350,169750,51.410838,-0.055001
NA,20/07/2018 11:33,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED UNDER DECKING   LEAD TANGLED AND WRAPPED AROUND POST,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000171,CLEVELAND,E09000009,EALING,Ealing,NULL,HARROW VIEW ROAD,20600833,W5,NULL,NULL,516950,182250,NULL,NULL
NA,20/07/2018 13:01,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN GUTTERING    REQUESTED BY RSPCA WHO ARE ON SCENE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000452,SYDENHAM,E09000023,LEWISHAM,Beckenham,NULL,NEWLANDS PARK,22004449,SE26,NULL,NULL,535550,171350,NULL,NULL
NA,21/07/2018 13:16,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON BALCONY    RSPCA IN ATTENDANCE.,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Clapham,NULL,THESSALY ROAD,22905303,SW8,NULL,NULL,529350,176950,NULL,NULL
NA,21/07/2018 13:39,2018,2018/19,Special Service,1,1,333,333,BIRD STUCK IN CHIMNEY  RSPCA REFUSING TO ATTEND,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,NORTH BIRKBECK ROAD,22861800,E11,NULL,NULL,538750,186050,NULL,NULL
NA,21/07/2018 18:56,2018,2018/19,Special Service,1,1,333,333,DOG WITH HEAD TRAPPED IN METAL STAIRS,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000259,PALACE RIVERSIDE,E09000013,HAMMERSMITH AND FULHAM,Fulham,34076784,FULHAM PALACE ROAD,21000356,SW6,524025,176701,524050,176750,51.47569249,-0.215521755
NA,22/07/2018 02:47,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,BLAGROVE ROAD,21700813,W10,NULL,NULL,524550,181550,NULL,NULL
NA,22/07/2018 09:06,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN WALL,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000122,ORPINGTON,E09000006,BROMLEY,Orpington,NULL,AVALON CLOSE,20300004,BR6,NULL,NULL,547650,165450,NULL,NULL
NA,22/07/2018 13:24,2018,2018/19,Special Service,1,1,333,333,Redacted,Dog,Person (mobile),Cemetery,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000105,WILLESDEN GREEN,E09000005,BRENT,Willesden,202095051,GLEBE ROAD,20200664,NW10,522015,184556,522050,184550,51.54671876,-0.241737422
NA,23/07/2018 12:49,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BETWEEN SHED AND WALL - OWNER ON SCENE,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000478,CANNING TOWN SOUTH,E09000025,NEWHAM,Plaistow,10090852160,HAMMERSLEY ROAD,22208226,E16,540188,181679,540150,181650,51.51665515,0.019034882
NA,24/07/2018 03:15,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE  -  CAT FALLEN AND IS ON FIRST FLOOR WINDOW LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000619,NORTHCOTE,E09000032,WANDSWORTH,Battersea,NULL,AUCKLAND ROAD,22900184,SW11,NULL,NULL,527350,175050,NULL,NULL
NA,24/07/2018 08:42,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,NULL,TUDOR DRIVE,21800983,KT2,NULL,NULL,518550,171050,NULL,NULL
NA,24/07/2018 10:36,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON WINDOW LEDGE   RSPCA IN ATTENDANCE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000373,HIGHBURY WEST,E09000019,ISLINGTON,Islington,NULL,HIGHBURY HILL,21603358,N5,NULL,NULL,531550,185750,NULL,NULL
NA,24/07/2018 12:25,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN ROOM,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011467,Crystal Palace & Upper Norwood,E09000008,CROYDON,West Norwood,NULL,MATILDA CLOSE,20502905,SE19,NULL,NULL,532750,170250,NULL,NULL
NA,24/07/2018 15:25,2018,2018/19,Special Service,2,4,333,1332,Redacted,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000111,CHISLEHURST,E09000006,BROMLEY,Sidcup,10025188606,POND PATH,20304039,BR7,544028,170752,544050,170750,51.41749989,0.069900275
NA,24/07/2018 20:53,2018,2018/19,Special Service,1,2,333,666,BIRD TRAPPED IN NETTING   ALP REQUESTED FROM SCENE,Bird,Person (mobile),Furniture warehouse,Non Residential,Animal rescue from water,Animal rescue from water - Bird,E05000090,FRYENT,E09000005,BRENT,Stanmore,202099002,EDGWARE ROAD,20201631,NW9,520743,189456,520750,189450,51.59103264,-0.258385308
NA,26/07/2018 11:52,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011106,North Bermondsey,E09000028,SOUTHWARK,Dockhead,NULL,LAYARD SQUARE,22503047,SE16,NULL,NULL,534750,178950,NULL,NULL
NA,26/07/2018 14:18,2018,2018/19,Special Service,1,1,333,333,CAT ON ROOF    ASSIST RSPCA,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000439,BROCKLEY,E09000023,LEWISHAM,New Cross,NULL,MALPAS ROAD,22000662,SE4,NULL,NULL,536550,176150,NULL,NULL
NA,26/07/2018 15:26,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000374,HILLRISE,E09000019,ISLINGTON,Holloway,5300004140,ASHLEY ROAD,21606730,N19,530373,187571,530350,187550,51.57194625,-0.120149579
NA,26/07/2018 15:33,2018,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN NETTING   RSPCA IN ATTENDANCE,Bird,Person (mobile),Pub/wine bar/bar,Non Residential,Other animal assistance,Assist trapped wild animal,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,200220980,HIGH ROAD,20022400,N12,526356,192155,526350,192150,51.61405711,-0.176430422
NA,26/07/2018 17:25,2018,2018/19,Special Service,1,2,333,666,RUNNING CALL TO DUCK TRAPPED IN MUD,Bird,Person (land line),River/canal,Outdoor,Other animal assistance,Animal harm involving wild animal,E05011106,North Bermondsey,E09000028,SOUTHWARK,Dockhead,2.00003E+11,SHAD THAMES,22502224,SE1,533809,179711,533850,179750,51.50051531,-0.073584172
NA,27/07/2018 22:29,2018,2018/19,Special Service,1,1,333,333,CAT ON ROOF FOURTH FLOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000272,HIGHGATE,E09000014,HARINGEY,Hornsey,NULL,JACKSONS LANE,21100481,N6,NULL,NULL,528550,187950,NULL,NULL
NA,28/07/2018 07:01,2018,2018/19,Special Service,1,1,333,333,CAT FALLEN FROM FOURTH FLOOR TO FIRST FLOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000176,ELTHORNE,E09000009,EALING,Ealing,NULL,SINGAPORE ROAD,20601555,W13,NULL,NULL,516350,180450,NULL,NULL
NA,28/07/2018 10:28,2018,2018/19,Special Service,1,1,333,333,RSPCA REQUEST - CAT TRAPPED ON ROOF - RSPCA ON SCENE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000272,HIGHGATE,E09000014,HARINGEY,Hornsey,NULL,JACKSONS LANE,21100481,N6,NULL,NULL,528550,187950,NULL,NULL
NA,28/07/2018 12:14,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED UNDER BATH,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000413,SURBITON HILL,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,ARLINGTON ROAD,21800074,KT6,NULL,NULL,517850,167050,NULL,NULL
NA,28/07/2018 14:17,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED ON ROOF,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000200,GRANGE,E09000010,ENFIELD,Edmonton,NULL,PARK AVENUE,20707523,EN1,NULL,NULL,532950,195450,NULL,NULL
NA,28/07/2018 16:07,2018,2018/19,Special Service,NULL,NULL,333,NULL,DOG TRAPPED DOWN HOLE IN GARDEN,Dog,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011247,Loxford,E09000026,REDBRIDGE,Ilford,10034912960,BANKSIDE ROAD,22306128,IG1,544488,185124,544450,185150,51.54652661,0.082386584
NA,28/07/2018 16:16,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING ON ROOF NEAR TO WARREN STREET STATION - CALLER WILL MEET YOU,Bird,Person (mobile),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000129,BLOOMSBURY,E09000007,CAMDEN,Euston,5042736,WARREN STREET,20400928,W1T,529145,182193,529150,182150,51.52390075,-0.139834517
NA,28/07/2018 19:08,2018,2018/19,Special Service,1,1,333,333,Redacted,Unknown - Domestic Animal Or Pet,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000592,CHAPEL END,E09000031,WALTHAM FOREST,Walthamstow,2.00001E+11,WADHAM ROAD,22884500,E17,537861,190948,537850,190950,51.60051202,-0.010841668
NA,28/07/2018 20:10,2018,2018/19,Special Service,1,1,333,333,CAT STUCK UP TREE RSPCA INSPECTOR REQUESTED ATTENDANCE AS CAT IS INJURED,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000322,SQUIRREL'S HEATH,E09000016,HAVERING,Hornchurch,1.00021E+11,SLEWINS CLOSE,21300585,RM11,553511,188635,553550,188650,51.57568657,0.213940416
NA,28/07/2018 21:40,2018,2018/19,Special Service,1,1,333,333,RABBIT STUCK BETWEEN WALLS OF EXTENSION,Rabbit,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000308,ELM PARK,E09000016,HAVERING,Hornchurch,NULL,COWDRAY WAY,21300301,RM12,NULL,NULL,552150,185750,NULL,NULL
NA,29/07/2018 17:08,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED UNDER RAILWAY BRIDGE OPPOSITE THE FALCON PUBLIC HOUSE  CALLER WILL MEET,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000421,FERNDALE,E09000022,LAMBETH,Clapham,10091121144,BEDFORD ROAD,21900157,SW4,530037,175551,530050,175550,51.46400649,-0.129427873
NA,29/07/2018 19:27,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON ROOF - OWNER ON SCENE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000112,CLOCK HOUSE,E09000006,BROMLEY,Beckenham,NULL,CHURCHFIELDS ROAD,20300883,BR3,NULL,NULL,536050,169350,NULL,NULL
NA,29/07/2018 23:22,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,BOURDON STREET,8400900,W1K,NULL,NULL,528850,180850,NULL,NULL
NA,30/07/2018 14:16,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND FENCE (POSSIBLY INJURED)  FLAT B,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000092,KENSAL GREEN,E09000005,BRENT,North Kensington,202085421,HILEY ROAD,20202450,NW10,523141,182961,523150,182950,51.53214438,-0.226059083
NA,30/07/2018 16:51,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000562,ST. HELIER,E09000029,SUTTON,Mitcham,NULL,WELBECK ROAD,22605655,SM5,NULL,NULL,527050,166150,NULL,NULL
NA,30/07/2018 20:50,2018,2018/19,Special Service,1,1,333,333,CAT STUCK UP INTERNAL FIREPLACE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000270,FORTIS GREEN,E09000014,HARINGEY,Hornsey,NULL,FORTISMERE AVENUE,21100315,N10,NULL,NULL,528350,189450,NULL,NULL
NA,31/07/2018 14:39,2018,2018/19,Special Service,2,3,333,999,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000145,WEST HAMPSTEAD,E09000007,CAMDEN,West Hampstead,NULL,FAWLEY ROAD,20400267,NW6,NULL,NULL,525550,185050,NULL,NULL
NA,01/08/2018 01:34,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,NULL,ATKINS SQUARE,20950344,E8,NULL,NULL,534750,185250,NULL,NULL
NA,01/08/2018 14:59,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN CAR ENGINE   RSPCA ON SCENE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000439,BROCKLEY,E09000023,LEWISHAM,New Cross,1.00023E+11,UPPER BROCKLEY ROAD,22001057,SE4,536665,176027,536650,176050,51.46672142,-0.033886865
NA,02/08/2018 09:19,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000208,SOUTHGATE,E09000010,ENFIELD,Southgate,207179215,HIGH STREET,20701608,N14,529706,194153,529750,194150,51.63125461,-0.127332233
NA,02/08/2018 21:25,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED BETWEEN FENCE AND SHED WALL  CONTACTED BY RSPCA,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000265,WORMHOLT AND WHITE CITY,E09000013,HAMMERSMITH AND FULHAM,Acton,34014743,ALDBOURNE ROAD,21000039,W12,522141,180296,522150,180250,51.50840641,-0.241390884
NA,03/08/2018 02:13,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED BEHIND COOKER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Plumstead,NULL,WOOLWICH COMMON,20801648,SE18,NULL,NULL,543250,177850,NULL,NULL
NA,03/08/2018 10:35,2018,2018/19,Special Service,1,1,333,333,RUNNING CALL CAT IN TREE,Cat,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,1.00022E+11,RICHMOND ROAD,21800806,KT2,517831,171264,517850,171250,51.42814438,-0.306480748
NA,03/08/2018 11:35,2018,2018/19,Special Service,1,2,333,666,CAT TRAPPED IN BLACK CAB ENGINE BAY  FRU REQUESTED WITH SNAKE EYE CAMERA,Cat,Person (mobile),Car,Road Vehicle,Animal rescue from height,Animal rescue from height - Domestic pet,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Chelsea,217092165,WESTGATE TERRACE,21700584,SW10,525841,177977,525850,177950,51.48675225,-0.188931834
NA,03/08/2018 18:45,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN A HOLE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000303,STANMORE PARK,E09000015,HARROW,Stanmore,NULL,HAMPTON ROAD,21202815,HA7,NULL,NULL,515350,193350,NULL,NULL
NA,04/08/2018 10:09,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON ROOF,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000279,STROUD GREEN,E09000014,HARINGEY,Hornsey,NULL,MOUNT VIEW ROAD,21103656,N4,NULL,NULL,530850,188050,NULL,NULL
NA,05/08/2018 15:05,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN FENCE,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000285,BELMONT,E09000015,HARROW,Stanmore,1.00021E+11,LAMORNA GROVE,21200990,HA7,517755,190423,517750,190450,51.60035672,-0.301178518
NA,05/08/2018 18:14,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN WINDOW AT FIRST FLOOR LEVEL   NEAR,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000108,BROMLEY COMMON AND KESTON,E09000006,BROMLEY,Bromley,NULL,ARCHERY LANE,20304203,BR2,NULL,NULL,541950,167150,NULL,NULL
NA,05/08/2018 19:26,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN FENCE,Cat,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000272,HIGHGATE,E09000014,HARINGEY,Hornsey,1.00021E+11,SOUTHWOOD LANE,21100786,N6,528377,187727,528350,187750,51.5738045,-0.1488837
NA,06/08/2018 10:37,2018,2018/19,Special Service,1,1,333,333,CAT WITH HEAD TRAPPED BEHIND SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009331,ST. PETER'S,E09000030,TOWER HAMLETS,Bethnal Green,NULL,ELLSWORTH STREET,22700463,E2,NULL,NULL,534750,182850,NULL,NULL
NA,06/08/2018 13:57,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011247,Loxford,E09000026,REDBRIDGE,Ilford,10093036268,ILFORD LANE,22302963,IG1,543973,185424,543950,185450,51.54934946,0.075079043
NA,06/08/2018 16:01,2018,2018/19,Special Service,1,3,333,999,SMALL ANIMAL RESCUE - CAT IN DISTRESS,Cat,Person (mobile),Restaurant/cafe,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011247,Loxford,E09000026,REDBRIDGE,Ilford,10093036268,ILFORD LANE,22302963,IG1,543973,185422,543950,185450,51.549333,0.0750759
NA,06/08/2018 19:33,2018,2018/19,Special Service,1,1,333,333,DOG ON WINDOW LEDGE ON THIRD FLOOR  AERIAL APPLIANCE REQUESTED FROM ON SCENE,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000635,HARROW ROAD,E09000033,WESTMINSTER,Paddington,NULL,LANHILL ROAD,8400494,W9,NULL,NULL,525250,182450,NULL,NULL
NA,06/08/2018 22:38,2018,2018/19,Special Service,1,1,333,333,CAT IN PRECARIOUS POSITION  CALLER STATES ON WINDOW SILL OF BUILDING OPPOSITE APPROX FIVE FLOORS UP,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,NULL,WEATHERLEY CLOSE,22702112,E3,NULL,NULL,536950,181850,NULL,NULL
NA,07/08/2018 16:54,2018,2018/19,Special Service,1,1,333,333,BIRD IN CHIMNEY  CALLER DIABLED - UNABLE TO WALK OR WRITE - DETAILS PASSED TO RSPCA,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011474,Old Coulsdon,E09000008,CROYDON,Purley,NULL,THE CROSSWAYS,20500347,CR5,NULL,NULL,530950,157750,NULL,NULL
NA,08/08/2018 22:01,2018,2018/19,Special Service,1,3,333,999,KITTEN STUCK IN PIPE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,NULL,LEA BRIDGE ROAD,22850300,E10,NULL,NULL,537450,187850,NULL,NULL
NA,09/08/2018 02:05,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - CAT TRAPPED,Cat,Person (mobile),Vehicle Repair Workshop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011109,Old Kent Road,E09000028,SOUTHWARK,New Cross,2.00003E+11,ILDERTON ROAD,22501322,SE15,535267,177561,535250,177550,51.48084558,-0.053413138
NA,09/08/2018 13:30,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN GAP BETWEEN TWO BUILDINGS  - OWNER ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011489,Woodside,E09000008,CROYDON,Woodside,NULL,NAPIER ROAD,20501218,SE25,NULL,NULL,534650,168050,NULL,NULL
NA,11/08/2018 03:26,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN VENT BEHIND KITCHEN APPLIANCES,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000042,WHALEBONE,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,HIGH ROAD,19900165,RM6,NULL,NULL,547850,187950,NULL,NULL
NA,11/08/2018 05:25,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED BEHIND KITCHEN CUPBOARDS,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000523,HEATHFIELD,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,FARM ROAD,22401470,TW4,NULL,NULL,512250,173450,NULL,NULL
NA,11/08/2018 10:55,2018,2018/19,Special Service,1,4,333,1332,HORSE IN DISTRESS,Horse,Person (mobile),Other building/use not known,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,1.00021E+11,BRAMBLEBURY ROAD,20800213,SE18,544442,178141,544450,178150,51.48378454,0.078857786
NA,13/08/2018 00:34,2018,2018/19,Special Service,1,1,333,333,CAT FALLEN INTO BASEMENT AREA   CAT IS POSSIBLY INJURED AND OWNERS UNABLE TO GET IT OUT,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000380,ST. PETER'S,E09000019,ISLINGTON,Islington,5300077614,REMINGTON STREET,21605789,N1,531899,183088,531850,183050,51.53131063,-0.099820646
NA,13/08/2018 02:34,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,West Hampstead,NULL,WILLOW ROAD,20400172,NW3,NULL,NULL,526650,185850,NULL,NULL
NA,13/08/2018 09:39,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH FOX IMPALED ON RAILINGS  RSPCA INSPECTOR GILL ON SCENE - THIS IS AT THE END OF THE,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000528,SOUTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,1.00022E+11,ALTON ROAD,22405311,TW9,518384,175215,518350,175250,51.4635425,-0.297205017
NA,13/08/2018 10:27,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED BEHIND KITCHEN SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011468,Fairfield,E09000008,CROYDON,Croydon,NULL,WATERWORKS YARD,20502803,CR0,NULL,NULL,532250,165450,NULL,NULL
NA,13/08/2018 11:06,2018,2018/19,Special Service,1,2,333,666,ASSIST RSPCA WITH PARROT ON ROOF   RSPCA IN ATTENDANCE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,NULL,CUCKOO HALL LANE,20703243,N9,NULL,NULL,535750,194850,NULL,NULL
NA,13/08/2018 12:10,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED INSIDE PROPERTY DEMOLITON SITE,Fox,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Assist trapped wild animal,E05000057,MILL HILL,E09000003,BARNET,Finchley,200220046,DOLLIS ROAD,20012840,NW7,524289,191123,524250,191150,51.60524528,-0.20662662
NA,13/08/2018 19:33,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING ON  BALCONY RSPCA ON SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000321,SOUTH HORNCHURCH,E09000016,HAVERING,Wennington,NULL,DUNEDIN ROAD,21300754,RM13,NULL,NULL,551750,182650,NULL,NULL
NA,13/08/2018 21:34,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009405,STANLEY,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,CARLYLE SQUARE,21700072,SW3,NULL,NULL,527050,178050,NULL,NULL
NA,13/08/2018 22:13,2018,2018/19,Special Service,1,1,333,333,KITTEN STUCK UNDERNEATH KITCHEN FLOORBOARD,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,STRATHMORE GARDENS,20041220,N3,NULL,NULL,525650,190550,NULL,NULL
NA,14/08/2018 09:25,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED INSIDE SOFA,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000088,DOLLIS HILL,E09000005,BRENT,Willesden,NULL,NORTH CIRCULAR ROAD,20200296,NW2,NULL,NULL,521850,186850,NULL,NULL
NA,14/08/2018 11:18,2018,2018/19,Special Service,1,3,333,999,ASSIST IN HELPING STAND UP A HORSE THAT IS DUE TO BE PUT DOWN THIS AFTERNOON BY LOCAL VET,Horse,Person (mobile),Other agricultural building,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,1.00021E+11,VICARAGE PARK,20801545,SE18,544446,178159,544450,178150,51.48395029,0.078918499
NA,14/08/2018 14:09,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000273,HORNSEY,E09000014,HARINGEY,Hornsey,NULL,PRIORY ROAD,21100710,N8,NULL,NULL,529950,189250,NULL,NULL
NA,16/08/2018 07:54,2018,2018/19,Special Service,1,1,333,333,DEER STUCK IN RAILINGS  - LIAISE WITH PARK WARDEN,Deer,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05000200,GRANGE,E09000010,ENFIELD,Enfield,207136923,CECIL ROAD,20702573,EN2,532374,196422,532350,196450,51.65102427,-0.087958991
NA,16/08/2018 19:55,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN CAR ENGINE GREY TOYOTA CALLER WILL FLAG YOU DOWN,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,10014033057,ANGEL LANE,22201284,E15,538776,184647,538750,184650,51.54367101,-0.000125622
NA,17/08/2018 09:11,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN GUTTERING ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,NULL,PARK FARM ROAD,21800744,KT2,NULL,NULL,518450,170350,NULL,NULL
NA,17/08/2018 17:40,2018,2018/19,Special Service,1,1,333,333,SQUIRREL HANGING FROM GUTTERING,Squirrel,Person (land line),Fence,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,200087310,OAKDENE PARK,20032260,N3,524945,191478,524950,191450,51.6082869,-0.197047
NA,18/08/2018 01:06,2018,2018/19,Special Service,1,1,333,333,CALLED X RSPCA - ASSIST WITH SMALL MAMMAL TRAPPED,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,NULL,MERTON ROAD,22903296,SW18,NULL,NULL,525250,174250,NULL,NULL
NA,18/08/2018 18:35,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON LEDGE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000188,SOUTH ACTON,E09000009,EALING,Acton,NULL,NEWTON AVENUE,20601243,W3,NULL,NULL,520250,179750,NULL,NULL
NA,18/08/2018 22:39,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN BUILDING,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011113,Rye Lane,E09000028,SOUTHWARK,Peckham,2.00003E+11,RYE LANE,22502166,SE15,534254,176358,534250,176350,51.47027507,-0.068450815
NA,19/08/2018 17:25,2018,2018/19,Special Service,1,3,333,999,Redacted,Horse,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,10009949007,PACKET BOAT LANE,21401472,UB8,505368,181061,505350,181050,51.51866096,-0.482751161
NA,20/08/2018 00:37,2018,2018/19,Special Service,1,2,333,666,CAT TRAPPED IN THICKET BEHIND HOUSE - OWNER ON SCENE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000434,THURLOW PARK,E09000022,LAMBETH,West Norwood,NULL,NORWOOD ROAD,21901036,SE24,NULL,NULL,531850,173650,NULL,NULL
NA,21/08/2018 12:30,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN WINDOW - POSSIBLE BROKEN LEG - OWNER ON SCENE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000494,WEST HAM,E09000025,NEWHAM,Stratford,NULL,MITRE ROAD,22201521,E15,NULL,NULL,539350,183450,NULL,NULL
NA,21/08/2018 15:28,2018,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000253,COLLEGE PARK AND OLD OAK,E09000013,HAMMERSMITH AND FULHAM,Acton,34136926,DU CANE ROAD,21000285,W12,521940,181090,521950,181050,51.51558628,-0.244016596
NA,21/08/2018 19:30,2018,2018/19,Special Service,1,1,333,333,CAT STUCK INSIDE SOFA,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000108,BROMLEY COMMON AND KESTON,E09000006,BROMLEY,Bromley,NULL,TURPINGTON LANE,20300668,BR2,NULL,NULL,542250,166950,NULL,NULL
NA,22/08/2018 14:08,2018,2018/19,Special Service,1,1,333,333,DOG IN PRECARIOUS POSITION ON PITCHED ROOF OPPOSITE TOWN HALL CALLER WILL MEET BRIGADE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000256,HAMMERSMITH BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,KING STREET,21000478,W6,NULL,NULL,522650,178550,NULL,NULL
NA,22/08/2018 19:08,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE  -  NETS OF BIRDS HAVE TRAPPED BY BUILDING WORKS,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000185,NORTHOLT WEST END,E09000009,EALING,Southall,NULL,DEHAVILLAND CLOSE,20600519,UB5,NULL,NULL,511650,182650,NULL,NULL
NA,22/08/2018 21:26,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000341,UXBRIDGE SOUTH,E09000017,HILLINGDON,Hillingdon,NULL,CHILTERN VIEW ROAD,21400386,UB8,NULL,NULL,505550,183250,NULL,NULL
NA,23/08/2018 01:24,2018,2018/19,Special Service,1,2,333,666,KITTENS TRAPPED UNDER DECKING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000329,EASTCOTE AND EAST RUISLIP,E09000017,HILLINGDON,Ruislip,NULL,MOUNT PARK ROAD,21401335,HA5,NULL,NULL,510250,188450,NULL,NULL
NA,24/08/2018 13:51,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND SHED,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,5870067582,MCRAE LANE,22600783,CR4,527784,166593,527750,166550,51.38401023,-0.165062115
NA,25/08/2018 11:13,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON A ROOF   ASSISTANCE REQUESTED FROM RSPCA OFFICER ON SCENE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000479,CUSTOM HOUSE,E09000025,NEWHAM,Plaistow,NULL,ST MICHAELS CLOSE,22207901,E16,NULL,NULL,541650,181550,NULL,NULL
NA,25/08/2018 12:19,2018,2018/19,Special Service,1,2,333,666,FOX TRAPPED ON ROOF,Fox,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05009371,DE BEAUVOIR,E09000012,HACKNEY,Islington,NULL,BALLS POND ROAD,20900101,N1,NULL,NULL,533050,184750,NULL,NULL
NA,25/08/2018 22:15,2018,2018/19,Special Service,1,1,333,333,DOG WITH ROD STUCK THROUGH STOMACH,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000342,WEST DRAYTON,E09000017,HILLINGDON,Hayes,NULL,THORNTON AVENUE,21401990,UB7,NULL,NULL,506850,179250,NULL,NULL
NA,27/08/2018 14:39,2018,2018/19,Special Service,1,4,333,1332,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011100,Dulwich Village,E09000028,SOUTHWARK,West Norwood,NULL,PICKWICK ROAD,22501976,SE21,NULL,NULL,533050,173950,NULL,NULL
NA,28/08/2018 09:32,2018,2018/19,Special Service,1,1,333,333,ASSIST RSCPA WITH CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000179,HANGER HILL,E09000009,EALING,Acton,12091983,DALLAS ROAD,20600504,W5,518722,181902,518750,181950,51.52356691,-0.290096303
NA,28/08/2018 21:56,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED DOWN MANHOLE - OUTSIDE RAVENSDALE MANSIONS,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000269,CROUCH END,E09000014,HARINGEY,Hornsey,NULL,HARINGEY PARK,21103376,N8,NULL,NULL,530450,188350,NULL,NULL
NA,29/08/2018 16:19,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING - RSPCA UNABLE TO ATTEND  BELIEVED TO BE UNDER HUNGERFORD BRIDGE ON BELVEDER,Bird,Person (land line),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000416,BISHOP'S,E09000022,LAMBETH,Lambeth,10001114017,LOWER MARSH,21900900,SE1,530968,179568,530950,179550,51.49988645,-0.114536212
NA,30/08/2018 22:02,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN SOFA MECHANISM,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011114,St. George's,E09000028,SOUTHWARK,Dowgate,NULL,LONDON ROAD,22501544,SE1,NULL,NULL,531850,179250,NULL,NULL
NA,31/08/2018 12:11,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH INJURED BIRD TRAPPED IN TELEPHONE WIRE    RSPCA IN ATTENDANCE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000036,MAYESBROOK,E09000002,BARKING AND DAGENHAM,Barking,NULL,FLAMSTEAD ROAD,19900484,RM9,NULL,NULL,547250,184350,NULL,NULL
NA,31/08/2018 23:18,2018,2018/19,Special Service,1,1,333,333,KITTEN STUCK IN HOLE BEHIND FRIDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,NULL,EDWARD STREET,22000361,SE8,NULL,NULL,537050,177550,NULL,NULL
NA,01/09/2018 17:17,2018,2018/19,Special Service,1,1,333,333,Redacted,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000334,MANOR,E09000017,HILLINGDON,Ruislip,1.00023E+11,WEST END ROAD,21402115,HA4,509373,186710,509350,186750,51.56867094,-0.423305578
NA,01/09/2018 20:10,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN BUILDERS WEBBING ON BUILDING SITE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,NULL,HARDWICKS SQUARE,22902216,SW18,NULL,NULL,525450,174650,NULL,NULL
NA,02/09/2018 15:43,2018,2018/19,Special Service,1,1,333,333,Redacted,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,10070018619,HIGH STREET,20302875,SE20,535510,170241,535550,170250,51.41500017,-0.052722326
NA,02/09/2018 17:16,2018,2018/19,Special Service,1,5,333,1665,HORSE STUCK IN DITCH AT REAR OF OCADO - OWNERS ON SCENE  RVP - CRABTREE MANOR WAY NORTH,Horse,Person (mobile),Wasteland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05011218,Belvedere,E09000004,BEXLEY,Erith,10023301848,CRABTREE MANORWAY NORTH TO FOOTPATH 3 FP242,20106242,DA17,550150,179495,550150,179450,51.49447323,0.161576005
NA,02/09/2018 18:13,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN STRING AT ROOF LEVEL ALSO HANGING UPSIDE DOWN CALLED BY THE RSPCA WHO WILL NOT BE AT,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000085,ALPERTON,E09000005,BRENT,Wembley,NULL,DOROTHY AVENUE,20201293,HA0,NULL,NULL,518350,184150,NULL,NULL
NA,02/09/2018 19:37,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON NAILED FENCE,Cat,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000256,HAMMERSMITH BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34026766,SOUTHERTON ROAD,21000745,W6,523046,178908,523050,178950,51.49573838,-0.228837444
NA,02/09/2018 23:35,2018,2018/19,Special Service,1,1,333,333,SMALL BIRD TRAPPED IN WIRING BEHIND A CUPBOARD,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000378,ST. GEORGE'S,E09000019,ISLINGTON,Holloway,NULL,HOLLOWAY ROAD,21606449,N19,NULL,NULL,530050,186450,NULL,NULL
NA,03/09/2018 02:47,2018,2018/19,Special Service,2,3,333,999,NULL,Unknown - Heavy Livestock Animal,Other FRS,Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving livestock,E05000559,CARSHALTON SOUTH AND CLOCKHOUSE,E09000029,SUTTON,Wallington,5870086811,LITTLE WOODCOTE LANE,22605502,CR8,529081,161589,529050,161550,51.33875101,-0.148257902
NA,03/09/2018 21:36,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED BEHIND BATH,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000437,BELLINGHAM,E09000023,LEWISHAM,Lewisham,NULL,BROADMEAD,22001272,SE6,NULL,NULL,537650,172250,NULL,NULL
NA,05/09/2018 13:41,2018,2018/19,Special Service,1,5,333,1665,ASSIST RSPCA WITH HORSE STUCK IN STREAM - WATER OPS LEVEL ONE,Horse,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05011218,Belvedere,E09000004,BEXLEY,Erith,1.0002E+11,ST THOMAS ROAD,20101393,DA17,550057,179737,550050,179750,51.49666637,0.160343336
NA,05/09/2018 14:53,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000431,STREATHAM SOUTH,E09000022,LAMBETH,Norbury,2.00001E+11,STREATHAM HIGH ROAD,21901326,SW16,530258,170737,530250,170750,51.42069521,-0.128010227
NA,05/09/2018 17:55,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BETWEEN WINDOW AND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000191,SOUTHFIELD,E09000009,EALING,Chiswick,NULL,CHURCH PATH,20600383,W4,NULL,NULL,520350,179250,NULL,NULL
NA,05/09/2018 22:21,2018,2018/19,Special Service,1,1,333,333,RUNNING CALL TO  PET  SNAKE  TRAPPED IN RING,Snake,Person (land line),Fire station,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000365,TURNHAM GREEN,E09000018,HOUNSLOW,Chiswick,1.00023E+11,HEATHFIELD GARDENS,21500589,W4,520270,178257,520250,178250,51.49048509,-0.2690352
NA,06/09/2018 16:01,2018,2018/19,Special Service,1,1,333,333,INJURED FOX TRAPPED BETWEEN WALL AND SHED,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,NULL,JANSON ROAD,22202021,E15,NULL,NULL,539250,185450,NULL,NULL
NA,06/09/2018 23:55,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON ROOF AREA,Cat,Person (mobile),Petrol station,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000299,QUEENSBURY,E09000015,HARROW,Stanmore,2E+11,HONEYPOT LANE,21200986,HA7,518251,189980,518250,189950,51.59626989,-0.29417034
NA,07/09/2018 07:56,2018,2018/19,Special Service,1,2,333,666,PARROT TRAPPED IN TREE - ALP REQUESTED,Bird,Person (mobile),Other car park structure,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000336,NORTHWOOD HILLS,E09000017,HILLINGDON,Ruislip,1.00023E+11,WINDSOR CLOSE,21402187,HA6,510136,190393,510150,190350,51.60162319,-0.411140294
NA,08/09/2018 08:12,2018,2018/19,Special Service,1,1,333,333,CAT UP TREE - RSPCA IN ATT,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000283,WHITE HART LANE,E09000014,HARINGEY,Tottenham,10003972871,LABURNUM AVENUE,21106065,N17,532732,191473,532750,191450,51.60646719,-0.084655529
NA,08/09/2018 14:05,2018,2018/19,Special Service,1,1,333,333,ASSSIST RSPCA WITH SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000170,ACTON CENTRAL,E09000009,EALING,Acton,NULL,ALFRED ROAD,20600036,W3,NULL,NULL,520450,180050,NULL,NULL
NA,08/09/2018 16:07,2018,2018/19,Special Service,1,2,333,666,ASSIST RSPCA WITH SMALL ANIMAL RESCUE CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000180,HOBBAYNE,E09000009,EALING,Ealing,NULL,WESTCOTT CRESCENT,20601872,W7,NULL,NULL,515750,181550,NULL,NULL
NA,08/09/2018 19:31,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000208,SOUTHGATE,E09000010,ENFIELD,Southgate,207039467,PRUDEN CLOSE,20706091,N14,529407,193770,529450,193750,51.62787708,-0.131798676
NA,09/09/2018 17:47,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON WINDOW LEDGE   FLAT B,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,NULL,PORTNALL ROAD,8400567,W9,NULL,NULL,524750,182550,NULL,NULL
NA,10/09/2018 22:05,2018,2018/19,Special Service,1,1,333,333,INJURED CAT STUCK UP TREE - OWNER ON SCENE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000124,PETTS WOOD AND KNOLL,E09000006,BROMLEY,Orpington,1.0002E+11,KNOLL RISE,20301175,BR6,545968,166197,545950,166150,51.37607691,0.095898332
NA,11/09/2018 12:12,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON ROOF LOOK LIKE HE IS WEDGED BETWEEN CHIMNEYS BEEN ON ROOF SINCE SATURDAY RSPCA INSP ON,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,NULL,HUXLEY STREET,8400828,W10,NULL,NULL,524050,182550,NULL,NULL
NA,11/09/2018 19:09,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (land line),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000522,HAMPTON WICK,E09000027,RICHMOND UPON THAMES,Twickenham,10070718915,HIGH STREET,22400303,KT1,517405,169775,517450,169750,51.41485298,-0.313092197
NA,12/09/2018 09:07,2018,2018/19,Special Service,1,3,333,999,Redacted,Horse,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05011222,Crayford,E09000004,BEXLEY,Bexley,10091897350,THAMES ROAD,20101429,DA1,553138,175523,553150,175550,51.45798004,0.20287323
NA,13/09/2018 12:38,2018,2018/19,Special Service,1,1,333,333,PUPPY TRAPPED UNDER LOG PILE,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000191,SOUTHFIELD,E09000009,EALING,Chiswick,12100114,WILKINSON WAY,20601904,W4,520574,179650,520550,179650,51.50293409,-0.264183216
NA,14/09/2018 21:23,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN WALL ALSFORD TIMBER MERCHANTS  CALLER WILL WAIT OUTSIDE AND DIRECT,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000529,SOUTH TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,1.00023E+11,HEATH ROAD,22403551,TW1,515930,173098,515950,173050,51.44502415,-0.33321439
NA,16/09/2018 10:40,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT STUCK IN TREE IN REAR GARDEN,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011096,Camberwell Green,E09000028,SOUTHWARK,Peckham,10009803979,THOMPSONS AVENUE,22502494,SE5,532109,177366,532150,177350,51.47983704,-0.098932525
NA,16/09/2018 14:55,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED UNDER FRONT DOOR STEP,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000599,HIGH STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,HAZELWOOD ROAD,22844150,E17,NULL,NULL,536150,188850,NULL,NULL
NA,17/09/2018 17:47,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN FENCE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000404,CHESSINGTON NORTH AND HOOK,E09000021,KINGSTON UPON THAMES,Surbiton,128043988,WOODGATE AVENUE,21801073,KT9,517670,164378,517650,164350,51.36628426,-0.311074317
NA,18/09/2018 00:32,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND WASHING MACHINE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,NULL,LOWER HAM ROAD,21800623,KT2,NULL,NULL,518050,170250,NULL,NULL
NA,18/09/2018 18:45,2018,2018/19,Special Service,1,2,333,666,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,NULL,ELTHAM ROAD,22005070,SE12,NULL,NULL,540050,174850,NULL,NULL
NA,19/09/2018 09:51,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN WIRE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,NULL,SEVEN SISTERS ROAD,21603790,N7,NULL,NULL,530650,186150,NULL,NULL
NA,19/09/2018 09:56,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON ROOF - REQUESTED BY RSPCA,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000461,GRAVENEY,E09000024,MERTON,Mitcham,NULL,LINKS ROAD,22103954,SW17,NULL,NULL,528550,170450,NULL,NULL
NA,19/09/2018 12:48,2018,2018/19,Special Service,1,1,333,333,CAT IN THE TREE   REQUESTED BY RSPCA WHO ARE ON SCENE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009373,HACKNEY DOWNS,E09000012,HACKNEY,Stoke Newington,2.00001E+11,BROOKE ROAD,20900177,E5,534766,186384,534750,186350,51.56024993,-0.057249438
NA,19/09/2018 13:00,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN ENGINE BAY OF CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000308,ELM PARK,E09000016,HAVERING,Hornchurch,1.00024E+11,WOOD LANE,21300665,RM12,552686,185034,552650,185050,51.5435556,0.200487757
NA,19/09/2018 21:55,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN PIPE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,NULL,ROSEMARY GARDENS,22406394,SW14,NULL,NULL,520350,175750,NULL,NULL
NA,20/09/2018 15:28,2018,2018/19,Special Service,1,1,333,333,KITTEN IN CAR BONNET - RSPCA ON SCENE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,1.00022E+11,THE COPPICE,21401943,UB7,506408,181452,506450,181450,51.52197265,-0.467651944
NA,20/09/2018 18:57,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000364,SYON,E09000018,HOUNSLOW,Heston,NULL,TALLOW ROAD,21501760,TW8,NULL,NULL,517450,177450,NULL,NULL
NA,21/09/2018 13:29,2018,2018/19,Special Service,1,1,333,333,DOG IN PRECARIOUS POSISTION ON ROOF,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000423,HERNE HILL,E09000022,LAMBETH,Brixton,NULL,HERNE HILL,21900703,SE24,NULL,NULL,532350,174950,NULL,NULL
NA,21/09/2018 20:57,2018,2018/19,Special Service,1,1,333,333,CAT FALLEN FROM PONTOON ONTO THAMES EMBANKMENT - STUCK IN MUD,Cat,Person (land line),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000622,ST. MARY'S PARK,E09000032,WANDSWORTH,Battersea,10008158601,LOMBARD ROAD,22902991,SW11,526608,176321,526650,176350,51.47170291,-0.17847995
NA,22/09/2018 09:51,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN VAN,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000572,WORCESTER PARK,E09000029,SUTTON,Sutton,5870015304,BOSCOMBE ROAD,22601298,KT4,523350,166399,523350,166350,51.38325213,-0.228825987
NA,24/09/2018 10:25,2018,2018/19,Special Service,1,2,333,666,RSPCA WITH REQUEST FOR ASSISTANCE IN SEARCH OF INJURED CAT IN SHRUBBERY  USING TIC     RSPCA CONTACT,Cat,Person (land line),Scrub land,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000048,EAST BARNET,E09000003,BARNET,Barnet,200103575,SHURLAND AVENUE,20039540,EN4,526836,195271,526850,195250,51.64194904,-0.168367364
NA,26/09/2018 15:29,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000424,KNIGHT'S HILL,E09000022,LAMBETH,West Norwood,NULL,SELSDON ROAD,21901222,SE27,NULL,NULL,531550,171850,NULL,NULL
NA,27/09/2018 17:23,2018,2018/19,Special Service,1,4,333,1332,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000257,MUNSTER,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,ROWALLAN ROAD,21000701,SW6,NULL,NULL,524050,177150,NULL,NULL
NA,28/09/2018 20:22,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE  - INJURED RABBIT TRAPPED IN CAVITY OF TWO FENCES,Rabbit,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,NULL,DURAND WAY,20202204,NW10,NULL,NULL,520050,184250,NULL,NULL
NA,29/09/2018 02:35,2018,2018/19,Special Service,1,1,333,333,DOG STUCK BY FENCE AND TIDE DEPTFORD GREEN BY THE AHOY CENTRE,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000222,GREENWICH WEST,E09000011,GREENWICH,Deptford,10010257764,BORTHWICK STREET,20800196,SE8,537222,178045,537250,178050,51.48472081,-0.02509723
NA,29/09/2018 14:57,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000251,ASKEW,E09000013,HAMMERSMITH AND FULHAM,Chiswick,NULL,EMLYN GARDENS,21001199,W12,NULL,NULL,521450,179450,NULL,NULL
NA,29/09/2018 15:13,2018,2018/19,Special Service,NULL,NULL,333,NULL,Redacted,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,2.00001E+11,WHITTLE CLOSE,22887050,E17,536321,188025,536350,188050,51.57462177,-0.034193288
NA,29/09/2018 16:49,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN BUSHES ON LAKE,Cat,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,10070001081,GLASSMILL LANE,20303922,BR2,539917,169050,539950,169050,51.40322961,0.010142562
NA,29/09/2018 21:10,2018,2018/19,Special Service,1,1,333,333,DOG WITH PAWS TRAPPED IN METAL TABLE LEG,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000177,GREENFORD BROADWAY,E09000009,EALING,Southall,NULL,FERRYMEAD AVENUE,20600665,UB6,NULL,NULL,513950,183050,NULL,NULL
NA,30/09/2018 17:25,2018,2018/19,Special Service,1,1,333,333,RUNNING CALL TO KITTEN STUCK IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,1.00022E+11,PARK ROAD,21880043,KT2,519000,170845,519050,170850,51.42413326,-0.289804675
NA,30/09/2018 21:39,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON ROOF OF HOUSE,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000043,BRUNSWICK PARK,E09000003,BARNET,Southgate,NULL,PINE ROAD,20034360,N11,NULL,NULL,528150,193850,NULL,NULL
NA,02/10/2018 01:27,2018,2018/19,Special Service,1,1,333,333,CAT STUCK BETWEEN TWO WALLS IN GARDEN AREA - CALLER WILL MEET YOU,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,LISSON GROVE,8400993,NW1,NULL,NULL,527450,181850,NULL,NULL
NA,02/10/2018 04:59,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000139,KENTISH TOWN,E09000007,CAMDEN,Kentish Town,NULL,COLLEGE YARD,20400222,NW5,NULL,NULL,528850,185550,NULL,NULL
NA,02/10/2018 12:20,2018,2018/19,Special Service,2,3,333,999,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000468,RAVENSBURY,E09000024,MERTON,Mitcham,48108879,MORDEN HALL ROAD,22104539,SM4,526221,168516,526250,168550,51.40164587,-0.186830577
NA,02/10/2018 18:54,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011466,COULSDON TOWN,E09000008,CROYDON,Purley,NULL,SOUTH DRIVE,20502511,CR5,NULL,NULL,529950,159950,NULL,NULL
NA,05/10/2018 02:47,2018,2018/19,Special Service,1,1,333,333,FOX WITH PAW TRAPPED  IN GARDEN,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011105,NEWINGTON,E09000028,SOUTHWARK,Old Kent Road,NULL,SUTHERLAND SQUARE,22502438,SE17,NULL,NULL,532250,178050,NULL,NULL
NA,05/10/2018 16:31,2018,2018/19,Special Service,1,1,333,333,DEER WITH HEAD TRAPPED IN FENCE  O.S. SKATE PARK,Deer,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,DAGNAM PARK DRIVE,21300071,RM3,554182,192330,554150,192350,51.60869976,0.225239236
NA,06/10/2018 14:32,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000367,BUNHILL,E09000019,ISLINGTON,Shoreditch,NULL,MORA STREET,21605467,EC1V,NULL,NULL,532450,182750,NULL,NULL
NA,06/10/2018 20:55,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,NULL,LEONARD ROAD,22207787,E7,NULL,NULL,540150,185650,NULL,NULL
NA,07/10/2018 07:13,2018,2018/19,Special Service,1,1,333,333,BIN ROOM ALIGHT,Unknown - Wild Animal,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000314,HEATON,E09000016,HAVERING,Harold Hill,10024390298,MIMOSA CLOSE,21301431,RM3,553335,191083,553350,191050,51.59773516,0.212471858
NA,07/10/2018 11:55,2018,2018/19,Special Service,1,1,333,333,DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000455,ABBEY,E09000024,MERTON,Wimbledon,48058726,QUICKS ROAD,22105269,SW19,525776,170394,525750,170350,51.41862375,-0.192556606
NA,07/10/2018 14:13,2018,2018/19,Special Service,1,2,333,666,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000309,EMERSON PARK,E09000016,HAVERING,Hornchurch,NULL,FAIRLAWNS CLOSE,21300055,RM11,NULL,NULL,554650,187850,NULL,NULL
NA,07/10/2018 19:30,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON ROOF     FIFTH FLOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,WERRINGTON STREET,20400792,NW1,NULL,NULL,529550,182950,NULL,NULL
NA,11/10/2018 15:39,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED UNDER FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,KINGSDALE ROAD,20800862,SE18,NULL,NULL,545650,177550,NULL,NULL
NA,11/10/2018 21:19,2018,2018/19,Special Service,2,6,333,1998,Redacted,Deer,Person (land line),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist trapped wild animal,E05000313,HAVERING PARK,E09000016,HAVERING,Romford,1.00021E+11,BROXHILL ROAD,21301041,RM4,551553,193320,551550,193350,51.61830858,0.187724767
NA,12/10/2018 11:10,2018,2018/19,Special Service,1,1,333,333,SQUIRREL IS TRAPPED UNDER THE TERRACE FLOORBOARDS OF PROPERTY,Squirrel,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000144,SWISS COTTAGE,E09000007,CAMDEN,West Hampstead,NULL,CANFIELD GARDENS,20400419,NW6,NULL,NULL,525750,184350,NULL,NULL
NA,13/10/2018 18:09,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,NULL,THE HIGHWAY,22701200,E1W,NULL,NULL,535250,180750,NULL,NULL
NA,14/10/2018 17:56,2018,2018/19,Special Service,1,2,333,666,FOX TRAPPED IN BARBED WIRE,Fox,Person (mobile),Other outdoor equipment/machinery,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,1.00021E+11,TEWSON ROAD,20801460,SE18,545348,178499,545350,178450,51.48677287,0.092051246
NA,15/10/2018 14:20,2018,2018/19,Special Service,1,1,333,333,DOG STUCK ON ROOF OF POST OFFICE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000426,OVAL,E09000022,LAMBETH,Lambeth,NULL,KENNINGTON ROAD,21900793,SE11,NULL,NULL,531250,177850,NULL,NULL
NA,15/10/2018 14:51,2018,2018/19,Special Service,1,1,333,333,"ASSIST RSPCA WITHN PIGEON IN NETTING, INSPECTOR ON SCENE",Bird,Person (mobile),Secondary school,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000047,COPPETTS,E09000003,BARNET,Southgate,200058625,HEMINGTON AVENUE,20021720,N11,527851,192407,527850,192450,51.61598511,-0.154762586
NA,15/10/2018 15:08,2018,2018/19,Special Service,NULL,NULL,333,NULL,CAT STUCK UNDERNEATH BATHTUB,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011099,DULWICH HILL,E09000028,SOUTHWARK,Forest Hill,NULL,FRIERN ROAD,22501002,SE22,NULL,NULL,534150,173950,NULL,NULL
NA,15/10/2018 19:58,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000091,HARLESDEN,E09000005,BRENT,Park Royal,NULL,ST MARYS ROAD,20202537,NW10,NULL,NULL,521350,183750,NULL,NULL
NA,16/10/2018 14:10,2018,2018/19,Special Service,1,1,333,333,KITTEN FALLEN THROUGH GARAGE ROOF AND TRAPPED,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011234,ALDBOROUGH,E09000026,REDBRIDGE,Ilford,1.00022E+11,ASHURST DRIVE,22302589,IG2,544032,188687,544050,188650,51.5786505,0.0772616
NA,18/10/2018 12:34,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000314,HEATON,E09000016,HAVERING,Harold Hill,1.00021E+11,SNOWDROP PATH,21301601,RM3,553523,191226,553550,191250,51.59896427,0.215245156
NA,19/10/2018 09:53,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - DOG STUCK IN BRAMBLES - DISTRESSED,Dog,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011223,CROOK LOG,E09000004,BEXLEY,Bexley,10011844850,BEECHWOOD CRESCENT,20100112,DA7,548026,175846,548050,175850,51.46223843,0.129484573
NA,19/10/2018 17:35,2018,2018/19,Special Service,1,1,333,333,DEER STUCK IN METAL FENCE      ENTER VIA PETERSFIELD AVENUE,Deer,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,10024390526,COLLERNE STREET,21320280,RM3,554326,192212,554350,192250,51.60760106,0.22726324
NA,20/10/2018 12:42,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - PIGEON STUCK IN NETTING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Finchley,NULL,CHURCH CRESCENT,20008600,N3,NULL,NULL,524750,190650,NULL,NULL
NA,22/10/2018 13:22,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED IN BETWEEN FENCES,Fox,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000361,HOUNSLOW WEST,E09000018,HOUNSLOW,Feltham,1.00022E+11,THE GREENWAY,21500516,TW4,512626,175375,512650,175350,51.46615129,-0.380003659
NA,22/10/2018 21:08,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED IN PIPEWORK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011109,OLD KENT ROAD,E09000028,SOUTHWARK,Old Kent Road,NULL,COOPERS ROAD,22500590,SE1,NULL,NULL,533850,178250,NULL,NULL
NA,22/10/2018 23:41,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN A TREE - HAVING BREATHING PROBLEMS,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,5105657,PHOENIX ROAD,20400807,NW1,529710,183067,529750,183050,51.53162612,-0.131374456
NA,23/10/2018 23:26,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN STUD PARTITION WALL`,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000189,SOUTHALL BROADWAY,E09000009,EALING,Southall,NULL,ST JOSEPHS DRIVE,20601600,UB1,NULL,NULL,512750,180250,NULL,NULL
NA,24/10/2018 18:54,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED BEHIND GATE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000052,GARDEN SUBURB,E09000003,BARNET,Finchley,NULL,SOUTHWAY,20040180,NW11,NULL,NULL,525950,188550,NULL,NULL
NA,25/10/2018 22:19,2018,2018/19,Special Service,1,1,333,333,CAT STUCK BETWEEN TWO HOUSES,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011226,FALCONWOOD & WELLING,E09000004,BEXLEY,Bexley,NULL,DANSON CRESCENT,20100423,DA16,NULL,NULL,546550,175650,NULL,NULL
NA,26/10/2018 11:27,2018,2018/19,Special Service,1,1,333,333,DOG WITH LEG TRAPPED IN CAR SEAT,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000041,VILLAGE,E09000002,BARKING AND DAGENHAM,Dagenham,100048649,ST GILES CLOSE,19900579,RM10,549838,184424,549850,184450,51.53883619,0.159180714
NA,26/10/2018 14:40,2018,2018/19,Special Service,1,1,333,333,DOP TRAPPED IN METAL FENCING  AT REAR OF GARAGES     RSPCA ATTENDING,Unknown - Domestic Animal Or Pet,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,10010225322,BLISS CRESCENT,20800185,SE13,537736,176442,537750,176450,51.47019399,-0.018315422
NA,27/10/2018 10:39,2018,2018/19,Special Service,1,1,333,333,GULL TRAPPED ON ROOF  RSPCA IN ATTENDANCE,Bird,Person (land line),Large supermarket,Non Residential,Animal rescue from height,Wild animal rescue from height,E05011476,PURLEY & WOODCOTE,E09000008,CROYDON,Purley,1.00023E+11,PURLEY ROAD,20502281,CR8,531261,161445,531250,161450,51.336951,-0.117036
NA,28/10/2018 11:38,2018,2018/19,Special Service,1,1,333,333,DOG STUCK IN STREAM        YOU WILL BE MET IN THE CAR PARK ON THE RIGHT BEFORE THE OPTIMIST PH    CO,Dog,Person (land line),Heathland,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000323,UPMINSTER,E09000016,HAVERING,Hornchurch,10033423523,HACTON LANE,21300391,RM14,554863,185877,554850,185850,51.55053972,0.232222434
NA,28/10/2018 20:30,2018,2018/19,Special Service,1,1,333,333,BIRD STUCK IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,NULL,THE BROADWAY,22306023,IG8,NULL,NULL,540950,191850,NULL,NULL
NA,30/10/2018 20:37,2018,2018/19,Special Service,1,1,333,333,CAT SHUT IN GARAGE FOR TWO DAYS,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000214,ABBEY WOOD,E09000011,GREENWICH,Plumstead,10010230800,VIOLA AVENUE,20801552,SE2,546697,178546,546650,178550,51.48685163,0.111486555
NA,30/10/2018 23:34,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000529,SOUTH TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,KNOWLE ROAD,22403597,TW2,NULL,NULL,515450,173150,NULL,NULL
NA,01/11/2018 12:55,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE    CAT TRAPPED BETWEEN TWO OUTBUILDINGS,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000533,WHITTON,E09000027,RICHMOND UPON THAMES,Twickenham,1.00022E+11,ROSECROFT GARDENS,22401799,TW2,515016,173483,515050,173450,51.44866921,-0.346231717
NA,01/11/2018 13:39,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000369,CANONBURY,E09000019,ISLINGTON,Islington,5300030383,ELMORE STREET,21604800,N1,532755,184225,532750,184250,51.54132811,-0.087057622
NA,01/11/2018 14:09,2018,2018/19,Special Service,1,1,333,333,DOG IN PRECARIOUS POSITION ON WINDOWSILL,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,NULL,COLDBATH STREET,20800354,SE13,NULL,NULL,537750,176550,NULL,NULL
NA,02/11/2018 23:17,2018,2018/19,Special Service,1,1,333,333,ANIMAL IN DISTRESS  -  CAT   LOCKED IN THE BIN ROOM  -  CALLER WILL MEET AT MAIN GATE BY THE BIN ROO,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,NULL,WHINCHAT ROAD,20801605,SE28,NULL,NULL,544650,179250,NULL,NULL
NA,03/11/2018 12:18,2018,2018/19,Special Service,1,1,333,333,ASSIST  RSPCA  WITH   INJURED GULL  IN CANAL RVP  WITH  RSPCA AT  LASCAR WHARF,Bird,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009329,ST. DUNSTAN'S,E09000030,TOWER HAMLETS,Poplar,6151232,PARNHAM STREET,22700918,E14,536443,181374,536450,181350,51.51482797,-0.035019217
NA,03/11/2018 13:12,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON ROOF OF FIVE STOREY BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000633,CHURCHILL,E09000033,WESTMINSTER,Lambeth,NULL,CHURCHILL GARDENS ESTATE,8401852,SW1V,NULL,NULL,529350,178050,NULL,NULL
NA,03/11/2018 14:01,2018,2018/19,Special Service,NULL,NULL,333,NULL,KITTEN  TRAPPED IN ROOF VOID,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000199,ENFIELD LOCK,E09000010,ENFIELD,Enfield,NULL,PARK ROAD,20702363,EN3,NULL,NULL,536250,199150,NULL,NULL
NA,03/11/2018 21:25,2018,2018/19,Special Service,1,1,333,333,DOG FALLEN IN MANHOLE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000122,ORPINGTON,E09000006,BROMLEY,Orpington,NULL,RAMSDEN ROAD,20300960,BR5,NULL,NULL,547150,166450,NULL,NULL
NA,04/11/2018 12:50,2018,2018/19,Special Service,1,1,333,333,DOG WITH PAW TRAPPED IN CAR SEAT MECHANISM,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000411,ST. JAMES,E09000021,KINGSTON UPON THAMES,New Malden,1.00022E+11,BODLEY ROAD,21800152,KT3,521499,167665,521450,167650,51.39502419,-0.254970637
NA,06/11/2018 15:53,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,1.00022E+11,LINKS ROAD,22305148,IG8,540077,192240,540050,192250,51.61157753,0.021647783
NA,06/11/2018 22:54,2018,2018/19,Special Service,2,3,333,999,Redacted,Dog,Person (land line),Bus/coach,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009376,HOMERTON,E09000012,HACKNEY,Homerton,1.00023E+11,HOMERTON HIGH STREET,20900533,E9,535746,185109,535750,185150,51.54855616,-0.043617977
NA,09/11/2018 10:16,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000343,WEST RUISLIP,E09000017,HILLINGDON,Ruislip,1.00021E+11,STANDALE GROVE,21401849,HA4,508346,188635,508350,188650,51.58616704,-0.437524169
NA,09/11/2018 15:49,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN METAL FENCE   BEST ACCESS SPENCER GROVE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05009369,CLISSOLD,E09000012,HACKNEY,Stoke Newington,10008305386,ALLEN ROAD,20900055,N16,533203,185719,533250,185750,51.5546405,-0.0800453
NA,10/11/2018 00:20,2018,2018/19,Special Service,1,1,333,333,PUPPY WITH PAW TRAPPED IN CANDELABRA,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000027,ALIBON,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,PETTITS ROAD,19900289,RM10,NULL,NULL,549050,185150,NULL,NULL
NA,11/11/2018 07:23,2018,2018/19,Special Service,1,1,333,333,DEER STUCK IN FENCE NEAR THE MY SPACE CALLER WILL MEET YOU AND SHOW YOU,Deer,Person (mobile),Scrub land,Outdoor,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,DAGNAM PARK DRIVE,21300071,RM3,554174,192334,554150,192350,51.60874324,0.225120285
NA,12/11/2018 02:20,2018,2018/19,Special Service,1,1,333,333,FOX IN WATER CITY ROAD BASIN,Fox,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000380,ST. PETER'S,E09000019,ISLINGTON,Islington,5300101470,CITY ROAD,21604520,N1,532141,182959,532150,182950,51.53009314,-0.096377977
NA,12/11/2018 04:20,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED IN CANAL,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000527,ST. MARGARETS AND NORTH TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,10091161372,BREWERY LANE,22407081,TW1,515980,173693,515950,173650,51.45035975,-0.332290493
NA,12/11/2018 04:20,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED INBETWEEN  FENCE  PANELS,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Chelsea,217031691,FINBOROUGH ROAD,21700211,SW10,525925,177832,525950,177850,51.48543305,-0.187774467
NA,13/11/2018 14:18,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - RVP GAP ROAD AND HAYDENS ROAD    CAT IN TREE - CALLER STATES THAT CAT IS PHYSI,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000474,WIMBLEDON PARK,E09000024,MERTON,Wimbledon,48034736,HAYDONS ROAD,22103210,SW19,525836,171368,525850,171350,51.42735676,-0.191354298
NA,14/11/2018 11:22,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000454,WHITEFOOT,E09000023,LEWISHAM,Bromley,1.00022E+11,UNDERSHAW ROAD,22001989,BR1,539658,172110,539650,172150,51.43079614,0.007631035
NA,15/11/2018 14:53,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED  BETWEEN WALL AND FENCE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000441,CROFTON PARK,E09000023,LEWISHAM,Forest Hill,1.00022E+11,EWART ROAD,22001450,SE23,535762,173515,535750,173550,51.44436255,-0.047836293
NA,16/11/2018 12:13,2018,2018/19,Special Service,1,2,333,666,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,NULL,SUTHERLAND ROAD,21105113,N17,NULL,NULL,534250,191050,NULL,NULL
NA,16/11/2018 17:06,2018,2018/19,Special Service,1,1,333,333,PIGEON  TRAPPED IN CABLE ON ROOF     RIVER ISLAND,Bird,Person (mobile),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000275,NOEL PARK,E09000014,HARINGEY,Hornsey,10022944507,HIGH ROAD,21104652,N22,531181,190048,531150,190050,51.594017,-0.1075794
NA,19/11/2018 05:34,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN ELECTRICAL CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000597,HALE END AND HIGHAMS PARK,E09000031,WALTHAM FOREST,Walthamstow,NULL,PENTIRE ROAD,22865250,E17,NULL,NULL,538650,190650,NULL,NULL
NA,20/11/2018 14:36,2018,2018/19,Special Service,1,1,333,333,CAT SHUT IN ELECTRICAL METER CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000597,HALE END AND HIGHAMS PARK,E09000031,WALTHAM FOREST,Walthamstow,NULL,PENTIRE ROAD,22865250,E17,NULL,NULL,538650,190650,NULL,NULL
NA,20/11/2018 16:35,2018,2018/19,Special Service,1,1,333,333,DEER STUCK IN RAILINGS,Deer,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,2.00003E+11,SETTLE ROAD,21301584,RM3,555015,192480,555050,192450,51.6098196,0.237316
NA,21/11/2018 16:45,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,NULL,NAVARINO ROAD,20900727,E8,NULL,NULL,534350,184850,NULL,NULL
NA,21/11/2018 20:47,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000528,SOUTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,NULL,HYDE ROAD,22403572,TW10,NULL,NULL,518550,174950,NULL,NULL
NA,21/11/2018 21:30,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,121007942,GARRATT LANE,22901883,SW18,525601,174506,525650,174550,51.45561287,-0.193619416
NA,22/11/2018 10:53,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT STUCK UP CHIMNEY,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,NULL,NAVARINO ROAD,20900727,E8,NULL,NULL,534350,184850,NULL,NULL
NA,24/11/2018 13:42,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE  -  CAT HAS FALLEN THROUGH ROOF OF DERELICT BUILDING. OWNER IS WAITING OUTSIDE A,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,POWIS MEWS,21700973,W11,NULL,NULL,524950,181350,NULL,NULL
NA,26/11/2018 15:16,2018,2018/19,Special Service,1,1,333,333,Redacted,Dog,Person (mobile),Tree scrub,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,207015274,GALLIARD ROAD,20704240,N9,534476,194687,534450,194650,51.6349305,-0.058248631
NA,27/11/2018 13:51,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH KITTEN ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,NULL,AVOCET MEWS,20800099,SE28,NULL,NULL,544850,179450,NULL,NULL
NA,29/11/2018 16:17,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN INBETWEEN WINDOW LEDGE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011250,NEWBURY,E09000026,REDBRIDGE,Ilford,NULL,CRANLEY DRIVE,22302763,IG2,NULL,NULL,544250,187850,NULL,NULL
NA,01/12/2018 06:50,2018,2018/19,Special Service,1,1,333,333,CAT STUCK UP CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000227,SHOOTERS HILL,E09000011,GREENWICH,Eltham,NULL,MOORDOWN,20801049,SE18,NULL,NULL,543650,176750,NULL,NULL
NA,01/12/2018 10:26,2018,2018/19,Special Service,1,2,333,666,PIGEON TRAPPED IN NETTING AT FRONT OF BUILDING APPROX SECOND FLOOR   REQUESTED TO ATTEND BY RSPCA,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009378,HOXTON WEST,E09000012,HACKNEY,Shoreditch,1.00024E+11,CRANWOOD STREET,20900286,EC1V,532823,182621,532850,182650,51.52689,-0.086691
NA,01/12/2018 11:55,2018,2018/19,Special Service,1,1,333,333,CAT WITH PAW TRAPPED IN GRILL OF WINDOW,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,NULL,AISLIBIE ROAD,22001150,SE12,NULL,NULL,539450,175050,NULL,NULL
NA,03/12/2018 10:18,2018,2018/19,Special Service,1,1,333,333,DOG POSSIBLY FALLEN INTO BADGER HOLE  CALLER IS AT GOLF COURSE AREA ON FOOTPATH,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011481,SELSDON VALE & FORESTDALE,E09000008,CROYDON,Addington,2.00001E+11,ADDINGTON ROAD,20502645,CR2,535300,161755,535350,161750,51.33879899,-0.05895798
NA,03/12/2018 20:46,2018,2018/19,Special Service,1,1,333,333,FOX STUCK UNDER FENCE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000180,HOBBAYNE,E09000009,EALING,Ealing,NULL,TEMPLEMAN ROAD,20601699,W7,NULL,NULL,515750,181750,NULL,NULL
NA,04/12/2018 18:19,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND WALL NEXT TO BIN CHUTE COMMUNAL AREA GROUND FLOOR,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000381,TOLLINGTON,E09000019,ISLINGTON,Holloway,NULL,SUSSEX CLOSE,21606975,N19,NULL,NULL,530250,186750,NULL,NULL
NA,05/12/2018 20:41,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN HOLE IN BACK GARDEN,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011488,WEST THORNTON,E09000008,CROYDON,Norbury,1.00021E+11,MAYFIELD ROAD,20502591,CR7,530820,167873,530850,167850,51.39482068,-0.120998773
NA,08/12/2018 12:23,2018,2018/19,Special Service,1,1,333,333,DEER TRAPPED IN RAILINGS ON THE THE BROCKLEY HILL ESTATE,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000286,CANONS,E09000015,HARROW,Stanmore,10034787215,AUGUSTUS CLOSE,21202569,HA7,517803,193068,517850,193050,51.62411438,-0.299589476
NA,08/12/2018 15:51,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - CAT TRAPPED BEHIND TOILET,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000038,RIVER,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,OVAL ROAD NORTH,19900900,RM10,NULL,NULL,549750,183750,NULL,NULL
NA,09/12/2018 04:40,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA RETRIEVE CAT FROM BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000595,FOREST,E09000031,WALTHAM FOREST,Leyton,NULL,LEA BRIDGE ROAD,22850350,E17,NULL,NULL,538450,188650,NULL,NULL
NA,09/12/2018 23:05,2018,2018/19,Special Service,1,1,333,333,CAT PHYSICALLY TRAPPED BEHIND OVEN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000042,WHALEBONE,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,HIGH ROAD,19900165,RM6,NULL,NULL,547850,187950,NULL,NULL
NA,10/12/2018 10:19,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Plumstead,NULL,WHITWORTH ROAD,20801611,SE18,NULL,NULL,543350,177350,NULL,NULL
NA,10/12/2018 19:07,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN GAP BETWEEN ROOF AND TILES,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000034,HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,COOTE ROAD,19900087,RM8,NULL,NULL,548850,186350,NULL,NULL
NA,10/12/2018 20:39,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - CAT TRAPPED IN CAR ENGINE BELIEVED INJURED  OWNER ON SCENE - CAR IS LOCATED IN,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000208,SOUTHGATE,E09000010,ENFIELD,Southgate,207083452,CHASE SIDE,20701407,N14,529360,194475,529350,194450,51.63422871,-0.132210507
NA,12/12/2018 13:07,2018,2018/19,Special Service,1,1,333,333,RSPCA ON SITE WITH DEER LEG TRAPPED IN GATE,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000055,HENDON,E09000003,BARNET,Hendon,200064180,HILLTOP GARDENS,20023080,NW4,522772,190453,522750,190450,51.59955423,-0.228759227
NA,13/12/2018 11:51,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000250,ADDISON,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34062880,SINCLAIR ROAD,21000737,W14,524088,179443,524050,179450,51.50031694,-0.213656376
NA,14/12/2018 06:15,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED UNDER BATHROOM SINK,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000453,TELEGRAPH HILL,E09000023,LEWISHAM,New Cross,NULL,ASPINALL ROAD,22000059,SE4,NULL,NULL,535850,175850,NULL,NULL
NA,14/12/2018 20:15,2018,2018/19,Special Service,1,0,333,0,RUNNING CALL TO DOG LOOSE IN THE ROADWAY,Dog,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000320,ST. ANDREW'S,E09000016,HAVERING,Hornchurch,1.00023E+11,NORTH STREET,21300513,RM11,553981,187413,553950,187450,51.56458492,0.220182145
NA,15/12/2018 13:46,2018,2018/19,Special Service,1,2,333,666,CAT TRAPPED UNDER FLOORBOARDS - RSPCA ON SCENE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000110,CHELSFIELD AND PRATTS BOTTOM,E09000006,BROMLEY,Orpington,NULL,WINDSOR DRIVE,20301476,BR6,NULL,NULL,546650,163950,NULL,NULL
NA,16/12/2018 07:10,2018,2018/19,Special Service,1,1,333,333,KITTEN STUCK BEHIND FRIDGE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Chiswick,NULL,HARTSWOOD ROAD,21000421,W12,NULL,NULL,521650,179450,NULL,NULL
NA,16/12/2018 08:03,2018,2018/19,Special Service,1,1,333,333,CAT STUCK UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011477,PURLEY OAKS & RIDDLESDOWN,E09000008,CROYDON,Purley,NULL,CHRISTCHURCH ROAD,20501976,CR8,NULL,NULL,531550,161850,NULL,NULL
NA,16/12/2018 17:33,2018,2018/19,Special Service,1,1,333,333,CAT STUCK BETWEEN WALL AND HEAVY UNIT   ELDERLY OWNER ON SITE UNABLE TO FREE CAT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,NULL,ALEXANDRA PARK ROAD,21106086,N10,NULL,NULL,529050,190450,NULL,NULL
NA,16/12/2018 23:32,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED IN PANEL BEHIND TOILET,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009336,WHITECHAPEL,E09000030,TOWER HAMLETS,Whitechapel,NULL,BUCKLE STREET,22700221,E1,NULL,NULL,534050,181350,NULL,NULL
NA,17/12/2018 16:39,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - BIRD CAUGHT IN NETTING,Bird,Person (land line),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000124,PETTS WOOD AND KNOLL,E09000006,BROMLEY,Orpington,1.00023E+11,QUEENSWAY,20300576,BR5,544412,167486,544450,167450,51.38805017,0.074084198
NA,18/12/2018 12:39,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011469,KENLEY,E09000008,CROYDON,Purley,1.00024E+11,HAYES LANE,20502124,CR3,532524,157490,532550,157450,51.30111451,-0.100368723
NA,22/12/2018 14:18,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BETWEEN LEDGE ON WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009319,BOW EAST,E09000030,TOWER HAMLETS,Poplar,NULL,BRYMAY CLOSE,22700218,E3,NULL,NULL,537550,183250,NULL,NULL
NA,23/12/2018 15:29,2018,2018/19,Special Service,1,1,333,333,"CAT UP TREE, BEEN UP THERE FOR OVER A DAY - INFORMANT WAITING TO MEET YOU",Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Shadwell,6651849,GREEN BANK,22700575,E1W,534821,180099,534850,180050,51.50375517,-0.058863362
NA,24/12/2018 17:17,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000137,HIGHGATE,E09000007,CAMDEN,Kentish Town,NULL,LISSENDEN GARDENS,20400214,NW5,NULL,NULL,528350,185850,NULL,NULL
NA,24/12/2018 23:40,2018,2018/19,Special Service,1,1,333,333,DOG FALLEN INTO THE RIVER - CHELSEA HARBOUR,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000262,SANDS END,E09000013,HAMMERSMITH AND FULHAM,Chelsea,34133700,CHELSEA HARBOUR DRIVE,21000208,SW10,526471,176659,526450,176650,51.47477258,-0.18033847
NA,25/12/2018 09:02,2018,2018/19,Special Service,2,3,333,999,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Assist trapped wild animal,E05011485,SOUTH NORWOOD,E09000008,CROYDON,Woodside,1.00024E+11,WOODVALE AVENUE,20501596,SE25,533970,169293,533950,169250,51.4068491,-0.075204248
NA,25/12/2018 15:58,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN RECLINER CHAIR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000318,RAINHAM AND WENNINGTON,E09000016,HAVERING,Wennington,NULL,MITCHELL CLOSE,21310044,RM13,NULL,NULL,553450,183350,NULL,NULL
NA,26/12/2018 16:37,2018,2018/19,Special Service,1,1,333,333,DOG STUCK IN METAL GATE BY SHOULDER - OWNER ON SCENE,Dog,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,10022801801,CHESTNUT AVENUE,21400376,UB7,507049,180549,507050,180550,51.51373623,-0.458701242
NA,29/12/2018 15:39,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH FOX TRAPPED IN NETTING,Fox,Person (mobile),Other outdoor equipment/machinery,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000198,ENFIELD HIGHWAY,E09000010,ENFIELD,Enfield,207008321,ACCESS ROAD FROM BELL LANE TO 133 BELL LANE,20700061,EN3,535964,198222,535950,198250,51.66634115,-0.035400387
NA,30/12/2018 17:26,2018,2018/19,Special Service,1,1,333,333,Redacted,Squirrel,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000044,BURNT OAK,E09000003,BARNET,Mill Hill,NULL,HANSHAW DRIVE,20020860,HA8,NULL,NULL,520650,190850,NULL,NULL
NA,31/12/2018 01:01,2018,2018/19,Special Service,1,1,333,333,KITTENS TRAPPED UNDER THE FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011463,ADDISCOMBE WEST,E09000008,CROYDON,Croydon,NULL,CEDAR ROAD,20500767,CR0,NULL,NULL,533150,165950,NULL,NULL
NA,31/12/2018 11:13,2018,2018/19,Special Service,1,3,333,999,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011489,WOODSIDE,E09000008,CROYDON,Woodside,NULL,NAPIER ROAD,20501218,SE25,NULL,NULL,534650,168050,NULL,NULL
NA,01/01/2019 15:09,2019,2018/19,Special Service,1,1,333,333,INVESTIGATE POSSIBLE BABY LOCKED IN ALONE,Unknown - Domestic Animal Or Pet,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009405,STANLEY,E09000020,KENSINGTON AND CHELSEA,Chelsea,217064603,PARK WALK,21700392,SW10,526603,177763,526650,177750,51.48465988,-0.178044712
NA,02/01/2019 13:04,2019,2018/19,Special Service,1,4,333,1332,Redacted,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05009379,KING'S PARK,E09000012,HACKNEY,Homerton,10008329457,HOMERTON ROAD,20900534,E9,536975,185988,536950,185950,51.55615804,-0.025559021
NA,04/01/2019 09:04,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND SINK IN BATHROOM,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011109,OLD KENT ROAD,E09000028,SOUTHWARK,Old Kent Road,NULL,HAYMERLE ROAD,22501223,SE15,NULL,NULL,534150,177650,NULL,NULL
NA,04/01/2019 12:58,2019,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN MESH SINCE YESTERDAY,Bird,Person (mobile),Hospital,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000416,BISHOP'S,E09000022,LAMBETH,Lambeth,10000444919,WESTMINSTER BRIDGE ROAD,21901482,SE1,530702,179518,530750,179550,51.49950222,-0.118387508
NA,04/01/2019 15:56,2019,2018/19,Special Service,2,4,333,1332,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000213,WINCHMORE HILL,E09000010,ENFIELD,Southgate,207188076,THE BOURNE,20705011,N14,530606,194312,530650,194350,51.63247073,-0.114275052
NA,05/01/2019 00:54,2019,2018/19,Special Service,1,1,333,333,FOX TRAPPED IN GARDEN GATE CALLED BY THE RSPCA WHO ARE NOT ON SCENE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011488,WEST THORNTON,E09000008,CROYDON,Norbury,NULL,RAYMEAD AVENUE,20501329,CR7,NULL,NULL,531450,167950,NULL,NULL
NA,05/01/2019 10:51,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN RAILINGS,Cat,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000334,MANOR,E09000017,HILLINGDON,Ruislip,1.00021E+11,PARK WAY,21401490,HA4,510306,187381,510350,187350,51.57452401,-0.409642705
NA,06/01/2019 11:50,2019,2018/19,Special Service,1,2,333,666,SWAN TRAPPED IN BUSH,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05011485,SOUTH NORWOOD,E09000008,CROYDON,Woodside,1.00021E+11,AVENUE ROAD,20500652,SE25,533980,169175,533950,169150,51.40578623,-0.07510978
NA,06/01/2019 20:10,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND KITCHEN CABINETS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009331,ST. PETER'S,E09000030,TOWER HAMLETS,Bethnal Green,NULL,ELLSWORTH STREET,22700463,E2,NULL,NULL,534750,182750,NULL,NULL
NA,07/01/2019 05:47,2019,2018/19,Special Service,2,5,333,1665,HORSE FALLEN INTO RIVER       WATER RESCUE LEVEL  IMPLEMENTED,Horse,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207187270,WHARF ROAD,20702448,EN3,536440,195707,536450,195750,51.64361936,-0.029495258
NA,07/01/2019 14:28,2019,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,NULL,ROLVENDEN PLACE,21104992,N17,NULL,NULL,534450,191050,NULL,NULL
NA,10/01/2019 11:44,2019,2018/19,Special Service,1,2,333,666,CAT FALLEN BEHIND BUILT-IN FRIDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009319,BOW EAST,E09000030,TOWER HAMLETS,Bethnal Green,NULL,MOSTYN GROVE,22700849,E3,NULL,NULL,537050,183050,NULL,NULL
NA,11/01/2019 11:43,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN GARAGES - REQUESTED BY RSPCA,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000600,HIGHAM HILL,E09000031,WALTHAM FOREST,Walthamstow,1.00023E+11,GUILDSWAY,22841800,E17,536551,190689,536550,190650,51.5985011,-0.0298541
NA,11/01/2019 12:13,2019,2018/19,Special Service,1,1,333,333,CAT IN DISTRESS UP TREE BEING ATTACKED BY BIRDS,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000270,FORTIS GREEN,E09000014,HARINGEY,Finchley,1.00023E+11,COPPETTS ROAD,21101165,N10,527971,190876,527950,190850,51.60220393,-0.153582849
NA,11/01/2019 21:16,2019,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING     ON THE WESTBOUND PLAFORM BY THE TICKET BOOTH,Bird,Person (land line),Train station - platform (at ground level or elevated),Non Residential,Other animal assistance,Assist trapped wild animal,E05000305,WEST HARROW,E09000015,HARROW,Harrow,2.00004E+11,THE GARDENS,21202022,HA1,514228,188061,514250,188050,51.579847,-0.3528532
NA,13/01/2019 11:02,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000590,CANN HALL,E09000031,WALTHAM FOREST,Leytonstone,1.00023E+11,SANSOM ROAD,22872800,E11,539465,186593,539450,186550,51.56098826,0.010576587
NA,13/01/2019 19:20,2019,2018/19,Special Service,1,2,333,666,CAT TRAPPED BETWEEN RAILWAY TRACKS,Cat,Person (mobile),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000131,CANTELOWES,E09000007,CAMDEN,Kentish Town,5120975,BAYNES STREET,20400661,NW1,529349,184146,529350,184150,51.5414076,-0.136167736
NA,14/01/2019 00:27,2019,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,NULL,WESTFIELDS AVENUE,22405246,SW13,NULL,NULL,521650,176050,NULL,NULL
NA,14/01/2019 13:13,2019,2018/19,Special Service,1,1,333,333,CAT SHUT IN WASHING MACHINE &  UNABLE TO OPEN AS HANDLE IS BROKEN,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000597,HALE END AND HIGHAMS PARK,E09000031,WALTHAM FOREST,Walthamstow,NULL,LONGACRE ROAD,22853700,E17,NULL,NULL,538650,190850,NULL,NULL
NA,15/01/2019 02:03,2019,2018/19,Special Service,1,1,333,333,DEER TRAPPED IN METAL FENCE FRONT GARDEN,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000332,HILLINGDON EAST,E09000017,HILLINGDON,Hillingdon,1.00021E+11,POLE HILL ROAD,21401551,UB10,507921,182455,507950,182450,51.53070708,-0.445547282
NA,16/01/2019 10:33,2019,2018/19,Special Service,1,1,333,333,RUNNING CALL TO CAT STUCK IN ROOF VOID,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011472,NORBURY & POLLARDS HILL,E09000008,CROYDON,Norbury,NULL,NORBURY COURT ROAD,20501229,SW16,NULL,NULL,530750,169150,NULL,NULL
NA,16/01/2019 20:48,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON VINES THREE STOREYS UP - OWNER ON SCENE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000615,FURZEDOWN,E09000032,WANDSWORTH,Tooting,1.00023E+11,CLAIRVIEW ROAD,22900957,SW16,528934,171495,528950,171450,51.42780221,-0.146768206
NA,17/01/2019 13:34,2019,2018/19,Special Service,1,1,333,333,FOX TRAPPED IN FENCE,Fox,Person (mobile),Garden equipment,Outdoor Structure,Other animal assistance,Animal harm involving wild animal,E05000186,NORWOOD GREEN,E09000009,EALING,Southall,12037489,NORWOOD ROAD,20602330,UB2,513135,178211,513150,178250,51.49154498,-0.371771323
NA,17/01/2019 18:43,2019,2018/19,Special Service,1,1,333,333,INJURED CAT TRAPPED IN CHIMNEY ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000423,HERNE HILL,E09000022,LAMBETH,Brixton,NULL,BICKNELL ROAD,21900184,SE5,NULL,NULL,532250,175550,NULL,NULL
NA,18/01/2019 12:58,2019,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - RSPCA ON SCENE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000258,NORTH END,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,NORTH END ROAD,21000597,W14,NULL,NULL,524850,177850,NULL,NULL
NA,18/01/2019 14:05,2019,2018/19,Special Service,1,1,333,333,SQUIRREL TRAPPED BETWEEN PIPES  A WALL AND THE ROOF,Squirrel,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000445,GROVE PARK,E09000023,LEWISHAM,Lee Green,NULL,BARING ROAD,22005120,SE12,NULL,NULL,540550,172550,NULL,NULL
NA,18/01/2019 16:48,2019,2018/19,Special Service,1,1,333,333,PARROT TRAPPED IN NETTING ON MAIN ROAD OUTSIDE NURSING HOME,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000104,WEMBLEY CENTRAL,E09000005,BRENT,Wembley,NULL,CHAPLIN ROAD,20202398,HA0,NULL,NULL,518050,184850,NULL,NULL
NA,19/01/2019 23:17,2019,2018/19,Special Service,1,1,333,333,CAT STUCK IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,GILBERT STREET,8401343,W1K,NULL,NULL,528450,181050,NULL,NULL
NA,20/01/2019 20:33,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND WALL,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009387,WOODBERRY DOWN,E09000012,HACKNEY,Stoke Newington,NULL,WOODBERRY DOWN,20901092,N4,NULL,NULL,532150,187450,NULL,NULL
NA,21/01/2019 12:08,2019,2018/19,Special Service,1,2,333,666,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,NULL,NORMANS CLOSE,20200612,NW10,NULL,NULL,520550,184650,NULL,NULL
NA,23/01/2019 00:23,2019,2018/19,Special Service,1,1,333,333,DEER WITH HEAD STUCK IN RAILINGS    OUTSIDE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist  trapped livestock animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,10025149964,DAGNAM PARK DRIVE,21300071,RM3,554465,192391,554450,192350,51.60917725,0.229349506
NA,26/01/2019 11:25,2019,2018/19,Special Service,1,1,333,333,SMALL ANIMAL TRAPPED BEHIND WALL  CALLER SPEAKS LITTLE ENGLISH,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,NULL,SCRATTONS TERRACE,19900573,IG11,NULL,NULL,548050,183450,NULL,NULL
NA,26/01/2019 12:55,2019,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000441,CROFTON PARK,E09000023,LEWISHAM,Forest Hill,1.00022E+11,BROCKLEY PARK,22001276,SE23,536380,173569,536350,173550,51.44470654,-0.038928088
NA,30/01/2019 11:26,2019,2018/19,Special Service,2,3,333,999,ASSIST KENT FRS WITH LARGE ANIMAL RESCUE,Unknown - Animal rescue from water - Farm animal,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,NA,NA,NA,NA,Kent,44003798,MEAD WALL,32102382,ME3,571394,177648,571350,177650,51.47184494,0.466420909
NA,31/01/2019 10:29,2019,2018/19,Special Service,1,1,333,333,SWAN IN DISTRESS TRAPPED IN ICE IN POND   LEVEL TWO WATER OPS,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000036,MAYESBROOK,E09000002,BARKING AND DAGENHAM,Barking,100036344,LODGE AVENUE,19900241,IG11,546324,184566,546350,184550,51.54103634,0.108610476
NA,31/01/2019 23:17,2019,2018/19,Special Service,1,1,333,333,ASSIST WILDLIFE REPS WITH SMALL ANIMAL RESCUE      REPS WILL MEET CREWS IN CAR PARK,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,NULL,THE BROADWAY,22306023,IG8,NULL,NULL,540950,191850,NULL,NULL
NA,02/02/2019 11:02,2019,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED UNDER COOKER,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000289,HARROW ON THE HILL,E09000015,HARROW,Northolt,NULL,COWEN AVENUE,21201206,HA2,NULL,NULL,514550,186650,NULL,NULL
NA,04/02/2019 12:24,2019,2018/19,Special Service,1,2,333,666,CAT STUCK UP A DRAIN   FRU REQUESTED FROM SCENE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,202140963,LAWRENCE AVENUE,20204111,NW10,520673,183772,520650,183750,51.53996336,-0.261344341
NA,05/02/2019 07:30,2019,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH FOX TRAPPED IN STATUE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011466,COULSDON TOWN,E09000008,CROYDON,Purley,NULL,THE NETHERLANDS,20502519,CR5,NULL,NULL,529350,157750,NULL,NULL
NA,06/02/2019 22:36,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN WHEEL OF VAN,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,1.00023E+11,HIGH ROAD,21106680,N17,533915,190787,533950,190750,51.60001675,-0.067850892
NA,07/02/2019 21:41,2019,2018/19,Special Service,1,2,333,666,HORSE STUCK IN DITCH - WATER OPS LEVEL TWO IMPLEMENTED CALLER SAYS IT IS BEHIND FENCING OF ASDAS CAR,Horse,Person (mobile),Other outdoor location,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05011218,BELVEDERE,E09000004,BEXLEY,Erith,2.00003E+11,NORMAN ROAD,20101027,DA17,549733,179950,549750,179950,51.49866811,0.155771365
NA,09/02/2019 21:07,2019,2018/19,Special Service,1,1,333,333,KITTEN STUCK UP ON ROOF OF HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,NULL,CLYDE ROAD,21101155,N22,NULL,NULL,529650,190750,NULL,NULL
NA,11/02/2019 01:18,2019,2018/19,Special Service,NULL,NULL,333,NULL,DOG TRAPPED AT END OF GARDEN - CALLER IS DISABLED AND UNABLE TO RETRIEVE HIM,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000297,PINNER,E09000015,HARROW,Harrow,NULL,NORMAN CRESCENT,21201793,HA5,NULL,NULL,511350,190650,NULL,NULL
NA,12/02/2019 13:45,2019,2018/19,Special Service,1,1,333,333,CAT STUCK ON BALCONY  THREE FLOORS UP,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000375,HOLLOWAY,E09000019,ISLINGTON,Holloway,NULL,STURMER WAY,21603886,N7,NULL,NULL,530650,185450,NULL,NULL
NA,15/02/2019 13:37,2019,2018/19,Special Service,1,1,333,333,Redacted,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05011219,BEXLEYHEATH,E09000004,BEXLEY,Bexley,NULL,CHURCH ROAD,20100315,DA7,NULL,NULL,548650,175550,NULL,NULL
NA,16/02/2019 00:38,2019,2018/19,Special Service,1,1,333,333,Redacted,Squirrel,Person (land line),Self contained Sheltered Housing,Dwelling,Other animal assistance,Assist trapped wild animal,E05000194,BUSH HILL PARK,E09000010,ENFIELD,Edmonton,NULL,VILLAGE ROAD,20704874,EN1,NULL,NULL,533150,195850,NULL,NULL
NA,17/02/2019 18:03,2019,2018/19,Special Service,1,1,333,333,CAT STUCK IN PIPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000648,WESTBOURNE,E09000033,WESTMINSTER,Paddington,NULL,HARROW ROAD,8400530,W2,NULL,NULL,525550,181750,NULL,NULL
NA,18/02/2019 12:16,2019,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT UP A TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000333,ICKENHAM,E09000017,HILLINGDON,Hillingdon,1.00021E+11,SWAKELEYS ROAD,21401902,UB10,506332,185920,506350,185950,51.56215171,-0.467396395
NA,18/02/2019 13:04,2019,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - CAT STUCK INSIDE PIPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000648,WESTBOURNE,E09000033,WESTMINSTER,Paddington,NULL,HARROW ROAD,8400530,W2,NULL,NULL,525550,181750,NULL,NULL
NA,19/02/2019 12:28,2019,2018/19,Special Service,1,1,333,333,CAT STUCK IN RAILINGS LOCATED IN THE MOREMEAD PARK NR RIVER EDGE,Cat,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000527,ST. MARGARETS AND NORTH TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,1.00022E+11,COLE PARK ROAD,22403402,TW1,516347,173858,516350,173850,51.45176687,-0.326962086
NA,19/02/2019 20:35,2019,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000474,WIMBLEDON PARK,E09000024,MERTON,Wimbledon,NULL,STRATHEARN ROAD,22106033,SW19,NULL,NULL,525350,171650,NULL,NULL
NA,20/02/2019 11:45,2019,2018/19,Special Service,1,1,333,333,SWAN TRAPPED IN MUD,Bird,Person (mobile),Heathland,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000346,BEDFONT,E09000018,HOUNSLOW,Feltham,10025166897,CLOCKHOUSE LANE,21500287,TW14,507571,172471,507550,172450,51.44103678,-0.453639256
NA,20/02/2019 12:07,2019,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN BETWEEN SHED AND FENCE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000137,HIGHGATE,E09000007,CAMDEN,Kentish Town,NULL,BROOKFIELD PARK,20400098,NW5,NULL,NULL,528650,186350,NULL,NULL
NA,20/02/2019 19:07,2019,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,NULL,THE BROADWAY,22306023,IG8,NULL,NULL,540950,191850,NULL,NULL
NA,21/02/2019 12:01,2019,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT TRAPPED UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,207099682,CUCKOO HALL LANE,20703243,N9,535733,194873,535750,194850,51.63629735,-0.040030659
NA,24/02/2019 12:18,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED  BETWEEN TWO WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000333,ICKENHAM,E09000017,HILLINGDON,Hillingdon,NULL,HALFORD ROAD,21400866,UB10,NULL,NULL,507050,185650,NULL,NULL
NA,24/02/2019 13:59,2019,2018/19,Special Service,1,2,333,666,Redacted,Snake,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,NULL,HIGH STREET,22201445,E15,NULL,NULL,538850,184150,NULL,NULL
NA,24/02/2019 14:35,2019,2018/19,Special Service,1,1,333,333,CAT IMPALED ON METAL POLE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000230,WOOLWICH RIVERSIDE,E09000011,GREENWICH,East Greenwich,NULL,ANCHOR AND HOPE LANE,20800063,SE7,NULL,NULL,540950,179150,NULL,NULL
NA,25/02/2019 11:54,2019,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN NETTING    LONDON WILDLIFE PRPTECTION OFFICER ON SCENE,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000459,DUNDONALD,E09000024,MERTON,New Malden,48119014,RAYNES PARK BRIDGE,22105345,SW20,523306,169313,523350,169350,51.4094449,-0.228436126
NA,26/02/2019 17:24,2019,2018/19,Special Service,1,1,333,333,PUPPY STUCK BEHIND SHED,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000171,CLEVELAND,E09000009,EALING,Ealing,12189729,DRAYTON GROVE,20600563,W13,516410,180948,516450,180950,51.51547322,-0.323717229
NA,27/02/2019 14:16,2019,2018/19,Special Service,1,2,333,666,TO ASSIST RSPCA WITH CAT UP A TREE - AERIAL REQUESTED FROM SCENE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000333,ICKENHAM,E09000017,HILLINGDON,Hillingdon,10093734544,SWAKELEYS ROAD,21401902,UB10,506332,185919,506350,185950,51.56214596,-0.467406566
NA,27/02/2019 16:01,2019,2018/19,Special Service,1,1,333,333,CAT STUCK BETWEEN WALL AND HEATER,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009386,VICTORIA,E09000012,HACKNEY,Bethnal Green,NULL,TRYON CRESCENT,20901739,E9,NULL,NULL,535050,183950,NULL,NULL
NA,28/02/2019 07:46,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000055,HENDON,E09000003,BARNET,Hendon,200160009,BURROUGHS GARDENS,20006300,NW4,522684,189053,522650,189050,51.58699705,-0.230526524
NA,28/02/2019 15:03,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009395,EARL'S COURT,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,KENWAY ROAD,21700298,SW5,NULL,NULL,525550,178750,NULL,NULL
NA,28/02/2019 17:17,2019,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN GUTTERING   AT FRONT OF PREMISES     REQUESTED BY RSPCA,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,NULL,CAUSEYWARE ROAD,20704017,N9,NULL,NULL,535150,194750,NULL,NULL
NA,28/02/2019 17:32,2019,2018/19,Special Service,1,1,333,333,CAT LOCKED IN FLAT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Whitechapel,NULL,ST KATHARINES WAY,22701138,E1W,NULL,NULL,534050,180250,NULL,NULL
NA,28/02/2019 18:09,2019,2018/19,Special Service,1,2,333,666,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000628,WEST HILL,E09000032,WANDSWORTH,Wandsworth,NULL,PRINCES WAY,22903974,SW19,NULL,NULL,524150,173150,NULL,NULL
NA,28/02/2019 22:04,2019,2018/19,Special Service,1,1,333,333,RABBIT TRAPPED IN MECHANISM UNDER SETTEE,Rabbit,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000319,ROMFORD TOWN,E09000016,HAVERING,Hornchurch,NULL,BENJAMIN CLOSE,21300224,RM11,NULL,NULL,552350,188150,NULL,NULL
NA,01/03/2019 09:43,2019,2018/19,Special Service,1,1,333,333,PIGEONS TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,WORNINGTON ROAD,21701065,W10,NULL,NULL,524250,182050,NULL,NULL
NA,02/03/2019 12:03,2019,2018/19,Special Service,1,1,333,333,CAT STUCK BEHIND KITCHEN UNITS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,NULL,MOORE WALK,22207813,E7,NULL,NULL,540450,185550,NULL,NULL
NA,03/03/2019 11:37,2019,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT  STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,NULL,MOUNTBATTEN CLOSE,21900996,SE19,NULL,NULL,533250,171150,NULL,NULL
NA,03/03/2019 16:26,2019,2018/19,Special Service,1,1,333,333,FOX TRAPPED IN POND FILTER,Fox,Person (mobile),Garden equipment,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05011476,PURLEY & WOODCOTE,E09000008,CROYDON,Purley,1.00021E+11,FURZE LANE,20502085,CR8,530559,161905,530550,161950,51.3412492,-0.126927655
NA,03/03/2019 17:19,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN  MECHANISM OF RECLINER CHAIR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011220,BLACKFEN & LAMORBEY,E09000004,BEXLEY,Eltham,NULL,PARISH GATE DRIVE,20101098,DA15,NULL,NULL,545250,174250,NULL,NULL
NA,05/03/2019 16:37,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Other private non-residential building,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000424,KNIGHT'S HILL,E09000022,LAMBETH,West Norwood,1.00022E+11,CASEWICK ROAD,21901551,SE27,531637,171546,531650,171550,51.42764294,-0.10789136
NA,06/03/2019 07:20,2019,2018/19,Special Service,1,1,333,333,CAT STUCK ON BALCONY - OCCPANTS ON HOLIDAY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,NULL,DROOP STREET,8400073,W10,NULL,NULL,524250,182450,NULL,NULL
NA,08/03/2019 10:57,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009324,ISLAND GARDENS,E09000030,TOWER HAMLETS,Millwall,NULL,BARNFIELD PLACE,22702049,E14,NULL,NULL,537450,178650,NULL,NULL
NA,08/03/2019 12:23,2019,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN NETTING    BY RAILWAY BRIDGE    CALLER IS WAITING FOR YOU,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000469,RAYNES PARK,E09000024,MERTON,New Malden,48128491,COOMBE LANE,22101605,SW20,523291,169324,523250,169350,51.40955327,-0.228651838
NA,09/03/2019 19:32,2019,2018/19,Special Service,1,1,333,333,DOG TRAPPED ON NEIGHBOURING BALCONY,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,LONSDALE ROAD,21700934,W11,NULL,NULL,524950,181050,NULL,NULL
NA,10/03/2019 13:27,2019,2018/19,Special Service,1,1,333,333,ASSIST RSPCA,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,CHURCH STREET,8400794,NW8,NULL,NULL,527050,182150,NULL,NULL
NA,10/03/2019 15:14,2019,2018/19,Special Service,1,1,333,333,INJURED CAT STUCK ON  LEDGE OF APARTMENT - CALLER WILL MEET,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Clapham,NULL,ST JOSEPHS STREET,22904881,SW8,NULL,NULL,528850,176850,NULL,NULL
NA,10/03/2019 17:00,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000264,TOWN,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,DAIRY CLOSE,21001557,SW6,NULL,NULL,525050,176650,NULL,NULL
NA,11/03/2019 15:40,2019,2018/19,Special Service,1,1,333,333,RSPCA REQ ATT OF BRIGADE   CAT IN DISTRESS UP TREE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000103,WELSH HARP,E09000005,BRENT,Hendon,202200937,HOLDEN AVENUE,20202211,NW9,520280,187186,520250,187150,51.57073257,-0.265837592
NA,13/03/2019 12:59,2019,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING  ASSIST RSPCA  NEXT TO RECYCLING CENTRE,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,128021349,CHAPEL MILL ROAD,21880152,KT1,518880,168538,518850,168550,51.4034214,-0.2923166
NA,15/03/2019 11:24,2019,2018/19,Special Service,1,1,333,333,ANIMAL TRAPPED IN CHIMNEY,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,WYATTS LANE,22889550,E17,NULL,NULL,538250,189750,NULL,NULL
NA,15/03/2019 18:09,2019,2018/19,Special Service,1,3,333,999,INJURED SWAN TRAPPED IN TREE - APPROX FORTY FOOT UP,Bird,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05000520,HAMPTON,E09000027,RICHMOND UPON THAMES,Twickenham,10024345291,ASH ISLAND,22407002,KT8,514950,168862,514950,168850,51.40714729,-0.34868271
NA,16/03/2019 17:55,2019,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000453,TELEGRAPH HILL,E09000023,LEWISHAM,New Cross,NULL,ERLANGER ROAD,22000384,SE14,NULL,NULL,535850,176250,NULL,NULL
NA,17/03/2019 18:32,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000355,HESTON CENTRAL,E09000018,HOUNSLOW,Heston,10093257864,FOOTPATH FROM 340 GREAT WEST ROAD TO SUTTON SQUARE,21519949,TW5,512923,176640,512950,176650,51.47746446,-0.375328159
NA,17/03/2019 21:43,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND KITCHEN CABINET,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,NULL,THESIGER ROAD,20302124,SE20,NULL,NULL,535950,170250,NULL,NULL
NA,18/03/2019 12:47,2019,2018/19,Special Service,1,2,333,666,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05011226,FALCONWOOD & WELLING,E09000004,BEXLEY,Bexley,10011845442,DANSON ROAD,20100426,DA6,547343,174810,547350,174850,51.45311327,0.119220606
NA,19/03/2019 08:41,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,NULL,BRIAR WALK,8400753,W10,NULL,NULL,524250,182450,NULL,NULL
NA,19/03/2019 12:36,2019,2018/19,Special Service,1,2,333,666,HORSE FALLEN AND UNABLE TO GET UP,Horse,Person (mobile),Other agricultural building,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011478,SANDERSTEAD,E09000008,CROYDON,Croydon,1.00021E+11,PURLEY OAKS ROAD,20502277,CR2,533112,161941,533150,161950,51.34097989,-0.090288896
NA,20/03/2019 10:14,2019,2018/19,Special Service,1,1,333,333,CAT STUCK IN ELECTRICAL SUB STATION,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05009386,VICTORIA,E09000012,HACKNEY,Homerton,1.00023E+11,MARE STREET,20900658,E8,535011,184274,535050,184250,51.5412327,-0.054530777
NA,20/03/2019 21:43,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED UP TREE OVERHANGING RIVER,Cat,Person (land line),Canal/riverbank vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000326,BRUNEL,E09000017,HILLINGDON,Hillingdon,1.00021E+11,IVYBRIDGE CLOSE,21401049,UB8,506196,182883,506150,182850,51.5348816,-0.470275626
NA,21/03/2019 09:08,2019,2018/19,Special Service,1,2,333,666,Redacted,Horse,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000323,UPMINSTER,E09000016,HAVERING,Hornchurch,10015776172,ST MARYS LANE,21301899,RM14,558934,187079,558950,187050,51.56020451,0.291423812
NA,21/03/2019 11:29,2019,2018/19,Special Service,1,2,333,666,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000126,SHORTLANDS,E09000006,BROMLEY,Beckenham,1.0002E+11,WICKHAM WAY,20302048,BR3,538408,167861,538450,167850,51.39291677,-0.01199935
NA,22/03/2019 10:55,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011238,CHURCHFIELDS,E09000026,REDBRIDGE,Woodford,1.00022E+11,WENSLEY AVENUE,22305436,IG8,540145,191404,540150,191450,51.60404366,0.022289453
NA,22/03/2019 17:13,2019,2018/19,Special Service,1,1,333,333,PIGEON STUCK IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000608,WILLIAM MORRIS,E09000031,WALTHAM FOREST,Walthamstow,NULL,FOREST ROAD,22837350,E17,NULL,NULL,536650,189650,NULL,NULL
NA,23/03/2019 12:04,2019,2018/19,Special Service,1,1,333,333,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000115,CRAY VALLEY WEST,E09000006,BROMLEY,Orpington,NULL,ASCOT ROAD,20300001,BR5,NULL,NULL,545850,168150,NULL,NULL
NA,24/03/2019 19:44,2019,2018/19,Special Service,1,1,333,333,DOG WITH HEAD TRAPPED IN FENCE,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,1.00023E+11,MALTA ROAD,22855200,E10,537169,187545,537150,187550,51.57010025,-0.022160828
NA,27/03/2019 10:22,2019,2018/19,Special Service,1,2,333,666,Redacted,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,NULL,ST JAMES'S PLACE,8400875,SW1A,NULL,NULL,529150,180150,NULL,NULL
NA,27/03/2019 10:58,2019,2018/19,Special Service,1,1,333,333,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000119,HAYES AND CONEY HALL,E09000006,BROMLEY,Bromley,10070008611,PICKHURST LANE,20302961,BR2,540241,166228,540250,166250,51.37779389,0.013689658
NA,28/03/2019 17:23,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000590,CANN HALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,ODESSA ROAD,22863000,E7,NULL,NULL,539850,185950,NULL,NULL
NA,29/03/2019 12:15,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED UNDER NEATH KICTHEN CUPBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,NULL,COLMORE ROAD,20702189,EN3,NULL,NULL,535450,196250,NULL,NULL
NA,29/03/2019 21:28,2019,2018/19,Special Service,1,1,333,333,DOG OR FOX TRAPPED IN EXTRACTION VENT,Dog,Person (mobile),Restaurant/cafe,Non Residential,Other animal assistance,Assist trapped wild animal,E05000042,WHALEBONE,E09000002,BARKING AND DAGENHAM,Dagenham,100029314,STATION ROAD,19900343,RM6,547750,187805,547750,187850,51.5697641,0.1305088
NA,30/03/2019 17:19,2019,2018/19,Special Service,1,2,333,666,SWAN IN DISTRESS ON DISUSED BOAT,Bird,Person (mobile),Barge,Boat,Other animal assistance,Assist trapped wild animal,E05000347,BRENTFORD,E09000018,HOUNSLOW,Chiswick,1.00024E+11,HIGH STREET,21500607,TW8,518403,177819,518450,177850,51.48694373,-0.296056086
NA,31/03/2019 03:48,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000277,ST. ANN'S,E09000014,HARINGEY,Tottenham,NULL,EDGECOT GROVE,21106435,N15,NULL,NULL,533050,188850,NULL,NULL
NA,31/03/2019 16:16,2019,2018/19,Special Service,1,1,333,333,ANIMAL OR BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000599,HIGH STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,NORTHCOTE ROAD,22862000,E17,NULL,NULL,536350,189050,NULL,NULL
NA,01/04/2019 05:11,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER KITCHEN UNITS,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011095,BOROUGH & BANKSIDE,E09000028,SOUTHWARK,Dowgate,NULL,AYRES STREET,22500122,SE1,NULL,NULL,532350,179950,NULL,NULL
NA,01/04/2019 14:20,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING ABOVE,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,NULL,THE BROADWAY,22306023,IG8,NULL,NULL,540950,191850,NULL,NULL
NA,01/04/2019 16:35,2019,2019/20,Special Service,1,2,339,678,CAT OR DOG TRAPPED INSIDE STORAGE UNIT IN QUEENSWAY MARKET  CALLER ON SCENE WILL MEET YOU,Cat,Person (mobile),Indoor Market,Non Residential,Other animal assistance,Assist trapped wild animal,E05000638,LANCASTER GATE,E09000033,WESTMINSTER,Paddington,10033542052,QUEENSWAY,8401512,W2,525780,181072,525750,181050,51.5145804,-0.1887159
NA,01/04/2019 20:37,2019,2019/20,Special Service,1,1,339,339,DOG STUCK IN THE MUD NEXT TO CANNAL,Dog,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,10008985450,CANNING ROAD,22207641,E15,538882,183106,538850,183150,51.52979263,0.00079877
NA,02/04/2019 19:46,2019,2019/20,Special Service,1,1,339,339,INJURED CAT STUCK UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011230,SIDCUP,E09000004,BEXLEY,Sidcup,1.0002E+11,MALLARD WALK,20100909,DA14,547229,170979,547250,170950,51.41871986,0.115989637
NA,06/04/2019 02:39,2019,2019/20,Special Service,1,1,339,339,CAT STUCK BEHIND SINK - HAVING DIFFICULTY BREATHING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000225,PENINSULA,E09000011,GREENWICH,East Greenwich,NULL,PEARTREE WAY,20801696,SE10,NULL,NULL,540250,179050,NULL,NULL
NA,06/04/2019 19:56,2019,2019/20,Special Service,1,3,339,1017,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000213,WINCHMORE HILL,E09000010,ENFIELD,Southgate,207094702,WOODLAND WAY,20704952,N21,531104,193726,531150,193750,51.62709232,-0.107314052
NA,07/04/2019 11:26,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED BEHIND CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011224,EAST WICKHAM,E09000004,BEXLEY,Plumstead,NULL,MILTON ROAD,20100984,DA16,NULL,NULL,545650,176850,NULL,NULL
NA,07/04/2019 13:48,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000211,TURKEY STREET,E09000010,ENFIELD,Enfield,NULL,WINDSOR ROAD,20702451,EN3,NULL,NULL,535550,199350,NULL,NULL
NA,07/04/2019 17:49,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT AT THIRD FLOOR LEVEL  RSPCA OFFICER IN ATTENDANCE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,FRESTON ROAD,21700874,W11,NULL,NULL,523750,180650,NULL,NULL
NA,07/04/2019 20:24,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000570,WALLINGTON SOUTH,E09000029,SUTTON,Wallington,768018088,DOWER AVENUE,22605457,SM6,528882,162767,528850,162750,51.3493783,-0.15068951
NA,08/04/2019 23:26,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN BUSH,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000405,CHESSINGTON SOUTH,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,CHESSINGTON HILL PARK,21800236,KT9,NULL,NULL,519050,164050,NULL,NULL
NA,09/04/2019 15:31,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000099,QUEENSBURY,E09000005,BRENT,Stanmore,NULL,ALPINE ROAD,20204265,NW9,NULL,NULL,518950,189350,NULL,NULL
NA,10/04/2019 07:47,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000442,DOWNHAM,E09000023,LEWISHAM,Bromley,1.00022E+11,CONISTON ROAD,22004352,BR1,539119,170752,539150,170750,51.41872532,-0.000656349
NA,11/04/2019 08:22,2019,2019/20,Special Service,1,2,339,678,DOG TRAPPED DOWN HOLE,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000259,PALACE RIVERSIDE,E09000013,HAMMERSMITH AND FULHAM,Fulham,34142080,PUTNEY BRIDGE APPROACH,21000658,SW6,524292,175922,524250,175950,51.4686265,-0.211951221
NA,12/04/2019 10:43,2019,2019/20,Special Service,1,3,339,1017,Redacted,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009379,KING'S PARK,E09000012,HACKNEY,Homerton,10008329457,HOMERTON ROAD,20900534,E9,536964,185643,536950,185650,51.55306484,-0.02584976
NA,12/04/2019 14:49,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN FENCE - CALLER WILL MEET YOU AND DIRECT YOU,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000184,NORTHOLT MANDEVILLE,E09000009,EALING,Northolt,10091851669,MANDEVILLE ROAD,20601113,UB5,513165,184536,513150,184550,51.54838402,-0.369318287
NA,13/04/2019 23:16,2019,2019/20,Special Service,1,1,339,339,KITTEN UP TREE FOR TWO DAYS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011476,PURLEY & WOODCOTE,E09000008,CROYDON,Purley,NULL,PHEASANT CLOSE,20502267,CR8,NULL,NULL,531850,160850,NULL,NULL
NA,14/04/2019 05:08,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011232,THAMESMEAD EAST,E09000004,BEXLEY,Erith,NULL,KALE ROAD,20100778,DA18,NULL,NULL,548050,179550,NULL,NULL
NA,14/04/2019 09:44,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER SINK,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000602,LARKSWOOD,E09000031,WALTHAM FOREST,Walthamstow,NULL,BEAUFORT CLOSE,22813500,E4,NULL,NULL,537950,191750,NULL,NULL
NA,15/04/2019 10:42,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN SOFABED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000475,BECKTON,E09000025,NEWHAM,East Ham,NULL,PEPPER CLOSE,22200951,E6,NULL,NULL,542950,181750,NULL,NULL
NA,15/04/2019 14:08,2019,2019/20,Special Service,1,1,339,339,TO ASSIST RSPCA WITH PIGEON CAUGHT IN TREE NEAR BOAT POND     ALP REQUIRED,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,10010226397,PARK VISTA,20801142,SE10,538808,177738,538850,177750,51.4815755,-0.002378299
NA,15/04/2019 21:56,2019,2019/20,Special Service,1,1,339,339,FERRET TRAPPED IN RADIATOR,Ferret,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,NULL,HUTCHINS ROAD,20801672,SE28,NULL,NULL,546350,180450,NULL,NULL
NA,16/04/2019 06:12,2019,2019/20,Special Service,1,1,339,339,INJURED CAT TRAPPED AT LOWER GROUND LEVEL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,OLD BROMPTON ROAD,21700372,SW5,NULL,NULL,525850,178350,NULL,NULL
NA,16/04/2019 08:36,2019,2019/20,Special Service,1,1,339,339,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011224,EAST WICKHAM,E09000004,BEXLEY,Plumstead,NULL,LOVEL AVENUE,20100881,DA16,NULL,NULL,546350,176350,NULL,NULL
NA,16/04/2019 08:53,2019,2019/20,Special Service,1,1,339,339,CAT STUCK DOWN MAN HOLE,Cat,Person (mobile),Veterinary surgery,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000092,KENSAL GREEN,E09000005,BRENT,Willesden,202201829,LEGHORN ROAD,20201220,NW10,521936,183446,521950,183450,51.53676599,-0.24325475
NA,17/04/2019 11:23,2019,2019/20,Special Service,1,1,339,339,ASSIST BLUE CROSS WITH CAT ON LEDGE,Cat,Person (mobile),Train station - elsewhere,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000647,WARWICK,E09000033,WESTMINSTER,Chelsea,10033642688,TERMINUS PLACE,8401056,SW1V,528932,178897,528950,178850,51.49433132,-0.144107283
NA,17/04/2019 12:18,2019,2019/20,Special Service,1,1,339,339,CAT STUCK ON ROOF      REQUESTED TO ATTEND BY RSPCA   RSPCA IN ATTENDANCE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000350,CRANFORD,E09000018,HOUNSLOW,Feltham,NULL,ASTON GREEN,21500054,TW4,NULL,NULL,511050,176450,NULL,NULL
NA,17/04/2019 19:07,2019,2019/20,Special Service,1,2,339,678,PIGEON TRAPPED IN CHIMNEY - NO OTHER AGENCIES ATTENDING,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000349,CHISWICK RIVERSIDE,E09000018,HOUNSLOW,Chiswick,NULL,FITZROY CRESCENT,21501613,W4,NULL,NULL,520550,177350,NULL,NULL
NA,18/04/2019 06:53,2019,2019/20,Special Service,1,1,339,339,BIRD STUCK IN NETTING    GROUND FLOOR FLAT,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000264,TOWN,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,MUNSTER ROAD,21000580,SW6,NULL,NULL,524650,176550,NULL,NULL
NA,18/04/2019 15:43,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED BETWEEN SHED AND FENCE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,ULLESWATER ROAD,20704867,N14,NULL,NULL,530450,193150,NULL,NULL
NA,18/04/2019 16:18,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000562,ST. HELIER,E09000029,SUTTON,Sutton,NULL,WRYTHE LANE,22605653,SM5,NULL,NULL,526250,166450,NULL,NULL
NA,19/04/2019 11:44,2019,2019/20,Special Service,1,1,339,339,PUPPY LOCKED IN PLAYGROUND,Dog,Person (land line),Pre School/nursery,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05011102,FARADAY,E09000028,SOUTHWARK,Old Kent Road,2.00003E+11,DAWES STREET,22500720,SE17,532938,178318,532950,178350,51.48819706,-0.086651989
NA,19/04/2019 14:15,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA GAIN ENTRY TO FREE DOG LOCKED IN,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011106,NORTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,NULL,SOUTHWARK PARK ROAD,22502293,SE16,NULL,NULL,534750,179450,NULL,NULL
NA,19/04/2019 17:48,2019,2019/20,Special Service,1,1,339,339,FOX CUB TRAPPED IN NETTING,Fox,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000192,WALPOLE,E09000009,EALING,Ealing,NULL,LAMMAS PARK ROAD,20601014,W5,NULL,NULL,517550,179950,NULL,NULL
NA,19/04/2019 17:53,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000218,ELTHAM NORTH,E09000011,GREENWICH,Eltham,NULL,WESTMOUNT ROAD,20801601,SE9,NULL,NULL,542750,176250,NULL,NULL
NA,20/04/2019 05:23,2019,2019/20,Special Service,1,1,339,339,CAT STUCK ON SIXTH FLOOR BALCONY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000420,COLDHARBOUR,E09000022,LAMBETH,Brixton,NULL,COLDHARBOUR LANE,21900369,SW9,NULL,NULL,531750,175650,NULL,NULL
NA,20/04/2019 08:12,2019,2019/20,Special Service,1,1,339,339,CAT  TRAPPED IN TREE IN WOODED  AREA  CALLER WILL MEET YOU IN ALLEYWAY  AT END OF CRESCENT,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000602,LARKSWOOD,E09000031,WALTHAM FOREST,Chingford,1.00023E+11,LARKSHALL CRESCENT,22849700,E4,538398,192732,538350,192750,51.61640915,-0.002384901
NA,20/04/2019 08:38,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,NULL,RITHERDON ROAD,22904302,SW17,NULL,NULL,528550,172550,NULL,NULL
NA,20/04/2019 12:08,2019,2019/20,Special Service,1,1,339,339,CAT IN PRECARIOUS POSITION ON FOURTH FLOOR  UNABLE TO BACK INSIDE BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009375,HAGGERSTON,E09000012,HACKNEY,Shoreditch,NULL,LOVELACE STREET,20950325,E8,NULL,NULL,533750,183850,NULL,NULL
NA,20/04/2019 19:18,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000288,GREENHILL,E09000015,HARROW,Harrow,1.00021E+11,HEADSTONE ROAD,21201047,HA1,514979,188554,514950,188550,51.58413148,-0.341844726
NA,21/04/2019 12:41,2019,2019/20,Special Service,1,2,339,678,ASSIST RSPCA   TWO PIGEONS HEADS STUCK IN SOLAR PANEL BY GUTTER,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Chingford,NULL,CONNINGTON CRESCENT,22828750,E4,NULL,NULL,538750,193250,NULL,NULL
NA,21/04/2019 13:08,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER DECKING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,NULL,STRATHVILLE ROAD,22905028,SW18,NULL,NULL,525750,173050,NULL,NULL
NA,21/04/2019 19:24,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN RAZOR WIRE,Dog,Person (mobile),Scrub land,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000465,LOWER MORDEN,E09000024,MERTON,Sutton,48108486,LOWER MORDEN LANE,22104038,SM4,524282,167247,524250,167250,51.39066481,-0.215138895
NA,22/04/2019 09:02,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA CAT UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000406,COOMBE HILL,E09000021,KINGSTON UPON THAMES,New Malden,1.00022E+11,COOMBE END,21800271,KT2,520806,170248,520850,170250,51.41839213,-0.264051658
NA,22/04/2019 10:08,2019,2019/20,Special Service,2,3,339,1017,DOG SHUT IN LIFT   LEAD CAUGHT IN DOORS,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,NULL,MACE STREET,22700764,E2,NULL,NULL,535750,183150,NULL,NULL
NA,22/04/2019 11:10,2019,2019/20,Special Service,1,1,339,339,BIRD ENTANGLED IN WIRE    SECOND FLOOR LEVEL   CALLER WILL MEET BRIGADE,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011110,PECKHAM,E09000028,SOUTHWARK,Peckham,NULL,PECKHAM HILL STREET,22501926,SE15,NULL,NULL,534150,176750,NULL,NULL
NA,22/04/2019 16:46,2019,2019/20,Special Service,NULL,NULL,339,NULL,Redacted,Snake,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05011219,BEXLEYHEATH,E09000004,BEXLEY,Bexley,1.00023E+11,ERITH ROAD,20100511,DA7,549692,175781,549650,175750,51.46122158,0.15340913
NA,22/04/2019 17:03,2019,2019/20,Special Service,1,1,339,339,PIGEON STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000291,HATCH END,E09000015,HARROW,Harrow,NULL,NUGENTS PARK,21201146,HA5,NULL,NULL,512550,190750,NULL,NULL
NA,23/04/2019 07:40,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000047,COPPETTS,E09000003,BARNET,Southgate,NULL,BEACONSFIELD ROAD,20002720,N11,NULL,NULL,528350,192550,NULL,NULL
NA,23/04/2019 17:01,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING UNDER BRIDGE - CALLER WILL FLAG YOU DOWN,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,121033252,CAVENDISH ROAD,22900824,SW12,529134,172999,529150,172950,51.4412732,-0.1433545
NA,24/04/2019 10:25,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000184,NORTHOLT MANDEVILLE,E09000009,EALING,Northolt,NULL,WILSMERE DRIVE,20601914,UB5,NULL,NULL,512450,185050,NULL,NULL
NA,24/04/2019 15:46,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND KITCHEN UNIT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009378,HOXTON WEST,E09000012,HACKNEY,Islington,NULL,PRESTWOOD STREET,20900818,N1,NULL,NULL,532350,183050,NULL,NULL
NA,25/04/2019 00:59,2019,2019/20,Special Service,1,1,339,339,DOG STUCK BETWEEN FENCE AND WALL        BACKING ONTO RAILWAY LINE,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000377,MILDMAY,E09000019,ISLINGTON,Islington,5300038559,GROSVENOR AVENUE,21603291,N5,532280,185049,532250,185050,51.54884192,-0.093602207
NA,25/04/2019 20:45,2019,2019/20,Special Service,1,1,339,339,KITTEN POSSIBLY TRAPPED IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000280,TOTTENHAM GREEN,E09000014,HARINGEY,Tottenham,10022938583,WEST GREEN ROAD,21104071,N15,533594,188980,533550,188950,51.58385581,-0.073161712
NA,26/04/2019 17:47,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER BONNET OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal harm involving domestic animal,E05000378,ST. GEORGE'S,E09000019,ISLINGTON,Holloway,10012786048,TABLEY ROAD,21603902,N7,530178,185851,530150,185850,51.55653662,-0.123591193
NA,27/04/2019 12:55,2019,2019/20,Special Service,1,1,339,339,WILD BIRD TRAPPED INSIDE BUILDING   CALLER WILL MEET YOU,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000623,SHAFTESBURY,E09000032,WANDSWORTH,Clapham,NULL,CROSLAND PLACE,22906487,SW11,NULL,NULL,528450,175750,NULL,NULL
NA,28/04/2019 13:43,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000097,PRESTON,E09000005,BRENT,Wembley,202061830,OAKINGTON AVENUE,20202423,HA9,518740,186353,518750,186350,51.56356979,-0.288339538
NA,29/04/2019 20:47,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED WITHIN BUSHES BEHIND GRATE NEAR POND,Cat,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05011487,WADDON,E09000008,CROYDON,Croydon,1.00024E+11,WADDON COURT ROAD,20502402,CR0,530998,165021,530950,165050,51.36915281,-0.119487375
NA,30/04/2019 11:31,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Self contained Sheltered Housing,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000254,FULHAM BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,FARM LANE,21000330,SW6,NULL,NULL,525350,177450,NULL,NULL
NA,30/04/2019 11:44,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND WASHING MACHINE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000417,BRIXTON HILL,E09000022,LAMBETH,West Norwood,NULL,BRIXTON HILL,21900236,SW2,NULL,NULL,530450,174050,NULL,NULL
NA,01/05/2019 13:36,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Finchley,NULL,MOAT CRESCENT,20029860,N3,NULL,NULL,525550,189750,NULL,NULL
NA,02/05/2019 11:49,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009374,HACKNEY WICK,E09000012,HACKNEY,Homerton,NULL,BRADSTOCK ROAD,20900160,E9,NULL,NULL,535950,184650,NULL,NULL
NA,02/05/2019 12:36,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000617,LATCHMERE,E09000032,WANDSWORTH,Battersea,NULL,MAYSOULE ROAD,22903249,SW11,NULL,NULL,526550,175450,NULL,NULL
NA,02/05/2019 13:37,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN TREE   RSPCA IN ATTENDANCE   JUNC COWLEY ROAD,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000436,VASSALL,E09000022,LAMBETH,Brixton,10001113492,CANCELL ROAD,21900282,SW9,531341,176940,531350,176950,51.47618445,-0.110145471
NA,02/05/2019 13:44,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,NULL,ANDOVER ROAD,21602833,N7,NULL,NULL,530650,186650,NULL,NULL
NA,03/05/2019 19:23,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011115,ST. GILES,E09000028,SOUTHWARK,Peckham,NULL,PECKHAM ROAD,22501935,SE5,NULL,NULL,533150,176650,NULL,NULL
NA,03/05/2019 19:25,2019,2019/20,Special Service,1,1,339,339,PARROT STUCK IN TREE  ATTACHED BY HARNESS,Bird,Person (mobile),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000217,COLDHARBOUR AND NEW ELTHAM,E09000011,GREENWICH,Eltham,1.00021E+11,SPEKEHILL,20801390,SE9,542884,172108,542850,172150,51.42997559,0.0539967
NA,05/05/2019 16:57,2019,2019/20,Special Service,1,1,339,339,RUNNING CALL TO PARAKEET TRAPPED IN NETTING OF FIRE STATION TOWER,Bird,Person (land line),Fire station,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000286,CANONS,E09000015,HARROW,Stanmore,10002294818,HONEYPOT LANE,21200986,HA7,517992,190795,517950,190750,51.60364698,-0.297636313
NA,05/05/2019 18:20,2019,2019/20,Special Service,1,1,339,339,FOX CUB TRAPPED BETWEEN CONCRETE POSTS - RSPCA ON SCENE -,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000217,COLDHARBOUR AND NEW ELTHAM,E09000011,GREENWICH,Eltham,1.00021E+11,BIRBETTS ROAD,20800167,SE9,542640,172573,542650,172550,51.43420939,0.050686131
NA,05/05/2019 19:59,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING UNDER BRIDGE - CALLER WILL MEET YOU,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000469,RAYNES PARK,E09000024,MERTON,New Malden,48128490,COOMBE LANE,22101605,SW20,523282,169316,523250,169350,51.40947958,-0.228781229
NA,05/05/2019 21:26,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN GARDEN,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000303,STANMORE PARK,E09000015,HARROW,Stanmore,1.00021E+11,ADELAIDE CLOSE,21202157,HA7,516315,192607,516350,192650,51.62028002,-0.321232568
NA,08/05/2019 13:06,2019,2019/20,Special Service,1,1,339,339,POSSIBLY CAT STUCK IN CHIMNEY  FRU  REQUIRED WITH SNAKE EYE  CAMERA,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000568,THE WRYTHE,E09000029,SUTTON,Sutton,NULL,TEMPLE WAY,22601066,SM1,NULL,NULL,526750,165150,NULL,NULL
NA,09/05/2019 10:48,2019,2019/20,Special Service,1,1,339,339,CROW STUCK IN NETTING - RSPCA INSPECTOR ON SCENE,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000353,HANWORTH,E09000018,HOUNSLOW,Feltham,1.00022E+11,NALLHEAD ROAD,21500784,TW13,511577,171174,511550,171150,51.42859915,-0.396432817
NA,09/05/2019 13:59,2019,2019/20,Special Service,1,1,339,339,SWAN STUCK ON BALCONY   ASSIST RSPCA,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011483,SHIRLY SOUTH,E09000008,CROYDON,Woodside,NULL,FARM DRIVE,20500447,CR0,NULL,NULL,536950,165750,NULL,NULL
NA,10/05/2019 14:18,2019,2019/20,Special Service,1,1,339,339,SQUIRREL TRAPPED IN VENT INSIDE PARK    CALLER WILL MEET LFB  SECOND ENTRANCE AFTER BP GARAGE ON RIG,Squirrel,Person (mobile),Park,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000184,NORTHOLT MANDEVILLE,E09000009,EALING,Northolt,12141341,EASTCOTE LANE,20600590,UB5,512715,184422,512750,184450,51.54744556,-0.375844697
NA,11/05/2019 15:10,2019,2019/20,Special Service,1,3,339,1017,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000086,BARNHILL,E09000005,BRENT,Wembley,NULL,SALMON STREET,20200954,NW9,NULL,NULL,519950,187050,NULL,NULL
NA,11/05/2019 19:48,2019,2019/20,Special Service,1,1,339,339,Redacted,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000353,HANWORTH,E09000018,HOUNSLOW,Twickenham,1.00022E+11,STOURTON AVENUE,21501300,TW13,512662,171954,512650,171950,51.43539286,-0.380573952
NA,12/05/2019 13:35,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA GAIN ACCESS TO DUCKLINGS,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000475,BECKTON,E09000025,NEWHAM,East Ham,NULL,FROBISHER YARD,22208305,E16,NULL,NULL,544150,180650,NULL,NULL
NA,12/05/2019 14:14,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL CAUGHT BEHIND BATHROOM WALL,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000482,EAST HAM SOUTH,E09000025,NEWHAM,East Ham,NULL,LONSDALE AVENUE,22200818,E6,NULL,NULL,542150,182450,NULL,NULL
NA,12/05/2019 17:28,2019,2019/20,Special Service,1,1,339,339,PARROT TRAPPED IN BASEMENT,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,LISSON GROVE,8400993,NW1,NULL,NULL,527350,181850,NULL,NULL
NA,13/05/2019 10:22,2019,2019/20,Special Service,1,1,339,339,STARLING   TRAPPED IN EAVES OF ROOF,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009373,HACKNEY DOWNS,E09000012,HACKNEY,Homerton,NULL,QUEENSDOWN ROAD,20900834,E5,NULL,NULL,534750,185650,NULL,NULL
NA,13/05/2019 12:20,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL TRAPPED BEHIND BATHROOM WALL,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000482,EAST HAM SOUTH,E09000025,NEWHAM,East Ham,NULL,LONSDALE AVENUE,22200818,E6,NULL,NULL,542150,182450,NULL,NULL
NA,13/05/2019 14:54,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000195,CHASE,E09000010,ENFIELD,Enfield,NULL,GOAT LANE,20702707,EN1,NULL,NULL,533750,198350,NULL,NULL
NA,14/05/2019 14:50,2019,2019/20,Special Service,1,1,339,339,CAT STUCK ON FITH FLOOR WINDOW LEDGE  NO ANSWER FROM FLAT OWNERS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000648,WESTBOURNE,E09000033,WESTMINSTER,Paddington,NULL,ELMFIELD WAY,8401841,W9,NULL,NULL,525250,181850,NULL,NULL
NA,14/05/2019 15:01,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000604,LEYTON,E09000031,WALTHAM FOREST,Leyton,NULL,VILLIERS CLOSE,22884200,E10,NULL,NULL,537350,186850,NULL,NULL
NA,15/05/2019 19:49,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000359,HOUNSLOW HEATH,E09000018,HOUNSLOW,Heston,NULL,ANDERSON PLACE,21500037,TW3,NULL,NULL,513750,175250,NULL,NULL
NA,15/05/2019 23:41,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED IN DEEP EXCAVATION HOLE OUTSIDE HOUSE  - CALLER STATES ANIMAL IS DISTRESSED,Fox,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000340,UXBRIDGE NORTH,E09000017,HILLINGDON,Hillingdon,1.00021E+11,LANCRESSE CLOSE,21401120,UB8,505673,184641,505650,184650,51.5507749,-0.477295026
NA,17/05/2019 10:29,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN TREE   CALLED BY RSPCA OFFICER WHO IS ON SCENE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000373,HIGHBURY WEST,E09000019,ISLINGTON,Holloway,5300001735,AMBLER ROAD,21602827,N4,531553,186350,531550,186350,51.56070481,-0.103587086
NA,17/05/2019 16:31,2019,2019/20,Special Service,1,1,339,339,SEAGULL CAUGHT IN NETTING ON ROOF,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009301,CORNHILL,E09000001,CITY OF LONDON,Dowgate,1.00024E+11,CORNHILL,8100150,EC3V,532936,181131,532950,181150,51.51347926,-0.085614967
NA,17/05/2019 18:18,2019,2019/20,Special Service,1,1,339,339,CAT STUCK UP TREE - REQUESTED BY RSPCA,Cat,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000423,HERNE HILL,E09000022,LAMBETH,Brixton,1.00022E+11,DEERDALE ROAD,21900450,SE24,532157,175434,532150,175450,51.4624637,-0.098963546
NA,17/05/2019 21:49,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL TRAPPED BEHIND KITCHEN UNIT,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000333,ICKENHAM,E09000017,HILLINGDON,Ruislip,NULL,CLOVELLY AVENUE,21400429,UB10,NULL,NULL,508150,185750,NULL,NULL
NA,18/05/2019 07:23,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED BETWEEN PATIO BALCONY AND WALL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,NULL,WARD ROAD,22201656,E15,NULL,NULL,538550,183950,NULL,NULL
NA,18/05/2019 13:17,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - PIGEON TRAPPED IN NETTING  THIS IS BETWEEN LOWER MASH AND UPPER MARSH,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011114,ST. GEORGE'S,E09000028,SOUTHWARK,Dowgate,2.00003E+11,LONDON ROAD,22501544,SE1,531669,179415,531650,179450,51.49835683,-0.104505122
NA,18/05/2019 15:31,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011472,NORBURY & POLLARDS HILL,E09000008,CROYDON,Norbury,NULL,DARCY ROAD,20500853,SW16,NULL,NULL,530050,169150,NULL,NULL
NA,18/05/2019 17:30,2019,2019/20,Special Service,1,1,339,339,CALF TRAPPED IN DRINKING WATER TROUGH  BEHIND WEST MIDDLESEX HOSPITAL TURN LEFT INTO SYON PARK BARE,Unknown - Heavy Livestock Animal,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000364,SYON,E09000018,HOUNSLOW,Heston,10091690311,LONDON ROAD,21519757,TW8,516991,176886,516950,176850,51.47884447,-0.316696587
NA,18/05/2019 19:07,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009373,HACKNEY DOWNS,E09000012,HACKNEY,Stoke Newington,NULL,NAPOLEON ROAD,20900724,E5,NULL,NULL,534650,186050,NULL,NULL
NA,18/05/2019 21:42,2019,2019/20,Special Service,1,1,339,339,MAGPIE WITH HEAD STUCK IN ROOF VOID      AT SIDE OF HOUSE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000192,WALPOLE,E09000009,EALING,Ealing,NULL,LOVEDAY ROAD,20601087,W13,NULL,NULL,516850,179850,NULL,NULL
NA,19/05/2019 12:54,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000205,PALMERS GREEN,E09000010,ENFIELD,Southgate,NULL,LODGE DRIVE,20704449,N13,NULL,NULL,531150,192750,NULL,NULL
NA,19/05/2019 15:41,2019,2019/20,Special Service,1,1,339,339,KITTEN STUCK UNDERNEATH RUBBLE IN REAR GARDEN,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000189,SOUTHALL BROADWAY,E09000009,EALING,Southall,NULL,BEACONSFIELD ROAD,20600131,UB1,NULL,NULL,512450,179950,NULL,NULL
NA,19/05/2019 18:26,2019,2019/20,Special Service,1,1,339,339,CAT STUCK ON THE ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009386,VICTORIA,E09000012,HACKNEY,Homerton,NULL,MOULINS ROAD,20900710,E9,NULL,NULL,535450,184050,NULL,NULL
NA,20/05/2019 17:23,2019,2019/20,Special Service,1,1,339,339,KITTENS TRAPPED IN UNDERGROWTH,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000487,LITTLE ILFORD,E09000025,NEWHAM,East Ham,10009002773,NORTH CIRCULAR ROAD,22202013,E12,543371,185309,543350,185350,51.5484659,0.066363777
NA,22/05/2019 00:00,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN DRAINPIPE ATTACHED TO PROPERTY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009333,SPITALFIELDS & BANGLATOWN,E09000030,TOWER HAMLETS,Whitechapel,NULL,FOURNIER STREET,22700517,E1,NULL,NULL,533750,181850,NULL,NULL
NA,22/05/2019 06:40,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000033,GORESBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,GORESBROOK ROAD,19900853,RM9,NULL,NULL,549050,183650,NULL,NULL
NA,22/05/2019 12:27,2019,2019/20,Special Service,1,1,339,339,CAT AND KITTENS UNDER WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011231,SLADE GREEN & NORTHEND,E09000004,BEXLEY,Bexley,NULL,DALE VIEW,20100416,DA8,NULL,NULL,552050,176350,NULL,NULL
NA,22/05/2019 17:55,2019,2019/20,Special Service,1,1,339,339,CHICKS TRAPPED BETWEEN GATES IN PARK NEXT TO,Unknown - Wild Animal,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05009336,WHITECHAPEL,E09000030,TOWER HAMLETS,Shadwell,6150723,THE HIGHWAY,22701200,E1,534363,180808,534350,180850,51.51023815,-0.065193371
NA,22/05/2019 20:55,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000567,SUTTON WEST,E09000029,SUTTON,Sutton,5870034010,MULGRAVE ROAD,22605508,SM2,525650,163655,525650,163650,51.35808225,-0.196762197
NA,23/05/2019 18:16,2019,2019/20,Special Service,1,1,339,339,HORSE IN DISTRESS STUCK IN METAL FRAME     IN A  FIELD ON LEFT HAND SIDE COMING FROM LIMPSFIELD ROAD,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011478,SANDERSTEAD,E09000008,CROYDON,Purley,1.00021E+11,MITCHLEY AVENUE,20503090,CR2,533473,160810,533450,160850,51.33073554,-0.085523951
NA,24/05/2019 09:24,2019,2019/20,Special Service,1,NULL,339,NULL,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,HUXLEY ROAD,20704356,N18,NULL,NULL,533150,192950,NULL,NULL
NA,24/05/2019 18:20,2019,2019/20,Special Service,1,1,339,339,INJURED KITTEN TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000445,GROVE PARK,E09000023,LEWISHAM,Eltham,NULL,HENRY COOPER WAY,22003400,SE9,NULL,NULL,541650,172050,NULL,NULL
NA,25/05/2019 10:36,2019,2019/20,Special Service,1,1,339,339,BIRD STUCK IN FIRE PLACE,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000111,CHISLEHURST,E09000006,BROMLEY,Sidcup,NULL,BROMLEY LANE,20302113,BR7,NULL,NULL,544450,170250,NULL,NULL
NA,25/05/2019 13:08,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED BETWEEN EAVES AND GUTTER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000144,SWISS COTTAGE,E09000007,CAMDEN,West Hampstead,NULL,ACOL ROAD,20400559,NW6,NULL,NULL,525550,184150,NULL,NULL
NA,25/05/2019 14:56,2019,2019/20,Special Service,1,1,339,339,BIRDS STUCK IN NETTING - ASSIST RSPCA,Bird,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000358,HOUNSLOW CENTRAL,E09000018,HOUNSLOW,Heston,1.00023E+11,DOUGLAS ROAD,21500365,TW3,513876,175653,513850,175650,51.46840455,-0.361930383
NA,25/05/2019 17:46,2019,2019/20,Special Service,1,1,339,339,DOG LOCKED INSIDE CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving wild animal - Other action,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,46250363,THE GROVE,22201626,E15,539125,184668,539150,184650,51.5437701,0.004917027
NA,25/05/2019 20:44,2019,2019/20,Special Service,1,1,339,339,MAGPIE TRAPPED IN GUTTER,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000413,SURBITON HILL,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,SAXON CLOSE,21800841,KT6,NULL,NULL,517950,167150,NULL,NULL
NA,26/05/2019 13:17,2019,2019/20,Special Service,1,1,339,339,PIGEON STUCK IN NETTING,Bird,Person (mobile),"Takeaway, fast food",Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,100070213,FARR AVENUE,19900881,IG11,546063,183234,546050,183250,51.52913593,0.104299587
NA,26/05/2019 17:38,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,NULL,DARLINGTON PATH,21300074,RM3,NULL,NULL,553750,192350,NULL,NULL
NA,27/05/2019 20:15,2019,2019/20,Special Service,1,3,339,1017,FOX FALLEN IN RIVER  CALLER WILL MEET YOU,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000353,HANWORTH,E09000018,HOUNSLOW,Twickenham,1.00022E+11,STOURTON AVENUE,21501300,TW13,512734,171880,512750,171850,51.43471409,-0.37956785
NA,27/05/2019 21:40,2019,2019/20,Special Service,1,1,339,339,PIGEON STUCK IN CHIMNEY AREA,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011105,NEWINGTON,E09000028,SOUTHWARK,Lambeth,NULL,CRAMPTON STREET,22500633,SE17,NULL,NULL,531950,178550,NULL,NULL
NA,28/05/2019 19:20,2019,2019/20,Special Service,1,3,339,1017,DOG TRAPPED IN FOX HOLE      SNAKE EYE REQUIRED    WIMBLEDON COMMON   NEAR WAR MEMORIAL      TERRIER,Dog,Person (mobile),Tree scrub,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000472,VILLAGE,E09000024,MERTON,Wimbledon,48102622,YPROW8,22100224,SW15,521679,171519,521650,171550,51.42962386,-0.251065518
NA,29/05/2019 16:32,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN WINDOW IN DISTRESS - CALLER IS PASSER BY - NO ONE ANSWERING DOOR,Dog,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000283,WHITE HART LANE,E09000014,HARINGEY,Tottenham,1.00021E+11,HENNINGHAM ROAD,21104642,N17,532855,190969,532850,190950,51.60190452,-0.083066792
NA,29/05/2019 19:29,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000478,CANNING TOWN SOUTH,E09000025,NEWHAM,Plaistow,10090848761,ROBERTSON ROAD,22208227,E16,540330,181605,540350,181650,51.51595059,0.021051712
NA,30/05/2019 17:18,2019,2019/20,Special Service,1,1,339,339,RUNNING CALL TO CAT TRAPPED IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,10010194718,TWIN TUMPS WAY,20801831,SE28,546233,180859,546250,180850,51.50774745,0.105759711
NA,01/06/2019 15:01,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH KITTEN FALLEN DOWN CAVITY OF WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000352,FELTHAM WEST,E09000018,HOUNSLOW,Feltham,NULL,HIGH STREET,21500609,TW13,NULL,NULL,510450,172950,NULL,NULL
NA,01/06/2019 15:13,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED IN FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000137,HIGHGATE,E09000007,CAMDEN,Kentish Town,NULL,THE GROVE,20400011,N6,NULL,NULL,528150,187450,NULL,NULL
NA,02/06/2019 11:47,2019,2019/20,Special Service,1,1,339,339,RABBIT WITH HEAD STUCK IN CAGE DOOR,Rabbit,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000360,HOUNSLOW SOUTH,E09000018,HOUNSLOW,Heston,NULL,CHATSWORTH CRESCENT,21500236,TW3,NULL,NULL,514550,175450,NULL,NULL
NA,02/06/2019 12:28,2019,2019/20,Special Service,1,1,339,339,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000211,TURKEY STREET,E09000010,ENFIELD,Enfield,NULL,HOE LANE,20702753,EN3,NULL,NULL,535150,198150,NULL,NULL
NA,02/06/2019 14:42,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN VELUX WINDOW,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist  trapped livestock animal,E05000627,WANDSWORTH COMMON,E09000032,WANDSWORTH,Wandsworth,NULL,BARMOUTH ROAD,22900280,SW18,NULL,NULL,526350,174150,NULL,NULL
NA,02/06/2019 22:36,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED POSSIBLY IN DRAN,Cat,Person (mobile),Pre School/nursery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000493,WALL END,E09000025,NEWHAM,East Ham,46256926,CANBERRA ROAD,22200441,E6,542952,183588,542950,183550,51.53311141,0.059619816
NA,02/06/2019 23:14,2019,2019/20,Special Service,1,1,339,339,BABY FOX TRAPPED IN NETTING,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,46079826,WESTPORT ROAD,22201211,E13,540706,182092,540750,182050,51.52023703,0.026661866
NA,03/06/2019 16:29,2019,2019/20,Special Service,2,3,339,1017,HORSE TRAPPED IN BARBED WIRE IN FIELD OPPOSITE,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011480,SELSDON & ADDINGTON VILLAGE,E09000008,CROYDON,Addington,1.00021E+11,HUNTINGFIELD,20501750,CR0,536966,163404,536950,163450,51.35321712,-0.034429168
NA,03/06/2019 16:46,2019,2019/20,Special Service,1,1,339,339,CAT STUCK UP TREE   REQUESTED BY RSPCA (ON SITE),Cat,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011481,SELSDON VALE & FORESTDALE,E09000008,CROYDON,Addington,1.00021E+11,KERSEY DRIVE,20501757,CR2,535399,161249,535350,161250,51.33422533,-0.057741515
NA,03/06/2019 21:10,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN BOARDED UP DISUSED BUILDING,Cat,Person (mobile),Shelter,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000141,KING'S CROSS,E09000007,CAMDEN,Euston,5105342,LOXHAM STREET,20400896,WC1H,530388,182701,530350,182750,51.52818365,-0.121735956
NA,04/06/2019 16:25,2019,2019/20,Special Service,1,1,339,339,ANIMAL IN DISTRESS - FOX INJURED ON PREMISES,Fox,Person (land line),Central Government Office,Non Residential,Other animal assistance,Animal harm involving wild animal,E05000637,KNIGHTSBRIDGE AND BELGRAVIA,E09000033,WESTMINSTER,Kensington,1.00023E+11,KNIGHTSBRIDGE,8400987,SW1X,527937,179797,527950,179750,51.50264607,-0.158108725
NA,05/06/2019 18:09,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN RAILINGS,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,1.00022E+11,KEMPTHORNE ROAD,22000572,SE8,536495,178750,536450,178750,51.4912354,-0.035278618
NA,05/06/2019 20:57,2019,2019/20,Special Service,1,2,339,678,SMALL ANIMAL RESCUE - CAT AND KITTENS STUCK BEHIND BATH,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000107,BIGGIN HILL,E09000006,BROMLEY,Biggin Hill,NULL,NORHEADS LANE,20301455,TN16,NULL,NULL,540950,159050,NULL,NULL
NA,06/06/2019 21:07,2019,2019/20,Special Service,1,1,339,339,CAT POSSIBLY TRAPPED BY COLLAR IN TREE VERY HIGH UP,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000424,KNIGHT'S HILL,E09000022,LAMBETH,West Norwood,1.00022E+11,THURLBY ROAD,21901378,SE27,531486,171747,531450,171750,51.42948314,-0.109997415
NA,07/06/2019 09:26,2019,2019/20,Special Service,1,1,339,339,MUNTJAC TRAPPED IN METAL GATES,Unknown - Wild Animal,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000196,COCKFOSTERS,E09000010,ENFIELD,Southgate,207016293,BOLINGBROKE CLOSE,20700878,EN4,527759,196747,527750,196750,51.65500702,-0.154499646
NA,07/06/2019 17:23,2019,2019/20,Special Service,1,1,339,339,SQUIRREL TRAPPED IN TOILET,Squirrel,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000360,HOUNSLOW SOUTH,E09000018,HOUNSLOW,Heston,NULL,SUSSEX AVENUE,21501086,TW7,NULL,NULL,515250,175650,NULL,NULL
NA,07/06/2019 18:37,2019,2019/20,Special Service,1,1,339,339,ANIMAL OR BIRD TRAPPED BEHIND GAS FIRE IN BEDROOM,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000215,BLACKHEATH WESTCOMBE,E09000011,GREENWICH,East Greenwich,NULL,VANBRUGH PARK,20801536,SE3,NULL,NULL,540050,177350,NULL,NULL
NA,07/06/2019 18:59,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT TRAPPED INSIDE ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009379,KING'S PARK,E09000012,HACKNEY,Homerton,1.00023E+11,PEDRO STREET,20900791,E5,535942,185902,535950,185950,51.55563429,-0.040480299
NA,07/06/2019 19:52,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000493,WALL END,E09000025,NEWHAM,East Ham,46010226,CALEDON ROAD,22200435,E6,542697,183963,542650,183950,51.53654247,0.05609693
NA,08/06/2019 19:16,2019,2019/20,Special Service,1,1,339,339,RUNNING CALL TO PIGEON TRAPPED IN NETTING - RSCPA REQUESTED,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,10010441203,UPPER STREET,21606199,N1,531670,184237,531650,184250,51.54168472,-0.102686222
NA,09/06/2019 00:08,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN WATER FEATURE,Cat,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Assist trapped domestic animal,E05009380,LEA BRIDGE,E09000012,HACKNEY,Homerton,10008315971,LEA BRIDGE ROAD,20900602,E5,535557,186503,535550,186550,51.56112771,-0.045805743
NA,09/06/2019 03:45,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000557,BELMONT,E09000029,SUTTON,Sutton,NULL,HULVERSTON CLOSE,22602404,SM2,NULL,NULL,525650,162450,NULL,NULL
NA,09/06/2019 09:50,2019,2019/20,Special Service,1,1,339,339,FERRET WITH HEAD TRAPPED IN METAL GRILL ON CARRIER,Ferret,Person (land line),Other outdoor equipment/machinery,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000526,NORTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,1.00022E+11,TOWNSHEND TERRACE,22406024,TW9,518811,175298,518850,175250,51.46419293,-0.29103095
NA,09/06/2019 18:59,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05009321,BROMLEY NORTH,E09000030,TOWER HAMLETS,Bethnal Green,NULL,TOMLINS GROVE,22701222,E3,NULL,NULL,537250,182750,NULL,NULL
NA,09/06/2019 20:58,2019,2019/20,Special Service,1,1,339,339,KITTEN STUCK IN FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,200154296,MORPHOU ROAD,20030210,NW7,524324,191807,524350,191850,51.61137941,-0.205879515
NA,10/06/2019 18:44,2019,2019/20,Special Service,1,1,339,339,KITTEN STUCK IN FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,200154296,MORPHOU ROAD,20030210,NW7,524332,191816,524350,191850,51.61146244,-0.205775007
NA,10/06/2019 20:02,2019,2019/20,Special Service,1,1,339,339,CAT STUCK ON ROOF DRAIN AT FOURTH FLOOR LEVEL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000601,HOE STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,FOREST ROAD,22837350,E17,NULL,NULL,537450,189750,NULL,NULL
NA,10/06/2019 21:29,2019,2019/20,Special Service,1,1,339,339,CAT WEDGED IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000488,MANOR PARK,E09000025,NEWHAM,East Ham,NULL,SNOWSHILL ROAD,22200252,E12,NULL,NULL,542250,185350,NULL,NULL
NA,11/06/2019 23:15,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE CAT TRAPPED AT ROOF LEVEL DUE TO RAIN WATER - CALLER NOT SURE IF INJURED,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,NULL,FAIRFIELD ROAD,20704203,N18,NULL,NULL,534350,192450,NULL,NULL
NA,12/06/2019 09:47,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000278,SEVEN SISTERS,E09000014,HARINGEY,Tottenham,10003981460,CROWLAND ROAD,21103171,N15,534191,188594,534150,188550,51.5802422,-0.064706251
NA,12/06/2019 13:44,2019,2019/20,Special Service,1,2,339,678,SMALL ANIMAL RESCUE  - BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000593,CHINGFORD GREEN,E09000031,WALTHAM FOREST,Chingford,NULL,THE DRIVE,22832150,E4,NULL,NULL,538950,194850,NULL,NULL
NA,13/06/2019 13:25,2019,2019/20,Special Service,1,1,339,339,SQUIRREL STUCK IN GUTTERING,Squirrel,Person (mobile),Other entertainment venue,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Kentish Town,5145653,CHALK FARM ROAD,20400624,NW1,528542,184228,528550,184250,51.54232943,-0.147772448
NA,14/06/2019 07:40,2019,2019/20,Special Service,1,2,339,678,CAT TRAPPED IN CAR WHEEL BY LEG -  RSPCA ON SCENE  VW BLACK POLO IN CAR PARK AT THE END OF FLOOD WAL,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,217095302,CHELSEA MANOR STREET,21700083,SW3,527296,177928,527250,177950,51.48599319,-0.168003293
NA,14/06/2019 09:06,2019,2019/20,Special Service,1,1,339,339,CAT WITH HAND TRAPPED IN METAL BARS OF WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009371,DE BEAUVOIR,E09000012,HACKNEY,Shoreditch,NULL,TOTTENHAM ROAD,20901008,N1,NULL,NULL,533350,184750,NULL,NULL
NA,14/06/2019 12:21,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Dagenham,10090633176,MERRIELANDS CRESCENT,19900534,RM9,548884,183483,548850,183450,51.53063943,0.145043323
NA,14/06/2019 22:17,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,1.00023E+11,MUSWELL HILL BROADWAY,21106387,N10,528730,189578,528750,189550,51.5903584,-0.1431149
NA,15/06/2019 11:28,2019,2019/20,Special Service,1,1,339,339,DUCKLINGS STUCK BEHIND WALL        BEHIND HALIFAX J/O HIGHT STREET,Bird,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000056,HIGH BARNET,E09000003,BARNET,Barnet,200150338,SALISBURY ROAD,20038600,EN5,524430,196660,524450,196650,51.6549737,-0.20263194
NA,15/06/2019 13:41,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000445,GROVE PARK,E09000023,LEWISHAM,Lee Green,NULL,SANDSTONE ROAD,22003646,SE12,NULL,NULL,540650,172650,NULL,NULL
NA,15/06/2019 23:18,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT STUCK AT HEIGHT,Cat,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000364,SYON,E09000018,HOUNSLOW,Heston,1.00022E+11,BRICKFIELD CLOSE,21500153,TW8,517118,177302,517150,177350,51.48255997,-0.314727291
NA,16/06/2019 15:00,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN EXTRACTOR FAN,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000186,NORWOOD GREEN,E09000009,EALING,Southall,NULL,BLANDFORD ROAD,20600190,UB2,NULL,NULL,513050,178850,NULL,NULL
NA,16/06/2019 16:03,2019,2019/20,Special Service,1,1,339,339,CAT IN ENGINE COMPARTMENT - O/S PETS AT HOME,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Dagenham,10090633176,MERRIELANDS CRESCENT,19900534,RM9,548920,183479,548950,183450,51.53058905,0.145550855
NA,16/06/2019 23:12,2019,2019/20,Special Service,1,1,339,339,FOX STUCK IN FENCE,Fox,Person (land line),Fence,Outdoor Structure,Other animal assistance,Animal harm involving wild animal,E05000282,WEST GREEN,E09000014,HARINGEY,Tottenham,1.00021E+11,SOMERSET CLOSE,21105057,N17,532846,190460,532850,190450,51.5973292,-0.0834014
NA,18/06/2019 20:37,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN DRAINPIPE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000195,CHASE,E09000010,ENFIELD,Enfield,NULL,MELLING DRIVE,20706105,EN1,NULL,NULL,534050,197850,NULL,NULL
NA,18/06/2019 20:57,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,10008983267,BARKING ROAD,22208079,E13,541028,182872,541050,182850,51.52716381,0.031618387
NA,18/06/2019 22:03,2019,2019/20,Special Service,1,1,339,339,SQUIRREL CAUGHT BEHIND PIPEWORK,Squirrel,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000033,GORESBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,DARCY GARDENS,19900465,RM9,NULL,NULL,548950,183950,NULL,NULL
NA,19/06/2019 23:06,2019,2019/20,Special Service,1,1,339,339,INJURED BIRD TRAPPED BEHIND BIRD PROOFING   SERVICE YARD ENTRANCE OPPOSITE IMMAGRATION CENTRE,Bird,Person (mobile),Underground car park,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011104,LONDON BRIDGE & WEST BERMONDSEY,E09000028,SOUTHWARK,Dockhead,10094086243,LONDON BRIDGE STREET,22501542,SE1,532944,180147,532950,180150,51.50463573,-0.085873459
NA,20/06/2019 10:34,2019,2019/20,Special Service,1,1,339,339,DUCKLING FALLEN INTO DRAIN   CALLER WILL LIASE AT LOCATION,Bird,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011232,THAMESMEAD EAST,E09000004,BEXLEY,Erith,10025467017,BAZALGETTE WAY TO NORMAN ROAD FP2,20106002,DA18,549212,179937,549250,179950,51.49868772,0.148264916
NA,22/06/2019 06:00,2019,2019/20,Special Service,1,1,339,339,MUNTJAC DEER STUCK IN METAL RAILINGS     RSPCA OFFICER ON SCENE,Deer,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000059,TOTTERIDGE,E09000003,BARNET,Barnet,200105054,SOUTHOVER,20040140,N12,525127,193232,525150,193250,51.62401306,-0.19379099
NA,22/06/2019 09:50,2019,2019/20,Special Service,1,1,339,339,Redacted,Hamster,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000346,BEDFONT,E09000018,HOUNSLOW,Feltham,NULL,PAGE ROAD,21500849,TW14,NULL,NULL,509050,173850,NULL,NULL
NA,22/06/2019 11:03,2019,2019/20,Special Service,1,1,339,339,DOG IMPALED ON FENCE  VET ON SCENE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000225,PENINSULA,E09000011,GREENWICH,East Greenwich,NULL,COLOMB STREET,20800361,SE10,NULL,NULL,539450,178150,NULL,NULL
NA,22/06/2019 11:23,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND WARDROBE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000460,FIGGE'S MARSH,E09000024,MERTON,Mitcham,NULL,SPENCER ROAD,22105931,CR4,NULL,NULL,528350,168850,NULL,NULL
NA,22/06/2019 13:52,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING    ABOVE THE CAR WASH UNDER THE BRIDGE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,10008463814,MARE STREET,20900658,E8,534951,184921,534950,184950,51.54705912,-0.055152666
NA,22/06/2019 16:59,2019,2019/20,Special Service,1,1,339,339,HORSE STUCK IN DITCH,Horse,Person (land line),Other animal boarding/breeding establishment,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000309,EMERSON PARK,E09000016,HAVERING,Hornchurch,10033424304,WINGLETYE LANE,21300661,RM11,555513,188357,555550,188350,51.57263976,0.242687585
NA,23/06/2019 16:57,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000134,GOSPEL OAK,E09000007,CAMDEN,Kentish Town,NULL,FLEET ROAD,20400195,NW3,NULL,NULL,527650,185350,NULL,NULL
NA,23/06/2019 17:38,2019,2019/20,Special Service,1,1,339,339,CAT STUCK UNDER CONCRETE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000214,ABBEY WOOD,E09000011,GREENWICH,Plumstead,NULL,CHALCOMBE ROAD,20800307,SE2,NULL,NULL,546850,179150,NULL,NULL
NA,23/06/2019 18:24,2019,2019/20,Special Service,1,2,339,678,DOG TRAPPED ON ROOF - ALP REQUESTED FROM INC,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009368,CAZENOVE,E09000012,HACKNEY,Stoke Newington,NULL,ALKHAM ROAD,20900053,N16,NULL,NULL,533950,186750,NULL,NULL
NA,23/06/2019 18:41,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED UNDER FLOORING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000592,CHAPEL END,E09000031,WALTHAM FOREST,Walthamstow,NULL,FULBOURNE ROAD,22838500,E17,NULL,NULL,538250,190050,NULL,NULL
NA,23/06/2019 23:09,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED UNDER FLOORBOARD,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000592,CHAPEL END,E09000031,WALTHAM FOREST,Walthamstow,NULL,FULBOURNE ROAD,22838500,E17,NULL,NULL,538250,190050,NULL,NULL
NA,24/06/2019 06:53,2019,2019/20,Special Service,1,1,339,339,HORSE STUCK IN DYKE OFF NORMAN ROAD   CALLER AT ROUNDABOUT TO DIRECT,Horse,Person (mobile),Scrub land,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05011218,BELVEDERE,E09000004,BEXLEY,Erith,2.00003E+11,NORMAN ROAD,20101027,DA17,549745,179917,549750,179950,51.49837216,0.155928139
NA,24/06/2019 11:10,2019,2019/20,Special Service,1,2,339,678,KITTEN TRAPPED ON ROOF - THREE DAYS,Cat,Person (mobile),House in Multiple Occupation - 3 or more storeys (not known if licensed),Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,NULL,RUSKIN ROAD,21105004,N17,NULL,NULL,533850,190850,NULL,NULL
NA,24/06/2019 12:43,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH TRAPPED DUCKLINGS,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,NULL,WATERSIDE WAY,21107091,N17,NULL,NULL,534650,189650,NULL,NULL
NA,24/06/2019 14:21,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN TREE RSPCA IN ATTENDANCE AT JUNCTION OF GUY STREET,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011104,LONDON BRIDGE & WEST BERMONDSEY,E09000028,SOUTHWARK,Dockhead,2.00003E+11,KIPLING STREET,22501429,SE1,532835,179658,532850,179650,51.50026646,-0.087631875
NA,24/06/2019 14:27,2019,2019/20,Special Service,1,1,339,339,CHICK TRAPPED IN LOFT,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Chingford,NULL,LITTLE FRIDAY ROAD,22853200,E4,NULL,NULL,539150,193550,NULL,NULL
NA,24/06/2019 14:58,2019,2019/20,Special Service,1,1,339,339,KITTEN STUCK IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000027,ALIBON,E09000002,BARKING AND DAGENHAM,Dagenham,100026182,ROCKWELL ROAD,19900300,RM10,549672,185221,549650,185250,51.54604605,0.157123126
NA,24/06/2019 17:16,2019,2019/20,Special Service,1,1,339,339,BIRDS TRAPPED IN ROOF SPACE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Chingford,NULL,LITTLE FRIDAY ROAD,22853200,E4,NULL,NULL,539150,193550,NULL,NULL
NA,25/06/2019 14:57,2019,2019/20,Special Service,1,2,339,678,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000306,BROOKLANDS,E09000016,HAVERING,Romford,1.00024E+11,MARKS ROAD,21301396,RM7,550835,188739,550850,188750,51.57734297,0.175396158
NA,25/06/2019 19:22,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND KITCHEN UNIT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011107,NORTH WALWORTH,E09000028,SOUTHWARK,Old Kent Road,NULL,HEYGATE STREET,22501239,SE17,NULL,NULL,532150,178750,NULL,NULL
NA,25/06/2019 21:17,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Common external bin storage area,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000275,NOEL PARK,E09000014,HARINGEY,Hornsey,1.00023E+11,HIGH ROAD,21104652,N22,531331,189912,531350,189950,51.59276052,-0.10545739
NA,26/06/2019 08:39,2019,2019/20,Special Service,1,1,339,339,DOG IN PRECARIOUS POSITION ON ROOF OF HOUSE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,FAWE PARK ROAD,22901648,SW15,NULL,NULL,524750,175050,NULL,NULL
NA,29/06/2019 16:26,2019,2019/20,Special Service,1,2,339,678,DOG TRAPPED ON THIRD FLOOR BALCONY,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000322,SQUIRREL'S HEATH,E09000016,HAVERING,Hornchurch,NULL,SLEWINS CLOSE,21300585,RM11,NULL,NULL,553450,188650,NULL,NULL
NA,29/06/2019 18:40,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH DOG ON BALCONY,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000322,SQUIRREL'S HEATH,E09000016,HAVERING,Hornchurch,NULL,SLEWINS CLOSE,21300585,RM11,NULL,NULL,553450,188650,NULL,NULL
NA,29/06/2019 21:34,2019,2019/20,Special Service,1,1,339,339,KITTEN CAUGHT BEHIND FREEZER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000479,CUSTOM HOUSE,E09000025,NEWHAM,Plaistow,NULL,LESLIE ROAD,22207789,E16,NULL,NULL,540850,181050,NULL,NULL
NA,30/06/2019 13:46,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BETWEEN FLOORBOARDS AND FLOOR,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000279,STROUD GREEN,E09000014,HARINGEY,Holloway,NULL,UPPER TOLLINGTON PARK,21104024,N4,NULL,NULL,531350,187650,NULL,NULL
NA,30/06/2019 17:18,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - BIRD TRAPPED IN GUTTERING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000630,ABBEY ROAD,E09000033,WESTMINSTER,West Hampstead,NULL,SPRINGFIELD ROAD,8400561,NW8,NULL,NULL,526350,183750,NULL,NULL
NA,30/06/2019 18:53,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Private Garden Shed,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000360,HOUNSLOW SOUTH,E09000018,HOUNSLOW,Heston,1.00022E+11,ELLERDINE ROAD,21500391,TW3,514835,175185,514850,175150,51.46399772,-0.348284528
NA,30/06/2019 22:29,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000402,BEVERLEY,E09000021,KINGSTON UPON THAMES,New Malden,1.00022E+11,BURLINGTON ROAD,21800185,KT3,521619,168105,521650,168150,51.3989494,-0.253093255
NA,01/07/2019 00:03,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05009404,ST. HELEN'S,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,OXFORD GARDENS,21700962,W10,NULL,NULL,523550,181250,NULL,NULL
NA,01/07/2019 09:13,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BETWEEN TWO WALLS  OWNER ON SCENE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000279,STROUD GREEN,E09000014,HARINGEY,Holloway,10093589920,CONNAUGHT ROAD,21103138,N4,531240,187814,531250,187850,51.57393244,-0.107561263
NA,01/07/2019 11:38,2019,2019/20,Special Service,1,1,339,339,ASSIST WITH BIRD TRAPPED IN NETTING    RSPCA ON SCENE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,NULL,GREAT CHERTSEY ROAD,21500503,W4,NULL,NULL,520450,176450,NULL,NULL
NA,02/07/2019 07:13,2019,2019/20,Special Service,1,1,339,339,RUNNING CALL FOX TRAPPED BEHIND WALL    ALLEY BETWEEN TRANMERE ROAD AND HARROW DRIVE,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,207002604,TRANMERE ROAD,20704859,N9,533775,194476,533750,194450,51.63320353,-0.068450792
NA,02/07/2019 12:37,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,10010226567,ROWNTREE PATH,20801297,SE28,546878,180514,546850,180550,51.50448363,0.114905783
NA,02/07/2019 14:17,2019,2019/20,Special Service,1,1,339,339,ANIMAL TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,BOUNCES ROAD,20703940,N9,NULL,NULL,535050,193950,NULL,NULL
NA,03/07/2019 09:14,2019,2019/20,Special Service,1,2,339,678,CAT TRAPPED BEHIND KITCHEN CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009324,ISLAND GARDENS,E09000030,TOWER HAMLETS,Millwall,NULL,GROSVENOR WHARF ROAD,22700581,E14,NULL,NULL,538650,178550,NULL,NULL
NA,03/07/2019 19:11,2019,2019/20,Special Service,1,2,339,678,CAT TRAPPED UNDER RECLINER    TAIL STUCK IN MOTOR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000177,GREENFORD BROADWAY,E09000009,EALING,Southall,NULL,STANHOPE ROAD,20601626,UB6,NULL,NULL,514250,181850,NULL,NULL
NA,04/07/2019 05:23,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000452,SYDENHAM,E09000023,LEWISHAM,Forest Hill,NULL,CHAMPION CRESCENT,22001332,SE26,NULL,NULL,536250,171950,NULL,NULL
NA,04/07/2019 11:32,2019,2019/20,Special Service,1,1,339,339,TWO DOGS STUCK IN DRAINPIPE,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000196,COCKFOSTERS,E09000010,ENFIELD,Barnet,207008280,FOOTPATH FROM PARKGATE CRESCENT TO BOROUGH BOUNDARY,20700234,EN4,526170,197358,526150,197350,51.66085514,-0.177245969
NA,04/07/2019 13:20,2019,2019/20,Special Service,1,1,339,339,"SQUIRREL IN EAVES OF ROOF, HANGING BY ONE LEG",Squirrel,Person (mobile),Secondary school,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000054,HALE,E09000003,BARNET,Mill Hill,200131283,WORCESTER CRESCENT,20047640,NW7,521319,193752,521350,193750,51.62951533,-0.24859343
NA,04/07/2019 19:27,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),Bungalow - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000526,NORTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,NULL,KINGSWAY,22405697,SW14,NULL,NULL,520050,175750,NULL,NULL
NA,05/07/2019 13:42,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH SMALL ANIMAL RESCUE   OFFICER AWAITING YOUR ARRIVAL TO DIRECT YOU,Unknown - Wild Animal,Person (mobile),Bulk waste storage,Non Residential,Other animal assistance,Assist trapped wild animal,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,128029886,CHAPEL MILL ROAD,21880152,KT1,518864,168560,518850,168550,51.4036225,-0.2925391
NA,05/07/2019 18:47,2019,2019/20,Special Service,1,1,339,339,DOG DISTRESSED ON WINDOW LEDGE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000633,CHURCHILL,E09000033,WESTMINSTER,Chelsea,NULL,PEABODY AVENUE,8401749,SW1V,NULL,NULL,528750,178150,NULL,NULL
NA,06/07/2019 17:07,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED IN UNACCESSIBLE COURTYARD AREA,Fox,Person (land line),Common external bin storage area,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011242,FAIRLOP,E09000026,REDBRIDGE,Hainault,10023773004,FENCEPIECE ROAD,22304983,IG6,544418,191360,544450,191350,51.602572,0.083941437
NA,06/07/2019 20:44,2019,2019/20,Special Service,1,1,339,339,POND FARM KENNELS - ANIMALS IN DISTRESS,Unknown - Domestic Animal Or Pet,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05011229,ST. MARY'S & ST. JAMES,E09000004,BEXLEY,Sidcup,10011845247,COCKSURE LANE,20100342,DA14,549096,172005,549050,172050,51.42744626,0.143244769
NA,07/07/2019 11:10,2019,2019/20,Special Service,1,1,339,339,DOG SHUT IN POWER STATION ENCLOSURE,Dog,Person (mobile),Electricity power station,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000630,ABBEY ROAD,E09000033,WESTMINSTER,Paddington,1.00023E+11,AVENUE ROAD,8400533,NW8,527245,183567,527250,183550,51.5366753,-0.1667137
NA,07/07/2019 11:26,2019,2019/20,Special Service,1,1,339,339,DOG SHUT IN POWER STATION ENCLOSURE,Dog,Person (mobile),Electricity power station,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000269,CROUCH END,E09000014,HARINGEY,Hornsey,10003978700,AVENUE ROAD,21100037,N6,529455,187843,529450,187850,51.5746007,-0.1332941
NA,07/07/2019 15:06,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000095,MAPESBURY,E09000005,BRENT,West Hampstead,202079062,KILBURN HIGH ROAD,20202523,NW6,524664,184622,524650,184650,51.54673803,-0.203531387
NA,07/07/2019 16:58,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Kentish Town,NULL,CASTLE ROAD,20400490,NW1,NULL,NULL,528750,184550,NULL,NULL
NA,07/07/2019 17:11,2019,2019/20,Special Service,1,1,339,339,DOG  STUCK ON ROOF,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000451,RUSHEY GREEN,E09000023,LEWISHAM,Lewisham,NULL,RUSHEY GREEN,22004211,SE6,NULL,NULL,537850,173950,NULL,NULL
NA,08/07/2019 13:08,2019,2019/20,Special Service,1,3,339,1017,Redacted,Unknown - Wild Animal,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05000200,GRANGE,E09000010,ENFIELD,Edmonton,207113903,PARK DRIVE,20704583,N21,532173,195126,532150,195150,51.63941904,-0.091339317
NA,09/07/2019 13:41,2019,2019/20,Special Service,1,1,339,339,BIRD STUCK IN CHIMMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000433,THORNTON,E09000022,LAMBETH,Tooting,NULL,POYNDERS GARDENS,21902132,SW4,NULL,NULL,529350,173950,NULL,NULL
NA,09/07/2019 18:10,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000118,FARNBOROUGH AND CROFTON,E09000006,BROMLEY,Orpington,NULL,NEWSTEAD AVENUE,20302906,BR6,NULL,NULL,544750,165250,NULL,NULL
NA,10/07/2019 01:18,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND SKIRTING BOARD STRUGGLING TO BREATHE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000411,ST. JAMES,E09000021,KINGSTON UPON THAMES,New Malden,NULL,GEORGE ROAD,21800435,KT3,NULL,NULL,521950,167950,NULL,NULL
NA,10/07/2019 08:58,2019,2019/20,Special Service,1,1,339,339,DOG STUCK IN EXERCISE MACHINE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000567,SUTTON WEST,E09000029,SUTTON,Sutton,10015376599,GREBE COURT,22606297,SM1,524522,164118,524550,164150,51.36249462,-0.212786028
NA,10/07/2019 16:17,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT STUCK IN ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009336,WHITECHAPEL,E09000030,TOWER HAMLETS,Whitechapel,6002554,EAST TENTER STREET,22700450,E1,533960,181130,533950,181150,51.51322873,-0.070871172
NA,11/07/2019 11:32,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH RETREAVAL OF ANIMAL STUCK ON ROOF,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000332,HILLINGDON EAST,E09000017,HILLINGDON,Hillingdon,NULL,RICHMOND AVENUE,21401627,UB10,NULL,NULL,507750,184550,NULL,NULL
NA,11/07/2019 22:27,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN PIT,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Soho,NULL,NORTHINGTON STREET,20401017,WC1N,NULL,NULL,530750,182050,NULL,NULL
NA,12/07/2019 10:26,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH BIRD STUCK IN TREE,Bird,Person (mobile),Common external bin storage area,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Soho,5014827,NORTHINGTON STREET,20401017,WC1N,530787,182005,530750,182050,51.5218293,-0.1162525
NA,12/07/2019 13:55,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (land line),Community centre/Hall,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000218,ELTHAM NORTH,E09000011,GREENWICH,Eltham,1.00023E+11,ELTHAM HIGH STREET,20800526,SE9,543233,174468,543250,174450,51.45109116,0.059973609
NA,12/07/2019 18:13,2019,2019/20,Special Service,1,1,339,339,PUPPY WITH HEAD STUCK IN FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011115,ST. GILES,E09000028,SOUTHWARK,Peckham,2.00003E+11,CAMBERWELL GREEN,22500399,SE5,532590,176893,532550,176850,51.47547075,-0.092185951
NA,12/07/2019 18:31,2019,2019/20,Special Service,1,1,339,339,COWS HEAD STUCK IN TROUGH - BRIGADE REQUESTED BY RSPCA WHO HAVE NO ONE TO SEND,Unknown - Heavy Livestock Animal,Person (land line),"Intensive Farming Sheds (chickens, pigs etc)",Non Residential,Other animal assistance,Assist  trapped livestock animal,E05000292,HEADSTONE NORTH,E09000015,HARROW,Harrow,10010598377,GEORGE V AVENUE,21201120,HA5,513143,190225,513150,190250,51.59952336,-0.367805041
NA,12/07/2019 20:50,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000212,UPPER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,AMERSHAM AVENUE,20703862,N18,NULL,NULL,532750,191850,NULL,NULL
NA,12/07/2019 21:20,2019,2019/20,Special Service,1,1,339,339,TWO PIGEONS TRAPPED IN SOLAR PANEL,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011255,WANSTEAD VILLAGE,E09000026,REDBRIDGE,Leytonstone,NULL,LONSDALE ROAD,22306048,E11,NULL,NULL,540050,187950,NULL,NULL
NA,13/07/2019 04:42,2019,2019/20,Special Service,1,1,339,339,LARGE ANIMAL RESCUE - DEER TRAPPED AND INJURED - CALLER WILL MEET YOU,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000313,HAVERING PARK,E09000016,HAVERING,Romford,1.00021E+11,TURPIN AVENUE,21301662,RM5,549578,191581,549550,191550,51.60321464,0.15848059
NA,13/07/2019 10:12,2019,2019/20,Special Service,1,2,339,678,CAT STUCK ON THIRD STORY ROOF.  ALP REQUESTED FROM SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,NULL,WHITE HART LANE,22405257,SW13,NULL,NULL,521350,175950,NULL,NULL
NA,13/07/2019 10:23,2019,2019/20,Special Service,1,1,339,339,CAT STUCK UNDERGROUND,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000336,NORTHWOOD HILLS,E09000017,HILLINGDON,Ruislip,1.00021E+11,CHAMBERLAIN WAY,21400337,HA5,511130,189703,511150,189750,51.59523302,-0.397012081
NA,13/07/2019 11:52,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009373,HACKNEY DOWNS,E09000012,HACKNEY,Stoke Newington,NULL,MUIR ROAD,20900716,E5,NULL,NULL,534350,186050,NULL,NULL
NA,13/07/2019 14:27,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000482,EAST HAM SOUTH,E09000025,NEWHAM,East Ham,46037550,HIGH STREET SOUTH,22200716,E6,542820,182436,542850,182450,51.5227894,0.0572475
NA,13/07/2019 18:15,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011218,BELVEDERE,E09000004,BEXLEY,Erith,NULL,CALDY ROAD,20100246,DA17,NULL,NULL,549650,179350,NULL,NULL
NA,13/07/2019 19:44,2019,2019/20,Special Service,1,1,339,339,CAT STUCK ON ROOF,Cat,Person (land line),Electricity power station,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000470,ST. HELIER,E09000024,MERTON,Mitcham,10025296214,BIRCHWOOD CLOSE,22100620,SM4,525689,168146,525650,168150,51.39843582,-0.194607594
NA,14/07/2019 12:47,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN HOLE BEHIND KITCHEN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000140,KILBURN,E09000007,CAMDEN,West Hampstead,NULL,MORTIMER CRESCENT,20400688,NW6,NULL,NULL,525750,183550,NULL,NULL
NA,14/07/2019 19:20,2019,2019/20,Special Service,1,1,339,339,PARROT ENTANGLED IN WIRE IN TREE,Bird,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,207099536,CHARLTON ROAD,20703193,N9,535743,195029,535750,195050,51.63770302,-0.039832067
NA,15/07/2019 10:39,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH BIRD TRAPPED NETTING  - RSPCA ON SCENE,Bird,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000198,ENFIELD HIGHWAY,E09000010,ENFIELD,Enfield,207152229,ELMORE ROAD,20702220,EN3,535522,198067,535550,198050,51.6650575,-0.041840514
NA,15/07/2019 10:45,2019,2019/20,Special Service,1,3,339,1017,LARGE ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000121,MOTTINGHAM AND CHISLEHURST NORTH,E09000006,BROMLEY,Lee Green,10003628194,MOTTINGHAM LANE,20301665,SE12,541161,173194,541150,173150,51.44016496,0.029668859
NA,15/07/2019 14:48,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED IN WHEEL OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011468,FAIRFIELD,E09000008,CROYDON,Croydon,1.00021E+11,WANDLE ROAD,20501532,CR0,532296,165028,532250,165050,51.36891647,-0.100841489
NA,15/07/2019 17:24,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN GUTTERING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000372,HIGHBURY EAST,E09000019,ISLINGTON,Stoke Newington,NULL,BIRCHMORE WALK,21602914,N5,NULL,NULL,532050,186050,NULL,NULL
NA,16/07/2019 10:43,2019,2019/20,Special Service,1,2,339,678,ASSIST RSPCA GAIN ACCESS TO GARAGE,Unknown - Wild Animal,Person (mobile),Bus/coach station/garage,Non Residential,Other animal assistance,Assist trapped wild animal,E05000046,COLINDALE,E09000003,BARNET,Mill Hill,10025292171,NEAR ACRE,20030800,NW9,521626,190565,521650,190550,51.60080739,-0.24525613
NA,16/07/2019 14:15,2019,2019/20,Special Service,NULL,NULL,339,NULL,DOG STUCK IN BRAMBLES AND WOODLAND BY LEAD    OWNER WILL MEET AT THE ENTRANCE TO PADDOCKS    JCT ALE,Dog,Person (mobile),Scrub land,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,10003975673,ALEXANDRA PALACE WAY,21106542,N10,530240,190320,530250,190350,51.59668035,-0.121044258
NA,16/07/2019 18:29,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN THE DRAIN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011097,CHAMPION HILL,E09000028,SOUTHWARK,Peckham,NULL,ALBRIGHTON ROAD,22500036,SE22,NULL,NULL,533350,175650,NULL,NULL
NA,16/07/2019 19:41,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING ON BALCONY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000629,WEST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,CARSLAKE ROAD,22900812,SW15,NULL,NULL,523250,174350,NULL,NULL
NA,16/07/2019 21:14,2019,2019/20,Special Service,1,1,339,339,PIGEON WITH LEG TRAPPED IN WINDOW  SECOND FLOOR,Bird,Person (mobile),Library,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009370,DALSTON,E09000012,HACKNEY,Homerton,10008322989,DALSTON LANE,20900310,E8,533658,184777,533650,184750,51.54607247,-0.073834937
NA,17/07/2019 16:16,2019,2019/20,Special Service,1,1,339,339,BIRDS TRAPPED IN METAL GRILLS DIRECTLY UNDERNEATH THE FLYOVER,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000032,GASCOIGNE,E09000002,BARKING AND DAGENHAM,Barking,10094445038,HIGHBRIDGE ROAD,19900700,IG11,543700,183949,543750,183950,51.53616709,0.070545452
NA,18/07/2019 00:40,2019,2019/20,Special Service,1,1,339,339,INJURED CAT TRAPPED IN HANGAR IN TIMBER YARD,Cat,Person (land line),Warehouse,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000558,CARSHALTON CENTRAL,E09000029,SUTTON,Sutton,5870048660,WESTMEAD ROAD,22602747,SM1,526810,164565,526850,164550,51.3659992,-0.179787
NA,18/07/2019 09:50,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH BIRD HANGING FROM GUTTERING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000375,HOLLOWAY,E09000019,ISLINGTON,Holloway,NULL,FREEGROVE ROAD,21603247,N7,NULL,NULL,530550,185450,NULL,NULL
NA,19/07/2019 08:51,2019,2019/20,Special Service,1,2,339,678,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05011112,ROTHERHITHE,E09000028,SOUTHWARK,Dockhead,10090284129,SURREY QUAYS ROAD,22502426,SE16,535531,179410,535550,179450,51.49739655,-0.048903422
NA,19/07/2019 15:30,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000268,BRUCE GROVE,E09000014,HARINGEY,Tottenham,NULL,PHILIP LANE,21106391,N15,NULL,NULL,533150,189550,NULL,NULL
NA,20/07/2019 21:04,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000617,LATCHMERE,E09000032,WANDSWORTH,Battersea,NULL,BATTERSEA PARK ROAD,22900337,SW11,NULL,NULL,527650,176450,NULL,NULL
NA,21/07/2019 17:07,2019,2019/20,Special Service,1,2,339,678,FOX TRAPPED ON LEDGE  RSCPA ON SCENE,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05011117,SURREY DOCKS,E09000028,SOUTHWARK,Dockhead,2.00003E+11,ROTHERHITHE STREET,22502127,SE16,535639,180250,535650,180250,51.50492288,-0.047030385
NA,21/07/2019 17:38,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000377,MILDMAY,E09000019,ISLINGTON,Stoke Newington,NULL,LECONFIELD ROAD,21603468,N5,NULL,NULL,532550,185550,NULL,NULL
NA,22/07/2019 03:06,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - CAT STUCK DOWN SIDE OF BOILER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000426,OVAL,E09000022,LAMBETH,Lambeth,NULL,WANDSWORTH ROAD,21901460,SW8,NULL,NULL,530150,177950,NULL,NULL
NA,22/07/2019 08:57,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN BEDROOM,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009368,CAZENOVE,E09000012,HACKNEY,Stoke Newington,NULL,GELDESTON ROAD,20900437,E5,NULL,NULL,534350,186950,NULL,NULL
NA,22/07/2019 13:34,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN CAR BOOT - IN CAR PARK,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000468,RAVENSBURY,E09000024,MERTON,Mitcham,48101401,MORDEN HALL ROAD,22104539,SM4,526113,168484,526150,168450,51.40137847,-0.188391999
NA,22/07/2019 18:51,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),Telephone exchange,Non Residential,Other animal assistance,Assist trapped wild animal,E05011246,ILFORD TOWN,E09000026,REDBRIDGE,Ilford,1.00023E+11,ILFORD HILL,22302962,IG1,543350,186298,543350,186250,51.55736333,0.066460321
NA,23/07/2019 10:18,2019,2019/20,Special Service,1,1,339,339,FERRET TRAPPED BETWEEN TWO PIPES IN KITCHEN,Ferret,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000608,WILLIAM MORRIS,E09000031,WALTHAM FOREST,Walthamstow,NULL,FOREST ROAD,22837350,E17,NULL,NULL,536050,189450,NULL,NULL
NA,25/07/2019 10:21,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH MAGPIE SHUT IN STAIRWELL  RSPCA IN ATTENDANCE,Bird,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Lambeth,10033540298,PALACE STREET,8401205,SW1E,529272,179310,529250,179350,51.49795845,-0.139054737
NA,25/07/2019 16:42,2019,2019/20,Special Service,1,1,339,339,PET SNAKE TRAPPED UNDER RAMP,Snake,Person (mobile),Church/Chapel,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009380,LEA BRIDGE,E09000012,HACKNEY,Homerton,10008233475,CHATSWORTH ROAD,20900224,E5,535570,185867,535550,185850,51.55540656,-0.045853625
NA,25/07/2019 20:32,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (mobile),Other private non-residential building,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000096,NORTHWICK PARK,E09000005,BRENT,Wembley,202134139,AUDREY GARDENS,20202246,HA0,516893,187047,516850,187050,51.57019634,-0.314733151
NA,27/07/2019 21:50,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMALE RESCUE - KITTEN STUCK BEHIND BATH PANEL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000253,COLLEGE PARK AND OLD OAK,E09000013,HAMMERSMITH AND FULHAM,North Kensington,NULL,WHITE CITY CLOSE,21000881,W12,NULL,NULL,523150,180650,NULL,NULL
NA,29/07/2019 01:15,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN BETWEEN WALLS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,LISSON GROVE,8400993,NW1,NULL,NULL,527450,181850,NULL,NULL
NA,29/07/2019 08:17,2019,2019/20,Special Service,1,1,339,339,TO ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000572,WORCESTER PARK,E09000029,SUTTON,Sutton,NULL,CHEAM COMMON ROAD,22601362,KT4,NULL,NULL,523050,165350,NULL,NULL
NA,29/07/2019 13:30,2019,2019/20,Special Service,1,1,339,339,PUPPY TRAPPED IN BUSHES,Dog,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,10010437384,COURT GARDENS,21610022,N7,531300,184637,531350,184650,51.54537089,-0.107881018
NA,29/07/2019 14:57,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000268,BRUCE GROVE,E09000014,HARINGEY,Tottenham,NULL,PHILIP LANE,21106391,N15,NULL,NULL,533150,189550,NULL,NULL
NA,29/07/2019 17:22,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Multi-Storey car park,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Kingston,128007048,WHEATFIELD WAY,21801044,KT1,518266,169239,518250,169250,51.40985298,-0.300895225
NA,29/07/2019 20:38,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (land line),Synagogue,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000050,EDGWARE,E09000003,BARNET,Mill Hill,200212663,HALE LANE,20020280,HA8,519509,192270,519550,192250,51.61658334,-0.275234691
NA,29/07/2019 23:04,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED/INJURED BEHIND WASHING MACHINE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,NULL,MANOR LANE,22001684,SE12,NULL,NULL,539650,173850,NULL,NULL
NA,29/07/2019 23:20,2019,2019/20,Special Service,1,1,339,339,CAT FALLEN FROM ROOF AND INJURED   POLICE ON SCENE AND UNABLE TO REACH,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009393,COURTFIELD,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,COURTFIELD GARDENS,21700123,SW5,NULL,NULL,525850,178750,NULL,NULL
NA,30/07/2019 07:33,2019,2019/20,Special Service,1,3,339,1017,HORSE IN DITCH,Horse,Person (mobile),Hedge,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011234,ALDBOROUGH,E09000026,REDBRIDGE,Hainault,10023192924,ALDBOROUGH ROAD NORTH,22301799,IG2,545790,189943,545750,189950,51.58948685,0.103146305
NA,30/07/2019 12:29,2019,2019/20,Special Service,1,1,339,339,DOG ON WINDOW LEDGE,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000531,TWICKENHAM RIVERSIDE,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,COPTHALL GARDENS,22403411,TW1,NULL,NULL,515950,173350,NULL,NULL
NA,30/07/2019 14:30,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN VOID OF CAVITY IN THE KITCHEN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000185,NORTHOLT WEST END,E09000009,EALING,Northolt,NULL,OLD RUISLIP ROAD,20601307,UB5,NULL,NULL,511150,183250,NULL,NULL
NA,30/07/2019 18:51,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,10091777532,MARKHOUSE ROAD,22856450,E17,536592,188197,536550,188150,51.5760985,-0.0302301
NA,31/07/2019 10:17,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN FOX DEN,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,NULL,EAST ROAD,21800353,KT2,NULL,NULL,518350,169850,NULL,NULL
NA,31/07/2019 16:50,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN VENT,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000210,TOWN,E09000010,ENFIELD,Enfield,NULL,RUSSELL ROAD,20702941,EN1,NULL,NULL,533550,198150,NULL,NULL
NA,31/07/2019 20:57,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED IN ROOM IN DISTRESS,Fox,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000616,GRAVENEY,E09000032,WANDSWORTH,Tooting,NULL,ESWYN ROAD,22901556,SW17,NULL,NULL,527750,171550,NULL,NULL
NA,01/08/2019 09:38,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,10091578181,LEAMINGTON ROAD,21300095,RM3,555094,191979,555050,191950,51.605301,0.23823343
NA,02/08/2019 07:56,2019,2019/20,Special Service,1,1,339,339,BABY FOX STUCK IN POND   CALLER STATES IN PARK OPPOSITE HOLU CAFE,Fox,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000519,"HAM, PETERSHAM AND RICHMOND RIVERSIDE",E09000027,RICHMOND UPON THAMES,Richmond,10025549748,RICHMOND HILL,22403768,TW10,518207,174157,518250,174150,51.45407153,-0.300112092
NA,02/08/2019 23:09,2019,2019/20,Special Service,1,2,339,678,ASSIST RSPCA WITH CAT STUCK IN TREE   TL REQUESTED AT SCENE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000593,CHINGFORD GREEN,E09000031,WALTHAM FOREST,Chingford,2.00001E+11,BURY ROAD,22821000,E4,539388,194876,539350,194850,51.63543512,0.012748054
NA,02/08/2019 23:49,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED BEHIND FENCE   NEAR TO ENTRANCE ON LYNDHURST WAY,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011115,ST. GILES,E09000028,SOUTHWARK,Peckham,10009791706,LYNDHURST WAY,22501612,SE15,533756,176261,533750,176250,51.46952091,-0.075651967
NA,03/08/2019 03:07,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN REAR GARDEN,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009319,BOW EAST,E09000030,TOWER HAMLETS,Bethnal Green,NULL,MCCULLUM ROAD,22700805,E3,NULL,NULL,536650,183550,NULL,NULL
NA,03/08/2019 14:19,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009382,SHACKLEWELL,E09000012,HACKNEY,Stoke Newington,NULL,DUNN STREET,20900348,E8,NULL,NULL,533650,185450,NULL,NULL
NA,04/08/2019 20:36,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009375,HAGGERSTON,E09000012,HACKNEY,Shoreditch,NULL,CLARISSA STREET,20900249,E8,NULL,NULL,533650,183750,NULL,NULL
NA,05/08/2019 00:15,2019,2019/20,Special Service,1,1,339,339,INJURED DUCKLING FALLEN ONTO BALCONY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009329,ST. DUNSTAN'S,E09000030,TOWER HAMLETS,Bethnal Green,NULL,CANDLE STREET,22702468,E1,NULL,NULL,536250,181750,NULL,NULL
NA,05/08/2019 11:21,2019,2019/20,Special Service,1,2,339,678,CAT TRAPPED ON WINDOW LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,NULL,DROOP STREET,8400073,W10,NULL,NULL,524250,182450,NULL,NULL
NA,05/08/2019 20:41,2019,2019/20,Special Service,1,2,339,678,CAT TRAPPED IN TREE POSSIBLY BY COLLAR,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011476,PURLEY & WOODCOTE,E09000008,CROYDON,Purley,1.00021E+11,FOXLEY HILL ROAD,20502079,CR8,531403,161219,531450,161250,51.3348919,-0.115069026
NA,06/08/2019 07:47,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED UNDER THE FLOOR,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000301,ROXBOURNE,E09000015,HARROW,Northolt,NULL,DRINKWATER ROAD,21201926,HA2,NULL,NULL,513750,186850,NULL,NULL
NA,06/08/2019 12:04,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN TREE  REQUESTED BY RSPCA ON SCENE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000533,WHITTON,E09000027,RICHMOND UPON THAMES,Heston,1.00022E+11,BRACKEN CLOSE,22406144,TW2,513194,173804,513150,173850,51.45191773,-0.372341147
NA,06/08/2019 13:21,2019,2019/20,Special Service,2,6,339,2034,FOX STUCK ON PONTOON - FRU REQUESTED WITH WATER ATTRIBUTE        LEVEL TWO WATER OPS  RSPCA INSPECTO,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05009325,LANSBURY,E09000030,TOWER HAMLETS,Poplar,6066053,BALLADIER WALK,22702015,E14,537653,181771,537650,181750,51.51810301,-0.017436844
NA,08/08/2019 11:08,2019,2019/20,Special Service,1,1,339,339,RABBIT TRAPPED IN GAP ON BALCONY,Rabbit,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Euston,NULL,HOPKINSON'S PLACE,20401359,NW1,NULL,NULL,528050,183950,NULL,NULL
NA,08/08/2019 17:17,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000094,KILBURN,E09000005,BRENT,Paddington,NULL,KILBURN PARK ROAD,20201396,NW6,NULL,NULL,525350,182850,NULL,NULL
NA,08/08/2019 23:52,2019,2019/20,Special Service,1,1,339,339,"CAT STUCK ON A LEDGE FOUR STOREYS UP AND NOT MOVING, UNABLE TO GAIN ACCESS",Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,NULL,ARUNDEL SQUARE,21604148,N7,NULL,NULL,531150,184650,NULL,NULL
NA,09/08/2019 09:53,2019,2019/20,Special Service,1,1,339,339,INJURED CAT TRAPPED UNDERNEATH FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000271,HARRINGAY,E09000014,HARINGEY,Hornsey,NULL,HEWITT ROAD,21103411,N8,NULL,NULL,531450,188750,NULL,NULL
NA,10/08/2019 14:07,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN WIRE MESH,Cat,Person (mobile),Other retail,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000460,FIGGE'S MARSH,E09000024,MERTON,Mitcham,48046421,MAJESTIC WAY,22104137,CR4,527862,169100,527850,169150,51.40652,-0.163052
NA,13/08/2019 13:14,2019,2019/20,Special Service,1,1,339,339,DEER TRAPPED IN RAILINGS   CALLER WILL LIASE WITH YOU AT MAIN GATE,Deer,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000198,ENFIELD HIGHWAY,E09000010,ENFIELD,Enfield,207002362,ACCESS ROAD FROM HERTFORD ROAD TO HERTFORD ROAD CEMETERY,20707384,EN3,535348,196961,535350,196950,51.65515728,-0.04477633
NA,13/08/2019 16:46,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000435,TULSE HILL,E09000022,LAMBETH,West Norwood,NULL,BRAILSFORD ROAD,21900220,SW2,NULL,NULL,531350,174450,NULL,NULL
NA,13/08/2019 18:20,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011486,THORNTON HEATH,E09000008,CROYDON,Woodside,NULL,ROSS ROAD,20501349,SE25,NULL,NULL,533150,168850,NULL,NULL
NA,14/08/2019 07:55,2019,2019/20,Special Service,1,1,339,339,HORSE TRAPPED IN BARBED WIRE,Horse,Person (land line),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000404,CHESSINGTON NORTH AND HOOK,E09000021,KINGSTON UPON THAMES,Surbiton,128039180,FOOTPATH F/O 23-28 FOXGLOVE LANE,21880255,KT9,519415,164444,519450,164450,51.36651763,-0.285991063
NA,15/08/2019 14:14,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED IN SMALL AREA UNDER SHED,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011463,ADDISCOMBE WEST,E09000008,CROYDON,Croydon,1.00021E+11,TURNPIKE LINK,20501509,CR0,533429,165531,533450,165550,51.37316634,-0.084399695
NA,16/08/2019 14:31,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA     CAT UP TREE,Cat,Person (land line),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist trapped domestic animal,E05011102,FARADAY,E09000028,SOUTHWARK,Old Kent Road,2.00003E+11,BEACONSFIELD ROAD,22500180,SE17,533086,178034,533050,178050,51.48561419,-0.08461883
NA,17/08/2019 14:54,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN SHED  CAT POSSIBLY CAUGHT UNDER EQUIPMENT,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000208,SOUTHGATE,E09000010,ENFIELD,Southgate,207115505,OLD FARM AVENUE,20701788,N14,529362,194770,529350,194750,51.63687192,-0.13207648
NA,18/08/2019 02:18,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT FALLEN OUT OF WINDOW - RSPCA IN ATTENDANCE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,NULL,DOWELLS STREET,20802135,SE10,NULL,NULL,537850,177750,NULL,NULL
NA,18/08/2019 11:11,2019,2019/20,Special Service,1,3,339,1017,LEVEL TWO WATER RESCUE - ASSIST RSPCA DUCKLINGS FROM A LOCK,Bird,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,6647171,CARPENTERS ROAD,22700273,E20,537671,184512,537650,184550,51.54272503,-0.016104849
NA,18/08/2019 16:53,2019,2019/20,Special Service,1,2,339,678,DOG TRAPPED IN BLACKBERRY BUSHES CALLER WILL MEET YOU BY RODING SCHOOL,Dog,Person (mobile),Heathland,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011236,BRIDGE,E09000026,REDBRIDGE,Woodford,10025288423,FOOTPATH FROM RAVENSBOURNE GARDENS THROUGH COCKED HAT PLANTATION TO RODING LANE NORTH,22390155,IG8,542805,191272,542850,191250,51.60219835,0.060623751
NA,19/08/2019 08:05,2019,2019/20,Special Service,1,1,339,339,INJURED DEER TRAPPED IN FENCE     CALLER WILL MEET IN CAR PARK AT BOTTOM OF BAKERS HILL,Deer,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05000196,COCKFOSTERS,E09000010,ENFIELD,Barnet,207001410,FOOTPATH FROM COVERT WAY TO COVERT WAY OPEN SPACE,20707128,EN4,526327,197081,526350,197050,51.65833379,-0.175066746
NA,19/08/2019 12:25,2019,2019/20,Special Service,1,1,339,339,CAT STUCK BEHIND WALL,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000140,KILBURN,E09000007,CAMDEN,West Hampstead,5079307,ROWLEY WAY,20402815,NW8,526168,183950,526150,183950,51.54036629,-0.182087091
NA,20/08/2019 17:02,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000568,THE WRYTHE,E09000029,SUTTON,Wallington,NULL,WRYTHE LANE,22605653,SM5,NULL,NULL,527150,165350,NULL,NULL
NA,20/08/2019 21:22,2019,2019/20,Special Service,2,3,339,1017,Redacted,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,6168485,URSULA GOULD WAY,22702630,E14,537294,181551,537250,181550,51.51620754,-0.022693542
NA,21/08/2019 18:40,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED BEHIND COPPER PIPES,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000405,CHESSINGTON SOUTH,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,FENGATE CLOSE,21880352,KT9,NULL,NULL,517750,163850,NULL,NULL
NA,22/08/2019 17:26,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000597,HALE END AND HIGHAMS PARK,E09000031,WALTHAM FOREST,Chingford,NULL,JACKS FARM WAY,22847360,E4,NULL,NULL,538250,191750,NULL,NULL
NA,23/08/2019 15:35,2019,2019/20,Special Service,NULL,NULL,339,NULL,DOG STUCK IN WATERWAY - INGREBOURNE RIVER  LOCATED BETWEEN BRIDGES AT HAROLD WOOD PARK AND THE GOLF,Dog,Person (mobile),Park,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000312,HAROLD WOOD,E09000016,HAVERING,Harold Hill,10091577192,SQUIRRELS HEATH ROAD,21300145,RM3,555181,190044,555150,190050,51.58788915,0.238637414
NA,23/08/2019 19:14,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Police,Car,Road Vehicle,Other animal assistance,Animal harm involving domestic animal,E05000225,PENINSULA,E09000011,GREENWICH,East Greenwich,10010259153,BROCKLEBANK ROAD,20800232,SE7,540606,178768,540650,178750,51.49038542,0.023894671
NA,23/08/2019 19:59,2019,2019/20,Special Service,1,1,339,339,SQUIRREL TRAPPED BETWEEN PIPE AND WALL,Squirrel,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000060,UNDERHILL,E09000003,BARNET,Barnet,NULL,CROCUS FIELD,20011100,EN5,NULL,NULL,524850,195450,NULL,NULL
NA,24/08/2019 12:34,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000452,SYDENHAM,E09000023,LEWISHAM,Beckenham,NULL,LAWRIE PARK ROAD,22004427,SE26,NULL,NULL,535150,171350,NULL,NULL
NA,24/08/2019 15:34,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CROW TRAPPED IN FENCING ON ROOF,Bird,Person (mobile),Gym,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000522,HAMPTON WICK,E09000027,RICHMOND UPON THAMES,Twickenham,1.00023E+11,PARK ROAD,22400459,KT1,517457,169590,517450,169550,51.41317378,-0.312412742
NA,24/08/2019 16:37,2019,2019/20,Special Service,1,1,339,339,GUINEA PIG TRAPPED UNDER CAR SEAT,Unknown - Domestic Animal Or Pet,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009369,CLISSOLD,E09000012,HACKNEY,Stoke Newington,1.00021E+11,WINSTON ROAD,20901091,N16,532841,185865,532850,185850,51.55604303,-0.085206906
NA,24/08/2019 16:55,2019,2019/20,Special Service,1,1,339,339,DOG STUCK IN GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009334,STEPNEY GREEN,E09000030,TOWER HAMLETS,Shadwell,NULL,JUBILEE STREET,22700688,E1,NULL,NULL,535150,181750,NULL,NULL
NA,25/08/2019 03:57,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED INSIDE CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009335,WEAVERS,E09000030,TOWER HAMLETS,Shoreditch,NULL,QUILTER STREET,22700984,E2,NULL,NULL,534150,182850,NULL,NULL
NA,25/08/2019 10:19,2019,2019/20,Special Service,1,2,339,678,PIGEON TRAPPED IN NETTING      INSIDE STATION,Bird,Person (land line),Train station - platform (at ground level or elevated),Non Residential,Other animal assistance,Assist trapped wild animal,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,10034920911,THE BROADWAY,22306023,IG8,540937,191754,540950,191750,51.6069938,0.0338614
NA,25/08/2019 11:19,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000635,HARROW ROAD,E09000033,WESTMINSTER,Paddington,NULL,CHIPPENHAM ROAD,8400793,W9,NULL,NULL,525150,182450,NULL,NULL
NA,25/08/2019 13:34,2019,2019/20,Special Service,1,1,339,339,DOG STUCK UNDER DECKING,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000112,CLOCK HOUSE,E09000006,BROMLEY,Beckenham,NULL,BIRKBECK ROAD,20301839,BR3,NULL,NULL,535650,169350,NULL,NULL
NA,25/08/2019 18:43,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING-OPPOSITE SAINSBURYS UNDER THE ARCHES,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011107,NORTH WALWORTH,E09000028,SOUTHWARK,Old Kent Road,10093342132,NEW KENT ROAD,22501783,SE1,532091,178986,532050,178950,51.49440257,-0.098595843
NA,26/08/2019 01:08,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000094,KILBURN,E09000005,BRENT,West Hampstead,NULL,ALDERSHOT ROAD,20200245,NW6,NULL,NULL,524950,183850,NULL,NULL
NA,26/08/2019 17:33,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND TOILET CISTERN,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000616,GRAVENEY,E09000032,WANDSWORTH,Tooting,NULL,COTEFORD STREET,22901055,SW17,NULL,NULL,528050,171650,NULL,NULL
NA,28/08/2019 07:20,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - CAT STUCK,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000619,NORTHCOTE,E09000032,WANDSWORTH,Battersea,NULL,GRANDISON ROAD,22902089,SW11,NULL,NULL,527850,174750,NULL,NULL
NA,28/08/2019 10:28,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED IN ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000596,GROVE GREEN,E09000031,WALTHAM FOREST,Leytonstone,NULL,ASHVILLE ROAD,22812000,E11,NULL,NULL,538850,186850,NULL,NULL
NA,29/08/2019 17:14,2019,2019/20,Special Service,1,1,339,339,INJURED CAT TRAPPED IN BUSHES - OWNER ON SCENE,Cat,Person (mobile),Hedge,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009325,LANSBURY,E09000030,TOWER HAMLETS,Poplar,6067533,CHRISP STREET,22700311,E14,537751,181509,537750,181550,51.51571775,-0.01612341
NA,30/08/2019 14:18,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH SMALL DEER TRAPPED IN RAILINGS OF A GATE   GO TO BEDWELL ROAD AT THE R/O THE SCHOO,Deer,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000283,WHITE HART LANE,E09000014,HARINGEY,Tottenham,1.00021E+11,THE ROUNDWAY,21106424,N17,533181,190796,533150,190750,51.60027225,-0.078429795
NA,30/08/2019 15:09,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA AT SCENE CAT STUCK ON ROOF,Cat,Person (mobile),Large supermarket,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000412,ST. MARK'S,E09000021,KINGSTON UPON THAMES,Surbiton,1.00023E+11,VICTORIA ROAD,21801000,KT6,517835,167182,517850,167150,51.391452,-0.307782
NA,01/09/2019 13:25,2019,2019/20,Special Service,1,1,339,339,ASSISTING RSPCA WITH CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000351,FELTHAM NORTH,E09000018,HOUNSLOW,Feltham,NULL,SPARROW FARM DRIVE,21501041,TW14,NULL,NULL,511450,173850,NULL,NULL
NA,03/09/2019 20:06,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED BEHIND TOILET,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000173,EALING BROADWAY,E09000009,EALING,Ealing,NULL,WINDSOR ROAD,20601929,W5,NULL,NULL,518050,180650,NULL,NULL
NA,03/09/2019 21:58,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05011246,ILFORD TOWN,E09000026,REDBRIDGE,Ilford,1.00022E+11,BALFOUR ROAD,22302601,IG1,543693,186784,543650,186750,51.56164582,0.071604872
NA,03/09/2019 21:59,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BETWEEN TWO FENCES,Cat,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000464,LONGTHORNTON,E09000024,MERTON,Norbury,48066859,STIRLING CLOSE,22106008,SW16,529510,169632,529550,169650,51.4109283,-0.139169902
NA,04/09/2019 17:50,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND KITCHEN UNIT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011116,SOUTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,NULL,DUNLOP PLACE,22500834,SE16,NULL,NULL,533950,179150,NULL,NULL
NA,04/09/2019 18:27,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND FIRE PANEL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000317,PETTITS,E09000016,HAVERING,Romford,NULL,WAYSIDE CLOSE,21301685,RM1,NULL,NULL,551650,189850,NULL,NULL
NA,04/09/2019 19:14,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,10091941543,THE BROADWAY,22306023,IG8,540936,191858,540950,191850,51.60793021,0.033885768
NA,06/09/2019 21:45,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,NULL,THE BROADWAY,22306023,IG8,NULL,NULL,540950,191850,NULL,NULL
NA,08/09/2019 13:04,2019,2019/20,Special Service,1,1,339,339,CATS PAW TRAPPED IN PLUG HOLE,cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000190,SOUTHALL GREEN,E09000009,EALING,Southall,NULL,GORDON ROAD,20600739,UB2,NULL,NULL,512350,178750,NULL,NULL
NA,09/09/2019 00:11,2019,2019/20,Special Service,1,1,339,339,CAT STUCK BEHIND CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,NULL,BLYTH ROAD,22816750,E17,NULL,NULL,536650,187650,NULL,NULL
NA,09/09/2019 19:26,2019,2019/20,Special Service,1,1,339,339,SQUIRREL TRAPPED BEHIND TOILET SOIL PIPE - HAS BEEN THERE SINCE EARLY THIS MORNING,Squirrel,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000220,ELTHAM WEST,E09000011,GREENWICH,Eltham,NULL,FOXHOLE ROAD,20800589,SE9,NULL,NULL,542150,174950,NULL,NULL
NA,11/09/2019 14:06,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,CHEYNE PLACE,21701182,SW3,NULL,NULL,527650,177950,NULL,NULL
NA,13/09/2019 10:54,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,1.00021E+11,GREENWICH HIGH ROAD,20800685,SE10,537920,177186,537950,177150,51.47683546,-0.015380157
NA,13/09/2019 11:47,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND THE FACIA OF SHOP,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000094,KILBURN,E09000005,BRENT,West Hampstead,202031114,WILLESDEN LANE,20201346,NW6,524763,183955,524750,183950,51.5407158,-0.2023438
NA,14/09/2019 12:54,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN BASEMENT BEHIND SPIKED RAILINGS    REQUEST FROM RSPCA OFFICER ON SCENE TO ASSIST,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,SHEPHERDS PLACE,8400166,W1K,NULL,NULL,528150,180850,NULL,NULL
NA,14/09/2019 13:43,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH FOX TRAPPED IN CONSTRUCTION SITE,Fox,Person (mobile),Other Dwelling,Dwelling,Other animal assistance,Assist trapped wild animal,E05000369,CANONBURY,E09000019,ISLINGTON,Islington,NULL,HALLIFORD STREET,21605047,N1,NULL,NULL,532450,184250,NULL,NULL
NA,14/09/2019 16:58,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN TREE - ACCESS VIA SUNNINGFIELDS ROAD,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000055,HENDON,E09000003,BARNET,Hendon,200111105,SUNNY GARDENS ROAD,20041580,NW4,522966,190167,522950,190150,51.59694033,-0.226057726
NA,15/09/2019 08:26,2019,2019/20,Special Service,1,2,339,678,DOG STUCK DOWN A HOLE   FRU REQUIRED WITH SNAKE EYE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,RAVENSCOURT AVENUE,21000673,W6,NULL,NULL,522450,178650,NULL,NULL
NA,15/09/2019 09:43,2019,2019/20,Special Service,1,1,339,339,"KITTENS UNDERNEATH CAR SOME BELIEVED TO BE INSIDE CAR ENGINE  PERSON ON SCENE, SILVER CORSA CAR",Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05011250,NEWBURY,E09000026,REDBRIDGE,Ilford,1.00022E+11,ALDBOROUGH ROAD SOUTH,22301800,IG3,545521,188264,545550,188250,51.57447567,0.098564267
NA,15/09/2019 17:56,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,10025176373,THE PROMENADE,21590226,W4,521402,176552,521450,176550,51.474916,-0.253325934
NA,15/09/2019 23:10,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED ON PIPE,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009318,BLACKWALL & CUBITT TOWN,E09000030,TOWER HAMLETS,Poplar,6083203,LOVEGROVE WALK,22700757,E14,538223,180170,538250,180150,51.50357032,-0.009847936
NA,16/09/2019 08:13,2019,2019/20,Special Service,1,2,339,678,KITTEN STUCK BEHIND FIREPLACE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000048,EAST BARNET,E09000003,BARNET,Barnet,NULL,ALVERSTONE AVENUE,20000900,EN4,NULL,NULL,527250,194650,NULL,NULL
NA,16/09/2019 13:04,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000436,VASSALL,E09000022,LAMBETH,Brixton,NULL,HALSMERE ROAD,21900656,SE5,NULL,NULL,531950,176850,NULL,NULL
NA,16/09/2019 19:26,2019,2019/20,Special Service,1,1,339,339,CAT STUCK BEHIND DISHWASHER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000052,GARDEN SUBURB,E09000003,BARNET,Finchley,NULL,WILLIFIELD WAY,20046400,NW11,NULL,NULL,525250,188750,NULL,NULL
NA,17/09/2019 11:11,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING NEXT TO BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,1.00023E+11,TURNHAM GREEN TERRACE,21501135,W4,521212,178797,521250,178750,51.49513367,-0.255293677
NA,18/09/2019 02:01,2019,2019/20,Special Service,1,2,339,678,CAT TRAPPED DOWN VENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000641,MARYLEBONE HIGH STREET,E09000033,WESTMINSTER,Euston,NULL,MARYLEBONE HIGH STREET,8401405,W1U,NULL,NULL,528350,181650,NULL,NULL
NA,18/09/2019 14:33,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING OUTSIDE                  NEAR SAINSBURYS,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,NULL,THE BROADWAY,22306023,IG8,NULL,NULL,540950,191850,NULL,NULL
NA,18/09/2019 17:39,2019,2019/20,Special Service,1,1,339,339,CAT STUCK  IN CAR ENGINE - CALLER WILL MAKE THEMSELVES KNOWN,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207032004,HIGH STREET,20705021,EN3,535260,195881,535250,195850,51.64547414,-0.046469884
NA,20/09/2019 23:22,2019,2019/20,Special Service,1,2,339,678,FOX TRAPPED BETWEEN TWO HOUSES,Fox,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Assist trapped wild animal,E05011113,RYE LANE,E09000028,SOUTHWARK,Peckham,2.00003E+11,DANBY STREET,22500699,SE15,533671,175869,533650,175850,51.46601375,-0.077022332
NA,21/09/2019 02:09,2019,2019/20,Special Service,1,4,339,1356,FOX TRAPPED IN BETWEEN TWO HOUSES FOLLOWING SM ASSESSMENT,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011113,RYE LANE,E09000028,SOUTHWARK,Peckham,NULL,DANBY STREET,22500699,SE15,NULL,NULL,533650,175850,NULL,NULL
NA,22/09/2019 17:34,2019,2019/20,Special Service,1,1,339,339,BIRD STUCK IN TREE BY WIRE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000336,NORTHWOOD HILLS,E09000017,HILLINGDON,Ruislip,1.00021E+11,SALISBURY ROAD,21401688,HA5,510050,189150,510050,189150,51.59047458,-0.412776765
NA,23/09/2019 18:44,2019,2019/20,Special Service,1,2,339,678,Redacted,Horse,Person (land line),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,NA,NA,NA,NA,Buckinghamshire,1.00081E+11,GEORGE GREEN ROAD,35200389,SL3,500025,181111,500050,181150,51.52007841,-0.559730194
NA,23/09/2019 19:57,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED BY HEAD IN MESH FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000410,OLD MALDEN,E09000021,KINGSTON UPON THAMES,New Malden,1.00022E+11,SHEEPHOUSE WAY,21800852,KT3,521284,166611,521250,166650,51.38559988,-0.258418655
NA,23/09/2019 23:10,2019,2019/20,Special Service,1,1,339,339,KITTEN  STUCK IN BASEMENT AREA - HAS FALLEN FROM BALCONY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,CHESTERTON ROAD,21700835,W10,NULL,NULL,524250,181750,NULL,NULL
NA,24/09/2019 09:40,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN COAL CELLAR,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000462,HILLSIDE,E09000024,MERTON,Wimbledon,NULL,RIDGWAY PLACE,22105412,SW19,NULL,NULL,524250,170550,NULL,NULL
NA,25/09/2019 13:07,2019,2019/20,Special Service,1,3,339,1017,HORSE CAST IN STABLE      VET ON SCENE,Horse,Person (mobile),Other private non-residential building,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011478,SANDERSTEAD,E09000008,CROYDON,Croydon,2.00001E+11,PURLEY OAKS ROAD,20502277,CR2,533119,161889,533150,161850,51.34051479,-0.0902084
NA,26/09/2019 03:09,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND BOILER PIPES,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009373,HACKNEY DOWNS,E09000012,HACKNEY,Stoke Newington,NULL,NARFORD ROAD,20900725,E5,NULL,NULL,534350,186550,NULL,NULL
NA,28/09/2019 16:29,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (land line),Scrub land,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000532,WEST TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,10070719848,UNNAMED FOOTPATH - MILL ROAD TO PERCY ROAD,22406500,TW2,514368,172843,514350,172850,51.44304372,-0.355754565
NA,29/09/2019 10:38,2019,2019/20,Special Service,1,1,339,339,HEDGEHOG TRAPPED BETWEEN WIRE FENCE - CALLER UNABLE TO GET TO IT TO RELEASE IT,Hedgehog,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped wild animal,E05000049,EAST FINCHLEY,E09000003,BARNET,Finchley,200161791,EAGANS CLOSE,20013360,N2,526887,189664,526850,189650,51.5915511,-0.169663587
NA,30/09/2019 08:26,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED DOWN HOLE  BIGGIN HILL RECREATION GROUND NEAR SWIMMIMG POOL      CALLER WILL LIASE WITH,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000107,BIGGIN HILL,E09000006,BROMLEY,Biggin Hill,10070018285,CHURCH ROAD,20301436,TN16,541956,159034,541950,159050,51.31272161,0.03544197
NA,02/10/2019 06:27,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED - OPP MORRISONS DOWN PRIVATE ROAD SEEN FROM BRIDGE - AT  FURTHEST LOCK FROM HIGH STREET,Fox,Person (land line),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000364,SYON,E09000018,HOUNSLOW,Chiswick,10093259173,FOOTPATH FROM DOCK ROAD TO CATHERINE WHEEL ROAD ALONG CANAL WAY,21519771,TW8,517825,177287,517850,177250,51.48227699,-0.304555772
NA,02/10/2019 12:32,2019,2019/20,Special Service,1,2,339,678,KITTEN TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000280,TOTTENHAM GREEN,E09000014,HARINGEY,Tottenham,1.00021E+11,COLLINGWOOD ROAD,21103130,N15,533238,189397,533250,189350,51.58768725,-0.078136739
NA,02/10/2019 16:06,2019,2019/20,Special Service,1,1,339,339,CAT WITH KITTENS IN A HOLE IN THE GARDEN - TOO DEEP TO REACH,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,NULL,LORDSHIP LANE,21106476,N17,NULL,NULL,533850,190650,NULL,NULL
NA,02/10/2019 22:09,2019,2019/20,Special Service,1,1,339,339,CAT IN DISTRESS TRAPPED BEHIND HIGH FENCE - OWNER ON SCENE,Cat,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000633,CHURCHILL,E09000033,WESTMINSTER,Chelsea,1.00023E+11,EBURY BRIDGE ROAD,8400824,SW1W,528554,178253,528550,178250,51.48862538,-0.149781226
NA,03/10/2019 10:12,2019,2019/20,Special Service,1,3,339,1017,HORSE STUCK IN OVERFLOW CHANNEL BETWEEN RESERVOIRS NEAR PONDERS END LOCK,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist trapped wild animal,E05000203,JUBILEE,E09000010,ENFIELD,Chingford,207169544,LEA VALLEY ROAD,20703459,E4,537303,195014,537350,195050,51.63718202,-0.017301322
NA,03/10/2019 13:28,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000275,NOEL PARK,E09000014,HARINGEY,Hornsey,1.00021E+11,TURNPIKE LANE,21104018,N8,531299,189588,531250,189550,51.58985564,-0.106041221
NA,03/10/2019 20:32,2019,2019/20,Special Service,1,1,339,339,RSPCA IN ATTENDANCE  CAT FALLEN FROM THIRD FLOOR BALCONY ONTO CONCRETE OVERHANG TO ENTRANCE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000639,LITTLE VENICE,E09000033,WESTMINSTER,Paddington,NULL,HALL PLACE,8400423,W2,NULL,NULL,526750,181850,NULL,NULL
NA,04/10/2019 16:50,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN GUTTERING OF LOCK UP GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011231,SLADE GREEN & NORTHEND,E09000004,BEXLEY,Bexley,10023303854,DALE VIEW,20100416,DA8,552020,176420,552050,176450,51.46634452,0.187177254
NA,06/10/2019 17:01,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND WALL OF SHED - BACKS ON TO RANMORE CLOSE,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,46039460,IDMISTON ROAD,22200075,E15,539593,185376,539550,185350,51.55002104,0.011930641
NA,07/10/2019 07:23,2019,2019/20,Special Service,1,2,339,678,SMALL ANIMAL RESCUE - FOX WITH TRAPPED LEG,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05009380,LEA BRIDGE,E09000012,HACKNEY,Homerton,1.00021E+11,POWERSCROFT ROAD,20900816,E5,535370,185744,535350,185750,51.55435678,-0.048785829
NA,07/10/2019 13:05,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Assist trapped wild animal,E05009291,BILLINGSGATE,E09000001,CITY OF LONDON,Dowgate,10091781655,GREAT TOWER STREET,8100251,EC3R,533187,180800,533150,180850,51.51044841,-0.082130589
NA,07/10/2019 22:29,2019,2019/20,Special Service,1,2,339,678,CAT STUCK ON LEDGE -  ** DO NOT CLOSE **,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,AVERY ROW,8400745,W1K,NULL,NULL,528750,180950,NULL,NULL
NA,10/10/2019 12:38,2019,2019/20,Special Service,1,2,339,678,"FOX TRAPPED ON PILLAR     CALLER WAITING FOR YOU ON PEDESTRIAN WALKWAY, SOUTHSIDE OF BLACKFRIARS BRI",Fox,Person (mobile),Bridge,Outdoor Structure,Animal rescue from water,Wild animal rescue from water or mud,E05011095,BOROUGH & BANKSIDE,E09000028,SOUTHWARK,Dowgate,2.00003E+11,BLACKFRIARS ROAD,22500249,SE1,531622,180554,531650,180550,51.50860098,-0.104759215
NA,10/10/2019 18:52,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED BETWEEN STEERING WHEEL AND DASH BOARD GREY  LEXUS CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000136,HAVERSTOCK,E09000007,CAMDEN,Kentish Town,5090679,HAVERSTOCK HILL,20400301,NW3,528079,184497,528050,184450,51.5448467,-0.154348608
NA,10/10/2019 21:37,2019,2019/20,Special Service,1,1,339,339,CAT STUCK ON WINDOW LEDGE AT HEIGHT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,NULL,CLOUDESDALE ROAD,22900995,SW17,NULL,NULL,528550,172750,NULL,NULL
NA,11/10/2019 22:30,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED BETWEEN FENCES,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000476,BOLEYN,E09000025,NEWHAM,East Ham,46018973,CREIGHTON AVENUE,22200519,E6,541735,183130,541750,183150,51.52930038,0.041903814
NA,12/10/2019 19:08,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN FACTORY BETWEEN SCAFFOLDING AND DRAPING IN THE THE VINYL FACTORY,Cat,Person (mobile),Factory,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000325,BOTWELL,E09000017,HILLINGDON,Hayes,2.00003E+11,CLAYTON ROAD,21400418,UB3,509343,179648,509350,179650,51.50520369,-0.425934777
NA,14/10/2019 07:04,2019,2019/20,Special Service,1,2,339,678,DEER CAUGHT IN RAILINGS,Deer,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05000353,HANWORTH,E09000018,HOUNSLOW,Feltham,1.00022E+11,BEACH GROVE,21500085,TW13,513324,172538,513350,172550,51.44051754,-0.370864564
NA,14/10/2019 14:39,2019,2019/20,Special Service,1,1,339,339,INJURED FOX SHUT BEHIND GATES    RSPCA ON SCENE,Fox,Person (mobile),Water works,Non Residential,Other animal assistance,Assist trapped wild animal,E05000528,SOUTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,1.00023E+11,RIVERSIDE,22407112,TW9,517638,174694,517650,174650,51.45901336,-0.308106819
NA,14/10/2019 19:25,2019,2019/20,Special Service,1,1,339,339,PUPPY WITH HEAD TRAPPED IN METAL CAGE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,NULL,STRATHVILLE ROAD,22905028,SW18,NULL,NULL,525850,173050,NULL,NULL
NA,16/10/2019 09:10,2019,2019/20,Special Service,1,1,339,339,HORSE TRAPPED IN FENCE,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance - Lift heavy wild animal,E05011231,SLADE GREEN & NORTHEND,E09000004,BEXLEY,Erith,10023303145,MOAT LANE,20100987,DA8,552972,176295,552950,176250,51.46495768,0.200818382
NA,16/10/2019 22:07,2019,2019/20,Special Service,1,2,339,678,Redacted,Dog,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000230,WOOLWICH RIVERSIDE,E09000011,GREENWICH,East Greenwich,1.00021E+11,WILLOW LANE,20801675,SE18,543073,178824,543050,178850,51.4902689,0.0594272
NA,17/10/2019 20:36,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN NET AT SECOND FLOOR LEVEL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000487,LITTLE ILFORD,E09000025,NEWHAM,Ilford,NULL,JACK CORNWELL STREET,22200193,E12,NULL,NULL,543250,185650,NULL,NULL
NA,17/10/2019 22:05,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000263,SHEPHERD'S BUSH GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34159547,SOUTH AFRICA ROAD,21000741,W12,523014,180626,523050,180650,51.511188,-0.228701
NA,18/10/2019 12:25,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011241,CRANBROOK,E09000026,REDBRIDGE,Ilford,NULL,BRAMLEY CRESCENT,22302643,IG2,NULL,NULL,543350,188250,NULL,NULL
NA,18/10/2019 17:40,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND KITCHEN CUPBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009320,BOW WEST,E09000030,TOWER HAMLETS,Bethnal Green,NULL,NEW GUN WHARF,22702725,E3,NULL,NULL,536450,183650,NULL,NULL
NA,19/10/2019 09:44,2019,2019/20,Special Service,1,1,339,339,DEER TRAPPED IN ROPE    CALLER WILL DIRECT YOU FROM THE JUNCTION,Deer,Other FRS,Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,10025149967,LOWER NOKE CLOSE,21300682,RM3,554669,193576,554650,193550,51.61976638,0.232809108
NA,19/10/2019 11:57,2019,2019/20,Special Service,1,1,339,339,DOG IN POND/SMALL LAKE BEHIND RAILINGS   OWNER ON SCENE,Dog,Person (mobile),Heathland,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000045,CHILDS HILL,E09000003,BARNET,West Hampstead,10025178856,ELM WALK,20014320,NW3,525622,186680,525650,186650,51.56501966,-0.18897956
NA,19/10/2019 18:52,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND WALL IN HALLWAY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000526,NORTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,NULL,TERSHA STREET,22407053,TW9,NULL,NULL,518550,175350,NULL,NULL
NA,19/10/2019 20:54,2019,2019/20,Special Service,1,1,339,339,ASSIST WOMAN WITH SQUIRREL IN SITTING ROOM,Squirrel,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000255,FULHAM REACH,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,COLWITH ROAD,21000230,W6,NULL,NULL,523350,177750,NULL,NULL
NA,20/10/2019 00:01,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000604,LEYTON,E09000031,WALTHAM FOREST,Leyton,NULL,BREWSTER ROAD,22818450,E10,NULL,NULL,537750,187250,NULL,NULL
NA,20/10/2019 11:36,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,NULL,THE AVENUE,21106439,N10,NULL,NULL,529350,190250,NULL,NULL
NA,21/10/2019 09:47,2019,2019/20,Special Service,1,1,339,339,DEER CAUGHT IN A TRAP,Deer,Person (land line),Heathland,Outdoor,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,10025145227,LOWER NOKE CLOSE,21300682,RM3,554790,193442,554750,193450,51.61852424,0.234502487
NA,22/10/2019 15:13,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009400,PEMBRIDGE,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,PEMBRIDGE SQUARE,21700756,W2,NULL,NULL,525450,180750,NULL,NULL
NA,23/10/2019 12:48,2019,2019/20,Special Service,1,1,339,339,FOX STUCK UNDER FENCE   IN BACK GARDEN,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000090,FRYENT,E09000005,BRENT,Stanmore,202109473,CRUNDALE AVENUE,20201132,NW9,519363,188548,519350,188550,51.5831668,-0.278608387
NA,23/10/2019 15:52,2019,2019/20,Special Service,1,1,339,339,RABBIT TRAPPED BEHIND SHED,Rabbit,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000268,BRUCE GROVE,E09000014,HARINGEY,Tottenham,NULL,HIGHAM ROAD,21103419,N17,NULL,NULL,532950,189950,NULL,NULL
NA,24/10/2019 18:43,2019,2019/20,Special Service,1,2,339,678,Redacted,Dog,Person (mobile),Hedge,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,LEA BRIDGE,E09000012,HACKNEY,Homerton,10008299053,LEA BRIDGE ROAD,20900602,E5,535579,186571,535550,186550,51.5617384,-0.045464073
NA,24/10/2019 23:06,2019,2019/20,Special Service,1,1,339,339,DEER WITH HEAD STUCK IN RAILINGS OPPOSITE THE PRIMARY SCHOOL,Deer,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,10025146314,DAGNAM PARK DRIVE,21300071,RM3,554229,192345,554250,192350,51.60882369,0.225914524
NA,25/10/2019 07:55,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN BASEMENET     RSPCA ON SCENE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000251,ASKEW,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,CURWEN ROAD,21000254,W12,NULL,NULL,522150,179750,NULL,NULL
NA,25/10/2019 13:48,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000250,ADDISON,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34032803,SPRING VALE TERRACE,21000747,W14,523842,179226,523850,179250,51.49842596,-0.217275184
NA,25/10/2019 15:50,2019,2019/20,Special Service,1,2,339,678,DOG STUCK UP TREE  CALLER STATES SHE WAS BY THE RIVER RODING,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011241,CRANBROOK,E09000026,REDBRIDGE,Ilford,1.00022E+11,WANSTEAD PARK ROAD,22303358,IG1,542138,187454,542150,187450,51.56805644,0.049460884
NA,25/10/2019 16:37,2019,2019/20,Special Service,1,1,339,339,KITTEN STUCK OUTSIDE WINDOW OF HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000227,SHOOTERS HILL,E09000011,GREENWICH,Plumstead,NULL,DALLIN ROAD,20800435,SE18,NULL,NULL,543950,177450,NULL,NULL
NA,25/10/2019 19:48,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN SOFA,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000405,CHESSINGTON SOUTH,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,REYNOLDS AVENUE,21800802,KT9,NULL,NULL,518350,163450,NULL,NULL
NA,26/10/2019 16:19,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN BADGER SETT   CALLER WILL MEET YOU ON THE FOOTPATH,Dog,Person (mobile),Golf course (not building on course),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000335,NORTHWOOD,E09000017,HILLINGDON,Ruislip,10090329808,CHESTNUT AVENUE,21400375,HA6,509596,190256,509550,190250,51.60049797,-0.418984419
NA,26/10/2019 16:55,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED BETWEEN GATE AND WALL,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000444,FOREST HILL,E09000023,LEWISHAM,Forest Hill,NULL,DUNOON ROAD,22001424,SE23,NULL,NULL,535450,173950,NULL,NULL
NA,26/10/2019 20:43,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN FIREPLACE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Hayes,NULL,MONKS WAY,21401313,UB7,NULL,NULL,506050,177850,NULL,NULL
NA,27/10/2019 21:42,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED BEHIND SHED,Dog,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000043,BRUNSWICK PARK,E09000003,BARNET,Southgate,200082208,MONKFRITH WAY,20029900,N14,528613,194732,528650,194750,51.63670725,-0.142905981
NA,29/10/2019 23:05,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000111,CHISLEHURST,E09000006,BROMLEY,Bromley,NULL,NORLANDS CRESCENT,20300522,BR7,NULL,NULL,543750,169550,NULL,NULL
NA,30/10/2019 15:19,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN DUCTING,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009387,WOODBERRY DOWN,E09000012,HACKNEY,Stoke Newington,NULL,BETHUNE ROAD,20900135,N16,NULL,NULL,532950,187650,NULL,NULL
NA,30/10/2019 18:44,2019,2019/20,Special Service,1,1,339,339,KITTEN POSSIBLY TRAPPED OR INJURED IN TREE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,NULL,WEST CLOSE,20704904,N9,NULL,NULL,533850,193350,NULL,NULL
NA,31/10/2019 13:43,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000305,WEST HARROW,E09000015,HARROW,Harrow,NULL,THE RETREAT,21202023,HA2,NULL,NULL,513450,187650,NULL,NULL
NA,02/11/2019 11:11,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED BETWEEN OUTHOUSE AND FENCE,Fox,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped wild animal,E05000332,HILLINGDON EAST,E09000017,HILLINGDON,Hillingdon,1.00023E+11,SUTTON COURT ROAD,21401899,UB10,507866,183597,507850,183550,51.54098264,-0.445989797
NA,02/11/2019 15:28,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009391,CHELSEA RIVERSIDE,E09000020,KENSINGTON AND CHELSEA,Chelsea,217088245,UPPER CHEYNE ROW,21700563,SW3,527082,177734,527050,177750,51.48429172,-0.171157198
NA,02/11/2019 23:12,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000352,FELTHAM WEST,E09000018,HOUNSLOW,Feltham,NULL,CHERTSEY ROAD,21500242,TW13,NULL,NULL,509350,171950,NULL,NULL
NA,04/11/2019 11:59,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED IN NETTING,Fox,Person (mobile),Secondary school,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000617,LATCHMERE,E09000032,WANDSWORTH,Battersea,1.00023E+11,BATTERSEA PARK ROAD,22900337,SW11,527923,176526,527950,176550,51.47324474,-0.159488874
NA,04/11/2019 21:31,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED IN FENCE,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,46006002,BISSON ROAD,22201310,E15,538478,183116,538450,183150,51.52998601,-0.005018049
NA,05/11/2019 21:46,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN A WELL,Cat,Person (mobile),Converted office,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000264,TOWN,E09000013,HAMMERSMITH AND FULHAM,Fulham,34092988,FULHAM HIGH STREET,21000355,SW6,524427,176093,524450,176050,51.47013579,-0.209947838
NA,07/11/2019 08:03,2019,2019/20,Special Service,1,1,339,339,DEER STUCK IN RAILINGS,Deer,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,2.00001E+11,LEA BRIDGE ROAD,22850300,E10,535912,186758,535950,186750,51.56334005,-0.040585667
NA,07/11/2019 17:54,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000314,HEATON,E09000016,HAVERING,Harold Hill,NULL,STRAIGHT ROAD,21301625,RM3,NULL,NULL,553050,191750,NULL,NULL
NA,07/11/2019 21:28,2019,2019/20,Special Service,1,2,339,678,PERSON WITH SQUIRREL TRAPPED BEHIND WARDROBE,Squirrel,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,NULL,RAVENSBOURNE AVENUE,20303571,BR2,NULL,NULL,538750,170050,NULL,NULL
NA,08/11/2019 08:20,2019,2019/20,Special Service,1,1,339,339,FOX STUCK IN GATE,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000476,BOLEYN,E09000025,NEWHAM,Plaistow,46250898,BOUNDARY ROAD,22200398,E13,541440,183161,541450,183150,51.52965787,0.037671653
NA,09/11/2019 22:32,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000626,TOOTING,E09000032,WANDSWORTH,Tooting,NULL,GILBEY ROAD,22902000,SW17,NULL,NULL,527250,171550,NULL,NULL
NA,10/11/2019 09:22,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER CAR BONNET,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011112,ROTHERHITHE,E09000028,SOUTHWARK,Dockhead,2.00004E+11,RENFORTH STREET,22502073,SE16,535387,179480,535350,179450,51.49806084,-0.050950467
NA,12/11/2019 11:34,2019,2019/20,Special Service,1,1,339,339,KITTEN STUCK BEHIND BRICK WORK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009378,HOXTON WEST,E09000012,HACKNEY,Islington,NULL,WIMBOURNE STREET,20901087,N1,NULL,NULL,532550,183350,NULL,NULL
NA,13/11/2019 09:09,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDERNEATH FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,BRUNSWICK STREET,22820250,E17,NULL,NULL,538150,188850,NULL,NULL
NA,13/11/2019 16:24,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED BEHIND FIREPLACE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000482,EAST HAM SOUTH,E09000025,NEWHAM,East Ham,NULL,EDWIN AVENUE,22200578,E6,NULL,NULL,543250,182950,NULL,NULL
NA,13/11/2019 19:12,2019,2019/20,Special Service,1,2,339,678,DOG IN FOX DEN,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000343,WEST RUISLIP,E09000017,HILLINGDON,Ruislip,1.00021E+11,BEMBRIDGE GARDENS,21400147,HA4,508990,186724,508950,186750,51.56886925,-0.428828149
NA,15/11/2019 12:15,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED INSIDE CAR    AT THE REAR OF THE CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000180,HOBBAYNE,E09000009,EALING,Ealing,12059310,SHAKESPEARE ROAD,20601535,W7,515871,180845,515850,180850,51.51466283,-0.331525757
NA,16/11/2019 17:13,2019,2019/20,Special Service,1,1,339,339,CAT STUCK ON HIGH FENCE    ASSISTANCE REQUESTED BY RSPCA OFFICER ON SCENE,Cat,Person (mobile),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,10034508567,FARADAY ROAD,22201400,E15,539689,184632,539650,184650,51.5433055,0.013018
NA,17/11/2019 03:23,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05009378,HOXTON WEST,E09000012,HACKNEY,Islington,NULL,WIMBOURNE STREET,20901087,N1,NULL,NULL,532650,183450,NULL,NULL
NA,17/11/2019 17:04,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED IN IRON GATE - PERSON ON SCENE FROM FOX PROJECT TO DEAL WITH FOX WHEN RELEASED,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011489,WOODSIDE,E09000008,CROYDON,Woodside,1.00021E+11,WOODSIDE ROAD,20501593,SE25,534705,167179,534750,167150,51.3876747,-0.0654549
NA,18/11/2019 01:30,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN BETWEEN A PANEL AND A WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,NULL,PAYNESFIELD AVENUE,22404871,SW14,NULL,NULL,520850,175550,NULL,NULL
NA,18/11/2019 01:36,2019,2019/20,Special Service,1,1,339,339,PERSON LOCKED OUT - DOG LOCKED IN,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000142,REGENT'S PARK,E09000007,CAMDEN,Euston,NULL,CHESTER TERRACE,20400821,NW1,NULL,NULL,528750,182850,NULL,NULL
NA,18/11/2019 11:56,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED BEHIND RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,10008985396,DONGOLA ROAD WEST,22200551,E13,540560,182646,540550,182650,51.52524433,0.024779439
NA,19/11/2019 14:57,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,10090849105,TEMPLE MILL LANE,22201738,E20,538233,185248,538250,185250,51.54920327,-0.007711702
NA,21/11/2019 11:16,2019,2019/20,Special Service,1,1,339,339,BIRD STUCK IN NETTING   RSPCA ON SCENE,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011468,FAIRFIELD,E09000008,CROYDON,Croydon,1.00023E+11,NORTH END,20501238,CR0,532191,165905,532150,165950,51.37682073,-0.10202979
NA,22/11/2019 07:46,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BETWEEN WALL AND FENCE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,VALLENTIN ROAD,22883400,E17,NULL,NULL,538250,189350,NULL,NULL
NA,22/11/2019 09:36,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000343,WEST RUISLIP,E09000017,HILLINGDON,Ruislip,1.00023E+11,RESERVOIR ROAD,21401623,HA4,508683,189227,508650,189250,51.59142519,-0.432475748
NA,22/11/2019 12:26,2019,2019/20,Special Service,1,1,339,339,KITTEN STUCK BEHIND KITCHEN UNIT,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009376,HOMERTON,E09000012,HACKNEY,Homerton,NULL,STEVENS AVENUE,20900974,E9,NULL,NULL,535350,184850,NULL,NULL
NA,22/11/2019 13:11,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,GRAFTON PLACE,20400856,NW1,NULL,NULL,529750,182750,NULL,NULL
NA,23/11/2019 20:54,2019,2019/20,Special Service,1,1,339,339,NULL,Unknown - Domestic Animal Or Pet,Other FRS,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000342,WEST DRAYTON,E09000017,HILLINGDON,Hayes,1.00024E+11,MILL ROAD,21401294,UB7,505440,179073,505450,179050,51.50077925,-0.48231471
NA,24/11/2019 03:12,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - DOG FALLEN IN HOLE,Dog,Person (mobile),Pipe or drain,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000045,CHILDS HILL,E09000003,BARNET,West Hampstead,200052627,GREENFIELD GARDENS,20019340,NW2,524457,186333,524450,186350,51.56215901,-0.205911543
NA,25/11/2019 20:26,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000178,GREENFORD GREEN,E09000009,EALING,Northolt,12176982,GREENFORD ROAD,20602353,UB6,515260,183932,515250,183950,51.54253233,-0.339316513
NA,26/11/2019 12:59,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000087,BRONDESBURY PARK,E09000005,BRENT,Willesden,202126588,BRONDESBURY PARK,20201215,NW6,524047,184128,524050,184150,51.542428,-0.212602
NA,26/11/2019 14:08,2019,2019/20,Special Service,1,1,339,339,SQUIRREL TRAPPED VIA LEGS INSIDE BIN - SQUIRREL IN DISTRESS - RSPCA CONTACTED,Squirrel,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Hendon,200065607,FREELAND PARK,20016560,NW4,524037,190319,524050,190350,51.59807855,-0.210547761
NA,28/11/2019 00:11,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED IN STEERING COLUMN,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05000050,EDGWARE,E09000003,BARNET,Mill Hill,200112983,TAYSIDE DRIVE,20042150,HA8,519822,193522,519850,193550,51.62776753,-0.270289448
NA,29/11/2019 14:21,2019,2019/20,Special Service,1,1,339,339,PIGEONS TRAPPED IN NETTING-NEXT TO CHICKEN SHOP IN MARKET,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009370,DALSTON,E09000012,HACKNEY,Stoke Newington,1.00024E+11,KINGSLAND HIGH STREET,20900579,E8,533587,184954,533550,184950,51.54767579,-0.074793683
NA,29/11/2019 22:05,2019,2019/20,Special Service,1,3,339,1017,CAT TRAPPED UNDER CAR    FRU REQUESTED FROM SCENE WITH SPECIALIST TOOLS TO EXTRICATE THE CAT  CALLER,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011229,ST. MARY'S & ST. JAMES,E09000004,BEXLEY,Bexley,10011844891,BEXLEY HIGH STREET,20100138,DA5,549513,173547,549550,173550,51.44119519,0.149892487
NA,30/11/2019 15:05,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,10034920938,THE BROADWAY,22306023,IG8,540937,191859,540950,191850,51.60793628,0.033899537
NA,01/12/2019 22:50,2019,2019/20,Special Service,1,1,339,339,ASSIST IN CAT RESCUE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000263,SHEPHERD'S BUSH GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,WARBECK ROAD,21000858,W12,NULL,NULL,522950,179850,NULL,NULL
NA,02/12/2019 20:37,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009386,VICTORIA,E09000012,HACKNEY,Bethnal Green,1.00024E+11,VICTORIA PARK ROAD,20901033,E9,535316,183838,535350,183850,51.53723556,-0.050294111
NA,02/12/2019 23:53,2019,2019/20,Special Service,NULL,NULL,339,NULL,DOG STUCK UNDER SHED,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000472,VILLAGE,E09000024,MERTON,New Malden,48018302,COTTENHAM PARK ROAD,22101632,SW20,522163,169857,522150,169850,51.41457936,-0.244677733
NA,03/12/2019 14:22,2019,2019/20,Special Service,1,1,339,339,BIRD HANGING BY NECK FROM RAILWAY BRIDGE   WILDLIFE RESCUE VOLUNTEER IN ATTENDANCE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,6155299,CABLE STREET,22700241,E1,534951,181004,534950,181050,51.51185921,-0.056643913
NA,03/12/2019 16:48,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED BEHIND WIRED FENCING - UPPER CAR PARK - NEAR OLD RUINS,Dog,Person (mobile),Heathland,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000116,CRYSTAL PALACE,E09000006,BROMLEY,Beckenham,1.0002E+11,CRYSTAL PALACE PARADE,20302173,SE19,534103,171195,534150,171150,51.42391468,-0.072571332
NA,05/12/2019 02:05,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000452,SYDENHAM,E09000023,LEWISHAM,Forest Hill,NULL,SYDENHAM ROAD,22001951,SE26,NULL,NULL,536350,171650,NULL,NULL
NA,05/12/2019 19:54,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN HOLE - CALLER WILL MEET YOU AT THE BP PETROL STATION,Dog,Person (mobile),Petrol station,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000055,HENDON,E09000003,BARNET,Hendon,200013819,BRENT STREET,20004600,NW4,523350,189298,523350,189250,51.58904765,-0.220827324
NA,07/12/2019 23:47,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000649,WEST END,E09000033,WESTMINSTER,Soho,1.00023E+11,MOUNT STREET,8401887,W1K,528453,180595,528450,180550,51.50970106,-0.150381176
NA,10/12/2019 11:37,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05011117,SURREY DOCKS,E09000028,SOUTHWARK,Deptford,2.00003E+11,TEAK CLOSE,22502476,SE16,536259,179981,536250,179950,51.50234886,-0.038210013
NA,10/12/2019 11:42,2019,2019/20,Special Service,1,1,339,339,CAT POSSIBLY TRAPPED  CALLER UNABLE TO COMMIUNICATE INFORMATION CLEARLY - DUE TO LANGUAGE BARRIER,Cat,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000615,FURZEDOWN,E09000032,WANDSWORTH,Tooting,1.00023E+11,SOUTHCROFT ROAD,22904728,SW17,528215,170809,528250,170850,51.42179972,-0.157350473
NA,10/12/2019 14:10,2019,2019/20,Special Service,1,1,339,339,RUNNING CALL TO CAT IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000445,GROVE PARK,E09000023,LEWISHAM,Eltham,NULL,CASTLETON ROAD,22004775,SE9,NULL,NULL,541550,171650,NULL,NULL
NA,12/12/2019 14:12,2019,2019/20,Special Service,1,3,339,1017,DOG TRAPPED IN BADGER HOLE - FRU REQUESTED FROM SCENE   **RVP HOGARTH LANE OPPOSITE CHISWICK SCHOOL*,Dog,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000349,CHISWICK RIVERSIDE,E09000018,HOUNSLOW,Chiswick,10093765957,CHISWICK HOUSE GROUNDS,21590044,W4,520810,177394,520850,177350,51.48260846,-0.261552199
NA,13/12/2019 15:22,2019,2019/20,Special Service,1,2,339,678,BIRD TRAPPED ON WINDOW LEDGE  LONDON WILDLIFE PROTECTION ON SCENE AL REQUESTED FROM SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,BROWN HART GARDENS,8401078,W1K,NULL,NULL,528350,181050,NULL,NULL
NA,16/12/2019 16:21,2019,2019/20,Special Service,1,2,339,678,DOG STUCK IN PIPE IN PARK ON BUILDING SITE CALLER WILL MEET BRIGADE NEAR UNLOADING AREA,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000347,BRENTFORD,E09000018,HOUNSLOW,Acton,10091692323,POPES LANE,20602411,W5,518614,179080,518650,179050,51.49822772,-0.29259438
NA,17/12/2019 17:53,2019,2019/20,Special Service,1,2,339,678,RUNNING CALL TO DOG TIED TO LAMPOST OUTSIDE STATION,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,1.00022E+11,LONGBRIDGE WAY,22003859,SE13,538158,174960,538150,174950,51.45676896,-0.012830778
NA,18/12/2019 14:31,2019,2019/20,Special Service,1,5,339,1695,Redacted,Unknown - Wild Animal,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05011218,BELVEDERE,E09000004,BEXLEY,Erith,10090793774,CLYDESDALE WAY,20101889,DA17,549828,179891,549850,179850,51.49811025,0.157111048
NA,19/12/2019 19:23,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,NULL,DACCA STREET,22000299,SE8,NULL,NULL,536950,177950,NULL,NULL
NA,20/12/2019 19:02,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND WASHING MACHINE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000175,EAST ACTON,E09000009,EALING,Park Royal,NULL,SEACOLE CLOSE,20602050,W3,NULL,NULL,520850,181550,NULL,NULL
NA,23/12/2019 10:13,2019,2019/20,Special Service,1,2,339,678,DEER WITH ANTLERS TRAPPED IN WASHING LINE,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,KINGS LYNN DRIVE,21300091,RM3,553827,191840,553850,191850,51.60439729,0.219895829
NA,24/12/2019 15:58,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009381,LONDON FIELDS,E09000012,HACKNEY,Shoreditch,1.00021E+11,FRESHFIELD AVENUE,20900427,E8,533634,184322,533650,184350,51.54199323,-0.074351626
NA,24/12/2019 20:34,2019,2019/20,Special Service,1,1,339,339,CAT STUCK BEHIND CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011116,SOUTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,NULL,DUNLOP PLACE,22500834,SE16,NULL,NULL,533950,179150,NULL,NULL
NA,25/12/2019 19:12,2019,2019/20,Special Service,1,1,339,339,BIRDS TRAPPED IN NETTING ON BALCONY AT FIRST FLOOR LEVEL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000278,SEVEN SISTERS,E09000014,HARINGEY,Tottenham,NULL,REMINGTON ROAD,21103796,N15,NULL,NULL,532850,188350,NULL,NULL
NA,26/12/2019 09:28,2019,2019/20,Special Service,1,1,339,339,COLLAPSED HORSE BLOCKING ROADWAY   JUNCTION OF CEMETERY LANE,Horse,Police,Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000216,CHARLTON,E09000011,GREENWICH,East Greenwich,10010225469,CHARLTON PARK ROAD,20800318,SE7,542175,177932,542150,177950,51.48248069,0.046155386
NA,27/12/2019 09:57,2019,2019/20,Special Service,1,1,339,339,ASSIST RAILWAY STAFF WITH CAT TRAPPED BEHIND RAILING,Cat,Person (mobile),Train station - platform (at ground level or elevated),Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011097,CHAMPION HILL,E09000028,SOUTHWARK,Peckham,2.00003E+11,WINDSOR WALK,22502725,SE5,532802,176067,532850,176050,51.46800036,-0.08945448
NA,27/12/2019 12:56,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011253,VALENTINES,E09000026,REDBRIDGE,Ilford,1.00022E+11,COURTLAND AVENUE,22302752,IG1,543018,187142,543050,187150,51.56502979,0.062020808
NA,28/12/2019 09:04,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000225,PENINSULA,E09000011,GREENWICH,East Greenwich,1.00021E+11,FINGAL STREET,20800573,SE10,539823,178322,539850,178350,51.48657857,0.012459035
NA,28/12/2019 11:32,2019,2019/20,Special Service,1,1,339,339,BIRD IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist  trapped livestock animal,E05000416,BISHOP'S,E09000022,LAMBETH,Lambeth,NULL,FRAZIER STREET,21900578,SE1,NULL,NULL,531350,179650,NULL,NULL
NA,29/12/2019 12:32,2019,2019/20,Special Service,1,2,339,678,CAT STUCK ON ROOF - RSPCA INSP ON SCENE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000351,FELTHAM NORTH,E09000018,HOUNSLOW,Feltham,NULL,HOUNSLOW ROAD,21500631,TW14,NULL,NULL,511050,174450,NULL,NULL
NA,29/12/2019 16:22,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED IN WALL CAVITY - RSPCA ON SCENE REQUESTING ASSISTANCE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000200,GRANGE,E09000010,ENFIELD,Southgate,NULL,THE COPPICE,20705063,EN2,NULL,NULL,531750,196350,NULL,NULL
NA,30/12/2019 15:01,2019,2019/20,Special Service,1,1,339,339,CATS TRAPPED ON ROOF - ONE BY COLLAR,cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000571,WANDLE VALLEY,E09000029,SUTTON,Wallington,NULL,OAK WALK,22605736,SM6,NULL,NULL,528450,166450,NULL,NULL
NA,31/12/2019 12:00,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING -WILDLIFE TRUST ON SCENE,Bird,Person (land line),Train station - elsewhere,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000095,MAPESBURY,E09000005,BRENT,West Hampstead,202079062,KILBURN HIGH ROAD,20202523,NW6,524625,184644,524650,184650,51.54694229,-0.204087847
NA,01/01/2020 15:23,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER FLOOR BOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000145,WEST HAMPSTEAD,E09000007,CAMDEN,West Hampstead,NULL,WEST END LANE,20499056,NW6,NULL,NULL,525550,184850,NULL,NULL
NA,01/01/2020 16:02,2020,2019/20,Special Service,1,1,339,339,ASSIST RSPCA TO GAIN ACCESS DUE TO INJURED FOX     HE IS PARKED ON GREEN HUNDRED ROAD,Fox,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05011109,OLD KENT ROAD,E09000028,SOUTHWARK,Old Kent Road,2.00003E+11,BIRD IN BUSH ROAD,22500242,SE15,534576,177468,534550,177450,51.48017239,-0.06339237
NA,02/01/2020 23:26,2020,2019/20,Special Service,1,1,339,339,DOG WITH HEAD TRAPPED BETWEEN STONE STEP AND SHED,Dog,Person (mobile),Outdoor storage,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000419,CLAPHAM TOWN,E09000022,LAMBETH,Clapham,1.00022E+11,FITZWILLIAM ROAD,21900564,SW4,529339,175781,529350,175750,51.46623119,-0.13938316
NA,03/01/2020 14:30,2020,2019/20,Special Service,1,1,339,339,KITTEN STUCK BEHIND WARDROBE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,TASH PLACE,20701956,N11,NULL,NULL,528750,192250,NULL,NULL
NA,05/01/2020 11:16,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED BETWEEN CHIMNEY ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,NULL,DYSART AVENUE,21800350,KT2,NULL,NULL,517550,171650,NULL,NULL
NA,06/01/2020 10:34,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Bird,Person (mobile),Railway building - other,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000416,BISHOP'S,E09000022,LAMBETH,Lambeth,2E+11,YORK ROAD,21901539,SE1,530962,180074,530950,180050,51.5044353,-0.1144383
NA,06/01/2020 14:34,2020,2019/20,Special Service,1,2,339,678,Redacted,Unknown - Animal rescue from below ground - Farm animal,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207169546,LEA VALLEY ROAD,20703459,EN3,536523,195619,536550,195650,51.64281072,-0.028328427
NA,07/01/2020 11:03,2020,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000251,ASKEW,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,GOODWIN ROAD,21000382,W12,NULL,NULL,522450,179750,NULL,NULL
NA,07/01/2020 20:32,2020,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH SMALL ANIMAL RESCUE - SQUIRREL STUCK IN DRAINPIPE,Squirrel,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000062,WEST HENDON,E09000003,BARNET,Hendon,NULL,PENDRAGON WALK,20034110,NW9,NULL,NULL,521250,188350,NULL,NULL
NA,07/01/2020 20:43,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED BETWEEN TWO EXTERNAL WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011217,BARNEHURST,E09000004,BEXLEY,Bexley,NULL,APPLEDORE AVENUE,20100042,DA7,NULL,NULL,550550,176550,NULL,NULL
NA,08/01/2020 15:50,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - CAT TRAPPED BEHIND COOKER,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,NULL,HIGH ROAD,21106680,N17,NULL,NULL,533850,191550,NULL,NULL
NA,08/01/2020 17:19,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN AIR VENT    AT THE REAR OF COSTA. ACCESS APPLETON WAY,Cat,Person (land line),Restaurant/cafe,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000320,ST. ANDREW'S,E09000016,HAVERING,Hornchurch,1.00024E+11,HIGH STREET,21300416,RM12,553993,187117,553950,187150,51.561923,0.220226
NA,08/01/2020 18:17,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011248,MAYFIELD,E09000026,REDBRIDGE,Ilford,NULL,TAVISTOCK GARDENS,22302466,IG3,NULL,NULL,545350,185650,NULL,NULL
NA,09/01/2020 11:39,2020,2019/20,Special Service,1,1,339,339,CAT STUCK UP TREE - UNABLE TO CONTACT RSPCA,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,CROUCH END,E09000014,HARINGEY,Hornsey,NULL,CRESCENT ROAD,21100205,N8,NULL,NULL,529750,187950,NULL,NULL
NA,10/01/2020 06:24,2020,2019/20,Special Service,1,1,339,339,DEER TRAPPED IN FENCE,Deer,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,SHEFFIELD DRIVE,21301590,RM3,555468,192213,555450,192250,51.60730241,0.243735362
NA,10/01/2020 12:10,2020,2019/20,Special Service,1,1,339,339,FOX TRAPPED IN FENCING,Fox,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000036,MAYESBROOK,E09000002,BARKING AND DAGENHAM,Barking,100052507,WOODWARD ROAD,19900629,RM9,547461,184366,547450,184350,51.53894456,0.124907175
NA,10/01/2020 13:06,2020,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING  -  J/O ELM ROAD    RSPCA ON SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,NULL,ST GEORGES ROAD,21800892,KT2,NULL,NULL,519050,170150,NULL,NULL
NA,11/01/2020 18:14,2020,2019/20,Special Service,1,2,339,678,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000174,EALING COMMON,E09000009,EALING,Acton,NULL,CEDAR GROVE,20600341,W5,NULL,NULL,518050,179450,NULL,NULL
NA,16/01/2020 15:51,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000439,BROCKLEY,E09000023,LEWISHAM,Lewisham,NULL,DRAKE ROAD,22000344,SE4,NULL,NULL,537250,175750,NULL,NULL
NA,18/01/2020 08:23,2020,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED UNDER FRIDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000429,STOCKWELL,E09000022,LAMBETH,Brixton,NULL,STOCKWELL ROAD,21901316,SW9,NULL,NULL,530650,176350,NULL,NULL
NA,20/01/2020 22:08,2020,2019/20,Special Service,1,1,339,339,RUNNING CALL TO  PARROT TRAPPED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011238,CHURCHFIELDS,E09000026,REDBRIDGE,Woodford,10090505643,HIGH ROAD,22306016,IG8,539985,191743,539950,191750,51.6071334,0.020119487
NA,21/01/2020 10:52,2020,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN ELECTRIC CHAIR,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011116,SOUTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,NULL,LUCEY WAY,22501595,SE16,NULL,NULL,534350,179050,NULL,NULL
NA,21/01/2020 21:44,2020,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED UNDER FLOOR BOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011472,NORBURY & POLLARDS HILL,E09000008,CROYDON,Norbury,NULL,NEWLANDS ROAD,20501223,SW16,NULL,NULL,530450,169150,NULL,NULL
NA,23/01/2020 09:52,2020,2019/20,Special Service,1,1,339,339,DOG TRAPPED BETWEEN FENCES,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,NULL,CHESTNUT WALK,22305469,IG8,NULL,NULL,540150,192450,NULL,NULL
NA,24/01/2020 22:58,2020,2019/20,Special Service,1,1,339,339,CAT STUCK UNDER WALKWAY   CALLER WILL WAIT TO DIRECT AT LOCATION,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000275,NOEL PARK,E09000014,HARINGEY,Hornsey,NULL,MAYES ROAD,21104796,N22,NULL,NULL,531050,190050,NULL,NULL
NA,25/01/2020 01:10,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER LIFT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,NULL,SPENCER WAY,22702473,E1,NULL,NULL,534950,181050,NULL,NULL
NA,25/01/2020 09:16,2020,2019/20,Special Service,1,1,339,339,DOG TRAPPED ON LEDGE NEAR WATER,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000615,FURZEDOWN,E09000032,WANDSWORTH,Tooting,NULL,SOUTHCROFT ROAD,22904728,SW17,NULL,NULL,528050,170850,NULL,NULL
NA,25/01/2020 17:16,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN WINDOW,Cat,Person (land line),Bungalow - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000058,OAKLEIGH,E09000003,BARNET,Barnet,NULL,OAKLEIGH PARK NORTH,20032540,N20,NULL,NULL,526750,194150,NULL,NULL
NA,25/01/2020 18:17,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011105,NEWINGTON,E09000028,SOUTHWARK,Lambeth,NULL,CRAMPTON STREET,22500633,SE17,NULL,NULL,532050,178450,NULL,NULL
NA,26/01/2020 09:03,2020,2019/20,Special Service,1,1,339,339,CAT STUCK BEHIND RAILING   CALLER WILL MEET YOU,Cat,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000643,REGENT'S PARK,E09000033,WESTMINSTER,Paddington,1.00023E+11,ABERDEEN PLACE,8400293,NW8,526754,182360,526750,182350,51.52593836,-0.174209502
NA,26/01/2020 11:09,2020,2019/20,Special Service,1,1,339,339,BIRD STUCK IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000127,WEST WICKHAM,E09000006,BROMLEY,Woodside,NULL,DIXON PLACE,20301884,BR4,NULL,NULL,537650,166050,NULL,NULL
NA,27/01/2020 14:09,2020,2019/20,Special Service,1,1,339,339,BIRD CAUGHT IN CABLE   RSPCA IN ATTENDANCE AND REQUEST ASSISTANCE,Bird,Person (mobile),Converted office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000300,RAYNERS LANE,E09000015,HARROW,Harrow,1.00023E+11,RAYNERS LANE,21201971,HA5,512935,187763,512950,187750,51.57743031,-0.37159057
NA,27/01/2020 17:44,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDERNEATH HOUSE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000329,EASTCOTE AND EAST RUISLIP,E09000017,HILLINGDON,Ruislip,NULL,SUTTON CLOSE,21401898,HA5,NULL,NULL,510450,188650,NULL,NULL
NA,28/01/2020 03:03,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN CLOTHING AIRER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000564,SUTTON CENTRAL,E09000029,SUTTON,Sutton,NULL,CROWN ROAD,22602247,SM1,NULL,NULL,525550,164850,NULL,NULL
NA,28/01/2020 23:46,2020,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000131,CANTELOWES,E09000007,CAMDEN,Kentish Town,NULL,CAMDEN PARK ROAD,20400532,NW1,NULL,NULL,529950,184650,NULL,NULL
NA,30/01/2020 08:36,2020,2019/20,Special Service,1,1,339,339,DOG TRAPPED UNDER SEAT IN VAN,Dog,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,207089318,BOUNCES ROAD,20703940,N9,534820,193981,534850,193950,51.62850853,-0.053557323
NA,30/01/2020 11:10,2020,2019/20,Special Service,1,1,339,339,SEAGULL TRAPPED IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,REDESDALE STREET,21700456,SW3,NULL,NULL,527550,177950,NULL,NULL
NA,31/01/2020 14:30,2020,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN WIRING AT FIRST FLOOR LEVEL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009320,BOW WEST,E09000030,TOWER HAMLETS,Bethnal Green,NULL,OLD FORD ROAD,22700893,E3,NULL,NULL,535950,183350,NULL,NULL
NA,31/01/2020 23:54,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER FLOORING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000208,SOUTHGATE,E09000010,ENFIELD,Southgate,NULL,DALRYMPLE CLOSE,20701473,N14,NULL,NULL,529650,195150,NULL,NULL
NA,04/02/2020 09:29,2020,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN FOX HOLE,Dog,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,10010225306,BLACKHEATH AVENUE,20800172,SE10,538963,177309,538950,177350,51.47768691,-0.000323235
NA,04/02/2020 11:40,2020,2019/20,Special Service,1,1,339,339,DOG STUCK IN RAILINGS     NEAR THE DUCK POND AND THE REAR OF THE BASKETBALL COURT,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34142492,RAVENSCOURT PARK,21000675,W6,522321,178745,522350,178750,51.49442884,-0.239341441
NA,04/02/2020 23:19,2020,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED IN GARDEN,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000250,ADDISON,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,LAKESIDE ROAD,21000485,W14,NULL,NULL,523750,179450,NULL,NULL
NA,05/02/2020 14:35,2020,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH    CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000217,COLDHARBOUR AND NEW ELTHAM,E09000011,GREENWICH,Eltham,1.00021E+11,BROWNSPRING DRIVE,20800245,SE9,543918,172009,543950,172050,51.42882527,0.06883247
NA,05/02/2020 18:28,2020,2019/20,Special Service,1,2,339,678,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,FLOOD STREET,21700213,SW3,NULL,NULL,527550,177850,NULL,NULL
NA,05/02/2020 20:01,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED ON NINTH FLOOR LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009378,HOXTON WEST,E09000012,HACKNEY,Islington,NULL,WIMBOURNE STREET,20901087,N1,NULL,NULL,532550,183350,NULL,NULL
NA,06/02/2020 22:38,2020,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000482,EAST HAM SOUTH,E09000025,NEWHAM,East Ham,10008995608,GOOSELEY LANE,22201752,E6,543652,182670,543650,182650,51.52468471,0.069328443
NA,07/02/2020 02:29,2020,2019/20,Special Service,1,1,339,339,DEER STUCK ON METAL FENCE    POLICE ON SCENE,Deer,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00024E+11,DAGNAM PARK DRIVE,21300071,RM3,554320,192363,554350,192350,51.60896296,0.227246831
NA,07/02/2020 11:38,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - CAT TRAPPED  BEHIND PIPEWORK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,GOLBORNE ROAD,21700877,W10,NULL,NULL,524550,182050,NULL,NULL
NA,07/02/2020 14:33,2020,2019/20,Special Service,1,1,339,339,KITTEN STUCK BEHIND KITCHEN CABINETS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,NULL,BIRNAM ROAD,21607114,N4,NULL,NULL,530650,186750,NULL,NULL
NA,07/02/2020 20:07,2020,2019/20,Special Service,1,1,339,339,KITTEN STUCK IN TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000339,TOWNFIELD,E09000017,HILLINGDON,Hayes,768010707,FULWOOD CLOSE,21400778,UB3,509856,181169,509850,181150,51.51877086,-0.418073254
NA,08/02/2020 12:58,2020,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT ON ROOF OF SECOND FLOOR   RSPCA OFFICER IN ATTENDANCE,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000215,BLACKHEATH WESTCOMBE,E09000011,GREENWICH,East Greenwich,NULL,WESTCOMBE HILL,20801590,SE3,NULL,NULL,540350,177550,NULL,NULL
NA,08/02/2020 14:49,2020,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NESTING BED BY CANAL AND ATTACHED TO REGENTS WHARF,Bird,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05000368,CALEDONIAN,E09000019,ISLINGTON,Euston,5300066495,NEW WHARF ROAD,21606910,N1,530514,183476,530550,183450,51.53511471,-0.119629116
NA,08/02/2020 18:21,2020,2019/20,Special Service,1,1,339,339,CAT  STUCK ON ROOF  CORNER OF  ST PANCRAS WAY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000131,CANTELOWES,E09000007,CAMDEN,Kentish Town,NULL,ROYAL COLLEGE STREET,20400512,NW1,NULL,NULL,529250,184050,NULL,NULL
NA,08/02/2020 22:12,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN BASEMENT OF BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000565,SUTTON NORTH,E09000029,SUTTON,Sutton,NULL,CHAUCER GARDENS,22602196,SM1,NULL,NULL,525350,165050,NULL,NULL
NA,09/02/2020 15:31,2020,2019/20,Special Service,1,2,339,678,PUPPY BELIEVED TO BE TRAPPED BEHIND A WALL,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000306,BROOKLANDS,E09000016,HAVERING,Romford,NULL,SPRING GARDENS,21301608,RM7,NULL,NULL,550250,188550,NULL,NULL
NA,12/02/2020 15:39,2020,2019/20,Special Service,1,1,339,339,RUNNING CALL TO SMALL ANIMAL RESCUE - CAT IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,NULL,WAVEL MEWS,21106917,N8,NULL,NULL,529850,189150,NULL,NULL
NA,13/02/2020 07:38,2020,2019/20,Special Service,1,2,339,678,CAT TRAPPED IN STAIRLIFT MECHANISM,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000441,CROFTON PARK,E09000023,LEWISHAM,Forest Hill,NULL,BEXHILL ROAD,22001229,SE4,NULL,NULL,536950,174350,NULL,NULL
NA,15/02/2020 16:57,2020,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011483,SHIRLY SOUTH,E09000008,CROYDON,Addington,NULL,BROOM ROAD,20500410,CR0,NULL,NULL,537350,165150,NULL,NULL
NA,17/02/2020 10:36,2020,2019/20,Special Service,1,1,339,339,ASSIST STAFF WITH PIGS AND SHEEP STRANDED IN FLOOD WATER     WATER OPS LEVEL ONE,Sheep,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Heathrow,1.00022E+11,BATH ROAD,21400116,UB7,504700,176682,504750,176650,51.4794205,-0.493680449
NA,19/02/2020 15:32,2020,2019/20,Special Service,1,1,339,339,CAT STUCK UP TREE - RSPCA UNABLE TO ATTEND,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000301,ROXBOURNE,E09000015,HARROW,Northolt,NULL,RAYNERS LANE,21201971,HA2,NULL,NULL,513550,186850,NULL,NULL
NA,20/02/2020 10:00,2020,2019/20,Special Service,1,1,339,339,CAT IMPALED IN BARBED WIRE,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011244,GOODMAYES,E09000026,REDBRIDGE,Ilford,1.00023E+11,ABERDOUR ROAD,22301786,IG3,546609,186951,546650,186950,51.56239509,0.113705195
NA,20/02/2020 14:47,2020,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011483,SHIRLY SOUTH,E09000008,CROYDON,Addington,NULL,LAUREL CRESCENT,20500488,CR0,NULL,NULL,537250,165050,NULL,NULL
NA,21/02/2020 00:50,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED INSIDE AIR BRICK,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,RIVERDALE ROAD,20801275,SE18,NULL,NULL,545550,178350,NULL,NULL
NA,22/02/2020 14:46,2020,2019/20,Special Service,1,1,339,339,DOG TRAPPED UNDERNEATH CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011480,SELSDON & ADDINGTON VILLAGE,E09000008,CROYDON,Addington,1.00023E+11,FREELANDS AVENUE,20501725,CR2,535733,162977,535750,162950,51.34967737,-0.052282362
NA,22/02/2020 16:22,2020,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011476,PURLEY & WOODCOTE,E09000008,CROYDON,Purley,NULL,BRIGHTON ROAD,20500187,CR8,NULL,NULL,531350,161650,NULL,NULL
NA,23/02/2020 00:30,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - HUSKY STUCK BY HOLE IN ROOF,Unknown - Domestic Animal Or Pet,Person (mobile),Private Garden Shed,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Hayes,1.00021E+11,ETON ROAD,21400689,UB3,509821,177105,509850,177150,51.4822489,-0.419829877
NA,24/02/2020 10:43,2020,2019/20,Special Service,1,1,339,339,PIGEONS STUCK IN NETTING  UNDER BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000267,BOUNDS GREEN,E09000014,HARINGEY,Southgate,10090481303,PINKHAM WAY,20034380,N11,529017,191815,529050,191850,51.61039633,-0.138138672
NA,24/02/2020 11:50,2020,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000095,MAPESBURY,E09000005,BRENT,West Hampstead,NULL,TEIGNMOUTH ROAD,20201460,NW2,NULL,NULL,524150,184950,NULL,NULL
NA,24/02/2020 13:38,2020,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000267,BOUNDS GREEN,E09000014,HARINGEY,Southgate,2.00001E+11,PINKHAM WAY,20034380,N11,529064,191831,529050,191850,51.61052884,-0.137459137
NA,24/02/2020 22:47,2020,2019/20,Special Service,1,2,339,678,CAT TRAPPED INSIDE EMPTY PROPERTY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,NULL,GRASMERE ROAD,21104585,N17,NULL,NULL,533950,191550,NULL,NULL
NA,25/02/2020 12:48,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - DOG TRAPPED IN NECK CHAIN,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000107,BIGGIN HILL,E09000006,BROMLEY,Biggin Hill,NULL,ARTHUR ROAD,20301440,TN16,NULL,NULL,541450,159250,NULL,NULL
NA,25/02/2020 20:01,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED BETWEEN A SHOP SIGN AND A LEDGE,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011117,SURREY DOCKS,E09000028,SOUTHWARK,Deptford,2.00003E+11,ELGAR STREET,22500893,SE16,536489,179395,536450,179350,51.4970267,-0.0351255
NA,26/02/2020 14:26,2020,2019/20,Special Service,1,2,339,678,PIGEON TRAPPED IN NETTING    WAITROSE SIDE OF THE BUILDING - NEAR VIRGIN ACTIVE SIGN - NEAR TO ROOF,Bird,Person (land line),Leisure Centre,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000057,MILL HILL,E09000003,BARNET,Finchley,200140920,LANGSTONE WAY,20025875,NW7,524039,191391,524050,191350,51.60770425,-0.210146868
NA,26/02/2020 14:46,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - CAT STUCK IN GUTTERING,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000555,BEDDINGTON NORTH,E09000029,SUTTON,Wallington,5870126179,RICHMOND GREEN,22604358,CR0,530576,165174,530550,165150,51.3706224,-0.1254905
NA,26/02/2020 16:59,2020,2019/20,Special Service,1,3,339,1017,HORSE STUCK IN CATTLEGATE RVP ALBURY DRIVE JO WOODHALL ROAD,Horse,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000297,PINNER,E09000015,HARROW,Harrow,1.00021E+11,WOODHALL ROAD,21201827,HA5,511618,191693,511650,191650,51.61301713,-0.389343819
NA,27/02/2020 13:48,2020,2019/20,Special Service,1,1,339,339,DOG WITH HEAD STUCK IN FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000637,KNIGHTSBRIDGE AND BELGRAVIA,E09000033,WESTMINSTER,Soho,10025398227,SERPENTINE ROAD,8401184,W2,526931,180259,526950,180250,51.50701674,-0.172423089
NA,27/02/2020 19:42,2020,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING AT THE JUNCTION CAVENDISH ROAD   ANIMAL LIFE WARDEN IS ON SCENE BUT UNABLE T,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,121033252,CAVENDISH ROAD,22900824,SW12,529130,173009,529150,173050,51.44136992,-0.143395436
NA,28/02/2020 07:26,2020,2019/20,Special Service,1,1,339,339,DOG LOCKED IN ALLOTMENT,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000313,HAVERING PARK,E09000016,HAVERING,Romford,NULL,BROOMFIELD CLOSE,21301038,RM5,NULL,NULL,550950,191450,NULL,NULL
NA,29/02/2020 08:11,2020,2019/20,Special Service,1,1,339,339,DOG STUCK IN A DOG CRATE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000454,WHITEFOOT,E09000023,LEWISHAM,Bromley,NULL,SHAW ROAD,22001887,BR1,NULL,NULL,539750,172150,NULL,NULL
NA,02/03/2020 10:36,2020,2019/20,Special Service,1,1,339,339,CAT STUCK UP A TREE - LADDER REQUIRED    ASSISTANCE REQUESTED BY RSPCA OFFICER IN ATTENDANCE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000378,ST. GEORGE'S,E09000019,ISLINGTON,Holloway,5300092767,TUFNELL PARK ROAD,21606493,N7,530155,185973,530150,185950,51.55763801,-0.123885212
NA,03/03/2020 14:52,2020,2019/20,Special Service,1,2,339,678,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000335,NORTHWOOD,E09000017,HILLINGDON,Ruislip,1.00021E+11,FRITHWOOD AVENUE,21400773,HA6,509580,191967,509550,191950,51.61588176,-0.418679326
NA,03/03/2020 18:59,2020,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,10013149326,MARKET SQUARE,20300493,BR1,540319,169271,540350,169250,51.40511521,0.016000348
NA,03/03/2020 23:06,2020,2019/20,Special Service,1,1,339,339,OPPOSITE BP PETROL STATION  - DOG STUCK BEHIND GREEN METAL FENCING AND IN BRAMBLES    OWNER UNABLE T,Dog,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000343,WEST RUISLIP,E09000017,HILLINGDON,Ruislip,1.00021E+11,BREAKSPEAR ROAD,21400231,HA4,508387,188770,508350,188750,51.58737492,-0.436895469
NA,04/03/2020 07:58,2020,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED BETWEEN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000476,BOLEYN,E09000025,NEWHAM,Plaistow,NULL,GREEN STREET,22208084,E13,NULL,NULL,541350,183550,NULL,NULL
NA,04/03/2020 22:10,2020,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Pub/wine bar/bar,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000365,TURNHAM GREEN,E09000018,HOUNSLOW,Chiswick,1.00023E+11,CHISWICK HIGH ROAD,21502481,W4,520427,178532,520450,178550,51.4929171,-0.266689041
NA,06/03/2020 11:19,2020,2019/20,Special Service,1,1,339,339,HORSE ENTANGLED IN CHAIN AROUND TREE,Horse,Person (mobile),Wasteland,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000356,HESTON EAST,E09000018,HOUNSLOW,Heston,10001272892,BISCOE CLOSE,21501369,TW5,513216,177903,513250,177950,51.48875244,-0.370707842
NA,06/03/2020 15:08,2020,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000592,CHAPEL END,E09000031,WALTHAM FOREST,Walthamstow,1.00023E+11,FOREST ROAD,22837350,E17,537462,189792,537450,189750,51.5902237,-0.01705065
NA,06/03/2020 15:39,2020,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED ABOVE AUTOMATIC DOORS,Bird,Person (mobile),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009333,SPITALFIELDS & BANGLATOWN,E09000030,TOWER HAMLETS,Bethnal Green,6017513,CAMBRIDGE HEATH ROAD,22700252,E1,534826,181992,534850,181950,51.520767,-0.058065
NA,06/03/2020 18:32,2020,2019/20,Special Service,1,1,339,339,DOG TRAPPED UNDER SHED IN BACK GARDEN,Dog,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000520,HAMPTON,E09000027,RICHMOND UPON THAMES,Twickenham,1.00022E+11,LINDEN ROAD,22400961,TW12,513156,169854,513150,169850,51.41642046,-0.374146883
NA,07/03/2020 14:25,2020,2019/20,Special Service,1,2,339,678,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000041,VILLAGE,E09000002,BARKING AND DAGENHAM,Dagenham,100072696,HEATHWAY,19900172,RM10,549132,184590,549150,184550,51.54051426,0.149080089
NA,07/03/2020 16:44,2020,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000344,YEADING,E09000017,HILLINGDON,Southall,NULL,YEADING LANE,21402234,UB4,NULL,NULL,511250,182450,NULL,NULL
NA,08/03/2020 10:22,2020,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000208,SOUTHGATE,E09000010,ENFIELD,Southgate,NULL,HIGH STREET,20701608,N14,NULL,NULL,529750,194150,NULL,NULL
NA,10/03/2020 09:45,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN NEIGHBOURS GARDEN INJURED AFTER FALLING FROM ROOF OF BUILDING   OWNER IN ATTENDANCE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000416,BISHOP'S,E09000022,LAMBETH,Lambeth,NULL,WESTMINSTER BRIDGE ROAD,21901482,SE1,NULL,NULL,531050,179550,NULL,NULL
NA,11/03/2020 07:26,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000054,HALE,E09000003,BARNET,Mill Hill,NULL,GIBBS GREEN,20017580,HA8,NULL,NULL,520450,192550,NULL,NULL
NA,11/03/2020 19:42,2020,2019/20,Special Service,1,1,339,339,CAT STUCK IN BARBED WIRE BEHIND FENCE,Cat,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000338,SOUTH RUISLIP,E09000017,HILLINGDON,Ruislip,10009948758,BOURNE COURT,21400214,HA4,511029,185316,511050,185350,51.55582048,-0.39986248
NA,12/03/2020 11:15,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND PIPES IN BATHROOM,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009325,LANSBURY,E09000030,TOWER HAMLETS,Poplar,NULL,UPPER NORTH STREET,22701256,E14,NULL,NULL,537550,181250,NULL,NULL
NA,12/03/2020 18:37,2020,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING - ALP REQUESTED,Bird,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000268,BRUCE GROVE,E09000014,HARINGEY,Tottenham,10003975506,HIGH ROAD,21106680,N17,533754,189787,533750,189750,51.59106849,-0.070547341
NA,13/03/2020 12:33,2020,2019/20,Special Service,1,1,339,339,ASSIST POLICE IN GAINING ENTRY FOR RSPCA,Unknown - Heavy Livestock Animal,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Hayes,10003002853,NEW ROAD,21401374,UB3,508386,177158,508350,177150,51.48300377,-0.440479013
NA,13/03/2020 14:38,2020,2019/20,Special Service,1,1,339,339,DOG STUCK IN TUNNEL    OWNER ON SCENE,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000056,HIGH BARNET,E09000003,BARNET,Barnet,200046312,GALDANA AVENUE,20017080,EN5,526239,196754,526250,196750,51.65541893,-0.176462166
NA,13/03/2020 16:56,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN BASEMENT   OWNER ON SCENE TO TRACK DOWN JUNCTION OF ST MICHAELS STREET,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000636,HYDE PARK,E09000033,WESTMINSTER,Paddington,NULL,BOUVERIE PLACE,8400891,W2,NULL,NULL,526950,181450,NULL,NULL
NA,13/03/2020 17:59,2020,2019/20,Special Service,1,1,339,339,CAT STUCK IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011225,ERITH,E09000004,BEXLEY,Erith,1.0002E+11,HOLLY HILL ROAD,20100737,DA8,550083,178283,550050,178250,51.48359229,0.160097639
NA,14/03/2020 21:35,2020,2019/20,Special Service,1,1,339,339,CAT STUCK ON EDGE OF ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000604,LEYTON,E09000031,WALTHAM FOREST,Leyton,NULL,PARK ROAD,22864350,E10,NULL,NULL,537350,187150,NULL,NULL
NA,15/03/2020 11:28,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - DOG STUCK UNDER DECKING,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000108,BROMLEY COMMON AND KESTON,E09000006,BROMLEY,Bromley,NULL,LABURNUM WAY,20300460,BR2,NULL,NULL,543550,166950,NULL,NULL
NA,15/03/2020 19:17,2020,2019/20,Special Service,1,2,339,678,CAT TRAPPED BETWEEN TWO SHEDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,NULL,TILSON ROAD,21105158,N17,NULL,NULL,534350,190550,NULL,NULL
NA,15/03/2020 22:40,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER CHAIR LIFT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,NULL,SPENCER WAY,22702473,E1,NULL,NULL,534950,181050,NULL,NULL
NA,17/03/2020 09:54,2020,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,NULL,LAZAR WALK,21606871,N7,NULL,NULL,530750,186750,NULL,NULL
NA,17/03/2020 17:24,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - DISTRESSED ELDERLY FEMALE WITH CAT TRAPPED BY BRANCH IN TREE  RSPOCA NOT ABLE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000429,STOCKWELL,E09000022,LAMBETH,Clapham,NULL,LARKHALL LANE,21900847,SW4,NULL,NULL,530150,176750,NULL,NULL
NA,19/03/2020 18:13,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED ON ROOF - CLIMBED UP SCAFFOLDING WHICH HAS NOW BEEN REMOVED BY BUILDERS - NO WAY FOR HIM,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,POWIS MEWS,21700973,W11,NULL,NULL,524950,181350,NULL,NULL
NA,19/03/2020 21:46,2020,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,LEDBURY ROAD,21700932,W11,NULL,NULL,524950,181350,NULL,NULL
NA,21/03/2020 18:31,2020,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000106,BICKLEY,E09000006,BROMLEY,Bromley,NULL,BLACKBROOK LANE,20300128,BR2,NULL,NULL,543050,167750,NULL,NULL
NA,22/03/2020 12:46,2020,2019/20,Special Service,1,1,339,339,CAT FALLEN FROM BALCONY ON THIRD FLOOR INTO BASEMENT AREA -  ADDITONAL PL REQUESTED FROM SCENE,Cat,Person (land line),Wasteland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Shoreditch,5156336,SAFFRON HILL,20401049,EC1N,531460,181912,531450,181950,51.52084141,-0.106586324
NA,23/03/2020 11:16,2020,2019/20,Special Service,1,1,339,339,BIRD CAUGHT ON NETTING    CAUGHT IN NETTING FIRST FLOOR,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05011483,SHIRLY SOUTH,E09000008,CROYDON,Addington,NULL,BROOM ROAD,20500410,CR0,NULL,NULL,537350,165150,NULL,NULL
NA,23/03/2020 22:44,2020,2019/20,Special Service,1,1,339,339,INJURED CAT STUCK ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000375,HOLLOWAY,E09000019,ISLINGTON,Holloway,NULL,EDEN GROVE,21603162,N7,NULL,NULL,530850,185250,NULL,NULL
NA,25/03/2020 21:46,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN ENGINE COMPARTMENT,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217112284,WAYNFLETE SQUARE,21701050,W10,523740,181007,523750,181050,51.51445476,-0.218113591
NA,27/03/2020 17:34,2020,2019/20,Special Service,1,1,339,339,PARROT ON ROOF    POSSIBLY INJURED  CALLED BY RSPCA WHO ARE UNABLE TO ATTEND AT THIS TIME,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011244,GOODMAYES,E09000026,REDBRIDGE,Ilford,NULL,HIGHBURY GARDENS,22302148,IG3,NULL,NULL,545250,186750,NULL,NULL
NA,28/03/2020 01:20,2020,2019/20,Special Service,1,1,339,339,HAMSTER TRAPPED BEHIND CUPBOARD,Hamster,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011483,SHIRLY SOUTH,E09000008,CROYDON,Addington,NULL,GORSE ROAD,20501734,CR0,NULL,NULL,537450,165050,NULL,NULL
NA,28/03/2020 11:03,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN LOCKED GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,10025176425,VERNON ROAD,22405207,SW14,520521,175645,520550,175650,51.46695123,-0.266308518
NA,28/03/2020 13:40,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED AT REAR OF,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011248,MAYFIELD,E09000026,REDBRIDGE,Ilford,NULL,HENLEY ROAD,22302138,IG1,NULL,NULL,544850,185950,NULL,NULL
NA,28/03/2020 17:53,2020,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,DRAYCOTT PLACE,21700154,SW3,NULL,NULL,527650,178550,NULL,NULL
NA,29/03/2020 14:30,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000612,EARLSFIELD,E09000032,WANDSWORTH,Tooting,NULL,QUINTON STREET,22904149,SW18,NULL,NULL,526250,172650,NULL,NULL
NA,29/03/2020 21:57,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN WIRE FENCE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,202130854,BECKETT CLOSE,20200229,NW10,521049,184624,521050,184650,51.54754397,-0.255632281
NA,31/03/2020 10:31,2020,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN RABBIT HOLE,Dog,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000318,RAINHAM AND WENNINGTON,E09000016,HAVERING,Wennington,10091576968,FERRY LANE,21300773,RM13,551727,179425,551750,179450,51.49341598,0.184242054
NA,31/03/2020 19:49,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,CONWAY ROAD,20704084,N14,NULL,NULL,530650,193450,NULL,NULL
NA,02/04/2020 04:53,2020,2020/21,Special Service,1,1,346,346,DEER STUCK IN METAL RAILINGS,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,2.00003E+11,SETTLE ROAD,21301584,RM3,555090,192383,555050,192350,51.60892773,0.238364363
NA,02/04/2020 15:53,2020,2020/21,Special Service,1,1,346,346,Redacted,Dog,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,HIGH ROAD LEYTONSTONE,22844950,E11,NULL,NULL,539350,186650,NULL,NULL
NA,04/04/2020 13:24,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009379,KING'S PARK,E09000012,HACKNEY,Homerton,NULL,ADLEY STREET,20900038,E5,NULL,NULL,536350,185450,NULL,NULL
NA,04/04/2020 20:08,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED BETWEEN WINDOWS,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009390,CAMPDEN,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,HORNTON STREET,21700720,W8,NULL,NULL,525350,179750,NULL,NULL
NA,05/04/2020 13:21,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED IN WALL BEHIND DRYER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011110,PECKHAM,E09000028,SOUTHWARK,Peckham,NULL,CHANDLER WAY,22502935,SE15,NULL,NULL,533550,177150,NULL,NULL
NA,05/04/2020 14:25,2020,2020/21,Special Service,1,1,346,346,FOX FALLEN INTO VIADUCT    MEET RSPCA OFFICRER IN THEIR VAN  IN THE CAR PARK,Fox,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Assist trapped wild animal,E05000338,SOUTH RUISLIP,E09000017,HILLINGDON,Ruislip,10022798372,WEST END ROAD,21402115,HA4,511003,184516,511050,184550,51.54862957,-0.400495169
NA,07/04/2020 09:30,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000461,GRAVENEY,E09000024,MERTON,Mitcham,NULL,SEELY ROAD,22105790,SW17,NULL,NULL,528150,170750,NULL,NULL
NA,07/04/2020 12:34,2020,2020/21,Special Service,1,1,346,346,CAT WITH PAW TRAPPED IN SINK  - RESIDENT NOT SELF ISOLATING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000275,NOEL PARK,E09000014,HARINGEY,Tottenham,NULL,GLADSTONE AVENUE,21104563,N22,NULL,NULL,531850,190550,NULL,NULL
NA,07/04/2020 13:41,2020,2020/21,Special Service,1,1,346,346,FOX CAUGHT IN NETTING OF GOAL POST,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,NULL,SWEET BRIAR GROVE,20704804,N9,NULL,NULL,533550,193150,NULL,NULL
NA,07/04/2020 14:33,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN NETTING THIRD FLOOR LEVEL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000046,COLINDALE,E09000003,BARNET,Mill Hill,NULL,LINKLEA CLOSE,20026840,NW9,NULL,NULL,521250,191050,NULL,NULL
NA,07/04/2020 15:31,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED AT THE BOTTOM OF THE SINK CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009334,STEPNEY GREEN,E09000030,TOWER HAMLETS,Shadwell,NULL,CLEARBROOK WAY,22700324,E1,NULL,NULL,535450,181350,NULL,NULL
NA,09/04/2020 21:13,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN TREE AVERY HILL PARK RSPCA IN ATTENDANCE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,10010246212,AVERY HILL ROAD,20800098,SE9,544543,174168,544550,174150,51.4480619,0.078685922
NA,10/04/2020 11:44,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000202,HIGHLANDS,E09000010,ENFIELD,Enfield,NULL,HIGH OAKS,20702749,EN2,NULL,NULL,530850,198050,NULL,NULL
NA,10/04/2020 19:54,2020,2020/21,Special Service,1,2,346,692,CAT STUCK BETWEEN WALLS        BETWEEN TWO BLOCKS OF FLATS      CONCIERGE WILL MEET BRIGADE  AT A HE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009370,DALSTON,E09000012,HACKNEY,Homerton,NULL,MARTEL PLACE,20900665,E8,NULL,NULL,533950,184950,NULL,NULL
NA,10/04/2020 22:15,2020,2020/21,Special Service,1,1,346,346,PERSON STUCK IN TREE  ATTEMPTING TO RESCUE CAT,Cat,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217023366,DARFIELD WAY,21700856,W10,523626,180987,523650,180950,51.51429877,-0.219762562
NA,12/04/2020 16:04,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,NULL,MARKHOUSE ROAD,22856450,E17,NULL,NULL,536550,188450,NULL,NULL
NA,12/04/2020 21:19,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000342,WEST DRAYTON,E09000017,HILLINGDON,Hayes,NULL,STATION ROAD,21401864,UB7,NULL,NULL,505950,179950,NULL,NULL
NA,12/04/2020 23:43,2020,2020/21,Special Service,1,1,346,346,ASSIST POLICE TO RESCUE CAT UP TREE WHO ARE IN ATTENDANCE WITH THE OWNER WHO HAS MENTAL HEALTH ISSUE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,10003976191,QUEENSWOOD ROAD,21106071,N6,528813,188611,528850,188650,51.5816581,-0.142268936
NA,13/04/2020 09:07,2020,2020/21,Special Service,1,2,346,692,Redacted,Horse,Person (mobile),Standing crop,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207180882,EAST DUCK LEES LANE,20706818,EN3,536671,195966,536650,195950,51.64589788,-0.026059285
NA,13/04/2020 18:54,2020,2020/21,Special Service,1,1,346,346,PIGEON IMPALED ON SPIKE  RSPCA IN ATTENDANCE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011484,SOUTH CROYDON,E09000008,CROYDON,Croydon,1.00021E+11,SELSDON ROAD,20502323,CR2,532844,163658,532850,163650,51.35647295,-0.093498073
NA,13/04/2020 23:29,2020,2020/21,Special Service,1,1,346,346,ANIMAL TRAPPED UNDER KITCHEN FLOOR,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,WESTERHAM AVENUE,20704910,N9,NULL,NULL,532950,193150,NULL,NULL
NA,15/04/2020 09:01,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011232,THAMESMEAD EAST,E09000004,BEXLEY,Plumstead,NULL,MARTHAM CLOSE,20100938,SE28,NULL,NULL,547750,180950,NULL,NULL
NA,15/04/2020 10:30,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UNDER SOLAR PANEL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000040,VALENCE,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,LYMINGTON ROAD,19900004,RM8,NULL,NULL,547850,187250,NULL,NULL
NA,15/04/2020 10:53,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009321,BROMLEY NORTH,E09000030,TOWER HAMLETS,Bethnal Green,NULL,WELLINGTON WAY,22701312,E3,NULL,NULL,537150,182550,NULL,NULL
NA,15/04/2020 20:00,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000042,WHALEBONE,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,SAVILLE ROAD,19900307,RM6,NULL,NULL,548750,187950,NULL,NULL
NA,17/04/2020 07:16,2020,2020/21,Special Service,1,1,346,346,FOAL STUCK IN DRAINAGE CHANNEL  CALLER WILL DIRECT YOU FROM WHARF ROAD,Horse,Person (mobile),Wasteland,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207007286,WHARF ROAD,20702448,EN3,536608,195899,536650,195850,51.64531072,-0.026997785
NA,17/04/2020 13:37,2020,2020/21,Special Service,1,4,346,1384,Redacted,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009378,HOXTON WEST,E09000012,HACKNEY,Islington,1.00024E+11,WENLOCK STREET,20901062,N1,532524,183108,532550,183150,51.53133896,-0.090805714
NA,18/04/2020 01:05,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING   CALLER WILL MEET BRIGADE,Bird,Person (mobile),Department Store,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000564,SUTTON CENTRAL,E09000029,SUTTON,Sutton,5870034325,HIGH STREET,22605497,SM1,525951,164129,525950,164150,51.36227589,-0.192270559
NA,18/04/2020 12:41,2020,2020/21,Special Service,1,1,346,346,PUPPY STUCK IN HOLE,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000196,COCKFOSTERS,E09000010,ENFIELD,Barnet,207078040,COVERT WAY,20701017,EN4,526638,197507,526650,197550,51.66209188,-0.170424193
NA,18/04/2020 14:51,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011099,DULWICH HILL,E09000028,SOUTHWARK,Forest Hill,NULL,DUNSTANS ROAD,22500836,SE22,NULL,NULL,534450,174150,NULL,NULL
NA,18/04/2020 15:45,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UP TREE  REQUESTED FROM RSPCA - WHO IS NOT ON SCENE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Lee Green,1.00021E+11,SIBTHORPE ROAD,20801356,SE12,540882,174021,540850,174050,51.44766632,0.025978125
NA,18/04/2020 21:17,2020,2020/21,Special Service,1,1,346,346,PIDGEON TRAPPED IN NETTING  - WILDLIFE TRUST ON SCENE,Bird,Person (mobile),"Tunnel, subway",Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,1.00024E+11,TRUNDLEYS ROAD,22001040,SE8,535973,177978,535950,177950,51.4844172,-0.0431001
NA,19/04/2020 12:49,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011111,PECKHAM RYE,E09000028,SOUTHWARK,New Cross,NULL,RYE HILL PARK,22502162,SE15,NULL,NULL,535150,175150,NULL,NULL
NA,19/04/2020 13:09,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05011112,ROTHERHITHE,E09000028,SOUTHWARK,Dockhead,NULL,MOODKEE STREET,22501741,SE16,NULL,NULL,535350,179450,NULL,NULL
NA,19/04/2020 16:31,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000425,LARKHALL,E09000022,LAMBETH,Clapham,10001113584,COTTAGE GROVE,21900391,SW4,530033,175573,530050,175550,51.46420608,-0.12947421
NA,20/04/2020 19:43,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Chingford,1.00023E+11,CONEY BURROWS,22828350,E4,539115,193911,539150,193950,51.62683233,0.008427898
NA,21/04/2020 03:42,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009334,STEPNEY GREEN,E09000030,TOWER HAMLETS,Shadwell,NULL,COMMERCIAL ROAD,22700346,E1,NULL,NULL,535850,181150,NULL,NULL
NA,21/04/2020 12:20,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN CHIMNEY   FLAT A    (TOP BELL),Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011099,DULWICH HILL,E09000028,SOUTHWARK,Forest Hill,NULL,DUNSTANS ROAD,22500836,SE22,NULL,NULL,534450,174150,NULL,NULL
NA,21/04/2020 12:24,2020,2020/21,Special Service,1,2,346,692,ASSIST RSCPA WITH CAT ON THE ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Hendon,NULL,HOLDERS HILL AVENUE,20023380,NW4,NULL,NULL,523850,189950,NULL,NULL
NA,21/04/2020 20:51,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NET ON THIRD FLOOR,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011234,ALDBOROUGH,E09000026,REDBRIDGE,Ilford,NULL,ROYAL CRESCENT,22306384,IG2,NULL,NULL,544650,188250,NULL,NULL
NA,22/04/2020 14:32,2020,2020/21,Special Service,1,1,346,346,DOG LOCKED IN CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Eltham,1.00021E+11,ELTHAM PALACE ROAD,20800528,SE9,542062,174144,542050,174150,51.44847553,0.043002775
NA,22/04/2020 18:01,2020,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000478,CANNING TOWN SOUTH,E09000025,NEWHAM,Poplar,10023996433,EAST INDIA DOCK ROAD,22201804,E14,539131,181345,539150,181350,51.51390645,0.00368577
NA,24/04/2020 10:53,2020,2020/21,Special Service,1,3,346,1038,KESTRAL TRAPPED IN BRANCHES,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000111,CHISLEHURST,E09000006,BROMLEY,Orpington,1.0002E+11,SOUTHFIELD ROAD,20301295,BR7,546020,168843,546050,168850,51.3998356,0.097742268
NA,24/04/2020 21:50,2020,2020/21,Special Service,1,1,346,346,PERSON STUCK IN TREE ATTEMPTING TO RETRIEVE PARROT,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,1.00023E+11,THIRD AVENUE,8401232,W10,524482,182639,524450,182650,51.52895345,-0.206850051
NA,25/04/2020 10:43,2020,2020/21,Special Service,1,1,346,346,DEER TRAPPED BETWEEN TWO GARAGES IN BACK GARDEN,Deer,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000320,ST. ANDREW'S,E09000016,HAVERING,Hornchurch,1.00021E+11,BENHURST AVENUE,21300223,RM12,552824,186089,552850,186050,51.55300058,0.202920997
NA,26/04/2020 09:38,2020,2020/21,Special Service,1,1,346,346,REQUESTED BY RSPCA TO CAT STUCK ON ROOF   RSPCA INSPECTOR NOT IN ATTENDANCE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000268,BRUCE GROVE,E09000014,HARINGEY,Tottenham,NULL,NAPIER ROAD,21104833,N17,NULL,NULL,533350,189750,NULL,NULL
NA,26/04/2020 10:10,2020,2020/21,Special Service,1,1,346,346,BIRD IMPALED ON SPIKES    IN REAR GARDEN,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,NULL,KINGS ROAD,21800550,KT2,NULL,NULL,519250,170350,NULL,NULL
NA,26/04/2020 13:12,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,ROSKELL ROAD,22904436,SW15,NULL,NULL,523550,175750,NULL,NULL
NA,27/04/2020 15:22,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN REAR GARDEN IN SOME SORT OF METAL CAGE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011099,DULWICH HILL,E09000028,SOUTHWARK,Peckham,NULL,BARRY ROAD,22500157,SE22,NULL,NULL,534250,174650,NULL,NULL
NA,27/04/2020 15:53,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011226,FALCONWOOD & WELLING,E09000004,BEXLEY,Plumstead,NULL,HOOK LANE,20100745,DA16,NULL,NULL,546450,175750,NULL,NULL
NA,27/04/2020 19:46,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING - LONDON WILDLIFE PROTECTION ON SCENE NEEDING ASSISTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000217,COLDHARBOUR AND NEW ELTHAM,E09000011,GREENWICH,Eltham,NULL,COURT ROAD,20800394,SE9,NULL,NULL,542250,172750,NULL,NULL
NA,28/04/2020 11:11,2020,2020/21,Special Service,1,1,346,346,RUNNING CALL FOR BIRD STUCK UNDER GUTTERING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,WOODLANDS ROAD,22888850,E17,NULL,NULL,538550,189750,NULL,NULL
NA,28/04/2020 13:22,2020,2020/21,Special Service,1,2,346,692,CAT TRAPPED IN ENGINE BLOCK OF CAR  - FRU REQUESTED,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,6039396,CYPRUS STREET,22700389,E2,535468,183101,535450,183150,51.53057768,-0.048400258
NA,29/04/2020 13:11,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Converted office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009325,LANSBURY,E09000030,TOWER HAMLETS,Poplar,6715635,MARKET SQUARE,22702010,E14,537814,181107,537850,181150,51.51209,-0.015381
NA,30/04/2020 16:04,2020,2020/21,Special Service,1,1,346,346,INJURED CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011231,SLADE GREEN & NORTHEND,E09000004,BEXLEY,Erith,NULL,MOAT LANE,20100987,DA8,NULL,NULL,552350,176550,NULL,NULL
NA,01/05/2020 05:52,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217048179,LADBROKE GROVE,21700912,W11,524449,181043,524450,181050,51.51461905,-0.20788451
NA,01/05/2020 18:18,2020,2020/21,Special Service,1,1,346,346,Redacted,Snake,Person (land line),Boarding House/B&B for homeless/asylum seekers,Other Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000284,WOODSIDE,E09000014,HARINGEY,Hornsey,1.00021E+11,PELLATT GROVE,21104917,N22,531012,190637,531050,190650,51.59935374,-0.10979562
NA,02/05/2020 15:01,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000472,VILLAGE,E09000024,MERTON,Wimbledon,48047745,MARRYAT ROAD,22104248,SW19,523937,171431,523950,171450,51.4283411,-0.218630976
NA,02/05/2020 16:14,2020,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009323,CANARY WHARF,E09000030,TOWER HAMLETS,Millwall,6065859,WESTFERRY ROAD,22701325,E14,537092,180093,537050,180050,51.5031554,-0.026167988
NA,03/05/2020 06:43,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000441,CROFTON PARK,E09000023,LEWISHAM,Forest Hill,NULL,BOVILL ROAD,22001255,SE23,NULL,NULL,535950,173850,NULL,NULL
NA,03/05/2020 12:45,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Warehouse,Non Residential,Other animal assistance,Assist trapped wild animal,E05000090,FRYENT,E09000005,BRENT,Stanmore,202217023,SILVERWORKS CLOSE,20204278,NW9,520765,189509,520750,189550,51.5915002,-0.2580573
NA,04/05/2020 13:22,2020,2020/21,Special Service,1,1,346,346,PIDGEON STUCK IN NETTING UNDER THE BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000459,DUNDONALD,E09000024,MERTON,New Malden,48119014,RAYNES PARK BRIDGE,22105345,SW20,523307,169306,523350,169350,51.40938141,-0.2284358
NA,04/05/2020 17:47,2020,2020/21,Special Service,1,1,346,346,CAT IS INJURED IN TREE UNABLE TO COME DOWN,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009382,SHACKLEWELL,E09000012,HACKNEY,Stoke Newington,1.00021E+11,PRINCESS MAY ROAD,20900823,N16,533293,185504,533250,185550,51.55269027,-0.078823198
NA,05/05/2020 13:18,2020,2020/21,Special Service,1,1,346,346,RSPCA IN ATTENDANCE -      FOX TRAPPED IN BRAMBLES ON TOP OF NETTING OF BASKET BALL COURT,Fox,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Wild animal rescue from height,E05000258,NORTH END,E09000013,HAMMERSMITH AND FULHAM,Fulham,34047897,ARCHEL ROAD,21000053,W14,524758,177973,524750,177950,51.48696225,-0.204518893
NA,05/05/2020 21:24,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN TELEGRAPH POLE,Bird,Person (land line),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000450,PERRY VALE,E09000023,LEWISHAM,Forest Hill,1.00023E+11,SIDDONS ROAD,22001894,SE23,535999,172746,535950,172750,51.43739938,-0.044728203
NA,06/05/2020 20:41,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED ON ROOF - RSPCA IN ATTENDANCE REQUESTING ASSISTANCE  - BEST ACCESS VIA KINGSWOOD CLOSE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000207,SOUTHBURY,E09000010,ENFIELD,Enfield,NULL,LINCOLN ROAD,20702802,EN1,NULL,NULL,533550,196050,NULL,NULL
NA,06/05/2020 22:40,2020,2020/21,Special Service,1,1,346,346,FOX POSSIBLY INJURED TRAPPED IN BASEMENT GARDEN,Fox,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000140,KILBURN,E09000007,CAMDEN,West Hampstead,NULL,WEST END LANE,20499056,NW6,NULL,NULL,525350,184250,NULL,NULL
NA,07/05/2020 03:30,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN REAR GARDEN,Fox,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05009324,ISLAND GARDENS,E09000030,TOWER HAMLETS,Millwall,6099921,TORRES SQUARE,22702327,E14,537410,178490,537450,178450,51.48867278,-0.022209928
NA,07/05/2020 10:12,2020,2020/21,Special Service,1,1,346,346,Redacted,Deer,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,1.00023E+11,BERRYS HILL,20301446,TN16,543749,159504,543750,159550,51.31649616,0.061347621
NA,07/05/2020 11:23,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOK - ASSIST RSPCA THEY ARE WAITING ON SCENE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000617,LATCHMERE,E09000032,WANDSWORTH,Battersea,NULL,FOWLER CLOSE,22901764,SW11,NULL,NULL,526750,175650,NULL,NULL
NA,07/05/2020 12:50,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA AND POLICE IN GAINING ENTRY  BOTH ON SCENE - KITTENS LOCKED IN BUILDING,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000193,BOWES,E09000010,ENFIELD,Southgate,NULL,GREEN LANES,20704279,N13,NULL,NULL,530950,192050,NULL,NULL
NA,08/05/2020 09:27,2020,2020/21,Special Service,1,1,346,346,DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,207037272,STREAMSIDE CLOSE,20704794,N9,533680,194123,533650,194150,51.63005187,-0.06996162
NA,08/05/2020 10:40,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (mobile),Recycling plant,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,1.00023E+11,TRUNDLEYS ROAD,22001040,SE8,535970,177981,535950,177950,51.48445346,-0.043132369
NA,08/05/2020 20:35,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000297,PINNER,E09000015,HARROW,Harrow,NULL,BRIDGE STREET,21201743,HA5,NULL,NULL,512150,189650,NULL,NULL
NA,08/05/2020 22:57,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN FOLDED FOOTBALL POST,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,46001381,ALLIANCE ROAD,22200318,E13,541060,181993,541050,181950,51.51925094,0.031725126
NA,09/05/2020 09:36,2020,2020/21,Special Service,1,1,346,346,PIGEON CAUGHT IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000487,LITTLE ILFORD,E09000025,NEWHAM,Ilford,NULL,GRANTHAM ROAD,22200183,E12,NULL,NULL,543350,185650,NULL,NULL
NA,09/05/2020 11:30,2020,2020/21,Special Service,1,1,346,346,DEER TRAPPED IN FENCING    THE MANOR ON  REGENTS DRIVE,Deer,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05011236,BRIDGE,E09000026,REDBRIDGE,Woodford,10034921095,CLARENCE GATE,22306209,IG8,543049,191372,543050,191350,51.60303151,0.064184605
NA,09/05/2020 12:46,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED BETWEEN FENCE,Fox,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000210,TOWN,E09000010,ENFIELD,Enfield,207140004,ROCHESTER CLOSE,20702932,EN1,533453,197719,533450,197750,51.66242008,-0.071864639
NA,09/05/2020 13:30,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA INSPECTOR WITH CAT STUCK BETWEEN A GARAGE AND A WALL,Cat,Person (mobile),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011101,DULWICH WOOD,E09000028,SOUTHWARK,West Norwood,2.00003E+11,COLLEGE ROAD,22500543,SE19,533713,171616,533750,171650,51.42778318,-0.078028608
NA,09/05/2020 15:25,2020,2020/21,Special Service,1,1,346,346,FOX STUCK IN CAR,Fox,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05009386,VICTORIA,E09000012,HACKNEY,Homerton,1.00023E+11,LODDIGES ROAD,20900628,E9,535060,184361,535050,184350,51.54199754,-0.053783141
NA,10/05/2020 09:20,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH CAT TRAPPED IN FOX HOLE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000613,EAST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,VIEWFIELD ROAD,22905847,SW18,NULL,NULL,524850,174050,NULL,NULL
NA,10/05/2020 23:39,2020,2020/21,Special Service,1,1,346,346,DEER TRAPPED IN FENCE,Deer,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist  trapped livestock animal,E05000195,CHASE,E09000010,ENFIELD,Enfield,207018481,CLAY HILL,20702606,EN2,532539,198671,532550,198650,51.67119121,-0.084709875
NA,11/05/2020 09:46,2020,2020/21,Special Service,1,1,346,346,PUPPY WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,1.00023E+11,HERRIES STREET,8400416,W10,524363,183019,524350,183050,51.5323919,-0.2084392
NA,11/05/2020 11:51,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000355,HESTON CENTRAL,E09000018,HOUNSLOW,Heston,NULL,SUTTON LANE,21501095,TW3,NULL,NULL,512850,176250,NULL,NULL
NA,11/05/2020 13:49,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000564,SUTTON CENTRAL,E09000029,SUTTON,Sutton,5870034325,HIGH STREET,22605497,SM1,525950,164128,525950,164150,51.3622689,-0.1922752
NA,12/05/2020 16:32,2020,2020/21,Special Service,1,1,346,346,DUCKS TRAPPED IN DRAIN,Bird,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011236,BRIDGE,E09000026,REDBRIDGE,Woodford,10023189460,CHAPELMOUNT ROAD,22304879,IG8,542796,191793,542750,191750,51.6068753,0.060705081
NA,13/05/2020 04:17,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000452,SYDENHAM,E09000023,LEWISHAM,Forest Hill,NULL,LONGTON AVENUE,22002662,SE26,NULL,NULL,534350,171650,NULL,NULL
NA,13/05/2020 14:36,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK IN SMALL SPACE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,CHURCH STREET,8400794,NW8,NULL,NULL,527050,182150,NULL,NULL
NA,13/05/2020 16:54,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK IN BATHROOM WALL CAVITY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000186,NORWOOD GREEN,E09000009,EALING,Southall,NULL,BLANDFORD ROAD,20600190,UB2,NULL,NULL,513050,178850,NULL,NULL
NA,13/05/2020 18:44,2020,2020/21,Special Service,1,1,346,346,PIDGEON TRAPPED IN TV AERIAL     CALLED BY RSPCA ON SCENE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011485,SOUTH NORWOOD,E09000008,CROYDON,Woodside,NULL,ROTHESAY ROAD,20501350,SE25,NULL,NULL,533050,168250,NULL,NULL
NA,14/05/2020 07:39,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED BETTWEN WINDOW AND THE RAILING,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,NULL,BELLING CRESCENT,20700957,EN3,NULL,NULL,535150,195950,NULL,NULL
NA,14/05/2020 23:21,2020,2020/21,Special Service,1,1,346,346,CAT IN DISTRESS IN TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000042,WHALEBONE,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,FARRANCE ROAD,19900126,RM6,NULL,NULL,548350,187950,NULL,NULL
NA,15/05/2020 05:24,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED UNDER BATH,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000227,SHOOTERS HILL,E09000011,GREENWICH,Plumstead,NULL,FLAXTON ROAD,20800577,SE18,NULL,NULL,545250,177350,NULL,NULL
NA,15/05/2020 17:32,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN THE MESH UNDER THE FOOTBRIDGE NEXT TO,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000122,ORPINGTON,E09000006,BROMLEY,Orpington,1.00023E+11,LYCH GATE ROAD,20301200,BR6,546426,166211,546450,166250,51.37608142,0.102486002
NA,15/05/2020 18:44,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,NULL,PARK ROAD,21880043,KT2,NULL,NULL,518850,171050,NULL,NULL
NA,16/05/2020 10:50,2020,2020/21,Special Service,1,1,346,346,BABY FOXES TRAPPED IN GARDEN - UNABLE TO GET BACK OUT,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000268,BRUCE GROVE,E09000014,HARINGEY,Tottenham,NULL,HARTHAM ROAD,21104621,N17,NULL,NULL,533650,190450,NULL,NULL
NA,16/05/2020 21:38,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011235,BARKINGSIDE,E09000026,REDBRIDGE,Ilford,1.00022E+11,EASTERN AVENUE,22302011,IG2,543527,188419,543550,188450,51.57637588,0.069878142
NA,17/05/2020 16:00,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000480,EAST HAM CENTRAL,E09000025,NEWHAM,East Ham,NULL,BARKING ROAD,22207589,E6,NULL,NULL,542750,183650,NULL,NULL
NA,17/05/2020 17:13,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED BEHIND WINDOW ON TOP FLOOR IN BUILDING UNDER RENOVATION - CALLER WILL MEET CREWS,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05009390,CAMPDEN,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,GLOUCESTER WALK,21700229,W8,NULL,NULL,525350,179950,NULL,NULL
NA,17/05/2020 17:21,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000482,EAST HAM SOUTH,E09000025,NEWHAM,East Ham,NULL,CHARLEMONT ROAD,22200458,E6,NULL,NULL,542850,182550,NULL,NULL
NA,17/05/2020 17:58,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UNDER DECKING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000225,PENINSULA,E09000011,GREENWICH,East Greenwich,NULL,WOOLWICH ROAD,20801725,SE7,NULL,NULL,540650,178350,NULL,NULL
NA,17/05/2020 19:26,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011244,GOODMAYES,E09000026,REDBRIDGE,Ilford,NULL,SEVEN KINGS ROAD,22302407,IG3,NULL,NULL,545550,186950,NULL,NULL
NA,18/05/2020 16:24,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000113,COPERS COPE,E09000006,BROMLEY,Beckenham,1.0002E+11,THE KNOLL,20301804,BR3,537894,169471,537850,169450,51.40750681,-0.018750773
NA,18/05/2020 17:42,2020,2020/21,Special Service,1,1,346,346,SWAN TRAPPED IN FENCE  CALLER WILL WILL MEET YOU,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000516,BARNES,E09000027,RICHMOND UPON THAMES,Hammersmith,10070719397,QUEEN ELIZABETH WALK,22404921,SW13,522813,176693,522850,176650,51.47587893,-0.232967857
NA,18/05/2020 22:33,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,NULL,GRESLEY CLOSE,22841125,E17,NULL,NULL,536250,188050,NULL,NULL
NA,19/05/2020 07:56,2020,2020/21,Special Service,NULL,NULL,346,NULL,CAT TRAPPED IN FURNITURE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,SEAFIELD ROAD,20701885,N11,NULL,NULL,529850,192450,NULL,NULL
NA,19/05/2020 13:19,2020,2020/21,Special Service,1,2,346,692,Redacted,Unknown - Wild Animal,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped wild animal,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,1.00023E+11,RAILWAY SIDE,22404932,SW13,521656,175830,521650,175850,51.46837436,-0.249913982
NA,19/05/2020 15:20,2020,2020/21,Special Service,2,3,346,1038,Redacted,Bird,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,1.00023E+11,RAILWAY SIDE,22404932,SW13,521655,175831,521650,175850,51.46838511,-0.249930391
NA,19/05/2020 16:17,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000283,WHITE HART LANE,E09000014,HARINGEY,Tottenham,NULL,LORDSHIP LANE,21106476,N17,NULL,NULL,532450,190550,NULL,NULL
NA,20/05/2020 11:58,2020,2020/21,Special Service,1,1,346,346,PIGEON CAUGHT IN NETTING ON BALCONY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011238,CHURCHFIELDS,E09000026,REDBRIDGE,Woodford,NULL,COOPERSALE CLOSE,22306027,IG8,NULL,NULL,541350,191150,NULL,NULL
NA,20/05/2020 14:12,2020,2020/21,Special Service,1,2,346,692,ANIMAL RESCUE LARGE - FOAL IN WATER    USE THE TOW PATH BY THE NAVIGATION INN. CALLER IS FISHING NEA,Horse,Person (mobile),Standing crop,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207184742,WHARF ROAD,20702448,EN3,536206,195490,536250,195450,51.64173077,-0.032965558
NA,21/05/2020 10:56,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH CAT RESCUE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011105,NEWINGTON,E09000028,SOUTHWARK,Lambeth,NULL,PENTON PLACE,22501965,SE17,NULL,NULL,531850,178450,NULL,NULL
NA,21/05/2020 11:21,2020,2020/21,Special Service,1,1,346,346,FOX CUB WITH HEAD STUCK IN ALLOY WHEEL - RSPCA ON SCENE,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05011463,ADDISCOMBE WEST,E09000008,CROYDON,Croydon,1.00021E+11,ADDISCOMBE COURT ROAD,20500605,CR0,533350,166069,533350,166050,51.37802429,-0.085323417
NA,21/05/2020 19:54,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN CHIMNEY AREA,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000098,QUEENS PARK,E09000005,BRENT,North Kensington,NULL,CREIGHTON ROAD,20202230,NW6,NULL,NULL,523950,183350,NULL,NULL
NA,22/05/2020 17:48,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000353,HANWORTH,E09000018,HOUNSLOW,Feltham,NULL,MAIN STREET,21500724,TW13,NULL,NULL,511950,171250,NULL,NULL
NA,22/05/2020 18:45,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000561,NONSUCH,E09000029,SUTTON,Sutton,5870119351,LONDON ROAD,22601662,SM3,523639,165173,523650,165150,51.37216222,-0.225099599
NA,23/05/2020 11:35,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000410,OLD MALDEN,E09000021,KINGSTON UPON THAMES,New Malden,NULL,LEYFIELD,21800602,KT4,NULL,NULL,521550,166050,NULL,NULL
NA,24/05/2020 09:34,2020,2020/21,Special Service,1,1,346,346,SQUIREL TRAPPED BETWEEN SATELLITE DISH,Squirrel,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000032,GASCOIGNE,E09000002,BARKING AND DAGENHAM,Barking,NULL,SUTTON ROAD,19900602,IG11,NULL,NULL,545450,183550,NULL,NULL
NA,24/05/2020 12:24,2020,2020/21,Special Service,1,1,346,346,CAT LOCKED IN NEIGHBOURS FLAT ON THIRD FLOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,NULL,WELWYN STREET,22701313,E2,NULL,NULL,535350,182950,NULL,NULL
NA,25/05/2020 10:47,2020,2020/21,Special Service,1,1,346,346,CAT STUCK BETWEEN CEILING TILE AND ROOF PETS   AT HOME     VETS  COMPANION CARE SITE,Cat,Person (land line),Veterinary surgery,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000364,SYON,E09000018,HOUNSLOW,Heston,1.00023E+11,LONDON ROAD,21519757,TW8,517148,177257,517150,177250,51.48215266,-0.314311692
NA,26/05/2020 07:36,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UNDER CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000098,QUEENS PARK,E09000005,BRENT,North Kensington,202079024,KILBURN LANE,20202035,W9,524503,183081,524550,183050,51.53292288,-0.206393217
NA,26/05/2020 08:01,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF OF HOUSE - UNABLE TO GET DOWN,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000419,CLAPHAM TOWN,E09000022,LAMBETH,Clapham,NULL,BRAYBURNE AVENUE,21900227,SW4,NULL,NULL,529450,176150,NULL,NULL
NA,26/05/2020 13:29,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE    CAT TRAPPED BEHIND A WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000531,TWICKENHAM RIVERSIDE,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,ST MARGARETS ROAD,22405949,TW1,NULL,NULL,517150,174150,NULL,NULL
NA,26/05/2020 14:50,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK IN TREE    RSPCA ON SCENE,Bird,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000213,WINCHMORE HILL,E09000010,ENFIELD,Southgate,207094305,FERNLEIGH ROAD,20704211,N21,531438,193899,531450,193850,51.62856752,-0.102415538
NA,26/05/2020 18:55,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000590,CANN HALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,HARROW GREEN,22843250,E11,NULL,NULL,539250,186250,NULL,NULL
NA,26/05/2020 19:19,2020,2020/21,Special Service,1,1,346,346,INJUIRED KITTEN STUCK IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011250,NEWBURY,E09000026,REDBRIDGE,Ilford,1.00022E+11,PERTH ROAD,22303147,IG2,544336,187651,544350,187650,51.56927215,0.081225027
NA,27/05/2020 13:06,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN WALL    CALLER WILL MEET YOU,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05009381,LONDON FIELDS,E09000012,HACKNEY,Shoreditch,NULL,GLEBE ROAD,20900446,E8,NULL,NULL,533550,184350,NULL,NULL
NA,27/05/2020 15:09,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN BARB WIRE    OWNER IN ATTENDANCE WILL LIASE WITH BRIGADE  ARRANDENE OPEN SPACE ACCESS,Dog,Person (land line),Heathland,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,200127721,WISE LANE,20046840,NW7,522306,191968,522350,191950,51.61327201,-0.234966062
NA,27/05/2020 16:19,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING   ASSIST RSPCA ON SCENE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Finchley,NULL,CHURCH CRESCENT,20008600,N3,NULL,NULL,524750,190650,NULL,NULL
NA,27/05/2020 17:14,2020,2020/21,Special Service,1,1,346,346,SQUIRREL TRAPPED IN SHELF BETWEEN WINDOW,Squirrel,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped wild animal,E05000530,TEDDINGTON,E09000027,RICHMOND UPON THAMES,Twickenham,1.00023E+11,HIGH STREET,22403558,TW11,516111,171140,516150,171150,51.42738248,-0.331255551
NA,27/05/2020 22:46,2020,2020/21,Special Service,1,1,346,346,Redacted,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000442,DOWNHAM,E09000023,LEWISHAM,Beckenham,NULL,CHESTNUT CLOSE,22001339,SE6,NULL,NULL,538350,171450,NULL,NULL
NA,28/05/2020 14:07,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN FENCE IN REAR GARDEN,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009380,LEA BRIDGE,E09000012,HACKNEY,Homerton,NULL,THORNBY ROAD,20901002,E5,NULL,NULL,535150,186250,NULL,NULL
NA,28/05/2020 15:18,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN NETTING ON BALCONY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011234,ALDBOROUGH,E09000026,REDBRIDGE,Ilford,NULL,MONARCH WAY,22306385,IG2,NULL,NULL,544850,188350,NULL,NULL
NA,28/05/2020 16:26,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN NETTING AT FRONT OF STORE,Bird,Person (mobile),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000170,ACTON CENTRAL,E09000009,EALING,Acton,12142724,KING STREET,20602382,W3,519972,180244,519950,180250,51.5084004,-0.2726579
NA,28/05/2020 17:55,2020,2020/21,Special Service,1,1,346,346,ANIMAL TRAPPED IN CHIMNEY,Unknown - Wild Animal,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000087,BRONDESBURY PARK,E09000005,BRENT,Willesden,NULL,BRYAN AVENUE,20201522,NW10,NULL,NULL,522850,184350,NULL,NULL
NA,29/05/2020 04:30,2020,2020/21,Special Service,1,1,346,346,PUPPY STUCK TO DOG CRATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000459,DUNDONALD,E09000024,MERTON,New Malden,NULL,VERNON AVENUE,22106453,SW20,NULL,NULL,523650,169150,NULL,NULL
NA,30/05/2020 07:26,2020,2020/21,Special Service,1,1,346,346,DEER STUCK IN GATE,Deer,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011236,BRIDGE,E09000026,REDBRIDGE,Woodford,NULL,SPRING GARDENS,22305325,IG8,NULL,NULL,541050,191450,NULL,NULL
NA,30/05/2020 16:02,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000565,SUTTON NORTH,E09000029,SUTTON,Sutton,NULL,COLLINGWOOD ROAD,22602218,SM1,NULL,NULL,525150,165150,NULL,NULL
NA,30/05/2020 20:41,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009390,CAMPDEN,E09000020,KENSINGTON AND CHELSEA,Kensington,217001205,AIRLIE GARDENS,21700692,W8,525098,180019,525050,180050,51.50526851,-0.198910522
NA,31/05/2020 05:03,2020,2020/21,Special Service,1,1,346,346,DOG FALLEN INTO POND,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05009374,HACKNEY WICK,E09000012,HACKNEY,Homerton,NULL,CADOGAN TERRACE,20900196,E9,NULL,NULL,536550,184650,NULL,NULL
NA,31/05/2020 09:18,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000137,HIGHGATE,E09000007,CAMDEN,Kentish Town,NULL,LISSENDEN GARDENS,20400214,NW5,NULL,NULL,528350,185850,NULL,NULL
NA,31/05/2020 11:55,2020,2020/21,Special Service,1,2,346,692,CAT TRAPPED BETWEEN TWO BRANCHES IN A TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000369,CANONBURY,E09000019,ISLINGTON,Islington,10001297848,ROTHERFIELD STREET,21605841,N1,532570,183962,532550,183950,51.53900221,-0.089829932
NA,31/05/2020 12:40,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE    BIRD TRAPPED BEHIND CUPBOARDS. RSPCA OFFICER IN ATTENDANCE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011112,ROTHERHITHE,E09000028,SOUTHWARK,Deptford,NULL,TIMBER POND ROAD,22502510,SE16,NULL,NULL,535950,180050,NULL,NULL
NA,31/05/2020 15:48,2020,2020/21,Special Service,1,1,346,346,FOX CUB WEDGED BETWEEN WALL OF HOUSE AND GARAGE   RSPCA IN ATTENDANCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011243,FULLWELL,E09000026,REDBRIDGE,Hainault,NULL,RYECROFT AVENUE,22305294,IG5,NULL,NULL,543650,190450,NULL,NULL
NA,31/05/2020 22:09,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000478,CANNING TOWN SOUTH,E09000025,NEWHAM,Plaistow,46027933,FRANK STREET,22200628,E13,540430,182231,540450,182250,51.52155418,0.022743332
NA,31/05/2020 22:12,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN GARAGE,Cat,Person (mobile),Underground car park,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,5034748,CAMDEN STREET,20400650,NW1,529190,183688,529150,183650,51.53732914,-0.138636741
NA,31/05/2020 22:17,2020,2020/21,Special Service,1,2,346,692,FOX WITH HEAD STUCK IN CAR WHEEL - REQUESTED BY RSPCA,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000317,PETTITS,E09000016,HAVERING,Romford,1.00021E+11,HAVERING ROAD,21300027,RM1,550766,190717,550750,190750,51.59513877,0.175256893
NA,01/06/2020 08:35,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000182,NORTHFIELD,E09000009,EALING,Ealing,NULL,DEVONSHIRE ROAD,20600535,W5,NULL,NULL,517250,179350,NULL,NULL
NA,01/06/2020 12:43,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN FOOTBALL GOAL NETTING ON SPORTS GROUND   OPP KNOCKED DOWN PUBLIC HOUSE,Fox,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000210,TOWN,E09000010,ENFIELD,Enfield,207136124,BELL ROAD,20702504,EN1,532840,197556,532850,197550,51.66109824,-0.080784413
NA,01/06/2020 22:21,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK ON CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000476,BOLEYN,E09000025,NEWHAM,East Ham,NULL,CENTRAL PARK ROAD,22200451,E6,NULL,NULL,541650,183050,NULL,NULL
NA,02/06/2020 16:20,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN TREE OPPOSITE WYATT COURT - CALLED BY LONDON WILDLIFE PROTECTION,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000188,SOUTH ACTON,E09000009,EALING,Acton,NULL,BOLLO BRIDGE ROAD,20600203,W3,NULL,NULL,520150,179450,NULL,NULL
NA,02/06/2020 18:02,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN WINDOW,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000480,EAST HAM CENTRAL,E09000025,NEWHAM,East Ham,NULL,ARTHUR ROAD,22207601,E6,NULL,NULL,542750,183250,NULL,NULL
NA,02/06/2020 18:52,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK BEHIND WARDROBE AND WALL  CALLER UNABLE TO MOVE WARDROBE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,NULL,ASHLIN ROAD,22203396,E15,NULL,NULL,538950,185450,NULL,NULL
NA,03/06/2020 09:32,2020,2020/21,Special Service,1,1,346,346,GUINEA PIG TRAPPED BEHIND KITCHEN UNITS   POSSIBLY INJURED,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000254,FULHAM BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,LILLIE ROAD,21000509,SW6,NULL,NULL,524750,177650,NULL,NULL
NA,03/06/2020 12:36,2020,2020/21,Special Service,1,1,346,346,RUNNING CALL TO CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000420,COLDHARBOUR,E09000022,LAMBETH,Brixton,1.00024E+11,WILTSHIRE ROAD,21901503,SW9,531250,175895,531250,175850,51.46681981,-0.111849653
NA,03/06/2020 15:19,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000193,BOWES,E09000010,ENFIELD,Southgate,NULL,HARDWICKE ROAD,20704304,N13,NULL,NULL,530250,192050,NULL,NULL
NA,03/06/2020 18:02,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED UNDER SINK,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000425,LARKHALL,E09000022,LAMBETH,Brixton,NULL,PRIDEAUX ROAD,21901118,SW9,NULL,NULL,530350,175850,NULL,NULL
NA,03/06/2020 18:43,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000220,ELTHAM WEST,E09000011,GREENWICH,Eltham,NULL,FOXHOLE ROAD,20800589,SE9,NULL,NULL,542150,174950,NULL,NULL
NA,05/06/2020 11:47,2020,2020/21,Special Service,1,1,346,346,FOX WITH HEAD STUCK BETWEEN TWO WALLS,Fox,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05009373,HACKNEY DOWNS,E09000012,HACKNEY,Stoke Newington,10008300545,RECTORY ROAD,20900843,N16,533940,186229,533950,186250,51.55905588,-0.069226694
NA,05/06/2020 14:30,2020,2020/21,Special Service,1,1,346,346,CAT POSSIBLY TRAPPED IN FENCING AT REAR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000279,STROUD GREEN,E09000014,HARINGEY,Holloway,NULL,MARQUIS ROAD,21103599,N4,NULL,NULL,530950,187350,NULL,NULL
NA,05/06/2020 17:48,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH GULL,Bird,Person (mobile),Vehicle Repair Workshop,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000207,SOUTHBURY,E09000010,ENFIELD,Enfield,207202200,CROWN ROAD,20702635,EN1,534817,196576,534850,196550,51.65182266,-0.052608347
NA,05/06/2020 19:47,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,CHURCH STREET,8400794,NW8,NULL,NULL,527050,182150,NULL,NULL
NA,06/06/2020 22:34,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF - ALLEYWAY AT SIDE OF BAPTIST CHURCH - CALLER WILL WAIT AND SHOW CREWS,Cat,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000045,CHILDS HILL,E09000003,BARNET,West Hampstead,200082864,MORTIMER CLOSE,20030220,NW2,524859,186464,524850,186450,51.5632456,-0.2000631
NA,07/06/2020 14:48,2020,2020/21,Special Service,1,1,346,346,DOG STUCK ON ROOF  CALL FROM POLICE - NOT ATTENDING,Dog,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000185,NORTHOLT WEST END,E09000009,EALING,Northolt,NULL,CHURCH ROAD,20600386,UB5,NULL,NULL,512450,183950,NULL,NULL
NA,07/06/2020 16:51,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN WALLS OF PROPERTY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011096,CAMBERWELL GREEN,E09000028,SOUTHWARK,Peckham,NULL,PICTON STREET,22501978,SE5,NULL,NULL,532850,177250,NULL,NULL
NA,07/06/2020 17:56,2020,2020/21,Special Service,1,1,346,346,INJURED CAT UP A TREE - CALLER STATED CAT IS HANGING OFF THE TREE,Cat,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,207044067,BRETTENHAM ROAD,20703959,N18,534736,192595,534750,192550,51.61606814,-0.055302254
NA,07/06/2020 18:50,2020,2020/21,Special Service,1,1,346,346,DEER WITH HEAD STUCK IN STORM DRAIN    CALLER STATES AS YOU COME INTO BREAM CLOSE ITS THE FIRST RIGH,Deer,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,10003980525,BREAM CLOSE,21106595,N17,534787,189379,534750,189350,51.58716199,-0.055800753
NA,07/06/2020 20:35,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED BEHIND TOILET CYSTERN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000202,HIGHLANDS,E09000010,ENFIELD,Enfield,NULL,HOLTWHITES HILL,20705071,EN2,NULL,NULL,531950,197550,NULL,NULL
NA,08/06/2020 10:39,2020,2020/21,Special Service,1,1,346,346,RUNNING CALL TO CROW TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,NULL,ALEXANDRA ROAD,20301692,SE26,NULL,NULL,535750,170850,NULL,NULL
NA,08/06/2020 16:34,2020,2020/21,Special Service,1,1,346,346,TO ASSIST RSPCA-CAT TRAPPED UNDER RAILWAY BRIDGE,Cat,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05011105,NEWINGTON,E09000028,SOUTHWARK,Old Kent Road,2.00003E+11,JOHN RUSKIN STREET,22501369,SE5,532322,177806,532350,177850,51.4837371,-0.0957146
NA,08/06/2020 19:28,2020,2020/21,Special Service,1,1,346,346,PERSON SHUT IN LIFT EMERGENCY   CAVENDISH HOUSE BOULEVARD DRIVE,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000046,COLINDALE,E09000003,BARNET,Mill Hill,NULL,BOULEVARD DRIVE,20004185,NW9,NULL,NULL,521850,190050,NULL,NULL
NA,08/06/2020 19:39,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN GUTTER,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000529,SOUTH TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,STRAWBERRY VALE,22403855,TW1,NULL,NULL,516150,172250,NULL,NULL
NA,08/06/2020 19:48,2020,2020/21,Special Service,1,2,346,692,DOG TRAPPED DOWN FOX HOLE - UNDERNEATH SHED,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000618,NIGHTINGALE,E09000032,WANDSWORTH,Tooting,NULL,BOUNDARIES ROAD,22900572,SW12,NULL,NULL,527850,172950,NULL,NULL
NA,09/06/2020 01:13,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND KITCHEN CABINETS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000320,ST. ANDREW'S,E09000016,HAVERING,Hornchurch,NULL,DEVONSHIRE ROAD,21300318,RM12,NULL,NULL,553550,186650,NULL,NULL
NA,09/06/2020 09:05,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN FOOTBALL NETS - ON THE FIELD,Fox,Person (land line),Other outdoor location,Outdoor,Other animal assistance,Assist trapped wild animal,E05000063,WOODHOUSE,E09000003,BARNET,Finchley,200144604,SCHOOL WAY,20038820,N12,526956,191578,526950,191550,51.60873919,-0.167984857
NA,09/06/2020 09:21,2020,2020/21,Special Service,1,2,346,692,RUNNING CALL TO SEAGULL WITH BROKEN WING - RSPCA REQUESTED TO ATTEND,Bird,Person (land line),Fire station,Non Residential,Other animal assistance,Animal harm involving wild animal,E05000482,EAST HAM SOUTH,E09000025,NEWHAM,East Ham,10008993909,HIGH STREET SOUTH,22200716,E6,542801,182520,542850,182550,51.5235531,0.0570087
NA,09/06/2020 20:27,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,46081917,WOODGRANGE ROAD,22200290,E7,540504,185090,540550,185050,51.54721932,0.024948789
NA,10/06/2020 13:56,2020,2020/21,Special Service,1,1,346,346,DOG WITH HEAD TRAPPED IN FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,NA,NA,NA,NA,Surbiton,2.00004E+11,GATLEY AVENUE,13900281,KT19,519636,164359,519650,164350,51.36570811,-0.282855266
NA,10/06/2020 18:07,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA ON SCENE WITH FOX TRAPPED IN NETTING,Fox,Person (mobile),Leisure Centre,Non Residential,Other animal assistance,Assist trapped wild animal,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Shadwell,6010921,TENCH STREET,22701195,E1W,534628,180216,534650,180250,51.504851,-0.061605
NA,11/06/2020 09:50,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Petrol station,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,10091940551,HIGH ROAD LEYTONSTONE,22844950,E11,539246,186487,539250,186450,51.56008725,0.007377774
NA,11/06/2020 11:14,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED UNDER PETROL STATION FORECOURT ROOF- RSPCA IN ATTENDANCE,Bird,Person (land line),Petrol station,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,1.00023E+11,HIGH ROAD LEYTONSTONE,22844950,E11,539242,186483,539250,186450,51.56005038,0.007322219
NA,11/06/2020 12:14,2020,2020/21,Special Service,1,1,346,346,BLACKBIRD TRAPPED IN NETTING   RSPCA ON SCENE,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000361,HOUNSLOW WEST,E09000018,HOUNSLOW,Heston,1.00023E+11,LAMPTON ROAD,21500676,TW3,513519,175719,513550,175750,51.46906678,-0.367040108
NA,11/06/2020 21:20,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN NETTING ON BALCONY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000347,BRENTFORD,E09000018,HOUNSLOW,Heston,NULL,BOSTON MANOR ROAD,21500133,TW8,NULL,NULL,517650,177650,NULL,NULL
NA,12/06/2020 22:25,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN ANTI BIRD NETTING CALL PASSED BY RSPCA WHO HAVE NO OFFICERS TO ATTEND,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000085,ALPERTON,E09000005,BRENT,Wembley,NULL,NORTHWICK ROAD,20201813,HA0,NULL,NULL,517850,183550,NULL,NULL
NA,13/06/2020 06:12,2020,2020/21,Special Service,1,1,346,346,YOUNG DEER WITH LEGS STUCK IN RAILINGS,Deer,Person (mobile),Heathland,Outdoor,Other animal assistance,Assist trapped wild animal,E05000353,HANWORTH,E09000018,HOUNSLOW,Feltham,1.00022E+11,HILL GROVE,21501717,TW13,512903,172699,512950,172650,51.44204098,-0.376880188
NA,13/06/2020 13:12,2020,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,NULL,HIGH STREET,22201445,E15,NULL,NULL,538050,183350,NULL,NULL
NA,13/06/2020 15:29,2020,2020/21,Special Service,1,1,346,346,DOG ON ROOF  OF HOUSE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000494,WEST HAM,E09000025,NEWHAM,Stratford,NULL,MEATH ROAD,22201517,E15,NULL,NULL,539550,183350,NULL,NULL
NA,13/06/2020 16:18,2020,2020/21,Special Service,1,1,346,346,CAT LOCKED IN STORAGE SHED   REAR OF,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,1.00023E+11,LANSDOWNE ROAD,21104730,N17,534082,190705,534050,190750,51.59924034,-0.065463959
NA,14/06/2020 11:35,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000046,COLINDALE,E09000003,BARNET,Mill Hill,NULL,THE CONCOURSE,20010000,NW9,NULL,NULL,521650,190750,NULL,NULL
NA,14/06/2020 12:05,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN WINDOW,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,NULL,PORTNALL ROAD,8400567,W9,NULL,NULL,524550,183050,NULL,NULL
NA,14/06/2020 12:45,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000562,ST. HELIER,E09000029,SUTTON,Sutton,NULL,GARENDON ROAD,22600569,SM4,NULL,NULL,525750,166550,NULL,NULL
NA,14/06/2020 21:47,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK ABOVE NETTING AT TESCO NEAR FRONT ENTRANCE OF THE STORE   WILD LIFE RESCUER ON SCENE WI,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05011476,PURLEY & WOODCOTE,E09000008,CROYDON,Purley,1.00023E+11,PURLEY ROAD,20502281,CR8,531235,161456,531250,161450,51.33706304,-0.117395736
NA,15/06/2020 08:56,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED IN REAR PATIO AREA   - CALLER WILL MEET CREWS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000366,BARNSBURY,E09000019,ISLINGTON,Islington,NULL,BROOKSBY STREET,21604364,N1,NULL,NULL,531350,184250,NULL,NULL
NA,15/06/2020 10:42,2020,2020/21,Special Service,1,1,346,346,DUCKLINGS TRAPPED IN GROOVE IN DRIVEWAY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000264,TOWN,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,PARSONS GREEN LANE,21000629,SW6,NULL,NULL,525050,176750,NULL,NULL
NA,15/06/2020 12:02,2020,2020/21,Special Service,1,1,346,346,DUCKLINGS TRAPPED IN GROOVE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000264,TOWN,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,PARSONS GREEN,21000628,SW6,NULL,NULL,525050,176450,NULL,NULL
NA,15/06/2020 17:58,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN CHIMNEY STACK,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,NULL,HEATHER LANE,21400935,UB7,NULL,NULL,506350,181350,NULL,NULL
NA,15/06/2020 21:27,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN DRAINAGE SYSTEM UNDER HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000351,FELTHAM NORTH,E09000018,HOUNSLOW,Feltham,NULL,BURNS AVENUE,21500182,TW14,NULL,NULL,510250,174150,NULL,NULL
NA,15/06/2020 23:29,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000365,TURNHAM GREEN,E09000018,HOUNSLOW,Chiswick,NULL,MARLBOROUGH ROAD,21500739,W4,NULL,NULL,520150,178450,NULL,NULL
NA,16/06/2020 16:44,2020,2020/21,Special Service,1,4,346,1384,ASSIST RSPCA OFFICER WITH CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal harm involving domestic animal,E05000141,KING'S CROSS,E09000007,CAMDEN,Euston,5119648,ACTON STREET,20400906,WC1X,530743,182740,530750,182750,51.52844552,-0.11661291
NA,17/06/2020 09:34,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000599,HIGH STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,WARNER ROAD,22885150,E17,NULL,NULL,536550,189250,NULL,NULL
NA,17/06/2020 10:03,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK IN NETTING,Bird,Person (land line),Self contained Sheltered Housing,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000044,BURNT OAK,E09000003,BARNET,Mill Hill,NULL,HANSHAW DRIVE,20020860,HA8,NULL,NULL,520650,190850,NULL,NULL
NA,18/06/2020 21:33,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BETWEEN WINDOW AND METAL GRILL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011465,BROAD GREEN,E09000008,CROYDON,Croydon,NULL,HANDCROFT ROAD,20501020,CR0,NULL,NULL,531750,166350,NULL,NULL
NA,19/06/2020 15:26,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000475,BECKTON,E09000025,NEWHAM,East Ham,46253169,CLAPS GATE LANE,22207527,E6,543772,182218,543750,182250,51.52059619,0.07087768
NA,19/06/2020 17:06,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,ABINGDON ROAD,20000160,N3,NULL,NULL,526250,190550,NULL,NULL
NA,19/06/2020 18:44,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000303,STANMORE PARK,E09000015,HARROW,Stanmore,10025300478,FOOTPATH 16 FROM END OF THE COMMON TO OLD LODGE WAY,21202859,HA7,515580,192566,515550,192550,51.62006257,-0.331853567
NA,19/06/2020 19:19,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN TREE    INSIDE THE PARK   CALLER WILL MEET YOU IN HEATH PASSAGE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,West Hampstead,5149796,HEATH PASSAGE,20400020,NW3,525952,186855,525950,186850,51.56651692,-0.184156848
NA,19/06/2020 21:08,2020,2020/21,Special Service,1,1,346,346,BABY PIGEON TRAPPED IN GUTTERING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000449,NEW CROSS,E09000023,LEWISHAM,New Cross,NULL,MYERS LANE,22000735,SE14,NULL,NULL,535650,177750,NULL,NULL
NA,20/06/2020 01:52,2020,2020/21,Special Service,1,1,346,346,DEER TRAPPED AND IMPAILED IN BETWEEN FENCES,Deer,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,WICKFORD CLOSE,21301698,RM3,554815,192385,554850,192350,51.60902344,0.234396085
NA,20/06/2020 10:26,2020,2020/21,Special Service,1,1,346,346,HORSE LOCKED IN HORSE TRAILER - OPPOSITE GLEBE FARM BUSINESS PARK,Horse,Person (mobile),Trailer (not attached to tractor unit),Road Vehicle,Other animal assistance,Assist  trapped livestock animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,10003629389,LEAVES GREEN ROAD,20300757,BR2,541935,162881,541950,162850,51.34729652,0.036682467
NA,20/06/2020 12:57,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED HANGING FROM ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000187,PERIVALE,E09000009,EALING,Wembley,NULL,EMPIRE ROAD,20600626,UB6,NULL,NULL,516850,183850,NULL,NULL
NA,20/06/2020 20:58,2020,2020/21,Special Service,1,3,346,1038,CAT TRAPPED BETWEEN BUILDINGS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011107,NORTH WALWORTH,E09000028,SOUTHWARK,Old Kent Road,NULL,OCCUPATION ROAD,22501834,SE17,NULL,NULL,532250,178250,NULL,NULL
NA,22/06/2020 10:30,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,NULL,ALEXANDRA ROAD,20301692,SE26,NULL,NULL,535750,170750,NULL,NULL
NA,22/06/2020 10:47,2020,2020/21,Special Service,1,1,346,346,BIRDS TRAPPED IN NETTING AT ROOF LEVEL - SEEN FROM TRENT HOUSE,Bird,Person (land line),Other retail warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000090,FRYENT,E09000005,BRENT,Stanmore,202099001,EDGWARE ROAD,20201631,NW9,520777,189479,520750,189450,51.59122865,-0.257882362
NA,22/06/2020 10:54,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED DOWN THE DRAIN CALLER WILL MEET AT LOCATION AND DIRECT YOU,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Chingford,1.00023E+11,NEWGATE STREET,22861050,E4,539489,193111,539450,193150,51.61954959,0.013508309
NA,22/06/2020 14:34,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Train station - platform (at ground level or elevated),Non Residential,Other animal assistance,Assist trapped wild animal,E05000462,HILLSIDE,E09000024,MERTON,Wimbledon,48086089,WIMBLEDON BRIDGE,22106759,SW19,524826,170657,524850,170650,51.42119192,-0.20612149
NA,22/06/2020 16:25,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011479,SELHURST,E09000008,CROYDON,Croydon,NULL,NEVILLE ROAD,20501220,CR0,NULL,NULL,532850,166750,NULL,NULL
NA,22/06/2020 22:36,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN BOILER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000477,CANNING TOWN NORTH,E09000025,NEWHAM,Plaistow,NULL,GRANT STREET,22200661,E13,NULL,NULL,540250,182550,NULL,NULL
NA,23/06/2020 12:29,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000365,TURNHAM GREEN,E09000018,HOUNSLOW,Chiswick,NULL,GROSVENOR ROAD,21500522,W4,NULL,NULL,520250,178150,NULL,NULL
NA,23/06/2020 13:50,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,WELLINGTON SQUARE,21700577,SW3,NULL,NULL,527650,178350,NULL,NULL
NA,23/06/2020 14:55,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05011222,CRAYFORD,E09000004,BEXLEY,Bexley,10011847436,THAMES ROAD,20101429,DA1,552753,175222,552750,175250,51.45538417,0.197195582
NA,23/06/2020 19:25,2020,2020/21,Special Service,1,1,346,346,KITTEN ON ROOF BEING ATTACKED BY CROWS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000442,DOWNHAM,E09000023,LEWISHAM,Bromley,NULL,BOURNESIDE GARDENS,22001252,SE6,NULL,NULL,538350,171350,NULL,NULL
NA,24/06/2020 10:46,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH PIDGEON TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011469,KENLEY,E09000008,CROYDON,Purley,NULL,FOXLEY ROAD,20500235,CR8,NULL,NULL,531950,159950,NULL,NULL
NA,24/06/2020 18:48,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED ON ROOF ALSO INJURED - RSPCA ON SCENE - ALP REQUESTED FROM SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000142,REGENT'S PARK,E09000007,CAMDEN,Euston,NULL,WILLIAM ROAD,20400835,NW1,NULL,NULL,529150,182550,NULL,NULL
NA,24/06/2020 20:54,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,West Hampstead,NULL,VALE OF HEALTH,20400078,NW3,NULL,NULL,526450,186450,NULL,NULL
NA,24/06/2020 23:42,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,CADOGAN SQUARE,21700060,SW1X,NULL,NULL,527850,178850,NULL,NULL
NA,25/06/2020 00:12,2020,2020/21,Special Service,1,1,346,346,RUBBISH ALIGHT,Bird,Person (mobile),Furniture warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000189,SOUTHALL BROADWAY,E09000009,EALING,Southall,10025481928,THE BROADWAY,20602249,UB1,511787,180601,511750,180650,51.51329058,-0.39043017
NA,25/06/2020 00:48,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK - CALLER WILL MEET,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal harm involving wild animal,E05000186,NORWOOD GREEN,E09000009,EALING,Southall,NULL,UXBRIDGE ROAD,20602324,UB1,NULL,NULL,514550,180250,NULL,NULL
NA,25/06/2020 12:22,2020,2020/21,Special Service,1,1,346,346,GOSLINGS STUCK AT WESTFIELD WEIR ENTRANCE DUE TO FLOW OF WATER,Unknown - Wild Animal,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,1.00023E+11,RAILWAY SIDE,22404932,SW13,521659,175828,521650,175850,51.46835668,-0.249878192
NA,25/06/2020 16:03,2020,2020/21,Special Service,1,1,346,346,TWO KITTENS TRAPPED UNDER CAR BONNET,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000342,WEST DRAYTON,E09000017,HILLINGDON,Hayes,1.00022E+11,LAUREL LANE,21401134,UB7,506228,178897,506250,178850,51.49904727,-0.471025927
NA,25/06/2020 16:27,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000044,BURNT OAK,E09000003,BARNET,Mill Hill,NULL,THIRLEBY ROAD,20042540,HA8,NULL,NULL,520550,190750,NULL,NULL
NA,26/06/2020 12:27,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000429,STOCKWELL,E09000022,LAMBETH,Clapham,NULL,BINFIELD ROAD,21900185,SW4,NULL,NULL,530450,176650,NULL,NULL
NA,26/06/2020 13:47,2020,2020/21,Special Service,1,3,346,1038,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000489,PLAISTOW NORTH,E09000025,NEWHAM,Plaistow,NULL,TWEEDMOUTH ROAD,22201160,E13,NULL,NULL,540650,183250,NULL,NULL
NA,26/06/2020 16:04,2020,2020/21,Special Service,1,1,346,346,CAT TAPPED UNDER FENCE AND IN BUSHES,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,NULL,GALLEONS DRIVE,19903950,IG11,NULL,NULL,546350,182450,NULL,NULL
NA,26/06/2020 20:51,2020,2020/21,Special Service,1,1,346,346,CAT  TRAPPED IN BUILDING UNDER RENOVATION,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,REDCLIFFE STREET,21700455,SW10,NULL,NULL,525850,177950,NULL,NULL
NA,27/06/2020 13:03,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED BETWEEN FENCE AND A WALL             ENTER VIA RAVENSCOURT SQUARE    CALLER WILL MEET AN,Fox,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34013192,RAVENSCOURT SQUARE,21000678,W6,522156,179194,522150,179150,51.4984963,-0.241562865
NA,27/06/2020 14:10,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED BEHIND COOKER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000406,COOMBE HILL,E09000021,KINGSTON UPON THAMES,New Malden,NULL,COOMBE HILL ROAD,21800274,KT2,NULL,NULL,521350,170350,NULL,NULL
NA,27/06/2020 15:23,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED UNDER THE GUTTER ON ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000605,LEYTONSTONE,E09000031,WALTHAM FOREST,Leytonstone,NULL,FAIRLOP ROAD,22835350,E11,NULL,NULL,538850,187650,NULL,NULL
NA,28/06/2020 00:14,2020,2020/21,Special Service,1,1,346,346,YOUNG DEER CAUGHT IN RAILINGS  O/S AMBERLEY HOUSE,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000321,SOUTH HORNCHURCH,E09000016,HAVERING,Wennington,10091576686,PASSIVE CLOSE,21320290,RM13,551755,182560,551750,182550,51.5215762,0.186006381
NA,28/06/2020 03:07,2020,2020/21,Special Service,1,1,346,346,KITTENS TRAPPED IN SHUTTER,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011096,CAMBERWELL GREEN,E09000028,SOUTHWARK,Peckham,2.00003E+11,COLDHARBOUR LANE,22502943,SE5,532481,176456,532450,176450,51.4715679,-0.0939317
NA,28/06/2020 12:37,2020,2020/21,Special Service,1,2,346,692,KITTEN TRAPPED IN CEILING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Hayes,NULL,OXFORD AVENUE,21401468,UB3,NULL,NULL,509450,177150,NULL,NULL
NA,28/06/2020 21:29,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING ON BALCONY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000347,BRENTFORD,E09000018,HOUNSLOW,Heston,NULL,BOSTON MANOR ROAD,21500133,TW8,NULL,NULL,517650,177650,NULL,NULL
NA,29/06/2020 06:58,2020,2020/21,Special Service,1,1,346,346,DOG WITH HEAD TRAPPED IN PLASTIC TOY - RESTRICTING BREATHING,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,NULL,PARISH LANE,20302157,SE20,NULL,NULL,535550,170650,NULL,NULL
NA,29/06/2020 23:20,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN WINDOW FRAME,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000279,STROUD GREEN,E09000014,HARINGEY,Holloway,NULL,FLORENCE ROAD,21103297,N4,NULL,NULL,531150,187550,NULL,NULL
NA,01/07/2020 08:05,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED UNDER KITCHEN CABINET,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000104,WEMBLEY CENTRAL,E09000005,BRENT,Wembley,NULL,JESMOND AVENUE,20200361,HA9,NULL,NULL,519050,184950,NULL,NULL
NA,01/07/2020 10:26,2020,2020/21,Special Service,1,1,346,346,BIRD OF PREY STUCK IN A TREE WRAPPED IN WIRE    RSPCA ON SCENE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000326,BRUNEL,E09000017,HILLINGDON,Hillingdon,1.00021E+11,UXBRIDGE ROAD,21402681,UB10,507291,182691,507250,182650,51.53294528,-0.454554687
NA,02/07/2020 02:02,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK BEHIND SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009395,EARL'S COURT,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,EARL'S COURT ROAD,21700166,SW5,NULL,NULL,525650,178450,NULL,NULL
NA,02/07/2020 11:06,2020,2020/21,Special Service,1,1,346,346,ANIMAL STUCK IN CHIMNEY,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000179,HANGER HILL,E09000009,EALING,Park Royal,NULL,HUXLEY GARDENS,20600932,NW10,NULL,NULL,518750,182950,NULL,NULL
NA,02/07/2020 11:46,2020,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000255,FULHAM REACH,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,RAINVILLE ROAD,21000669,W6,NULL,NULL,523350,177650,NULL,NULL
NA,02/07/2020 18:19,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE/ DUCKLING  IN DRAIN,Bird,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000056,HIGH BARNET,E09000003,BARNET,Barnet,10094709533,HIGH STREET,20022440,EN5,524531,196574,524550,196550,51.65417772,-0.201211907
NA,02/07/2020 20:02,2020,2020/21,Special Service,1,1,346,346,BIRD TANGLED IN ANTIBIRD NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000087,BRONDESBURY PARK,E09000005,BRENT,Willesden,NULL,WILLESDEN LANE,20201346,NW6,NULL,NULL,523750,184450,NULL,NULL
NA,03/07/2020 00:09,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011116,SOUTH BERMONDSEY,E09000028,SOUTHWARK,Old Kent Road,NULL,STEVENSON CRESCENT,22502808,SE16,NULL,NULL,534450,178350,NULL,NULL
NA,03/07/2020 22:40,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,NULL,LONG ACRE,8400849,WC2E,NULL,NULL,530250,180950,NULL,NULL
NA,03/07/2020 23:42,2020,2020/21,Special Service,1,1,346,346,FOX FALLEN INTO LIGHT WELL AT FRONT OF PROPERTY,Fox,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000260,PARSONS GREEN AND WALHAM,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,RYECROFT STREET,21000708,SW6,NULL,NULL,525550,176550,NULL,NULL
NA,04/07/2020 12:07,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,NULL,LONG ACRE,8400849,WC2E,NULL,NULL,530250,180950,NULL,NULL
NA,04/07/2020 14:04,2020,2020/21,Special Service,1,1,346,346,CALL FROM LONDON WILDLIFE PROTECTION - BABY BIRD STUCK ON BALCONY OF VACANT FLAT - FIRST FLOOR LEVEL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009336,WHITECHAPEL,E09000030,TOWER HAMLETS,Whitechapel,NULL,HOOPER STREET,22700652,E1,NULL,NULL,534150,181050,NULL,NULL
NA,04/07/2020 15:05,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF   REQ BY RSPCA,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000590,CANN HALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,CANN HALL ROAD,22822050,E11,NULL,NULL,539550,186050,NULL,NULL
NA,04/07/2020 19:36,2020,2020/21,Special Service,1,1,346,346,SEAGULL TRAPPED IN NETTING - BETWEEN NETTING AND A GLASS BALCONY  ON THIRD FLOOR       ON THE ROAD S,Bird,Person (mobile),Hotel/motel,Other Residential,Animal rescue from height,Wild animal rescue from height,E05000102,TOKYNGTON,E09000005,BRENT,Wembley,202200942,LAKESIDE WAY,20201216,HA9,519170,185682,519150,185650,51.557444,-0.28237
NA,05/07/2020 08:04,2020,2020/21,Special Service,1,1,346,346,CAT STUCK IN ENGINE COMPARTMENT OF CAR,Cat,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000418,CLAPHAM COMMON,E09000022,LAMBETH,Clapham,10008787927,MAUD CHADBURN PLACE,21903337,SW4,528857,174270,528850,174250,51.45276174,-0.146865438
NA,05/07/2020 12:34,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK BEHIND CAR RADIATOR - RED DACIA DUSTER,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000621,ROEHAMPTON AND PUTNEY HEATH,E09000032,WANDSWORTH,Wandsworth,10070248563,HIGHCROSS WAY,22906517,SW15,522395,173469,522350,173450,51.44699956,-0.240101545
NA,05/07/2020 17:16,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN FIRE ESCAPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011108,NUNHEAD & QUEEN'S ROAD,E09000028,SOUTHWARK,New Cross,NULL,CANDLE GROVE,22503216,SE15,NULL,NULL,534950,175650,NULL,NULL
NA,05/07/2020 22:47,2020,2020/21,Special Service,1,1,346,346,Redacted,Fox,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped wild animal,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,10008997626,UPTON LANE,22201168,E7,540560,184798,540550,184750,51.54458945,0.02564682
NA,06/07/2020 21:21,2020,2020/21,Special Service,1,3,346,1038,BABY SEAGULL TRAPPED ON SPIKE ON ROOF,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000128,BELSIZE,E09000007,CAMDEN,Kentish Town,NULL,GLENMORE ROAD,20400304,NW3,NULL,NULL,527250,184950,NULL,NULL
NA,07/07/2020 10:08,2020,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000128,BELSIZE,E09000007,CAMDEN,Kentish Town,NULL,GLENMORE ROAD,20400304,NW3,NULL,NULL,527250,184950,NULL,NULL
NA,07/07/2020 11:06,2020,2020/21,Special Service,1,1,346,346,CAT UP TREE   RSPCA IN ATTENDANCE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Hayes,1.00022E+11,ZEALAND AVENUE,21402241,UB7,506132,177209,506150,177250,51.48389508,-0.472913454
NA,07/07/2020 22:19,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED UP CHIMNEY,Cat,Person (mobile),Self contained Sheltered Housing,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011487,WADDON,E09000008,CROYDON,Croydon,NULL,COSEDGE CRESCENT,20502000,CR0,NULL,NULL,531450,164450,NULL,NULL
NA,08/07/2020 10:52,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000423,HERNE HILL,E09000022,LAMBETH,Brixton,NULL,MAYALL ROAD,21900934,SE24,NULL,NULL,531650,174850,NULL,NULL
NA,10/07/2020 12:45,2020,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,NULL,SLINGSBY PLACE,8400250,WC2E,NULL,NULL,530150,180950,NULL,NULL
NA,10/07/2020 21:16,2020,2020/21,Special Service,1,1,346,346,CAT WITH LEG TRAPPED IN TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000441,CROFTON PARK,E09000023,LEWISHAM,Forest Hill,NULL,BEXHILL ROAD,22001229,SE4,NULL,NULL,536850,174150,NULL,NULL
NA,11/07/2020 01:29,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BETWEEN SHUTTERS AND WINDOW - THIS IS A BUTCHERS SHOP NR THE CLOCK TOWER,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,1.00023E+11,LEWISHAM HIGH STREET,22004177,SE13,538304,175570,538350,175550,51.46221867,-0.0104889
NA,11/07/2020 15:08,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN PRECARIOUS POSITION    RSPCA INSPECTOR ON SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000141,KING'S CROSS,E09000007,CAMDEN,Euston,NULL,HUNTER STREET,20400990,WC1N,NULL,NULL,530250,182450,NULL,NULL
NA,11/07/2020 17:38,2020,2020/21,Special Service,1,1,346,346,BIRDS TRAPPED IN NETTING - JUNCTION CAMBERWELL STATION ROAD  - VOLUNTEER ON SCENE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000436,VASSALL,E09000022,LAMBETH,Brixton,1.00024E+11,DENMARK ROAD,21900455,SE5,532095,176569,532050,176550,51.4726813,-0.099438613
NA,12/07/2020 08:44,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - BIRD TRAPPED IN VENTILATION SHAFT,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000091,HARLESDEN,E09000005,BRENT,Willesden,NULL,MARIAN WAY,20200900,NW10,NULL,NULL,521650,184050,NULL,NULL
NA,12/07/2020 10:40,2020,2020/21,Special Service,1,1,346,346,DOG WITH HEAD TRAPPED BETWEEN RAILINGS   CALLER REMAINING ON SCENE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000637,KNIGHTSBRIDGE AND BELGRAVIA,E09000033,WESTMINSTER,Paddington,10033529604,BAYSWATER ROAD,8400628,W2,526559,180723,526550,180750,51.51127428,-0.177619736
NA,12/07/2020 16:52,2020,2020/21,Special Service,1,1,346,346,SQUIRREL TRAPPED IN CHIMNEY,Squirrel,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011486,THORNTON HEATH,E09000008,CROYDON,Woodside,NULL,CRANLEIGH GARDENS,20500825,SE25,NULL,NULL,533450,168850,NULL,NULL
NA,13/07/2020 12:29,2020,2020/21,Special Service,1,1,346,346,FOX STUCK IN FENCE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000440,CATFORD SOUTH,E09000023,LEWISHAM,Lee Green,NULL,MINARD ROAD,22001714,SE6,NULL,NULL,539150,172950,NULL,NULL
NA,13/07/2020 14:10,2020,2020/21,Special Service,1,2,346,692,ANIMAL TRAPPED BEHIND FIRE PLACE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000326,BRUNEL,E09000017,HILLINGDON,Hillingdon,NULL,ST CHRISTOPHER ROAD,21401810,UB8,NULL,NULL,505950,181450,NULL,NULL
NA,13/07/2020 15:53,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN WALL,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000273,HORNSEY,E09000014,HARINGEY,Hornsey,NULL,HAROLD ROAD,21103379,N8,NULL,NULL,530550,188950,NULL,NULL
NA,13/07/2020 18:42,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK IN VENT,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011232,THAMESMEAD EAST,E09000004,BEXLEY,Plumstead,NULL,FAIRWAY DRIVE,20101772,SE28,NULL,NULL,547850,181350,NULL,NULL
NA,14/07/2020 14:02,2020,2020/21,Special Service,1,2,346,692,ASSIST RSPCA - CAT STUCK BEHIND CLADDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000262,SANDS END,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,TOWNMEAD ROAD,21000826,SW6,NULL,NULL,525850,175750,NULL,NULL
NA,14/07/2020 18:56,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN VOID IN BEDROOM,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000108,BROMLEY COMMON AND KESTON,E09000006,BROMLEY,Bromley,NULL,SOUTHLANDS ROAD,20300621,BR2,NULL,NULL,541450,167950,NULL,NULL
NA,14/07/2020 20:16,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH PARAKEET STUCK IN CABLE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000359,HOUNSLOW HEATH,E09000018,HOUNSLOW,Feltham,NULL,MIDSUMMER AVENUE,21500757,TW4,NULL,NULL,512650,174950,NULL,NULL
NA,15/07/2020 15:34,2020,2020/21,Special Service,1,1,346,346,PIGEON CAUGHT IN NETTING OF BRIDGE   CALLER REMAINING ON SCENE TO DIRECT,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,6188679,COMMERCIAL ROAD,22700346,E14,536438,181122,536450,181150,51.51256125,-0.035186381
NA,15/07/2020 18:12,2020,2020/21,Special Service,1,1,346,346,ANIMAL TRAPPED IN DRAIN OUTSIDE OF,Unknown - Wild Animal,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000277,ST. ANN'S,E09000014,HARINGEY,Tottenham,1.00021E+11,WOODLANDS PARK ROAD,21104106,N15,532064,188867,532050,188850,51.58320011,-0.095281254
NA,15/07/2020 19:26,2020,2020/21,Special Service,1,3,346,1038,KITTEN TRAPPED BEHIND BEAM IN ATTIC -    SNAKE EYE CAMERA REQUESTED FROM SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,BULWER ROAD,20703992,N18,NULL,NULL,533350,192850,NULL,NULL
NA,15/07/2020 20:40,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK IN DRAIN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000200,GRANGE,E09000010,ENFIELD,Enfield,NULL,LONDON ROAD,20702812,EN2,NULL,NULL,532950,196150,NULL,NULL
NA,16/07/2020 11:04,2020,2020/21,Special Service,1,1,346,346,CAT UP TREE BEING ATTACKED BY BIRDS,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000172,DORMERS WELLS,E09000009,EALING,Southall,12015920,WHITECOTE ROAD,20601893,UB1,514035,181080,514050,181050,51.51714924,-0.357888532
NA,16/07/2020 15:45,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000254,FULHAM BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,ST JOHN'S CLOSE,21000757,SW6,NULL,NULL,525050,177350,NULL,NULL
NA,16/07/2020 20:27,2020,2020/21,Special Service,1,1,346,346,FOX IN RIVER THAMES IN THE SIDE OF LOCK,Fox,Person (land line),Hedge,Outdoor,Other animal assistance,Assist trapped wild animal,E05000519,"HAM, PETERSHAM AND RICHMOND RIVERSIDE",E09000027,RICHMOND UPON THAMES,Kingston,10002263285,RIVERSIDE DRIVE,22403779,TW10,516373,172295,516350,172250,51.43771678,-0.327095673
NA,17/07/2020 20:58,2020,2020/21,Special Service,1,1,346,346,ANIMAL TRAPPED IN A CHIMMNEY - CALLER IS SHIELDING,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000464,LONGTHORNTON,E09000024,MERTON,Norbury,NULL,LYNDHURST AVENUE,22104056,SW16,NULL,NULL,529850,169150,NULL,NULL
NA,18/07/2020 07:49,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON FIRST FLOOR WINDOW LEDGE - OFF DUTY POLICE OFFICER ON SCENE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,NULL,ST MARTIN'S LANE,8400618,WC2N,NULL,NULL,530050,180750,NULL,NULL
NA,18/07/2020 08:43,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - BIRD TRAPPED INSIDE CHIMNEY,Bird,Person (mobile),Self contained Sheltered Housing,Dwelling,Other animal assistance,Assist trapped wild animal,E05011246,ILFORD TOWN,E09000026,REDBRIDGE,Ilford,NULL,BALFOUR ROAD,22302601,IG1,NULL,NULL,543750,186750,NULL,NULL
NA,18/07/2020 08:55,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN FENCE,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000481,EAST HAM NORTH,E09000025,NEWHAM,East Ham,46024516,ESSEX ROAD,22200593,E12,542509,184792,542550,184750,51.54403809,0.053725223
NA,18/07/2020 16:03,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000649,WEST END,E09000033,WESTMINSTER,Euston,NULL,FOLEY STREET,8400008,W1W,NULL,NULL,529150,181650,NULL,NULL
NA,18/07/2020 17:28,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - INJURED BIRD,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,NULL,SOUTH GROVE,22875850,E17,NULL,NULL,536550,188650,NULL,NULL
NA,19/07/2020 10:37,2020,2020/21,Special Service,1,2,346,692,HORSE COLLAPSED IN FIELD,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011224,EAST WICKHAM,E09000004,BEXLEY,Plumstead,2.00002E+11,UPPER WICKHAM LANE,20101499,DA16,546688,176954,546650,176950,51.47254554,0.110690783
NA,19/07/2020 15:44,2020,2020/21,Special Service,1,3,346,1038,SEAGULL TRAPPED IN NETTING    HAS BEEN THERE SINCE LAST NIGHT    CALLER WILL MEET YOU ON SCENE,Bird,Person (mobile),Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009325,LANSBURY,E09000030,TOWER HAMLETS,Poplar,6135945,DEE STREET,22700402,E14,538409,181250,538450,181250,51.51323109,-0.006749358
NA,19/07/2020 23:17,2020,2020/21,Special Service,1,1,346,346,INJURED FOX TRAPPED IN METAL FENCE   BROKEN FOOT,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving wild animal,E05000626,TOOTING,E09000032,WANDSWORTH,Tooting,NULL,BRUDENELL ROAD,22900667,SW17,NULL,NULL,528150,171850,NULL,NULL
NA,20/07/2020 16:07,2020,2020/21,Special Service,1,3,346,1038,FOX TRAPPED BETWEEN TWO HOUSES   CALL FROM RSPCA,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000322,SQUIRREL'S HEATH,E09000016,HAVERING,Romford,NULL,CROSSWAYS,21300062,RM2,NULL,NULL,552750,189850,NULL,NULL
NA,20/07/2020 18:33,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000170,ACTON CENTRAL,E09000009,EALING,Acton,NULL,ALFRED ROAD,20600036,W3,NULL,NULL,520450,180150,NULL,NULL
NA,20/07/2020 22:09,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009367,BROWNSWOOD,E09000012,HACKNEY,Holloway,NULL,GLOUCESTER DRIVE,20900448,N4,NULL,NULL,531950,186950,NULL,NULL
NA,21/07/2020 14:28,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN ROOF   RSPCA ON SCENE REQUIRING ASSISTANCE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Woodford,NULL,HENRYS AVENUE,22844450,IG8,NULL,NULL,539550,191950,NULL,NULL
NA,21/07/2020 21:30,2020,2020/21,Special Service,1,1,346,346,PARROT STUCK ON ROOF,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000185,NORTHOLT WEST END,E09000009,EALING,Northolt,NULL,COMPTON CRESCENT,20600425,UB5,NULL,NULL,511950,184050,NULL,NULL
NA,22/07/2020 18:55,2020,2020/21,Special Service,1,2,346,692,KITTENS STUCK BEHIND WALL    RSCPA ON SCENE,Cat,Person (mobile),Department Store,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000618,NIGHTINGALE,E09000032,WANDSWORTH,Tooting,1.00023E+11,BALHAM HIGH ROAD,22900217,SW12,528481,173328,528450,173350,51.44438022,-0.152622368
NA,23/07/2020 21:13,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,NULL,AIREDALE AVENUE,21500012,W4,NULL,NULL,521750,178250,NULL,NULL
NA,24/07/2020 18:35,2020,2020/21,Special Service,1,1,346,346,BIRDS TRAPPED BEHIND NETTING UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000410,OLD MALDEN,E09000021,KINGSTON UPON THAMES,New Malden,128012637,MALDEN ROAD,21800637,KT4,522224,166090,522250,166050,51.38071039,-0.245099915
NA,25/07/2020 11:37,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000419,CLAPHAM TOWN,E09000022,LAMBETH,Clapham,NULL,RECTORY GROVE,21901147,SW4,NULL,NULL,529250,175950,NULL,NULL
NA,25/07/2020 19:34,2020,2020/21,Special Service,2,3,346,1038,DOG STUCK IN MIDDLE OF THE CANAL NEXT TO THE BRIDGE - WATER OPERATIONS LEVEL TWO    FRU WITH BOAT RE,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009329,ST. DUNSTAN'S,E09000030,TOWER HAMLETS,Bethnal Green,10025541719,BEN JONSON ROAD,22700144,E3,536360,181796,536350,181750,51.51863743,-0.036049569
NA,26/07/2020 20:22,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED IN DRAIN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Shadwell,NULL,WAPPING WALL,22701295,E1W,NULL,NULL,535350,180550,NULL,NULL
NA,26/07/2020 22:22,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - FOX WITH LEG TRAPPED IN WIRING,Fox,Person (land line),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000467,POLLARDS HILL,E09000024,MERTON,Norbury,NULL,CHESTNUT GROVE,22101386,CR4,NULL,NULL,529950,168150,NULL,NULL
NA,26/07/2020 22:57,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UNDER FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000049,EAST FINCHLEY,E09000003,BARNET,Finchley,NULL,SEDGEMERE AVENUE,20038900,N2,NULL,NULL,526250,189750,NULL,NULL
NA,27/07/2020 02:43,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,10090500380,GARRATT LANE,22901883,SW18,525652,174417,525650,174450,51.4548035,-0.192912333
NA,27/07/2020 13:46,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BETWEEN DOOR POSSIBLY INJURED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011489,WOODSIDE,E09000008,CROYDON,Woodside,NULL,PORTLAND ROAD,20501311,SE25,NULL,NULL,534550,167950,NULL,NULL
NA,28/07/2020 11:11,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011463,ADDISCOMBE WEST,E09000008,CROYDON,Croydon,1.00021E+11,GRANVILLE CLOSE,20500998,CR0,533114,165465,533150,165450,51.37264938,-0.088947382
NA,28/07/2020 12:48,2020,2020/21,Special Service,1,1,346,346,CAT FALLEN INTO ABANDONED COURTYARD,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000366,BARNSBURY,E09000019,ISLINGTON,Islington,NULL,BARNSBURY STREET,21604203,N1,NULL,NULL,531350,184150,NULL,NULL
NA,28/07/2020 15:01,2020,2020/21,Special Service,1,2,346,692,CAT ON ROOF INACCESSIBLE FROM REST OF BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,GREAT MARLBOROUGH STREET,8401373,W1F,NULL,NULL,529350,181150,NULL,NULL
NA,28/07/2020 16:38,2020,2020/21,Special Service,1,2,346,692,BIRDS TRAPPED IN LOFT,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000449,NEW CROSS,E09000023,LEWISHAM,New Cross,NULL,MYERS LANE,22000735,SE14,NULL,NULL,535650,177850,NULL,NULL
NA,28/07/2020 19:02,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000632,BRYANSTON AND DORSET SQUARE,E09000033,WESTMINSTER,Paddington,1.00023E+11,BRYANSTON MEWS WEST,8401140,W1H,527629,181457,527650,181450,51.5176278,-0.161939285
NA,29/07/2020 02:23,2020,2020/21,Special Service,1,1,346,346,FOX BELIEVED TRAPPED IN METAL FENCE BY THE WATERWORKS - CALLER DOES NOT WANT BRIGADE TO KNOCK,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000026,ABBEY,E09000002,BARKING AND DAGENHAM,Barking,100055287,COWBRIDGE LANE,19900672,IG11,543685,184343,543650,184350,51.53971004,0.07049203
NA,29/07/2020 06:41,2020,2020/21,Special Service,1,1,346,346,FOX CUB STUCK IN FENCE,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000269,CROUCH END,E09000014,HARINGEY,Hornsey,NULL,WESTON PARK,21104076,N8,NULL,NULL,530350,188450,NULL,NULL
NA,29/07/2020 12:13,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000425,LARKHALL,E09000022,LAMBETH,Brixton,1.00023E+11,LANDOR ROAD,21900834,SW9,530201,175676,530250,175650,51.46508989,-0.127021537
NA,29/07/2020 16:35,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN METAL GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000280,TOTTENHAM GREEN,E09000014,HARINGEY,Tottenham,NULL,GROVE PARK ROAD,21103358,N15,NULL,NULL,533350,189150,NULL,NULL
NA,29/07/2020 18:41,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ROOF IN PRECARIOUS POSITION,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011245,HAINAULT,E09000026,REDBRIDGE,Hainault,NULL,POLLARD CLOSE,22304702,IG7,NULL,NULL,546050,192250,NULL,NULL
NA,30/07/2020 12:26,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED BEHIND A METAL GRILL IN WINDOW    CALLER ON SCENE AND WILL DIRECT YOU,Bird,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011102,FARADAY,E09000028,SOUTHWARK,Old Kent Road,2.00003E+11,LIVERPOOL GROVE,22501528,SE17,532525,178129,532550,178150,51.48659534,-0.092658881
NA,31/07/2020 10:12,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN WINDOW GRILL  WILD LIFE TEAM ON SITE,Bird,Person (mobile),Church/Chapel,Non Residential,Other animal assistance,Assist trapped wild animal,E05011102,FARADAY,E09000028,SOUTHWARK,Old Kent Road,10013533004,LIVERPOOL GROVE,22501528,SE17,532559,178130,532550,178150,51.4865993,-0.0921685
NA,31/07/2020 13:33,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN NETTING   RSPCA IN ATTENDANCE   ACCESS VIA WALKWAY NEXT TO GYM,Bird,Person (mobile),Water works,Non Residential,Other animal assistance,Assist trapped wild animal,E05011468,FAIRFIELD,E09000008,CROYDON,Croydon,2.00001E+11,WATERWORKS YARD,20502803,CR0,532219,165392,532250,165350,51.37220731,-0.101823279
NA,31/07/2020 13:41,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK BEHIND WINDOW GRILL     FACING THE CHURCH TO THE RIGHT IN THE TOP WINDOW BEFORE THE SEP,Bird,Person (mobile),Church/Chapel,Non Residential,Other animal assistance,Assist trapped wild animal,E05011102,FARADAY,E09000028,SOUTHWARK,Old Kent Road,10013533004,LIVERPOOL GROVE,22501528,SE17,532559,178130,532550,178150,51.4865993,-0.0921685
NA,01/08/2020 08:53,2020,2020/21,Special Service,1,1,346,346,KITTEN LOCKED IN BEDROOM    FURNITURE FALLEN AGAINST DOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011102,FARADAY,E09000028,SOUTHWARK,Old Kent Road,NULL,WOOLER STREET,22502758,SE17,NULL,NULL,532750,178250,NULL,NULL
NA,01/08/2020 13:17,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011101,DULWICH WOOD,E09000028,SOUTHWARK,West Norwood,10000816583,BOWEN DRIVE,22500298,SE21,533396,171650,533350,171650,51.42816374,-0.082562444
NA,01/08/2020 18:26,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN NETTING AT BRIDGE - RSPCA IN ATTENDANCE - WILL MEET YOU AT  BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000114,CRAY VALLEY EAST,E09000006,BROMLEY,Orpington,10013149863,CRAY AVENUE,20301080,BR5,547016,168294,547050,168250,51.39464478,0.111820702
NA,01/08/2020 22:33,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - KITTEN STUCK IN CAR EXHAUST,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011250,NEWBURY,E09000026,REDBRIDGE,Ilford,1.00022E+11,COLENSO ROAD,22301952,IG2,545144,187454,545150,187450,51.56729265,0.092790251
NA,02/08/2020 00:28,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000225,PENINSULA,E09000011,GREENWICH,East Greenwich,NULL,TUSKAR STREET,20801523,SE10,NULL,NULL,539150,178050,NULL,NULL
NA,03/08/2020 02:34,2020,2020/21,Special Service,1,1,346,346,WOOD PIGEON TRAPPED BEHIND CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000111,CHISLEHURST,E09000006,BROMLEY,Sidcup,NULL,GREEN LANE,20302333,BR7,NULL,NULL,544050,170950,NULL,NULL
NA,03/08/2020 09:24,2020,2020/21,Special Service,1,1,346,346,RUNNING CALL TO TRAPPED FOX,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000450,PERRY VALE,E09000023,LEWISHAM,Forest Hill,NULL,KEMBLE ROAD,22001596,SE23,NULL,NULL,535950,173250,NULL,NULL
NA,03/08/2020 12:22,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH FOX STRANDED ON FORESHORE - RSPCA INSPECTOR IS EMILY     THIS IS NEAR TO WANDSWORT,Fox,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000262,SANDS END,E09000013,HAMMERSMITH AND FULHAM,Fulham,34154479,CENTRAL AVENUE,21001652,SW6,526188,175831,526150,175850,51.46739635,-0.184707255
NA,03/08/2020 16:13,2020,2020/21,Special Service,1,1,346,346,CAT IN TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000347,BRENTFORD,E09000018,HOUNSLOW,Ealing,10091693063,BOSTON MANOR ROAD,21500133,TW8,516980,178093,516950,178050,51.48969643,-0.316452401
NA,04/08/2020 07:46,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - CAT WITH LEG TRAPPED IN BATH VERY DISTRESSED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Kentish Town,NULL,CAMDEN GARDENS,20400648,NW1,NULL,NULL,528950,184150,NULL,NULL
NA,04/08/2020 17:58,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH SMALL ANIMAL RESCUE - RSPCA OFFICER ON SCENE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011469,KENLEY,E09000008,CROYDON,Purley,NULL,GODSTONE ROAD,20503071,CR8,NULL,NULL,531950,160550,NULL,NULL
NA,04/08/2020 18:17,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN DRAIN PIPE - LOCATED ON THE ROOF OF HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000472,VILLAGE,E09000024,MERTON,New Malden,NULL,ELLERTON ROAD,22102397,SW20,NULL,NULL,522150,170350,NULL,NULL
NA,05/08/2020 15:07,2020,2020/21,Special Service,1,1,346,346,DOG WITH FOOT TRAPPED IN SEAT OF CAR  BLACK AND GREY SMART CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal harm involving domestic animal,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,6041166,OLD FORD ROAD,22700893,E2,535472,183146,535450,183150,51.53098116,-0.048323841
NA,05/08/2020 16:39,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000106,BICKLEY,E09000006,BROMLEY,Bromley,1.0002E+11,PARKSIDE AVENUE,20300559,BR1,542304,168202,542350,168250,51.39501881,0.044091722
NA,05/08/2020 18:35,2020,2020/21,Special Service,1,1,346,346,Redacted,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000530,TEDDINGTON,E09000027,RICHMOND UPON THAMES,Twickenham,10002251960,BUSHY PARK,22407070,TW11,515295,169725,515250,169750,51.41483118,-0.343437209
NA,05/08/2020 19:09,2020,2020/21,Special Service,1,1,346,346,PUPPY TRAPPED IN CHIMNEY,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011100,DULWICH VILLAGE,E09000028,SOUTHWARK,West Norwood,NULL,AYSGARTH ROAD,22500123,SE21,NULL,NULL,533050,173950,NULL,NULL
NA,05/08/2020 23:01,2020,2020/21,Special Service,1,1,346,346,CAT FALLEN INTO A COURTYARD AND NOW TRAPPED UNABLE TO GET OUT.,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000632,BRYANSTON AND DORSET SQUARE,E09000033,WESTMINSTER,Paddington,NULL,BOSTON PLACE,8400897,NW1,NULL,NULL,527550,182250,NULL,NULL
NA,06/08/2020 00:21,2020,2020/21,Special Service,1,1,346,346,CAT STUCK BETWEEN WALL,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011465,BROAD GREEN,E09000008,CROYDON,Croydon,10093755716,WATTEAU SQUARE,20503294,CR0,531531,166021,531550,166050,51.3780175,-0.1114712
NA,06/08/2020 09:08,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED ON ROOF   STARLIGHT TRUST REPRESENTIVE IN ATTENDANCE,Bird,Person (mobile),Other Dwelling,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000569,WALLINGTON NORTH,E09000029,SUTTON,Wallington,NULL,HARCOURT AVENUE,22602368,SM6,NULL,NULL,528950,164650,NULL,NULL
NA,07/08/2020 12:34,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN VENT,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,NULL,MAIDEN ROAD,22201492,E15,NULL,NULL,539350,184350,NULL,NULL
NA,07/08/2020 20:36,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UNDER PATIO,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011239,CLAYHALL,E09000026,REDBRIDGE,Ilford,NULL,MERRIVALE AVENUE,22303070,IG4,NULL,NULL,541750,189050,NULL,NULL
NA,08/08/2020 09:44,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF - OWNER ON SCENE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000597,HALE END AND HIGHAMS PARK,E09000031,WALTHAM FOREST,Walthamstow,NULL,HILLCREST ROAD,22845550,E17,NULL,NULL,538850,190250,NULL,NULL
NA,08/08/2020 16:17,2020,2020/21,Special Service,1,1,346,346,INJURED CAT TRAPPED DOWN CRATER IN BACK GARDEN,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000363,OSTERLEY AND SPRING GROVE,E09000018,HOUNSLOW,Heston,1.00022E+11,APLIN WAY,21501436,TW7,515181,176789,515150,176750,51.47834359,-0.342776716
NA,08/08/2020 19:55,2020,2020/21,Special Service,1,1,346,346,CAT ON ROOF POSSIBLY INJURED,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Finchley,NULL,HENDON LANE,20021800,N3,NULL,NULL,524950,190450,NULL,NULL
NA,09/08/2020 08:53,2020,2020/21,Special Service,1,2,346,692,CAT TRAPPED IN GRATE ON ROOF,Cat,Person (mobile),Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000485,GREEN STREET EAST,E09000025,NEWHAM,Stratford,10008997118,SANDRINGHAM ROAD,22207876,E7,541177,185018,541150,185050,51.5464038,0.0346155
NA,09/08/2020 10:50,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED IN ENGINE OF CAR - CARPARK FOR GROUP PAMA HOUSE,Cat,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000058,OAKLEIGH,E09000003,BARNET,Barnet,200107674,STATION ROAD,20040880,EN5,526291,196083,526250,196050,51.64937066,-0.175951503
NA,09/08/2020 14:47,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED AND INJURED UNDER BRIDGE NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,128012373,LOWER HAM ROAD,21800623,KT2,517966,169602,517950,169650,51.41317842,-0.305090726
NA,09/08/2020 15:00,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH BIRD THAT IS TRAPPED BY LEG AND WING IN NETTING     RSPCA OFFICER ON SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011241,CRANBROOK,E09000026,REDBRIDGE,Ilford,NULL,CLARENCE AVENUE,22302721,IG2,NULL,NULL,543150,188450,NULL,NULL
NA,10/08/2020 19:25,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000028,BECONTREE,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,FARMWAY,19900129,RM8,NULL,NULL,547250,185950,NULL,NULL
NA,10/08/2020 21:03,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000639,LITTLE VENICE,E09000033,WESTMINSTER,Paddington,NULL,WARWICK AVENUE,8400268,W9,NULL,NULL,525750,182250,NULL,NULL
NA,10/08/2020 22:20,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000254,FULHAM BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,KNIVET ROAD,21000484,SW6,NULL,NULL,525150,177550,NULL,NULL
NA,10/08/2020 22:30,2020,2020/21,Special Service,2,3,346,1038,FOX IN DISTRESS DOWN A HOLE ON THE CREEK BANK,Fox,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,1.00023E+11,NORMAN ROAD,20801089,SE10,537823,177583,537850,177550,51.48042181,-0.01662415
NA,11/08/2020 10:25,2020,2020/21,Special Service,1,1,346,346,DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000100,STONEBRIDGE,E09000005,BRENT,Wembley,202072007,LANSBURY CLOSE,20202621,NW10,520339,185119,520350,185150,51.55214404,-0.265704221
NA,11/08/2020 11:47,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000487,LITTLE ILFORD,E09000025,NEWHAM,Ilford,NULL,DERSINGHAM AVENUE,22200157,E12,NULL,NULL,543050,185650,NULL,NULL
NA,11/08/2020 11:51,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009385,STOKE NEWINGTON,E09000012,HACKNEY,Stoke Newington,NULL,GARNHAM CLOSE,20901684,N16,NULL,NULL,533750,186550,NULL,NULL
NA,11/08/2020 12:47,2020,2020/21,Special Service,1,1,346,346,PIDGEON IN FIRE PLACE  CALLER UNABLE TO REMOVE BOARDS TO REMOVE BIRD,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000487,LITTLE ILFORD,E09000025,NEWHAM,Ilford,NULL,DERSINGHAM AVENUE,22200157,E12,NULL,NULL,543050,185650,NULL,NULL
NA,11/08/2020 14:31,2020,2020/21,Special Service,1,1,346,346,DOG ON ROOF         ESCAPED VIA SKY LIGHT,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000473,WEST BARNES,E09000024,MERTON,New Malden,NULL,CROSSWAY,22101752,SW20,NULL,NULL,523150,168250,NULL,NULL
NA,11/08/2020 19:10,2020,2020/21,Special Service,1,1,346,346,DOG WITH PAW STUCK IN DRAIN,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000437,BELLINGHAM,E09000023,LEWISHAM,Beckenham,NULL,LUSHINGTON ROAD,22001674,SE6,NULL,NULL,537550,171550,NULL,NULL
NA,12/08/2020 15:34,2020,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (mobile),Factory,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000325,BOTWELL,E09000017,HILLINGDON,Hayes,2.00002E+11,BLYTH ROAD,21400193,UB3,509355,179622,509350,179650,51.5049621,-0.4257716
NA,12/08/2020 20:57,2020,2020/21,Special Service,1,1,346,346,DOG STUCK IN MARSHLAND,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000308,ELM PARK,E09000016,HAVERING,Hornchurch,10091578110,SQUADRONS APPROACH,21300594,RM12,553694,184770,553650,184750,51.54091393,0.214893796
NA,13/08/2020 11:44,2020,2020/21,Special Service,1,1,346,346,AT BACK OF TENNIS COURTS NEAR THE CAFE - DOG TRAPPED WITH LEAD AROUND TREE BASE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000555,BEDDINGTON NORTH,E09000029,SUTTON,Wallington,5870114171,CHURCH ROAD,22600429,SM6,529476,165598,529450,165550,51.37468409,-0.141127695
NA,13/08/2020 12:15,2020,2020/21,Special Service,1,1,346,346,DOG WITH LEG TRAPPED IN BANNISTER,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,NULL,HILLFIELD PARK,21100429,N10,NULL,NULL,528750,189350,NULL,NULL
NA,13/08/2020 15:22,2020,2020/21,Special Service,1,1,346,346,DOG STUCK IN IRON TABLE IN REAR GARDEN,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000170,ACTON CENTRAL,E09000009,EALING,Acton,NULL,GROVE ROAD,20600785,W3,NULL,NULL,520350,180050,NULL,NULL
NA,13/08/2020 21:09,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000101,SUDBURY,E09000005,BRENT,Wembley,NULL,MAYBANK AVENUE,20200048,HA0,NULL,NULL,516250,185350,NULL,NULL
NA,13/08/2020 21:54,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN HOLE IN GARDEN,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000475,BECKTON,E09000025,NEWHAM,East Ham,46055164,OLIVER GARDENS,22200918,E6,542299,181582,542250,181550,51.51525218,0.049403064
NA,13/08/2020 22:46,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000114,CRAY VALLEY EAST,E09000006,BROMLEY,Orpington,NULL,SHEEPCOTE LANE,20300972,BR5,NULL,NULL,548850,168050,NULL,NULL
NA,14/08/2020 01:42,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED,Fox,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000472,VILLAGE,E09000024,MERTON,New Malden,48060657,ROKEBY PLACE,22105460,SW20,522826,170309,522850,170350,51.41849983,-0.235000202
NA,15/08/2020 11:31,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN GARAGE   OWNER ON SCENE WILL DIRECT,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000127,WEST WICKHAM,E09000006,BROMLEY,Beckenham,1.0002E+11,KENT ROAD,20301417,BR4,537990,166169,537950,166150,51.37781487,-0.018658248
NA,15/08/2020 13:35,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH CAT IN TREE - RSPCA ON SCENE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000270,FORTIS GREEN,E09000014,HARINGEY,Hornsey,1.00021E+11,EASTWOOD ROAD,21101220,N10,528106,190040,528150,190050,51.59465355,-0.151937188
NA,15/08/2020 17:07,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED INBETWEEN TWO WALLS,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000527,ST. MARGARETS AND NORTH TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,BURNSIDE CLOSE,22403334,TW1,NULL,NULL,516050,174450,NULL,NULL
NA,15/08/2020 18:02,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN TREE    CALLED BY RSPCA ON SCENE,Bird,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,2.00004E+11,WELSTEAD WAY,21590241,W4,521711,178922,521750,178950,51.49615258,-0.248056535
NA,15/08/2020 21:47,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED INSIDE CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000100,STONEBRIDGE,E09000005,BRENT,Wembley,202072142,LILBURNE WALK,20200785,NW10,520239,184756,520250,184750,51.54889953,-0.267269994
NA,16/08/2020 06:33,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED BEHIND WALL,Fox,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000251,ASKEW,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,CATHNOR ROAD,21000191,W12,NULL,NULL,522650,179550,NULL,NULL
NA,16/08/2020 13:25,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000418,CLAPHAM COMMON,E09000022,LAMBETH,Clapham,NULL,CLAPHAM COMMON SOUTH SIDE,21900340,SW4,NULL,NULL,529050,174550,NULL,NULL
NA,17/08/2020 11:14,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON LOW FLAT ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000635,HARROW ROAD,E09000033,WESTMINSTER,North Kensington,NULL,HARROW ROAD,8400530,W10,NULL,NULL,524550,182350,NULL,NULL
NA,18/08/2020 12:05,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN NETTING WILDLIFE REP WILL MEET AT SAINSBURYS AND DIRECT BRIGADE,Bird,Person (mobile),Other outdoor sporting venue,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009394,DALGARNO,E09000020,KENSINGTON AND CHELSEA,North Kensington,217118148,CANAL CLOSE,21701120,W10,523826,182315,523850,182350,51.52619068,-0.216414315
NA,18/08/2020 19:09,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN TREE - HANGING BY WING ATTACHED TO SOMETHING IN TREE - CALLER WILL MEET YOU AT NUMBE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000297,PINNER,E09000015,HARROW,Harrow,NULL,PINNER HILL ROAD,21201803,HA5,NULL,NULL,511250,190150,NULL,NULL
NA,19/08/2020 17:45,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000557,BELMONT,E09000029,SUTTON,Sutton,NULL,EGMONT ROAD,22602284,SM2,NULL,NULL,526450,163150,NULL,NULL
NA,20/08/2020 06:36,2020,2020/21,Special Service,1,1,346,346,KITTEN  STUCK IN TREE  CAUGHT ON BRANCHES BY COLLAR AND LEAD,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000288,GREENHILL,E09000015,HARROW,Harrow,10000006039,HINDES ROAD,21201050,HA1,515383,188625,515350,188650,51.58468523,-0.336002973
NA,20/08/2020 08:39,2020,2020/21,Special Service,1,2,346,692,KITTEN TRAPPED BEHIND KITCHEN UNITS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000113,COPERS COPE,E09000006,BROMLEY,Beckenham,NULL,CRESCENT ROAD,20301870,BR3,NULL,NULL,537950,169250,NULL,NULL
NA,21/08/2020 13:08,2020,2020/21,Special Service,1,1,346,346,Redacted,Horse,Person (mobile),Scrub land,Outdoor,Other animal assistance,Assist trapped wild animal,E05000317,PETTITS,E09000016,HAVERING,Romford,1.00021E+11,GARRY CLOSE,21301217,RM1,551381,191160,551350,191150,51.59894989,0.184313658
NA,21/08/2020 17:14,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED BETWEEN METAL GRILL AND WINDOW,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000381,TOLLINGTON,E09000019,ISLINGTON,Holloway,NULL,HANLEY ROAD,21603308,N4,NULL,NULL,530750,187350,NULL,NULL
NA,21/08/2020 17:21,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000139,KENTISH TOWN,E09000007,CAMDEN,Kentish Town,5028891,FORTESS ROAD,20400362,NW5,529056,185658,529050,185650,51.55505942,-0.139848459
NA,21/08/2020 22:12,2020,2020/21,Special Service,1,1,346,346,CAT STUCK BEHIND FRIDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009375,HAGGERSTON,E09000012,HACKNEY,Shoreditch,NULL,HACKNEY ROAD,20900479,E2,NULL,NULL,533650,183050,NULL,NULL
NA,21/08/2020 23:25,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK IN DISHWASHER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000092,KENSAL GREEN,E09000005,BRENT,North Kensington,NULL,EARLSMEAD ROAD,20202141,NW10,NULL,NULL,523050,182950,NULL,NULL
NA,22/08/2020 08:31,2020,2020/21,Special Service,1,2,346,692,CAT TRAPPED IN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011467,CRYSTAL PALACE & UPPER NORWOOD,E09000008,CROYDON,West Norwood,NULL,HAROLD ROAD,20500071,SE19,NULL,NULL,532950,170250,NULL,NULL
NA,22/08/2020 17:45,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,10091184787,UPPER WALTHAMSTOW ROAD,22883300,E17,538474,189398,538450,189350,51.5864334,-0.00260358
NA,22/08/2020 18:39,2020,2020/21,Special Service,1,1,346,346,CAT STUCK IN WALL BEHIND WASHING MACHINE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000426,OVAL,E09000022,LAMBETH,Lambeth,NULL,SOUTH LAMBETH ROAD,21901248,SW8,NULL,NULL,530350,177350,NULL,NULL
NA,22/08/2020 18:56,2020,2020/21,Special Service,2,3,346,1038,PIDGEON TRAPPED IN NETTING  ON BRIDGE BETWEEN SHOPPING CENTER AND CAR PARK ABOVE KENTISH WAY   RSPCA,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,10003628663,KENTISH WAY,20302059,BR1,540507,169149,540550,169150,51.40398032,0.018659324
NA,23/08/2020 13:55,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON RAILINGS   NEXT TO THE SCHOOL,Cat,Person (mobile),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000600,HIGHAM HILL,E09000031,WALTHAM FOREST,Walthamstow,2.00001E+11,WALTHAM PARK WAY,22884700,E17,537023,190994,537050,190950,51.601133,-0.02291845
NA,25/08/2020 15:57,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED INSIDE DERELICT BUILDING - CALLER WILL MEET CREWS,Cat,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Soho,1.00024E+11,GREAT MARLBOROUGH STREET,8401373,W1F,529341,181167,529350,181150,51.51463833,-0.13738072
NA,25/08/2020 18:07,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000407,COOMBE VALE,E09000021,KINGSTON UPON THAMES,New Malden,NULL,BEECHCROFT AVENUE,21800125,KT3,NULL,NULL,520250,169350,NULL,NULL
NA,26/08/2020 10:58,2020,2020/21,Special Service,NULL,NULL,346,NULL,Redacted,Dog,Coastguard,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000427,PRINCE'S,E09000022,LAMBETH,Lambeth,2E+11,ALBERT EMBANKMENT,21900079,SE1,530323,178213,530350,178250,51.48786109,-0.124327096
NA,26/08/2020 15:37,2020,2020/21,Special Service,1,1,346,346,DOG WITH HEAD STUCK IN PRAM,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009397,HOLLAND,E09000020,KENSINGTON AND CHELSEA,Hammersmith,NULL,ADDISON ROAD,21700789,W14,NULL,NULL,524650,179250,NULL,NULL
NA,26/08/2020 22:22,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN ROOF SPACE OF BUNGALOW,Fox,Person (mobile),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000267,BOUNDS GREEN,E09000014,HARINGEY,Southgate,NULL,FINSBURY COTTAGES,21104526,N22,NULL,NULL,530450,191050,NULL,NULL
NA,27/08/2020 08:42,2020,2020/21,Special Service,1,2,346,692,CAT TRAPPED BY RISING TIDE,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000225,PENINSULA,E09000011,GREENWICH,East Greenwich,10010247581,RIVER GARDENS WALK,20800008,SE10,539050,178452,539050,178450,51.48793482,0.001383886
NA,27/08/2020 10:00,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,BLACK PRINCE STREET,20800016,SE18,NULL,NULL,545950,177650,NULL,NULL
NA,27/08/2020 12:48,2020,2020/21,Special Service,1,1,346,346,SQUIRRELS TRAPPED IN ROOF,Squirrel,Person (land line),Pub/wine bar/bar,Non Residential,Other animal assistance,Assist trapped wild animal,E05000230,WOOLWICH RIVERSIDE,E09000011,GREENWICH,Plumstead,10010228928,NO 1 STREET,20801881,SE18,543801,179038,543850,179050,51.4920077,0.069996
NA,27/08/2020 12:58,2020,2020/21,Special Service,1,1,346,346,CAT STUCK IN ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011230,SIDCUP,E09000004,BEXLEY,Sidcup,10011846997,SIDCUP HIGH STREET,20101300,DA14,546399,171758,546350,171750,51.4259268,0.104385019
NA,29/08/2020 00:34,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN BARBED WIRE AT FRONT OF HOUSE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000486,GREEN STREET WEST,E09000025,NEWHAM,Stratford,46039642,ISMAILIA ROAD,22207763,E7,540644,184385,540650,184350,51.54084933,0.026685099
NA,29/08/2020 08:26,2020,2020/21,Special Service,1,2,346,692,CAT LOCKED IN BUILDING,Cat,Person (mobile),Engineering manufacturing plant,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,207036039,HIGH ROAD,20701606,N11,528916,192233,528950,192250,51.61417604,-0.139456588
NA,29/08/2020 13:02,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE    BIRD TRAPPED IN CHIMNEY. ACCESSIBLE FROM INSIDE THE PROPERTY,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000528,SOUTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,NULL,LANCASTER PARK,22403605,TW10,NULL,NULL,518150,174550,NULL,NULL
NA,29/08/2020 19:45,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK ON ELECTRICITY CABLE,Bird,Person (mobile),Cables,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000205,PALMERS GREEN,E09000010,ENFIELD,Edmonton,207058487,HAZEL CLOSE,20704319,N13,532534,193165,532550,193150,51.62171417,-0.086877076
NA,29/08/2020 23:19,2020,2020/21,Special Service,1,1,346,346,MUNTJAC DEER TRAPPED IN FENCE,Deer,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000199,ENFIELD LOCK,E09000010,ENFIELD,Enfield,207004128,Y025,20706639,EN3,536993,198322,536950,198350,51.66698944,-0.020477727
NA,30/08/2020 22:49,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - CAT TRAPPED IN CAGE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000296,MARLBOROUGH,E09000015,HARROW,Stanmore,NULL,ARCHERY CLOSE,21201646,HA3,NULL,NULL,515950,189950,NULL,NULL
NA,31/08/2020 13:33,2020,2020/21,Special Service,1,1,346,346,PUPPY TRAPPED BETWEEN WALL AND FENCE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,NULL,TRETAWN PARK,20043140,NW7,NULL,NULL,521250,192950,NULL,NULL
NA,31/08/2020 16:09,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000085,ALPERTON,E09000005,BRENT,Wembley,NULL,NORTHWICK ROAD,20201813,HA0,NULL,NULL,517850,183550,NULL,NULL
NA,01/09/2020 09:55,2020,2020/21,Special Service,1,1,346,346,CAT LOCKED IN CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000627,WANDSWORTH COMMON,E09000032,WANDSWORTH,Tooting,1.00023E+11,BRIGHTMAN ROAD,22900621,SW18,526558,173315,526550,173350,51.44469887,-0.180281899
NA,01/09/2020 16:21,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN HOLE,Dog,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Heathrow,10093738174,SPEEDBIRD WAY,21402697,UB7,505173,177524,505150,177550,51.48690382,-0.486622004
NA,01/09/2020 18:24,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON CHIMNEY    ABOVE ONE STOP  RSPCA DECLINED TO ATTEND   ACCESS VIA BACK OF SHOP,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011222,CRAYFORD,E09000004,BEXLEY,Bexley,NULL,MAYPLACE ROAD EAST,20100956,DA7,NULL,NULL,550250,175750,NULL,NULL
NA,01/09/2020 19:52,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000639,LITTLE VENICE,E09000033,WESTMINSTER,Paddington,NULL,WARWICK AVENUE,8400268,W9,NULL,NULL,525850,182150,NULL,NULL
NA,03/09/2020 01:18,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000376,JUNCTION,E09000019,ISLINGTON,Kentish Town,NULL,TREMLETT GROVE,21600876,N19,NULL,NULL,529150,186450,NULL,NULL
NA,03/09/2020 09:12,2020,2020/21,Special Service,1,1,346,346,Redacted,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,NULL,HOLLYBUSH ROAD,21800515,KT2,NULL,NULL,518550,170850,NULL,NULL
NA,04/09/2020 08:55,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - KITTEN TRAPPED UNDERNEATH FLOORBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000223,KIDBROOKE WITH HORNFAIR,E09000011,GREENWICH,Eltham,NULL,DURSLEY ROAD,20800486,SE3,NULL,NULL,541450,176450,NULL,NULL
NA,04/09/2020 11:28,2020,2020/21,Special Service,1,1,346,346,ASSISIT RSPCA WITH CAT RETRIEVAL - RSPCA REP ON SCENE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000614,FAIRFIELD,E09000032,WANDSWORTH,Battersea,1.00023E+11,GARRICK CLOSE,22901948,SW18,526392,175071,526350,175050,51.46051897,-0.182039847
NA,04/09/2020 16:40,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN NETTING ON THE BUILDING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011484,SOUTH CROYDON,E09000008,CROYDON,Croydon,NULL,BRIGHTON ROAD,20501945,CR2,NULL,NULL,532450,162950,NULL,NULL
NA,04/09/2020 20:03,2020,2020/21,Special Service,1,1,346,346,BABY PIGEON TRAPPED - CALL FROM RSPCA - UNABLE TO ATTEND    NEAR THE CENTRE CLOSE TO THE OFF LEAD DO,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000524,KEW,E09000027,RICHMOND UPON THAMES,Richmond,1.00022E+11,ATWOOD AVENUE,22405321,TW9,519251,176177,519250,176150,51.47200436,-0.284401956
NA,04/09/2020 20:10,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN SHED BELIEVED INJURED   RSPCA UNABLE TO ASSIST,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,10023600540,LAWES WAY,19904091,IG11,546105,182655,546150,182650,51.52392268,0.104654497
NA,05/09/2020 18:32,2020,2020/21,Special Service,1,1,346,346,DOG WITH HEAD TRAPPED IN WINE RACK,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,NULL,WOLSELEY ROAD,22601161,CR4,NULL,NULL,528250,166650,NULL,NULL
NA,05/09/2020 19:35,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED     CALLER BELIEVES CATS LEG IS BROKEN AS ITS TWISTED WITH FLYS AROUND IT    CAT IS VERY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000324,BARNHILL,E09000017,HILLINGDON,Hillingdon,NULL,GLEDWOOD GARDENS,21400810,UB4,NULL,NULL,510050,181650,NULL,NULL
NA,06/09/2020 09:34,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000355,HESTON CENTRAL,E09000018,HOUNSLOW,Heston,NULL,WESLEY AVENUE,21501184,TW3,NULL,NULL,512550,176250,NULL,NULL
NA,06/09/2020 18:26,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000132,FORTUNE GREEN,E09000007,CAMDEN,West Hampstead,5033357,GONDAR GARDENS,20400245,NW6,524861,185383,524850,185350,51.55352819,-0.200424134
NA,07/09/2020 21:41,2020,2020/21,Special Service,1,1,346,346,CAT WITH PAW STUCK IN SINK PLUG HOLE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011471,NEW ADDINGTON SOUTH,E09000008,CROYDON,Addington,NULL,SHAXTON CRESCENT,20501826,CR0,NULL,NULL,538350,162650,NULL,NULL
NA,08/09/2020 09:15,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,CHURCH STREET,8400794,NW8,NULL,NULL,527050,182150,NULL,NULL
NA,09/09/2020 08:05,2020,2020/21,Special Service,1,1,346,346,FOX WITH HEAD STUCK RAILINGS,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000308,ELM PARK,E09000016,HAVERING,Hornchurch,1.00021E+11,CARNFORTH GARDENS,21300267,RM12,552486,185490,552450,185450,51.54770713,0.197788967
NA,09/09/2020 11:21,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011116,SOUTH BERMONDSEY,E09000028,SOUTHWARK,Old Kent Road,NULL,ABERCORN WAY,22502815,SE1,NULL,NULL,534350,178350,NULL,NULL
NA,09/09/2020 14:52,2020,2020/21,Special Service,1,1,346,346,CAT STUCK IN AN AIR VENT ON FIRST FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011101,DULWICH WOOD,E09000028,SOUTHWARK,Forest Hill,NULL,CRESCENT WOOD ROAD,22500645,SE26,NULL,NULL,534050,172250,NULL,NULL
NA,10/09/2020 08:54,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000121,MOTTINGHAM AND CHISLEHURST NORTH,E09000006,BROMLEY,Eltham,NULL,DUNKERY ROAD,20302283,SE9,NULL,NULL,542250,172150,NULL,NULL
NA,10/09/2020 09:32,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ROOF OWNERS ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000098,QUEENS PARK,E09000005,BRENT,North Kensington,NULL,HARVIST ROAD,20201304,NW6,NULL,NULL,524050,183050,NULL,NULL
NA,10/09/2020 09:48,2020,2020/21,Special Service,1,1,346,346,ASSIST WILDLIFE PROTECTION WITH A PIGEON TRAPPED IN BUILDING,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009367,BROWNSWOOD,E09000012,HACKNEY,Stoke Newington,NULL,BROWNSWOOD ROAD,20900183,N4,NULL,NULL,532150,186650,NULL,NULL
NA,10/09/2020 10:35,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000121,MOTTINGHAM AND CHISLEHURST NORTH,E09000006,BROMLEY,Eltham,NULL,DUNKERY ROAD,20302283,SE9,NULL,NULL,542250,172150,NULL,NULL
NA,10/09/2020 18:50,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING NEAR BRAMLEY CHEMIST SHOP,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009404,ST. HELEN'S,E09000020,KENSINGTON AND CHELSEA,North Kensington,217107613,BRAMLEY ROAD,21700822,W10,523796,181180,523750,181150,51.51599284,-0.21724415
NA,10/09/2020 20:49,2020,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (mobile),Multi-Storey car park,Non Residential,Other animal assistance,Assist trapped wild animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,10023998240,MONTFICHET ROAD,22208219,E20,538310,184593,538350,184550,0,0
NA,11/09/2020 07:31,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED UNDER DECKING,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000613,EAST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,WEST HILL ROAD,22906081,SW18,NULL,NULL,524850,174250,NULL,NULL
NA,11/09/2020 18:01,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND FRIDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000491,ROYAL DOCKS,E09000025,NEWHAM,Plaistow,NULL,STARBOARD WAY,22208320,E16,NULL,NULL,541050,179850,NULL,NULL
NA,12/09/2020 00:06,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011238,CHURCHFIELDS,E09000026,REDBRIDGE,Woodford,1.00022E+11,AVONDALE COURT,22304803,E18,540543,190882,540550,190850,51.59925778,0.027831522
NA,12/09/2020 09:29,2020,2020/21,Special Service,1,1,346,346,INJURED CAT - FALLEN OFF HIGH FENCE INTO BACK GARDEN    OWNER UNABLE TO REACH CAT,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000278,SEVEN SISTERS,E09000014,HARINGEY,Tottenham,NULL,HILLSIDE ROAD,21106683,N15,NULL,NULL,533550,188050,NULL,NULL
NA,12/09/2020 10:16,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY BREAST,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000336,NORTHWOOD HILLS,E09000017,HILLINGDON,Ruislip,NULL,WILTSHIRE LANE,21402176,HA5,NULL,NULL,509950,189550,NULL,NULL
NA,12/09/2020 13:24,2020,2020/21,Special Service,1,1,346,346,BIRDS TRAPPED BHIND MESH WIRING -,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009404,ST. HELEN'S,E09000020,KENSINGTON AND CHELSEA,North Kensington,217107613,BRAMLEY ROAD,21700822,W10,523790,181185,523750,181150,51.51604003,-0.217338857
NA,13/09/2020 11:28,2020,2020/21,Special Service,1,1,346,346,GOAT STUCK IN RAILINGS,Goat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist  trapped livestock animal,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Hayes,1.00021E+11,VICTORIA LANE,21402043,UB3,508534,178043,508550,178050,51.49093009,-0.438068603
NA,13/09/2020 12:07,2020,2020/21,Special Service,NULL,NULL,346,NULL,DOG WITH LEG STUCK IN BENCH   NEAR NURSERY MRS DELANSEY SCHOOL,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217112078,AVONDALE PARK ROAD,21700803,W11,524091,180671,524050,180650,51.51135251,-0.213182067
NA,13/09/2020 13:53,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED IN CAR SPEAKER,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011236,BRIDGE,E09000026,REDBRIDGE,Woodford,10034919449,BRANDESBURY SQUARE,22306397,IG8,543450,191509,543450,191550,51.60416326,0.07003494
NA,13/09/2020 15:41,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN ROOF TILES,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000059,TOTTERIDGE,E09000003,BARNET,Finchley,NULL,BUDD CLOSE,20005980,N12,NULL,NULL,525750,192750,NULL,NULL
NA,14/09/2020 16:03,2020,2020/21,Special Service,1,1,346,346,SQUIRREL TRAPPED IN EAVES OF A ROOF     FIRST FLOOR LEVEL,Squirrel,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000493,WALL END,E09000025,NEWHAM,East Ham,NULL,WATSON AVENUE,22201199,E6,NULL,NULL,543250,184350,NULL,NULL
NA,15/09/2020 08:59,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ABANONDED COURT YARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Soho,NULL,DRURY LANE,20401232,WC2B,NULL,NULL,530250,181350,NULL,NULL
NA,16/09/2020 09:51,2020,2020/21,Special Service,1,1,346,346,MAGPIE STUCK IN GUTTERING AT SECOND FLOOR    RSPCA ON SCENE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000519,"HAM, PETERSHAM AND RICHMOND RIVERSIDE",E09000027,RICHMOND UPON THAMES,Kingston,NULL,LAMMAS ROAD,22403600,TW10,NULL,NULL,517250,171550,NULL,NULL
NA,16/09/2020 12:19,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000140,KILBURN,E09000007,CAMDEN,West Hampstead,NULL,ABBEY ROAD,20400561,NW8,NULL,NULL,525850,183750,NULL,NULL
NA,18/09/2020 04:01,2020,2020/21,Special Service,1,1,346,346,FOX FALLEN FROM HEIGHT AND TRAPPED,Fox,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000369,CANONBURY,E09000019,ISLINGTON,Islington,NULL,DOWNHAM ROAD,21604726,N1,NULL,NULL,532850,184050,NULL,NULL
NA,18/09/2020 11:46,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED BEHIND WASHING MACHINE  HAVING DIFFICULTY BREATHING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000308,ELM PARK,E09000016,HAVERING,Hornchurch,NULL,EGBERT CLOSE,21320246,RM12,NULL,NULL,551750,185450,NULL,NULL
NA,18/09/2020 19:16,2020,2020/21,Special Service,1,2,346,692,HORSE STUCK IN PONDERS END DITCH - JUST NORTH OF NAVIGATION PUB ON THE TOW PATH ON THE EAST SIDE,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207184743,WHARF ROAD,20702448,EN3,536335,195538,536350,195550,51.64212673,-0.031071584
NA,19/09/2020 20:56,2020,2020/21,Special Service,1,1,346,346,Redacted,Fox,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000613,EAST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,BURSTON ROAD,22900734,SW15,NULL,NULL,523850,174950,NULL,NULL
NA,20/09/2020 04:09,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND WARDROBE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000309,EMERSON PARK,E09000016,HAVERING,Hornchurch,NULL,WINGLETYE LANE,21300661,RM11,NULL,NULL,554850,189350,NULL,NULL
NA,20/09/2020 17:58,2020,2020/21,Special Service,1,1,346,346,BIRDS TRAPPED IN CRANE NETTING - CALLER WILL MEET YOU,Bird,Person (land line),Other outdoor equipment/machinery,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000362,ISLEWORTH,E09000018,HOUNSLOW,Heston,10015417296,BRIDGE WHARF ROAD,21501580,TW7,516676,175940,516650,175950,51.4704103,-0.321543335
NA,22/09/2020 07:41,2020,2020/21,Special Service,1,1,346,346,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,10090500610,NEVILLE GILL CLOSE,22903484,SW18,525496,174408,525450,174450,51.45475637,-0.195172828
NA,22/09/2020 08:09,2020,2020/21,Special Service,1,1,346,346,FOX WITH LEG TRAPPED IN A TRAMPOLINE,Fox,Person (land line),Garden equipment,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000359,HOUNSLOW HEATH,E09000018,HOUNSLOW,Feltham,10001267036,BLACKBURN WAY,21501782,TW4,512539,174552,512550,174550,51.4587713,-0.3815225
NA,22/09/2020 18:45,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,NULL,CHISWICK HIGH ROAD,21502481,W4,NULL,NULL,521650,178650,NULL,NULL
NA,23/09/2020 11:35,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN LIFT AREA,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000132,FORTUNE GREEN,E09000007,CAMDEN,West Hampstead,NULL,BARLOW ROAD,20401354,NW6,NULL,NULL,524850,184750,NULL,NULL
NA,23/09/2020 12:51,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND SPIKED FENCING  CALLER WILL LIASE WITH BRIGADE AT ADDRESS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,MARLOWE ROAD,22856650,E17,NULL,NULL,538350,189450,NULL,NULL
NA,23/09/2020 18:08,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - CAT UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000214,ABBEY WOOD,E09000011,GREENWICH,Plumstead,NULL,BOSTALL HILL,20800197,SE2,NULL,NULL,546350,178350,NULL,NULL
NA,24/09/2020 17:18,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH CAT TRAPPED,Cat,Person (mobile),Vehicle sales building,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000480,EAST HAM CENTRAL,E09000025,NEWHAM,East Ham,10008994144,BARKING ROAD,22207589,E6,542252,183454,542250,183450,51.53208226,0.049485937
NA,24/09/2020 20:39,2020,2020/21,Special Service,1,1,346,346,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000034,HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,OGLETHORPE ROAD,19900263,RM10,NULL,NULL,548950,186450,NULL,NULL
NA,25/09/2020 19:56,2020,2020/21,Special Service,1,2,346,692,CAT STUCK IN CAR ENGINE - TOYOTA PRUIS,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000467,POLLARDS HILL,E09000024,MERTON,Norbury,48064697,SHERWOOD PARK ROAD,22105823,CR4,529813,168403,529850,168450,51.39981962,-0.135269269
NA,25/09/2020 22:38,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND BUILT IN FRIDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009335,WEAVERS,E09000030,TOWER HAMLETS,Shoreditch,NULL,AVANTGARDE PLACE,22702704,E1,NULL,NULL,533750,182350,NULL,NULL
NA,26/09/2020 06:31,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN DRAIN,Fox,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,BRAMHAM GARDENS,21700043,SW5,NULL,NULL,525750,178350,NULL,NULL
NA,26/09/2020 09:36,2020,2020/21,Special Service,1,1,346,346,Redacted,Fox,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,BRAMHAM GARDENS,21700043,SW5,NULL,NULL,525750,178450,NULL,NULL
NA,27/09/2020 15:38,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN BUSHES - UNABLE TO GET OUT      ADVISED TO CONTACT LFB BY RSPCA,Cat,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000381,TOLLINGTON,E09000019,ISLINGTON,Holloway,10001297679,GRENVILLE ROAD,21603288,N19,530351,187231,530350,187250,51.56890101,-0.120585567
NA,27/09/2020 16:54,2020,2020/21,Special Service,1,1,346,346,Redacted,Fox,Person (mobile),TV/film/music/art studio,Non Residential,Animal rescue from height,Wild animal rescue from height,E05011106,NORTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,10094742756,DRUMMOND ROAD,22500813,SE16,534669,179035,534650,179050,51.4942282,-0.0614625
NA,27/09/2020 22:04,2020,2020/21,Special Service,1,1,346,346,CAT STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000279,STROUD GREEN,E09000014,HARINGEY,Hornsey,NULL,MOUNT VIEW ROAD,21103656,N4,NULL,NULL,531250,188250,NULL,NULL
NA,28/09/2020 15:32,2020,2020/21,Special Service,1,1,346,346,PIGEONS TRAPPED IN NETTING UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,2.00001E+11,WOOD STREET,22888350,E17,538465,189407,538450,189450,51.58651728,-0.002742098
NA,29/09/2020 01:08,2020,2020/21,Special Service,1,1,346,346,CAT FALLEN ONTO LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000306,BROOKLANDS,E09000016,HAVERING,Romford,NULL,LONDON ROAD,21301367,RM7,NULL,NULL,550050,188450,NULL,NULL
NA,29/09/2020 16:47,2020,2020/21,Special Service,2,3,346,1038,CAT STUCK IN CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,NETHER STREET,20030910,N12,NULL,NULL,525850,192150,NULL,NULL
NA,30/09/2020 19:03,2020,2020/21,Special Service,1,1,346,346,SWAN IN DISTRESS ON THE LONGFORD RIVER - SWAN TRAPPED IN NETTING ABOVE THE RIVER,Bird,Person (land line),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Heathrow,10059100274,SOUTHERN PERIMETER ROAD,21401785,TW6,505666,174741,505650,174750,51.46179941,-0.480361843
NA,01/10/2020 00:26,2020,2020/21,Special Service,1,1,346,346,CAT  TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,SHAWFIELD STREET,21700486,SW3,NULL,NULL,527450,178050,NULL,NULL
NA,01/10/2020 18:29,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED UNDERNEITH SEAT IN CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000063,WOODHOUSE,E09000003,BARNET,Finchley,200072646,LEISURE WAY,20026390,N12,526535,191150,526550,191150,51.60498289,-0.174212308
NA,01/10/2020 22:42,2020,2020/21,Special Service,1,1,346,346,DOG STUCK BETWEEN BOAT AND RIVER BANK - OWNER UNABLE TO LIFT OUT,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000326,BRUNEL,E09000017,HILLINGDON,Hillingdon,10092980277,PACKET BOAT LANE,21401472,UB8,505348,181257,505350,181250,51.52042284,-0.482982882
NA,02/10/2020 12:38,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED DOWN EMBANKMENT BESIDE RAILWAY LINE    THIS IS NEAR TO WARREN FARM CALLER SAID IF BRIGAD,Dog,Person (mobile),Railway,Outdoor,Other animal assistance,Assist trapped domestic animal,NA,NA,NA,NA,Surrey,10025167084,BRAMLEY ROAD,13900063,SM3,523473,162849,523450,162850,51.35131526,-0.228278517
NA,03/10/2020 02:48,2020,2020/21,Special Service,1,2,346,692,DOG TRAPPED IN REAR GARDEN  - NO COVID,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000184,NORTHOLT MANDEVILLE,E09000009,EALING,Northolt,12046922,MANDEVILLE ROAD,20601113,UB5,513451,184644,513450,184650,51.5492948,-0.365152372
NA,03/10/2020 15:06,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CLOTHING RECYCLING BIN - OWNER WAS UNAWARE CAT CLIMBED INTO  BAG,Cat,Person (land line),"Large refuse/rubbish container (eg skip, paladin)",Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,1.0002E+11,PAWLEYNE CLOSE,20302159,SE20,535477,170059,535450,170050,51.41337266,-0.053260383
NA,03/10/2020 21:55,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN WALL IN HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,WELLINGTON SQUARE,21700577,SW3,NULL,NULL,527750,178250,NULL,NULL
NA,04/10/2020 12:35,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN FENCING TO ELECTRICAL SUB STATION BEHIND THE TIP,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000112,CLOCK HOUSE,E09000006,BROMLEY,Beckenham,1.00023E+11,CHURCHFIELDS ROAD,20300883,BR3,535908,169109,535950,169150,51.40473975,-0.047429086
NA,05/10/2020 17:12,2020,2020/21,Special Service,1,1,346,346,PIGEONS TRAPPED IN NETTING ON THE WOOD STREET RAILWAY BRIDGE - BY THE ORANGE BICYCLE STAND,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,2.00001E+11,WOOD STREET,22888350,E17,538464,189399,538450,189350,51.58644283,-0.002746789
NA,05/10/2020 23:04,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN ENGINE OF CAR   OWNER ON SCENE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000623,SHAFTESBURY,E09000032,WANDSWORTH,Clapham,10090498952,PAGE MEWS,22906892,SW11,528385,176024,528350,176050,51.46863743,-0.153028504
NA,09/10/2020 07:40,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UP ON TELEGRAPH POLE - O/S THIS NUMBER,Cat,Person (mobile),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000036,MAYESBROOK,E09000002,BARKING AND DAGENHAM,Barking,100039245,BROMHALL ROAD,19900452,RM8,546808,184798,546850,184750,51.54299441,0.115673664
NA,10/10/2020 22:50,2020,2020/21,Special Service,1,1,346,346,CAT STUCK IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000254,FULHAM BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,LILLIE ROAD,21000509,SW6,NULL,NULL,524750,177650,NULL,NULL
NA,11/10/2020 19:53,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Secondary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,10090196375,GIPSY ROAD,21900607,SE27,532328,171637,532350,171650,51.428296,-0.097934
NA,12/10/2020 16:14,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - KITTEN TRAPPED ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011246,ILFORD TOWN,E09000026,REDBRIDGE,Ilford,NULL,HIGH ROAD,22306010,IG1,NULL,NULL,544850,186850,NULL,NULL
NA,12/10/2020 16:27,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED UNDER DRAIN,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009335,WEAVERS,E09000030,TOWER HAMLETS,Shoreditch,NULL,GASCOIGNE PLACE,22700530,E2,NULL,NULL,533750,182750,NULL,NULL
NA,12/10/2020 16:41,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,BOLTON GARDENS,21700039,SW5,NULL,NULL,525850,178350,NULL,NULL
NA,12/10/2020 19:18,2020,2020/21,Special Service,1,2,346,692,KITTEN TRAPPED IN BACK OF THE LIFT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011487,WADDON,E09000008,CROYDON,Croydon,NULL,VIOLET LANE,20502401,CR0,NULL,NULL,531950,164850,NULL,NULL
NA,13/10/2020 11:24,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA - LARGE CARP POND THAT REQUIRES WATER,Unknown - Wild Animal,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000325,BOTWELL,E09000017,HILLINGDON,Hillingdon,2.00002E+11,BURBAGE CLOSE,21402811,UB3,508929,181019,508950,181050,51.51760554,-0.431463719
NA,14/10/2020 02:25,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK BEHIND CABINET,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011251,SEVEN KINGS,E09000026,REDBRIDGE,Ilford,NULL,ST ALBANS ROAD,22302431,IG3,NULL,NULL,545550,187250,NULL,NULL
NA,14/10/2020 07:05,2020,2020/21,Special Service,1,1,346,346,SQUIRREL IN BEDROOM,Squirrel,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Finchley,NULL,HENDON LANE,20021800,N3,NULL,NULL,524750,190250,NULL,NULL
NA,14/10/2020 21:18,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000632,BRYANSTON AND DORSET SQUARE,E09000033,WESTMINSTER,Paddington,NULL,GLOUCESTER PLACE,8401354,W1U,NULL,NULL,527750,181750,NULL,NULL
NA,15/10/2020 07:27,2020,2020/21,Special Service,1,1,346,346,FOX WITH HEAD STUCK IN FENCE,Fox,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000489,PLAISTOW NORTH,E09000025,NEWHAM,Plaistow,10008984743,KIRTON ROAD,22200782,E13,541164,183495,541150,183450,51.53272636,0.033830384
NA,16/10/2020 11:55,2020,2020/21,Special Service,1,1,346,346,DOG FALLEN DOWN STEPS - OWNER ON SCENE,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,NULL,KNOX ROAD,22207779,E7,NULL,NULL,540250,184750,NULL,NULL
NA,16/10/2020 16:30,2020,2020/21,Special Service,1,1,346,346,DEER STUCK IN FENCE ENTER VIA FOREST ROAD CALLR WILL MEET BRIGADE,Deer,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000597,HALE END AND HIGHAMS PARK,E09000031,WALTHAM FOREST,Woodford,10009142414,FOREST ROAD,22837350,E17,539290,190398,539250,190350,51.59521595,0.009563592
NA,17/10/2020 10:54,2020,2020/21,Special Service,2,5,346,1730,Redacted,Fox,Person (land line),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05011106,NORTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,2.00004E+11,SHAD THAMES,22502224,SE1,533898,179833,533850,179850,51.50158444,-0.072255604
NA,18/10/2020 08:17,2020,2020/21,Special Service,1,1,346,346,DEER TRAPPED IN FENCE,Deer,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,10024389225,MAWBERY GROVE,21320235,RM3,555464,192203,555450,192250,51.60720903,0.24367811
NA,18/10/2020 20:54,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN METAL FENCE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000270,FORTIS GREEN,E09000014,HARINGEY,Finchley,1.00021E+11,RINGWOOD AVENUE,21101587,N2,527897,189890,527850,189850,51.59335375,-0.155018469
NA,18/10/2020 20:59,2020,2020/21,Special Service,1,1,346,346,DISTRESSED FOX TRAPPED IN A SCHOOL BUILDING,Fox,Person (mobile),College/University,Non Residential,Animal rescue from height,Wild animal rescue from height,E05011115,ST. GILES,E09000028,SOUTHWARK,Peckham,2.00003E+11,WILSON ROAD,22502720,SE5,533025,176614,533050,176650,51.47286707,-0.086038355
NA,19/10/2020 08:10,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH FOX RESCUE,Fox,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000412,ST. MARK'S,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,SEETHING WELLS LANE,21800843,KT6,NULL,NULL,517450,167350,NULL,NULL
NA,22/10/2020 09:39,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BETWEEN SHED AND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,CHURCH STREET,8400794,NW8,NULL,NULL,527050,182150,NULL,NULL
NA,22/10/2020 16:11,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK IN CHIMNEY   CALLER STATES AT GROUND LEVEL BEHIND GAS FIRE CALLER STATES GAS HAS BEEN DIS,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000048,EAST BARNET,E09000003,BARNET,Barnet,NULL,FORDHAM ROAD,20016220,EN4,NULL,NULL,527050,196550,NULL,NULL
NA,22/10/2020 18:42,2020,2020/21,Special Service,1,1,346,346,PIGEON CAUGHT UP IN NETTING AT FOURTH FLOOR LEVEL   TL REQ FROM SCENE,Bird,Person (mobile),Cinema,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000432,STREATHAM WELLS,E09000022,LAMBETH,Tooting,2E+11,STREATHAM HIGH ROAD,21901326,SW16,530224,172005,530250,172050,51.43209478,-0.128034387
NA,23/10/2020 09:51,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE  CAT TRAPPED BEHIND KITCHEN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000267,BOUNDS GREEN,E09000014,HARINGEY,Southgate,NULL,QUEENS ROAD,21106070,N11,NULL,NULL,530150,191550,NULL,NULL
NA,24/10/2020 21:41,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - CAT TRAPPED BEHIND WALL BETWEEN WOODEN PANELS AND TOILET  CAT IS BELIEVE INJUR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000452,SYDENHAM,E09000023,LEWISHAM,Beckenham,NULL,HOMECROFT ROAD,22001568,SE26,NULL,NULL,535450,171350,NULL,NULL
NA,25/10/2020 17:24,2020,2020/21,Special Service,1,2,346,692,CAT STUCK ON CHIMNEY - ALP REQUESTED FROM INCIDENT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,CHESTER ROAD,20704042,N9,NULL,NULL,534950,194050,NULL,NULL
NA,26/10/2020 11:34,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN BUILDING,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,NULL,UPPER RICHMOND ROAD WEST,22406036,SW14,NULL,NULL,520250,175350,NULL,NULL
NA,26/10/2020 12:38,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,NULL,TUSCAN ROAD,20801522,SE18,NULL,NULL,544850,178050,NULL,NULL
NA,26/10/2020 16:40,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011231,SLADE GREEN & NORTHEND,E09000004,BEXLEY,Erith,NULL,MANOR ROAD,20100917,DA8,NULL,NULL,551950,177750,NULL,NULL
NA,27/10/2020 08:33,2020,2020/21,Special Service,1,1,346,346,INJURED PARROT TRAPPED ON WIRE IN TREE,Bird,Person (mobile),"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)",Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000306,BROOKLANDS,E09000016,HAVERING,Dagenham,1.00021E+11,EDDY CLOSE,21301165,RM7,549635,188111,549650,188150,51.57202525,0.157824974
NA,27/10/2020 16:09,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011110,PECKHAM,E09000028,SOUTHWARK,Peckham,NULL,SHURLAND GARDENS,22502243,SE15,NULL,NULL,533950,177350,NULL,NULL
NA,27/10/2020 22:44,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BETWEEN FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,NULL,CRANLEY GARDENS,21100200,N10,NULL,NULL,529150,189050,NULL,NULL
NA,28/10/2020 00:06,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON PILLARS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000170,ACTON CENTRAL,E09000009,EALING,Acton,NULL,LEXDEN ROAD,20601052,W3,NULL,NULL,519950,180450,NULL,NULL
NA,28/10/2020 07:28,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,NULL,CRANLEY GARDENS,21100200,N10,NULL,NULL,529150,189050,NULL,NULL
NA,28/10/2020 14:42,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UP CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000256,HAMMERSMITH BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,TABOR ROAD,21000807,W6,NULL,NULL,522850,179150,NULL,NULL
NA,30/10/2020 12:33,2020,2020/21,Special Service,1,2,346,692,ASSIST RSPCA  WITH CAT TRAPPED IN NETTING ON ROOF OF BUILDING,Cat,Person (mobile),Large supermarket,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Battersea,1.00023E+11,BATTERSEA PARK ROAD,22900337,SW11,528404,176693,528450,176650,51.47463972,-0.152510233
NA,31/10/2020 15:51,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN WIRE - WILDLIFE PROTECTION ON SCENE,Bird,Person (mobile),Train station - platform (below ground),Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000632,BRYANSTON AND DORSET SQUARE,E09000033,WESTMINSTER,Paddington,1.00023E+11,CABBELL STREET,8400346,NW1,527200,181689,527250,181650,51.5198078,-0.1680402
NA,01/11/2020 06:31,2020,2020/21,Special Service,1,1,346,346,PUPPY WITH HEAD STUCK IN MESH OF CAGE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,CHARLWOOD ROAD,22900866,SW15,NULL,NULL,523750,175450,NULL,NULL
NA,01/11/2020 12:21,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011236,BRIDGE,E09000026,REDBRIDGE,Woodford,NULL,BRANDESBURY SQUARE,22306397,IG8,NULL,NULL,543550,191450,NULL,NULL
NA,02/11/2020 02:54,2020,2020/21,Special Service,1,2,346,692,Redacted,Fox,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,HAMLET GARDENS,21000406,W6,NULL,NULL,522150,178750,NULL,NULL
NA,02/11/2020 10:23,2020,2020/21,Special Service,1,3,346,1038,ASSIST RSPCA WITH TRAPPED FOX,Fox,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,HAMLET GARDENS,21000406,W6,NULL,NULL,522150,178750,NULL,NULL
NA,02/11/2020 17:21,2020,2020/21,Special Service,1,1,346,346,ASSIST POLICE RECOVER DOG FROM VEHICLE,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011247,LOXFORD,E09000026,REDBRIDGE,Ilford,1.00022E+11,TWYFORD ROAD,22303332,IG1,544250,185462,544250,185450,51.54961829,0.079096559
NA,04/11/2020 13:28,2020,2020/21,Special Service,1,1,346,346,PERSON ON ROOF ATTEMPTING TO RESCUE CAT   ADVISED SITE IS NOT SAFE AND SHOULD HAVE BEEN SECURED (POS,Cat,Person (mobile),Other building/use not known,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,10070721887,UPPER RICHMOND ROAD WEST,22406036,SW14,520249,175358,520250,175350,51.4644286,-0.270327644
NA,04/11/2020 13:57,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000176,ELTHORNE,E09000009,EALING,Ealing,NULL,ENDSLEIGH ROAD,20600628,W13,NULL,NULL,516150,180550,NULL,NULL
NA,04/11/2020 14:51,2020,2020/21,Special Service,1,1,346,346,CROW STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000122,ORPINGTON,E09000006,BROMLEY,Orpington,NULL,TOWER CLOSE,20301323,BR6,NULL,NULL,545850,165550,NULL,NULL
NA,05/11/2020 19:31,2020,2020/21,Special Service,1,1,346,346,DOG STUCK BETWEEN WALLS,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000211,TURKEY STREET,E09000010,ENFIELD,Enfield,NULL,CHARNWOOD ROAD,20702582,EN1,NULL,NULL,534950,199250,NULL,NULL
NA,05/11/2020 19:57,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000425,LARKHALL,E09000022,LAMBETH,Brixton,NULL,STOCKWELL ROAD,21901316,SW9,NULL,NULL,530450,176350,NULL,NULL
NA,06/11/2020 08:51,2020,2020/21,Special Service,1,1,346,346,SWAN TRAPPED IN ELECTRICAL SUBSTATION,Bird,Person (land line),Electricity power station,Non Residential,Other animal assistance,Assist trapped wild animal,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,10025311721,LINTON MEAD ROAD ACCESS TO PUMPING STATION,20801829,SE28,546631,180883,546650,180850,51.5078648,0.1115038
NA,07/11/2020 14:07,2020,2020/21,Special Service,1,2,346,692,CAT STUCK IN FOXES BURROW IN CAR PARK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011116,SOUTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,NULL,ENID STREET,22500914,SE16,NULL,NULL,534050,179250,NULL,NULL
NA,07/11/2020 16:33,2020,2020/21,Special Service,1,1,346,346,ASSIST LONDON WILDLIFE PROTECTION WITH GAINING ACCESS,Bird,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,10094608145,SEVEN SISTERS ROAD,21603790,N7,530939,186477,530950,186450,51.56198509,-0.112392893
NA,08/11/2020 16:02,2020,2020/21,Special Service,2,3,346,1038,DOG STUCK IN HOLE UNDER A TREE     SMALL ANIMAL RESCUE       IN RICHMOND PARK    NEXT TO GOLF COURSE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000519,"HAM, PETERSHAM AND RICHMOND RIVERSIDE",E09000027,RICHMOND UPON THAMES,Kingston,10070719914,UNNAMED ROAD - RICHMOND PARK - HAM GATE TO PARK,22406888,TW10,518936,171748,518950,171750,51.43225986,-0.290434165
NA,11/11/2020 01:44,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN GATE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000342,WEST DRAYTON,E09000017,HILLINGDON,Hayes,1.00022E+11,WISE LANE,21402194,UB7,505705,178960,505750,178950,51.4997144,-0.47853494
NA,11/11/2020 08:59,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000297,PINNER,E09000015,HARROW,Harrow,NULL,PINNER HILL ROAD,21201803,HA5,NULL,NULL,511350,190150,NULL,NULL
NA,11/11/2020 09:28,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND FIRE PLACE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,NULL,ALDWORTH ROAD,22201280,E15,NULL,NULL,539150,184250,NULL,NULL
NA,11/11/2020 15:10,2020,2020/21,Special Service,1,1,346,346,BIRD POSSIBLY TRAPPED ON CURTAIN POLE,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011477,PURLEY OAKS & RIDDLESDOWN,E09000008,CROYDON,Croydon,NULL,KENDALL AVENUE,20502165,CR2,NULL,NULL,532650,162650,NULL,NULL
NA,12/11/2020 08:33,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN ENGINE OF VEHICLE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000330,HAREFIELD,E09000017,HILLINGDON,Ruislip,10009949065,HILL END ROAD,21400974,UB9,505294,190972,505250,190950,51.60775163,-0.480853437
NA,13/11/2020 15:17,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN LIGHT WELL    RSPCA ON SCENE (PHILLIP HAYES),Fox,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000636,HYDE PARK,E09000033,WESTMINSTER,Paddington,NULL,CHILWORTH STREET,8400791,W2,NULL,NULL,526450,181150,NULL,NULL
NA,13/11/2020 15:23,2020,2020/21,Special Service,1,1,346,346,TO ASSIST RSPCA WITH BIRD OF PREY TRAPPED BEHIND CUPBOARD,Bird,Person (mobile),Hospital,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000595,FOREST,E09000031,WALTHAM FOREST,Leytonstone,10091184802,WHIPPS CROSS ROAD,22886800,E11,538923,188409,538950,188450,51.5774342,0.0034702
NA,13/11/2020 16:28,2020,2020/21,Special Service,2,4,346,1384,LOVE BIRD TRAPPED IN LIFT    RSPCA ON SCENE - PHILLIPS HAYES,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000636,HYDE PARK,E09000033,WESTMINSTER,Paddington,10033626484,KINGDOM STREET,8402127,W2,526263,181630,526250,181650,51.51948895,-0.181549178
NA,14/11/2020 23:13,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009405,STANLEY,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,KING'S ROAD,21700299,SW3,NULL,NULL,527250,178050,NULL,NULL
NA,15/11/2020 00:31,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009374,HACKNEY WICK,E09000012,HACKNEY,Homerton,NULL,CADOGAN TERRACE,20900196,E9,NULL,NULL,536550,184650,NULL,NULL
NA,15/11/2020 10:30,2020,2020/21,Special Service,1,2,346,692,LARGE ANIMAL RESCUE   HORSE STRADDLED OVER BARB WIRE FENCING - CALLER WILL MEET YOU ON ARRIVAL,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000119,HAYES AND CONEY HALL,E09000006,BROMLEY,Bromley,10034788950,HAYES LANE,20300405,BR2,541123,167422,541150,167450,51.38830405,0.026819848
NA,15/11/2020 13:47,2020,2020/21,Special Service,2,8,346,2768,Redacted,Horse,Person (mobile),Wasteland,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05011218,BELVEDERE,E09000004,BEXLEY,Erith,1.00024E+11,CHURCH MANORWAY,20100314,DA8,550614,179656,550650,179650,51.49579248,0.1683246
NA,15/11/2020 14:23,2020,2020/21,Special Service,1,1,346,346,Redacted,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,10014035637,QUEEN ELIZABETH OLYMPIC PARK,22208180,E20,537503,184040,537550,184050,51.53852854,-0.018706461
NA,15/11/2020 21:42,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN THE ENGINE GUARD OF A CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000357,HESTON WEST,E09000018,HOUNSLOW,Southall,1.00022E+11,CONVENT WAY,21501310,UB2,511804,178465,511850,178450,51.49408968,-0.390854388
NA,17/11/2020 13:22,2020,2020/21,Special Service,1,1,346,346,PIGEONS TRAPPED IN NETTING UNDER BRIDGE    CALLER WILL MEET BRIGADE,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,1.00023E+11,DEPTFORD HIGH STREET,22000328,SE8,537144,177404,537150,177450,51.47898309,-0.026459566
NA,18/11/2020 20:54,2020,2020/21,Special Service,NULL,NULL,346,NULL,ANIMAL RESCUE - DISTRESSED FOX IN TRAPPED IN STAIRWELL  RSCPA HAVE NO RESOURCES TO ATTEND,Fox,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,HANDYSIDE STREET,20400024,N1C,NULL,NULL,530250,183650,NULL,NULL
NA,21/11/2020 10:30,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH FOX TRAPPED IN NETTING - ACCESS VIA MOUNT PLEASANT,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000085,ALPERTON,E09000005,BRENT,Wembley,202025709,VINCENT ROAD,20200002,HA0,518771,184057,518750,184050,51.542928,-0.288664559
NA,22/11/2020 08:42,2020,2020/21,Special Service,1,2,346,692,COW STUCK IN DITCH    LOCATED JUST NORTH OF THE CAR PARK ON DITCHES LANE,Cow,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011474,OLD COULSDON,E09000008,CROYDON,Purley,10001002126,DITCHES LANE,20500221,CR5,530214,157050,530250,157050,51.2976946,-0.133656717
NA,22/11/2020 20:49,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN LIGHTWELL    CALL FROM RSPCA    RSPCA ON SCENE   - CO OP,Cat,Person (mobile),Common external bin storage area,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000141,KING'S CROSS,E09000007,CAMDEN,Euston,5172338,GRAY'S INN ROAD,20400895,WC1X,530488,182819,530450,182850,51.52922053,-0.120257329
NA,23/11/2020 16:16,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN NETTING BY ITS WING  UP A TREE      CALLER WILL DIRECT FROM THE CAR PARK ON DOCTOR J,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,10025174808,TOOTING BEC ROAD TO BEDFORD HILL,22906782,SW12,529106,172096,529150,172050,51.43316831,-0.144075244
NA,23/11/2020 21:20,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011227,LONGLANDS,E09000004,BEXLEY,Sidcup,10025183574,RECREATION ROAD,20101647,DA15,545490,172287,545450,172250,51.43091643,0.091533829
NA,23/11/2020 22:32,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN A GATE,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05011229,ST. MARY'S & ST. JAMES,E09000004,BEXLEY,Bexley,1.0002E+11,MANOR ROAD,20100915,DA5,549835,173457,549850,173450,51.44029766,0.154484865
NA,24/11/2020 03:43,2020,2020/21,Special Service,1,1,346,346,FOX IN HOLE,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000176,ELTHORNE,E09000009,EALING,Ealing,12179996,KIRN ROAD,20601003,W13,516686,180505,516650,180550,51.51143843,-0.319895773
NA,24/11/2020 14:41,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED BETWEEN CONSERVATORY AND FENCE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,NULL,CARDINAL AVENUE,21800205,KT2,NULL,NULL,518250,171150,NULL,NULL
NA,26/11/2020 14:20,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED IN GATE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000433,THORNTON,E09000022,LAMBETH,Tooting,NULL,POYNDERS GARDENS,21902132,SW4,NULL,NULL,529350,173950,NULL,NULL
NA,27/11/2020 11:10,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK ON THE ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,BULWER ROAD,20703992,N18,NULL,NULL,533350,192850,NULL,NULL
NA,28/11/2020 00:08,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000188,SOUTH ACTON,E09000009,EALING,Acton,NULL,MEON ROAD,20601167,W3,NULL,NULL,520350,179750,NULL,NULL
NA,28/11/2020 00:48,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN GATE,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05011100,DULWICH VILLAGE,E09000028,SOUTHWARK,Brixton,2.00003E+11,HERNE HILL,22503024,SE24,532300,174749,532350,174750,51.45627541,-0.09716388
NA,28/11/2020 10:55,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN GARAGE  ACCESS REAR OF DUDLEY ROAD,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,128041722,ACCESS ROAD FOR MILLFIELD,21880427,KT1,518565,168972,518550,168950,51.40738919,-0.296696889
NA,28/11/2020 12:37,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING   CALLER WILL LIASE AT LOCATION,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217006818,BRAMLEY ROAD,21700822,W10,523762,180884,523750,180850,51.51334017,-0.217834722
NA,29/11/2020 02:00,2020,2020/21,Special Service,1,1,346,346,PUPPY WITH PAW STUCK IN BARS OF CAGE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000516,BARNES,E09000027,RICHMOND UPON THAMES,Richmond,NULL,CHURCH ROAD,22404264,SW13,NULL,NULL,521950,176450,NULL,NULL
NA,30/11/2020 12:25,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CEILING - STAFF WILL MEET BRIGADE,Cat,Person (land line),Veterinary surgery,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011234,ALDBOROUGH,E09000026,REDBRIDGE,Ilford,1.00024E+11,HORNS ROAD,22302956,IG2,544548,188263,544550,188250,51.574714,0.084539352
NA,30/11/2020 20:34,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CAR ENGINE   CAR IN CAR PARK    OWNER ON SCENE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000282,WEST GREEN,E09000014,HARINGEY,Tottenham,1.00021E+11,GLOUCESTER ROAD,21106469,N17,532936,190209,532950,190250,51.59505715,-0.082197799
NA,30/11/2020 21:35,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - CAT TRAPPED BEHIND DOOR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,NULL,DOWSETT ROAD,21104472,N17,NULL,NULL,534350,190250,NULL,NULL
NA,01/12/2020 03:11,2020,2020/21,Special Service,1,1,346,346,DEER WEDGED IN METAL GATES,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000054,HALE,E09000003,BARNET,Mill Hill,200083763,NAN CLARKS LANE,20030700,NW7,521534,193908,521550,193950,51.63087712,-0.245433479
NA,01/12/2020 10:44,2020,2020/21,Special Service,1,2,346,692,RUNNING CALL TO ASSIST RSPCA WITH TRAPPED SEAGULL - NO FURTHER BRIGADE ATTENDANCE REQUIRED,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000303,STANMORE PARK,E09000015,HARROW,Stanmore,10025300478,FOOTPATH 16 FROM END OF THE COMMON TO OLD LODGE WAY,21202859,HA7,515573,192701,515550,192750,51.6212797,-0.331908332
NA,02/12/2020 10:19,2020,2020/21,Special Service,2,6,346,2076,LARGE ANIMAL RESCUE   HORSE FALLEN IN FIELD AND UNABLE TO LIFT,Horse,Person (mobile),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000309,EMERSON PARK,E09000016,HAVERING,Hornchurch,1.00021E+11,WINGLETYE LANE,21300661,RM11,555411,188369,555450,188350,51.572783,0.24121
NA,02/12/2020 12:09,2020,2020/21,Special Service,1,2,346,692,CAT IN TREE   RSPCA ON SCENE    ACCESS VIA JERNINGHAM ROAD,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000453,TELEGRAPH HILL,E09000023,LEWISHAM,New Cross,1.00023E+11,MUSGROVE ROAD,22000734,SE14,536091,176639,536050,176650,51.47235956,-0.041917453
NA,02/12/2020 12:48,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING   SECOND FLOOR,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000359,HOUNSLOW HEATH,E09000018,HOUNSLOW,Feltham,NULL,ALLIANCE CLOSE,21501776,TW4,NULL,NULL,512550,174750,NULL,NULL
NA,03/12/2020 08:23,2020,2020/21,Special Service,1,1,346,346,Redacted,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000119,HAYES AND CONEY HALL,E09000006,BROMLEY,Bromley,10003628309,HAYES LANE,20300405,BR2,540743,167397,540750,167350,51.38817646,0.021352045
NA,03/12/2020 19:18,2020,2020/21,Special Service,1,1,346,346,DOG WITH CHAIN STUCK AROUND NECK,Dog,Person (land line),Fire station,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000034,HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,100024983,RAINHAM ROAD NORTH,19900293,RM10,549609,186697,549650,186650,51.55932104,0.15685519
NA,04/12/2020 15:38,2020,2020/21,Special Service,1,1,346,346,DOG HAS IT HEAD STUCK IN A HOLE IN THE GATE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000333,ICKENHAM,E09000017,HILLINGDON,Hillingdon,1.00021E+11,THE DRIVE,21401950,UB10,506129,185914,506150,185950,51.56213362,-0.470329964
NA,05/12/2020 16:38,2020,2020/21,Special Service,1,3,346,1038,LARGE ANIMAL RESCUE - HORSE,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011234,ALDBOROUGH,E09000026,REDBRIDGE,Ilford,1.00022E+11,BILLET ROAD,22304527,RM6,546936,189450,546950,189450,51.5847648,0.119472358
NA,05/12/2020 20:23,2020,2020/21,Special Service,1,2,346,692,DISTRESSED KITTEN STUCK ON THIRD FLOOR BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,NULL,TARVES WAY,20801453,SE10,NULL,NULL,537950,177450,NULL,NULL
NA,06/12/2020 15:39,2020,2020/21,Special Service,1,1,346,346,LARGE ANIMAL RESCUE  HORSE WITH LEGS TRAPPED IN FENCE,Horse,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000029,CHADWELL HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,10090629511,FOOTPATH 108 FROM ENTRANCE OF WARREN FARM TO MEET FOOTPATH 107,19904011,RM6,548512,189299,548550,189250,51.58298902,0.14213365
NA,06/12/2020 23:13,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN CABLE,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,NULL,COOPERS YARD,21610093,N1,NULL,NULL,531650,184350,NULL,NULL
NA,07/12/2020 22:28,2020,2020/21,Special Service,1,3,346,1038,CAT STUCK ON ROOF - AL REQUESTED FROM SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,NULL,ROSEBERY AVENUE,21104995,N17,NULL,NULL,534350,190450,NULL,NULL
NA,08/12/2020 09:26,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH CAT RESCUE - CAT ON ROOF      CALL RECEIVED FROM RSPCA REPRESENTATIVE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,NULL,ROSEBERY AVENUE,21104995,N17,NULL,NULL,534350,190450,NULL,NULL
NA,09/12/2020 11:07,2020,2020/21,Special Service,1,5,346,1730,CAT STUCK INSIDE WALLS OF PROPERTY   ACCESS VIA REAR OF BEDAR AVENUE  FRU REQUESTED WITH SNAKE EYE C,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,NULL,HOE STREET,22845700,E17,NULL,NULL,537350,188850,NULL,NULL
NA,11/12/2020 11:50,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000353,HANWORTH,E09000018,HOUNSLOW,Feltham,2.00004E+11,HOUNSLOW ROAD,21500632,TW13,512013,172054,512050,172050,51.43642455,-0.389879217
NA,12/12/2020 00:34,2020,2020/21,Special Service,1,1,346,346,Redacted,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000104,WEMBLEY CENTRAL,E09000005,BRENT,Wembley,202128053,BRAEMAR AVENUE,20200556,HA0,517967,184199,517950,184150,51.54437346,-0.300203048
NA,12/12/2020 05:24,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Train station - concourse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,5088423,EUSTON ROAD,20400889,N1C,530076,183055,530050,183050,51.5314295,-0.1261073
NA,12/12/2020 10:59,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000605,LEYTONSTONE,E09000031,WALTHAM FOREST,Leytonstone,2.00001E+11,KINGSWOOD ROAD,22848850,E11,539403,187670,539450,187650,51.57068051,0.010107683
NA,12/12/2020 12:59,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011472,NORBURY & POLLARDS HILL,E09000008,CROYDON,Norbury,NULL,ARDFERN AVENUE,20500634,SW16,NULL,NULL,531050,168850,NULL,NULL
NA,12/12/2020 18:00,2020,2020/21,Special Service,1,1,346,346,CAT FALLEN THROUGH GARAGE ROOF CALLER WILL MEET YOU ON SCENE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000029,CHADWELL HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,100062846,EAGLE AVENUE,19900766,RM6,548177,188187,548150,188150,51.57308841,0.136836848
NA,13/12/2020 21:12,2020,2020/21,Special Service,1,1,346,346,FOX FALLEN INTO A WELL OUTSIDE - CALLER WILL MEET THE BRIGADE,Fox,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,HAMLET GARDENS,21000406,W6,NULL,NULL,522150,178750,NULL,NULL
NA,14/12/2020 00:58,2020,2020/21,Special Service,1,1,346,346,DEER STUCK IN GATES OF THE HEALTH CARE CENTRE AT THE BACK,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,COLNE DRIVE,21301095,RM3,554938,191752,554950,191750,51.60330618,0.235893378
NA,15/12/2020 15:40,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING ON BRIDGE - CALLER WILL MEET,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000292,HEADSTONE NORTH,E09000015,HARROW,Harrow,1.00023E+11,STATION ROAD,21201360,HA2,513569,188579,513550,188550,51.58463855,-0.362192326
NA,15/12/2020 16:12,2020,2020/21,Special Service,1,1,346,346,INJURED CAT CAUGHT IN WIRE ON FIRST FLOOR SCAFFOLDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011486,THORNTON HEATH,E09000008,CROYDON,Norbury,NULL,GILLETT ROAD,20500975,CR7,NULL,NULL,532450,168250,NULL,NULL
NA,16/12/2020 00:23,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CLOTHES AIRER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000482,EAST HAM SOUTH,E09000025,NEWHAM,East Ham,NULL,LONSDALE AVENUE,22200818,E6,NULL,NULL,542650,182250,NULL,NULL
NA,16/12/2020 20:21,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON RIVERBANK,Cat,Person (mobile),Wasteland,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,1.00021E+11,REEDHAM CLOSE,21103792,N17,534698,189408,534650,189450,51.58744413,-0.057073775
NA,16/12/2020 22:00,2020,2020/21,Special Service,1,1,346,346,CAT IN PRECARIOUS POSITION ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000610,BALHAM,E09000032,WANDSWORTH,Tooting,NULL,LAITWOOD ROAD,22902761,SW12,NULL,NULL,528750,173450,NULL,NULL
NA,17/12/2020 10:56,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Euston,NULL,WHITTLEBURY MEWS EAST,20499405,NW1,NULL,NULL,528250,184150,NULL,NULL
NA,17/12/2020 15:08,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011117,SURREY DOCKS,E09000028,SOUTHWARK,Dockhead,2.00003E+11,ROTHERHITHE STREET,22502127,SE16,536049,180435,536050,180450,51.50648085,-0.041057516
NA,18/12/2020 11:23,2020,2020/21,Special Service,1,2,346,692,CAT TRAPPED ON THE ROOF - RSPCA IN ATTENDANCE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000369,CANONBURY,E09000019,ISLINGTON,Islington,NULL,MORTON ROAD,21605476,N1,NULL,NULL,532550,184250,NULL,NULL
NA,19/12/2020 10:24,2020,2020/21,Special Service,1,2,346,692,INJURED GOOSE - FOOT TRAPPED IN HOLE IN TREE - LINE OPERATIONS LEVEL ONE,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000637,KNIGHTSBRIDGE AND BELGRAVIA,E09000033,WESTMINSTER,Kensington,10033558241,LANCASTER WALK,8401939,W2,526434,180332,526450,180350,51.5077862,-0.179555431
NA,19/12/2020 11:23,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,NULL,HORACE ROAD,22207759,E7,NULL,NULL,540550,185750,NULL,NULL
NA,19/12/2020 15:02,2020,2020/21,Special Service,1,1,346,346,PARROT TRAPPED BEHIND FURNITURE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000475,BECKTON,E09000025,NEWHAM,East Ham,NULL,WINSOR TERRACE,22201228,E6,NULL,NULL,543650,181650,NULL,NULL
NA,19/12/2020 15:10,2020,2020/21,Special Service,1,2,346,692,TO ASSIST RSPCA WITH HORSE IN WATER -WATER OERATIONS LEVEL ONE IMPLEMENTED   RPCA ARE NOT SCENE,Horse,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207169485,LOWER HALL LANE,20705126,EN3,536481,195476,536450,195450,51.64154228,-0.029000221
NA,20/12/2020 08:58,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BY SCHOOL SHED   CALLER WILL MEET & DIRECT,Cat,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011463,ADDISCOMBE WEST,E09000008,CROYDON,Croydon,1.00021E+11,OVAL ROAD,20501270,CR0,533129,166074,533150,166050,51.3781165,-0.088505
NA,20/12/2020 09:16,2020,2020/21,Special Service,1,2,346,692,DOG WITH HEAD TRAPPED IN FENCE BARS - CRYSTAL PALACE PARK UPPER TERRACE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000116,CRYSTAL PALACE,E09000006,BROMLEY,Beckenham,10003626411,CRYSTAL PALACE PARADE,20302173,SE19,533888,171003,533850,171050,51.42223249,-0.075747116
NA,20/12/2020 13:35,2020,2020/21,Special Service,1,2,346,692,LARGE ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),"Grassland, pasture, grazing etc",Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000313,HAVERING PARK,E09000016,HAVERING,Romford,1.00021E+11,ROWLAND WALK,21301566,RM4,551425,193299,551450,193250,51.61816295,0.185872344
NA,20/12/2020 16:45,2020,2020/21,Special Service,1,1,346,346,CAT STUCK BEHIND OVEN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000095,MAPESBURY,E09000005,BRENT,Willesden,NULL,CHICHELE ROAD,20202353,NW2,NULL,NULL,523650,185350,NULL,NULL
NA,21/12/2020 11:55,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH SMALL ANIMAL RESCUE     RSPCA ON SCENE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,6067325,STAINSBY ROAD,22701152,E14,537162,181320,537150,181350,51.51416551,-0.024676124
NA,21/12/2020 14:33,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE  BIRD TRAPPED BY WING IN RAFTERS OF PROPERTY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000063,WOODHOUSE,E09000003,BARNET,Finchley,NULL,ETCHINGHAM PARK ROAD,20014780,N3,NULL,NULL,526050,190950,NULL,NULL
NA,23/12/2020 11:20,2020,2020/21,Special Service,1,1,346,346,CAT INJURED AND TRAPPED ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000410,OLD MALDEN,E09000021,KINGSTON UPON THAMES,New Malden,NULL,SOUTH LANE,21800871,KT3,NULL,NULL,521550,166650,NULL,NULL
NA,23/12/2020 11:43,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011468,FAIRFIELD,E09000008,CROYDON,Croydon,NULL,SALEM PLACE,20501354,CR0,NULL,NULL,532050,165150,NULL,NULL
NA,23/12/2020 22:43,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE  CAT TRAPPED BETWEEN THE WALL OF TWO HOUSES,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000635,HARROW ROAD,E09000033,WESTMINSTER,North Kensington,NULL,HARROW ROAD,8400530,W9,NULL,NULL,524750,182150,NULL,NULL
NA,24/12/2020 04:11,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN GARDEN BELOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000369,CANONBURY,E09000019,ISLINGTON,Islington,NULL,DOWNHAM ROAD,21604726,N1,NULL,NULL,532850,184050,NULL,NULL
NA,24/12/2020 08:58,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011468,FAIRFIELD,E09000008,CROYDON,Croydon,NULL,SALEM PLACE,20501354,CR0,NULL,NULL,532050,165150,NULL,NULL
NA,24/12/2020 22:37,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000487,LITTLE ILFORD,E09000025,NEWHAM,East Ham,NULL,CHURCH ROAD,22200141,E12,NULL,NULL,542550,185250,NULL,NULL
NA,25/12/2020 16:47,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000122,ORPINGTON,E09000006,BROMLEY,Orpington,NULL,THE DRIVE,20300984,BR6,NULL,NULL,546050,165750,NULL,NULL
NA,26/12/2020 17:56,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000370,CLERKENWELL,E09000019,ISLINGTON,Shoreditch,NULL,CLERKENWELL CLOSE,21604536,EC1R,NULL,NULL,531450,182150,NULL,NULL
NA,26/12/2020 20:45,2020,2020/21,Special Service,1,1,346,346,FOX STUCK IN GUTTER PIPE,Fox,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05009325,LANSBURY,E09000030,TOWER HAMLETS,Poplar,6718297,ACCRA CLOSE,22702913,E14,538586,181248,538550,181250,51.51317326,-0.00420009
NA,27/12/2020 08:11,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE      KITTEN TRAPPED BEHIND WARDROBE ENTRANCE  AT  REAR OF CHINESE BUFFET,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000364,SYON,E09000018,HOUNSLOW,Heston,NULL,HIGH STREET,21500607,TW8,NULL,NULL,517450,177350,NULL,NULL
NA,27/12/2020 10:55,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN GUTTER    CALLER WILL MEET BRIGADE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,TURNER ROAD,22882850,E17,NULL,NULL,538250,189550,NULL,NULL
NA,27/12/2020 14:46,2020,2020/21,Special Service,1,1,346,346,DOG ON BANK OF RIVER THAMES       BY THE TRAVELODGE   IN WATERMANS PARK  BY THE WATERMANS ART CENTRE,Dog,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000347,BRENTFORD,E09000018,HOUNSLOW,Chiswick,10091693256,HIGH STREET,21500607,TW8,518354,177725,518350,177750,51.48610498,-0.296803874
NA,27/12/2020 17:17,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED UNDER SINK UNABLE TO MOVE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000493,WALL END,E09000025,NEWHAM,East Ham,NULL,BARKING ROAD,22207589,E6,NULL,NULL,543350,183750,NULL,NULL
NA,27/12/2020 20:28,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED BETWEEN GARDEN SHED AND WALL,Fox,Person (land line),Outdoor storage,Outdoor Structure,Other animal assistance,Assist  trapped livestock animal,E05000282,WEST GREEN,E09000014,HARINGEY,Hornsey,1.00023E+11,CARLINGFORD ROAD,21103058,N15,531791,189630,531750,189650,51.59012281,-0.098922184
NA,28/12/2020 07:45,2020,2020/21,Special Service,1,2,346,692,CAT TRAPPED IN A HOLE ABOVE KITCHEN SINK  FRU REQUESTED WITH SNAKE EYE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000493,WALL END,E09000025,NEWHAM,East Ham,NULL,BARKING ROAD,22207589,E6,NULL,NULL,543350,183750,NULL,NULL
NA,28/12/2020 10:41,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN PIPE ON THE SIDE OF THE BUILDING,Bird,Person (mobile),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000364,SYON,E09000018,HOUNSLOW,Heston,10093259175,THE HAM,21500543,TW8,517506,177163,517550,177150,51.48123434,-0.309197366
NA,28/12/2020 13:10,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN CABLING ON WALL AT REAR OF GARDEN,Bird,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217127931,LOCKTON STREET,21701312,W10,523740,180889,523750,180850,51.51339113,-0.218158047
NA,28/12/2020 13:16,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN BASEMENT LIGHT WELL AT SIDE OF HOUSE,Fox,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000630,ABBEY ROAD,E09000033,WESTMINSTER,West Hampstead,NULL,CLIFTON HILL,8400800,NW8,NULL,NULL,525850,183350,NULL,NULL
NA,28/12/2020 15:00,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000428,ST. LEONARD'S,E09000022,LAMBETH,Tooting,NULL,WOODFIELD AVENUE,21901521,SW16,NULL,NULL,529850,172450,NULL,NULL
NA,28/12/2020 15:37,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN VOID IN WALL       FOLLOWING EARLIER INCIDENT CAT LOCATION NOW IDENTIFIED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000493,WALL END,E09000025,NEWHAM,East Ham,NULL,BARKING ROAD,22207589,E6,NULL,NULL,543350,183750,NULL,NULL
NA,29/12/2020 13:25,2020,2020/21,Special Service,1,2,346,692,HORSE STUCK IN DRAINAGE OVERFLOW CHANNEL    NEXT TO THE RIVER LEE   CALLER WILL MEET AND DIRECT,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207007287,WHARF ROAD,20702448,EN3,536381,195485,536350,195450,51.64164631,-0.03043902
NA,30/12/2020 18:53,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,NULL,SHELLNESS ROAD,20900912,E5,NULL,NULL,534850,185350,NULL,NULL
NA,01/01/2021 12:09,2021,2020/21,Special Service,1,2,346,692,KITTEN STUCK UP TREE  AL REQUESTED FROM SCENE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000357,HESTON WEST,E09000018,HOUNSLOW,Southall,1.00022E+11,HOLLY FARM ROAD,21500619,UB2,512009,178286,512050,178250,51.49244181,-0.387967616
NA,01/01/2021 14:06,2021,2020/21,Special Service,1,1,346,346,Redacted,Sheep,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000203,JUBILEE,E09000010,ENFIELD,Chingford,207185205,LOWER HALL LANE,20705126,E4,537266,195047,537250,195050,51.63749131,-0.017824704
NA,03/01/2021 18:40,2021,2020/21,Special Service,1,1,346,346,CAT WITH LEG TRAPPED IN BATH PLUGHOLE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000614,FAIRFIELD,E09000032,WANDSWORTH,Battersea,NULL,VARDENS ROAD,22905780,SW11,NULL,NULL,526950,175050,NULL,NULL
NA,04/01/2021 13:39,2021,2020/21,Special Service,1,1,346,346,Redacted,Fox,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Assist trapped wild animal,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Soho,5087941,STONE BUILDINGS,20401158,WC2A,530933,181553,530950,181550,51.5177335,-0.1143172
NA,06/01/2021 10:22,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Soho,5105007,BETTERTON STREET,20401231,WC2H,530269,181206,530250,181250,51.51477674,-0.124005278
NA,06/01/2021 13:09,2021,2020/21,Special Service,2,4,346,1384,CAT IN DISTRESS ON ROOF - ADDITIONAL APPLIANCE REQUESTED FROM SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000199,ENFIELD LOCK,E09000010,ENFIELD,Enfield,NULL,MANTON ROAD,20706864,EN3,NULL,NULL,537550,198250,NULL,NULL
NA,06/01/2021 20:35,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN FOX HOLE  - MEET AT CLUB HOUSE,Dog,Person (mobile),Golf course (not building on course),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000111,CHISLEHURST,E09000006,BROMLEY,Bromley,1.00023E+11,CAMDEN PARK ROAD,20302990,BR7,543703,170048,543750,170050,51.41125552,0.064940725
NA,07/01/2021 23:50,2021,2020/21,Special Service,1,1,346,346,KITTEN STUCK BETWEEN WALL AND ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009378,HOXTON WEST,E09000012,HACKNEY,Islington,NULL,SHAFTESBURY STREET,20900904,N1,NULL,NULL,532450,183150,NULL,NULL
NA,09/01/2021 08:01,2021,2020/21,Special Service,1,1,346,346,DOG STUCK IN TRENCH,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000401,BERRYLANDS,E09000021,KINGSTON UPON THAMES,Surbiton,128001560,BERRYLANDS,21800134,KT5,519011,167417,519050,167450,51.39332408,-0.290804918
NA,10/01/2021 19:27,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009328,POPLAR,E09000030,TOWER HAMLETS,Poplar,NULL,SALTWELL STREET,22701057,E14,NULL,NULL,537350,180850,NULL,NULL
NA,12/01/2021 11:39,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011471,NEW ADDINGTON SOUTH,E09000008,CROYDON,Addington,1.00021E+11,ROWDOWN CRESCENT,20501817,CR0,538917,162736,538950,162750,51.34674034,-0.006691875
NA,12/01/2021 22:38,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN DITCH,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000145,WEST HAMPSTEAD,E09000007,CAMDEN,West Hampstead,5057574,SANDWELL CRESCENT,20400408,NW6,525421,184913,525450,184950,51.54918533,-0.192513522
NA,16/01/2021 18:05,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN PORTER CABIN,Dog,Person (mobile),Other private non-residential building,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000034,HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,10091589745,RAINHAM ROAD NORTH,19900293,RM10,549922,186394,549950,186350,51.55651723,0.161224295
NA,17/01/2021 16:09,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN WAREHOUSE AREA - CALLER BELIEVES DOG IS INJURED - ACCESS VIA LINLEY CRESCENT,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000316,MAWNEYS,E09000016,HAVERING,Romford,1.00021E+11,LINLEY CRESCENT,21301362,RM7,549934,189861,549950,189850,51.58766466,0.162875698
NA,17/01/2021 17:09,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN NETTING    CALLER WILL MEET YOU,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000319,ROMFORD TOWN,E09000016,HAVERING,Romford,1.00023E+11,THE LIBERTY,21301354,RM1,551370,188803,551350,188850,51.57777879,0.183142388
NA,18/01/2021 15:17,2021,2020/21,Special Service,1,1,346,346,CAT STUCK IN TREE BEING ATTACKED BY CROWS,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000629,WEST PUTNEY,E09000032,WANDSWORTH,Wandsworth,1.00023E+11,WESTLEIGH AVENUE,22906123,SW15,523222,174549,523250,174550,51.45652351,-0.227832429
NA,18/01/2021 17:06,2021,2020/21,Special Service,1,1,346,346,ASSIST RSPCA - SMALL ANIMAL RESUE - BIRD ENTANGLED IN NETTING,Bird,Person (mobile),Furniture warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000090,FRYENT,E09000005,BRENT,Stanmore,202099001,EDGWARE ROAD,20201631,NW9,520774,189483,520750,189450,51.59127178,-0.257934412
NA,19/01/2021 18:28,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Plumstead,NULL,LLANOVER ROAD,20800932,SE18,NULL,NULL,543250,177550,NULL,NULL
NA,19/01/2021 20:24,2021,2020/21,Special Service,1,1,346,346,Redacted,Horse,Person (land line),Scrub land,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000318,RAINHAM AND WENNINGTON,E09000016,HAVERING,Wennington,10033419935,FERRY LANE,21300773,RM13,551542,181415,551550,181450,51.51135379,0.182436899
NA,19/01/2021 20:36,2021,2020/21,Special Service,1,1,346,346,RUNNING CALL AT ON ROOF,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000409,NORBITON,E09000021,KINGSTON UPON THAMES,Kingston,NULL,BONNER HILL ROAD,21800156,KT1,NULL,NULL,518850,168950,NULL,NULL
NA,20/01/2021 09:35,2021,2020/21,Special Service,1,1,346,346,CAT STUCK BETWEEN TREE BRANCHES,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000446,LADYWELL,E09000023,LEWISHAM,Lewisham,NULL,EMBLETON ROAD,22000377,SE13,NULL,NULL,537650,175150,NULL,NULL
NA,21/01/2021 13:15,2021,2020/21,Special Service,1,1,346,346,SWAN TRAPPED IN NETTING,Bird,Person (land line),Airfield/runway,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Heathrow,10059101553,WOODCOCK ROAD,21402989,TW19,504909,175484,504950,175450,51.46861273,-0.491024744
NA,21/01/2021 18:23,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,NULL,WHITELEY ROAD,21901493,SE19,NULL,NULL,532950,171150,NULL,NULL
NA,22/01/2021 14:22,2021,2020/21,Special Service,NULL,NULL,346,NULL,CAT TRAPPED BETWEEN WALL AND FENCE,Cat,Person (mobile),Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000113,COPERS COPE,E09000006,BROMLEY,Beckenham,10070012641,HIGH STREET,20301747,BR3,537381,169522,537350,169550,51.4080907,-0.026111084
NA,23/01/2021 10:18,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009398,NORLAND,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,CLARENDON ROAD,21700837,W11,NULL,NULL,524350,180650,NULL,NULL
NA,23/01/2021 15:43,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011226,FALCONWOOD & WELLING,E09000004,BEXLEY,Eltham,NULL,TYRRELL AVENUE,20101490,DA16,NULL,NULL,546750,174650,NULL,NULL
NA,23/01/2021 17:16,2021,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000263,SHEPHERD'S BUSH GREEN,E09000013,HAMMERSMITH AND FULHAM,North Kensington,NULL,QUEENSDALE CRESCENT,21000663,W11,NULL,NULL,523850,180350,NULL,NULL
NA,25/01/2021 12:02,2021,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH FOX STUCK DOWN CULVERT,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05009319,BOW EAST,E09000030,TOWER HAMLETS,Homerton,6647935,SMEED ROAD,22701107,E3,537116,184150,537150,184150,51.53961279,-0.024238454
NA,26/01/2021 13:42,2021,2020/21,Special Service,1,1,346,346,DOG STUCK IN RAILINGS - CALLER WILL MEET YOU,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,1.00021E+11,AVERY HILL ROAD,20800098,SE9,544707,174192,544750,174150,51.44823467,0.08106256
NA,26/01/2021 18:21,2021,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000214,ABBEY WOOD,E09000011,GREENWICH,Plumstead,10010244821,LONGLEIGH LANE,20800936,SE2,547074,177963,547050,177950,51.48150712,0.116663295
NA,26/01/2021 22:44,2021,2020/21,Special Service,1,1,346,346,BIRDS TRAPPED IN BASKETBALL COURT CALLER IS ON A BIKE AND WILL MEET YOU AND DIRECT YOU FROM THE LOCA,Bird,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,10012787266,CORKER WALK,21606796,N7,530939,186473,530950,186450,51.56195457,-0.112396507
NA,26/01/2021 23:35,2021,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN FENCE IN ALLEYWAY NEXT TO,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011226,FALCONWOOD & WELLING,E09000004,BEXLEY,Eltham,NULL,WESTWOOD LANE,20101579,DA16,NULL,NULL,546150,175050,NULL,NULL
NA,27/01/2021 09:18,2021,2020/21,Special Service,1,1,346,346,CAT STUCK IN TREE - ATTENDED YESTERDAY AND ADVISED TO RECONTACT THIS MORNING IF CAT REMAINED IN SITU,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000214,ABBEY WOOD,E09000011,GREENWICH,Plumstead,10010226159,LONGLEIGH LANE,20800936,SE2,546919,177978,546950,177950,51.48168486,0.11444617
NA,27/01/2021 10:12,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED BY LEG IN A TREE - RSPCA IN ATTENDANCE    **AERIAL APPLIANCE REQUESTED FROM SCENE - TO,Bird,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05000380,ST. PETER'S,E09000019,ISLINGTON,Islington,5300095282,VINCENT TERRACE,21607012,N1,531758,183260,531750,183250,51.53288794,-0.101785925
NA,27/01/2021 15:22,2021,2020/21,Special Service,1,1,346,346,CAT UP TREE   ASSIST RSPCA,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000452,SYDENHAM,E09000023,LEWISHAM,Forest Hill,1.00022E+11,LOCKWOOD CLOSE,22001659,SE26,535972,171727,535950,171750,51.42824528,-0.045504343
NA,29/01/2021 10:47,2021,2020/21,Special Service,1,1,346,346,TRAPPED FOX IN FENCE  IN REAR GARDEN,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000054,HALE,E09000003,BARNET,Mill Hill,NULL,GIBBS GREEN,20017580,HA8,NULL,NULL,520450,192550,NULL,NULL
NA,30/01/2021 14:53,2021,2020/21,Special Service,1,1,346,346,CAT STUCK UNDER SHED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000449,NEW CROSS,E09000023,LEWISHAM,New Cross,NULL,ROBERT LOWE CLOSE,22000867,SE14,NULL,NULL,535950,177350,NULL,NULL
NA,30/01/2021 15:28,2021,2020/21,Special Service,1,1,346,346,BIRD CAUGHT IN NETTING - RSPCA ON SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011097,CHAMPION HILL,E09000028,SOUTHWARK,Peckham,NULL,CHAMPION HILL,22500477,SE5,NULL,NULL,533150,175650,NULL,NULL
NA,30/01/2021 17:54,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED UNDER CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000627,WANDSWORTH COMMON,E09000032,WANDSWORTH,Tooting,10070248175,SANDGATE LANE,22904517,SW18,527247,173114,527250,173150,51.44274157,-0.170446211
NA,31/01/2021 12:53,2021,2020/21,Special Service,1,1,346,346,CAT STUCK UP TREE - RSPCA ON SCENE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000195,CHASE,E09000010,ENFIELD,Enfield,207156651,BRODIE ROAD,20702534,EN2,532453,198177,532450,198150,51.66676966,-0.086152895
NA,31/01/2021 13:48,2021,2020/21,Special Service,1,1,346,346,INJURED CAT STUCK IN GREEN AREA AT REAR OF,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000613,EAST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,WEST HILL,22906045,SW15,NULL,NULL,524850,174550,NULL,NULL
NA,01/02/2021 09:49,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011486,THORNTON HEATH,E09000008,CROYDON,Norbury,NULL,PARCHMORE ROAD,20501277,CR7,NULL,NULL,532250,168650,NULL,NULL
NA,01/02/2021 10:03,2021,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE   CAT TRAPPED IN CAVITY WALL IN BATHROOM,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,FINBOROUGH ROAD,21700211,SW10,NULL,NULL,526050,177650,NULL,NULL
NA,02/02/2021 14:40,2021,2020/21,Special Service,1,1,346,346,LARGE ANIMAL RESCUE - DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000286,CANONS,E09000015,HARROW,Stanmore,2E+11,CANONS DRIVE,21200870,HA8,518220,191912,518250,191950,51.61364342,-0.293968915
NA,02/02/2021 17:20,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN WIRE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,BOUNCES ROAD,20703940,N9,NULL,NULL,535250,193950,NULL,NULL
NA,02/02/2021 20:13,2021,2020/21,Special Service,1,1,346,346,KITTEN POSSIBLY TRAPPED IN BRANCHES IN TREE - ALSO POSSIBLY INJURED,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000058,OAKLEIGH,E09000003,BARNET,Southgate,200032073,DENHAM ROAD,20012180,N20,527633,193496,527650,193450,51.62581749,-0.157503973
NA,03/02/2021 20:24,2021,2020/21,Special Service,2,5,346,1730,Redacted,Dog,Person (mobile),Scrub land,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011483,SHIRLY SOUTH,E09000008,CROYDON,Addington,1.00023E+11,SHIRLEY HILLS ROAD,20501831,CR0,535137,164275,535150,164250,51.36147843,-0.060352874
NA,04/02/2021 09:37,2021,2020/21,Special Service,1,1,346,346,PIDGEON STUCK IN NETTING UNDER BRIDGE - CALLER IS WAITING THERE TO SHOW YOU,Bird,Person (mobile),Underground car park,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011487,WADDON,E09000008,CROYDON,Croydon,1.00021E+11,LOWER CHURCH STREET,20501161,CR0,531857,165650,531850,165650,51.37460893,-0.106922341
NA,04/02/2021 09:50,2021,2020/21,Special Service,1,1,346,346,DOG IN RIVER THAMES     ON THE TOW PATH NEAR RIVERSIDE AVENUE,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000524,KEW,E09000027,RICHMOND UPON THAMES,Richmond,10070719479,ROYAL BOTANIC GARDENS,22407049,TW9,518478,177474,518450,177450,51.48381961,-0.295097335
NA,05/02/2021 20:22,2021,2020/21,Special Service,1,1,346,346,DOG  TRAPPED INSIDE SCHOOL PLAYGROUND  - CALLER WILL MEET YOU AT GATES,Dog,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,2.00001E+11,DOWNSELL ROAD,22831900,E15,538810,185846,538850,185850,51.5544313,0.0008295
NA,06/02/2021 14:16,2021,2020/21,Special Service,1,1,346,346,KITEEN TRAPPED UNDER KITCHEN SINK,Unknown - Domestic Animal Or Pet,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000140,KILBURN,E09000007,CAMDEN,West Hampstead,NULL,WEST END LANE,20499056,NW6,NULL,NULL,525450,184350,NULL,NULL
NA,10/02/2021 11:55,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND PIPES UNDER SINK,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000440,CATFORD SOUTH,E09000023,LEWISHAM,Lewisham,NULL,MUNCIES MEWS,22001732,SE6,NULL,NULL,538450,172950,NULL,NULL
NA,11/02/2021 15:52,2021,2020/21,Special Service,1,1,346,346,DOG STUCK IN OPEN WINDOW,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000320,ST. ANDREW'S,E09000016,HAVERING,Hornchurch,NULL,DIBAN AVENUE,21300320,RM12,NULL,NULL,552650,185750,NULL,NULL
NA,13/02/2021 15:46,2021,2020/21,Special Service,1,1,346,346,INJURED CAT IN TREE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000270,FORTIS GREEN,E09000014,HARINGEY,Hornsey,1.00021E+11,LYNMOUTH ROAD,21100553,N2,527882,189325,527850,189350,51.58828022,-0.155433784
NA,14/02/2021 12:43,2021,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE  CAT TRAPPED IN BATHROOM,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000372,HIGHBURY EAST,E09000019,ISLINGTON,Islington,NULL,ABERDEEN ROAD,21602796,N5,NULL,NULL,532150,185650,NULL,NULL
NA,15/02/2021 09:53,2021,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN TREE HANGING UPSIDE DOWN  CALLER WILL MEET YOU AT THE ENTRANCE OF COLERIDGE HOUSE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000633,CHURCHILL,E09000033,WESTMINSTER,Lambeth,2.00003E+11,CHURCHILL GARDENS ESTATE,8401852,SW1V,529249,178037,529250,178050,51.48652947,-0.13985501
NA,15/02/2021 13:34,2021,2020/21,Special Service,1,1,346,346,CAT STUCK ON TOP OF CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000346,BEDFONT,E09000018,HOUNSLOW,Feltham,NULL,OAK WAY,21500828,TW14,NULL,NULL,509450,173150,NULL,NULL
NA,15/02/2021 13:42,2021,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN NETTING - RSPCA ON SCENE,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000300,RAYNERS LANE,E09000015,HARROW,Harrow,1.00021E+11,RAYNERS LANE,21201971,HA5,512934,187757,512950,187750,51.57738268,-0.371615878
NA,15/02/2021 19:54,2021,2020/21,Special Service,1,1,346,346,CAT STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011477,PURLEY OAKS & RIDDLESDOWN,E09000008,CROYDON,Purley,NULL,GRASMERE ROAD,20502102,CR8,NULL,NULL,531850,161750,NULL,NULL
NA,16/02/2021 00:05,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN ABANDONED BUILDING   CALLER STATED ITS THE ABANDONED BUILDING AT THE END OF MENDORA R,Cat,Person (land line),Other building/use not known,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000254,FULHAM BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Fulham,34115023,RYLSTON ROAD,21000711,SW6,524627,177494,524650,177450,51.48268643,-0.206586249
NA,17/02/2021 08:47,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CAT FLAP - CAT FLAP JAMMED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000441,CROFTON PARK,E09000023,LEWISHAM,Forest Hill,NULL,RAVENSBOURNE ROAD,22001833,SE6,NULL,NULL,536650,173550,NULL,NULL
NA,17/02/2021 20:20,2021,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED IN CHIMNEY BEHIND ELECTRICAL FIRE PLACE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000453,TELEGRAPH HILL,E09000023,LEWISHAM,New Cross,NULL,OMMANEY ROAD,22000770,SE14,NULL,NULL,536050,176550,NULL,NULL
NA,18/02/2021 16:14,2021,2020/21,Special Service,1,1,346,346,CAT FALLEN IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000469,RAYNES PARK,E09000024,MERTON,Wimbledon,NULL,DURHAM ROAD,22102223,SW20,NULL,NULL,522950,169350,NULL,NULL
NA,18/02/2021 19:16,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000418,CLAPHAM COMMON,E09000022,LAMBETH,Clapham,1.00022E+11,NARBONNE AVENUE,21901004,SW4,529147,174560,529150,174550,51.45530442,-0.142590827
NA,19/02/2021 09:27,2021,2020/21,Special Service,1,1,346,346,KITTEN IN RIVER   FRU REQUESTED FROM SCENE,Cat,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,207012839,VICTORIA ROAD,20704873,N18,533911,192518,533950,192550,51.61557829,-0.067235941
NA,19/02/2021 12:29,2021,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000059,TOTTERIDGE,E09000003,BARNET,Barnet,200215159,TOTTERIDGE LANE,20043080,N20,525954,193943,525950,193950,51.63021366,-0.181589552
NA,20/02/2021 10:53,2021,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - CAT TRAPPED BEHIND WASHINE MACHINE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000428,ST. LEONARD'S,E09000022,LAMBETH,Norbury,NULL,ESTREHAM ROAD,21900528,SW16,NULL,NULL,529750,170850,NULL,NULL
NA,20/02/2021 15:32,2021,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED BEHIND NETTING ON RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,2.00001E+11,WOOD STREET,22888350,E17,538472,189398,538450,189350,51.58643842,-0.002639492
NA,21/02/2021 08:44,2021,2020/21,Special Service,1,1,346,346,CAT OVER SIDE OF BALCONY - SITTING ON LEDGE OF BALCONY UNABLE TO GET BACK OVER BALCONY - SECOND FLOO,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,MILE END,E09000030,TOWER HAMLETS,Bethnal Green,NULL,COPPERFIELD ROAD,22700356,E3,NULL,NULL,536350,181950,NULL,NULL
NA,21/02/2021 10:34,2021,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000381,TOLLINGTON,E09000019,ISLINGTON,Holloway,5300091411,TOLLINGTON PARK,21603933,N4,530751,186843,530750,186850,51.56531702,-0.11496689
NA,21/02/2021 13:49,2021,2020/21,Special Service,1,1,346,346,CAT STUCK IN BARS OF CRATE INSIDE PROPERTY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009378,HOXTON WEST,E09000012,HACKNEY,Shoreditch,NULL,WENLOCK STREET,20901062,N1,NULL,NULL,532750,183150,NULL,NULL
NA,21/02/2021 15:37,2021,2020/21,Special Service,1,1,346,346,KITTEN FALLEN DOWN INTO BASEMENT BELIEVED TO BE INJURED,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009401,QUEEN'S GATE,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,KENSINGTON GATE,21700292,W8,NULL,NULL,526150,179450,NULL,NULL
NA,21/02/2021 22:27,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON SECOND FLOOR WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000434,THURLOW PARK,E09000022,LAMBETH,West Norwood,NULL,HARPENDEN ROAD,21900674,SE27,NULL,NULL,531650,172650,NULL,NULL
NA,22/02/2021 00:12,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000184,NORTHOLT MANDEVILLE,E09000009,EALING,Northolt,12201111,ROWDELL ROAD,20601480,UB5,513155,183701,513150,183750,51.54088323,-0.369725437
NA,22/02/2021 14:54,2021,2020/21,Special Service,1,1,346,346,DOG STUCK ON FORESHORE,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000524,KEW,E09000027,RICHMOND UPON THAMES,Richmond,10070719479,ROYAL BOTANIC GARDENS,22407049,TW9,518324,177413,518350,177450,51.48330999,-0.297328658
NA,23/02/2021 08:38,2021,2020/21,Special Service,2,3,346,1038,Redacted,Fox,Person (land line),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000222,GREENWICH WEST,E09000011,GREENWICH,Deptford,10010260540,ALDER PATH,20802032,SE8,537780,177614,537750,177650,51.48071333,-0.017227128
NA,23/02/2021 16:44,2021,2020/21,Special Service,1,1,346,346,ASSIST RSPCA  CAT ON ROOF OF GARAGES,Cat,Person (mobile),Private garage,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000335,NORTHWOOD,E09000017,HILLINGDON,Ruislip,10091098473,WESTMINSTER CLOSE,21403394,HA6,508322,191419,508350,191450,51.61119639,-0.437004352
NA,23/02/2021 18:08,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED BEHIND NETTING - LONDON WILDLIFE PROTECTION ON SCENE,Bird,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217069956,PORTOBELLO ROAD,21700970,W10,524335,181754,524350,181750,51.52103431,-0.209286849
NA,23/02/2021 18:53,2021,2020/21,Special Service,1,2,346,692,CAT TRAPPED MID BROOK AT REAR,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000097,PRESTON,E09000005,BRENT,Wembley,202100729,ELMSTEAD AVENUE,20200435,HA9,518684,186833,518650,186850,51.56789556,-0.288978385
NA,23/02/2021 20:52,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000459,DUNDONALD,E09000024,MERTON,Wimbledon,48033007,HARTFIELD CRESCENT,22103117,SW19,524749,170271,524750,170250,51.41774528,-0.2073704
NA,24/02/2021 15:33,2021,2020/21,Special Service,1,1,346,346,KITTEN STUCK IN WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,BLACK PRINCE STREET,20800016,SE18,NULL,NULL,545950,177650,NULL,NULL
NA,24/02/2021 17:24,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED UNDER HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,NULL,THE AVENUE,21106439,N10,NULL,NULL,529350,190250,NULL,NULL
NA,26/02/2021 09:01,2021,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN HOARDING OF FLAT,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000177,GREENFORD BROADWAY,E09000009,EALING,Southall,12052279,RUISLIP ROAD EAST,20602095,UB6,514434,182258,514450,182250,51.52765491,-0.351754991
NA,26/02/2021 13:49,2021,2020/21,Special Service,1,2,346,692,FOX TRAPPED IN STORE ROOM,Fox,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05009376,HOMERTON,E09000012,HACKNEY,Homerton,1.00023E+11,FRAMPTON PARK ROAD,20900423,E9,535158,184473,535150,184450,51.542979,-0.052341
NA,28/02/2021 08:14,2021,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000363,OSTERLEY AND SPRING GROVE,E09000018,HOUNSLOW,Heston,1.00022E+11,THE GROVE,21500523,TW7,515380,176478,515350,176450,51.47551317,-0.340022536
NA,28/02/2021 11:47,2021,2020/21,Special Service,1,1,346,346,PARROT TRAPPED IN TREE - RSPCA ON SCENE     IN FRONT OF MAIN ENTRANCE LOOK TO RIGHT,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000047,COPPETTS,E09000003,BARNET,Southgate,200209428,ROYAL DRIVE,20037410,N11,528280,192157,528250,192150,51.61363875,-0.148657145
NA,28/02/2021 18:05,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED UNDER DECKING,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009334,STEPNEY GREEN,E09000030,TOWER HAMLETS,Bethnal Green,NULL,STEPNEY GREEN,22701160,E1,NULL,NULL,535750,181750,NULL,NULL
NA,01/03/2021 10:34,2021,2020/21,Special Service,1,1,346,346,ASSIST RSPCA REP WITH BIRD TRAPPED IN NETTING,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000632,BRYANSTON AND DORSET SQUARE,E09000033,WESTMINSTER,Paddington,NULL,CHAGFORD STREET,8400930,NW1,NULL,NULL,527750,182150,NULL,NULL
NA,02/03/2021 13:43,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED BETWEEN CHIMNEYS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009331,ST. PETER'S,E09000030,TOWER HAMLETS,Bethnal Green,NULL,HAGUE STREET,22700589,E2,NULL,NULL,534450,182550,NULL,NULL
NA,02/03/2021 15:09,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN A FENCE     NATIONAL GRID BUILDING SITE - CALLER WILL MEET CREW,Bird,Person (mobile),River/canal,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000380,ST. PETER'S,E09000019,ISLINGTON,Islington,10090552582,CITY ROAD,21604520,EC1V,532089,182978,532050,182950,51.53027371,-0.097126095
NA,03/03/2021 10:43,2021,2020/21,Special Service,1,1,346,346,CAT STUCK UP TREE - RSPCA ON SCENE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009388,ABINGDON,E09000020,KENSINGTON AND CHELSEA,Kensington,217118035,SOUTH EDWARDES SQUARE,21700673,W8,524979,179077,524950,179050,51.49683553,-0.200949226
NA,03/03/2021 11:38,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011488,WEST THORNTON,E09000008,CROYDON,Norbury,1.00021E+11,HARCOURT ROAD,20501023,CR7,531090,167176,531050,167150,51.3884955,-0.117369498
NA,03/03/2021 18:23,2021,2020/21,Special Service,1,1,346,346,SMALL ANIMAL TRAPPED IN CHIMNEY,Unknown - Wild Animal,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011467,CRYSTAL PALACE & UPPER NORWOOD,E09000008,CROYDON,Beckenham,NULL,MABERLEY ROAD,20501167,SE19,NULL,NULL,534050,169950,NULL,NULL
NA,05/03/2021 08:46,2021,2020/21,Special Service,1,1,346,346,CAT STUCK BETWEEN EXTENSION AND EXTERIOR WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000558,CARSHALTON CENTRAL,E09000029,SUTTON,Wallington,NULL,SUTTON GROVE,22602650,SM1,NULL,NULL,526750,164350,NULL,NULL
NA,05/03/2021 12:31,2021,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000102,TOKYNGTON,E09000005,BRENT,Wembley,NULL,EMPIRE WAY,20201371,HA9,NULL,NULL,518950,185750,NULL,NULL
NA,05/03/2021 16:18,2021,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,10070248797,DR JOHNSON AVENUE TO TOOTING BEC ROAD,22906781,SW17,529043,172087,529050,172050,51.43310024,-0.144992207
NA,06/03/2021 13:43,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN CRATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011217,BARNEHURST,E09000004,BEXLEY,Bexley,NULL,HURSTWOOD AVENUE,20101844,DA7,NULL,NULL,551350,176650,NULL,NULL
NA,06/03/2021 20:35,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,SALISBURY STREET,8401547,NW8,NULL,NULL,527050,182150,NULL,NULL
NA,07/03/2021 08:39,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,BOURNE HILL,20703944,N13,NULL,NULL,530550,193750,NULL,NULL
NA,07/03/2021 14:28,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN GATE  CALLER STATES IN CHILDRENS PLAYGROUND   NEAR RIVERSIDE WALK NEAR TO MICHAELS BR,Dog,Person (land line),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000059,TOTTERIDGE,E09000003,BARNET,Finchley,200121208,WALMINGTON FOLD,20044460,N12,525367,192071,525350,192050,51.61352561,-0.190731057
NA,07/03/2021 21:58,2021,2020/21,Special Service,1,1,346,346,CAT STUCK IN WALL CAVITY IN BASEMENT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000284,WOODSIDE,E09000014,HARINGEY,Hornsey,NULL,PARK AVENUE,21104893,N22,NULL,NULL,530550,190750,NULL,NULL
NA,08/03/2021 15:56,2021,2020/21,Special Service,1,2,346,692,BIRD TRAPPED IN KITE NET - PERSONS ON SCENE WAITING FOR CREWS,Bird,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000085,ALPERTON,E09000005,BRENT,Wembley,202126825,BRIDGEWATER ROAD,20200329,HA0,517393,184361,517350,184350,51.5459467,-0.308428651
NA,08/03/2021 16:32,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED UNDER A CAR - NEAR TO THE HARRIS ACADEMY,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal harm involving domestic animal,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Eltham,10010225703,ELTHAM HILL,20800527,SE9,541624,174623,541650,174650,51.45288972,0.036899556
NA,08/03/2021 21:16,2021,2020/21,Special Service,1,1,346,346,DOG STUCK IN METAL FENCE,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000215,BLACKHEATH WESTCOMBE,E09000011,GREENWICH,Lee Green,NULL,POND ROAD,20801195,SE3,NULL,NULL,539950,176050,NULL,NULL
NA,09/03/2021 05:07,2021,2020/21,Special Service,1,1,346,346,ANIMAL RESCUE - FOX STUCK BETWEEN SHED & WALL,Fox,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000173,EALING BROADWAY,E09000009,EALING,Ealing,12075598,DENBIGH ROAD,20600525,W13,516990,181097,516950,181050,51.51669472,-0.315316074
NA,09/03/2021 10:02,2021,2020/21,Special Service,1,1,346,346,PIGEON CAUGHT IN NETTING UNDER FOOTBRIDGE BETWEEN THE PAVILIONS SHOPPING CENTRE AND MULTI STORY CAR,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,10003640627,STOCKWELL CLOSE,20300634,BR1,540592,169175,540550,169150,51.4041894,0.019886361
NA,09/03/2021 11:59,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND BUILT IN  WARDROBE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000572,WORCESTER PARK,E09000029,SUTTON,Sutton,NULL,CHEAM COMMON ROAD,22601362,KT4,NULL,NULL,523250,165250,NULL,NULL
NA,09/03/2021 17:30,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000449,NEW CROSS,E09000023,LEWISHAM,New Cross,NULL,HUNSDON ROAD,22000543,SE14,NULL,NULL,535550,177550,NULL,NULL
NA,10/03/2021 04:37,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN SOFA,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000489,PLAISTOW NORTH,E09000025,NEWHAM,Plaistow,NULL,HELENA ROAD,22201443,E13,NULL,NULL,540050,183150,NULL,NULL
NA,11/03/2021 13:05,2021,2020/21,Special Service,1,1,346,346,TRAPPED BIRD IN DISUSED BUILDING - RSPCA INSPECTOR ON SITE,Bird,Person (mobile),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009376,HOMERTON,E09000012,HACKNEY,Homerton,1.00023E+11,ELSDALE STREET,20900382,E9,535396,184519,535350,184550,51.54334127,-0.048893517
NA,11/03/2021 17:14,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN GUTTERING OF A EMPTY HOUSE -,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000316,MAWNEYS,E09000016,HAVERING,Romford,NULL,CROSS ROAD,21301119,RM7,NULL,NULL,549450,189450,NULL,NULL
NA,13/03/2021 14:09,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED UNDER RECLINER SOFA,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,NULL,BULRUSH TERRACE,19910338,IG11,NULL,NULL,547450,182750,NULL,NULL
NA,15/03/2021 18:24,2021,2020/21,Special Service,1,1,346,346,FOX CUB TRAPPED UNDER DECKING,Fox,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000121,MOTTINGHAM AND CHISLEHURST NORTH,E09000006,BROMLEY,Eltham,1.0002E+11,WAYSIDE GROVE,20302467,SE9,542658,171534,542650,171550,51.42486709,0.050518345
NA,15/03/2021 21:00,2021,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED BEHIND FIRE PLACE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011251,SEVEN KINGS,E09000026,REDBRIDGE,Ilford,NULL,PRIMROSE AVENUE,22306059,RM6,NULL,NULL,546550,187950,NULL,NULL
NA,15/03/2021 22:16,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN CAGE,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000047,COPPETTS,E09000003,BARNET,Southgate,NULL,ROYAL DRIVE,20037410,N11,NULL,NULL,528350,192150,NULL,NULL
NA,16/03/2021 14:05,2021,2020/21,Special Service,1,1,346,346,CAT STUCK IN GATE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000600,HIGHAM HILL,E09000031,WALTHAM FOREST,Walthamstow,NULL,SHAW SQUARE,22874250,E17,NULL,NULL,536550,190750,NULL,NULL
NA,16/03/2021 23:50,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000212,UPPER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,WATERMILL LANE,20704891,N18,NULL,NULL,533150,192250,NULL,NULL
NA,17/03/2021 09:48,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN HOLE IN PARK  GO UP THE MAIN HILL WHERE THE BARRIERS ARE AT THE TOP OF THE PARK BESID,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,10010234100,BLACKHEATH AVENUE,20800172,SE10,539006,177622,539050,177650,51.48048248,0.000414812
NA,19/03/2021 10:35,2021,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000227,SHOOTERS HILL,E09000011,GREENWICH,Eltham,NULL,RED LION LANE,20801247,SE18,NULL,NULL,543250,176850,NULL,NULL
NA,19/03/2021 10:42,2021,2020/21,Special Service,1,1,346,346,CAT STUCK UP A CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000227,SHOOTERS HILL,E09000011,GREENWICH,Plumstead,NULL,ALABAMA STREET,20800046,SE18,NULL,NULL,544950,177450,NULL,NULL
NA,19/03/2021 17:21,2021,2020/21,Special Service,1,1,346,346,PUPPY WITH HEAD STUCK IN WINE RACK -  FLAT A,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000279,STROUD GREEN,E09000014,HARINGEY,Hornsey,NULL,STAPLETON HALL ROAD,21103929,N4,NULL,NULL,531350,188150,NULL,NULL
NA,21/03/2021 07:08,2021,2020/21,Special Service,1,1,346,346,FOX TRAPPED BETWEEN CONCRETE AND METAL POST CALLER WILL MEET YOU AT ESSO GARAGE,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000176,ELTHORNE,E09000009,EALING,Ealing,12057481,TOWNHOLM CRESCENT,20601763,W7,515721,179572,515750,179550,51.50324873,-0.334097772
NA,21/03/2021 14:17,2021,2020/21,Special Service,NULL,NULL,346,NULL,REQUEST FROM PLA - AS DISCUSSED    ATTEND SOUTHSIDE,Unknown - Wild Animal,Person (land line),Secondary school,Non Residential,Other animal assistance,Animal harm involving wild animal,E05000516,BARNES,E09000027,RICHMOND UPON THAMES,Hammersmith,1.00023E+11,LONSDALE ROAD,22404716,SW13,522469,177999,522450,177950,51.48769035,-0.237462009
NA,22/03/2021 13:07,2021,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED UNDER FLOOR   RSPCA ON SCENE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000041,VILLAGE,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,LEYS AVENUE,19900859,RM10,NULL,NULL,550450,183950,NULL,NULL
NA,23/03/2021 01:07,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN SIDE OF WARDROBE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000105,WILLESDEN GREEN,E09000005,BRENT,Willesden,NULL,KINGS ROAD,20202255,NW10,NULL,NULL,522550,184250,NULL,NULL
NA,23/03/2021 07:40,2021,2020/21,Special Service,1,1,346,346,DEER TRAPPED IN METAL FENCE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000048,EAST BARNET,E09000003,BARNET,Barnet,200007104,BARING ROAD,20002300,EN4,526971,196721,526950,196750,51.65495627,-0.165905618
NA,24/03/2021 17:17,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN BADGER HOLE,Dog,Person (mobile),"Grassland, pasture, grazing etc",Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,1.00024E+11,BLACKNESS LANE,20300053,BR2,541406,162874,541450,162850,51.34737164,0.029084848
NA,25/03/2021 05:16,2021,2020/21,Special Service,1,1,346,346,DOG WITH PAW TRAPPED IN DOG CAGE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000438,BLACKHEATH,E09000023,LEWISHAM,Greenwich,NULL,BEECHWOOD PLACE,22007197,SE10,NULL,NULL,538150,176550,NULL,NULL
NA,26/03/2021 10:20,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED BEHIND GAS FIRE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000468,RAVENSBURY,E09000024,MERTON,Mitcham,NULL,MIDDLETON ROAD,22104395,SM4,NULL,NULL,526450,167050,NULL,NULL
NA,26/03/2021 13:21,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN KITCHEN,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Finchley,NULL,REGENTS PARK ROAD,20036360,N3,NULL,NULL,524850,189850,NULL,NULL
NA,26/03/2021 16:04,2021,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED IN CEILING - ASSISTING RSCPA ON SCENE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000481,EAST HAM NORTH,E09000025,NEWHAM,East Ham,NULL,GOLDSMITH AVENUE,22200653,E12,NULL,NULL,542550,184850,NULL,NULL
NA,26/03/2021 16:44,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000278,SEVEN SISTERS,E09000014,HARINGEY,Tottenham,1.00021E+11,HERMITAGE ROAD,21103408,N4,532289,188003,532250,188050,51.5753812,-0.092356517
NA,27/03/2021 13:16,2021,2020/21,Special Service,1,2,346,692,CAT TRAPPED IN YARD OF ART STUDIO,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000278,SEVEN SISTERS,E09000014,HARINGEY,Tottenham,NULL,HERMITAGE ROAD,21103408,N4,NULL,NULL,532250,187950,NULL,NULL
NA,27/03/2021 16:05,2021,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING ON RAILWAY BRIDGE    NEAR STATION    RSPCA IN ATTENDANCE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,10009144359,HIGH ROAD LEYTONSTONE,22844950,E11,539380,186813,539350,186850,51.56298399,0.009432004
NA,28/03/2021 19:43,2021,2020/21,Special Service,1,1,346,346,ANIMAL BELIEVED TRAPPED IN LOFT,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000181,LADY MARGARET,E09000009,EALING,Southall,NULL,WESTBURY AVENUE,20601870,UB1,NULL,NULL,513250,182150,NULL,NULL
NA,28/03/2021 21:01,2021,2020/21,Special Service,1,1,346,346,KITTEN STUCK UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000434,THURLOW PARK,E09000022,LAMBETH,West Norwood,NULL,CROXTED ROAD,21900423,SE24,NULL,NULL,532150,174050,NULL,NULL
NA,29/03/2021 05:41,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND RAILINGS NEXT TO STAIRWELL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000458,CRICKET GREEN,E09000024,MERTON,Mitcham,NULL,LONDON ROAD,22103995,CR4,NULL,NULL,527250,168250,NULL,NULL
NA,29/03/2021 21:25,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000032,GASCOIGNE,E09000002,BARKING AND DAGENHAM,Barking,NULL,BARKING WHARF SQUARE,19910373,IG11,NULL,NULL,543950,183850,NULL,NULL
NA,29/03/2021 21:51,2021,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009375,HAGGERSTON,E09000012,HACKNEY,Shoreditch,NULL,DAWSON STREET,20900316,E2,NULL,NULL,533850,183150,NULL,NULL
NA,30/03/2021 17:09,2021,2020/21,Special Service,1,1,346,346,CAT ON ROOF - STUCK BETWEEN TILES,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011476,PURLEY & WOODCOTE,E09000008,CROYDON,Purley,NULL,BOUVERIE GARDENS,20502892,CR8,NULL,NULL,530550,160250,NULL,NULL
NA,30/03/2021 17:36,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED BETWEEN BRANCHES,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000062,WEST HENDON,E09000003,BARNET,Hendon,NULL,STURGESS AVENUE,20041340,NW4,NULL,NULL,522750,187850,NULL,NULL
NA,30/03/2021 22:52,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000352,FELTHAM WEST,E09000018,HOUNSLOW,Feltham,NULL,ASHFORD ROAD,21500051,TW13,NULL,NULL,508750,171850,NULL,NULL
NA,31/03/2021 05:20,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000638,LANCASTER GATE,E09000033,WESTMINSTER,Paddington,NULL,WESTBOURNE GROVE,8400743,W2,NULL,NULL,525650,181150,NULL,NULL
NA,31/03/2021 09:55,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05011236,BRIDGE,E09000026,REDBRIDGE,Woodford,NULL,MEADWAY,22305183,IG8,NULL,NULL,541350,192450,NULL,NULL
NA,31/03/2021 16:03,2021,2020/21,Special Service,1,2,346,692,KITTEN BELIEVED TO BE TRAPPED UNDER FLOORBOARDS - FRU REQUESTED WITH SNAKE EYE CAMERA,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000212,UPPER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,UPTON ROAD,20704870,N18,NULL,NULL,534650,192250,NULL,NULL
NA,01/04/2021 00:14,2021,2021/22,Special Service,1,1,352,352,Redacted,Deer,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000122,ORPINGTON,E09000006,BROMLEY,Orpington,1.0002E+11,SPUR ROAD,20301297,BR6,546542,165869,546550,165850,51.37298218,0.10400749
NA,01/04/2021 10:04,2021,2021/22,Special Service,1,1,352,352,SMALL ANIMAL RESCUE - PIGEON TANGLED IN NETTING AT THIRD FLOOR LEVEL   ADDITIONAL PL REQUESTED FROM,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000359,HOUNSLOW HEATH,E09000018,HOUNSLOW,Feltham,NULL,BLACKBURN WAY,21501782,TW4,NULL,NULL,512450,174650,NULL,NULL
NA,01/04/2021 19:27,2021,2021/22,Special Service,1,1,352,352,DOG TRAPPED IN LIFT SHAFT,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000524,KEW,E09000027,RICHMOND UPON THAMES,Richmond,NULL,MELLISS AVENUE,22407046,TW9,NULL,NULL,519650,176850,NULL,NULL
NA,02/04/2021 17:03,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED ON FIRST FLOOR BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011098,CHAUCER,E09000028,SOUTHWARK,Dowgate,NULL,SWAN STREET,22502444,SE1,NULL,NULL,532250,179450,NULL,NULL
NA,02/04/2021 23:12,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED BETWEEN FENCE AND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,PARK GROVE ROAD,22864300,E11,NULL,NULL,539250,186750,NULL,NULL
NA,03/04/2021 19:08,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000522,HAMPTON WICK,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,SEYMOUR ROAD,22400527,KT1,NULL,NULL,517550,169850,NULL,NULL
NA,05/04/2021 00:52,2021,2021/22,Special Service,1,1,352,352,DOG IN FOX HOLE,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000171,CLEVELAND,E09000009,EALING,Ealing,12142471,PERIVALE GARDENS,20601367,W13,517032,182541,517050,182550,51.52966567,-0.314225853
NA,05/04/2021 10:13,2021,2021/22,Special Service,1,1,352,352,CAT STUCK IN TREE   REQUESTED BY RSPCA,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000287,EDGWARE,E09000015,HARROW,Stanmore,1.00021E+11,THE HIGHLANDS,21202387,HA8,519900,190537,519950,190550,51.60092594,-0.270178823
NA,05/04/2021 11:58,2021,2021/22,Special Service,1,1,352,352,CAT STUCK ON ROOF UNDER SOLAR PANELS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,LONG LANE,20027220,N3,NULL,NULL,526050,190550,NULL,NULL
NA,05/04/2021 15:11,2021,2021/22,Special Service,1,2,352,704,Redacted,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,10033527302,THE MALL,8401058,SW1A,529329,179662,529350,179650,51.50111403,-0.138110634
NA,05/04/2021 18:10,2021,2021/22,Special Service,1,1,352,352,DOG TRAPPED IN FOX HOLE,Dog,Person (mobile),Golf course (not building on course),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000343,WEST RUISLIP,E09000017,HILLINGDON,Ruislip,10025312422,CLACK LANE PUBLIC FOOTPATH TO THE GREENWAY,21403082,UB9,508184,187115,508150,187150,51.57253709,-0.440327706
NA,05/04/2021 21:25,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000488,MANOR PARK,E09000025,NEWHAM,Stratford,46033700,HALLEY ROAD,22208155,E12,541913,184989,541950,184950,51.54596179,0.045212785
NA,06/04/2021 00:03,2021,2021/22,Special Service,1,1,352,352,FOX STUCK IN WATER OF CHURCH GARDENS     CALLER WAITING TO DIRECT,Fox,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000435,TULSE HILL,E09000022,LAMBETH,Brixton,2E+11,BRIXTON HILL,21900236,SW2,530986,175142,530950,175150,51.46010956,-0.115918543
NA,06/04/2021 17:23,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011468,FAIRFIELD,E09000008,CROYDON,Croydon,1.00023E+11,NORTH END,20501238,CR0,532185,165905,532150,165950,51.376818,-0.102124
NA,07/04/2021 17:22,2021,2021/22,Special Service,1,1,352,352,BIRD STUCK IN NETTING - REQUESTD BY RSPCA,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Clapham,10008157949,QUEENSTOWN ROAD,22904132,SW8,528738,176823,528750,176850,51.47573447,-0.147646733
NA,08/04/2021 23:30,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED BEHIND WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,CHURCH STREET,8400794,NW8,NULL,NULL,527050,182150,NULL,NULL
NA,09/04/2021 09:17,2021,2021/22,Special Service,1,1,352,352,RUNNING CALL TO CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000102,TOKYNGTON,E09000005,BRENT,Wembley,202192157,DAGMAR AVENUE,20200339,HA9,518598,185599,518550,185550,51.55681973,-0.29063872
NA,09/04/2021 16:08,2021,2021/22,Special Service,1,1,352,352,RUNNING CALL TO DUCK IMPALED,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011106,NORTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,10000818280,BERMONDSEY WALL WEST,22500227,SE16,534212,179823,534250,179850,51.50142328,-0.067736187
NA,09/04/2021 18:37,2021,2021/22,Special Service,1,1,352,352,DOG TRAPPED BENEATH SUMMER HOUSE IN REAR GARDEN,Dog,Person (land line),Private Summer house,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000124,PETTS WOOD AND KNOLL,E09000006,BROMLEY,Orpington,1.0002E+11,BIRCHWOOD ROAD,20302912,BR5,544806,168196,544850,168150,51.39432989,0.080033626
NA,09/04/2021 19:54,2021,2021/22,Special Service,1,1,352,352,ASSIST LONDON WILDLIFE PROTECTION OFFICER IN ATTENDANCE WITH BIRDS IN DANGER,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000053,GOLDERS GREEN,E09000003,BARNET,Hendon,200062733,HIGHCROFT GARDENS,20022240,NW11,524821,188268,524850,188250,51.57946588,-0.199966533
NA,09/04/2021 22:39,2021,2021/22,Special Service,1,1,352,352,DOG TRAPPED IN BUSHES AT REAR OF - CALLER WILL MEET YOU TO ASSIST,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,100045597,MAYBURY ROAD,19900536,IG11,546019,183385,546050,183350,51.53050822,0.103728053
NA,10/04/2021 00:55,2021,2021/22,Special Service,1,1,352,352,ANIMAL TRAPPED INSIDE GARAGE DOOR - J/O HARTLAND ROAD,Unknown - Wild Animal,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped wild animal,E05000047,COPPETTS,E09000003,BARNET,Southgate,200069086,KENNARD ROAD,20025020,N11,527743,192183,527750,192150,51.61399648,-0.156395
NA,10/04/2021 10:53,2021,2021/22,Special Service,1,1,352,352,BIRD STUCK IN NETTING,Bird,Person (mobile),Multi-Storey car park,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Eltham,10010222078,ELTHAM HILL,20800527,SE9,542318,174448,542350,174450,51.45113993,0.046812085
NA,10/04/2021 11:06,2021,2021/22,Special Service,1,2,352,704,CAT TRAPPED BETWEEN WALL IN BIN CUPBOARD OUTSIDE  *FRU WITH SNAKE EYE REQUESTED FROM SCENE*,Cat,Person (mobile),Common external bin storage area,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000355,HESTON CENTRAL,E09000018,HOUNSLOW,Feltham,1.00024E+11,CHURCH CLOSE,21501639,TW3,512527,176005,512550,176050,51.47183006,-0.381235413
NA,10/04/2021 11:19,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),Train station - concourse,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,LEA BRIDGE,E09000012,HACKNEY,Stoke Newington,1.00023E+11,UPPER CLAPTON ROAD,20901025,E5,534779,186513,534750,186550,51.5614013,-0.0570231
NA,11/04/2021 14:18,2021,2021/22,Special Service,1,1,352,352,ASSIST LIFT ENGINEER WITH SMALL ANIMAL RESCUE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000636,HYDE PARK,E09000033,WESTMINSTER,Paddington,NULL,HERMITAGE STREET,8400414,W2,NULL,NULL,526650,181550,NULL,NULL
NA,11/04/2021 16:06,2021,2021/22,Special Service,1,1,352,352,PIGEON TRAPPED IN TREE    RSPCA ON SCENE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000636,HYDE PARK,E09000033,WESTMINSTER,Paddington,10033546872,SUSSEX GARDENS,8401585,W2,527243,181478,527250,181450,51.51790913,-0.167496087
NA,11/04/2021 18:16,2021,2021/22,Special Service,1,1,352,352,Redacted,Unknown - Wild Animal,Person (running call),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,NULL,EFFINGHAM ROAD,22001433,SE12,NULL,NULL,539450,174650,NULL,NULL
NA,11/04/2021 20:17,2021,2021/22,Special Service,1,1,352,352,CAT FALLEN AND TRAPPED IN RIVER BANK - QUAGGY RIVER,Cat,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,1.00022E+11,LONGHURST ROAD,22001668,SE13,539126,174628,539150,174650,51.45355524,0.000962671
NA,11/04/2021 23:19,2021,2021/22,Special Service,1,1,352,352,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000297,PINNER,E09000015,HARROW,Harrow,NULL,PINNER HILL ROAD,21201803,HA5,NULL,NULL,511350,190150,NULL,NULL
NA,12/04/2021 08:19,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,1.00022E+11,LONGHURST ROAD,22001668,SE13,539114,174638,539150,174650,51.45364876,0.000801434
NA,12/04/2021 09:45,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000367,BUNHILL,E09000019,ISLINGTON,Shoreditch,NULL,BUNHILL ROW,21604385,EC1Y,NULL,NULL,532550,182350,NULL,NULL
NA,12/04/2021 18:33,2021,2021/22,Special Service,1,1,352,352,Redacted,Bird,Person (mobile),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000294,KENTON EAST,E09000015,HARROW,Stanmore,10070269249,HONEYPOT LANE,21200986,NW9,519070,188805,519050,188850,51.5855328,-0.2827536
NA,12/04/2021 19:29,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),Other outdoor equipment/machinery,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009336,WHITECHAPEL,E09000030,TOWER HAMLETS,Whitechapel,10025543883,ROYAL MINT STREET,22701043,E1,534110,180870,534150,180850,51.51085062,-0.068804812
NA,13/04/2021 18:24,2021,2021/22,Special Service,1,1,352,352,DOG WITH LEG STUCK IN METAL TRAINING CAGE,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,LEWIS CUBITT SQUARE,20499409,N1C,NULL,NULL,529950,183650,NULL,NULL
NA,13/04/2021 21:01,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED ON ROOF,Cat,Person (mobile),Licensed House in Multiple Occupation - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,WINTHORPE ROAD,22906300,SW15,NULL,NULL,524450,175250,NULL,NULL
NA,14/04/2021 11:04,2021,2021/22,Special Service,1,1,352,352,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING ON BALCONY   RSPCA ON SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000628,WEST HILL,E09000032,WANDSWORTH,Wandsworth,NULL,CHOBHAM GARDENS,22900923,SW19,NULL,NULL,523750,172750,NULL,NULL
NA,14/04/2021 12:09,2021,2021/22,Special Service,1,1,352,352,PIGEON TRAPPED AND HANGING FROM TOP FLOOR,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000273,HORNSEY,E09000014,HARINGEY,Hornsey,NULL,MIDDLE LANE,21103615,N8,NULL,NULL,530050,188750,NULL,NULL
NA,14/04/2021 16:30,2021,2021/22,Special Service,1,1,352,352,KITTEN STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011097,CHAMPION HILL,E09000028,SOUTHWARK,Peckham,NULL,BROMAR ROAD,22500346,SE5,NULL,NULL,533450,175750,NULL,NULL
NA,14/04/2021 18:52,2021,2021/22,Special Service,1,1,352,352,DOVE TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,NULL,WRAYSBURY DRIVE,21402993,UB7,NULL,NULL,505650,180450,NULL,NULL
NA,15/04/2021 10:30,2021,2021/22,Special Service,1,1,352,352,Redacted,Bird,Person (land line),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000438,BLACKHEATH,E09000023,LEWISHAM,Lee Green,1.00022E+11,LEE HIGH ROAD,22004135,SE12,539825,175022,539850,175050,51.4569159,0.0111709
NA,15/04/2021 14:52,2021,2021/22,Special Service,1,1,352,352,KITTEN TRAPPED UNDER BRICKWORK,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000424,KNIGHT'S HILL,E09000022,LAMBETH,West Norwood,NULL,LEIGHAM COURT ROAD,21900860,SW16,NULL,NULL,531050,172150,NULL,NULL
NA,15/04/2021 16:27,2021,2021/22,Special Service,1,1,352,352,CAT FALLEN INTO OUTSIDE BASEMENT AREA UNABLE TO GET OUT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000649,WEST END,E09000033,WESTMINSTER,Euston,NULL,HANSON STREET,8400438,W1W,NULL,NULL,529050,181750,NULL,NULL
NA,17/04/2021 00:17,2021,2021/22,Special Service,1,2,352,704,RUNNING CALL TO HORSE ON ROADWAY - NO FURTHER LFB ATTENDANCE REQUIRED,Horse,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000318,RAINHAM AND WENNINGTON,E09000016,HAVERING,Wennington,1.00024E+11,WENNINGTON ROAD,21300921,RM13,554193,180954,554150,180950,51.50648981,0.22041394
NA,17/04/2021 11:00,2021,2021/22,Special Service,1,1,352,352,RUNNING CALL TO ASSIST WITH CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000102,TOKYNGTON,E09000005,BRENT,Wembley,202230594,DAGMAR AVENUE,20200339,HA9,518594,185600,518550,185650,51.5568303,-0.290689421
NA,17/04/2021 12:27,2021,2021/22,Special Service,1,1,352,352,ANIMAL RESCUE SMALL - POSSIBLY A PIGEON TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000215,BLACKHEATH WESTCOMBE,E09000011,GREENWICH,East Greenwich,NULL,HUMBER ROAD,20800800,SE3,NULL,NULL,540150,178050,NULL,NULL
NA,17/04/2021 13:33,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000285,BELMONT,E09000015,HARROW,Stanmore,NULL,CROWSHOTT AVENUE,21200970,HA7,NULL,NULL,517850,190650,NULL,NULL
NA,17/04/2021 16:29,2021,2021/22,Special Service,1,1,352,352,KITTEN STUCK UNDER FLOOR BOARDS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000531,TWICKENHAM RIVERSIDE,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,RICHMOND ROAD,22403771,TW1,NULL,NULL,516650,173650,NULL,NULL
NA,17/04/2021 17:16,2021,2021/22,Special Service,1,1,352,352,CAT IN PRECARIOUS POSITION ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Clapham,NULL,TENNYSON STREET,22905245,SW8,NULL,NULL,528650,176050,NULL,NULL
NA,17/04/2021 19:59,2021,2021/22,Special Service,1,1,352,352,KITTTEN TRAPPED BETWEEN CUPBOARD AND WALL,Unknown - Domestic Animal Or Pet,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000319,ROMFORD TOWN,E09000016,HAVERING,Romford,NULL,EASTERN ROAD,21301162,RM1,NULL,NULL,551850,188750,NULL,NULL
NA,17/04/2021 22:06,2021,2021/22,Special Service,1,1,352,352,DISTRESSED DOG  STUCK IN PLAYGROUND,Dog,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000028,BECONTREE,E09000002,BARKING AND DAGENHAM,Barking,10023598990,SCHOLARS WAY,19904082,RM8,546584,185717,546550,185750,51.55131011,0.112834799
NA,18/04/2021 10:28,2021,2021/22,Special Service,1,1,352,352,Redacted,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Lee Green,1.00023E+11,LEE ROAD,22001639,SE3,539879,174995,539850,174950,51.4566599,0.011937
NA,18/04/2021 11:35,2021,2021/22,Special Service,1,2,352,704,Redacted,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000555,BEDDINGTON NORTH,E09000029,SUTTON,Wallington,5870094183,BLOXWORTH CLOSE,22600334,SM6,529326,165092,529350,165050,51.37017279,-0.143464518
NA,18/04/2021 21:01,2021,2021/22,Special Service,1,1,352,352,DISTRESSED KITTEN STUCK ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000459,DUNDONALD,E09000024,MERTON,Wimbledon,NULL,KINGSTON ROAD,22103623,SW20,NULL,NULL,524150,169350,NULL,NULL
NA,19/04/2021 07:56,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED BEHIND A WALL/CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000299,QUEENSBURY,E09000015,HARROW,Stanmore,NULL,ST AUSTELL CLOSE,21202360,HA8,NULL,NULL,518750,190150,NULL,NULL
NA,19/04/2021 14:52,2021,2021/22,Special Service,1,1,352,352,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000491,ROYAL DOCKS,E09000025,NEWHAM,East Ham,NULL,RYMILL STREET,22207872,E16,NULL,NULL,543150,180050,NULL,NULL
NA,19/04/2021 16:37,2021,2021/22,Special Service,1,1,352,352,DOG TRAPPED IN FENCING,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000041,VILLAGE,E09000002,BARKING AND DAGENHAM,Dagenham,10023593285,BALLARDS ROAD,19900399,RM10,549997,184157,549950,184150,51.53639916,0.161355237
NA,19/04/2021 19:15,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED IN BARBED WIRE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000477,CANNING TOWN NORTH,E09000025,NEWHAM,Plaistow,NULL,LESTER AVENUE,22207790,E15,NULL,NULL,539450,182550,NULL,NULL
NA,19/04/2021 21:56,2021,2021/22,Special Service,1,1,352,352,KITTEN POSSIBLY TRAPPED BEHIND INTERIOR WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000200,GRANGE,E09000010,ENFIELD,Southgate,NULL,VERA AVENUE,20703026,N21,NULL,NULL,531750,195450,NULL,NULL
NA,20/04/2021 00:10,2021,2021/22,Special Service,1,2,352,704,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000356,HESTON EAST,E09000018,HOUNSLOW,Heston,NULL,HESTON ROAD,21500600,TW5,NULL,NULL,513050,177450,NULL,NULL
NA,21/04/2021 15:15,2021,2021/22,Special Service,1,2,352,704,FOX TRAPPED IN RAILINGS OPPOSITE,Fox,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217117508,LADBROKE GROVE,21700912,W10,524307,181357,524350,181350,51.51747248,-0.209825706
NA,22/04/2021 19:32,2021,2021/22,Special Service,1,1,352,352,ASSIST WILDLIFE OFFICER IN ATTENDANCE FOR STUCK BIRD,Bird,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000467,POLLARDS HILL,E09000024,MERTON,Norbury,48085107,SOUTH LODGE AVENUE,22105877,CR4,530267,168346,530250,168350,51.3992045,-0.128758866
NA,24/04/2021 15:10,2021,2021/22,Special Service,1,1,352,352,BABY DEER STUCK IN RAILINGS     IN CARPARK,Deer,Person (mobile),Secondary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000330,HAREFIELD,E09000017,HILLINGDON,Ruislip,1.00023E+11,NORTHWOOD WAY,21401429,UB9,505882,190824,505850,190850,51.60631825,-0.472412318
NA,24/04/2021 20:37,2021,2021/22,Special Service,1,1,352,352,CAT STUCK ON ROOF - BEEN UP THERE SINCE WEDNESDAY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000108,BROMLEY COMMON AND KESTON,E09000006,BROMLEY,Biggin Hill,NULL,WESTERHAM ROAD,20302896,BR2,NULL,NULL,542050,164650,NULL,NULL
NA,24/04/2021 21:51,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,1.00022E+11,HOMEFIELD ROAD,21500622,W4,521622,178309,521650,178350,51.49066025,-0.249550823
NA,25/04/2021 10:04,2021,2021/22,Special Service,1,1,352,352,DEER TRAPPED IN METAL GATE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000054,HALE,E09000003,BARNET,Mill Hill,200083757,NAN CLARKS LANE,20030700,NW7,521617,193830,521650,193850,51.63015245,-0.244254771
NA,25/04/2021 12:12,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED IN FENCE WIRING   RSPCA ON SCENE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000302,ROXETH,E09000015,HARROW,Northolt,NULL,PARKFIELD ROAD,21202130,HA2,NULL,NULL,514350,186350,NULL,NULL
NA,25/04/2021 17:31,2021,2021/22,Special Service,1,1,352,352,CAT UP TREE       AT REQUEST OF RSPCA,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,2.00004E+11,THAMES CRESCENT,21590212,W4,521494,177250,521450,177250,51.48117312,-0.251760444
NA,26/04/2021 00:50,2021,2021/22,Special Service,1,1,352,352,CAT STUCK IN GUTTERING ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000629,WEST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,NEWNES PATH,22903499,SW15,NULL,NULL,522550,175350,NULL,NULL
NA,27/04/2021 01:22,2021,2021/22,Special Service,1,1,352,352,FOX CUB TRAPPED AND IN DISTRESS IN SHED,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011487,WADDON,E09000008,CROYDON,Croydon,NULL,BARROW ROAD,20501907,CR0,NULL,NULL,531450,164050,NULL,NULL
NA,27/04/2021 10:11,2021,2021/22,Special Service,1,1,352,352,KITTEN STUCK IN CAGE,Cat,Person (land line),Fire station,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000567,SUTTON WEST,E09000029,SUTTON,Sutton,5870021065,ST DUNSTANS HILL,22605525,SM1,524617,164843,524650,164850,51.36898499,-0.211172616
NA,27/04/2021 17:30,2021,2021/22,Special Service,1,1,352,352,DOG WITH GLASS JAR  STUCK ON ITS HEAD,Dog,Person (land line),Veterinary surgery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217085551,TALBOT ROAD,21701031,W11,524975,181314,524950,181350,51.51693994,-0.200218436
NA,27/04/2021 17:52,2021,2021/22,Special Service,1,1,352,352,BIRD/ANIMAL TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011482,SHIRLEY NORTH,E09000008,CROYDON,Woodside,NULL,CORNFLOWER LANE,20500426,CR0,NULL,NULL,535750,166250,NULL,NULL
NA,27/04/2021 19:55,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED ON ROOF LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011096,CAMBERWELL GREEN,E09000028,SOUTHWARK,Peckham,NULL,LOMOND GROVE,22501538,SE5,NULL,NULL,532550,177150,NULL,NULL
NA,27/04/2021 22:17,2021,2021/22,Special Service,1,2,352,704,CAT TRAPPED IN BUILDING UNDER RENOVATION,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000623,SHAFTESBURY,E09000032,WANDSWORTH,Battersea,NULL,DOROTHY ROAD,22901245,SW11,NULL,NULL,527650,175650,NULL,NULL
NA,28/04/2021 12:16,2021,2021/22,Special Service,1,2,352,704,CAT TRAPPED UNDER FLOOR BOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000185,NORTHOLT WEST END,E09000009,EALING,Northolt,NULL,WEST END GARDENS,20601860,UB5,NULL,NULL,511350,183050,NULL,NULL
NA,29/04/2021 13:01,2021,2021/22,Special Service,1,1,352,352,PUPPY TRAPPED IN FOXHOLE,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000193,BOWES,E09000010,ENFIELD,Southgate,207101429,KELVIN AVENUE,20704376,N13,530848,191865,530850,191850,51.61042588,-0.111697038
NA,29/04/2021 15:28,2021,2021/22,Special Service,1,1,352,352,FOX STUCK IN WIRE,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000273,HORNSEY,E09000014,HARINGEY,Hornsey,1.00021E+11,FERME PARK ROAD,21106156,N8,530509,188581,530550,188550,51.58099506,-0.117806165
NA,29/04/2021 17:13,2021,2021/22,Special Service,1,1,352,352,SQUIRREL STUCK BETWEEN WALL AND GUTTERING BEING ATTACKED BY BIRDS,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000350,CRANFORD,E09000018,HOUNSLOW,Feltham,NULL,SQUIRREL CLOSE,21501349,TW4,NULL,NULL,511450,175950,NULL,NULL
NA,30/04/2021 09:45,2021,2021/22,Special Service,1,1,352,352,HORSE DOWN IN PADDOCK,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05011229,ST. MARY'S & ST. JAMES,E09000004,BEXLEY,Sidcup,10023304295,NORTH CRAY ROAD,20101032,DA14,548763,172019,548750,172050,51.42766436,0.138470724
NA,30/04/2021 12:31,2021,2021/22,Special Service,1,1,352,352,RSPCA ON SCENE - BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000557,BELMONT,E09000029,SUTTON,Sutton,NULL,BANSTEAD ROAD SOUTH,22602088,SM2,NULL,NULL,526850,162750,NULL,NULL
NA,30/04/2021 12:44,2021,2021/22,Special Service,1,1,352,352,DOG WITH PAW TRAPPED IN GRILL IN GARDEN,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000227,SHOOTERS HILL,E09000011,GREENWICH,Eltham,NULL,CLEANTHUS ROAD,20800349,SE18,NULL,NULL,543850,176650,NULL,NULL
NA,30/04/2021 23:52,2021,2021/22,Special Service,1,2,352,704,CAT TRAPPED IN WIRED FENCING - OPPOSITE THIS ADDRESS - CALLER WILL MEET YOU,Cat,Person (mobile),Railway trackside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000175,EAST ACTON,E09000009,EALING,Acton,12105407,THE CRESCENT,20601717,W3,520990,181221,520950,181250,51.51697163,-0.257652538
NA,01/05/2021 12:14,2021,2021/22,Special Service,1,1,352,352,SMALL ANIMAL RESCUE SQUIRREL BEHIND FIREPLACE,Squirrel,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000613,EAST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,MELROSE ROAD,22903287,SW18,NULL,NULL,524850,174150,NULL,NULL
NA,01/05/2021 12:48,2021,2021/22,Special Service,NULL,NULL,352,NULL,FOX TRAPPED IN BARBED WIRE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000208,SOUTHGATE,E09000010,ENFIELD,Southgate,NULL,BLAGDENS CLOSE,20701341,N14,NULL,NULL,529450,193750,NULL,NULL
NA,02/05/2021 10:49,2021,2021/22,Special Service,1,1,352,352,SMALL ANIMAL RESCUE  CAT TRAPPED BETWEEN WALL AND BASEMENT FLOORBOARDS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,NULL,BODNEY ROAD,20900148,E8,NULL,NULL,534550,185350,NULL,NULL
NA,02/05/2021 11:19,2021,2021/22,Special Service,1,1,352,352,ASSIST  LONDON WILDLIFE PROTECTION  VOLUNTEEER ON SCENE WITH PARROT TRAPPED IN GUTTERING ON ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011227,LONGLANDS,E09000004,BEXLEY,Sidcup,NULL,KINGSGROVE CLOSE,20101909,DA14,NULL,NULL,545950,171650,NULL,NULL
NA,02/05/2021 13:58,2021,2021/22,Special Service,1,2,352,704,KITTEN IN PRECARIOUS POSITION     AERIAL APPLIANCE TO ENTER VIA GRAYS INN ROAD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000141,KING'S CROSS,E09000007,CAMDEN,Euston,NULL,FREDERICK STREET,20400908,WC1X,NULL,NULL,530750,182650,NULL,NULL
NA,02/05/2021 16:06,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED IN SHED    CALLER WILL MEET YOU ON HARCOURT ROAD,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011488,WEST THORNTON,E09000008,CROYDON,Norbury,1.00021E+11,HARCOURT ROAD,20501023,CR7,530931,167295,530950,167250,51.38960082,-0.119613432
NA,02/05/2021 21:54,2021,2021/22,Special Service,1,1,352,352,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000141,KING'S CROSS,E09000007,CAMDEN,Euston,NULL,FREDERICK STREET,20400908,WC1X,NULL,NULL,530750,182650,NULL,NULL
NA,02/05/2021 22:23,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED AND INJURED IN GARDEN,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009384,STAMFORD HILL WEST,E09000012,HACKNEY,Stoke Newington,1.00021E+11,WEST BANK,20901063,N16,533229,187649,533250,187650,51.57198508,-0.078940241
NA,03/05/2021 11:31,2021,2021/22,Special Service,1,1,352,352,TWO CYGNETS TRAPPED IN GRATE    FRU REQUIRED FOR WATER RESCUE,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009320,BOW WEST,E09000030,TOWER HAMLETS,Bethnal Green,6135457,CLINTON ROAD,22700328,E3,536177,182698,536150,182650,51.52678985,-0.03833142
NA,03/05/2021 14:35,2021,2021/22,Special Service,1,1,352,352,Redacted,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011218,BELVEDERE,E09000004,BEXLEY,Erith,NULL,BEVERCOTE WALK,20100134,DA17,NULL,NULL,548550,177950,NULL,NULL
NA,03/05/2021 21:49,2021,2021/22,Special Service,1,1,352,352,KITTEN TRAPPED UNDERNEATH BOILER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,CHARING CROSS ROAD,8400626,WC2H,NULL,NULL,529850,181150,NULL,NULL
NA,04/05/2021 20:08,2021,2021/22,Special Service,1,1,352,352,CAT STUCK IN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000572,WORCESTER PARK,E09000029,SUTTON,Sutton,NULL,CONRAD DRIVE,22601399,KT4,NULL,NULL,523550,166350,NULL,NULL
NA,04/05/2021 21:57,2021,2021/22,Special Service,1,1,352,352,CAT IN PRECARIOUS  POSITION ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009376,HOMERTON,E09000012,HACKNEY,Homerton,NULL,CHATHAM PLACE,20900223,E9,NULL,NULL,535250,184850,NULL,NULL
NA,04/05/2021 23:55,2021,2021/22,Special Service,1,1,352,352,CAT STUCK BEHIND  WALL   BUILDERS BOARDED UP WALL AND HAVE NOW LEFT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000460,FIGGE'S MARSH,E09000024,MERTON,Mitcham,NULL,CLARENDON GROVE,22101470,CR4,NULL,NULL,527950,168950,NULL,NULL
NA,05/05/2021 18:46,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011466,COULSDON TOWN,E09000008,CROYDON,Purley,NULL,REDDOWN ROAD,20500317,CR5,NULL,NULL,529850,158850,NULL,NULL
NA,05/05/2021 21:42,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED INSIDE BLOCKED FIRE PLACE,Cat,Person (mobile),Restaurant/cafe,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Euston,10033535032,GREAT PORTLAND STREET,8400603,W1W,528958,181704,528950,181750,51.51954786,-0.142703802
NA,07/05/2021 06:43,2021,2021/22,Special Service,1,1,352,352,DEER TRAPPED IN SIDE GATE OF SCHOOL,Deer,Person (land line),Pre School/nursery,Non Residential,Other animal assistance,Assist trapped wild animal,E05000030,EASTBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,100007499,DAGENHAM ROAD,19901047,RM7,551007,187181,551050,187150,51.5633003,0.1772123
NA,07/05/2021 07:58,2021,2021/22,Special Service,1,1,352,352,DUCK TRAPPED IN WIRE IN TREE BY LAKE - FRU REQUIRED,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000456,CANNON HILL,E09000024,MERTON,New Malden,48011970,CANNON HILL LANE,22101155,SW20,524024,168501,524050,168550,51.40198841,-0.218401393
NA,07/05/2021 13:28,2021,2021/22,Special Service,1,1,352,352,DUCKLINGS STUCK IN THE DRAIN,Bird,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000055,HENDON,E09000003,BARNET,Hendon,200157527,ST JOSEPHS GROVE,20038150,NW4,522583,189215,522550,189250,51.58846684,-0.231926364
NA,07/05/2021 15:12,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000600,HIGHAM HILL,E09000031,WALTHAM FOREST,Walthamstow,NULL,MILLFIELD AVENUE,22858800,E17,NULL,NULL,536650,190450,NULL,NULL
NA,07/05/2021 16:39,2021,2021/22,Special Service,1,1,352,352,DOG ON ROOF OF FLATS,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,NULL,KEATS CLOSE,20706073,EN3,NULL,NULL,535650,195750,NULL,NULL
NA,08/05/2021 07:12,2021,2021/22,Special Service,1,2,352,704,TWO FOXES STUCK IN FOUNTAIN,Fox,Person (land line),Other outdoor location,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000519,"HAM, PETERSHAM AND RICHMOND RIVERSIDE",E09000027,RICHMOND UPON THAMES,Richmond,10025549748,RICHMOND HILL,22403768,TW10,518207,174157,518250,174150,51.45406677,-0.30010783
NA,08/05/2021 17:20,2021,2021/22,Special Service,1,1,352,352,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000277,ST. ANN'S,E09000014,HARINGEY,Tottenham,NULL,RUTLAND GARDENS,21103834,N4,NULL,NULL,532150,188450,NULL,NULL
NA,09/05/2021 07:39,2021,2021/22,Special Service,1,1,352,352,FOX TRAPPED IN FENCE RAILINGS,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000326,BRUNEL,E09000017,HILLINGDON,Hillingdon,768012097,THE CHANTRY,21401936,UB8,506859,182799,506850,182750,51.53400059,-0.460747172
NA,09/05/2021 09:46,2021,2021/22,Special Service,1,3,352,1056,DEER STUCK IN WATER    TWIN RIVERS OPPOSITE ESSO GARAGE - HAL STAFF TO MEET BRIGADE,Deer,Person (land line),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,NA,NA,NA,NA,Heathrow,33043760,SOUTHERN PERIMETER ROAD,21401785,TW6,505266,174763,505250,174750,51.4620701,-0.486104001
NA,09/05/2021 15:13,2021,2021/22,Special Service,1,1,352,352,DOG TRAPPED ON LEDGE ON FIRST FLOOR.  OCCUPIER NOT AT HOME,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000262,SANDS END,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,CARNWATH ROAD,21000185,SW6,NULL,NULL,525550,175650,NULL,NULL
NA,09/05/2021 21:34,2021,2021/22,Special Service,1,4,352,1408,Redacted,Unknown - Wild Animal,Person (land line),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000526,NORTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,10070721878,RANELAGH DRIVE,22405873,TW9,517002,175043,517050,175050,51.46227737,-0.317144477
NA,09/05/2021 22:24,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,NULL,BARNARD HILL,21101041,N10,NULL,NULL,528550,190550,NULL,NULL
NA,10/05/2021 09:58,2021,2021/22,Special Service,1,1,352,352,DOG IN DISTRESS LOCKED IN BATHROOM,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,LANCASTER WEST ESTATE,21701277,W11,NULL,NULL,524150,181050,NULL,NULL
NA,10/05/2021 15:30,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED IN WALL CAVITY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000251,ASKEW,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,BECKLOW ROAD,21000108,W12,NULL,NULL,522150,179950,NULL,NULL
NA,10/05/2021 16:09,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED BEHIND FIREPLACE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011470,NEW ADDINGTON NORTH,E09000008,CROYDON,Addington,NULL,KING HENRY'S DRIVE,20501759,CR0,NULL,NULL,537950,162750,NULL,NULL
NA,10/05/2021 16:50,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000252,AVONMORE AND BROOK GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34028029,SAMUEL'S CLOSE,21001137,W6,523461,178968,523450,178950,51.4961841,-0.222844596
NA,11/05/2021 12:17,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000454,WHITEFOOT,E09000023,LEWISHAM,Bromley,NULL,REIGATE ROAD,22003617,BR1,NULL,NULL,540250,172550,NULL,NULL
NA,11/05/2021 14:51,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED IN NETTING,Bird,Person (mobile),"Takeaway, fast food",Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,10034920939,THE BROADWAY,22306023,IG8,540947,191860,540950,191850,51.60794965,0.03404456
NA,12/05/2021 13:57,2021,2021/22,Special Service,1,1,352,352,SQUIRREL TRAPPED BETWEEN BLINDS IN BATHROOM,Squirrel,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011098,CHAUCER,E09000028,SOUTHWARK,Dowgate,NULL,BROCKHAM STREET,22500337,SE1,NULL,NULL,532350,179350,NULL,NULL
NA,12/05/2021 22:02,2021,2021/22,Special Service,1,2,352,704,CAT STUCK IN TREE - AERIAL REQUESTED FROM SCENE RUNNING CALL,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,1.00022E+11,DERBY ROAD,22405516,SW14,519813,175088,519850,175050,51.46209467,-0.276692546
NA,13/05/2021 22:00,2021,2021/22,Special Service,1,1,352,352,WOOD PIGEON STUCK IN CHIMNEY BEHIND CAST IRON FIREPLACE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000125,PLAISTOW AND SUNDRIDGE,E09000006,BROMLEY,Bromley,NULL,PARK ROAD,20300557,BR1,NULL,NULL,540850,169650,NULL,NULL
NA,14/05/2021 07:46,2021,2021/22,Special Service,1,2,352,704,SEAGULL TRAPPED IN NETTING  AERIAL REQUESTED FROM SCENE,Bird,Person (land line),Warehouse,Non Residential,Other animal assistance,Assist trapped wild animal,E05000175,EAST ACTON,E09000009,EALING,Acton,12152493,CONCORD ROAD,20600427,W3,519551,182044,519550,182050,51.52467073,-0.278111173
NA,14/05/2021 14:37,2021,2021/22,Special Service,1,1,352,352,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (mobile),"Takeaway, fast food",Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000638,LANCASTER GATE,E09000033,WESTMINSTER,Paddington,1.00023E+11,QUEENSWAY,8401512,W2,525855,180778,525850,180750,51.511922,-0.187741
NA,14/05/2021 17:51,2021,2021/22,Special Service,1,1,352,352,INJURED PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000378,ST. GEORGE'S,E09000019,ISLINGTON,Holloway,NULL,TUFNELL PARK ROAD,21606493,N7,NULL,NULL,530050,185950,NULL,NULL
NA,14/05/2021 22:33,2021,2021/22,Special Service,1,1,352,352,PUPPY WITH HEAD STUCK IN SOFA,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000223,KIDBROOKE WITH HORNFAIR,E09000011,GREENWICH,Eltham,NULL,LANGBROOK ROAD,20800886,SE3,NULL,NULL,542050,176050,NULL,NULL
NA,14/05/2021 22:48,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Battersea,NULL,CHARLOTTE DESPARD AVENUE,22900858,SW11,NULL,NULL,528350,176650,NULL,NULL
NA,15/05/2021 09:24,2021,2021/22,Special Service,1,1,352,352,PIGEON TRAPPED IN CHIMNEY BEHIND GAS FIRE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000338,SOUTH RUISLIP,E09000017,HILLINGDON,Ruislip,NULL,ACORN GROVE,21400016,HA4,NULL,NULL,509650,185750,NULL,NULL
NA,15/05/2021 13:39,2021,2021/22,Special Service,1,1,352,352,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000487,LITTLE ILFORD,E09000025,NEWHAM,Ilford,NULL,GRANTHAM ROAD,22200183,E12,NULL,NULL,543350,185650,NULL,NULL
NA,15/05/2021 19:55,2021,2021/22,Special Service,1,1,352,352,CAT POSSIBLY STUCK UP A CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000264,TOWN,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,BARCLAY ROAD,21000090,SW6,NULL,NULL,525350,177150,NULL,NULL
NA,15/05/2021 22:32,2021,2021/22,Special Service,1,1,352,352,DOG TRAPPED IN GARDEN - PROPERTY IS VACANT,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009391,CHELSEA RIVERSIDE,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,OLD CHURCH STREET,21700373,SW3,NULL,NULL,527050,177650,NULL,NULL
NA,16/05/2021 13:08,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED IN WIRE - RSPCA ON SCENE - BEST ACCESS IS MURTRIX ROAD,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000140,KILBURN,E09000007,CAMDEN,West Hampstead,NULL,BIRCHINGTON ROAD,20400674,NW6,NULL,NULL,525350,183850,NULL,NULL
NA,16/05/2021 13:32,2021,2021/22,Special Service,1,1,352,352,Redacted,Bird,Person (mobile),Restaurant/cafe,Non Residential,Other animal assistance,Assist trapped wild animal,E05000256,HAMMERSMITH BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34160526,FULHAM PALACE ROAD,21000356,W6,523489,178207,523450,178250,51.4893433,-0.2227147
NA,17/05/2021 12:08,2021,2021/22,Special Service,1,2,352,704,BIRDS TRAPPED IN WIRE UNDER BRIDGE - CALLER WILL MEET YOU ON PICKETTS LOCK LANE BY THE BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,207170280,PICKETTS LOCK LANE,20705131,N9,535608,193774,535650,193750,51.62645872,-0.042264388
NA,17/05/2021 18:46,2021,2021/22,Special Service,1,1,352,352,PARROT TRAPPED IN NETTING,Bird,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000377,MILDMAY,E09000019,ISLINGTON,Stoke Newington,5300066651,NEWINGTON GREEN,21603608,N16,532910,185371,532950,185350,51.55158588,-0.084394582
NA,17/05/2021 20:11,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED BEHIND SHUTTERS,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,1.00023E+11,LEWISHAM HIGH STREET,22004177,SE13,538293,175571,538250,175550,51.46222543,-0.010639968
NA,18/05/2021 09:09,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000288,GREENHILL,E09000015,HARROW,Harrow,10010598072,GAYTON ROAD,21201035,HA1,516002,188160,516050,188150,51.58037751,-0.327217925
NA,18/05/2021 12:40,2021,2021/22,Special Service,1,1,352,352,FOX CUB TRAPPED IN STREAM   RSPCA ON SCENE,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,1.0002E+11,MARTINS ROAD,20301901,BR2,539666,169259,539650,169250,51.40517608,0.006626105
NA,18/05/2021 17:24,2021,2021/22,Special Service,1,1,352,352,KITTEN TRAPPED IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000193,BOWES,E09000010,ENFIELD,Edmonton,207046874,PRINCES AVENUE,20704626,N13,531553,192043,531550,192050,51.61186679,-0.10145459
NA,19/05/2021 19:34,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED IN WHEEL OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011252,SOUTH WOODFORD,E09000026,REDBRIDGE,Leytonstone,1.00022E+11,SOUTH VIEW DRIVE,22303249,E18,540911,189610,540950,189650,51.5877339,0.032624203
NA,20/05/2021 12:59,2021,2021/22,Special Service,1,1,352,352,RUNNING CALL TO BIRD STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000125,PLAISTOW AND SUNDRIDGE,E09000006,BROMLEY,Bromley,NULL,FAIRFIELD ROAD,20302301,BR1,NULL,NULL,540150,170250,NULL,NULL
NA,21/05/2021 05:53,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED INBETWEEN DOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000170,ACTON CENTRAL,E09000009,EALING,Acton,NULL,SUMMERLANDS AVENUE,20601662,W3,NULL,NULL,520150,180550,NULL,NULL
NA,21/05/2021 15:32,2021,2021/22,Special Service,1,1,352,352,DOG STUCK IN HOLE,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000349,CHISWICK RIVERSIDE,E09000018,HOUNSLOW,Chiswick,10093765957,CHISWICK HOUSE GROUNDS,21590044,W4,521026,177676,521050,177650,51.48510163,-0.258353744
NA,21/05/2021 22:09,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED UNDER FLOOR,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000643,REGENT'S PARK,E09000033,WESTMINSTER,Paddington,NULL,AVENUE ROAD,8400533,NW8,NULL,NULL,527350,183450,NULL,NULL
NA,22/05/2021 03:03,2021,2021/22,Special Service,1,1,352,352,Redacted,Snake,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000449,NEW CROSS,E09000023,LEWISHAM,New Cross,NULL,HATFIELD CLOSE,22004809,SE14,NULL,NULL,535550,177150,NULL,NULL
NA,22/05/2021 10:25,2021,2021/22,Special Service,1,1,352,352,PIGEON TRAPPED IN EXTRACTOR,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000349,CHISWICK RIVERSIDE,E09000018,HOUNSLOW,Chiswick,NULL,OXFORD ROAD SOUTH,21584700,W4,NULL,NULL,519650,178050,NULL,NULL
NA,22/05/2021 12:55,2021,2021/22,Special Service,1,2,352,704,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,NULL,ALBERT ROAD,22801100,E17,NULL,NULL,537350,188750,NULL,NULL
NA,22/05/2021 19:47,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED IN DERELICT BUILDING AT JUNCTION OF HURST ROAD    CALLER WILL MEET AND DIRECT,Cat,Person (mobile),Swimming Pool,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011220,BLACKFEN & LAMORBEY,E09000004,BEXLEY,Sidcup,1.00023E+11,STATION ROAD,20101349,DA15,546163,172805,546150,172850,51.4353954,0.1014163
NA,22/05/2021 22:40,2021,2021/22,Special Service,1,1,352,352,SMALL ANIMAL RESCUE - DOG WITH CHAIN WRAPPED AROUND NECK AND LEG - OWNER ON SCENE,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,KASHGAR ROAD,20800829,SE18,NULL,NULL,545750,178550,NULL,NULL
NA,23/05/2021 14:22,2021,2021/22,Special Service,1,1,352,352,CAT STUCK DOWN SHAFT ON SIXTH FLOOR,Cat,Person (land line),Hotel/motel,Other Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000173,EALING BROADWAY,E09000009,EALING,Ealing,12183619,UXBRIDGE ROAD,20602194,W5,517256,180636,517250,180650,51.51250059,-0.311641048
NA,23/05/2021 15:07,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000365,TURNHAM GREEN,E09000018,HOUNSLOW,Chiswick,NULL,WELLESLEY ROAD,21501177,W4,NULL,NULL,519850,178250,NULL,NULL
NA,23/05/2021 15:13,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,CHURCH STREET,8400794,NW8,NULL,NULL,527050,182150,NULL,NULL
NA,23/05/2021 16:32,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (land line),Hotel/motel,Other Residential,Other animal assistance,Assist trapped domestic animal,E05000173,EALING BROADWAY,E09000009,EALING,Ealing,12183619,UXBRIDGE ROAD,20602194,W5,517254,180638,517250,180650,51.51251447,-0.311670974
NA,24/05/2021 05:04,2021,2021/22,Special Service,1,1,352,352,POSSIBLY INJURED CAT STUCK ON THIRD FLOOR    CALLER WILL MEET BRIGADE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000275,NOEL PARK,E09000014,HARINGEY,Hornsey,NULL,MAYES ROAD,21104796,N22,NULL,NULL,531050,190050,NULL,NULL
NA,25/05/2021 07:06,2021,2021/22,Special Service,1,1,352,352,PIGEON STUCK IN NETTING - AT TOP AND END OF BUILDING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000200,GRANGE,E09000010,ENFIELD,Enfield,NULL,LONDON ROAD,20702812,EN2,NULL,NULL,532950,196150,NULL,NULL
NA,25/05/2021 07:20,2021,2021/22,Special Service,1,1,352,352,SMALL DEER TRAPPED IN SCHOOL GATE,Deer,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000341,UXBRIDGE SOUTH,E09000017,HILLINGDON,Hillingdon,1.00023E+11,COWLEY ROAD,21400501,UB8,505466,183481,505450,183450,51.54038675,-0.480625471
NA,25/05/2021 15:19,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED BEHIND MESH FOLLOWING WORKS ON RAILWAY BRIDGE OPPOSITE - JCTN OF ROPER LANE AND DRUID S,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011104,LONDON BRIDGE & WEST BERMONDSEY,E09000028,SOUTHWARK,Dockhead,10009806810,TOWER BRIDGE ROAD,22502529,SE1,533487,179811,533450,179850,51.5014816,-0.0781888
NA,26/05/2021 22:19,2021,2021/22,Special Service,1,1,352,352,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000353,HANWORTH,E09000018,HOUNSLOW,Feltham,NULL,BARNLEA CLOSE,21500076,TW13,NULL,NULL,512450,172450,NULL,NULL
NA,26/05/2021 23:55,2021,2021/22,Special Service,1,1,352,352,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000595,FOREST,E09000031,WALTHAM FOREST,Leyton,NULL,COLCHESTER ROAD,22827800,E10,NULL,NULL,538250,187850,NULL,NULL
NA,27/05/2021 15:08,2021,2021/22,Special Service,1,1,352,352,FOX STUCK IN GARAGE   RSPCA ON SCENE,Fox,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped wild animal,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,6131065,SCEPTRE ROAD,22701069,E2,535188,182718,535150,182750,51.5272007,-0.052579314
NA,28/05/2021 13:07,2021,2021/22,Special Service,1,1,352,352,ASSIST RSPCA WITH BIRD TRAPPED IN GUTTERING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000431,STREATHAM SOUTH,E09000022,LAMBETH,Norbury,NULL,GREYHOUND LANE,21900642,SW16,NULL,NULL,529950,170550,NULL,NULL
NA,28/05/2021 14:49,2021,2021/22,Special Service,1,1,352,352,CAT STUCK IN WHEEL OF BUS,Cat,Person (mobile),Bus/coach station/garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000599,HIGH STREET,E09000031,WALTHAM FOREST,Walthamstow,10091987569,VINER PLACE,22884308,E17,537232,189057,537250,189050,51.58367739,-0.02065325
NA,28/05/2021 22:13,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000312,HAROLD WOOD,E09000016,HAVERING,Harold Hill,NULL,ARLINGTON GARDENS,21300957,RM3,NULL,NULL,554350,190850,NULL,NULL
NA,29/05/2021 11:15,2021,2021/22,Special Service,1,1,352,352,FOX IN DISTRESS TRAPPED IN GOAL NETTING       AT REAR OF VACCINATION CENTRE     GOAL NEAREST TO THE,Fox,Person (mobile),Secondary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000210,TOWN,E09000010,ENFIELD,Enfield,207182937,CHURCHBURY LANE,20702602,EN1,533084,197599,533050,197550,51.66142935,-0.077254384
NA,29/05/2021 15:18,2021,2021/22,Special Service,1,1,352,352,CAT FALLEN FROM WINDOW ONTO ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,NULL,WHITSTONE LANE,20304075,BR3,NULL,NULL,537950,167750,NULL,NULL
NA,29/05/2021 21:41,2021,2021/22,Special Service,1,1,352,352,SMALL ANIMAL RESCUE  CAT TRAPPED BEHIND GATE - BEHIND THE ROYAL MAIL AND FORD DEPOT,Cat,Person (mobile),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000364,SYON,E09000018,HOUNSLOW,Heston,10093258175,LONDON ROAD,21519757,TW8,517062,177238,517050,177250,51.48199377,-0.315558576
NA,29/05/2021 22:20,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011116,SOUTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,NULL,BOMBAY STREET,22500279,SE16,NULL,NULL,534550,178850,NULL,NULL
NA,30/05/2021 12:58,2021,2021/22,Special Service,1,1,352,352,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011100,DULWICH VILLAGE,E09000028,SOUTHWARK,Brixton,10025177987,ELMWOOD ROAD,22500904,SE24,532696,174764,532650,174750,51.45631467,-0.091464162
NA,30/05/2021 16:12,2021,2021/22,Special Service,1,1,352,352,ASSIST RSPCA WITH FOX TRAPPED BETWEEN FENCE AND WALL,Fox,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05009385,STOKE NEWINGTON,E09000012,HACKNEY,Stoke Newington,1.00021E+11,FOULDEN ROAD,20900420,N16,533735,185872,533750,185850,51.55589708,-0.072313276
NA,30/05/2021 17:10,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED UNDER WHEEL ARCH   SILVER CAR,Cat,Person (land line),Car,Road Vehicle,Animal rescue from water,Animal rescue from water - Domestic pet,E05000647,WARWICK,E09000033,WESTMINSTER,Lambeth,1.00023E+11,HUGH STREET,8400826,SW1V,528842,178677,528850,178650,51.49236935,-0.145485362
NA,31/05/2021 01:17,2021,2021/22,Special Service,1,1,352,352,POSSIBLY INJURED CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011219,BEXLEYHEATH,E09000004,BEXLEY,Bexley,10025288069,BROOMFIELD ROAD,20100214,DA6,549341,174605,549350,174650,51.45074354,0.147866818
NA,31/05/2021 11:18,2021,2021/22,Special Service,1,1,352,352,INJURED  CAT  TRAPPED BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000033,GORESBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,COLEMAN ROAD,19900450,RM9,NULL,NULL,548450,184550,NULL,NULL
NA,31/05/2021 14:42,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000193,BOWES,E09000010,ENFIELD,Edmonton,207046874,PRINCES AVENUE,20704626,N13,531553,192036,531550,192050,51.61180374,-0.101453845
NA,31/05/2021 15:47,2021,2021/22,Special Service,1,1,352,352,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000252,AVONMORE AND BROOK GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,ADDISON BRIDGE PLACE,21000028,W14,NULL,NULL,524550,178950,NULL,NULL
NA,31/05/2021 18:20,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED IN FAN    IN ATTIC,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000178,GREENFORD GREEN,E09000009,EALING,Northolt,NULL,GREENFORD ROAD,20602353,UB6,NULL,NULL,514950,183550,NULL,NULL
NA,31/05/2021 19:00,2021,2021/22,Special Service,1,1,352,352,PIGEON WITH FOOT TRAPPED IN GUTTER ABOVE QUICK FIT- CALLER ON SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000600,HIGHAM HILL,E09000031,WALTHAM FOREST,Walthamstow,NULL,BILLET ROAD,22816150,E17,NULL,NULL,537250,190950,NULL,NULL

================================================
FILE: 2023年/2023.10.08 灯芯柱状图/df_animals_sum.csv
================================================
cal_year,n
2009,568
2010,611
2011,620
2012,603
2013,585
2014,583
2015,540
2016,604
2017,539
2018,610
2019,604
2020,758


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================================================
FILE: 2023年/2023.10.08 灯芯柱状图/灯芯柱状图.r
================================================
# 代码来源于https://github.com/z3tt/TidyTuesday/blob/main/R/2021_27_AnimalRescues.Rmd
# 与源代码有所改动，数据读取代码进行了改动。
library(tidyverse)
library(ggtext)
library(ggrepel)
library(patchwork)
library(systemfonts)


# 主题设置 ====== 
theme_set(theme_minimal(base_size = 19))

theme_update(
  text = element_text(color = "grey12"),
  axis.title = element_blank(),
  axis.text.x = element_text(),
  axis.text.y = element_blank(),
  panel.grid.major.y = element_blank(),
  panel.grid.minor = element_blank(),
  plot.margin = margin(20, 5, 10, 10),
  plot.subtitle = element_textbox_simple(size = 14,
                                         lineheight = 1.6),
  plot.title.position = "plot",
  plot.caption = element_text( color = "#b40059", hjust = .5,
                              size = 10, margin = margin(35, 0, 0, 0))
)

# 数据读取+处理 ========
df_animals <- readr::read_csv('animal_rescues.txt')

df_animals_agg <-
  df_animals %>% 
  mutate(
    animal_group_aggregated = case_when(
      str_detect(animal_group_parent, "Domestic|Livestock|Farm|Horse|Cow|Sheep|Goat|Lamb|Bull") ~ "Other Domestic Animals",
      animal_group_parent %in% c("Cat", "cat") ~ "Cats",
      animal_group_parent %in% c("Bird", "Budgie") ~ "Birds",
      animal_group_parent == "Dog" ~ "Dogs",
      animal_group_parent == "Fox" ~ "Foxes",
      TRUE ~ "Other Wild Animals"
    )
  ) %>% 
  count(cal_year, animal_group_aggregated) %>% 
  group_by(animal_group_aggregated) %>% 
  mutate(
    total = sum(n),
    current = n[which(cal_year == 2021)]
  ) %>% 
  ungroup() %>% 
  mutate(
    animal_group_aggregated = fct_reorder(animal_group_aggregated, total),
    animal_group_aggregated = fct_relevel(animal_group_aggregated, "Other Domestic Animals", after = 0),
    animal_group_aggregated = fct_relevel(animal_group_aggregated, "Other Wild Animals", after = 0)
  )

df_animals_labs <-
  df_animals_agg %>% 
  filter(cal_year == 2016) %>% 
  group_by(animal_group_aggregated) %>% 
  mutate(n = case_when(
    animal_group_aggregated == "Cats" ~ 320,
    animal_group_aggregated %in% c("Birds", "Dogs") ~ 135,
    TRUE ~ 55
  ))

df_animals_annotate <-
  df_animals_agg %>% 
  mutate(label = "\n\n← Number of Rescues in 2021 so far.") %>% 
  filter(cal_year == 2021 & animal_group_aggregated == "Cats")

df_animals_sum <-
  df_animals_agg %>% 
  filter(cal_year < 2021) %>% 
  group_by(cal_year) %>% 
  summarize(n = sum(n))

# write_csv(df_animals_sum,"df_animals_sum.csv")
# df_animals_sum = read.csv("df_animals_sum.csv")
## 绘图 ==============

  df_animals_sum %>% 
  ggplot(aes(cal_year, n)) +
  geom_col(aes(fill = factor(cal_year)), width = .85) +
  geom_col(
    data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
    aes(alpha = cal_year == 2020), # 这里有细节！
    fill = "white", width = .5 # 宽度和透明度设置
  ) +
  geom_text( #这里的数据处理：添加文本+加入rescues
    data = df_animals_sum %>% 
      mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nRescues"), as.character(n))),
    aes(label = n_lab), size = 4.3, lineheight = .8, 
    nudge_y = 12, vjust = 0, color = "grey12", fontface = "bold"
  ) +
  geom_text( #添加文本+加入cats
    data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021) %>% 
      mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, "\nCats"), as.character(n))), 
    aes(label = n_lab), 
    color = "white", lineheight = .8, size = 4.3, 
    nudge_y = 12, vjust = 0, fontface = "bold"
  ) +
  geom_text( # 手动添加年份标签
    data = df_animals_agg %>% filter(animal_group_aggregated == "Cats" & cal_year < 2021),
    aes(y = -15, label = cal_year, color = factor(cal_year)), 
    size = 6, hjust = .5, vjust = 1
  ) +
  coord_cartesian(clip = "off") +#这想干嘛？
  scale_y_continuous(limits = c(-15, NA)) +
  scale_color_manual(values = c(rep("grey30", 11), "#b40059"), guide = "none") +
  scale_fill_manual(values = c(rep("grey30", 11), "#b40059"), guide = "none") +
  scale_alpha_manual(values = c(.25, .4), guide = "none") +
  theme(
    # plot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = "black"),
    # plot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),
    panel.grid.major = element_blank(),
    axis.text.x = element_blank()
  )





================================================
FILE: 2023年/2023.12.02 分面中添加不同表格/.Rhistory
================================================
head(myData)
# https://stackoverflow.com/questions/25554548/adding-sub-tables-on-each-panel-of-a-facet-ggplot-in-r/58464596#58464596?newreg=9fc3d38711e945d1b5d37847f0f5d0af
library(ggpmisc)
library(dplyr)
library(tibble)
myData <- filter(mpg, manufacturer == "audi" | manufacturer == "chevrolet")
head(myData)
gg <- ggplot(myData, aes(x=hwy, y=cty, colour=model)) +
facet_wrap(~ manufacturer) +
geom_point(size = 3) +
geom_smooth(stat="identity")
gg
tb <- myData %>%
group_by(manufacturer, model) %>%
summarize(var1 = round(mean(displ)), var2 = round(mean(cyl))) %>%
ungroup()
tb
mpg
tb
tbs <- lapply(split(tb, tb$manufacturer), "[", -1)
tbs
tbs
df
df <- tibble(x = rep(-Inf, length(tbs)),
y = rep(Inf, length(tbs)),
manufacturer = levels(as.factor(tb$manufacturer)),
tbl = tbs)
df
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = 0, vjust = 1)
rep(-Inf, length(tbs)
rep(-Inf, length(tbs))
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = 1, vjust = 1)
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = 0, vjust = 0)
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = 0, vjust = 1)
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = 0, vjust = 12)
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = 0, vjust = 2)
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = 0, vjust = 1.2)
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = 0.2, vjust = 1.2)
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = -0。2, vjust = 1.2)
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = -0.2, vjust = 1.2)
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = -0.2, vjust = 1.2)
library(viridis)
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = -0.2, vjust = 1.2) +
scale_color_viridis(discrete = TRUE)
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = -0.2, vjust = 1.2) +
scale_color_viridis(discrete = TRUE) + theme_bw() +
theme(panel.grid = element_blank())
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = -0.2, vjust = 1.2) +
scale_color_viridis(discrete = TRUE) + theme_bw() +
theme(panel.grid = element_blank(),legend.position = "bottom")
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = -0.2, vjust = 1.2) +
scale_color_viridis(discrete = TRUE) + theme_bw() +
theme(panel.grid = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal")
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = -0.2, vjust = 1.2) +
scale_color_viridis(discrete = TRUE) + theme_bw() +
theme(panel.grid = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal") +
guides(fill=guide_legend(nrow=1))
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = -0.2, vjust = 1.2) +
scale_color_viridis(discrete = TRUE) + theme_bw() +
theme(panel.grid = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal") +
guides(color=guide_legend(nrow=1))
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = -0.2, vjust = 1.2,
table.theme = ttheme_gtstripes) +
scale_color_viridis(discrete = TRUE) + theme_bw() +
theme(panel.grid = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal") +
guides(color=guide_legend(nrow=1))
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = -0.2, vjust = 1.2,
table.theme = ttheme_gtminimal) +
scale_color_viridis(discrete = TRUE) + theme_bw() +
theme(panel.grid = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal") +
guides(color=guide_legend(nrow=1))
# https://stackoverflow.com/questions/25554548/adding-sub-tables-on-each-panel-of-a-facet-ggplot-in-r/58464596#58464596?newreg=9fc3d38711e945d1b5d37847f0f5d0af
# library(ggpmisc)
library(dplyr)
library(tibble)
myData <- filter(mpg, manufacturer == "audi" | manufacturer == "chevrolet")
head(myData)
gg <- ggplot(myData, aes(x=hwy, y=cty, colour=model)) +
facet_wrap(~ manufacturer) +
geom_point(size = 3) +
geom_smooth(stat="identity")
tb <- myData %>%
group_by(manufacturer, model) %>%
summarize(var1 = round(mean(displ)), var2 = round(mean(cyl))) %>%
ungroup()
tbs <- lapply(split(tb, tb$manufacturer), "[", -1)
df <- tibble(x = rep(-Inf, length(tbs)),
y = rep(Inf, length(tbs)),
manufacturer = levels(as.factor(tb$manufacturer)),
tbl = tbs)
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = -0.2, vjust = 1.2)
library(viridis)
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = -0.2, vjust = 1.2,
table.theme = ttheme_gtstripes) +
scale_color_viridis(discrete = TRUE) + theme_bw() +
theme(panel.grid = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal") +
guides(color=guide_legend(nrow=1))
# https://stackoverflow.com/questions/25554548/adding-sub-tables-on-each-panel-of-a-facet-ggplot-in-r/58464596#58464596?newreg=9fc3d38711e945d1b5d37847f0f5d0af
# library(ggpmisc)
library(ggpp)
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
hjust = -0.2, vjust = 1.2,
table.theme = ttheme_gtstripes) +
scale_color_viridis(discrete = TRUE) + theme_bw() +
theme(panel.grid = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal") +
guides(color=guide_legend(nrow=1))


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FILE: 2023年/2023.12.02 分面中添加不同表格/.Rproj.user/C6560784/sources/per/t/23FB0CA3-contents
================================================
# https://stackoverflow.com/questions/25554548/adding-sub-tables-on-each-panel-of-a-facet-ggplot-in-r/58464596#58464596?newreg=9fc3d38711e945d1b5d37847f0f5d0af
# library(ggpmisc)
library(ggpp)
library(dplyr)
library(tibble)

myData <- filter(mpg, manufacturer == "audi" | manufacturer == "chevrolet")
head(myData)

gg <- ggplot(myData, aes(x=hwy, y=cty, colour=model)) + 
  facet_wrap(~ manufacturer) + 
  geom_point(size = 3) +
  geom_smooth(stat="identity")

tb <- myData %>%
  group_by(manufacturer, model) %>%
  summarize(var1 = round(mean(displ)), var2 = round(mean(cyl))) %>%
  ungroup() 

tbs <- lapply(split(tb, tb$manufacturer), "[", -1)
df <- tibble(x = rep(-Inf, length(tbs)), 
             y = rep(Inf, length(tbs)), 
             manufacturer = levels(as.factor(tb$manufacturer)), 
             tbl = tbs)

gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
                hjust = -0.2, vjust = 1.2) 

library(viridis)
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
                hjust = -0.2, vjust = 1.2,
                table.theme = ttheme_gtstripes) + 
  scale_color_viridis(discrete = TRUE) + theme_bw() +
  theme(panel.grid = element_blank(),
        legend.position = "bottom",
        legend.direction = "horizontal") +
  guides(color=guide_legend(nrow=1))
  




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LaTeX: XeLaTeX

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================================================
FILE: 2023年/2023.12.02 分面中添加不同表格/分面中添加不同表格.R
================================================
# https://stackoverflow.com/questions/25554548/adding-sub-tables-on-each-panel-of-a-facet-ggplot-in-r/58464596#58464596?newreg=9fc3d38711e945d1b5d37847f0f5d0af
# library(ggpmisc)
library(ggpp)
library(dplyr)
library(tibble)

myData <- filter(mpg, manufacturer == "audi" | manufacturer == "chevrolet")
head(myData)

gg <- ggplot(myData, aes(x=hwy, y=cty, colour=model)) + 
  facet_wrap(~ manufacturer) + 
  geom_point(size = 3) +
  geom_smooth(stat="identity")

tb <- myData %>%
  group_by(manufacturer, model) %>%
  summarize(var1 = round(mean(displ)), var2 = round(mean(cyl))) %>%
  ungroup() 

tbs <- lapply(split(tb, tb$manufacturer), "[", -1)
df <- tibble(x = rep(-Inf, length(tbs)), 
             y = rep(Inf, length(tbs)), 
             manufacturer = levels(as.factor(tb$manufacturer)), 
             tbl = tbs)

gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
                hjust = -0.2, vjust = 1.2) 

library(viridis)
gg + geom_table(data = df, aes(x = x, y = y, label = tbl),
                hjust = -0.2, vjust = 1.2,
                table.theme = ttheme_gtstripes) + 
  scale_color_viridis(discrete = TRUE) + theme_bw() +
  theme(panel.grid = element_blank(),
        legend.position = "bottom",
        legend.direction = "horizontal") +
  guides(color=guide_legend(nrow=1))
  




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FILE: 2023年/2023.12.16 分面中添加拟合曲线/.Rproj.user/C6560784/sources/session-427bca66/AB1928BC-contents
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library(ggplot2)
library(tidyverse)
library(viridis)
time2 = seq(3,20,3) #希望展示的数据点（离散）
load("true_data.RData")
cal_data
load("data_fit.RData")
data_fit

boxplot.path.fit = function(data_fit = data_fit, cal_data = cal_data, leg.pos = "none"){
  
  merged_df2 <- bind_rows(data_fit, .id = "Unit") #合并数据
  merged_df2$Unit = rep(c("Low","Mean","Up"),each = length(0:m))
  mer_dat = merged_df2 %>% pivot_longer(cols = !c(Time,Unit), names_to = "PC", values_to = "Value")
  # 数据筛选，用于画直线
  mer_dat1 = mer_dat[mer_dat$"Time" %in% time2 & mer_dat$"Unit" == "Low", 2:4]; colnames(mer_dat1) = c("Unit","PC","value")
  mer_dat2 = mer_dat[mer_dat$"Time" %in% time2 & mer_dat$"Unit" == "Mean", 2:4]; colnames(mer_dat2) = c("Unit","PC","value")
  mer_dat3 = mer_dat[mer_dat$"Time" %in% time2 & mer_dat$"Unit" == "Up", 2:4]; colnames(mer_dat3) = c("Unit","PC","value")
  
  p1 = ggplot() + 
    geom_boxplot(data = cal_data, aes(factor(Unit,levels = time2),value,fill=factor(Unit,levels = time2))) +
    geom_smooth(data= mer_dat1, aes(factor(Unit,levels = time2),value,group=1),
                color="#EE81C3", method="loess", linetype = 2,se = FALSE) +
    geom_smooth(data= mer_dat2, aes(factor(Unit,levels = time2),value,group=1),
                color="#DC3F20", method="loess",linetype = 1,se = FALSE) +
    geom_smooth(data= mer_dat3, aes(factor(Unit,levels = time2),value,group=1),
                color="#EE81C3", method="loess",linetype = 2,se = FALSE) +
    facet_wrap(vars(PC),scale="free") +
    scale_fill_viridis(discrete = TRUE,alpha = 0.8) + 
    theme_bw() + theme(panel.grid = element_blank(),legend.position = leg.pos) +
    xlab("Time") + ylab("Y(t)")
  
  return(p1)
}

boxplot.path.fit(data_fit = data_fit, cal_data = cal_data, leg.pos = "none")
ggsave("boxplot_path_fit.pdf")


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FILE: 2023年/2023.12.16 分面中添加拟合曲线/分面中添加拟合曲线.R
================================================
library(ggplot2)
library(tidyverse)
library(viridis)
time2 = seq(3,20,3) #希望展示的数据点（离散）
load("true_data.RData")
cal_data
load("data_fit.RData")
data_fit

boxplot.path.fit = function(data_fit = data_fit, cal_data = cal_data, leg.pos = "none"){
  
  merged_df2 <- bind_rows(data_fit, .id = "Unit") #合并数据
  merged_df2$Unit = rep(c("Low","Mean","Up"),each = length(0:m))
  mer_dat = merged_df2 %>% pivot_longer(cols = !c(Time,Unit), names_to = "PC", values_to = "Value")
  # 数据筛选，用于画直线
  mer_dat1 = mer_dat[mer_dat$"Time" %in% time2 & mer_dat$"Unit" == "Low", 2:4]; colnames(mer_dat1) = c("Unit","PC","value")
  mer_dat2 = mer_dat[mer_dat$"Time" %in% time2 & mer_dat$"Unit" == "Mean", 2:4]; colnames(mer_dat2) = c("Unit","PC","value")
  mer_dat3 = mer_dat[mer_dat$"Time" %in% time2 & mer_dat$"Unit" == "Up", 2:4]; colnames(mer_dat3) = c("Unit","PC","value")
  
  p1 = ggplot() + 
    geom_boxplot(data = cal_data, aes(factor(Unit,levels = time2),value,fill=factor(Unit,levels = time2))) +
    geom_smooth(data= mer_dat1, aes(factor(Unit,levels = time2),value,group=1),
                color="#EE81C3", method="loess", linetype = 2,se = FALSE) +
    geom_smooth(data= mer_dat2, aes(factor(Unit,levels = time2),value,group=1),
                color="#DC3F20", method="loess",linetype = 1,se = FALSE) +
    geom_smooth(data= mer_dat3, aes(factor(Unit,levels = time2),value,group=1),
                color="#EE81C3", method="loess",linetype = 2,se = FALSE) +
    facet_wrap(vars(PC),scale="free") +
    scale_fill_viridis(discrete = TRUE,alpha = 0.8) + 
    theme_bw() + theme(panel.grid = element_blank(),legend.position = leg.pos) +
    xlab("Time") + ylab("Y(t)")
  
  return(p1)
}

boxplot.path.fit(data_fit = data_fit, cal_data = cal_data, leg.pos = "none")



================================================
FILE: 2023年/合并图形+共享图例/合并图形-共享图例.R
================================================
library(ggplot2)
library(viridis)
## 创建图形
plot1 <- ggplot(data = mpg, aes(x = displ, y = hwy, color = class)) +
  geom_point(size=1.7) + scale_color_viridis(discrete = T) + 
  theme_bw() + theme(panel.grid = element_blank())  
  
plot2 <- ggplot(data = mpg, aes(x = cty, y = hwy, color = class)) +
  geom_point(size=1.7) + scale_color_viridis(discrete = T) +
  theme_bw() + theme(panel.grid = element_blank())

ggarrange(plot1, plot2)


# 合并图形并共享图例
## 方法一
library(ggpubr)
ggarrange(plot1, plot2, common.legend = TRUE, legend="top")
## 方法二
library(cowplot)
combined_plot <- plot_grid(plot1 + theme(legend.position = 'none'), plot2 + theme(legend.position = 'none'), ncol = 2)
# 将图例添加到合并后的图形中
plot_grid(combined_plot, get_legend(plot1),
          rel_widths = c(4, 1))
## 方法三
library(patchwork)
plot1 + plot2 + plot_layout(guides = "collect") &
  theme(legend.position='bottom')



================================================
FILE: 2024年/2024.02.07 多分类的条形图并标记数字/.Rhistory
================================================
# 科研论文中常用的图形（可靠性）
# 多分类条形图
library(ggplot2)
library(viridis)
library(forcats)
# 示例数据
R_all <- data.frame(
Scen = rep(c("A", "B", "C"), each = 5),
Model = rep(c("M1", "M2", "M3", "M4", "M5"), 3),
value = c(10, 15, 8, 12, 7, 9, 14, 18, 5, 10, 8, 11, 6, 8, 12)
)
# 设置 Model 列的因子顺序
model_order <- c("M1", "M2", "M3", "M4", "M5")
# 绘制柱状图（版本一）
ggplot(R_all, aes(x = Scen, y = value, fill = fct_relevel(Model, model_order))) +
geom_col(position = "dodge", alpha = 0.8) +
geom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.5) +  # 添加标签
scale_fill_viridis(discrete = TRUE, name = "Model") +
theme_bw() +
theme(panel.grid = element_blank(), legend.position = "bottom")
#更加复杂的场景(多一个case变量，使用分面)
# 示例数据
R_all2 = rbind(R_all,R_all)
R_all2$case = rep(c("Case 1", "Case 2"),each=15)
R_all2$value = round(R_all2$value + 1* rnorm(30,0,1),2)
ggplot(R_all2, aes(x = Scen, y = value, fill = fct_relevel(Model, model_order))) +
geom_col(position = "dodge", alpha = 0.8) +
geom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.5) +  # 添加标签
facet_wrap(vars(factor(case))) +
scale_fill_viridis(discrete = TRUE, name = "Model") +
theme_bw() +
theme(panel.grid = element_blank(), legend.position = "bottom")
# 科研论文中常用的图形（可靠性）
# 多分类条形图
library(ggplot2)
library(viridis)
library(forcats)
# 示例数据
R_all <- data.frame(
Scen = rep(c("A", "B", "C"), each = 5),
Model = rep(c("M1", "M2", "M3", "M4", "M5"), 3),
value = c(10, 15, 8, 12, 7, 9, 14, 18, 5, 10, 8, 11, 6, 8, 12)
)
# 设置 Model 列的因子顺序
model_order <- c("M1", "M2", "M3", "M4", "M5")
# 绘制柱状图（版本一）
ggplot(R_all, aes(x = Scen, y = value, fill = fct_relevel(Model, model_order))) +
geom_col(position = "dodge", alpha = 0.8) +
geom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.5) +  # 添加标签
scale_fill_viridis(discrete = TRUE, name = "Model") +
theme_bw() +
theme(panel.grid = element_blank(), legend.position = "bottom")
#更加复杂的场景(多一个case变量，使用分面)
# 示例数据
R_all2 = rbind(R_all,R_all)
R_all2$case = rep(c("Case 1", "Case 2"),each=15)
R_all2$value = round(R_all2$value + 1* rnorm(30,0,1),2)
ggplot(R_all2, aes(x = Scen, y = value, fill = fct_relevel(Model, model_order))) +
geom_col(position = "dodge", alpha = 0.8) +
geom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.5) +  # 添加标签
facet_wrap(vars(factor(case))) +
scale_fill_viridis(discrete = TRUE, name = "Model") +
theme_bw() +
theme(panel.grid = element_blank(), legend.position = "bottom")


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================================================
# 科研论文中常用的图形（可靠性） =======
# 推文：《ggplot 绘制多分类条形图并标记数字》
# 链接：https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247520840&idx=1&sn=1a13fdace99353fb984d8b1ff9ae5c99&chksm=ea276dacdd50e4ba2b668a0ccb2feb87079cf0bb7b8eae26fe51122e5031cd5ca4e289f6e0f8&token=1416591993&lang=zh_CN#rd

# 多分类条形图
library(ggplot2)
library(viridis)
library(forcats)
# 示例数据
R_all <- data.frame(
  Scen = rep(c("A", "B", "C"), each = 5),
  Model = rep(c("M1", "M2", "M3", "M4", "M5"), 3),
  value = c(10, 15, 8, 12, 7, 9, 14, 18, 5, 10, 8, 11, 6, 8, 12)
)

# 设置 Model 列的因子顺序
model_order <- c("M1", "M2", "M3", "M4", "M5")

# 绘制柱状图（版本一）
ggplot(R_all, aes(x = Scen, y = value, fill = fct_relevel(Model, model_order))) +
  geom_col(position = "dodge", alpha = 0.8) +
  geom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.5) +  # 添加标签
  scale_fill_viridis(discrete = TRUE, name = "Model") +
  theme_bw() +
  theme(panel.grid = element_blank(), legend.position = "bottom")


#更加复杂的场景(多一个case变量，使用分面)

# 示例数据
R_all2 = rbind(R_all,R_all)
R_all2$case = rep(c("Case 1", "Case 2"),each=15)
R_all2$value = round(R_all2$value + 1* rnorm(30,0,1),2)

ggplot(R_all2, aes(x = Scen, y = value, fill = fct_relevel(Model, model_order))) +
  geom_col(position = "dodge", alpha = 0.8) +
  geom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.5) +  # 添加标签
  facet_wrap(vars(factor(case))) + 
  scale_fill_viridis(discrete = TRUE, name = "Model") +
  theme_bw() +
  theme(panel.grid = element_blank(), legend.position = "bottom")




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FILE: 2024年/2024.02.07 多分类的条形图并标记数字/多分类条形图并标记数字.R
================================================
# 科研论文中常用的图形（可靠性） =======
# 推文：《ggplot 绘制多分类条形图并标记数字》
# 链接：https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247520840&idx=1&sn=1a13fdace99353fb984d8b1ff9ae5c99&chksm=ea276dacdd50e4ba2b668a0ccb2feb87079cf0bb7b8eae26fe51122e5031cd5ca4e289f6e0f8&token=1416591993&lang=zh_CN#rd

# 多分类条形图
library(ggplot2)
library(viridis)
library(forcats)
# 示例数据
R_all <- data.frame(
  Scen = rep(c("A", "B", "C"), each = 5),
  Model = rep(c("M1", "M2", "M3", "M4", "M5"), 3),
  value = c(10, 15, 8, 12, 7, 9, 14, 18, 5, 10, 8, 11, 6, 8, 12)
)

# 设置 Model 列的因子顺序
model_order <- c("M1", "M2", "M3", "M4", "M5")

# 绘制柱状图（版本一）
ggplot(R_all, aes(x = Scen, y = value, fill = fct_relevel(Model, model_order))) +
  geom_col(position = "dodge", alpha = 0.8) +
  geom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.5) +  # 添加标签
  scale_fill_viridis(discrete = TRUE, name = "Model") +
  theme_bw() +
  theme(panel.grid = element_blank(), legend.position = "bottom")


#更加复杂的场景(多一个case变量，使用分面)

# 示例数据
R_all2 = rbind(R_all,R_all)
R_all2$case = rep(c("Case 1", "Case 2"),each=15)
R_all2$value = round(R_all2$value + 1* rnorm(30,0,1),2)

ggplot(R_all2, aes(x = Scen, y = value, fill = fct_relevel(Model, model_order))) +
  geom_col(position = "dodge", alpha = 0.8) +
  geom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.5) +  # 添加标签
  facet_wrap(vars(factor(case))) + 
  scale_fill_viridis(discrete = TRUE, name = "Model") +
  theme_bw() +
  theme(panel.grid = element_blank(), legend.position = "bottom")




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FILE: 2024年/2024.02.12 TiKZ绘图/tutorial.tex
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\quad

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FILE: 2024年/2024.02.19 柱状图+分面/.Rproj.user/C6560784/sources/session-fd2b58b4/1FF7DD4B-contents
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library(tidyverse)
library(viridis)
data = read.csv("demo_dat.csv")
head(data) 

toselect <- c("ananas","rongdianer","rainie.tian", "vivakatt","nya", "liangliang","nannan", "joly","lizlu")

data2 <- as_tibble(data)  %>% filter(NAME %in% toselect) %>%
  arrange(match(NAME, toselect)) %>% 
  pivot_longer(-NAME, names_to = "centrality" , values_to = "value") %>% 
  group_by(centrality) %>% 
  arrange(desc(value)) %>% 
  mutate(order = row_number()) %>% 
  ungroup %>% mutate(NAME = factor(NAME, levels = toselect)) %>% 
  mutate(centrality = factor(centrality, levels = c("A" , "B", "C" , "D")))

data2 %>%
  ggplot(aes(factor(NAME,levels = toselect), value, fill = NAME)) + 
  geom_bar(stat = "identity") + 
  facet_wrap(vars(centrality), scales = "free" ,nrow = 1) + 
  coord_flip() + 
  geom_label(aes(label = order), colour = "white", size = 3) + 
  scale_x_discrete(limits = rev(levels(data2$NAME))) + 
  scale_fill_viridis(discrete = TRUE) +
  theme_bw() +
  theme(plot.title = element_blank(),
        panel.grid = element_blank(),
        axis.title.x = element_blank(), 
        axis.title.y = element_blank(), 
        panel.spacing.x = unit(4, "mm"), 
        legend.position = "none")


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FILE: 2024年/2024.02.19 柱状图+分面/demo_dat.csv
================================================
NAME,A,B,C,D
steamed.bun,35,43.07327007,0.330225175,488.3134921
YCC,47,100,4.326085414,19035.97772
sword.sister,47,49.42830214,0.68037295,797.1241113
qianhu.changsheng,18,43.18077589,1.77364794,287.5858994
yihua,15,27.14853047,2.374004417,8281.834945
rex,30,64.11946526,5.283012691,0
xiaozhuang,23,38.3994967,0.674876203,3571.711403
father.rabbit,43,80.71350494,0.90036657,2893.305091
advenced.hania,16,29.01789266,0.592592443,954.8038248
ananas,48,93.48849769,6.031732638,0
Miss.xu,34,66.77709531,3.841465863,2034.987784
teresa,13,25.60066856,1.760791184,8498.542422
sweeter,25,44.43754034,2.665977469,5999.390437
wen.buding,54,65.36168342,0.341463415,7933.670757
kawkao,25,85.82311134,0.838447011,22156.41883
zzreal,19,89.44727102,1.223651352,17756.39133
pony,39,34.72956598,1.127418586,557.32665
echo.liang,8,81.83585971,0.318021201,0
lizi.don.t.have.small.waist,32,14.46027951,1.27973252,2442.932665
suzy_z,26,60.17982578,2.27205517,321.484904
lizlu,17,47.56799364,3.640444023,9011.031245
oh.emma,16,38.1624698,1.803064081,341.7474488
nya,26,31.55011987,3.094480257,10586.24213
austin,35,49.81429148,1.574941864,4521.797339
qiala.fang,43,64.90544892,3.101730833,405.9931735
qiuqiu,23,82.29267411,1.120421247,2049.991219
nicole,35,70.8466772,2.631761941,2849.652445
shian.cheng,33,67.45075028,2.65187965,1707.417564
nannan,21,37.43177419,1.340004358,970.7874774
malicious.girl,17,31.96709284,0.788080064,409.108063
liangliang,22,75.47390896,4.686843639,953.950377
father.xian,27,68.38136332,0.382504026,808.3814131
vivakatt,31,25.79500899,0.319391635,4487.43143
shiyin,23,28.58403995,2.374630135,3462.449352
rainie.tian,39,72.16853763,0.431840485,10951.40967
samchak,19,6.441398435,4.671480096,571.5805792
Old.liu,14,24.25692704,1.463194315,540.8838545
an.pan,20,38.12269694,5.347062632,637.1347646
handsome.maodan,11,15.82109163,2.619991687,0
vickysoupsss,14,27.0200177,1.815183324,4844.607512
clever.dang,40,72.80533262,0.584335384,4747.497759
rongdianer,40,42.73742596,4.296965496,7579.09566
xiran.fu,38,61.74085799,1.815183324,8227.066978
zlks,15,55.34274479,1.507494108,243.0790859
be.bald.for.900.years,14,39.8404343,1.050649095,1029.805877
pink.li,36,72.94033991,1.731313444,3848.163219
vivi,5,35.59707319,1.050649095,7.692307692
joly,18,32.75056853,0.625731023,468.7201906
chifan.chen,9,17.00064587,0.335664336,0
hu,14,21.51426748,1.075503418,1425.479608


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FILE: 2024年/2024.02.19 柱状图+分面/柱状图+分面.R
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# ggplot 绘制分面条形图并标记数字 ====
# 来自公众号《庄闪闪的R语言手册》——庄闪闪
# 数据导入
library(tidyverse)
library(viridis)
data = read.csv("demo_dat.csv")
head(data) 

# 数据处理
toselect <- c("ananas","rongdianer","rainie.tian", "vivakatt","nya", "liangliang","nannan", "joly","lizlu")
data2 <- as_tibble(data)  %>% filter(NAME %in% toselect) %>%
  arrange(match(NAME, toselect)) %>% 
  pivot_longer(-NAME, names_to = "centrality" , values_to = "value") %>% 
  group_by(centrality) %>% 
  arrange(desc(value)) %>% 
  mutate(order = row_number()) %>% 
  ungroup %>% mutate(NAME = factor(NAME, levels = toselect)) %>% 
  mutate(centrality = factor(centrality, levels = c("A" , "B", "C" , "D")))

# 绘图
data2 %>%
  ggplot(aes(factor(NAME,levels = toselect), value, fill = NAME)) + 
  geom_bar(stat = "identity") + 
  facet_wrap(vars(centrality), scales = "free" ,nrow = 1) + 
  coord_flip() + 
  geom_label(aes(label = order), colour = "white", size = 3) + 
  scale_x_discrete(limits = rev(levels(data2$NAME))) + 
  scale_fill_viridis(discrete = TRUE) +
  theme_bw() +
  theme(plot.title = element_blank(),
        panel.grid = element_blank(),
        axis.title.x = element_blank(), 
        axis.title.y = element_blank(), 
        panel.spacing.x = unit(4, "mm"), 
        legend.position = "none")


================================================
FILE: README.md
================================================
# 公众号资料留档

## 简介

公众号对应的资料（代码+数据），如有错误，欢迎大家批评指正。

作者：庄闪闪/庄亮亮

微信公众号：[庄闪闪的R语言手册](https://mp.weixin.qq.com/mp/appmsgalbum?__biz=MzI1NjUwMjQxMQ==&action=getalbum&album_id=1476187590709002243&scene=173&from_msgid=2247486060&from_itemidx=1&count=3#wechat_redirect) 

合作请加微信号：a641292753



[TOC]

-------

# R可视乎系列

- 高级棒棒图

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/image-20230513214535597.png)

- [ggparty包：构建个性化树状图形](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247517054&idx=1&sn=664b88b42061fb2f2979b01833c363bf&chksm=ea277c9add50f58c172f08b246d4f88d507c0dacda981796599efeeb48d5298eb5cb72ab7af4&token=1444533026&lang=zh_CN#rd)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514093057588.png)

- [如何一步步提高图形 B 格？以 ggplot 绘图为例](https://mp.weixin.qq.com/s/k5VgsK7e4d9RDrTkctJP6g)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102342404.png)

- [基于 R 语言的科研论文绘图技巧汇总](https://mp.weixin.qq.com/s/j9ribyJ809pErDGQXL_Usw)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/figure_example.png)

> 代码参考：[scifig_plot_examples_R: Scientific publication figure plotting examples with R](https://github.com/marco-meer/scifig_plot_examples_R)



- [使用 ggTimeSeries 包构建日历图](https://mp.weixin.qq.com/s/rAlPbNh2bYuNkqyT9dxosQ)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/%E5%9B%BE%E5%BD%A21.jpeg)

- [基于 ggdensity 包的等高线绘制](https://mp.weixin.qq.com/s/IRSvobzOfqmYtfBXDpbdJw)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514093725312.png)

- [基于 ggridges 绘制剩余使用寿命密度图](https://mp.weixin.qq.com/s/ECquLeoHaJ2byWgrRpqftw)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514093728804.png)

- [使用 ggcharts 包高亮部分内容](https://mp.weixin.qq.com/s/Rml11BcLE5_fLKckfob9Wg)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514093824511.png)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102409266.png)



- [2023年日历大派送](https://mp.weixin.qq.com/s/BdE2Zb97iDyp2NpsoELouQ)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102415734.jpeg)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514094407585.png)

- [使用 ggplot2 绘制比较各省份及其区县的详细数据](https://mp.weixin.qq.com/s/KzuT4nnGbRqccBccVXlouQ)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102425063.png)

- [使用 ggplot2 绘制单个和多个省份地图](https://mp.weixin.qq.com/s/9Y7z0XVHv-sMRSiwY6PnBw)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514094600488.png)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514094602623.png)

- [R 语言绘制十段线地图，给特定省份填色](https://mp.weixin.qq.com/s/ZjUuFtRT1j3ksaGD-hHnLg)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102438571.png)

- [R绘图案例｜基于分面的面积图绘制](https://mp.weixin.qq.com/s/alyuUumkwqC08RJilXmfeA)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102446221.png)

- [分面中添加不同的直线](https://mp.weixin.qq.com/s/JdfKl9vEqkE0rcEfz6lBBg)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102453306.png)



- [R绘图案例｜基于分面的折线图绘制](https://mp.weixin.qq.com/s/apu5-4ykHe0QTUbdJKy-MQ)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102459934.png)

- [如何在分面中添加数学表达式标签?](https://mp.weixin.qq.com/s/Dbx9J_flkTtyi7LtRAdRPQ)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102505599.png)



- [ggplot 分面的细节调整汇总](https://mp.weixin.qq.com/s/fzcGJugs0VxJpM6nb25hLA)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100200680.png)

- [老板让你复现一个图片，你会使用什么软件？](https://mp.weixin.qq.com/s/V8H-U719PjCgsn1lVY_PFg)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100153931.png)

- [R 案例｜绘制不同分布的 QQ 图](https://mp.weixin.qq.com/s/ncorK41sntA4iBJWBCdVkA)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100148744.png)

- [绘制混合密度函数图以及添加分位数线](https://mp.weixin.qq.com/s/bQ_aefTxk32x30Avfzv5kw)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100144955.png)

- [如何使用 ggplot2 绘制双轴分离图？](https://mp.weixin.qq.com/s/QiMHA10X8nGtK5iOH4armQ)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100140481.png)



- igraph包——绘制网络图 [2020.07.13Network_igraph](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=100000088&idx=1&sn=3b7b79c2587e930a79cf11a84bcdb3e4&chksm=6a24febc5d5377aa3615fb6a0e3ec4e383162d694ad50d6a414482f8244de447ca5b2922c352&mpshare=1&scene=1&srcid=0714Vu2WALNMi27VcRfIFXH9&sharer_sharetime=1594708423633&sharer_shareid=ee38888b33e1d0070e96aeb454518587&key=3c22c88777da856c2f08a877b1939ceea75ba76d52067d706d44418948b4568c7e4e0ed9e4f0d00539c1e2cbbf51700d4f0b4f2642d757270ed6eeca83bbffa59947c9f38399e0e6cd58299f9d1d61e6&ascene=1&uin=OTk1MTUyNzI2&devicetype=Windows+10+x64&version=62090529&lang=zh_CN&exportkey=Ax5Q4wOen8wg5nPK10PFOqA%3D&pass_ticket=phh%2BTyTOwain33l3gWNzH4Aki97YE7dlcnlLuCxtFuuIrAtl234GrZ237NODA6HD)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100135963.jpeg)


- 国内疫情图绘制（静态）  [2020.07.14China_map](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247483799&idx=1&sn=da1acf1004a6d2f25cc296bcaf972063&chksm=ea24fe73dd537765132f64e79f7ba86ec225ecf6e81c43ba4d7aec9a75df42062728e110ad0e&mpshare=1&scene=1&srcid=07142HT0hZAFaVLTlpac4zAV&sharer_sharetime=1594708107631&sharer_shareid=ee38888b33e1d0070e96aeb454518587&key=65fe14863667915bc7e0151836657ceb6c523eaf07ca95785ed82332713ffd495369b1fd5b97cefc7cf7a8e1c66a5f880921fba19ce1d0e4832cb61609b1bcc76d99204dbc6dd77b97c47ad62d3e0e56&ascene=1&uin=OTk1MTUyNzI2&devicetype=Windows+10+x64&version=62090529&lang=zh_CN&exportkey=Aw4fL5T557lBWbSWfFSE37M%3D&pass_ticket=phh%2BTyTOwain33l3gWNzH4Aki97YE7dlcnlLuCxtFuuIrAtl234GrZ237NODA6HD)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100128773.jpeg)


- R中坐标轴截断的不同实现方式 [2020.07.22坐标轴截断画图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=100000243&idx=1&sn=119c4039f27b351a367ca40d1a54f0a7&chksm=6a24fe175d5377014aa610e3b5080d08e5e7f58447ca89f8c4806d418df61a8027353dd8156f#rd)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514095854330.png)


- R可视乎|饼图[2020.07.24饼图与圆环图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484046&idx=1&sn=cc087b09448e75ed54b5364accfb3bff&chksm=ea24fd6add53747ca57137b964bdb0db371220737398e4276ee1ede27df7f09be0afd14185f8&mpshare=1&scene=1&srcid=0816aG9PZi3BixNvL0cNcVhp&sharer_sharetime=1597541989820&sharer_shareid=ee38888b33e1d0070e96aeb454518587&key=b936ead840dca7f6e5a6a9b2f01076516390ed96ff9f73bc52a446143a88f2b01f9b378ea6423ce540c7591d1f324e00481858da6cb7dc5d8b4bf6d6cab3e79763433d2818f9952e1ff4ed49e93412026b6771123e151a7f6242d0b63a58e8da7f010dd41e4e833ef9f4766ee2e8130626e801b5a3cd32b43c38a1a9809099d9&ascene=1&uin=OTk1MTUyNzI2&devicetype=Windows+10+x64&version=62090529&lang=zh_CN&exportkey=A0VSObDydAW7hVulHfsWDDE%3D&pass_ticket=LZqYAanUOJecq6uEoCOQznjsykTBUbS15CZIvi%2FJtgBFhDLyBvXsaDBBnPRaSb0n)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100123331.png)


- R可视乎|分面一页多面[2020.08.15分面](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484186&idx=1&sn=c913a65f88132b3611e580b0318404d9&chksm=ea24fcfedd5375e87adfc3028850ee4034a0a0d34dd3855cf155b28eea9c71bfedb381d2c9e9&token=222682915&lang=zh_CN#rd)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100108861.png)


- R可视乎|马赛克图[2020.08.25马赛克](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484309&idx=1&sn=d79ce748d43fe066a0bad0221ae8e068&chksm=ea24fc71dd537567c3ca322cb216e92c2ca4bdd96c20c9873e762d81cfb892583af10198b757&token=682523778&lang=zh_CN#rd)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/11861684029650_.pic.jpg)


- R可视乎|混合多个图形[2020.08.26混合多个图形](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484367&idx=1&sn=30a54bd7dbf44852c380192d11a10ab9&chksm=ea24fc2bdd53753d239bbe995dfcb88232223e0a24f595acb343433fd4a053116e9f26dbf983&token=682523778&lang=zh_CN#rd)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/11871684029811_.pic.jpg)


- R可视乎|华夫饼图[2020.09.01华夫饼图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484631&idx=1&sn=6402ba80ab417c1d8a99c7778a48c7db&chksm=ea24fb33dd537225a7488a578b876b6da2f2157e0048b9515100d4ab64301fad2ad4f460e822&token=682523778&lang=zh_CN#rd)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/11881684029842_.pic.jpg)


- R可视乎|散点图系列（1）[2020.10.09散点图系列一](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485142&idx=1&sn=564bffc9e7765ebae9b9b81a17a188d9&chksm=ea24f932dd5370241a05c75975ff24a34423a8f182bf6c716c8c9ed981788d0492dcb268248a&token=682523778&lang=zh_CN#rd)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/11891684029866_.pic.jpg)


- R可视乎|散点图系列（2）[2020.10.27散点图系列二](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485276&idx=1&sn=f98a2aede13555fa1c372f08c3cdec44&chksm=ea24f8b8dd5371ae9e13f3df41ff73e070775eb1ab871370783bb3f396a0a9c29d42c6a89210&token=682523778&lang=zh_CN#rd)

> 以下几篇内容视为《R语言数据可视化之美》书籍的学习笔记。

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/11901684029902_.pic.jpg)


- R可视乎|气泡图[2020.11.12气泡图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486060&idx=1&sn=b613c8d0239c93185641c1bb6a062f7b&chksm=ea24f588dd537c9e2d7a33dd5e9f32b34d0a72d5396cdb52211c51f3998269a4d843011abf9b&token=682523778&lang=zh_CN#rd)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100544500.png)


- R可视乎|三维散点图[2020.11.22三维散点图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486366&idx=1&sn=b47d8f89f032bbf1ead4f0db75d45ef7&chksm=ea24f47add537d6c5d50a73bd64318f473071e1474be95c9d5c2f250a39a64a6877ba4d61b68&token=761595288&lang=zh_CN#rd)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102548544.png)


- R可视乎|等高线图[2020.11.28等高线](https://mp.weixin.qq.com/s/eI_7qkItkqa6kHwRWM1CXA)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100817869.png)


- R可视乎|瀑布图[2020.12.07瀑布图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487383&idx=1&sn=43e2eaf6b7c6b24510ccadb79e766f07&chksm=ea24f073dd53796546fd4145ded4cddfe8779464ad9d0d674900a65484966cd5c3f6367dcecf&token=86432493&lang=zh_CN#rd)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100832157.png)


- R可视乎|私人定制专属日历[2020.12.05日历](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487814&idx=1&sn=aa58149b66ce8b6d1c6210ded418c71a&chksm=ea24eea2dd5367b4a24a670b9e78d377f1506399be8dd0d0a35230407e3dc131f6b07ab3d9ca&token=86432493&lang=zh_CN#rd)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100900010.png)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100924520.png)


- R可视乎|峰峦图[2020.12.25峰峦图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488248&idx=1&sn=6b71d7adba5ea796fdfe8f49fe232d94&chksm=ea24ed1cdd53640a1e30271584458097fce82a732f9d63ce2084dc63fbae6fb028fd7f813bb7&token=86432493&lang=zh_CN#rd)

> 这篇内容很多，点击查看

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102607135.png)

![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102614445.png)



![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102620236.png)



-----

# R包介绍

- reticulate包:数据科学者的福音[2020.09.15reticulate](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484515&idx=1&sn=26b03b6ad26f2315cdc04049f740f1c0&chksm=ea24fb87dd537291d5184c28a9c9f2cdda591e4c17a7e7daaff34a9a1c3949ee0e86f9b355b7&token=682523778&lang=zh_CN#rd)

- ggpubr包:制可以学术发表的图 [2020.10.30ggpubr](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485615&idx=1&sn=47ac21f131bf2ac6c90c50fb9fb7966b&chksm=ea24f74bdd537e5d74f60919388f683dfe779fe8a2d11999e55e290d4bdb25c64e36cc74ccc1&token=682523778&lang=zh_CN#rd)


- gghalves包：你五毛我五毛[2020.11.14gghalves](https://mp.weixin.qq.com/s/WaMCCmT2eAP9DmCjPOtcMg)

- flexdashboard包：用于R的简单交互式仪表盘[2020.11.16flexdashboard](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486237&idx=1&sn=571544510c7e3e48a280dd4d677656e5&chksm=ea24f4f9dd537defa493c419973f75943159316765ac61093a195b83fde314dd7fffe61349cd&token=1801328502&lang=zh_CN#rd)

- esquisse包:不写代码生成ggplot图[2020.12.21esquisse包](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488200&idx=1&sn=3a058480b104165118975b2d908dff72&chksm=ea24ed2cdd53643a9deb58069cd8d0e9933fc165994a2bb7a6f7d4651c7796b839fc781ec86d&token=86432493&lang=zh_CN#rd)

- ggridges包:绘制峰峦图[2020.12.25峰峦图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488248&idx=1&sn=6b71d7adba5ea796fdfe8f49fe232d94&chksm=ea24ed1cdd53640a1e30271584458097fce82a732f9d63ce2084dc63fbae6fb028fd7f813bb7&token=86432493&lang=zh_CN#rd)

- [ggvis包—数据可视化交互](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488405&idx=1&sn=271fc88b523e738a6a1d92504dbce330&chksm=ea24ec71dd5365671bb66cbb50afdb0b00762b7581b9d3e5060a4b59021485364093f1c8b963&scene=21#wechat_redirect)

- [calendR包—私人定制专属日历](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487814&idx=1&sn=aa58149b66ce8b6d1c6210ded418c71a&chksm=ea24eea2dd5367b4a24a670b9e78d377f1506399be8dd0d0a35230407e3dc131f6b07ab3d9ca&scene=21#wechat_redirect)

- [corrplot包：相关性矩阵可视化](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487625&idx=1&sn=3102c4afb0cf97904d810579af386eb6&chksm=ea24ef6ddd53667b887d11e7013589f796c8ff4f9e1e9b6e8df817ea7b223baadbc19dfbaaa5&scene=21#wechat_redirect)

- [cowplot包：用R添加水印](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486838&idx=2&sn=21ee1c8b683e7d27373f3e1f40901428&chksm=ea24f292dd537b843db330a88161ce6f89227418f64515164615c3f63721df6464b6d91a2b1a&scene=21#wechat_redirect)

- [flexdashboard包：用于R的简单交互式仪表盘](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486237&idx=1&sn=571544510c7e3e48a280dd4d677656e5&chksm=ea24f4f9dd537defa493c419973f75943159316765ac61093a195b83fde314dd7fffe61349cd&scene=21#wechat_redirect)

- [igraph包——绘制网络图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247483780&idx=1&sn=46ce562ed91ec2d08d7669477160c249&chksm=ea24fe60dd53777615de14ec0ad087c1bbc56d46d73eb51e13fdeeaa60633063b4bd415a7d01&scene=21#wechat_redirect)

------------

# R文档沟通

- [R Markdown 指南](https://cosname.github.io/rmarkdown-guide/)

- [R Markdown 入门教程](https://cosx.org/2021/04/rmarkdown-introduction/)

- [Rmarkdown 制作重复性报告](https://github.com/liangliangzhuang/R_example/tree/master/2023.04.12%20Rmarkdown%E9%87%8D%E5%A4%8D%E6%80%A7%E6%8A%A5%E5%91%8A)



# R案例分析

- 亚马逊产品的推荐算法[2020.08.14amazon](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484112&idx=1&sn=ffcb0b6ed0efb64ab25b6a76d9dba654&chksm=ea24fd34dd537422ed28f04d438b066dca643986d990711b9c9c1bdb53ba8aa38dcfef655822&mpshare=1&scene=1&srcid=081525ytkp6KQM65Wx6GX5E7&sharer_sharetime=1597501747183&sharer_shareid=ee38888b33e1d0070e96aeb454518587&key=872f9623724a6dd292ceaa369df1008b9de4f20ddfb7de21277ad15de2f0b0c0ea578533c0b11558a838423e8f5ada1126e44e9d47b11556890d71177b7641e2a7efa73be1185a7984515d48939fab85840b9561a0c846fa95d3634939a056d8fe84cbf0a6cc6d7758e0f1c09107fd171046d1580e33298cebbf3c1675e37dd1&ascene=1&uin=OTk1MTUyNzI2&devicetype=Windows+10+x64&version=62090529&lang=zh_CN&exportkey=A0brKWx%2BS97cOS6wsgyDKSQ%3D&pass_ticket=LZqYAanUOJecq6uEoCOQznjsykTBUbS15CZIvi%2FJtgBFhDLyBvXsaDBBnPRaSb0n)

- 志愿填报|温州大学"非官方"招办专家帮你支招！[2020.07.29温大招生](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247483954&idx=1&sn=047a1b1b3e9c5ce46e3876bb05b54282&chksm=ea24fdd6dd5374c0c119c680cbd595b77a0bc7479889b34d378ae3282bce5e07eff93a5d08e5&mpshare=1&scene=1&srcid=0729IxeNwC6yyOwYcJh7C7Px&sharer_sharetime=1596027172994&sharer_shareid=ee38888b33e1d0070e96aeb454518587&key=d3c7cb8eeb23bc8322a73076864458cac5ef3808dd487c233e6eda4d377bdf5bb87322ee7ffc1b6b68f86e3799092502da7e5839e8080dc5d265dbdc5a880b93c2c286acab339bbe8bf9a06458dfa653&ascene=1&uin=OTk1MTUyNzI2&devicetype=Windows+10+x64&version=62090529&lang=zh_CN&exportkey=A1EDrAZkVUF2VzXm2Nm2UpQ%3D&pass_ticket=GHX0j6fsfiEATjqcMrcVQQYSihtF3L6yDim2tm78a1XP0v2qucpofrFRF8%2Bz4zjt)

- 协同过滤算法—MovieLense数据集分析[2020.07.22Movielense](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247483889&idx=1&sn=b56b5ee3374bb8e2ec0e5643f62cd4bb&chksm=ea24fe15dd5377034e245dddb4c47ae5fc4a07ffc0d117734ec81910d733173b729e11a4fd29&token=1309493585&lang=zh_CN#rd)

# R数据科学系列

> 该系列代码数据都在[R数据科学系列](xx)

- 几何对象[2020.08.23几何对象](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484261&idx=1&sn=6b451f752f86a284717d958e13738686&chksm=ea24fc81dd537597080427826e52dd4c1dac24e9cee9cc07897497f0dd9649ff63cc6a50f232&token=682523778&lang=zh_CN#rd)

- R速查表分享[2020.08.23速查表](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484290&idx=1&sn=edfe8a910e87353dc8e68f3f42e34c7c&chksm=ea24fc66dd537570f2bcda65b1ae0457ac9906fd14e74da6a0ffd3ee62a304ed465fd8ea9637&token=1309493585&lang=zh_CN#rd)

- tidyverse数据清洗案例详解[2020.09.27tidyverse数据清洗](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484881&idx=1&sn=2f5330b23e376ceb2ef746184935174f&chksm=ea24fa35dd53732319b16bed64e42a8dfa2da2a578cc2f7d4ef043f000bff81d5113c4e9dca1&token=682523778&lang=zh_CN#rd)

- 数据处理|数据按从小到大分成n类[2020.11.22数据处理ntile()](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486366&idx=2&sn=839daca8d94687bb2d2c07b4d61ee505&chksm=ea24f47add537d6c1601e07d3692c28a9d1ff89215478c9f1e0a0dd8c020f0bbd6f7d902f7d9&token=761595288&lang=zh_CN#rd)

----

## 学习笔记

1. [R数据科学|第八章内容介绍](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247489655&idx=1&sn=0fb004c7434066ef8777749670a47879&chksm=ea24e793dd536e856e19476a2c2807e271d94503de6f81a455761d8543087cb6d65d781ae6b8&token=261038642&lang=zh_CN#rd)
2. [R数据科学|第七章内容介绍](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488790&idx=1&sn=7d9bea14b8d5f65aa0d6f7f2243aba0a&chksm=ea24eaf2dd5363e4aabf912c0bfea863d0929b37c4eb26ebf0c64c4c13b851445128e40c8ed1&token=261038642&lang=zh_CN#rd)
3. [R数据科学|5.5.3内容介绍](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488658&idx=2&sn=0e52bfe7d2845ee5080d0f34ef39321a&chksm=ea24eb76dd536260db4060ba3cc109d571c1e5440f23a3d2ca37eec43b74d838a40a7d64a4da&token=261038642&lang=zh_CN#rd)
4. [R数据科学|5.5.2内容介绍及课后习题解答](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488450&idx=1&sn=1d67f85351b5c6a4fbac9b9b391c8c37&chksm=ea24ec26dd53653072f30e3352597f000fd274913b67fb90809f1bf729fde2e9420ad9d3d624&token=261038642&lang=zh_CN#rd)
5. [R数据科学|5.5.1 内容介绍](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488291&idx=1&sn=26ab9e40c82e19b580cc49c8b94830a5&chksm=ea24ecc7dd5365d1288ed40377dceb2f46838ddbdd375779c299346f403adce30cf3cb979a61&scene=21#wechat_redirect)
6. [R数据科学|5.5.1 习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488291&idx=2&sn=f3b6c7ec6e0380e4aa7df6a662574c14&chksm=ea24ecc7dd5365d12a7799865c040991124893ac5a9daa2cacb0ab88e5f00d9e1f5ac8ea21d6&scene=21#wechat_redirect)
7. [R数据科学|5.4内容介绍及习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488201&idx=1&sn=d10dfd154c074df858ec0a23df5ea095&chksm=ea24ed2ddd53643bd2e815a0285d03edc2ee1faf2ba92e6a90e09fa819d9c4458a060130266d&scene=21#wechat_redirect)
8. [R数据科学|5.3内容介绍](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488092&idx=1&sn=1e8a03e95af9c15b375214954f9143e0&chksm=ea24edb8dd5364ae3df6554e02d76c8129204687ff187640a0e3b9cb8f03bc67513425b017ef&scene=21#wechat_redirect)
9. [R数据科学|5.3课后习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488092&idx=2&sn=f247a53556885912a6bdf01a1dac82ee&chksm=ea24edb8dd5364ae8739e3de0856be14250ffc4a71b09f27ae526d01dd6e3f1dd4d49bf07c0c&scene=21#wechat_redirect)
10. [R数据科学|3.7内容介绍及习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487680&idx=1&sn=1713a96af27fe980917684a79ae6f9b3&chksm=ea24ef24dd5366322365ebad1056d19ce116b8d3fb9089be27e54865bb5d3b4063c9eda60245&scene=21#wechat_redirect)
11. [R数据科学|3.6习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486565&idx=2&sn=9528c701d54da29740de2d60516e8ad2&chksm=ea24f381dd537a97c61beb59c35ede88198ba40a915505b266ad3324769b730615f65d8a851d&scene=21#wechat_redirect)
12. [R数据科学|3.6内容介绍](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487324&idx=1&sn=4a7a1261a79894a960d674cddba8ca68&chksm=ea24f0b8dd5379aead668460edffcb128400df78cbfd930a9e37ea58c48da468227221353b7b&scene=21#wechat_redirect)
13. [R数据科学|3.5内容介绍及习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487001&idx=1&sn=1001c3ab89e0e4e3ee841c9c061bbdfc&chksm=ea24f1fddd5378ebf5f6c6d1e6c63c1e1781ae34a8585d5b76b031a975470f836aa15405bc78&scene=21#wechat_redirect)
14. [R数据科学|3.4内容介绍及习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487680&idx=2&sn=4ed84cbe90bf14cf6b913b7957892a91&chksm=ea24ef24dd5366324ec0e1689955384572b765463f4cfbc6c59d7ed5c7b6c1af180414031a4b&scene=21#wechat_redirect)
15. [R数据科学|3.3课后习题](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486565&idx=2&sn=9528c701d54da29740de2d60516e8ad2&chksm=ea24f381dd537a97c61beb59c35ede88198ba40a915505b266ad3324769b730615f65d8a851d&scene=21#wechat_redirect)
16. [R数据科学|3.2.4课后习题](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486530&idx=1&sn=cb7ca8c7a29afc24d901a8ffe7ed64ca&chksm=ea24f3a6dd537ab035a0e2e50b0321f7a3366f06f798af8a1e6ae724670f9916f72b82809bae&scene=21#wechat_redirect)


----------
- 欢迎关注我的公众号[庄闪闪的R语言手册]，创作不易，感谢打赏

> ![微信扫码即可](https://gitee.com/zhuang_liang_liang0825/other/raw/master/vcode.jpg)



================================================
FILE: 公众号资料/年度总结/2020年公众号文章汇总.md
================================================
## 小编自我介绍

大家好，我是**庄闪闪**，今天是2020最后一天就不带来干货啦，来介绍下我和我们的公众号。我是一名**应用统计研究生**。由于数学功底较差，只好往统计应用方向靠近，喜欢瞎折腾各种东西（调R包做统计分析，用最简单的和弦吉他弹唱，业余的手机摄影爱好者，折腾一些金鱼）这个公众号早在2016年就已经申请了，本打算记录一下自己的学习笔记，学习心得。但是那是水平不高（太差），没多久就放弃了。如今删除了以前所有推文，打算重新来过，记录下**平常学习到的统计知识与研究生期间科研中有趣的干货**。

## 公众号总结

我一直认为我是一个幸运的人，我有一个好导师，也有一群可爱的朋友。经过各位朋友的不断“努力”下，公众号从今年8月下旬开始到如今4个多月，发原创推文**58篇**，粉丝超过了**1500+**。在此，感谢各位同道中人的**点赞、分享、在看和收藏**以及给我**建设性建议**的仁兄们。2021年我会坚持做下去，继续学下去，通过公众号与大家分享优质内容。

## 2020年推文汇总

今年推送主要包含以下几方面：

- **R语言可视化**：

各个图的详细讲解，该系列还会继续下去，还有好多图没有介绍。

1.  [R可视乎|2021年日历大派送](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487678&idx=1&sn=4ddebccf2214e8e20a2a798ab23d002f&chksm=ea24ef5add53664c274d47a677804251052135c609cd6207e17c94b49ed6b7908321c8b7890f&scene=21#wechat_redirect)

2.  [R可视乎|瀑布图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487383&idx=1&sn=43e2eaf6b7c6b24510ccadb79e766f07&chksm=ea24f073dd53796546fd4145ded4cddfe8779464ad9d0d674900a65484966cd5c3f6367dcecf&scene=21#wechat_redirect)

3.  [R可视乎|等高线图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486838&idx=1&sn=6da5284c2b15d10b374d1fa5fc69a716&chksm=ea24f292dd537b8446f7555a862ad08cd30f2c252e4ebf39033bddd088b2ca016601f3bf2949&scene=21#wechat_redirect)

4.  [R可视乎|手把手绘制散点图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486565&idx=1&sn=937bc16b4c400109386cddaa0d083b0d&chksm=ea24f381dd537a97d9489ceed1243d74e001db454d16c121304254dafcbd547468dde3109b5d&scene=21#wechat_redirect)

5.  [R可视乎|dotchart点阵图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486437&idx=2&sn=ae6df80e6061089a7ebec1789c3bfa2d&chksm=ea24f401dd537d17affd48ef99971e3573e4c8b7a44226448feb232bfd8c0f95b67b889b2e92&scene=21#wechat_redirect)

6.  [R可视乎|三维散点图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486366&idx=1&sn=b47d8f89f032bbf1ead4f0db75d45ef7&chksm=ea24f47add537d6c5d50a73bd64318f473071e1474be95c9d5c2f250a39a64a6877ba4d61b68&scene=21#wechat_redirect)

7.  [R可视乎|蜂群图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486214&idx=2&sn=1d1ebfd76d8383ecd998561ffe884667&chksm=ea24f4e2dd537df4520ae79929abe24ace6bf029a78707ce3f85130ab638f9d0d72f4929acd0&scene=21#wechat_redirect)

8.  [R可视乎|气泡图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486060&idx=1&sn=b613c8d0239c93185641c1bb6a062f7b&chksm=ea24f588dd537c9e2d7a33dd5e9f32b34d0a72d5396cdb52211c51f3998269a4d843011abf9b&scene=21#wechat_redirect)

9.  [R可视乎|向日葵图和星星图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485615&idx=2&sn=6a9b895acccacf2ddb01c2eaa5383e00&chksm=ea24f74bdd537e5dec9c29de638caede3ff2c401f5b49ee798447e0bf4eb53914318667cca54&scene=21#wechat_redirect)

10.  [R可视乎 | 散点图系列（2）](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485276&idx=1&sn=f98a2aede13555fa1c372f08c3cdec44&chksm=ea24f8b8dd5371ae9e13f3df41ff73e070775eb1ab871370783bb3f396a0a9c29d42c6a89210&scene=21#wechat_redirect)

11.  [R可视乎 | 散点图系列（1）](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485142&idx=1&sn=564bffc9e7765ebae9b9b81a17a188d9&chksm=ea24f932dd5370241a05c75975ff24a34423a8f182bf6c716c8c9ed981788d0492dcb268248a&scene=21#wechat_redirect)

12.  [R 可视乎 | 华夫饼图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484631&idx=1&sn=6402ba80ab417c1d8a99c7778a48c7db&chksm=ea24fb33dd537225a7488a578b876b6da2f2157e0048b9515100d4ab64301fad2ad4f460e822&scene=21#wechat_redirect)

13.  [R可视乎|混合多个图形](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484367&idx=1&sn=30a54bd7dbf44852c380192d11a10ab9&chksm=ea24fc2bdd53753d239bbe995dfcb88232223e0a24f595acb343433fd4a053116e9f26dbf983&scene=21#wechat_redirect)

14.  [R可视乎|马赛克图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484309&idx=1&sn=d79ce748d43fe066a0bad0221ae8e068&chksm=ea24fc71dd537567c3ca322cb216e92c2ca4bdd96c20c9873e762d81cfb892583af10198b757&scene=21#wechat_redirect)

15.  [R可视乎|复合饼图系列](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484188&idx=1&sn=daeb6b1997f5ccaa1db56fc7698aeb1c&chksm=ea24fcf8dd5375eebbe2ee48594bc9670525ddb1111da56a658012d4970e73bbf2a8ff866330&scene=21#wechat_redirect)

16.  [R可视乎|分面一页多图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484186&idx=1&sn=c913a65f88132b3611e580b0318404d9&chksm=ea24fcfedd5375e87adfc3028850ee4034a0a0d34dd3855cf155b28eea9c71bfedb381d2c9e9&scene=21#wechat_redirect)

17.  [R可视乎|圆环图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484133&idx=1&sn=885a57209345d88e6574d42a2192394b&chksm=ea24fd01dd537417af4111dbe641943ffd530db1300da08332d5f56c2ef690fe7bcf66cf423e&scene=21#wechat_redirect)

18.  [R可视乎|饼图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484046&idx=1&sn=cc087b09448e75ed54b5364accfb3bff&chksm=ea24fd6add53747ca57137b964bdb0db371220737398e4276ee1ede27df7f09be0afd14185f8&scene=21#wechat_redirect)

19.  [R可视乎|3D曲面图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484046&idx=2&sn=57cf10eb09be797eeaf7572a579c6922&chksm=ea24fd6add53747c2a8708031b847c3d83892313a45af6617a158deb9b48a9ea96a26ee79498&scene=21#wechat_redirect)

- **数据科学手册**

基于《R数据科学》进行学习并分享每节主要内容和课后练习答案——系列由师妹赵茜茜负责）。

1.  [R数据科学|5.5.1 内容介绍](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488291&idx=1&sn=26ab9e40c82e19b580cc49c8b94830a5&chksm=ea24ecc7dd5365d1288ed40377dceb2f46838ddbdd375779c299346f403adce30cf3cb979a61&scene=21#wechat_redirect)

2.  [R数据科学|5.5.1 习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488291&idx=2&sn=f3b6c7ec6e0380e4aa7df6a662574c14&chksm=ea24ecc7dd5365d12a7799865c040991124893ac5a9daa2cacb0ab88e5f00d9e1f5ac8ea21d6&scene=21#wechat_redirect)

3.  [R数据科学|5.4内容介绍及习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488201&idx=1&sn=d10dfd154c074df858ec0a23df5ea095&chksm=ea24ed2ddd53643bd2e815a0285d03edc2ee1faf2ba92e6a90e09fa819d9c4458a060130266d&scene=21#wechat_redirect)

4.  [R数据科学|5.3内容介绍](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488092&idx=1&sn=1e8a03e95af9c15b375214954f9143e0&chksm=ea24edb8dd5364ae3df6554e02d76c8129204687ff187640a0e3b9cb8f03bc67513425b017ef&scene=21#wechat_redirect)

5.  [R数据科学|5.3课后习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488092&idx=2&sn=f247a53556885912a6bdf01a1dac82ee&chksm=ea24edb8dd5364ae8739e3de0856be14250ffc4a71b09f27ae526d01dd6e3f1dd4d49bf07c0c&scene=21#wechat_redirect)

6.  [R数据科学|3.7内容介绍及习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487680&idx=1&sn=1713a96af27fe980917684a79ae6f9b3&chksm=ea24ef24dd5366322365ebad1056d19ce116b8d3fb9089be27e54865bb5d3b4063c9eda60245&scene=21#wechat_redirect)

7.  [R数据科学|3.6习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486565&idx=2&sn=9528c701d54da29740de2d60516e8ad2&chksm=ea24f381dd537a97c61beb59c35ede88198ba40a915505b266ad3324769b730615f65d8a851d&scene=21#wechat_redirect)

8.  [R数据科学|3.6内容介绍](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487324&idx=1&sn=4a7a1261a79894a960d674cddba8ca68&chksm=ea24f0b8dd5379aead668460edffcb128400df78cbfd930a9e37ea58c48da468227221353b7b&scene=21#wechat_redirect)

9.  [R数据科学|3.5内容介绍及习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487001&idx=1&sn=1001c3ab89e0e4e3ee841c9c061bbdfc&chksm=ea24f1fddd5378ebf5f6c6d1e6c63c1e1781ae34a8585d5b76b031a975470f836aa15405bc78&scene=21#wechat_redirect)

10.  [R数据科学|3.4内容介绍及习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487680&idx=2&sn=4ed84cbe90bf14cf6b913b7957892a91&chksm=ea24ef24dd5366324ec0e1689955384572b765463f4cfbc6c59d7ed5c7b6c1af180414031a4b&scene=21#wechat_redirect)

11.  [R数据科学|3.3课后习题](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486565&idx=2&sn=9528c701d54da29740de2d60516e8ad2&chksm=ea24f381dd537a97c61beb59c35ede88198ba40a915505b266ad3324769b730615f65d8a851d&scene=21#wechat_redirect)

12.  [R数据科学|3.2.4课后习题](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486530&idx=1&sn=cb7ca8c7a29afc24d901a8ffe7ed64ca&chksm=ea24f3a6dd537ab035a0e2e50b0321f7a3366f06f798af8a1e6ae724670f9916f72b82809bae&scene=21#wechat_redirect)

- **R资料分享**

1.  [](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486530&idx=1&sn=cb7ca8c7a29afc24d901a8ffe7ed64ca&chksm=ea24f3a6dd537ab035a0e2e50b0321f7a3366f06f798af8a1e6ae724670f9916f72b82809bae&scene=21#wechat_redirect)[Rstudio常用快捷键以及窗口操作有用技巧](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487180&idx=1&sn=43f64d3a9859c7e12562806de901e683&chksm=ea24f128dd53783e62d504190665a06cdd66432ba5532ae87b019c99a63ffa0b96e0fd4be377&scene=21#wechat_redirect)

2.  [R爱好者福利|免费线上资源分享](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486437&idx=1&sn=93c9250cda3a467b85f12e8b750e1096&chksm=ea24f401dd537d17d1004e0d8dc70d88fd503706ff3b9888d2cc4ceafc4497f114d92c4fac92&scene=21#wechat_redirect)  

3.  [Bookdown优质免费书籍分享](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486727&idx=1&sn=43a9b4cdc93bd38f179230bf4f0abc2e&chksm=ea24f2e3dd537bf515b3fe89f89158329479f6f550eaf58bdb24f9c83c2643753a4764154e73&scene=21#wechat_redirect)

4.  [【小白必看】可视化图形推文汇总](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485242&idx=1&sn=6616ae433fc59bb1e766f978a4b0e061&chksm=ea24f8dedd5371c874a30a4600d659d3a04d0186a48d4965d27aa574a602717d769f395ea0bb&scene=21#wechat_redirect)

5.  [R语言数据处理120题](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484633&idx=1&sn=09ddb4127cd8167acbb933c55b720403&chksm=ea24fb3ddd53722b7531f64046c494399012237f7229ca40f68b6a24471166b22c24a17fd8f1&scene=21#wechat_redirect)

6.  [R速查表分享](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484290&idx=1&sn=edfe8a910e87353dc8e68f3f42e34c7c&chksm=ea24fc66dd537570f2bcda65b1ae0457ac9906fd14e74da6a0ffd3ee62a304ed465fd8ea9637&scene=21#wechat_redirect)

7.  [Rmarkdown写日记真的香！](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484161&idx=1&sn=5510c69afff62f729bfe91a409511509&chksm=ea24fce5dd5375f3168d0a2a2aed9fe9f9b427494f3bdbbe8b198cad094bdce09ee18639ec5d&scene=21#wechat_redirect)

8.  [志愿填报|温州大学"非官方"招办专家帮你支招！](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247483954&idx=1&sn=047a1b1b3e9c5ce46e3876bb05b54282&chksm=ea24fdd6dd5374c0c119c680cbd595b77a0bc7479889b34d378ae3282bce5e07eff93a5d08e5&scene=21#wechat_redirect)

- **有趣R包介绍**

介绍一些ggplot2扩展可视化包以及其他实用的包。

1.  [ggvis包—数据可视化交互](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488405&idx=1&sn=271fc88b523e738a6a1d92504dbce330&chksm=ea24ec71dd5365671bb66cbb50afdb0b00762b7581b9d3e5060a4b59021485364093f1c8b963&scene=21#wechat_redirect)

2.  [ggridges包—峰峦图详细介绍](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488248&idx=1&sn=6b71d7adba5ea796fdfe8f49fe232d94&chksm=ea24ed1cdd53640a1e30271584458097fce82a732f9d63ce2084dc63fbae6fb028fd7f813bb7&scene=21#wechat_redirect)

3.  [esquisse包—不写代码生成ggplot图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488200&idx=1&sn=3a058480b104165118975b2d908dff72&chksm=ea24ed2cdd53643a9deb58069cd8d0e9933fc165994a2bb7a6f7d4651c7796b839fc781ec86d&scene=21#wechat_redirect)

4.  [calendR包—私人定制专属日历](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487814&idx=1&sn=aa58149b66ce8b6d1c6210ded418c71a&chksm=ea24eea2dd5367b4a24a670b9e78d377f1506399be8dd0d0a35230407e3dc131f6b07ab3d9ca&scene=21#wechat_redirect)

5.  [corrplot包：相关性矩阵可视化](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487625&idx=1&sn=3102c4afb0cf97904d810579af386eb6&chksm=ea24ef6ddd53667b887d11e7013589f796c8ff4f9e1e9b6e8df817ea7b223baadbc19dfbaaa5&scene=21#wechat_redirect)

6.  [cowplot包：用R添加水印](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486838&idx=2&sn=21ee1c8b683e7d27373f3e1f40901428&chksm=ea24f292dd537b843db330a88161ce6f89227418f64515164615c3f63721df6464b6d91a2b1a&scene=21#wechat_redirect)

7.  [flexdashboard包：用于R的简单交互式仪表盘](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486237&idx=1&sn=571544510c7e3e48a280dd4d677656e5&chksm=ea24f4f9dd537defa493c419973f75943159316765ac61093a195b83fde314dd7fffe61349cd&scene=21#wechat_redirect)

8.  [gghalves包-你五毛我五毛](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486214&idx=1&sn=7ff5d7375c615d20cffea6329cccff37&chksm=ea24f4e2dd537df4a5ebe66a441b2fb0ee0599a05a700e1d199f729c497a68857e8031cd2e61&scene=21#wechat_redirect)

9.  [用ggpubr包制图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485615&idx=1&sn=47ac21f131bf2ac6c90c50fb9fb7966b&chksm=ea24f74bdd537e5d74f60919388f683dfe779fe8a2d11999e55e290d4bdb25c64e36cc74ccc1&scene=21#wechat_redirect)

10.  [reticulate包——数据科学者的福音](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484515&idx=1&sn=26b03b6ad26f2315cdc04049f740f1c0&chksm=ea24fb87dd537291d5184c28a9c9f2cdda591e4c17a7e7daaff34a9a1c3949ee0e86f9b355b7&scene=21#wechat_redirect)

11.  [igraph包——绘制网络图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247483780&idx=1&sn=46ce562ed91ec2d08d7669477160c249&chksm=ea24fe60dd53777615de14ec0ad087c1bbc56d46d73eb51e13fdeeaa60633063b4bd415a7d01&scene=21#wechat_redirect)

- **R数据分析**

1.  [数据处理|数据按从小到大分成n类](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486366&idx=2&sn=839daca8d94687bb2d2c07b4d61ee505&chksm=ea24f47add537d6c1601e07d3692c28a9d1ff89215478c9f1e0a0dd8c020f0bbd6f7d902f7d9&scene=21#wechat_redirect)

2.  [冗余分析](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485653&idx=1&sn=28ac9c2fb8a756bb0e2aecb53992c74f&chksm=ea24f731dd537e2771dbf39f9d534a0fb96000913b6010346cfd6f500fb21a463dbad98ec888&scene=21#wechat_redirect)

3.  [主成分分析](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485340&idx=1&sn=6f1dc4e3447a9e18ef56df92c215b477&chksm=ea24f878dd53716ec9fe64d0fca6d4587d751103982d5a18cb637a6c611a502b893de8b96851&scene=21#wechat_redirect)

4.  [看篮球学R语言：探索约基奇数据](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484905&idx=1&sn=8393cb4eb893eaed82b588a166599733&chksm=ea24fa0ddd53731b8af04333530bddbf0b292f8949f5d75185389c10ddf996324ea9250caa60&scene=21#wechat_redirect)

5.  [亚马逊产品的推荐算法](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484112&idx=1&sn=ffcb0b6ed0efb64ab25b6a76d9dba654&chksm=ea24fd34dd537422ed28f04d438b066dca643986d990711b9c9c1bdb53ba8aa38dcfef655822&scene=21#wechat_redirect)

6.  [协同过滤算法—MovieLense数据集分析](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247483889&idx=1&sn=b56b5ee3374bb8e2ec0e5643f62cd4bb&chksm=ea24fe15dd5377034e245dddb4c47ae5fc4a07ffc0d117734ec81910d733173b729e11a4fd29&scene=21#wechat_redirect)

- **其他科研实用推文**

1.  [推荐几个R数据可视化用的配色网站](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488405&idx=2&sn=540f4118d1d7c94d4a77b0fc513d855d&chksm=ea24ec71dd5365679340315d53e4abae0d3871313b5d60cdeb295847f2cd1a01607faeabf66b&scene=21#wechat_redirect)
2.  [小伙伴们，圣诞节快乐！](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488248&idx=2&sn=53014031470fbc033d64326e1a87f311&chksm=ea24ed1cdd53640a12c6c23a58be7ae80fa0df34e4ade64ed34ab4f4688212a0b2d7eafe1cc8&scene=21#wechat_redirect)
3.  [好用又免费的文献软件 不知道这些你就OUT啦](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487883&idx=2&sn=d78dd20986eb2eaa3efba20f3b3658af&chksm=ea24ee6fdd536779bbb108cc8c73fd36ad76150bd17c68ca3c714d327f98bc363258076ca31e&scene=21#wechat_redirect)
4.  [一键帮你解决SCI论文语法错误—Grammarly英文润色软件](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487324&idx=2&sn=e5b05c9a8ac9a9ea3717e12a2d75a202&chksm=ea24f0b8dd5379ae3c9de7f2739460488f2f7c1ca44fcdc34fa62f8896df2762ef95e402dc79&scene=21#wechat_redirect)
5.  [加载Windows系统字体到图上，解决PDF导出字体无法显示的问题](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487180&idx=2&sn=4d98c30116032c2995ed67233e77333f&chksm=ea24f128dd53783e55b6308d5afac933ec635ec754e9571ecf09eda328f9e454dbd5a33044df&scene=21#wechat_redirect)
6.  [微软推出的新版Edge浏览器，让我抛弃用了5年的Chrome！](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486980&idx=1&sn=2145f5a4b485829c0a7654b3baa16643&chksm=ea24f1e0dd5378f6035f4cd07e8d01cf9f339e5b36f18201d02035cddc57ba65065442bb375c&scene=21#wechat_redirect)
7.  [AI不会，没关系，R可以导出PPT格式的图形啦](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485653&idx=2&sn=8c085a31c654ed8237948fbd342c2fca&chksm=ea24f731dd537e2722acee14c91c7f4a6e183480cc1e06b135476286f875cc86da2475b0b4b6&scene=21#wechat_redirect)
8.  [R中输出常见位图和矢量图格式总结](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485740&idx=2&sn=9e9658cea1fbcfa6eb19d155c8b7a7c5&chksm=ea24f6c8dd537fde4b65b022ddc16af5137504ab764b2744e2dd821712ce36d89088473ea2cc&scene=21#wechat_redirect)
9.  [R中坐标轴截断的不同实现方式](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247483923&idx=1&sn=1494f3b69e7388e9a71b995e3b09f473&chksm=ea24fdf7dd5374e13f386521eef9d16bebf8e0dca2cef4b7036e5a7407f560d05de7ed6e390d&scene=21#wechat_redirect)

## 2021年预想计划  

- **基于Rmarkdown的不同呈现介绍**

我是谢益辉的小粉丝也是Rmarkdwon的小粉丝，这系列对不同报告版式进行呈现（使用Rmarkdown制作pdf，word，ppt，简历，书籍，博客等）。这个内容不需要任何专业知识，任何人都可以入门。

- **数据科学手册\(继续推进\)**

  - 数据处理: tibble包，readr包，dplyr包，stringr包，forcats包，lubridate包
  - 编程：magrittr包，purrr包
  - 模型：tidymodel包
  - 可视化：本公众号主要内容
  - 沟通：Rmarkdwon系列学习（前面提过）

- **shiny界面学习与应用**

shiny我也正在学习，借此机会（flag）总结学习内容与分享自己做的“无用”案例。

- **可视化系列（继续推进，不限R）**

可视化除了用R当然可以用其他软件，做一些更加简单的教程吧，毕竟R学习需要门槛，作为科研人，最主要的是把自己想要的结果通过图来表示，也没说一定要用什么软件。

- **深度学习与工业相结合**

我的研究方向是工业统计，主要与老师做可靠性方向。以后有意将深度学习与工业相结合，所以这方向也可以一直推进下去。

---

================================================
FILE: 公众号资料/资料获取.md
================================================
作为处女座的我，喜欢储存各类小玩意，包括学习中的好用的资料，好用的软件。只要公众号不停，我会一直在后台更新相关的学习资料。

### 获取方式

输入相应的指令获取我存放的资料链接:
| 命令      | 描述 |        
| :--------- | :--: |
| R    |  返回 R 资料列表  |  
| Py|  返回 Python 资料列表 | 
| 可靠性统计  | 返回可靠性统计资料  |
|文献|返回搜文献网站|
| 仓库    |  返回公众号 GitHub 仓库  |  
|知乎|返回「庄闪闪」个人知乎链接|

### 专题整理

[R线上开源资料分享（可跳转）](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486437&idx=1&sn=93c9250cda3a467b85f12e8b750e1096&chksm=ea24f401dd537d17d1004e0d8dc70d88fd503706ff3b9888d2cc4ceafc4497f114d92c4fac92&scene=21&token=405533159&lang=zh_CN#wechat_redirect)


> 由于版权原因，书籍不包括一些没有作者授权的pdf（如有需要，可私聊小编）。不过开源的网站以及github中大佬的教程也再好不过了。建议网站用电脑浏览器（google）打开，微信直接打开可能会比较慢。



>>> ### 联系方式
>>>
>>> 商业合作，开白，转载，咨询可加小编微信，并备注单位+姓名
>>>
>>> [Github](https://github.com/liangliangzhuang "Github")、[知乎](https://www.zhihu.com/people/zhuangshanshan "知乎")、[CSDN](https://i.csdn.net/#/uc/profile "CSDN")
>>>
>>> ![小编微信：庄闪闪](https://imgkr2.cn-bj.ufileos.com/d994c29f-59be-4a35-9444-f57407a89f01.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=cgimoe4NXQMRX04H4PraZaejZCQ%253D&Expires=1606203711)

<span style="display:block;text-align:center;color:green;">拉上你的小伙伴和闪闪一起学习吧！</span>

================================================
FILE: 资料分享.md
================================================
作为处女座的我，喜欢储存各类小玩意，包括学习中的好用的资料，好用的软件。只要公众号不停，我会一直在后台更新相关的学习资料。

### 获取方式

输入相应的指令获取我存放的资料链接:
| 命令      | 描述 |        
| :--------- | :--: |
| R    |  返回 R 资料列表  |  
| Py|  返回 Python 资料列表 | 
| 可靠性统计  | 返回可靠性统计资料  |
|文献|返回搜文献网站|
| 仓库    |  返回公众号 GitHub 仓库  |  
|知乎|返回「庄闪闪」个人知乎链接|

### 专题整理

- [R线上开源资料分享](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486437&idx=1&sn=93c9250cda3a467b85f12e8b750e1096&chksm=ea24f401dd537d17d1004e0d8dc70d88fd503706ff3b9888d2cc4ceafc4497f114d92c4fac92&scene=21&token=405533159&lang=zh_CN#wechat_redirect)

- [Bookdown优质免费书籍分享](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486727&idx=1&sn=43a9b4cdc93bd38f179230bf4f0abc2e&chksm=ea24f2e3dd537bf515b3fe89f89158329479f6f550eaf58bdb24f9c83c2643753a4764154e73&scene=21&token=806552290&lang=zh_CN#wechat_redirect)

- [R语言数据处理120题](https://mp.weixin.qq.com/s/nf_f4zGT5GMjcZXV-Xm3XQ)

- [Rstudio常用快捷键以及窗口操作有用技巧](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487180&idx=1&sn=43f64d3a9859c7e12562806de901e683&chksm=ea24f128dd53783e62d504190665a06cdd66432ba5532ae87b019c99a63ffa0b96e0fd4be377&token=241520229&lang=zh_CN#rd)

> 由于版权原因，书籍不包括一些没有作者授权的pdf（如有需要，可私聊小编）。不过开源的网站以及github中大佬的教程也再好不过了。建议网站用电脑浏览器（google）打开，微信直接打开可能会比较慢。



>>> ### 联系方式
>>>
>>> 商业合作，开白，转载，咨询可加小编微信，并备注单位+姓名
>>>
>>> [Github](https://github.com/liangliangzhuang "Github")、[知乎](https://www.zhihu.com/people/zhuangshanshan "知乎")、[CSDN](https://i.csdn.net/#/uc/profile "CSDN")
>>>

<span style="display:block;text-align:center;c;">**拉上你的小伙伴和闪闪一起学习吧！**</span>